{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'PDF TO Markdown' && linkText !== 'PDF TO Markdown' ) { link.textContent = 'PDF TO Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== 'Voice Cloning' ) { link.textContent = 'Voice Cloning'; link.href = 'https://vibevoice.info/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'PDF TO Markdown'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \n ''')\n index_fo.write('\\n'.join(toc))\n index_fo.write('\\n'.join(data))\n \n index_fo.write('''\n\n \n ''') \n index_fo.close()\n \n print 'HTML charts generated into:', index_fo.name \n os.chdir(old_dir)\n \n\ndef load_gunit_file(gunit_file):\n fi = open(gunit_file, 'r')\n test_cases = {}\n section = None\n for line in fi:\n l = line.strip()\n if not l or l[:2] == '//':\n continue\n parts = split_line(l)\n if len(parts) == 1 and parts[0][-1] == ':':\n section = parts[0][:-1]\n test_cases.setdefault(section, [])\n elif len(parts) > 1 and parts[1].lower() != 'fails':\n query = parts[0]\n query = query.replace('\\\\\\\"', '\"').replace('\\\\\\'', '\\'').replace('\\\\\\\\', '\\\\')\n test_cases[section].append(query)\n fi.close()\n return test_cases\n \n \ndef split_line(line):\n line = line.replace('->', '')\n start = 0\n last_pos = None\n parts = []\n while line.find('\"', start) > -1:\n p = line.index('\"', start)\n start = p+1\n if line[p-1] != '\\\\':\n if last_pos is None:\n last_pos = p\n else:\n parts.append(line[last_pos+1:p])\n parts.append(line[p+1:].strip())\n last_pos = None\n break\n if not parts:\n parts.append(line.strip())\n return parts\n\n\nif __name__ == '__main__':\n if len(sys.argv) == 1:\n sys.argv.insert(1, \"StandardLuceneGrammar\")\n run(*sys.argv[1:])\n"},"avg_line_length":{"kind":"number","value":30.7227722772,"string":"30.722772"},"max_line_length":{"kind":"number","value":144,"string":"144"},"alphanum_fraction":{"kind":"number","value":0.4966161779,"string":"0.496616"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1529,"string":"1,529"},"score_documentation":{"kind":"number","value":0.24637447631324524,"string":"0.246374"}}},{"rowIdx":3207,"cells":{"hexsha":{"kind":"string","value":"92d5a318d2e721b05edd8c4dc433e4875c24b448"},"size":{"kind":"number","value":6318,"string":"6,318"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"visual_perception/Detection/yolov4/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"SSusantAchary/Visual-Perception"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b81ffe69ab85e9afb7ee6eece43ac83c8f292285"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"visual_perception/Detection/yolov4/__init__.py"},"max_issues_repo_name":{"kind":"string","value":"SSusantAchary/Visual-Perception"},"max_issues_repo_head_hexsha":{"kind":"string","value":"b81ffe69ab85e9afb7ee6eece43ac83c8f292285"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"visual_perception/Detection/yolov4/__init__.py"},"max_forks_repo_name":{"kind":"string","value":"SSusantAchary/Visual-Perception"},"max_forks_repo_head_hexsha":{"kind":"string","value":"b81ffe69ab85e9afb7ee6eece43ac83c8f292285"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"\nMIT License\n\nCopyright (c) 2020 Susant Achary \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\n\nfrom visual_perception.Detection.yolov4.tf import YOLOv4 as yolo_main\nimport numpy as np\nimport cv2\n\nlabels = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat',\n 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog',\n 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella',\n 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite',\n 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass',\n 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange',\n 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant',\n 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone',\n 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',\n 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}\n\nclass YOLOv4:\n def __init__(self):\n self.weights_path = \"\"\n self.model = None\n self.yolo_classes = \"\"\n self.iou = 0\n self.score = 0\n self.input_shape = 0\n self.output_path = \"\"\n \n def load_model(self, weights_path:str = None, classes_path:str = None, input_shape:int = 608):\n if (weights_path is None) or (classes_path is None):\n raise RuntimeError ('weights_path AND classes_path should not be None.')\n \n self.yolo_classes = classes_path\n self.weights_path = weights_path\n self.input_shape = input_shape\n \n self.model = yolo_main(shape = self.input_shape)\n self.model.classes = self.yolo_classes\n self.model.make_model()\n self.model.load_weights(self.weights_path, weights_type = 'yolo')\n \n def predict(self, img:np.ndarray, output_path:str, iou = 0.45, score = 0.25, custom_objects:dict = None, \n debug=True):\n\n self.output_path = output_path\n self.iou = iou\n self.score = score \n #img = np.array(Image.open(img))[..., ::-1] \n pred_bboxes = self.model.predict(img, iou_threshold = self.iou, score_threshold = self.score)\n boxes = []\n if (custom_objects != None):\n for i in range(len(pred_bboxes)):\n check_name = labels[pred_bboxes[i][4]]\n check = custom_objects.get(check_name, 'invalid')\n if check == 'invalid':\n continue\n elif check == 'valid':\n boxes.append(list(pred_bboxes[i]))\n boxes = np.array(boxes)\n res = self.model.draw_bboxes(img, boxes)\n if debug:\n cv2.imwrite(self.output_path, res)\n\n else:\n res = self.model.draw_bboxes(img, pred_bboxes)\n if debug:\n cv2.imwrite(self.output_path, res)\n \n return res\n \nclass TinyYOLOv4:\n def __init__(self):\n self.weights_path = \"\"\n self.model = None\n self.yolo_classes = \"\"\n self.iou = 0\n self.score = 0\n self.input_shape = 0\n self.output_path = \"\"\n \n def load_model(self, weights_path:str = None, classes_path:str = None, input_shape:int = 0):\n if (weights_path is None) or (classes_path is None):\n raise RuntimeError ('weights_path AND classes_path should not be None.')\n \n self.yolo_classes = classes_path\n self.weights_path = weights_path\n self.input_shape = input_shape\n self.model = yolo_main(tiny = True, shape = self.input_shape)\n self.model.classes = self.yolo_classes\n self.model.make_model()\n self.model.load_weights(self.weights_path, weights_type = 'yolo')\n \n def predict(self, img:np.ndarray, output_path:str, iou = 0.4, score = 0.07, custom_objects:dict = None,\n debug=True):\n\n self.output_path = output_path\n self.iou = iou\n self.score = score \n #img = np.array(Image.open(img))[..., ::-1] \n pred_bboxes = self.model.predict(img, iou_threshold = self.iou, score_threshold = self.score)\n \n boxes = []\n if (custom_objects != None):\n for i in range(len(pred_bboxes)):\n check_name = labels[pred_bboxes[i][4]]\n check = custom_objects.get(check_name, 'invalid')\n if check == 'invalid':\n continue\n elif check == 'valid':\n boxes.append(list(pred_bboxes[i]))\n boxes = np.array(boxes)\n res = self.model.draw_bboxes(img, boxes)\n if debug:\n cv2.imwrite(self.output_path, res)\n\n else:\n res = self.model.draw_bboxes(img, pred_bboxes)\n if debug:\n cv2.imwrite(self.output_path, res)\n \n return res\n "},"avg_line_length":{"kind":"number","value":43.875,"string":"43.875"},"max_line_length":{"kind":"number","value":141,"string":"141"},"alphanum_fraction":{"kind":"number","value":0.60889522,"string":"0.608895"},"count_classes":{"kind":"number","value":3811,"string":"3,811"},"score_classes":{"kind":"number","value":0.6031972143083254,"string":"0.603197"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2065,"string":"2,065"},"score_documentation":{"kind":"number","value":0.32684393795504907,"string":"0.326844"}}},{"rowIdx":3208,"cells":{"hexsha":{"kind":"string","value":"92d6916cbd5aa31c26daff18d295d7d026f17d82"},"size":{"kind":"number","value":517,"string":"517"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"server/mqtt/handler.py"},"max_stars_repo_name":{"kind":"string","value":"rishab-rb/MyIOTMap"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2018-10-08T06:11:20.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2018-10-08T06:11:20.000Z"},"max_issues_repo_path":{"kind":"string","value":"server/mqtt/handler.py"},"max_issues_repo_name":{"kind":"string","value":"rishab-rb/MyIOTMap"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"server/mqtt/handler.py"},"max_forks_repo_name":{"kind":"string","value":"rishab-rb/MyIOTMap"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":2,"string":"2"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2018-07-30T08:18:22.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2018-10-11T08:04:58.000Z"},"content":{"kind":"string","value":"import paho.client as mqtt\n\nHOST = 'localhost'\nPORT = 1883\n\nclass MQTTConnector:\n \n def __init__(self, host, port):\n host = host\n port = port\n client = mqtt.Client()\n\n def connect():\n self.client.connect(self.host, self.port, 60) \n\n def run(self):\n self.client.loop_forever()\n\nclass MQTTSubscriber:\n \n def __init__(self, *args, **kwargs): \n super(MQTTSubscriber, self).__init__(*args, **kwargs)\n\nclass MQTTPublisher:\n \n def __init__(self, host)"},"avg_line_length":{"kind":"number","value":19.8846153846,"string":"19.884615"},"max_line_length":{"kind":"number","value":61,"string":"61"},"alphanum_fraction":{"kind":"number","value":0.6092843327,"string":"0.609284"},"count_classes":{"kind":"number","value":453,"string":"453"},"score_classes":{"kind":"number","value":0.8762088974854932,"string":"0.876209"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":11,"string":"11"},"score_documentation":{"kind":"number","value":0.02127659574468085,"string":"0.021277"}}},{"rowIdx":3209,"cells":{"hexsha":{"kind":"string","value":"92d713c9c1666b57fdf260fc3597ec5bb433209c"},"size":{"kind":"number","value":1886,"string":"1,886"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"scripts/spacy_files/similarity_replacement.py"},"max_stars_repo_name":{"kind":"string","value":"HighDeFing/thesis_v4"},"max_stars_repo_head_hexsha":{"kind":"string","value":"2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"scripts/spacy_files/similarity_replacement.py"},"max_issues_repo_name":{"kind":"string","value":"HighDeFing/thesis_v4"},"max_issues_repo_head_hexsha":{"kind":"string","value":"2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"scripts/spacy_files/similarity_replacement.py"},"max_forks_repo_name":{"kind":"string","value":"HighDeFing/thesis_v4"},"max_forks_repo_head_hexsha":{"kind":"string","value":"2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/bin/env python\nfrom black import main\nimport spacy\nimport json\nfrom spacy import displacy\nimport unidecode\nimport pandas as pd\nimport numpy as np\nimport os\n\n\ncsv_source = \"scripts/spacy_files/data/thesis_200_with_school.csv\"\ndf = pd.read_csv(csv_source)\ndf = df[df['isScan']==False]\ndf = df.sort_values('isScan', ascending=False)\n\ntext1= \"Escuela de Enfermería\"\n\ntext2 = \"ESCUELA DE ENFERMERIA\"\n\nfile = open(\"scripts/spacy_files/data/escuelas.json\", \"r\")\nfile = json.load(file)\ntemp_list = []\nfor facultad in file:\n temp_list.append(facultad['escuela'])\n#print(facultad['escuela'])\nescuelas = [item for sublist in temp_list for item in sublist] # make the list flat\n\n#print(escuelas)\n\ntext1_u = unidecode.unidecode(text1)\ntext1_l_u = text1_u.lower()\ntext2_l_u = unidecode.unidecode(text2).lower()\nprint(text1_l_u, \"<-->\", text2_l_u)\nif text1_l_u == text2_l_u:\n print(text1, \" is correct.\")\n\ndef unaccent_list(accent_list):\n unaccented_schools = []\n for sch in accent_list:\n unaccented_schools.append(unidecode.unidecode(sch).lower())\n return unaccented_schools\n\ndef set_school_to_unaccent(escuelas):\n escuelas = unaccent_list(escuelas)\n return escuelas\n\ndef create_dictionary(schools):\n myDict = dict((e,i) for i,e in enumerate(schools))\n return myDict\n\ndef set_schools_accents(row, dict, dict_c):\n index = dict.get(row.lower())\n key_list = list(dict_c.keys())\n val_list = list(dict_c.values())\n\n try:\n position = val_list.index(index)\n key_list[position]\n except:\n return None\n\nif __name__ == \"__main__\":\n u_escuelas = set_school_to_unaccent(escuelas)\n u_escuelas_dict = create_dictionary(u_escuelas)\n escuelas_dict = create_dictionary(escuelas)\n print(u_escuelas_dict)\n print(escuelas_dict)\n print(set_schools_accents(\"No school\", u_escuelas_dict, escuelas_dict))\n\n\n\n\n"},"avg_line_length":{"kind":"number","value":25.4864864865,"string":"25.486486"},"max_line_length":{"kind":"number","value":83,"string":"83"},"alphanum_fraction":{"kind":"number","value":0.7189819724,"string":"0.718982"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":290,"string":"290"},"score_documentation":{"kind":"number","value":0.15368309485956544,"string":"0.153683"}}},{"rowIdx":3210,"cells":{"hexsha":{"kind":"string","value":"92d920562d22f1142cab1ea79e81051636bf317f"},"size":{"kind":"number","value":7212,"string":"7,212"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"test/unittest_base.py"},"max_stars_repo_name":{"kind":"string","value":"dat-boris/tensorforce"},"max_stars_repo_head_hexsha":{"kind":"string","value":"d777121b1c971da5500572c5f83173b9229f7370"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"test/unittest_base.py"},"max_issues_repo_name":{"kind":"string","value":"dat-boris/tensorforce"},"max_issues_repo_head_hexsha":{"kind":"string","value":"d777121b1c971da5500572c5f83173b9229f7370"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"test/unittest_base.py"},"max_forks_repo_name":{"kind":"string","value":"dat-boris/tensorforce"},"max_forks_repo_head_hexsha":{"kind":"string","value":"d777121b1c971da5500572c5f83173b9229f7370"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# Copyright 2018 Tensorforce Team. 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\nfrom copy import deepcopy\nfrom datetime import datetime\nimport os\nimport sys\nimport warnings\n\nfrom tensorforce import TensorforceError\nfrom tensorforce.agents import Agent\nfrom tensorforce.core.layers import Layer\nfrom tensorforce.environments import Environment\nfrom tensorforce.execution import Runner\nfrom test.unittest_environment import UnittestEnvironment\n\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\nclass UnittestBase(object):\n \"\"\"\n Unit-test base class.\n \"\"\"\n\n # Unittest\n num_updates = None\n num_episodes = None\n num_timesteps = None\n\n # Environment\n min_timesteps = 1\n states = dict(\n bool_state=dict(type='bool', shape=(1,)),\n int_state=dict(type='int', shape=(2,), num_values=4),\n float_state=dict(type='float', shape=(1, 1, 2)),\n bounded_state=dict(type='float', shape=(), min_value=-0.5, max_value=0.5)\n )\n actions = dict(\n bool_action=dict(type='bool', shape=(1,)),\n int_action=dict(type='int', shape=(2,), num_values=4),\n float_action=dict(type='float', shape=(1, 1)),\n bounded_action=dict(type='float', shape=(2,), min_value=-0.5, max_value=0.5)\n )\n\n # Exclude action types\n exclude_bool_action = False\n exclude_int_action = False\n exclude_float_action = False\n exclude_bounded_action = False\n\n # Agent\n agent = dict(\n update=4, policy=dict(network=dict(type='auto', size=8, depth=1, internal_rnn=2)),\n objective='policy_gradient', reward_estimation=dict(horizon=3)\n )\n\n # Tensorforce config\n require_observe = False\n require_all = False\n\n def setUp(self):\n warnings.filterwarnings(\n action='ignore',\n message='Converting sparse IndexedSlices to a dense Tensor of unknown shape'\n )\n\n def start_tests(self, name=None):\n \"\"\"\n Start unit-test method.\n \"\"\"\n if name is None:\n sys.stdout.write('\\n{} {}: '.format(\n datetime.now().strftime('%H:%M:%S'), self.__class__.__name__[4:]\n ))\n else:\n sys.stdout.write('\\n{} {} ({}): '.format(\n datetime.now().strftime('%H:%M:%S'), self.__class__.__name__[4:], name\n ))\n sys.stdout.flush()\n\n def finished_test(self, assertion=None):\n \"\"\"\n Finished unit-test.\n \"\"\"\n if assertion is None:\n assertion = True\n else:\n self.assertTrue(expr=assertion)\n if assertion:\n sys.stdout.write('.')\n sys.stdout.flush()\n\n def prepare(\n self, environment=None, min_timesteps=None, states=None, actions=None,\n exclude_bool_action=False, exclude_int_action=False, exclude_float_action=False,\n exclude_bounded_action=False, require_observe=False, require_all=False, **agent\n ):\n \"\"\"\n Generic unit-test preparation.\n \"\"\"\n Layer.layers = None\n\n if environment is None:\n if states is None:\n states = deepcopy(self.__class__.states)\n\n if actions is None:\n actions = deepcopy(self.__class__.actions)\n if exclude_bool_action or self.__class__.exclude_bool_action:\n actions.pop('bool_action')\n if exclude_int_action or self.__class__.exclude_int_action:\n actions.pop('int_action')\n if exclude_float_action or self.__class__.exclude_float_action:\n actions.pop('float_action')\n if exclude_bounded_action or self.__class__.exclude_bounded_action:\n actions.pop('bounded_action')\n\n if min_timesteps is None:\n min_timesteps = self.__class__.min_timesteps\n\n environment = UnittestEnvironment(\n states=states, actions=actions, min_timesteps=min_timesteps\n )\n\n elif min_timesteps is not None:\n raise TensorforceError.unexpected()\n\n environment = Environment.create(environment=environment, max_episode_timesteps=5)\n\n for key, value in self.__class__.agent.items():\n if key not in agent:\n agent[key] = value\n\n if self.__class__.require_all or require_all:\n config = None\n elif self.__class__.require_observe or require_observe:\n config = dict(api_functions=['reset', 'act', 'observe'])\n else:\n config = dict(api_functions=['reset', 'act'])\n\n agent = Agent.create(agent=agent, environment=environment, config=config)\n\n return agent, environment\n\n def unittest(\n self, num_updates=None, num_episodes=None, num_timesteps=None, environment=None,\n min_timesteps=None, states=None, actions=None, exclude_bool_action=False,\n exclude_int_action=False, exclude_float_action=False, exclude_bounded_action=False,\n require_observe=False, require_all=False, **agent\n ):\n \"\"\"\n Generic unit-test.\n \"\"\"\n agent, environment = self.prepare(\n environment=environment, min_timesteps=min_timesteps, states=states, actions=actions,\n exclude_bool_action=exclude_bool_action, exclude_int_action=exclude_int_action,\n exclude_float_action=exclude_float_action,\n exclude_bounded_action=exclude_bounded_action, require_observe=require_observe,\n require_all=require_all, **agent\n )\n\n self.runner = Runner(agent=agent, environment=environment)\n\n assert (num_updates is not None) + (num_episodes is not None) + \\\n (num_timesteps is not None) <= 1\n if num_updates is None and num_episodes is None and num_timesteps is None:\n num_updates = self.__class__.num_updates\n num_episodes = self.__class__.num_episodes\n num_timesteps = self.__class__.num_timesteps\n if num_updates is None and num_episodes is None and num_timesteps is None:\n num_updates = 2\n assert (num_updates is not None) + (num_episodes is not None) + \\\n (num_timesteps is not None) == 1\n\n evaluation = not any([\n require_all, require_observe, self.__class__.require_all,\n self.__class__.require_observe\n ])\n self.runner.run(\n num_episodes=num_episodes, num_timesteps=num_timesteps, num_updates=num_updates,\n use_tqdm=False, evaluation=evaluation\n )\n self.runner.close()\n agent.close()\n environment.close()\n\n self.finished_test()\n"},"avg_line_length":{"kind":"number","value":35.8805970149,"string":"35.880597"},"max_line_length":{"kind":"number","value":97,"string":"97"},"alphanum_fraction":{"kind":"number","value":0.6344980588,"string":"0.634498"},"count_classes":{"kind":"number","value":6120,"string":"6,120"},"score_classes":{"kind":"number","value":0.848585690515807,"string":"0.848586"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1276,"string":"1,276"},"score_documentation":{"kind":"number","value":0.1769273433166944,"string":"0.176927"}}},{"rowIdx":3211,"cells":{"hexsha":{"kind":"string","value":"92dadd375392924dd104471b2991098481772c78"},"size":{"kind":"number","value":132,"string":"132"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"mspray/apps/reveal/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"onaio/mspray"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b3e0f4b5855abbf0298de6b66f2e9f472f2bf838"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"mspray/apps/reveal/__init__.py"},"max_issues_repo_name":{"kind":"string","value":"onaio/mspray"},"max_issues_repo_head_hexsha":{"kind":"string","value":"b3e0f4b5855abbf0298de6b66f2e9f472f2bf838"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":76,"string":"76"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-03-15T09:37:56.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2019-05-15T12:45:51.000Z"},"max_forks_repo_path":{"kind":"string","value":"mspray/apps/reveal/__init__.py"},"max_forks_repo_name":{"kind":"string","value":"onaio/mspray"},"max_forks_repo_head_hexsha":{"kind":"string","value":"b3e0f4b5855abbf0298de6b66f2e9f472f2bf838"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-10-31T07:15:22.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-10-31T07:15:22.000Z"},"content":{"kind":"string","value":"\"\"\"init module for reveal app\"\"\"\n# pylint: disable=invalid-name\ndefault_app_config = \"mspray.apps.reveal.apps.RevealConfig\" # noqa\n"},"avg_line_length":{"kind":"number","value":33,"string":"33"},"max_line_length":{"kind":"number","value":67,"string":"67"},"alphanum_fraction":{"kind":"number","value":0.7575757576,"string":"0.757576"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":106,"string":"106"},"score_documentation":{"kind":"number","value":0.803030303030303,"string":"0.80303"}}},{"rowIdx":3212,"cells":{"hexsha":{"kind":"string","value":"92dbf0257f2cb3330104352818f4db40e27c4927"},"size":{"kind":"number","value":513,"string":"513"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"guifw/models/port.py"},"max_stars_repo_name":{"kind":"string","value":"luizerico/PyGuiFW"},"max_stars_repo_head_hexsha":{"kind":"string","value":"d79347db7d4bd9e09fbc53215d79c06ccf16bad5"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2021-02-05T02:58:19.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-02-05T02:58:19.000Z"},"max_issues_repo_path":{"kind":"string","value":"guifw/models/port.py"},"max_issues_repo_name":{"kind":"string","value":"luizerico/PyGuiFW"},"max_issues_repo_head_hexsha":{"kind":"string","value":"d79347db7d4bd9e09fbc53215d79c06ccf16bad5"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-11-09T16:10:50.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2018-11-09T16:10:50.000Z"},"max_forks_repo_path":{"kind":"string","value":"guifw/models/port.py"},"max_forks_repo_name":{"kind":"string","value":"luizerico/PyGuiFW"},"max_forks_repo_head_hexsha":{"kind":"string","value":"d79347db7d4bd9e09fbc53215d79c06ccf16bad5"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from django.db import models\nfrom django import forms\nfrom audit_log.models.managers import AuditLog\n\n\n# Create your models here.\n\n\nclass Port(models.Model):\n name = models.CharField(max_length=250)\n port = models.CharField(max_length=250)\n description = models.TextField(blank=True)\n\n audit_log = AuditLog()\n #icon = models.ImageField(upload_to='images', blank=True)\n\n def __str__(self):\n return self.name\n\n\nclass FormPort(forms.ModelForm):\n pass\n\n class Meta:\n model = Port"},"avg_line_length":{"kind":"number","value":20.52,"string":"20.52"},"max_line_length":{"kind":"number","value":61,"string":"61"},"alphanum_fraction":{"kind":"number","value":0.7076023392,"string":"0.707602"},"count_classes":{"kind":"number","value":378,"string":"378"},"score_classes":{"kind":"number","value":0.7368421052631579,"string":"0.736842"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":83,"string":"83"},"score_documentation":{"kind":"number","value":0.1617933723196881,"string":"0.161793"}}},{"rowIdx":3213,"cells":{"hexsha":{"kind":"string","value":"92dc4e7fbdb299e18e1175c0718307d433b0cb15"},"size":{"kind":"number","value":386,"string":"386"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"app/backend/arm/migrations/0002_auto_20190924_1712.py"},"max_stars_repo_name":{"kind":"string","value":"karstenv/nmp-arm"},"max_stars_repo_head_hexsha":{"kind":"string","value":"47e45f0391820000f461ab6e994e20eacfffb457"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-08-12T22:06:23.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2019-10-22T20:50:32.000Z"},"max_issues_repo_path":{"kind":"string","value":"app/backend/arm/migrations/0002_auto_20190924_1712.py"},"max_issues_repo_name":{"kind":"string","value":"karstenv/nmp-arm"},"max_issues_repo_head_hexsha":{"kind":"string","value":"47e45f0391820000f461ab6e994e20eacfffb457"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":12,"string":"12"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-07-10T18:11:31.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-02-10T08:47:57.000Z"},"max_forks_repo_path":{"kind":"string","value":"app/backend/arm/migrations/0002_auto_20190924_1712.py"},"max_forks_repo_name":{"kind":"string","value":"karstenv/nmp-arm"},"max_forks_repo_head_hexsha":{"kind":"string","value":"47e45f0391820000f461ab6e994e20eacfffb457"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":6,"string":"6"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-07-03T21:24:11.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-04-29T17:31:34.000Z"},"content":{"kind":"string","value":"# Generated by Django 2.2.5 on 2019-09-25 00:12\r\n\r\nfrom django.db import migrations\r\n\r\n\r\nclass Migration(migrations.Migration):\r\n\r\n dependencies = [\r\n ('arm', '0001_initial'),\r\n ]\r\n\r\n operations = [\r\n migrations.DeleteModel(\r\n name='CautionMessage',\r\n ),\r\n migrations.DeleteModel(\r\n name='RiskRatingValue',\r\n ),\r\n ]\r\n"},"avg_line_length":{"kind":"number","value":19.3,"string":"19.3"},"max_line_length":{"kind":"number","value":48,"string":"48"},"alphanum_fraction":{"kind":"number","value":0.5466321244,"string":"0.546632"},"count_classes":{"kind":"number","value":295,"string":"295"},"score_classes":{"kind":"number","value":0.7642487046632125,"string":"0.764249"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":100,"string":"100"},"score_documentation":{"kind":"number","value":0.25906735751295334,"string":"0.259067"}}},{"rowIdx":3214,"cells":{"hexsha":{"kind":"string","value":"92dc54efa676f164aaadbce167924df2d1df95ab"},"size":{"kind":"number","value":7112,"string":"7,112"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"webcam_demo.py"},"max_stars_repo_name":{"kind":"string","value":"taranek/tennis-stats-provider"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e95093679a194d30d0727ec8e11d44fc462f6adc"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"webcam_demo.py"},"max_issues_repo_name":{"kind":"string","value":"taranek/tennis-stats-provider"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e95093679a194d30d0727ec8e11d44fc462f6adc"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"webcam_demo.py"},"max_forks_repo_name":{"kind":"string","value":"taranek/tennis-stats-provider"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e95093679a194d30d0727ec8e11d44fc462f6adc"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import tensorflow as tf\nimport json\nimport math\nimport cv2\nimport time\nimport argparse\nimport concurrent.futures\nimport posenet\nimport keyboard\nimport sys\nimport numpy as np\nfrom threading import Thread\nfrom slugify import slugify\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model', type=int, default=101)\nparser.add_argument('--cam_id', type=int, default=0)\nparser.add_argument('--cam_width', type=int, default=1280)\nparser.add_argument('--cam_height', type=int, default=720)\nparser.add_argument('--scale_factor', type=float, default=0.7125)\nparser.add_argument('--file', type=str, default=None, help=\"Optionally use a video file instead of a live camera\")\nargs = parser.parse_args()\n\n\ndef main():\n # tf.config.threading.set_inter_op_parallelism_threads(0)\n # tf.config.threading.set_intra_op_parallelism_threads(0)\n # print(tf.config.threading.get_inter_op_parallelism_threads())\n # print(tf.config.threading.get_intra_op_parallelism_threads())\n with tf.compat.v1.Session() as sess:\n model_cfg, model_outputs = posenet.load_model(args.model, sess)\n output_stride = model_cfg['output_stride']\n\n if args.file is not None:\n cap = cv2.VideoCapture(args.file)\n else:\n cap = cv2.VideoCapture(args.cam_id)\n cap.set(3, args.cam_width)\n cap.set(4, args.cam_height)\n\n\n\n start = time.time()\n frame_count = 0\n recording = True\n # ret,frame1 = cap.read()\n # ret,frame2 = cap.read()\n file_content = []\n while True:\n # diff = cv2.absdiff(frame1,frame2)\n # gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)\n # blur = cv2.GaussianBlur(gray,(15,15),0)\n # _, thresh = cv2.threshold(blur,20,255,cv2.THRESH_BINARY)\n # dilated = cv2.dilate(thresh,None, iterations=3)\n # contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n # # if(len(contours)>0):\n # # print(\"One:\")\n # # print(dir(contours[0]))\n # # print(\"One it is.\")\n # for contour in contours:\n # (x,y,w,h) = cv2.boundingRect(contour)\n # if(cv2.contourArea(contour)>400):\n # continue\n # cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,0),2)\n # # cv2.drawContours(frame1,contours, -1,(0,255,0),2)\n # cv2.imshow(\"feed\",frame1)\n # frame1 = frame2\n # ret, frame2 = cap.read() \n input_image, display_image, output_scale = posenet.read_cap(cap, scale_factor=args.scale_factor, output_stride=output_stride)\n\n heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = sess.run(\n model_outputs,\n feed_dict={'image:0': input_image}\n ) \n \n pose_scores, keypoint_scores, keypoint_coords = posenet.decode_multi.decode_multiple_poses(\n heatmaps_result.squeeze(axis=0),\n offsets_result.squeeze(axis=0),\n displacement_fwd_result.squeeze(axis=0),\n displacement_bwd_result.squeeze(axis=0),\n output_stride=output_stride,\n max_pose_detections=1,\n min_pose_score=0.15) \n keypoint_coords *= output_scale\n\n # TODO this isn't particularly fast, use GL for drawing and display someday...\n # print(\"\\n ===================================== \\n\")\n \n \n img = posenet.draw_skel_and_kp(\n display_image, pose_scores, keypoint_scores, keypoint_coords,\n min_pose_score=0.15, min_part_score=0.15)\n \n \n\n cv2.imshow('posenet', img)\n\n frame_count += 1\n\n if(recording):\n normalize_poses(keypoint_coords)\n results = json.dumps({\n \"timestamp\":time.time() - start,\n \"pose_scores\":pose_scores.tolist(),\n \"keypoint_scores\":keypoint_scores.tolist(),\n \"scores\": keypoint_scores.size,\n \"keypoint_coords\":normalize_poses(keypoint_coords),\n \"coords\": keypoint_coords.size\n })\n file_content.append(results)\n file_content = file_content[-30:]\n \n if cv2.waitKey(1) & keyboard.is_pressed('w'):\n print('you pressed w - service it was!')\n time.sleep(0.5)\n path = \"collected/serves/\"\n filename = str(slugify(\"s-\"+str(time.time()))+\".txt\")\n x = Thread(target=save_to_file, args=(str(path+filename),str(file_content)))\n x.start() \n x.join()\n file_content = []\n\n if cv2.waitKey(1) & keyboard.is_pressed('d'):\n print('you pressed d - forehand it was!')\n time.sleep(0.5)\n path = \"collected/forehand/\"\n filename = str(slugify(\"f-\"+str(time.time()))+\".txt\")\n x = Thread(target=save_to_file, args=(str(path+filename),str(file_content)))\n x.start() \n x.join()\n file_content = []\n \n if cv2.waitKey(1) & keyboard.is_pressed('a'):\n print('you pressed a - backhand it was!')\n time.sleep(0.5)\n path = \"collected/backhand/\"\n filename = str(slugify(\"b-\"+str(time.time()))+\".txt\")\n x = Thread(target=save_to_file, args=(str(path+filename),str(file_content)))\n x.start() \n x.join()\n file_content = []\n \n\n if cv2.waitKey(1) & keyboard.is_pressed('q'):\n print('you pressed q - quitting!')\n cv2.destroyAllWindows()\n break\n \n print('Average FPS: ', frame_count / (time.time() - start))\n return 0\n\ndef my_function(toPrint):\n print(toPrint)\n\ndef save_to_file(filename,data):\n file = open(filename,'w')\n file.write(data) \n file.close() \n\n\ndef find_middle(left,right):\n x = (left[0]+right[0])/2.0\n y = (left[1]+right[1])/2.0\n return [x,y]\ndef find_distance(pointA,pointB):\n dist = math.sqrt((pointB[0] - pointA[0])**2 + (pointB[1] - pointA[1])**2) \n return dist \n\ndef normalize_poses(poses):\n leftShoulderCords = poses[0][5]\n rightShoulderCords = poses[0][6]\n middleShoulderPoint = find_middle(leftShoulderCords,rightShoulderCords)\n leftHipCords = poses[0][11]\n rightHipCords = poses[0][12]\n middleHipPoint = find_middle(leftHipCords,rightHipCords)\n\n armHipDistance = find_distance(middleHipPoint,middleShoulderPoint);\n\n normalized = []\n for pose in poses[0]:\n normalized.append(\n [(pose[0]-middleHipPoint[0])/armHipDistance,\n (pose[1]-middleHipPoint[1])/armHipDistance]\n )\n return normalized\n\nif __name__ == \"__main__\":\n main()"},"avg_line_length":{"kind":"number","value":37.829787234,"string":"37.829787"},"max_line_length":{"kind":"number","value":137,"string":"137"},"alphanum_fraction":{"kind":"number","value":0.5643982002,"string":"0.564398"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1647,"string":"1,647"},"score_documentation":{"kind":"number","value":0.23158042744656918,"string":"0.23158"}}},{"rowIdx":3215,"cells":{"hexsha":{"kind":"string","value":"92df29892405e44dded087915f2a1792a9fb1160"},"size":{"kind":"number","value":6265,"string":"6,265"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"otcextensions/tests/unit/osclient/dcs/v1/fakes.py"},"max_stars_repo_name":{"kind":"string","value":"zsoltn/python-otcextensions"},"max_stars_repo_head_hexsha":{"kind":"string","value":"4c0fa22f095ebd5f9636ae72acbae5048096822c"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"otcextensions/tests/unit/osclient/dcs/v1/fakes.py"},"max_issues_repo_name":{"kind":"string","value":"zsoltn/python-otcextensions"},"max_issues_repo_head_hexsha":{"kind":"string","value":"4c0fa22f095ebd5f9636ae72acbae5048096822c"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"otcextensions/tests/unit/osclient/dcs/v1/fakes.py"},"max_forks_repo_name":{"kind":"string","value":"zsoltn/python-otcextensions"},"max_forks_repo_head_hexsha":{"kind":"string","value":"4c0fa22f095ebd5f9636ae72acbae5048096822c"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# 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# import datetime\nimport random\nimport uuid\n\nimport mock\n\nfrom openstackclient.tests.unit import utils\nfrom otcextensions.tests.unit.osclient import test_base\n\nfrom otcextensions.sdk.dcs.v1 import backup\nfrom otcextensions.sdk.dcs.v1 import config\nfrom otcextensions.sdk.dcs.v1 import instance\nfrom otcextensions.sdk.dcs.v1 import restore\nfrom otcextensions.sdk.dcs.v1 import statistic\n\n\nclass TestDCS(utils.TestCommand):\n\n def setUp(self):\n super(TestDCS, self).setUp()\n\n self.app.client_manager.dcs = mock.Mock()\n self.client = self.app.client_manager.dcs\n\n self.client.get_instance = mock.Mock()\n self.client.find_instance = mock.Mock()\n self.client.instances = mock.Mock()\n self.client.delete_instance = mock.Mock()\n self.client.update_instance = mock.Mock()\n self.client.create_instance = mock.Mock()\n self.client.extend_instance = mock.Mock()\n\n\nclass FakeInstance(test_base.Fake):\n \"\"\"Fake one or more Instance\"\"\"\n\n @classmethod\n def generate(cls):\n object_info = {\n 'name': 'group-' + uuid.uuid4().hex,\n 'id': 'id-' + uuid.uuid4().hex,\n 'description': 'SOME description',\n 'status': random.choice(['CREATING', 'CREATEFILED',\n 'RUNNING', 'ERROR', 'STARTING',\n 'RESTARTING', 'CLOSING', 'CLOSED',\n 'EXTENDING']),\n 'engine': uuid.uuid4().hex,\n 'capacity': random.randint(1, 100),\n 'ip': uuid.uuid4().hex,\n 'port': random.randint(1, 65535),\n 'resource_spec_code': random.choice(['dcs.single_node',\n 'dcs.master_standby',\n 'dcs.cluster'\n ]),\n 'engine_version': uuid.uuid4().hex,\n 'internal_version': uuid.uuid4().hex,\n 'charging_mode': random.randint(0, 10),\n 'vpc_id': uuid.uuid4().hex,\n 'vpc_name': uuid.uuid4().hex,\n 'subnet_id': uuid.uuid4().hex,\n 'subnet_name': uuid.uuid4().hex,\n 'subnet_cidr': uuid.uuid4().hex,\n 'security_group_id': uuid.uuid4().hex,\n 'security_group_name': uuid.uuid4().hex,\n 'created_at': uuid.uuid4().hex,\n 'error_code': uuid.uuid4().hex,\n 'product_id': random.choice(['OTC_DCS_SINGLE',\n 'OTC_DCS_MS',\n 'OTC_DCS_CL']),\n 'available_zones': uuid.uuid4().hex,\n 'max_memory': random.randint(0, 10),\n 'used_memory': random.randint(0, 10),\n 'user_id': uuid.uuid4().hex,\n 'user_name': uuid.uuid4().hex,\n 'order_id': uuid.uuid4().hex,\n 'maintain_begin': uuid.uuid4().hex,\n 'maintain_end': uuid.uuid4().hex,\n }\n obj = instance.Instance.existing(**object_info)\n return obj\n\n\nclass FakeStatistic(test_base.Fake):\n \"\"\"Fake one or more Statistic\"\"\"\n\n @classmethod\n def generate(cls):\n object_info = {\n 'instance_id': 'instance_id-' + uuid.uuid4().hex,\n 'max_memory': random.randint(1, 65535),\n 'used_memory': random.randint(1, 65535),\n 'cmd_get_count': random.randint(1, 65535),\n 'cmd_set_count': random.randint(1, 65535),\n 'used_cpu': 'cpu-' + uuid.uuid4().hex,\n 'input_kbps': 'input-' + uuid.uuid4().hex,\n 'output_kbps': 'output-' + uuid.uuid4().hex,\n\n }\n obj = statistic.Statistic.existing(**object_info)\n return obj\n\n\nclass FakeBackup(test_base.Fake):\n \"\"\"Fake one or more Backup\"\"\"\n\n @classmethod\n def generate(cls):\n object_info = {\n 'instance_id': 'instance_id-' + uuid.uuid4().hex,\n 'id': 'id-' + uuid.uuid4().hex,\n 'size': random.randint(1, 65535),\n 'period': uuid.uuid4().hex,\n 'description': uuid.uuid4().hex,\n 'progress': uuid.uuid4().hex,\n 'created_at': uuid.uuid4().hex,\n 'updated_at': uuid.uuid4().hex,\n 'type': uuid.uuid4().hex,\n 'name': uuid.uuid4().hex,\n 'error_code': uuid.uuid4().hex,\n 'is_restorable': True,\n\n }\n obj = backup.Backup.existing(**object_info)\n return obj\n\n\nclass FakeRestore(test_base.Fake):\n \"\"\"Fake one or more Restore\"\"\"\n\n @classmethod\n def generate(cls):\n object_info = {\n 'instance_id': 'instance_id-' + uuid.uuid4().hex,\n 'max_memory': random.randint(1, 65535),\n 'used_memory': random.randint(1, 65535),\n 'cmd_get_count': random.randint(1, 65535),\n 'cmd_set_count': random.randint(1, 65535),\n 'used_cpu': 'cpu-' + uuid.uuid4().hex,\n 'input_kbps': 'input-' + uuid.uuid4().hex,\n 'output_kbps': 'output-' + uuid.uuid4().hex\n }\n obj = restore.Restore.existing(**object_info)\n return obj\n\n\nclass FakeConfig(test_base.Fake):\n \"\"\"Fake one or more Config\"\"\"\n\n @classmethod\n def generate(cls):\n object_info = {\n 'instance_id': 'instance_id-' + uuid.uuid4().hex,\n 'id': uuid.uuid4().hex,\n 'name': uuid.uuid4().hex,\n 'value': uuid.uuid4().hex,\n 'value_type': uuid.uuid4().hex,\n 'value_range': uuid.uuid4().hex,\n 'default_value': uuid.uuid4().hex,\n 'description': uuid.uuid4().hex\n }\n obj = config.Config.existing(**object_info)\n return obj\n"},"avg_line_length":{"kind":"number","value":36.0057471264,"string":"36.005747"},"max_line_length":{"kind":"number","value":77,"string":"77"},"alphanum_fraction":{"kind":"number","value":0.5551476457,"string":"0.555148"},"count_classes":{"kind":"number","value":5298,"string":"5,298"},"score_classes":{"kind":"number","value":0.8456504389465284,"string":"0.84565"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":4388,"string":"4,388"},"score_decorators":{"kind":"number","value":0.7003990422984836,"string":"0.700399"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1804,"string":"1,804"},"score_documentation":{"kind":"number","value":0.2879489225857941,"string":"0.287949"}}},{"rowIdx":3216,"cells":{"hexsha":{"kind":"string","value":"92df7af4ff3a17d2d4ea5d2cff4ae315569f3502"},"size":{"kind":"number","value":830,"string":"830"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"tests/dummy_repo/tvm/python/tvm/api.py"},"max_stars_repo_name":{"kind":"string","value":"csullivan/ffi-navigator"},"max_stars_repo_head_hexsha":{"kind":"string","value":"ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":148,"string":"148"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-12-28T19:02:17.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-27T07:30:13.000Z"},"max_issues_repo_path":{"kind":"string","value":"tests/dummy_repo/tvm/python/tvm/api.py"},"max_issues_repo_name":{"kind":"string","value":"csullivan/ffi-navigator"},"max_issues_repo_head_hexsha":{"kind":"string","value":"ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":21,"string":"21"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-12-28T17:29:24.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-11-24T09:59:35.000Z"},"max_forks_repo_path":{"kind":"string","value":"tests/dummy_repo/tvm/python/tvm/api.py"},"max_forks_repo_name":{"kind":"string","value":"csullivan/ffi-navigator"},"max_forks_repo_head_hexsha":{"kind":"string","value":"ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":17,"string":"17"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-12-29T01:46:13.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-01-10T09:56:46.000Z"},"content":{"kind":"string","value":"from ._ffi.base import string_types\nfrom ._ffi.object import register_object, Object\nfrom ._ffi.node import register_node, NodeBase\nfrom ._ffi.node import convert_to_node as _convert_to_node\nfrom ._ffi.node_generic import _scalar_type_inference\nfrom ._ffi.function import Function\nfrom ._ffi.function import _init_api, register_func, get_global_func, extract_ext_funcs\nfrom ._ffi.function import convert_to_tvm_func as _convert_tvm_func\nfrom ._ffi.runtime_ctypes import TVMType\nfrom . import _api_internal\nfrom . import make as _make\nfrom . import expr as _expr\nfrom . import tensor as _tensor\nfrom . import schedule as _schedule\nfrom . import container as _container\nfrom . import tag as _tag\n\nint8 = \"int8\"\nint32 = \"int32\"\nfloat32 = \"float32\"\nhandle = \"handle\"\n\n\ndef min_value(dtype):\n return _api_internal._min_value(dtype)\n"},"avg_line_length":{"kind":"number","value":31.9230769231,"string":"31.923077"},"max_line_length":{"kind":"number","value":87,"string":"87"},"alphanum_fraction":{"kind":"number","value":0.8120481928,"string":"0.812048"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":30,"string":"30"},"score_documentation":{"kind":"number","value":0.03614457831325301,"string":"0.036145"}}},{"rowIdx":3217,"cells":{"hexsha":{"kind":"string","value":"92dfa8870f87fbcfb31691bd442140d0c802358d"},"size":{"kind":"number","value":4121,"string":"4,121"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"torchattacks/attacks/multiattack.py"},"max_stars_repo_name":{"kind":"string","value":"Harry24k/adversarial-attacks-pytorch"},"max_stars_repo_head_hexsha":{"kind":"string","value":"bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":782,"string":"782"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-03-28T01:56:36.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-31T14:54:02.000Z"},"max_issues_repo_path":{"kind":"string","value":"torchattacks/attacks/multiattack.py"},"max_issues_repo_name":{"kind":"string","value":"Harry24k/adversarial-attacks-pytorch"},"max_issues_repo_head_hexsha":{"kind":"string","value":"bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":48,"string":"48"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-04-18T23:06:30.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-24T01:54:50.000Z"},"max_forks_repo_path":{"kind":"string","value":"torchattacks/attacks/multiattack.py"},"max_forks_repo_name":{"kind":"string","value":"Harry24k/adversarial-attacks-pytorch"},"max_forks_repo_head_hexsha":{"kind":"string","value":"bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":197,"string":"197"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-03-31T05:21:02.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-31T15:24:29.000Z"},"content":{"kind":"string","value":"import copy\nimport torch\n\nfrom ..attack import Attack\n\n\nclass MultiAttack(Attack):\n r\"\"\"\n MultiAttack is a class to attack a model with various attacks agains same images and labels.\n\n Arguments:\n model (nn.Module): model to attack.\n attacks (list): list of attacks.\n\n Examples::\n >>> atk1 = torchattacks.PGD(model, eps=8/255, alpha=2/255, iters=40, random_start=True)\n >>> atk2 = torchattacks.PGD(model, eps=8/255, alpha=2/255, iters=40, random_start=True)\n >>> atk = torchattacks.MultiAttack([atk1, atk2])\n >>> adv_images = attack(images, labels)\n\n \"\"\"\n def __init__(self, attacks, verbose=False):\n\n # Check validity\n ids = []\n for attack in attacks:\n ids.append(id(attack.model))\n\n if len(set(ids)) != 1:\n raise ValueError(\"At least one of attacks is referencing a different model.\")\n\n super().__init__(\"MultiAttack\", attack.model)\n self.attacks = attacks\n self.verbose = verbose\n self._accumulate_multi_atk_records = False\n self._multi_atk_records = [0.0]\n self._supported_mode = ['default']\n\n def forward(self, images, labels):\n r\"\"\"\n Overridden.\n \"\"\"\n batch_size = images.shape[0]\n fails = torch.arange(batch_size).to(self.device)\n final_images = images.clone().detach().to(self.device)\n labels = labels.clone().detach().to(self.device)\n\n multi_atk_records = [batch_size]\n\n for _, attack in enumerate(self.attacks):\n adv_images = attack(images[fails], labels[fails])\n\n outputs = self.model(adv_images)\n _, pre = torch.max(outputs.data, 1)\n\n corrects = (pre == labels[fails])\n wrongs = ~corrects\n\n succeeds = torch.masked_select(fails, wrongs)\n succeeds_of_fails = torch.masked_select(torch.arange(fails.shape[0]).to(self.device), wrongs)\n\n final_images[succeeds] = adv_images[succeeds_of_fails]\n\n fails = torch.masked_select(fails, corrects)\n multi_atk_records.append(len(fails))\n\n if len(fails) == 0:\n break\n\n if self.verbose:\n print(self._return_sr_record(multi_atk_records))\n\n if self._accumulate_multi_atk_records:\n self._update_multi_atk_records(multi_atk_records)\n\n return final_images\n\n def _clear_multi_atk_records(self):\n self._multi_atk_records = [0.0]\n\n def _covert_to_success_rates(self, multi_atk_records):\n sr = [((1-multi_atk_records[i]/multi_atk_records[0])*100) for i in range(1, len(multi_atk_records))]\n return sr\n\n def _return_sr_record(self, multi_atk_records):\n sr = self._covert_to_success_rates(multi_atk_records)\n return \"Attack success rate: \"+\" | \".join([\"%2.2f %%\"%item for item in sr])\n\n def _update_multi_atk_records(self, multi_atk_records):\n for i, item in enumerate(multi_atk_records):\n self._multi_atk_records[i] += item\n\n def save(self, data_loader, save_path=None, verbose=True, return_verbose=False):\n r\"\"\"\n Overridden.\n \"\"\"\n self._clear_multi_atk_records()\n verbose = self.verbose\n self.verbose = False\n self._accumulate_multi_atk_records = True\n\n for i, attack in enumerate(self.attacks):\n self._multi_atk_records.append(0.0)\n\n rob_acc, l2, elapsed_time = super().save(data_loader, save_path, verbose, return_verbose)\n sr = self._covert_to_success_rates(self._multi_atk_records)\n\n self._clear_multi_atk_records()\n self._accumulate_multi_atk_records = False\n self.verbose = verbose\n\n if return_verbose:\n return rob_acc, sr, l2, elapsed_time\n\n def _save_print(self, progress, rob_acc, l2, elapsed_time, end):\n r\"\"\"\n Overridden.\n \"\"\"\n print(\"- Save progress: %2.2f %% / Robust accuracy: %2.2f %%\"%(progress, rob_acc)+\\\n \" / \"+self._return_sr_record(self._multi_atk_records)+\\\n ' / L2: %1.5f (%2.3f it/s) \\t'%(l2, elapsed_time), end=end)\n"},"avg_line_length":{"kind":"number","value":33.7786885246,"string":"33.778689"},"max_line_length":{"kind":"number","value":108,"string":"108"},"alphanum_fraction":{"kind":"number","value":0.6260616355,"string":"0.626062"},"count_classes":{"kind":"number","value":4064,"string":"4,064"},"score_classes":{"kind":"number","value":0.9861684057267653,"string":"0.986168"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":857,"string":"857"},"score_documentation":{"kind":"number","value":0.20795923319582627,"string":"0.207959"}}},{"rowIdx":3218,"cells":{"hexsha":{"kind":"string","value":"92e0877363cacd633cbbf12e0ee4175cb9564598"},"size":{"kind":"number","value":2627,"string":"2,627"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py"},"max_stars_repo_name":{"kind":"string","value":"wotchin/openGauss-server"},"max_stars_repo_head_hexsha":{"kind":"string","value":"ebd92e92b0cfd76b121d98e4c57a22d334573159"},"max_stars_repo_licenses":{"kind":"list like","value":["MulanPSL-1.0"],"string":"[\n \"MulanPSL-1.0\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-06-30T15:00:50.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-06-30T15:00:50.000Z"},"max_issues_repo_path":{"kind":"string","value":"src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py"},"max_issues_repo_name":{"kind":"string","value":"wotchin/openGauss-server"},"max_issues_repo_head_hexsha":{"kind":"string","value":"ebd92e92b0cfd76b121d98e4c57a22d334573159"},"max_issues_repo_licenses":{"kind":"list like","value":["MulanPSL-1.0"],"string":"[\n \"MulanPSL-1.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py"},"max_forks_repo_name":{"kind":"string","value":"wotchin/openGauss-server"},"max_forks_repo_head_hexsha":{"kind":"string","value":"ebd92e92b0cfd76b121d98e4c57a22d334573159"},"max_forks_repo_licenses":{"kind":"list like","value":["MulanPSL-1.0"],"string":"[\n \"MulanPSL-1.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# -*- coding:utf-8 -*-\n# Copyright (c) 2020 Huawei Technologies Co.,Ltd.\n#\n# openGauss is licensed under Mulan PSL v2.\n# You can use this software according to the terms\n# and conditions of the Mulan PSL v2.\n# You may obtain a copy of Mulan PSL v2 at:\n#\n# http://license.coscl.org.cn/MulanPSL2\n#\n# THIS SOFTWARE IS PROVIDED ON AN \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OF ANY KIND,\n# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,\n# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.\n# See the Mulan PSL v2 for more details.\n# ----------------------------------------------------------------------------\nimport subprocess\nfrom gspylib.inspection.common.CheckItem import BaseItem\nfrom gspylib.inspection.common.CheckResult import ResultStatus\n\n\nclass CheckPortConflict(BaseItem):\n def __init__(self):\n super(CheckPortConflict, self).__init__(self.__class__.__name__)\n\n def doCheck(self):\n cmd = \"netstat -apn | grep 'tcp' \" \\\n \"| grep 'LISTEN'| awk -F ' ' '$4 ~ /25[0-9][0-9][0-9]/'\"\n (status, output) = subprocess.getstatusoutput(cmd)\n if (status != 0):\n self.result.rst = ResultStatus.NG\n self.result.val = \"Failed to excuted commands: %s\\noutput:%s \" % (\n cmd, output)\n else:\n if (output.strip() == \"\"):\n self.result.rst = ResultStatus.OK\n self.result.val = \"ports is normal\"\n else:\n self.result.rst = ResultStatus.NG\n self.result.val = output\n self.result.raw = \"checked ports: (25000-26000)\\n\" + output\n\n def doSet(self):\n pidList = []\n cmd = \"netstat -apn| grep 'tcp'\" \\\n \"| grep 'LISTEN'| awk -F ' ' '$4 ~ /25[0-9][0-9][0-9]/'\" \\\n \"| awk '{print $NF}'\"\n (status, output) = subprocess.getstatusoutput(cmd)\n if (status == 0 and output != \"\"):\n for line in output.split('\\n'):\n if (line.find('/') > 0):\n pid = line.split('/')[0].strip()\n if (pid.isdigit()):\n pidList.append(pid)\n if (pidList):\n cmd = \"kill -9\"\n for pid in pidList:\n cmd += \" %s\" % pid\n (status, output) = subprocess.getstatusoutput(cmd)\n if (status != \"\"):\n self.result.val = \"Failed to kill process.Error:%s\\n\" % output\n self.result.val += \"The cmd is %s \" % cmd\n else:\n self.result.val = \\\n \"Successfully killed the process with occupies the port.\\n\"\n"},"avg_line_length":{"kind":"number","value":39.803030303,"string":"39.80303"},"max_line_length":{"kind":"number","value":79,"string":"79"},"alphanum_fraction":{"kind":"number","value":0.5344499429,"string":"0.53445"},"count_classes":{"kind":"number","value":1851,"string":"1,851"},"score_classes":{"kind":"number","value":0.7046060144651694,"string":"0.704606"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1039,"string":"1,039"},"score_documentation":{"kind":"number","value":0.3955081842405786,"string":"0.395508"}}},{"rowIdx":3219,"cells":{"hexsha":{"kind":"string","value":"92e16c1fa8d877e82eb805100d17b73907afb25e"},"size":{"kind":"number","value":646,"string":"646"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"_scripts/_build.py"},"max_stars_repo_name":{"kind":"string","value":"dfreeman06/wxyz"},"max_stars_repo_head_hexsha":{"kind":"string","value":"663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2021-06-20T12:21:27.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-06-20T12:21:27.000Z"},"max_issues_repo_path":{"kind":"string","value":"_scripts/_build.py"},"max_issues_repo_name":{"kind":"string","value":"nrbgt/wxyz"},"max_issues_repo_head_hexsha":{"kind":"string","value":"663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"_scripts/_build.py"},"max_forks_repo_name":{"kind":"string","value":"nrbgt/wxyz"},"max_forks_repo_head_hexsha":{"kind":"string","value":"663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import subprocess\nimport sys\n\nfrom . import ROOT, PY_SRC, _run, PY, DIST\n\n\nCONDA_ORDER = [\n \"core\",\n \"html\",\n \"lab\",\n \"datagrid\",\n \"svg\",\n \"tpl-jjinja\"\n \"yaml\"\n]\n\nCONDA_BUILD_ARGS = [\n \"conda-build\", \"-c\", \"conda-forge\", \"--output-folder\", DIST / \"conda-bld\",\n]\n\n\nif __name__ == \"__main__\":\n for pkg in PY_SRC.glob(\"wxyz_*\"):\n _run([PY, \"setup.py\", \"sdist\", \"--dist-dir\", DIST / \"sdist\"], cwd=str(pkg))\n\n try:\n _run([*CONDA_BUILD_ARGS, \"--skip-existing\", \".\"], cwd=ROOT / \"recipes\")\n except:\n for pkg in CONDA_ORDER:\n _run([*CONDA_BUILD_ARGS, f\"wxyz-{pkg}\"], cwd=ROOT / \"recipes\")\n"},"avg_line_length":{"kind":"number","value":20.8387096774,"string":"20.83871"},"max_line_length":{"kind":"number","value":83,"string":"83"},"alphanum_fraction":{"kind":"number","value":0.560371517,"string":"0.560372"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":213,"string":"213"},"score_documentation":{"kind":"number","value":0.32972136222910214,"string":"0.329721"}}},{"rowIdx":3220,"cells":{"hexsha":{"kind":"string","value":"92e1c91fec4c34f39e9e2622024fad4489b61749"},"size":{"kind":"number","value":5279,"string":"5,279"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"scripts/C189/C189Checkin.py"},"max_stars_repo_name":{"kind":"string","value":"xiaopowanyi/py_scripts"},"max_stars_repo_head_hexsha":{"kind":"string","value":"29f240800eefd6e0f91fd098c35ac3c451172ff8"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-11-14T05:42:49.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-11-14T05:43:13.000Z"},"max_issues_repo_path":{"kind":"string","value":"scripts/C189/C189Checkin.py"},"max_issues_repo_name":{"kind":"string","value":"J220541674/py_scripts"},"max_issues_repo_head_hexsha":{"kind":"string","value":"2b72e23041392a2e5f0a7305d7e9802054978384"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"scripts/C189/C189Checkin.py"},"max_forks_repo_name":{"kind":"string","value":"J220541674/py_scripts"},"max_forks_repo_head_hexsha":{"kind":"string","value":"2b72e23041392a2e5f0a7305d7e9802054978384"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import requests, time, re, rsa, json, base64\nfrom urllib import parse\n\ns = requests.Session()\n\nusername = \"\"\npassword = \"\"\n\nif(username == \"\" or password == \"\"):\n username = input(\"账号:\")\n password = input(\"密码:\")\n\ndef main():\n login(username, password)\n rand = str(round(time.time()*1000))\n surl = f'https://api.cloud.189.cn/mkt/userSign.action?rand={rand}&clientType=TELEANDROID&version=8.6.3&model=SM-G930K'\n url = f'https://m.cloud.189.cn/v2/drawPrizeMarketDetails.action?taskId=TASK_SIGNIN&activityId=ACT_SIGNIN'\n url2 = f'https://m.cloud.189.cn/v2/drawPrizeMarketDetails.action?taskId=TASK_SIGNIN_PHOTOS&activityId=ACT_SIGNIN'\n headers = {\n 'User-Agent':'Mozilla/5.0 (Linux; Android 5.1.1; SM-G930K Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/74.0.3729.136 Mobile Safari/537.36 Ecloud/8.6.3 Android/22 clientId/355325117317828 clientModel/SM-G930K imsi/460071114317824 clientChannelId/qq proVersion/1.0.6',\n \"Referer\" : \"https://m.cloud.189.cn/zhuanti/2016/sign/index.jsp?albumBackupOpened=1\",\n \"Host\" : \"m.cloud.189.cn\",\n \"Accept-Encoding\" : \"gzip, deflate\",\n }\n response = s.get(surl,headers=headers)\n netdiskBonus = response.json()['netdiskBonus']\n if(response.json()['isSign'] == \"false\"):\n print(f\"未签到,签到获得{netdiskBonus}M空间\")\n else:\n print(f\"已经签到过了,签到获得{netdiskBonus}M空间\")\n headers = {\n 'User-Agent':'Mozilla/5.0 (Linux; Android 5.1.1; SM-G930K Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/74.0.3729.136 Mobile Safari/537.36 Ecloud/8.6.3 Android/22 clientId/355325117317828 clientModel/SM-G930K imsi/460071114317824 clientChannelId/qq proVersion/1.0.6',\n \"Referer\" : \"https://m.cloud.189.cn/zhuanti/2016/sign/index.jsp?albumBackupOpened=1\",\n \"Host\" : \"m.cloud.189.cn\",\n \"Accept-Encoding\" : \"gzip, deflate\",\n }\n response = s.get(url,headers=headers)\n try:\n if (\"errorCode\" in response.text):\n print(response.json()['errorCode'])\n elif (response.json().has_key('description')):\n description = response.json()['description']\n print(f\"抽奖获得{description}\")\n except:\n print(f\"抽奖1完成,解析时失败\")\n \n try:\n response2 = s.get(url2,headers=headers)\n if (\"errorCode\" in response2.text):\n print(response.json()['errorCode'])\n elif (response2.json().has_key('description')):\n description = response2.json()['description']\n print(f\"抽奖2获得{description}\")\n except:\n print(f\"抽奖2完成,解析时失败\")\n\nBI_RM = list(\"0123456789abcdefghijklmnopqrstuvwxyz\")\ndef int2char(a):\n return BI_RM[a]\n\nb64map = \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\"\ndef b64tohex(a):\n d = \"\"\n e = 0\n c = 0\n for i in range(len(a)):\n if list(a)[i] != \"=\":\n v = b64map.index(list(a)[i])\n if 0 == e:\n e = 1\n d += int2char(v >> 2)\n c = 3 & v\n elif 1 == e:\n e = 2\n d += int2char(c << 2 | v >> 4)\n c = 15 & v\n elif 2 == e:\n e = 3\n d += int2char(c)\n d += int2char(v >> 2)\n c = 3 & v\n else:\n e = 0\n d += int2char(c << 2 | v >> 4)\n d += int2char(15 & v)\n if e == 1:\n d += int2char(c << 2)\n return d\n\n\ndef rsa_encode(j_rsakey, string):\n rsa_key = f\"-----BEGIN PUBLIC KEY-----\\n{j_rsakey}\\n-----END PUBLIC KEY-----\"\n pubkey = rsa.PublicKey.load_pkcs1_openssl_pem(rsa_key.encode())\n result = b64tohex((base64.b64encode(rsa.encrypt(f'{string}'.encode(), pubkey))).decode())\n return result\n\ndef calculate_md5_sign(params):\n return hashlib.md5('&'.join(sorted(params.split('&'))).encode('utf-8')).hexdigest()\n\ndef login(username, password):\n url = \"https://cloud.189.cn/udb/udb_login.jsp?pageId=1&redirectURL=/main.action\"\n r = s.get(url)\n captchaToken = re.findall(r\"captchaToken' value='(.+?)'\", r.text)[0]\n lt = re.findall(r'lt = \"(.+?)\"', r.text)[0]\n returnUrl = re.findall(r\"returnUrl = '(.+?)'\", r.text)[0]\n paramId = re.findall(r'paramId = \"(.+?)\"', r.text)[0]\n j_rsakey = re.findall(r'j_rsaKey\" value=\"(\\S+)\"', r.text, re.M)[0]\n s.headers.update({\"lt\": lt})\n\n username = rsa_encode(j_rsakey, username)\n password = rsa_encode(j_rsakey, password)\n url = \"https://open.e.189.cn/api/logbox/oauth2/loginSubmit.do\"\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:74.0) Gecko/20100101 Firefox/76.0',\n 'Referer': 'https://open.e.189.cn/',\n }\n data = {\n \"appKey\": \"cloud\",\n \"accountType\": '01',\n \"userName\": f\"{{RSA}}{username}\",\n \"password\": f\"{{RSA}}{password}\",\n \"validateCode\": \"\",\n \"captchaToken\": captchaToken,\n \"returnUrl\": returnUrl,\n \"mailSuffix\": \"@189.cn\",\n \"paramId\": paramId\n }\n r = s.post(url, data=data, headers=headers, timeout=5)\n if(r.json()['result'] == 0):\n print(r.json()['msg'])\n else:\n print(r.json()['msg'])\n redirect_url = r.json()['toUrl']\n r = s.get(redirect_url)\n return s\n \n\nif __name__ == \"__main__\":\n main()\n"},"avg_line_length":{"kind":"number","value":37.7071428571,"string":"37.707143"},"max_line_length":{"kind":"number","value":305,"string":"305"},"alphanum_fraction":{"kind":"number","value":0.5866641409,"string":"0.586664"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2331,"string":"2,331"},"score_documentation":{"kind":"number","value":0.4322269608752086,"string":"0.432227"}}},{"rowIdx":3221,"cells":{"hexsha":{"kind":"string","value":"92e2096dcbe8b31e8b6213b7078b62e4efd23dd0"},"size":{"kind":"number","value":3318,"string":"3,318"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"Mmint/CGratio.py"},"max_stars_repo_name":{"kind":"string","value":"lijiacd985/Mplot"},"max_stars_repo_head_hexsha":{"kind":"string","value":"adea07aa78a5495cf3551618f6ec2c08fa7c1029"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":5,"string":"5"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2018-07-02T16:33:23.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-03-23T00:32:31.000Z"},"max_issues_repo_path":{"kind":"string","value":"Mmint/CGratio.py"},"max_issues_repo_name":{"kind":"string","value":"lijiacd985/Mplot"},"max_issues_repo_head_hexsha":{"kind":"string","value":"adea07aa78a5495cf3551618f6ec2c08fa7c1029"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2017-09-19T19:46:11.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-02-28T05:00:49.000Z"},"max_forks_repo_path":{"kind":"string","value":"Mmint/CGratio.py"},"max_forks_repo_name":{"kind":"string","value":"lijiacd985/Mplot"},"max_forks_repo_head_hexsha":{"kind":"string","value":"adea07aa78a5495cf3551618f6ec2c08fa7c1029"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":4,"string":"4"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2017-11-16T15:26:24.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-02-13T16:25:25.000Z"},"content":{"kind":"string","value":"import subprocess\nfrom .Genome_fasta import get_fasta\nimport matplotlib\nmatplotlib.use('Agg')\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport pysam\n\ndef run(parser):\n args = parser.parse_args()\n bases,chrs = get_fasta(args.genome)\n l={}\n for c in chrs:\n l[c]=len(bases[c])\n chrs = set(chrs)\n #p = subprocess.Popen('bamToBed -i '+args.bamfile,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n reads_num=0\n reads_cg_num=[0,0,0] #CG,cg,Cg\n cgnum_per_read=[]\n\n with pysam.AlignmentFile(args.bamfile) as f:\n for line in f:\n #t = line.decode('utf-8').strip().split()\n chr = line.reference_name#t[0]\n start= line.reference_start\n end= line.reference_end\n strand= not line.is_reverse # True +strand; False -strand\n if not chr in chrs: continue\n end=min(end+1,l[chr])\n reads_num+=1\n if strand:#=='+':\n cg=[bases[chr].count('CG',start,end)+bases[chr].count('Cg',start,end),bases[chr].count('cG',start,end)+bases[chr].count('cg',start,end)]\n else:\n cg=[bases[chr].count('GC',start,end)+bases[chr].count('gC',start,end),bases[chr].count('Gc',start,end)+bases[chr].count('gc',start,end)]\n#We need to consider strand specific situation.\n#'+' strand we have CG but '-' we should count 'GC'.\n #print cg\n# for i in range(1,ls):\n# r2=read[i]\n# r1=read[i-1]\n# if 'G'==r2 or 'g'==r2:\n# if 'C'==r1: cg[0]+=1\n# if 'c'==r1: cg[1]+=1\n\n#count = int(cg[0]>0)+int(cg[1]>0)\n if cg[0]+cg[1]==0: continue\n#print cg\n cgnum_per_read.append(sum(cg))\n if cg[0]>0 and cg[1]>0:\n reads_cg_num[2]+=1\n continue\n if cg[0]>0:\n reads_cg_num[0]+=1\n else: \n reads_cg_num[1]+=1\n\n #print reads_cg_num\n #print reads_num\n\n plt.figure()\n plt.subplot(211)\n labels = ['noCG','NonRepeat CG','Repeat cg','CGcg mix']\n colors = ['r','b','g','y']\n explode=(0.05,0,0,0)\n sizes=[reads_num-sum(reads_cg_num)]+reads_cg_num\n patches,l_text,p_text = plt.pie(sizes,explode=explode,labels=labels,colors=colors, labeldistance = 1.1,autopct = '%3.1f%%',shadow = False, startangle = 90,pctdistance = 0.6)\n\n plt.axis('equal')\n #plt.legend(loc=2,bbox_to_anchor=(0, 0))\n ax=plt.subplot(212)\n t=np.zeros(20)\n for num in cgnum_per_read:\n t[min(num-1,19)]+=1\n labels = list(map(str,np.arange(1,20)))+['20+']\n #print(t)\n t = (np.array(t).astype(float)/sum(reads_cg_num))*100\n plt.bar(np.arange(20),t)\n ax.set_xticks(np.arange(20))\n ax.set_xticklabels(labels)\n ax.set_ylabel('Percentage of reads including CG')\n ax.set_xlabel('CG number per read')\n plt.text(4,max(t)+4,'All reads including CG site: '+str(sum(reads_cg_num)))\n #print args.output+'.pdf'\n plt.savefig(args.output+'.pdf')\n\nif __name__==\"__main__\":\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('-b','--bamfile',help=\"bam file name\", metavar=\"FILE\")\n parser.add_argument('-g','--genome',help=\"Genome fasta file path\")\n parser.add_argument('-o','--output',help=\"pie figure's filename\")\n run(parser)\n\n\n\n\n\n"},"avg_line_length":{"kind":"number","value":33.8571428571,"string":"33.857143"},"max_line_length":{"kind":"number","value":177,"string":"177"},"alphanum_fraction":{"kind":"number","value":0.5898131404,"string":"0.589813"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":968,"string":"968"},"score_documentation":{"kind":"number","value":0.2917420132610006,"string":"0.291742"}}},{"rowIdx":3222,"cells":{"hexsha":{"kind":"string","value":"92e278de46c7d8190178a6e51a0f4e234995f14e"},"size":{"kind":"number","value":1536,"string":"1,536"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/furo/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"sethmlarson/furo"},"max_stars_repo_head_hexsha":{"kind":"string","value":"1257d884dae9040248380595e06d7d2a1e6eba39"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"A clean customisable Sphinx documentation theme.\"\"\"\n\n__version__ = \"2020.9.8.beta2\"\n\nfrom pathlib import Path\n\nfrom .body import wrap_tables\nfrom .code import get_pygments_style_colors\nfrom .navigation import get_navigation_tree\nfrom .toc import should_hide_toc\n\n\ndef _html_page_context(app, pagename, templatename, context, doctree):\n if app.config.html_theme != \"furo\":\n return\n\n # Custom Navigation Tree (adds checkboxes and labels)\n toctree = context.get(\"toctree\", lambda **kwargs: \"\")\n toctree_html = toctree(\n collapse=False, titles_only=True, maxdepth=-1, includehidden=True\n )\n context[\"furo_navigation_tree\"] = get_navigation_tree(toctree_html)\n\n # Custom \"should hide ToC\" logic\n context[\"furo_hide_toc\"] = should_hide_toc(context.get(\"toc\", \"\"))\n # Allow for hiding toc via ToC in page-wide metadata.\n if \"hide-toc\" in (context.get(\"meta\", None) or {}):\n context[\"furo_hide_toc\"] = True\n\n # Inject information about styles\n colors = get_pygments_style_colors(\n app.builder.highlighter.formatter_args[\"style\"],\n fallbacks={\"foreground\": \"#000000\", \"background\": \"#FFFFFF\"},\n )\n context[\"furo_pygments\"] = colors\n\n # Patch the content\n if \"body\" in context:\n context[\"body\"] = wrap_tables(context[\"body\"])\n\n\ndef setup(app):\n \"\"\"Entry point for sphinx theming.\"\"\"\n theme_path = (Path(__file__).parent / \"theme\").resolve()\n app.add_html_theme(\"furo\", str(theme_path))\n\n app.connect(\"html-page-context\", _html_page_context)\n"},"avg_line_length":{"kind":"number","value":32,"string":"32"},"max_line_length":{"kind":"number","value":73,"string":"73"},"alphanum_fraction":{"kind":"number","value":0.6940104167,"string":"0.69401"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":503,"string":"503"},"score_documentation":{"kind":"number","value":0.3274739583333333,"string":"0.327474"}}},{"rowIdx":3223,"cells":{"hexsha":{"kind":"string","value":"92e3577604795bc43851e0afe7af80fe0fe26bbf"},"size":{"kind":"number","value":2059,"string":"2,059"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"experiments/mix_down.py"},"max_stars_repo_name":{"kind":"string","value":"fretboardfreak/potty_oh"},"max_stars_repo_head_hexsha":{"kind":"string","value":"70b752c719576c0975e1d2af5aca2fc7abc8abcc"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"experiments/mix_down.py"},"max_issues_repo_name":{"kind":"string","value":"fretboardfreak/potty_oh"},"max_issues_repo_head_hexsha":{"kind":"string","value":"70b752c719576c0975e1d2af5aca2fc7abc8abcc"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2016-05-04T03:51:36.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2016-05-16T19:08:02.000Z"},"max_forks_repo_path":{"kind":"string","value":"experiments/mix_down.py"},"max_forks_repo_name":{"kind":"string","value":"fretboardfreak/potty_oh"},"max_forks_repo_head_hexsha":{"kind":"string","value":"70b752c719576c0975e1d2af5aca2fc7abc8abcc"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python3\n# Copyright 2016 Curtis Sand\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\"\"\"A test for what happens when two waveforms are averaged together.\"\"\"\n\nfrom potty_oh import common\nfrom potty_oh.wav_file import wav_file_context\nfrom potty_oh.waveform import mix_down\nfrom potty_oh.signal_generator import Generator\nfrom potty_oh.music.pitch import Key\nfrom potty_oh.music.interval import Interval\n\n\ndef main():\n parser = common.get_cmd_line_parser(description=__doc__)\n common.ParserArguments.filename(parser)\n common.ParserArguments.length(parser)\n common.ParserArguments.framerate(parser)\n common.ParserArguments.set_defaults(parser, type='constant',\n length=2.0)\n args = parser.parse_args()\n common.defaults.framerate = args.framerate\n\n sg = Generator(length=args.length, verbose=args.debug)\n\n key = Key()\n unison = sg.sin_constant(key.interval(Interval.unison))\n maj_third = sg.sin_constant(key.interval(Interval.major_third))\n min_third = sg.sin_constant(key.interval(Interval.minor_third))\n fifth = sg.sin_constant(key.interval(Interval.fifth))\n\n powerchord = unison.mix_down(fifth)\n\n maj_triad = powerchord.mix_down(maj_third)\n min_triad = mix_down(powerchord, min_third)\n\n with wav_file_context(args.filename) as fout:\n fout.write_frames(powerchord.frames)\n fout.write_frames(maj_triad.frames)\n fout.write_frames(min_triad.frames)\n\n return 0\n\n\nif __name__ == \"__main__\":\n common.call_main(main)\n"},"avg_line_length":{"kind":"number","value":34.8983050847,"string":"34.898305"},"max_line_length":{"kind":"number","value":78,"string":"78"},"alphanum_fraction":{"kind":"number","value":0.7367654201,"string":"0.736765"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":695,"string":"695"},"score_documentation":{"kind":"number","value":0.3375424963574551,"string":"0.337542"}}},{"rowIdx":3224,"cells":{"hexsha":{"kind":"string","value":"92e37ec4545956a8e8242b1871fea16288a1a867"},"size":{"kind":"number","value":8704,"string":"8,704"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"tests/test_hrepr.py"},"max_stars_repo_name":{"kind":"string","value":"fabaff/hrepr"},"max_stars_repo_head_hexsha":{"kind":"string","value":"f6de915f1d34c47ceab11f5f70e433a30e6de174"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"tests/test_hrepr.py"},"max_issues_repo_name":{"kind":"string","value":"fabaff/hrepr"},"max_issues_repo_head_hexsha":{"kind":"string","value":"f6de915f1d34c47ceab11f5f70e433a30e6de174"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"tests/test_hrepr.py"},"max_forks_repo_name":{"kind":"string","value":"fabaff/hrepr"},"max_forks_repo_head_hexsha":{"kind":"string","value":"f6de915f1d34c47ceab11f5f70e433a30e6de174"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from dataclasses import dataclass\n\nfrom hrepr import H\nfrom hrepr import hrepr as real_hrepr\nfrom hrepr.h import styledir\n\nfrom .common import one_test_per_assert\n\ncss_hrepr = open(f\"{styledir}/hrepr.css\", encoding=\"utf-8\").read()\nhrepr = real_hrepr.variant(fill_resources=False)\n\n\n@dataclass\nclass Point:\n x: int\n y: int\n\n\nclass Opaque:\n pass\n\n\ndef hshort(x, **kw):\n return hrepr(x, max_depth=0, **kw)\n\n\n@one_test_per_assert\ndef test_singletons():\n assert hrepr(True) == H.span[\"hreprv-True\"](\"True\")\n assert hrepr(False) == H.span[\"hreprv-False\"](\"False\")\n assert hrepr(None) == H.span[\"hreprv-None\"](\"None\")\n\n\n@one_test_per_assert\ndef test_numbers():\n assert hrepr(123) == H.span[\"hreprt-int\"](\"123\")\n assert hrepr(1.25) == H.span[\"hreprt-float\"](\"1.25\")\n\n\n@one_test_per_assert\ndef test_string():\n assert hshort(\"hello\") == H.span[\"hreprt-str\"](\"hello\")\n assert hrepr(\"3 spaces\") == H.span[\"hreprt-str\"](\"3 spaces\")\n assert hrepr(\"hello this is a bit long\") == H.span[\"hreprt-str\"](\n \"hello this is a bit long\"\n )\n assert hshort(\"hello this is a bit long\") == H.span[\"hreprt-str\"](\n \"hello this is a b...\"\n )\n assert hshort(\"hello this is a bit long\", string_cutoff=10) == H.span[\n \"hreprt-str\"\n ](\"hello t...\")\n assert hshort(\"hello this is a bit long\", string_cutoff=5) == H.span[\n \"hreprt-str\"\n ](\"he...\")\n assert hshort(\"hello this is a bit long\", string_cutoff=10000) == H.span[\n \"hreprt-str\"\n ](\"hello this is a bit long\")\n\n\n@one_test_per_assert\ndef test_bytes():\n assert hrepr(b\"hello\") == H.span[\"hreprt-bytes\"](\"68656c6c6f\")\n assert hshort(b\"hello\") == H.span[\"hreprt-bytes\"](\"68656c6c6f\")\n assert hrepr(b\"hello this is a bit long\") == H.span[\"hreprt-bytes\"](\n \"68656c6c6f2074686973206973206120626974206c6f6e67\"\n )\n assert hshort(b\"hello this is a bit long\") == H.span[\"hreprt-bytes\"](\n \"68656c6c6f2074686...\"\n )\n\n\ndef test_function():\n assert hrepr(Opaque) == H.span[\"hreprk-class\"](\n H.span[\"hrepr-defn-key\"](\"class\"),\n \" \",\n H.span[\"hrepr-defn-name\"](\"Opaque\"),\n )\n\n\ndef test_structures():\n for typ, o, c in (\n (tuple, \"(\", \")\"),\n (list, \"[\", \"]\"),\n (set, \"{\", \"}\"),\n (frozenset, \"{\", \"}\"),\n ):\n clsname = typ.__name__\n assert hrepr(typ((1, 2))) == H.div[\n f\"hreprt-{clsname}\", \"hrepr-bracketed\"\n ](\n H.div[\"hrepr-open\"](o),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(H.span[\"hreprt-int\"](\"1\")),\n H.div(H.span[\"hreprt-int\"](\"2\")),\n ),\n H.div[\"hrepr-close\"](c),\n )\n\n\ndef test_short_structures():\n for val, o, c in (\n ((1, 2), \"(\", \")\"),\n ([1, 2], \"[\", \"]\"),\n ({1, 2}, \"{\", \"}\"),\n (frozenset({1, 2}), \"{\", \"}\"),\n ({\"x\": 1, \"y\": 2}, \"{\", \"}\"),\n ):\n clsname = type(val).__name__\n assert hrepr(val, max_depth=0) == H.div[\n f\"hreprt-{clsname}\", \"hrepr-bracketed\"\n ](\n H.div[\"hrepr-open\"](o),\n H.div[\"hreprl-s\", \"hrepr-body\"](H.div(\"...\")),\n H.div[\"hrepr-close\"](c),\n )\n\n\ndef test_dict():\n pt = {\"x\": 1, \"y\": 2}\n assert hrepr(pt) == H.div[\"hreprt-dict\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"{\"),\n H.table[\"hrepr-body\"](\n H.tr(\n H.td(H.span[\"hreprt-str\"](\"x\")),\n H.td[\"hrepr-delim\"](\": \"),\n H.td(H.span[\"hreprt-int\"](\"1\")),\n ),\n H.tr(\n H.td(H.span[\"hreprt-str\"](\"y\")),\n H.td[\"hrepr-delim\"](\": \"),\n H.td(H.span[\"hreprt-int\"](\"2\")),\n ),\n ),\n H.div[\"hrepr-close\"](\"}\"),\n )\n\n\ndef test_dataclass():\n pt = Point(1, 2)\n\n assert hrepr(pt) == H.div[\"hreprt-Point\", \"hrepr-instance\", \"hreprl-v\"](\n H.div[\"hrepr-title\"](\"Point\"),\n H.table[\"hrepr-body\"](\n H.tr(\n H.td(H.span[\"hreprt-symbol\"](\"x\")),\n H.td[\"hrepr-delim\"](\"=\"),\n H.td(H.span[\"hreprt-int\"](\"1\")),\n ),\n H.tr(\n H.td(H.span[\"hreprt-symbol\"](\"y\")),\n H.td[\"hrepr-delim\"](\"=\"),\n H.td(H.span[\"hreprt-int\"](\"2\")),\n ),\n ),\n )\n\n assert hrepr(pt, max_depth=0) == H.div[\n \"hreprt-Point\", \"hrepr-instance\", \"hreprl-s\"\n ](\n H.div[\"hrepr-title\"](\"Point\"),\n H.div[\"hreprl-s\", \"hrepr-body\"](H.div(\"...\")),\n )\n\n\ndef test_tag():\n tg = H.span[\"hello\"](1, 2, H.b(\"there\"))\n assert hrepr(tg) == tg\n\n\ndef test_multiref():\n li = [1, 2]\n lili = [li, li]\n\n assert hrepr(lili) == H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(\n H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"#\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(H.span[\"hreprt-int\"](\"1\")),\n H.div(H.span[\"hreprt-int\"](\"2\")),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n ),\n H.div(\n H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"#\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-s\", \"hrepr-body\"](H.div(\"...\"),),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n ),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n )\n\n assert hrepr(lili, shortrefs=True) == H.div[\n \"hreprt-list\", \"hrepr-bracketed\"\n ](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(\n H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"#\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(H.span[\"hreprt-int\"](\"1\")),\n H.div(H.span[\"hreprt-int\"](\"2\")),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n ),\n H.div(H.span[\"hrepr-ref\"](\"#\", 1)),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n )\n\n\ndef test_recursive():\n li = [1]\n li.append(li)\n\n assert hrepr(li) == H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"#\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(H.span[\"hreprt-int\"](\"1\")),\n H.div(\n H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"⟳\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-s\", \"hrepr-body\"](H.div(\"...\"),),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n ),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n\n assert hrepr(li, shortrefs=True) == H.div[\"hrepr-refbox\"](\n H.span[\"hrepr-ref\"](\"#\", 1, \"=\"),\n H.div[\"hreprt-list\", \"hrepr-bracketed\"](\n H.div[\"hrepr-open\"](\"[\"),\n H.div[\"hreprl-h\", \"hrepr-body\"](\n H.div(H.span[\"hreprt-int\"](\"1\")),\n H.div(H.span[\"hrepr-ref\"](\"⟳\", 1)),\n ),\n H.div[\"hrepr-close\"](\"]\"),\n ),\n )\n\n\ndef test_unsupported():\n assert hshort(Opaque()) == H.span[\"hreprt-Opaque\"](\n \"<\", \"tests.test_hrepr.Opaque\", \">\"\n )\n\n\ndef test_as_page():\n utf8 = H.meta(\n {\"http-equiv\": \"Content-type\"}, content=\"text/html\", charset=\"UTF-8\"\n )\n assert real_hrepr.page(1) == H.inline(\n H.raw(\"\"),\n H.html(H.head(utf8, H.style(css_hrepr)), H.body(real_hrepr(1)),),\n )\n\n\ndef test_hrepr_multiarg():\n assert hrepr(1, 2) == H.inline(\n H.span[\"hreprt-int\"](\"1\"), H.span[\"hreprt-int\"](\"2\"),\n )\n\n\ndef test_preprocess():\n assert hrepr(1, preprocess=lambda x, hrepr: x + 1) == H.span[\"hreprt-int\"](\n \"2\"\n )\n\n\ndef test_postprocess():\n assert hrepr(1, postprocess=lambda x, obj, hrepr: x[\"newclass\"]) == H.span[\n \"newclass\", \"hreprt-int\"\n ](\"1\")\n"},"avg_line_length":{"kind":"number","value":29.110367893,"string":"29.110368"},"max_line_length":{"kind":"number","value":79,"string":"79"},"alphanum_fraction":{"kind":"number","value":0.4439338235,"string":"0.443934"},"count_classes":{"kind":"number","value":56,"string":"56"},"score_classes":{"kind":"number","value":0.006430868167202572,"string":"0.006431"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":1577,"string":"1,577"},"score_decorators":{"kind":"number","value":0.18109784106568672,"string":"0.181098"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2632,"string":"2,632"},"score_documentation":{"kind":"number","value":0.3022508038585209,"string":"0.302251"}}},{"rowIdx":3225,"cells":{"hexsha":{"kind":"string","value":"92e459320d22b81d1d537a46bdb22eb8751da72d"},"size":{"kind":"number","value":6218,"string":"6,218"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"sympy/assumptions/assume.py"},"max_stars_repo_name":{"kind":"string","value":"shivangdubey/sympy"},"max_stars_repo_head_hexsha":{"kind":"string","value":"bd3ddd4c71d439c8b623f69a02274dd8a8a82198"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-07-27T16:36:27.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-12-29T22:28:37.000Z"},"max_issues_repo_path":{"kind":"string","value":"sympy/assumptions/assume.py"},"max_issues_repo_name":{"kind":"string","value":"shivangdubey/sympy"},"max_issues_repo_head_hexsha":{"kind":"string","value":"bd3ddd4c71d439c8b623f69a02274dd8a8a82198"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":2,"string":"2"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-08-18T15:21:59.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-08-18T19:35:29.000Z"},"max_forks_repo_path":{"kind":"string","value":"sympy/assumptions/assume.py"},"max_forks_repo_name":{"kind":"string","value":"shivangdubey/sympy"},"max_forks_repo_head_hexsha":{"kind":"string","value":"bd3ddd4c71d439c8b623f69a02274dd8a8a82198"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":2,"string":"2"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-01-08T23:03:23.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-01-13T18:57:02.000Z"},"content":{"kind":"string","value":"import inspect\nfrom sympy.core.cache import cacheit\nfrom sympy.core.singleton import S\nfrom sympy.core.sympify import _sympify\nfrom sympy.logic.boolalg import Boolean\nfrom sympy.utilities.source import get_class\nfrom contextlib import contextmanager\n\n\nclass AssumptionsContext(set):\n \"\"\"Set representing assumptions.\n\n This is used to represent global assumptions, but you can also use this\n class to create your own local assumptions contexts. It is basically a thin\n wrapper to Python's set, so see its documentation for advanced usage.\n\n Examples\n ========\n\n >>> from sympy import Q\n >>> from sympy.assumptions.assume import global_assumptions\n >>> global_assumptions\n AssumptionsContext()\n >>> from sympy.abc import x\n >>> global_assumptions.add(Q.real(x))\n >>> global_assumptions\n AssumptionsContext({Q.real(x)})\n >>> global_assumptions.remove(Q.real(x))\n >>> global_assumptions\n AssumptionsContext()\n >>> global_assumptions.clear()\n\n \"\"\"\n\n def add(self, *assumptions):\n \"\"\"Add an assumption.\"\"\"\n for a in assumptions:\n super().add(a)\n\n def _sympystr(self, printer):\n if not self:\n return \"%s()\" % self.__class__.__name__\n return \"{}({})\".format(self.__class__.__name__, printer._print_set(self))\n\nglobal_assumptions = AssumptionsContext()\n\n\nclass AppliedPredicate(Boolean):\n \"\"\"The class of expressions resulting from applying a Predicate.\n\n Examples\n ========\n\n >>> from sympy import Q, Symbol\n >>> x = Symbol('x')\n >>> Q.integer(x)\n Q.integer(x)\n >>> type(Q.integer(x))\n \n\n \"\"\"\n __slots__ = ()\n\n def __new__(cls, predicate, arg):\n arg = _sympify(arg)\n return Boolean.__new__(cls, predicate, arg)\n\n is_Atom = True # do not attempt to decompose this\n\n @property\n def arg(self):\n \"\"\"\n Return the expression used by this assumption.\n\n Examples\n ========\n\n >>> from sympy import Q, Symbol\n >>> x = Symbol('x')\n >>> a = Q.integer(x + 1)\n >>> a.arg\n x + 1\n\n \"\"\"\n return self._args[1]\n\n @property\n def args(self):\n return self._args[1:]\n\n @property\n def func(self):\n return self._args[0]\n\n @cacheit\n def sort_key(self, order=None):\n return (self.class_key(), (2, (self.func.name, self.arg.sort_key())),\n S.One.sort_key(), S.One)\n\n def __eq__(self, other):\n if type(other) is AppliedPredicate:\n return self._args == other._args\n return False\n\n def __hash__(self):\n return super().__hash__()\n\n def _eval_ask(self, assumptions):\n return self.func.eval(self.arg, assumptions)\n\n @property\n def binary_symbols(self):\n from sympy.core.relational import Eq, Ne\n if self.func.name in ['is_true', 'is_false']:\n i = self.arg\n if i.is_Boolean or i.is_Symbol or isinstance(i, (Eq, Ne)):\n return i.binary_symbols\n return set()\n\n\nclass Predicate(Boolean):\n \"\"\"A predicate is a function that returns a boolean value.\n\n Predicates merely wrap their argument and remain unevaluated:\n\n >>> from sympy import Q, ask\n >>> type(Q.prime)\n \n >>> Q.prime.name\n 'prime'\n >>> Q.prime(7)\n Q.prime(7)\n >>> _.func.name\n 'prime'\n\n To obtain the truth value of an expression containing predicates, use\n the function ``ask``:\n\n >>> ask(Q.prime(7))\n True\n\n The tautological predicate ``Q.is_true`` can be used to wrap other objects:\n\n >>> from sympy.abc import x\n >>> Q.is_true(x > 1)\n Q.is_true(x > 1)\n\n \"\"\"\n\n is_Atom = True\n\n def __new__(cls, name, handlers=None):\n obj = Boolean.__new__(cls)\n obj.name = name\n obj.handlers = handlers or []\n return obj\n\n def _hashable_content(self):\n return (self.name,)\n\n def __getnewargs__(self):\n return (self.name,)\n\n def __call__(self, expr):\n return AppliedPredicate(self, expr)\n\n def add_handler(self, handler):\n self.handlers.append(handler)\n\n def remove_handler(self, handler):\n self.handlers.remove(handler)\n\n @cacheit\n def sort_key(self, order=None):\n return self.class_key(), (1, (self.name,)), S.One.sort_key(), S.One\n\n def eval(self, expr, assumptions=True):\n \"\"\"\n Evaluate self(expr) under the given assumptions.\n\n This uses only direct resolution methods, not logical inference.\n \"\"\"\n res, _res = None, None\n mro = inspect.getmro(type(expr))\n for handler in self.handlers:\n cls = get_class(handler)\n for subclass in mro:\n eval_ = getattr(cls, subclass.__name__, None)\n if eval_ is None:\n continue\n res = eval_(expr, assumptions)\n # Do not stop if value returned is None\n # Try to check for higher classes\n if res is None:\n continue\n if _res is None:\n _res = res\n elif res is None:\n # since first resolutor was conclusive, we keep that value\n res = _res\n else:\n # only check consistency if both resolutors have concluded\n if _res != res:\n raise ValueError('incompatible resolutors')\n break\n return res\n\n\n@contextmanager\ndef assuming(*assumptions):\n \"\"\" Context manager for assumptions\n\n Examples\n ========\n\n >>> from sympy.assumptions import assuming, Q, ask\n >>> from sympy.abc import x, y\n\n >>> print(ask(Q.integer(x + y)))\n None\n\n >>> with assuming(Q.integer(x), Q.integer(y)):\n ... print(ask(Q.integer(x + y)))\n True\n \"\"\"\n old_global_assumptions = global_assumptions.copy()\n global_assumptions.update(assumptions)\n try:\n yield\n finally:\n global_assumptions.clear()\n global_assumptions.update(old_global_assumptions)\n"},"avg_line_length":{"kind":"number","value":26.686695279,"string":"26.686695"},"max_line_length":{"kind":"number","value":81,"string":"81"},"alphanum_fraction":{"kind":"number","value":0.5874879382,"string":"0.587488"},"count_classes":{"kind":"number","value":5328,"string":"5,328"},"score_classes":{"kind":"number","value":0.8568671598584754,"string":"0.856867"},"count_generators":{"kind":"number","value":569,"string":"569"},"score_generators":{"kind":"number","value":0.09150852364104213,"string":"0.091509"},"count_decorators":{"kind":"number","value":1590,"string":"1,590"},"score_decorators":{"kind":"number","value":0.255709231264072,"string":"0.255709"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2684,"string":"2,684"},"score_documentation":{"kind":"number","value":0.43165004824702474,"string":"0.43165"}}},{"rowIdx":3226,"cells":{"hexsha":{"kind":"string","value":"92e5ae34177c1ed1dca21481a52e063cdd40f997"},"size":{"kind":"number","value":5794,"string":"5,794"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"distancematrix/tests/consumer/test_distance_matrix.py"},"max_stars_repo_name":{"kind":"string","value":"IDLabResearch/seriesdistancematrix"},"max_stars_repo_head_hexsha":{"kind":"string","value":"c0e666d036f24184511e766cee9fdfa55f41df97"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":12,"string":"12"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-11-22T14:34:51.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-05-04T19:23:55.000Z"},"max_issues_repo_path":{"kind":"string","value":"distancematrix/tests/consumer/test_distance_matrix.py"},"max_issues_repo_name":{"kind":"string","value":"predict-idlab/seriesdistancematrix"},"max_issues_repo_head_hexsha":{"kind":"string","value":"c0e666d036f24184511e766cee9fdfa55f41df97"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-04-28T07:59:03.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-04-28T07:59:03.000Z"},"max_forks_repo_path":{"kind":"string","value":"distancematrix/tests/consumer/test_distance_matrix.py"},"max_forks_repo_name":{"kind":"string","value":"IDLabResearch/seriesdistancematrix"},"max_forks_repo_head_hexsha":{"kind":"string","value":"c0e666d036f24184511e766cee9fdfa55f41df97"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":3,"string":"3"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-03-02T12:39:00.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-03-22T13:36:25.000Z"},"content":{"kind":"string","value":"import numpy as np\nfrom unittest import TestCase\nimport numpy.testing as npt\n\nfrom distancematrix.util import diag_indices_of\nfrom distancematrix.consumer.distance_matrix import DistanceMatrix\n\n\nclass TestContextualMatrixProfile(TestCase):\n\n def setUp(self):\n self.dist_matrix = np.array([\n [8.67, 1.10, 1.77, 1.26, 1.91, 4.29, 6.32, 4.24, 4.64, 5.06, 6.41, 4.07, 4.67, 9.32, 5.09],\n [4.33, 4.99, 0.14, 2.79, 2.10, 6.26, 9.40, 4.14, 5.53, 4.26, 8.21, 5.91, 6.83, 9.26, 6.19],\n [0.16, 9.05, 1.35, 4.78, 7.01, 4.36, 5.24, 8.81, 7.90, 5.84, 8.90, 7.88, 3.37, 4.70, 6.94],\n [0.94, 8.70, 3.87, 6.29, 0.32, 1.79, 5.80, 2.61, 1.43, 6.32, 1.62, 0.20, 2.28, 7.11, 2.15],\n [9.90, 4.51, 2.11, 2.83, 5.52, 8.55, 6.90, 0.24, 1.58, 4.26, 8.75, 3.71, 9.93, 8.33, 0.38],\n [7.30, 5.84, 9.63, 1.95, 3.76, 3.61, 9.42, 5.56, 5.09, 7.07, 1.90, 4.78, 1.06, 0.69, 3.67],\n [2.17, 8.37, 3.99, 4.28, 4.37, 2.86, 8.61, 3.39, 8.37, 6.95, 6.57, 1.79, 7.40, 4.41, 7.64],\n [6.26, 0.29, 6.44, 8.84, 1.24, 2.52, 6.25, 3.07, 5.55, 3.19, 8.16, 5.32, 9.01, 0.39, 9.],\n [4.67, 8.88, 3.05, 3.06, 2.36, 8.34, 4.91, 5.46, 9.25, 9.78, 0.03, 5.64, 5.10, 3.58, 6.92],\n [1.01, 0.91, 6.28, 7.79, 0.68, 5.50, 6.72, 5.11, 0.80, 9.30, 9.77, 4.71, 3.26, 7.29, 6.26]])\n\n def mock_initialise(self, dm):\n dm.initialise(1, self.dist_matrix.shape[0], self.dist_matrix.shape[1])\n\n def test_process_diagonal(self):\n dm = DistanceMatrix()\n self.mock_initialise(dm)\n\n for diag in range(-self.dist_matrix.shape[0] + 1, self.dist_matrix.shape[1]):\n diag_ind = diag_indices_of(self.dist_matrix, diag)\n dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind]))\n\n npt.assert_equal(dm.distance_matrix, self.dist_matrix)\n\n def test_process_diagonal_partial_calculation(self):\n dm = DistanceMatrix()\n self.mock_initialise(dm)\n\n correct = np.full_like(self.dist_matrix, np.nan, dtype=float)\n\n for diag in range(-8, self.dist_matrix.shape[1], 3):\n diag_ind = diag_indices_of(self.dist_matrix, diag)\n dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind]))\n correct[diag_ind] = self.dist_matrix[diag_ind]\n\n npt.assert_equal(dm.distance_matrix, correct)\n\n def test_process_column(self):\n dm = DistanceMatrix()\n self.mock_initialise(dm)\n\n for column in range(0, self.dist_matrix.shape[1]):\n dm.process_column(column, np.atleast_2d(self.dist_matrix[:, column]))\n\n npt.assert_equal(dm.distance_matrix, self.dist_matrix)\n\n def test_process_column_partial_calculation(self):\n dm = DistanceMatrix()\n self.mock_initialise(dm)\n\n correct = np.full_like(self.dist_matrix, np.nan, dtype=float)\n\n for column in [2, 3, 4, 5, 10, 11, 12]:\n dm.process_column(column, np.atleast_2d(self.dist_matrix[:, column]))\n correct[:, column] = self.dist_matrix[:, column]\n\n npt.assert_equal(dm.distance_matrix, correct)\n\n def test_streaming_process_column(self):\n dm = DistanceMatrix()\n dm.initialise(1, 5, 5)\n\n dm.process_column(0, np.atleast_2d(self.dist_matrix[0, 0]))\n dm.process_column(1, np.atleast_2d(self.dist_matrix[:2, 1]))\n expected = np.full((5, 5), np.nan)\n expected[0, 0] = self.dist_matrix[0, 0]\n expected[:2, 1] = self.dist_matrix[:2, 1]\n npt.assert_equal(dm.distance_matrix, expected)\n\n for column in range(0, 5):\n dm.process_column(column, np.atleast_2d(self.dist_matrix[:5, :5][:, column]))\n npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5])\n\n dm.shift_query(1)\n dm.shift_series(3)\n\n correct = np.full((5, 5), np.nan)\n correct[0:4, 0:2] = self.dist_matrix[1:5, 3:5]\n npt.assert_equal(dm.distance_matrix, correct)\n\n for column in range(0, 5):\n dm.process_column(column, np.atleast_2d(self.dist_matrix[1:6, 3:8][:, column]))\n npt.assert_equal(dm.distance_matrix, self.dist_matrix[1:6, 3:8])\n\n dm.shift_query(2)\n dm.shift_series(1)\n dm.process_column(4, np.atleast_2d(self.dist_matrix[3:8, 8]))\n\n correct = np.full((5, 5), np.nan)\n correct[0:3, 0:4] = self.dist_matrix[3:6, 4:8]\n correct[:, 4] = self.dist_matrix[3:8, 8]\n npt.assert_equal(dm.distance_matrix, correct)\n\n def test_streaming_process_diagonal(self):\n dm = DistanceMatrix()\n dm.initialise(1, 5, 5)\n\n dm.process_diagonal(0, np.atleast_2d(self.dist_matrix[0, 0]))\n diag_ind = diag_indices_of(self.dist_matrix[:3, :3], 1)\n dm.process_diagonal(1, np.atleast_2d(np.atleast_2d(self.dist_matrix[diag_ind])))\n expected = np.full((5, 5), np.nan)\n expected[0, 0] = self.dist_matrix[0, 0]\n expected[0, 1] = self.dist_matrix[0, 1]\n expected[1, 2] = self.dist_matrix[1, 2]\n npt.assert_equal(dm.distance_matrix, expected)\n\n for diag in range(-4,5):\n diag_ind = diag_indices_of(self.dist_matrix[:5, :5], diag)\n dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind]))\n\n npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5])\n\n dm.shift_query(2)\n dm.shift_series(1)\n expected = self.dist_matrix[2:7, 1:6].copy()\n expected[-2:, :] = np.nan\n expected[:, -1:] = np.nan\n npt.assert_equal(dm.distance_matrix, expected)\n\n for diag in range(-4,5):\n diag_ind = diag_indices_of(self.dist_matrix[:5, :5], diag)\n dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind]))\n npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5])\n"},"avg_line_length":{"kind":"number","value":42.6029411765,"string":"42.602941"},"max_line_length":{"kind":"number","value":104,"string":"104"},"alphanum_fraction":{"kind":"number","value":0.6087331722,"string":"0.608733"},"count_classes":{"kind":"number","value":5598,"string":"5,598"},"score_classes":{"kind":"number","value":0.9661719019675526,"string":"0.966172"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":0,"string":"0"},"score_documentation":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":3227,"cells":{"hexsha":{"kind":"string","value":"92e5bc0e9b68f032b202632a0013f3e6bb85256a"},"size":{"kind":"number","value":11460,"string":"11,460"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"supervisor/const.py"},"max_stars_repo_name":{"kind":"string","value":"peddamat/home-assistant-supervisor-test"},"max_stars_repo_head_hexsha":{"kind":"string","value":"5da55772bcb2db3c6d8432cbc08e2ac9fbf480c4"},"max_stars_repo_licenses":{"kind":"list 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\"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"supervisor/const.py"},"max_forks_repo_name":{"kind":"string","value":"peddamat/home-assistant-supervisor-test"},"max_forks_repo_head_hexsha":{"kind":"string","value":"5da55772bcb2db3c6d8432cbc08e2ac9fbf480c4"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"Constants file for Supervisor.\"\"\"\nfrom enum import Enum\nfrom ipaddress import ip_network\nfrom pathlib import Path\n\nSUPERVISOR_VERSION = \"DEV\"\n\nURL_HASSIO_ADDONS = \"https://github.com/home-assistant/addons\"\nURL_HASSIO_APPARMOR = \"https://version.home-assistant.io/apparmor.txt\"\nURL_HASSIO_VERSION = \"https://version.home-assistant.io/{channel}.json\"\n\nSUPERVISOR_DATA = Path(\"/data\")\n\nFILE_HASSIO_ADDONS = Path(SUPERVISOR_DATA, \"addons.json\")\nFILE_HASSIO_AUTH = Path(SUPERVISOR_DATA, \"auth.json\")\nFILE_HASSIO_CONFIG = Path(SUPERVISOR_DATA, \"config.json\")\nFILE_HASSIO_DISCOVERY = Path(SUPERVISOR_DATA, \"discovery.json\")\nFILE_HASSIO_DOCKER = Path(SUPERVISOR_DATA, \"docker.json\")\nFILE_HASSIO_HOMEASSISTANT = Path(SUPERVISOR_DATA, \"homeassistant.json\")\nFILE_HASSIO_INGRESS = Path(SUPERVISOR_DATA, \"ingress.json\")\nFILE_HASSIO_SERVICES = Path(SUPERVISOR_DATA, \"services.json\")\nFILE_HASSIO_UPDATER = Path(SUPERVISOR_DATA, \"updater.json\")\n\nFILE_SUFFIX_CONFIGURATION = [\".yaml\", \".yml\", \".json\"]\n\nMACHINE_ID = Path(\"/etc/machine-id\")\nSOCKET_DBUS = Path(\"/run/dbus/system_bus_socket\")\nSOCKET_DOCKER = Path(\"/run/docker.sock\")\nRUN_SUPERVISOR_STATE = Path(\"/run/supervisor\")\nSYSTEMD_JOURNAL_PERSISTENT = Path(\"/var/log/journal\")\nSYSTEMD_JOURNAL_VOLATILE = Path(\"/run/log/journal\")\n\nDOCKER_NETWORK = \"hassio\"\nDOCKER_NETWORK_MASK = ip_network(\"172.30.32.0/23\")\nDOCKER_NETWORK_RANGE = ip_network(\"172.30.33.0/24\")\n\n# This needs to match the dockerd --cpu-rt-runtime= argument.\nDOCKER_CPU_RUNTIME_TOTAL = 950_000\n\n# The rt runtimes are guarantees, hence we cannot allocate more\n# time than available! Support up to 5 containers with equal time\n# allocated.\n# Note that the time is multiplied by CPU count. This means that\n# a single container can schedule up to 950/5*4 = 760ms in RT priority\n# on a quad core system.\nDOCKER_CPU_RUNTIME_ALLOCATION = int(DOCKER_CPU_RUNTIME_TOTAL / 5)\n\nDNS_SUFFIX = \"local.hass.io\"\n\nLABEL_ARCH = \"io.hass.arch\"\nLABEL_MACHINE = \"io.hass.machine\"\nLABEL_TYPE = \"io.hass.type\"\nLABEL_VERSION = \"io.hass.version\"\n\nMETA_ADDON = \"addon\"\nMETA_HOMEASSISTANT = \"homeassistant\"\nMETA_SUPERVISOR = \"supervisor\"\n\nJSON_DATA = \"data\"\nJSON_MESSAGE = \"message\"\nJSON_RESULT = \"result\"\n\nRESULT_ERROR = \"error\"\nRESULT_OK = \"ok\"\n\nCONTENT_TYPE_BINARY = \"application/octet-stream\"\nCONTENT_TYPE_JSON = \"application/json\"\nCONTENT_TYPE_PNG = \"image/png\"\nCONTENT_TYPE_TAR = \"application/tar\"\nCONTENT_TYPE_TEXT = \"text/plain\"\nCONTENT_TYPE_URL = \"application/x-www-form-urlencoded\"\nCOOKIE_INGRESS = \"ingress_session\"\n\nHEADER_TOKEN = \"X-Supervisor-Token\"\nHEADER_TOKEN_OLD = \"X-Hassio-Key\"\n\nENV_TIME = \"TZ\"\nENV_TOKEN = \"SUPERVISOR_TOKEN\"\nENV_TOKEN_HASSIO = \"HASSIO_TOKEN\"\n\nENV_HOMEASSISTANT_REPOSITORY = \"HOMEASSISTANT_REPOSITORY\"\nENV_SUPERVISOR_DEV = \"SUPERVISOR_DEV\"\nENV_SUPERVISOR_MACHINE = \"SUPERVISOR_MACHINE\"\nENV_SUPERVISOR_NAME = \"SUPERVISOR_NAME\"\nENV_SUPERVISOR_SHARE = \"SUPERVISOR_SHARE\"\nENV_SUPERVISOR_CPU_RT = \"SUPERVISOR_CPU_RT\"\n\nREQUEST_FROM = \"HASSIO_FROM\"\n\nATTR_ACCESS_TOKEN = \"access_token\"\nATTR_ACCESSPOINTS = \"accesspoints\"\nATTR_ACTIVE = \"active\"\nATTR_ADDON = \"addon\"\nATTR_ADDONS = \"addons\"\nATTR_ADDONS_CUSTOM_LIST = \"addons_custom_list\"\nATTR_ADDONS_REPOSITORIES = \"addons_repositories\"\nATTR_ADDRESS = \"address\"\nATTR_ADDRESS_DATA = \"address-data\"\nATTR_ADMIN = \"admin\"\nATTR_ADVANCED = \"advanced\"\nATTR_APPARMOR = \"apparmor\"\nATTR_APPLICATION = \"application\"\nATTR_ARCH = \"arch\"\nATTR_ARGS = \"args\"\nATTR_LABELS = \"labels\"\nATTR_AUDIO = \"audio\"\nATTR_AUDIO_INPUT = \"audio_input\"\nATTR_AUDIO_OUTPUT = \"audio_output\"\nATTR_AUTH = \"auth\"\nATTR_AUTH_API = \"auth_api\"\nATTR_AUTO_UPDATE = \"auto_update\"\nATTR_AVAILABLE = \"available\"\nATTR_BLK_READ = \"blk_read\"\nATTR_BLK_WRITE = \"blk_write\"\nATTR_BOARD = \"board\"\nATTR_BOOT = \"boot\"\nATTR_BRANCH = \"branch\"\nATTR_BUILD = \"build\"\nATTR_BUILD_FROM = \"build_from\"\nATTR_CARD = \"card\"\nATTR_CHANGELOG = \"changelog\"\nATTR_CHANNEL = \"channel\"\nATTR_CHASSIS = \"chassis\"\nATTR_CHECKS = \"checks\"\nATTR_CLI = \"cli\"\nATTR_CONFIG = \"config\"\nATTR_CONFIGURATION = \"configuration\"\nATTR_CONNECTED = \"connected\"\nATTR_CONNECTIONS = \"connections\"\nATTR_CONTAINERS = \"containers\"\nATTR_CPE = \"cpe\"\nATTR_CPU_PERCENT = \"cpu_percent\"\nATTR_CRYPTO = \"crypto\"\nATTR_DATA = \"data\"\nATTR_DATE = \"date\"\nATTR_DEBUG = \"debug\"\nATTR_DEBUG_BLOCK = \"debug_block\"\nATTR_DEFAULT = \"default\"\nATTR_DEPLOYMENT = \"deployment\"\nATTR_DESCRIPTON = \"description\"\nATTR_DETACHED = \"detached\"\nATTR_DEVICES = \"devices\"\nATTR_DEVICETREE = \"devicetree\"\nATTR_DIAGNOSTICS = \"diagnostics\"\nATTR_DISCOVERY = \"discovery\"\nATTR_DISK = \"disk\"\nATTR_DISK_FREE = \"disk_free\"\nATTR_DISK_LIFE_TIME = \"disk_life_time\"\nATTR_DISK_TOTAL = \"disk_total\"\nATTR_DISK_USED = \"disk_used\"\nATTR_DNS = \"dns\"\nATTR_DOCKER = \"docker\"\nATTR_DOCKER_API = \"docker_api\"\nATTR_DOCUMENTATION = \"documentation\"\nATTR_DOMAINS = \"domains\"\nATTR_ENABLE = \"enable\"\nATTR_ENABLED = \"enabled\"\nATTR_ENVIRONMENT = \"environment\"\nATTR_EVENT = \"event\"\nATTR_FEATURES = \"features\"\nATTR_FILENAME = \"filename\"\nATTR_FLAGS = \"flags\"\nATTR_FOLDERS = \"folders\"\nATTR_FREQUENCY = \"frequency\"\nATTR_FULL_ACCESS = \"full_access\"\nATTR_GATEWAY = \"gateway\"\nATTR_GPIO = \"gpio\"\nATTR_HASSIO_API = \"hassio_api\"\nATTR_HASSIO_ROLE = \"hassio_role\"\nATTR_HASSOS = \"hassos\"\nATTR_HEALTHY = \"healthy\"\nATTR_HOMEASSISTANT = \"homeassistant\"\nATTR_HOMEASSISTANT_API = \"homeassistant_api\"\nATTR_HOST = \"host\"\nATTR_HOST_DBUS = \"host_dbus\"\nATTR_HOST_INTERNET = \"host_internet\"\nATTR_HOST_IPC = \"host_ipc\"\nATTR_HOST_NETWORK = \"host_network\"\nATTR_HOST_PID = \"host_pid\"\nATTR_HOSTNAME = \"hostname\"\nATTR_ICON = \"icon\"\nATTR_ID = \"id\"\nATTR_IMAGE = \"image\"\nATTR_IMAGES = \"images\"\nATTR_INDEX = \"index\"\nATTR_INGRESS = \"ingress\"\nATTR_INGRESS_ENTRY = \"ingress_entry\"\nATTR_INGRESS_PANEL = \"ingress_panel\"\nATTR_INGRESS_PORT = \"ingress_port\"\nATTR_INGRESS_TOKEN = \"ingress_token\"\nATTR_INGRESS_URL = \"ingress_url\"\nATTR_INIT = \"init\"\nATTR_INITIALIZE = \"initialize\"\nATTR_INPUT = \"input\"\nATTR_INSTALLED = \"installed\"\nATTR_INTERFACE = \"interface\"\nATTR_INTERFACES = \"interfaces\"\nATTR_IP_ADDRESS = \"ip_address\"\nATTR_IPV4 = \"ipv4\"\nATTR_IPV6 = \"ipv6\"\nATTR_ISSUES = \"issues\"\nATTR_KERNEL = \"kernel\"\nATTR_KERNEL_MODULES = \"kernel_modules\"\nATTR_LAST_BOOT = \"last_boot\"\nATTR_LEGACY = \"legacy\"\nATTR_LOCALS = \"locals\"\nATTR_LOCATON = \"location\"\nATTR_LOGGING = \"logging\"\nATTR_LOGO = \"logo\"\nATTR_LONG_DESCRIPTION = \"long_description\"\nATTR_MAC = \"mac\"\nATTR_MACHINE = \"machine\"\nATTR_MAINTAINER = \"maintainer\"\nATTR_MAP = \"map\"\nATTR_MEMORY_LIMIT = \"memory_limit\"\nATTR_MEMORY_PERCENT = \"memory_percent\"\nATTR_MEMORY_USAGE = \"memory_usage\"\nATTR_MESSAGE = \"message\"\nATTR_METHOD = \"method\"\nATTR_MODE = \"mode\"\nATTR_MULTICAST = \"multicast\"\nATTR_NAME = \"name\"\nATTR_NAMESERVERS = \"nameservers\"\nATTR_NETWORK = \"network\"\nATTR_NETWORK_DESCRIPTION = \"network_description\"\nATTR_NETWORK_RX = \"network_rx\"\nATTR_NETWORK_TX = \"network_tx\"\nATTR_OBSERVER = \"observer\"\nATTR_OPERATING_SYSTEM = \"operating_system\"\nATTR_OPTIONS = \"options\"\nATTR_OTA = \"ota\"\nATTR_OUTPUT = \"output\"\nATTR_PANEL_ADMIN = \"panel_admin\"\nATTR_PANEL_ICON = \"panel_icon\"\nATTR_PANEL_TITLE = \"panel_title\"\nATTR_PANELS = \"panels\"\nATTR_PARENT = \"parent\"\nATTR_PASSWORD = \"password\"\nATTR_PORT = \"port\"\nATTR_PORTS = \"ports\"\nATTR_PORTS_DESCRIPTION = \"ports_description\"\nATTR_PREFIX = \"prefix\"\nATTR_PRIMARY = \"primary\"\nATTR_PRIORITY = \"priority\"\nATTR_PRIVILEGED = \"privileged\"\nATTR_PROTECTED = \"protected\"\nATTR_PROVIDERS = \"providers\"\nATTR_PSK = \"psk\"\nATTR_RATING = \"rating\"\nATTR_REALTIME = \"realtime\"\nATTR_REFRESH_TOKEN = \"refresh_token\"\nATTR_REGISTRIES = \"registries\"\nATTR_REGISTRY = \"registry\"\nATTR_REPOSITORIES = \"repositories\"\nATTR_REPOSITORY = \"repository\"\nATTR_SCHEMA = \"schema\"\nATTR_SECURITY = \"security\"\nATTR_SERIAL = \"serial\"\nATTR_SERVERS = \"servers\"\nATTR_SERVICE = \"service\"\nATTR_SERVICES = \"services\"\nATTR_SESSION = \"session\"\nATTR_SIGNAL = \"signal\"\nATTR_SIZE = \"size\"\nATTR_SLUG = \"slug\"\nATTR_SNAPSHOT_EXCLUDE = \"snapshot_exclude\"\nATTR_SNAPSHOTS = \"snapshots\"\nATTR_SOURCE = \"source\"\nATTR_SQUASH = \"squash\"\nATTR_SSD = \"ssid\"\nATTR_SSID = \"ssid\"\nATTR_SSL = \"ssl\"\nATTR_STAGE = \"stage\"\nATTR_STARTUP = \"startup\"\nATTR_STATE = \"state\"\nATTR_STATIC = \"static\"\nATTR_STDIN = \"stdin\"\nATTR_STORAGE = \"storage\"\nATTR_SUGGESTIONS = \"suggestions\"\nATTR_SUPERVISOR = \"supervisor\"\nATTR_SUPERVISOR_INTERNET = \"supervisor_internet\"\nATTR_SUPPORTED = \"supported\"\nATTR_SUPPORTED_ARCH = \"supported_arch\"\nATTR_SYSTEM = \"system\"\nATTR_JOURNALD = \"journald\"\nATTR_TIMEOUT = \"timeout\"\nATTR_TIMEZONE = \"timezone\"\nATTR_TITLE = \"title\"\nATTR_TMPFS = \"tmpfs\"\nATTR_TOTP = \"totp\"\nATTR_TRANSLATIONS = \"translations\"\nATTR_TYPE = \"type\"\nATTR_UART = \"uart\"\nATTR_UDEV = \"udev\"\nATTR_UNHEALTHY = \"unhealthy\"\nATTR_UNSAVED = \"unsaved\"\nATTR_UNSUPPORTED = \"unsupported\"\nATTR_UPDATE_AVAILABLE = \"update_available\"\nATTR_UPDATE_KEY = \"update_key\"\nATTR_URL = \"url\"\nATTR_USB = \"usb\"\nATTR_USER = \"user\"\nATTR_USERNAME = \"username\"\nATTR_UUID = \"uuid\"\nATTR_VALID = \"valid\"\nATTR_VALUE = \"value\"\nATTR_VERSION = \"version\"\nATTR_VERSION_LATEST = \"version_latest\"\nATTR_VIDEO = \"video\"\nATTR_VLAN = \"vlan\"\nATTR_VOLUME = \"volume\"\nATTR_VPN = \"vpn\"\nATTR_WAIT_BOOT = \"wait_boot\"\nATTR_WATCHDOG = \"watchdog\"\nATTR_WEBUI = \"webui\"\nATTR_WIFI = \"wifi\"\nATTR_CONTENT_TRUST = \"content_trust\"\nATTR_FORCE_SECURITY = \"force_security\"\n\nPROVIDE_SERVICE = \"provide\"\nNEED_SERVICE = \"need\"\nWANT_SERVICE = \"want\"\n\n\nMAP_CONFIG = \"config\"\nMAP_SSL = \"ssl\"\nMAP_ADDONS = \"addons\"\nMAP_BACKUP = \"backup\"\nMAP_SHARE = \"share\"\nMAP_MEDIA = \"media\"\n\nARCH_ARMHF = \"armhf\"\nARCH_ARMV7 = \"armv7\"\nARCH_AARCH64 = \"aarch64\"\nARCH_AMD64 = \"amd64\"\nARCH_I386 = \"i386\"\n\nARCH_ALL = [ARCH_ARMHF, ARCH_ARMV7, ARCH_AARCH64, ARCH_AMD64, ARCH_I386]\n\nREPOSITORY_CORE = \"core\"\nREPOSITORY_LOCAL = \"local\"\n\nFOLDER_HOMEASSISTANT = \"homeassistant\"\nFOLDER_SHARE = \"share\"\nFOLDER_ADDONS = \"addons/local\"\nFOLDER_SSL = \"ssl\"\nFOLDER_MEDIA = \"media\"\n\nSNAPSHOT_FULL = \"full\"\nSNAPSHOT_PARTIAL = \"partial\"\n\nCRYPTO_AES128 = \"aes128\"\n\nSECURITY_PROFILE = \"profile\"\nSECURITY_DEFAULT = \"default\"\nSECURITY_DISABLE = \"disable\"\n\nROLE_DEFAULT = \"default\"\nROLE_HOMEASSISTANT = \"homeassistant\"\nROLE_BACKUP = \"backup\"\nROLE_MANAGER = \"manager\"\nROLE_ADMIN = \"admin\"\n\nROLE_ALL = [ROLE_DEFAULT, ROLE_HOMEASSISTANT, ROLE_BACKUP, ROLE_MANAGER, ROLE_ADMIN]\n\n\nclass AddonBoot(str, Enum):\n \"\"\"Boot mode for the add-on.\"\"\"\n\n AUTO = \"auto\"\n MANUAL = \"manual\"\n\n\nclass AddonStartup(str, Enum):\n \"\"\"Startup types of Add-on.\"\"\"\n\n INITIALIZE = \"initialize\"\n SYSTEM = \"system\"\n SERVICES = \"services\"\n APPLICATION = \"application\"\n ONCE = \"once\"\n\n\nclass AddonStage(str, Enum):\n \"\"\"Stage types of add-on.\"\"\"\n\n STABLE = \"stable\"\n EXPERIMENTAL = \"experimental\"\n DEPRECATED = \"deprecated\"\n\n\nclass AddonState(str, Enum):\n \"\"\"State of add-on.\"\"\"\n\n STARTED = \"started\"\n STOPPED = \"stopped\"\n UNKNOWN = \"unknown\"\n ERROR = \"error\"\n\n\nclass UpdateChannel(str, Enum):\n \"\"\"Core supported update channels.\"\"\"\n\n STABLE = \"stable\"\n BETA = \"beta\"\n DEV = \"dev\"\n\n\nclass CoreState(str, Enum):\n \"\"\"Represent current loading state.\"\"\"\n\n INITIALIZE = \"initialize\"\n SETUP = \"setup\"\n STARTUP = \"startup\"\n RUNNING = \"running\"\n FREEZE = \"freeze\"\n SHUTDOWN = \"shutdown\"\n STOPPING = \"stopping\"\n CLOSE = \"close\"\n\n\nclass LogLevel(str, Enum):\n \"\"\"Logging level of system.\"\"\"\n\n DEBUG = \"debug\"\n INFO = \"info\"\n WARNING = \"warning\"\n ERROR = \"error\"\n CRITICAL = \"critical\"\n\n\nclass HostFeature(str, Enum):\n \"\"\"Host feature.\"\"\"\n\n HASSOS = \"hassos\"\n HOSTNAME = \"hostname\"\n NETWORK = \"network\"\n REBOOT = \"reboot\"\n SERVICES = \"services\"\n SHUTDOWN = \"shutdown\"\n"},"avg_line_length":{"kind":"number","value":25.9863945578,"string":"25.986395"},"max_line_length":{"kind":"number","value":84,"string":"84"},"alphanum_fraction":{"kind":"number","value":0.7555846422,"string":"0.755585"},"count_classes":{"kind":"number","value":1357,"string":"1,357"},"score_classes":{"kind":"number","value":0.11841186736474694,"string":"0.118412"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":4428,"string":"4,428"},"score_documentation":{"kind":"number","value":0.3863874345549738,"string":"0.386387"}}},{"rowIdx":3228,"cells":{"hexsha":{"kind":"string","value":"92e5e938e0e0af1229cd08971df68b5917c123c7"},"size":{"kind":"number","value":8334,"string":"8,334"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"quaesit/agent.py"},"max_stars_repo_name":{"kind":"string","value":"jgregoriods/quaesit"},"max_stars_repo_head_hexsha":{"kind":"string","value":"3846f5084ea4d6c1cbd9a93176ee9dee25e12105"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"quaesit/agent.py"},"max_issues_repo_name":{"kind":"string","value":"jgregoriods/quaesit"},"max_issues_repo_head_hexsha":{"kind":"string","value":"3846f5084ea4d6c1cbd9a93176ee9dee25e12105"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"quaesit/agent.py"},"max_forks_repo_name":{"kind":"string","value":"jgregoriods/quaesit"},"max_forks_repo_head_hexsha":{"kind":"string","value":"3846f5084ea4d6c1cbd9a93176ee9dee25e12105"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import inspect\n\nfrom math import hypot, sin, asin, cos, radians, degrees\nfrom abc import ABCMeta, abstractmethod\nfrom random import randint, choice\nfrom typing import Dict, List, Tuple, Union\n\n\nclass Agent(metaclass=ABCMeta):\n \"\"\"\n Class to represent an agent in an agent-based model.\n \"\"\"\n\n _id = 0\n colors = ['blue', 'brown', 'cyan', 'gray', 'green', 'magenta', 'orange',\n 'pink', 'purple', 'red', 'yellow']\n\n def __init__(self, world, coords: Tuple = None):\n self._id = Agent._id\n Agent._id += 1\n\n self.world = world\n self.coords = coords or (randint(0, self.world.width - 1),\n randint(0, self.world.height - 1))\n self.direction = 90\n self.breed = self.__class__.__name__.lower()\n self.icon = '.'\n self.color = choice(self.colors)\n\n self.world.add_agent(self)\n\n def die(self):\n \"\"\"\n Remove the agent from the world.\n \"\"\"\n\n del self.world.agents[self._id]\n self.world.grid[self.coords]['agents'].remove(self)\n del self\n\n def hatch(self):\n \"\"\"\n Creates an agent and initializes it with the same parameters as\n oneself.\n \"\"\"\n\n sig = inspect.signature(self.__init__)\n filter_keys = [param.name for param in sig.parameters.values()\n if param.kind == param.POSITIONAL_OR_KEYWORD]\n filtered_dict = {filter_key: self.__dict__[filter_key]\n for filter_key in filter_keys}\n return self.__class__(**filtered_dict)\n\n def move_to(self, coords: Tuple):\n \"\"\"\n Places the agent in a different cell of the world grid.\n \"\"\"\n\n self.world.remove_from_grid(self)\n self.coords = coords\n self.world.place_on_grid(self)\n\n def cell_here(self, layer = None):\n \"\"\"\n Returns the value of a layer in the model's grid for the cell\n where the agent is. If no layer is specified, the values of all\n layers are returned.\n \"\"\"\n if layer is not None:\n return self.world.grid[self.coords][layer]\n else:\n return self.world.grid[self.coords]\n\n def get_distance(self, coords: Tuple) -> int:\n \"\"\"\n Returns the distance (in cells) from the agent to a pair of\n coordinates.\n \"\"\"\n\n x, y = coords\n return round(hypot((x - self.coords[0]), (y - self.coords[1])))\n\n def cells_in_radius(self, radius: int) -> Dict:\n \"\"\"\n Returns all cells and respective attributes within a distance\n of the agent.\n \"\"\"\n\n if self.world.torus:\n neighborhood = {self.world.to_torus((x, y)):\n self.world.grid[self.world.to_torus((x, y))]\n for x in range(self.coords[0] - radius,\n self.coords[0] + radius + 1)\n for y in range(self.coords[1] - radius,\n self.coords[1] + radius + 1)\n if self.get_distance((x, y)) <= radius}\n else:\n neighborhood = {(x, y): self.world.grid[(x, y)]\n for x in range(self.coords[0] - radius,\n self.coords[0] + radius + 1)\n for y in range(self.coords[1] - radius,\n self.coords[1] + radius + 1)\n if (self.get_distance((x, y)) <= radius and\n (x, y) in self.world.grid)}\n\n return neighborhood\n\n def empty_cells_in_radius(self, radius: int) -> Dict:\n \"\"\"\n Returns all empty cells (with no agents on them) and respective\n attributes within a distance of the agent.\n \"\"\"\n\n if self.world.torus:\n neighborhood = {self.world.to_torus((x, y)):\n self.world.grid[self.world.to_torus((x, y))]\n for x in range(self.coords[0] - radius,\n self.coords[0] + radius + 1)\n for y in range(self.coords[1] - radius,\n self.coords[1] + radius + 1)\n if (self.get_distance((x, y)) <= radius and not\n self.world.grid[self.world.to_torus((x, y))]\n ['agents'])}\n else:\n neighborhood = {(x, y): self.world.grid[(x, y)]\n for x in range(self.coords[0] - radius,\n self.coords[0] + radius + 1)\n for y in range(self.coords[1] - radius,\n self.coords[1] + radius + 1)\n if (self.get_distance((x, y)) <= radius and\n (x, y) in self.world.grid and not\n self.world.grid[(x, y)]['agents'])}\n\n return neighborhood\n\n def nearest_cell(self, cells: Union[List, Dict]) -> Tuple:\n \"\"\"\n Given a list or dictionary of cells, returns the coordinates of\n the cell that is nearest to the agent.\n \"\"\"\n\n dists = {cell: self.get_distance(cell) for cell in cells}\n return min(dists, key=dists.get)\n\n def agents_in_radius(self, radius: int):\n \"\"\"\n Returns all agents within a distance of oneself.\n \"\"\"\n\n neighborhood = self.cells_in_radius(radius)\n neighbors = [agent for coords in neighborhood\n for agent in self.world.grid[coords]['agents']\n if agent is not self]\n return neighbors\n\n def agents_here(self) -> List:\n \"\"\"\n Returns all agents located on the same cell as oneself.\n \"\"\"\n\n return [agent for agent in self.world.grid[self.coords]['agents']\n if agent is not self]\n\n def nearest_agent(self, agents: List = None):\n \"\"\"\n Given a list of agents, returns the agent that is nearest to\n oneself. If no list is provided, all agents are evaluated.\n \"\"\"\n\n if agents is None:\n agents = [self.world.agents[_id] for _id in self.world.agents]\n dists = {agent: self.get_distance(agent.coords)\n for agent in agents if agent is not self}\n return min(dists, key=dists.get)\n\n def turn_right(self, angle: int = 90):\n \"\"\"\n Rotates the agent's direction a number of degrees to the right.\n \"\"\"\n\n self.direction = round((self.direction - angle) % 360)\n\n def turn_left(self, angle: int = 90):\n \"\"\"\n Rotates the agent's direction a number of degrees to the left.\n \"\"\"\n\n self.direction = round((self.direction + angle) % 360)\n\n def forward(self, n_steps: int = 1):\n \"\"\"\n Moves the agent a number of cells forward in the direction it\n is currently facing.\n \"\"\"\n\n x = round(self.coords[0] + cos(radians(self.direction)) * n_steps)\n y = round(self.coords[1] + sin(radians(self.direction)) * n_steps)\n\n if self.world.torus:\n self.move_to(self.world.to_torus((x, y)))\n elif (x, y) in self.world.grid:\n self.move_to((x, y))\n\n def face_towards(self, coords: Tuple):\n \"\"\"\n Turns the agent's direction towards a given pair of coordinates.\n \"\"\"\n\n if coords != self.coords:\n xdif = coords[0] - self.coords[0]\n ydif = coords[1] - self.coords[1]\n dist = hypot(xdif, ydif)\n angle = degrees(asin(ydif / dist))\n\n if xdif < 0:\n self.direction = round(180 - angle)\n else:\n self.direction = round((360 + angle) % 360)\n\n def random_walk(self, n_steps: int = 1):\n \"\"\"\n Moves the agent one cell forward in a random direction for a\n number of times.\n \"\"\"\n\n for i in range(n_steps):\n self.turn_right(randint(0, 360))\n self.forward()\n\n @abstractmethod\n def step(self):\n \"\"\"\n Methods to be performed by the agent at each step of the\n simulation.\n \"\"\"\n\n raise NotImplementedError\n"},"avg_line_length":{"kind":"number","value":35.0168067227,"string":"35.016807"},"max_line_length":{"kind":"number","value":76,"string":"76"},"alphanum_fraction":{"kind":"number","value":0.5176385889,"string":"0.517639"},"count_classes":{"kind":"number","value":8139,"string":"8,139"},"score_classes":{"kind":"number","value":0.9766018718502519,"string":"0.976602"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":179,"string":"179"},"score_decorators":{"kind":"number","value":0.021478281737461004,"string":"0.021478"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1981,"string":"1,981"},"score_documentation":{"kind":"number","value":0.2377009839212863,"string":"0.237701"}}},{"rowIdx":3229,"cells":{"hexsha":{"kind":"string","value":"92e5fb97c8f7793e1b150c9be5289156548c78e6"},"size":{"kind":"number","value":15337,"string":"15,337"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"models/LRF_COCO_300.py"},"max_stars_repo_name":{"kind":"string","value":"vaesl/LRF-Net"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e44b120dd55288c02852f8e58cda31313525d748"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":180,"string":"180"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-10-24T01:55:54.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-02-07T11:26:49.000Z"},"max_issues_repo_path":{"kind":"string","value":"models/LRF_COCO_300.py"},"max_issues_repo_name":{"kind":"string","value":"CV-Rookie/LRF-Net"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e44b120dd55288c02852f8e58cda31313525d748"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":11,"string":"11"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-11-06T08:46:00.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-06-20T02:30:32.000Z"},"max_forks_repo_path":{"kind":"string","value":"models/LRF_COCO_300.py"},"max_forks_repo_name":{"kind":"string","value":"CV-Rookie/LRF-Net"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e44b120dd55288c02852f8e58cda31313525d748"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":29,"string":"29"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-10-28T03:26:27.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-05-03T02:32:06.000Z"},"content":{"kind":"string","value":"import torch\nimport torch.nn as nn\nimport os\nimport torch.nn.functional as F\n\n\nclass LDS(nn.Module):\n def __init__(self,):\n super(LDS, self).__init__()\n self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)\n self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0)\n self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=1)\n\n def forward(self, x):\n x_pool1 = self.pool1(x)\n x_pool2 = self.pool2(x_pool1)\n x_pool3 = self.pool3(x_pool2)\n return x_pool3\n\n\nclass ConvBlock(nn.Module):\n def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):\n super(ConvBlock, self).__init__()\n self.out_channels = out_planes\n self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)\n self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None\n self.relu = nn.ReLU(inplace=False) if relu else None\n\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x = self.bn(x)\n if self.relu is not None:\n x = self.relu(x)\n return x\n\n\nclass LSN_init(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1):\n super(LSN_init, self).__init__()\n self.out_channels = out_planes\n inter_planes = out_planes // 4\n self.part_a = nn.Sequential(\n ConvBlock(in_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1),\n ConvBlock(inter_planes, inter_planes, kernel_size=1, stride=1),\n ConvBlock(inter_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1)\n )\n self.part_b = ConvBlock(inter_planes, out_planes, kernel_size=1, stride=1, relu=False)\n\n def forward(self, x):\n out1 = self.part_a(x)\n out2 = self.part_b(out1)\n return out1, out2\n\n\nclass LSN_later(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1):\n super(LSN_later, self).__init__()\n self.out_channels = out_planes\n inter_planes = out_planes // 4\n self.part_a = ConvBlock(in_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1)\n self.part_b = ConvBlock(inter_planes, out_planes, kernel_size=1, stride=1, relu=False)\n\n def forward(self, x):\n out1 = self.part_a(x)\n out2 = self.part_b(out1)\n return out1, out2\n\n\nclass IBN(nn.Module):\n def __init__(self, out_planes, bn=True):\n super(IBN, self).__init__()\n self.out_channels = out_planes\n self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None\n\n def forward(self, x):\n if self.bn is not None:\n x = self.bn(x)\n return x\n\n\nclass One_Three_Conv(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1):\n super(One_Three_Conv, self).__init__()\n self.out_channels = out_planes\n inter_planes = in_planes // 4\n self.single_branch = nn.Sequential(\n ConvBlock(in_planes, inter_planes, kernel_size=1, stride=1),\n ConvBlock(inter_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=1, relu=False)\n )\n\n def forward(self, x):\n out = self.single_branch(x)\n return out\n\n\nclass Relu_Conv(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1):\n super(Relu_Conv, self).__init__()\n self.out_channels = out_planes\n self.relu = nn.ReLU(inplace=False)\n self.single_branch = nn.Sequential(\n ConvBlock(in_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=1)\n )\n\n def forward(self, x):\n x = self.relu(x)\n out = self.single_branch(x)\n return out\n\n\nclass Ds_Conv(nn.Module):\n def __init__(self, in_planes, out_planes, stride=1, padding=(1, 1)):\n super(Ds_Conv, self).__init__()\n self.out_channels = out_planes\n self.single_branch = nn.Sequential(\n ConvBlock(in_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=padding, relu=False)\n )\n\n def forward(self, x):\n out = self.single_branch(x)\n return out\n\n\nclass LRFNet(nn.Module):\n \"\"\"LRFNet for object detection\n The network is based on the SSD architecture.\n Each multibox layer branches into\n 1) conv2d for class conf scores\n 2) conv2d for localization predictions\n 3) associated priorbox layer to produce default bounding\n boxes specific to the layer's feature map size.\n\n Args:\n phase: (string) Can be \"test\" or \"train\"\n base: VGG16 layers for input, size of either 300 or 512\n extras: extra layers that feed to multibox loc and conf layers\n head: \"multibox head\" consists of loc and conf conv layers\n \"\"\"\n\n def __init__(self, phase, size, base, extras, head, num_classes):\n super(LRFNet, self).__init__()\n self.phase = phase\n self.num_classes = num_classes\n self.size = size\n\n # vgg network\n self.base = nn.ModuleList(base)\n\n self.lds = LDS()\n\n # convs for merging the lsn and ssd features\n self.Norm1 = Relu_Conv(512, 512, stride=1)\n self.Norm2 = Relu_Conv(1024, 1024, stride=1)\n self.Norm3 = Relu_Conv(512, 512, stride=1)\n self.Norm4 = Relu_Conv(256, 256, stride=1)\n\n # convs for generate the lsn features\n self.icn1 = LSN_init(3, 512, stride=1)\n self.icn2 = LSN_later(128, 1024, stride=2)\n self.icn3 = LSN_later(256, 512, stride=2)\n\n # convs with s=2 to downsample the features\n self.dsc1 = Ds_Conv(512, 1024, stride=2, padding=(1, 1))\n self.dsc2 = Ds_Conv(1024, 512, stride=2, padding=(1, 1))\n self.dsc3 = Ds_Conv(512, 256, stride=2, padding=(1, 1))\n\n # convs to reduce the feature dimensions of current level\n self.agent1 = ConvBlock(512, 256, kernel_size=1, stride=1)\n self.agent2 = ConvBlock(1024, 512, kernel_size=1, stride=1)\n self.agent3 = ConvBlock(512, 256, kernel_size=1, stride=1)\n\n # convs to reduce the feature dimensions of other levels\n self.proj1 = ConvBlock(1024, 128, kernel_size=1, stride=1)\n self.proj2 = ConvBlock(512, 128, kernel_size=1, stride=1)\n self.proj3 = ConvBlock(256, 128, kernel_size=1, stride=1)\n\n # convs to reduce the feature dimensions of other levels\n self.convert1 = ConvBlock(384, 256, kernel_size=1)\n self.convert2 = ConvBlock(256, 512, kernel_size=1)\n self.convert3 = ConvBlock(128, 256, kernel_size=1)\n\n # convs to merge the features of the current and higher level features\n self.merge1 = ConvBlock(512, 512, kernel_size=3, stride=1, padding=1)\n self.merge2 = ConvBlock(1024, 1024, kernel_size=3, stride=1, padding=1)\n self.merge3 = ConvBlock(512, 512, kernel_size=3, stride=1, padding=1)\n\n self.ibn1 = IBN(512, bn=True)\n self.ibn2 = IBN(1024, bn=True)\n\n self.relu = nn.ReLU(inplace=False)\n\n self.extras = nn.ModuleList(extras)\n self.loc = nn.ModuleList(head[0])\n self.conf = nn.ModuleList(head[1])\n if self.phase == 'test':\n self.softmax = nn.Softmax()\n\n def forward(self, x):\n \"\"\"Applies network layers and ops on input image(s) x.\n\n Args:\n x: input image or batch of images. Shape: [batch,3,300,300].\n\n Return:\n Depending on phase:\n test:\n list of concat outputs from:\n 1: softmax layers, Shape: [batch*num_priors,num_classes]\n 2: localization layers, Shape: [batch,num_priors*4]\n 3: priorbox layers, Shape: [2,num_priors*4]\n\n train:\n list of concat outputs from:\n 1: confidence layers, Shape: [batch*num_priors,num_classes]\n 2: localization layers, Shape: [batch,num_priors*4]\n 3: priorbox layers, Shape: [2,num_priors*4]\n \"\"\"\n sources = list()\n loc = list()\n conf = list()\n new_sources = list()\n\n # apply lds to the initial image\n x_pool = self.lds(x)\n\n # apply vgg up to conv4_3\n for k in range(22):\n x = self.base[k](x)\n conv4_3_bn = self.ibn1(x)\n x_pool1_skip, x_pool1_icn = self.icn1(x_pool)\n s = self.Norm1(conv4_3_bn * x_pool1_icn)\n\n # apply vgg up to fc7\n for k in range(22, 34):\n x = self.base[k](x)\n conv7_bn = self.ibn2(x)\n x_pool2_skip, x_pool2_icn = self.icn2(x_pool1_skip)\n p = self.Norm2(self.dsc1(s) + conv7_bn * x_pool2_icn)\n\n x = self.base[34](x)\n\n # apply extra layers and cache source layer outputs\n for k, v in enumerate(self.extras):\n x = v(x)\n if k == 0:\n x_pool3_skip, x_pool3_icn = self.icn3(x_pool2_skip)\n w = self.Norm3(self.dsc2(p) + x * x_pool3_icn)\n elif k == 2:\n q = self.Norm4(self.dsc3(w) + x)\n sources.append(q)\n elif k == 5 or k == 7:\n sources.append(x)\n else:\n pass\n\n # project the forward features into lower dimension.\n tmp1 = self.proj1(p)\n tmp2 = self.proj2(w)\n tmp3 = self.proj3(q)\n\n # The conv4_3 level\n proj1 = F.upsample(tmp1, size=(38, 38), mode='bilinear')\n proj2 = F.upsample(tmp2, size=(38, 38), mode='bilinear')\n proj3 = F.upsample(tmp3, size=(38, 38), mode='bilinear')\n proj = torch.cat([proj1, proj2, proj3], dim=1)\n\n agent1 = self.agent1(s)\n convert1 = self.convert1(proj)\n pred1 = torch.cat([agent1, convert1], dim=1)\n pred1 = self.merge1(pred1)\n new_sources.append(pred1)\n\n # The fc_7 level\n proj2 = F.upsample(tmp2, size=(19, 19), mode='bilinear')\n proj3 = F.upsample(tmp3, size=(19, 19), mode='bilinear')\n proj = torch.cat([proj2, proj3], dim=1)\n\n agent2 = self.agent2(p)\n convert2 = self.convert2(proj)\n pred2 = torch.cat([agent2, convert2], dim=1)\n pred2 = self.merge2(pred2)\n new_sources.append(pred2)\n\n # The conv8 level\n proj3 = F.upsample(tmp3, size=(10, 10), mode='bilinear')\n proj = proj3\n\n agent3 = self.agent3(w)\n convert3 = self.convert3(proj)\n pred3 = torch.cat([agent3, convert3], dim=1)\n pred3 = self.merge3(pred3)\n new_sources.append(pred3)\n\n for prediction in sources:\n new_sources.append(prediction)\n\n # apply multibox head to source layers\n for (x, l, c) in zip(new_sources, self.loc, self.conf):\n loc.append(l(x).permute(0, 2, 3, 1).contiguous())\n conf.append(c(x).permute(0, 2, 3, 1).contiguous())\n\n loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)\n conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)\n\n if self.phase == \"test\":\n output = (\n loc.view(loc.size(0), -1, 4), # loc preds\n self.softmax(conf.view(-1, self.num_classes)), # conf preds\n )\n else:\n output = (\n loc.view(loc.size(0), -1, 4),\n conf.view(conf.size(0), -1, self.num_classes),\n )\n return output\n\n def load_weights(self, base_file):\n other, ext = os.path.splitext(base_file)\n if ext == '.pkl' or '.pth':\n print('Loading weights into state dict...')\n self.load_state_dict(torch.load(base_file))\n print('Finished!')\n else:\n print('Sorry only .pth and .pkl files supported.')\n\n\ndef vgg(cfg, i, batch_norm=False):\n layers = []\n in_channels = i\n for v in cfg:\n if v == 'M':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n elif v == 'C':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]\n else:\n conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n if batch_norm:\n layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=False)]\n else:\n layers += [conv2d, nn.ReLU(inplace=False)]\n in_channels = v\n pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)\n conv7 = nn.Conv2d(1024, 1024, kernel_size=1)\n layers += [pool5, conv6,\n nn.ReLU(inplace=False), conv7, nn.ReLU(inplace=False)]\n return layers\n\nbase = {\n '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',\n 512, 512, 512]}\n\n\ndef add_extras(size, cfg, i, batch_norm=False):\n # Extra layers added to VGG for feature scaling\n layers = []\n in_channels = i\n flag = False\n for k, v in enumerate(cfg):\n if in_channels != 'S':\n if v == 'S':\n if in_channels == 256 and size == 512:\n layers += [One_Three_Conv(in_channels, cfg[k+1], stride=2), nn.ReLU(inplace=False)]\n else:\n layers += [One_Three_Conv(in_channels, cfg[k+1], stride=2), nn.ReLU(inplace=False)]\n in_channels = v\n layers += [ConvBlock(256, 128, kernel_size=1,stride=1)]\n layers += [ConvBlock(128, 256, kernel_size=3,stride=1)]\n layers += [ConvBlock(256, 128, kernel_size=1,stride=1)]\n layers += [ConvBlock(128, 256, kernel_size=3,stride=1)]\n return layers\n\n\nextras = {\n '300': [1024, 'S', 512, 'S', 256]}\n\n\ndef multibox(size, vgg, extra_layers, cfg, num_classes):\n loc_layers = []\n conf_layers = []\n vgg_source = [1, -2]\n for k, v in enumerate(vgg_source):\n if k == 0:\n loc_layers += [nn.Conv2d(512,\n cfg[k] * 4, kernel_size=3, padding=1)]\n conf_layers +=[nn.Conv2d(512,\n cfg[k] * num_classes, kernel_size=3, padding=1)]\n else:\n loc_layers += [nn.Conv2d(vgg[v].out_channels,\n cfg[k] * 4, kernel_size=3, padding=1)]\n conf_layers += [nn.Conv2d(vgg[v].out_channels,\n cfg[k] * num_classes, kernel_size=3, padding=1)]\n i = 2\n indicator = 3\n\n for k, v in enumerate(extra_layers):\n if (k < indicator+1 and k % 2 == 0) or (k > indicator+1 and k % 2 != 0):\n loc_layers += [nn.Conv2d(v.out_channels, cfg[i]\n * 4, kernel_size=3, padding=1)]\n conf_layers += [nn.Conv2d(v.out_channels, cfg[i]\n * num_classes, kernel_size=3, padding=1)]\n i += 1\n\n return vgg, extra_layers, (loc_layers, conf_layers)\n\n\nmbox = {\n '300': [6, 6, 6, 6, 4, 4]}\n\n\ndef build_net(phase, size=300, num_classes=81):\n if size != 300:\n print(\"Error: The input image size is not supported!\")\n return\n\n return LRFNet(phase, size, *multibox(size, vgg(base[str(size)], 3),\n add_extras(size, extras[str(size)], 1024),\n mbox[str(size)], num_classes), num_classes)\n"},"avg_line_length":{"kind":"number","value":36.5166666667,"string":"36.516667"},"max_line_length":{"kind":"number","value":154,"string":"154"},"alphanum_fraction":{"kind":"number","value":0.5797744018,"string":"0.579774"},"count_classes":{"kind":"number","value":11783,"string":"11,783"},"score_classes":{"kind":"number","value":0.7682728043293995,"string":"0.768273"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2342,"string":"2,342"},"score_documentation":{"kind":"number","value":0.15270261459216275,"string":"0.152703"}}},{"rowIdx":3230,"cells":{"hexsha":{"kind":"string","value":"92e751e7128a30f8b366e1182af0f9e14b4591cd"},"size":{"kind":"number","value":25418,"string":"25,418"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"tests/test.py"},"max_stars_repo_name":{"kind":"string","value":"chromia/wandplus"},"max_stars_repo_head_hexsha":{"kind":"string","value":"815127aeee85dbac3bc8fca35971d2153b1898a9"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"tests/test.py"},"max_forks_repo_name":{"kind":"string","value":"chromia/wandplus"},"max_forks_repo_head_hexsha":{"kind":"string","value":"815127aeee85dbac3bc8fca35971d2153b1898a9"},"max_forks_repo_licenses":{"kind":"list like","value":["ImageMagick","MIT"],"string":"[\n \"ImageMagick\",\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python\n\nfrom wand.image import Image\nfrom wand.drawing import Drawing\nfrom wand.color import Color\nimport wandplus.image as wpi\nfrom wandplus.textutil import calcSuitableFontsize, calcSuitableImagesize\nimport os\nimport unittest\n\n\ntmpdir = '_tmp/'\n\n\ndef save(img, function, channel=False, ext='.png'):\n if channel:\n path = tmpdir + function.__name__ + \"_ch\" + ext\n else:\n path = tmpdir + function.__name__ + ext\n # print(path)\n img.save(filename=path)\n\n\nclass CheckImage(unittest.TestCase):\n\n @classmethod\n def setUpClass(self):\n os.mkdir(tmpdir)\n self.rose = Image(filename='rose:')\n self.grad = Image(filename='gradient:', width=400, height=400)\n self.logo = Image(filename='logo:')\n self.text = Image(filename='label:Confirm', width=200, height=60)\n self.text_a = Image(width=70, height=60)\n with Drawing() as draw:\n draw.font = 'Arial'\n draw.font_size = 50\n draw.gravity = 'center'\n draw.fill_color = Color('white')\n draw.stroke_color = Color('black')\n draw.text(0, 0, 'A')\n draw(self.text_a)\n\n self.rose.save(filename=tmpdir + 'rose.png')\n self.grad.save(filename=tmpdir + 'grad.png')\n self.logo.save(filename=tmpdir + 'logo.png')\n self.text.save(filename=tmpdir + 'text.png')\n self.text_a.save(filename=tmpdir + 'a.png')\n\n @classmethod\n def tearDownClass(self):\n self.rose.destroy()\n self.grad.destroy()\n self.logo.destroy()\n self.text.destroy()\n self.text_a.destroy()\n\n def test_adaptiveblur(self):\n f = wpi.adaptiveblur\n with self.rose.clone() as t:\n f(t, 5.0, 3.0)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 5.0, 3.0, channel='red')\n save(t, f, True)\n\n def test_adaptiveresize(self):\n f = wpi.adaptiveresize\n with self.rose.clone() as t:\n f(t, int(t.width*1.5), int(t.height*2.0))\n save(t, f)\n\n def test_adaptivesharpen(self):\n f = wpi.adaptivesharpen\n with self.rose.clone() as t:\n f(t, 5, 5)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 5, 5, channel='red')\n save(t, f, True)\n\n def test_adaptivethreshold(self):\n f = wpi.adaptivethreshold\n with self.logo.clone() as t:\n f(t, 20, 20, int(0.1*t.quantum_range))\n save(t, f)\n\n def test_addnoise(self):\n f = wpi.addnoise\n with self.grad.clone() as t:\n f(t, 'gaussian')\n save(t, f)\n with self.grad.clone() as t:\n f(t, 'gaussian', channel='red')\n save(t, f, True)\n\n def test_affinetransform(self):\n f = wpi.affinetransform\n with self.rose.clone() as t:\n with Drawing() as d:\n d.affine([2.0, 0.0, 0.0, 2.0, 0.0, 0.0])\n f(t, d) # not work correctly (IM<6.9.9-36)\n save(t, f)\n\n def test_autogamma(self):\n f = wpi.autogamma\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n with self.rose.clone() as t:\n f(t, channel='red')\n save(t, f, True)\n\n def test_autolevel(self):\n f = wpi.autolevel\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n with self.rose.clone() as t:\n f(t, channel='red')\n save(t, f, True)\n\n def test_blackthreshold(self):\n f = wpi.blackthreshold\n with self.grad.clone() as t:\n f(t, Color('gray(50%)'))\n save(t, f)\n\n def test_blueshift(self):\n f = wpi.blueshift\n with self.logo.clone() as t:\n f(t, 0.5)\n save(t, f)\n\n def test_brightnesscontrast(self):\n f = wpi.brightnesscontrast\n with self.rose.clone() as t:\n f(t, -30, 0)\n save(t, f)\n with self.rose.clone() as t:\n f(t, -30, 0, channel='red')\n save(t, f, True)\n\n def test_blur(self):\n f = wpi.blur\n with self.rose.clone() as t:\n f(t, 0, 3)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 0, 3, channel='red')\n save(t, f, True)\n\n def test_charcoal(self):\n f = wpi.charcoal\n with self.rose.clone() as t:\n f(t, 5, 1)\n save(t, f)\n\n def test_chop(self):\n f = wpi.chop\n with self.grad.clone() as t:\n t.gravity = 'north_west'\n f(t, 0, 00, 200, 200)\n save(t, f)\n\n def test_clamp(self):\n f = wpi.clamp # TODO: more useful code\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n with self.rose.clone() as t:\n f(t, channel='red')\n save(t, f, True)\n\n def test_clip(self): # NOTE: result is always FAILED.\n f = wpi.clip # I don't have an image which has clipping path\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n\n def test_clippath(self): # NOTE: result is always FAILED.\n f = wpi.clippath\n with self.rose.clone() as t:\n f(t, '#1', True)\n save(t, f)\n\n def test_clut(self):\n f = wpi.clut\n with Image(filename='gradient:red-blue', width=1, height=100) as p:\n p.rotate(90)\n with self.grad.clone() as t:\n f(t, p)\n save(t, f)\n with self.grad.clone() as t:\n f(t, p, channel='green')\n save(t, f, True)\n\n def test_coalesce(self): # TODO: input optimized .gif file.\n f = wpi.coalesce\n with Image() as t:\n with self.rose.clone() as p:\n for i in range(5):\n wpi.blur(p, 0, 1)\n wpi.add(t, p)\n with f(t) as p:\n save(p, f)\n\n def test_colordecisionlist(self):\n xml = \"\"\"\n \n \n \n 0.9 1.2 0.5 \n 0.4 -0.5 0.6 \n 1.0 0.8 1.5 \n \n \n 0.85 \n \n \n \n \"\"\"\n f = wpi.colordecisionlist\n with self.rose.clone() as t:\n f(t, xml)\n save(t, f)\n\n def test_colorize(self):\n f = wpi.colorize\n with self.grad.clone() as t:\n f(t, Color('red'), Color('gray(25%)'))\n save(t, f)\n\n def test_colormatrix(self):\n f = wpi.colormatrix\n with self.logo.clone() as t:\n kernel = [\n 0.5, 0.0, 0.0, 0.0, 0.0,\n 0.0, 1.5, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.5, 0.0, 0.0,\n 0.0, 0.0, 0.0, 1.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 1.0\n ]\n f(t, 5, 5, kernel)\n save(t, f)\n\n def test_combine(self):\n f = wpi.combine\n with Image() as t:\n w = 100\n h = 100\n black = Color('black')\n white = Color('white')\n with Image(width=w, height=w, background=black) as b:\n with Image(width=h, height=h, background=white) as w:\n wpi.add(t, b) # add image for red channel\n wpi.add(t, b) # add image for green channel\n wpi.add(t, w) # add image for blue channel\n wpi.setfirstiterator(t) # rewind the index pointer\n channel = 1 + 2 + 4 # R + G + B\n with f(t, channel) as q:\n save(q, f)\n\n def test_comment(self):\n f = wpi.comment\n with self.grad.clone() as t:\n f(t, 'hello')\n save(t, f)\n\n def test_compare(self):\n f = wpi.compare\n with self.rose.clone() as t:\n with t.clone() as p:\n (c, d) = f(t, p, metric='absolute')\n save(c, f)\n c.destroy()\n with self.rose.clone() as t:\n with t.clone() as p:\n (c, d) = f(t, p, metric='absolute', channel='red')\n save(c, f, True)\n c.destroy()\n\n def test_comparelayer(self):\n f = wpi.comparelayer\n with Image() as t:\n with Image(width=50, height=50, background=Color('red')) as p:\n wpi.add(t, p)\n with Image(width=25, height=25, background=Color('green1')) as q:\n for i in range(4):\n with q.clone() as qq:\n wpi.resetpage(qq, 5*(i+1), 5*(i+1))\n wpi.add(t, qq)\n with f(t, 'compareany') as r:\n save(r, f, ext='.gif')\n\n def test_constitute(self):\n f = wpi.constitute\n with Image() as t:\n w = 2\n h = 2\n b = [0, 0, 0,\n 255, 255, 255,\n 255, 0, 0,\n 0, 255, 0]\n f(t, w, h, 'RGB', 'char', b)\n save(t, f)\n\n def test_contrast(self):\n f = wpi.contrast\n with self.rose.clone() as t:\n f(t, False)\n save(t, f)\n\n def test_convolve(self):\n f = wpi.convolve\n kernel = [1/16, 2/16, 1/16,\n 2/16, 4/16, 2/16,\n 1/16, 2/16, 1/16]\n with self.rose.clone() as t:\n f(t, 3, kernel)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 3, kernel, channel='red')\n save(t, f, True)\n\n def test_cyclecolormap(self):\n f = wpi.cyclecolormap\n with self.logo.clone() as t:\n f(t, 5)\n save(t, f)\n\n def test_cipher(self):\n f = wpi.encipher\n with self.rose.clone() as t:\n f(t, 'password')\n save(t, f)\n f = wpi.decipher\n f(t, 'password')\n save(t, f)\n\n def test_deskew(self):\n f = wpi.deskew\n with Image(width=80, height=40, background=Color('black')) as t:\n f(t, 0.5*t.quantum_range) # TODO: find an skewed image as sample\n save(t, f)\n\n def test_despeckle(self):\n f = wpi.despeckle\n with self.rose.clone() as t:\n # TODO: add speckle noise\n f(t)\n save(t, f)\n\n def test_edge(self):\n f = wpi.edge\n with self.logo.clone() as t:\n f(t, 3)\n save(t, f)\n\n def test_emboss(self):\n f = wpi.emboss\n with self.logo.clone() as t:\n f(t, 0, 3)\n save(t, f)\n\n def test_enhance(self):\n f = wpi.enhance\n with Image(filename='plasma:', width=100, height=100) as t:\n f(t)\n save(t, f)\n\n def test_equalize(self):\n f = wpi.equalize\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n with self.rose.clone() as t:\n f(t, channel='red')\n save(t, f, True)\n\n def test_exportpixels(self):\n w = 1\n h = 1\n channels = 'RGB'\n with Image(width=w, height=h, background=Color('red')) as t:\n r = wpi.exportpixels(t, 0, 0, w, h, channels, 'double')\n self.assertEqual(r[0], 1.0)\n self.assertEqual(r[1], 0.0)\n self.assertEqual(r[2], 0.0)\n\n def test_extent(self):\n f = wpi.extent\n with self.rose.clone() as t:\n t.gravity = 'center'\n t.background_color = Color('blue')\n f(t, -10, -10, t.width+20, t.height+20)\n save(t, f)\n\n def test_filterimage(self):\n f = wpi.filterimage\n kernel = [ # Sobel filter\n -1.0, 0.0, 1.0,\n -2.0, 0.0, 2.0,\n -1.0, 0.0, 1.0,\n ]\n with self.rose.clone() as t:\n f(t, 3, 3, kernel)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 3, 3, kernel, channel='red')\n save(t, f, True)\n\n def test_floodfillpaint(self):\n f = wpi.floodfillpaint\n with self.logo.clone() as t:\n f(t, Color('green'), 0.10*t.quantum_range, Color('white'), 0, 0)\n save(t, f)\n\n def test_fft(self):\n f = wpi.forwardfouriertransform # require IM build option '--with-fftw'\n with self.logo.clone() as t: # I couldn't build on Windows...\n f(t, True)\n save(t, f) # includes two images(magnitude&phase)\n f = wpi.inversefouriertransform\n with t.sequence[0].clone() as mag:\n with t.sequence[1].clone() as phase:\n wpi.blur(mag, 0, 0.5) # as degradation\n t2 = mag\n f(t2, phase, True)\n save(t2, f)\n\n def test_haldclut(self):\n f = wpi.haldclut # TODO: more useful code\n with Image(filename='hald:12') as p:\n with self.rose.clone() as t:\n f(t, p)\n save(t, f)\n with self.rose.clone() as t:\n f(t, p, channel='red')\n save(t, f, True)\n\n def test_implode(self):\n f = wpi.implode\n with self.rose.clone() as t:\n f(t, 1.0)\n save(t, f)\n\n def test_importpixels(self):\n f = wpi.importpixels\n with Image(width=4, height=4, background=Color('red')) as t:\n w = 2\n h = 2\n b = [0, 0, 0,\n 255, 255, 255,\n 255, 0, 0,\n 0, 255, 0]\n f(t, 1, 1, w, h, 'RGB', 'char', b)\n save(t, f)\n\n def test_label(self):\n f = wpi.label\n with self.rose.clone() as t:\n f(t, 'hello')\n save(t, f)\n\n def test_localcontrast(self):\n f = wpi.localcontrast\n with self.logo.clone() as t:\n f(t, 5, 30)\n save(t, f)\n\n def test_magnify(self):\n f = wpi.magnify\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n\n def test_minify(self):\n f = wpi.minify\n with self.rose.clone() as t:\n f(t)\n save(t, f)\n\n def test_montage(self):\n f = wpi.montage\n with self.rose.clone() as base:\n with Image() as dst:\n rows = 2\n columns = 3\n for i in range(rows * columns):\n wpi.add(dst, base)\n\n tile = \"{0}x{1}+0+0\".format(columns, rows)\n thumb = \"80x50+4+3\"\n frame = \"15x15+3+3\"\n mode = \"frame\"\n with Drawing() as d:\n with f(dst, d, tile, thumb, mode, frame) as result:\n save(result, f)\n\n def test_morph(self):\n f = wpi.morph\n color = Color('white')\n with self.rose.clone() as t:\n with Image(width=t.width, height=t.height, background=color) as p:\n wpi.add(t, p)\n wpi.setfirstiterator(t)\n wpi.setdelay(t, 60)\n with f(t, 5) as q:\n save(q, f, ext='.gif')\n\n def test_morphology(self):\n f = wpi.morphology\n with self.logo.clone() as t:\n f(t, 'dilate', 1, 'Diamond')\n save(t, f)\n with self.logo.clone() as t:\n f(t, 'dilate', 1, 'Diamond', channel='red')\n save(t, f, True)\n\n def test_motionblur(self):\n f = wpi.motionblur\n with self.logo.clone() as t:\n f(t, 30, 10, 45)\n save(t, f)\n with self.logo.clone() as t:\n f(t, 30, 10, 45, channel='red')\n save(t, f, True)\n\n def test_oilpaint(self):\n f = wpi.oilpaint\n with self.rose.clone() as t:\n f(t, 2.0)\n save(t, f)\n\n def test_opaquepaint(self):\n f = wpi.opaquepaint\n with self.logo.clone() as t:\n f(t, Color('red'), Color('blue'), 1.0, False)\n save(t, f)\n with self.logo.clone() as t:\n f(t, Color('red'), Color('blue'), 1.0, False, channel='blue')\n save(t, f, True)\n\n def test_orderedposterize(self):\n f = wpi.orderedposterize\n with self.grad.clone() as t:\n f(t, 'o4x4,3,3')\n save(t, f)\n with self.grad.clone() as t:\n f(t, 'o4x4,3,3', channel='red')\n save(t, f, True)\n\n def test_polaroid(self):\n f = wpi.polaroid\n with self.logo.clone() as t:\n with Drawing() as d:\n f(t, d, 1.0)\n save(t, f)\n\n def test_posterize(self):\n f = wpi.posterize\n with self.rose.clone() as t:\n f(t, 3, True)\n save(t, f)\n\n def test_raiseimage(self):\n f = wpi.raiseimage\n with self.rose.clone() as t:\n f(t, 10, 10, 10, 10, True)\n save(t, f)\n\n def test_randomthreshold(self):\n f = wpi.randomthreshold\n with self.text_a.clone() as t:\n rng = t.quantum_range\n f(t, int(rng * 0.05), int(rng * 0.95))\n save(t, f)\n with self.text_a.clone() as t:\n rng = t.quantum_range\n f(t, int(rng * 0.05), int(rng * 0.95), channel='red')\n save(t, f, True)\n\n def test_remap(self):\n f = wpi.remap\n with self.logo.clone() as t:\n with self.rose.clone() as p:\n f(t, p, 'nodither')\n save(t, f)\n\n def test_resample(self):\n f = wpi.resample\n with self.rose.clone() as t:\n dpi = 72 * 2\n f(t, dpi, dpi, 'lanczos', 1.0)\n save(t, f)\n\n def test_roll(self):\n f = wpi.roll\n with self.rose.clone() as t:\n f(t, 10, 10)\n save(t, f)\n\n def test_rotationalblur(self):\n f = wpi.rotationalblur\n with self.rose.clone() as t:\n f(t, 45)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 45, channel='red')\n save(t, f, True)\n\n def test_scale(self):\n f = wpi.scale\n with self.rose.clone() as t:\n f(t, t.width*2, t.height*2)\n save(t, f)\n\n def test_segment(self):\n f = wpi.segment\n with self.logo.clone() as t:\n f(t, 'rgb', False, 5, 20)\n save(t, f)\n\n def test_selectiveblur(self):\n f = wpi.selectiveblur\n with self.logo.clone() as t:\n f(t, 20, 20, 0.5*t.quantum_range)\n save(t, f)\n with self.logo.clone() as t:\n f(t, 20, 20, 0.5*t.quantum_range, channel='red')\n save(t, f, True)\n\n def test_separate_channel(self):\n f = wpi.separate_channel\n with self.rose.clone() as t:\n f(t, 'red')\n save(t, f)\n\n def test_sepiatone(self):\n f = wpi.sepiatone\n with self.rose.clone() as t:\n f(t, 0.5*t.quantum_range)\n save(t, f)\n\n def test_shade(self):\n f = wpi.shade\n with self.logo.clone() as t:\n f(t, True, 45, 135)\n save(t, f)\n\n def test_shadow(self):\n f = wpi.shadow\n with self.text.clone() as t:\n with self.text.clone() as p:\n p.negate()\n f(p, 100, 2, 10, 10)\n t.composite_channel('default_channels', p, 'overlay')\n save(t, f)\n\n def test_sharpen(self):\n f = wpi.sharpen\n with self.rose.clone() as t:\n f(t, 3, 3)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 3, 3, channel='red')\n save(t, f, True)\n\n def test_shave(self):\n f = wpi.shave\n with self.logo.clone() as t:\n f(t, 100, 100)\n save(t, f)\n\n def test_shear(self):\n f = wpi.shear\n with self.grad.clone() as t:\n f(t, Color('red'), 0, 10)\n save(t, f)\n\n def test_sigmoidalcontrast(self):\n f = wpi.sigmoidalcontrast\n with self.rose.clone() as t:\n f(t, True, 3, 3)\n save(t, f)\n with self.rose.clone() as t:\n f(t, True, 3, 3, channel='red')\n save(t, f, True)\n\n def test_sketch(self):\n f = wpi.sketch\n with self.logo.clone() as t:\n f(t, 10, 10, 45)\n save(t, f)\n\n def test_smush(self):\n f = wpi.smush\n\n def makeletter(letter, w, h):\n img = Image(width=w, height=h)\n with Drawing() as d:\n d.font = 'Arial'\n d.font_size = 24\n d.gravity = 'center'\n d.text(0, 0, letter)\n d(img)\n return img\n with Image() as t:\n with makeletter('A', 50, 30) as a:\n with makeletter('B', 50, 30) as b:\n wpi.add(t, a)\n wpi.add(t, b)\n wpi.setfirstiterator(t)\n with f(t, False, -3) as p:\n save(p, f)\n\n def test_solarize(self):\n f = wpi.solarize\n with self.rose.clone() as t:\n f(t, 0.4*t.quantum_range)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 0.4*t.quantum_range, channel='red')\n save(t, f, True)\n\n def test_splice(self):\n f = wpi.splice\n with self.rose.clone() as t:\n t.gravity = 'center'\n f(t, t.width//2, t.height//2, 20, 20)\n save(t, f)\n\n def test_sparsecolor(self):\n f = wpi.sparsecolor\n with Image(width=100, height=100, background=Color('black')) as t:\n f(t, 'default_channels', 'bilinear',\n [0, 0, 1.0, 0.0, 0.0, 1.0,\n 100, 100, 0.0, 1.0, 1.0, 1.0])\n save(t, f)\n\n def test_spread(self):\n f = wpi.spread\n with self.logo.clone() as t:\n f(t, 20)\n save(t, f)\n\n def test_statistic(self):\n f = wpi.statistic\n with self.rose.clone() as t:\n f(t, 'gradient', 4, 4)\n save(t, f)\n with self.rose.clone() as t:\n f(t, 'gradient', 4, 4, channel='red')\n save(t, f, True)\n\n def test_stegano(self):\n f = wpi.stegano\n with self.rose.clone() as t:\n w = 50\n h = 40\n offset = 15\n tmpfile = 'tmp.png'\n with Image(width=w, height=h, background=Color('white')) as p:\n with Drawing() as d:\n d.gravity = 'center'\n d.fill_color = Color('black')\n d.text(0, 0, 'Watch\\nthe\\nPidgeon')\n d(p)\n with f(t, p, offset) as q:\n q.save(filename=tmpfile)\n try:\n with Image() as q:\n wpi.setsizeoffset(q, w, h, offset)\n q.read(filename='stegano:' + tmpfile)\n save(q, f)\n except Exception:\n raise\n finally:\n os.remove(tmpfile)\n\n def test_stereo(self):\n f = wpi.stereo\n with self.rose.clone() as t:\n with self.rose.clone() as p:\n p.negate()\n with f(t, p) as q:\n save(q, f)\n\n def test_swirl(self):\n f = wpi.swirl\n with self.rose.clone() as t:\n f(t, 180)\n save(t, f)\n\n def test_texture(self):\n f = wpi.texture\n with Image(width=300, height=200) as t:\n with self.rose.clone() as p:\n with f(t, p) as q:\n save(q, f)\n\n def test_thumbnail(self):\n f = wpi.thumbnail\n with self.logo.clone() as t:\n f(t, 100, 100)\n save(t, f)\n\n def test_tint(self):\n f = wpi.tint\n with self.rose.clone() as t:\n f(t, Color('rgb'), Color('gray(25%)'))\n save(t, f)\n\n def test_vignette(self):\n f = wpi.vignette\n with self.logo.clone() as t:\n wpi.minify(t)\n t.background_color = Color('black')\n f(t, 0, 10, 20, 20)\n save(t, f)\n\n def test_wave(self):\n f = wpi.wave\n with self.grad.clone() as t:\n f(t, 40, 200)\n save(t, f)\n\n def test_whitethreshold(self):\n f = wpi.whitethreshold\n with self.grad.clone() as t:\n f(t, Color('gray(50%)'))\n save(t, f)\n\n\nclass CheckTextUtil(unittest.TestCase):\n\n def test_imagesize(self):\n with Drawing() as d:\n text = 'check'\n d.font = 'Arial'\n d.font_size = 36\n size = calcSuitableImagesize(d, text)\n print('calcSuitableImagesize: ', size)\n self.assertTrue(size[0] > 0 and size[1] > 0)\n\n def test_fontsize(self):\n w = 100\n h = 100\n with Drawing() as d:\n text = 'check'\n d.font = 'Arial'\n fontsize = calcSuitableFontsize(d, text, width=w)\n print('calcSuitableImagesize[W]: ', fontsize)\n self.assertTrue(fontsize > 0)\n fontsize = calcSuitableFontsize(d, text, height=h)\n print('calcSuitableImagesize[H]: ', fontsize)\n self.assertTrue(fontsize > 0)\n\n\nif __name__ == '__main__':\n 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\"CNRI-Python\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-04-07T22:30:23.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-04-08T00:55:36.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/librender/tests/test_mesh.py"},"max_forks_repo_name":{"kind":"string","value":"tizian/layer-laboratory"},"max_forks_repo_head_hexsha":{"kind":"string","value":"008cc94b76127e9eb74227fcd3d0145da8ddec30"},"max_forks_repo_licenses":{"kind":"list like","value":["CNRI-Python"],"string":"[\n \"CNRI-Python\"\n]"},"max_forks_count":{"kind":"number","value":2,"string":"2"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-06-08T08:25:09.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-04-05T22:13:08.000Z"},"content":{"kind":"string","value":"import mitsuba\nimport pytest\nimport enoki as ek\nfrom enoki.dynamic import Float32 as Float\n\nfrom mitsuba.python.test.util import fresolver_append_path\nfrom mitsuba.python.util import traverse\n\n\ndef test01_create_mesh(variant_scalar_rgb):\n from mitsuba.core import Struct, float_dtype\n from mitsuba.render import Mesh\n\n m = Mesh(\"MyMesh\", 3, 2)\n m.vertex_positions_buffer()[:] = [0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.2, 1.0, 0.0]\n m.faces_buffer()[:] = [0, 1, 2, 1, 2, 0]\n m.parameters_changed()\n\n assert str(m) == \"\"\"Mesh[\n name = \"MyMesh\",\n bbox = BoundingBox3f[\n min = [0, 0, 0],\n max = [1, 1, 0]\n ],\n vertex_count = 3,\n vertices = [36 B of vertex data],\n face_count = 2,\n faces = [24 B of face data],\n disable_vertex_normals = 0,\n surface_area = 0.96\n]\"\"\"\n\n\n@fresolver_append_path\ndef test02_ply_triangle(variant_scalar_rgb):\n from mitsuba.core import UInt32, Vector3f\n from mitsuba.core.xml import load_string\n\n m = load_string(\"\"\"\n \n \n \n \n \"\"\")\n\n positions = m.vertex_positions_buffer()\n faces = m.faces_buffer()\n\n assert not m.has_vertex_normals()\n assert ek.slices(positions) == 9\n assert ek.allclose(positions[0:3], [0, 0, 0])\n assert ek.allclose(positions[3:6], [0, 0, 1])\n assert ek.allclose(positions[6:9], [0, 1, 0])\n assert ek.slices(faces) == 3\n assert faces[0] == UInt32(0)\n assert faces[1] == UInt32(1)\n assert faces[2] == UInt32(2)\n\n\n@fresolver_append_path\ndef test03_ply_computed_normals(variant_scalar_rgb):\n from mitsuba.core import Vector3f\n from mitsuba.core.xml import load_string\n\n \"\"\"Checks(automatic) vertex normal computation for a PLY file that\n doesn't have them.\"\"\"\n shape = load_string(\"\"\"\n \n \n \n \"\"\")\n normals = shape.vertex_normals_buffer()\n assert shape.has_vertex_normals()\n # Normals are stored in half precision\n assert ek.allclose(normals[0:3], [-1, 0, 0])\n assert ek.allclose(normals[3:6], [-1, 0, 0])\n assert ek.allclose(normals[6:9], [-1, 0, 0])\n\n\ndef test04_normal_weighting_scheme(variant_scalar_rgb):\n from mitsuba.core import Struct, float_dtype, Vector3f\n from mitsuba.render import Mesh\n import numpy as np\n\n \"\"\"Tests the weighting scheme that is used to compute surface normals.\"\"\"\n m = Mesh(\"MyMesh\", 5, 2, has_vertex_normals=True)\n\n vertices = m.vertex_positions_buffer()\n normals = m.vertex_normals_buffer()\n\n a, b = 1.0, 0.5\n vertices[:] = [0, 0, 0, -a, 1, 0, a, 1, 0, -b, 0, 1, b, 0, 1]\n\n n0 = Vector3f(0.0, 0.0, -1.0)\n n1 = Vector3f(0.0, 1.0, 0.0)\n angle_0 = ek.pi / 2.0\n angle_1 = ek.acos(3.0 / 5.0)\n n2 = n0 * angle_0 + n1 * angle_1\n n2 /= ek.norm(n2)\n n = np.vstack([n2, n0, n0, n1, n1]).transpose()\n\n m.faces_buffer()[:] = [0, 1, 2, 0, 3, 4]\n\n m.recompute_vertex_normals()\n for i in range(5):\n assert ek.allclose(normals[i*3:(i+1)*3], n[:, i], 5e-4)\n\n\n@fresolver_append_path\ndef test05_load_simple_mesh(variant_scalar_rgb):\n from mitsuba.core.xml import load_string\n\n \"\"\"Tests the OBJ and PLY loaders on a simple example.\"\"\"\n for mesh_format in [\"obj\", \"ply\"]:\n shape = load_string(\"\"\"\n \n \n \n \"\"\".format(mesh_format))\n\n positions = shape.vertex_positions_buffer()\n faces = shape.faces_buffer()\n\n assert shape.has_vertex_normals()\n assert ek.slices(positions) == 72\n assert ek.slices(faces) == 36\n assert ek.allclose(faces[6:9], [4, 5, 6])\n assert ek.allclose(positions[:5], [130, 165, 65, 82, 165])\n\n\n@pytest.mark.parametrize('mesh_format', ['obj', 'ply', 'serialized'])\n@pytest.mark.parametrize('features', ['normals', 'uv', 'normals_uv'])\n@pytest.mark.parametrize('face_normals', [True, False])\ndef test06_load_various_features(variant_scalar_rgb, mesh_format, features, face_normals):\n \"\"\"Tests the OBJ & PLY loaders with combinations of vertex / face normals,\n presence and absence of UVs, etc.\n \"\"\"\n from mitsuba.core.xml import load_string\n\n def test():\n shape = load_string(\"\"\"\n \n \n \n \n \"\"\".format(mesh_format, features, str(face_normals).lower()))\n assert shape.has_vertex_normals() == (not face_normals)\n\n positions = shape.vertex_positions_buffer()\n normals = shape.vertex_normals_buffer()\n texcoords = shape.vertex_texcoords_buffer()\n faces = shape.faces_buffer()\n\n (v0, v2, v3) = [positions[i*3:(i+1)*3] for i in [0, 2, 3]]\n\n assert ek.allclose(v0, [-2.85, 0.0, -7.600000], atol=1e-3)\n assert ek.allclose(v2, [ 2.85, 0.0, 0.599999], atol=1e-3)\n assert ek.allclose(v3, [ 2.85, 0.0, -7.600000], atol=1e-3)\n\n if 'uv' in features:\n assert shape.has_vertex_texcoords()\n (uv0, uv2, uv3) = [texcoords[i*2:(i+1)*2] for i in [0, 2, 3]]\n # For OBJs (and .serialized generated from OBJ), UV.y is flipped.\n if mesh_format in ['obj', 'serialized']:\n assert ek.allclose(uv0, [0.950589, 1-0.988416], atol=1e-3)\n assert ek.allclose(uv2, [0.025105, 1-0.689127], atol=1e-3)\n assert ek.allclose(uv3, [0.950589, 1-0.689127], atol=1e-3)\n else:\n assert ek.allclose(uv0, [0.950589, 0.988416], atol=1e-3)\n assert ek.allclose(uv2, [0.025105, 0.689127], atol=1e-3)\n assert ek.allclose(uv3, [0.950589, 0.689127], atol=1e-3)\n\n if shape.has_vertex_normals():\n for n in [normals[i*3:(i+1)*3] for i in [0, 2, 3]]:\n assert ek.allclose(n, [0.0, 1.0, 0.0])\n\n return fresolver_append_path(test)()\n\n\n@fresolver_append_path\ndef test07_ply_stored_attribute(variant_scalar_rgb):\n from mitsuba.core import Vector3f\n from mitsuba.core.xml import load_string\n\n m = load_string(\"\"\"\n \n \n \n \"\"\")\n\n assert str(m) == \"\"\"PLYMesh[\n name = \"triangle_face_colors.ply\",\n bbox = BoundingBox3f[\n min = [0, 0, 0],\n max = [0, 1, 1]\n ],\n vertex_count = 3,\n vertices = [72 B of vertex data],\n face_count = 1,\n faces = [24 B of face data],\n disable_vertex_normals = 0,\n surface_area = 0,\n mesh attributes = [\n face_color: 3 floats\n ]\n]\"\"\"\n\n\ndef test08_mesh_add_attribute(variant_scalar_rgb):\n from mitsuba.core import Struct, float_dtype\n from mitsuba.render import Mesh\n\n m = Mesh(\"MyMesh\", 3, 2)\n m.vertex_positions_buffer()[:] = [0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.2, 1.0, 0.0]\n m.faces_buffer()[:] = [0, 1, 2, 1, 2, 0]\n m.parameters_changed()\n\n m.add_attribute(\"vertex_color\", 3)[:] = [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0]\n\n assert str(m) == \"\"\"Mesh[\n name = \"MyMesh\",\n bbox = BoundingBox3f[\n min = [0, 0, 0],\n max = [1, 1, 0]\n ],\n vertex_count = 3,\n vertices = [72 B of vertex data],\n face_count = 2,\n faces = [24 B of face data],\n disable_vertex_normals = 0,\n surface_area = 0.96,\n mesh attributes = [\n vertex_color: 3 floats\n ]\n]\"\"\""},"avg_line_length":{"kind":"number","value":32.5617021277,"string":"32.561702"},"max_line_length":{"kind":"number","value":93,"string":"93"},"alphanum_fraction":{"kind":"number","value":0.6119968636,"string":"0.611997"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":5213,"string":"5,213"},"score_decorators":{"kind":"number","value":0.6812598013591218,"string":"0.68126"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2384,"string":"2,384"},"score_documentation":{"kind":"number","value":0.31155253528489285,"string":"0.311553"}}},{"rowIdx":3232,"cells":{"hexsha":{"kind":"string","value":"92e9c5118907200831bee6234267cd344285472f"},"size":{"kind":"number","value":1457,"string":"1,457"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"agsadmin/sharing_admin/community/groups/Group.py"},"max_stars_repo_name":{"kind":"string","value":"christopherblanchfield/agsadmin"},"max_stars_repo_head_hexsha":{"kind":"string","value":"989cb3795aacf285ccf74ee51b0de26bf2f48bc3"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2015-12-07T05:53:29.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-09-13T18:12:15.000Z"},"max_issues_repo_path":{"kind":"string","value":"agsadmin/sharing_admin/community/groups/Group.py"},"max_issues_repo_name":{"kind":"string","value":"christopherblanchfield/agsadmin"},"max_issues_repo_head_hexsha":{"kind":"string","value":"989cb3795aacf285ccf74ee51b0de26bf2f48bc3"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":4,"string":"4"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2015-03-09T05:59:14.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2018-01-09T00:12:56.000Z"},"max_forks_repo_path":{"kind":"string","value":"agsadmin/sharing_admin/community/groups/Group.py"},"max_forks_repo_name":{"kind":"string","value":"christopherblanchfield/agsadmin"},"max_forks_repo_head_hexsha":{"kind":"string","value":"989cb3795aacf285ccf74ee51b0de26bf2f48bc3"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":5,"string":"5"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2015-03-09T01:05:24.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2019-09-09T23:01:21.000Z"},"content":{"kind":"string","value":"from __future__ import (absolute_import, division, print_function, unicode_literals)\nfrom builtins import (ascii, bytes, chr, dict, filter, hex, input, int, map, next, oct, open, pow, range, round, str,\n super, zip)\n\nfrom ...._utils import send_session_request\nfrom ..._PortalEndpointBase import PortalEndpointBase\nfrom .CreateUpdateGroupParams import CreateUpdateGroupParams\n\n\nclass Group(PortalEndpointBase):\n @property\n def id(self):\n return self._pdata[\"id\"]\n\n @property\n def _url_full(self):\n return \"{0}/{1}\".format(self._url_base, self.id)\n\n def __init__(self, requests_session, url_base, id):\n super().__init__(requests_session, url_base)\n\n self._pdata = {\"id\": id}\n\n def get_properties(self):\n \"\"\"\n Gets the properties of the item.\n \"\"\"\n return self._get()\n\n def update(self, update_group_params, clear_empty_fields=False):\n \"\"\"\n Updates the group properties.\n \"\"\"\n\n update_group_params = update_group_params._get_params() if isinstance(\n update_group_params, CreateUpdateGroupParams) else update_group_params.copy()\n\n if not \"clearEmptyFields\" in update_group_params:\n update_group_params[\"clearEmptyFields\"] = clear_empty_fields\n\n r = self._create_operation_request(self, \"update\", method=\"POST\", data=update_group_params)\n\n return send_session_request(self._session, r).json()"},"avg_line_length":{"kind":"number","value":33.8837209302,"string":"33.883721"},"max_line_length":{"kind":"number","value":117,"string":"117"},"alphanum_fraction":{"kind":"number","value":0.683596431,"string":"0.683596"},"count_classes":{"kind":"number","value":1058,"string":"1,058"},"score_classes":{"kind":"number","value":0.726149622512011,"string":"0.72615"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":151,"string":"151"},"score_decorators":{"kind":"number","value":0.10363761153054221,"string":"0.103638"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":176,"string":"176"},"score_documentation":{"kind":"number","value":0.12079615648592999,"string":"0.120796"}}},{"rowIdx":3233,"cells":{"hexsha":{"kind":"string","value":"92ea3eda1c775e0583e47210352c08da3ae6793c"},"size":{"kind":"number","value":3995,"string":"3,995"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"amy/workshops/migrations/0191_auto_20190809_0936.py"},"max_stars_repo_name":{"kind":"string","value":"code-review-doctor/amy"},"max_stars_repo_head_hexsha":{"kind":"string","value":"268c1a199510457891459f3ddd73fcce7fe2b974"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":53,"string":"53"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2015-01-10T17:39:19.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2019-06-12T17:36:34.000Z"},"max_issues_repo_path":{"kind":"string","value":"amy/workshops/migrations/0191_auto_20190809_0936.py"},"max_issues_repo_name":{"kind":"string","value":"code-review-doctor/amy"},"max_issues_repo_head_hexsha":{"kind":"string","value":"268c1a199510457891459f3ddd73fcce7fe2b974"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1176,"string":"1,176"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2015-01-02T06:32:47.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2019-06-18T11:57:47.000Z"},"max_forks_repo_path":{"kind":"string","value":"amy/workshops/migrations/0191_auto_20190809_0936.py"},"max_forks_repo_name":{"kind":"string","value":"code-review-doctor/amy"},"max_forks_repo_head_hexsha":{"kind":"string","value":"268c1a199510457891459f3ddd73fcce7fe2b974"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":44,"string":"44"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2015-01-03T15:08:56.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2019-06-09T05:33:08.000Z"},"content":{"kind":"string","value":"# Generated by Django 2.1.7 on 2019-08-09 09:36\n\nfrom django.db import migrations, models\n\n\ndef migrate_public_event(apps, schema_editor):\n \"\"\"Migrate options previously with no contents (displayed as \"Other:\")\n to a new contents (\"other\").\n The field containing these options is in CommonRequest abstract model,\n implemented in WorkshopRequest, WorkshopInquiryRequest, and\n SelfOrganizedSubmission models.\"\"\"\n WorkshopRequest = apps.get_model('workshops', 'WorkshopRequest')\n WorkshopInquiryRequest = apps.get_model('extrequests',\n 'WorkshopInquiryRequest')\n SelfOrganizedSubmission = apps.get_model('extrequests',\n 'SelfOrganizedSubmission')\n \n WorkshopRequest.objects.filter(public_event=\"\") \\\n .update(public_event=\"other\")\n WorkshopInquiryRequest.objects.filter(public_event=\"\") \\\n .update(public_event=\"other\")\n SelfOrganizedSubmission.objects.filter(public_event=\"\") \\\n .update(public_event=\"other\")\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('workshops', '0190_auto_20190728_1118'),\n ('extrequests', '0008_auto_20190809_1004'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='workshoprequest',\n name='host_responsibilities',\n field=models.BooleanField(default=False, verbose_name='I understand the responsibilities of the workshop host, including recruiting local helpers to support the workshop (1 helper for every 8-10 learners).'),\n ),\n migrations.AlterField(\n model_name='workshoprequest',\n name='requested_workshop_types',\n field=models.ManyToManyField(help_text='If your learners are new to programming and primarily interested in working with data, Data Carpentry is likely the best choice. If your learners are interested in learning more about programming, including version control and automation, Software Carpentry is likely the best match. If your learners are people working in library and information related roles interested in learning data and software skills, Library Carpentry is the best choice. Please visit the Software Carpentry lessons page, Data Carpentry lessons page, or the Library Carpentry lessons page for more information about any of our lessons.', limit_choices_to={'active': True}, to='workshops.Curriculum', verbose_name='Which Carpentries workshop are you requesting?'),\n ),\n migrations.AlterField(\n model_name='workshoprequest',\n name='scholarship_circumstances',\n field=models.TextField(blank=True, help_text='Required only if you request a scholarship.', verbose_name='Please explain the circumstances for your scholarship request and let us know what budget you have towards The Carpentries workshop fees.'),\n ),\n migrations.AlterField(\n model_name='workshoprequest',\n name='public_event',\n field=models.CharField(blank=True, choices=[('invite', 'This event is open to learners by invitation only.'), ('closed', 'This event is open to learners inside of my institution.'), ('public', 'This event is open to learners outside of my institution.'), ('other', 'Other:')], default='', help_text='Many of our workshops restrict registration to learners from the hosting institution. If your workshop will be open to registrants outside of your institution please let us know below.', max_length=20, verbose_name='Is this workshop open to the public?'),\n ),\n migrations.RunPython(migrate_public_event),\n ]\n"},"avg_line_length":{"kind":"number","value":71.3392857143,"string":"71.339286"},"max_line_length":{"kind":"number","value":949,"string":"949"},"alphanum_fraction":{"kind":"number","value":0.7023779725,"string":"0.702378"},"count_classes":{"kind":"number","value":2869,"string":"2,869"},"score_classes":{"kind":"number","value":0.7181476846057572,"string":"0.718148"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2432,"string":"2,432"},"score_documentation":{"kind":"number","value":0.6087609511889862,"string":"0.608761"}}},{"rowIdx":3234,"cells":{"hexsha":{"kind":"string","value":"92ec1a79aa56994e71f763b1fea1ca3f88478806"},"size":{"kind":"number","value":1278,"string":"1,278"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"pix2pix/Discriminator.py"},"max_stars_repo_name":{"kind":"string","value":"yubin1219/GAN"},"max_stars_repo_head_hexsha":{"kind":"string","value":"8345095f9816e548c968492efbe92b427b0e06a3"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"pix2pix/Discriminator.py"},"max_issues_repo_name":{"kind":"string","value":"yubin1219/GAN"},"max_issues_repo_head_hexsha":{"kind":"string","value":"8345095f9816e548c968492efbe92b427b0e06a3"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"pix2pix/Discriminator.py"},"max_forks_repo_name":{"kind":"string","value":"yubin1219/GAN"},"max_forks_repo_head_hexsha":{"kind":"string","value":"8345095f9816e548c968492efbe92b427b0e06a3"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-09-17T01:28:50.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-09-17T01:28:50.000Z"},"content":{"kind":"string","value":"import torch\nimport torch.nn as nn\n\nclass Discriminator(nn.Module):\n def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False) :\n super(Discriminator, self).__init__()\n self.conv1 = nn.Sequential(\n nn.Conv2d(input_nc, ndf, kernel_size=4, stride=2, padding=1),\n nn.LeakyReLU(0.2, True)\n )\n self.conv2 = nn.Sequential(\n nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1),\n norm_layer(ndf * 2),\n nn.LeakyReLU(0.2, True)\n )\n self.conv3 = nn.Sequential(\n nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1),\n norm_layer(ndf * 4),\n nn.LeakyReLU(0.2, True)\n )\n self.conv4 = nn.Sequential(\n nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),\n norm_layer(ndf * 8),\n nn.LeakyReLU(0.2, True)\n )\n \n if use_sigmoid:\n self.conv5 = nn.Sequential(\n nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1),\n nn.Sigmoid()\n )\n else:\n self.conv5 = nn.Sequential(\n nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1)\n )\n \n def forward(self, x):\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.conv3(x)\n x = self.conv4(x)\n x = self.conv5(x)\n return x\n"},"avg_line_length":{"kind":"number","value":29.0454545455,"string":"29.045455"},"max_line_length":{"kind":"number","value":86,"string":"86"},"alphanum_fraction":{"kind":"number","value":0.5821596244,"string":"0.58216"},"count_classes":{"kind":"number","value":1241,"string":"1,241"},"score_classes":{"kind":"number","value":0.9710485133020345,"string":"0.971049"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":0,"string":"0"},"score_documentation":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":3235,"cells":{"hexsha":{"kind":"string","value":"92ec31910f4ccb9a9e9fdaf1976491caf430c06d"},"size":{"kind":"number","value":1067,"string":"1,067"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"tests/slicebuilders/subpopulations/test_length.py"},"max_stars_repo_name":{"kind":"string","value":"ANarayan/robustness-gym"},"max_stars_repo_head_hexsha":{"kind":"string","value":"eed2800985631fbbe6491b5f6f0731a067eef78e"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"tests/slicebuilders/subpopulations/test_length.py"},"max_issues_repo_name":{"kind":"string","value":"ANarayan/robustness-gym"},"max_issues_repo_head_hexsha":{"kind":"string","value":"eed2800985631fbbe6491b5f6f0731a067eef78e"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"tests/slicebuilders/subpopulations/test_length.py"},"max_forks_repo_name":{"kind":"string","value":"ANarayan/robustness-gym"},"max_forks_repo_head_hexsha":{"kind":"string","value":"eed2800985631fbbe6491b5f6f0731a067eef78e"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from unittest import TestCase\n\nimport numpy as np\n\nfrom robustnessgym.cachedops.spacy import Spacy\nfrom robustnessgym.slicebuilders.subpopulations.length import LengthSubpopulation\nfrom tests.testbeds import MockTestBedv0\n\n\nclass TestLengthSubpopulation(TestCase):\n def setUp(self):\n self.testbed = MockTestBedv0()\n self.testbed.dataset = Spacy()(self.testbed.dataset, columns=[\"text\"])\n\n def test_score(self):\n # Create the length subpopulation\n length = LengthSubpopulation(intervals=[(1, 3), (4, 5)])\n\n # Compute scores\n scores = length.score(self.testbed.dataset[:], columns=[\"text\"])\n self.assertTrue(np.allclose(scores, np.array([5, 5, 5, 5, 5, 5])))\n\n print(self.testbed.dataset.column_names)\n print(Spacy.retrieve(self.testbed.dataset[:], [\"text\"]))\n\n # Apply the subpopulation\n slices, slice_matrix = length(self.testbed.dataset, columns=[\"text\"])\n\n # Check that the slice membership lines up\n self.assertTrue(np.allclose(slice_matrix, np.array([[0, 1]] * 6)))\n"},"avg_line_length":{"kind":"number","value":34.4193548387,"string":"34.419355"},"max_line_length":{"kind":"number","value":81,"string":"81"},"alphanum_fraction":{"kind":"number","value":0.6860356139,"string":"0.686036"},"count_classes":{"kind":"number","value":842,"string":"842"},"score_classes":{"kind":"number","value":0.7891283973758201,"string":"0.789128"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":140,"string":"140"},"score_documentation":{"kind":"number","value":0.13120899718837864,"string":"0.131209"}}},{"rowIdx":3236,"cells":{"hexsha":{"kind":"string","value":"92eca5c1a6337291d863c933685487ea52da0c9b"},"size":{"kind":"number","value":1146,"string":"1,146"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py"},"max_stars_repo_name":{"kind":"string","value":"NickSwainston/pulsar_spectra"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py"},"max_issues_repo_name":{"kind":"string","value":"NickSwainston/pulsar_spectra"},"max_issues_repo_head_hexsha":{"kind":"string","value":"b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":4,"string":"4"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-12-17T04:24:13.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-02-24T14:51:18.000Z"},"max_forks_repo_path":{"kind":"string","value":"pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py"},"max_forks_repo_name":{"kind":"string","value":"NickSwainston/pulsar_spectra"},"max_forks_repo_head_hexsha":{"kind":"string","value":"b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import json\nfrom astroquery.vizier import Vizier\n\nwith open(\"Jankowski_2018_raw.txt\", \"r\") as raw_file:\n lines = raw_file.readlines()\n print(lines)\n\npulsar_dict = {}\nfor row in lines[3:]:\n row = row.split(\"|\")\n print(row)\n pulsar = row[0].strip().replace(\"−\", \"-\")\n freqs = []\n fluxs = []\n flux_errs = []\n # If no error means it's an upper limit andnow sure how to handle it\n if row[1].strip() != \"\" and row[2].strip() != \"\":\n freqs.append(728)\n fluxs.append(float(row[1].strip()))\n flux_errs.append(float(row[2].strip()))\n if row[3].strip() != \"\" and row[4].strip() != \"\":\n freqs.append(1382)\n fluxs.append(float(row[3].strip()))\n flux_errs.append(float(row[4].strip()))\n if row[5].strip() != \"\" and row[6].strip() != \"\":\n freqs.append(3100)\n fluxs.append(float(row[5].strip()))\n flux_errs.append(float(row[6].strip()))\n pulsar_dict[pulsar] = {\"Frequency MHz\":freqs, \"Flux Density mJy\":fluxs, \"Flux Density error mJy\":flux_errs}\n\nwith open(\"Jankowski_2018.yaml\", \"w\") as cat_file:\n cat_file.write(json.dumps(pulsar_dict))\nprint(pulsar_dict)"},"avg_line_length":{"kind":"number","value":34.7272727273,"string":"34.727273"},"max_line_length":{"kind":"number","value":111,"string":"111"},"alphanum_fraction":{"kind":"number","value":0.6047120419,"string":"0.604712"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":199,"string":"199"},"score_documentation":{"kind":"number","value":0.17334494773519163,"string":"0.173345"}}},{"rowIdx":3237,"cells":{"hexsha":{"kind":"string","value":"92ee36608ac8edb00b879a89f8f1eafb4cb4fb04"},"size":{"kind":"number","value":15018,"string":"15,018"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"integration-tests/run-intg-test.py"},"max_stars_repo_name":{"kind":"string","value":"NishikaDeSilva/identity-test-integration"},"max_stars_repo_head_hexsha":{"kind":"string","value":"dbd1db07aa6d4f4942d772cd56c0b06c355bd43b"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":4,"string":"4"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2017-10-23T05:25:27.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2018-01-10T08:00:14.000Z"},"max_issues_repo_path":{"kind":"string","value":"integration-tests/run-intg-test.py"},"max_issues_repo_name":{"kind":"string","value":"NishikaDeSilva/identity-test-integration"},"max_issues_repo_head_hexsha":{"kind":"string","value":"dbd1db07aa6d4f4942d772cd56c0b06c355bd43b"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":42,"string":"42"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-05-21T12:55:49.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-01-17T06:40:25.000Z"},"max_forks_repo_path":{"kind":"string","value":"integration-tests/run-intg-test.py"},"max_forks_repo_name":{"kind":"string","value":"NishikaDeSilva/identity-test-integration"},"max_forks_repo_head_hexsha":{"kind":"string","value":"dbd1db07aa6d4f4942d772cd56c0b06c355bd43b"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":46,"string":"46"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2017-10-04T05:45:52.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2018-05-05T14:32:26.000Z"},"content":{"kind":"string","value":"# Copyright (c) 2018, WSO2 Inc. (http://wso2.com) 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# importing required modules\nimport sys\nfrom xml.etree import ElementTree as ET\nimport toml\nimport subprocess\nimport wget\nimport logging\nimport inspect\nimport os\nimport shutil\nimport pymysql\nimport sqlparse\nimport re\nfrom pathlib import Path\nimport urllib.request as urllib2\nfrom xml.dom import minidom\nimport intg_test_manager as cm\nfrom subprocess import Popen, PIPE\nimport os\n\nfrom prod_test_constant import DB_META_DATA, DIST_POM_PATH, INTEGRATION_PATH, DISTRIBUTION_PATH, \\\n DATASOURCE_PATHS, LIB_PATH, WSO2SERVER, M2_PATH, ARTIFACT_REPORTS_PATHS, POM_FILE_PATHS\n\nfrom intg_test_constant import NS, ZIP_FILE_EXTENSION, CARBON_NAME, VALUE_TAG, SURFACE_PLUGIN_ARTIFACT_ID, \\\n DEPLOYMENT_PROPERTY_FILE_NAME, LOG_FILE_NAME, PRODUCT_STORAGE_DIR_NAME, \\\n DEFAULT_DB_USERNAME, LOG_STORAGE, TEST_OUTPUT_DIR_NAME, DEFAULT_ORACLE_SID, MYSQL_DB_ENGINE, \\\n ORACLE_DB_ENGINE, PRODUCT_STORAGE_DIR_NAME, MSSQL_DB_ENGINE\n\ndatabase_names = []\ndb_engine = None\nsql_driver_location = None\nidentity_db_url = None\nidentity_db_username = None\nidentity_db_password = None\nidentity_db_driver = None\nshared_db_url = None\nshared_db_username = None\nshared_db_password = None\nshared_db_driver = None\nidentity_db = \"WSO2_IDENTITY_DB\"\nshared_db = \"WSO2_SHARED_DB\"\ndef get_db_meta_data(argument):\n switcher = DB_META_DATA\n return switcher.get(argument, False)\n\ndef add_environmental_variables():\n if MYSQL_DB_ENGINE == cm.database_config['db_engine'].upper():\n identity_url = cm.database_config[\n 'url'] + \"/\" + identity_db + \"?useSSL=false&amp;autoReconnect=true&amp;requireSSL=false\" \\\n \"&amp;verifyServerCertificate=false\"\n shared_url = cm.database_config[\n 'url'] + \"/\" + shared_db + \\\n \"?useSSL=false&amp;autoReconnect=true&amp;requireSSL=false\" \\\n \"&amp;verifyServerCertificate=false\"\n user = cm.database_config['user']\n elif ORACLE_DB_ENGINE == cm.database_config['db_engine'].upper():\n identity_url= cm.database_config['url'] + \"/\" + DEFAULT_ORACLE_SID\n shared_url= cm.database_config['url'] + \"/\" + DEFAULT_ORACLE_SID\n user = cm.database_config['user']\n elif MSSQL_DB_ENGINE == cm.database_config['db_engine'].upper():\n identity_url = cm.database_config['url'] + \";\" + \"databaseName=\" + identity_db\n shared_url = cm.database_config['url'] + \";\" + \"databaseName=\" + shared_db\n user = cm.database_config['user']\n else:\n shared_url = cm.database_config['url'] + \"/\" + shared_db\n identity_url = cm.database_config['url'] + \"/\" + identity_db\n user = cm.database_config['user']\n password = cm.database_config['password']\n driver_class_name = cm.database_config['driver_class_name']\n\n os.environ[\"SHARED_DATABASE_URL\"] = shared_url\n os.environ[\"SHARED_DATABASE_USERNAME\"] = user\n os.environ[\"SHARED_DATABASE_PASSWORD\"] = password\n os.environ[\"SHARED_DATABASE_DRIVER\"] = driver_class_name\n os.environ[\"IDENTITY_DATABASE_URL\"] = identity_url\n os.environ[\"IDENTITY_DATABASE_USERNAME\"] = user\n os.environ[\"IDENTITY_DATABASE_PASSWORD\"] = password\n os.environ[\"IDENTITY_DATABASE_DRIVER\"] = driver_class_name\n logger.info(\"Added environmental variables for integration test\")\n\n\ndef modify_datasources():\n file_path = Path(storage_dist_abs_path / datasource_path)\n if sys.platform.startswith('win'):\n file_path = cm.winapi_path(file_path)\n logger.info(\"Modifying datasource: \" + str(file_path))\n deployment_toml_config = toml.load(file_path)\n logger.info(\"loading dep,loyment.toml file\")\n logger.info(deployment_toml_config)\n\n for key in deployment_toml_config:\n if key == 'database':\n database_config = deployment_toml_config[key]\n for key in database_config:\n if key == 'identity_db':\n identity_db_config = database_config['identity_db']\n identity_db_config ['url'] = \"$env{IDENTITY_DATABASE_URL}\"\n identity_db_config ['username'] = \"$env{IDENTITY_DATABASE_USERNAME}\"\n identity_db_config ['password'] = \"$env{IDENTITY_DATABASE_PASSWORD}\"\n identity_db_config ['driver'] = \"$env{IDENTITY_DATABASE_DRIVER}\"\n database_names.append(identity_db)\n\n if key == 'shared_db':\n shared_db_config = database_config['shared_db']\n shared_db_config ['url'] = \"$env{SHARED_DATABASE_URL}\"\n shared_db_config ['username'] = \"$env{SHARED_DATABASE_USERNAME}\"\n shared_db_config ['password'] = \"$env{SHARED_DATABASE_PASSWORD}\"\n shared_db_config ['driver'] = \"$env{SHARED_DATABASE_DRIVER}\"\n database_names.append(shared_db)\n\n with open(file_path, 'w') as writer:\n writer.write(toml.dumps(deployment_toml_config))\n\n# Since we have added a method to clone a given git branch and checkout to the latest released tag it is not required to\n# modify pom files. Hence in the current implementation this method is not using.\n# However, in order to execute this method you can define pom file paths in const_.py as a constant\n# and import it to run-intg-test.py. Thereafter assign it to global variable called pom_file_paths in the\n# configure_product method and call the modify_pom_files method.\ndef modify_pom_files():\n for pom in POM_FILE_PATHS:\n file_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + pom)\n if sys.platform.startswith('win'):\n file_path = cm.winapi_path(file_path)\n logger.info(\"Modifying pom file: \" + str(file_path))\n ET.register_namespace('', NS['d'])\n artifact_tree = ET.parse(file_path)\n artifarct_root = artifact_tree.getroot()\n data_sources = artifarct_root.find('d:build', NS)\n plugins = data_sources.find('d:plugins', NS)\n for plugin in plugins.findall('d:plugin', NS):\n artifact_id = plugin.find('d:artifactId', NS)\n if artifact_id is not None and artifact_id.text == SURFACE_PLUGIN_ARTIFACT_ID:\n configuration = plugin.find('d:configuration', NS)\n system_properties = configuration.find('d:systemProperties', NS)\n for neighbor in system_properties.iter('{' + NS['d'] + '}' + CARBON_NAME):\n neighbor.text = cm.modify_distribution_name(neighbor)\n for prop in system_properties:\n name = prop.find('d:name', NS)\n if name is not None and name.text == CARBON_NAME:\n for data in prop:\n if data.tag == VALUE_TAG:\n data.text = cm.modify_distribution_name(data)\n break\n artifact_tree.write(file_path)\n\n\n#TODO: Improve the method in generic way to support all products\ndef save_log_files():\n log_storage = Path(cm.workspace + \"/\" + LOG_STORAGE)\n if not Path.exists(log_storage):\n Path(log_storage).mkdir(parents=True, exist_ok=True)\n log_file_paths = ARTIFACT_REPORTS_PATHS\n if log_file_paths:\n for file in log_file_paths:\n absolute_file_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + file)\n if Path.exists(absolute_file_path):\n cm.copy_file(absolute_file_path, log_storage)\n else:\n logger.error(\"File doesn't contain in the given location: \" + str(absolute_file_path))\n\n\n#TODO: Improve the method in generic way to support all products\ndef save_test_output():\n report_folder = Path(cm.workspace + \"/\" + TEST_OUTPUT_DIR_NAME)\n logger.info(str(report_folder))\n if Path.exists(report_folder):\n shutil.rmtree(report_folder)\n logger.info(str(ARTIFACT_REPORTS_PATHS))\n logger.info(str(type(ARTIFACT_REPORTS_PATHS)))\n report_file_paths = ARTIFACT_REPORTS_PATHS\n for key, value in report_file_paths.items():\n for file in value:\n absolute_file_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + file)\n if Path.exists(absolute_file_path):\n report_storage = Path(cm.workspace + \"/\" + TEST_OUTPUT_DIR_NAME + \"/\" + key)\n cm.copy_file(absolute_file_path, report_storage)\n logger.info(\"Report successfully copied\")\n else:\n logger.error(\"File doesn't contain in the given location: \" + str(absolute_file_path))\n\n\n#TODO: Improve the method in generic way to support all products\n# def set_custom_testng():\n# if cm.use_custom_testng_file == \"TRUE\":\n# testng_source = Path(cm.workspace + \"/\" + \"testng.xml\")\n# testng_destination = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + TESTNG_DIST_XML_PATHS)\n# testng_server_mgt_source = Path(cm.workspace + \"/\" + \"testng-server-mgt.xml\")\n# testng_server_mgt_destination = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + TESTNG_SERVER_MGT_DIST)\n# # replace testng source\n# cm.replace_file(testng_source, testng_destination)\n# # replace testng server mgt source\n# cm.replace_file(testng_server_mgt_source, testng_server_mgt_destination)\n\n\ndef configure_product():\n try:\n global datasource_path\n global target_dir_abs_path\n global storage_dist_abs_path\n global pom_file_paths\n\n datasource_path = DATASOURCE_PATHS\n zip_name = dist_name + ZIP_FILE_EXTENSION\n\n storage_dir_abs_path = Path(cm.workspace + \"/\" + PRODUCT_STORAGE_DIR_NAME)\n target_dir_abs_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + DISTRIBUTION_PATH)\n storage_dist_abs_path = Path(storage_dir_abs_path / dist_name)\n storage_zip_abs_path = Path(storage_dir_abs_path / zip_name)\n configured_dist_storing_loc = Path(target_dir_abs_path / dist_name)\n script_name = Path(WSO2SERVER)\n\n script_path = Path(storage_dist_abs_path / script_name)\n cm.extract_product(storage_dir_abs_path, storage_zip_abs_path)\n cm.attach_jolokia_agent(script_path)\n cm.copy_jar_file(Path(cm.database_config['sql_driver_location']), Path(storage_dist_abs_path / LIB_PATH))\n if datasource_path is not None:\n modify_datasources()\n else:\n logger.info(\"Datasource paths are not defined in the config file\")\n os.remove(str(storage_zip_abs_path))\n cm.compress_distribution(configured_dist_storing_loc, storage_dir_abs_path)\n cm.add_distribution_to_m2(storage_dir_abs_path, M2_PATH)\n shutil.rmtree(configured_dist_storing_loc, onerror=cm.on_rm_error)\n return database_names\n except FileNotFoundError as e:\n logger.error(\"Error occurred while finding files\", exc_info=True)\n except IOError as e:\n logger.error(\"Error occurred while accessing files\", exc_info=True)\n except Exception as e:\n logger.error(\"Error occurred while configuring the product\", exc_info=True)\n\n\ndef build_source_without_tests(source_path):\n \"\"\"Build the product-source.\n \"\"\"\n logger.info('Building the source skipping tests')\n if sys.platform.startswith('win'):\n subprocess.call(['mvn', 'clean', 'install', '-B', '-e','-Dmaven.test.skip=true'], shell=True, cwd=source_path)\n else:\n subprocess.call(['mvn', 'clean', 'install', '-B', '-e', '-Dmaven.test.skip=true'], cwd=source_path)\n logger.info('Module build is completed. Module: ' + str(source_path))\n\ndef main():\n try:\n global logger\n global dist_name\n logger = cm.function_logger(logging.DEBUG, logging.DEBUG)\n if sys.version_info < (3, 6):\n raise Exception(\n \"To run run-intg-test.py script you must have Python 3.6 or latest. Current version info: \" + sys.version_info)\n cm.read_property_files()\n if not cm.validate_property_readings():\n raise Exception(\n \"Property file doesn't have mandatory key-value pair. Please verify the content of the property file \"\n \"and the format\")\n\n # get properties assigned to local variables\n pom_path = DIST_POM_PATH\n engine = cm.db_engine.upper()\n db_meta_data = get_db_meta_data(engine)\n distribution_path = DISTRIBUTION_PATH\n\n # construct the database configurations\n cm.construct_db_config(db_meta_data)\n\n # clone the repository\n cm.clone_repo()\n\n if cm.test_mode == \"RELEASE\":\n cm.checkout_to_tag()\n # product name retrieve from product pom files\n dist_name = cm.get_dist_name(pom_path)\n # build the product without test once to make samples and required artifacts to be available.\n build_source_without_tests(cm.workspace + \"/\" + cm.product_id + \"/\")\n cm.get_latest_released_dist()\n elif cm.test_mode == \"SNAPSHOT\":\n # product name retrieve from product pom files\n dist_name = cm.get_dist_name(pom_path)\n cm.build_snapshot_dist(distribution_path)\n elif cm.test_mode == \"WUM\":\n dist_name = cm.get_dist_name_wum()\n\n # populate databases\n db_names = configure_product()\n if db_names is None or not db_names:\n raise Exception(\"Failed the product configuring\")\n cm.setup_databases(db_names, db_meta_data)\n # run integration tests\n # Buld Common module\n add_environmental_variables()\n module_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + 'modules/integration/tests-common')\n logger.info('Building common module. Build path: '+ str(module_path) + ' \\n')\n\n cm.build_module(module_path)\n intg_module_path = Path(cm.workspace + \"/\" + cm.product_id + \"/\" + INTEGRATION_PATH)\n logger.info('Building integration module. Build path: '+ str(intg_module_path) + ' \\n')\n cm.build_module(intg_module_path)\n save_test_output()\n cm.create_output_property_fle()\n except Exception as e:\n logger.error(\"Error occurred while running the run-intg-test.py script\", exc_info=True)\n except BaseException as e:\n logger.error(\"Error occurred while doing the configuration\", exc_info=True)\n\n\nif __name__ == \"__main__\":\n main()\n"},"avg_line_length":{"kind":"number","value":46.6397515528,"string":"46.639752"},"max_line_length":{"kind":"number","value":127,"string":"127"},"alphanum_fraction":{"kind":"number","value":0.6711945665,"string":"0.671195"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":4624,"string":"4,624"},"score_documentation":{"kind":"number","value":0.3078971900386203,"string":"0.307897"}}},{"rowIdx":3238,"cells":{"hexsha":{"kind":"string","value":"92eed01036cb07058175a69126f2f5a418891a9a"},"size":{"kind":"number","value":2376,"string":"2,376"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/pytest_notification/sound.py"},"max_stars_repo_name":{"kind":"string","value":"rhpvorderman/pytest-notification"},"max_stars_repo_head_hexsha":{"kind":"string","value":"3f322ab04914f52525e1b07bc80537d5f9a00250"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-08-27T03:14:05.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-10-24T17:17:36.000Z"},"max_issues_repo_path":{"kind":"string","value":"src/pytest_notification/sound.py"},"max_issues_repo_name":{"kind":"string","value":"rhpvorderman/pytest-notification"},"max_issues_repo_head_hexsha":{"kind":"string","value":"3f322ab04914f52525e1b07bc80537d5f9a00250"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":5,"string":"5"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-12-02T08:49:15.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-06-22T08:38:34.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/pytest_notification/sound.py"},"max_forks_repo_name":{"kind":"string","value":"rhpvorderman/pytest-notification"},"max_forks_repo_head_hexsha":{"kind":"string","value":"3f322ab04914f52525e1b07bc80537d5f9a00250"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# Copyright (c) 2019 Leiden University Medical Center\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nSOUNDS_DIR = (Path(__file__).parent / Path(\"sounds\")).absolute()\nDEFAULT_SUCCESS_SOUND = SOUNDS_DIR / Path(\"applause\")\nDEFAULT_FAIL_SOUND = SOUNDS_DIR / Path(\"buzzer\")\n\n\ndef play_sound(sound_file: Path):\n if sys.platform == \"linux\":\n # paplay comes from PulseAudio and should be installed by default on\n # most systems.\n _play_sound_unix(sound_file.with_suffix(\".oga\"), program=\"paplay\")\n elif sys.platform == \"darwin\":\n # Afplay comes installed by default on Macintosh\n _play_sound_unix(sound_file.with_suffix(\".mp3\"), program=\"afplay\")\n else:\n # A windows implementation should be possible with the winsound\n # implementation, but that does not play ogg audio.\n raise NotImplementedError(\n \"Playing sounds not supported by pytest-notification on {}\"\n \"\".format(sys.platform))\n\n\ndef _play_sound_unix(sound_file: Path, program):\n \"\"\"\n Play a sound file on unix with the program.\n :param sound_file: Path to the sound file.\n :param program: Which program to use.\n :return: No returns. Plays a sound file.\n \"\"\"\n # Play the sound non blocking, use Popen.\n subprocess.Popen([program, str(sound_file)])\n"},"avg_line_length":{"kind":"number","value":43.2,"string":"43.2"},"max_line_length":{"kind":"number","value":79,"string":"79"},"alphanum_fraction":{"kind":"number","value":0.7314814815,"string":"0.731481"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1701,"string":"1,701"},"score_documentation":{"kind":"number","value":0.7159090909090909,"string":"0.715909"}}},{"rowIdx":3239,"cells":{"hexsha":{"kind":"string","value":"92ef37eb449c4f50b5c90c7a720a5f53652a647c"},"size":{"kind":"number","value":420,"string":"420"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"7KYU/next_prime.py"},"max_stars_repo_name":{"kind":"string","value":"yaznasivasai/python_codewars"},"max_stars_repo_head_hexsha":{"kind":"string","value":"25493591dde4649dc9c1ec3bece8191a3bed6818"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":4,"string":"4"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2021-07-17T22:48:03.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-25T14:10:58.000Z"},"max_issues_repo_path":{"kind":"string","value":"7KYU/next_prime.py"},"max_issues_repo_name":{"kind":"string","value":"yaznasivasai/python_codewars"},"max_issues_repo_head_hexsha":{"kind":"string","value":"25493591dde4649dc9c1ec3bece8191a3bed6818"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"7KYU/next_prime.py"},"max_forks_repo_name":{"kind":"string","value":"yaznasivasai/python_codewars"},"max_forks_repo_head_hexsha":{"kind":"string","value":"25493591dde4649dc9c1ec3bece8191a3bed6818"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":3,"string":"3"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-06-14T14:18:16.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-16T06:02:02.000Z"},"content":{"kind":"string","value":"from math import sqrt\n\n\ndef is_simple(n: int) -> bool:\n if n % 2 == 0 and n != 2:\n return False\n for i in range (3, int(sqrt(n)) + 2, 2):\n if n % i == 0 and n != i:\n return False\n return True\n\ndef next_prime(n: int) -> int:\n n += 1\n if n <= 2:\n return 2\n else:\n if n % 2 == 0:\n n += 1\n while not is_simple(n):\n n += 2\n return n"},"avg_line_length":{"kind":"number","value":20,"string":"20"},"max_line_length":{"kind":"number","value":44,"string":"44"},"alphanum_fraction":{"kind":"number","value":0.4357142857,"string":"0.435714"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":0,"string":"0"},"score_documentation":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":3240,"cells":{"hexsha":{"kind":"string","value":"92ef91238a4d28bed6389f80b7547828e84737ba"},"size":{"kind":"number","value":6622,"string":"6,622"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py"},"max_stars_repo_name":{"kind":"string","value":"manxueitp/cozmo-test"},"max_stars_repo_head_hexsha":{"kind":"string","value":"a91b1a4020544cb622bd67385f317931c095d2e8"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py"},"max_issues_repo_name":{"kind":"string","value":"manxueitp/cozmo-test"},"max_issues_repo_head_hexsha":{"kind":"string","value":"a91b1a4020544cb622bd67385f317931c095d2e8"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py"},"max_forks_repo_name":{"kind":"string","value":"manxueitp/cozmo-test"},"max_forks_repo_head_hexsha":{"kind":"string","value":"a91b1a4020544cb622bd67385f317931c095d2e8"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python3\n\n# Copyright (c) 2016 Anki, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License in the file LICENSE.txt or at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n'''\"If This Then That\" Gmail example\n\nThis example demonstrates how \"If This Then That\" (http://ifttt.com) can be used\nmake Cozmo respond when a Gmail account receives an email. Instructions below\nwill lead you through setting up an applet on the IFTTT website. When the applet\ntrigger is called (which sends a web request received by the web server started\nin this example), Cozmo will play an animation, speak the email sender's name and\nshow a mailbox image on his face.\n\nPlease place Cozmo on the charger for this example. When necessary, he will be\nrolled off and back on.\n\nFollow these steps to set up and run the example:\n 1) Provide a a static ip, URL or similar that can be reached from the If This\n Then That server. One easy way to do this is with ngrok, which sets up\n a secure tunnel to localhost running on your machine.\n\n To set up ngrok:\n a) Follow instructions here to download and install:\n https://ngrok.com/download\n b) Run this command to create a secure public URL for port 8080:\n ./ngrok http 8080\n c) Note the HTTP forwarding address shown in the terminal (e.g., http://55e57164.ngrok.io).\n You will use this address in your applet, below.\n\n WARNING: Using ngrok exposes your local web server to the internet. See the ngrok\n documentation for more information: https://ngrok.com/docs\n\n 2) Set up your applet on the \"If This Then That\" website.\n a) Sign up and sign into https://ifttt.com\n b) Create an applet: https://ifttt.com/create\n c) Set up your trigger.\n 1. Click \"this\".\n 2. Select \"Gmail\" as your service. If prompted, click \"Connect\",\n select your Gmail account, and click “Allow” to provide permissions\n to IFTTT for your email account. Click \"Done\".\n 3. Under \"Choose a Trigger\", select “Any new email in inbox\".\n d) Set up your action.\n 1. Click “that\".\n 2. Select “Maker\" to set it as your action channel. Connect to the Maker channel if prompted.\n 3. Click “Make a web request\" and fill out the fields as follows. Remember your publicly\n accessible URL from above (e.g., http://55e57164.ngrok.io) and use it in the URL field,\n followed by \"/iftttGmail\" as shown below:\n\n URL: http://55e57164.ngrok.io/iftttGmail\n Method: POST\n Content Type: application/json\n Body: {\"FromAddress\":\"{{FromAddress}}\"}\n\n 5. Click “Create Action\" then “Finish\".\n\n 3) Test your applet.\n a) Run this script at the command line: ./ifttt_gmail.py\n b) On ifttt.com, on your applet page, click “Check now”. See that IFTTT confirms that the applet\n was checked.\n c) Send an email to the Gmail account in your recipe\n d) On your IFTTT applet webpage, again click “Check now”. This should cause IFTTT to detect that\n the email was received and send a web request to the ifttt_gmail.py script.\n e) In response to the ifttt web request, Cozmo should roll off the charger, raise and lower\n his lift, announce the email, and then show a mailbox image on his face.\n'''\n\nimport asyncio\nimport re\nimport sys\n\n\ntry:\n from aiohttp import web\nexcept ImportError:\n sys.exit(\"Cannot import from aiohttp. Do `pip3 install --user aiohttp` to install\")\n\nimport cozmo\n\nfrom common import IFTTTRobot\n\n\napp = web.Application()\n\n\nasync def serve_gmail(request):\n '''Define an HTTP POST handler for receiving requests from If This Then That.\n\n You may modify this method to change how Cozmo reacts to the email\n being received.\n '''\n\n json_object = await request.json()\n\n # Extract the name of the email sender.\n from_email_address = json_object[\"FromAddress\"]\n\n # Use a regular expression to break apart pieces of the email address\n match_object = re.search(r'([\\w.]+)@([\\w.]+)', from_email_address)\n email_local_part = match_object.group(1)\n\n robot = request.app['robot']\n async def read_name():\n try:\n async with robot.perform_off_charger():\n '''If necessary, Move Cozmo's Head and Lift to make it easy to see Cozmo's face.'''\n await robot.get_in_position()\n\n # First, have Cozmo play animation \"ID_pokedB\", which tells\n # Cozmo to raise and lower his lift. To change the animation,\n # you may replace \"ID_pokedB\" with another animation. Run\n # remote_control_cozmo.py to see a list of animations.\n await robot.play_anim(name='ID_pokedB').wait_for_completed()\n\n # Next, have Cozmo speak the name of the email sender.\n await robot.say_text(\"Email from \" + email_local_part).wait_for_completed()\n\n # Last, have Cozmo display an email image on his face.\n robot.display_image_file_on_face(\"../face_images/ifttt_gmail.png\")\n\n except cozmo.RobotBusy:\n cozmo.logger.warning(\"Robot was busy so didn't read email address: \"+ from_email_address)\n\n # Perform Cozmo's task in the background so the HTTP server responds immediately.\n asyncio.ensure_future(read_name())\n\n return web.Response(text=\"OK\")\n\n# Attach the function as an HTTP handler.\napp.router.add_post('/iftttGmail', serve_gmail)\n\n\nif __name__ == '__main__':\n cozmo.setup_basic_logging()\n cozmo.robot.Robot.drive_off_charger_on_connect = False\n\n # Use our custom robot class with extra helper methods\n cozmo.conn.CozmoConnection.robot_factory = IFTTTRobot\n\n try:\n sdk_conn = cozmo.connect_on_loop(app.loop)\n # Wait for the robot to become available and add it to the app object.\n app['robot'] = app.loop.run_until_complete(sdk_conn.wait_for_robot())\n except cozmo.ConnectionError as e:\n sys.exit(\"A connection error occurred: %s\" % e)\n\n web.run_app(app)\n"},"avg_line_length":{"kind":"number","value":41.3875,"string":"41.3875"},"max_line_length":{"kind":"number","value":105,"string":"105"},"alphanum_fraction":{"kind":"number","value":0.6745696164,"string":"0.67457"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":1806,"string":"1,806"},"score_async_functions":{"kind":"number","value":0.27174240144447787,"string":"0.271742"},"count_documentation":{"kind":"number","value":5140,"string":"5,140"},"score_documentation":{"kind":"number","value":0.7733975323502859,"string":"0.773398"}}},{"rowIdx":3241,"cells":{"hexsha":{"kind":"string","value":"92f0c7d812707a316f1c04c4ec3e35722444b8b5"},"size":{"kind":"number","value":13843,"string":"13,843"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"plotutils.py"},"max_stars_repo_name":{"kind":"string","value":"parkus/mypy"},"max_stars_repo_head_hexsha":{"kind":"string","value":"21043c559dca14abe7508e0f6b2f8053bf376bb8"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2015-11-06T06:27:59.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2015-11-06T06:27:59.000Z"},"max_issues_repo_path":{"kind":"string","value":"plotutils.py"},"max_issues_repo_name":{"kind":"string","value":"parkus/mypy"},"max_issues_repo_head_hexsha":{"kind":"string","value":"21043c559dca14abe7508e0f6b2f8053bf376bb8"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"plotutils.py"},"max_forks_repo_name":{"kind":"string","value":"parkus/mypy"},"max_forks_repo_head_hexsha":{"kind":"string","value":"21043c559dca14abe7508e0f6b2f8053bf376bb8"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 30 17:15:27 2014\n\n@author: Parke\n\"\"\"\n\nfrom __future__ import division, print_function, absolute_import\nimport numpy as np\nimport matplotlib as mplot\nimport matplotlib.pyplot as plt\nimport mypy.my_numpy as mnp\n\ndpi = 100\nfullwidth = 10.0\nhalfwidth = 5.0\n\n# use these with line.set_dashes and iterate through more linestyles than come with matplotlib\n# consider ussing a ::2 slice for fewer\ndashes = [[],\n [30, 10],\n [20, 8],\n [10, 5],\n [3, 2],\n [30, 5, 3, 5, 10, 5, 3, 5],\n [15] + [5, 3]*3 + [5],\n [15] + [5, 3]*2 + [5],\n [15] + [5, 3] + [5]]\n\n\ndef click_coords(fig=None, timeout=600.):\n if fig is None:\n fig = plt.gcf()\n\n xy = []\n def onclick(event):\n if not event.inaxes:\n fig.canvas.stop_event_loop()\n else:\n xy.append([event.xdata, event.ydata])\n\n print(\"Gathering coordinates of mouse clicks. Click outside of the axes \" \\\n \"when done.\")\n cid = fig.canvas.mpl_connect('button_press_event', onclick)\n fig.canvas.start_event_loop(timeout=timeout)\n fig.canvas.mpl_disconnect(cid)\n return np.array(xy)\n\n\ndef common_axes(fig, pos=None):\n if pos is None:\n bigax = fig.add_subplot(111)\n else:\n bigax = fig.add_axes(pos)\n [bigax.spines[s].set_visible(False) for s in ['top', 'bottom', 'left', 'right']]\n bigax.tick_params(labelleft=False, labelbottom=False, left='off', bottom='off')\n bigax.set_zorder(-10)\n return bigax\n\n\ndef log_frac(x, frac):\n l0, l1 = list(map(np.log10, x))\n ld = l1 - l0\n l = ld*frac + l0\n return 10**l\n\n\ndef log2linear(x, errneg=None, errpos=None):\n xl = 10**x\n result = [xl]\n if errneg is not None:\n xn = xl - 10**(x - np.abs(errneg))\n result.append(xn)\n if errpos is not None:\n xp = 10**(x + errpos) - xl\n result.append(xp)\n return result\n\n\ndef linear2log(x, errneg=None, errpos=None):\n xl = np.log10(x)\n result = [x]\n if errneg is not None:\n xn = xl - np.log10(x - np.abs(errneg))\n result.append(xn)\n if errpos is not None:\n xp = np.log10(x + errpos) - xl\n result.append(xp)\n return result\n\n\ndef step(*args, **kwargs):\n edges, values = args[0], args[1]\n\n # deal with potentially gappy 2-column bin specifications\n edges = np.asarray(edges)\n if edges.ndim == 2:\n if np.any(edges[1:,0] < edges[:-1,1]):\n raise ValueError('Some bins overlap')\n if np.any(edges[1:,0] < edges[:-1,0]):\n raise ValueError('Bins must be in increasing order.')\n gaps = edges[1:,0] > edges[:-1,1]\n edges = np.unique(edges)\n if np.any(gaps):\n values = np.insert(values, np.nonzero(gaps), np.nan)\n\n edges = mnp.lace(edges[:-1], edges[1:])\n values = mnp.lace(values, values)\n args = list(args)\n args[0], args[1] = edges, values\n ax = kwargs.pop('ax', plt.gca())\n return ax.plot(*args, **kwargs)\n\n\ndef point_along_line(x, y, xfrac=None, xlbl=None, scale='linear'):\n if scale == 'log':\n lx, ly = point_along_line(np.log10(x), np.log10(y), xfrac, xlbl, ylbl, scale)\n return 10 ** lx, 10 ** ly\n if xfrac is not None:\n if xfrac == 0:\n return x[0], y[0]\n if xfrac == 1:\n return x[-1], y[-1]\n else:\n d = np.cumsum(np.sqrt(np.diff(x)**2 + np.diff(y)**2))\n d = np.insert(d, 0, 0)\n f = d/d[-1]\n xp, yp = [np.interp(xfrac, f, a) for a in [x,y]]\n return xp, yp\n if xlbl is not None:\n return xlbl, np.interp(xlbl, x, y)\n\n\ndef textSize(ax_or_fig=None, coordinate='data'):\n \"\"\"\n Return x & y scale factors for converting text sizes in points to another coordinate. Useful for properly spacing\n text labels and such when you need to know sizes before the text is made (otherwise you can use textBoxSize).\n\n Coordinate can be 'data', 'axes', or 'figure'.\n\n If data coordinates are requested and the data is plotted on a log scale, then the factor will be given in dex.\n\n \"\"\"\n if ax_or_fig is None:\n fig = plt.gcf()\n ax = fig.gca()\n else:\n if isinstance(ax_or_fig, plt.Figure):\n fig = ax_or_fig\n ax = fig.gca()\n elif isinstance(ax_or_fig, plt.Axes):\n ax = ax_or_fig\n fig = ax.get_figure()\n else:\n raise TypeError('ax_or_fig must be a Figure or Axes instance, if given.')\n\n w_fig_in, h_fig_in = ax.get_figure().get_size_inches()\n\n if coordinate == 'fig':\n return 1.0/(w_fig_in*72), 1.0/(h_fig_in*72)\n\n w_ax_norm, h_ax_norm = ax.get_position().size\n w_ax_in = w_ax_norm * w_fig_in\n h_ax_in = h_ax_norm * h_fig_in\n w_ax_pts, h_ax_pts = w_ax_in*72, h_ax_in*72\n\n if coordinate == 'axes':\n return 1.0/w_ax_pts, 1.0/h_ax_pts\n\n if coordinate == 'data':\n xlim = ax.get_xlim()\n ylim = ax.get_ylim()\n if ax.get_xscale() == 'log': xlim = np.log10(xlim)\n if ax.get_yscale() == 'log': ylim = np.log10(ylim)\n w_ax_data = xlim[1] - xlim[0]\n h_ax_data = ylim[1] - ylim[0]\n return w_ax_data/w_ax_pts, h_ax_data/h_ax_pts\n\n\ndef tight_axis_limits(ax=None, xory='both', margin=0.05):\n if ax is None: ax = plt.gca()\n\n def newlim(oldlim):\n delta = abs(oldlim[1] - oldlim[0])\n pad = delta*margin\n if oldlim[1] > oldlim[0]:\n return (oldlim[0] - pad, oldlim[1] + pad)\n else:\n return (oldlim[0] + pad, oldlim[1] - pad)\n\n def newlim_log(oldlim):\n loglim = [np.log10(l) for l in oldlim]\n newloglim = newlim(loglim)\n return (10.0**newloglim[0], 10.0**newloglim[1])\n\n def newlim_either(oldlim,axlim,scale):\n if axlim[1] < axlim [0]: oldlim = oldlim[::-1]\n if scale == 'linear':\n return newlim(oldlim)\n elif scale == 'log':\n return newlim_log(oldlim)\n elif scale == 'symlog':\n raise NotImplementedError('Past Parke to future Parke, you did\\'t write an implementation for symlog'\n 'scaled axes.')\n\n if xory == 'x' or xory == 'both':\n datalim = ax.dataLim.extents[[0,2]]\n axlim = ax.get_xlim()\n scale = ax.get_xscale()\n ax.set_xlim(newlim_either(datalim,axlim,scale))\n if xory == 'y' or xory == 'both':\n datalim = ax.dataLim.extents[[1,3]]\n axlim = ax.get_ylim()\n scale = ax.get_yscale()\n ax.set_ylim(newlim_either(datalim,axlim,scale))\n\n#TODO: discard this function?\ndef standard_figure(app, slideAR=1.6, height=1.0):\n \"\"\"Generate a figure of standard size for publishing.\n\n implemented values for app (application) are:\n 'fullslide'\n\n height is the fractional height of the figure relative to the \"standard\"\n height. For slides the standard is the full height of a slide.\n\n returns the figure object and default font size\n \"\"\"\n\n if app == 'fullslide':\n fontsize = 20\n figsize = [fullwidth, fullwidth/slideAR*height]\n fig = mplot.pyplot.figure(figsize=figsize, dpi=dpi)\n\n mplot.rcParams.update({'font.size': fontsize})\n return fig, fontsize\n\n\ndef pcolor_reg(x, y, z, **kw):\n \"\"\"\n Similar to `pcolor`, but assume that the grid is uniform,\n and do plotting with the (much faster) `imshow` function.\n\n \"\"\"\n x, y, z = np.asarray(x), np.asarray(y), np.asarray(z)\n if x.ndim != 1 or y.ndim != 1:\n raise ValueError(\"x and y should be 1-dimensional\")\n if z.ndim != 2 or z.shape != (y.size, x.size):\n raise ValueError(\"z.shape should be (y.size, x.size)\")\n dx = np.diff(x)\n dy = np.diff(y)\n if not np.allclose(dx, dx[0], 1e-2) or not np.allclose(dy, dy[0], 1e-2):\n raise ValueError(\"The grid must be uniform\")\n\n if np.issubdtype(z.dtype, np.complexfloating):\n zp = np.zeros(z.shape, float)\n zp[...] = z[...]\n z = zp\n\n plt.imshow(z, origin='lower',\n extent=[x.min(), x.max(), y.min(), y.max()],\n interpolation='nearest',\n aspect='auto',\n **kw)\n plt.axis('tight')\n\n\ndef errorpoly(x, y, yerr, fmt=None, ecolor=None, ealpha=0.5, ax=None, **kw):\n if ax is None: ax = plt.gca()\n p = ax.plot(x, y, **kw) if fmt is None else ax.plot(x, y, fmt, **kw)\n if len(yerr.shape) == 2:\n ylo = y - yerr[0,:]\n yhi = y + yerr[1,:]\n else:\n ylo, yhi = y - yerr, y + yerr\n if ecolor is None: ecolor = p[0].get_color()\n\n # deal with matplotlib sometimes not showing polygon when it extends beyond plot range\n xlim = ax.get_xlim()\n inrange = mnp.inranges(x, xlim)\n if not np.all(inrange):\n n = np.sum(inrange)\n yends = np.interp(xlim, x, y)\n yloends = np.interp(xlim, x, ylo)\n yhiends = np.interp(xlim, x, yhi)\n x = np.insert(x[inrange], [0, n], xlim)\n y = np.insert(y[inrange], [0, n], yends)\n ylo = np.insert(ylo[inrange], [0, n], yloends)\n yhi = np.insert(yhi[inrange], [0, n], yhiends)\n\n f = ax.fill_between(x,ylo,yhi,color=ecolor,alpha=ealpha)\n return p[0],f\n\n\ndef onscreen_pres(mpl, screenwidth=1200):\n \"\"\"\n Set matplotlibrc values so that plots are readable as they are created\n and maximized for an audience far from a screen.\n\n Parameters\n ----------\n mpl : module\n Current matplotlib module. Use 'import matplotlib as mpl'.\n screewidth : int\n Width of the screen in question in pixels.\n\n Returns\n -------\n None\n \"\"\"\n mpl.rcParams['lines.linewidth'] = 2\n fontsize = round(14 / (800.0 / screenwidth))\n mpl.rcParams['font.size'] = fontsize\n\n\ndef textBoxSize(txt, transformation=None, figure=None):\n \"\"\"Get the width and height of a text object's bounding box transformed to the desired coordinates. Defaults to\n figure coordinates if transformation is None.\"\"\"\n\n fig= txt.get_figure() if figure is None else figure\n if transformation is None:\n transformation = fig.transFigure\n\n coordConvert = transformation.inverted().transform\n bboxDisp = txt.get_window_extent(fig.canvas.renderer)\n bboxConv = coordConvert(bboxDisp)\n w = bboxConv[1,0] - bboxConv[0,0]\n h = bboxConv[1,1] - bboxConv[0,1]\n return w, h\n\n\ndef stars3d(ra, dec, dist, T=5000.0, r=1.0, labels='', view=None, size=(800,800), txt_scale=1.0):\n \"\"\"\n Make a 3D diagram of stars positions relative to the Sun, with\n semi-accurate colors and distances as desired. Coordinates must be in\n degrees. Distance is assumed to be in pc (for axes labels).\n\n Meant to be used with only a handful of stars.\n \"\"\"\n from mayavi import mlab\n from color.maps import true_temp\n\n n = len(ra)\n dec, ra = dec*np.pi/180.0, ra*np.pi/180.0\n makearr = lambda v: np.array([v] * n) if np.isscalar(v) else v\n T, r, labels = list(map(makearr, (T, r, labels)))\n\n # add the sun\n ra, dec, dist = list(map(np.append, (ra, dec, dist), (0.0, 0.0, 0.0)))\n r, T, labels = list(map(np.append, (r, T, labels), (1.0, 5780.0, 'Sun')))\n\n # get xyz coordinates\n z = dist * np.sin(dec)\n h = dist * np.cos(dec)\n x = h * np.cos(ra)\n y = h * np.sin(ra)\n\n # make figure\n fig = mlab.figure(bgcolor=(0,0,0), fgcolor=(1,1,1), size=size)\n\n # plot lines down to the dec=0 plane for all but the sun\n lines = []\n for x1, y1, z1 in list(zip(x, y, z))[:-1]:\n xx, yy, zz = [x1, x1], [y1, y1], [0.0, z1]\n line = mlab.plot3d(xx, yy, zz, color=(0.7,0.7,0.7), line_width=0.5,\n figure=fig)\n lines.append(line)\n\n # plot spheres\n r_factor = np.max(dist) / 30.0\n pts = mlab.quiver3d(x, y, z, r, r, r, scalars=T, mode='sphere',\n scale_factor=r_factor, figure=fig, resolution=100)\n pts.glyph.color_mode = 'color_by_scalar'\n\n # center the glyphs on the data point\n pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]\n\n # set a temperature colormap\n cmap = true_temp(T)\n pts.module_manager.scalar_lut_manager.lut.table = cmap\n\n # set the camera view\n mlab.view(focalpoint=(0.0, 0.0, 0.0), figure=fig)\n if view is not None:\n mlab.view(*view, figure=fig)\n\n ## add labels\n # unit vec to camera\n view = mlab.view()\n az, el = view[:2]\n hc = np.sin(el * np.pi / 180.0)\n xc = hc * np.cos(az * np.pi / 180.0)\n yc = hc * np.sin(az * np.pi / 180.0)\n zc = -np.cos(el * np.pi / 180.0)\n\n # unit vec orthoganal to camera\n if xc**2 + yc**2 == 0.0:\n xoff = 1.0\n yoff = 0.0\n zoff = 0.0\n else:\n xoff = yc / np.sqrt(xc**2 + yc**2)\n yoff = np.sqrt(1.0 - xoff**2)\n zoff = 0.0\n\n # xoff, yoff, zoff = xc, yc, zc\n\n # scale orthogonal vec by sphere size\n r_label = 1.0 * r_factor\n xoff, yoff, zoff = [r_label * v for v in [xoff, yoff, zoff]]\n\n # plot labels\n size = r_factor * txt_scale * 0.75\n for xx, yy, zz, label in zip(x, y, z, labels):\n mlab.text3d(xx + xoff, yy + yoff, zz + zoff, label, figure=fig,\n color=(1,1,1), scale=size)\n\n ## add translucent dec=0 surface\n n = 101\n t = np.linspace(0.0, 2*np.pi, n)\n r = np.max(dist * np.cos(dec))\n x, y = r*np.cos(t), r*np.sin(t)\n z = np.zeros(n+1)\n x, y = [np.insert(a, 0, 0.0) for a in [x,y]]\n triangles = [(0, i, i + 1) for i in range(1, n)]\n mlab.triangular_mesh(x, y, z, triangles, color=(1,1,1), opacity=0.3, figure=fig)\n\n ## add ra=0 line\n line = mlab.plot3d([0, r], [0, 0], [0, 0], color=(1,1,1), line_width=1, figure=fig)\n rtxt = '{:.1f} pc'.format(r)\n orientation=np.array([180.0, 180.0, 0.0])\n mlab.text3d(r, 0, 0, rtxt, figure=fig, scale=size*1.25, orient_to_camera=False, orientation=orientation)\n\n if view is not None:\n mlab.view(*view, figure=fig)\n\n return fig\n\n\n"},"avg_line_length":{"kind":"number","value":31.4613636364,"string":"31.461364"},"max_line_length":{"kind":"number","value":117,"string":"117"},"alphanum_fraction":{"kind":"number","value":0.5823882106,"string":"0.582388"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":3099,"string":"3,099"},"score_documentation":{"kind":"number","value":0.2238676587444918,"string":"0.223868"}}},{"rowIdx":3242,"cells":{"hexsha":{"kind":"string","value":"92f10be0db1bfd9a43f5256577f3d2fdfd1c920b"},"size":{"kind":"number","value":13185,"string":"13,185"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"marvel_world/views.py"},"max_stars_repo_name":{"kind":"string","value":"xiaoranppp/si664-final"},"max_stars_repo_head_hexsha":{"kind":"string","value":"f5545c04452fd674ddf1d078444e79ea58385e7e"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"marvel_world/views.py"},"max_issues_repo_name":{"kind":"string","value":"xiaoranppp/si664-final"},"max_issues_repo_head_hexsha":{"kind":"string","value":"f5545c04452fd674ddf1d078444e79ea58385e7e"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-11-25T21:07:37.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2018-11-25T21:07:37.000Z"},"max_forks_repo_path":{"kind":"string","value":"marvel_world/views.py"},"max_forks_repo_name":{"kind":"string","value":"xiaoranppp/si664-final"},"max_forks_repo_head_hexsha":{"kind":"string","value":"f5545c04452fd674ddf1d078444e79ea58385e7e"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2018-12-21T12:06:03.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2018-12-21T12:06:03.000Z"},"content":{"kind":"string","value":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.views import generic\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom .models import Character,Comic,Power,CharacterPower,CharacterComic\nfrom django_filters.views import FilterView\nfrom .filters import Marvel_worldFilter,Marvel_comicFilter\nfrom .forms import CharacterForm,PowerForm,ComicForm\nfrom django.urls import reverse,reverse_lazy\ndef index(request):\n\treturn HttpResponse(\"Hello, world. You're at the marvel world super hero\")\n\n\nclass AboutPageView(generic.TemplateView):\n\ttemplate_name = 'marvel_world/about.html'\n\n\nclass HomePageView(generic.TemplateView):\n\ttemplate_name = 'marvel_world/home.html'\n\n@method_decorator(login_required, name='dispatch')\nclass CharacterListView(generic.ListView):\n\tmodel = Character\n\tcontext_object_name = 'characters'\n\ttemplate_name = 'marvel_world/characters.html'\n\tpaginate_by = 50\n\n\tdef get_queryset(self):\n\t\treturn Character.objects.all().select_related('alignment','eye_color','skin_color','hair_color','race','gender','publisher').order_by('character_name')\n@method_decorator(login_required, name='dispatch')\nclass CharacterDetailView(generic.DetailView):\n model = Character\n context_object_name= 'character'\n template_name = 'marvel_world/character_information.html'\n@method_decorator(login_required, name='dispatch')\nclass ComicListView(generic.ListView):\n\tmodel = Comic\n\tcontext_object_name = 'comics'\n\ttemplate_name = 'marvel_world/comics.html'\n\tpaginate_by = 600\n\n\tdef get_queryset(self):\n\t\treturn Comic.objects.all().order_by('comic_name')\n@method_decorator(login_required, name='dispatch')\nclass ComicDetailView(generic.DetailView):\n model = Comic\n context_object_name= 'comic'\n template_name = 'marvel_world/comic_information.html'\n@method_decorator(login_required, name='dispatch')\nclass PowerListView(generic.ListView):\n\tmodel = Power\n\tcontext_object_name = 'powers'\n\ttemplate_name = 'marvel_world/super_power.html'\n\tpaginate_by = 50\n\n\tdef get_queryset(self):\n\t\treturn Power.objects.all().order_by('power_name')\n@method_decorator(login_required, name='dispatch')\nclass PowerDetailView(generic.DetailView):\n model = Power\n context_object_name= 'power'\n template_name = 'marvel_world/super_power_information.html'\n@method_decorator(login_required, name='dispatch')\nclass CharacterFilterView(FilterView):\n\tfilterset_class = Marvel_worldFilter\n\ttemplate_name = 'marvel_world/character_filter.html'\n@method_decorator(login_required, name='dispatch')\nclass ComicFilterView(FilterView):\n\tfilterset_class = Marvel_comicFilter\n\ttemplate_name = 'marvel_world/comic_filter.html'\n@method_decorator(login_required, name='dispatch')\nclass CharacterCreateView(generic.View):\n\tmodel = Character\n\tform_class = CharacterForm\n\tsuccess_message = \"Character created successfully\"\n\ttemplate_name = 'marvel_world/character_new.html'\n\t# fields = '__all__' <-- superseded by form_class\n\t# success_url = reverse_lazy('heritagesites/site_list')\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef post(self, request):\n\t\tform = CharacterForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tcharacter = form.save(commit=False)\n\t\t\tcharacter.save()\n\t\t\tfor power in form.cleaned_data['super_power']:\n\t\t\t\tCharacterPower.objects.create(character=character, power=power)\n\t\t\tfor comic in form.cleaned_data['comics']:\n\t\t\t\tCharacterComic.objects.create(character=character, comic=comic)\n\t\t\treturn redirect(character) # shortcut to object's get_absolute_url()\n\t\t\t# return HttpResponseRedirect(site.get_absolute_url())\n\t\treturn render(request, 'marvel_world/character_new.html', {'form': form})\n\n\tdef get(self, request):\n\t\tform = CharacterForm()\n\t\treturn render(request, 'marvel_world/character_new.html', {'form': form})\n@method_decorator(login_required, name='dispatch')\nclass PowerCreateView(generic.View):\n\tmodel = Power\n\tform_class = PowerForm\n\tsuccess_message = \"Super power created successfully\"\n\ttemplate_name = 'marvel_world/power_new.html'\n\t# fields = '__all__' <-- superseded by form_class\n\t# success_url = reverse_lazy('heritagesites/site_list')\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef post(self, request):\n\t\tform = PowerForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tpower = form.save(commit=False)\n\t\t\tpower.save()\n\t\t\tfor character in form.cleaned_data['character']:\n\t\t\t\tCharacterPower.objects.create(character=character, power=power)\n\t\t\treturn redirect(power) # shortcut to object's get_absolute_url()\n\t\t\t# return HttpResponseRedirect(site.get_absolute_url())\n\t\treturn render(request, 'marvel_world/power_new.html', {'form': form})\n\n\tdef get(self, request):\n\t\tform = PowerForm()\n\t\treturn render(request, 'marvel_world/power_new.html', {'form': form})\n@method_decorator(login_required, name='dispatch')\nclass ComicCreateView(generic.View):\n\tmodel = Comic\n\tform_class = ComicForm\n\tsuccess_message = \"Comic created successfully\"\n\ttemplate_name = 'marvel_world/comic_new.html'\n\t# fields = '__all__' <-- superseded by form_class\n\t# success_url = reverse_lazy('heritagesites/site_list')\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef post(self, request):\n\t\tform = ComicForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tcomic = form.save(commit=False)\n\t\t\tcomic.save()\n\t\t\tfor character in form.cleaned_data['character']:\n\t\t\t\tCharacterComic.objects.create(character=character, comic=comic)\n\t\t\treturn redirect(comic) # shortcut to object's get_absolute_url()\n\t\t\t# return HttpResponseRedirect(site.get_absolute_url())\n\t\treturn render(request, 'marvel_world/comic_new.html', {'form': form})\n\n\tdef get(self, request):\n\t\tform = ComicForm()\n\t\treturn render(request, 'marvel_world/comic_new.html', {'form': form})\n#class CharacterDetailView(generic.DetailView):model = Characters context_object_name= 'character'template_name='marvel_world/character_information.html'\n\n@method_decorator(login_required, name='dispatch')\nclass CharacterUpdateView(generic.UpdateView):\n\tmodel = Character\n\tform_class = CharacterForm\n\t# fields = '__all__' <-- superseded by form_class\n\tcontext_object_name = 'character'\n\t# pk_url_kwarg = 'site_pk'\n\tsuccess_message = \"Character updated successfully\"\n\ttemplate_name = 'marvel_world/character_update.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef form_valid(self, form):\n\t\tcharacter = form.save(commit=False)\n\t\t# site.updated_by = self.request.user\n\t\t# site.date_updated = timezone.now()\n\t\tcharacter.save()\n\n\t\t# Current country_area_id values linked to site\n\t\told_ids = CharacterPower.objects\\\n\t\t\t.values_list('power_id', flat=True)\\\n\t\t\t.filter(character_id=character.character_id)\n\n\t\t# New countries list\n\t\tnew_powers = form.cleaned_data['super_power']\n\n\t\t# TODO can these loops be refactored?\n\n\t\t# New ids\n\t\tnew_ids = []\n\n\t\t# Insert new unmatched country entries\n\t\tfor power in new_powers:\n\t\t\tnew_id = power.power_id\n\t\t\tnew_ids.append(new_id)\n\t\t\tif new_id in old_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterPower.objects \\\n\t\t\t\t\t.create(character=character, power=power)\n\n\t\t# Delete old unmatched country entries\n\t\tfor old_id in old_ids:\n\t\t\tif old_id in new_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterPower.objects \\\n\t\t\t\t\t.filter(character_id=character.character_id, power_id=old_id) \\\n\t\t\t\t\t.delete()\n\n\n\t\told_ids1 = CharacterComic.objects\\\n\t\t\t.values_list('comic_id', flat=True)\\\n\t\t\t.filter(character_id=character.character_id)\n\n\t\t# New countries list\n\t\tnew_comics = form.cleaned_data['comics']\n\n\t\t# TODO can these loops be refactored?\n\n\t\t# New ids\n\t\tnew_ids1 = []\n\n\t\t# Insert new unmatched country entries\n\t\tfor comic in new_comics:\n\t\t\tnew_id1 = comic.comic_id\n\t\t\tnew_ids1.append(new_id1)\n\t\t\tif new_id1 in old_ids1:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterComic.objects \\\n\t\t\t\t\t.create(character=character, comic=comic)\n\n\t\t# Delete old unmatched country entries\n\t\tfor old_id1 in old_ids1:\n\t\t\tif old_id1 in new_ids1:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterComic.objects \\\n\t\t\t\t\t.filter(character_id=character.character_id, comic_id=old_id1) \\\n\t\t\t\t\t.delete()\n\n\t\treturn HttpResponseRedirect(character.get_absolute_url())\n@method_decorator(login_required, name='dispatch')\nclass PowerUpdateView(generic.UpdateView):\n\tmodel = Power\n\tform_class = PowerForm\n\t# fields = '__all__' <-- superseded by form_class\n\tcontext_object_name = 'power'\n\t# pk_url_kwarg = 'site_pk'\n\tsuccess_message = \"Super power updated successfully\"\n\ttemplate_name = 'marvel_world/power_update.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef form_valid(self, form):\n\t\tpower = form.save(commit=False)\n\t\t# site.updated_by = self.request.user\n\t\t# site.date_updated = timezone.now()\n\t\tpower.save()\n\n\t\t# Current country_area_id values linked to site\n\t\told_ids = CharacterPower.objects\\\n\t\t\t.values_list('character_id', flat=True)\\\n\t\t\t.filter(power_id=power.power_id)\n\n\t\t# New countries list\n\t\tnew_chs = form.cleaned_data['character']\n\n\t\t# TODO can these loops be refactored?\n\n\t\t# New ids\n\t\tnew_ids = []\n\n\t\t# Insert new unmatched country entries\n\t\tfor character in new_chs:\n\t\t\tnew_id = character.character_id\n\t\t\tnew_ids.append(new_id)\n\t\t\tif new_id in old_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterPower.objects \\\n\t\t\t\t\t.create(character=character, power=power)\n\n\t\t# Delete old unmatched country entries\n\t\tfor old_id in old_ids:\n\t\t\tif old_id in new_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterPower.objects \\\n\t\t\t\t\t.filter(character_id=old_id, power_id=power.power_id) \\\n\t\t\t\t\t.delete()\n\n\n\t\t\n\n\t\treturn HttpResponseRedirect(power.get_absolute_url())\n\t\t# return redirect('heritagesites/site_detail', pk=site.pk)\n@method_decorator(login_required, name='dispatch')\nclass ComicUpdateView(generic.UpdateView):\n\tmodel = Comic\n\tform_class = ComicForm\n\t# fields = '__all__' <-- superseded by form_class\n\tcontext_object_name = 'comic'\n\t# pk_url_kwarg = 'site_pk'\n\tsuccess_message = \"Comic updated successfully\"\n\ttemplate_name = 'marvel_world/comic_update.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef form_valid(self, form):\n\t\tcomic = form.save(commit=False)\n\t\t# site.updated_by = self.request.user\n\t\t# site.date_updated = timezone.now()\n\t\tcomic.save()\n\n\t\t# Current country_area_id values linked to site\n\t\told_ids = CharacterComic.objects\\\n\t\t\t.values_list('character_id', flat=True)\\\n\t\t\t.filter(comic_id=comic.comic_id)\n\n\t\t# New countries list\n\t\tnew_chs = form.cleaned_data['character']\n\n\t\t# TODO can these loops be refactored?\n\n\t\t# New ids\n\t\tnew_ids = []\n\n\t\t# Insert new unmatched country entries\n\t\tfor character in new_chs:\n\t\t\tnew_id = character.character_id\n\t\t\tnew_ids.append(new_id)\n\t\t\tif new_id in old_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterComic.objects \\\n\t\t\t\t\t.create(character=character, comic=comic)\n\n\t\t# Delete old unmatched country entries\n\t\tfor old_id in old_ids:\n\t\t\tif old_id in new_ids:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tCharacterComic.objects \\\n\t\t\t\t\t.filter(character_id=old_id, comic_id=comic.comic_id) \\\n\t\t\t\t\t.delete()\n\n\n\t\t\n\n\t\treturn HttpResponseRedirect(comic.get_absolute_url())\n@method_decorator(login_required, name='dispatch')\nclass CharacterDeleteView(generic.DeleteView):\n\tmodel =Character\n\tsuccess_message = \"Character deleted successfully\"\n\tsuccess_url = reverse_lazy('characters')\n\tcontext_object_name = 'character'\n\ttemplate_name = 'marvel_world/character_delete.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef delete(self, request, *args, **kwargs):\n\t\tself.object = self.get_object()\n\n\t\t# Delete HeritageSiteJurisdiction entries\n\t\tCharacterPower.objects \\\n\t\t\t.filter(character_id=self.object.character_id) \\\n\t\t\t.delete()\n\t\tCharacterComic.objects \\\n\t\t\t.filter(character_id=self.object.character_id) \\\n\t\t\t.delete()\n\n\t\tself.object.delete()\n\n\t\treturn HttpResponseRedirect(self.get_success_url())\n@method_decorator(login_required, name='dispatch')\nclass PowerDeleteView(generic.DeleteView):\n\tmodel =Power\n\tsuccess_message = \"Super power deleted successfully\"\n\tsuccess_url = reverse_lazy('super_power')\n\tcontext_object_name = 'power'\n\ttemplate_name = 'marvel_world/power_delete.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef delete(self, request, *args, **kwargs):\n\t\tself.object = self.get_object()\n\n\t\t# Delete HeritageSiteJurisdiction entries\n\t\tCharacterPower.objects \\\n\t\t\t.filter(power_id=self.object.power_id) \\\n\t\t\t.delete()\n\n\t\tself.object.delete()\n\n\t\treturn HttpResponseRedirect(self.get_success_url())\n@method_decorator(login_required, name='dispatch')\nclass ComicDeleteView(generic.DeleteView):\n\tmodel =Comic\n\tsuccess_message = \"Comic deleted successfully\"\n\tsuccess_url = reverse_lazy('comics')\n\tcontext_object_name = 'comic'\n\ttemplate_name = 'marvel_world/comic_delete.html'\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super().dispatch(*args, **kwargs)\n\n\tdef delete(self, request, *args, **kwargs):\n\t\tself.object = self.get_object()\n\n\t\t# Delete HeritageSiteJurisdiction entries\n\t\tCharacterComic.objects \\\n\t\t\t.filter(comic_id=self.object.comic_id) \\\n\t\t\t.delete()\n\n\t\tself.object.delete()\n\n\t\treturn HttpResponseRedirect(self.get_success_url())"},"avg_line_length":{"kind":"number","value":31.618705036,"string":"31.618705"},"max_line_length":{"kind":"number","value":154,"string":"154"},"alphanum_fraction":{"kind":"number","value":0.7548729617,"string":"0.754873"},"count_classes":{"kind":"number","value":11523,"string":"11,523"},"score_classes":{"kind":"number","value":0.8739476678043231,"string":"0.873948"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":12222,"string":"12,222"},"score_decorators":{"kind":"number","value":0.926962457337884,"string":"0.926962"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":3805,"string":"3,805"},"score_documentation":{"kind":"number","value":0.28858551384148656,"string":"0.288586"}}},{"rowIdx":3243,"cells":{"hexsha":{"kind":"string","value":"92f1aa0fa9769c9eeef09b7c084da22948285538"},"size":{"kind":"number","value":267,"string":"267"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/rpi/fwd.py"},"max_stars_repo_name":{"kind":"string","value":"au-chrismor/selfdrive"},"max_stars_repo_head_hexsha":{"kind":"string","value":"31325dd7a173bbb16a13e3de4c9598aab0a50632"},"max_stars_repo_licenses":{"kind":"list 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\"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":6,"string":"6"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-03-15T05:23:55.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2018-10-26T10:28:47.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/rpi/fwd.py"},"max_forks_repo_name":{"kind":"string","value":"au-chrismor/selfdrive"},"max_forks_repo_head_hexsha":{"kind":"string","value":"31325dd7a173bbb16a13e3de4c9598aab0a50632"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"Set-up and execute the main loop\"\"\"\nimport RPi.GPIO as GPIO\nimport time\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\n\n#Right motor input A\nGPIO.setup(18,GPIO.OUT)\n#Right motor input B\nGPIO.setup(23,GPIO.OUT)\n\nGPIO.output(18,GPIO.HIGH)\nGPIO.output(23,GPIO.LOW)\n\n"},"avg_line_length":{"kind":"number","value":16.6875,"string":"16.6875"},"max_line_length":{"kind":"number","value":38,"string":"38"},"alphanum_fraction":{"kind":"number","value":0.7490636704,"string":"0.749064"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":78,"string":"78"},"score_documentation":{"kind":"number","value":0.29213483146067415,"string":"0.292135"}}},{"rowIdx":3244,"cells":{"hexsha":{"kind":"string","value":"92f3155f2bddf2f3a6336a7c75d33f3d299e7e91"},"size":{"kind":"number","value":931,"string":"931"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"util/get_from_db.py"},"max_stars_repo_name":{"kind":"string","value":"Abel-Huang/simple-image-classifier"},"max_stars_repo_head_hexsha":{"kind":"string","value":"89d2822c2b06cdec728f734d43d9638f4b601348"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":4,"string":"4"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2017-05-17T08:01:38.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2018-07-22T11:13:55.000Z"},"max_issues_repo_path":{"kind":"string","value":"util/get_from_db.py"},"max_issues_repo_name":{"kind":"string","value":"Abel-Huang/ImageClassifier"},"max_issues_repo_head_hexsha":{"kind":"string","value":"89d2822c2b06cdec728f734d43d9638f4b601348"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"util/get_from_db.py"},"max_forks_repo_name":{"kind":"string","value":"Abel-Huang/ImageClassifier"},"max_forks_repo_head_hexsha":{"kind":"string","value":"89d2822c2b06cdec728f734d43d9638f4b601348"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import pymysql\n\n# 连接配置信息\nconfig = {\n 'host': '127.0.0.1',\n 'port': 3306,\n 'user': 'root',\n 'password': '',\n 'db': 'classdata',\n 'charset': 'utf8',\n 'cursorclass': pymysql.cursors.DictCursor,\n}\n\n\ndef get_summary_db(unitag):\n # 创建连接\n conn = pymysql.connect(**config)\n cur = conn.cursor()\n # 执行sql语句\n try:\n # 执行sql语句,进行查询\n sql = 'SELECT * FROM summary where unitag= %s'\n cur.execute(sql,unitag)\n # 获取查询结果\n result = cur.fetchall()\n return result\n finally:\n cur.close()\n conn.close()\n\ndef get_result_db(unitag):\n # 创建连接\n conn = pymysql.connect(**config)\n cur = conn.cursor()\n # 执行sql语句\n try:\n # 执行sql语句,进行查询\n sql = 'SELECT * FROM result where unitag= %s'\n cur.execute(sql,unitag)\n # 获取查询结果\n result = cur.fetchall()\n return result\n finally:\n cur.close()\n conn.close()\n"},"avg_line_length":{"kind":"number","value":20.2391304348,"string":"20.23913"},"max_line_length":{"kind":"number","value":54,"string":"54"},"alphanum_fraction":{"kind":"number","value":0.5413533835,"string":"0.541353"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":355,"string":"355"},"score_documentation":{"kind":"number","value":0.34299516908212563,"string":"0.342995"}}},{"rowIdx":3245,"cells":{"hexsha":{"kind":"string","value":"92f361f8ed017247a610edc6823b43b8f1e0fefd"},"size":{"kind":"number","value":290,"string":"290"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"registerapp/api.py"},"max_stars_repo_name":{"kind":"string","value":"RajapandiR/django-register"},"max_stars_repo_head_hexsha":{"kind":"string","value":"cf20829fe3515bdd3112a88a890d83d852f09bde"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"registerapp/api.py"},"max_issues_repo_name":{"kind":"string","value":"RajapandiR/django-register"},"max_issues_repo_head_hexsha":{"kind":"string","value":"cf20829fe3515bdd3112a88a890d83d852f09bde"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":6,"string":"6"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-03-19T04:17:51.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-09-22T19:07:17.000Z"},"max_forks_repo_path":{"kind":"string","value":"registerapp/api.py"},"max_forks_repo_name":{"kind":"string","value":"RajapandiR/django-register"},"max_forks_repo_head_hexsha":{"kind":"string","value":"cf20829fe3515bdd3112a88a890d83d852f09bde"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from rest_framework import viewsets\nfrom rest_framework.views import APIView\n\nfrom registerapp import serializers\nfrom registerapp import models\n\nclass RegisterViewSet(viewsets.ModelViewSet):\n\tserializer_class = serializers.RegisterSerializer\n\tqueryset = models.RegisterPage.objects.all()\n\n"},"avg_line_length":{"kind":"number","value":26.3636363636,"string":"26.363636"},"max_line_length":{"kind":"number","value":50,"string":"50"},"alphanum_fraction":{"kind":"number","value":0.8551724138,"string":"0.855172"},"count_classes":{"kind":"number","value":142,"string":"142"},"score_classes":{"kind":"number","value":0.4896551724137931,"string":"0.489655"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":0,"string":"0"},"score_documentation":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":3246,"cells":{"hexsha":{"kind":"string","value":"92f638d897dda2bf328a3077b43f492f38f39bb7"},"size":{"kind":"number","value":1412,"string":"1,412"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"jduck/robot.py"},"max_stars_repo_name":{"kind":"string","value":"luutp/jduck"},"max_stars_repo_head_hexsha":{"kind":"string","value":"3c60a79c926bb9452777cddbebe28982273068a6"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"jduck/robot.py"},"max_issues_repo_name":{"kind":"string","value":"luutp/jduck"},"max_issues_repo_head_hexsha":{"kind":"string","value":"3c60a79c926bb9452777cddbebe28982273068a6"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"jduck/robot.py"},"max_forks_repo_name":{"kind":"string","value":"luutp/jduck"},"max_forks_repo_head_hexsha":{"kind":"string","value":"3c60a79c926bb9452777cddbebe28982273068a6"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\njduck.py\nDescription:\n\nAuthor: luutp\nContact: luubot2207@gmail.com\nCreated on: 2021/02/27\n\"\"\"\n\n# Utilities\n# %%\n# ================================IMPORT PACKAGES====================================\n\n# Utilities\nfrom traitlets.config.configurable import SingletonConfigurable\n\n# Custom Packages\nfrom jduck.DCMotor import DCMotor\n\n\n# ================================================================================\n\n\nclass JDuck(SingletonConfigurable):\n def __init__(self, *args, **kwargs):\n self.left_motor = DCMotor(32, 36, 38, alpha=1.0)\n self.right_motor = DCMotor(33, 35, 37, alpha=1.0)\n self.left_motor.set_speed(50)\n self.right_motor.set_speed(50)\n\n def set_speeds(self, left_speed, right_speed):\n self.left_motor.set_speed(left_speed)\n self.right_motor.set_speed(right_speed)\n\n def move_forward(self):\n self.left_motor.rotate_forward()\n self.right_motor.rotate_forward()\n\n def move_backward(self):\n self.left_motor.rotate_backward()\n self.right_motor.rotate_backward()\n\n def turn_left(self):\n self.left_motor.rotate_backward()\n self.right_motor.rotate_forward()\n\n def turn_right(self):\n self.left_motor.rotate_forward()\n self.right_motor.rotate_backward()\n\n def stop(self):\n self.left_motor.stop()\n self.right_motor.stop()\n"},"avg_line_length":{"kind":"number","value":25.2142857143,"string":"25.214286"},"max_line_length":{"kind":"number","value":85,"string":"85"},"alphanum_fraction":{"kind":"number","value":0.6111898017,"string":"0.61119"},"count_classes":{"kind":"number","value":946,"string":"946"},"score_classes":{"kind":"number","value":0.669971671388102,"string":"0.669972"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":351,"string":"351"},"score_documentation":{"kind":"number","value":0.24858356940509915,"string":"0.248584"}}},{"rowIdx":3247,"cells":{"hexsha":{"kind":"string","value":"92f8d1944416ddff1cb95f31ed4c8d93f364c956"},"size":{"kind":"number","value":5193,"string":"5,193"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/nebulo/gql/alias.py"},"max_stars_repo_name":{"kind":"string","value":"olirice/nebulo"},"max_stars_repo_head_hexsha":{"kind":"string","value":"de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":76,"string":"76"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-04-03T01:21:47.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-12-06T02:54:53.000Z"},"max_issues_repo_path":{"kind":"string","value":"src/nebulo/gql/alias.py"},"max_issues_repo_name":{"kind":"string","value":"olirice/nebulo"},"max_issues_repo_head_hexsha":{"kind":"string","value":"de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":7,"string":"7"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-04-06T04:44:10.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-05-17T12:38:15.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/nebulo/gql/alias.py"},"max_forks_repo_name":{"kind":"string","value":"olirice/nebulo"},"max_forks_repo_head_hexsha":{"kind":"string","value":"de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":2,"string":"2"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-10-23T10:25:16.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-10-28T14:16:57.000Z"},"content":{"kind":"string","value":"# pylint: disable=missing-class-docstring,invalid-name\nimport typing\n\nfrom graphql.language import (\n InputObjectTypeDefinitionNode,\n InputObjectTypeExtensionNode,\n ObjectTypeDefinitionNode,\n ObjectTypeExtensionNode,\n)\nfrom graphql.type import (\n GraphQLArgument,\n GraphQLBoolean,\n GraphQLEnumType,\n GraphQLEnumValue,\n GraphQLField,\n GraphQLFieldMap,\n GraphQLFloat,\n GraphQLID,\n GraphQLInputFieldMap,\n GraphQLInputObjectType,\n GraphQLInt,\n GraphQLInterfaceType,\n GraphQLIsTypeOfFn,\n GraphQLList,\n GraphQLNonNull,\n GraphQLObjectType,\n GraphQLResolveInfo,\n GraphQLScalarType,\n GraphQLSchema,\n GraphQLString,\n GraphQLType,\n Thunk,\n)\nfrom graphql.type.definition import GraphQLInputFieldOutType\nfrom nebulo.sql.composite import CompositeType as SQLACompositeType\n\n# Handle name changes from graphql-core and graphql-core-next\ntry:\n from graphql.type import GraphQLInputObjectField as GraphQLInputField\nexcept ImportError:\n from graphql.type import GraphQLInputField\n\nType = GraphQLType\nList = GraphQLList\nNonNull = GraphQLNonNull\nArgument = GraphQLArgument\nBoolean = GraphQLBoolean\nString = GraphQLString\nScalarType = GraphQLScalarType\nID = GraphQLID\nInterfaceType = GraphQLInterfaceType\nInt = GraphQLInt\nInputField = GraphQLInputField\nResolveInfo = GraphQLResolveInfo\nEnumType = GraphQLEnumType\nEnumValue = GraphQLEnumValue\nSchema = GraphQLSchema\nField = GraphQLField\nFloat = GraphQLFloat\n\nEnumType = GraphQLEnumType\n\n\nclass HasSQLAModel: # pylint: disable= too-few-public-methods\n sqla_table = None\n\n\nclass HasSQLFunction: # pylint: disable= too-few-public-methods\n sql_function = None\n\n\nclass HasSQLAComposite: # pylint: disable= too-few-public-methods\n sqla_composite: SQLACompositeType\n\n\nclass ObjectType(GraphQLObjectType, HasSQLAModel):\n def __init__(\n self,\n name: str,\n fields: Thunk[GraphQLFieldMap],\n interfaces: typing.Optional[Thunk[typing.Collection[\"GraphQLInterfaceType\"]]] = None,\n is_type_of: typing.Optional[GraphQLIsTypeOfFn] = None,\n extensions: typing.Optional[typing.Dict[str, typing.Any]] = None,\n description: typing.Optional[str] = None,\n ast_node: typing.Optional[ObjectTypeDefinitionNode] = None,\n extension_ast_nodes: typing.Optional[typing.Collection[ObjectTypeExtensionNode]] = None,\n sqla_model=None,\n ) -> None:\n super().__init__(\n name=name,\n fields=fields,\n interfaces=interfaces,\n is_type_of=is_type_of,\n extensions=extensions,\n description=description,\n ast_node=ast_node,\n extension_ast_nodes=extension_ast_nodes,\n )\n self.sqla_model = sqla_model\n\n\nclass ConnectionType(ObjectType):\n pass\n\n\nclass EdgeType(ObjectType):\n pass\n\n\nclass TableType(ObjectType):\n pass\n\n\nclass CompositeType(ObjectType, HasSQLAComposite):\n pass\n\n\nclass MutationPayloadType(ObjectType):\n pass\n\n\nclass CreatePayloadType(MutationPayloadType):\n pass\n\n\nclass UpdatePayloadType(MutationPayloadType):\n pass\n\n\nclass DeletePayloadType(MutationPayloadType):\n pass\n\n\nclass FunctionPayloadType(MutationPayloadType, HasSQLFunction):\n pass\n\n\nclass InputObjectType(GraphQLInputObjectType, HasSQLAModel):\n def __init__(\n self,\n name: str,\n fields: Thunk[GraphQLInputFieldMap],\n description: typing.Optional[str] = None,\n out_type: typing.Optional[GraphQLInputFieldOutType] = None,\n extensions: typing.Optional[typing.Dict[str, typing.Any]] = None,\n ast_node: typing.Optional[InputObjectTypeDefinitionNode] = None,\n extension_ast_nodes: typing.Optional[typing.Collection[InputObjectTypeExtensionNode]] = None,\n sqla_model=None,\n ) -> None:\n super().__init__(\n name=name,\n fields=fields,\n description=description,\n out_type=out_type,\n extensions=extensions,\n ast_node=ast_node,\n extension_ast_nodes=extension_ast_nodes,\n )\n self.sqla_model = sqla_model\n\n\nclass CreateInputType(InputObjectType):\n pass\n\n\nclass TableInputType(InputObjectType):\n pass\n\n\nclass UpdateInputType(InputObjectType):\n pass\n\n\nclass DeleteInputType(InputObjectType):\n pass\n\n\nclass FunctionInputType(GraphQLInputObjectType):\n def __init__(\n self,\n name: str,\n fields: Thunk[GraphQLInputFieldMap],\n description: typing.Optional[str] = None,\n out_type: typing.Optional[GraphQLInputFieldOutType] = None,\n extensions: typing.Optional[typing.Dict[str, typing.Any]] = None,\n ast_node: typing.Optional[InputObjectTypeDefinitionNode] = None,\n extension_ast_nodes: typing.Optional[typing.Collection[InputObjectTypeExtensionNode]] = None,\n sql_function=None,\n ) -> None:\n super().__init__(\n name=name,\n fields=fields,\n description=description,\n out_type=out_type,\n extensions=extensions,\n ast_node=ast_node,\n extension_ast_nodes=extension_ast_nodes,\n )\n self.sql_function = sql_function\n"},"avg_line_length":{"kind":"number","value":25.965,"string":"25.965"},"max_line_length":{"kind":"number","value":101,"string":"101"},"alphanum_fraction":{"kind":"number","value":0.7069131523,"string":"0.706913"},"count_classes":{"kind":"number","value":3638,"string":"3,638"},"score_classes":{"kind":"number","value":0.7005584440593106,"string":"0.700558"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":260,"string":"260"},"score_documentation":{"kind":"number","value":0.05006739842095128,"string":"0.050067"}}},{"rowIdx":3248,"cells":{"hexsha":{"kind":"string","value":"92f9c4373d43c67eefcb0f04052b7d238d59ad11"},"size":{"kind":"number","value":2297,"string":"2,297"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py"},"max_stars_repo_name":{"kind":"string","value":"rise-lang/iree"},"max_stars_repo_head_hexsha":{"kind":"string","value":"46ad3fe392d38ce3df6eff7826cc1ab331a40b72"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-08-13T09:25:59.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-08-13T09:25:59.000Z"},"max_issues_repo_path":{"kind":"string","value":"integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py"},"max_issues_repo_name":{"kind":"string","value":"rise-lang/iree"},"max_issues_repo_head_hexsha":{"kind":"string","value":"46ad3fe392d38ce3df6eff7826cc1ab331a40b72"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py"},"max_forks_repo_name":{"kind":"string","value":"rise-lang/iree"},"max_forks_repo_head_hexsha":{"kind":"string","value":"46ad3fe392d38ce3df6eff7826cc1ab331a40b72"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# Copyright 2019 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\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport importlib\nimport os\nimport sys\nimport tempfile\n\nfrom pyiree.tf import compiler\n\n# Dynamically import tensorflow.\ntry:\n # Use a dynamic import so as to avoid hermetic dependency analysis\n # (i.e. we only want the tensorflow from the environment).\n tf = importlib.import_module(\"tensorflow\")\n # Just in case if linked against a pre-V2 defaulted version.\n if hasattr(tf, \"enable_v2_behavior\"):\n tf.enable_v2_behavior()\n tf = tf.compat.v2\nexcept ImportError:\n print(\"Not running tests because tensorflow is not available\")\n sys.exit(0)\n\n\nclass StatelessModule(tf.Module):\n\n def __init__(self):\n pass\n\n @tf.function(input_signature=[\n tf.TensorSpec([4], tf.float32),\n tf.TensorSpec([4], tf.float32)\n ])\n def add(self, a, b):\n return tf.tanh(a + b)\n\n\nclass RuntimeTest(tf.test.TestCase):\n\n def testLoadSavedModelToXlaPipeline(self):\n \"\"\"Tests that a basic saved model to XLA workflow grossly functions.\n\n This is largely here to verify that everything is linked in that needs to be\n and that there are not no-ops, etc.\n \"\"\"\n with tempfile.TemporaryDirectory() as temp_dir:\n sm_dir = os.path.join(temp_dir, \"simple.sm\")\n print(\"Saving to:\", sm_dir)\n my_module = StatelessModule()\n options = tf.saved_model.SaveOptions(save_debug_info=True)\n tf.saved_model.save(my_module, sm_dir, options=options)\n\n # Load it up.\n input_module = compiler.tf_load_saved_model(sm_dir)\n xla_asm = input_module.to_asm()\n print(\"XLA ASM:\", xla_asm)\n self.assertRegex(xla_asm, \"mhlo.tanh\")\n\n\nif __name__ == \"__main__\":\n tf.test.main()\n"},"avg_line_length":{"kind":"number","value":29.8311688312,"string":"29.831169"},"max_line_length":{"kind":"number","value":80,"string":"80"},"alphanum_fraction":{"kind":"number","value":0.7270352634,"string":"0.727035"},"count_classes":{"kind":"number","value":1008,"string":"1,008"},"score_classes":{"kind":"number","value":0.4388332607749238,"string":"0.438833"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":159,"string":"159"},"score_decorators":{"kind":"number","value":0.06922072268175881,"string":"0.069221"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1131,"string":"1,131"},"score_documentation":{"kind":"number","value":0.49238136700043533,"string":"0.492381"}}},{"rowIdx":3249,"cells":{"hexsha":{"kind":"string","value":"92f9e01e28f69fd9e75ceb00e00af2fc86419292"},"size":{"kind":"number","value":1618,"string":"1,618"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"api/models/indicator/child_objects/properties.py"},"max_stars_repo_name":{"kind":"string","value":"taco-chainalysis/pypulsedive"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"api/models/indicator/child_objects/properties.py"},"max_issues_repo_name":{"kind":"string","value":"taco-chainalysis/pypulsedive"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"api/models/indicator/child_objects/properties.py"},"max_forks_repo_name":{"kind":"string","value":"taco-chainalysis/pypulsedive"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-11-10T16:50:32.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-11-10T16:50:32.000Z"},"content":{"kind":"string","value":"from .grandchild_objects import Cookies\nfrom .grandchild_objects import Dns\nfrom .grandchild_objects import Dom\nfrom .grandchild_objects import Geo\n#from .grandchild_objects import Http\n#from .grandchild_objects import Meta\nfrom .grandchild_objects import Ssl\n#from .grandchild_objects import WhoIs\n\nclass Properties(object):\n\n FIELD_MAP = {\n \"cookies\": \"cookies\",\n \"dns\": \"dns\",\n \"dom\": \"dom\",\n \"geo\": \"geo\",\n \"http\": \"http\",\n \"meta\": \"meta\",\n \"ssl\": \"ssl\",\n \"whois\": \"whois\"\n }\n\n def __init__(self):\n self.cookies = \"\"\n self.dns = \"\"\n self.dom = \"\"\n self.geo = \"\"\n self.http = \"\"\n self.meta = \"\"\n self.ssl = \"\"\n self.whois = \"\"\n\n @staticmethod\n def from_dictionary(properties_dict: dict):\n properties = Properties()\n field_map = getattr(properties.__class__, \"FIELD_MAP\")\n for key_name in field_map:\n if key_name in properties_dict:\n setattr(properties, field_map[key_name], properties_dict[key_name])\n properties.cookies = Cookies.from_dictionary(properties.cookies)\n properties.dns = Dns.from_dictionary(properties.dns)\n properties.dom = Dom.from_dictionary(properties.dom)\n properties.geo = Geo.from_dictionary(properties.geo)\n #properties.http = Http.from_dictionary(properties.http)\n #properties.meta = Meta.from_dictionary(properties.meta)\n properties.ssl = Ssl.from_dictionary(properties.ssl)\n #properties.whois = WhoIs.from_dictionary(properties.whois)\n return properties"},"avg_line_length":{"kind":"number","value":33.7083333333,"string":"33.708333"},"max_line_length":{"kind":"number","value":83,"string":"83"},"alphanum_fraction":{"kind":"number","value":0.6427688504,"string":"0.642769"},"count_classes":{"kind":"number","value":1318,"string":"1,318"},"score_classes":{"kind":"number","value":0.8145859085290482,"string":"0.814586"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":862,"string":"862"},"score_decorators":{"kind":"number","value":0.5327564894932015,"string":"0.532756"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":406,"string":"406"},"score_documentation":{"kind":"number","value":0.25092707045735474,"string":"0.250927"}}},{"rowIdx":3250,"cells":{"hexsha":{"kind":"string","value":"92fa506f1dc831d005f72a65f033e46f94fe54e8"},"size":{"kind":"number","value":9084,"string":"9,084"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"iRep/gc_skew.py"},"max_stars_repo_name":{"kind":"string","value":"scottdaniel/iRep"},"max_stars_repo_head_hexsha":{"kind":"string","value":"5d31688eeeab057ce54f39698e3f9cc5738e05ad"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":55,"string":"55"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2016-06-17T17:31:48.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-01-19T08:24:43.000Z"},"max_issues_repo_path":{"kind":"string","value":"iRep/gc_skew.py"},"max_issues_repo_name":{"kind":"string","value":"scottdaniel/iRep"},"max_issues_repo_head_hexsha":{"kind":"string","value":"5d31688eeeab057ce54f39698e3f9cc5738e05ad"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":35,"string":"35"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2016-06-24T17:19:04.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-11-06T16:08:43.000Z"},"max_forks_repo_path":{"kind":"string","value":"iRep/gc_skew.py"},"max_forks_repo_name":{"kind":"string","value":"scottdaniel/iRep"},"max_forks_repo_head_hexsha":{"kind":"string","value":"5d31688eeeab057ce54f39698e3f9cc5738e05ad"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":14,"string":"14"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2016-07-21T17:34:16.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-03-18T03:45:55.000Z"},"content":{"kind":"string","value":"#!/usr/bin/env python3\n\n\"\"\"\nscript for calculating gc skew\n\nChris Brown\nctb@berkeley.edu\n\"\"\"\n\n# python modules\nimport os\nimport sys\nimport argparse\nimport numpy as np\nfrom scipy import signal\nfrom itertools import cycle, product\n\n# plotting modules\nfrom matplotlib import use as mplUse\nmplUse('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\nplt.rcParams['pdf.fonttype'] = 42\nfrom matplotlib import rc\nrc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n\n# ctb\nfrom ctbBio.fasta import iterate_fasta as parse_fasta\n\ndef plot_two(title, subtitle, A, B, labels, legend, vert = False):\n \"\"\"\n plot with differnt y axes\n title = title for chart\n A = data for left axis [[x], [y]]\n B = data for right axis\n lables = [left label, right label, x label]\n legend = [[left legend], [right legend]]\n \"\"\"\n fig, ax1 = plt.subplots()\n colors = ['0.75', 'b', 'r', 'c', 'y', 'm', 'k', 'g']\n a_colors = cycle(colors)\n b_colors = cycle(colors[::-1])\n a_label = cycle(legend[0])\n b_label = cycle(legend[1])\n # plot left axis and x - axis\n for a in A:\n x, y = a\n ax1.set_ylabel(labels[0], labelpad = 3)\n ax1.set_xlabel(labels[-1])\n ax1.plot(x, y, c = next(a_colors), marker = 'o', ms = 4, label = next(a_label))\n # add vertical lines\n if vert is not False:\n for i in vert:\n x, c = i\n ax1.axvline(x = x, c = c, label = next(a_label), linewidth = 2)\n # plot right axis\n ax2 = ax1.twinx()\n for b in B:\n x, y = b\n ax2.set_ylabel(labels[1], labelpad = 8)\n ax2.plot(x, y, c = next(b_colors), linewidth = 2, label = next(b_label))\n xmin = min([min(i[1]) for i in A] + [min(i[0]) for i in B])\n xmax = max([max(i[0]) for i in A] + [max(i[0]) for i in B])\n ax2.set_xlim(xmin, xmax)\n # title\n plt.suptitle(title, fontsize = 16)\n plt.title(subtitle, fontsize = 10)\n # legend\n ax1.legend(loc = 'upper left', \\\n bbox_to_anchor=(0.55, -0.125), \\\n prop = {'size':8}, \\\n framealpha = 0.0\n )\n plt.legend(loc = 'upper right', \\\n bbox_to_anchor=(0.45, -0.125), \\\n prop = {'size':8}, \\\n framealpha = 0.0\\\n )\n # save\n pdf = PdfPages('%s.pdf' % title.replace(' ', '_'))\n pdf.savefig(bbox_inches = 'tight')\n plt.close()\n pdf.close()\n\ndef check_peaks(peaks, length):\n \"\"\"\n select pair of min and max that are not too close or\n too far apart and have greatest y distance between one another\n \"\"\"\n # if ori/ter peaks are too close or too far apart, they are probably wrong\n closest, farthest = int(length * float(0.45)), int(length * float(0.55))\n pairs = []\n for pair in list(product(*peaks)):\n ### added this to make sure gets origin and ter right\n tr, pk = sorted(list(pair), key = lambda x: x[1], reverse = False) # trough and peak\n a = (tr[0] - pk[0]) % length\n b = (pk[0] - tr[0]) % length\n pt = abs(tr[1] - pk[1]) # distance between values\n if (a <= farthest and a >= closest) or (b <=farthest and b >= closest):\n pairs.append([pt, tr, pk])\n if len(pairs) == 0:\n return [False, False]\n pt, tr, pk = sorted(pairs, reverse = True)[0]\n return [tr[0], pk[0]]\n\ndef find_ori_ter(c_skew, length):\n \"\"\"\n find origin and terminus of replication based on \n cumulative GC Skew\n \"\"\"\n # find origin and terminus of replication based on \n # cumulative gc skew min and max peaks\n c_skew_min = signal.argrelextrema(np.asarray(c_skew[1]), np.less, order = 1)[0].tolist()\n c_skew_max = signal.argrelextrema(np.asarray(c_skew[1]), np.greater, order = 1)[0].tolist()\n # return False if no peaks were detected\n if len(c_skew_min) == 0 or len(c_skew_min) == 0:\n return [False, False]\n else:\n c_skew_min = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_min]\n c_skew_max = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_max]\n ori, ter = check_peaks([c_skew_min, c_skew_max], length)\n return ori, ter\n\ndef gc_skew(name, length, seq, window, slide, plot_skew):\n \"\"\"\n calculate gc skew and cumulative sum of gc skew over sequence windows\n gc skew = ((G - C) / (G + C)) * window size * genome length\n \"\"\"\n # convert to G - C\n replacements = {'G':1, 'C':-1, 'A':0, 'T':0, 'N':0}\n gmc = [] # G - C\n for base in seq:\n try:\n gmc.append(replacements[base])\n except:\n gmc.append(0)\n # convert to G + C\n gpc = [abs(i) for i in gmc] # G + C\n # calculate sliding windows for (G - C) and (G + C)\n weights = np.ones(window)/window\n gmc = [[i, c] for i, c in enumerate(signal.fftconvolve(gmc, weights, 'same').tolist())]\n gpc = [[i, c] for i, c in enumerate(signal.fftconvolve(gpc, weights, 'same').tolist())]\n # calculate gc skew and cummulative gc skew sum\n skew = [[], []] # x and y for gc skew\n c_skew = [[], []] # x and y for gc skew cummulative sums\n cs = 0 # cummulative sum\n # select windows to use based on slide\n for i, m in gmc[0::slide]:\n p = gpc[i][1]\n if p == 0:\n gcs = 0\n else:\n gcs = m/p\n cs += gcs\n skew[0].append(i)\n c_skew[0].append(i)\n skew[1].append(gcs)\n c_skew[1].append(cs)\n ori, ter = find_ori_ter(c_skew, length)\n # plot data\n if plot_skew is True:\n title = '%s GC Skew' % (name)\n subtitle = '(window = %s, slide = %s)' % (window, slide)\n labels = ['GC Skew', 'Cumulative GC Skew', 'Position on Genome (bp)']\n # remove some points for plotting (approx. 1,000 datapoints)\n N = int(len(skew[0])/1000)\n if N != 0:\n skew = [skew[0][0::N], skew[1][0::N]]\n if ori is False:\n plot_two(title, subtitle, [skew], [c_skew], labels, \\\n [[labels[0]], [labels[1]]])\n else:\n plot_two(title, subtitle, [skew], [c_skew], labels, \\\n [[labels[0], 'Ori:%s' % ('{:,}'.format(ori)), \\\n 'Ter:%s' % ('{:,}'.format(ter))], [labels[1]]], \\\n vert = [(ori, 'r'), (ter, 'b')])\n return ori, ter, skew, c_skew\n\ndef parse_genomes(fastas, single):\n \"\"\"\n generator for parsing fastas\n if single is True, combine sequences in multifasta file\n \"\"\"\n if single is True:\n for genome in fastas:\n sequence = []\n for seq in parse_fasta(genome): \n sequence.extend(list(seq[1].upper()))\n yield (genome.name.rsplit('.', 1)[0], len(sequence), sequence)\n else:\n for genome in fastas:\n for seq in parse_fasta(genome):\n ID = seq[0].split('>', 1)[1].split()[0]\n yield (ID, len(seq[1]), list(seq[1].upper()))\n\ndef open_files(files):\n \"\"\"\n open files in list, use stdin if first \n item in list is '-'\n \"\"\"\n if files is None:\n return files\n if files[0] == '-':\n return (sys.stdin)\n return (open(i) for i in files)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description = \\\n '# calculate gc skew and find Ori and Ter of replication')\n parser.add_argument(\\\n '-f', nargs = '*', action = 'store', required = True, \\\n help = 'fasta(s)')\n parser.add_argument(\\\n '-l', default = False, type = int, \\\n help = 'minimum contig length (default = 10 x window)')\n parser.add_argument(\\\n '-w', default = 1000, type = int, \\\n help = 'window length (default = 1000)')\n parser.add_argument(\\\n '-s', default = 10, type = int, \\\n help = 'slide length (default = 10)')\n parser.add_argument(\\\n '--single', action = 'store_true', \\\n help = 'combine multi-fasta sequences into single genome')\n parser.add_argument(\\\n '--no-plot', action = 'store_false', \\\n help = 'do not generate plots, print GC Skew to stdout')\n args = vars(parser.parse_args())\n fastas = open_files(args['f'])\n single, plot_skew = args['single'], args['no_plot']\n window, slide = args['w'], args['s']\n min_len = args['l']\n if min_len is False:\n min_len = 10 * window\n for name, length, seq in parse_genomes(fastas, single):\n if length < min_len:\n print('%s: Too Short' % (name), file=sys.stderr)\n continue\n ori, ter, skew, c_skew = gc_skew(name, length, seq, window, slide, plot_skew)\n if ori == False:\n ori, ter = 'n/a', 'n/a'\n else:\n ori, ter = '{:,}'.format(ori), '{:,}'.format(ter)\n print('%s -> Origin: %s Terminus: %s' \\\n % (name, ori, ter), file=sys.stderr)\n if plot_skew is False:\n print('\\t'.join(['# Name', 'Position', 'GC Skew', 'Cumulative GC Skew']))\n for i, pos in enumerate(skew[0]):\n out = [name, pos, skew[1][i], c_skew[1][i]]\n print('\\t'.join([str(i) for i in out]))\n"},"avg_line_length":{"kind":"number","value":36.1912350598,"string":"36.191235"},"max_line_length":{"kind":"number","value":95,"string":"95"},"alphanum_fraction":{"kind":"number","value":0.5529502422,"string":"0.55295"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":598,"string":"598"},"score_generators":{"kind":"number","value":0.0658300308234258,"string":"0.06583"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2457,"string":"2,457"},"score_documentation":{"kind":"number","value":0.2704755614266843,"string":"0.270476"}}},{"rowIdx":3251,"cells":{"hexsha":{"kind":"string","value":"92fa730397bfd4949cfd5d8aa12c70a6b5cb5576"},"size":{"kind":"number","value":2429,"string":"2,429"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"examples/send_governance_vote_transaction.py"},"max_stars_repo_name":{"kind":"string","value":"Algofiorg/algofi-py-sdk"},"max_stars_repo_head_hexsha":{"kind":"string","value":"6100a6726d36db4d4d3287064f0ad1d0b9a05e03"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":4,"string":"4"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-11-03T00:14:46.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-28T02:17:33.000Z"},"max_forks_repo_path":{"kind":"string","value":"examples/send_governance_vote_transaction.py"},"max_forks_repo_name":{"kind":"string","value":"Algofiorg/algofi-py-sdk"},"max_forks_repo_head_hexsha":{"kind":"string","value":"6100a6726d36db4d4d3287064f0ad1d0b9a05e03"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":8,"string":"8"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-12-15T05:29:55.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-02-08T03:45:11.000Z"},"content":{"kind":"string","value":"# This sample is provided for demonstration purposes only.\n# It is not intended for production use.\n# This example does not constitute trading advice.\nimport os\nfrom dotenv import dotenv_values\nfrom algosdk import mnemonic, account\nfrom algofi.v1.asset import Asset\nfrom algofi.v1.client import AlgofiTestnetClient, AlgofiMainnetClient\nfrom algofi.utils import get_ordered_symbols, prepare_payment_transaction, get_new_account\n\nfrom example_utils import print_market_state, print_user_state\n\n### run setup.py before proceeding. make sure the .env file is set with mnemonic + storage_mnemonic.\n\n# Hardcoding account keys is not a great practice. This is for demonstration purposes only.\n# See the README & Docs for alternative signing methods.\nmy_path = os.path.abspath(os.path.dirname(__file__))\nENV_PATH = os.path.join(my_path, \".env\")\n\n# load user passphrase\nuser = dotenv_values(ENV_PATH)\nsender = mnemonic.to_public_key(user['mnemonic'])\nkey = mnemonic.to_private_key(user['mnemonic'])\n\n# IS_MAINNET\nIS_MAINNET = False\nclient = AlgofiMainnetClient(user_address=sender) if IS_MAINNET else AlgofiTestnetClient(user_address=sender)\n\n# NOTE: Get the live governance address at https://governance.algorand.foundation/api/periods/\n# under \"sign_up_address\" for the relevant governance period\n# Specify your vote according to the formats that are permissible in the Algorand Foundation Spec\n# https://github.com/algorandfoundation/governance/blob/main/af-gov1-spec.md\n# Get the idx, vote choices based on the relevant voting session from https://governance.algorand.foundation/api/periods/\n\naddress = sender\ngovernance_address = \"\"\nvote_note = b'af/gov1:j[6,\"a\",\"c\"]' # NOTE: an example, not to be used in live voting necessarily\n\nvault_address = client.manager.get_storage_address(address)\n\nprint(\"~\"*100)\nprint(\"Processing send_governance_vote_transaction transaction for vault address \" + vault_address)\nprint(\"~\"*100)\n\ntxn = client.prepare_send_governance_vote_transactions(governance_address, note=vote_note, address=address)\ntxn.sign_with_private_key(sender, key)\ntxn.submit(client.algod, wait=True)\n\n# After sending, check your vote at\n# https://governance.algorand.foundation/api/periods//governors/\n# to confirm successful vote in voting session\n\n# print final state\nprint(\"~\"*100)\nprint(\"Final State\")\nprint(\"Sent governance transaction with note: \" + str(vote_note))\nprint(\"~\"*100)"},"avg_line_length":{"kind":"number","value":42.6140350877,"string":"42.614035"},"max_line_length":{"kind":"number","value":121,"string":"121"},"alphanum_fraction":{"kind":"number","value":0.7999176616,"string":"0.799918"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1334,"string":"1,334"},"score_documentation":{"kind":"number","value":0.5491972004940304,"string":"0.549197"}}},{"rowIdx":3252,"cells":{"hexsha":{"kind":"string","value":"92fa7f11780de4e7d336cb67c51c29ac5c8fbc36"},"size":{"kind":"number","value":9059,"string":"9,059"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"bid/inventoryClient.py"},"max_stars_repo_name":{"kind":"string","value":"franklx/SOAPpy-py3"},"max_stars_repo_head_hexsha":{"kind":"string","value":"f25afba322e9300ba4ebdd281118b629ca63ba24"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":7,"string":"7"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2018-01-03T18:24:43.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-07T04:34:01.000Z"},"max_issues_repo_path":{"kind":"string","value":"bid/inventoryClient.py"},"max_issues_repo_name":{"kind":"string","value":"franklx/SOAPpy-py3"},"max_issues_repo_head_hexsha":{"kind":"string","value":"f25afba322e9300ba4ebdd281118b629ca63ba24"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"bid/inventoryClient.py"},"max_forks_repo_name":{"kind":"string","value":"franklx/SOAPpy-py3"},"max_forks_repo_head_hexsha":{"kind":"string","value":"f25afba322e9300ba4ebdd281118b629ca63ba24"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":18,"string":"18"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2018-08-06T11:30:16.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-09T11:24:24.000Z"},"content":{"kind":"string","value":"#!/usr/bin/env python\n\n\nimport getopt\nimport sys\nimport string\nimport re\nimport time\nsys.path.insert(1,\"..\")\nfrom SOAPpy import SOAP\nimport traceback\n\nDEFAULT_SERVERS_FILE = './inventory.servers'\n\nDEFAULT_METHODS = ('SimpleBuy', 'RequestForQuote','Buy','Ping')\n\ndef usage (error = None):\n sys.stdout = sys.stderr\n\n if error != None:\n print(error)\n\n print(\"\"\"usage: %s [options] [server ...]\n If a long option shows an argument is mandatory, it's mandatory for the\n equivalent short option also.\n\n -?, --help display this usage\n -d, --debug turn on debugging in the SOAP library\n -i, --invert test servers *not* in the list of servers given\n -m, --method=METHOD#[,METHOD#...]\n call only the given methods, specify a METHOD# of ?\n for the list of method numbers\n -o, --output=TYPE turn on output, TYPE is one or more of s(uccess),\n f(ailure), n(ot implemented), F(ailed (as expected)),\n a(ll)\n [f]\n -s, --servers=FILE use FILE as list of servers to test [%s]\n -t, --stacktrace print a stack trace on each unexpected failure\n -T, --always-stacktrace\n print a stack trace on any failure\n\"\"\" % (sys.argv[0], DEFAULT_SERVERS_FILE), end=' ')\n\n sys.exit (0)\n \n\ndef methodUsage ():\n sys.stdout = sys.stderr\n\n print(\"Methods are specified by number. Multiple methods can be \" \\\n \"specified using a\\ncomma-separated list of numbers or ranges. \" \\\n \"For example 1,4-6,8 specifies\\nmethods 1, 4, 5, 6, and 8.\\n\")\n\n print(\"The available methods are:\\n\")\n\n half = (len (DEFAULT_METHODS) + 1) / 2\n for i in range (half):\n print(\"%4d. %-25s\" % (i + 1, DEFAULT_METHODS[i]), end=' ')\n if i + half < len (DEFAULT_METHODS):\n print(\"%4d. %-25s\" % (i + 1 + half, DEFAULT_METHODS[i + half]), end=' ')\n print()\n\n sys.exit (0)\n\n\ndef readServers (file):\n servers = []\n f = open (file, 'r')\n\n while 1:\n line = f.readline ()\n\n if line == '':\n break\n\n if line[0] in ('#', '\\n') or line[0] in string.whitespace:\n continue\n\n cur = {'nonfunctional': {}}\n tag = None\n servers.append (cur)\n\n while 1:\n if line[0] in string.whitespace:\n if tag == 'nonfunctional':\n value = method + ' ' + cur[tag][method]\n else:\n value = cur[tag]\n value += ' ' + line.strip ()\n else:\n tag, value = line.split (':', 1)\n\n tag = tag.strip ().lower ()\n value = value.strip ()\n\n if value[0] == '\"' and value[-1] == '\"':\n value = value[1:-1]\n\n if tag == 'nonfunctional':\n value = value.split (' ', 1) + ['']\n\n method = value[0]\n cur[tag][method] = value[1]\n else:\n cur[tag] = value\n\n line = f.readline ()\n\n if line == '' or line[0] == '\\n':\n break\n\n return servers\n \ndef str2list (s):\n l = {}\n\n for i in s.split (','):\n if i.find ('-') != -1:\n i = i.split ('-')\n for i in range (int (i[0]),int (i[1]) + 1):\n l[i] = 1\n else:\n l[int (i)] = 1\n\n l = list(l.keys ())\n l.sort ()\n\n return l\n\ndef SimpleBuy(serv, sa, epname):\n serv = serv._sa (sa % {'methodname':'SimpleBuy'})\n return serv.SimpleBuy(ProductName=\"widget\", Quantity = 50, Address = \"this is my address\") #JHawk, Phalanx require this order of params\n\n\ndef RequestForQuote(serv, sa, epname):\n serv = serv._sa (sa % {'methodname':'RequestForQuote'})\n return serv.RequestForQuote(Quantity=3, ProductName = \"thing\") # for Phalanx, JHawk\n\n\ndef Buy(serv, sa, epname):\n import copy\n serv = serv._sa (sa % {'methodname':'Buy'})\n billTo_d = {\"name\":\"Buyer One\", \"address\":\"1 1st Street\",\n \"city\":\"New York\", \"state\":\"NY\", \"zipCode\":\"10000\"}\n shipTo_d = {\"name\":\"Buyer One \", \"address\":\"1 1st Street \",\n \"city\":\"New York \", \"state\":\"NY \", \"zipCode\":\"10000 \"}\n \n for k,v in list(shipTo_d.items()):\n shipTo_d[k] = v[:-1]\n \n itemd1 = SOAP.structType( {\"name\":\"widg1\",\"quantity\":200,\"price\":SOAP.decimalType(45.99), \"_typename\":\"LineItem\"})\n itemd2 = SOAP.structType( {\"name\":\"widg2\",\"quantity\":400,\"price\":SOAP.decimalType(33.45), \"_typename\":\"LineItem\"})\n\n items_d = SOAP.arrayType( [itemd1, itemd2] )\n items_d._ns = \"http://www.soapinterop.org/Bid\"\n po_d = SOAP.structType( data = {\"poID\":\"myord\",\"createDate\":SOAP.dateTimeType(),\"shipTo\":shipTo_d, \"billTo\":billTo_d, \"items\":items_d})\n try:\n # it's called PO by MST (MS SOAP Toolkit), JHawk (.NET Remoting),\n # Idoox WASP, Paul (SOAP::Lite), PranishK (ATL), GLUE, Aumsoft,\n # HP, EasySoap, and Jake (Frontier). [Actzero accepts either]\n return serv.Buy(PO=po_d) \n except:\n # called PurchaseOrder by KeithBa \n return serv.Buy(PurchaseOrder=po_d) \n\n\ndef Ping(serv, sa, epname):\n serv = serv._sa (sa % {'methodname':'Ping'})\n return serv.Ping()\n\ndef main():\n servers = DEFAULT_SERVERS_FILE\n methodnums = None\n output = 'f'\n invert = 0\n succeed = 0\n printtrace = 0\n stats = 1\n total = 0\n fail = 0\n failok = 0\n notimp = 0\n\n try:\n opts,args = getopt.getopt (sys.argv[1:], '?dm:io:s:t',\n ['help', 'method', 'debug', 'invert',\n 'output', 'servers='])\n for opt, arg in opts:\n if opt in ('-?', '--help'):\n usage ()\n elif opt in ('-d', '--debug'):\n SOAP.Config.debug = 1\n elif opt in ('-i', '--invert'):\n invert = 1\n elif opt in ('-m', '--method'):\n if arg == '?':\n methodUsage ()\n methodnums = str2list (arg)\n elif opt in ('-o', '--output'):\n output = arg\n elif opt in ('-s', '--servers'):\n servers = arg\n else:\n raise AttributeError(\"Recognized but unimplemented option `%s'\" % opt)\n except SystemExit:\n raise\n except:\n usage (sys.exc_info ()[1])\n\n if 'a' in output:\n output = 'fFns'\n\n servers = readServers(servers)\n\n if methodnums == None:\n methodnums = list(range(1, len (DEFAULT_METHODS) + 1))\n \n limitre = re.compile ('|'.join (args), re.IGNORECASE)\n \n for s in servers:\n if (not not limitre.match (s['name'])) == invert:\n continue\n \n serv = SOAP.SOAPProxy(s['endpoint'], namespace = s['namespace'])\n\n for num in (methodnums):\n if num > len(DEFAULT_METHODS):\n break\n\n total += 1\n\n name = DEFAULT_METHODS[num - 1]\n\n title = '%s: %s (#%d)' % (s['name'], name, num)\n\n try:\n fn = globals ()[name]\n except KeyboardInterrupt:\n raise\n except:\n if 'n' in output:\n print(title, \"test not yet implemented\")\n notimp += 1\n continue\n\n try:\n res = fn (serv, s['soapaction'], s['name'])\n if name in s['nonfunctional']:\n print(title, \"succeeded despite marked nonfunctional\")\n elif 's' in output:\n print(title, \"succeeded \")\n succeed += 1\n except KeyboardInterrupt:\n print(\"fail\")\n raise\n except:\n if name in s['nonfunctional']:\n if 'F' in output:\n t = 'as expected'\n if s['nonfunctional'][name] != '':\n t += ', ' + s['nonfunctional'][name]\n print(title, \"failed (%s) -\" %t, sys.exc_info()[1])\n failok += 1\n else:\n if 'f' in output:\n print(title, \"failed -\", str (sys.exc_info()[1]))\n fail += 1\n\n if stats:\n print(\" Tests ended at:\", time.ctime (time.time()))\n if stats > 0:\n print(\" Total tests: %d\" % total)\n print(\" Successes: %d (%3.2f%%)\" % \\\n (succeed, 100.0 * succeed / total))\n if stats > 0 or fail > 0:\n print(\"Failed unexpectedly: %d (%3.2f%%)\" % \\\n (fail, 100.0 * fail / total))\n if stats > 0:\n print(\" Failed as expected: %d (%3.2f%%)\" % \\\n (failok, 100.0 * failok / total))\n if stats > 0 or notimp > 0:\n print(\" Not implemented: %d (%3.2f%%)\" % \\\n (notimp, 100.0 * notimp / total))\n\n return fail + notimp\n \n \n\nif __name__ == \"__main__\":\n main()\n\n"},"avg_line_length":{"kind":"number","value":31.1305841924,"string":"31.130584"},"max_line_length":{"kind":"number","value":139,"string":"139"},"alphanum_fraction":{"kind":"number","value":0.486919086,"string":"0.486919"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2769,"string":"2,769"},"score_documentation":{"kind":"number","value":0.3056628766972072,"string":"0.305663"}}},{"rowIdx":3253,"cells":{"hexsha":{"kind":"string","value":"92fb1af4be141cb39cbab935a9b9551b1ec5b453"},"size":{"kind":"number","value":934,"string":"934"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/compile.py"},"max_stars_repo_name":{"kind":"string","value":"Pixxeasy/WinTools"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import os\nimport json\nimport shutil\n\nwith open(\"entry.tp\") as entry:\n entry = json.loads(entry.read())\n\nstartcmd = entry['plugin_start_cmd'].split(\"%TP_PLUGIN_FOLDER%\")[1].split(\"\\\\\")\nfiledirectory = startcmd[0]\nfileName = startcmd[1]\n \nif os.path.exists(filedirectory):\n os.remove(os.path.join(os.getcwd(), \"WinTools\"))\nelse:\n os.makedirs(\"temp/\"+filedirectory)\n\n\nfor file in os.listdir(\".\"):\n if file not in [\"compile.py\", \"utils\", \"requirements.txt\", \"build\", \"dist\", \"main.py\", \"main.spec\", \"__pycache__\", \"temp\"]:\n print(\"copying\", file)\n shutil.copy(os.path.join(os.getcwd(), file), os.path.join(\"temp\", filedirectory))\n \nos.rename(\"dist\\Main.exe\", \"dist\\WinTools.exe\")\nshutil.copy(os.path.join(os.getcwd(), r\"dist\\WinTools.exe\"), \"temp/\"+filedirectory)\n\nshutil.make_archive(base_name=\"WinTools\", format='zip', root_dir=\"temp\", base_dir=\"WinTools\") \nos.rename(\"WinTools.zip\", \"WinTools.tpp\")\n"},"avg_line_length":{"kind":"number","value":33.3571428571,"string":"33.357143"},"max_line_length":{"kind":"number","value":127,"string":"127"},"alphanum_fraction":{"kind":"number","value":0.6745182013,"string":"0.674518"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":296,"string":"296"},"score_documentation":{"kind":"number","value":0.3169164882226981,"string":"0.316916"}}},{"rowIdx":3254,"cells":{"hexsha":{"kind":"string","value":"92fca9c0141bc42e92af9526839fedc292014b9b"},"size":{"kind":"number","value":292,"string":"292"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"suda/1121/12.py"},"max_stars_repo_name":{"kind":"string","value":"tusikalanse/acm-icpc"},"max_stars_repo_head_hexsha":{"kind":"string","value":"20150f42752b85e286d812e716bb32ae1fa3db70"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-09-08T12:00:05.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-09-08T14:52:30.000Z"},"max_forks_repo_path":{"kind":"string","value":"suda/1121/12.py"},"max_forks_repo_name":{"kind":"string","value":"tusikalanse/acm-icpc"},"max_forks_repo_head_hexsha":{"kind":"string","value":"20150f42752b85e286d812e716bb32ae1fa3db70"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"for _ in range(int(input())):\n\tx, y = list(map(int, input().split()))\n\tflag = 1\n\tfor i in range(x, y + 1):\n\t\tn = i * i + i + 41\n\t\tfor j in range(2, n):\n\t\t\tif j * j > n:\n\t\t\t\tbreak\n\t\t\tif n % j == 0:\n\t\t\t\tflag = 0\n\t\t\t\tbreak\n\t\tif flag == 0:\n\t\t\tbreak\n\tif flag:\n\t\tprint(\"OK\")\n\telse:\n\t\tprint(\"Sorry\")"},"avg_line_length":{"kind":"number","value":17.1764705882,"string":"17.176471"},"max_line_length":{"kind":"number","value":39,"string":"39"},"alphanum_fraction":{"kind":"number","value":0.4897260274,"string":"0.489726"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":11,"string":"11"},"score_documentation":{"kind":"number","value":0.03767123287671233,"string":"0.037671"}}},{"rowIdx":3255,"cells":{"hexsha":{"kind":"string","value":"92feee19e193679cb75cdd6152ceb20caad92e8b"},"size":{"kind":"number","value":389,"string":"389"},"ext":{"kind":"string","value":"gyp"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"notification/app/node_modules/hiredis/binding.gyp"},"max_stars_repo_name":{"kind":"string","value":"c2gconsulting/bulkpay"},"max_stars_repo_head_hexsha":{"kind":"string","value":"224a52427f80a71f66613c367a5596cbd5e97294"},"max_stars_repo_licenses":{"kind":"list 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\"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":72,"string":"72"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2015-01-11T09:54:16.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2019-11-21T14:07:43.000Z"},"max_forks_repo_path":{"kind":"string","value":"binding.gyp"},"max_forks_repo_name":{"kind":"string","value":"badboy/hiredis-node-win"},"max_forks_repo_head_hexsha":{"kind":"string","value":"d113945a182ba1d616f8fba06e2d80dc9f09552b"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":55,"string":"55"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2015-01-10T20:54:13.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-02-02T14:08:01.000Z"},"content":{"kind":"string","value":"{\n 'targets': [\n {\n 'target_name': 'hiredis',\n 'sources': [\n 'src/hiredis.cc'\n , 'src/reader.cc'\n ],\n 'include_dirs': [\"= 0]\n\n # Find the closest prediction (w/o matching).\n for i, label in enumerate(labels):\n all_dists = np.zeros(K)\n\n idx_i = KP[k,label]\n assert(idx_i < n_points)\n p_i = P[k,idx_i]\n\n for j in range(K):\n idx_j = pred_KP[k,j]\n assert(idx_j < n_points)\n p_j = P[k,idx_j]\n\n all_dists[j] = np.linalg.norm(p_i - p_j)\n\n j = np.argmin(all_dists)\n dists_info.append((k, i, j, all_dists[j]))\n\n dists_info = np.array(dists_info)\n\n return dists_info\n\n\ndef evaluate_PCK_after_label_basis_matching(P, KP, pred_KP):\n n_data = P.shape[0]\n n_points = P.shape[1]\n n_labels = KP.shape[1]\n K = pred_KP.shape[1]\n\n # Find the best mapping from labels to bases.\n all_dists = np.zeros((n_data, n_labels, K))\n label_counts = np.zeros(n_labels)\n\n for k in range(n_data):\n for i in range(n_labels):\n\n # NOTE:\n # Skip if the keypoint does not exist.\n if KP[k,i] < 0: continue\n\n idx_i = KP[k,i]\n assert(idx_i < n_points)\n p_i = P[k,idx_i]\n\n label_counts[i] += 1.\n\n for j in range(K):\n idx_j = pred_KP[k,j]\n assert(idx_j < n_points)\n p_j = P[k,idx_j]\n\n all_dists[k,i,j] += np.linalg.norm(p_i - p_j)\n\n mean_dists = np.sum(all_dists, axis=0) / \\\n np.expand_dims(label_counts, axis=-1)\n row_ind, col_ind = linear_sum_assignment(mean_dists)\n\n\n # dists_info: (point_cloud_index, label, basis_index, distance)\n dists_info = []\n\n for k in range(n_data):\n for (i, j) in zip(row_ind, col_ind):\n if KP[k,i] < 0: continue\n dists_info.append((k, i, j, all_dists[k,i,j]))\n\n dists_info = np.array(dists_info)\n\n return dists_info\n\n\ndef save_results(dists_info, out_dir, postfix=None):\n # dists_info: (point_cloud_index, label, basis_index, distance)\n dists = dists_info[:,3]\n\n if postfix is not None:\n out_file = os.path.join(out_dir, 'distances_{}.npy'.format(postfix))\n else:\n out_file = os.path.join(out_dir, 'distances.npy')\n\n np.save(out_file, dists)\n print(\"Saved '{}'.\".format(out_file))\n\n '''\n # Draw plot.\n n_matches = dists.size\n\n x_list = np.linspace(0.0, 0.1, 20 + 1)\n counts = np.zeros(x_list.size, dtype=int)\n\n for i in range(x_list.size):\n counts[i] = np.sum(dists <= x_list[i])\n\n y_list = counts.astype(x_list.dtype) / float(n_matches)\n\n plt.clf()\n plt.plot(x_list, y_list)\n plt.ylim(0., 1.)\n plt.yticks(np.linspace(0., 1., 10 + 1))\n\n if postfix is not None:\n out_file = os.path.join(out_dir, 'pck_{}.png'.format(postfix))\n else:\n out_file = os.path.join(out_dir, 'pck.png')\n\n plt.savefig(out_file)\n print(\"Saved '{}'.\".format(out_file))\n '''\n\n\ndef evaluate(sess, net, data, out_dir):\n if not os.path.exists(out_dir): os.makedirs(out_dir)\n\n P, KP, pred_KP = compute_all_keypoints(sess, net, data)\n\n dists = evaluate_PCK(P, KP, pred_KP)\n save_results(dists, out_dir)\n\n dists_after_matching = evaluate_PCK_after_label_basis_matching(\n P, KP, pred_KP)\n save_results(dists_after_matching, out_dir, postfix='after_matching')\n\n"},"avg_line_length":{"kind":"number","value":26.4385964912,"string":"26.438596"},"max_line_length":{"kind":"number","value":76,"string":"76"},"alphanum_fraction":{"kind":"number","value":0.601857996,"string":"0.601858"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1145,"string":"1,145"},"score_documentation":{"kind":"number","value":0.25326255253262553,"string":"0.253263"}}},{"rowIdx":3258,"cells":{"hexsha":{"kind":"string","value":"13008c4023106e4274d2b92d9aa79a58e4551138"},"size":{"kind":"number","value":2388,"string":"2,388"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"recipes/cxxopts/all/conanfile.py"},"max_stars_repo_name":{"kind":"string","value":"dvirtz/conan-center-index"},"max_stars_repo_head_hexsha":{"kind":"string","value":"2e7a6337804325616f8d97e3a5b6f66cc72699cb"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":9799,"string":"9,799"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-09-04T12:02:11.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-31T23:55:45.000Z"},"max_forks_repo_path":{"kind":"string","value":"recipes/cxxopts/all/conanfile.py"},"max_forks_repo_name":{"kind":"string","value":"dvirtz/conan-center-index"},"max_forks_repo_head_hexsha":{"kind":"string","value":"2e7a6337804325616f8d97e3a5b6f66cc72699cb"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1126,"string":"1,126"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-09-04T11:57:46.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-31T16:43:38.000Z"},"content":{"kind":"string","value":"import os\nfrom conans import ConanFile, tools\nfrom conans.errors import ConanInvalidConfiguration\n\n\nclass CxxOptsConan(ConanFile):\n name = \"cxxopts\"\n homepage = \"https://github.com/jarro2783/cxxopts\"\n url = \"https://github.com/conan-io/conan-center-index\"\n description = \"Lightweight C++ option parser library, supporting the standard GNU style syntax for options.\"\n license = \"MIT\"\n topics = (\"conan\", \"option-parser\", \"positional-arguments \", \"header-only\")\n settings = \"compiler\"\n options = { \"unicode\": [True, False] }\n default_options = { \"unicode\": False }\n no_copy_source = True\n \n @property\n def _source_subfolder(self):\n return \"source_subfolder\"\n\n @property\n def _minimum_cpp_standard(self):\n return 11\n\n @property\n def _minimum_compilers_version(self):\n return {\n \"Visual Studio\": \"14\",\n \"gcc\": \"5\",\n \"clang\": \"3.9\",\n \"apple-clang\": \"8\",\n }\n\n def configure(self):\n if self.settings.compiler.get_safe(\"cppstd\"):\n tools.check_min_cppstd(self, self._minimum_cpp_standard)\n min_version = self._minimum_compilers_version.get(str(self.settings.compiler))\n if not min_version:\n self.output.warn(\"{} recipe lacks information about the {} compiler support.\".format(\n self.name, self.settings.compiler))\n else:\n if tools.Version(self.settings.compiler.version) < min_version:\n raise ConanInvalidConfiguration(\"{} requires C++{} support. The current compiler {} {} does not support it.\".format(\n self.name, self._minimum_cpp_standard, self.settings.compiler, self.settings.compiler.version))\n\n def requirements(self):\n if self.options.unicode:\n self.requires(\"icu/64.2\")\n\n def source(self):\n tools.get(**self.conan_data[\"sources\"][self.version])\n os.rename(\"{}-{}\".format(self.name, self.version), self._source_subfolder)\n\n def package(self):\n self.copy(\"LICENSE\", dst=\"licenses\", src=self._source_subfolder)\n self.copy(\"{}.hpp\".format(self.name), dst=\"include\", src=os.path.join(self._source_subfolder, \"include\"))\n\n def package_id(self):\n self.info.header_only()\n\n def package_info(self):\n if self.options.unicode:\n self.cpp_info.defines = [\"CXXOPTS_USE_UNICODE\"]\n"},"avg_line_length":{"kind":"number","value":36.7384615385,"string":"36.738462"},"max_line_length":{"kind":"number","value":132,"string":"132"},"alphanum_fraction":{"kind":"number","value":0.6411222781,"string":"0.641122"},"count_classes":{"kind":"number","value":2287,"string":"2,287"},"score_classes":{"kind":"number","value":0.9577051926298158,"string":"0.957705"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":337,"string":"337"},"score_decorators":{"kind":"number","value":0.14112227805695143,"string":"0.141122"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":589,"string":"589"},"score_documentation":{"kind":"number","value":0.2466499162479062,"string":"0.24665"}}},{"rowIdx":3259,"cells":{"hexsha":{"kind":"string","value":"1300c8abfbfcff2fad07bdd38a7b66244215a15d"},"size":{"kind":"number","value":1868,"string":"1,868"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"p_030_039/problem31.py"},"max_stars_repo_name":{"kind":"string","value":"ericgreveson/projecteuler"},"max_stars_repo_head_hexsha":{"kind":"string","value":"1844bf383fca871b82d88ef1eb3a9b1a0e363054"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"p_030_039/problem31.py"},"max_issues_repo_name":{"kind":"string","value":"ericgreveson/projecteuler"},"max_issues_repo_head_hexsha":{"kind":"string","value":"1844bf383fca871b82d88ef1eb3a9b1a0e363054"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"p_030_039/problem31.py"},"max_forks_repo_name":{"kind":"string","value":"ericgreveson/projecteuler"},"max_forks_repo_head_hexsha":{"kind":"string","value":"1844bf383fca871b82d88ef1eb3a9b1a0e363054"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"class CoinArray(list):\n \"\"\"\n Coin list that is hashable for storage in sets\n The 8 entries are [1p count, 2p count, 5p count, ... , 200p count]\n \"\"\"\n def __hash__(self):\n \"\"\"\n Hash this as a string\n \"\"\"\n return hash(\" \".join([str(i) for i in self]))\n\ndef main():\n \"\"\"\n Entry point\n \"\"\"\n # Important: sorted smallest to largest\n coins = [1, 2, 5, 10, 20, 50, 100, 200]\n coin_index = {coin: index for index, coin in enumerate(coins)}\n \n # How many ways are there of making each number from 1 to 200 from these values?\n # Building up from 1 means we can re-use earlier results\n # e.g.:\n # 1p: [{1}]\n # 2p: [{1,1}, {2}]\n # 3p: [{1,1,1}, {2,1}]\n # 4p: [{1,1,1,1}, {2,1,1}, {2,2}]\n # etc\n way_sets = [None]\n for i in range(1, 201):\n way_set_i = set()\n\n # Try using 1 of each coin and then all the ways of the remainder, if > 0\n for coin in coins:\n remainder = i - coin\n\n if remainder == 0:\n # We can make this with exactly this coin alone - but no larger coins\n coin_count = [0 for i in coins]\n coin_count[coin_index[coin]] = 1\n way_set_i.add(CoinArray(coin_count))\n break\n elif remainder > 0:\n # We can use this coin and whatever the options for the smaller value are\n for rem_list in way_sets[remainder]:\n new_coin_count = [c for c in rem_list]\n new_coin_count[coin_index[coin]] += 1\n way_set_i.add(CoinArray(new_coin_count))\n else:\n # Can't use any bigger coins\n break\n\n way_sets.append(way_set_i)\n\n print(f\"Number of ways of making £2: {len(way_sets[200])}\")\n return\n\nif __name__ == \"__main__\":\n main()\n"},"avg_line_length":{"kind":"number","value":31.6610169492,"string":"31.661017"},"max_line_length":{"kind":"number","value":89,"string":"89"},"alphanum_fraction":{"kind":"number","value":0.534261242,"string":"0.534261"},"count_classes":{"kind":"number","value":292,"string":"292"},"score_classes":{"kind":"number","value":0.15623327982878546,"string":"0.156233"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":785,"string":"785"},"score_documentation":{"kind":"number","value":0.4200107009095773,"string":"0.420011"}}},{"rowIdx":3260,"cells":{"hexsha":{"kind":"string","value":"1300e7747076d34572209fef1029da836f1dbf7b"},"size":{"kind":"number","value":2358,"string":"2,358"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"video/cloud-client/quickstart/quickstart.py"},"max_stars_repo_name":{"kind":"string","value":"nasirdec/GCP-AppEngine-Example"},"max_stars_repo_head_hexsha":{"kind":"string","value":"3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-11-17T08:59:14.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2019-11-17T08:59:14.000Z"},"max_issues_repo_path":{"kind":"string","value":"video/cloud-client/quickstart/quickstart.py"},"max_issues_repo_name":{"kind":"string","value":"nasirdec/GCP-AppEngine-Example"},"max_issues_repo_head_hexsha":{"kind":"string","value":"3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":16,"string":"16"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-06-15T00:02:56.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-03-25T23:22:38.000Z"},"max_forks_repo_path":{"kind":"string","value":"video/cloud-client/quickstart/quickstart.py"},"max_forks_repo_name":{"kind":"string","value":"nasirdec/GCP-AppEngine-Example"},"max_forks_repo_head_hexsha":{"kind":"string","value":"3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":3,"string":"3"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-02-11T16:16:11.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2019-04-19T21:34:37.000Z"},"content":{"kind":"string","value":"#!/usr/bin/env python\n\n# Copyright 2017 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"This application demonstrates label detection on a demo video using\nthe Google Cloud API.\n\nUsage:\n python quickstart.py\n\n\"\"\"\n\n\ndef run_quickstart():\n # [START video_quickstart]\n from google.cloud import videointelligence\n\n video_client = videointelligence.VideoIntelligenceServiceClient()\n features = [videointelligence.enums.Feature.LABEL_DETECTION]\n operation = video_client.annotate_video(\n 'gs://demomaker/cat.mp4', features=features)\n print('\\nProcessing video for label annotations:')\n\n result = operation.result(timeout=120)\n print('\\nFinished processing.')\n\n # first result is retrieved because a single video was processed\n segment_labels = result.annotation_results[0].segment_label_annotations\n for i, segment_label in enumerate(segment_labels):\n print('Video label description: {}'.format(\n segment_label.entity.description))\n for category_entity in segment_label.category_entities:\n print('\\tLabel category description: {}'.format(\n category_entity.description))\n\n for i, segment in enumerate(segment_label.segments):\n start_time = (segment.segment.start_time_offset.seconds +\n segment.segment.start_time_offset.nanos / 1e9)\n end_time = (segment.segment.end_time_offset.seconds +\n segment.segment.end_time_offset.nanos / 1e9)\n positions = '{}s to {}s'.format(start_time, end_time)\n confidence = segment.confidence\n print('\\tSegment {}: {}'.format(i, positions))\n print('\\tConfidence: {}'.format(confidence))\n print('\\n')\n # [END video_quickstart]\n\n\nif __name__ == '__main__':\n run_quickstart()\n"},"avg_line_length":{"kind":"number","value":37.4285714286,"string":"37.428571"},"max_line_length":{"kind":"number","value":75,"string":"75"},"alphanum_fraction":{"kind":"number","value":0.6972010178,"string":"0.697201"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1064,"string":"1,064"},"score_documentation":{"kind":"number","value":0.45122985581000846,"string":"0.45123"}}},{"rowIdx":3261,"cells":{"hexsha":{"kind":"string","value":"13027944554eb9d8705c77a8073c891a250b3842"},"size":{"kind":"number","value":1853,"string":"1,853"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"ally/instrument.py"},"max_stars_repo_name":{"kind":"string","value":"platformmaster9/PyAlly"},"max_stars_repo_head_hexsha":{"kind":"string","value":"55400e0835ae3ac5b3cf58e0e8214c6244aeb149"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"ally/instrument.py"},"max_issues_repo_name":{"kind":"string","value":"platformmaster9/PyAlly"},"max_issues_repo_head_hexsha":{"kind":"string","value":"55400e0835ae3ac5b3cf58e0e8214c6244aeb149"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"ally/instrument.py"},"max_forks_repo_name":{"kind":"string","value":"platformmaster9/PyAlly"},"max_forks_repo_head_hexsha":{"kind":"string","value":"55400e0835ae3ac5b3cf58e0e8214c6244aeb149"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from . import utils\n#################################################\n\"\"\" INSTRUMENT \"\"\"\n#################################################\ndef Instrument(symbol):\n symbol = str(symbol).upper()\n return {\n '__symbol' : symbol,\n 'Sym' : symbol,\n 'SecTyp' : 'CS',\n '__type' : 'equity'\n }\n\n#################################################\ndef Equity(symbol):\n return Instrument(symbol)\n\n#################################################\ndef Option (instrument, maturity_date, strike):\n return {\n **{\n 'MatDt' : str(maturity_date) + 'T00:00:00.000-05:00',\n 'StrkPx' : str(int(strike)),\n 'SecTyp' : 'OPT',\n '__maturity' : str(maturity_date),\n '__strike' : str(int(strike))\n },\n **instrument\n }\n\n#################################################\ndef Call (instrument, maturity_date, strike):\n # Let Option do some lifting\n x = {\n **{ 'CFI':'OC' },\n **Option(instrument, maturity_date, strike)\n }\n x['__underlying'] = x['Sym']\n x['__type'] = 'call'\n x['__symbol'] = utils.option_format(\n symbol = x['Sym'],\n exp_date = x['__maturity'],\n strike = x['__strike'],\n direction = 'C'\n )\n return x\n \n#################################################\ndef Put (instrument, maturity_date, strike):\n # Let Option do some lifting\n x = {\n **{ 'CFI':'OP' },\n **Option(instrument, maturity_date, strike)\n }\n x['__underlying'] = x['Sym']\n x['__type'] = 'put'\n x['__symbol'] = utils.option_format(\n symbol = x['Sym'],\n exp_date = x['__maturity'],\n strike = x['__strike'],\n direction = 'P'\n )\n return x"},"avg_line_length":{"kind":"number","value":29.4126984127,"string":"29.412698"},"max_line_length":{"kind":"number","value":70,"string":"70"},"alphanum_fraction":{"kind":"number","value":0.3993524015,"string":"0.399352"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":671,"string":"671"},"score_documentation":{"kind":"number","value":0.36211548839719376,"string":"0.362115"}}},{"rowIdx":3262,"cells":{"hexsha":{"kind":"string","value":"1302b2f57e10ec891cc57b121da1cf9b5593731f"},"size":{"kind":"number","value":432,"string":"432"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py"},"max_stars_repo_name":{"kind":"string","value":"onaio/airbyte"},"max_stars_repo_head_hexsha":{"kind":"string","value":"38302e82a25f1b66742c3febfbff0668556920f2"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":116,"string":"116"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-08-27T01:11:27.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-09-19T02:47:52.000Z"},"max_forks_repo_path":{"kind":"string","value":"airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py"},"max_forks_repo_name":{"kind":"string","value":"onaio/airbyte"},"max_forks_repo_head_hexsha":{"kind":"string","value":"38302e82a25f1b66742c3febfbff0668556920f2"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2020-09-15T06:10:01.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-09-15T06:10:01.000Z"},"content":{"kind":"string","value":"#\n# Copyright (c) 2022 Airbyte, Inc., all rights reserved.\n#\n\n\nimport pytest\n\npytest_plugins = (\"source_acceptance_test.plugin\",)\n\n\n@pytest.fixture(scope=\"session\", autouse=True)\ndef connector_setup():\n \"\"\"This fixture is a placeholder for external resources that acceptance test might require.\"\"\"\n # TODO: setup test dependencies if needed. otherwise remove the TODO comments\n yield\n # TODO: clean up test dependencies\n"},"avg_line_length":{"kind":"number","value":25.4117647059,"string":"25.411765"},"max_line_length":{"kind":"number","value":98,"string":"98"},"alphanum_fraction":{"kind":"number","value":0.7384259259,"string":"0.738426"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":252,"string":"252"},"score_generators":{"kind":"number","value":0.5833333333333334,"string":"0.583333"},"count_decorators":{"kind":"number","value":299,"string":"299"},"score_decorators":{"kind":"number","value":0.6921296296296297,"string":"0.69213"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":303,"string":"303"},"score_documentation":{"kind":"number","value":0.7013888888888888,"string":"0.701389"}}},{"rowIdx":3263,"cells":{"hexsha":{"kind":"string","value":"13037ede58d748bec28747c289956464268c2ceb"},"size":{"kind":"number","value":118,"string":"118"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"ddt/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"GawenChen/test_pytest"},"max_stars_repo_head_hexsha":{"kind":"string","value":"da7a29dc43e8027d3fd1a05054480ed7007131c3"},"max_stars_repo_licenses":{"kind":"list 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810400085\n\"\"\""},"avg_line_length":{"kind":"number","value":14.75,"string":"14.75"},"max_line_length":{"kind":"number","value":23,"string":"23"},"alphanum_fraction":{"kind":"number","value":0.5762711864,"string":"0.576271"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":129,"string":"129"},"score_documentation":{"kind":"number","value":0.9923076923076923,"string":"0.992308"}}},{"rowIdx":3264,"cells":{"hexsha":{"kind":"string","value":"13058e5281c2e6d308c1c802707f6f83b62cdc9b"},"size":{"kind":"number","value":1809,"string":"1,809"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"darts/models/linear_regression_model.py"},"max_stars_repo_name":{"kind":"string","value":"BiancaMT25/darts"},"max_stars_repo_head_hexsha":{"kind":"string","value":"bb550dede6d8927a45aea0d9f3df53de32a6eee2"},"max_stars_repo_licenses":{"kind":"list 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\"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"darts/models/linear_regression_model.py"},"max_forks_repo_name":{"kind":"string","value":"BiancaMT25/darts"},"max_forks_repo_head_hexsha":{"kind":"string","value":"bb550dede6d8927a45aea0d9f3df53de32a6eee2"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"\nStandard Regression model\n-------------------------\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\nfrom typing import Union\nfrom ..logging import get_logger\nfrom .regression_model import RegressionModel\nfrom sklearn.linear_model import LinearRegression\n\nlogger = get_logger(__name__)\n\n\nclass LinearRegressionModel(RegressionModel):\n\n def __init__(self,\n lags: Union[int, list] = None,\n lags_exog: Union[int, list, bool] = None,\n **kwargs):\n \"\"\"\n Simple wrapper for the linear regression model in scikit-learn, LinearRegression().\n\n Parameters\n ----------\n lags : Union[int, list]\n Number of lagged target values used to predict the next time step. If an integer is given\n the last `lags` lags are used (inclusive). Otherwise a list of integers with lags is required.\n lags_exog : Union[int, list, bool]\n Number of lagged exogenous values used to predict the next time step. If an integer is given\n the last `lags_exog` lags are used (inclusive). Otherwise a list of integers with lags is required.\n If True `lags` will be used to determine lags_exog. If False, the values of all exogenous variables\n at the current time `t`. This might lead to leakage if for predictions the values of the exogenous\n variables at time `t` are not known.\n **kwargs\n Additional keyword arguments passed to `sklearn.linear_model.LinearRegression`.\n \"\"\"\n self.kwargs = kwargs\n super().__init__(\n lags=lags,\n lags_exog=lags_exog,\n model=LinearRegression(**kwargs)\n )\n\n def __str__(self):\n return 'LinearRegression(lags={}, lags_exog={})'.format(self.lags, self.lags_exog)"},"avg_line_length":{"kind":"number","value":38.4893617021,"string":"38.489362"},"max_line_length":{"kind":"number","value":111,"string":"111"},"alphanum_fraction":{"kind":"number","value":0.64013267,"string":"0.640133"},"count_classes":{"kind":"number","value":1522,"string":"1,522"},"score_classes":{"kind":"number","value":0.8413488114980652,"string":"0.841349"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1128,"string":"1,128"},"score_documentation":{"kind":"number","value":0.6235489220563848,"string":"0.623549"}}},{"rowIdx":3265,"cells":{"hexsha":{"kind":"string","value":"130613c0dd1daf7edf4aa6e30bb0303b2984e2ce"},"size":{"kind":"number","value":4862,"string":"4,862"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"hail/python/test/hailtop/utils/test_utils.py"},"max_stars_repo_name":{"kind":"string","value":"vrautela/hail"},"max_stars_repo_head_hexsha":{"kind":"string","value":"7db6189b5b1feafa88452b8470e497d9505d9a46"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"hail/python/test/hailtop/utils/test_utils.py"},"max_issues_repo_name":{"kind":"string","value":"vrautela/hail"},"max_issues_repo_head_hexsha":{"kind":"string","value":"7db6189b5b1feafa88452b8470e497d9505d9a46"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"hail/python/test/hailtop/utils/test_utils.py"},"max_forks_repo_name":{"kind":"string","value":"vrautela/hail"},"max_forks_repo_head_hexsha":{"kind":"string","value":"7db6189b5b1feafa88452b8470e497d9505d9a46"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from hailtop.utils import (partition, url_basename, url_join, url_scheme,\n url_and_params, parse_docker_image_reference)\n\n\ndef test_partition_zero_empty():\n assert list(partition(0, [])) == []\n\n\ndef test_partition_even_small():\n assert list(partition(3, range(3))) == [range(0, 1), range(1, 2), range(2, 3)]\n\n\ndef test_partition_even_big():\n assert list(partition(3, range(9))) == [range(0, 3), range(3, 6), range(6, 9)]\n\n\ndef test_partition_uneven_big():\n assert list(partition(2, range(9))) == [range(0, 5), range(5, 9)]\n\n\ndef test_partition_toofew():\n assert list(partition(6, range(3))) == [range(0, 1), range(1, 2), range(2, 3),\n range(3, 3), range(3, 3), range(3, 3)]\n\n\ndef test_url_basename():\n assert url_basename('/path/to/file') == 'file'\n assert url_basename('https://hail.is/path/to/file') == 'file'\n\n\ndef test_url_join():\n assert url_join('/path/to', 'file') == '/path/to/file'\n assert url_join('/path/to/', 'file') == '/path/to/file'\n assert url_join('/path/to/', '/absolute/file') == '/absolute/file'\n assert url_join('https://hail.is/path/to', 'file') == 'https://hail.is/path/to/file'\n assert url_join('https://hail.is/path/to/', 'file') == 'https://hail.is/path/to/file'\n assert url_join('https://hail.is/path/to/', '/absolute/file') == 'https://hail.is/absolute/file'\n\n\ndef test_url_scheme():\n assert url_scheme('https://hail.is/path/to') == 'https'\n assert url_scheme('/path/to') == ''\n\ndef test_url_and_params():\n assert url_and_params('https://example.com/') == ('https://example.com/', {})\n assert url_and_params('https://example.com/foo?') == ('https://example.com/foo', {})\n assert url_and_params('https://example.com/foo?a=b&c=d') == ('https://example.com/foo', {'a': 'b', 'c': 'd'})\n\ndef test_parse_docker_image_reference():\n x = parse_docker_image_reference('animage')\n assert x.domain is None\n assert x.path == 'animage'\n assert x.tag is None\n assert x.digest is None\n assert x.name() == 'animage'\n assert str(x) == 'animage'\n\n x = parse_docker_image_reference('hailgenetics/animage')\n assert x.domain == 'hailgenetics'\n assert x.path == 'animage'\n assert x.tag is None\n assert x.digest is None\n assert x.name() == 'hailgenetics/animage'\n assert str(x) == 'hailgenetics/animage'\n\n x = parse_docker_image_reference('localhost:5000/animage')\n assert x.domain == 'localhost:5000'\n assert x.path == 'animage'\n assert x.tag is None\n assert x.digest is None\n assert x.name() == 'localhost:5000/animage'\n assert str(x) == 'localhost:5000/animage'\n\n x = parse_docker_image_reference('localhost:5000/a/b/name')\n assert x.domain == 'localhost:5000'\n assert x.path == 'a/b/name'\n assert x.tag is None\n assert x.digest is None\n assert x.name() == 'localhost:5000/a/b/name'\n assert str(x) == 'localhost:5000/a/b/name'\n\n x = parse_docker_image_reference('localhost:5000/a/b/name:tag')\n assert x.domain == 'localhost:5000'\n assert x.path == 'a/b/name'\n assert x.tag == 'tag'\n assert x.digest is None\n assert x.name() == 'localhost:5000/a/b/name'\n assert str(x) == 'localhost:5000/a/b/name:tag'\n\n x = parse_docker_image_reference('localhost:5000/a/b/name:tag@sha256:abc123')\n assert x.domain == 'localhost:5000'\n assert x.path == 'a/b/name'\n assert x.tag == 'tag'\n assert x.digest == 'sha256:abc123'\n assert x.name() == 'localhost:5000/a/b/name'\n assert str(x) == 'localhost:5000/a/b/name:tag@sha256:abc123'\n\n x = parse_docker_image_reference('localhost:5000/a/b/name@sha256:abc123')\n assert x.domain == 'localhost:5000'\n assert x.path == 'a/b/name'\n assert x.tag is None\n assert x.digest == 'sha256:abc123'\n assert x.name() == 'localhost:5000/a/b/name'\n assert str(x) == 'localhost:5000/a/b/name@sha256:abc123'\n\n x = parse_docker_image_reference('name@sha256:abc123')\n assert x.domain is None\n assert x.path == 'name'\n assert x.tag is None\n assert x.digest == 'sha256:abc123'\n assert x.name() == 'name'\n assert str(x) == 'name@sha256:abc123'\n\n x = parse_docker_image_reference('gcr.io/hail-vdc/batch-worker:123fds312')\n assert x.domain == 'gcr.io'\n assert x.path == 'hail-vdc/batch-worker'\n assert x.tag == '123fds312'\n assert x.digest is None\n assert x.name() == 'gcr.io/hail-vdc/batch-worker'\n assert str(x) == 'gcr.io/hail-vdc/batch-worker:123fds312'\n\n x = parse_docker_image_reference('us-docker.pkg.dev/my-project/my-repo/test-image')\n assert x.domain == 'us-docker.pkg.dev'\n assert x.path == 'my-project/my-repo/test-image'\n assert x.tag is None\n assert x.digest is None\n assert x.name() == 'us-docker.pkg.dev/my-project/my-repo/test-image'\n assert str(x) == 'us-docker.pkg.dev/my-project/my-repo/test-image'\n"},"avg_line_length":{"kind":"number","value":37.6899224806,"string":"37.689922"},"max_line_length":{"kind":"number","value":113,"string":"113"},"alphanum_fraction":{"kind":"number","value":0.6443850267,"string":"0.644385"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1722,"string":"1,722"},"score_documentation":{"kind":"number","value":0.35417523652817773,"string":"0.354175"}}},{"rowIdx":3266,"cells":{"hexsha":{"kind":"string","value":"13064197a568b4ea0fdb674d3a8685e3b27e92eb"},"size":{"kind":"number","value":863,"string":"863"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"hood/urls.py"},"max_stars_repo_name":{"kind":"string","value":"wadi-1000/Vicinity"},"max_stars_repo_head_hexsha":{"kind":"string","value":"a41f6ec2c532cb06f7444b55073b6879a1fce63a"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"hood/urls.py"},"max_issues_repo_name":{"kind":"string","value":"wadi-1000/Vicinity"},"max_issues_repo_head_hexsha":{"kind":"string","value":"a41f6ec2c532cb06f7444b55073b6879a1fce63a"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"hood/urls.py"},"max_forks_repo_name":{"kind":"string","value":"wadi-1000/Vicinity"},"max_forks_repo_head_hexsha":{"kind":"string","value":"a41f6ec2c532cb06f7444b55073b6879a1fce63a"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from django.urls import path,include\nfrom . import views\n\nurlpatterns = [\n path('home/', views.home, name = 'home'),\n path('add_hood/',views.uploadNeighbourhood, name = 'add_hood'),\n path('viewhood/',views.viewHood, name = 'viewhood'),\n path('hood//',views.hood, name = 'hood'),\n path('add_bizna/',views.uploadBuisness, name = 'add_bizna'),\n path('bizna/',views.viewBizna, name = 'view_bizna'),\n path('viewbizna//',views.bizna, name = 'bizna'),\n path('post/',views.create_post, name = 'post'),\n path('posts/',views.viewPost, name = 'posts'),\n path('searchbizna/', views.searchBizna, name=\"search_results\"),\n path('searchhood/', views.searchHood, name=\"search_res\"),\n path('join_hood/', views.join_neighbourhood, name='join-hood'),\n path('leave_hood/', views.leave_neighbourhood, name='leave-hood'),\n]"},"avg_line_length":{"kind":"number","value":47.9444444444,"string":"47.944444"},"max_line_length":{"kind":"number","value":74,"string":"74"},"alphanum_fraction":{"kind":"number","value":0.6651216686,"string":"0.665122"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":287,"string":"287"},"score_documentation":{"kind":"number","value":0.3325608342989571,"string":"0.332561"}}},{"rowIdx":3267,"cells":{"hexsha":{"kind":"string","value":"1306e647595d1f2f64e2d6dd214b9b25580f3ed1"},"size":{"kind":"number","value":8305,"string":"8,305"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/licensedcode/tokenize.py"},"max_stars_repo_name":{"kind":"string","value":"chetanya-shrimali/scancode-toolkit"},"max_stars_repo_head_hexsha":{"kind":"string","value":"a1a22fb225cbeb211bd6f92272a46f1351f57d6b"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0","CC0-1.0"],"string":"[\n \"Apache-2.0\",\n \"CC0-1.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"src/licensedcode/tokenize.py"},"max_issues_repo_name":{"kind":"string","value":"chetanya-shrimali/scancode-toolkit"},"max_issues_repo_head_hexsha":{"kind":"string","value":"a1a22fb225cbeb211bd6f92272a46f1351f57d6b"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0","CC0-1.0"],"string":"[\n \"Apache-2.0\",\n \"CC0-1.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"src/licensedcode/tokenize.py"},"max_forks_repo_name":{"kind":"string","value":"chetanya-shrimali/scancode-toolkit"},"max_forks_repo_head_hexsha":{"kind":"string","value":"a1a22fb225cbeb211bd6f92272a46f1351f57d6b"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0","CC0-1.0"],"string":"[\n \"Apache-2.0\",\n \"CC0-1.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2017 nexB Inc. and others. All rights reserved.\n# http://nexb.com and https://github.com/nexB/scancode-toolkit/\n# The ScanCode software is licensed under the Apache License version 2.0.\n# Data generated with ScanCode require an acknowledgment.\n# ScanCode is a trademark of nexB Inc.\n#\n# You may not use this software except in compliance with the License.\n# You may obtain a copy of the License at: http://apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software distributed\n# under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n# CONDITIONS OF ANY KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations under the License.\n#\n# When you publish or redistribute any data created with ScanCode or any ScanCode\n# derivative work, you must accompany this data with the following acknowledgment:\n#\n# Generated with ScanCode and provided on an \"AS IS\" BASIS, WITHOUT WARRANTIES\n# OR CONDITIONS OF ANY KIND, either express or implied. No content created from\n# ScanCode should be considered or used as legal advice. Consult an Attorney\n# for any legal advice.\n# ScanCode is a free software code scanning tool from nexB Inc. and others.\n# Visit https://github.com/nexB/scancode-toolkit/ for support and download.\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom itertools import islice\nfrom itertools import izip\nimport re\nfrom zlib import crc32\n\nfrom textcode.analysis import text_lines\n\n\n\"\"\"\nUtilities to break texts in lines and tokens (aka. words) with specialized version\nfor queries and rules texts.\n\"\"\"\n\ndef query_lines(location=None, query_string=None, strip=True):\n \"\"\"\n Return an iterable of text lines given a file at `location` or a\n `query string`. Include empty lines.\n \"\"\"\n # TODO: OPTIMIZE: tokenizing line by line may be rather slow\n # we could instead get lines and tokens at once in a batch?\n lines = []\n if location:\n lines = text_lines(location, demarkup=False)\n elif query_string:\n if strip:\n keepends = False\n else:\n keepends = True\n lines = query_string.splitlines(keepends)\n\n for line in lines:\n if strip:\n yield line.strip()\n else:\n yield line\n\n\n# Split on whitespace and punctuations: keep only characters\n# and + in the middle or end of a word.\n# Keeping the trailing + is important for licenses name such as GPL2+\nquery_pattern = '[^\\W_]+\\+?[^\\W_]*'\nword_splitter = re.compile(query_pattern, re.UNICODE).findall\n\ndef query_tokenizer(text, lower=True):\n \"\"\"\n Return an iterable of tokens from a unicode query text.\n \"\"\"\n if not text:\n return []\n text = lower and text.lower() or text\n return (token for token in word_splitter(text) if token)\n\n\n# Alternate pattern used for matched text collection\nnot_query_pattern = '[\\W_+]+[\\W_]?'\n\n# collect tokens and non-token texts in two different groups\n_text_capture_pattern = '(?P' + query_pattern + ')' + '|' + '(?P' + not_query_pattern + ')'\ntokens_and_non_tokens = re.compile(_text_capture_pattern, re.UNICODE).finditer\n\ndef matched_query_text_tokenizer(text):\n \"\"\"\n Return an iterable of tokens and non-tokens from a unicode query text keeping\n everything (including punctuations, line endings, etc.)\n The returned iterable contains 2-tuples of:\n - True if the string is a text token or False if this is not (such as punctuation, spaces, etc).\n - the corresponding string\n This is used to reconstruct the matched query text accurately.\n \"\"\"\n if not text:\n return\n for match in tokens_and_non_tokens(text):\n if not match:\n continue\n mgd = match.groupdict()\n token = mgd.get('token')\n punct = mgd.get('punct')\n if token or punct:\n yield (True, token) if token else (False, punct)\n\n\n# Template-aware splitter, keeping a templated part {{anything}} as a token.\n# This splitter yields plain token strings or double braces-enclosed strings\n# {{something}} for templates. curly barces are otherwise treated as punctuation.\n# A template part is anything enclosed in double braces\ntemplate_pattern = '\\{\\{[^{}]*\\}\\}'\nrule_pattern = '%s|%s+' % (query_pattern, template_pattern,)\ntemplate_splitter = re.compile(rule_pattern , re.UNICODE).findall\n\ndef rule_tokenizer(text, lower=True):\n \"\"\"\n Return an iterable of tokens from a unicode rule text, skipping templated\n parts, including leading and trailing templated parts.\n\n For example:\n >>> list(rule_tokenizer(''))\n []\n >>> list(rule_tokenizer('some Text with spAces! + _ -'))\n [u'some', u'text', u'with', u'spaces']\n\n Unbalanced templates are handled correctly:\n >>> list(rule_tokenizer('{{}some }}Text with spAces! + _ -'))\n [u'some', u'text', u'with', u'spaces']\n\n Templates are handled and skipped for templated sequences:\n >>> list(rule_tokenizer('{{Hi}}some {{}}Text with{{noth+-_!@ing}} {{junk}}spAces! + _ -{{}}'))\n [u'some', u'text', u'with', u'spaces']\n \"\"\"\n if not text:\n return []\n text = lower and text.lower() or text\n tokens = template_splitter(text)\n # skip templates\n return (token for token in tokens if token and not token.startswith('{{'))\n\n\ndef ngrams(iterable, ngram_length):\n \"\"\"\n Return an iterable of ngrams of length `ngram_length` given an iterable.\n Each ngram is a tuple of ngram_length items.\n The returned iterable is empty if the input iterable contains less than\n `ngram_length` items.\n\n Note: this is a fairly arcane but optimized way to compute ngrams.\n\n For example:\n >>> list(ngrams([1,2,3,4,5], 2))\n [(1, 2), (2, 3), (3, 4), (4, 5)]\n\n >>> list(ngrams([1,2,3,4,5], 4))\n [(1, 2, 3, 4), (2, 3, 4, 5)]\n\n >>> list(ngrams([1,2,3,4], 2))\n [(1, 2), (2, 3), (3, 4)]\n\n >>> list(ngrams([1,2,3], 2))\n [(1, 2), (2, 3)]\n\n >>> list(ngrams([1,2], 2))\n [(1, 2)]\n\n >>> list(ngrams([1], 2))\n []\n\n This also works with arrays or tuples:\n\n >>> from array import array\n >>> list(ngrams(array(b'h', [1,2,3,4,5]), 2))\n [(1, 2), (2, 3), (3, 4), (4, 5)]\n\n >>> list(ngrams(tuple([1,2,3,4,5]), 2))\n [(1, 2), (2, 3), (3, 4), (4, 5)]\n \"\"\"\n return izip(*(islice(iterable, i, None) for i in range(ngram_length)))\n\n\ndef select_ngrams(ngrams, with_pos=False):\n \"\"\"\n Return an iterable as a subset of a sequence of ngrams using the hailstorm\n algorithm. If `with_pos` is True also include the starting position for the ngram\n in the original sequence.\n\n Definition from the paper: http://www2009.eprints.org/7/1/p61.pdf\n The algorithm first fingerprints every token and then selects a shingle s if\n the minimum fingerprint value of all k tokens in s occurs at the first or the\n last position of s (and potentially also in between). Due to the\n probabilistic properties of Rabin fingerprints the probability that a shingle\n is chosen is 2/k if all tokens in the shingle are different.\n\n For example:\n >>> list(select_ngrams([(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)]))\n [(2, 1, 3), (1, 1, 3), (2, 6, 1), (7, 3, 4)]\n\n Positions can also be included. In this case, tuple of (pos, ngram) are returned:\n >>> list(select_ngrams([(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)], with_pos=True))\n [(0, (2, 1, 3)), (1, (1, 1, 3)), (3, (2, 6, 1)), (4, (7, 3, 4))]\n\n This works also from a generator:\n >>> list(select_ngrams(x for x in [(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)]))\n [(2, 1, 3), (1, 1, 3), (2, 6, 1), (7, 3, 4)]\n \"\"\"\n last = None\n for i, ngram in enumerate(ngrams):\n # FIXME: use a proper hash\n nghs = [crc32(str(ng)) for ng in ngram]\n min_hash = min(nghs)\n if with_pos:\n ngram = (i, ngram,)\n if nghs[0] == min_hash or nghs[-1] == min_hash:\n yield ngram\n last = ngram\n else:\n # always yield the first or last ngram too.\n if i == 0:\n yield ngram\n last = ngram\n if last != ngram:\n yield ngram\n"},"avg_line_length":{"kind":"number","value":36.4254385965,"string":"36.425439"},"max_line_length":{"kind":"number","value":105,"string":"105"},"alphanum_fraction":{"kind":"number","value":0.6461167971,"string":"0.646117"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":3247,"string":"3,247"},"score_generators":{"kind":"number","value":0.3909692956050572,"string":"0.390969"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":5809,"string":"5,809"},"score_documentation":{"kind":"number","value":0.6994581577363034,"string":"0.699458"}}},{"rowIdx":3268,"cells":{"hexsha":{"kind":"string","value":"1308f0dd9c15ba9ea58abe836ba21f50ef3863ef"},"size":{"kind":"number","value":679794,"string":"679,794"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"homework_05/graficos_3.py"},"max_stars_repo_name":{"kind":"string","value":"ufpa-organization-repositories/evolutionary-computing"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e16786f9619e2b357b94ab91ff3a7b352e6a0d92"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"homework_05/graficos_3.py"},"max_forks_repo_name":{"kind":"string","value":"ufpa-organization-repositories/evolutionary-computing"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e16786f9619e2b357b94ab91ff3a7b352e6a0d92"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# ensaio = [[[[1, 999.4951009067408, 999.495100909791, '1001100.11100010011001100001001', '100011.10010111010000111110100', '1', '1'], [2, 999.5052434400473, 999.5052434497359, '0000100.11100010011001100001001', '111011.10010111010000111110000', '1', '2'], [3, 999.51676448592, 999.516764613072, '0000100.11100010011001100001001', '011011.10010111010000111110100', '1', '3'], [4, 999.5986670278455, 999.5986691897469, '0000100.11100010011001100001001', '001011.10010111010000111110100', '1', '4'], [5, 999.8231043912172, 999.8231154915733, '0000100.11100010011001100001001', '000011.10010111010000111110100', '1', '5'], [6, 999.8507392915436, 999.8507498599146, '0000100.11101010011001100001001', '000011.10010111010000111110100', '1', '6'], [7, 999.8770250807991, 999.8770357110892, '0000100.11101010011001100001001', '000011.10011011010000111110110', '1', '7'], [8, 999.9035511429103, 999.9035563402527, '0000100.11111011011001100001001', '000011.10011011010000111110110', '1', '8'], [9, 999.8985266375895, 999.8985377843169, '0000100.11111011011001100001001', '000011.11011011010000111110110', '1', '9'], [10, 999.9175205241293, 999.9175299982971, '0000100.11111001011001100001001', '000011.11011011010000111110110', '1', '10']]], [[[999.7693956630327, 999.5712824548395, 999.5712938998612, '-1111.1100110000001100100111', '1100.0111101110100011011001101', '2', '1'], [999.772264826825, 999.722709716149, 999.7227150806862, '-1111.1100110000001100000111', '1100.0101101110011011011001101', '2', '2'], [999.772309110711, 999.7568109989896, 999.7568135752206, '-1111.1100110000001100000101', '1100.0101111110011011011001101', '2', '3'], [999.7723098264468, 999.7626773116484, 999.762679240235, '-1111.1100110010001000000111', '1100.0101111110011011011001101', '2', '4'], [999.772309861109, 999.7628189867448, 999.7628208837242, '-1111.1100110010101100000111', '1100.0101111111011011011001101', '2', '5'], [999.7723098614766, 999.7575339099722, 999.7575364450361, '-1111.1100110010011100000111', '1100.0101111111001011011001101', '2', '6'], [999.7723098614781, 999.7330120594124, 999.7330190985555, '-1111.1100110010011100000111', '1100.0101111111001011011101101', '2', '7'], [999.7723098614792, 999.7425919826176, 999.7425978232334, '-1111.1100110010011100000011', '1100.0101111111001011011101101', '2', '8'], [999.7723098614795, 999.7521383886242, 999.7521421922053, '-1111.1100110010011100000011', '1100.0101111111001011011111101', '2', '9'], [999.7723098614797, 999.7479881509024, 999.7479935597561, '-1111.1100110010011100000010', '1100.0101111111001011011111101', '2', '10']]]]\nensaio = [[[[999.5055172457077, 999.4950089601775, 999.4950089625081, '1000110.11100010011001100001001', '110011.10010111010000111110100', '1', '1'], [999.5061559955897, 999.5023090273177, 999.502309034424, '1000100.01100010011001100001001', '110111.10010111010000111110110', '1', '2'], [999.5064522811073, 999.503907723421, 999.503907729031, '1000110.11110010001001100001001', '110011.11010111010000111110110', '1', '3'], [999.5121423697065, 999.5035214104588, 999.5035214189329, '1000100.01100010001001100001001', '010111.11010111010000111110110', '1', '4'], [999.5129192569484, 999.5073323118404, 999.5073323302529, '1000100.01100010001001100001001', '010111.01010111010000111110110', '1', '5'], [999.5129217445863, 999.5088300870693, 999.5088303022028, '1000100.01100010001001100001001', '010111.01010011010000111100000', '1', '6'], [999.5129221606866, 999.5109518591257, 999.5109518758048, '1000100.01100010001001100001001', '010111.01010010010000111100000', '1', '7'], [999.5129230124272, 999.512166424112, 999.5121664266934, '1000100.01100000001001100001001', '010111.01010011010000111100000', '1', '8'], [999.5129230131629, 999.5124005902164, 999.5124005938198, '1000100.01100000001001100001001', '010111.01010011010010111100000', '1', '9'], [999.5129230148639, 999.5103974240145, 999.5103976354789, '1000100.01100000001011100001001', '010111.01010011010010111110000', '1', '10'], [999.5129230160566, 999.5122248009202, 999.5122248072961, '1000100.01100000001011100001001', '010111.01010011011010111110000', '1', '11'], [999.5129230160816, 999.5098512171988, 999.5098514317501, '1000100.01100000001011101001001', '010111.01010011011010111111010', '1', '12'], [999.5129230161706, 999.5117481943298, 999.5117482051002, '1000100.01100000001011100001101', '010111.01010011011110111110000', '1', '13'], [999.512923016171, 999.5123140953751, 999.5123140998094, '1000100.01100000001011100000101', '010111.01010011011110111110000', '1', '14'], [999.5129230161734, 999.5118310368094, 999.5118310431579, '1000100.01100000001011100000101', '010111.01010011011110101100000', '1', '15'], [999.5129230161741, 999.5121210327977, 999.5121210375976, '1000100.01100000001011100000101', '010111.01010011011110011000000', '1', '16'], [999.5129230161741, 999.5094388460799, 999.5094390635309, '1000100.01100000001011100001101', '010111.01010011011110011000000', '1', '17'], [999.6136428564632, 999.5131524131325, 999.5131524699873, '0000100.01100000001011100000111', '011111.01010011011110011000000', '1', '18'], [999.6171227592827, 999.5505819522255, 999.5505841236918, '0000100.00100000001011100000111', '011111.01010011011110011000000', '1', '19'], [999.6260388885507, 999.6021087614022, 999.6021098942948, '0000100.00100000001011100001111', '011111.00010011011110011000000', '1', '20'], [999.6264039047687, 999.6120960828682, 999.6120968350307, '0000100.01000000001011100001111', '011111.00010011011110011000000', '1', '21'], [999.8211587180026, 999.6202616252683, 999.6202623370596, '0000100.01000000101011100001111', '001111.00010011011110011000000', '1', '22'], [999.8217737535546, 999.697569365876, 999.6975767463806, '0000100.01100000101011100001111', '001111.00010011001110011000000', '1', '23'], [999.821774465545, 999.7883631635897, 999.7883680067487, '0000100.01100000101011100001111', '001111.00010011001010011000000', '1', '24'], [999.82177567708, 999.7936505713287, 999.7936562996696, '0000100.01100000101011100001111', '001111.00010011000010011000000', '1', '25'], [999.8217757540084, 999.7949952836876, 999.7949996579591, '0000100.01100000101001100001111', '001111.00010011000010011000000', '1', '26'], [999.8217757867357, 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'10111.011110001101110111111111', '-1001.0000111100100000111', '50', '85'], [999.6878968594276, 999.6708282159406, 999.6708307565932, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '86'], [999.6878968594276, 999.6786322471293, 999.6786335350438, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '87'], [999.6878968594276, 999.6705502080745, 999.6705525462352, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '88'], [999.6878968594276, 999.6733558435163, 999.6733578660389, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '89'], [999.6878968594276, 999.6647669025892, 999.6647701528194, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '90'], [999.6878968594276, 999.6637718138106, 999.6637750563322, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '91'], [999.6878968594276, 999.6702059379244, 999.6702088069798, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '92'], [999.6878968594276, 999.6687775107825, 999.6687802569869, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '93'], [999.6878968594276, 999.6624347881049, 999.6624382564946, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '94'], [999.6878968594276, 999.6596444722741, 999.6596485096564, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '95'], [999.6878968594276, 999.6722319737632, 999.6722345087489, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '96'], [999.6878968594276, 999.6619993921432, 999.6620030672873, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '97'], [999.6878968594276, 999.6767364740043, 999.6767381465787, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '98'], [999.6878968594276, 999.6674371363, 999.6674397163673, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '99'], [999.6878968594276, 999.6818107105054, 999.6818115173727, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '100']]]]\n\nfitness = []\nmedia = []\ndesvio = []\nexperimento_plot = []\n\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nfor index, experimento in enumerate(ensaio):\n for populacao in experimento:\n for x, individuo in enumerate(populacao):\n fitness.append(individuo[0])\n media.append(individuo[1])\n desvio.append(individuo[2])\n\n x = np.arange(1, fitness.__len__() + 1)\n plt.plot(x, fitness)\n\n\n\n experimento_plot.append([fitness, media, desvio])\n fitness = []\n media = []\n desvio = []\n\nplt.show()"},"avg_line_length":{"kind":"number","value":23441.1724137931,"string":"23,441.172414"},"max_line_length":{"kind":"number","value":676649,"string":"676,649"},"alphanum_fraction":{"kind":"number","value":0.7763807859,"string":"0.776381"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":346369,"string":"346,369"},"score_documentation":{"kind":"number","value":0.5095205312197518,"string":"0.509521"}}},{"rowIdx":3269,"cells":{"hexsha":{"kind":"string","value":"13099ef7ac061602aaff6bfaa4733bfcc5e92bd8"},"size":{"kind":"number","value":225,"string":"225"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"etest_test/fixtures_test/ebuilds_test/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"alunduil/etest"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e5f06d7e8c83be369576976f239668545bcbfffd"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":6,"string":"6"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2015-01-08T13:56:50.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2018-01-08T00:53:08.000Z"},"max_issues_repo_path":{"kind":"string","value":"etest_test/fixtures_test/ebuilds_test/__init__.py"},"max_issues_repo_name":{"kind":"string","value":"alunduil/etest"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e5f06d7e8c83be369576976f239668545bcbfffd"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":64,"string":"64"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2015-01-01T23:10:07.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-06-12T06:55:31.000Z"},"max_forks_repo_path":{"kind":"string","value":"etest_test/fixtures_test/ebuilds_test/__init__.py"},"max_forks_repo_name":{"kind":"string","value":"alunduil/etest"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e5f06d7e8c83be369576976f239668545bcbfffd"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":2,"string":"2"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2015-05-08T01:32:48.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2015-05-30T15:38:31.000Z"},"content":{"kind":"string","value":"\"\"\"Ebuild Test Fixtures.\"\"\"\nimport os\nfrom typing import Any, Dict, List\n\nfrom etest_test import helpers_test\n\nEBUILDS: Dict[str, List[Dict[str, Any]]] = {}\n\nhelpers_test.import_directory(__name__, os.path.dirname(__file__))\n"},"avg_line_length":{"kind":"number","value":22.5,"string":"22.5"},"max_line_length":{"kind":"number","value":66,"string":"66"},"alphanum_fraction":{"kind":"number","value":0.7555555556,"string":"0.755556"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":27,"string":"27"},"score_documentation":{"kind":"number","value":0.12,"string":"0.12"}}},{"rowIdx":3270,"cells":{"hexsha":{"kind":"string","value":"130a61bee54706bd995afa354e7681d8726e5d5d"},"size":{"kind":"number","value":3764,"string":"3,764"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/model.py"},"max_stars_repo_name":{"kind":"string","value":"palucki/RememberIt"},"max_stars_repo_head_hexsha":{"kind":"string","value":"1d66616d4bb1bca026dda031d876dca226ba71ad"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"src/model.py"},"max_issues_repo_name":{"kind":"string","value":"palucki/RememberIt"},"max_issues_repo_head_hexsha":{"kind":"string","value":"1d66616d4bb1bca026dda031d876dca226ba71ad"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"src/model.py"},"max_forks_repo_name":{"kind":"string","value":"palucki/RememberIt"},"max_forks_repo_head_hexsha":{"kind":"string","value":"1d66616d4bb1bca026dda031d876dca226ba71ad"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import random\nfrom pymongo import MongoClient\n\nfrom observable import Observable\nfrom phrase import Phrase\n\nclass MongoDbProxy:\n \"\"\"Proxy for MongoDB\"\"\"\n \n def __init__(self, url, dbName, tableName):\n self.client = MongoClient(url)\n self.db = self.client[dbName]\n self.table = tableName\n self.count = self.db[self.table].find().count()\n \n def get_db(self):\n return self.db\n \n def add_phrase(self, phrase):\n #[{ \"english\": eng, \"polish\" : pl}]\n record = {\"english\" : phrase.eng, \"polish\" : phrase.meanings}\n self.db[self.table].insert(record)\n self.count = self.db[self.table].find().count()\n \n def show_one(self, phrase):\n print(\"eng: \\'%s\\' pol: \\'%s\\'\" % (phrase[\"english\"], phrase[\"polish\"]))\n \n def get_all(self):\n #define your data struct here\n words = {}\n for i, phrase in enumerate(self.db[self.table].find()):\n eng = phrase[\"english\"]\n #lang = phrase[\"lang\"]\n meaning = phrase[\"polish\"]\n \n words[eng] = meaning\n\n return words\n \n def show_all(self):\n if self.count > 0:\n for i, phrase in enumerate(self.db[self.table].find()):\n print(i, end=\" \")\n self.show_one(phrase)\n else:\n print(\"Database is empty\")\n\n def show_random(self):\n entries = self.db[self.table].find()\n self.count = entries.count()\n if self.count > 0:\n self.show_one(entries[random.randrange(self.count)])\n else:\n print(\"Database is empty\")\n \n def record_exists(self, eng):\n if self.db[self.table].find_one({\"english\" : eng}):\n return True\n else:\n return False\n def drop_record(self, eng):\n self.db[self.table].delete_one({\"english\":eng}) \n \n def drop_db(self):\n print(\"Dropping\")\n self.db.self.table.drop()\n self.count = self.db[self.table].find().count()\n\nclass Model:\n \"\"\"That needs a table of pairs - eng and its meanings\"\"\"\n def __init__(self):\n self.phrases = Observable({})\n self.db = MongoDbProxy(\"mongodb://localhost:27017/\", \"RepeatItDb\", \"phrases\")\n \n data = self.db.get_all()\n self.phrases.setData(data)\n \n def addWord(self, key, lang, meanings):\n newData = self.phrases.getData()\n newData[key] = meanings\n self.phrases.setData(newData)\n \n def getAllWords(self):\n return self.phrases.getData()\n \n def removeWord(self, key):\n newData = self.phrases.getData()\n newData.pop(key)\n self.phrases.setData(newData)\n \n def saveWord(self, wordAndMeaning):\n word = wordAndMeaning[0]\n meaning = wordAndMeaning[1]\n self.addWord(word, \"pl\", meaning)\n \n def saveDb(self):\n dbData = self.db.get_all()\n modelData = self.getAllWords()\n \n #That's for future optimization: update db instead of adding it all\n \n dbKeysSet = set(dbData.keys())\n dbValuesSet = set(dbData.values())\n \n modelKeysSet = set(modelData.keys())\n modelValuesSet = set(modelData.values())\n\n newRecordsKeys = modelKeysSet - dbKeysSet\n deletedRecordsKeys = dbKeysSet - modelKeysSet\n \n if len(newRecordsKeys):\n for newKey in newRecordsKeys:\n self.db.add_phrase(Phrase(newKey, \"pl\", modelData[newKey]))\n\n if len(deletedRecordsKeys):\n for deletedKey in deletedRecordsKeys:\n self.db.drop_record(deletedKey)\n \n #Handle also value update\n \n print(\"Saving database...\")\n"},"avg_line_length":{"kind":"number","value":30.8524590164,"string":"30.852459"},"max_line_length":{"kind":"number","value":85,"string":"85"},"alphanum_fraction":{"kind":"number","value":0.5629649309,"string":"0.562965"},"count_classes":{"kind":"number","value":3653,"string":"3,653"},"score_classes":{"kind":"number","value":0.970510095642933,"string":"0.97051"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":479,"string":"479"},"score_documentation":{"kind":"number","value":0.12725823591923485,"string":"0.127258"}}},{"rowIdx":3271,"cells":{"hexsha":{"kind":"string","value":"130ad5b5c3caa22c7668a018ea30cf4d2bc3c2f4"},"size":{"kind":"number","value":1381,"string":"1,381"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"sampleApplication/clientGenerator.py"},"max_stars_repo_name":{"kind":"string","value":"chall68/BlackWatch"},"max_stars_repo_head_hexsha":{"kind":"string","value":"0b95d69e4b7de9213a031557e9aff54ce35b12dd"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"sampleApplication/clientGenerator.py"},"max_issues_repo_name":{"kind":"string","value":"chall68/BlackWatch"},"max_issues_repo_head_hexsha":{"kind":"string","value":"0b95d69e4b7de9213a031557e9aff54ce35b12dd"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"sampleApplication/clientGenerator.py"},"max_forks_repo_name":{"kind":"string","value":"chall68/BlackWatch"},"max_forks_repo_head_hexsha":{"kind":"string","value":"0b95d69e4b7de9213a031557e9aff54ce35b12dd"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!flask/bin/python\n#from user import User\nfrom sampleObjects.User import User\nfrom datetime import datetime\nfrom sampleObjects.DetectionPoint import DetectionPoint\n\nimport time, requests, random, atexit\n\ndef requestGenerator():\n userObject = randomUser()\n detectionPointObject = randomDetectionPoint()\n\n req = requests.post('http://localhost:5000/addevent', json = {\"User\": userObject.__dict__, \"DetectionPoint\" : detectionPointObject.__dict__, \"Time\" : str(datetime.now().isoformat())})\n print (req.text)\n\n checkResp = requests.get('http://localhost:5000/getResponses')\n print (checkResp.text)\n\ndef randomUser():\n user = random.randint(1,3)\n attacker=0\n if (user==1):\n attacker = User(\"Phillipo\", \"255.255.255.101\", \"xxxx\")\n elif (user==2):\n attacker = User(\"Sergio\", \"109.123.234.1\", \"yyyy\")\n elif (user==3):\n attacker = User(\"Anonymous\", \"101.101.101.87\", \"354343jjk23\")\n\n return attacker\n\n\n\ndef randomDetectionPoint():\n rand = random.randint(1,2)\n dp=0\n if (rand==1):\n dp = DetectionPoint(\"HTTP Verb\", \"GET Request used where POST is expected\")\n elif (rand==2):\n dp = DetectionPoint(\"Login Page\", \"Hidden field altered within the login form\")\n return dp\n\n\nfor i in range (50):\n requestGenerator()\n time.sleep(1.5)\ndef closingTime():\n print (\"Exiting\")\n\natexit.register(closingTime)\n"},"avg_line_length":{"kind":"number","value":27.62,"string":"27.62"},"max_line_length":{"kind":"number","value":187,"string":"187"},"alphanum_fraction":{"kind":"number","value":0.6748732802,"string":"0.674873"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":355,"string":"355"},"score_documentation":{"kind":"number","value":0.25706010137581464,"string":"0.25706"}}},{"rowIdx":3272,"cells":{"hexsha":{"kind":"string","value":"130c49099f8aa40a9dd92ff170ecb6c15b43d8f9"},"size":{"kind":"number","value":1873,"string":"1,873"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"news_collector/collector/consumers.py"},"max_stars_repo_name":{"kind":"string","value":"ridwaniyas/channels-examples"},"max_stars_repo_head_hexsha":{"kind":"string","value":"9e6a26c8e6404483695cbd96ebf12fc4ed9956b2"},"max_stars_repo_licenses":{"kind":"list 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\"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"news_collector/collector/consumers.py"},"max_forks_repo_name":{"kind":"string","value":"ridwaniyas/channels-examples"},"max_forks_repo_head_hexsha":{"kind":"string","value":"9e6a26c8e6404483695cbd96ebf12fc4ed9956b2"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import asyncio\nimport json\nimport datetime\nfrom aiohttp import ClientSession\nfrom channels.generic.http import AsyncHttpConsumer\nfrom .constants import BLOGS\n\n\nclass NewsCollectorAsyncConsumer(AsyncHttpConsumer):\n \"\"\"\n Async HTTP consumer that fetches URLs.\n \"\"\"\n\n async def handle(self, body):\n\n # Adapted from:\n # \"Making 1 million requests with python-aiohttp\"\n # https://pawelmhm.github.io/asyncio/python/aiohttp/2016/04/22/asyncio-aiohttp.html\n async def fetch(url, session):\n async with session.get(url) as response:\n return await response.read()\n\n tasks = []\n loop = asyncio.get_event_loop()\n\n # aiohttp allows a ClientSession object to link all requests together\n t0 = datetime.datetime.now()\n async with ClientSession() as session:\n for name, url in BLOGS.items():\n print('Start downloading \"%s\"' % name)\n # Launch a coroutine for each URL fetch\n task = loop.create_task(fetch(url, session))\n tasks.append(task)\n\n # Wait on, and then gather, all responses\n responses = await asyncio.gather(*tasks)\n dt = (datetime.datetime.now() - t0).total_seconds()\n print('All downloads completed; elapsed time: {} [s]'.format(dt))\n\n # asyncio.gather returns results in the order of the original sequence,\n # so we can safely zip these together.\n data = dict(zip(BLOGS.keys(), [r.decode('utf-8') for r in responses]))\n text = json.dumps(data)\n\n # We have to send a response using send_response rather than returning\n # it in Channels' async HTTP consumer\n await self.send_response(200,\n text.encode(),\n headers=[\n (\"Content-Type\", \"application/json\"),\n ]\n )\n"},"avg_line_length":{"kind":"number","value":35.3396226415,"string":"35.339623"},"max_line_length":{"kind":"number","value":91,"string":"91"},"alphanum_fraction":{"kind":"number","value":0.6150560598,"string":"0.615056"},"count_classes":{"kind":"number","value":1712,"string":"1,712"},"score_classes":{"kind":"number","value":0.9140416444207154,"string":"0.914042"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":1595,"string":"1,595"},"score_async_functions":{"kind":"number","value":0.8515750133475708,"string":"0.851575"},"count_documentation":{"kind":"number","value":676,"string":"676"},"score_documentation":{"kind":"number","value":0.3609183128670582,"string":"0.360918"}}},{"rowIdx":3273,"cells":{"hexsha":{"kind":"string","value":"130d77d6c796e047f21c43df476be8389b35aecb"},"size":{"kind":"number","value":737,"string":"737"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/randomcsv/FileUtils.py"},"max_stars_repo_name":{"kind":"string","value":"PhilipBuhr/randomCsv"},"max_stars_repo_head_hexsha":{"kind":"string","value":"34b1da62134077dfe4db2682ee0da386ef380c1d"},"max_stars_repo_licenses":{"kind":"list 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pass\n"},"avg_line_length":{"kind":"number","value":23.03125,"string":"23.03125"},"max_line_length":{"kind":"number","value":80,"string":"80"},"alphanum_fraction":{"kind":"number","value":0.591587517,"string":"0.591588"},"count_classes":{"kind":"number","value":41,"string":"41"},"score_classes":{"kind":"number","value":0.05563093622795115,"string":"0.055631"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":63,"string":"63"},"score_documentation":{"kind":"number","value":0.08548168249660787,"string":"0.085482"}}},{"rowIdx":3274,"cells":{"hexsha":{"kind":"string","value":"130e4d54c2deae9e851943dbae96e38707ce57ce"},"size":{"kind":"number","value":46,"string":"46"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"stringtoiso/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"vats98754/stringtoiso"},"max_stars_repo_head_hexsha":{"kind":"string","value":"985da5efa26111ef1d92b7026b5d5d68f0101ef1"},"max_stars_repo_licenses":{"kind":"list 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convert"},"avg_line_length":{"kind":"number","value":46,"string":"46"},"max_line_length":{"kind":"number","value":46,"string":"46"},"alphanum_fraction":{"kind":"number","value":0.9130434783,"string":"0.913043"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":0,"string":"0"},"score_documentation":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":3275,"cells":{"hexsha":{"kind":"string","value":"130ec5dfef9f34118ca5d16b6a8a1a90a53517da"},"size":{"kind":"number","value":5495,"string":"5,495"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py"},"max_stars_repo_name":{"kind":"string","value":"PNNL-Comp-Mass-Spec/DtaRefinery"},"max_stars_repo_head_hexsha":{"kind":"string","value":"609cc90d0322af69aea43c2fc21d9cf05a06797a"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-2-Clause"],"string":"[\n \"BSD-2-Clause\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py"},"max_issues_repo_name":{"kind":"string","value":"PNNL-Comp-Mass-Spec/DtaRefinery"},"max_issues_repo_head_hexsha":{"kind":"string","value":"609cc90d0322af69aea43c2fc21d9cf05a06797a"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-2-Clause"],"string":"[\n \"BSD-2-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py"},"max_forks_repo_name":{"kind":"string","value":"PNNL-Comp-Mass-Spec/DtaRefinery"},"max_forks_repo_head_hexsha":{"kind":"string","value":"609cc90d0322af69aea43c2fc21d9cf05a06797a"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-2-Clause"],"string":"[\n \"BSD-2-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from aux_sys_err_prediction_module.additive.R_runmed_spline.my_R_runmed_spline_fit import R_runmed_smooth_spline\r\nfrom numpy import random, array, median, zeros, arange, hstack\r\n\r\nfrom win32com.client import Dispatch\r\nimport math\r\n\r\nmyName = 'R_runmed_spline'\r\nuseMAD = True # use median absolute deviations instead of sum of squared residues\r\n\r\n\r\n# -----------------------------------------------------------------------\r\ndef R_runmed_spline_MAIN(ARG3, Controller):\r\n pars = Controller.updatedSettings['refiningPars']['regressionSettings'][myName]\r\n\r\n # ARG3\r\n x = ARG3[0][0]\r\n y = ARG3[0][1]\r\n\r\n sc = Dispatch(\"StatConnectorSrv.StatConnector\")\r\n sc.Init(\"R\")\r\n\r\n # get the best smoothing parameter\r\n bestSpar = R_runmed_spline_KCV_OPTIMIZATION(x, y, sc=sc, **pars)\r\n\r\n # get the prediction error for this smoothing parameter\r\n bestPredErr = R_runmed_spline_KCV_predErr(x, y, spar=bestSpar, sc=sc, **pars)\r\n\r\n # compare with original SSE\r\n # is fit successful?\r\n # return isSuccessfulFit, yFit, yEval, runMedData\r\n SSE = sum(y ** 2)\r\n MAD = 1.4826 * median(abs(y))\r\n if useMAD:\r\n SSE = MAD\r\n if bestPredErr < SSE:\r\n isSuccessfulFit = True\r\n #\r\n ppmArrs = [[] for i in range(len(ARG3))]\r\n for ind in range(len(ARG3)):\r\n x = ARG3[ind][0]\r\n y = ARG3[ind][1]\r\n xEval = ARG3[ind][2]\r\n #\r\n yFit, runMedData = R_runmed_smooth_spline(x, y, x, spar=bestSpar, sc=sc, **pars)\r\n yEval, runMedData = R_runmed_smooth_spline(x, y, xEval, spar=bestSpar, sc=sc, **pars)\r\n #\r\n ppmArrs[ind] = [yFit, yEval]\r\n else:\r\n isSuccessfulFit = False\r\n #\r\n ppmArrs = [[] for i in range(len(ARG3))]\r\n for ind in range(len(ARG3)):\r\n x = ARG3[ind][0]\r\n y = ARG3[ind][1]\r\n xEval = ARG3[ind][2]\r\n #\r\n yFit = zeros(len(x), 'd')\r\n yEval = zeros(len(xEval), 'd')\r\n #\r\n ppmArrs[ind] = [yFit, yEval]\r\n\r\n sc.Close()\r\n\r\n return isSuccessfulFit, bestPredErr, ppmArrs\r\n\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n\r\n# -----------------------------------------------------------------------\r\ndef R_runmed_spline_KCV_OPTIMIZATION(x, y, sc, **pars):\r\n sparRange = array([float(i) for i in pars['spar range'].split(',')])\r\n sparStepsNum = int(pars['spar steps number'])\r\n sparStep = round((sparRange[1] - sparRange[0]) / sparStepsNum, 5)\r\n sparSet = arange(sparRange[0], sparRange[1], sparStep)\r\n\r\n predErrSet = zeros(len(sparSet), 'd')\r\n for i in range(len(sparSet)):\r\n predErr = R_runmed_spline_KCV_predErr(x, y, spar=sparSet[i], sc=sc, **pars)\r\n predErrSet[i] = predErr\r\n\r\n ## p(zip(sparSet, predErrSet))\r\n spar = sparSet[predErrSet == min(predErrSet)][-1] # take the last one (smoothest) if there are few\r\n ## print('spar ', spar)\r\n\r\n return spar\r\n\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n\r\n# -----------------------------------------------------------------------\r\ndef R_runmed_spline_KCV_predErr(x, y, **kwargs):\r\n \"\"\"\r\n just returns the prediction error\r\n \"\"\"\r\n K = int(kwargs['K'])\r\n # --Related to K-fold CV---------------------------\r\n L = len(x)\r\n N = L / K ##min length of pieces\r\n W = list(range(L))\r\n Z = list(range(1, K + 1))\r\n Z = [N for j in Z]\r\n R = L % K\r\n Z[0:R] = [j + 1 for j in Z[0:R]] # length of the pieces\r\n random.shuffle(W)\r\n ind = 0\r\n predErr = 0\r\n allResiduals = array([])\r\n SSE = sum(y ** 2) # VLAD. Why do I need this???\r\n # ---running through K training/testings-------------\r\n for val in Z:\r\n j = math.floor(val)\r\n\r\n # ---making training/testing subsets-------------\r\n test = W[ind:ind + j]\r\n test.sort()\r\n train = W[0:ind] + W[ind + j:]\r\n train.sort()\r\n ind += j\r\n # -----------------------------------------------\r\n\r\n # ---fit runmed_spline here----------------------\r\n yFit, runMed = R_runmed_smooth_spline(x[train], y[train], x[test], **kwargs)\r\n residualsTest = y[test] - yFit\r\n predErr += sum(residualsTest ** 2)\r\n allResiduals = hstack((allResiduals, residualsTest))\r\n # -----------------------------------------------\r\n\r\n if useMAD:\r\n predErr = 1.4826 * median(abs(allResiduals))\r\n\r\n return predErr\r\n\r\n\r\n# -----------------------------------------------------------------------\r\n\r\n\r\nif __name__ == '__main__':\r\n from numpy import linspace, cos, lexsort, zeros, sin\r\n from pylab import plot, show, subplot, savefig, clf, ylim\r\n from pprint import pprint as p\r\n from time import clock as c\r\n\r\n x1 = linspace(0, 30, 300)\r\n ## y1 = cos(x1)\r\n ## y1 = zeros(len(x1),'d') #nice test\r\n y1 = x1 * 0.03\r\n\r\n y1 += random.normal(scale=0.2, size=y1.shape)\r\n ind = lexsort(keys=(y1, x1))\r\n x1 = x1[ind]\r\n y1 = y1[ind]\r\n\r\n t1 = c()\r\n isSuccessfulFit, yFit, yEval, runMedData, predErr = \\\r\n R_runmed_spline_MAIN(x1, y1, x1, runMedSpan=0.01, K=10, sparRange=[0.6, 1.1, 0.1])\r\n t2 = c()\r\n print('done in %s seconds' % (t2 - t1))\r\n\r\n subplot(211)\r\n plot(x1, y1, 'bo')\r\n plot(runMedData[0], runMedData[1], 'y^')\r\n plot(x1, yEval, 'r+-')\r\n ylim([-1.5, +1.5])\r\n subplot(212)\r\n plot(x1, y1 - yEval, 'go')\r\n ylim([-1.5, +1.5])\r\n show()\r\n"},"avg_line_length":{"kind":"number","value":32.3235294118,"string":"32.323529"},"max_line_length":{"kind":"number","value":113,"string":"113"},"alphanum_fraction":{"kind":"number","value":0.4993630573,"string":"0.499363"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1503,"string":"1,503"},"score_documentation":{"kind":"number","value":0.2735213830755232,"string":"0.273521"}}},{"rowIdx":3276,"cells":{"hexsha":{"kind":"string","value":"130f0d527db89218f9714b016db75a6b60750779"},"size":{"kind":"number","value":2721,"string":"2,721"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"setup.py"},"max_stars_repo_name":{"kind":"string","value":"Ms2ger/python-zstandard"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b8ea1f6722a710e252b452554442b84c81049439"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"setup.py"},"max_issues_repo_name":{"kind":"string","value":"Ms2ger/python-zstandard"},"max_issues_repo_head_hexsha":{"kind":"string","value":"b8ea1f6722a710e252b452554442b84c81049439"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"setup.py"},"max_forks_repo_name":{"kind":"string","value":"Ms2ger/python-zstandard"},"max_forks_repo_head_hexsha":{"kind":"string","value":"b8ea1f6722a710e252b452554442b84c81049439"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python\n# Copyright (c) 2016-present, Gregory Szorc\n# All rights reserved.\n#\n# This software may be modified and distributed under the terms\n# of the BSD license. See the LICENSE file for details.\n\nimport os\nimport sys\nfrom setuptools import setup\n\ntry:\n import cffi\nexcept ImportError:\n cffi = None\n\nimport setup_zstd\n\nSUPPORT_LEGACY = False\nSYSTEM_ZSTD = False\nWARNINGS_AS_ERRORS = False\n\nif os.environ.get('ZSTD_WARNINGS_AS_ERRORS', ''):\n WARNINGS_AS_ERRORS = True\n\nif '--legacy' in sys.argv:\n SUPPORT_LEGACY = True\n sys.argv.remove('--legacy')\n\nif '--system-zstd' in sys.argv:\n SYSTEM_ZSTD = True\n sys.argv.remove('--system-zstd')\n\nif '--warnings-as-errors' in sys.argv:\n WARNINGS_AS_ERRORS = True\n sys.argv.remote('--warning-as-errors')\n\n# Code for obtaining the Extension instance is in its own module to\n# facilitate reuse in other projects.\nextensions = [\n setup_zstd.get_c_extension(name='zstd',\n support_legacy=SUPPORT_LEGACY,\n system_zstd=SYSTEM_ZSTD,\n warnings_as_errors=WARNINGS_AS_ERRORS),\n]\n\ninstall_requires = []\n\nif cffi:\n import make_cffi\n extensions.append(make_cffi.ffi.distutils_extension())\n\n # Need change in 1.10 for ffi.from_buffer() to handle all buffer types\n # (like memoryview).\n # Need feature in 1.11 for ffi.gc() to declare size of objects so we avoid\n # garbage collection pitfalls.\n install_requires.append('cffi>=1.11')\n\nversion = None\n\nwith open('c-ext/python-zstandard.h', 'r') as fh:\n for line in fh:\n if not line.startswith('#define PYTHON_ZSTANDARD_VERSION'):\n continue\n\n version = line.split()[2][1:-1]\n break\n\nif not version:\n raise Exception('could not resolve package version; '\n 'this should never happen')\n\nsetup(\n name='zstandard',\n version=version,\n description='Zstandard bindings for Python',\n long_description=open('README.rst', 'r').read(),\n url='https://github.com/indygreg/python-zstandard',\n author='Gregory Szorc',\n author_email='gregory.szorc@gmail.com',\n license='BSD',\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: C',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n ],\n keywords='zstandard zstd compression',\n packages=['zstandard'],\n ext_modules=extensions,\n test_suite='tests',\n 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\"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py"},"max_forks_repo_name":{"kind":"string","value":"c4st1lh0/Projetos-de-Aula"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import math\nnum = float(input('Digite um numero real qualquer: '))\nprint('O numero: {} tem a parte inteira {}'.format(num, math.trunc(num)))"},"avg_line_length":{"kind":"number","value":46.6666666667,"string":"46.666667"},"max_line_length":{"kind":"number","value":73,"string":"73"},"alphanum_fraction":{"kind":"number","value":0.7,"string":"0.7"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":71,"string":"71"},"score_documentation":{"kind":"number","value":0.5071428571428571,"string":"0.507143"}}},{"rowIdx":3278,"cells":{"hexsha":{"kind":"string","value":"1310f38eea5f3635000289a9e4355d5205ba5668"},"size":{"kind":"number","value":551,"string":"551"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"mmdet/ops/orn/functions/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"JarvisUSTC/DARDet"},"max_stars_repo_head_hexsha":{"kind":"string","value":"debbf476e9750030db67f030a40cf8d4f03e46ee"},"max_stars_repo_licenses":{"kind":"list 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\"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":136,"string":"136"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-07-11T11:26:54.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-31T02:45:34.000Z"},"max_forks_repo_path":{"kind":"string","value":"mmdet/ops/orn/functions/__init__.py"},"max_forks_repo_name":{"kind":"string","value":"JarvisUSTC/DARDet"},"max_forks_repo_head_hexsha":{"kind":"string","value":"debbf476e9750030db67f030a40cf8d4f03e46ee"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"number","value":84,"string":"84"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-05-29T06:58:14.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-31T07:44:10.000Z"},"content":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport torch\nfrom .active_rotating_filter import active_rotating_filter\nfrom .active_rotating_filter import ActiveRotatingFilter\nfrom .rotation_invariant_encoding import rotation_invariant_encoding\nfrom .rotation_invariant_encoding import RotationInvariantEncoding\nfrom .rotation_invariant_pooling import RotationInvariantPooling\n\n__all__ = ['ActiveRotatingFilter', 'active_rotating_filter', 'rotation_invariant_encoding', 'RotationInvariantEncoding', 'RotationInvariantPooling']"},"avg_line_length":{"kind":"number","value":61.2222222222,"string":"61.222222"},"max_line_length":{"kind":"number","value":148,"string":"148"},"alphanum_fraction":{"kind":"number","value":0.8747731397,"string":"0.874773"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":199,"string":"199"},"score_documentation":{"kind":"number","value":0.36116152450090744,"string":"0.361162"}}},{"rowIdx":3279,"cells":{"hexsha":{"kind":"string","value":"131271d5663df6a63ce374c8c4958678283dd5fc"},"size":{"kind":"number","value":218,"string":"218"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"sympy/core/tests/test_cache.py"},"max_stars_repo_name":{"kind":"string","value":"eriknw/sympy"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b7544e2bb74c011f6098a7e886fd77f41776c2c4"},"max_stars_repo_licenses":{"kind":"list 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\"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-02-19T04:56:04.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2018-02-19T04:56:04.000Z"},"max_forks_repo_path":{"kind":"string","value":"sympy/core/tests/test_cache.py"},"max_forks_repo_name":{"kind":"string","value":"pbeltran/sympy-1"},"max_forks_repo_head_hexsha":{"kind":"string","value":"94f92b36731c2bebe6de1037c063c2a258a8a399"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2016-04-24T14:39:22.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2016-04-24T14:39:22.000Z"},"content":{"kind":"string","value":"from sympy.core.cache import cacheit\n\n\ndef test_cacheit_doc():\n @cacheit\n def testfn():\n \"test docstring\"\n pass\n\n assert testfn.__doc__ == \"test docstring\"\n assert testfn.__name__ == \"testfn\"\n"},"avg_line_length":{"kind":"number","value":18.1666666667,"string":"18.166667"},"max_line_length":{"kind":"number","value":45,"string":"45"},"alphanum_fraction":{"kind":"number","value":0.6467889908,"string":"0.646789"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":64,"string":"64"},"score_decorators":{"kind":"number","value":0.29357798165137616,"string":"0.293578"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":40,"string":"40"},"score_documentation":{"kind":"number","value":0.1834862385321101,"string":"0.183486"}}},{"rowIdx":3280,"cells":{"hexsha":{"kind":"string","value":"1312f8f0f49eb471bc17c6830c67ae3b593f1370"},"size":{"kind":"number","value":8694,"string":"8,694"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"mmdet/models/losses/ranking_losses.py"},"max_stars_repo_name":{"kind":"string","value":"VietDunghacker/VarifocalNet"},"max_stars_repo_head_hexsha":{"kind":"string","value":"f57917afb3c29ceba1d3c4f824d10b9cc53aaa40"},"max_stars_repo_licenses":{"kind":"list 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\"Apache-2.0\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"mmdet/models/losses/ranking_losses.py"},"max_forks_repo_name":{"kind":"string","value":"VietDunghacker/VarifocalNet"},"max_forks_repo_head_hexsha":{"kind":"string","value":"f57917afb3c29ceba1d3c4f824d10b9cc53aaa40"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import torch\n\nclass RankSort(torch.autograd.Function):\n\t@staticmethod\n\tdef forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): \n\n\t\tclassification_grads=torch.zeros(logits.shape).cuda()\n\t\t\n\t\t#Filter fg logits\n\t\tfg_labels = (targets > 0.)\n\t\tfg_logits = logits[fg_labels]\n\t\tfg_targets = targets[fg_labels]\n\t\tfg_num = len(fg_logits)\n\n\t\t#Do not use bg with scores less than minimum fg logit\n\t\t#since changing its score does not have an effect on precision\n\t\tthreshold_logit = torch.min(fg_logits)-delta_RS\n\t\trelevant_bg_labels=((targets==0) & (logits>=threshold_logit))\n\t\t\n\t\trelevant_bg_logits = logits[relevant_bg_labels] \n\t\trelevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()\n\t\tsorting_error=torch.zeros(fg_num).cuda()\n\t\tranking_error=torch.zeros(fg_num).cuda()\n\t\tfg_grad=torch.zeros(fg_num).cuda()\n\t\t\n\t\t#sort the fg logits\n\t\torder=torch.argsort(fg_logits)\n\t\t#Loops over each positive following the order\n\t\tfor ii in order:\n\t\t\t# Difference Transforms (x_ij)\n\t\t\tfg_relations=fg_logits-fg_logits[ii] \n\t\t\tbg_relations=relevant_bg_logits-fg_logits[ii]\n\n\t\t\tif delta_RS > 0:\n\t\t\t\tfg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1)\n\t\t\t\tbg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1)\n\t\t\telse:\n\t\t\t\tfg_relations = (fg_relations >= 0).float()\n\t\t\t\tbg_relations = (bg_relations >= 0).float()\n\n\t\t\t# Rank of ii among pos and false positive number (bg with larger scores)\n\t\t\trank_pos=torch.sum(fg_relations)\n\t\t\tFP_num=torch.sum(bg_relations)\n\n\t\t\t# Rank of ii among all examples\n\t\t\trank=rank_pos+FP_num\n\t\t\t\t\t\t\t\n\t\t\t# Ranking error of example ii. target_ranking_error is always 0. (Eq. 7)\n\t\t\tranking_error[ii]=FP_num/rank\t \n\n\t\t\t# Current sorting error of example ii. (Eq. 7)\n\t\t\tcurrent_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos\n\n\t\t\t#Find examples in the target sorted order for example ii\t\t \n\t\t\tiou_relations = (fg_targets >= fg_targets[ii])\n\t\t\ttarget_sorted_order = iou_relations * fg_relations\n\n\t\t\t#The rank of ii among positives in sorted order\n\t\t\trank_pos_target = torch.sum(target_sorted_order)\n\n\t\t\t#Compute target sorting error. (Eq. 8)\n\t\t\t#Since target ranking error is 0, this is also total target error \n\t\t\ttarget_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target\n\n\t\t\t#Compute sorting error on example ii\n\t\t\tsorting_error[ii] = current_sorting_error - target_sorting_error\n \n\t\t\t#Identity Update for Ranking Error \n\t\t\tif FP_num > eps:\n\t\t\t\t#For ii the update is the ranking error\n\t\t\t\tfg_grad[ii] -= ranking_error[ii]\n\t\t\t\t#For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num)\n\t\t\t\trelevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num))\n\n\t\t\t#Find the positives that are misranked (the cause of the error)\n\t\t\t#These are the ones with smaller IoU but larger logits\n\t\t\tmissorted_examples = (~ iou_relations) * fg_relations\n\n\t\t\t#Denominotor of sorting pmf \n\t\t\tsorting_pmf_denom = torch.sum(missorted_examples)\n\n\t\t\t#Identity Update for Sorting Error \n\t\t\tif sorting_pmf_denom > eps:\n\t\t\t\t#For ii the update is the sorting error\n\t\t\t\tfg_grad[ii] -= sorting_error[ii]\n\t\t\t\t#For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom)\n\t\t\t\tfg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom))\n\n\t\t#Normalize gradients by number of positives \n\t\tclassification_grads[fg_labels]= (fg_grad/fg_num)\n\t\tclassification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num)\n\n\t\tctx.save_for_backward(classification_grads)\n\n\t\treturn ranking_error.mean(), sorting_error.mean()\n\n\t@staticmethod\n\tdef backward(ctx, out_grad1, out_grad2):\n\t\tg1, =ctx.saved_tensors\n\t\treturn g1*out_grad1, None, None, None\n\nclass aLRPLoss(torch.autograd.Function):\n\t@staticmethod\n\tdef forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): \n\t\tclassification_grads=torch.zeros(logits.shape).cuda()\n\t\t\n\t\t#Filter fg logits\n\t\tfg_labels = (targets == 1)\n\t\tfg_logits = logits[fg_labels]\n\t\tfg_num = len(fg_logits)\n\n\t\t#Do not use bg with scores less than minimum fg logit\n\t\t#since changing its score does not have an effect on precision\n\t\tthreshold_logit = torch.min(fg_logits)-delta\n\n\t\t#Get valid bg logits\n\t\trelevant_bg_labels=((targets==0)&(logits>=threshold_logit))\n\t\trelevant_bg_logits=logits[relevant_bg_labels] \n\t\trelevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()\n\t\trank=torch.zeros(fg_num).cuda()\n\t\tprec=torch.zeros(fg_num).cuda()\n\t\tfg_grad=torch.zeros(fg_num).cuda()\n\t\t\n\t\tmax_prec=0\t\t\t\t\t\t\t\t\t\t \n\t\t#sort the fg logits\n\t\torder=torch.argsort(fg_logits)\n\t\t#Loops over each positive following the order\n\t\tfor ii in order:\n\t\t\t#x_ij s as score differences with fgs\n\t\t\tfg_relations=fg_logits-fg_logits[ii] \n\t\t\t#Apply piecewise linear function and determine relations with fgs\n\t\t\tfg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)\n\t\t\t#Discard i=j in the summation in rank_pos\n\t\t\tfg_relations[ii]=0\n\n\t\t\t#x_ij s as score differences with bgs\n\t\t\tbg_relations=relevant_bg_logits-fg_logits[ii]\n\t\t\t#Apply piecewise linear function and determine relations with bgs\n\t\t\tbg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)\n\n\t\t\t#Compute the rank of the example within fgs and number of bgs with larger scores\n\t\t\trank_pos=1+torch.sum(fg_relations)\n\t\t\tFP_num=torch.sum(bg_relations)\n\t\t\t#Store the total since it is normalizer also for aLRP Regression error\n\t\t\trank[ii]=rank_pos+FP_num\n\t\t\t\t\t\t\t\n\t\t\t#Compute precision for this example to compute classification loss \n\t\t\tprec[ii]=rank_pos/rank[ii]\t\t\t\t\n\t\t\t#For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads\n\t\t\tif FP_num > eps: \n\t\t\t\tfg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii]\n\t\t\t\trelevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) \n\t\t\t\t\t\n\t\t#aLRP with grad formulation fg gradient\n\t\tclassification_grads[fg_labels]= fg_grad\n\t\t#aLRP with grad formulation bg gradient\n\t\tclassification_grads[relevant_bg_labels]= relevant_bg_grad \n \n\t\tclassification_grads /= (fg_num)\n\t\n\t\tcls_loss=1-prec.mean()\n\t\tctx.save_for_backward(classification_grads)\n\n\t\treturn cls_loss, rank, order\n\n\t@staticmethod\n\tdef backward(ctx, out_grad1, out_grad2, out_grad3):\n\t\tg1, =ctx.saved_tensors\n\t\treturn g1*out_grad1, None, None, None, None\n\t\n\t\nclass APLoss(torch.autograd.Function):\n\t@staticmethod\n\tdef forward(ctx, logits, targets, delta=1.): \n\t\tclassification_grads=torch.zeros(logits.shape).cuda()\n\t\t\n\t\t#Filter fg logits\n\t\tfg_labels = (targets == 1)\n\t\tfg_logits = logits[fg_labels]\n\t\tfg_num = len(fg_logits)\n\n\t\t#Do not use bg with scores less than minimum fg logit\n\t\t#since changing its score does not have an effect on precision\n\t\tthreshold_logit = torch.min(fg_logits)-delta\n\n\t\t#Get valid bg logits\n\t\trelevant_bg_labels=((targets==0)&(logits>=threshold_logit))\n\t\trelevant_bg_logits=logits[relevant_bg_labels] \n\t\trelevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()\n\t\trank=torch.zeros(fg_num).cuda()\n\t\tprec=torch.zeros(fg_num).cuda()\n\t\tfg_grad=torch.zeros(fg_num).cuda()\n\t\t\n\t\tmax_prec=0\t\t\t\t\t\t\t\t\t\t \n\t\t#sort the fg logits\n\t\torder=torch.argsort(fg_logits)\n\t\t#Loops over each positive following the order\n\t\tfor ii in order:\n\t\t\t#x_ij s as score differences with fgs\n\t\t\tfg_relations=fg_logits-fg_logits[ii] \n\t\t\t#Apply piecewise linear function and determine relations with fgs\n\t\t\tfg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)\n\t\t\t#Discard i=j in the summation in rank_pos\n\t\t\tfg_relations[ii]=0\n\n\t\t\t#x_ij s as score differences with bgs\n\t\t\tbg_relations=relevant_bg_logits-fg_logits[ii]\n\t\t\t#Apply piecewise linear function and determine relations with bgs\n\t\t\tbg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)\n\n\t\t\t#Compute the rank of the example within fgs and number of bgs with larger scores\n\t\t\trank_pos=1+torch.sum(fg_relations)\n\t\t\tFP_num=torch.sum(bg_relations)\n\t\t\t#Store the total since it is normalizer also for aLRP Regression error\n\t\t\trank[ii]=rank_pos+FP_num\n\t\t\t\t\t\t\t\n\t\t\t#Compute precision for this example \n\t\t\tcurrent_prec=rank_pos/rank[ii]\n\t\t\t\n\t\t\t#Compute interpolated AP and store gradients for relevant bg examples\n\t\t\tif (max_prec<=current_prec):\n\t\t\t\tmax_prec=current_prec\n\t\t\t\trelevant_bg_grad += (bg_relations/rank[ii])\n\t\t\telse:\n\t\t\t\trelevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec)))\n\t\t\t\n\t\t\t#Store fg gradients\n\t\t\tfg_grad[ii]=-(1-max_prec)\n\t\t\tprec[ii]=max_prec \n\n\t\t#aLRP with grad formulation fg gradient\n\t\tclassification_grads[fg_labels]= fg_grad\n\t\t#aLRP with grad formulation bg gradient\n\t\tclassification_grads[relevant_bg_labels]= relevant_bg_grad \n \n\t\tclassification_grads /= fg_num\n\t\n\t\tcls_loss=1-prec.mean()\n\t\tctx.save_for_backward(classification_grads)\n\n\t\treturn cls_loss\n\n\t@staticmethod\n\tdef backward(ctx, out_grad1):\n\t\tg1, =ctx.saved_tensors\n\t\treturn g1*out_grad1, None, None\n"},"avg_line_length":{"kind":"number","value":35.1983805668,"string":"35.198381"},"max_line_length":{"kind":"number","value":96,"string":"96"},"alphanum_fraction":{"kind":"number","value":0.7439613527,"string":"0.743961"},"count_classes":{"kind":"number","value":8672,"string":"8,672"},"score_classes":{"kind":"number","value":0.9974695192086497,"string":"0.99747"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":8539,"string":"8,539"},"score_decorators":{"kind":"number","value":0.9821716126063952,"string":"0.982172"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":2846,"string":"2,846"},"score_documentation":{"kind":"number","value":0.3273521969174143,"string":"0.327352"}}},{"rowIdx":3281,"cells":{"hexsha":{"kind":"string","value":"131342de18ae50cff3d8d09f0b5c640ef367d9c5"},"size":{"kind":"number","value":997,"string":"997"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"tests/test_dcd_api.py"},"max_stars_repo_name":{"kind":"string","value":"sadamek/pyIMX"},"max_stars_repo_head_hexsha":{"kind":"string","value":"52af15e656b400f0812f16cf31d9bf6edbe631ad"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"tests/test_dcd_api.py"},"max_issues_repo_name":{"kind":"string","value":"sadamek/pyIMX"},"max_issues_repo_head_hexsha":{"kind":"string","value":"52af15e656b400f0812f16cf31d9bf6edbe631ad"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"tests/test_dcd_api.py"},"max_forks_repo_name":{"kind":"string","value":"sadamek/pyIMX"},"max_forks_repo_head_hexsha":{"kind":"string","value":"52af15e656b400f0812f16cf31d9bf6edbe631ad"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# Copyright (c) 2017-2018 Martin Olejar\n#\n# SPDX-License-Identifier: BSD-3-Clause\n# The BSD-3-Clause license for this file can be found in the LICENSE file included with this distribution\n# or at https://spdx.org/licenses/BSD-3-Clause.html#licenseText\n\nimport os\nimport pytest\nfrom imx import img\n\n# Used Directories\nDATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')\n\n# Test Files\nDCD_TXT = os.path.join(DATA_DIR, 'dcd_test.txt')\nDCD_BIN = os.path.join(DATA_DIR, 'dcd_test.bin')\n\n\ndef setup_module(module):\n # Prepare test environment\n pass\n\n\ndef teardown_module(module):\n # Clean test environment\n pass\n\n\ndef test_txt_parser():\n\n with open(DCD_TXT, 'r') as f:\n dcd_obj = img.SegDCD.parse_txt(f.read())\n\n assert dcd_obj is not None\n assert len(dcd_obj) == 12\n\n\ndef test_bin_parser():\n\n with open(DCD_BIN, 'rb') as f:\n dcd_obj = img.SegDCD.parse(f.read())\n\n assert dcd_obj is not None\n assert len(dcd_obj) == 12\n"},"avg_line_length":{"kind":"number","value":22.1555555556,"string":"22.155556"},"max_line_length":{"kind":"number","value":105,"string":"105"},"alphanum_fraction":{"kind":"number","value":0.6910732197,"string":"0.691073"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":368,"string":"368"},"score_documentation":{"kind":"number","value":0.3691073219658977,"string":"0.369107"}}},{"rowIdx":3282,"cells":{"hexsha":{"kind":"string","value":"1313abf2371aefa93bced19321156374195a59c5"},"size":{"kind":"number","value":9387,"string":"9,387"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"recentjson.py"},"max_stars_repo_name":{"kind":"string","value":"nydailynews/feedutils"},"max_stars_repo_head_hexsha":{"kind":"string","value":"8cb18b26ebf70033df420f3fece8c2cac363f918"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"recentjson.py"},"max_issues_repo_name":{"kind":"string","value":"nydailynews/feedutils"},"max_issues_repo_head_hexsha":{"kind":"string","value":"8cb18b26ebf70033df420f3fece8c2cac363f918"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2017-07-11T17:37:50.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2017-07-11T17:37:50.000Z"},"max_forks_repo_path":{"kind":"string","value":"recentjson.py"},"max_forks_repo_name":{"kind":"string","value":"nydailynews/feedutils"},"max_forks_repo_head_hexsha":{"kind":"string","value":"8cb18b26ebf70033df420f3fece8c2cac363f918"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Return recent items from a json feed. Recent means \"In the last X days.\"\nimport os\nimport doctest\nimport json\nimport urllib2\nimport argparse\nimport types\nimport gzip\nfrom datetime import datetime, timedelta\nfrom time import mktime\n\nclass RecentJson:\n \"\"\" Methods for ingesting and publishing JSON feeds.\n >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628'\n >>> parser = build_parser()\n >>> args = parser.parse_args([url])\n >>> rj = RecentJson(args)\n \"\"\"\n\n def __init__(self, args={}):\n self.args = args\n if not hasattr(self.args, 'days'):\n self.args.days = 0\n self.days = self.args.days\n self.date_format = '%a, %d %b %Y %X'\n\n def get(self, url):\n \"\"\" Wrapper for API requests. Take a URL, return a json array.\n >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628'\n >>> parser = build_parser()\n >>> args = parser.parse_args([url])\n >>> rj = RecentJson(args)\n >>> rj.get(url)\n True\n \"\"\"\n response = urllib2.urlopen(url)\n if int(response.code) >= 400:\n if 'verbose' in self.args and self.args.verbose:\n print \"URL: %s\" % url\n raise ValueError(\"URL %s response: %s\" % (url, response['status']))\n self.xml = response.read()\n return True\n\n def parse(self):\n \"\"\" Turn the xml into an object.\n >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628'\n >>> parser = build_parser()\n >>> args = parser.parse_args([url])\n >>> rj = RecentJson(args)\n >>> rj.get(url)\n True\n >>> xml = rj.parse()\n >>> print len(xml)\n 50\n \"\"\"\n try:\n p = json.loads(self.xml)\n except:\n # Sometimes we download gzipped documents from the web.\n fh = open('json.gz', 'wb')\n fh.write(self.xml)\n fh.close()\n try:\n gz = gzip.GzipFile('json.gz', 'r').read()\n p = json.loads(gzip.GzipFile('json.gz', 'r').read())\n except IOError:\n return None\n self.p = p\n return p\n\n def recently(self):\n \"\"\" Return a feedparser entry object for the last X days of feed entries.\n >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628'\n >>> parser = build_parser()\n >>> args = parser.parse_args([url])\n >>> rj = RecentJson(args)\n >>> rj.get(url)\n True\n >>> xml = rj.parse()\n >>> articles = rj.recently()\n \"\"\"\n items = []\n for item in self.p:\n # print item.keys()\n # [u'body', u'tags', u'url', u'contentId', u'abstract', u'author', u'lastUpdated', u'mobileTitle', u'mobileUrl', u'publish_date', u'images', u'title', u'type', u'categories']\n # print item['publish_date']\n # Fri, 7 Jul 2017 15:16:38 -0400\n #dt = datetime.strptime(item['publish_date'], '%a, %d %b %Y %X %z')\n dt = datetime.strptime(' '.join(item['publish_date'].split(' ')[:5]), self.date_format)\n delta = datetime.today() - dt\n\n if delta.days > int(self.days):\n continue\n items.append(item)\n if 'verbose' in self.args and self.args.verbose:\n print delta.days, dt\n self.items = items\n return items\n\ndef pretty_date(ago):\n \"\"\" Process a timedelta object.\n From https://stackoverflow.com/questions/1551382/user-friendly-time-format-in-python\n \"\"\"\n second_diff = ago.seconds\n day_diff = ago.days\n\n if day_diff < 0:\n return ''\n\n if day_diff == 0:\n if second_diff < 10:\n return \"just now\"\n if second_diff < 60:\n return str(second_diff) + \" seconds ago\"\n if second_diff < 120:\n return \"a minute ago\"\n if second_diff < 3600:\n return str(second_diff / 60) + \" minutes ago\"\n if second_diff < 7200:\n return \"an hour ago\"\n if second_diff < 86400:\n return str(second_diff / 3600) + \" hours ago\"\n if day_diff == 1:\n return \"Yesterday\"\n if day_diff < 7:\n return str(day_diff) + \" days ago\"\n if day_diff < 31:\n if day_diff / 7 == 1:\n return str(day_diff / 7) + \" week ago\"\n return str(day_diff / 7) + \" weeks ago\"\n if day_diff < 365:\n if day_diff / 30 == 1:\n return str(day_diff / 30) + \" month ago\"\n return str(day_diff / 30) + \" months ago\"\n if day_diff / 365 == 1:\n return str(day_diff / 365) + \" year ago\"\n return str(day_diff / 365) + \" years ago\"\n\ndef main(args):\n \"\"\" For command-line use.\n \"\"\"\n rj = RecentJson(args)\n if args:\n articles = []\n for arg in args.urls[0]:\n if args.verbose:\n print arg\n rj.get(arg)\n try:\n p = rj.parse()\n except:\n continue\n if not p:\n continue\n articles.append(rj.recently())\n\n if len(articles) is 0:\n return None\n for i, article in enumerate(articles[0]):\n if i >= args.limit and args.limit > 0:\n break \n\n dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X')\n ago = datetime.now() - dt\n # print ago\n # 2 days, 15:57:48.578638\n if args.output == 'html':\n if type(article['title']) is types.UnicodeType:\n article['title'] = article['title'].encode('utf-8', 'replace')\n if args.listitem == True:\n print '
  • {1} ({2})
  • '.format(article['url'], article['title'], pretty_date(ago).lower())\n elif args.nostamp == True:\n print '
  • {1}
  • '.format(article['url'], article['title'], pretty_date(ago).lower())\n else:\n print '{1} ({2})'.format(article['url'], article['title'], pretty_date(ago).lower())\n if args.output == 'js':\n if type(article['title']) is types.UnicodeType:\n article['title'] = article['title'].encode('utf-8', 'replace')\n print 'var hed = \"{1} ({2})\";'.format(article['url'], article['title'].replace('\"', '\\\\\\\\\"'), pretty_date(ago).lower())\n elif args.output == 'json':\n print json.dumps({'title': article['title'],\n 'id': article['id'],\n 'description': article['description']})\n elif args.output == 'csv':\n dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X')\n article['datetime'] = '%s-%s-%s' % (dt.year, dt.month, dt.day)\n if dt.month < 10:\n article['datetime'] = '%d-0%d-%d' % (dt.year, dt.month, dt.day)\n if dt.day < 10:\n article['datetime'] = '%d-0%d-0%d' % (dt.year, dt.month, dt.day)\n article['slug'] = article['title'].lower().replace(' ', '-').replace('--', '-').replace(':', '')\n article['iframe_url'] = article['media_player']['url']\n article['image_url'] = article['media_thumbnail'][0]['url']\n article['image_large_url'] = article['media_thumbnail'][1]['url']\n article['description'] = article['description'].replace('\"', \"'\")\n # date,title,id,slug,player_url,image_url,image_large_url,keywords,description\n print '%(datetime)s,\"%(title)s\",%(id)s,%(slug)s,%(iframe_url)s,%(image_url)s,%(image_large_url)s,\"%(media_keywords)s\",\"%(description)s\"' % article\n\n\ndef build_parser():\n \"\"\" We put the argparse in a method so we can test it\n outside of the command-line.\n \"\"\"\n parser = argparse.ArgumentParser(usage='$ python recentjson.py http://domain.com/json/',\n description='''Takes a list of URLs passed as args.\n Returns the items published today unless otherwise specified.''',\n epilog='')\n parser.add_argument(\"-v\", \"--verbose\", dest=\"verbose\", default=False, action=\"store_true\")\n parser.add_argument(\"--test\", dest=\"test\", default=False, action=\"store_true\")\n parser.add_argument(\"-d\", \"--days\", dest=\"days\", default=0)\n parser.add_argument(\"-l\", \"--limit\", dest=\"limit\", default=0, type=int)\n parser.add_argument(\"-o\", \"--output\", dest=\"output\", default=\"html\", type=str)\n parser.add_argument(\"--li\", dest=\"listitem\", default=False, action=\"store_true\")\n parser.add_argument(\"--ns\", dest=\"nostamp\", default=False, action=\"store_true\")\n parser.add_argument(\"urls\", action=\"append\", nargs=\"*\")\n return parser\n\nif __name__ == '__main__':\n \"\"\" \n \"\"\"\n parser = build_parser()\n args = parser.parse_args()\n\n if args.test:\n doctest.testmod(verbose=args.verbose)\n\n main(args)\n"},"avg_line_length":{"kind":"number","value":40.8130434783,"string":"40.813043"},"max_line_length":{"kind":"number","value":186,"string":"186"},"alphanum_fraction":{"kind":"number","value":0.520080963,"string":"0.520081"},"count_classes":{"kind":"number","value":3325,"string":"3,325"},"score_classes":{"kind":"number","value":0.3542132736763609,"string":"0.354213"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":3823,"string":"3,823"},"score_documentation":{"kind":"number","value":0.4072653669969106,"string":"0.407265"}}},{"rowIdx":3283,"cells":{"hexsha":{"kind":"string","value":"1314a6b3e97ad080ab7cf47017455ad35f9e033a"},"size":{"kind":"number","value":34521,"string":"34,521"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"maint/MultiStage2.py"},"max_stars_repo_name":{"kind":"string","value":"Liastre/pcre2"},"max_stars_repo_head_hexsha":{"kind":"string","value":"ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"maint/MultiStage2.py"},"max_issues_repo_name":{"kind":"string","value":"Liastre/pcre2"},"max_issues_repo_head_hexsha":{"kind":"string","value":"ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-04-07T10:42:22.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2020-04-07T10:42:22.000Z"},"max_forks_repo_path":{"kind":"string","value":"maint/MultiStage2.py"},"max_forks_repo_name":{"kind":"string","value":"Liastre/pcre2"},"max_forks_repo_head_hexsha":{"kind":"string","value":"ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#! /usr/bin/python\n\n# Multistage table builder\n# (c) Peter Kankowski, 2008\n\n##############################################################################\n# This script was submitted to the PCRE project by Peter Kankowski as part of\n# the upgrading of Unicode property support. The new code speeds up property\n# matching many times. The script is for the use of PCRE maintainers, to\n# generate the pcre2_ucd.c file that contains a digested form of the Unicode\n# data tables. A number of extensions have been added to the original script.\n#\n# The script has now been upgraded to Python 3 for PCRE2, and should be run in\n# the maint subdirectory, using the command\n#\n# [python3] ./MultiStage2.py >../src/pcre2_ucd.c\n#\n# It requires six Unicode data tables: DerivedGeneralCategory.txt,\n# GraphemeBreakProperty.txt, Scripts.txt, ScriptExtensions.txt,\n# CaseFolding.txt, and emoji-data.txt. These must be in the\n# maint/Unicode.tables subdirectory.\n#\n# DerivedGeneralCategory.txt is found in the \"extracted\" subdirectory of the\n# Unicode database (UCD) on the Unicode web site; GraphemeBreakProperty.txt is\n# in the \"auxiliary\" subdirectory. Scripts.txt, ScriptExtensions.txt, and\n# CaseFolding.txt are directly in the UCD directory. The emoji-data.txt file is\n# in files associated with Unicode Technical Standard #51 (\"Unicode Emoji\"),\n# for example:\n#\n# http://unicode.org/Public/emoji/11.0/emoji-data.txt\n#\n# -----------------------------------------------------------------------------\n# Minor modifications made to this script:\n# Added #! line at start\n# Removed tabs\n# Made it work with Python 2.4 by rewriting two statements that needed 2.5\n# Consequent code tidy\n# Adjusted data file names to take from the Unicode.tables directory\n# Adjusted global table names by prefixing _pcre_.\n# Commented out stuff relating to the casefolding table, which isn't used;\n# removed completely in 2012.\n# Corrected size calculation\n# Add #ifndef SUPPORT_UCP to use dummy tables when no UCP support is needed.\n# Update for PCRE2: name changes, and SUPPORT_UCP is abolished.\n#\n# Major modifications made to this script:\n# Added code to add a grapheme break property field to records.\n#\n# Added code to search for sets of more than two characters that must match\n# each other caselessly. A new table is output containing these sets, and\n# offsets into the table are added to the main output records. This new\n# code scans CaseFolding.txt instead of UnicodeData.txt, which is no longer\n# used.\n#\n# Update for Python3:\n# . Processed with 2to3, but that didn't fix everything\n# . Changed string.strip to str.strip\n# . Added encoding='utf-8' to the open() call\n# . Inserted 'int' before blocksize/ELEMS_PER_LINE because an int is\n# required and the result of the division is a float\n#\n# Added code to scan the emoji-data.txt file to find the Extended Pictographic\n# property, which is used by PCRE2 as a grapheme breaking property. This was\n# done when updating to Unicode 11.0.0 (July 2018).\n#\n# Added code to add a Script Extensions field to records. This has increased\n# their size from 8 to 12 bytes, only 10 of which are currently used.\n#\n# 01-March-2010: Updated list of scripts for Unicode 5.2.0\n# 30-April-2011: Updated list of scripts for Unicode 6.0.0\n# July-2012: Updated list of scripts for Unicode 6.1.0\n# 20-August-2012: Added scan of GraphemeBreakProperty.txt and added a new\n# field in the record to hold the value. Luckily, the\n# structure had a hole in it, so the resulting table is\n# not much bigger than before.\n# 18-September-2012: Added code for multiple caseless sets. This uses the\n# final hole in the structure.\n# 30-September-2012: Added RegionalIndicator break property from Unicode 6.2.0\n# 13-May-2014: Updated for PCRE2\n# 03-June-2014: Updated for Python 3\n# 20-June-2014: Updated for Unicode 7.0.0\n# 12-August-2014: Updated to put Unicode version into the file\n# 19-June-2015: Updated for Unicode 8.0.0\n# 02-July-2017: Updated for Unicode 10.0.0\n# 03-July-2018: Updated for Unicode 11.0.0\n# 07-July-2018: Added code to scan emoji-data.txt for the Extended\n# Pictographic property.\n# 01-October-2018: Added the 'Unknown' script name\n# 03-October-2018: Added new field for Script Extensions\n# 27-July-2019: Updated for Unicode 12.1.0\n# ----------------------------------------------------------------------------\n#\n#\n# The main tables generated by this script are used by macros defined in\n# pcre2_internal.h. They look up Unicode character properties using short\n# sequences of code that contains no branches, which makes for greater speed.\n#\n# Conceptually, there is a table of records (of type ucd_record), containing a\n# script number, script extension value, character type, grapheme break type,\n# offset to caseless matching set, offset to the character's other case, for\n# every Unicode character. However, a real table covering all Unicode\n# characters would be far too big. It can be efficiently compressed by\n# observing that many characters have the same record, and many blocks of\n# characters (taking 128 characters in a block) have the same set of records as\n# other blocks. This leads to a 2-stage lookup process.\n#\n# This script constructs six tables. The ucd_caseless_sets table contains\n# lists of characters that all match each other caselessly. Each list is\n# in order, and is terminated by NOTACHAR (0xffffffff), which is larger than\n# any valid character. The first list is empty; this is used for characters\n# that are not part of any list.\n#\n# The ucd_digit_sets table contains the code points of the '9' characters in\n# each set of 10 decimal digits in Unicode. This is used to ensure that digits\n# in script runs all come from the same set. The first element in the vector\n# contains the number of subsequent elements, which are in ascending order.\n#\n# The ucd_script_sets vector contains lists of script numbers that are the\n# Script Extensions properties of certain characters. Each list is terminated\n# by zero (ucp_Unknown). A character with more than one script listed for its\n# Script Extension property has a negative value in its record. This is the\n# negated offset to the start of the relevant list in the ucd_script_sets\n# vector.\n#\n# The ucd_records table contains one instance of every unique record that is\n# required. The ucd_stage1 table is indexed by a character's block number,\n# which is the character's code point divided by 128, since 128 is the size\n# of each block. The result of a lookup in ucd_stage1 a \"virtual\" block number.\n#\n# The ucd_stage2 table is a table of \"virtual\" blocks; each block is indexed by\n# the offset of a character within its own block, and the result is the index\n# number of the required record in the ucd_records vector.\n#\n# The following examples are correct for the Unicode 11.0.0 database. Future\n# updates may make change the actual lookup values.\n#\n# Example: lowercase \"a\" (U+0061) is in block 0\n# lookup 0 in stage1 table yields 0\n# lookup 97 (0x61) in the first table in stage2 yields 17\n# record 17 is { 34, 5, 12, 0, -32, 34, 0 }\n# 34 = ucp_Latin => Latin script\n# 5 = ucp_Ll => Lower case letter\n# 12 = ucp_gbOther => Grapheme break property \"Other\"\n# 0 => Not part of a caseless set\n# -32 (-0x20) => Other case is U+0041\n# 34 = ucp_Latin => No special Script Extension property\n# 0 => Dummy value, unused at present\n#\n# Almost all lowercase latin characters resolve to the same record. One or two\n# are different because they are part of a multi-character caseless set (for\n# example, k, K and the Kelvin symbol are such a set).\n#\n# Example: hiragana letter A (U+3042) is in block 96 (0x60)\n# lookup 96 in stage1 table yields 90\n# lookup 66 (0x42) in table 90 in stage2 yields 564\n# record 564 is { 27, 7, 12, 0, 0, 27, 0 }\n# 27 = ucp_Hiragana => Hiragana script\n# 7 = ucp_Lo => Other letter\n# 12 = ucp_gbOther => Grapheme break property \"Other\"\n# 0 => Not part of a caseless set\n# 0 => No other case\n# 27 = ucp_Hiragana => No special Script Extension property\n# 0 => Dummy value, unused at present\n#\n# Example: vedic tone karshana (U+1CD0) is in block 57 (0x39)\n# lookup 57 in stage1 table yields 55\n# lookup 80 (0x50) in table 55 in stage2 yields 458\n# record 458 is { 28, 12, 3, 0, 0, -101, 0 }\n# 28 = ucp_Inherited => Script inherited from predecessor\n# 12 = ucp_Mn => Non-spacing mark\n# 3 = ucp_gbExtend => Grapheme break property \"Extend\"\n# 0 => Not part of a caseless set\n# 0 => No other case\n# -101 => Script Extension list offset = 101\n# 0 => Dummy value, unused at present\n#\n# At offset 101 in the ucd_script_sets vector we find the list 3, 15, 107, 29,\n# and terminator 0. This means that this character is expected to be used with\n# any of those scripts, which are Bengali, Devanagari, Grantha, and Kannada.\n#\n# Philip Hazel, 03 July 2008\n# Last Updated: 07 October 2018\n##############################################################################\n\n\nimport re\nimport string\nimport sys\n\nMAX_UNICODE = 0x110000\nNOTACHAR = 0xffffffff\n\n\n# Parse a line of Scripts.txt, GraphemeBreakProperty.txt or DerivedGeneralCategory.txt\ndef make_get_names(enum):\n return lambda chardata: enum.index(chardata[1])\n\n# Parse a line of CaseFolding.txt\ndef get_other_case(chardata):\n if chardata[1] == 'C' or chardata[1] == 'S':\n return int(chardata[2], 16) - int(chardata[0], 16)\n return 0\n\n# Parse a line of ScriptExtensions.txt\ndef get_script_extension(chardata):\n this_script_list = list(chardata[1].split(' '))\n if len(this_script_list) == 1:\n return script_abbrevs.index(this_script_list[0])\n\n script_numbers = []\n for d in this_script_list:\n script_numbers.append(script_abbrevs.index(d))\n script_numbers.append(0)\n script_numbers_length = len(script_numbers)\n\n for i in range(1, len(script_lists) - script_numbers_length + 1):\n for j in range(0, script_numbers_length):\n found = True\n if script_lists[i+j] != script_numbers[j]:\n found = False\n break\n if found:\n return -i\n\n # Not found in existing lists\n\n return_value = len(script_lists)\n script_lists.extend(script_numbers)\n return -return_value\n\n# Read the whole table in memory, setting/checking the Unicode version\ndef read_table(file_name, get_value, default_value):\n global unicode_version\n\n f = re.match(r'^[^/]+/([^.]+)\\.txt$', file_name)\n file_base = f.group(1)\n version_pat = r\"^# \" + re.escape(file_base) + r\"-(\\d+\\.\\d+\\.\\d+)\\.txt$\"\n file = open(file_name, 'r', encoding='utf-8')\n f = re.match(version_pat, file.readline())\n version = f.group(1)\n if unicode_version == \"\":\n unicode_version = version\n elif unicode_version != version:\n print(\"WARNING: Unicode version differs in %s\", file_name, file=sys.stderr)\n\n table = [default_value] * MAX_UNICODE\n for line in file:\n line = re.sub(r'#.*', '', line)\n chardata = list(map(str.strip, line.split(';')))\n if len(chardata) <= 1:\n continue\n value = get_value(chardata)\n m = re.match(r'([0-9a-fA-F]+)(\\.\\.([0-9a-fA-F]+))?$', chardata[0])\n char = int(m.group(1), 16)\n if m.group(3) is None:\n last = char\n else:\n last = int(m.group(3), 16)\n for i in range(char, last + 1):\n # It is important not to overwrite a previously set\n # value because in the CaseFolding file there are lines\n # to be ignored (returning the default value of 0)\n # which often come after a line which has already set\n # data.\n if table[i] == default_value:\n table[i] = value\n file.close()\n return table\n\n# Get the smallest possible C language type for the values\ndef get_type_size(table):\n type_size = [(\"uint8_t\", 1), (\"uint16_t\", 2), (\"uint32_t\", 4),\n (\"signed char\", 1), (\"pcre_int16\", 2), (\"pcre_int32\", 4)]\n limits = [(0, 255), (0, 65535), (0, 4294967295),\n (-128, 127), (-32768, 32767), (-2147483648, 2147483647)]\n minval = min(table)\n maxval = max(table)\n for num, (minlimit, maxlimit) in enumerate(limits):\n if minlimit <= minval and maxval <= maxlimit:\n return type_size[num]\n else:\n raise OverflowError(\"Too large to fit into C types\")\n\ndef get_tables_size(*tables):\n total_size = 0\n for table in tables:\n type, size = get_type_size(table)\n total_size += size * len(table)\n return total_size\n\n# Compress the table into the two stages\ndef compress_table(table, block_size):\n blocks = {} # Dictionary for finding identical blocks\n stage1 = [] # Stage 1 table contains block numbers (indices into stage 2 table)\n stage2 = [] # Stage 2 table contains the blocks with property values\n table = tuple(table)\n for i in range(0, len(table), block_size):\n block = table[i:i+block_size]\n start = blocks.get(block)\n if start is None:\n # Allocate a new block\n start = len(stage2) / block_size\n stage2 += block\n blocks[block] = start\n stage1.append(start)\n\n return stage1, stage2\n\n# Print a table\ndef print_table(table, table_name, block_size = None):\n type, size = get_type_size(table)\n ELEMS_PER_LINE = 16\n\n s = \"const %s %s[] = { /* %d bytes\" % (type, table_name, size * len(table))\n if block_size:\n s += \", block = %d\" % block_size\n print(s + \" */\")\n table = tuple(table)\n if block_size is None:\n fmt = \"%3d,\" * ELEMS_PER_LINE + \" /* U+%04X */\"\n mult = MAX_UNICODE / len(table)\n for i in range(0, len(table), ELEMS_PER_LINE):\n print(fmt % (table[i:i+ELEMS_PER_LINE] +\n (int(i * mult),)))\n else:\n if block_size > ELEMS_PER_LINE:\n el = ELEMS_PER_LINE\n else:\n el = block_size\n fmt = \"%3d,\" * el + \"\\n\"\n if block_size > ELEMS_PER_LINE:\n fmt = fmt * int(block_size / ELEMS_PER_LINE)\n for i in range(0, len(table), block_size):\n print((\"/* block %d */\\n\" + fmt) % ((i / block_size,) + table[i:i+block_size]))\n print(\"};\\n\")\n\n# Extract the unique combinations of properties into records\ndef combine_tables(*tables):\n records = {}\n index = []\n for t in zip(*tables):\n i = records.get(t)\n if i is None:\n i = records[t] = len(records)\n index.append(i)\n return index, records\n\ndef get_record_size_struct(records):\n size = 0\n structure = '/* When recompiling tables with a new Unicode version, please check the\\n' + \\\n 'types in this structure definition from pcre2_internal.h (the actual\\n' + \\\n 'field names will be different):\\n\\ntypedef struct {\\n'\n for i in range(len(records[0])):\n record_slice = [record[i] for record in records]\n slice_type, slice_size = get_type_size(record_slice)\n # add padding: round up to the nearest power of slice_size\n size = (size + slice_size - 1) & -slice_size\n size += slice_size\n structure += '%s property_%d;\\n' % (slice_type, i)\n\n # round up to the first item of the next structure in array\n record_slice = [record[0] for record in records]\n slice_type, slice_size = get_type_size(record_slice)\n size = (size + slice_size - 1) & -slice_size\n\n structure += '} ucd_record;\\n*/\\n'\n return size, structure\n\ndef test_record_size():\n tests = [ \\\n ( [(3,), (6,), (6,), (1,)], 1 ), \\\n ( [(300,), (600,), (600,), (100,)], 2 ), \\\n ( [(25, 3), (6, 6), (34, 6), (68, 1)], 2 ), \\\n ( [(300, 3), (6, 6), (340, 6), (690, 1)], 4 ), \\\n ( [(3, 300), (6, 6), (6, 340), (1, 690)], 4 ), \\\n ( [(300, 300), (6, 6), (6, 340), (1, 690)], 4 ), \\\n ( [(3, 100000), (6, 6), (6, 123456), (1, 690)], 8 ), \\\n ( [(100000, 300), (6, 6), (123456, 6), (1, 690)], 8 ), \\\n ]\n for test in tests:\n size, struct = get_record_size_struct(test[0])\n assert(size == test[1])\n #print struct\n\ndef print_records(records, record_size):\n print('const ucd_record PRIV(ucd_records)[] = { ' + \\\n '/* %d bytes, record size %d */' % (len(records) * record_size, record_size))\n\n records = list(zip(list(records.keys()), list(records.values())))\n records.sort(key = lambda x: x[1])\n for i, record in enumerate(records):\n print((' {' + '%6d, ' * len(record[0]) + '}, /* %3d */') % (record[0] + (i,)))\n print('};\\n')\n\nscript_names = ['Unknown', 'Arabic', 'Armenian', 'Bengali', 'Bopomofo', 'Braille', 'Buginese', 'Buhid', 'Canadian_Aboriginal',\n 'Cherokee', 'Common', 'Coptic', 'Cypriot', 'Cyrillic', 'Deseret', 'Devanagari', 'Ethiopic', 'Georgian',\n 'Glagolitic', 'Gothic', 'Greek', 'Gujarati', 'Gurmukhi', 'Han', 'Hangul', 'Hanunoo', 'Hebrew', 'Hiragana',\n 'Inherited', 'Kannada', 'Katakana', 'Kharoshthi', 'Khmer', 'Lao', 'Latin', 'Limbu', 'Linear_B', 'Malayalam',\n 'Mongolian', 'Myanmar', 'New_Tai_Lue', 'Ogham', 'Old_Italic', 'Old_Persian', 'Oriya', 'Osmanya', 'Runic',\n 'Shavian', 'Sinhala', 'Syloti_Nagri', 'Syriac', 'Tagalog', 'Tagbanwa', 'Tai_Le', 'Tamil', 'Telugu', 'Thaana',\n 'Thai', 'Tibetan', 'Tifinagh', 'Ugaritic', 'Yi',\n# New for Unicode 5.0\n 'Balinese', 'Cuneiform', 'Nko', 'Phags_Pa', 'Phoenician',\n# New for Unicode 5.1\n 'Carian', 'Cham', 'Kayah_Li', 'Lepcha', 'Lycian', 'Lydian', 'Ol_Chiki', 'Rejang', 'Saurashtra', 'Sundanese', 'Vai',\n# New for Unicode 5.2\n 'Avestan', 'Bamum', 'Egyptian_Hieroglyphs', 'Imperial_Aramaic',\n 'Inscriptional_Pahlavi', 'Inscriptional_Parthian',\n 'Javanese', 'Kaithi', 'Lisu', 'Meetei_Mayek',\n 'Old_South_Arabian', 'Old_Turkic', 'Samaritan', 'Tai_Tham', 'Tai_Viet',\n# New for Unicode 6.0.0\n 'Batak', 'Brahmi', 'Mandaic',\n# New for Unicode 6.1.0\n 'Chakma', 'Meroitic_Cursive', 'Meroitic_Hieroglyphs', 'Miao', 'Sharada', 'Sora_Sompeng', 'Takri',\n# New for Unicode 7.0.0\n 'Bassa_Vah', 'Caucasian_Albanian', 'Duployan', 'Elbasan', 'Grantha', 'Khojki', 'Khudawadi',\n 'Linear_A', 'Mahajani', 'Manichaean', 'Mende_Kikakui', 'Modi', 'Mro', 'Nabataean',\n 'Old_North_Arabian', 'Old_Permic', 'Pahawh_Hmong', 'Palmyrene', 'Psalter_Pahlavi',\n 'Pau_Cin_Hau', 'Siddham', 'Tirhuta', 'Warang_Citi',\n# New for Unicode 8.0.0\n 'Ahom', 'Anatolian_Hieroglyphs', 'Hatran', 'Multani', 'Old_Hungarian',\n 'SignWriting',\n# New for Unicode 10.0.0\n 'Adlam', 'Bhaiksuki', 'Marchen', 'Newa', 'Osage', 'Tangut', 'Masaram_Gondi',\n 'Nushu', 'Soyombo', 'Zanabazar_Square',\n# New for Unicode 11.0.0\n 'Dogra', 'Gunjala_Gondi', 'Hanifi_Rohingya', 'Makasar', 'Medefaidrin',\n 'Old_Sogdian', 'Sogdian',\n# New for Unicode 12.0.0\n 'Elymaic', 'Nandinagari', 'Nyiakeng_Puachue_Hmong', 'Wancho'\n ]\n\nscript_abbrevs = [\n 'Zzzz', 'Arab', 'Armn', 'Beng', 'Bopo', 'Brai', 'Bugi', 'Buhd', 'Cans',\n 'Cher', 'Zyyy', 'Copt', 'Cprt', 'Cyrl', 'Dsrt', 'Deva', 'Ethi', 'Geor',\n 'Glag', 'Goth', 'Grek', 'Gujr', 'Guru', 'Hani', 'Hang', 'Hano', 'Hebr',\n 'Hira', 'Zinh', 'Knda', 'Kana', 'Khar', 'Khmr', 'Laoo', 'Latn', 'Limb',\n 'Linb', 'Mlym', 'Mong', 'Mymr', 'Talu', 'Ogam', 'Ital', 'Xpeo', 'Orya',\n 'Osma', 'Runr', 'Shaw', 'Sinh', 'Sylo', 'Syrc', 'Tglg', 'Tagb', 'Tale',\n 'Taml', 'Telu', 'Thaa', 'Thai', 'Tibt', 'Tfng', 'Ugar', 'Yiii',\n#New for Unicode 5.0\n 'Bali', 'Xsux', 'Nkoo', 'Phag', 'Phnx',\n#New for Unicode 5.1\n 'Cari', 'Cham', 'Kali', 'Lepc', 'Lyci', 'Lydi', 'Olck', 'Rjng', 'Saur',\n 'Sund', 'Vaii',\n#New for Unicode 5.2\n 'Avst', 'Bamu', 'Egyp', 'Armi', 'Phli', 'Prti', 'Java', 'Kthi', 'Lisu',\n 'Mtei', 'Sarb', 'Orkh', 'Samr', 'Lana', 'Tavt',\n#New for Unicode 6.0.0\n 'Batk', 'Brah', 'Mand',\n#New for Unicode 6.1.0\n 'Cakm', 'Merc', 'Mero', 'Plrd', 'Shrd', 'Sora', 'Takr',\n#New for Unicode 7.0.0\n 'Bass', 'Aghb', 'Dupl', 'Elba', 'Gran', 'Khoj', 'Sind', 'Lina', 'Mahj',\n 'Mani', 'Mend', 'Modi', 'Mroo', 'Nbat', 'Narb', 'Perm', 'Hmng', 'Palm',\n 'Phlp', 'Pauc', 'Sidd', 'Tirh', 'Wara',\n#New for Unicode 8.0.0\n 'Ahom', 'Hluw', 'Hatr', 'Mult', 'Hung', 'Sgnw',\n#New for Unicode 10.0.0\n 'Adlm', 'Bhks', 'Marc', 'Newa', 'Osge', 'Tang', 'Gonm', 'Nshu', 'Soyo',\n 'Zanb',\n#New for Unicode 11.0.0\n 'Dogr', 'Gong', 'Rohg', 'Maka', 'Medf', 'Sogo', 'Sogd',\n#New for Unicode 12.0.0\n 'Elym', 'Nand', 'Hmnp', 'Wcho'\n ]\n\ncategory_names = ['Cc', 'Cf', 'Cn', 'Co', 'Cs', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu',\n 'Mc', 'Me', 'Mn', 'Nd', 'Nl', 'No', 'Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps',\n 'Sc', 'Sk', 'Sm', 'So', 'Zl', 'Zp', 'Zs' ]\n\n# The Extended_Pictographic property is not found in the file where all the\n# others are (GraphemeBreakProperty.txt). It comes from the emoji-data.txt\n# file, but we list it here so that the name has the correct index value.\n\nbreak_property_names = ['CR', 'LF', 'Control', 'Extend', 'Prepend',\n 'SpacingMark', 'L', 'V', 'T', 'LV', 'LVT', 'Regional_Indicator', 'Other',\n 'ZWJ', 'Extended_Pictographic' ]\n\ntest_record_size()\nunicode_version = \"\"\n\nscript = read_table('Unicode.tables/Scripts.txt', make_get_names(script_names), script_names.index('Unknown'))\ncategory = read_table('Unicode.tables/DerivedGeneralCategory.txt', make_get_names(category_names), category_names.index('Cn'))\nbreak_props = read_table('Unicode.tables/GraphemeBreakProperty.txt', make_get_names(break_property_names), break_property_names.index('Other'))\nother_case = read_table('Unicode.tables/CaseFolding.txt', get_other_case, 0)\n\n# The grapheme breaking rules were changed for Unicode 11.0.0 (June 2018). Now\n# we need to find the Extended_Pictographic property for emoji characters. This\n# can be set as an additional grapheme break property, because the default for\n# all the emojis is \"other\". We scan the emoji-data.txt file and modify the\n# break-props table.\n\nfile = open('Unicode.tables/emoji-data.txt', 'r', encoding='utf-8')\nfor line in file:\n line = re.sub(r'#.*', '', line)\n chardata = list(map(str.strip, line.split(';')))\n if len(chardata) <= 1:\n continue\n\n if chardata[1] != \"Extended_Pictographic\":\n continue\n\n m = re.match(r'([0-9a-fA-F]+)(\\.\\.([0-9a-fA-F]+))?$', chardata[0])\n char = int(m.group(1), 16)\n if m.group(3) is None:\n last = char\n else:\n last = int(m.group(3), 16)\n for i in range(char, last + 1):\n if break_props[i] != break_property_names.index('Other'):\n print(\"WARNING: Emoji 0x%x has break property %s, not 'Other'\",\n i, break_property_names[break_props[i]], file=sys.stderr)\n break_props[i] = break_property_names.index('Extended_Pictographic')\nfile.close()\n\n# The Script Extensions property default value is the Script value. Parse the\n# file, setting 'Unknown' as the default (this will never be a Script Extension\n# value), then scan it and fill in the default from Scripts. Code added by PH\n# in October 2018. Positive values are used for just a single script for a\n# code point. Negative values are negated offsets in a list of lists of\n# multiple scripts. Initialize this list with a single entry, as the zeroth\n# element is never used.\n\nscript_lists = [0]\nscript_abbrevs_default = script_abbrevs.index('Zzzz')\nscriptx = read_table('Unicode.tables/ScriptExtensions.txt', get_script_extension, script_abbrevs_default)\n\nfor i in range(0, MAX_UNICODE):\n if scriptx[i] == script_abbrevs_default:\n scriptx[i] = script[i]\n\n# With the addition of the new Script Extensions field, we need some padding\n# to get the Unicode records up to 12 bytes (multiple of 4). Set a value\n# greater than 255 to make the field 16 bits.\n\npadding_dummy = [0] * MAX_UNICODE\npadding_dummy[0] = 256\n\n# This block of code was added by PH in September 2012. I am not a Python\n# programmer, so the style is probably dreadful, but it does the job. It scans\n# the other_case table to find sets of more than two characters that must all\n# match each other caselessly. Later in this script a table of these sets is\n# written out. However, we have to do this work here in order to compute the\n# offsets in the table that are inserted into the main table.\n\n# The CaseFolding.txt file lists pairs, but the common logic for reading data\n# sets only one value, so first we go through the table and set \"return\"\n# offsets for those that are not already set.\n\nfor c in range(MAX_UNICODE):\n if other_case[c] != 0 and other_case[c + other_case[c]] == 0:\n other_case[c + other_case[c]] = -other_case[c]\n\n# Now scan again and create equivalence sets.\n\nsets = []\n\nfor c in range(MAX_UNICODE):\n o = c + other_case[c]\n\n # Trigger when this character's other case does not point back here. We\n # now have three characters that are case-equivalent.\n\n if other_case[o] != -other_case[c]:\n t = o + other_case[o]\n\n # Scan the existing sets to see if any of the three characters are already\n # part of a set. If so, unite the existing set with the new set.\n\n appended = 0\n for s in sets:\n found = 0\n for x in s:\n if x == c or x == o or x == t:\n found = 1\n\n # Add new characters to an existing set\n\n if found:\n found = 0\n for y in [c, o, t]:\n for x in s:\n if x == y:\n found = 1\n if not found:\n s.append(y)\n appended = 1\n\n # If we have not added to an existing set, create a new one.\n\n if not appended:\n sets.append([c, o, t])\n\n# End of loop looking for caseless sets.\n\n# Now scan the sets and set appropriate offsets for the characters.\n\ncaseless_offsets = [0] * MAX_UNICODE\n\noffset = 1;\nfor s in sets:\n for x in s:\n caseless_offsets[x] = offset\n offset += len(s) + 1\n\n# End of block of code for creating offsets for caseless matching sets.\n\n\n# Combine the tables\n\ntable, records = combine_tables(script, category, break_props,\n caseless_offsets, other_case, scriptx, padding_dummy)\n\nrecord_size, record_struct = get_record_size_struct(list(records.keys()))\n\n# Find the optimum block size for the two-stage table\nmin_size = sys.maxsize\nfor block_size in [2 ** i for i in range(5,10)]:\n size = len(records) * record_size\n stage1, stage2 = compress_table(table, block_size)\n size += get_tables_size(stage1, stage2)\n #print \"/* block size %5d => %5d bytes */\" % (block_size, size)\n if size < min_size:\n min_size = size\n min_stage1, min_stage2 = stage1, stage2\n min_block_size = block_size\n\nprint(\"/* This module is generated by the maint/MultiStage2.py script.\")\nprint(\"Do not modify it by hand. Instead modify the script and run it\")\nprint(\"to regenerate this code.\")\nprint()\nprint(\"As well as being part of the PCRE2 library, this module is #included\")\nprint(\"by the pcre2test program, which redefines the PRIV macro to change\")\nprint(\"table names from _pcre2_xxx to xxxx, thereby avoiding name clashes\")\nprint(\"with the library. At present, just one of these tables is actually\")\nprint(\"needed. */\")\nprint()\nprint(\"#ifndef PCRE2_PCRE2TEST\")\nprint()\nprint(\"#ifdef HAVE_CONFIG_H\")\nprint(\"#include \\\"config.h\\\"\")\nprint(\"#endif\")\nprint()\nprint(\"#include \\\"pcre2_internal.h\\\"\")\nprint()\nprint(\"#endif /* PCRE2_PCRE2TEST */\")\nprint()\nprint(\"/* Unicode character database. */\")\nprint(\"/* This file was autogenerated by the MultiStage2.py script. */\")\nprint(\"/* Total size: %d bytes, block size: %d. */\" % (min_size, min_block_size))\nprint()\nprint(\"/* The tables herein are needed only when UCP support is built,\")\nprint(\"and in PCRE2 that happens automatically with UTF support.\")\nprint(\"This module should not be referenced otherwise, so\")\nprint(\"it should not matter whether it is compiled or not. However\")\nprint(\"a comment was received about space saving - maybe the guy linked\")\nprint(\"all the modules rather than using a library - so we include a\")\nprint(\"condition to cut out the tables when not needed. But don't leave\")\nprint(\"a totally empty module because some compilers barf at that.\")\nprint(\"Instead, just supply some small dummy tables. */\")\nprint()\nprint(\"#ifndef SUPPORT_UNICODE\")\nprint(\"const ucd_record PRIV(ucd_records)[] = {{0,0,0,0,0,0,0 }};\")\nprint(\"const uint16_t PRIV(ucd_stage1)[] = {0};\")\nprint(\"const uint16_t PRIV(ucd_stage2)[] = {0};\")\nprint(\"const uint32_t PRIV(ucd_caseless_sets)[] = {0};\")\nprint(\"#else\")\nprint()\nprint(\"const char *PRIV(unicode_version) = \\\"{}\\\";\".format(unicode_version))\nprint()\nprint(\"/* If the 32-bit library is run in non-32-bit mode, character values\")\nprint(\"greater than 0x10ffff may be encountered. For these we set up a\")\nprint(\"special record. */\")\nprint()\nprint(\"#if PCRE2_CODE_UNIT_WIDTH == 32\")\nprint(\"const ucd_record PRIV(dummy_ucd_record)[] = {{\")\nprint(\" ucp_Unknown, /* script */\")\nprint(\" ucp_Cn, /* type unassigned */\")\nprint(\" ucp_gbOther, /* grapheme break property */\")\nprint(\" 0, /* case set */\")\nprint(\" 0, /* other case */\")\nprint(\" ucp_Unknown, /* script extension */\")\nprint(\" 0, /* dummy filler */\")\nprint(\" }};\")\nprint(\"#endif\")\nprint()\nprint(record_struct)\n\n# --- Added by PH: output the table of caseless character sets ---\n\nprint(\"/* This table contains lists of characters that are caseless sets of\")\nprint(\"more than one character. Each list is terminated by NOTACHAR. */\\n\")\n\nprint(\"const uint32_t PRIV(ucd_caseless_sets)[] = {\")\nprint(\" NOTACHAR,\")\nfor s in sets:\n s = sorted(s)\n for x in s:\n print(' 0x%04x,' % x, end=' ')\n print(' NOTACHAR,')\nprint('};')\nprint()\n\n# ------\n\nprint(\"/* When #included in pcre2test, we don't need the table of digit\")\nprint(\"sets, nor the the large main UCD tables. */\")\nprint()\nprint(\"#ifndef PCRE2_PCRE2TEST\")\nprint()\n\n# --- Added by PH: read Scripts.txt again for the sets of 10 digits. ---\n\ndigitsets = []\nfile = open('Unicode.tables/Scripts.txt', 'r', encoding='utf-8')\n\nfor line in file:\n m = re.match(r'([0-9a-fA-F]+)\\.\\.([0-9a-fA-F]+)\\s+;\\s+\\S+\\s+#\\s+Nd\\s+', line)\n if m is None:\n continue\n first = int(m.group(1),16)\n last = int(m.group(2),16)\n if ((last - first + 1) % 10) != 0:\n print(\"ERROR: %04x..%04x does not contain a multiple of 10 characters\" % (first, last),\n file=sys.stderr)\n while first < last:\n digitsets.append(first + 9)\n first += 10\nfile.close()\ndigitsets.sort()\n\nprint(\"/* This table lists the code points for the '9' characters in each\")\nprint(\"set of decimal digits. It is used to ensure that all the digits in\")\nprint(\"a script run come from the same set. */\\n\")\nprint(\"const uint32_t PRIV(ucd_digit_sets)[] = {\")\n\nprint(\" %d, /* Number of subsequent values */\" % len(digitsets), end='')\ncount = 8\nfor d in digitsets:\n if count == 8:\n print(\"\\n \", end='')\n count = 0\n print(\" 0x%05x,\" % d, end='')\n count += 1\nprint(\"\\n};\\n\")\n\nprint(\"/* This vector is a list of lists of scripts for the Script Extension\")\nprint(\"property. Each sublist is zero-terminated. */\\n\")\nprint(\"const uint8_t PRIV(ucd_script_sets)[] = {\")\n\ncount = 0\nprint(\" /* 0 */\", end='')\nfor d in script_lists:\n print(\" %3d,\" % d, end='')\n count += 1\n if d == 0:\n print(\"\\n /* %3d */\" % count, end='')\nprint(\"\\n};\\n\")\n\n# Output the main UCD tables.\n\nprint(\"/* These are the main two-stage UCD tables. The fields in each record are:\")\nprint(\"script (8 bits), character type (8 bits), grapheme break property (8 bits),\")\nprint(\"offset to multichar other cases or zero (8 bits), offset to other case\")\nprint(\"or zero (32 bits, signed), script extension (16 bits, signed), and a dummy\")\nprint(\"16-bit field to make the whole thing a multiple of 4 bytes. */\\n\")\n\nprint_records(records, record_size)\nprint_table(min_stage1, 'PRIV(ucd_stage1)')\nprint_table(min_stage2, 'PRIV(ucd_stage2)', min_block_size)\nprint(\"#if UCD_BLOCK_SIZE != %d\" % min_block_size)\nprint(\"#error Please correct UCD_BLOCK_SIZE in pcre2_internal.h\")\nprint(\"#endif\")\nprint(\"#endif /* SUPPORT_UNICODE */\")\nprint()\nprint(\"#endif /* PCRE2_PCRE2TEST */\")\n\n\n# This code was part of the original contribution, but is commented out as it\n# was never used. A two-stage table has sufficed.\n\n\"\"\"\n\n# Three-stage tables:\n\n# Find the optimum block size for 3-stage table\nmin_size = sys.maxint\nfor stage3_block in [2 ** i for i in range(2,6)]:\n stage_i, stage3 = compress_table(table, stage3_block)\n for stage2_block in [2 ** i for i in range(5,10)]:\n size = len(records) * 4\n stage1, stage2 = compress_table(stage_i, stage2_block)\n size += get_tables_size(stage1, stage2, stage3)\n # print \"/* %5d / %3d => %5d bytes */\" % (stage2_block, stage3_block, size)\n if size < min_size:\n min_size = size\n min_stage1, min_stage2, min_stage3 = stage1, stage2, stage3\n min_stage2_block, min_stage3_block = stage2_block, stage3_block\n\nprint \"/* Total size: %d bytes\" % min_size */\nprint_records(records)\nprint_table(min_stage1, 'ucd_stage1')\nprint_table(min_stage2, 'ucd_stage2', min_stage2_block)\nprint_table(min_stage3, 'ucd_stage3', min_stage3_block)\n\n\"\"\"\n"},"avg_line_length":{"kind":"number","value":42.724009901,"string":"42.72401"},"max_line_length":{"kind":"number","value":143,"string":"143"},"alphanum_fraction":{"kind":"number","value":0.6256481562,"string":"0.625648"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":22202,"string":"22,202"},"score_documentation":{"kind":"number","value":0.6431447524695113,"string":"0.643145"}}},{"rowIdx":3284,"cells":{"hexsha":{"kind":"string","value":"1314ffbb2b5a881e8cbdb62ecc8a53c659f4f382"},"size":{"kind":"number","value":638,"string":"638"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"setup.py"},"max_stars_repo_name":{"kind":"string","value":"ihayhurst/RetroBioCat"},"max_stars_repo_head_hexsha":{"kind":"string","value":"d674897459c0ab65faad5ed3017c55cf51bcc020"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":9,"string":"9"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2020-12-01T16:33:02.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-01-19T20:02:42.000Z"},"max_issues_repo_path":{"kind":"string","value":"setup.py"},"max_issues_repo_name":{"kind":"string","value":"ihayhurst/RetroBioCat"},"max_issues_repo_head_hexsha":{"kind":"string","value":"d674897459c0ab65faad5ed3017c55cf51bcc020"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":4,"string":"4"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-10-02T14:38:32.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-08-02T09:23:58.000Z"},"max_forks_repo_path":{"kind":"string","value":"setup.py"},"max_forks_repo_name":{"kind":"string","value":"ihayhurst/RetroBioCat"},"max_forks_repo_head_hexsha":{"kind":"string","value":"d674897459c0ab65faad5ed3017c55cf51bcc020"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":6,"string":"6"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-01-14T07:48:36.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-20T17:34:27.000Z"},"content":{"kind":"string","value":"from setuptools import setup, find_packages\nfrom retrobiocat_web import __version__\n\nwith open('requirements.txt') as f:\n requirements = f.read().splitlines()\n\nsetup(\n name = 'retrobiocat_web',\n packages = find_packages(),\n include_package_data=True,\n version = __version__,\n license='',\n description = 'Retrosynthesis',\n author = 'William Finnigan',\n author_email = 'wjafinnigan@gmail.com',\n url = '',\n download_url = '',\n keywords = ['enzyme'],\n install_requires=requirements,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3'],\n)"},"avg_line_length":{"kind":"number","value":26.5833333333,"string":"26.583333"},"max_line_length":{"kind":"number","value":45,"string":"45"},"alphanum_fraction":{"kind":"number","value":0.6943573668,"string":"0.694357"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":216,"string":"216"},"score_documentation":{"kind":"number","value":0.3385579937304075,"string":"0.338558"}}},{"rowIdx":3285,"cells":{"hexsha":{"kind":"string","value":"131631df01aa9316264d6c8b1aaa6ecfd1254785"},"size":{"kind":"number","value":1748,"string":"1,748"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py"},"max_stars_repo_name":{"kind":"string","value":"Lung-Yi/rxn_yield_context"},"max_stars_repo_head_hexsha":{"kind":"string","value":"116d6f21a1b6dc39016d87c001dc5b142cfb697a"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py"},"max_issues_repo_name":{"kind":"string","value":"Lung-Yi/rxn_yield_context"},"max_issues_repo_head_hexsha":{"kind":"string","value":"116d6f21a1b6dc39016d87c001dc5b142cfb697a"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py"},"max_forks_repo_name":{"kind":"string","value":"Lung-Yi/rxn_yield_context"},"max_forks_repo_head_hexsha":{"kind":"string","value":"116d6f21a1b6dc39016d87c001dc5b142cfb697a"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"# -*- coding: utf-8 -*-\r\n\r\nimport pickle\r\nimport numpy as np\r\nfrom rdkit import Chem\r\nfrom rdkit.Chem import AllChem,DataStructs\r\n\r\n\r\ndef get_classes(path):\r\n f = open(path, 'rb')\r\n dict_ = pickle.load(f)\r\n f.close()\r\n classes = sorted(dict_.items(), key=lambda d: d[1],reverse=True)\r\n classes = [(x,y) for x,y in classes]\r\n return classes\r\n\r\ndef create_rxn_Morgan2FP_concatenate(rsmi, psmi, rxnfpsize=16384, pfpsize=16384, useFeatures=False, calculate_rfp=True, useChirality=True):\r\n # Similar as the above function but takes smiles separately and returns pfp and rfp separately\r\n\r\n rsmi = rsmi.encode('utf-8')\r\n psmi = psmi.encode('utf-8')\r\n try:\r\n mol = Chem.MolFromSmiles(rsmi)\r\n except Exception as e:\r\n print(e)\r\n return\r\n try:\r\n fp_bit = AllChem.GetMorganFingerprintAsBitVect(\r\n mol=mol, radius=2, nBits=rxnfpsize, useFeatures=useFeatures, useChirality=useChirality)\r\n fp = np.empty(rxnfpsize, dtype='float32')\r\n DataStructs.ConvertToNumpyArray(fp_bit, fp)\r\n except Exception as e:\r\n print(\"Cannot build reactant fp due to {}\".format(e))\r\n return\r\n rfp = fp\r\n\r\n try:\r\n mol = Chem.MolFromSmiles(psmi)\r\n except Exception as e:\r\n return\r\n try:\r\n fp_bit = AllChem.GetMorganFingerprintAsBitVect(\r\n mol=mol, radius=2, nBits=pfpsize, useFeatures=useFeatures, useChirality=useChirality)\r\n fp = np.empty(pfpsize, dtype='float32')\r\n DataStructs.ConvertToNumpyArray(fp_bit, fp)\r\n except Exception as e:\r\n print(\"Cannot build product fp due to {}\".format(e))\r\n return\r\n pfp = fp\r\n rxn_fp = pfp - rfp\r\n final_fp = np.concatenate((pfp, rxn_fp))\r\n return final_fp"},"avg_line_length":{"kind":"number","value":33.6153846154,"string":"33.615385"},"max_line_length":{"kind":"number","value":140,"string":"140"},"alphanum_fraction":{"kind":"number","value":0.6424485126,"string":"0.642449"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":226,"string":"226"},"score_documentation":{"kind":"number","value":0.12929061784897025,"string":"0.129291"}}},{"rowIdx":3286,"cells":{"hexsha":{"kind":"string","value":"131665ba7b9465c31b9a3f7865c4b018c27a3aec"},"size":{"kind":"number","value":6434,"string":"6,434"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/webstruct-demo/__init__.py"},"max_stars_repo_name":{"kind":"string","value":"zanachka/webstruct-demo"},"max_stars_repo_head_hexsha":{"kind":"string","value":"f5b5081760d9a2b7924704041cd74748a5c98664"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":5,"string":"5"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-04-15T14:54:23.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2020-10-03T04:47:12.000Z"},"max_issues_repo_path":{"kind":"string","value":"src/webstruct-demo/__init__.py"},"max_issues_repo_name":{"kind":"string","value":"zanachka/webstruct-demo"},"max_issues_repo_head_hexsha":{"kind":"string","value":"f5b5081760d9a2b7924704041cd74748a5c98664"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":2,"string":"2"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-06-01T22:49:44.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-12-13T19:51:11.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/webstruct-demo/__init__.py"},"max_forks_repo_name":{"kind":"string","value":"zanachka/webstruct-demo"},"max_forks_repo_head_hexsha":{"kind":"string","value":"f5b5081760d9a2b7924704041cd74748a5c98664"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":3,"string":"3"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-06-25T10:31:30.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2020-10-03T04:49:01.000Z"},"content":{"kind":"string","value":"import functools\nimport logging\nimport random\n\nfrom flask import Flask, render_template, request\nimport joblib\nfrom lxml.html import html5parser\nimport lxml.html\nimport requests\nimport yarl\n\nimport webstruct.model\nimport webstruct.sequence_encoding\nimport webstruct.webannotator\n\n\nwebstruct_demo = Flask(__name__, instance_relative_config=True)\nwebstruct_demo.config.from_pyfile('config.py')\n\n\ndef absolutize_link(link, base_url):\n if link.startswith('#'):\n return link\n\n try:\n target_url = yarl.URL(link)\n except:\n return link\n\n if target_url.is_absolute() and target_url.scheme:\n return link\n\n if target_url.is_absolute() and not target_url.scheme:\n target_url = target_url.with_scheme(base_url.scheme)\n return str(target_url)\n\n try:\n target_url = base_url.join(target_url)\n except:\n return link\n\n return str(target_url)\n\n\ndef absolute_links(tree, url):\n _LINK_SOURCES = ['src', 'href']\n\n try:\n base_url = yarl.URL(url)\n except:\n return tree\n\n for _, element in lxml.html.etree.iterwalk(tree, events=('start', )):\n if not isinstance(element.tag, str):\n continue\n\n for attr in _LINK_SOURCES:\n if attr not in element.attrib:\n continue\n\n element.attrib[attr] = absolutize_link(element.attrib[attr], base_url)\n\n return tree\n\n\ndef parent_links(tree, base_url):\n base_url = yarl.URL(base_url)\n for _, element in lxml.html.etree.iterwalk(tree, events=('start', )):\n if not isinstance(element.tag, str):\n continue\n\n if element.tag != 'a':\n continue\n\n if 'href' not in element.attrib:\n continue\n\n url = element.attrib['href']\n\n if url.startswith('#'):\n continue\n\n element.attrib['target'] = '_parent'\n element.attrib['href'] = str(base_url.update_query(url=url))\n\n return tree\n\n\ndef remove_namespace(tree):\n _NS=\"{http://www.w3.org/1999/xhtml}\"\n for _, element in lxml.html.etree.iterwalk(tree, events=('start', )):\n if not isinstance(element.tag, str):\n continue\n if not element.tag.startswith(_NS):\n continue\n element.tag = element.tag[len(_NS):]\n\n return tree\n\n\n_TOKENS_PER_PART = 2000\ndef run_model(tree, model):\n html_tokens, _ = model.html_tokenizer.tokenize_single(tree)\n if not html_tokens:\n return tree, list(), list()\n tree = html_tokens[0].elem.getroottree().getroot()\n tags = model.model.predict([html_tokens[i:i+_TOKENS_PER_PART] for i in range(0, len(html_tokens), _TOKENS_PER_PART)])\n tags = [i for t in tags for i in t]\n return tree, html_tokens, tags\n\n\ndef download(url):\n splash_url = webstruct_demo.config.get('SPLASH_URL', None)\n splash_user = webstruct_demo.config.get('SPLASH_USER', None)\n splash_pass = webstruct_demo.config.get('SPLASH_PASS', None)\n\n is_splash = functools.reduce(lambda x,y: x and y is not None,\n [splash_url, splash_user, splash_pass],\n True)\n\n if not is_splash:\n response = requests.get(url)\n return response.content, response.url\n\n load = {'url': url,\n 'images': 0,\n 'base_url': url}\n response = requests.post(splash_url + '/render.html',\n json=load,\n auth=requests.auth.HTTPBasicAuth(splash_user, splash_pass))\n\n return response.content, url\n\n\ndef extract_ner(response_content, response_url, base_url):\n url = response_url\n tree = html5parser.document_fromstring(response_content)\n tree = remove_namespace(tree)\n tree = absolute_links(tree, url)\n tree = parent_links(tree, base_url)\n\n title = tree.xpath('//title')[0].text\n\n model = joblib.load(webstruct_demo.config['MODEL_PATH'])\n tree, tokens, tags = run_model(tree, model)\n tree = model.html_tokenizer.detokenize_single(tokens, tags)\n tree = webstruct.webannotator.to_webannotator(\n tree,\n entity_colors=model.entity_colors,\n url=url\n )\n content = lxml.html.tostring(tree, encoding='utf-8').decode('utf-8')\n entities = webstruct.sequence_encoding.IobEncoder.group(zip(tokens, tags))\n entities = webstruct.model._drop_empty(\n (model.build_entity(tokens), tag)\n for (tokens, tag) in entities if tag != 'O'\n )\n groups = webstruct.model.extract_entitiy_groups(\n tokens,\n tags,\n dont_penalize=None,\n join_tokens=model.build_entity\n )\n\n return content, title, entities, groups\n\n\ndef sample_entities(entities):\n unique = list(set(entities))\n random.shuffle(unique)\n sampled = unique[:5]\n sampled = sorted(sampled, key=lambda e:(e[1], e[0]))\n return sampled\n\n\ndef sample_groups(groups):\n groups = [tuple(sorted(g)) for g in groups]\n sampled = sorted(list(set(groups)), key=lambda g:-len(g))\n return sampled[:2]\n\n\n@webstruct_demo.route('/')\ndef index():\n url = request.args.get('url', 'http://en.wikipedia.org/')\n output = request.args.get('output', 'html')\n\n try:\n response_content, response_url = download(url)\n content, title, entities, groups = extract_ner(response_content,\n response_url,\n request.url)\n except:\n logging.exception('Got exception')\n content = None\n title = 'Error during obtaining %s' % (url, )\n entities = []\n groups = []\n\n _TEMPLATE_MAPPING = {'html': 'main.html',\n 'entities': 'entities.html',\n 'groups': 'groups.html'}\n\n template = _TEMPLATE_MAPPING.get(output, _TEMPLATE_MAPPING['html'])\n\n sampled_entities = sample_entities(entities)\n sampled_groups = sample_groups(groups)\n\n base_url = yarl.URL(request.url)\n routing = {t: str(base_url.update_query(output=t)) for t in ['html', 'entities', 'groups']}\n\n values = {'url': url,\n 'title': title,\n 'entities': entities,\n 'sampled_entities': sampled_entities,\n 'sampled_groups': sampled_groups,\n 'routing': routing,\n 'srcdoc': content,\n 'groups': groups,\n 'output': output}\n\n return render_template(template, **values)\n"},"avg_line_length":{"kind":"number","value":28.981981982,"string":"28.981982"},"max_line_length":{"kind":"number","value":121,"string":"121"},"alphanum_fraction":{"kind":"number","value":0.6176562014,"string":"0.617656"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":1421,"string":"1,421"},"score_decorators":{"kind":"number","value":0.2208579421821573,"string":"0.220858"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":504,"string":"504"},"score_documentation":{"kind":"number","value":0.07833385141436121,"string":"0.078334"}}},{"rowIdx":3287,"cells":{"hexsha":{"kind":"string","value":"13178607e92d499e0a8fa091130826ae93f57d37"},"size":{"kind":"number","value":757,"string":"757"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"setup.py"},"max_stars_repo_name":{"kind":"string","value":"Liang813/einops"},"max_stars_repo_head_hexsha":{"kind":"string","value":"9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":120,"string":"120"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2018-10-30T09:04:01.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-27T11:27:30.000Z"},"max_forks_repo_path":{"kind":"string","value":"setup.py"},"max_forks_repo_name":{"kind":"string","value":"Liang813/einops"},"max_forks_repo_head_hexsha":{"kind":"string","value":"9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":216,"string":"216"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2018-11-09T02:50:30.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-30T05:46:44.000Z"},"content":{"kind":"string","value":"__author__ = 'Alex Rogozhnikov'\n\nfrom setuptools import setup\n\nsetup(\n name=\"einops\",\n version='0.3.2',\n description=\"A new flavour of deep learning operations\",\n long_description=open('README.md', encoding='utf-8').read(),\n long_description_content_type='text/markdown',\n url='https://github.com/arogozhnikov/einops',\n author='Alex Rogozhnikov',\n\n packages=['einops', 'einops.layers'],\n\n classifiers=[\n 'Intended Audience :: Science/Research',\n 'Programming Language :: Python :: 3 ',\n ],\n keywords='deep learning, neural networks, tensor manipulation, machine learning, '\n 'scientific computations, einops',\n install_requires=[\n # no run-time or installation-time dependencies\n ],\n)\n"},"avg_line_length":{"kind":"number","value":29.1153846154,"string":"29.115385"},"max_line_length":{"kind":"number","value":86,"string":"86"},"alphanum_fraction":{"kind":"number","value":0.6684280053,"string":"0.668428"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":420,"string":"420"},"score_documentation":{"kind":"number","value":0.5548216644649934,"string":"0.554822"}}},{"rowIdx":3288,"cells":{"hexsha":{"kind":"string","value":"1317d06f323723e4a4f4ac9a34fb6dfc7aa40fb0"},"size":{"kind":"number","value":1146,"string":"1,146"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"website/migrations/0084_auto_20210215_1401.py"},"max_stars_repo_name":{"kind":"string","value":"czhu1217/cmimc-online"},"max_stars_repo_head_hexsha":{"kind":"string","value":"5ef49ceec0bb86d8ae120a6ecfd723532e277821"},"max_stars_repo_licenses":{"kind":"list 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\"MIT\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2022-01-23T21:08:12.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-01-23T21:08:12.000Z"},"max_forks_repo_path":{"kind":"string","value":"website/migrations/0084_auto_20210215_1401.py"},"max_forks_repo_name":{"kind":"string","value":"czhu1217/cmimc-online"},"max_forks_repo_head_hexsha":{"kind":"string","value":"5ef49ceec0bb86d8ae120a6ecfd723532e277821"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":1,"string":"1"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2021-10-17T17:11:42.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-10-17T17:11:42.000Z"},"content":{"kind":"string","value":"# Generated by Django 3.1.6 on 2021-02-15 19:01\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('website', '0083_remove_aisubmission_code'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='exam',\n name='division',\n field=models.IntegerField(default=1),\n preserve_default=False,\n ),\n migrations.CreateModel(\n name='ExamPair',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=100, unique=True)),\n ('contest', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='exampairs', to='website.contest')),\n ],\n ),\n migrations.AddField(\n model_name='exam',\n name='exampair',\n field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='exams', to='website.exampair'),\n ),\n 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like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2022-03-06T06:59:42.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-06T06:59:42.000Z"},"max_issues_repo_path":{"kind":"string","value":"ldp/tasks/dlp.py"},"max_issues_repo_name":{"kind":"string","value":"evandez/low-dimensional-probing"},"max_issues_repo_head_hexsha":{"kind":"string","value":"3e4af6644a4db7fdf48bc40c5de4815f9db52a6e"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"ldp/tasks/dlp.py"},"max_forks_repo_name":{"kind":"string","value":"evandez/low-dimensional-probing"},"max_forks_repo_head_hexsha":{"kind":"string","value":"3e4af6644a4db7fdf48bc40c5de4815f9db52a6e"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"Core experiments for the dependency label prediction task.\"\"\"\nimport collections\nimport copy\nimport logging\nfrom typing import (Any, Dict, Iterator, Optional, Sequence, Set, Tuple, Type,\n Union)\n\nfrom ldp import datasets, learning\nfrom ldp.models import probes, projections\nfrom ldp.parse import ptb\nfrom ldp.parse import representations as reps\nfrom ldp.utils.typing import Device\n\nimport numpy\nimport torch\nimport wandb\n\nUNK = 'unk'\n\n\nclass DLPIndexer:\n \"\"\"Map pairs of words to their syntactic relationship, if any.\"\"\"\n\n def __init__(self, samples: Sequence[ptb.Sample], unk: str = UNK):\n \"\"\"Map each relation label to an integer.\n\n Args:\n samples (Sequence[ptb.Sample]): The samples from which to determine\n possible relations.\n unk (str): Label to use when un-indexed dependency label is\n encountered.\n\n \"\"\"\n labels = {rel for sample in samples for rel in sample.relations}\n self.indexer = {unk: 0}\n for label in sorted(labels):\n self.indexer[label] = len(self.indexer)\n self.unk = unk\n\n def __call__(self, sample: ptb.Sample) -> torch.Tensor:\n \"\"\"Map all possible (word, word) pairs to labels.\n\n Args:\n sample (ptb.Sample): The sample to label.\n\n Returns:\n torch.Tensor: For length W sentence, returns shape (W, W) matrix\n where element (v, w) is the index of the label describing\n the relationship between word v and w, if any. Defaults to\n the \"unk\" label, even if there is no relationship between\n v and w.\n\n \"\"\"\n heads, relations = sample.heads, sample.relations\n labels = torch.empty(len(heads), len(heads), dtype=torch.long)\n labels.fill_(self.indexer[self.unk])\n for word, (head, rel) in enumerate(zip(heads, relations)):\n if head == -1:\n labels[word, word] = self.indexer[rel]\n else:\n label = self.indexer.get(rel, self.indexer[self.unk])\n labels[word, head] = label\n return labels\n\n def __len__(self) -> int:\n \"\"\"Return the number of unique labels for this task.\"\"\"\n return len(self.indexer)\n\n\nclass ControlDLPIndexer:\n \"\"\"Map pairs of words to arbitrary syntactic relationships.\"\"\"\n\n def __init__(self,\n samples: Sequence[ptb.Sample],\n dist: Optional[Union[numpy.ndarray, Sequence[float]]] = None):\n \"\"\"Map each relation label to an arbitrary (integer) label.\n\n We only do this for pairs of words which have a head-dependent\n relationship in the original dataset.\n\n Args:\n samples (Sequence[ptb.Samples]): The samples from which to pull\n possible word pairs.\n dist (Optional[Union[numpy.ndarray, Sequence[float]]], optional): A\n distribution to use when sampling tags per word type.\n By default, is computed from the list of samples.\n\n \"\"\"\n if dist is None:\n counts: Dict[str, int] = collections.defaultdict(lambda: 0)\n for sample in samples:\n for relation in sample.relations:\n counts[relation] += 1\n dist = numpy.array([float(count) for count in counts.values()])\n dist /= numpy.sum(dist)\n assert dist is not None, 'uninitialized distribution?'\n self.dist = dist\n\n self.rels: Dict[Tuple[str, str], int] = {}\n for sample in samples:\n sentence = sample.sentence\n heads = sample.heads\n for dep, head in enumerate(heads):\n if head == -1:\n head = dep\n words = (sentence[dep], sentence[head])\n if words not in self.rels:\n # Add one so that 0 is reserved for \"no relationship\" tag.\n rel = numpy.random.choice(len(dist), p=dist) + 1\n self.rels[words] = rel\n\n def __call__(self, sample: ptb.Sample) -> torch.Tensor:\n \"\"\"Map all possible (word, word) pairs to labels.\n\n Args:\n sample (ptb.Sample): The sample to label.\n\n Returns:\n torch.Tensor: For length W sentence, returns shape (W, W) matrix\n where element (v, w) is the index of the label describing\n the relationship between word v and w, if any. Defaults to\n the \"unk\" label, even if there is no relationship between\n v and w.\n\n \"\"\"\n heads = sample.heads\n labels = torch.zeros(len(heads), len(heads), dtype=torch.long)\n for dep, head in enumerate(heads):\n if head == -1:\n head = dep\n words = (sample.sentence[dep], sample.sentence[head])\n labels[dep, head] = self.rels.get(words, 0)\n return labels\n\n def __len__(self) -> int:\n \"\"\"Return the number of relationships, including the null one.\"\"\"\n return len(self.dist) + 1\n\n\nclass DLPTaskDataset(datasets.TaskDataset):\n \"\"\"Iterate over (word representation pair, dependency label) pairs.\"\"\"\n\n def __init__(\n self,\n representations: reps.RepresentationLayerDataset,\n annotations: Sequence[ptb.Sample],\n indexer: Type[Union[DLPIndexer, ControlDLPIndexer]] = DLPIndexer,\n **kwargs: Any,\n ):\n \"\"\"Initialize dataset by mapping each dependency label to an index.\n\n The kwargs are forwarded to indexer when it is instantiated.\n\n Args:\n representations (representations.RepresentationsLayerDataset): Word\n representations corresponding to the words to be paired and\n labeled.\n annotations (Sequence[ptb.PTBSample]): The PTB annotations from\n which to pull dependency labels.\n indexer (Union[DLPIndexer, ControlDLPIndexer]): Type of the indexer\n to use for mapping PTB dependency label annotations to integer\n tensors. Instantiated with given annotations unless the\n samples keyword is set in kwargs.\n\n Raises:\n ValueError: If number of representations/annotations do not match.\n\n \"\"\"\n if len(representations) != len(annotations):\n raise ValueError(f'got {len(representations)} representations '\n f'but {len(annotations)} annotations')\n\n self.representations = representations\n self.annotations = annotations\n\n kwargs = kwargs.copy()\n kwargs.setdefault('samples', annotations)\n self.indexer = indexer(**kwargs)\n\n def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Return (representations, integral POS tags) for index'th sentence.\n\n Args:\n index (int): Index of the sentence in the dataset.\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: First tensor is shape\n (sentence_length, representation_dimension) containing word\n representations, and second is shape (sentence_length,)\n containing integral POS tags.\n\n \"\"\"\n representations = self.representations[index]\n annotations = self.annotations[index]\n assert len(representations) == len(\n annotations.sentence), 'diff sentence lengths?'\n rels = self.indexer(annotations)\n\n # Find all pairs of words sharing an edge.\n indexes = set(range(len(representations)))\n pairs = [(i, j) for i in indexes for j in indexes if rels[i, j]]\n assert pairs and len(pairs) == len(representations), 'missing edges?'\n\n # Stack everything before returning it.\n bigrams = torch.stack([\n torch.stack((representations[i], representations[j]))\n for i, j in pairs\n ])\n labels = torch.stack([rels[i, j] for i, j in pairs])\n\n return bigrams, labels\n\n def __iter__(self) -> Iterator[Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"Yield all (sentence representations, sentence POS tags) samples.\"\"\"\n for index in range(len(self)):\n yield self[index]\n\n def __len__(self) -> int:\n \"\"\"Return the number of sentences (batches) in the dataset.\"\"\"\n return len(self.annotations)\n\n @property\n def sample_representations_shape(self) -> Sequence[int]:\n \"\"\"Return the dimensionality of the representation pairs.\"\"\"\n return (2, self.representations.dataset.dimension)\n\n @property\n def sample_features_shape(self) -> Sequence[int]:\n \"\"\"Return the shape of each individual POS tag.\n\n Since POS tags are integral scalars, there is no such shape!\n \"\"\"\n return ()\n\n def count_samples(self) -> int:\n \"\"\"Return the number of words in the dataset.\"\"\"\n return sum(\n self.representations.dataset.length(index)\n for index in range(len(self.representations)))\n\n def count_unique_features(self) -> int:\n \"\"\"Return number of unique POS seen in data.\"\"\"\n return len(self.indexer)\n\n\n# Define the valid probe types for this task.\nProbe = Union[probes.Linear, probes.MLP]\n\n\ndef train(train_dataset: datasets.TaskDataset,\n dev_dataset: datasets.TaskDataset,\n test_dataset: datasets.TaskDataset,\n probe_t: Type[Probe] = probes.Linear,\n project_to: Optional[int] = None,\n share_projection: bool = False,\n epochs: int = 25,\n patience: int = 4,\n lr: float = 1e-3,\n device: Optional[Device] = None,\n also_log_to_wandb: bool = False) -> Tuple[Probe, float]:\n \"\"\"Train a probe on dependency label prediction.\n\n Args:\n train_dataset (TaskDataset): Training data for probe.\n dev_dataset (TaskDataset): Validation data for probe, used for early\n stopping.\n test_dataset (TaskDataset): Test data for probe, used to compute\n final accuracy after training.\n probe_t (Type[Probe], optional): Probe type to train.\n Defaults to probes.Linear.\n project_to (Optional[int], optional): Project representations to this\n dimensionality. Defaults to no projection.\n share_projection (bool): If set, project the left and right components\n of pairwise probes with the same projection. E.g. if the probe is\n bilinear of the form xAy, we will always compute (Px)A(Py) as\n opposed to (Px)A(Qy) for distinct projections P, Q. Defaults to NOT\n shared.\n epochs (int, optional): Maximum passes through the training dataset.\n Defaults to 25.\n patience (int, optional): Allow dev loss to not improve for this many\n epochs, then stop training. Defaults to 4.\n lr (float, optional): Learning rate for optimizer. Defaults to 1e-3.\n device (Optional[Device], optional): Torch device on which to\n train probe. Defaults to CPU.\n also_log_to_wandb (Optional[pathlib.Path], optional): If set, log\n training data to wandb. By default, wandb is not used.\n\n Returns:\n Tuple[Probe, float]: The trained probe and its test accuracy.\n\n \"\"\"\n log = logging.getLogger(__name__)\n\n device = device or 'cpu'\n\n ndims = train_dataset.sample_representations_shape[-1]\n log.info('representations have dimension %d', ndims)\n\n ntags = train_dataset.count_unique_features()\n assert ntags is not None, 'no label count, is dataset for different task?'\n log.info('dependency labeling task has %d tags', ntags)\n\n if project_to is None or ndims == project_to:\n logging.info('projection dim = reps dim, not projecting')\n projection = None\n elif share_projection:\n projection = projections.Projection(ndims, project_to)\n else:\n projection = projections.Projection(2 * ndims, 2 * project_to)\n\n probe = probe_t(2 * (project_to or ndims), ntags, project=projection)\n learning.train(probe,\n train_dataset,\n dev_dataset=dev_dataset,\n stopper=learning.EarlyStopping(patience=patience),\n epochs=epochs,\n lr=lr,\n device=device,\n also_log_to_wandb=also_log_to_wandb)\n accuracy = learning.test(probe, test_dataset, device=device)\n return probe, accuracy\n\n\n# TODO(evandez): May as well commonize this, since it's shared with POS.\ndef axis_alignment(\n probe: Probe,\n dev_dataset: datasets.TaskDataset,\n test_dataset: datasets.TaskDataset,\n device: Optional[Device] = None,\n also_log_to_wandb: bool = False) -> Sequence[Tuple[int, float]]:\n \"\"\"Measure whether the given probe is axis aligned.\n\n Args:\n probe (Probe): The probe to evaluate.\n dev_dataset (datasets.TaskDataset): Data used to determine which axes\n to cut.\n test_dataset (datasets.TaskDataset): Data used to determine the effect\n of cutting an axis.\n device (Optional[Device], optional): Torch device on which to\n train probe. Defaults to CPU.\n also_log_to_wandb (bool, optional): If set, log results to wandb.\n\n Returns:\n Sequence[Tuple[int, float]]: The ablated axes paired with optimal probe\n accuracy after that axis is zeroed.\n\n \"\"\"\n log = logging.getLogger(__name__)\n\n projection = probe.project\n assert projection is not None, 'no projection?'\n\n axes = set(range(projection.project.in_features))\n ablated: Set[int] = set()\n accuracies = []\n while axes:\n best_model, best_axis, best_accuracy = probe, -1, -1.\n for axis in axes:\n model = copy.deepcopy(best_model).eval()\n assert model.project is not None, 'no projection?'\n model.project.project.weight.data[:, sorted(ablated | {axis})] = 0\n accuracy = learning.test(model, dev_dataset, device=device)\n if accuracy > best_accuracy:\n best_model = model\n best_axis = axis\n best_accuracy = accuracy\n accuracy = learning.test(best_model, test_dataset, device=device)\n\n log.info('ablating axis %d, test accuracy %f', best_axis, accuracy)\n if also_log_to_wandb:\n wandb.log({\n 'axis': best_axis,\n 'dev accuracy': best_accuracy,\n 'test accuracy': accuracy,\n })\n\n axes.remove(best_axis)\n ablated.add(best_axis)\n accuracies.append((best_axis, accuracy))\n\n return tuple(accuracies)\n"},"avg_line_length":{"kind":"number","value":38.3952879581,"string":"38.395288"},"max_line_length":{"kind":"number","value":79,"string":"79"},"alphanum_fraction":{"kind":"number","value":0.614986023,"string":"0.614986"},"count_classes":{"kind":"number","value":8684,"string":"8,684"},"score_classes":{"kind":"number","value":0.592077452785164,"string":"0.592077"},"count_generators":{"kind":"number","value":214,"string":"214"},"score_generators":{"kind":"number","value":0.014590577486875297,"string":"0.014591"},"count_decorators":{"kind":"number","value":417,"string":"417"},"score_decorators":{"kind":"number","value":0.028431172018817754,"string":"0.028431"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":6764,"string":"6,764"},"score_documentation":{"kind":"number","value":0.46117133701506785,"string":"0.461171"}}},{"rowIdx":3290,"cells":{"hexsha":{"kind":"string","value":"1318a68dfab5df9c8cd4a02ab91e86cccb7f469d"},"size":{"kind":"number","value":23613,"string":"23,613"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"pycquery_krb/common/ccache.py"},"max_stars_repo_name":{"kind":"string","value":"naver/PyCQuery"},"max_stars_repo_head_hexsha":{"kind":"string","value":"a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2021-11-17T03:13:16.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-12-03T05:30:22.000Z"},"max_issues_repo_path":{"kind":"string","value":"pycquery_krb/common/ccache.py"},"max_issues_repo_name":{"kind":"string","value":"naver/PyCQuery"},"max_issues_repo_head_hexsha":{"kind":"string","value":"a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":1,"string":"1"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2021-05-04T06:02:40.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-05-04T06:02:40.000Z"},"max_forks_repo_path":{"kind":"string","value":"pycquery_krb/common/ccache.py"},"max_forks_repo_name":{"kind":"string","value":"naver/PyCQuery"},"max_forks_repo_head_hexsha":{"kind":"string","value":"a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/usr/bin/env python3\n#\n# Author:\n# Tamas Jos (@skelsec)\n#\n\nimport os\nimport io\nimport datetime\nimport glob\nimport hashlib\n\nfrom pycquery_krb.protocol.asn1_structs import Ticket, EncryptedData, \\\n\tkrb5_pvno, KrbCredInfo, EncryptionKey, KRBCRED, TicketFlags, EncKrbCredPart\nfrom pycquery_krb.common.utils import dt_to_kerbtime, TGSTicket2hashcat\nfrom pycquery_krb.protocol.constants import EncryptionType, MESSAGE_TYPE\nfrom pycquery_krb import logger\nfrom asn1crypto import core\n\n\n\n# http://repo.or.cz/w/krb5dissect.git/blob_plain/HEAD:/ccache.txt\nclass Header:\n\tdef __init__(self):\n\t\tself.tag = None\n\t\tself.taglen = None\n\t\tself.tagdata = None\n\n\t@staticmethod\n\tdef parse(data):\n\t\t\"\"\"\n\t\treturns a list of header tags\n\t\t\"\"\"\n\t\treader = io.BytesIO(data)\n\t\theaders = []\n\t\twhile reader.tell() < len(data):\n\t\t\th = Header()\n\t\t\th.tag = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\t\th.taglen = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\t\th.tagdata = reader.read(h.taglen)\n\t\t\theaders.append(h)\n\t\treturn headers\n\n\tdef to_bytes(self):\n\t\tt = self.tag.to_bytes(2, byteorder='big', signed=False)\n\t\tt += len(self.tagdata).to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.tagdata\n\t\treturn t\n\n\tdef __str__(self):\n\t\tt = 'tag: %s\\n' % self.tag\n\t\tt += 'taglen: %s\\n' % self.taglen\n\t\tt += 'tagdata: %s\\n' % self.tagdata\n\t\treturn t\n\nclass DateTime:\n\tdef __init__(self):\n\t\tself.time_offset = None\n\t\tself.usec_offset = None\n\n\t@staticmethod\n\tdef parse(reader):\n\t\td = DateTime()\n\t\td.time_offset = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\td.usec_offset = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\treturn d\n\n\tdef to_bytes(self):\n\t\tt = self.time_offset.to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.usec_offset.to_bytes(4, byteorder='big', signed=False)\n\t\treturn t\n\n\n\nclass Credential:\n\tdef __init__(self):\n\t\tself.client = None\n\t\tself.server = None\n\t\tself.key = None\n\t\tself.time = None\n\t\tself.is_skey = None\n\t\tself.tktflags = None\n\t\tself.num_address = None\n\t\tself.addrs = []\n\t\tself.num_authdata = None\n\t\tself.authdata = []\n\t\tself.ticket = None\n\t\tself.second_ticket = None\n\n\tdef to_hash(self):\n\t\tres = Ticket.load(self.ticket.to_asn1()).native\n\t\ttgs_encryption_type = int(res['enc-part']['etype'])\n\t\tt = len(res['sname']['name-string'])\n\t\tif t == 1:\n\t\t\ttgs_name_string = res['sname']['name-string'][0]\n\t\telse:\n\t\t\ttgs_name_string = res['sname']['name-string'][1]\n\t\ttgs_realm = res['realm']\n\t\tif tgs_encryption_type == EncryptionType.AES256_CTS_HMAC_SHA1_96.value:\n\t\t\ttgs_checksum = res['enc-part']['cipher'][-12:]\n\t\t\ttgs_encrypted_data2 = res['enc-part']['cipher'][:-12]\n\t\t\treturn '$krb5tgs$%s$%s$%s$%s$%s' % (tgs_encryption_type,tgs_name_string,tgs_realm, tgs_checksum.hex(), tgs_encrypted_data2.hex() )\n\t\telse:\n\t\t\ttgs_checksum = res['enc-part']['cipher'][:16]\n\t\t\ttgs_encrypted_data2 = res['enc-part']['cipher'][16:]\n\t\t\treturn '$krb5tgs$%s$*%s$%s$spn*$%s$%s' % (tgs_encryption_type,tgs_name_string,tgs_realm, tgs_checksum.hex(), tgs_encrypted_data2.hex() )\n\n\tdef to_tgt(self):\n\t\t\"\"\"\n\t\tReturns the native format of an AS_REP message and the sessionkey in EncryptionKey native format\n\t\t\"\"\"\n\t\tenc_part = EncryptedData({'etype': 1, 'cipher': b''})\n\n\t\ttgt_rep = {}\n\t\ttgt_rep['pvno'] = krb5_pvno\n\t\ttgt_rep['msg-type'] = MESSAGE_TYPE.KRB_AS_REP.value\n\t\ttgt_rep['crealm'] = self.server.realm.to_string()\n\t\ttgt_rep['cname'] = self.client.to_asn1()[0]\n\t\ttgt_rep['ticket'] = Ticket.load(self.ticket.to_asn1()).native\n\t\ttgt_rep['enc-part'] = enc_part.native\n\n\t\tt = EncryptionKey(self.key.to_asn1()).native\n\n\t\treturn tgt_rep, t\n\n\tdef to_tgs(self):\n\t\t\"\"\"\n\t\tReturns the native format of an AS_REP message and the sessionkey in EncryptionKey native format\n\t\t\"\"\"\n\t\tenc_part = EncryptedData({'etype': 1, 'cipher': b''})\n\n\t\ttgt_rep = {}\n\t\ttgt_rep['pvno'] = krb5_pvno\n\t\ttgt_rep['msg-type'] = MESSAGE_TYPE.KRB_AS_REP.value\n\t\ttgt_rep['crealm'] = self.server.realm.to_string()\n\t\ttgt_rep['cname'] = self.client.to_asn1()[0]\n\t\ttgt_rep['ticket'] = Ticket.load(self.ticket.to_asn1()).native\n\t\ttgt_rep['enc-part'] = enc_part.native\n\n\t\tt = EncryptionKey(self.key.to_asn1()).native\n\n\t\treturn tgt_rep, t\n\n\tdef to_kirbi(self):\n\t\tfilename = '%s@%s_%s' % (self.client.to_string() , self.server.to_string(), hashlib.sha1(self.ticket.to_asn1()).hexdigest()[:8])\n\t\tkrbcredinfo = {}\n\t\tkrbcredinfo['key'] = EncryptionKey(self.key.to_asn1())\n\t\tkrbcredinfo['prealm'] = self.client.realm.to_string()\n\t\tkrbcredinfo['pname'] = self.client.to_asn1()[0]\n\t\tkrbcredinfo['flags'] = core.IntegerBitString(self.tktflags).cast(TicketFlags)\n\t\tif self.time.authtime != 0: #this parameter is not mandatory, and most of the time not present\n\t\t\tkrbcredinfo['authtime'] = datetime.datetime.fromtimestamp(self.time.authtime, datetime.timezone.utc)\n\t\tif self.time.starttime != 0:\n\t\t\tkrbcredinfo['starttime'] = datetime.datetime.fromtimestamp(self.time.starttime, datetime.timezone.utc)\n\t\tif self.time.endtime != 0:\n\t\t\tkrbcredinfo['endtime'] = datetime.datetime.fromtimestamp(self.time.endtime, datetime.timezone.utc)\n\t\tif self.time.renew_till != 0: #this parameter is not mandatory, and sometimes it's not present\n\t\t\tkrbcredinfo['renew-till'] = datetime.datetime.fromtimestamp(self.time.authtime, datetime.timezone.utc)\n\t\tkrbcredinfo['srealm'] = self.server.realm.to_string()\n\t\tkrbcredinfo['sname'] = self.server.to_asn1()[0]\n\n\t\tenc_krbcred = {}\n\t\tenc_krbcred['ticket-info'] = [KrbCredInfo(krbcredinfo)]\n\n\t\tkrbcred = {}\n\t\tkrbcred['pvno'] = krb5_pvno\n\t\tkrbcred['msg-type'] = MESSAGE_TYPE.KRB_CRED.value\n\t\tkrbcred['tickets'] = [Ticket.load(self.ticket.to_asn1())]\n\t\tkrbcred['enc-part'] = EncryptedData({'etype': EncryptionType.NULL.value, 'cipher': EncKrbCredPart(enc_krbcred).dump()})\n\n\n\n\t\tkirbi = KRBCRED(krbcred)\n\t\treturn kirbi, filename\n\n\t@staticmethod\n\tdef from_asn1(ticket, data):\n\t\t###\n\t\t# data = KrbCredInfo\n\t\t###\n\t\tc = Credential()\n\t\tc.client = CCACHEPrincipal.from_asn1(data['pname'], data['prealm'])\n\t\tc.server = CCACHEPrincipal.from_asn1(data['sname'], data['srealm'])\n\t\tc.key = Keyblock.from_asn1(data['key'])\n\t\tc.is_skey = 0 #not sure!\n\n\t\tc.tktflags = TicketFlags(data['flags']).cast(core.IntegerBitString).native\n\t\tc.num_address = 0\n\t\tc.num_authdata = 0\n\t\tc.ticket = CCACHEOctetString.from_asn1(ticket['enc-part']['cipher'])\n\t\tc.second_ticket = CCACHEOctetString.empty()\n\t\treturn c\n\n\t@staticmethod\n\tdef parse(reader):\n\t\tc = Credential()\n\t\tc.client = CCACHEPrincipal.parse(reader)\n\t\tc.server = CCACHEPrincipal.parse(reader)\n\t\tc.key = Keyblock.parse(reader)\n\t\tc.time = Times.parse(reader)\n\t\tc.is_skey = int.from_bytes(reader.read(1), byteorder='big', signed=False)\n\t\tc.tktflags = int.from_bytes(reader.read(4), byteorder='little', signed=False)\n\t\tc.num_address = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tfor _ in range(c.num_address):\n\t\t\tc.addrs.append(Address.parse(reader))\n\t\tc.num_authdata = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tfor _ in range(c.num_authdata):\n\t\t\tc.authdata.append(Authdata.parse(reader))\n\t\tc.ticket = CCACHEOctetString.parse(reader)\n\t\tc.second_ticket = CCACHEOctetString.parse(reader)\n\t\treturn c\n\n\t@staticmethod\n\tdef summary_header():\n\t\treturn ['client','server','starttime','endtime','renew-till']\n\n\tdef summary(self):\n\t\treturn [\n\t\t\t'%s@%s' % (self.client.to_string(separator='/'), self.client.realm.to_string()),\n\t\t\t'%s@%s' % (self.server.to_string(separator='/'), self.server.realm.to_string()),\n\t\t\tdatetime.datetime.fromtimestamp(self.time.starttime).isoformat() if self.time.starttime != 0 else 'N/A',\n\t\t\tdatetime.datetime.fromtimestamp(self.time.endtime).isoformat() if self.time.endtime != 0 else 'N/A',\n\t\t\tdatetime.datetime.fromtimestamp(self.time.renew_till).isoformat() if self.time.renew_till != 0 else 'N/A',\n\n\t\t]\n\n\tdef to_bytes(self):\n\t\tt = self.client.to_bytes()\n\t\tt += self.server.to_bytes()\n\t\tt += self.key.to_bytes()\n\t\tt += self.time.to_bytes()\n\t\tt += self.is_skey.to_bytes(1, byteorder='big', signed=False)\n\t\tt += self.tktflags.to_bytes(4, byteorder='little', signed=False)\n\t\tt += self.num_address.to_bytes(4, byteorder='big', signed=False)\n\t\tfor addr in self.addrs:\n\t\t\tt += addr.to_bytes()\n\t\tt += self.num_authdata.to_bytes(4, byteorder='big', signed=False)\n\t\tfor ad in self.authdata:\n\t\t\tt += ad.to_bytes()\n\t\tt += self.ticket.to_bytes()\n\t\tt += self.second_ticket.to_bytes()\n\t\treturn t\n\nclass Keyblock:\n\tdef __init__(self):\n\t\tself.keytype = None\n\t\tself.etype = None\n\t\tself.keylen = None\n\t\tself.keyvalue = None\n\n\t@staticmethod\n\tdef from_asn1(data):\n\t\tk = Keyblock()\n\t\tk.keytype = data['keytype']\n\t\tk.etype = 0 # not sure\n\t\tk.keylen = len(data['keyvalue'])\n\t\tk.keyvalue = data['keyvalue']\n\n\t\treturn k\n\n\tdef to_asn1(self):\n\t\tt = {}\n\t\tt['keytype'] = self.keytype\n\t\tt['keyvalue'] = self.keyvalue\n\n\t\treturn t\n\n\t@staticmethod\n\tdef parse(reader):\n\t\tk = Keyblock()\n\t\tk.keytype = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\tk.etype = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\tk.keylen = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\tk.keyvalue = reader.read(k.keylen)\n\t\treturn k\n\n\tdef to_bytes(self):\n\t\tt = self.keytype.to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.etype.to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.keylen.to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.keyvalue\n\t\treturn t\n\n\nclass Times:\n\tdef __init__(self):\n\t\tself.authtime = None\n\t\tself.starttime = None\n\t\tself.endtime = None\n\t\tself.renew_till = None\n\n\t@staticmethod\n\tdef from_asn1(enc_as_rep_part):\n\t\tt = Times()\n\t\tt.authtime = dt_to_kerbtime(enc_as_rep_part['authtime']) \\\n\t\t\tif 'authtime' in enc_as_rep_part and enc_as_rep_part['authtime'] else 0\n\t\tt.starttime = dt_to_kerbtime(enc_as_rep_part['starttime']) \\\n\t\t\tif 'starttime' in enc_as_rep_part and enc_as_rep_part['starttime'] else 0\n\t\tt.endtime = dt_to_kerbtime(enc_as_rep_part['endtime']) \\\n\t\t\tif 'endtime' in enc_as_rep_part and enc_as_rep_part['endtime'] else 0\n\t\tt.renew_till = dt_to_kerbtime(enc_as_rep_part['renew_till']) \\\n\t\t\tif 'renew_till' in enc_as_rep_part and enc_as_rep_part['renew_till'] else 0\n\t\treturn t\n\n\t@staticmethod\n\tdef dummy_time(start= datetime.datetime.now(datetime.timezone.utc)):\n\t\tt = Times()\n\t\tt.authtime = dt_to_kerbtime(start)\n\t\tt.starttime = dt_to_kerbtime(start )\n\t\tt.endtime = dt_to_kerbtime(start + datetime.timedelta(days=1))\n\t\tt.renew_till = dt_to_kerbtime(start + datetime.timedelta(days=2))\n\t\treturn t\n\n\t@staticmethod\n\tdef parse(reader):\n\t\tt = Times()\n\t\tt.authtime = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tt.starttime = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tt.endtime = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tt.renew_till = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\treturn t\n\n\tdef to_bytes(self):\n\t\tt = self.authtime.to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.starttime.to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.endtime.to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.renew_till.to_bytes(4, byteorder='big', signed=False)\n\t\treturn t\n\nclass Address:\n\tdef __init__(self):\n\t\tself.addrtype = None\n\t\tself.addrdata = None\n\n\t@staticmethod\n\tdef parse(reader):\n\t\ta = Address()\n\t\ta.addrtype = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\ta.addrdata = CCACHEOctetString.parse(reader)\n\t\treturn a\n\n\tdef to_bytes(self):\n\t\tt = self.addrtype.to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.addrdata.to_bytes()\n\t\treturn t\n\nclass Authdata:\n\tdef __init__(self):\n\t\tself.authtype = None\n\t\tself.authdata = None\n\n\t@staticmethod\n\tdef parse(reader):\n\t\ta = Authdata()\n\t\ta.authtype = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\ta.authdata = CCACHEOctetString.parse(reader)\n\t\treturn a\n\n\tdef to_bytes(self):\n\t\tt = self.authtype.to_bytes(2, byteorder='big', signed=False)\n\t\tt += self.authdata.to_bytes()\n\t\treturn t\n\nclass CCACHEPrincipal:\n\tdef __init__(self):\n\t\tself.name_type = None\n\t\tself.num_components = None\n\t\tself.realm = None\n\t\tself.components = []\n\n\t@staticmethod\n\tdef from_asn1(principal, realm):\n\t\tp = CCACHEPrincipal()\n\t\tp.name_type = principal['name-type']\n\t\tp.num_components = len(principal['name-string'])\n\t\tp.realm = CCACHEOctetString.from_string(realm)\n\t\tfor comp in principal['name-string']:\n\t\t\tp.components.append(CCACHEOctetString.from_asn1(comp))\n\n\t\treturn p\n\n\t@staticmethod\n\tdef dummy():\n\t\tp = CCACHEPrincipal()\n\t\tp.name_type = 1\n\t\tp.num_components = 1\n\t\tp.realm = CCACHEOctetString.from_string('kerbi.corp')\n\t\tfor _ in range(1):\n\t\t\tp.components.append(CCACHEOctetString.from_string('kerbi'))\n\n\t\treturn p\n\n\tdef to_string(self, separator='-'):\n\t\treturn separator.join([c.to_string() for c in self.components])\n\n\tdef to_asn1(self):\n\t\tt = {'name-type': self.name_type, 'name-string': [name.to_string() for name in self.components]}\n\t\treturn t, self.realm.to_string()\n\n\t@staticmethod\n\tdef parse(reader):\n\t\tp = CCACHEPrincipal()\n\t\tp.name_type = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tp.num_components = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\tp.realm = CCACHEOctetString.parse(reader)\n\t\tfor _ in range(p.num_components):\n\t\t\tp.components.append(CCACHEOctetString.parse(reader))\n\t\treturn p\n\n\tdef to_bytes(self):\n\t\tt = self.name_type.to_bytes(4, byteorder='big', signed=False)\n\t\tt += len(self.components).to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.realm.to_bytes()\n\t\tfor com in self.components:\n\t\t\tt += com.to_bytes()\n\t\treturn t\n\nclass CCACHEOctetString:\n\tdef __init__(self):\n\t\tself.length = None\n\t\tself.data = None\n\n\t@staticmethod\n\tdef empty():\n\t\to = CCACHEOctetString()\n\t\to.length = 0\n\t\to.data = b''\n\t\treturn o\n\n\tdef to_asn1(self):\n\t\treturn self.data\n\n\tdef to_string(self):\n\t\treturn self.data.decode()\n\n\t@staticmethod\n\tdef from_string(data):\n\t\to = CCACHEOctetString()\n\t\to.data = data.encode()\n\t\to.length = len(o.data)\n\t\treturn o\n\n\t@staticmethod\n\tdef from_asn1(data):\n\t\to = CCACHEOctetString()\n\t\to.length = len(data)\n\t\tif isinstance(data,str):\n\t\t\to.data = data.encode()\n\t\telse:\n\t\t\to.data = data\n\t\treturn o\n\n\t@staticmethod\n\tdef parse(reader):\n\t\to = CCACHEOctetString()\n\t\to.length = int.from_bytes(reader.read(4), byteorder='big', signed=False)\n\t\to.data = reader.read(o.length)\n\t\treturn o\n\n\tdef to_bytes(self):\n\t\tif isinstance(self.data,str):\n\t\t\tself.data = self.data.encode()\n\t\t\tself.length = len(self.data)\n\t\tt = len(self.data).to_bytes(4, byteorder='big', signed=False)\n\t\tt += self.data\n\t\treturn t\n\n\nclass CCACHE:\n\t\"\"\"\n\tAs the header is rarely used -mostly static- you'd need to init this object with empty = True to get an object without header already present\n\t\"\"\"\n\tdef __init__(self, empty = False):\n\t\tself.file_format_version = None #0x0504\n\t\tself.headers = []\n\t\tself.primary_principal = None\n\t\tself.credentials = []\n\n\t\tif empty == False:\n\t\t\tself.__setup()\n\n\tdef __setup(self):\n\t\tself.file_format_version = 0x0504\n\n\t\theader = Header()\n\t\theader.tag = 1\n\t\theader.taglen = 8\n\t\t#header.tagdata = b'\\xff\\xff\\xff\\xff\\x00\\x00\\x00\\x00'\n\t\theader.tagdata = b'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n\t\tself.headers.append(header)\n\n\t\t#t_hdr = b''\n\t\t#for header in self.headers:\n\t\t#\tt_hdr += header.to_bytes()\n\t\t#self.headerlen = 1 #size of the entire header in bytes, encoded in 2 byte big-endian unsigned int\n\n\t\tself.primary_principal = CCACHEPrincipal.dummy()\n\n\tdef __str__(self):\n\t\tt = '== CCACHE ==\\n'\n\t\tt+= 'file_format_version : %s\\n' % self.file_format_version\n\t\tfor header in self.headers:\n\t\t\tt+= '%s\\n' % header\n\t\tt+= 'primary_principal : %s\\n' % self.primary_principal\n\t\treturn t\n\n\tdef add_tgt(self, as_rep, enc_as_rep_part, override_pp = True): #from AS_REP\n\t\t\"\"\"\n\t\tCreates credential object from the TGT and adds to the ccache file\n\t\tThe TGT is basically the native representation of the asn1 encoded AS_REP data that the AD sends upon a succsessful TGT request.\n\n\t\tThis function doesn't do decryption of the encrypted part of the as_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part\n\n\t\toverride_pp: bool to determine if client principal should be used as the primary principal for the ccache file\n\t\t\"\"\"\n\t\tc = Credential()\n\t\tc.client = CCACHEPrincipal.from_asn1(as_rep['cname'], as_rep['crealm'])\n\t\tif override_pp == True:\n\t\t\tself.primary_principal = c.client\n\t\tc.server = CCACHEPrincipal.from_asn1(enc_as_rep_part['sname'], enc_as_rep_part['srealm'])\n\t\tc.time = Times.from_asn1(enc_as_rep_part)\n\t\tc.key = Keyblock.from_asn1(enc_as_rep_part['key'])\n\t\tc.is_skey = 0 #not sure!\n\n\t\tc.tktflags = TicketFlags(enc_as_rep_part['flags']).cast(core.IntegerBitString).native\n\t\tc.num_address = 0\n\t\tc.num_authdata = 0\n\t\tc.ticket = CCACHEOctetString.from_asn1(Ticket(as_rep['ticket']).dump())\n\t\tc.second_ticket = CCACHEOctetString.empty()\n\n\t\tself.credentials.append(c)\n\n\tdef add_tgs(self, tgs_rep, enc_tgs_rep_part, override_pp = False): #from AS_REP\n\t\t\"\"\"\n\t\tCreates credential object from the TGS and adds to the ccache file\n\t\tThe TGS is the native representation of the asn1 encoded TGS_REP data when the user requests a tgs to a specific service principal with a valid TGT\n\n\t\tThis function doesn't do decryption of the encrypted part of the tgs_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part\n\n\t\toverride_pp: bool to determine if client principal should be used as the primary principal for the ccache file\n\t\t\"\"\"\n\t\tc = Credential()\n\t\tc.client = CCACHEPrincipal.from_asn1(tgs_rep['cname'], tgs_rep['crealm'])\n\t\tif override_pp == True:\n\t\t\tself.primary_principal = c.client\n\t\tc.server = CCACHEPrincipal.from_asn1(enc_tgs_rep_part['sname'], enc_tgs_rep_part['srealm'])\n\t\tc.time = Times.from_asn1(enc_tgs_rep_part)\n\t\tc.key = Keyblock.from_asn1(enc_tgs_rep_part['key'])\n\t\tc.is_skey = 0 #not sure!\n\n\t\tc.tktflags = TicketFlags(enc_tgs_rep_part['flags']).cast(core.IntegerBitString).native\n\t\tc.num_address = 0\n\t\tc.num_authdata = 0\n\t\tc.ticket = CCACHEOctetString.from_asn1(Ticket(tgs_rep['ticket']).dump())\n\t\tc.second_ticket = CCACHEOctetString.empty()\n\n\t\tself.credentials.append(c)\n\n\n\tdef add_kirbi(self, krbcred, override_pp = True, include_expired = False):\n\t\tc = Credential()\n\t\tenc_credinfo = EncKrbCredPart.load(krbcred['enc-part']['cipher']).native\n\t\tticket_info = enc_credinfo['ticket-info'][0]\n\n\t\t\"\"\"\n\t\tif ticket_info['endtime'] < datetime.datetime.now(datetime.timezone.utc):\n\t\t\tif include_expired == True:\n\t\t\t\tlogging.debug('This ticket has most likely expired, but include_expired is forcing me to add it to cache! This can cause problems!')\n\t\t\telse:\n\t\t\t\tlogging.debug('This ticket has most likely expired, skipping')\n\t\t\t\treturn\n\t\t\"\"\"\n\n\t\tc.client = CCACHEPrincipal.from_asn1(ticket_info['pname'], ticket_info['prealm'])\n\t\tif override_pp == True:\n\t\t\tself.primary_principal = c.client\n\n\t\t#yaaaaay 4 additional weirdness!!!!\n\t\t#if sname name-string contains a realm as well htne impacket will crash miserably :(\n\t\tif len(ticket_info['sname']['name-string']) > 2 and ticket_info['sname']['name-string'][-1].upper() == ticket_info['srealm'].upper():\n\t\t\tlogger.debug('SNAME contains the realm as well, trimming it')\n\t\t\tt = ticket_info['sname']\n\t\t\tt['name-string'] = t['name-string'][:-1]\n\t\t\tc.server = CCACHEPrincipal.from_asn1(t, ticket_info['srealm'])\n\t\telse:\n\t\t\tc.server = CCACHEPrincipal.from_asn1(ticket_info['sname'], ticket_info['srealm'])\n\n\n\t\tc.time = Times.from_asn1(ticket_info)\n\t\tc.key = Keyblock.from_asn1(ticket_info['key'])\n\t\tc.is_skey = 0 #not sure!\n\n\t\tc.tktflags = TicketFlags(ticket_info['flags']).cast(core.IntegerBitString).native\n\t\tc.num_address = 0\n\t\tc.num_authdata = 0\n\t\tc.ticket = CCACHEOctetString.from_asn1(Ticket(krbcred['tickets'][0]).dump()) #kirbi only stores one ticket per file\n\t\tc.second_ticket = CCACHEOctetString.empty()\n\n\t\tself.credentials.append(c)\n\n\t@staticmethod\n\tdef from_kirbi(kirbidata):\n\t\tkirbi = KRBCRED.load(kirbidata).native\n\t\tcc = CCACHE()\n\t\tcc.add_kirbi(kirbi)\n\t\treturn cc\n\n\tdef get_all_tgt(self):\n\t\t\"\"\"\n\t\tReturns a list of AS_REP tickets in native format (dict).\n\t\tTo determine which ticket are AP_REP we check for the server principal to be the kerberos service\n\t\t\"\"\"\n\t\ttgts = []\n\t\tfor cred in self.credentials:\n\t\t\tif cred.server.to_string(separator='/').lower().find('krbtgt') != -1:\n\t\t\t\ttgt = [cred.to_tgt(), cred.time]\n\t\t\t\ttgts.append(tgt)\n\n\t\treturn tgts\n\n\tdef get_all_tgs(self):\n\t\ttgss = []\n\t\tfor cred in self.credentials:\n\t\t\tif cred.server.to_string(separator = '/').lower().find('krbtgt') == -1:\n\t\t\t\ttgss.append(cred.to_tgs())\n\n\t\treturn tgss\n\n\tdef get_hashes(self, all_hashes = False):\n\t\t\"\"\"\n\t\tReturns a list of hashes in hashcat-firendly format for tickets with encryption type 23 (which is RC4)\n\t\tall_hashes: overrides the encryption type filtering and returns hash for all tickets\n\n\t\t\"\"\"\n\t\thashes = []\n\t\tfor cred in self.credentials:\n\t\t\tres = Ticket.load(cred.ticket.to_asn1()).native\n\t\t\tif int(res['enc-part']['etype']) == 23 or all_hashes == True:\n\t\t\t\thashes.append(cred.to_hash())\n\n\t\treturn hashes\n\n\t@staticmethod\n\tdef parse(reader):\n\t\tc = CCACHE(True)\n\t\tc.file_format_version = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\n\t\thdr_size = int.from_bytes(reader.read(2), byteorder='big', signed=False)\n\t\tc.headers = Header.parse(reader.read(hdr_size))\n\n\t\t#c.headerlen =\n\t\t#for i in range(c.headerlen):\n\t\t#\tc.headers.append(Header.parse(reader))\n\n\t\tc.primary_principal = CCACHEPrincipal.parse(reader)\n\t\tpos = reader.tell()\n\t\treader.seek(-1,2)\n\t\teof = reader.tell()\n\t\treader.seek(pos,0)\n\t\twhile reader.tell() < eof:\n\t\t\tcred = Credential.parse(reader)\n\t\t\tif not (len(cred.server.components) > 0 and cred.server.components[0].to_string() == 'krb5_ccache_conf_data'\n\t\t\t\t\tand cred.server.realm.to_string() == 'X-CACHECONF:'):\n\t\t\t\tc.credentials.append(cred)\n\n\t\treturn c\n\n\tdef to_bytes(self):\n\t\tt = self.file_format_version.to_bytes(2, byteorder='big', signed=False)\n\n\t\tt_hdr = b''\n\t\tfor header in self.headers:\n\t\t\tt_hdr += header.to_bytes()\n\n\t\tt += len(t_hdr).to_bytes(2, byteorder='big', signed=False)\n\t\tt += t_hdr\n\n\t\tt += self.primary_principal.to_bytes()\n\t\tfor cred in self.credentials:\n\t\t\tt += cred.to_bytes()\n\t\treturn t\n\n\t@staticmethod\n\tdef from_kirbifile(kirbi_filename):\n\t\tkf_abs = os.path.abspath(kirbi_filename)\n\t\tkirbidata = None\n\t\twith open(kf_abs, 'rb') as f:\n\t\t\tkirbidata = f.read()\n\n\t\treturn CCACHE.from_kirbi(kirbidata)\n\n\t@staticmethod\n\tdef from_kirbidir(directory_path):\n\t\t\"\"\"\n\t\tIterates trough all .kirbi files in a given directory and converts all of them into one CCACHE object\n\t\t\"\"\"\n\t\tcc = CCACHE()\n\t\tdir_path = os.path.join(os.path.abspath(directory_path), '*.kirbi')\n\t\tfor filename in glob.glob(dir_path):\n\t\t\twith open(filename, 'rb') as f:\n\t\t\t\tkirbidata = f.read()\n\t\t\t\tkirbi = KRBCRED.load(kirbidata).native\n\t\t\t\tcc.add_kirbi(kirbi)\n\n\t\treturn cc\n\n\tdef to_kirbidir(self, directory_path):\n\t\t\"\"\"\n\t\tConverts all credential object in the CCACHE object to the kirbi file format used by mimikatz.\n\t\tThe kirbi file format supports one credential per file, so prepare for a lot of files being generated.\n\n\t\tdirectory_path: str the directory to write the kirbi files to\n\t\t\"\"\"\n\t\tkf_abs = os.path.abspath(directory_path)\n\t\tfor cred in self.credentials:\n\t\t\tkirbi, filename = cred.to_kirbi()\n\t\t\tfilename = '%s.kirbi' % filename.replace('..','!')\n\t\t\tfilepath = os.path.join(kf_abs, filename)\n\t\t\twith open(filepath, 'wb') as o:\n\t\t\t\to.write(kirbi.dump())\n\n\t@staticmethod\n\tdef from_file(filename):\n\t\t\"\"\"\n\t\tParses the ccache file and returns a CCACHE object\n\t\t\"\"\"\n\t\twith open(filename, 'rb') as f:\n\t\t\treturn CCACHE.parse(f)\n\n\tdef to_file(self, filename):\n\t\t\"\"\"\n\t\tWrites the contents of the CCACHE object to a file\n\t\t\"\"\"\n\t\twith open(filename, 'wb') as f:\n\t\t\tf.write(self.to_bytes())\n\n\t@staticmethod\n\tdef from_bytes(data):\n\t\treturn CCACHE.parse(io.BytesIO(data))\n"},"avg_line_length":{"kind":"number","value":31.9094594595,"string":"31.909459"},"max_line_length":{"kind":"number","value":151,"string":"151"},"alphanum_fraction":{"kind":"number","value":0.7079151315,"string":"0.707915"},"count_classes":{"kind":"number","value":23041,"string":"23,041"},"score_classes":{"kind":"number","value":0.9757760555626138,"string":"0.975776"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":7518,"string":"7,518"},"score_decorators":{"kind":"number","value":0.3183839410494219,"string":"0.318384"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":5146,"string":"5,146"},"score_documentation":{"kind":"number","value":0.21793080083005123,"string":"0.217931"}}},{"rowIdx":3291,"cells":{"hexsha":{"kind":"string","value":"131a0e5ecdfc260b3827d4c50aa8606f1a20747d"},"size":{"kind":"number","value":3054,"string":"3,054"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"getUniformSmiles.py"},"max_stars_repo_name":{"kind":"string","value":"OpenEye-Contrib/Molecular-List-Logic"},"max_stars_repo_head_hexsha":{"kind":"string","value":"82caf41f7d8b94e7448d8e839bdbc0620a8666d7"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":2,"string":"2"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2016-01-21T19:50:12.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2021-04-10T13:27:40.000Z"},"max_issues_repo_path":{"kind":"string","value":"getUniformSmiles.py"},"max_issues_repo_name":{"kind":"string","value":"OpenEye-Contrib/Molecular-List-Logic"},"max_issues_repo_head_hexsha":{"kind":"string","value":"82caf41f7d8b94e7448d8e839bdbc0620a8666d7"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"getUniformSmiles.py"},"max_forks_repo_name":{"kind":"string","value":"OpenEye-Contrib/Molecular-List-Logic"},"max_forks_repo_head_hexsha":{"kind":"string","value":"82caf41f7d8b94e7448d8e839bdbc0620a8666d7"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"#!/opt/az/psf/python/2.7/bin/python\nfrom openeye.oechem import *\n\nimport cgi\n\n#creates a list of smiles of the syntax [smiles|molId,smiles|molId]\ndef process_smiles(smiles):\n smiles = smiles.split('\\n')\n mol = OEGraphMol()\n smiles_list=[]\n for line in smiles:\n if len(line.rstrip())>0:\n line = line.split()\n smi = line[0]\n molId = \"\"\n if len(line)>1:\n molId = line[1].replace(\" \",\"|\").rstrip()\n if(OEParseSmiles(mol,smi)):\n smi = OECreateSmiString(mol)\n mol.Clear()\n smiles_list.append(smi + \"|\" + molId) #can't send spaces or new lines\n\n return smiles_list\n\n#takes a list of smiles and writes it as sdf using a memory buffer\ndef write_sdf(smiles_list):\n sdfs = []\n ofs = oemolostream()\n ofs.SetFormat(OEFormat_SDF)\n ofs.openstring()\n mol = OEGraphMol()\n for smiles in smiles_list:\n if(OEParseSmiles(mol,smiles.replace(\"|\",\" \"))):\n OEWriteMolecule(ofs,mol)\n sdfs.append(ofs.GetString())\n mol.Clear()\n ofs.SetString(\"\")\n return sdfs\n\n#creates a list of smiles of the syntax [smiles|molId,smiles|molId]\ndef read_sdf(sdf_data):\n ifs = oemolistream()\n ifs.SetFormat(OEFormat_SDF)\n ifs.openstring(sdf_data)\n smiles_list = []\n for mol in ifs.GetOEGraphMols():\n smiles = OECreateSmiString(mol)\n smiles_list.append(smiles + \"|\" + mol.GetTitle())\n return smiles_list\n\nif __name__ == \"__main__\":\n print \"Content-Type: text/html\\r\\n\\r\\n\"\n form = cgi.FieldStorage()\n\n extension = form.getvalue(\"extension\")\n dataA = form.getvalue(\"dataA\") \n operator = form.getvalue(\"smiles_operator\") \n sdf_output = form.getvalue(\"sdf_output\")\n \n if(extension==\"smi\"):\n list_A = process_smiles(dataA)\n else:\n list_A = read_sdf(dataA)\n \n\n outputString = \"\"\n if(operator==\"UNI\"): #if only one file is supplied\n outputString = \"*\".join(set(list_A)) #removes all doubles using the set() function\n else:\n dataB = form.getvalue(\"dataB\") #if two files are supplied\n if(extension==\"smi\"):\n list_B = process_smiles(dataB)\n else:\n list_B = read_sdf(dataB)\n \n if(operator==\"AND\"):\n outputString = \"*\".join(set(list_A) & set(list_B))\n elif(operator==\"OR\"):\n outputString = \"*\".join(set(list_A) | set(list_B))\n elif(operator==\"NOT\"):\n outputString = \"*\".join(set(list_A) - set(list_B))\n\n\n if(sdf_output==\"on\"): #if we want the output as sdf\n sdfs = write_sdf(outputString.replace(\"|\",\" \").split(\"*\"))\n outputString = \"*\".join(sdfs)\n outputString = outputString.replace(\"\\n\",\"!\").replace(\" \",\"|\")\n\n #sends the output to index.html using javascript\n print \"\"\"\n \n \n \n \n\n \n \n \"\"\" \n"},"avg_line_length":{"kind":"number","value":30.2376237624,"string":"30.237624"},"max_line_length":{"kind":"number","value":90,"string":"90"},"alphanum_fraction":{"kind":"number","value":0.5864440079,"string":"0.586444"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":833,"string":"833"},"score_documentation":{"kind":"number","value":0.2727570399476097,"string":"0.272757"}}},{"rowIdx":3292,"cells":{"hexsha":{"kind":"string","value":"131a15a3a8feaf503c53fb7ce15a6a9a4850f413"},"size":{"kind":"number","value":37988,"string":"37,988"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"mfem/_par/gridfunc.py"},"max_stars_repo_name":{"kind":"string","value":"mfem/PyMFEM"},"max_stars_repo_head_hexsha":{"kind":"string","value":"b7b7c3d3de1082eac1015e3a313cf513db06fd7b"},"max_stars_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_stars_count":{"kind":"number","value":93,"string":"93"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2017-03-01T16:45:33.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-03-27T22:10:33.000Z"},"max_issues_repo_path":{"kind":"string","value":"mfem/_par/gridfunc.py"},"max_issues_repo_name":{"kind":"string","value":"mfem/PyMFEM"},"max_issues_repo_head_hexsha":{"kind":"string","value":"b7b7c3d3de1082eac1015e3a313cf513db06fd7b"},"max_issues_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_issues_count":{"kind":"number","value":64,"string":"64"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2017-03-15T21:47:31.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-31T23:59:00.000Z"},"max_forks_repo_path":{"kind":"string","value":"mfem/_par/gridfunc.py"},"max_forks_repo_name":{"kind":"string","value":"mfem/PyMFEM"},"max_forks_repo_head_hexsha":{"kind":"string","value":"b7b7c3d3de1082eac1015e3a313cf513db06fd7b"},"max_forks_repo_licenses":{"kind":"list like","value":["BSD-3-Clause"],"string":"[\n \"BSD-3-Clause\"\n]"},"max_forks_count":{"kind":"number","value":32,"string":"32"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2017-03-02T22:13:38.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2022-03-26T13:09:31.000Z"},"content":{"kind":"string","value":"# This file was automatically generated by SWIG (http://www.swig.org).\n# Version 4.0.2\n#\n# Do not make changes to this file unless you know what you are doing--modify\n# the SWIG interface file instead.\n\nfrom sys import version_info as _swig_python_version_info\nif _swig_python_version_info < (2, 7, 0):\n raise RuntimeError(\"Python 2.7 or later required\")\n\n# Import the low-level C/C++ module\nif __package__ or \".\" in __name__:\n from . import _gridfunc\nelse:\n import _gridfunc\n\ntry:\n import builtins as __builtin__\nexcept ImportError:\n import __builtin__\n\n_swig_new_instance_method = _gridfunc.SWIG_PyInstanceMethod_New\n_swig_new_static_method = _gridfunc.SWIG_PyStaticMethod_New\n\ndef _swig_repr(self):\n try:\n strthis = \"proxy of \" + self.this.__repr__()\n except __builtin__.Exception:\n strthis = \"\"\n return \"<%s.%s; %s >\" % (self.__class__.__module__, self.__class__.__name__, strthis,)\n\n\ndef _swig_setattr_nondynamic_instance_variable(set):\n def set_instance_attr(self, name, value):\n if name == \"thisown\":\n self.this.own(value)\n elif name == \"this\":\n set(self, name, value)\n elif hasattr(self, name) and isinstance(getattr(type(self), name), property):\n set(self, name, value)\n else:\n raise AttributeError(\"You cannot add instance attributes to %s\" % self)\n return set_instance_attr\n\n\ndef _swig_setattr_nondynamic_class_variable(set):\n def set_class_attr(cls, name, value):\n if hasattr(cls, name) and not isinstance(getattr(cls, name), property):\n set(cls, name, value)\n else:\n raise AttributeError(\"You cannot add class attributes to %s\" % cls)\n return set_class_attr\n\n\ndef _swig_add_metaclass(metaclass):\n \"\"\"Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass\"\"\"\n def wrapper(cls):\n return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())\n return wrapper\n\n\nclass _SwigNonDynamicMeta(type):\n \"\"\"Meta class to enforce nondynamic attributes (no new attributes) for a class\"\"\"\n __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)\n\n\nimport weakref\n\nimport mfem._par.array\nimport mfem._par.mem_manager\nimport mfem._par.vector\nimport mfem._par.coefficient\nimport mfem._par.globals\nimport mfem._par.matrix\nimport mfem._par.operators\nimport mfem._par.intrules\nimport mfem._par.sparsemat\nimport mfem._par.densemat\nimport mfem._par.eltrans\nimport mfem._par.fe\nimport mfem._par.geom\nimport mfem._par.fespace\nimport mfem._par.mesh\nimport mfem._par.sort_pairs\nimport mfem._par.ncmesh\nimport mfem._par.vtk\nimport mfem._par.element\nimport mfem._par.table\nimport mfem._par.hash\nimport mfem._par.vertex\nimport mfem._par.fe_coll\nimport mfem._par.lininteg\nimport mfem._par.handle\nimport mfem._par.hypre\nimport mfem._par.restriction\nimport mfem._par.bilininteg\nimport mfem._par.linearform\nimport mfem._par.nonlininteg\nclass GridFunction(mfem._par.vector.Vector):\n r\"\"\"Proxy of C++ mfem::GridFunction class.\"\"\"\n\n thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc=\"The membership flag\")\n __repr__ = _swig_repr\n\n def MakeOwner(self, fec_):\n r\"\"\"MakeOwner(GridFunction self, FiniteElementCollection fec_)\"\"\"\n return _gridfunc.GridFunction_MakeOwner(self, fec_)\n MakeOwner = _swig_new_instance_method(_gridfunc.GridFunction_MakeOwner)\n\n def OwnFEC(self):\n r\"\"\"OwnFEC(GridFunction self) -> FiniteElementCollection\"\"\"\n return _gridfunc.GridFunction_OwnFEC(self)\n OwnFEC = _swig_new_instance_method(_gridfunc.GridFunction_OwnFEC)\n\n def VectorDim(self):\n r\"\"\"VectorDim(GridFunction self) -> int\"\"\"\n return _gridfunc.GridFunction_VectorDim(self)\n VectorDim = _swig_new_instance_method(_gridfunc.GridFunction_VectorDim)\n\n def GetTrueVector(self, *args):\n r\"\"\"\n GetTrueVector(GridFunction self) -> Vector\n GetTrueVector(GridFunction self) -> Vector\n \"\"\"\n return _gridfunc.GridFunction_GetTrueVector(self, *args)\n GetTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_GetTrueVector)\n\n def GetTrueDofs(self, tv):\n r\"\"\"GetTrueDofs(GridFunction self, Vector tv)\"\"\"\n return _gridfunc.GridFunction_GetTrueDofs(self, tv)\n GetTrueDofs = _swig_new_instance_method(_gridfunc.GridFunction_GetTrueDofs)\n\n def SetTrueVector(self):\n r\"\"\"SetTrueVector(GridFunction self)\"\"\"\n return _gridfunc.GridFunction_SetTrueVector(self)\n SetTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_SetTrueVector)\n\n def SetFromTrueDofs(self, tv):\n r\"\"\"SetFromTrueDofs(GridFunction self, Vector tv)\"\"\"\n return _gridfunc.GridFunction_SetFromTrueDofs(self, tv)\n SetFromTrueDofs = _swig_new_instance_method(_gridfunc.GridFunction_SetFromTrueDofs)\n\n def SetFromTrueVector(self):\n r\"\"\"SetFromTrueVector(GridFunction self)\"\"\"\n return _gridfunc.GridFunction_SetFromTrueVector(self)\n SetFromTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_SetFromTrueVector)\n\n def GetValue(self, *args):\n r\"\"\"\n GetValue(GridFunction self, int i, IntegrationPoint ip, int vdim=1) -> double\n GetValue(GridFunction self, ElementTransformation T, IntegrationPoint ip, int comp=0, Vector tr=None) -> double\n \"\"\"\n return _gridfunc.GridFunction_GetValue(self, *args)\n GetValue = _swig_new_instance_method(_gridfunc.GridFunction_GetValue)\n\n def GetVectorValue(self, *args):\n r\"\"\"\n GetVectorValue(GridFunction self, int i, IntegrationPoint ip, Vector val)\n GetVectorValue(GridFunction self, ElementTransformation T, IntegrationPoint ip, Vector val, Vector tr=None)\n \"\"\"\n return _gridfunc.GridFunction_GetVectorValue(self, *args)\n GetVectorValue = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorValue)\n\n def GetValues(self, *args):\n r\"\"\"\n GetValues(GridFunction self, int i, IntegrationRule ir, Vector vals, int vdim=1)\n GetValues(GridFunction self, int i, IntegrationRule ir, Vector vals, DenseMatrix tr, int vdim=1)\n GetValues(GridFunction self, ElementTransformation T, IntegrationRule ir, Vector vals, int comp=0, DenseMatrix tr=None)\n \"\"\"\n return _gridfunc.GridFunction_GetValues(self, *args)\n GetValues = _swig_new_instance_method(_gridfunc.GridFunction_GetValues)\n\n def GetVectorValues(self, *args):\n r\"\"\"\n GetVectorValues(GridFunction self, int i, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr)\n GetVectorValues(GridFunction self, ElementTransformation T, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr=None)\n \"\"\"\n return _gridfunc.GridFunction_GetVectorValues(self, *args)\n GetVectorValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorValues)\n\n def GetFaceValues(self, i, side, ir, vals, tr, vdim=1):\n r\"\"\"GetFaceValues(GridFunction self, int i, int side, IntegrationRule ir, Vector vals, DenseMatrix tr, int vdim=1) -> int\"\"\"\n return _gridfunc.GridFunction_GetFaceValues(self, i, side, ir, vals, tr, vdim)\n GetFaceValues = _swig_new_instance_method(_gridfunc.GridFunction_GetFaceValues)\n\n def GetFaceVectorValues(self, i, side, ir, vals, tr):\n r\"\"\"GetFaceVectorValues(GridFunction self, int i, int side, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr) -> int\"\"\"\n return _gridfunc.GridFunction_GetFaceVectorValues(self, i, side, ir, vals, tr)\n GetFaceVectorValues = _swig_new_instance_method(_gridfunc.GridFunction_GetFaceVectorValues)\n\n def GetLaplacians(self, *args):\n r\"\"\"\n GetLaplacians(GridFunction self, int i, IntegrationRule ir, Vector laps, int vdim=1)\n GetLaplacians(GridFunction self, int i, IntegrationRule ir, Vector laps, DenseMatrix tr, int vdim=1)\n \"\"\"\n return _gridfunc.GridFunction_GetLaplacians(self, *args)\n GetLaplacians = _swig_new_instance_method(_gridfunc.GridFunction_GetLaplacians)\n\n def GetHessians(self, *args):\n r\"\"\"\n GetHessians(GridFunction self, int i, IntegrationRule ir, DenseMatrix hess, int vdim=1)\n GetHessians(GridFunction self, int i, IntegrationRule ir, DenseMatrix hess, DenseMatrix tr, int vdim=1)\n \"\"\"\n return _gridfunc.GridFunction_GetHessians(self, *args)\n GetHessians = _swig_new_instance_method(_gridfunc.GridFunction_GetHessians)\n\n def GetValuesFrom(self, orig_func):\n r\"\"\"GetValuesFrom(GridFunction self, GridFunction orig_func)\"\"\"\n return _gridfunc.GridFunction_GetValuesFrom(self, orig_func)\n GetValuesFrom = _swig_new_instance_method(_gridfunc.GridFunction_GetValuesFrom)\n\n def GetBdrValuesFrom(self, orig_func):\n r\"\"\"GetBdrValuesFrom(GridFunction self, GridFunction orig_func)\"\"\"\n return _gridfunc.GridFunction_GetBdrValuesFrom(self, orig_func)\n GetBdrValuesFrom = _swig_new_instance_method(_gridfunc.GridFunction_GetBdrValuesFrom)\n\n def GetVectorFieldValues(self, i, ir, vals, tr, comp=0):\n r\"\"\"GetVectorFieldValues(GridFunction self, int i, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr, int comp=0)\"\"\"\n return _gridfunc.GridFunction_GetVectorFieldValues(self, i, ir, vals, tr, comp)\n GetVectorFieldValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorFieldValues)\n\n def ReorderByNodes(self):\n r\"\"\"ReorderByNodes(GridFunction self)\"\"\"\n return _gridfunc.GridFunction_ReorderByNodes(self)\n ReorderByNodes = _swig_new_instance_method(_gridfunc.GridFunction_ReorderByNodes)\n\n def GetNodalValues(self, *args):\n '''\n GetNodalValues(i) -> GetNodalValues(vector, vdim)\n GetNodalValues(i, array, vdim)\n '''\n from .vector import Vector\n if len(args) == 1:\n vec = Vector()\n _gridfunc.GridFunction_GetNodalValues(self, vec, args[0])\n vec.thisown = 0\n return vec.GetDataArray()\n else:\n return _gridfunc.GridFunction_GetNodalValues(self, *args)\n\n\n\n def GetVectorFieldNodalValues(self, val, comp):\n r\"\"\"GetVectorFieldNodalValues(GridFunction self, Vector val, int comp)\"\"\"\n return _gridfunc.GridFunction_GetVectorFieldNodalValues(self, val, comp)\n GetVectorFieldNodalValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorFieldNodalValues)\n\n def ProjectVectorFieldOn(self, vec_field, comp=0):\n r\"\"\"ProjectVectorFieldOn(GridFunction self, GridFunction vec_field, int comp=0)\"\"\"\n return _gridfunc.GridFunction_ProjectVectorFieldOn(self, vec_field, comp)\n ProjectVectorFieldOn = _swig_new_instance_method(_gridfunc.GridFunction_ProjectVectorFieldOn)\n\n def GetDerivative(self, comp, der_comp, der):\n r\"\"\"GetDerivative(GridFunction self, int comp, int der_comp, GridFunction der)\"\"\"\n return _gridfunc.GridFunction_GetDerivative(self, comp, der_comp, der)\n GetDerivative = _swig_new_instance_method(_gridfunc.GridFunction_GetDerivative)\n\n def GetDivergence(self, tr):\n r\"\"\"GetDivergence(GridFunction self, ElementTransformation tr) -> double\"\"\"\n return _gridfunc.GridFunction_GetDivergence(self, tr)\n GetDivergence = _swig_new_instance_method(_gridfunc.GridFunction_GetDivergence)\n\n def GetCurl(self, tr, curl):\n r\"\"\"GetCurl(GridFunction self, ElementTransformation tr, Vector curl)\"\"\"\n return _gridfunc.GridFunction_GetCurl(self, tr, curl)\n GetCurl = _swig_new_instance_method(_gridfunc.GridFunction_GetCurl)\n\n def GetGradient(self, tr, grad):\n r\"\"\"GetGradient(GridFunction self, ElementTransformation tr, Vector grad)\"\"\"\n return _gridfunc.GridFunction_GetGradient(self, tr, grad)\n GetGradient = _swig_new_instance_method(_gridfunc.GridFunction_GetGradient)\n\n def GetGradients(self, *args):\n r\"\"\"\n GetGradients(GridFunction self, ElementTransformation tr, IntegrationRule ir, DenseMatrix grad)\n GetGradients(GridFunction self, int const elem, IntegrationRule ir, DenseMatrix grad)\n \"\"\"\n return _gridfunc.GridFunction_GetGradients(self, *args)\n GetGradients = _swig_new_instance_method(_gridfunc.GridFunction_GetGradients)\n\n def GetVectorGradient(self, tr, grad):\n r\"\"\"GetVectorGradient(GridFunction self, ElementTransformation tr, DenseMatrix grad)\"\"\"\n return _gridfunc.GridFunction_GetVectorGradient(self, tr, grad)\n GetVectorGradient = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorGradient)\n\n def GetElementAverages(self, avgs):\n r\"\"\"GetElementAverages(GridFunction self, GridFunction avgs)\"\"\"\n return _gridfunc.GridFunction_GetElementAverages(self, avgs)\n GetElementAverages = _swig_new_instance_method(_gridfunc.GridFunction_GetElementAverages)\n\n def GetElementDofValues(self, el, dof_vals):\n r\"\"\"GetElementDofValues(GridFunction self, int el, Vector dof_vals)\"\"\"\n return _gridfunc.GridFunction_GetElementDofValues(self, el, dof_vals)\n GetElementDofValues = _swig_new_instance_method(_gridfunc.GridFunction_GetElementDofValues)\n\n def ImposeBounds(self, *args):\n r\"\"\"\n ImposeBounds(GridFunction self, int i, Vector weights, Vector lo_, Vector hi_)\n ImposeBounds(GridFunction self, int i, Vector weights, double min_=0.0, double max_=mfem::infinity())\n \"\"\"\n return _gridfunc.GridFunction_ImposeBounds(self, *args)\n ImposeBounds = _swig_new_instance_method(_gridfunc.GridFunction_ImposeBounds)\n\n def RestrictConforming(self):\n r\"\"\"RestrictConforming(GridFunction self)\"\"\"\n return _gridfunc.GridFunction_RestrictConforming(self)\n RestrictConforming = _swig_new_instance_method(_gridfunc.GridFunction_RestrictConforming)\n\n def ProjectGridFunction(self, src):\n r\"\"\"ProjectGridFunction(GridFunction self, GridFunction src)\"\"\"\n return _gridfunc.GridFunction_ProjectGridFunction(self, src)\n ProjectGridFunction = _swig_new_instance_method(_gridfunc.GridFunction_ProjectGridFunction)\n\n def ProjectCoefficient(self, *args):\n r\"\"\"\n ProjectCoefficient(GridFunction self, Coefficient coeff)\n ProjectCoefficient(GridFunction self, Coefficient coeff, intArray dofs, int vd=0)\n ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff)\n ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff, intArray dofs)\n ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff, int attribute)\n ProjectCoefficient(GridFunction self, mfem::Coefficient *[] coeff)\n \"\"\"\n return _gridfunc.GridFunction_ProjectCoefficient(self, *args)\n ProjectCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectCoefficient)\n ARITHMETIC = _gridfunc.GridFunction_ARITHMETIC\n \n HARMONIC = _gridfunc.GridFunction_HARMONIC\n \n\n def ProjectDiscCoefficient(self, *args):\n r\"\"\"\n ProjectDiscCoefficient(GridFunction self, VectorCoefficient coeff)\n ProjectDiscCoefficient(GridFunction self, Coefficient coeff, mfem::GridFunction::AvgType type)\n ProjectDiscCoefficient(GridFunction self, VectorCoefficient coeff, mfem::GridFunction::AvgType type)\n \"\"\"\n return _gridfunc.GridFunction_ProjectDiscCoefficient(self, *args)\n ProjectDiscCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectDiscCoefficient)\n\n def ProjectBdrCoefficient(self, *args):\n r\"\"\"\n ProjectBdrCoefficient(GridFunction self, Coefficient coeff, intArray attr)\n ProjectBdrCoefficient(GridFunction self, VectorCoefficient vcoeff, intArray attr)\n ProjectBdrCoefficient(GridFunction self, mfem::Coefficient *[] coeff, intArray attr)\n \"\"\"\n return _gridfunc.GridFunction_ProjectBdrCoefficient(self, *args)\n ProjectBdrCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficient)\n\n def ProjectBdrCoefficientNormal(self, vcoeff, bdr_attr):\n r\"\"\"ProjectBdrCoefficientNormal(GridFunction self, VectorCoefficient vcoeff, intArray bdr_attr)\"\"\"\n return _gridfunc.GridFunction_ProjectBdrCoefficientNormal(self, vcoeff, bdr_attr)\n ProjectBdrCoefficientNormal = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficientNormal)\n\n def ProjectBdrCoefficientTangent(self, vcoeff, bdr_attr):\n r\"\"\"ProjectBdrCoefficientTangent(GridFunction self, VectorCoefficient vcoeff, intArray bdr_attr)\"\"\"\n return _gridfunc.GridFunction_ProjectBdrCoefficientTangent(self, vcoeff, bdr_attr)\n ProjectBdrCoefficientTangent = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficientTangent)\n\n def ComputeL2Error(self, *args):\n r\"\"\"\n ComputeL2Error(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeL2Error(GridFunction self, mfem::Coefficient *[] exsol, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeL2Error(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0, intArray elems=None) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeL2Error(self, *args)\n ComputeL2Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeL2Error)\n\n def ComputeGradError(self, exgrad, irs=0):\n r\"\"\"ComputeGradError(GridFunction self, VectorCoefficient exgrad, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeGradError(self, exgrad, irs)\n ComputeGradError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeGradError)\n\n def ComputeCurlError(self, excurl, irs=0):\n r\"\"\"ComputeCurlError(GridFunction self, VectorCoefficient excurl, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeCurlError(self, excurl, irs)\n ComputeCurlError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeCurlError)\n\n def ComputeDivError(self, exdiv, irs=0):\n r\"\"\"ComputeDivError(GridFunction self, Coefficient exdiv, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeDivError(self, exdiv, irs)\n ComputeDivError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeDivError)\n\n def ComputeDGFaceJumpError(self, *args):\n r\"\"\"\n ComputeDGFaceJumpError(GridFunction self, Coefficient exsol, Coefficient ell_coeff, JumpScaling jump_scaling, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeDGFaceJumpError(GridFunction self, Coefficient exsol, Coefficient ell_coeff, double Nu, mfem::IntegrationRule const *[] irs=0) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeDGFaceJumpError(self, *args)\n ComputeDGFaceJumpError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeDGFaceJumpError)\n\n def ComputeH1Error(self, *args):\n r\"\"\"\n ComputeH1Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, Coefficient ell_coef, double Nu, int norm_type) -> double\n ComputeH1Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, mfem::IntegrationRule const *[] irs=0) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeH1Error(self, *args)\n ComputeH1Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeH1Error)\n\n def ComputeHDivError(self, exsol, exdiv, irs=0):\n r\"\"\"ComputeHDivError(GridFunction self, VectorCoefficient exsol, Coefficient exdiv, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeHDivError(self, exsol, exdiv, irs)\n ComputeHDivError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeHDivError)\n\n def ComputeHCurlError(self, exsol, excurl, irs=0):\n r\"\"\"ComputeHCurlError(GridFunction self, VectorCoefficient exsol, VectorCoefficient excurl, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeHCurlError(self, exsol, excurl, irs)\n ComputeHCurlError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeHCurlError)\n\n def ComputeMaxError(self, *args):\n r\"\"\"\n ComputeMaxError(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeMaxError(GridFunction self, mfem::Coefficient *[] exsol, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeMaxError(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeMaxError(self, *args)\n ComputeMaxError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeMaxError)\n\n def ComputeW11Error(self, exsol, exgrad, norm_type, elems=None, irs=0):\n r\"\"\"ComputeW11Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, int norm_type, intArray elems=None, mfem::IntegrationRule const *[] irs=0) -> double\"\"\"\n return _gridfunc.GridFunction_ComputeW11Error(self, exsol, exgrad, norm_type, elems, irs)\n ComputeW11Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeW11Error)\n\n def ComputeL1Error(self, *args):\n r\"\"\"\n ComputeL1Error(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeL1Error(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeL1Error(self, *args)\n ComputeL1Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeL1Error)\n\n def ComputeLpError(self, *args):\n r\"\"\"\n ComputeLpError(GridFunction self, double const p, Coefficient exsol, Coefficient weight=None, mfem::IntegrationRule const *[] irs=0) -> double\n ComputeLpError(GridFunction self, double const p, VectorCoefficient exsol, Coefficient weight=None, VectorCoefficient v_weight=None, mfem::IntegrationRule const *[] irs=0) -> double\n \"\"\"\n return _gridfunc.GridFunction_ComputeLpError(self, *args)\n ComputeLpError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeLpError)\n\n def ComputeElementLpErrors(self, *args):\n r\"\"\"\n ComputeElementLpErrors(GridFunction self, double const p, Coefficient exsol, Vector error, Coefficient weight=None, mfem::IntegrationRule const *[] irs=0)\n ComputeElementLpErrors(GridFunction self, double const p, VectorCoefficient exsol, Vector error, Coefficient weight=None, VectorCoefficient v_weight=None, mfem::IntegrationRule const *[] irs=0)\n \"\"\"\n return _gridfunc.GridFunction_ComputeElementLpErrors(self, *args)\n ComputeElementLpErrors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementLpErrors)\n\n def ComputeElementL1Errors(self, *args):\n r\"\"\"\n ComputeElementL1Errors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n ComputeElementL1Errors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n \"\"\"\n return _gridfunc.GridFunction_ComputeElementL1Errors(self, *args)\n ComputeElementL1Errors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementL1Errors)\n\n def ComputeElementL2Errors(self, *args):\n r\"\"\"\n ComputeElementL2Errors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n ComputeElementL2Errors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n \"\"\"\n return _gridfunc.GridFunction_ComputeElementL2Errors(self, *args)\n ComputeElementL2Errors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementL2Errors)\n\n def ComputeElementMaxErrors(self, *args):\n r\"\"\"\n ComputeElementMaxErrors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n ComputeElementMaxErrors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0)\n \"\"\"\n return _gridfunc.GridFunction_ComputeElementMaxErrors(self, *args)\n ComputeElementMaxErrors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementMaxErrors)\n\n def ComputeFlux(self, blfi, flux, wcoef=True, subdomain=-1):\n r\"\"\"ComputeFlux(GridFunction self, BilinearFormIntegrator blfi, GridFunction flux, bool wcoef=True, int subdomain=-1)\"\"\"\n return _gridfunc.GridFunction_ComputeFlux(self, blfi, flux, wcoef, subdomain)\n ComputeFlux = _swig_new_instance_method(_gridfunc.GridFunction_ComputeFlux)\n\n def Assign(self, *args):\n r\"\"\"\n Assign(GridFunction self, GridFunction rhs) -> GridFunction\n Assign(GridFunction self, double value) -> GridFunction\n Assign(GridFunction self, Vector v) -> GridFunction\n \"\"\"\n return _gridfunc.GridFunction_Assign(self, *args)\n Assign = _swig_new_instance_method(_gridfunc.GridFunction_Assign)\n\n def Update(self):\n r\"\"\"Update(GridFunction self)\"\"\"\n return _gridfunc.GridFunction_Update(self)\n Update = _swig_new_instance_method(_gridfunc.GridFunction_Update)\n\n def FESpace(self, *args):\n r\"\"\"\n FESpace(GridFunction self) -> FiniteElementSpace\n FESpace(GridFunction self) -> FiniteElementSpace\n \"\"\"\n return _gridfunc.GridFunction_FESpace(self, *args)\n FESpace = _swig_new_instance_method(_gridfunc.GridFunction_FESpace)\n\n def SetSpace(self, f):\n r\"\"\"SetSpace(GridFunction self, FiniteElementSpace f)\"\"\"\n return _gridfunc.GridFunction_SetSpace(self, f)\n SetSpace = _swig_new_instance_method(_gridfunc.GridFunction_SetSpace)\n\n def MakeRef(self, *args):\n r\"\"\"\n MakeRef(GridFunction self, Vector base, int offset, int size)\n MakeRef(GridFunction self, Vector base, int offset)\n MakeRef(GridFunction self, FiniteElementSpace f, double * v)\n MakeRef(GridFunction self, FiniteElementSpace f, Vector v, int v_offset)\n \"\"\"\n return _gridfunc.GridFunction_MakeRef(self, *args)\n MakeRef = _swig_new_instance_method(_gridfunc.GridFunction_MakeRef)\n\n def MakeTRef(self, *args):\n r\"\"\"\n MakeTRef(GridFunction self, FiniteElementSpace f, double * tv)\n MakeTRef(GridFunction self, FiniteElementSpace f, Vector tv, int tv_offset)\n \"\"\"\n return _gridfunc.GridFunction_MakeTRef(self, *args)\n MakeTRef = _swig_new_instance_method(_gridfunc.GridFunction_MakeTRef)\n\n def SaveVTK(self, out, field_name, ref):\n r\"\"\"SaveVTK(GridFunction self, std::ostream & out, std::string const & field_name, int ref)\"\"\"\n return _gridfunc.GridFunction_SaveVTK(self, out, field_name, ref)\n SaveVTK = _swig_new_instance_method(_gridfunc.GridFunction_SaveVTK)\n\n def SaveSTL(self, out, TimesToRefine=1):\n r\"\"\"SaveSTL(GridFunction self, std::ostream & out, int TimesToRefine=1)\"\"\"\n return _gridfunc.GridFunction_SaveSTL(self, out, TimesToRefine)\n SaveSTL = _swig_new_instance_method(_gridfunc.GridFunction_SaveSTL)\n __swig_destroy__ = _gridfunc.delete_GridFunction\n\n def __init__(self, *args):\n r\"\"\"\n __init__(GridFunction self) -> GridFunction\n __init__(GridFunction self, GridFunction orig) -> GridFunction\n __init__(GridFunction self, FiniteElementSpace f) -> GridFunction\n __init__(GridFunction self, FiniteElementSpace f, double * data) -> GridFunction\n __init__(GridFunction self, Mesh m, std::istream & input) -> GridFunction\n __init__(GridFunction self, Mesh m, mfem::GridFunction *[] gf_array, int num_pieces) -> GridFunction\n __init__(GridFunction self, FiniteElementSpace fes, Vector v, int offset) -> GridFunction\n \"\"\"\n _gridfunc.GridFunction_swiginit(self, _gridfunc.new_GridFunction(*args))\n\n def SaveToFile(self, gf_file, precision):\n r\"\"\"SaveToFile(GridFunction self, char const * gf_file, int const precision)\"\"\"\n return _gridfunc.GridFunction_SaveToFile(self, gf_file, precision)\n SaveToFile = _swig_new_instance_method(_gridfunc.GridFunction_SaveToFile)\n\n def WriteToStream(self, StringIO):\n r\"\"\"WriteToStream(GridFunction self, PyObject * StringIO) -> PyObject *\"\"\"\n return _gridfunc.GridFunction_WriteToStream(self, StringIO)\n WriteToStream = _swig_new_instance_method(_gridfunc.GridFunction_WriteToStream)\n\n def iadd(self, c):\n r\"\"\"iadd(GridFunction self, GridFunction c) -> GridFunction\"\"\"\n return _gridfunc.GridFunction_iadd(self, c)\n iadd = _swig_new_instance_method(_gridfunc.GridFunction_iadd)\n\n def isub(self, *args):\n r\"\"\"\n isub(GridFunction self, GridFunction c) -> GridFunction\n isub(GridFunction self, double c) -> GridFunction\n \"\"\"\n return _gridfunc.GridFunction_isub(self, *args)\n isub = _swig_new_instance_method(_gridfunc.GridFunction_isub)\n\n def imul(self, c):\n r\"\"\"imul(GridFunction self, double c) -> GridFunction\"\"\"\n return _gridfunc.GridFunction_imul(self, c)\n imul = _swig_new_instance_method(_gridfunc.GridFunction_imul)\n\n def idiv(self, c):\n r\"\"\"idiv(GridFunction self, double c) -> GridFunction\"\"\"\n return _gridfunc.GridFunction_idiv(self, c)\n idiv = _swig_new_instance_method(_gridfunc.GridFunction_idiv)\n\n def Save(self, *args):\n r\"\"\"\n Save(GridFunction self, std::ostream & out)\n Save(GridFunction self, char const * fname, int precision=16)\n Save(GridFunction self, char const * file, int precision=16)\n \"\"\"\n return _gridfunc.GridFunction_Save(self, *args)\n Save = _swig_new_instance_method(_gridfunc.GridFunction_Save)\n\n def SaveGZ(self, file, precision=16):\n r\"\"\"SaveGZ(GridFunction self, char const * file, int precision=16)\"\"\"\n return _gridfunc.GridFunction_SaveGZ(self, file, precision)\n SaveGZ = _swig_new_instance_method(_gridfunc.GridFunction_SaveGZ)\n\n# Register GridFunction in _gridfunc:\n_gridfunc.GridFunction_swigregister(GridFunction)\n\nclass JumpScaling(object):\n r\"\"\"Proxy of C++ mfem::JumpScaling class.\"\"\"\n\n thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc=\"The membership flag\")\n __repr__ = _swig_repr\n CONSTANT = _gridfunc.JumpScaling_CONSTANT\n \n ONE_OVER_H = _gridfunc.JumpScaling_ONE_OVER_H\n \n P_SQUARED_OVER_H = _gridfunc.JumpScaling_P_SQUARED_OVER_H\n \n\n def __init__(self, *args, **kwargs):\n r\"\"\"__init__(JumpScaling self, double nu_=1.0, mfem::JumpScaling::JumpScalingType type_=CONSTANT) -> JumpScaling\"\"\"\n _gridfunc.JumpScaling_swiginit(self, _gridfunc.new_JumpScaling(*args, **kwargs))\n\n def Eval(self, h, p):\n r\"\"\"Eval(JumpScaling self, double h, int p) -> double\"\"\"\n return _gridfunc.JumpScaling_Eval(self, h, p)\n Eval = _swig_new_instance_method(_gridfunc.JumpScaling_Eval)\n __swig_destroy__ = _gridfunc.delete_JumpScaling\n\n# Register JumpScaling in _gridfunc:\n_gridfunc.JumpScaling_swigregister(JumpScaling)\n\nclass QuadratureFunction(mfem._par.vector.Vector):\n r\"\"\"Proxy of C++ mfem::QuadratureFunction class.\"\"\"\n\n thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc=\"The membership flag\")\n __repr__ = _swig_repr\n\n def __init__(self, *args):\n r\"\"\"\n __init__(QuadratureFunction self) -> QuadratureFunction\n __init__(QuadratureFunction self, QuadratureFunction orig) -> QuadratureFunction\n __init__(QuadratureFunction self, QuadratureSpace qspace_, int vdim_=1) -> QuadratureFunction\n __init__(QuadratureFunction self, QuadratureSpace qspace_, double * qf_data, int vdim_=1) -> QuadratureFunction\n __init__(QuadratureFunction self, Mesh mesh, std::istream & _in) -> QuadratureFunction\n \"\"\"\n _gridfunc.QuadratureFunction_swiginit(self, _gridfunc.new_QuadratureFunction(*args))\n __swig_destroy__ = _gridfunc.delete_QuadratureFunction\n\n def GetSpace(self):\n r\"\"\"GetSpace(QuadratureFunction self) -> QuadratureSpace\"\"\"\n return _gridfunc.QuadratureFunction_GetSpace(self)\n GetSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetSpace)\n\n def SetSpace(self, *args):\n r\"\"\"\n SetSpace(QuadratureFunction self, QuadratureSpace qspace_, int vdim_=-1)\n SetSpace(QuadratureFunction self, QuadratureSpace qspace_, double * qf_data, int vdim_=-1)\n \"\"\"\n return _gridfunc.QuadratureFunction_SetSpace(self, *args)\n SetSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetSpace)\n\n def GetVDim(self):\n r\"\"\"GetVDim(QuadratureFunction self) -> int\"\"\"\n return _gridfunc.QuadratureFunction_GetVDim(self)\n GetVDim = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetVDim)\n\n def SetVDim(self, vdim_):\n r\"\"\"SetVDim(QuadratureFunction self, int vdim_)\"\"\"\n return _gridfunc.QuadratureFunction_SetVDim(self, vdim_)\n SetVDim = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetVDim)\n\n def OwnsSpace(self):\n r\"\"\"OwnsSpace(QuadratureFunction self) -> bool\"\"\"\n return _gridfunc.QuadratureFunction_OwnsSpace(self)\n OwnsSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_OwnsSpace)\n\n def SetOwnsSpace(self, own):\n r\"\"\"SetOwnsSpace(QuadratureFunction self, bool own)\"\"\"\n return _gridfunc.QuadratureFunction_SetOwnsSpace(self, own)\n SetOwnsSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetOwnsSpace)\n\n def GetElementIntRule(self, idx):\n r\"\"\"GetElementIntRule(QuadratureFunction self, int idx) -> IntegrationRule\"\"\"\n return _gridfunc.QuadratureFunction_GetElementIntRule(self, idx)\n GetElementIntRule = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetElementIntRule)\n\n def GetElementValues(self, *args):\n r\"\"\"\n GetElementValues(QuadratureFunction self, int idx, Vector values)\n GetElementValues(QuadratureFunction self, int idx, Vector values)\n GetElementValues(QuadratureFunction self, int idx, int const ip_num, Vector values)\n GetElementValues(QuadratureFunction self, int idx, int const ip_num, Vector values)\n GetElementValues(QuadratureFunction self, int idx, DenseMatrix values)\n GetElementValues(QuadratureFunction self, int idx, DenseMatrix values)\n \"\"\"\n return _gridfunc.QuadratureFunction_GetElementValues(self, *args)\n GetElementValues = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetElementValues)\n\n def Save(self, *args):\n r\"\"\"\n Save(QuadratureFunction self, std::ostream & out)\n Save(QuadratureFunction self, char const * file, int precision=16)\n \"\"\"\n return _gridfunc.QuadratureFunction_Save(self, *args)\n Save = _swig_new_instance_method(_gridfunc.QuadratureFunction_Save)\n\n def SaveGZ(self, file, precision=16):\n r\"\"\"SaveGZ(QuadratureFunction self, char const * file, int precision=16)\"\"\"\n return _gridfunc.QuadratureFunction_SaveGZ(self, file, precision)\n SaveGZ = _swig_new_instance_method(_gridfunc.QuadratureFunction_SaveGZ)\n\n# Register QuadratureFunction in _gridfunc:\n_gridfunc.QuadratureFunction_swigregister(QuadratureFunction)\n\n\ndef __lshift__(*args):\n r\"\"\"\n __lshift__(std::ostream & os, SparseMatrix mat) -> std::ostream\n __lshift__(std::ostream & out, Mesh mesh) -> std::ostream\n __lshift__(std::ostream & out, GridFunction sol) -> std::ostream\n __lshift__(std::ostream & out, QuadratureFunction qf) -> std::ostream &\n \"\"\"\n return _gridfunc.__lshift__(*args)\n__lshift__ = _gridfunc.__lshift__\n\ndef ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags=None, with_subdomains=1, with_coeff=False):\n r\"\"\"ZZErrorEstimator(BilinearFormIntegrator blfi, GridFunction u, GridFunction flux, Vector error_estimates, intArray aniso_flags=None, int with_subdomains=1, bool with_coeff=False) -> double\"\"\"\n return _gridfunc.ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags, with_subdomains, with_coeff)\nZZErrorEstimator = _gridfunc.ZZErrorEstimator\n\ndef ComputeElementLpDistance(p, i, gf1, gf2):\n r\"\"\"ComputeElementLpDistance(double p, int i, GridFunction gf1, GridFunction gf2) -> double\"\"\"\n return _gridfunc.ComputeElementLpDistance(p, i, gf1, gf2)\nComputeElementLpDistance = _gridfunc.ComputeElementLpDistance\nclass ExtrudeCoefficient(mfem._par.coefficient.Coefficient):\n r\"\"\"Proxy of C++ mfem::ExtrudeCoefficient class.\"\"\"\n\n thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc=\"The membership flag\")\n __repr__ = _swig_repr\n\n def __init__(self, m, s, n_):\n r\"\"\"__init__(ExtrudeCoefficient self, Mesh m, Coefficient s, int n_) -> ExtrudeCoefficient\"\"\"\n _gridfunc.ExtrudeCoefficient_swiginit(self, _gridfunc.new_ExtrudeCoefficient(m, s, n_))\n\n def Eval(self, T, ip):\n r\"\"\"Eval(ExtrudeCoefficient self, ElementTransformation T, IntegrationPoint ip) -> double\"\"\"\n return _gridfunc.ExtrudeCoefficient_Eval(self, T, ip)\n Eval = _swig_new_instance_method(_gridfunc.ExtrudeCoefficient_Eval)\n __swig_destroy__ = _gridfunc.delete_ExtrudeCoefficient\n\n# Register ExtrudeCoefficient in _gridfunc:\n_gridfunc.ExtrudeCoefficient_swigregister(ExtrudeCoefficient)\n\n\ndef Extrude1DGridFunction(mesh, mesh2d, sol, ny):\n r\"\"\"Extrude1DGridFunction(Mesh mesh, Mesh mesh2d, GridFunction sol, int const ny) -> GridFunction\"\"\"\n return _gridfunc.Extrude1DGridFunction(mesh, mesh2d, sol, ny)\nExtrude1DGridFunction = _gridfunc.Extrude1DGridFunction\n\ndef __iadd__(self, v):\n ret = _gridfunc.GridFunction_iadd(self, v)\n ret.thisown = 0\n return self\ndef __isub__(self, v):\n ret = _gridfunc.GridFunction_isub(self, v)\n ret.thisown = 0\n return self\ndef __idiv__(self, v):\n ret = _gridfunc.GridFunction_idiv(self, v)\n ret.thisown = 0\n return self\ndef __imul__(self, v):\n ret = _gridfunc.GridFunction_imul(self, v)\n ret.thisown = 0\n return self\n\nGridFunction.__iadd__ = __iadd__\nGridFunction.__idiv__ = __idiv__\nGridFunction.__isub__ = __isub__\nGridFunction.__imul__ = __imul__ \n\n\n\n"},"avg_line_length":{"kind":"number","value":49.3350649351,"string":"49.335065"},"max_line_length":{"kind":"number","value":201,"string":"201"},"alphanum_fraction":{"kind":"number","value":0.7400758134,"string":"0.740076"},"count_classes":{"kind":"number","value":32840,"string":"32,840"},"score_classes":{"kind":"number","value":0.86448352111193,"string":"0.864484"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":15335,"string":"15,335"},"score_documentation":{"kind":"number","value":0.40368010950826577,"string":"0.40368"}}},{"rowIdx":3293,"cells":{"hexsha":{"kind":"string","value":"131ada8dd58eaa29a8303d1a7138ffe5d3485877"},"size":{"kind":"number","value":6861,"string":"6,861"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/tracks/settings.py"},"max_stars_repo_name":{"kind":"string","value":"adcarmichael/tracks"},"max_stars_repo_head_hexsha":{"kind":"string","value":"04108bbdaf8554e57e278c1556efa9c5b9603973"},"max_stars_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"src/tracks/settings.py"},"max_issues_repo_name":{"kind":"string","value":"adcarmichael/tracks"},"max_issues_repo_head_hexsha":{"kind":"string","value":"04108bbdaf8554e57e278c1556efa9c5b9603973"},"max_issues_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_issues_count":{"kind":"number","value":41,"string":"41"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2019-06-14T21:19:31.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-02-10T14:41:00.000Z"},"max_forks_repo_path":{"kind":"string","value":"src/tracks/settings.py"},"max_forks_repo_name":{"kind":"string","value":"adcarmichael/tracks"},"max_forks_repo_head_hexsha":{"kind":"string","value":"04108bbdaf8554e57e278c1556efa9c5b9603973"},"max_forks_repo_licenses":{"kind":"list like","value":["Apache-2.0"],"string":"[\n \"Apache-2.0\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import os\nimport sentry_sdk\nfrom sentry_sdk.integrations.django import DjangoIntegration\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nPWA_SERVICE_WORKER_PATH = os.path.join(\n BASE_DIR, 'routes/static/routes/js', 'serviceworker.js')\nprint(os.path.join(\n BASE_DIR, 'routes/static/routes/js', 'serviceworker.js'))\n\nDEBUG = int(os.environ.get(\"DEBUG\", default=0))\n\nSECRET_KEY = os.environ.get(\"SECRET_KEY\", 'asdfkhbsadgui87gjsbdfui')\n\n# 'DJANGO_ALLOWED_HOSTS' should be a single string of hosts with a space between each.\n# For example: 'DJANGO_ALLOWED_HOSTS=localhost 127.0.0.1 [::1]'\nALLOWED_HOSTS = os.environ.get(\"DJANGO_ALLOWED_HOSTS\", 'localhost').split(\" \")\n\n# Application definition\n\nINSTALLED_APPS = [\n 'routes',\n 'accounts',\n 'dashboard.apps.DashboardConfig',\n 'api.apps.ApiConfig',\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'widget_tweaks',\n 'rest_framework',\n 'pwa',\n]\n# 'celery',\n\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'tracks.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'tracks.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/2.2/ref/settings/#databases\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": os.environ.get(\"SQL_ENGINE\", \"django.db.backends.sqlite3\"),\n \"NAME\": os.environ.get(\"SQL_DATABASE\", os.path.join(BASE_DIR, \"db.sqlite3\")),\n \"USER\": os.environ.get(\"SQL_USER\", \"user\"),\n \"PASSWORD\": os.environ.get(\"SQL_PASSWORD\", \"password\"),\n \"HOST\": os.environ.get(\"SQL_HOST\", \"localhost\"),\n \"PORT\": os.environ.get(\"SQL_PORT\", \"5432\"),\n }\n}\n\n# Password validation\n# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.2/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.2/howto/static-files/\n\nSTATIC_URL = '/static/'\nMEDIA_URL = '/media/'\n\nSTATIC_ROOT = './static/'\nMEDIA_ROOT = './media/'\n\nLOGIN_REDIRECT_URL = 'home'\nLOGOUT_REDIRECT_URL = 'home'\n\n# no email for localhost or staging\nEMAIL_USE_TLS = os.environ.get(\"EMAIL_USE_TLS\")\nEMAIL_HOST = os.environ.get(\"EMAIL_HOST\")\nEMAIL_HOST_USER = os.environ.get(\"EMAIL_HOST_USER\")\nEMAIL_HOST_PASSWORD = os.environ.get(\"EMAIL_HOST_PASSWORD\")\nEMAIL_PORT = os.environ.get(\"EMAIL_PORT\")\nEMAIL_BACKEND = os.environ.get(\"EMAIL_BACKEND\")\nDEFAULT_FROM_EMAIL = 'chalktracks@gmail.com'\n# CELERY\n# CELERY_BROKER_URL = 'redis://redis:6379/0'\n# CELERY_RESULT_BACKEND = 'redis://redis:6379/0'\n# BROKER_URL = 'redis://localhost:6379/0'\n# CELERY_RESULT_BACKEND = 'redis://localhost:6379/'\n# CELERY_ACCEPT_CONTENT = ['application/json']\n# CELERY_TASK_SERIALIZER = 'json'\n# CELERY_RESULT_SERIALIZER = 'json'\n\nREST_FRAMEWORK = {\n # Use Django's standard `django.contrib.auth` permissions,\n # or allow read-only access for unauthenticated users.\n 'DEFAULT_PERMISSION_CLASSES': [\n 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'\n ],\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.TokenAuthentication',\n 'rest_framework.authentication.SessionAuthentication',\n ),\n 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',\n 'PAGE_SIZE': 10\n}\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'console': {\n 'format': '%(levelname)s %(asctime)s %(module)s: %(message)s'\n },\n\n },\n 'handlers': {\n 'console': {\n 'class': 'logging.StreamHandler',\n 'formatter': 'console'\n },\n },\n 'loggers': {\n '': {\n 'handlers': ['console'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n 'django': {\n 'handlers': ['console'],\n 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n },\n 'django.request': {\n 'level': 'INFO',\n 'handlers': ['console']\n }\n # 'celery': {\n # 'handlers': ['console'],\n # 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n # },\n },\n}\n\n\n# STATICFILES_DIRS = [\n# os.path.join(BASE_DIR, 'static'),\n# ]\n\nPWA_APP_NAME = 'ChalkTracks'\nPWA_APP_DESCRIPTION = \"Indoor Climbing Tracker\"\nPWA_APP_THEME_COLOR = '#000000'\nPWA_APP_BACKGROUND_COLOR = '#000000'\nPWA_APP_DISPLAY = 'standalone'\nPWA_APP_SCOPE = '/'\nPWA_APP_ORIENTATION = 'portrait'\nPWA_APP_START_URL = '/'\nPWA_APP_ICONS = [\n {\n 'src': '/static/routes/favicon_io/favicon-32x32.png',\n 'sizes': '32x32',\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n }, {\n \"src\": \"/static/routes/favicon_io/android-chrome-192x192.png\",\n \"sizes\": \"192x192\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n }, {\n \"src\": \"/static/routes/favicon_io/android-chrome-512x512.png\",\n \"sizes\": \"512x512\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n }\n]\n\nPWA_APP_DIR = 'ltr'\nPWA_APP_LANG = 'en-US'\n\nsentry_sdk.init(\n dsn=\"https://09ce3488b18c4db19820b873eecc30c4@sentry.io/1878812\",\n integrations=[DjangoIntegration()],\n\n # If you wish to associate users to errors (assuming you are using\n # django.contrib.auth) you may enable sending PII data.\n send_default_pii=True\n)\n"},"avg_line_length":{"kind":"number","value":28.5875,"string":"28.5875"},"max_line_length":{"kind":"number","value":91,"string":"91"},"alphanum_fraction":{"kind":"number","value":0.6557353156,"string":"0.655735"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":4261,"string":"4,261"},"score_documentation":{"kind":"number","value":0.6210464946800758,"string":"0.621046"}}},{"rowIdx":3294,"cells":{"hexsha":{"kind":"string","value":"131b0afb1746ef9363aae186aba698e6731a895a"},"size":{"kind":"number","value":2647,"string":"2,647"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"examples/04-lights/plotter_builtins.py"},"max_stars_repo_name":{"kind":"string","value":"akeshavan/pyvista"},"max_stars_repo_head_hexsha":{"kind":"string","value":"45fe8b1c38712776f9b628a60a8662d0716dd52b"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"examples/04-lights/plotter_builtins.py"},"max_issues_repo_name":{"kind":"string","value":"akeshavan/pyvista"},"max_issues_repo_head_hexsha":{"kind":"string","value":"45fe8b1c38712776f9b628a60a8662d0716dd52b"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":6,"string":"6"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2022-03-11T23:21:22.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2022-03-25T03:32:21.000Z"},"max_forks_repo_path":{"kind":"string","value":"examples/04-lights/plotter_builtins.py"},"max_forks_repo_name":{"kind":"string","value":"akeshavan/pyvista"},"max_forks_repo_head_hexsha":{"kind":"string","value":"45fe8b1c38712776f9b628a60a8662d0716dd52b"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"\"\"\"\nPlotter Lighting Systems\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe :class:`pyvista.Plotter` class comes with three options for the default\nlighting system:\n\n * a light kit consisting of a headlight and four camera lights,\n * an illumination system containing three lights arranged around the camera,\n * no lighting.\n\nWith meshes that don't have depth information encoded in their color the\nimportance of an appropriate lighting setup becomes paramount for accurate\nvisualization.\n\nLight kit\n=========\n\nThe default ``lighting='light kit'`` option recreates a lighting setup that\ncorresponds to a ``vtk.vtkLightKit``. We can check what type of lights this\nlighting comprises:\n\"\"\"\n# sphinx_gallery_thumbnail_number = 3\nimport pyvista as pv\nfrom pyvista import examples\n\n# default: light kit\nplotter = pv.Plotter()\nlight_types = [light.light_type for light in plotter.renderer.lights]\n\n# Remove from plotters so output is not produced in docs\npv.plotting._ALL_PLOTTERS.clear()\n\nlight_types\n\n\n###############################################################################\n# Add a white terrain to the scene:\n\nmesh = examples.download_st_helens().warp_by_scalar()\nplotter = pv.Plotter()\nplotter.add_mesh(mesh, color='white')\nplotter.show()\n\n\n###############################################################################\n# Three-lights illumination\n# =========================\n#\n# Switching to three-lights illumination gives a different character to the\n# figure, in this case showing less contrast when viewing the mountain from\n# the top, but having more contrast with views closer to the side. This becomes\n# especially clear when exploring the figures interactively.\n\nplotter = pv.Plotter(lighting='three lights')\nplotter.add_mesh(mesh, color='white')\nplotter.show()\n\n###############################################################################\n# Again we can check what kind of lights this setting uses:\n\nplotter = pv.Plotter(lighting='three lights')\nlight_types = [light.light_type for light in plotter.renderer.lights]\n\n# Remove from plotters so output is not produced in docs\npv.plotting._ALL_PLOTTERS.clear()\n\nlight_types\n\n\n###############################################################################\n# Custom lighting\n# ===============\n#\n# We can introduce our own lighting from scratch by disabling any lighting\n# on plotter initialization. Adding a single scene light to a scene will\n# often result in ominous visuals due to objects having larger regions in\n# shadow:\n\nplotter = pv.Plotter(lighting='none')\nplotter.add_mesh(mesh, color='white')\nlight = pv.Light()\nlight.set_direction_angle(30, 0)\nplotter.add_light(light)\nplotter.show()\n"},"avg_line_length":{"kind":"number","value":30.7790697674,"string":"30.77907"},"max_line_length":{"kind":"number","value":79,"string":"79"},"alphanum_fraction":{"kind":"number","value":0.663770306,"string":"0.66377"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":1913,"string":"1,913"},"score_documentation":{"kind":"number","value":0.7227049489988666,"string":"0.722705"}}},{"rowIdx":3295,"cells":{"hexsha":{"kind":"string","value":"131c13cd6c6c6b833141fea96f58ed4c3b53dc38"},"size":{"kind":"number","value":272,"string":"272"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"src/swimport/tests/15_char_arrays/main.py"},"max_stars_repo_name":{"kind":"string","value":"talos-gis/swimport"},"max_stars_repo_head_hexsha":{"kind":"string","value":"e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-03-07T20:43:42.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2019-03-07T20:43:42.000Z"},"max_issues_repo_path":{"kind":"string","value":"src/swimport/tests/15_char_arrays/main.py"},"max_issues_repo_name":{"kind":"string","value":"talos-gis/swimport"},"max_issues_repo_head_hexsha":{"kind":"string","value":"e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"src/swimport/tests/15_char_arrays/main.py"},"max_forks_repo_name":{"kind":"string","value":"talos-gis/swimport"},"max_forks_repo_head_hexsha":{"kind":"string","value":"e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"from swimport.all import *\n\n\nsrc = FileSource('src.h')\nswim = Swim('example')\n\nswim(pools.c_string)\nswim(pools.numpy_arrays(r\"../resources\", allow_char_arrays=True))\nswim(pools.include(src))\n\nassert swim(Function.Behaviour()(src)) > 0\n\nswim.write('example.i')\nprint('ok!')"},"avg_line_length":{"kind":"number","value":19.4285714286,"string":"19.428571"},"max_line_length":{"kind":"number","value":65,"string":"65"},"alphanum_fraction":{"kind":"number","value":0.7205882353,"string":"0.720588"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":47,"string":"47"},"score_documentation":{"kind":"number","value":0.17279411764705882,"string":"0.172794"}}},{"rowIdx":3296,"cells":{"hexsha":{"kind":"string","value":"131c14184c1fb810c136590d143d0fdf3f83e6df"},"size":{"kind":"number","value":4523,"string":"4,523"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"ipyvolume/astro.py"},"max_stars_repo_name":{"kind":"string","value":"larsoner/ipyvolume"},"max_stars_repo_head_hexsha":{"kind":"string","value":"8603a47aff4531df69ace44efdcf6b85d6e51e51"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":1,"string":"1"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2019-04-09T11:57:07.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2019-04-09T11:57:07.000Z"},"max_issues_repo_path":{"kind":"string","value":"ipyvolume/astro.py"},"max_issues_repo_name":{"kind":"string","value":"larsoner/ipyvolume"},"max_issues_repo_head_hexsha":{"kind":"string","value":"8603a47aff4531df69ace44efdcf6b85d6e51e51"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"ipyvolume/astro.py"},"max_forks_repo_name":{"kind":"string","value":"larsoner/ipyvolume"},"max_forks_repo_head_hexsha":{"kind":"string","value":"8603a47aff4531df69ace44efdcf6b85d6e51e51"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"import numpy as np\n\nimport PIL.Image\nimport pythreejs\n\nimport ipyvolume as ipv\nfrom .datasets import UrlCached\n\ndef _randomSO3():\n \"\"\"return random rotatation matrix, algo by James Arvo\"\"\"\n u1 = np.random.random()\n u2 = np.random.random()\n u3 = np.random.random()\n R = np.array([[np.cos(2*np.pi*u1), np.sin(2*np.pi*u1), 0], [-np.sin(2*np.pi*u1), np.cos(2*np.pi*u1), 0], [0, 0, 1]])\n v = np.array([np.cos(2*np.pi*u2)*np.sqrt(u3), np.sin(2*np.pi*u2)*np.sqrt(u3), np.sqrt(1-u3)])\n H = np.identity(3)-2*v*np.transpose([v])\n return - np.dot(H, R)\n\ndef spherical_galaxy_orbit(orbit_x, orbit_y, orbit_z, N_stars=100, sigma_r=1, orbit_visible=False, orbit_line_interpolate=5, N_star_orbits=10, color=[255, 220, 200], size_star=1, scatter_kwargs={}):\n \"\"\"Create a fake galaxy around the points orbit_x/y/z with N_stars around it\"\"\"\n if orbit_line_interpolate > 1:\n import scipy.interpolate\n x = np.linspace(0, 1, len(orbit_x))\n x_smooth = np.linspace(0, 1, len(orbit_x)*orbit_line_interpolate)\n kind = 'quadratic'\n orbit_x_line = scipy.interpolate.interp1d(x, orbit_x, kind)(x_smooth)\n orbit_y_line = scipy.interpolate.interp1d(x, orbit_y, kind)(x_smooth)\n orbit_z_line = scipy.interpolate.interp1d(x, orbit_z, kind)(x_smooth)\n else:\n orbit_x_line = orbit_x\n orbit_y_line = orbit_y\n orbit_z_line = orbit_z\n line = ipv.plot(orbit_x_line, orbit_y_line, orbit_z_line, visible=orbit_visible)\n x = np.repeat(orbit_x, N_stars).reshape((-1, N_stars))\n y = np.repeat(orbit_y, N_stars).reshape((-1, N_stars))\n z = np.repeat(orbit_z, N_stars).reshape((-1, N_stars))\n xr, yr, zr = np.random.normal(0, scale=sigma_r, size=(3, N_stars))# + \n r = np.sqrt(xr**2 + yr**2 + zr**2)\n \n for i in range(N_stars):\n a = np.linspace(0, 1, x.shape[0]) * 2 * np.pi * N_star_orbits\n xo = r[i] * np.sin(a)\n yo = r[i] * np.cos(a)\n zo = a * 0\n xo, yo, zo = np.dot(_randomSO3(), [xo, yo, zo])\n #print(x.shape, xo.shape)\n x[:, i] += xo\n y[:, i] += yo\n z[:, i] += zo\n \n \n sprite = ipv.scatter(x, y, z, texture=radial_sprite((64, 64), color), marker='square_2d', size=size_star, **scatter_kwargs)\n with sprite.material.hold_sync():\n sprite.material.blending = pythreejs.BlendingMode.CustomBlending\n sprite.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor\n sprite.material.blendDst = pythreejs.BlendFactors.OneFactor\n sprite.material.blendEquation = 'AddEquation'\n sprite.material.transparent = True\n sprite.material.depthWrite = False\n sprite.material.alphaTest = 0.1\n return sprite, line\n\ndef radial_sprite(shape, color):\n color = np.array(color)\n ara = np.zeros(shape[:2] + (4,), dtype=np.uint8)\n x = np.linspace(-1, 1, shape[0])\n y = np.linspace(-1, 1, shape[1])\n x, y = np.meshgrid(x, y)\n s = 0.5\n radius = np.sqrt(x**2+y**2)\n amplitude = np.maximum(0, np.exp(-radius**2/s**2)).T\n ara[...,3] = (amplitude * 255)\n ara[...,:3] = color * amplitude.reshape(shape + (1,))\n im = PIL.Image.fromarray(ara, 'RGBA')\n return im\n\ndef stars(N=1000, radius=100000, thickness=3, seed=42, color=[255, 240, 240]):\n import ipyvolume as ipv\n rng = np.random.RandomState(seed)\n x, y, z = rng.normal(size=(3, N))\n r = np.sqrt(x**2 + y**2 + z**2)/(radius + thickness * radius * np.random.random(N))\n x /= r\n y /= r\n z /= r\n return ipv.scatter(x, y, z, texture=radial_sprite((64, 64), color), marker='square_2d', grow_limits=False, size=radius*0.7/100)\n\n\nmilkyway_url = 'https://www.nasa.gov/sites/default/files/images/620057main_milkyway_full.jpg'\nmilkyway_image = UrlCached(milkyway_url)\n\n\n\ndef plot_milkyway(R_sun=8, size=100):\n mw_image = PIL.Image.open(milkyway_image.fetch())\n rescale = 40\n t = np.linspace(0, 1, 100)\n xmw = np.linspace(0, 1, 10)\n ymw = np.linspace(0, 1, 10)\n xmw, ymw = np.meshgrid(xmw, ymw)\n zmw = xmw * 0 + 0.01\n mw = mesh = ipv.plot_mesh((xmw-0.5)*rescale, (ymw-0.5)*rescale+R_sun, zmw, u=xmw, v=ymw, texture=mw_image, wireframe=False)\n mw.material.blending = pythreejs.BlendingMode.CustomBlending\n mw.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor\n mw.material.blendDst = pythreejs.BlendFactors.OneFactor\n mw.material.blendEquation = 'AddEquation'\n mw.material.transparent = True\n mw.material.depthWrite = False\n mw.material.alphaTest = 0.1\n ipv.xyzlim(size)\n return mesh"},"avg_line_length":{"kind":"number","value":41.1181818182,"string":"41.118182"},"max_line_length":{"kind":"number","value":198,"string":"198"},"alphanum_fraction":{"kind":"number","value":0.6409462746,"string":"0.640946"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":308,"string":"308"},"score_documentation":{"kind":"number","value":0.06809639619721423,"string":"0.068096"}}},{"rowIdx":3297,"cells":{"hexsha":{"kind":"string","value":"131e1d61812e10d7ea42b3ca418199cd229845a3"},"size":{"kind":"number","value":1157,"string":"1,157"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"deepfunning/function.py"},"max_stars_repo_name":{"kind":"string","value":"Zrealshadow/DeepFunning"},"max_stars_repo_head_hexsha":{"kind":"string","value":"5c44210a6b30ea57a0be5f930da4ada540e7e3d0"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"null"},"max_stars_repo_stars_event_min_datetime":{"kind":"null"},"max_stars_repo_stars_event_max_datetime":{"kind":"null"},"max_issues_repo_path":{"kind":"string","value":"deepfunning/function.py"},"max_issues_repo_name":{"kind":"string","value":"Zrealshadow/DeepFunning"},"max_issues_repo_head_hexsha":{"kind":"string","value":"5c44210a6b30ea57a0be5f930da4ada540e7e3d0"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"null"},"max_issues_repo_issues_event_min_datetime":{"kind":"null"},"max_issues_repo_issues_event_max_datetime":{"kind":"null"},"max_forks_repo_path":{"kind":"string","value":"deepfunning/function.py"},"max_forks_repo_name":{"kind":"string","value":"Zrealshadow/DeepFunning"},"max_forks_repo_head_hexsha":{"kind":"string","value":"5c44210a6b30ea57a0be5f930da4ada540e7e3d0"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"null"},"max_forks_repo_forks_event_min_datetime":{"kind":"null"},"max_forks_repo_forks_event_max_datetime":{"kind":"null"},"content":{"kind":"string","value":"'''\n * @author Waldinsamkeit\n * @email Zenglz_pro@163.com\n * @create date 2020-09-25 14:33:38\n * @desc \n'''\n\nimport torch\n\n\n'''--------------------- Weighted Binary cross Entropy ----------------------'''\n\n'''\nIn Torch BCELoss, weight is set to every element of input instead of to every class\n'''\ndef weighted_binary_cross_entropy(output, target, weights=None):\n if weights is not None:\n assert len(weights) == 2\n \n loss = weights[1] * (target * torch.log(output)) + \\\n weights[0] * ((1 - target) * torch.log(1 - output))\n else:\n loss = target * torch.log(output) + (1 - target) * torch.log(1 - output)\n\n return torch.neg(torch.mean(loss))\n\n\n''' ---------------------- Binary focal loss function -------------------------- '''\n\n'''\nIn some degree, it can reduce the influence of imbalanced dataset\n'''\n\ndef focal_loss(y_true,y_pred,device):\n alpha,gamma = torch.tensor(0.25).to(device) , torch.tensor(2.0).to(device)\n y_pred=torch.clamp(y_pred,1e-7,1-1e-7)\n return - alpha * y_true * torch.log(y_pred) * (1 - y_pred) ** gamma\\\n - (1 - alpha) * (1 - y_true) * torch.log(1 - y_pred) * y_pred\n\n"},"avg_line_length":{"kind":"number","value":28.925,"string":"28.925"},"max_line_length":{"kind":"number","value":84,"string":"84"},"alphanum_fraction":{"kind":"number","value":0.577355229,"string":"0.577355"},"count_classes":{"kind":"number","value":0,"string":"0"},"score_classes":{"kind":"number","value":0,"string":"0"},"count_generators":{"kind":"number","value":0,"string":"0"},"score_generators":{"kind":"number","value":0,"string":"0"},"count_decorators":{"kind":"number","value":0,"string":"0"},"score_decorators":{"kind":"number","value":0,"string":"0"},"count_async_functions":{"kind":"number","value":0,"string":"0"},"score_async_functions":{"kind":"number","value":0,"string":"0"},"count_documentation":{"kind":"number","value":435,"string":"435"},"score_documentation":{"kind":"number","value":0.3759723422644771,"string":"0.375972"}}},{"rowIdx":3298,"cells":{"hexsha":{"kind":"string","value":"131e36d011ba94f7784c802143deb17326553c0e"},"size":{"kind":"number","value":7055,"string":"7,055"},"ext":{"kind":"string","value":"py"},"lang":{"kind":"string","value":"Python"},"max_stars_repo_path":{"kind":"string","value":"dlms_cosem/hdlc/address.py"},"max_stars_repo_name":{"kind":"string","value":"pwitab/dlms-cosem"},"max_stars_repo_head_hexsha":{"kind":"string","value":"aa9e18e6ef8a4fee30da8b797dad03b0b7847780"},"max_stars_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_stars_count":{"kind":"number","value":35,"string":"35"},"max_stars_repo_stars_event_min_datetime":{"kind":"string","value":"2018-05-24T08:16:35.000Z"},"max_stars_repo_stars_event_max_datetime":{"kind":"string","value":"2022-02-24T16:07:07.000Z"},"max_issues_repo_path":{"kind":"string","value":"dlms_cosem/hdlc/address.py"},"max_issues_repo_name":{"kind":"string","value":"Layty/dlms-cosem"},"max_issues_repo_head_hexsha":{"kind":"string","value":"95b67054a1dfb928e960547b0246b7b6794f0594"},"max_issues_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_issues_count":{"kind":"number","value":41,"string":"41"},"max_issues_repo_issues_event_min_datetime":{"kind":"string","value":"2020-12-18T16:31:40.000Z"},"max_issues_repo_issues_event_max_datetime":{"kind":"string","value":"2021-12-13T20:59:42.000Z"},"max_forks_repo_path":{"kind":"string","value":"dlms_cosem/hdlc/address.py"},"max_forks_repo_name":{"kind":"string","value":"Layty/dlms-cosem"},"max_forks_repo_head_hexsha":{"kind":"string","value":"95b67054a1dfb928e960547b0246b7b6794f0594"},"max_forks_repo_licenses":{"kind":"list like","value":["MIT"],"string":"[\n \"MIT\"\n]"},"max_forks_count":{"kind":"number","value":19,"string":"19"},"max_forks_repo_forks_event_min_datetime":{"kind":"string","value":"2019-04-02T14:32:01.000Z"},"max_forks_repo_forks_event_max_datetime":{"kind":"string","value":"2021-12-14T13:24:29.000Z"},"content":{"kind":"string","value":"from typing import *\n\nimport attr\n\nfrom dlms_cosem.hdlc import validators\n\n\n@attr.s(auto_attribs=True)\nclass HdlcAddress:\n \"\"\"\n A client address shall always be expressed on one byte.\n To enable addressing more than one logical device within a single physical device\n and to support the multi-drop configuration the server address may be divided in\n two parts– may be divided into two parts:\n The logical address to address a logical device (separate addressable entity\n within a physical device) makes up the upper HDLC address\n The logical address must always be present.\n The physical address is used to address a physical device ( a physical device on\n a multi-drop)\n The physical address can be omitted it not used.\n \"\"\"\n\n logical_address: int = attr.ib(validator=[validators.validate_hdlc_address])\n physical_address: Optional[int] = attr.ib(\n default=None, validator=[validators.validate_hdlc_address]\n )\n address_type: str = attr.ib(\n default=\"client\", validator=[validators.validate_hdlc_address_type]\n )\n\n @property\n def length(self):\n \"\"\"\n The number of bytes the address makes up.\n :return:\n \"\"\"\n return len(self.to_bytes())\n\n def to_bytes(self):\n out: List[Optional[int]] = list()\n if self.address_type == \"client\":\n # shift left 1 bit and set the lsb to mark end of address.\n out.append(((self.logical_address << 1) | 0b00000001))\n else:\n # server address type\n\n logical_higher, logical_lower = self._split_address(self.logical_address)\n\n if self.physical_address:\n physical_higher, physical_lower = self._split_address(\n self.physical_address\n )\n # mark physical lower as end\n physical_lower = physical_lower | 0b00000001\n out.extend(\n [logical_higher, logical_lower, physical_higher, physical_lower]\n )\n else:\n # no physical address so mark the logial as end.\n logical_lower = logical_lower | 0b00000001\n out.extend([logical_higher, logical_lower])\n\n out_bytes = list()\n for address in out:\n if address:\n out_bytes.append(address.to_bytes(1, \"big\"))\n\n return b\"\".join(out_bytes)\n\n @staticmethod\n def _split_address(address: int) -> Tuple[Optional[int], int]:\n higher: Optional[int]\n lower: int\n\n if address > 0b01111111:\n lower = (address & 0b0000000001111111) << 1\n higher = (address & 0b0011111110000000) >> 6\n\n else:\n lower = address << 1\n higher = None\n\n return higher, lower\n\n @staticmethod\n def _address_to_byte(address: int) -> bytes:\n return address.to_bytes(1, \"big\")\n\n @classmethod\n def destination_from_bytes(cls, frame_bytes: bytes, address_type: str):\n destination_address_data, _ = HdlcAddress.find_address_in_frame_bytes(\n frame_bytes\n )\n (\n destination_logical,\n destination_physical,\n destination_length,\n ) = destination_address_data\n\n return cls(destination_logical, destination_physical, address_type)\n\n @classmethod\n def source_from_bytes(cls, frame_bytes: bytes, address_type: str):\n _, source_address_data = HdlcAddress.find_address_in_frame_bytes(frame_bytes)\n\n source_logical, source_physical, source_length = source_address_data\n return cls(source_logical, source_physical, address_type)\n\n @staticmethod\n def find_address_in_frame_bytes(\n hdlc_frame_bytes: bytes,\n ) -> Tuple[Tuple[int, Optional[int], int], Tuple[int, Optional[int], int]]:\n \"\"\"\n address can be 1, 2 or 4 bytes long. the end byte is indicated by the of\n the last byte LSB being 1\n The first address is the destination address and the seconds is the\n source address.\n :param frame_bytes:\n :return:\n \"\"\"\n\n # Find destination address.\n destination_length: int = 1\n destination_logical: int = 0\n destination_physical: Optional[int] = 0\n destination_positions_list: List[Tuple[int, int]] = [(3, 1), (4, 2), (6, 4)]\n address_bytes: bytes\n for pos, _length in destination_positions_list:\n end_byte = hdlc_frame_bytes[pos]\n if bool(end_byte & 0b00000001):\n # Found end byte:\n destination_length = _length\n break\n continue\n if destination_length == 1:\n address_bytes = hdlc_frame_bytes[3].to_bytes(1, \"big\")\n destination_logical = address_bytes[0] >> 1\n destination_physical = None\n\n elif destination_length == 2:\n address_bytes = hdlc_frame_bytes[3:5]\n destination_logical = address_bytes[0] >> 1\n destination_physical = address_bytes[1] >> 1\n\n elif destination_length == 4:\n address_bytes = hdlc_frame_bytes[3:7]\n destination_logical = HdlcAddress.parse_two_byte_address(address_bytes[:2])\n destination_physical = HdlcAddress.parse_two_byte_address(address_bytes[3:])\n\n # Find source address\n source_length: int = 1\n source_logical: int = 0\n source_physical: Optional[int] = 0\n source_position_list: List[Tuple[int, int]] = [\n (item[0] + destination_length, item[1])\n for item in destination_positions_list\n ]\n for pos, _length in source_position_list:\n end_byte = hdlc_frame_bytes[pos]\n if bool(end_byte & 0b00000001):\n # Found end byte:\n source_length = _length\n break\n continue\n if source_length == 1:\n address_bytes = hdlc_frame_bytes[3 + destination_length].to_bytes(1, \"big\")\n source_logical = address_bytes[0] >> 1\n source_physical = None\n\n elif source_length == 2:\n address_bytes = hdlc_frame_bytes[3 + destination_length : 5 + source_length]\n source_logical = address_bytes[0] >> 1\n source_physical = address_bytes[1] >> 1\n\n elif destination_length == 4:\n address_bytes = hdlc_frame_bytes[3 + destination_length : 7 + source_length]\n source_logical = HdlcAddress.parse_two_byte_address(address_bytes[:2])\n source_physical = HdlcAddress.parse_two_byte_address(address_bytes[3:])\n\n return (\n (destination_logical, destination_physical, destination_length),\n (source_logical, source_physical, source_length),\n )\n\n @staticmethod\n def parse_two_byte_address(address_bytes: bytes):\n if address_bytes != 2:\n raise ValueError(f\"Can only parse 2 bytes for address\")\n upper = address_bytes[0] >> 1\n lower = address_bytes[1] >> 1\n\n return lower + (upper << 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self.b, equal_var=False)\n\n def time_ttest_ind_diff_var(self):\n # test different sized sample with different variances\n stats.ttest_ind(self.a, self.c)\n stats.ttest_ind(self.a, self.c, equal_var=False)\n\n\nclass Distribution(Benchmark):\n param_names = ['distribution', 'properties'] \n params = [\n ['cauchy', 'gamma', 'beta'],\n ['pdf', 'cdf', 'rvs', 'fit']\n ]\n \n def setup(self, distribution, properties):\n np.random.seed(12345678)\n self.x = np.random.rand(100)\n\n def time_distribution(self, distribution, properties):\n if distribution == 'gamma':\n if properties == 'pdf':\n stats.gamma.pdf(self.x, a=5, loc=4, scale=10)\n elif properties == 'cdf':\n stats.gamma.cdf(self.x, a=5, loc=4, scale=10)\n elif properties == 'rvs':\n stats.gamma.rvs(size=1000, a=5, loc=4, scale=10)\n elif properties == 'fit':\n stats.gamma.fit(self.x, loc=4, scale=10)\n elif distribution == 'cauchy':\n if properties == 'pdf':\n stats.cauchy.pdf(self.x, loc=4, scale=10)\n elif properties == 'cdf':\n 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    def main(request, response): headers = [("Content-type", "text/html;charset=shift-jis")] # Shift-JIS bytes for katakana TE SU TO ('test') content = chr(0x83) + chr(0x65) + chr(0x83) + chr(0x58) + chr(0x83) + chr(0x67); return headers, content
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    import re from setuptools import setup, find_packages import sys if sys.version_info < (3, 5): raise 'must use Python version 3.5 or higher' with open('./gmailapi_backend/__init__.py', 'r') as f: MATCH_EXPR = "__version__[^'\"]+(['\"])([^'\"]+)" VERSION = re.search(MATCH_EXPR, f.read()).group(2).strip() setup( name='django-gmailapi-backend', version=VERSION, packages=find_packages(), author="Michele Dolfi", author_email="[email protected]", license="Apache License 2.0", entry_points={ 'console_scripts': [ 'gmail_oauth2 = gmailapi_backend.bin.gmail_oauth2:main', ] }, install_requires=[ 'google-api-python-client~=2.0', 'google-auth>=1.16.0,<3.0.0dev', ], url="https://github.com/dolfim/django-gmailapi-backend", long_description_content_type='text/markdown', long_description=open('README.md').read(), description='Email backend for Django which sends email via the Gmail API', classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Framework :: Django', 'Topic :: Communications :: Email', 'Development Status :: 4 - Beta' ], )
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    null
    null
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    940a04a88e8f78e2d010dc912ad6905ae363503c
    [ "Apache-2.0" ]
    null
    null
    null
    openpeerpower/scripts/ensure_config.py
    OpenPeerPower/openpeerpower
    940a04a88e8f78e2d010dc912ad6905ae363503c
    [ "Apache-2.0" ]
    1
    2019-04-24T14:10:08.000Z
    2019-04-24T14:10:08.000Z
    """Script to ensure a configuration file exists.""" import argparse import os import openpeerpower.config as config_util from openpeerpower.core import OpenPeerPower # mypy: allow-untyped-calls, allow-untyped-defs def run(args): """Handle ensure config commandline script.""" parser = argparse.ArgumentParser( description=( "Ensure a Open Peer Power config exists, creates one if necessary." ) ) parser.add_argument( "-c", "--config", metavar="path_to_config_dir", default=config_util.get_default_config_dir(), help="Directory that contains the Open Peer Power configuration", ) parser.add_argument("--script", choices=["ensure_config"]) args = parser.parse_args() config_dir = os.path.join(os.getcwd(), args.config) # Test if configuration directory exists if not os.path.isdir(config_dir): print("Creating directory", config_dir) os.makedirs(config_dir) opp = OpenPeerPower() opp.config.config_dir = config_dir config_path = opp.loop.run_until_complete(async_run(opp)) print("Configuration file:", config_path) return 0 async def async_run(opp): """Make sure config exists.""" path = await config_util.async_ensure_config_exists(opp) await opp.async_stop(force=True) return path
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    atcoder/abc132A_fifty_fifty.py
    uninhm/kyopro
    bf6ed9cbf6a5e46cde0291f7aa9d91a8ddf1f5a3
    [ "BSD-3-Clause" ]
    31
    2020-05-13T01:07:55.000Z
    2021-07-13T07:53:26.000Z
    atcoder/abc132A_fifty_fifty.py
    uninhm/kyopro
    bf6ed9cbf6a5e46cde0291f7aa9d91a8ddf1f5a3
    [ "BSD-3-Clause" ]
    10
    2020-05-20T07:22:09.000Z
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    atcoder/abc132A_fifty_fifty.py
    uninhm/kyopro
    bf6ed9cbf6a5e46cde0291f7aa9d91a8ddf1f5a3
    [ "BSD-3-Clause" ]
    14
    2020-05-11T05:58:36.000Z
    2021-12-07T03:20:43.000Z
    # Vicfred # https://atcoder.jp/contests/abc132/tasks/abc132_a # implementation S = list(input()) if len(set(S)) == 2: if S.count(S[0]) == 2: print("Yes") quit() print("No")
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    ebc4686c7b16c011bf5266cb6335221309aacb80
    [ "BSD-3-Clause" ]
    500
    2019-04-01T13:50:18.000Z
    2022-03-07T01:50:45.000Z
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    nrohan09-cloud/dabl
    ebc4686c7b16c011bf5266cb6335221309aacb80
    [ "BSD-3-Clause" ]
    111
    2019-04-01T17:48:40.000Z
    2020-03-27T16:39:19.000Z
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    nrohan09-cloud/dabl
    ebc4686c7b16c011bf5266cb6335221309aacb80
    [ "BSD-3-Clause" ]
    60
    2019-04-01T14:58:35.000Z
    2021-08-13T02:58:20.000Z
    import pytest import numpy as np import pandas as pd import matplotlib.pyplot as plt import itertools from sklearn.datasets import (make_regression, make_blobs, load_digits, fetch_openml, load_diabetes) from sklearn.preprocessing import KBinsDiscretizer from dabl.preprocessing import clean, detect_types, guess_ordinal from dabl.plot.supervised import ( plot, plot_classification_categorical, plot_classification_continuous, plot_regression_categorical, plot_regression_continuous) from dabl.utils import data_df_from_bunch from dabl import set_config # FIXME: check that target is not y but a column name @pytest.mark.filterwarnings('ignore:the matrix subclass') @pytest.mark.parametrize("continuous_features, categorical_features, task", itertools.product([0, 1, 3, 100], [0, 1, 3, 100], ['classification', 'regression'])) def test_plots_smoke(continuous_features, categorical_features, task): # simple smoke test # should be parametrized n_samples = 100 X_cont, y_cont = make_regression( n_samples=n_samples, n_features=continuous_features, n_informative=min(continuous_features, 2)) X_cat, y_cat = make_regression( n_samples=n_samples, n_features=categorical_features, n_informative=min(categorical_features, 2)) if X_cat.shape[1] > 0: X_cat = KBinsDiscretizer(encode='ordinal').fit_transform(X_cat) cont_columns = ["asdf_%d_cont" % i for i in range(continuous_features)] df_cont = pd.DataFrame(X_cont, columns=cont_columns) if categorical_features > 0: cat_columns = ["asdf_%d_cat" % i for i in range(categorical_features)] df_cat = pd.DataFrame(X_cat, columns=cat_columns).astype('int') df_cat = df_cat.astype("category") X_df = pd.concat([df_cont, df_cat], axis=1) else: X_df = df_cont assert(X_df.shape[1] == continuous_features + categorical_features) X_clean = clean(X_df.copy()) y = y_cont + y_cat if X_df.shape[1] == 0: y = np.random.uniform(size=n_samples) if task == "classification": y = np.digitize(y, np.percentile(y, [5, 10, 60, 85])) X_clean['target'] = y if task == "classification": X_clean['target'] = X_clean['target'].astype('category') types = detect_types(X_clean) column_types = types.T.idxmax() assert np.all(column_types[:continuous_features] == 'continuous') assert np.all(column_types[continuous_features:-1] == 'categorical') if task == "classification": assert column_types[-1] == 'categorical' else: assert column_types[-1] == 'continuous' plot(X_clean, target_col='target') plt.close("all") @pytest.mark.parametrize("add, feature_type, target_type", itertools.product([0, .1], ['continuous', 'categorical'], ['continuous', 'categorical'])) def test_type_hints(add, feature_type, target_type): X = pd.DataFrame(np.random.randint(4, size=100)) + add X['target'] = np.random.uniform(size=100) plot(X, type_hints={0: feature_type, 'target': target_type}, target_col='target') # get title of figure text = plt.gcf()._suptitle.get_text() assert feature_type.capitalize() in text ax = plt.gca() # one of the labels is 'target' iif regression labels = ax.get_ylabel() + ax.get_xlabel() assert ('target' in labels) == (target_type == 'continuous') plt.close("all") def test_float_classification_target(): # check we can plot even if we do classification with a float target X, y = make_blobs() data = pd.DataFrame(X) data['target'] = y.astype(np.float) types = detect_types(data) assert types.categorical['target'] plot(data, target_col='target') # same with "actual float" - we need to specify classification for that :-/ data['target'] = y.astype(np.float) + .2 plot(data, target_col='target', type_hints={'target': 'categorical'}) plt.close("all") @pytest.mark.filterwarnings('ignore:Discarding near-constant') def test_plot_classification_n_classes(): X, y = make_blobs() X = pd.DataFrame(X) X['target'] = 0 with pytest.raises(ValueError, match="Less than two classes"): plot_classification_categorical(X, 'target') with pytest.raises(ValueError, match="Less than two classes"): plot_classification_continuous(X, 'target') def test_plot_wrong_target_type(): X, y = make_blobs() X = pd.DataFrame(X) X['target'] = y with pytest.raises(ValueError, match="need continuous"): plot_regression_categorical(X, 'target') with pytest.raises(ValueError, match="need continuous"): plot_regression_continuous(X, 'target') X['target'] = X[0] with pytest.raises(ValueError, match="need categorical"): plot_classification_categorical(X, 'target') with pytest.raises(ValueError, match="need categorical"): plot_classification_continuous(X, 'target') def test_plot_target_low_card_int(): data = load_digits() df = data_df_from_bunch(data) plot(df[::10], target_col='target') def test_plot_X_y(): X, y = make_blobs() X = pd.DataFrame(X) plot(X, y) def test_plot_regression_numpy(): X, y = make_regression() plot(X, y) def test_plot_lda_binary(): X, y = make_blobs(centers=2) X = pd.DataFrame(X) plot(X, y, univariate_plot='kde') def test_plot_int_column_name(): X, y = make_blobs() X = pd.DataFrame(X) X[3] = y plot(X, target_col=3) def test_negative_ordinal(): # check that a low card int with negative values is plotted correctly data = pd.DataFrame([np.random.randint(0, 10, size=1000) - 5, np.random.randint(0, 2, size=1000)]).T # ensure first column is low_card_int assert (detect_types(data).T.idxmax() == ['low_card_int', 'categorical']).all() assert guess_ordinal(data[0]) # smoke test plot(data, target_col=1) def test_large_ordinal(): # check that large integers don't bring us down (bincount memory error) # here some random phone numbers assert not guess_ordinal(pd.Series([6786930208, 2142878625, 9106275431])) def test_plot_classification_continuous(): data = fetch_openml('MiceProtein') df = data_df_from_bunch(data) # only univariate plots figures = plot_classification_continuous(df, target_col='target', plot_pairwise=False) assert len(figures) == 1 # top 10 axes assert len(figures[0].get_axes()) == 10 # six is the minimum number of features for histograms # (last column is target) figures = plot_classification_continuous(df.iloc[:, -7:], target_col='target', plot_pairwise=False) assert len(figures) == 1 assert len(figures[0].get_axes()) == 6 # for 5 features, do full pairplot figures = plot_classification_continuous(df.iloc[:, -6:], target_col='target', plot_pairwise=False) assert len(figures) == 1 # diagonal has twin axes assert len(figures[0].get_axes()) == 5 * 5 + 5 # also do pairwise plots figures = plot_classification_continuous(df, target_col='target', random_state=42) # univariate, pairwise, pca, lda assert len(figures) == 4 # univariate axes = figures[0].get_axes() assert len(axes) == 10 # known result assert axes[0].get_xlabel() == "SOD1_N" # bar plot never has ylabel assert axes[0].get_ylabel() == "" # pairwise axes = figures[1].get_axes() assert len(axes) == 4 # known result assert axes[0].get_xlabel() == "SOD1_N" assert axes[0].get_ylabel() == 'S6_N' # PCA axes = figures[2].get_axes() assert len(axes) == 4 # known result assert axes[0].get_xlabel() == "PCA 1" assert axes[0].get_ylabel() == 'PCA 5' # LDA axes = figures[3].get_axes() assert len(axes) == 4 # known result assert axes[0].get_xlabel() == "LDA 0" assert axes[0].get_ylabel() == 'LDA 1' def test_plot_string_target(): X, y = make_blobs(n_samples=30) data = pd.DataFrame(X) y = pd.Series(y) y[y == 0] = 'a' y[y == 1] = 'b' y[y == 2] = 'c' data['target'] = y plot(data, target_col='target') def test_na_vals_reg_plot_raise_warning(): X, y = load_diabetes(return_X_y=True) X = pd.DataFrame(X) y[::50] = np.NaN X['target_col'] = y with pytest.warns(UserWarning, match="Missing values in target_col have " "been removed for regression"): plot(X, 'target_col') with pytest.warns(UserWarning, match="Missing values in target_col have " "been removed for regression"): plot_regression_continuous(X, 'target_col') with pytest.warns(UserWarning, match="Missing values in target_col have " "been removed for regression"): plot_regression_categorical(X, 'target_col') def test_plot_regression_continuous_with_target_outliers(): df = pd.DataFrame( data={ "feature": np.random.randint(low=1, high=100, size=200), # target values are bound between 50 and 100 "target": np.random.randint(low=50, high=100, size=200) } ) # append single outlier record with target value 0 df = df.append({"feature": 50, "target": 0}, ignore_index=True) with pytest.warns( UserWarning, match="Dropped 1 outliers in column target." ): plot_regression_continuous(df, 'target') def test_plot_regression_categorical_missing_value(): df = pd.DataFrame({'y': np.random.normal(size=300)}) df.loc[100:200, 'y'] += 1 df.loc[200:300, 'y'] += 2 df['x'] = 'a' df.loc[100:200, 'x'] = 'b' df.loc[200:300, 'x'] = np.NaN res = plot(df, target_col='y') assert len(res[1][0, 0].get_yticklabels()) == 3 assert res[1][0, 0].get_yticklabels()[2].get_text() == 'dabl_mi...' def test_label_truncation(): a = ('a_really_long_name_that_would_mess_up_the_layout_a_lot' '_by_just_being_very_long') b = ('the_target_that_has_an_equally_long_name_which_would_' 'mess_up_everything_as_well_but_in_different_places') df = pd.DataFrame({a: np.random.uniform(0, 1, 1000)}) df[b] = df[a] + np.random.uniform(0, 0.1, 1000) res = plot_regression_continuous(df, target_col=b) assert res[0, 0].get_ylabel() == 'the_target_that_h...' assert res[0, 0].get_xlabel() == 'a_really_long_nam...' set_config(truncate_labels=False) res = plot_regression_continuous(df, target_col=b) assert res[0, 0].get_ylabel() == b assert res[0, 0].get_xlabel() == a set_config(truncate_labels=True)
    35.610224
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    0
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    3,371
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    2,441
    0.219002
    92d3e306e086847f38535479f8de8893955d728c
    4,480
    py
    Python
    scripts/calculate_rank.py
    daniel-theis/multicore-test-harness
    d0ff54ef1c9f9637dd16dd8b85ac1cee8dc49e19
    [ "MIT" ]
    15
    2018-05-06T20:54:41.000Z
    2020-12-04T05:36:11.000Z
    scripts/calculate_rank.py
    daniel-theis/multicore-test-harness
    d0ff54ef1c9f9637dd16dd8b85ac1cee8dc49e19
    [ "MIT" ]
    null
    null
    null
    scripts/calculate_rank.py
    daniel-theis/multicore-test-harness
    d0ff54ef1c9f9637dd16dd8b85ac1cee8dc49e19
    [ "MIT" ]
    3
    2020-12-04T05:36:13.000Z
    2021-09-08T11:53:16.000Z
    ################################################################################ # Copyright (c) 2017 Dan Iorga, Tyler Sorenson, Alastair Donaldson # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all #copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ################################################################################ import sys import json from pprint import pprint class CalculateRank(object): def __init__(self, input_file): self._input_file = input_file def get_rank(self): # Read the configuration in the JSON file with open(self._input_file) as data_file: experiments_object = json.load(data_file) # Sort all the configurations in a list dict_list = list() for experiment in experiments_object: ranked_list = experiments_object[experiment]["it"] od = list(sorted(ranked_list.values(), key=lambda x:x['q_value'], reverse=True)) dict_list.append(od) # for it in dict_list: # print() # print() # for i in range(len(it)): # print(it[i]['mapping']) # print(it[i]['q_value']) # For each environment. get the rank in the other experiments and store in 'rank' for it in dict_list[0]: environment = it['mapping'] rank_list = list() # Look it up for each victim(experiment) for it2 in dict_list: # Find its rank there for i in range(len(it2)): env = it2[i]['mapping'] if environment == env: rank_here = i break rank_list.append(rank_here) it['rank'] = rank_list # Identify the ones that are not Pareto optimal rank_list_bad = list() for it1 in dict_list[0]: for it2 in dict_list[0]: if len([i for i, j in zip(it1['rank'], it2['rank']) if i > j]) == len(it1['rank']): rank_list_bad.append(it1) # Put the Pareto Optimal in a list paretto_optimal = list() for it in dict_list[0]: if not (it in rank_list_bad): paretto_optimal.append(it) # If there are ties, try to break them at fewer comparisons if len(paretto_optimal) > 1: rank_list_bad = list() for it1 in paretto_optimal: for it2 in paretto_optimal: if len([i for i, j in zip(it1['rank'], it2['rank']) if i > j]) == len(it1['rank']) - 1: rank_list_bad.append(it1) # Put the tie broken ones in a list paretto_optimal_tie_break = list() for it in paretto_optimal: if not (it in rank_list_bad): paretto_optimal_tie_break.append(it) print("With no tie breaking") for i in range(len(paretto_optimal)): print(paretto_optimal[i]['mapping']) print("With tie breaking") for i in range(len(paretto_optimal_tie_break)): print(paretto_optimal_tie_break[i]['mapping']) else: print(paretto_optimal[0]['mapping']) print("There was no tie breaking") if __name__ == "__main__": if len(sys.argv) != 2: print("usage: " + sys.argv[0] + " <ranked_environments>.json\n") exit(1) rank = CalculateRank(sys.argv[1]) rank.get_rank()
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    92d42a0f362d457b4431027767a7c26d248663c3
    6,206
    py
    Python
    contrib/antlrqueryparser/src/python/generate_asts.py
    marblestation/montysolr
    50917b4d53caac633fe9d1965f175401b3edc77d
    [ "Apache-2.0" ]
    24
    2015-03-23T17:57:47.000Z
    2021-11-01T17:08:58.000Z
    contrib/antlrqueryparser/src/python/generate_asts.py
    marblestation/montysolr
    50917b4d53caac633fe9d1965f175401b3edc77d
    [ "Apache-2.0" ]
    138
    2015-01-02T16:47:51.000Z
    2022-02-26T02:44:07.000Z
    contrib/antlrqueryparser/src/python/generate_asts.py
    marblestation/montysolr
    50917b4d53caac633fe9d1965f175401b3edc77d
    [ "Apache-2.0" ]
    10
    2015-03-11T19:49:51.000Z
    2020-12-09T09:22:02.000Z
    import sys import subprocess as sub import os """ Simple utility script to generate HTML charts of how ANTLR parses every query and what is the resulting AST. """ def run(grammar_name, basedir='', cp='.:/dvt/antlr-142/lib/antlr-3.4-complete.jar:/x/dev/antlr-34/lib/antlr-3.4-complete.jar', grammardir='', java_executable='java', dot_executable='dot' ): if not basedir: basedir = os.path.abspath('../../../../../../../../../../bin') old_dir = os.getcwd() thisdir = grammardir if not thisdir: thisdir = os.path.dirname(os.path.abspath(__file__)) os.chdir(thisdir) cp += os.pathsep + basedir #print "We'll generate ANTLR graphs\ngramar: %s\nbasedir: %s\nclasspath: %s\nparserdir: %s" % (grammar_name, basedir, cp, thisdir) grammar_file = os.path.join(thisdir, grammar_name + '.g') if not os.path.exists(grammar_file): raise Exception('Grammar %s does not exist in classpath: %s' % (grammar_file, cp)) tmp_file = os.path.join(basedir, 'ast-tree.dot') index_file = os.path.join(basedir, '%s.html' % grammar_name) gunit_file = os.path.join(thisdir, grammar_name + '.gunit') generate_ast_command = '%s -cp %s org.apache.lucene.queryparser.flexible.aqp.parser.BuildAST %s "%%s"' % (java_executable, cp, grammar_name) generate_svg_command = '%s -Tsvg %s' % (dot_executable, tmp_file) test_cases = load_gunit_file(gunit_file) index_fo = open(index_file, 'w') index_fo.write('<h1>Test cases generated from grammar: %s</h1>\n' % grammar_name) out_lines = [] i = 0 cmds = generate_ast_command.split() cmds_svg = generate_svg_command.split() total = sum(map(lambda x: len(x), test_cases.values())) toc = [] data = [] toc.append('<a name="toc" />') for section,values in test_cases.items(): output = tree = svg = '' toc.append('The rule: <a href="#anchor%s"><pre>%s</pre></a><br/>' % (section, section)) # generate AST tree for query in values: i += 1 cmds[-1] = query #tmp_dot = os.path.join(basedir, 'tmp-%s.dot' % i) tmp_dot = tmp_file if os.path.exists(tmp_dot): os.remove(tmp_dot) toc.append('%s. <a href="#anchor%s"><pre>%s</pre></a><br/>' % (i, i, query)) print '// %s/%s :: %s' % (i, total, query) #generate graph p = sub.Popen(cmds,stdout=sub.PIPE,stderr=sub.PIPE) output, errors = p.communicate() if output: fo = open(tmp_dot, 'w') fo.write(output) fo.close() else: print 'Error generating AST for: ' + query print errors if 'java.lang.ClassNotFoundException' in errors: raise Exception('Please fix your classpath') continue #generate tree cmds.append(section) cmds.append("tree") p = sub.Popen(cmds,stdout=sub.PIPE,stderr=sub.PIPE) tree, errors = p.communicate() if tree: q = query.replace('\\', '\\\\').replace('"', '\\"').replace('\'', '\\\'') t = tree.strip().replace('\\', '\\\\').replace('"', '\\"').replace("'", "\\'") print "\"%s\" -> \"%s\"" % (q, t) else: print 'Error generating AST for: ' + query print errors tree = errors cmds.pop() cmds.pop() cmds_svg[-1] = tmp_dot try: p = sub.Popen(cmds_svg,stdout=sub.PIPE,stderr=sub.PIPE) except Exception, e: print "The following command failed:" print ' '.join(cmds_svg) raise e output, errors = p.communicate() data.append(' <a name="anchor%s"/><h3>%s. <pre">%s</pre>&nbsp;&nbsp; <a href="#toc">^</a> </h3>' % (i, i, query)) data.append(output) data.append('<br/><pre>' + tree + '</pre>') data.append('<br/>') index_fo.write(''' <html> <head> <meta http-equiv="Content-Type" content="text/html;charset=utf-8" /> <style type="text/css"> pre {display:inline;} </style> </head> </body> ''') index_fo.write('\n'.join(toc)) index_fo.write('\n'.join(data)) index_fo.write(''' </body> </html> ''') index_fo.close() print 'HTML charts generated into:', index_fo.name os.chdir(old_dir) def load_gunit_file(gunit_file): fi = open(gunit_file, 'r') test_cases = {} section = None for line in fi: l = line.strip() if not l or l[:2] == '//': continue parts = split_line(l) if len(parts) == 1 and parts[0][-1] == ':': section = parts[0][:-1] test_cases.setdefault(section, []) elif len(parts) > 1 and parts[1].lower() != 'fails': query = parts[0] query = query.replace('\\\"', '"').replace('\\\'', '\'').replace('\\\\', '\\') test_cases[section].append(query) fi.close() return test_cases def split_line(line): line = line.replace('->', '') start = 0 last_pos = None parts = [] while line.find('"', start) > -1: p = line.index('"', start) start = p+1 if line[p-1] != '\\': if last_pos is None: last_pos = p else: parts.append(line[last_pos+1:p]) parts.append(line[p+1:].strip()) last_pos = None break if not parts: parts.append(line.strip()) return parts if __name__ == '__main__': if len(sys.argv) == 1: sys.argv.insert(1, "StandardLuceneGrammar") run(*sys.argv[1:])
    30.722772
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    0.496616
    0
    0
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    0
    0
    1,529
    0.246374
    92d5a318d2e721b05edd8c4dc433e4875c24b448
    6,318
    py
    Python
    visual_perception/Detection/yolov4/__init__.py
    SSusantAchary/Visual-Perception
    b81ffe69ab85e9afb7ee6eece43ac83c8f292285
    [ "MIT" ]
    null
    null
    null
    visual_perception/Detection/yolov4/__init__.py
    SSusantAchary/Visual-Perception
    b81ffe69ab85e9afb7ee6eece43ac83c8f292285
    [ "MIT" ]
    null
    null
    null
    visual_perception/Detection/yolov4/__init__.py
    SSusantAchary/Visual-Perception
    b81ffe69ab85e9afb7ee6eece43ac83c8f292285
    [ "MIT" ]
    null
    null
    null
    """ MIT License Copyright (c) 2020 Susant Achary <[email protected]> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from visual_perception.Detection.yolov4.tf import YOLOv4 as yolo_main import numpy as np import cv2 labels = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'} class YOLOv4: def __init__(self): self.weights_path = "" self.model = None self.yolo_classes = "" self.iou = 0 self.score = 0 self.input_shape = 0 self.output_path = "" def load_model(self, weights_path:str = None, classes_path:str = None, input_shape:int = 608): if (weights_path is None) or (classes_path is None): raise RuntimeError ('weights_path AND classes_path should not be None.') self.yolo_classes = classes_path self.weights_path = weights_path self.input_shape = input_shape self.model = yolo_main(shape = self.input_shape) self.model.classes = self.yolo_classes self.model.make_model() self.model.load_weights(self.weights_path, weights_type = 'yolo') def predict(self, img:np.ndarray, output_path:str, iou = 0.45, score = 0.25, custom_objects:dict = None, debug=True): self.output_path = output_path self.iou = iou self.score = score #img = np.array(Image.open(img))[..., ::-1] pred_bboxes = self.model.predict(img, iou_threshold = self.iou, score_threshold = self.score) boxes = [] if (custom_objects != None): for i in range(len(pred_bboxes)): check_name = labels[pred_bboxes[i][4]] check = custom_objects.get(check_name, 'invalid') if check == 'invalid': continue elif check == 'valid': boxes.append(list(pred_bboxes[i])) boxes = np.array(boxes) res = self.model.draw_bboxes(img, boxes) if debug: cv2.imwrite(self.output_path, res) else: res = self.model.draw_bboxes(img, pred_bboxes) if debug: cv2.imwrite(self.output_path, res) return res class TinyYOLOv4: def __init__(self): self.weights_path = "" self.model = None self.yolo_classes = "" self.iou = 0 self.score = 0 self.input_shape = 0 self.output_path = "" def load_model(self, weights_path:str = None, classes_path:str = None, input_shape:int = 0): if (weights_path is None) or (classes_path is None): raise RuntimeError ('weights_path AND classes_path should not be None.') self.yolo_classes = classes_path self.weights_path = weights_path self.input_shape = input_shape self.model = yolo_main(tiny = True, shape = self.input_shape) self.model.classes = self.yolo_classes self.model.make_model() self.model.load_weights(self.weights_path, weights_type = 'yolo') def predict(self, img:np.ndarray, output_path:str, iou = 0.4, score = 0.07, custom_objects:dict = None, debug=True): self.output_path = output_path self.iou = iou self.score = score #img = np.array(Image.open(img))[..., ::-1] pred_bboxes = self.model.predict(img, iou_threshold = self.iou, score_threshold = self.score) boxes = [] if (custom_objects != None): for i in range(len(pred_bboxes)): check_name = labels[pred_bboxes[i][4]] check = custom_objects.get(check_name, 'invalid') if check == 'invalid': continue elif check == 'valid': boxes.append(list(pred_bboxes[i])) boxes = np.array(boxes) res = self.model.draw_bboxes(img, boxes) if debug: cv2.imwrite(self.output_path, res) else: res = self.model.draw_bboxes(img, pred_bboxes) if debug: cv2.imwrite(self.output_path, res) return res
    43.875
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    2,065
    0.326844
    92d6916cbd5aa31c26daff18d295d7d026f17d82
    517
    py
    Python
    server/mqtt/handler.py
    rishab-rb/MyIOTMap
    e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa
    [ "MIT" ]
    1
    2018-10-08T06:11:20.000Z
    2018-10-08T06:11:20.000Z
    server/mqtt/handler.py
    rishab-rb/MyIOTMap
    e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa
    [ "MIT" ]
    null
    null
    null
    server/mqtt/handler.py
    rishab-rb/MyIOTMap
    e27a73b58cd3a9aba558ebacfb2bf8b6ef4761aa
    [ "MIT" ]
    2
    2018-07-30T08:18:22.000Z
    2018-10-11T08:04:58.000Z
    import paho.client as mqtt HOST = 'localhost' PORT = 1883 class MQTTConnector: def __init__(self, host, port): host = host port = port client = mqtt.Client() def connect(): self.client.connect(self.host, self.port, 60) def run(self): self.client.loop_forever() class MQTTSubscriber: def __init__(self, *args, **kwargs): super(MQTTSubscriber, self).__init__(*args, **kwargs) class MQTTPublisher: def __init__(self, host)
    19.884615
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    11
    0.021277
    92d713c9c1666b57fdf260fc3597ec5bb433209c
    1,886
    py
    Python
    scripts/spacy_files/similarity_replacement.py
    HighDeFing/thesis_v4
    2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7
    [ "Apache-2.0" ]
    null
    null
    null
    scripts/spacy_files/similarity_replacement.py
    HighDeFing/thesis_v4
    2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7
    [ "Apache-2.0" ]
    null
    null
    null
    scripts/spacy_files/similarity_replacement.py
    HighDeFing/thesis_v4
    2dc9288af75a8b51fe54ed66f520e8aa8a0ab3c7
    [ "Apache-2.0" ]
    null
    null
    null
    #!/bin/env python from black import main import spacy import json from spacy import displacy import unidecode import pandas as pd import numpy as np import os csv_source = "scripts/spacy_files/data/thesis_200_with_school.csv" df = pd.read_csv(csv_source) df = df[df['isScan']==False] df = df.sort_values('isScan', ascending=False) text1= "Escuela de Enfermería" text2 = "ESCUELA DE ENFERMERIA" file = open("scripts/spacy_files/data/escuelas.json", "r") file = json.load(file) temp_list = [] for facultad in file: temp_list.append(facultad['escuela']) #print(facultad['escuela']) escuelas = [item for sublist in temp_list for item in sublist] # make the list flat #print(escuelas) text1_u = unidecode.unidecode(text1) text1_l_u = text1_u.lower() text2_l_u = unidecode.unidecode(text2).lower() print(text1_l_u, "<-->", text2_l_u) if text1_l_u == text2_l_u: print(text1, " is correct.") def unaccent_list(accent_list): unaccented_schools = [] for sch in accent_list: unaccented_schools.append(unidecode.unidecode(sch).lower()) return unaccented_schools def set_school_to_unaccent(escuelas): escuelas = unaccent_list(escuelas) return escuelas def create_dictionary(schools): myDict = dict((e,i) for i,e in enumerate(schools)) return myDict def set_schools_accents(row, dict, dict_c): index = dict.get(row.lower()) key_list = list(dict_c.keys()) val_list = list(dict_c.values()) try: position = val_list.index(index) key_list[position] except: return None if __name__ == "__main__": u_escuelas = set_school_to_unaccent(escuelas) u_escuelas_dict = create_dictionary(u_escuelas) escuelas_dict = create_dictionary(escuelas) print(u_escuelas_dict) print(escuelas_dict) print(set_schools_accents("No school", u_escuelas_dict, escuelas_dict))
    25.486486
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    290
    0.153683
    92d920562d22f1142cab1ea79e81051636bf317f
    7,212
    py
    Python
    test/unittest_base.py
    dat-boris/tensorforce
    d777121b1c971da5500572c5f83173b9229f7370
    [ "Apache-2.0" ]
    null
    null
    null
    test/unittest_base.py
    dat-boris/tensorforce
    d777121b1c971da5500572c5f83173b9229f7370
    [ "Apache-2.0" ]
    null
    null
    null
    test/unittest_base.py
    dat-boris/tensorforce
    d777121b1c971da5500572c5f83173b9229f7370
    [ "Apache-2.0" ]
    null
    null
    null
    # Copyright 2018 Tensorforce Team. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from copy import deepcopy from datetime import datetime import os import sys import warnings from tensorforce import TensorforceError from tensorforce.agents import Agent from tensorforce.core.layers import Layer from tensorforce.environments import Environment from tensorforce.execution import Runner from test.unittest_environment import UnittestEnvironment os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' class UnittestBase(object): """ Unit-test base class. """ # Unittest num_updates = None num_episodes = None num_timesteps = None # Environment min_timesteps = 1 states = dict( bool_state=dict(type='bool', shape=(1,)), int_state=dict(type='int', shape=(2,), num_values=4), float_state=dict(type='float', shape=(1, 1, 2)), bounded_state=dict(type='float', shape=(), min_value=-0.5, max_value=0.5) ) actions = dict( bool_action=dict(type='bool', shape=(1,)), int_action=dict(type='int', shape=(2,), num_values=4), float_action=dict(type='float', shape=(1, 1)), bounded_action=dict(type='float', shape=(2,), min_value=-0.5, max_value=0.5) ) # Exclude action types exclude_bool_action = False exclude_int_action = False exclude_float_action = False exclude_bounded_action = False # Agent agent = dict( update=4, policy=dict(network=dict(type='auto', size=8, depth=1, internal_rnn=2)), objective='policy_gradient', reward_estimation=dict(horizon=3) ) # Tensorforce config require_observe = False require_all = False def setUp(self): warnings.filterwarnings( action='ignore', message='Converting sparse IndexedSlices to a dense Tensor of unknown shape' ) def start_tests(self, name=None): """ Start unit-test method. """ if name is None: sys.stdout.write('\n{} {}: '.format( datetime.now().strftime('%H:%M:%S'), self.__class__.__name__[4:] )) else: sys.stdout.write('\n{} {} ({}): '.format( datetime.now().strftime('%H:%M:%S'), self.__class__.__name__[4:], name )) sys.stdout.flush() def finished_test(self, assertion=None): """ Finished unit-test. """ if assertion is None: assertion = True else: self.assertTrue(expr=assertion) if assertion: sys.stdout.write('.') sys.stdout.flush() def prepare( self, environment=None, min_timesteps=None, states=None, actions=None, exclude_bool_action=False, exclude_int_action=False, exclude_float_action=False, exclude_bounded_action=False, require_observe=False, require_all=False, **agent ): """ Generic unit-test preparation. """ Layer.layers = None if environment is None: if states is None: states = deepcopy(self.__class__.states) if actions is None: actions = deepcopy(self.__class__.actions) if exclude_bool_action or self.__class__.exclude_bool_action: actions.pop('bool_action') if exclude_int_action or self.__class__.exclude_int_action: actions.pop('int_action') if exclude_float_action or self.__class__.exclude_float_action: actions.pop('float_action') if exclude_bounded_action or self.__class__.exclude_bounded_action: actions.pop('bounded_action') if min_timesteps is None: min_timesteps = self.__class__.min_timesteps environment = UnittestEnvironment( states=states, actions=actions, min_timesteps=min_timesteps ) elif min_timesteps is not None: raise TensorforceError.unexpected() environment = Environment.create(environment=environment, max_episode_timesteps=5) for key, value in self.__class__.agent.items(): if key not in agent: agent[key] = value if self.__class__.require_all or require_all: config = None elif self.__class__.require_observe or require_observe: config = dict(api_functions=['reset', 'act', 'observe']) else: config = dict(api_functions=['reset', 'act']) agent = Agent.create(agent=agent, environment=environment, config=config) return agent, environment def unittest( self, num_updates=None, num_episodes=None, num_timesteps=None, environment=None, min_timesteps=None, states=None, actions=None, exclude_bool_action=False, exclude_int_action=False, exclude_float_action=False, exclude_bounded_action=False, require_observe=False, require_all=False, **agent ): """ Generic unit-test. """ agent, environment = self.prepare( environment=environment, min_timesteps=min_timesteps, states=states, actions=actions, exclude_bool_action=exclude_bool_action, exclude_int_action=exclude_int_action, exclude_float_action=exclude_float_action, exclude_bounded_action=exclude_bounded_action, require_observe=require_observe, require_all=require_all, **agent ) self.runner = Runner(agent=agent, environment=environment) assert (num_updates is not None) + (num_episodes is not None) + \ (num_timesteps is not None) <= 1 if num_updates is None and num_episodes is None and num_timesteps is None: num_updates = self.__class__.num_updates num_episodes = self.__class__.num_episodes num_timesteps = self.__class__.num_timesteps if num_updates is None and num_episodes is None and num_timesteps is None: num_updates = 2 assert (num_updates is not None) + (num_episodes is not None) + \ (num_timesteps is not None) == 1 evaluation = not any([ require_all, require_observe, self.__class__.require_all, self.__class__.require_observe ]) self.runner.run( num_episodes=num_episodes, num_timesteps=num_timesteps, num_updates=num_updates, use_tqdm=False, evaluation=evaluation ) self.runner.close() agent.close() environment.close() self.finished_test()
    35.880597
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    1,276
    0.176927
    92dadd375392924dd104471b2991098481772c78
    132
    py
    Python
    mspray/apps/reveal/__init__.py
    onaio/mspray
    b3e0f4b5855abbf0298de6b66f2e9f472f2bf838
    [ "Apache-2.0" ]
    null
    null
    null
    mspray/apps/reveal/__init__.py
    onaio/mspray
    b3e0f4b5855abbf0298de6b66f2e9f472f2bf838
    [ "Apache-2.0" ]
    76
    2018-03-15T09:37:56.000Z
    2019-05-15T12:45:51.000Z
    mspray/apps/reveal/__init__.py
    onaio/mspray
    b3e0f4b5855abbf0298de6b66f2e9f472f2bf838
    [ "Apache-2.0" ]
    1
    2020-10-31T07:15:22.000Z
    2020-10-31T07:15:22.000Z
    """init module for reveal app""" # pylint: disable=invalid-name default_app_config = "mspray.apps.reveal.apps.RevealConfig" # noqa
    33
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    106
    0.80303
    92dbf0257f2cb3330104352818f4db40e27c4927
    513
    py
    Python
    guifw/models/port.py
    luizerico/PyGuiFW
    d79347db7d4bd9e09fbc53215d79c06ccf16bad5
    [ "MIT" ]
    1
    2021-02-05T02:58:19.000Z
    2021-02-05T02:58:19.000Z
    guifw/models/port.py
    luizerico/PyGuiFW
    d79347db7d4bd9e09fbc53215d79c06ccf16bad5
    [ "MIT" ]
    1
    2018-11-09T16:10:50.000Z
    2018-11-09T16:10:50.000Z
    guifw/models/port.py
    luizerico/PyGuiFW
    d79347db7d4bd9e09fbc53215d79c06ccf16bad5
    [ "MIT" ]
    null
    null
    null
    from django.db import models from django import forms from audit_log.models.managers import AuditLog # Create your models here. class Port(models.Model): name = models.CharField(max_length=250) port = models.CharField(max_length=250) description = models.TextField(blank=True) audit_log = AuditLog() #icon = models.ImageField(upload_to='images', blank=True) def __str__(self): return self.name class FormPort(forms.ModelForm): pass class Meta: model = Port
    20.52
    61
    0.707602
    378
    0.736842
    0
    0
    0
    0
    0
    0
    83
    0.161793
    92dc4e7fbdb299e18e1175c0718307d433b0cb15
    386
    py
    Python
    app/backend/arm/migrations/0002_auto_20190924_1712.py
    karstenv/nmp-arm
    47e45f0391820000f461ab6e994e20eacfffb457
    [ "Apache-2.0" ]
    2
    2019-08-12T22:06:23.000Z
    2019-10-22T20:50:32.000Z
    app/backend/arm/migrations/0002_auto_20190924_1712.py
    karstenv/nmp-arm
    47e45f0391820000f461ab6e994e20eacfffb457
    [ "Apache-2.0" ]
    12
    2019-07-10T18:11:31.000Z
    2022-02-10T08:47:57.000Z
    app/backend/arm/migrations/0002_auto_20190924_1712.py
    karstenv/nmp-arm
    47e45f0391820000f461ab6e994e20eacfffb457
    [ "Apache-2.0" ]
    6
    2019-07-03T21:24:11.000Z
    2021-04-29T17:31:34.000Z
    # Generated by Django 2.2.5 on 2019-09-25 00:12 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('arm', '0001_initial'), ] operations = [ migrations.DeleteModel( name='CautionMessage', ), migrations.DeleteModel( name='RiskRatingValue', ), ]
    19.3
    48
    0.546632
    295
    0.764249
    0
    0
    0
    0
    0
    0
    100
    0.259067
    92dc54efa676f164aaadbce167924df2d1df95ab
    7,112
    py
    Python
    webcam_demo.py
    taranek/tennis-stats-provider
    e95093679a194d30d0727ec8e11d44fc462f6adc
    [ "Apache-2.0" ]
    null
    null
    null
    webcam_demo.py
    taranek/tennis-stats-provider
    e95093679a194d30d0727ec8e11d44fc462f6adc
    [ "Apache-2.0" ]
    null
    null
    null
    webcam_demo.py
    taranek/tennis-stats-provider
    e95093679a194d30d0727ec8e11d44fc462f6adc
    [ "Apache-2.0" ]
    null
    null
    null
    import tensorflow as tf import json import math import cv2 import time import argparse import concurrent.futures import posenet import keyboard import sys import numpy as np from threading import Thread from slugify import slugify parser = argparse.ArgumentParser() parser.add_argument('--model', type=int, default=101) parser.add_argument('--cam_id', type=int, default=0) parser.add_argument('--cam_width', type=int, default=1280) parser.add_argument('--cam_height', type=int, default=720) parser.add_argument('--scale_factor', type=float, default=0.7125) parser.add_argument('--file', type=str, default=None, help="Optionally use a video file instead of a live camera") args = parser.parse_args() def main(): # tf.config.threading.set_inter_op_parallelism_threads(0) # tf.config.threading.set_intra_op_parallelism_threads(0) # print(tf.config.threading.get_inter_op_parallelism_threads()) # print(tf.config.threading.get_intra_op_parallelism_threads()) with tf.compat.v1.Session() as sess: model_cfg, model_outputs = posenet.load_model(args.model, sess) output_stride = model_cfg['output_stride'] if args.file is not None: cap = cv2.VideoCapture(args.file) else: cap = cv2.VideoCapture(args.cam_id) cap.set(3, args.cam_width) cap.set(4, args.cam_height) start = time.time() frame_count = 0 recording = True # ret,frame1 = cap.read() # ret,frame2 = cap.read() file_content = [] while True: # diff = cv2.absdiff(frame1,frame2) # gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY) # blur = cv2.GaussianBlur(gray,(15,15),0) # _, thresh = cv2.threshold(blur,20,255,cv2.THRESH_BINARY) # dilated = cv2.dilate(thresh,None, iterations=3) # contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # # if(len(contours)>0): # # print("One:") # # print(dir(contours[0])) # # print("One it is.") # for contour in contours: # (x,y,w,h) = cv2.boundingRect(contour) # if(cv2.contourArea(contour)>400): # continue # cv2.rectangle(frame1,(x,y),(x+w,y+h),(0,255,0),2) # # cv2.drawContours(frame1,contours, -1,(0,255,0),2) # cv2.imshow("feed",frame1) # frame1 = frame2 # ret, frame2 = cap.read() input_image, display_image, output_scale = posenet.read_cap(cap, scale_factor=args.scale_factor, output_stride=output_stride) heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = sess.run( model_outputs, feed_dict={'image:0': input_image} ) pose_scores, keypoint_scores, keypoint_coords = posenet.decode_multi.decode_multiple_poses( heatmaps_result.squeeze(axis=0), offsets_result.squeeze(axis=0), displacement_fwd_result.squeeze(axis=0), displacement_bwd_result.squeeze(axis=0), output_stride=output_stride, max_pose_detections=1, min_pose_score=0.15) keypoint_coords *= output_scale # TODO this isn't particularly fast, use GL for drawing and display someday... # print("\n ===================================== \n") img = posenet.draw_skel_and_kp( display_image, pose_scores, keypoint_scores, keypoint_coords, min_pose_score=0.15, min_part_score=0.15) cv2.imshow('posenet', img) frame_count += 1 if(recording): normalize_poses(keypoint_coords) results = json.dumps({ "timestamp":time.time() - start, "pose_scores":pose_scores.tolist(), "keypoint_scores":keypoint_scores.tolist(), "scores": keypoint_scores.size, "keypoint_coords":normalize_poses(keypoint_coords), "coords": keypoint_coords.size }) file_content.append(results) file_content = file_content[-30:] if cv2.waitKey(1) & keyboard.is_pressed('w'): print('you pressed w - service it was!') time.sleep(0.5) path = "collected/serves/" filename = str(slugify("s-"+str(time.time()))+".txt") x = Thread(target=save_to_file, args=(str(path+filename),str(file_content))) x.start() x.join() file_content = [] if cv2.waitKey(1) & keyboard.is_pressed('d'): print('you pressed d - forehand it was!') time.sleep(0.5) path = "collected/forehand/" filename = str(slugify("f-"+str(time.time()))+".txt") x = Thread(target=save_to_file, args=(str(path+filename),str(file_content))) x.start() x.join() file_content = [] if cv2.waitKey(1) & keyboard.is_pressed('a'): print('you pressed a - backhand it was!') time.sleep(0.5) path = "collected/backhand/" filename = str(slugify("b-"+str(time.time()))+".txt") x = Thread(target=save_to_file, args=(str(path+filename),str(file_content))) x.start() x.join() file_content = [] if cv2.waitKey(1) & keyboard.is_pressed('q'): print('you pressed q - quitting!') cv2.destroyAllWindows() break print('Average FPS: ', frame_count / (time.time() - start)) return 0 def my_function(toPrint): print(toPrint) def save_to_file(filename,data): file = open(filename,'w') file.write(data) file.close() def find_middle(left,right): x = (left[0]+right[0])/2.0 y = (left[1]+right[1])/2.0 return [x,y] def find_distance(pointA,pointB): dist = math.sqrt((pointB[0] - pointA[0])**2 + (pointB[1] - pointA[1])**2) return dist def normalize_poses(poses): leftShoulderCords = poses[0][5] rightShoulderCords = poses[0][6] middleShoulderPoint = find_middle(leftShoulderCords,rightShoulderCords) leftHipCords = poses[0][11] rightHipCords = poses[0][12] middleHipPoint = find_middle(leftHipCords,rightHipCords) armHipDistance = find_distance(middleHipPoint,middleShoulderPoint); normalized = [] for pose in poses[0]: normalized.append( [(pose[0]-middleHipPoint[0])/armHipDistance, (pose[1]-middleHipPoint[1])/armHipDistance] ) return normalized if __name__ == "__main__": main()
    37.829787
    137
    0.564398
    0
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    0
    0
    0
    0
    1,647
    0.23158
    92df29892405e44dded087915f2a1792a9fb1160
    6,265
    py
    Python
    otcextensions/tests/unit/osclient/dcs/v1/fakes.py
    zsoltn/python-otcextensions
    4c0fa22f095ebd5f9636ae72acbae5048096822c
    [ "Apache-2.0" ]
    null
    null
    null
    otcextensions/tests/unit/osclient/dcs/v1/fakes.py
    zsoltn/python-otcextensions
    4c0fa22f095ebd5f9636ae72acbae5048096822c
    [ "Apache-2.0" ]
    null
    null
    null
    otcextensions/tests/unit/osclient/dcs/v1/fakes.py
    zsoltn/python-otcextensions
    4c0fa22f095ebd5f9636ae72acbae5048096822c
    [ "Apache-2.0" ]
    null
    null
    null
    # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # import datetime import random import uuid import mock from openstackclient.tests.unit import utils from otcextensions.tests.unit.osclient import test_base from otcextensions.sdk.dcs.v1 import backup from otcextensions.sdk.dcs.v1 import config from otcextensions.sdk.dcs.v1 import instance from otcextensions.sdk.dcs.v1 import restore from otcextensions.sdk.dcs.v1 import statistic class TestDCS(utils.TestCommand): def setUp(self): super(TestDCS, self).setUp() self.app.client_manager.dcs = mock.Mock() self.client = self.app.client_manager.dcs self.client.get_instance = mock.Mock() self.client.find_instance = mock.Mock() self.client.instances = mock.Mock() self.client.delete_instance = mock.Mock() self.client.update_instance = mock.Mock() self.client.create_instance = mock.Mock() self.client.extend_instance = mock.Mock() class FakeInstance(test_base.Fake): """Fake one or more Instance""" @classmethod def generate(cls): object_info = { 'name': 'group-' + uuid.uuid4().hex, 'id': 'id-' + uuid.uuid4().hex, 'description': 'SOME description', 'status': random.choice(['CREATING', 'CREATEFILED', 'RUNNING', 'ERROR', 'STARTING', 'RESTARTING', 'CLOSING', 'CLOSED', 'EXTENDING']), 'engine': uuid.uuid4().hex, 'capacity': random.randint(1, 100), 'ip': uuid.uuid4().hex, 'port': random.randint(1, 65535), 'resource_spec_code': random.choice(['dcs.single_node', 'dcs.master_standby', 'dcs.cluster' ]), 'engine_version': uuid.uuid4().hex, 'internal_version': uuid.uuid4().hex, 'charging_mode': random.randint(0, 10), 'vpc_id': uuid.uuid4().hex, 'vpc_name': uuid.uuid4().hex, 'subnet_id': uuid.uuid4().hex, 'subnet_name': uuid.uuid4().hex, 'subnet_cidr': uuid.uuid4().hex, 'security_group_id': uuid.uuid4().hex, 'security_group_name': uuid.uuid4().hex, 'created_at': uuid.uuid4().hex, 'error_code': uuid.uuid4().hex, 'product_id': random.choice(['OTC_DCS_SINGLE', 'OTC_DCS_MS', 'OTC_DCS_CL']), 'available_zones': uuid.uuid4().hex, 'max_memory': random.randint(0, 10), 'used_memory': random.randint(0, 10), 'user_id': uuid.uuid4().hex, 'user_name': uuid.uuid4().hex, 'order_id': uuid.uuid4().hex, 'maintain_begin': uuid.uuid4().hex, 'maintain_end': uuid.uuid4().hex, } obj = instance.Instance.existing(**object_info) return obj class FakeStatistic(test_base.Fake): """Fake one or more Statistic""" @classmethod def generate(cls): object_info = { 'instance_id': 'instance_id-' + uuid.uuid4().hex, 'max_memory': random.randint(1, 65535), 'used_memory': random.randint(1, 65535), 'cmd_get_count': random.randint(1, 65535), 'cmd_set_count': random.randint(1, 65535), 'used_cpu': 'cpu-' + uuid.uuid4().hex, 'input_kbps': 'input-' + uuid.uuid4().hex, 'output_kbps': 'output-' + uuid.uuid4().hex, } obj = statistic.Statistic.existing(**object_info) return obj class FakeBackup(test_base.Fake): """Fake one or more Backup""" @classmethod def generate(cls): object_info = { 'instance_id': 'instance_id-' + uuid.uuid4().hex, 'id': 'id-' + uuid.uuid4().hex, 'size': random.randint(1, 65535), 'period': uuid.uuid4().hex, 'description': uuid.uuid4().hex, 'progress': uuid.uuid4().hex, 'created_at': uuid.uuid4().hex, 'updated_at': uuid.uuid4().hex, 'type': uuid.uuid4().hex, 'name': uuid.uuid4().hex, 'error_code': uuid.uuid4().hex, 'is_restorable': True, } obj = backup.Backup.existing(**object_info) return obj class FakeRestore(test_base.Fake): """Fake one or more Restore""" @classmethod def generate(cls): object_info = { 'instance_id': 'instance_id-' + uuid.uuid4().hex, 'max_memory': random.randint(1, 65535), 'used_memory': random.randint(1, 65535), 'cmd_get_count': random.randint(1, 65535), 'cmd_set_count': random.randint(1, 65535), 'used_cpu': 'cpu-' + uuid.uuid4().hex, 'input_kbps': 'input-' + uuid.uuid4().hex, 'output_kbps': 'output-' + uuid.uuid4().hex } obj = restore.Restore.existing(**object_info) return obj class FakeConfig(test_base.Fake): """Fake one or more Config""" @classmethod def generate(cls): object_info = { 'instance_id': 'instance_id-' + uuid.uuid4().hex, 'id': uuid.uuid4().hex, 'name': uuid.uuid4().hex, 'value': uuid.uuid4().hex, 'value_type': uuid.uuid4().hex, 'value_range': uuid.uuid4().hex, 'default_value': uuid.uuid4().hex, 'description': uuid.uuid4().hex } obj = config.Config.existing(**object_info) return obj
    36.005747
    77
    0.555148
    5,298
    0.84565
    0
    0
    4,388
    0.700399
    0
    0
    1,804
    0.287949
    92df7af4ff3a17d2d4ea5d2cff4ae315569f3502
    830
    py
    Python
    tests/dummy_repo/tvm/python/tvm/api.py
    csullivan/ffi-navigator
    ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb
    [ "Apache-2.0" ]
    148
    2019-12-28T19:02:17.000Z
    2022-03-27T07:30:13.000Z
    tests/dummy_repo/tvm/python/tvm/api.py
    csullivan/ffi-navigator
    ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb
    [ "Apache-2.0" ]
    21
    2019-12-28T17:29:24.000Z
    2021-11-24T09:59:35.000Z
    tests/dummy_repo/tvm/python/tvm/api.py
    csullivan/ffi-navigator
    ed47678f9cb8c6d3637bf3219d3cf7b2754b84bb
    [ "Apache-2.0" ]
    17
    2019-12-29T01:46:13.000Z
    2022-01-10T09:56:46.000Z
    from ._ffi.base import string_types from ._ffi.object import register_object, Object from ._ffi.node import register_node, NodeBase from ._ffi.node import convert_to_node as _convert_to_node from ._ffi.node_generic import _scalar_type_inference from ._ffi.function import Function from ._ffi.function import _init_api, register_func, get_global_func, extract_ext_funcs from ._ffi.function import convert_to_tvm_func as _convert_tvm_func from ._ffi.runtime_ctypes import TVMType from . import _api_internal from . import make as _make from . import expr as _expr from . import tensor as _tensor from . import schedule as _schedule from . import container as _container from . import tag as _tag int8 = "int8" int32 = "int32" float32 = "float32" handle = "handle" def min_value(dtype): return _api_internal._min_value(dtype)
    31.923077
    87
    0.812048
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    0
    0
    0
    0
    0
    0
    0
    30
    0.036145
    92dfa8870f87fbcfb31691bd442140d0c802358d
    4,121
    py
    Python
    torchattacks/attacks/multiattack.py
    Harry24k/adversarial-attacks-pytorch
    bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb
    [ "MIT" ]
    782
    2020-03-28T01:56:36.000Z
    2022-03-31T14:54:02.000Z
    torchattacks/attacks/multiattack.py
    Harry24k/adversarial-attacks-pytorch
    bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb
    [ "MIT" ]
    48
    2020-04-18T23:06:30.000Z
    2022-03-24T01:54:50.000Z
    torchattacks/attacks/multiattack.py
    Harry24k/adversarial-attacks-pytorch
    bfa2aa8d6f0c3b8086718f9f31526fcafa6995bb
    [ "MIT" ]
    197
    2020-03-31T05:21:02.000Z
    2022-03-31T15:24:29.000Z
    import copy import torch from ..attack import Attack class MultiAttack(Attack): r""" MultiAttack is a class to attack a model with various attacks agains same images and labels. Arguments: model (nn.Module): model to attack. attacks (list): list of attacks. Examples:: >>> atk1 = torchattacks.PGD(model, eps=8/255, alpha=2/255, iters=40, random_start=True) >>> atk2 = torchattacks.PGD(model, eps=8/255, alpha=2/255, iters=40, random_start=True) >>> atk = torchattacks.MultiAttack([atk1, atk2]) >>> adv_images = attack(images, labels) """ def __init__(self, attacks, verbose=False): # Check validity ids = [] for attack in attacks: ids.append(id(attack.model)) if len(set(ids)) != 1: raise ValueError("At least one of attacks is referencing a different model.") super().__init__("MultiAttack", attack.model) self.attacks = attacks self.verbose = verbose self._accumulate_multi_atk_records = False self._multi_atk_records = [0.0] self._supported_mode = ['default'] def forward(self, images, labels): r""" Overridden. """ batch_size = images.shape[0] fails = torch.arange(batch_size).to(self.device) final_images = images.clone().detach().to(self.device) labels = labels.clone().detach().to(self.device) multi_atk_records = [batch_size] for _, attack in enumerate(self.attacks): adv_images = attack(images[fails], labels[fails]) outputs = self.model(adv_images) _, pre = torch.max(outputs.data, 1) corrects = (pre == labels[fails]) wrongs = ~corrects succeeds = torch.masked_select(fails, wrongs) succeeds_of_fails = torch.masked_select(torch.arange(fails.shape[0]).to(self.device), wrongs) final_images[succeeds] = adv_images[succeeds_of_fails] fails = torch.masked_select(fails, corrects) multi_atk_records.append(len(fails)) if len(fails) == 0: break if self.verbose: print(self._return_sr_record(multi_atk_records)) if self._accumulate_multi_atk_records: self._update_multi_atk_records(multi_atk_records) return final_images def _clear_multi_atk_records(self): self._multi_atk_records = [0.0] def _covert_to_success_rates(self, multi_atk_records): sr = [((1-multi_atk_records[i]/multi_atk_records[0])*100) for i in range(1, len(multi_atk_records))] return sr def _return_sr_record(self, multi_atk_records): sr = self._covert_to_success_rates(multi_atk_records) return "Attack success rate: "+" | ".join(["%2.2f %%"%item for item in sr]) def _update_multi_atk_records(self, multi_atk_records): for i, item in enumerate(multi_atk_records): self._multi_atk_records[i] += item def save(self, data_loader, save_path=None, verbose=True, return_verbose=False): r""" Overridden. """ self._clear_multi_atk_records() verbose = self.verbose self.verbose = False self._accumulate_multi_atk_records = True for i, attack in enumerate(self.attacks): self._multi_atk_records.append(0.0) rob_acc, l2, elapsed_time = super().save(data_loader, save_path, verbose, return_verbose) sr = self._covert_to_success_rates(self._multi_atk_records) self._clear_multi_atk_records() self._accumulate_multi_atk_records = False self.verbose = verbose if return_verbose: return rob_acc, sr, l2, elapsed_time def _save_print(self, progress, rob_acc, l2, elapsed_time, end): r""" Overridden. """ print("- Save progress: %2.2f %% / Robust accuracy: %2.2f %%"%(progress, rob_acc)+\ " / "+self._return_sr_record(self._multi_atk_records)+\ ' / L2: %1.5f (%2.3f it/s) \t'%(l2, elapsed_time), end=end)
    33.778689
    108
    0.626062
    4,064
    0.986168
    0
    0
    0
    0
    0
    0
    857
    0.207959
    92e0877363cacd633cbbf12e0ee4175cb9564598
    2,627
    py
    Python
    src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py
    wotchin/openGauss-server
    ebd92e92b0cfd76b121d98e4c57a22d334573159
    [ "MulanPSL-1.0" ]
    1
    2020-06-30T15:00:50.000Z
    2020-06-30T15:00:50.000Z
    src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py
    wotchin/openGauss-server
    ebd92e92b0cfd76b121d98e4c57a22d334573159
    [ "MulanPSL-1.0" ]
    null
    null
    null
    src/manager/om/script/gspylib/inspection/items/os/CheckPortConflict.py
    wotchin/openGauss-server
    ebd92e92b0cfd76b121d98e4c57a22d334573159
    [ "MulanPSL-1.0" ]
    null
    null
    null
    # -*- coding:utf-8 -*- # Copyright (c) 2020 Huawei Technologies Co.,Ltd. # # openGauss is licensed under Mulan PSL v2. # You can use this software according to the terms # and conditions of the Mulan PSL v2. # You may obtain a copy of Mulan PSL v2 at: # # http://license.coscl.org.cn/MulanPSL2 # # THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, # WITHOUT WARRANTIES OF ANY KIND, # EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, # MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the Mulan PSL v2 for more details. # ---------------------------------------------------------------------------- import subprocess from gspylib.inspection.common.CheckItem import BaseItem from gspylib.inspection.common.CheckResult import ResultStatus class CheckPortConflict(BaseItem): def __init__(self): super(CheckPortConflict, self).__init__(self.__class__.__name__) def doCheck(self): cmd = "netstat -apn | grep 'tcp' " \ "| grep 'LISTEN'| awk -F ' ' '$4 ~ /25[0-9][0-9][0-9]/'" (status, output) = subprocess.getstatusoutput(cmd) if (status != 0): self.result.rst = ResultStatus.NG self.result.val = "Failed to excuted commands: %s\noutput:%s " % ( cmd, output) else: if (output.strip() == ""): self.result.rst = ResultStatus.OK self.result.val = "ports is normal" else: self.result.rst = ResultStatus.NG self.result.val = output self.result.raw = "checked ports: (25000-26000)\n" + output def doSet(self): pidList = [] cmd = "netstat -apn| grep 'tcp'" \ "| grep 'LISTEN'| awk -F ' ' '$4 ~ /25[0-9][0-9][0-9]/'" \ "| awk '{print $NF}'" (status, output) = subprocess.getstatusoutput(cmd) if (status == 0 and output != ""): for line in output.split('\n'): if (line.find('/') > 0): pid = line.split('/')[0].strip() if (pid.isdigit()): pidList.append(pid) if (pidList): cmd = "kill -9" for pid in pidList: cmd += " %s" % pid (status, output) = subprocess.getstatusoutput(cmd) if (status != ""): self.result.val = "Failed to kill process.Error:%s\n" % output self.result.val += "The cmd is %s " % cmd else: self.result.val = \ "Successfully killed the process with occupies the port.\n"
    39.80303
    79
    0.53445
    1,851
    0.704606
    0
    0
    0
    0
    0
    0
    1,039
    0.395508
    92e16c1fa8d877e82eb805100d17b73907afb25e
    646
    py
    Python
    _scripts/_build.py
    dfreeman06/wxyz
    663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd
    [ "BSD-3-Clause" ]
    1
    2021-06-20T12:21:27.000Z
    2021-06-20T12:21:27.000Z
    _scripts/_build.py
    nrbgt/wxyz
    663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd
    [ "BSD-3-Clause" ]
    null
    null
    null
    _scripts/_build.py
    nrbgt/wxyz
    663cf6593f4c0ca12f7b94b61e34c0a8d3cbcdfd
    [ "BSD-3-Clause" ]
    null
    null
    null
    import subprocess import sys from . import ROOT, PY_SRC, _run, PY, DIST CONDA_ORDER = [ "core", "html", "lab", "datagrid", "svg", "tpl-jjinja" "yaml" ] CONDA_BUILD_ARGS = [ "conda-build", "-c", "conda-forge", "--output-folder", DIST / "conda-bld", ] if __name__ == "__main__": for pkg in PY_SRC.glob("wxyz_*"): _run([PY, "setup.py", "sdist", "--dist-dir", DIST / "sdist"], cwd=str(pkg)) try: _run([*CONDA_BUILD_ARGS, "--skip-existing", "."], cwd=ROOT / "recipes") except: for pkg in CONDA_ORDER: _run([*CONDA_BUILD_ARGS, f"wxyz-{pkg}"], cwd=ROOT / "recipes")
    20.83871
    83
    0.560372
    0
    0
    0
    0
    0
    0
    0
    0
    213
    0.329721
    92e1c91fec4c34f39e9e2622024fad4489b61749
    5,279
    py
    Python
    scripts/C189/C189Checkin.py
    xiaopowanyi/py_scripts
    29f240800eefd6e0f91fd098c35ac3c451172ff8
    [ "MIT" ]
    2
    2020-11-14T05:42:49.000Z
    2020-11-14T05:43:13.000Z
    scripts/C189/C189Checkin.py
    J220541674/py_scripts
    2b72e23041392a2e5f0a7305d7e9802054978384
    [ "MIT" ]
    null
    null
    null
    scripts/C189/C189Checkin.py
    J220541674/py_scripts
    2b72e23041392a2e5f0a7305d7e9802054978384
    [ "MIT" ]
    null
    null
    null
    import requests, time, re, rsa, json, base64 from urllib import parse s = requests.Session() username = "" password = "" if(username == "" or password == ""): username = input("账号:") password = input("密码:") def main(): login(username, password) rand = str(round(time.time()*1000)) surl = f'https://api.cloud.189.cn/mkt/userSign.action?rand={rand}&clientType=TELEANDROID&version=8.6.3&model=SM-G930K' url = f'https://m.cloud.189.cn/v2/drawPrizeMarketDetails.action?taskId=TASK_SIGNIN&activityId=ACT_SIGNIN' url2 = f'https://m.cloud.189.cn/v2/drawPrizeMarketDetails.action?taskId=TASK_SIGNIN_PHOTOS&activityId=ACT_SIGNIN' headers = { 'User-Agent':'Mozilla/5.0 (Linux; Android 5.1.1; SM-G930K Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/74.0.3729.136 Mobile Safari/537.36 Ecloud/8.6.3 Android/22 clientId/355325117317828 clientModel/SM-G930K imsi/460071114317824 clientChannelId/qq proVersion/1.0.6', "Referer" : "https://m.cloud.189.cn/zhuanti/2016/sign/index.jsp?albumBackupOpened=1", "Host" : "m.cloud.189.cn", "Accept-Encoding" : "gzip, deflate", } response = s.get(surl,headers=headers) netdiskBonus = response.json()['netdiskBonus'] if(response.json()['isSign'] == "false"): print(f"未签到,签到获得{netdiskBonus}M空间") else: print(f"已经签到过了,签到获得{netdiskBonus}M空间") headers = { 'User-Agent':'Mozilla/5.0 (Linux; Android 5.1.1; SM-G930K Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/74.0.3729.136 Mobile Safari/537.36 Ecloud/8.6.3 Android/22 clientId/355325117317828 clientModel/SM-G930K imsi/460071114317824 clientChannelId/qq proVersion/1.0.6', "Referer" : "https://m.cloud.189.cn/zhuanti/2016/sign/index.jsp?albumBackupOpened=1", "Host" : "m.cloud.189.cn", "Accept-Encoding" : "gzip, deflate", } response = s.get(url,headers=headers) try: if ("errorCode" in response.text): print(response.json()['errorCode']) elif (response.json().has_key('description')): description = response.json()['description'] print(f"抽奖获得{description}") except: print(f"抽奖1完成,解析时失败") try: response2 = s.get(url2,headers=headers) if ("errorCode" in response2.text): print(response.json()['errorCode']) elif (response2.json().has_key('description')): description = response2.json()['description'] print(f"抽奖2获得{description}") except: print(f"抽奖2完成,解析时失败") BI_RM = list("0123456789abcdefghijklmnopqrstuvwxyz") def int2char(a): return BI_RM[a] b64map = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" def b64tohex(a): d = "" e = 0 c = 0 for i in range(len(a)): if list(a)[i] != "=": v = b64map.index(list(a)[i]) if 0 == e: e = 1 d += int2char(v >> 2) c = 3 & v elif 1 == e: e = 2 d += int2char(c << 2 | v >> 4) c = 15 & v elif 2 == e: e = 3 d += int2char(c) d += int2char(v >> 2) c = 3 & v else: e = 0 d += int2char(c << 2 | v >> 4) d += int2char(15 & v) if e == 1: d += int2char(c << 2) return d def rsa_encode(j_rsakey, string): rsa_key = f"-----BEGIN PUBLIC KEY-----\n{j_rsakey}\n-----END PUBLIC KEY-----" pubkey = rsa.PublicKey.load_pkcs1_openssl_pem(rsa_key.encode()) result = b64tohex((base64.b64encode(rsa.encrypt(f'{string}'.encode(), pubkey))).decode()) return result def calculate_md5_sign(params): return hashlib.md5('&'.join(sorted(params.split('&'))).encode('utf-8')).hexdigest() def login(username, password): url = "https://cloud.189.cn/udb/udb_login.jsp?pageId=1&redirectURL=/main.action" r = s.get(url) captchaToken = re.findall(r"captchaToken' value='(.+?)'", r.text)[0] lt = re.findall(r'lt = "(.+?)"', r.text)[0] returnUrl = re.findall(r"returnUrl = '(.+?)'", r.text)[0] paramId = re.findall(r'paramId = "(.+?)"', r.text)[0] j_rsakey = re.findall(r'j_rsaKey" value="(\S+)"', r.text, re.M)[0] s.headers.update({"lt": lt}) username = rsa_encode(j_rsakey, username) password = rsa_encode(j_rsakey, password) url = "https://open.e.189.cn/api/logbox/oauth2/loginSubmit.do" headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:74.0) Gecko/20100101 Firefox/76.0', 'Referer': 'https://open.e.189.cn/', } data = { "appKey": "cloud", "accountType": '01', "userName": f"{{RSA}}{username}", "password": f"{{RSA}}{password}", "validateCode": "", "captchaToken": captchaToken, "returnUrl": returnUrl, "mailSuffix": "@189.cn", "paramId": paramId } r = s.post(url, data=data, headers=headers, timeout=5) if(r.json()['result'] == 0): print(r.json()['msg']) else: print(r.json()['msg']) redirect_url = r.json()['toUrl'] r = s.get(redirect_url) return s if __name__ == "__main__": main()
    37.707143
    305
    0.586664
    0
    0
    0
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    0
    0
    0
    2,331
    0.432227
    92e2096dcbe8b31e8b6213b7078b62e4efd23dd0
    3,318
    py
    Python
    Mmint/CGratio.py
    lijiacd985/Mplot
    adea07aa78a5495cf3551618f6ec2c08fa7c1029
    [ "MIT" ]
    5
    2018-07-02T16:33:23.000Z
    2021-03-23T00:32:31.000Z
    Mmint/CGratio.py
    lijiacd985/Mplot
    adea07aa78a5495cf3551618f6ec2c08fa7c1029
    [ "MIT" ]
    1
    2017-09-19T19:46:11.000Z
    2020-02-28T05:00:49.000Z
    Mmint/CGratio.py
    lijiacd985/Mplot
    adea07aa78a5495cf3551618f6ec2c08fa7c1029
    [ "MIT" ]
    4
    2017-11-16T15:26:24.000Z
    2020-02-13T16:25:25.000Z
    import subprocess from .Genome_fasta import get_fasta import matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt import numpy as np import pysam def run(parser): args = parser.parse_args() bases,chrs = get_fasta(args.genome) l={} for c in chrs: l[c]=len(bases[c]) chrs = set(chrs) #p = subprocess.Popen('bamToBed -i '+args.bamfile,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) reads_num=0 reads_cg_num=[0,0,0] #CG,cg,Cg cgnum_per_read=[] with pysam.AlignmentFile(args.bamfile) as f: for line in f: #t = line.decode('utf-8').strip().split() chr = line.reference_name#t[0] start= line.reference_start end= line.reference_end strand= not line.is_reverse # True +strand; False -strand if not chr in chrs: continue end=min(end+1,l[chr]) reads_num+=1 if strand:#=='+': cg=[bases[chr].count('CG',start,end)+bases[chr].count('Cg',start,end),bases[chr].count('cG',start,end)+bases[chr].count('cg',start,end)] else: cg=[bases[chr].count('GC',start,end)+bases[chr].count('gC',start,end),bases[chr].count('Gc',start,end)+bases[chr].count('gc',start,end)] #We need to consider strand specific situation. #'+' strand we have CG but '-' we should count 'GC'. #print cg # for i in range(1,ls): # r2=read[i] # r1=read[i-1] # if 'G'==r2 or 'g'==r2: # if 'C'==r1: cg[0]+=1 # if 'c'==r1: cg[1]+=1 #count = int(cg[0]>0)+int(cg[1]>0) if cg[0]+cg[1]==0: continue #print cg cgnum_per_read.append(sum(cg)) if cg[0]>0 and cg[1]>0: reads_cg_num[2]+=1 continue if cg[0]>0: reads_cg_num[0]+=1 else: reads_cg_num[1]+=1 #print reads_cg_num #print reads_num plt.figure() plt.subplot(211) labels = ['noCG','NonRepeat CG','Repeat cg','CGcg mix'] colors = ['r','b','g','y'] explode=(0.05,0,0,0) sizes=[reads_num-sum(reads_cg_num)]+reads_cg_num patches,l_text,p_text = plt.pie(sizes,explode=explode,labels=labels,colors=colors, labeldistance = 1.1,autopct = '%3.1f%%',shadow = False, startangle = 90,pctdistance = 0.6) plt.axis('equal') #plt.legend(loc=2,bbox_to_anchor=(0, 0)) ax=plt.subplot(212) t=np.zeros(20) for num in cgnum_per_read: t[min(num-1,19)]+=1 labels = list(map(str,np.arange(1,20)))+['20+'] #print(t) t = (np.array(t).astype(float)/sum(reads_cg_num))*100 plt.bar(np.arange(20),t) ax.set_xticks(np.arange(20)) ax.set_xticklabels(labels) ax.set_ylabel('Percentage of reads including CG') ax.set_xlabel('CG number per read') plt.text(4,max(t)+4,'All reads including CG site: '+str(sum(reads_cg_num))) #print args.output+'.pdf' plt.savefig(args.output+'.pdf') if __name__=="__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument('-b','--bamfile',help="bam file name", metavar="FILE") parser.add_argument('-g','--genome',help="Genome fasta file path") parser.add_argument('-o','--output',help="pie figure's filename") run(parser)
    33.857143
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    0.589813
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    0
    0
    0
    0
    0
    0
    0
    968
    0.291742
    92e278de46c7d8190178a6e51a0f4e234995f14e
    1,536
    py
    Python
    src/furo/__init__.py
    sethmlarson/furo
    1257d884dae9040248380595e06d7d2a1e6eba39
    [ "MIT" ]
    null
    null
    null
    src/furo/__init__.py
    sethmlarson/furo
    1257d884dae9040248380595e06d7d2a1e6eba39
    [ "MIT" ]
    null
    null
    null
    src/furo/__init__.py
    sethmlarson/furo
    1257d884dae9040248380595e06d7d2a1e6eba39
    [ "MIT" ]
    null
    null
    null
    """A clean customisable Sphinx documentation theme.""" __version__ = "2020.9.8.beta2" from pathlib import Path from .body import wrap_tables from .code import get_pygments_style_colors from .navigation import get_navigation_tree from .toc import should_hide_toc def _html_page_context(app, pagename, templatename, context, doctree): if app.config.html_theme != "furo": return # Custom Navigation Tree (adds checkboxes and labels) toctree = context.get("toctree", lambda **kwargs: "") toctree_html = toctree( collapse=False, titles_only=True, maxdepth=-1, includehidden=True ) context["furo_navigation_tree"] = get_navigation_tree(toctree_html) # Custom "should hide ToC" logic context["furo_hide_toc"] = should_hide_toc(context.get("toc", "")) # Allow for hiding toc via ToC in page-wide metadata. if "hide-toc" in (context.get("meta", None) or {}): context["furo_hide_toc"] = True # Inject information about styles colors = get_pygments_style_colors( app.builder.highlighter.formatter_args["style"], fallbacks={"foreground": "#000000", "background": "#FFFFFF"}, ) context["furo_pygments"] = colors # Patch the content if "body" in context: context["body"] = wrap_tables(context["body"]) def setup(app): """Entry point for sphinx theming.""" theme_path = (Path(__file__).parent / "theme").resolve() app.add_html_theme("furo", str(theme_path)) app.connect("html-page-context", _html_page_context)
    32
    73
    0.69401
    0
    0
    0
    0
    0
    0
    0
    0
    503
    0.327474
    92e3577604795bc43851e0afe7af80fe0fe26bbf
    2,059
    py
    Python
    experiments/mix_down.py
    fretboardfreak/potty_oh
    70b752c719576c0975e1d2af5aca2fc7abc8abcc
    [ "Apache-2.0" ]
    null
    null
    null
    experiments/mix_down.py
    fretboardfreak/potty_oh
    70b752c719576c0975e1d2af5aca2fc7abc8abcc
    [ "Apache-2.0" ]
    1
    2016-05-04T03:51:36.000Z
    2016-05-16T19:08:02.000Z
    experiments/mix_down.py
    fretboardfreak/potty_oh
    70b752c719576c0975e1d2af5aca2fc7abc8abcc
    [ "Apache-2.0" ]
    null
    null
    null
    #!/usr/bin/env python3 # Copyright 2016 Curtis Sand # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """A test for what happens when two waveforms are averaged together.""" from potty_oh import common from potty_oh.wav_file import wav_file_context from potty_oh.waveform import mix_down from potty_oh.signal_generator import Generator from potty_oh.music.pitch import Key from potty_oh.music.interval import Interval def main(): parser = common.get_cmd_line_parser(description=__doc__) common.ParserArguments.filename(parser) common.ParserArguments.length(parser) common.ParserArguments.framerate(parser) common.ParserArguments.set_defaults(parser, type='constant', length=2.0) args = parser.parse_args() common.defaults.framerate = args.framerate sg = Generator(length=args.length, verbose=args.debug) key = Key() unison = sg.sin_constant(key.interval(Interval.unison)) maj_third = sg.sin_constant(key.interval(Interval.major_third)) min_third = sg.sin_constant(key.interval(Interval.minor_third)) fifth = sg.sin_constant(key.interval(Interval.fifth)) powerchord = unison.mix_down(fifth) maj_triad = powerchord.mix_down(maj_third) min_triad = mix_down(powerchord, min_third) with wav_file_context(args.filename) as fout: fout.write_frames(powerchord.frames) fout.write_frames(maj_triad.frames) fout.write_frames(min_triad.frames) return 0 if __name__ == "__main__": common.call_main(main)
    34.898305
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    0.736765
    0
    0
    0
    0
    0
    0
    0
    0
    695
    0.337542
    92e37ec4545956a8e8242b1871fea16288a1a867
    8,704
    py
    Python
    tests/test_hrepr.py
    fabaff/hrepr
    f6de915f1d34c47ceab11f5f70e433a30e6de174
    [ "MIT" ]
    null
    null
    null
    tests/test_hrepr.py
    fabaff/hrepr
    f6de915f1d34c47ceab11f5f70e433a30e6de174
    [ "MIT" ]
    null
    null
    null
    tests/test_hrepr.py
    fabaff/hrepr
    f6de915f1d34c47ceab11f5f70e433a30e6de174
    [ "MIT" ]
    null
    null
    null
    from dataclasses import dataclass from hrepr import H from hrepr import hrepr as real_hrepr from hrepr.h import styledir from .common import one_test_per_assert css_hrepr = open(f"{styledir}/hrepr.css", encoding="utf-8").read() hrepr = real_hrepr.variant(fill_resources=False) @dataclass class Point: x: int y: int class Opaque: pass def hshort(x, **kw): return hrepr(x, max_depth=0, **kw) @one_test_per_assert def test_singletons(): assert hrepr(True) == H.span["hreprv-True"]("True") assert hrepr(False) == H.span["hreprv-False"]("False") assert hrepr(None) == H.span["hreprv-None"]("None") @one_test_per_assert def test_numbers(): assert hrepr(123) == H.span["hreprt-int"]("123") assert hrepr(1.25) == H.span["hreprt-float"]("1.25") @one_test_per_assert def test_string(): assert hshort("hello") == H.span["hreprt-str"]("hello") assert hrepr("3 spaces") == H.span["hreprt-str"]("3 spaces") assert hrepr("hello this is a bit long") == H.span["hreprt-str"]( "hello this is a bit long" ) assert hshort("hello this is a bit long") == H.span["hreprt-str"]( "hello this is a b..." ) assert hshort("hello this is a bit long", string_cutoff=10) == H.span[ "hreprt-str" ]("hello t...") assert hshort("hello this is a bit long", string_cutoff=5) == H.span[ "hreprt-str" ]("he...") assert hshort("hello this is a bit long", string_cutoff=10000) == H.span[ "hreprt-str" ]("hello this is a bit long") @one_test_per_assert def test_bytes(): assert hrepr(b"hello") == H.span["hreprt-bytes"]("68656c6c6f") assert hshort(b"hello") == H.span["hreprt-bytes"]("68656c6c6f") assert hrepr(b"hello this is a bit long") == H.span["hreprt-bytes"]( "68656c6c6f2074686973206973206120626974206c6f6e67" ) assert hshort(b"hello this is a bit long") == H.span["hreprt-bytes"]( "68656c6c6f2074686..." ) def test_function(): assert hrepr(Opaque) == H.span["hreprk-class"]( H.span["hrepr-defn-key"]("class"), " ", H.span["hrepr-defn-name"]("Opaque"), ) def test_structures(): for typ, o, c in ( (tuple, "(", ")"), (list, "[", "]"), (set, "{", "}"), (frozenset, "{", "}"), ): clsname = typ.__name__ assert hrepr(typ((1, 2))) == H.div[ f"hreprt-{clsname}", "hrepr-bracketed" ]( H.div["hrepr-open"](o), H.div["hreprl-h", "hrepr-body"]( H.div(H.span["hreprt-int"]("1")), H.div(H.span["hreprt-int"]("2")), ), H.div["hrepr-close"](c), ) def test_short_structures(): for val, o, c in ( ((1, 2), "(", ")"), ([1, 2], "[", "]"), ({1, 2}, "{", "}"), (frozenset({1, 2}), "{", "}"), ({"x": 1, "y": 2}, "{", "}"), ): clsname = type(val).__name__ assert hrepr(val, max_depth=0) == H.div[ f"hreprt-{clsname}", "hrepr-bracketed" ]( H.div["hrepr-open"](o), H.div["hreprl-s", "hrepr-body"](H.div("...")), H.div["hrepr-close"](c), ) def test_dict(): pt = {"x": 1, "y": 2} assert hrepr(pt) == H.div["hreprt-dict", "hrepr-bracketed"]( H.div["hrepr-open"]("{"), H.table["hrepr-body"]( H.tr( H.td(H.span["hreprt-str"]("x")), H.td["hrepr-delim"](": "), H.td(H.span["hreprt-int"]("1")), ), H.tr( H.td(H.span["hreprt-str"]("y")), H.td["hrepr-delim"](": "), H.td(H.span["hreprt-int"]("2")), ), ), H.div["hrepr-close"]("}"), ) def test_dataclass(): pt = Point(1, 2) assert hrepr(pt) == H.div["hreprt-Point", "hrepr-instance", "hreprl-v"]( H.div["hrepr-title"]("Point"), H.table["hrepr-body"]( H.tr( H.td(H.span["hreprt-symbol"]("x")), H.td["hrepr-delim"]("="), H.td(H.span["hreprt-int"]("1")), ), H.tr( H.td(H.span["hreprt-symbol"]("y")), H.td["hrepr-delim"]("="), H.td(H.span["hreprt-int"]("2")), ), ), ) assert hrepr(pt, max_depth=0) == H.div[ "hreprt-Point", "hrepr-instance", "hreprl-s" ]( H.div["hrepr-title"]("Point"), H.div["hreprl-s", "hrepr-body"](H.div("...")), ) def test_tag(): tg = H.span["hello"](1, 2, H.b("there")) assert hrepr(tg) == tg def test_multiref(): li = [1, 2] lili = [li, li] assert hrepr(lili) == H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div( H.div["hrepr-refbox"]( H.span["hrepr-ref"]("#", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div(H.span["hreprt-int"]("1")), H.div(H.span["hreprt-int"]("2")), ), H.div["hrepr-close"]("]"), ), ) ), H.div( H.div["hrepr-refbox"]( H.span["hrepr-ref"]("#", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-s", "hrepr-body"](H.div("..."),), H.div["hrepr-close"]("]"), ), ) ), ), H.div["hrepr-close"]("]"), ) assert hrepr(lili, shortrefs=True) == H.div[ "hreprt-list", "hrepr-bracketed" ]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div( H.div["hrepr-refbox"]( H.span["hrepr-ref"]("#", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div(H.span["hreprt-int"]("1")), H.div(H.span["hreprt-int"]("2")), ), H.div["hrepr-close"]("]"), ), ) ), H.div(H.span["hrepr-ref"]("#", 1)), ), H.div["hrepr-close"]("]"), ) def test_recursive(): li = [1] li.append(li) assert hrepr(li) == H.div["hrepr-refbox"]( H.span["hrepr-ref"]("#", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div(H.span["hreprt-int"]("1")), H.div( H.div["hrepr-refbox"]( H.span["hrepr-ref"]("⟳", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-s", "hrepr-body"](H.div("..."),), H.div["hrepr-close"]("]"), ), ) ), ), H.div["hrepr-close"]("]"), ), ) assert hrepr(li, shortrefs=True) == H.div["hrepr-refbox"]( H.span["hrepr-ref"]("#", 1, "="), H.div["hreprt-list", "hrepr-bracketed"]( H.div["hrepr-open"]("["), H.div["hreprl-h", "hrepr-body"]( H.div(H.span["hreprt-int"]("1")), H.div(H.span["hrepr-ref"]("⟳", 1)), ), H.div["hrepr-close"]("]"), ), ) def test_unsupported(): assert hshort(Opaque()) == H.span["hreprt-Opaque"]( "<", "tests.test_hrepr.Opaque", ">" ) def test_as_page(): utf8 = H.meta( {"http-equiv": "Content-type"}, content="text/html", charset="UTF-8" ) assert real_hrepr.page(1) == H.inline( H.raw("<!DOCTYPE html>"), H.html(H.head(utf8, H.style(css_hrepr)), H.body(real_hrepr(1)),), ) def test_hrepr_multiarg(): assert hrepr(1, 2) == H.inline( H.span["hreprt-int"]("1"), H.span["hreprt-int"]("2"), ) def test_preprocess(): assert hrepr(1, preprocess=lambda x, hrepr: x + 1) == H.span["hreprt-int"]( "2" ) def test_postprocess(): assert hrepr(1, postprocess=lambda x, obj, hrepr: x["newclass"]) == H.span[ "newclass", "hreprt-int" ]("1")
    29.110368
    79
    0.443934
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    2,632
    0.302251
    92e459320d22b81d1d537a46bdb22eb8751da72d
    6,218
    py
    Python
    sympy/assumptions/assume.py
    shivangdubey/sympy
    bd3ddd4c71d439c8b623f69a02274dd8a8a82198
    [ "BSD-3-Clause" ]
    2
    2020-07-27T16:36:27.000Z
    2020-12-29T22:28:37.000Z
    sympy/assumptions/assume.py
    shivangdubey/sympy
    bd3ddd4c71d439c8b623f69a02274dd8a8a82198
    [ "BSD-3-Clause" ]
    2
    2020-08-18T15:21:59.000Z
    2020-08-18T19:35:29.000Z
    sympy/assumptions/assume.py
    shivangdubey/sympy
    bd3ddd4c71d439c8b623f69a02274dd8a8a82198
    [ "BSD-3-Clause" ]
    2
    2021-01-08T23:03:23.000Z
    2021-01-13T18:57:02.000Z
    import inspect from sympy.core.cache import cacheit from sympy.core.singleton import S from sympy.core.sympify import _sympify from sympy.logic.boolalg import Boolean from sympy.utilities.source import get_class from contextlib import contextmanager class AssumptionsContext(set): """Set representing assumptions. This is used to represent global assumptions, but you can also use this class to create your own local assumptions contexts. It is basically a thin wrapper to Python's set, so see its documentation for advanced usage. Examples ======== >>> from sympy import Q >>> from sympy.assumptions.assume import global_assumptions >>> global_assumptions AssumptionsContext() >>> from sympy.abc import x >>> global_assumptions.add(Q.real(x)) >>> global_assumptions AssumptionsContext({Q.real(x)}) >>> global_assumptions.remove(Q.real(x)) >>> global_assumptions AssumptionsContext() >>> global_assumptions.clear() """ def add(self, *assumptions): """Add an assumption.""" for a in assumptions: super().add(a) def _sympystr(self, printer): if not self: return "%s()" % self.__class__.__name__ return "{}({})".format(self.__class__.__name__, printer._print_set(self)) global_assumptions = AssumptionsContext() class AppliedPredicate(Boolean): """The class of expressions resulting from applying a Predicate. Examples ======== >>> from sympy import Q, Symbol >>> x = Symbol('x') >>> Q.integer(x) Q.integer(x) >>> type(Q.integer(x)) <class 'sympy.assumptions.assume.AppliedPredicate'> """ __slots__ = () def __new__(cls, predicate, arg): arg = _sympify(arg) return Boolean.__new__(cls, predicate, arg) is_Atom = True # do not attempt to decompose this @property def arg(self): """ Return the expression used by this assumption. Examples ======== >>> from sympy import Q, Symbol >>> x = Symbol('x') >>> a = Q.integer(x + 1) >>> a.arg x + 1 """ return self._args[1] @property def args(self): return self._args[1:] @property def func(self): return self._args[0] @cacheit def sort_key(self, order=None): return (self.class_key(), (2, (self.func.name, self.arg.sort_key())), S.One.sort_key(), S.One) def __eq__(self, other): if type(other) is AppliedPredicate: return self._args == other._args return False def __hash__(self): return super().__hash__() def _eval_ask(self, assumptions): return self.func.eval(self.arg, assumptions) @property def binary_symbols(self): from sympy.core.relational import Eq, Ne if self.func.name in ['is_true', 'is_false']: i = self.arg if i.is_Boolean or i.is_Symbol or isinstance(i, (Eq, Ne)): return i.binary_symbols return set() class Predicate(Boolean): """A predicate is a function that returns a boolean value. Predicates merely wrap their argument and remain unevaluated: >>> from sympy import Q, ask >>> type(Q.prime) <class 'sympy.assumptions.assume.Predicate'> >>> Q.prime.name 'prime' >>> Q.prime(7) Q.prime(7) >>> _.func.name 'prime' To obtain the truth value of an expression containing predicates, use the function ``ask``: >>> ask(Q.prime(7)) True The tautological predicate ``Q.is_true`` can be used to wrap other objects: >>> from sympy.abc import x >>> Q.is_true(x > 1) Q.is_true(x > 1) """ is_Atom = True def __new__(cls, name, handlers=None): obj = Boolean.__new__(cls) obj.name = name obj.handlers = handlers or [] return obj def _hashable_content(self): return (self.name,) def __getnewargs__(self): return (self.name,) def __call__(self, expr): return AppliedPredicate(self, expr) def add_handler(self, handler): self.handlers.append(handler) def remove_handler(self, handler): self.handlers.remove(handler) @cacheit def sort_key(self, order=None): return self.class_key(), (1, (self.name,)), S.One.sort_key(), S.One def eval(self, expr, assumptions=True): """ Evaluate self(expr) under the given assumptions. This uses only direct resolution methods, not logical inference. """ res, _res = None, None mro = inspect.getmro(type(expr)) for handler in self.handlers: cls = get_class(handler) for subclass in mro: eval_ = getattr(cls, subclass.__name__, None) if eval_ is None: continue res = eval_(expr, assumptions) # Do not stop if value returned is None # Try to check for higher classes if res is None: continue if _res is None: _res = res elif res is None: # since first resolutor was conclusive, we keep that value res = _res else: # only check consistency if both resolutors have concluded if _res != res: raise ValueError('incompatible resolutors') break return res @contextmanager def assuming(*assumptions): """ Context manager for assumptions Examples ======== >>> from sympy.assumptions import assuming, Q, ask >>> from sympy.abc import x, y >>> print(ask(Q.integer(x + y))) None >>> with assuming(Q.integer(x), Q.integer(y)): ... print(ask(Q.integer(x + y))) True """ old_global_assumptions = global_assumptions.copy() global_assumptions.update(assumptions) try: yield finally: global_assumptions.clear() global_assumptions.update(old_global_assumptions)
    26.686695
    81
    0.587488
    5,328
    0.856867
    569
    0.091509
    1,590
    0.255709
    0
    0
    2,684
    0.43165
    92e5ae34177c1ed1dca21481a52e063cdd40f997
    5,794
    py
    Python
    distancematrix/tests/consumer/test_distance_matrix.py
    IDLabResearch/seriesdistancematrix
    c0e666d036f24184511e766cee9fdfa55f41df97
    [ "MIT" ]
    12
    2019-11-22T14:34:51.000Z
    2021-05-04T19:23:55.000Z
    distancematrix/tests/consumer/test_distance_matrix.py
    predict-idlab/seriesdistancematrix
    c0e666d036f24184511e766cee9fdfa55f41df97
    [ "MIT" ]
    1
    2020-04-28T07:59:03.000Z
    2020-04-28T07:59:03.000Z
    distancematrix/tests/consumer/test_distance_matrix.py
    IDLabResearch/seriesdistancematrix
    c0e666d036f24184511e766cee9fdfa55f41df97
    [ "MIT" ]
    3
    2020-03-02T12:39:00.000Z
    2021-03-22T13:36:25.000Z
    import numpy as np from unittest import TestCase import numpy.testing as npt from distancematrix.util import diag_indices_of from distancematrix.consumer.distance_matrix import DistanceMatrix class TestContextualMatrixProfile(TestCase): def setUp(self): self.dist_matrix = np.array([ [8.67, 1.10, 1.77, 1.26, 1.91, 4.29, 6.32, 4.24, 4.64, 5.06, 6.41, 4.07, 4.67, 9.32, 5.09], [4.33, 4.99, 0.14, 2.79, 2.10, 6.26, 9.40, 4.14, 5.53, 4.26, 8.21, 5.91, 6.83, 9.26, 6.19], [0.16, 9.05, 1.35, 4.78, 7.01, 4.36, 5.24, 8.81, 7.90, 5.84, 8.90, 7.88, 3.37, 4.70, 6.94], [0.94, 8.70, 3.87, 6.29, 0.32, 1.79, 5.80, 2.61, 1.43, 6.32, 1.62, 0.20, 2.28, 7.11, 2.15], [9.90, 4.51, 2.11, 2.83, 5.52, 8.55, 6.90, 0.24, 1.58, 4.26, 8.75, 3.71, 9.93, 8.33, 0.38], [7.30, 5.84, 9.63, 1.95, 3.76, 3.61, 9.42, 5.56, 5.09, 7.07, 1.90, 4.78, 1.06, 0.69, 3.67], [2.17, 8.37, 3.99, 4.28, 4.37, 2.86, 8.61, 3.39, 8.37, 6.95, 6.57, 1.79, 7.40, 4.41, 7.64], [6.26, 0.29, 6.44, 8.84, 1.24, 2.52, 6.25, 3.07, 5.55, 3.19, 8.16, 5.32, 9.01, 0.39, 9.], [4.67, 8.88, 3.05, 3.06, 2.36, 8.34, 4.91, 5.46, 9.25, 9.78, 0.03, 5.64, 5.10, 3.58, 6.92], [1.01, 0.91, 6.28, 7.79, 0.68, 5.50, 6.72, 5.11, 0.80, 9.30, 9.77, 4.71, 3.26, 7.29, 6.26]]) def mock_initialise(self, dm): dm.initialise(1, self.dist_matrix.shape[0], self.dist_matrix.shape[1]) def test_process_diagonal(self): dm = DistanceMatrix() self.mock_initialise(dm) for diag in range(-self.dist_matrix.shape[0] + 1, self.dist_matrix.shape[1]): diag_ind = diag_indices_of(self.dist_matrix, diag) dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind])) npt.assert_equal(dm.distance_matrix, self.dist_matrix) def test_process_diagonal_partial_calculation(self): dm = DistanceMatrix() self.mock_initialise(dm) correct = np.full_like(self.dist_matrix, np.nan, dtype=float) for diag in range(-8, self.dist_matrix.shape[1], 3): diag_ind = diag_indices_of(self.dist_matrix, diag) dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind])) correct[diag_ind] = self.dist_matrix[diag_ind] npt.assert_equal(dm.distance_matrix, correct) def test_process_column(self): dm = DistanceMatrix() self.mock_initialise(dm) for column in range(0, self.dist_matrix.shape[1]): dm.process_column(column, np.atleast_2d(self.dist_matrix[:, column])) npt.assert_equal(dm.distance_matrix, self.dist_matrix) def test_process_column_partial_calculation(self): dm = DistanceMatrix() self.mock_initialise(dm) correct = np.full_like(self.dist_matrix, np.nan, dtype=float) for column in [2, 3, 4, 5, 10, 11, 12]: dm.process_column(column, np.atleast_2d(self.dist_matrix[:, column])) correct[:, column] = self.dist_matrix[:, column] npt.assert_equal(dm.distance_matrix, correct) def test_streaming_process_column(self): dm = DistanceMatrix() dm.initialise(1, 5, 5) dm.process_column(0, np.atleast_2d(self.dist_matrix[0, 0])) dm.process_column(1, np.atleast_2d(self.dist_matrix[:2, 1])) expected = np.full((5, 5), np.nan) expected[0, 0] = self.dist_matrix[0, 0] expected[:2, 1] = self.dist_matrix[:2, 1] npt.assert_equal(dm.distance_matrix, expected) for column in range(0, 5): dm.process_column(column, np.atleast_2d(self.dist_matrix[:5, :5][:, column])) npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5]) dm.shift_query(1) dm.shift_series(3) correct = np.full((5, 5), np.nan) correct[0:4, 0:2] = self.dist_matrix[1:5, 3:5] npt.assert_equal(dm.distance_matrix, correct) for column in range(0, 5): dm.process_column(column, np.atleast_2d(self.dist_matrix[1:6, 3:8][:, column])) npt.assert_equal(dm.distance_matrix, self.dist_matrix[1:6, 3:8]) dm.shift_query(2) dm.shift_series(1) dm.process_column(4, np.atleast_2d(self.dist_matrix[3:8, 8])) correct = np.full((5, 5), np.nan) correct[0:3, 0:4] = self.dist_matrix[3:6, 4:8] correct[:, 4] = self.dist_matrix[3:8, 8] npt.assert_equal(dm.distance_matrix, correct) def test_streaming_process_diagonal(self): dm = DistanceMatrix() dm.initialise(1, 5, 5) dm.process_diagonal(0, np.atleast_2d(self.dist_matrix[0, 0])) diag_ind = diag_indices_of(self.dist_matrix[:3, :3], 1) dm.process_diagonal(1, np.atleast_2d(np.atleast_2d(self.dist_matrix[diag_ind]))) expected = np.full((5, 5), np.nan) expected[0, 0] = self.dist_matrix[0, 0] expected[0, 1] = self.dist_matrix[0, 1] expected[1, 2] = self.dist_matrix[1, 2] npt.assert_equal(dm.distance_matrix, expected) for diag in range(-4,5): diag_ind = diag_indices_of(self.dist_matrix[:5, :5], diag) dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind])) npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5]) dm.shift_query(2) dm.shift_series(1) expected = self.dist_matrix[2:7, 1:6].copy() expected[-2:, :] = np.nan expected[:, -1:] = np.nan npt.assert_equal(dm.distance_matrix, expected) for diag in range(-4,5): diag_ind = diag_indices_of(self.dist_matrix[:5, :5], diag) dm.process_diagonal(diag, np.atleast_2d(self.dist_matrix[diag_ind])) npt.assert_equal(dm.distance_matrix, self.dist_matrix[:5, :5])
    42.602941
    104
    0.608733
    5,598
    0.966172
    0
    0
    0
    0
    0
    0
    0
    0
    92e5bc0e9b68f032b202632a0013f3e6bb85256a
    11,460
    py
    Python
    supervisor/const.py
    peddamat/home-assistant-supervisor-test
    5da55772bcb2db3c6d8432cbc08e2ac9fbf480c4
    [ "Apache-2.0" ]
    null
    null
    null
    supervisor/const.py
    peddamat/home-assistant-supervisor-test
    5da55772bcb2db3c6d8432cbc08e2ac9fbf480c4
    [ "Apache-2.0" ]
    null
    null
    null
    supervisor/const.py
    peddamat/home-assistant-supervisor-test
    5da55772bcb2db3c6d8432cbc08e2ac9fbf480c4
    [ "Apache-2.0" ]
    null
    null
    null
    """Constants file for Supervisor.""" from enum import Enum from ipaddress import ip_network from pathlib import Path SUPERVISOR_VERSION = "DEV" URL_HASSIO_ADDONS = "https://github.com/home-assistant/addons" URL_HASSIO_APPARMOR = "https://version.home-assistant.io/apparmor.txt" URL_HASSIO_VERSION = "https://version.home-assistant.io/{channel}.json" SUPERVISOR_DATA = Path("/data") FILE_HASSIO_ADDONS = Path(SUPERVISOR_DATA, "addons.json") FILE_HASSIO_AUTH = Path(SUPERVISOR_DATA, "auth.json") FILE_HASSIO_CONFIG = Path(SUPERVISOR_DATA, "config.json") FILE_HASSIO_DISCOVERY = Path(SUPERVISOR_DATA, "discovery.json") FILE_HASSIO_DOCKER = Path(SUPERVISOR_DATA, "docker.json") FILE_HASSIO_HOMEASSISTANT = Path(SUPERVISOR_DATA, "homeassistant.json") FILE_HASSIO_INGRESS = Path(SUPERVISOR_DATA, "ingress.json") FILE_HASSIO_SERVICES = Path(SUPERVISOR_DATA, "services.json") FILE_HASSIO_UPDATER = Path(SUPERVISOR_DATA, "updater.json") FILE_SUFFIX_CONFIGURATION = [".yaml", ".yml", ".json"] MACHINE_ID = Path("/etc/machine-id") SOCKET_DBUS = Path("/run/dbus/system_bus_socket") SOCKET_DOCKER = Path("/run/docker.sock") RUN_SUPERVISOR_STATE = Path("/run/supervisor") SYSTEMD_JOURNAL_PERSISTENT = Path("/var/log/journal") SYSTEMD_JOURNAL_VOLATILE = Path("/run/log/journal") DOCKER_NETWORK = "hassio" DOCKER_NETWORK_MASK = ip_network("172.30.32.0/23") DOCKER_NETWORK_RANGE = ip_network("172.30.33.0/24") # This needs to match the dockerd --cpu-rt-runtime= argument. DOCKER_CPU_RUNTIME_TOTAL = 950_000 # The rt runtimes are guarantees, hence we cannot allocate more # time than available! Support up to 5 containers with equal time # allocated. # Note that the time is multiplied by CPU count. This means that # a single container can schedule up to 950/5*4 = 760ms in RT priority # on a quad core system. DOCKER_CPU_RUNTIME_ALLOCATION = int(DOCKER_CPU_RUNTIME_TOTAL / 5) DNS_SUFFIX = "local.hass.io" LABEL_ARCH = "io.hass.arch" LABEL_MACHINE = "io.hass.machine" LABEL_TYPE = "io.hass.type" LABEL_VERSION = "io.hass.version" META_ADDON = "addon" META_HOMEASSISTANT = "homeassistant" META_SUPERVISOR = "supervisor" JSON_DATA = "data" JSON_MESSAGE = "message" JSON_RESULT = "result" RESULT_ERROR = "error" RESULT_OK = "ok" CONTENT_TYPE_BINARY = "application/octet-stream" CONTENT_TYPE_JSON = "application/json" CONTENT_TYPE_PNG = "image/png" CONTENT_TYPE_TAR = "application/tar" CONTENT_TYPE_TEXT = "text/plain" CONTENT_TYPE_URL = "application/x-www-form-urlencoded" COOKIE_INGRESS = "ingress_session" HEADER_TOKEN = "X-Supervisor-Token" HEADER_TOKEN_OLD = "X-Hassio-Key" ENV_TIME = "TZ" ENV_TOKEN = "SUPERVISOR_TOKEN" ENV_TOKEN_HASSIO = "HASSIO_TOKEN" ENV_HOMEASSISTANT_REPOSITORY = "HOMEASSISTANT_REPOSITORY" ENV_SUPERVISOR_DEV = "SUPERVISOR_DEV" ENV_SUPERVISOR_MACHINE = "SUPERVISOR_MACHINE" ENV_SUPERVISOR_NAME = "SUPERVISOR_NAME" ENV_SUPERVISOR_SHARE = "SUPERVISOR_SHARE" ENV_SUPERVISOR_CPU_RT = "SUPERVISOR_CPU_RT" REQUEST_FROM = "HASSIO_FROM" ATTR_ACCESS_TOKEN = "access_token" ATTR_ACCESSPOINTS = "accesspoints" ATTR_ACTIVE = "active" ATTR_ADDON = "addon" ATTR_ADDONS = "addons" ATTR_ADDONS_CUSTOM_LIST = "addons_custom_list" ATTR_ADDONS_REPOSITORIES = "addons_repositories" ATTR_ADDRESS = "address" ATTR_ADDRESS_DATA = "address-data" ATTR_ADMIN = "admin" ATTR_ADVANCED = "advanced" ATTR_APPARMOR = "apparmor" ATTR_APPLICATION = "application" ATTR_ARCH = "arch" ATTR_ARGS = "args" ATTR_LABELS = "labels" ATTR_AUDIO = "audio" ATTR_AUDIO_INPUT = "audio_input" ATTR_AUDIO_OUTPUT = "audio_output" ATTR_AUTH = "auth" ATTR_AUTH_API = "auth_api" ATTR_AUTO_UPDATE = "auto_update" ATTR_AVAILABLE = "available" ATTR_BLK_READ = "blk_read" ATTR_BLK_WRITE = "blk_write" ATTR_BOARD = "board" ATTR_BOOT = "boot" ATTR_BRANCH = "branch" ATTR_BUILD = "build" ATTR_BUILD_FROM = "build_from" ATTR_CARD = "card" ATTR_CHANGELOG = "changelog" ATTR_CHANNEL = "channel" ATTR_CHASSIS = "chassis" ATTR_CHECKS = "checks" ATTR_CLI = "cli" ATTR_CONFIG = "config" ATTR_CONFIGURATION = "configuration" ATTR_CONNECTED = "connected" ATTR_CONNECTIONS = "connections" ATTR_CONTAINERS = "containers" ATTR_CPE = "cpe" ATTR_CPU_PERCENT = "cpu_percent" ATTR_CRYPTO = "crypto" ATTR_DATA = "data" ATTR_DATE = "date" ATTR_DEBUG = "debug" ATTR_DEBUG_BLOCK = "debug_block" ATTR_DEFAULT = "default" ATTR_DEPLOYMENT = "deployment" ATTR_DESCRIPTON = "description" ATTR_DETACHED = "detached" ATTR_DEVICES = "devices" ATTR_DEVICETREE = "devicetree" ATTR_DIAGNOSTICS = "diagnostics" ATTR_DISCOVERY = "discovery" ATTR_DISK = "disk" ATTR_DISK_FREE = "disk_free" ATTR_DISK_LIFE_TIME = "disk_life_time" ATTR_DISK_TOTAL = "disk_total" ATTR_DISK_USED = "disk_used" ATTR_DNS = "dns" ATTR_DOCKER = "docker" ATTR_DOCKER_API = "docker_api" ATTR_DOCUMENTATION = "documentation" ATTR_DOMAINS = "domains" ATTR_ENABLE = "enable" ATTR_ENABLED = "enabled" ATTR_ENVIRONMENT = "environment" ATTR_EVENT = "event" ATTR_FEATURES = "features" ATTR_FILENAME = "filename" ATTR_FLAGS = "flags" ATTR_FOLDERS = "folders" ATTR_FREQUENCY = "frequency" ATTR_FULL_ACCESS = "full_access" ATTR_GATEWAY = "gateway" ATTR_GPIO = "gpio" ATTR_HASSIO_API = "hassio_api" ATTR_HASSIO_ROLE = "hassio_role" ATTR_HASSOS = "hassos" ATTR_HEALTHY = "healthy" ATTR_HOMEASSISTANT = "homeassistant" ATTR_HOMEASSISTANT_API = "homeassistant_api" ATTR_HOST = "host" ATTR_HOST_DBUS = "host_dbus" ATTR_HOST_INTERNET = "host_internet" ATTR_HOST_IPC = "host_ipc" ATTR_HOST_NETWORK = "host_network" ATTR_HOST_PID = "host_pid" ATTR_HOSTNAME = "hostname" ATTR_ICON = "icon" ATTR_ID = "id" ATTR_IMAGE = "image" ATTR_IMAGES = "images" ATTR_INDEX = "index" ATTR_INGRESS = "ingress" ATTR_INGRESS_ENTRY = "ingress_entry" ATTR_INGRESS_PANEL = "ingress_panel" ATTR_INGRESS_PORT = "ingress_port" ATTR_INGRESS_TOKEN = "ingress_token" ATTR_INGRESS_URL = "ingress_url" ATTR_INIT = "init" ATTR_INITIALIZE = "initialize" ATTR_INPUT = "input" ATTR_INSTALLED = "installed" ATTR_INTERFACE = "interface" ATTR_INTERFACES = "interfaces" ATTR_IP_ADDRESS = "ip_address" ATTR_IPV4 = "ipv4" ATTR_IPV6 = "ipv6" ATTR_ISSUES = "issues" ATTR_KERNEL = "kernel" ATTR_KERNEL_MODULES = "kernel_modules" ATTR_LAST_BOOT = "last_boot" ATTR_LEGACY = "legacy" ATTR_LOCALS = "locals" ATTR_LOCATON = "location" ATTR_LOGGING = "logging" ATTR_LOGO = "logo" ATTR_LONG_DESCRIPTION = "long_description" ATTR_MAC = "mac" ATTR_MACHINE = "machine" ATTR_MAINTAINER = "maintainer" ATTR_MAP = "map" ATTR_MEMORY_LIMIT = "memory_limit" ATTR_MEMORY_PERCENT = "memory_percent" ATTR_MEMORY_USAGE = "memory_usage" ATTR_MESSAGE = "message" ATTR_METHOD = "method" ATTR_MODE = "mode" ATTR_MULTICAST = "multicast" ATTR_NAME = "name" ATTR_NAMESERVERS = "nameservers" ATTR_NETWORK = "network" ATTR_NETWORK_DESCRIPTION = "network_description" ATTR_NETWORK_RX = "network_rx" ATTR_NETWORK_TX = "network_tx" ATTR_OBSERVER = "observer" ATTR_OPERATING_SYSTEM = "operating_system" ATTR_OPTIONS = "options" ATTR_OTA = "ota" ATTR_OUTPUT = "output" ATTR_PANEL_ADMIN = "panel_admin" ATTR_PANEL_ICON = "panel_icon" ATTR_PANEL_TITLE = "panel_title" ATTR_PANELS = "panels" ATTR_PARENT = "parent" ATTR_PASSWORD = "password" ATTR_PORT = "port" ATTR_PORTS = "ports" ATTR_PORTS_DESCRIPTION = "ports_description" ATTR_PREFIX = "prefix" ATTR_PRIMARY = "primary" ATTR_PRIORITY = "priority" ATTR_PRIVILEGED = "privileged" ATTR_PROTECTED = "protected" ATTR_PROVIDERS = "providers" ATTR_PSK = "psk" ATTR_RATING = "rating" ATTR_REALTIME = "realtime" ATTR_REFRESH_TOKEN = "refresh_token" ATTR_REGISTRIES = "registries" ATTR_REGISTRY = "registry" ATTR_REPOSITORIES = "repositories" ATTR_REPOSITORY = "repository" ATTR_SCHEMA = "schema" ATTR_SECURITY = "security" ATTR_SERIAL = "serial" ATTR_SERVERS = "servers" ATTR_SERVICE = "service" ATTR_SERVICES = "services" ATTR_SESSION = "session" ATTR_SIGNAL = "signal" ATTR_SIZE = "size" ATTR_SLUG = "slug" ATTR_SNAPSHOT_EXCLUDE = "snapshot_exclude" ATTR_SNAPSHOTS = "snapshots" ATTR_SOURCE = "source" ATTR_SQUASH = "squash" ATTR_SSD = "ssid" ATTR_SSID = "ssid" ATTR_SSL = "ssl" ATTR_STAGE = "stage" ATTR_STARTUP = "startup" ATTR_STATE = "state" ATTR_STATIC = "static" ATTR_STDIN = "stdin" ATTR_STORAGE = "storage" ATTR_SUGGESTIONS = "suggestions" ATTR_SUPERVISOR = "supervisor" ATTR_SUPERVISOR_INTERNET = "supervisor_internet" ATTR_SUPPORTED = "supported" ATTR_SUPPORTED_ARCH = "supported_arch" ATTR_SYSTEM = "system" ATTR_JOURNALD = "journald" ATTR_TIMEOUT = "timeout" ATTR_TIMEZONE = "timezone" ATTR_TITLE = "title" ATTR_TMPFS = "tmpfs" ATTR_TOTP = "totp" ATTR_TRANSLATIONS = "translations" ATTR_TYPE = "type" ATTR_UART = "uart" ATTR_UDEV = "udev" ATTR_UNHEALTHY = "unhealthy" ATTR_UNSAVED = "unsaved" ATTR_UNSUPPORTED = "unsupported" ATTR_UPDATE_AVAILABLE = "update_available" ATTR_UPDATE_KEY = "update_key" ATTR_URL = "url" ATTR_USB = "usb" ATTR_USER = "user" ATTR_USERNAME = "username" ATTR_UUID = "uuid" ATTR_VALID = "valid" ATTR_VALUE = "value" ATTR_VERSION = "version" ATTR_VERSION_LATEST = "version_latest" ATTR_VIDEO = "video" ATTR_VLAN = "vlan" ATTR_VOLUME = "volume" ATTR_VPN = "vpn" ATTR_WAIT_BOOT = "wait_boot" ATTR_WATCHDOG = "watchdog" ATTR_WEBUI = "webui" ATTR_WIFI = "wifi" ATTR_CONTENT_TRUST = "content_trust" ATTR_FORCE_SECURITY = "force_security" PROVIDE_SERVICE = "provide" NEED_SERVICE = "need" WANT_SERVICE = "want" MAP_CONFIG = "config" MAP_SSL = "ssl" MAP_ADDONS = "addons" MAP_BACKUP = "backup" MAP_SHARE = "share" MAP_MEDIA = "media" ARCH_ARMHF = "armhf" ARCH_ARMV7 = "armv7" ARCH_AARCH64 = "aarch64" ARCH_AMD64 = "amd64" ARCH_I386 = "i386" ARCH_ALL = [ARCH_ARMHF, ARCH_ARMV7, ARCH_AARCH64, ARCH_AMD64, ARCH_I386] REPOSITORY_CORE = "core" REPOSITORY_LOCAL = "local" FOLDER_HOMEASSISTANT = "homeassistant" FOLDER_SHARE = "share" FOLDER_ADDONS = "addons/local" FOLDER_SSL = "ssl" FOLDER_MEDIA = "media" SNAPSHOT_FULL = "full" SNAPSHOT_PARTIAL = "partial" CRYPTO_AES128 = "aes128" SECURITY_PROFILE = "profile" SECURITY_DEFAULT = "default" SECURITY_DISABLE = "disable" ROLE_DEFAULT = "default" ROLE_HOMEASSISTANT = "homeassistant" ROLE_BACKUP = "backup" ROLE_MANAGER = "manager" ROLE_ADMIN = "admin" ROLE_ALL = [ROLE_DEFAULT, ROLE_HOMEASSISTANT, ROLE_BACKUP, ROLE_MANAGER, ROLE_ADMIN] class AddonBoot(str, Enum): """Boot mode for the add-on.""" AUTO = "auto" MANUAL = "manual" class AddonStartup(str, Enum): """Startup types of Add-on.""" INITIALIZE = "initialize" SYSTEM = "system" SERVICES = "services" APPLICATION = "application" ONCE = "once" class AddonStage(str, Enum): """Stage types of add-on.""" STABLE = "stable" EXPERIMENTAL = "experimental" DEPRECATED = "deprecated" class AddonState(str, Enum): """State of add-on.""" STARTED = "started" STOPPED = "stopped" UNKNOWN = "unknown" ERROR = "error" class UpdateChannel(str, Enum): """Core supported update channels.""" STABLE = "stable" BETA = "beta" DEV = "dev" class CoreState(str, Enum): """Represent current loading state.""" INITIALIZE = "initialize" SETUP = "setup" STARTUP = "startup" RUNNING = "running" FREEZE = "freeze" SHUTDOWN = "shutdown" STOPPING = "stopping" CLOSE = "close" class LogLevel(str, Enum): """Logging level of system.""" DEBUG = "debug" INFO = "info" WARNING = "warning" ERROR = "error" CRITICAL = "critical" class HostFeature(str, Enum): """Host feature.""" HASSOS = "hassos" HOSTNAME = "hostname" NETWORK = "network" REBOOT = "reboot" SERVICES = "services" SHUTDOWN = "shutdown"
    25.986395
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    4,428
    0.386387
    92e5e938e0e0af1229cd08971df68b5917c123c7
    8,334
    py
    Python
    quaesit/agent.py
    jgregoriods/quaesit
    3846f5084ea4d6c1cbd9a93176ee9dee25e12105
    [ "MIT" ]
    null
    null
    null
    quaesit/agent.py
    jgregoriods/quaesit
    3846f5084ea4d6c1cbd9a93176ee9dee25e12105
    [ "MIT" ]
    null
    null
    null
    quaesit/agent.py
    jgregoriods/quaesit
    3846f5084ea4d6c1cbd9a93176ee9dee25e12105
    [ "MIT" ]
    null
    null
    null
    import inspect from math import hypot, sin, asin, cos, radians, degrees from abc import ABCMeta, abstractmethod from random import randint, choice from typing import Dict, List, Tuple, Union class Agent(metaclass=ABCMeta): """ Class to represent an agent in an agent-based model. """ _id = 0 colors = ['blue', 'brown', 'cyan', 'gray', 'green', 'magenta', 'orange', 'pink', 'purple', 'red', 'yellow'] def __init__(self, world, coords: Tuple = None): self._id = Agent._id Agent._id += 1 self.world = world self.coords = coords or (randint(0, self.world.width - 1), randint(0, self.world.height - 1)) self.direction = 90 self.breed = self.__class__.__name__.lower() self.icon = '.' self.color = choice(self.colors) self.world.add_agent(self) def die(self): """ Remove the agent from the world. """ del self.world.agents[self._id] self.world.grid[self.coords]['agents'].remove(self) del self def hatch(self): """ Creates an agent and initializes it with the same parameters as oneself. """ sig = inspect.signature(self.__init__) filter_keys = [param.name for param in sig.parameters.values() if param.kind == param.POSITIONAL_OR_KEYWORD] filtered_dict = {filter_key: self.__dict__[filter_key] for filter_key in filter_keys} return self.__class__(**filtered_dict) def move_to(self, coords: Tuple): """ Places the agent in a different cell of the world grid. """ self.world.remove_from_grid(self) self.coords = coords self.world.place_on_grid(self) def cell_here(self, layer = None): """ Returns the value of a layer in the model's grid for the cell where the agent is. If no layer is specified, the values of all layers are returned. """ if layer is not None: return self.world.grid[self.coords][layer] else: return self.world.grid[self.coords] def get_distance(self, coords: Tuple) -> int: """ Returns the distance (in cells) from the agent to a pair of coordinates. """ x, y = coords return round(hypot((x - self.coords[0]), (y - self.coords[1]))) def cells_in_radius(self, radius: int) -> Dict: """ Returns all cells and respective attributes within a distance of the agent. """ if self.world.torus: neighborhood = {self.world.to_torus((x, y)): self.world.grid[self.world.to_torus((x, y))] for x in range(self.coords[0] - radius, self.coords[0] + radius + 1) for y in range(self.coords[1] - radius, self.coords[1] + radius + 1) if self.get_distance((x, y)) <= radius} else: neighborhood = {(x, y): self.world.grid[(x, y)] for x in range(self.coords[0] - radius, self.coords[0] + radius + 1) for y in range(self.coords[1] - radius, self.coords[1] + radius + 1) if (self.get_distance((x, y)) <= radius and (x, y) in self.world.grid)} return neighborhood def empty_cells_in_radius(self, radius: int) -> Dict: """ Returns all empty cells (with no agents on them) and respective attributes within a distance of the agent. """ if self.world.torus: neighborhood = {self.world.to_torus((x, y)): self.world.grid[self.world.to_torus((x, y))] for x in range(self.coords[0] - radius, self.coords[0] + radius + 1) for y in range(self.coords[1] - radius, self.coords[1] + radius + 1) if (self.get_distance((x, y)) <= radius and not self.world.grid[self.world.to_torus((x, y))] ['agents'])} else: neighborhood = {(x, y): self.world.grid[(x, y)] for x in range(self.coords[0] - radius, self.coords[0] + radius + 1) for y in range(self.coords[1] - radius, self.coords[1] + radius + 1) if (self.get_distance((x, y)) <= radius and (x, y) in self.world.grid and not self.world.grid[(x, y)]['agents'])} return neighborhood def nearest_cell(self, cells: Union[List, Dict]) -> Tuple: """ Given a list or dictionary of cells, returns the coordinates of the cell that is nearest to the agent. """ dists = {cell: self.get_distance(cell) for cell in cells} return min(dists, key=dists.get) def agents_in_radius(self, radius: int): """ Returns all agents within a distance of oneself. """ neighborhood = self.cells_in_radius(radius) neighbors = [agent for coords in neighborhood for agent in self.world.grid[coords]['agents'] if agent is not self] return neighbors def agents_here(self) -> List: """ Returns all agents located on the same cell as oneself. """ return [agent for agent in self.world.grid[self.coords]['agents'] if agent is not self] def nearest_agent(self, agents: List = None): """ Given a list of agents, returns the agent that is nearest to oneself. If no list is provided, all agents are evaluated. """ if agents is None: agents = [self.world.agents[_id] for _id in self.world.agents] dists = {agent: self.get_distance(agent.coords) for agent in agents if agent is not self} return min(dists, key=dists.get) def turn_right(self, angle: int = 90): """ Rotates the agent's direction a number of degrees to the right. """ self.direction = round((self.direction - angle) % 360) def turn_left(self, angle: int = 90): """ Rotates the agent's direction a number of degrees to the left. """ self.direction = round((self.direction + angle) % 360) def forward(self, n_steps: int = 1): """ Moves the agent a number of cells forward in the direction it is currently facing. """ x = round(self.coords[0] + cos(radians(self.direction)) * n_steps) y = round(self.coords[1] + sin(radians(self.direction)) * n_steps) if self.world.torus: self.move_to(self.world.to_torus((x, y))) elif (x, y) in self.world.grid: self.move_to((x, y)) def face_towards(self, coords: Tuple): """ Turns the agent's direction towards a given pair of coordinates. """ if coords != self.coords: xdif = coords[0] - self.coords[0] ydif = coords[1] - self.coords[1] dist = hypot(xdif, ydif) angle = degrees(asin(ydif / dist)) if xdif < 0: self.direction = round(180 - angle) else: self.direction = round((360 + angle) % 360) def random_walk(self, n_steps: int = 1): """ Moves the agent one cell forward in a random direction for a number of times. """ for i in range(n_steps): self.turn_right(randint(0, 360)) self.forward() @abstractmethod def step(self): """ Methods to be performed by the agent at each step of the simulation. """ raise NotImplementedError
    35.016807
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    0.976602
    0
    0
    179
    0.021478
    0
    0
    1,981
    0.237701
    92e5fb97c8f7793e1b150c9be5289156548c78e6
    15,337
    py
    Python
    models/LRF_COCO_300.py
    vaesl/LRF-Net
    e44b120dd55288c02852f8e58cda31313525d748
    [ "MIT" ]
    180
    2019-10-24T01:55:54.000Z
    2022-02-07T11:26:49.000Z
    models/LRF_COCO_300.py
    CV-Rookie/LRF-Net
    e44b120dd55288c02852f8e58cda31313525d748
    [ "MIT" ]
    11
    2019-11-06T08:46:00.000Z
    2020-06-20T02:30:32.000Z
    models/LRF_COCO_300.py
    CV-Rookie/LRF-Net
    e44b120dd55288c02852f8e58cda31313525d748
    [ "MIT" ]
    29
    2019-10-28T03:26:27.000Z
    2021-05-03T02:32:06.000Z
    import torch import torch.nn as nn import os import torch.nn.functional as F class LDS(nn.Module): def __init__(self,): super(LDS, self).__init__() self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0) self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=0) self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2, padding=1) def forward(self, x): x_pool1 = self.pool1(x) x_pool2 = self.pool2(x_pool1) x_pool3 = self.pool3(x_pool2) return x_pool3 class ConvBlock(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False): super(ConvBlock, self).__init__() self.out_channels = out_planes self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias) self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None self.relu = nn.ReLU(inplace=False) if relu else None def forward(self, x): x = self.conv(x) if self.bn is not None: x = self.bn(x) if self.relu is not None: x = self.relu(x) return x class LSN_init(nn.Module): def __init__(self, in_planes, out_planes, stride=1): super(LSN_init, self).__init__() self.out_channels = out_planes inter_planes = out_planes // 4 self.part_a = nn.Sequential( ConvBlock(in_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1), ConvBlock(inter_planes, inter_planes, kernel_size=1, stride=1), ConvBlock(inter_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1) ) self.part_b = ConvBlock(inter_planes, out_planes, kernel_size=1, stride=1, relu=False) def forward(self, x): out1 = self.part_a(x) out2 = self.part_b(out1) return out1, out2 class LSN_later(nn.Module): def __init__(self, in_planes, out_planes, stride=1): super(LSN_later, self).__init__() self.out_channels = out_planes inter_planes = out_planes // 4 self.part_a = ConvBlock(in_planes, inter_planes, kernel_size=(3, 3), stride=stride, padding=1) self.part_b = ConvBlock(inter_planes, out_planes, kernel_size=1, stride=1, relu=False) def forward(self, x): out1 = self.part_a(x) out2 = self.part_b(out1) return out1, out2 class IBN(nn.Module): def __init__(self, out_planes, bn=True): super(IBN, self).__init__() self.out_channels = out_planes self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None def forward(self, x): if self.bn is not None: x = self.bn(x) return x class One_Three_Conv(nn.Module): def __init__(self, in_planes, out_planes, stride=1): super(One_Three_Conv, self).__init__() self.out_channels = out_planes inter_planes = in_planes // 4 self.single_branch = nn.Sequential( ConvBlock(in_planes, inter_planes, kernel_size=1, stride=1), ConvBlock(inter_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=1, relu=False) ) def forward(self, x): out = self.single_branch(x) return out class Relu_Conv(nn.Module): def __init__(self, in_planes, out_planes, stride=1): super(Relu_Conv, self).__init__() self.out_channels = out_planes self.relu = nn.ReLU(inplace=False) self.single_branch = nn.Sequential( ConvBlock(in_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=1) ) def forward(self, x): x = self.relu(x) out = self.single_branch(x) return out class Ds_Conv(nn.Module): def __init__(self, in_planes, out_planes, stride=1, padding=(1, 1)): super(Ds_Conv, self).__init__() self.out_channels = out_planes self.single_branch = nn.Sequential( ConvBlock(in_planes, out_planes, kernel_size=(3, 3), stride=stride, padding=padding, relu=False) ) def forward(self, x): out = self.single_branch(x) return out class LRFNet(nn.Module): """LRFNet for object detection The network is based on the SSD architecture. Each multibox layer branches into 1) conv2d for class conf scores 2) conv2d for localization predictions 3) associated priorbox layer to produce default bounding boxes specific to the layer's feature map size. Args: phase: (string) Can be "test" or "train" base: VGG16 layers for input, size of either 300 or 512 extras: extra layers that feed to multibox loc and conf layers head: "multibox head" consists of loc and conf conv layers """ def __init__(self, phase, size, base, extras, head, num_classes): super(LRFNet, self).__init__() self.phase = phase self.num_classes = num_classes self.size = size # vgg network self.base = nn.ModuleList(base) self.lds = LDS() # convs for merging the lsn and ssd features self.Norm1 = Relu_Conv(512, 512, stride=1) self.Norm2 = Relu_Conv(1024, 1024, stride=1) self.Norm3 = Relu_Conv(512, 512, stride=1) self.Norm4 = Relu_Conv(256, 256, stride=1) # convs for generate the lsn features self.icn1 = LSN_init(3, 512, stride=1) self.icn2 = LSN_later(128, 1024, stride=2) self.icn3 = LSN_later(256, 512, stride=2) # convs with s=2 to downsample the features self.dsc1 = Ds_Conv(512, 1024, stride=2, padding=(1, 1)) self.dsc2 = Ds_Conv(1024, 512, stride=2, padding=(1, 1)) self.dsc3 = Ds_Conv(512, 256, stride=2, padding=(1, 1)) # convs to reduce the feature dimensions of current level self.agent1 = ConvBlock(512, 256, kernel_size=1, stride=1) self.agent2 = ConvBlock(1024, 512, kernel_size=1, stride=1) self.agent3 = ConvBlock(512, 256, kernel_size=1, stride=1) # convs to reduce the feature dimensions of other levels self.proj1 = ConvBlock(1024, 128, kernel_size=1, stride=1) self.proj2 = ConvBlock(512, 128, kernel_size=1, stride=1) self.proj3 = ConvBlock(256, 128, kernel_size=1, stride=1) # convs to reduce the feature dimensions of other levels self.convert1 = ConvBlock(384, 256, kernel_size=1) self.convert2 = ConvBlock(256, 512, kernel_size=1) self.convert3 = ConvBlock(128, 256, kernel_size=1) # convs to merge the features of the current and higher level features self.merge1 = ConvBlock(512, 512, kernel_size=3, stride=1, padding=1) self.merge2 = ConvBlock(1024, 1024, kernel_size=3, stride=1, padding=1) self.merge3 = ConvBlock(512, 512, kernel_size=3, stride=1, padding=1) self.ibn1 = IBN(512, bn=True) self.ibn2 = IBN(1024, bn=True) self.relu = nn.ReLU(inplace=False) self.extras = nn.ModuleList(extras) self.loc = nn.ModuleList(head[0]) self.conf = nn.ModuleList(head[1]) if self.phase == 'test': self.softmax = nn.Softmax() def forward(self, x): """Applies network layers and ops on input image(s) x. Args: x: input image or batch of images. Shape: [batch,3,300,300]. Return: Depending on phase: test: list of concat outputs from: 1: softmax layers, Shape: [batch*num_priors,num_classes] 2: localization layers, Shape: [batch,num_priors*4] 3: priorbox layers, Shape: [2,num_priors*4] train: list of concat outputs from: 1: confidence layers, Shape: [batch*num_priors,num_classes] 2: localization layers, Shape: [batch,num_priors*4] 3: priorbox layers, Shape: [2,num_priors*4] """ sources = list() loc = list() conf = list() new_sources = list() # apply lds to the initial image x_pool = self.lds(x) # apply vgg up to conv4_3 for k in range(22): x = self.base[k](x) conv4_3_bn = self.ibn1(x) x_pool1_skip, x_pool1_icn = self.icn1(x_pool) s = self.Norm1(conv4_3_bn * x_pool1_icn) # apply vgg up to fc7 for k in range(22, 34): x = self.base[k](x) conv7_bn = self.ibn2(x) x_pool2_skip, x_pool2_icn = self.icn2(x_pool1_skip) p = self.Norm2(self.dsc1(s) + conv7_bn * x_pool2_icn) x = self.base[34](x) # apply extra layers and cache source layer outputs for k, v in enumerate(self.extras): x = v(x) if k == 0: x_pool3_skip, x_pool3_icn = self.icn3(x_pool2_skip) w = self.Norm3(self.dsc2(p) + x * x_pool3_icn) elif k == 2: q = self.Norm4(self.dsc3(w) + x) sources.append(q) elif k == 5 or k == 7: sources.append(x) else: pass # project the forward features into lower dimension. tmp1 = self.proj1(p) tmp2 = self.proj2(w) tmp3 = self.proj3(q) # The conv4_3 level proj1 = F.upsample(tmp1, size=(38, 38), mode='bilinear') proj2 = F.upsample(tmp2, size=(38, 38), mode='bilinear') proj3 = F.upsample(tmp3, size=(38, 38), mode='bilinear') proj = torch.cat([proj1, proj2, proj3], dim=1) agent1 = self.agent1(s) convert1 = self.convert1(proj) pred1 = torch.cat([agent1, convert1], dim=1) pred1 = self.merge1(pred1) new_sources.append(pred1) # The fc_7 level proj2 = F.upsample(tmp2, size=(19, 19), mode='bilinear') proj3 = F.upsample(tmp3, size=(19, 19), mode='bilinear') proj = torch.cat([proj2, proj3], dim=1) agent2 = self.agent2(p) convert2 = self.convert2(proj) pred2 = torch.cat([agent2, convert2], dim=1) pred2 = self.merge2(pred2) new_sources.append(pred2) # The conv8 level proj3 = F.upsample(tmp3, size=(10, 10), mode='bilinear') proj = proj3 agent3 = self.agent3(w) convert3 = self.convert3(proj) pred3 = torch.cat([agent3, convert3], dim=1) pred3 = self.merge3(pred3) new_sources.append(pred3) for prediction in sources: new_sources.append(prediction) # apply multibox head to source layers for (x, l, c) in zip(new_sources, self.loc, self.conf): loc.append(l(x).permute(0, 2, 3, 1).contiguous()) conf.append(c(x).permute(0, 2, 3, 1).contiguous()) loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1) conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1) if self.phase == "test": output = ( loc.view(loc.size(0), -1, 4), # loc preds self.softmax(conf.view(-1, self.num_classes)), # conf preds ) else: output = ( loc.view(loc.size(0), -1, 4), conf.view(conf.size(0), -1, self.num_classes), ) return output def load_weights(self, base_file): other, ext = os.path.splitext(base_file) if ext == '.pkl' or '.pth': print('Loading weights into state dict...') self.load_state_dict(torch.load(base_file)) print('Finished!') else: print('Sorry only .pth and .pkl files supported.') def vgg(cfg, i, batch_norm=False): layers = [] in_channels = i for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] elif v == 'C': layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=False)] else: layers += [conv2d, nn.ReLU(inplace=False)] in_channels = v pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6) conv7 = nn.Conv2d(1024, 1024, kernel_size=1) layers += [pool5, conv6, nn.ReLU(inplace=False), conv7, nn.ReLU(inplace=False)] return layers base = { '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M', 512, 512, 512]} def add_extras(size, cfg, i, batch_norm=False): # Extra layers added to VGG for feature scaling layers = [] in_channels = i flag = False for k, v in enumerate(cfg): if in_channels != 'S': if v == 'S': if in_channels == 256 and size == 512: layers += [One_Three_Conv(in_channels, cfg[k+1], stride=2), nn.ReLU(inplace=False)] else: layers += [One_Three_Conv(in_channels, cfg[k+1], stride=2), nn.ReLU(inplace=False)] in_channels = v layers += [ConvBlock(256, 128, kernel_size=1,stride=1)] layers += [ConvBlock(128, 256, kernel_size=3,stride=1)] layers += [ConvBlock(256, 128, kernel_size=1,stride=1)] layers += [ConvBlock(128, 256, kernel_size=3,stride=1)] return layers extras = { '300': [1024, 'S', 512, 'S', 256]} def multibox(size, vgg, extra_layers, cfg, num_classes): loc_layers = [] conf_layers = [] vgg_source = [1, -2] for k, v in enumerate(vgg_source): if k == 0: loc_layers += [nn.Conv2d(512, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers +=[nn.Conv2d(512, cfg[k] * num_classes, kernel_size=3, padding=1)] else: loc_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * num_classes, kernel_size=3, padding=1)] i = 2 indicator = 3 for k, v in enumerate(extra_layers): if (k < indicator+1 and k % 2 == 0) or (k > indicator+1 and k % 2 != 0): loc_layers += [nn.Conv2d(v.out_channels, cfg[i] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(v.out_channels, cfg[i] * num_classes, kernel_size=3, padding=1)] i += 1 return vgg, extra_layers, (loc_layers, conf_layers) mbox = { '300': [6, 6, 6, 6, 4, 4]} def build_net(phase, size=300, num_classes=81): if size != 300: print("Error: The input image size is not supported!") return return LRFNet(phase, size, *multibox(size, vgg(base[str(size)], 3), add_extras(size, extras[str(size)], 1024), mbox[str(size)], num_classes), num_classes)
    36.516667
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    0.579774
    11,783
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    2,342
    0.152703
    92e751e7128a30f8b366e1182af0f9e14b4591cd
    25,418
    py
    Python
    tests/test.py
    chromia/wandplus
    815127aeee85dbac3bc8fca35971d2153b1898a9
    [ "ImageMagick", "MIT" ]
    null
    null
    null
    tests/test.py
    chromia/wandplus
    815127aeee85dbac3bc8fca35971d2153b1898a9
    [ "ImageMagick", "MIT" ]
    null
    null
    null
    tests/test.py
    chromia/wandplus
    815127aeee85dbac3bc8fca35971d2153b1898a9
    [ "ImageMagick", "MIT" ]
    null
    null
    null
    #!/usr/bin/env python from wand.image import Image from wand.drawing import Drawing from wand.color import Color import wandplus.image as wpi from wandplus.textutil import calcSuitableFontsize, calcSuitableImagesize import os import unittest tmpdir = '_tmp/' def save(img, function, channel=False, ext='.png'): if channel: path = tmpdir + function.__name__ + "_ch" + ext else: path = tmpdir + function.__name__ + ext # print(path) img.save(filename=path) class CheckImage(unittest.TestCase): @classmethod def setUpClass(self): os.mkdir(tmpdir) self.rose = Image(filename='rose:') self.grad = Image(filename='gradient:', width=400, height=400) self.logo = Image(filename='logo:') self.text = Image(filename='label:Confirm', width=200, height=60) self.text_a = Image(width=70, height=60) with Drawing() as draw: draw.font = 'Arial' draw.font_size = 50 draw.gravity = 'center' draw.fill_color = Color('white') draw.stroke_color = Color('black') draw.text(0, 0, 'A') draw(self.text_a) self.rose.save(filename=tmpdir + 'rose.png') self.grad.save(filename=tmpdir + 'grad.png') self.logo.save(filename=tmpdir + 'logo.png') self.text.save(filename=tmpdir + 'text.png') self.text_a.save(filename=tmpdir + 'a.png') @classmethod def tearDownClass(self): self.rose.destroy() self.grad.destroy() self.logo.destroy() self.text.destroy() self.text_a.destroy() def test_adaptiveblur(self): f = wpi.adaptiveblur with self.rose.clone() as t: f(t, 5.0, 3.0) save(t, f) with self.rose.clone() as t: f(t, 5.0, 3.0, channel='red') save(t, f, True) def test_adaptiveresize(self): f = wpi.adaptiveresize with self.rose.clone() as t: f(t, int(t.width*1.5), int(t.height*2.0)) save(t, f) def test_adaptivesharpen(self): f = wpi.adaptivesharpen with self.rose.clone() as t: f(t, 5, 5) save(t, f) with self.rose.clone() as t: f(t, 5, 5, channel='red') save(t, f, True) def test_adaptivethreshold(self): f = wpi.adaptivethreshold with self.logo.clone() as t: f(t, 20, 20, int(0.1*t.quantum_range)) save(t, f) def test_addnoise(self): f = wpi.addnoise with self.grad.clone() as t: f(t, 'gaussian') save(t, f) with self.grad.clone() as t: f(t, 'gaussian', channel='red') save(t, f, True) def test_affinetransform(self): f = wpi.affinetransform with self.rose.clone() as t: with Drawing() as d: d.affine([2.0, 0.0, 0.0, 2.0, 0.0, 0.0]) f(t, d) # not work correctly (IM<6.9.9-36) save(t, f) def test_autogamma(self): f = wpi.autogamma with self.rose.clone() as t: f(t) save(t, f) with self.rose.clone() as t: f(t, channel='red') save(t, f, True) def test_autolevel(self): f = wpi.autolevel with self.rose.clone() as t: f(t) save(t, f) with self.rose.clone() as t: f(t, channel='red') save(t, f, True) def test_blackthreshold(self): f = wpi.blackthreshold with self.grad.clone() as t: f(t, Color('gray(50%)')) save(t, f) def test_blueshift(self): f = wpi.blueshift with self.logo.clone() as t: f(t, 0.5) save(t, f) def test_brightnesscontrast(self): f = wpi.brightnesscontrast with self.rose.clone() as t: f(t, -30, 0) save(t, f) with self.rose.clone() as t: f(t, -30, 0, channel='red') save(t, f, True) def test_blur(self): f = wpi.blur with self.rose.clone() as t: f(t, 0, 3) save(t, f) with self.rose.clone() as t: f(t, 0, 3, channel='red') save(t, f, True) def test_charcoal(self): f = wpi.charcoal with self.rose.clone() as t: f(t, 5, 1) save(t, f) def test_chop(self): f = wpi.chop with self.grad.clone() as t: t.gravity = 'north_west' f(t, 0, 00, 200, 200) save(t, f) def test_clamp(self): f = wpi.clamp # TODO: more useful code with self.rose.clone() as t: f(t) save(t, f) with self.rose.clone() as t: f(t, channel='red') save(t, f, True) def test_clip(self): # NOTE: result is always FAILED. f = wpi.clip # I don't have an image which has clipping path with self.rose.clone() as t: f(t) save(t, f) def test_clippath(self): # NOTE: result is always FAILED. f = wpi.clippath with self.rose.clone() as t: f(t, '#1', True) save(t, f) def test_clut(self): f = wpi.clut with Image(filename='gradient:red-blue', width=1, height=100) as p: p.rotate(90) with self.grad.clone() as t: f(t, p) save(t, f) with self.grad.clone() as t: f(t, p, channel='green') save(t, f, True) def test_coalesce(self): # TODO: input optimized .gif file. f = wpi.coalesce with Image() as t: with self.rose.clone() as p: for i in range(5): wpi.blur(p, 0, 1) wpi.add(t, p) with f(t) as p: save(p, f) def test_colordecisionlist(self): xml = """ <ColorCorrectionCollection xmlns="urn:ASC:CDL:v1.2"> <ColorCorrection id="cc03345"> <SOPNode> <Slope> 0.9 1.2 0.5 </Slope> <Offset> 0.4 -0.5 0.6 </Offset> <Power> 1.0 0.8 1.5 </Power> </SOPNode> <SATNode> <Saturation> 0.85 </Saturation> </SATNode> </ColorCorrection> </ColorCorrectionCollection> """ f = wpi.colordecisionlist with self.rose.clone() as t: f(t, xml) save(t, f) def test_colorize(self): f = wpi.colorize with self.grad.clone() as t: f(t, Color('red'), Color('gray(25%)')) save(t, f) def test_colormatrix(self): f = wpi.colormatrix with self.logo.clone() as t: kernel = [ 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 1.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] f(t, 5, 5, kernel) save(t, f) def test_combine(self): f = wpi.combine with Image() as t: w = 100 h = 100 black = Color('black') white = Color('white') with Image(width=w, height=w, background=black) as b: with Image(width=h, height=h, background=white) as w: wpi.add(t, b) # add image for red channel wpi.add(t, b) # add image for green channel wpi.add(t, w) # add image for blue channel wpi.setfirstiterator(t) # rewind the index pointer channel = 1 + 2 + 4 # R + G + B with f(t, channel) as q: save(q, f) def test_comment(self): f = wpi.comment with self.grad.clone() as t: f(t, 'hello') save(t, f) def test_compare(self): f = wpi.compare with self.rose.clone() as t: with t.clone() as p: (c, d) = f(t, p, metric='absolute') save(c, f) c.destroy() with self.rose.clone() as t: with t.clone() as p: (c, d) = f(t, p, metric='absolute', channel='red') save(c, f, True) c.destroy() def test_comparelayer(self): f = wpi.comparelayer with Image() as t: with Image(width=50, height=50, background=Color('red')) as p: wpi.add(t, p) with Image(width=25, height=25, background=Color('green1')) as q: for i in range(4): with q.clone() as qq: wpi.resetpage(qq, 5*(i+1), 5*(i+1)) wpi.add(t, qq) with f(t, 'compareany') as r: save(r, f, ext='.gif') def test_constitute(self): f = wpi.constitute with Image() as t: w = 2 h = 2 b = [0, 0, 0, 255, 255, 255, 255, 0, 0, 0, 255, 0] f(t, w, h, 'RGB', 'char', b) save(t, f) def test_contrast(self): f = wpi.contrast with self.rose.clone() as t: f(t, False) save(t, f) def test_convolve(self): f = wpi.convolve kernel = [1/16, 2/16, 1/16, 2/16, 4/16, 2/16, 1/16, 2/16, 1/16] with self.rose.clone() as t: f(t, 3, kernel) save(t, f) with self.rose.clone() as t: f(t, 3, kernel, channel='red') save(t, f, True) def test_cyclecolormap(self): f = wpi.cyclecolormap with self.logo.clone() as t: f(t, 5) save(t, f) def test_cipher(self): f = wpi.encipher with self.rose.clone() as t: f(t, 'password') save(t, f) f = wpi.decipher f(t, 'password') save(t, f) def test_deskew(self): f = wpi.deskew with Image(width=80, height=40, background=Color('black')) as t: f(t, 0.5*t.quantum_range) # TODO: find an skewed image as sample save(t, f) def test_despeckle(self): f = wpi.despeckle with self.rose.clone() as t: # TODO: add speckle noise f(t) save(t, f) def test_edge(self): f = wpi.edge with self.logo.clone() as t: f(t, 3) save(t, f) def test_emboss(self): f = wpi.emboss with self.logo.clone() as t: f(t, 0, 3) save(t, f) def test_enhance(self): f = wpi.enhance with Image(filename='plasma:', width=100, height=100) as t: f(t) save(t, f) def test_equalize(self): f = wpi.equalize with self.rose.clone() as t: f(t) save(t, f) with self.rose.clone() as t: f(t, channel='red') save(t, f, True) def test_exportpixels(self): w = 1 h = 1 channels = 'RGB' with Image(width=w, height=h, background=Color('red')) as t: r = wpi.exportpixels(t, 0, 0, w, h, channels, 'double') self.assertEqual(r[0], 1.0) self.assertEqual(r[1], 0.0) self.assertEqual(r[2], 0.0) def test_extent(self): f = wpi.extent with self.rose.clone() as t: t.gravity = 'center' t.background_color = Color('blue') f(t, -10, -10, t.width+20, t.height+20) save(t, f) def test_filterimage(self): f = wpi.filterimage kernel = [ # Sobel filter -1.0, 0.0, 1.0, -2.0, 0.0, 2.0, -1.0, 0.0, 1.0, ] with self.rose.clone() as t: f(t, 3, 3, kernel) save(t, f) with self.rose.clone() as t: f(t, 3, 3, kernel, channel='red') save(t, f, True) def test_floodfillpaint(self): f = wpi.floodfillpaint with self.logo.clone() as t: f(t, Color('green'), 0.10*t.quantum_range, Color('white'), 0, 0) save(t, f) def test_fft(self): f = wpi.forwardfouriertransform # require IM build option '--with-fftw' with self.logo.clone() as t: # I couldn't build on Windows... f(t, True) save(t, f) # includes two images(magnitude&phase) f = wpi.inversefouriertransform with t.sequence[0].clone() as mag: with t.sequence[1].clone() as phase: wpi.blur(mag, 0, 0.5) # as degradation t2 = mag f(t2, phase, True) save(t2, f) def test_haldclut(self): f = wpi.haldclut # TODO: more useful code with Image(filename='hald:12') as p: with self.rose.clone() as t: f(t, p) save(t, f) with self.rose.clone() as t: f(t, p, channel='red') save(t, f, True) def test_implode(self): f = wpi.implode with self.rose.clone() as t: f(t, 1.0) save(t, f) def test_importpixels(self): f = wpi.importpixels with Image(width=4, height=4, background=Color('red')) as t: w = 2 h = 2 b = [0, 0, 0, 255, 255, 255, 255, 0, 0, 0, 255, 0] f(t, 1, 1, w, h, 'RGB', 'char', b) save(t, f) def test_label(self): f = wpi.label with self.rose.clone() as t: f(t, 'hello') save(t, f) def test_localcontrast(self): f = wpi.localcontrast with self.logo.clone() as t: f(t, 5, 30) save(t, f) def test_magnify(self): f = wpi.magnify with self.rose.clone() as t: f(t) save(t, f) def test_minify(self): f = wpi.minify with self.rose.clone() as t: f(t) save(t, f) def test_montage(self): f = wpi.montage with self.rose.clone() as base: with Image() as dst: rows = 2 columns = 3 for i in range(rows * columns): wpi.add(dst, base) tile = "{0}x{1}+0+0".format(columns, rows) thumb = "80x50+4+3" frame = "15x15+3+3" mode = "frame" with Drawing() as d: with f(dst, d, tile, thumb, mode, frame) as result: save(result, f) def test_morph(self): f = wpi.morph color = Color('white') with self.rose.clone() as t: with Image(width=t.width, height=t.height, background=color) as p: wpi.add(t, p) wpi.setfirstiterator(t) wpi.setdelay(t, 60) with f(t, 5) as q: save(q, f, ext='.gif') def test_morphology(self): f = wpi.morphology with self.logo.clone() as t: f(t, 'dilate', 1, 'Diamond') save(t, f) with self.logo.clone() as t: f(t, 'dilate', 1, 'Diamond', channel='red') save(t, f, True) def test_motionblur(self): f = wpi.motionblur with self.logo.clone() as t: f(t, 30, 10, 45) save(t, f) with self.logo.clone() as t: f(t, 30, 10, 45, channel='red') save(t, f, True) def test_oilpaint(self): f = wpi.oilpaint with self.rose.clone() as t: f(t, 2.0) save(t, f) def test_opaquepaint(self): f = wpi.opaquepaint with self.logo.clone() as t: f(t, Color('red'), Color('blue'), 1.0, False) save(t, f) with self.logo.clone() as t: f(t, Color('red'), Color('blue'), 1.0, False, channel='blue') save(t, f, True) def test_orderedposterize(self): f = wpi.orderedposterize with self.grad.clone() as t: f(t, 'o4x4,3,3') save(t, f) with self.grad.clone() as t: f(t, 'o4x4,3,3', channel='red') save(t, f, True) def test_polaroid(self): f = wpi.polaroid with self.logo.clone() as t: with Drawing() as d: f(t, d, 1.0) save(t, f) def test_posterize(self): f = wpi.posterize with self.rose.clone() as t: f(t, 3, True) save(t, f) def test_raiseimage(self): f = wpi.raiseimage with self.rose.clone() as t: f(t, 10, 10, 10, 10, True) save(t, f) def test_randomthreshold(self): f = wpi.randomthreshold with self.text_a.clone() as t: rng = t.quantum_range f(t, int(rng * 0.05), int(rng * 0.95)) save(t, f) with self.text_a.clone() as t: rng = t.quantum_range f(t, int(rng * 0.05), int(rng * 0.95), channel='red') save(t, f, True) def test_remap(self): f = wpi.remap with self.logo.clone() as t: with self.rose.clone() as p: f(t, p, 'nodither') save(t, f) def test_resample(self): f = wpi.resample with self.rose.clone() as t: dpi = 72 * 2 f(t, dpi, dpi, 'lanczos', 1.0) save(t, f) def test_roll(self): f = wpi.roll with self.rose.clone() as t: f(t, 10, 10) save(t, f) def test_rotationalblur(self): f = wpi.rotationalblur with self.rose.clone() as t: f(t, 45) save(t, f) with self.rose.clone() as t: f(t, 45, channel='red') save(t, f, True) def test_scale(self): f = wpi.scale with self.rose.clone() as t: f(t, t.width*2, t.height*2) save(t, f) def test_segment(self): f = wpi.segment with self.logo.clone() as t: f(t, 'rgb', False, 5, 20) save(t, f) def test_selectiveblur(self): f = wpi.selectiveblur with self.logo.clone() as t: f(t, 20, 20, 0.5*t.quantum_range) save(t, f) with self.logo.clone() as t: f(t, 20, 20, 0.5*t.quantum_range, channel='red') save(t, f, True) def test_separate_channel(self): f = wpi.separate_channel with self.rose.clone() as t: f(t, 'red') save(t, f) def test_sepiatone(self): f = wpi.sepiatone with self.rose.clone() as t: f(t, 0.5*t.quantum_range) save(t, f) def test_shade(self): f = wpi.shade with self.logo.clone() as t: f(t, True, 45, 135) save(t, f) def test_shadow(self): f = wpi.shadow with self.text.clone() as t: with self.text.clone() as p: p.negate() f(p, 100, 2, 10, 10) t.composite_channel('default_channels', p, 'overlay') save(t, f) def test_sharpen(self): f = wpi.sharpen with self.rose.clone() as t: f(t, 3, 3) save(t, f) with self.rose.clone() as t: f(t, 3, 3, channel='red') save(t, f, True) def test_shave(self): f = wpi.shave with self.logo.clone() as t: f(t, 100, 100) save(t, f) def test_shear(self): f = wpi.shear with self.grad.clone() as t: f(t, Color('red'), 0, 10) save(t, f) def test_sigmoidalcontrast(self): f = wpi.sigmoidalcontrast with self.rose.clone() as t: f(t, True, 3, 3) save(t, f) with self.rose.clone() as t: f(t, True, 3, 3, channel='red') save(t, f, True) def test_sketch(self): f = wpi.sketch with self.logo.clone() as t: f(t, 10, 10, 45) save(t, f) def test_smush(self): f = wpi.smush def makeletter(letter, w, h): img = Image(width=w, height=h) with Drawing() as d: d.font = 'Arial' d.font_size = 24 d.gravity = 'center' d.text(0, 0, letter) d(img) return img with Image() as t: with makeletter('A', 50, 30) as a: with makeletter('B', 50, 30) as b: wpi.add(t, a) wpi.add(t, b) wpi.setfirstiterator(t) with f(t, False, -3) as p: save(p, f) def test_solarize(self): f = wpi.solarize with self.rose.clone() as t: f(t, 0.4*t.quantum_range) save(t, f) with self.rose.clone() as t: f(t, 0.4*t.quantum_range, channel='red') save(t, f, True) def test_splice(self): f = wpi.splice with self.rose.clone() as t: t.gravity = 'center' f(t, t.width//2, t.height//2, 20, 20) save(t, f) def test_sparsecolor(self): f = wpi.sparsecolor with Image(width=100, height=100, background=Color('black')) as t: f(t, 'default_channels', 'bilinear', [0, 0, 1.0, 0.0, 0.0, 1.0, 100, 100, 0.0, 1.0, 1.0, 1.0]) save(t, f) def test_spread(self): f = wpi.spread with self.logo.clone() as t: f(t, 20) save(t, f) def test_statistic(self): f = wpi.statistic with self.rose.clone() as t: f(t, 'gradient', 4, 4) save(t, f) with self.rose.clone() as t: f(t, 'gradient', 4, 4, channel='red') save(t, f, True) def test_stegano(self): f = wpi.stegano with self.rose.clone() as t: w = 50 h = 40 offset = 15 tmpfile = 'tmp.png' with Image(width=w, height=h, background=Color('white')) as p: with Drawing() as d: d.gravity = 'center' d.fill_color = Color('black') d.text(0, 0, 'Watch\nthe\nPidgeon') d(p) with f(t, p, offset) as q: q.save(filename=tmpfile) try: with Image() as q: wpi.setsizeoffset(q, w, h, offset) q.read(filename='stegano:' + tmpfile) save(q, f) except Exception: raise finally: os.remove(tmpfile) def test_stereo(self): f = wpi.stereo with self.rose.clone() as t: with self.rose.clone() as p: p.negate() with f(t, p) as q: save(q, f) def test_swirl(self): f = wpi.swirl with self.rose.clone() as t: f(t, 180) save(t, f) def test_texture(self): f = wpi.texture with Image(width=300, height=200) as t: with self.rose.clone() as p: with f(t, p) as q: save(q, f) def test_thumbnail(self): f = wpi.thumbnail with self.logo.clone() as t: f(t, 100, 100) save(t, f) def test_tint(self): f = wpi.tint with self.rose.clone() as t: f(t, Color('rgb'), Color('gray(25%)')) save(t, f) def test_vignette(self): f = wpi.vignette with self.logo.clone() as t: wpi.minify(t) t.background_color = Color('black') f(t, 0, 10, 20, 20) save(t, f) def test_wave(self): f = wpi.wave with self.grad.clone() as t: f(t, 40, 200) save(t, f) def test_whitethreshold(self): f = wpi.whitethreshold with self.grad.clone() as t: f(t, Color('gray(50%)')) save(t, f) class CheckTextUtil(unittest.TestCase): def test_imagesize(self): with Drawing() as d: text = 'check' d.font = 'Arial' d.font_size = 36 size = calcSuitableImagesize(d, text) print('calcSuitableImagesize: ', size) self.assertTrue(size[0] > 0 and size[1] > 0) def test_fontsize(self): w = 100 h = 100 with Drawing() as d: text = 'check' d.font = 'Arial' fontsize = calcSuitableFontsize(d, text, width=w) print('calcSuitableImagesize[W]: ', fontsize) self.assertTrue(fontsize > 0) fontsize = calcSuitableFontsize(d, text, height=h) print('calcSuitableImagesize[H]: ', fontsize) self.assertTrue(fontsize > 0) if __name__ == '__main__': unittest.main()
    29.487239
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    0.083169
    92e78a29e0f69d74c35aa00744e686a1763079d2
    7,652
    py
    Python
    src/librender/tests/test_mesh.py
    tizian/layer-laboratory
    008cc94b76127e9eb74227fcd3d0145da8ddec30
    [ "CNRI-Python" ]
    7
    2020-07-24T03:19:59.000Z
    2022-03-30T10:56:12.000Z
    src/librender/tests/test_mesh.py
    tizian/layer-laboratory
    008cc94b76127e9eb74227fcd3d0145da8ddec30
    [ "CNRI-Python" ]
    1
    2021-04-07T22:30:23.000Z
    2021-04-08T00:55:36.000Z
    src/librender/tests/test_mesh.py
    tizian/layer-laboratory
    008cc94b76127e9eb74227fcd3d0145da8ddec30
    [ "CNRI-Python" ]
    2
    2020-06-08T08:25:09.000Z
    2021-04-05T22:13:08.000Z
    import mitsuba import pytest import enoki as ek from enoki.dynamic import Float32 as Float from mitsuba.python.test.util import fresolver_append_path from mitsuba.python.util import traverse def test01_create_mesh(variant_scalar_rgb): from mitsuba.core import Struct, float_dtype from mitsuba.render import Mesh m = Mesh("MyMesh", 3, 2) m.vertex_positions_buffer()[:] = [0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.2, 1.0, 0.0] m.faces_buffer()[:] = [0, 1, 2, 1, 2, 0] m.parameters_changed() assert str(m) == """Mesh[ name = "MyMesh", bbox = BoundingBox3f[ min = [0, 0, 0], max = [1, 1, 0] ], vertex_count = 3, vertices = [36 B of vertex data], face_count = 2, faces = [24 B of face data], disable_vertex_normals = 0, surface_area = 0.96 ]""" @fresolver_append_path def test02_ply_triangle(variant_scalar_rgb): from mitsuba.core import UInt32, Vector3f from mitsuba.core.xml import load_string m = load_string(""" <shape type="ply" version="0.5.0"> <string name="filename" value="data/triangle.ply"/> <boolean name="face_normals" value="true"/> </shape> """) positions = m.vertex_positions_buffer() faces = m.faces_buffer() assert not m.has_vertex_normals() assert ek.slices(positions) == 9 assert ek.allclose(positions[0:3], [0, 0, 0]) assert ek.allclose(positions[3:6], [0, 0, 1]) assert ek.allclose(positions[6:9], [0, 1, 0]) assert ek.slices(faces) == 3 assert faces[0] == UInt32(0) assert faces[1] == UInt32(1) assert faces[2] == UInt32(2) @fresolver_append_path def test03_ply_computed_normals(variant_scalar_rgb): from mitsuba.core import Vector3f from mitsuba.core.xml import load_string """Checks(automatic) vertex normal computation for a PLY file that doesn't have them.""" shape = load_string(""" <shape type="ply" version="0.5.0"> <string name="filename" value="data/triangle.ply"/> </shape> """) normals = shape.vertex_normals_buffer() assert shape.has_vertex_normals() # Normals are stored in half precision assert ek.allclose(normals[0:3], [-1, 0, 0]) assert ek.allclose(normals[3:6], [-1, 0, 0]) assert ek.allclose(normals[6:9], [-1, 0, 0]) def test04_normal_weighting_scheme(variant_scalar_rgb): from mitsuba.core import Struct, float_dtype, Vector3f from mitsuba.render import Mesh import numpy as np """Tests the weighting scheme that is used to compute surface normals.""" m = Mesh("MyMesh", 5, 2, has_vertex_normals=True) vertices = m.vertex_positions_buffer() normals = m.vertex_normals_buffer() a, b = 1.0, 0.5 vertices[:] = [0, 0, 0, -a, 1, 0, a, 1, 0, -b, 0, 1, b, 0, 1] n0 = Vector3f(0.0, 0.0, -1.0) n1 = Vector3f(0.0, 1.0, 0.0) angle_0 = ek.pi / 2.0 angle_1 = ek.acos(3.0 / 5.0) n2 = n0 * angle_0 + n1 * angle_1 n2 /= ek.norm(n2) n = np.vstack([n2, n0, n0, n1, n1]).transpose() m.faces_buffer()[:] = [0, 1, 2, 0, 3, 4] m.recompute_vertex_normals() for i in range(5): assert ek.allclose(normals[i*3:(i+1)*3], n[:, i], 5e-4) @fresolver_append_path def test05_load_simple_mesh(variant_scalar_rgb): from mitsuba.core.xml import load_string """Tests the OBJ and PLY loaders on a simple example.""" for mesh_format in ["obj", "ply"]: shape = load_string(""" <shape type="{0}" version="2.0.0"> <string name="filename" value="resources/data/tests/{0}/cbox_smallbox.{0}"/> </shape> """.format(mesh_format)) positions = shape.vertex_positions_buffer() faces = shape.faces_buffer() assert shape.has_vertex_normals() assert ek.slices(positions) == 72 assert ek.slices(faces) == 36 assert ek.allclose(faces[6:9], [4, 5, 6]) assert ek.allclose(positions[:5], [130, 165, 65, 82, 165]) @pytest.mark.parametrize('mesh_format', ['obj', 'ply', 'serialized']) @pytest.mark.parametrize('features', ['normals', 'uv', 'normals_uv']) @pytest.mark.parametrize('face_normals', [True, False]) def test06_load_various_features(variant_scalar_rgb, mesh_format, features, face_normals): """Tests the OBJ & PLY loaders with combinations of vertex / face normals, presence and absence of UVs, etc. """ from mitsuba.core.xml import load_string def test(): shape = load_string(""" <shape type="{0}" version="2.0.0"> <string name="filename" value="resources/data/tests/{0}/rectangle_{1}.{0}" /> <boolean name="face_normals" value="{2}" /> </shape> """.format(mesh_format, features, str(face_normals).lower())) assert shape.has_vertex_normals() == (not face_normals) positions = shape.vertex_positions_buffer() normals = shape.vertex_normals_buffer() texcoords = shape.vertex_texcoords_buffer() faces = shape.faces_buffer() (v0, v2, v3) = [positions[i*3:(i+1)*3] for i in [0, 2, 3]] assert ek.allclose(v0, [-2.85, 0.0, -7.600000], atol=1e-3) assert ek.allclose(v2, [ 2.85, 0.0, 0.599999], atol=1e-3) assert ek.allclose(v3, [ 2.85, 0.0, -7.600000], atol=1e-3) if 'uv' in features: assert shape.has_vertex_texcoords() (uv0, uv2, uv3) = [texcoords[i*2:(i+1)*2] for i in [0, 2, 3]] # For OBJs (and .serialized generated from OBJ), UV.y is flipped. if mesh_format in ['obj', 'serialized']: assert ek.allclose(uv0, [0.950589, 1-0.988416], atol=1e-3) assert ek.allclose(uv2, [0.025105, 1-0.689127], atol=1e-3) assert ek.allclose(uv3, [0.950589, 1-0.689127], atol=1e-3) else: assert ek.allclose(uv0, [0.950589, 0.988416], atol=1e-3) assert ek.allclose(uv2, [0.025105, 0.689127], atol=1e-3) assert ek.allclose(uv3, [0.950589, 0.689127], atol=1e-3) if shape.has_vertex_normals(): for n in [normals[i*3:(i+1)*3] for i in [0, 2, 3]]: assert ek.allclose(n, [0.0, 1.0, 0.0]) return fresolver_append_path(test)() @fresolver_append_path def test07_ply_stored_attribute(variant_scalar_rgb): from mitsuba.core import Vector3f from mitsuba.core.xml import load_string m = load_string(""" <shape type="ply" version="0.5.0"> <string name="filename" value="data/triangle_face_colors.ply"/> </shape> """) assert str(m) == """PLYMesh[ name = "triangle_face_colors.ply", bbox = BoundingBox3f[ min = [0, 0, 0], max = [0, 1, 1] ], vertex_count = 3, vertices = [72 B of vertex data], face_count = 1, faces = [24 B of face data], disable_vertex_normals = 0, surface_area = 0, mesh attributes = [ face_color: 3 floats ] ]""" def test08_mesh_add_attribute(variant_scalar_rgb): from mitsuba.core import Struct, float_dtype from mitsuba.render import Mesh m = Mesh("MyMesh", 3, 2) m.vertex_positions_buffer()[:] = [0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.2, 1.0, 0.0] m.faces_buffer()[:] = [0, 1, 2, 1, 2, 0] m.parameters_changed() m.add_attribute("vertex_color", 3)[:] = [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0] assert str(m) == """Mesh[ name = "MyMesh", bbox = BoundingBox3f[ min = [0, 0, 0], max = [1, 1, 0] ], vertex_count = 3, vertices = [72 B of vertex data], face_count = 2, faces = [24 B of face data], disable_vertex_normals = 0, surface_area = 0.96, mesh attributes = [ vertex_color: 3 floats ] ]"""
    32.561702
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    0
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    0.311553
    92e9c5118907200831bee6234267cd344285472f
    1,457
    py
    Python
    agsadmin/sharing_admin/community/groups/Group.py
    christopherblanchfield/agsadmin
    989cb3795aacf285ccf74ee51b0de26bf2f48bc3
    [ "BSD-3-Clause" ]
    2
    2015-12-07T05:53:29.000Z
    2020-09-13T18:12:15.000Z
    agsadmin/sharing_admin/community/groups/Group.py
    christopherblanchfield/agsadmin
    989cb3795aacf285ccf74ee51b0de26bf2f48bc3
    [ "BSD-3-Clause" ]
    4
    2015-03-09T05:59:14.000Z
    2018-01-09T00:12:56.000Z
    agsadmin/sharing_admin/community/groups/Group.py
    christopherblanchfield/agsadmin
    989cb3795aacf285ccf74ee51b0de26bf2f48bc3
    [ "BSD-3-Clause" ]
    5
    2015-03-09T01:05:24.000Z
    2019-09-09T23:01:21.000Z
    from __future__ import (absolute_import, division, print_function, unicode_literals) from builtins import (ascii, bytes, chr, dict, filter, hex, input, int, map, next, oct, open, pow, range, round, str, super, zip) from ...._utils import send_session_request from ..._PortalEndpointBase import PortalEndpointBase from .CreateUpdateGroupParams import CreateUpdateGroupParams class Group(PortalEndpointBase): @property def id(self): return self._pdata["id"] @property def _url_full(self): return "{0}/{1}".format(self._url_base, self.id) def __init__(self, requests_session, url_base, id): super().__init__(requests_session, url_base) self._pdata = {"id": id} def get_properties(self): """ Gets the properties of the item. """ return self._get() def update(self, update_group_params, clear_empty_fields=False): """ Updates the group properties. """ update_group_params = update_group_params._get_params() if isinstance( update_group_params, CreateUpdateGroupParams) else update_group_params.copy() if not "clearEmptyFields" in update_group_params: update_group_params["clearEmptyFields"] = clear_empty_fields r = self._create_operation_request(self, "update", method="POST", data=update_group_params) return send_session_request(self._session, r).json()
    33.883721
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    0.103638
    0
    0
    176
    0.120796
    92ea3eda1c775e0583e47210352c08da3ae6793c
    3,995
    py
    Python
    amy/workshops/migrations/0191_auto_20190809_0936.py
    code-review-doctor/amy
    268c1a199510457891459f3ddd73fcce7fe2b974
    [ "MIT" ]
    53
    2015-01-10T17:39:19.000Z
    2019-06-12T17:36:34.000Z
    amy/workshops/migrations/0191_auto_20190809_0936.py
    code-review-doctor/amy
    268c1a199510457891459f3ddd73fcce7fe2b974
    [ "MIT" ]
    1,176
    2015-01-02T06:32:47.000Z
    2019-06-18T11:57:47.000Z
    amy/workshops/migrations/0191_auto_20190809_0936.py
    code-review-doctor/amy
    268c1a199510457891459f3ddd73fcce7fe2b974
    [ "MIT" ]
    44
    2015-01-03T15:08:56.000Z
    2019-06-09T05:33:08.000Z
    # Generated by Django 2.1.7 on 2019-08-09 09:36 from django.db import migrations, models def migrate_public_event(apps, schema_editor): """Migrate options previously with no contents (displayed as "Other:") to a new contents ("other"). The field containing these options is in CommonRequest abstract model, implemented in WorkshopRequest, WorkshopInquiryRequest, and SelfOrganizedSubmission models.""" WorkshopRequest = apps.get_model('workshops', 'WorkshopRequest') WorkshopInquiryRequest = apps.get_model('extrequests', 'WorkshopInquiryRequest') SelfOrganizedSubmission = apps.get_model('extrequests', 'SelfOrganizedSubmission') WorkshopRequest.objects.filter(public_event="") \ .update(public_event="other") WorkshopInquiryRequest.objects.filter(public_event="") \ .update(public_event="other") SelfOrganizedSubmission.objects.filter(public_event="") \ .update(public_event="other") class Migration(migrations.Migration): dependencies = [ ('workshops', '0190_auto_20190728_1118'), ('extrequests', '0008_auto_20190809_1004'), ] operations = [ migrations.AlterField( model_name='workshoprequest', name='host_responsibilities', field=models.BooleanField(default=False, verbose_name='I understand <a href="https://docs.carpentries.org/topic_folders/hosts_instructors/hosts_instructors_checklist.html#host-checklist">the responsibilities of the workshop host</a>, including recruiting local helpers to support the workshop (1 helper for every 8-10 learners).'), ), migrations.AlterField( model_name='workshoprequest', name='requested_workshop_types', field=models.ManyToManyField(help_text='If your learners are new to programming and primarily interested in working with data, Data Carpentry is likely the best choice. If your learners are interested in learning more about programming, including version control and automation, Software Carpentry is likely the best match. If your learners are people working in library and information related roles interested in learning data and software skills, Library Carpentry is the best choice. Please visit the <a href="https://software-carpentry.org/lessons/">Software Carpentry lessons page</a>, <a href="http://www.datacarpentry.org/lessons/">Data Carpentry lessons page</a>, or the <a href="https://librarycarpentry.org/lessons/">Library Carpentry lessons page</a> for more information about any of our lessons.', limit_choices_to={'active': True}, to='workshops.Curriculum', verbose_name='Which Carpentries workshop are you requesting?'), ), migrations.AlterField( model_name='workshoprequest', name='scholarship_circumstances', field=models.TextField(blank=True, help_text='Required only if you request a scholarship.', verbose_name='Please explain the circumstances for your scholarship request and let us know what budget you have towards The Carpentries workshop fees.'), ), migrations.AlterField( model_name='workshoprequest', name='public_event', field=models.CharField(blank=True, choices=[('invite', 'This event is open to learners by invitation only.'), ('closed', 'This event is open to learners inside of my institution.'), ('public', 'This event is open to learners outside of my institution.'), ('other', 'Other:')], default='', help_text='Many of our workshops restrict registration to learners from the hosting institution. If your workshop will be open to registrants outside of your institution please let us know below.', max_length=20, verbose_name='Is this workshop open to the public?'), ), migrations.RunPython(migrate_public_event), ]
    71.339286
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    2,869
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    0
    0
    0
    0
    0
    0
    2,432
    0.608761
    92ec1a79aa56994e71f763b1fea1ca3f88478806
    1,278
    py
    Python
    pix2pix/Discriminator.py
    yubin1219/GAN
    8345095f9816e548c968492efbe92b427b0e06a3
    [ "MIT" ]
    null
    null
    null
    pix2pix/Discriminator.py
    yubin1219/GAN
    8345095f9816e548c968492efbe92b427b0e06a3
    [ "MIT" ]
    null
    null
    null
    pix2pix/Discriminator.py
    yubin1219/GAN
    8345095f9816e548c968492efbe92b427b0e06a3
    [ "MIT" ]
    1
    2021-09-17T01:28:50.000Z
    2021-09-17T01:28:50.000Z
    import torch import torch.nn as nn class Discriminator(nn.Module): def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False) : super(Discriminator, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(input_nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True) ) self.conv2 = nn.Sequential( nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1), norm_layer(ndf * 2), nn.LeakyReLU(0.2, True) ) self.conv3 = nn.Sequential( nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1), norm_layer(ndf * 4), nn.LeakyReLU(0.2, True) ) self.conv4 = nn.Sequential( nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1), norm_layer(ndf * 8), nn.LeakyReLU(0.2, True) ) if use_sigmoid: self.conv5 = nn.Sequential( nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1), nn.Sigmoid() ) else: self.conv5 = nn.Sequential( nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1) ) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) x = self.conv5(x) return x
    29.045455
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    0.971049
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    0
    0
    0
    0
    0
    0
    0
    92ec31910f4ccb9a9e9fdaf1976491caf430c06d
    1,067
    py
    Python
    tests/slicebuilders/subpopulations/test_length.py
    ANarayan/robustness-gym
    eed2800985631fbbe6491b5f6f0731a067eef78e
    [ "Apache-2.0" ]
    null
    null
    null
    tests/slicebuilders/subpopulations/test_length.py
    ANarayan/robustness-gym
    eed2800985631fbbe6491b5f6f0731a067eef78e
    [ "Apache-2.0" ]
    null
    null
    null
    tests/slicebuilders/subpopulations/test_length.py
    ANarayan/robustness-gym
    eed2800985631fbbe6491b5f6f0731a067eef78e
    [ "Apache-2.0" ]
    null
    null
    null
    from unittest import TestCase import numpy as np from robustnessgym.cachedops.spacy import Spacy from robustnessgym.slicebuilders.subpopulations.length import LengthSubpopulation from tests.testbeds import MockTestBedv0 class TestLengthSubpopulation(TestCase): def setUp(self): self.testbed = MockTestBedv0() self.testbed.dataset = Spacy()(self.testbed.dataset, columns=["text"]) def test_score(self): # Create the length subpopulation length = LengthSubpopulation(intervals=[(1, 3), (4, 5)]) # Compute scores scores = length.score(self.testbed.dataset[:], columns=["text"]) self.assertTrue(np.allclose(scores, np.array([5, 5, 5, 5, 5, 5]))) print(self.testbed.dataset.column_names) print(Spacy.retrieve(self.testbed.dataset[:], ["text"])) # Apply the subpopulation slices, slice_matrix = length(self.testbed.dataset, columns=["text"]) # Check that the slice membership lines up self.assertTrue(np.allclose(slice_matrix, np.array([[0, 1]] * 6)))
    34.419355
    81
    0.686036
    842
    0.789128
    0
    0
    0
    0
    0
    0
    140
    0.131209
    92eca5c1a6337291d863c933685487ea52da0c9b
    1,146
    py
    Python
    pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py
    NickSwainston/pulsar_spectra
    b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69
    [ "MIT" ]
    null
    null
    null
    pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py
    NickSwainston/pulsar_spectra
    b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69
    [ "MIT" ]
    4
    2021-12-17T04:24:13.000Z
    2022-02-24T14:51:18.000Z
    pulsar_spectra/catalogue_papers/Jankowski_2018_raw_to_yaml.py
    NickSwainston/pulsar_spectra
    b264aab3f8fc1bb3cad14ef1b93cab519ed5bc69
    [ "MIT" ]
    null
    null
    null
    import json from astroquery.vizier import Vizier with open("Jankowski_2018_raw.txt", "r") as raw_file: lines = raw_file.readlines() print(lines) pulsar_dict = {} for row in lines[3:]: row = row.split("|") print(row) pulsar = row[0].strip().replace("−", "-") freqs = [] fluxs = [] flux_errs = [] # If no error means it's an upper limit andnow sure how to handle it if row[1].strip() != "" and row[2].strip() != "": freqs.append(728) fluxs.append(float(row[1].strip())) flux_errs.append(float(row[2].strip())) if row[3].strip() != "" and row[4].strip() != "": freqs.append(1382) fluxs.append(float(row[3].strip())) flux_errs.append(float(row[4].strip())) if row[5].strip() != "" and row[6].strip() != "": freqs.append(3100) fluxs.append(float(row[5].strip())) flux_errs.append(float(row[6].strip())) pulsar_dict[pulsar] = {"Frequency MHz":freqs, "Flux Density mJy":fluxs, "Flux Density error mJy":flux_errs} with open("Jankowski_2018.yaml", "w") as cat_file: cat_file.write(json.dumps(pulsar_dict)) print(pulsar_dict)
    34.727273
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    0.604712
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    0
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    0
    199
    0.173345
    92ee36608ac8edb00b879a89f8f1eafb4cb4fb04
    15,018
    py
    Python
    integration-tests/run-intg-test.py
    NishikaDeSilva/identity-test-integration
    dbd1db07aa6d4f4942d772cd56c0b06c355bd43b
    [ "Apache-2.0" ]
    4
    2017-10-23T05:25:27.000Z
    2018-01-10T08:00:14.000Z
    integration-tests/run-intg-test.py
    NishikaDeSilva/identity-test-integration
    dbd1db07aa6d4f4942d772cd56c0b06c355bd43b
    [ "Apache-2.0" ]
    42
    2018-05-21T12:55:49.000Z
    2020-01-17T06:40:25.000Z
    integration-tests/run-intg-test.py
    NishikaDeSilva/identity-test-integration
    dbd1db07aa6d4f4942d772cd56c0b06c355bd43b
    [ "Apache-2.0" ]
    46
    2017-10-04T05:45:52.000Z
    2018-05-05T14:32:26.000Z
    # Copyright (c) 2018, WSO2 Inc. (http://wso2.com) All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # importing required modules import sys from xml.etree import ElementTree as ET import toml import subprocess import wget import logging import inspect import os import shutil import pymysql import sqlparse import re from pathlib import Path import urllib.request as urllib2 from xml.dom import minidom import intg_test_manager as cm from subprocess import Popen, PIPE import os from prod_test_constant import DB_META_DATA, DIST_POM_PATH, INTEGRATION_PATH, DISTRIBUTION_PATH, \ DATASOURCE_PATHS, LIB_PATH, WSO2SERVER, M2_PATH, ARTIFACT_REPORTS_PATHS, POM_FILE_PATHS from intg_test_constant import NS, ZIP_FILE_EXTENSION, CARBON_NAME, VALUE_TAG, SURFACE_PLUGIN_ARTIFACT_ID, \ DEPLOYMENT_PROPERTY_FILE_NAME, LOG_FILE_NAME, PRODUCT_STORAGE_DIR_NAME, \ DEFAULT_DB_USERNAME, LOG_STORAGE, TEST_OUTPUT_DIR_NAME, DEFAULT_ORACLE_SID, MYSQL_DB_ENGINE, \ ORACLE_DB_ENGINE, PRODUCT_STORAGE_DIR_NAME, MSSQL_DB_ENGINE database_names = [] db_engine = None sql_driver_location = None identity_db_url = None identity_db_username = None identity_db_password = None identity_db_driver = None shared_db_url = None shared_db_username = None shared_db_password = None shared_db_driver = None identity_db = "WSO2_IDENTITY_DB" shared_db = "WSO2_SHARED_DB" def get_db_meta_data(argument): switcher = DB_META_DATA return switcher.get(argument, False) def add_environmental_variables(): if MYSQL_DB_ENGINE == cm.database_config['db_engine'].upper(): identity_url = cm.database_config[ 'url'] + "/" + identity_db + "?useSSL=false&amp;autoReconnect=true&amp;requireSSL=false" \ "&amp;verifyServerCertificate=false" shared_url = cm.database_config[ 'url'] + "/" + shared_db + \ "?useSSL=false&amp;autoReconnect=true&amp;requireSSL=false" \ "&amp;verifyServerCertificate=false" user = cm.database_config['user'] elif ORACLE_DB_ENGINE == cm.database_config['db_engine'].upper(): identity_url= cm.database_config['url'] + "/" + DEFAULT_ORACLE_SID shared_url= cm.database_config['url'] + "/" + DEFAULT_ORACLE_SID user = cm.database_config['user'] elif MSSQL_DB_ENGINE == cm.database_config['db_engine'].upper(): identity_url = cm.database_config['url'] + ";" + "databaseName=" + identity_db shared_url = cm.database_config['url'] + ";" + "databaseName=" + shared_db user = cm.database_config['user'] else: shared_url = cm.database_config['url'] + "/" + shared_db identity_url = cm.database_config['url'] + "/" + identity_db user = cm.database_config['user'] password = cm.database_config['password'] driver_class_name = cm.database_config['driver_class_name'] os.environ["SHARED_DATABASE_URL"] = shared_url os.environ["SHARED_DATABASE_USERNAME"] = user os.environ["SHARED_DATABASE_PASSWORD"] = password os.environ["SHARED_DATABASE_DRIVER"] = driver_class_name os.environ["IDENTITY_DATABASE_URL"] = identity_url os.environ["IDENTITY_DATABASE_USERNAME"] = user os.environ["IDENTITY_DATABASE_PASSWORD"] = password os.environ["IDENTITY_DATABASE_DRIVER"] = driver_class_name logger.info("Added environmental variables for integration test") def modify_datasources(): file_path = Path(storage_dist_abs_path / datasource_path) if sys.platform.startswith('win'): file_path = cm.winapi_path(file_path) logger.info("Modifying datasource: " + str(file_path)) deployment_toml_config = toml.load(file_path) logger.info("loading dep,loyment.toml file") logger.info(deployment_toml_config) for key in deployment_toml_config: if key == 'database': database_config = deployment_toml_config[key] for key in database_config: if key == 'identity_db': identity_db_config = database_config['identity_db'] identity_db_config ['url'] = "$env{IDENTITY_DATABASE_URL}" identity_db_config ['username'] = "$env{IDENTITY_DATABASE_USERNAME}" identity_db_config ['password'] = "$env{IDENTITY_DATABASE_PASSWORD}" identity_db_config ['driver'] = "$env{IDENTITY_DATABASE_DRIVER}" database_names.append(identity_db) if key == 'shared_db': shared_db_config = database_config['shared_db'] shared_db_config ['url'] = "$env{SHARED_DATABASE_URL}" shared_db_config ['username'] = "$env{SHARED_DATABASE_USERNAME}" shared_db_config ['password'] = "$env{SHARED_DATABASE_PASSWORD}" shared_db_config ['driver'] = "$env{SHARED_DATABASE_DRIVER}" database_names.append(shared_db) with open(file_path, 'w') as writer: writer.write(toml.dumps(deployment_toml_config)) # Since we have added a method to clone a given git branch and checkout to the latest released tag it is not required to # modify pom files. Hence in the current implementation this method is not using. # However, in order to execute this method you can define pom file paths in const_<prod>.py as a constant # and import it to run-intg-test.py. Thereafter assign it to global variable called pom_file_paths in the # configure_product method and call the modify_pom_files method. def modify_pom_files(): for pom in POM_FILE_PATHS: file_path = Path(cm.workspace + "/" + cm.product_id + "/" + pom) if sys.platform.startswith('win'): file_path = cm.winapi_path(file_path) logger.info("Modifying pom file: " + str(file_path)) ET.register_namespace('', NS['d']) artifact_tree = ET.parse(file_path) artifarct_root = artifact_tree.getroot() data_sources = artifarct_root.find('d:build', NS) plugins = data_sources.find('d:plugins', NS) for plugin in plugins.findall('d:plugin', NS): artifact_id = plugin.find('d:artifactId', NS) if artifact_id is not None and artifact_id.text == SURFACE_PLUGIN_ARTIFACT_ID: configuration = plugin.find('d:configuration', NS) system_properties = configuration.find('d:systemProperties', NS) for neighbor in system_properties.iter('{' + NS['d'] + '}' + CARBON_NAME): neighbor.text = cm.modify_distribution_name(neighbor) for prop in system_properties: name = prop.find('d:name', NS) if name is not None and name.text == CARBON_NAME: for data in prop: if data.tag == VALUE_TAG: data.text = cm.modify_distribution_name(data) break artifact_tree.write(file_path) #TODO: Improve the method in generic way to support all products def save_log_files(): log_storage = Path(cm.workspace + "/" + LOG_STORAGE) if not Path.exists(log_storage): Path(log_storage).mkdir(parents=True, exist_ok=True) log_file_paths = ARTIFACT_REPORTS_PATHS if log_file_paths: for file in log_file_paths: absolute_file_path = Path(cm.workspace + "/" + cm.product_id + "/" + file) if Path.exists(absolute_file_path): cm.copy_file(absolute_file_path, log_storage) else: logger.error("File doesn't contain in the given location: " + str(absolute_file_path)) #TODO: Improve the method in generic way to support all products def save_test_output(): report_folder = Path(cm.workspace + "/" + TEST_OUTPUT_DIR_NAME) logger.info(str(report_folder)) if Path.exists(report_folder): shutil.rmtree(report_folder) logger.info(str(ARTIFACT_REPORTS_PATHS)) logger.info(str(type(ARTIFACT_REPORTS_PATHS))) report_file_paths = ARTIFACT_REPORTS_PATHS for key, value in report_file_paths.items(): for file in value: absolute_file_path = Path(cm.workspace + "/" + cm.product_id + "/" + file) if Path.exists(absolute_file_path): report_storage = Path(cm.workspace + "/" + TEST_OUTPUT_DIR_NAME + "/" + key) cm.copy_file(absolute_file_path, report_storage) logger.info("Report successfully copied") else: logger.error("File doesn't contain in the given location: " + str(absolute_file_path)) #TODO: Improve the method in generic way to support all products # def set_custom_testng(): # if cm.use_custom_testng_file == "TRUE": # testng_source = Path(cm.workspace + "/" + "testng.xml") # testng_destination = Path(cm.workspace + "/" + cm.product_id + "/" + TESTNG_DIST_XML_PATHS) # testng_server_mgt_source = Path(cm.workspace + "/" + "testng-server-mgt.xml") # testng_server_mgt_destination = Path(cm.workspace + "/" + cm.product_id + "/" + TESTNG_SERVER_MGT_DIST) # # replace testng source # cm.replace_file(testng_source, testng_destination) # # replace testng server mgt source # cm.replace_file(testng_server_mgt_source, testng_server_mgt_destination) def configure_product(): try: global datasource_path global target_dir_abs_path global storage_dist_abs_path global pom_file_paths datasource_path = DATASOURCE_PATHS zip_name = dist_name + ZIP_FILE_EXTENSION storage_dir_abs_path = Path(cm.workspace + "/" + PRODUCT_STORAGE_DIR_NAME) target_dir_abs_path = Path(cm.workspace + "/" + cm.product_id + "/" + DISTRIBUTION_PATH) storage_dist_abs_path = Path(storage_dir_abs_path / dist_name) storage_zip_abs_path = Path(storage_dir_abs_path / zip_name) configured_dist_storing_loc = Path(target_dir_abs_path / dist_name) script_name = Path(WSO2SERVER) script_path = Path(storage_dist_abs_path / script_name) cm.extract_product(storage_dir_abs_path, storage_zip_abs_path) cm.attach_jolokia_agent(script_path) cm.copy_jar_file(Path(cm.database_config['sql_driver_location']), Path(storage_dist_abs_path / LIB_PATH)) if datasource_path is not None: modify_datasources() else: logger.info("Datasource paths are not defined in the config file") os.remove(str(storage_zip_abs_path)) cm.compress_distribution(configured_dist_storing_loc, storage_dir_abs_path) cm.add_distribution_to_m2(storage_dir_abs_path, M2_PATH) shutil.rmtree(configured_dist_storing_loc, onerror=cm.on_rm_error) return database_names except FileNotFoundError as e: logger.error("Error occurred while finding files", exc_info=True) except IOError as e: logger.error("Error occurred while accessing files", exc_info=True) except Exception as e: logger.error("Error occurred while configuring the product", exc_info=True) def build_source_without_tests(source_path): """Build the product-source. """ logger.info('Building the source skipping tests') if sys.platform.startswith('win'): subprocess.call(['mvn', 'clean', 'install', '-B', '-e','-Dmaven.test.skip=true'], shell=True, cwd=source_path) else: subprocess.call(['mvn', 'clean', 'install', '-B', '-e', '-Dmaven.test.skip=true'], cwd=source_path) logger.info('Module build is completed. Module: ' + str(source_path)) def main(): try: global logger global dist_name logger = cm.function_logger(logging.DEBUG, logging.DEBUG) if sys.version_info < (3, 6): raise Exception( "To run run-intg-test.py script you must have Python 3.6 or latest. Current version info: " + sys.version_info) cm.read_property_files() if not cm.validate_property_readings(): raise Exception( "Property file doesn't have mandatory key-value pair. Please verify the content of the property file " "and the format") # get properties assigned to local variables pom_path = DIST_POM_PATH engine = cm.db_engine.upper() db_meta_data = get_db_meta_data(engine) distribution_path = DISTRIBUTION_PATH # construct the database configurations cm.construct_db_config(db_meta_data) # clone the repository cm.clone_repo() if cm.test_mode == "RELEASE": cm.checkout_to_tag() # product name retrieve from product pom files dist_name = cm.get_dist_name(pom_path) # build the product without test once to make samples and required artifacts to be available. build_source_without_tests(cm.workspace + "/" + cm.product_id + "/") cm.get_latest_released_dist() elif cm.test_mode == "SNAPSHOT": # product name retrieve from product pom files dist_name = cm.get_dist_name(pom_path) cm.build_snapshot_dist(distribution_path) elif cm.test_mode == "WUM": dist_name = cm.get_dist_name_wum() # populate databases db_names = configure_product() if db_names is None or not db_names: raise Exception("Failed the product configuring") cm.setup_databases(db_names, db_meta_data) # run integration tests # Buld Common module add_environmental_variables() module_path = Path(cm.workspace + "/" + cm.product_id + "/" + 'modules/integration/tests-common') logger.info('Building common module. Build path: '+ str(module_path) + ' \n') cm.build_module(module_path) intg_module_path = Path(cm.workspace + "/" + cm.product_id + "/" + INTEGRATION_PATH) logger.info('Building integration module. Build path: '+ str(intg_module_path) + ' \n') cm.build_module(intg_module_path) save_test_output() cm.create_output_property_fle() except Exception as e: logger.error("Error occurred while running the run-intg-test.py script", exc_info=True) except BaseException as e: logger.error("Error occurred while doing the configuration", exc_info=True) if __name__ == "__main__": main()
    46.639752
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    0.671195
    0
    0
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    0
    4,624
    0.307897
    92eed01036cb07058175a69126f2f5a418891a9a
    2,376
    py
    Python
    src/pytest_notification/sound.py
    rhpvorderman/pytest-notification
    3f322ab04914f52525e1b07bc80537d5f9a00250
    [ "MIT" ]
    2
    2020-08-27T03:14:05.000Z
    2020-10-24T17:17:36.000Z
    src/pytest_notification/sound.py
    rhpvorderman/pytest-notification
    3f322ab04914f52525e1b07bc80537d5f9a00250
    [ "MIT" ]
    5
    2019-12-02T08:49:15.000Z
    2020-06-22T08:38:34.000Z
    src/pytest_notification/sound.py
    rhpvorderman/pytest-notification
    3f322ab04914f52525e1b07bc80537d5f9a00250
    [ "MIT" ]
    null
    null
    null
    # Copyright (c) 2019 Leiden University Medical Center # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import subprocess import sys from pathlib import Path SOUNDS_DIR = (Path(__file__).parent / Path("sounds")).absolute() DEFAULT_SUCCESS_SOUND = SOUNDS_DIR / Path("applause") DEFAULT_FAIL_SOUND = SOUNDS_DIR / Path("buzzer") def play_sound(sound_file: Path): if sys.platform == "linux": # paplay comes from PulseAudio and should be installed by default on # most systems. _play_sound_unix(sound_file.with_suffix(".oga"), program="paplay") elif sys.platform == "darwin": # Afplay comes installed by default on Macintosh _play_sound_unix(sound_file.with_suffix(".mp3"), program="afplay") else: # A windows implementation should be possible with the winsound # implementation, but that does not play ogg audio. raise NotImplementedError( "Playing sounds not supported by pytest-notification on {}" "".format(sys.platform)) def _play_sound_unix(sound_file: Path, program): """ Play a sound file on unix with the program. :param sound_file: Path to the sound file. :param program: Which program to use. :return: No returns. Plays a sound file. """ # Play the sound non blocking, use Popen. subprocess.Popen([program, str(sound_file)])
    43.2
    79
    0.731481
    0
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    0
    0
    0
    0
    1,701
    0.715909
    92ef37eb449c4f50b5c90c7a720a5f53652a647c
    420
    py
    Python
    7KYU/next_prime.py
    yaznasivasai/python_codewars
    25493591dde4649dc9c1ec3bece8191a3bed6818
    [ "MIT" ]
    4
    2021-07-17T22:48:03.000Z
    2022-03-25T14:10:58.000Z
    7KYU/next_prime.py
    yaznasivasai/python_codewars
    25493591dde4649dc9c1ec3bece8191a3bed6818
    [ "MIT" ]
    null
    null
    null
    7KYU/next_prime.py
    yaznasivasai/python_codewars
    25493591dde4649dc9c1ec3bece8191a3bed6818
    [ "MIT" ]
    3
    2021-06-14T14:18:16.000Z
    2022-03-16T06:02:02.000Z
    from math import sqrt def is_simple(n: int) -> bool: if n % 2 == 0 and n != 2: return False for i in range (3, int(sqrt(n)) + 2, 2): if n % i == 0 and n != i: return False return True def next_prime(n: int) -> int: n += 1 if n <= 2: return 2 else: if n % 2 == 0: n += 1 while not is_simple(n): n += 2 return n
    20
    44
    0.435714
    0
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    0
    0
    0
    0
    0
    0
    0
    0
    92ef91238a4d28bed6389f80b7547828e84737ba
    6,622
    py
    Python
    cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py
    manxueitp/cozmo-test
    a91b1a4020544cb622bd67385f317931c095d2e8
    [ "MIT" ]
    null
    null
    null
    cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py
    manxueitp/cozmo-test
    a91b1a4020544cb622bd67385f317931c095d2e8
    [ "MIT" ]
    null
    null
    null
    cozmo_sdk_examples/if_this_then_that/ifttt_gmail.py
    manxueitp/cozmo-test
    a91b1a4020544cb622bd67385f317931c095d2e8
    [ "MIT" ]
    null
    null
    null
    #!/usr/bin/env python3 # Copyright (c) 2016 Anki, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License in the file LICENSE.txt or at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. '''"If This Then That" Gmail example This example demonstrates how "If This Then That" (http://ifttt.com) can be used make Cozmo respond when a Gmail account receives an email. Instructions below will lead you through setting up an applet on the IFTTT website. When the applet trigger is called (which sends a web request received by the web server started in this example), Cozmo will play an animation, speak the email sender's name and show a mailbox image on his face. Please place Cozmo on the charger for this example. When necessary, he will be rolled off and back on. Follow these steps to set up and run the example: 1) Provide a a static ip, URL or similar that can be reached from the If This Then That server. One easy way to do this is with ngrok, which sets up a secure tunnel to localhost running on your machine. To set up ngrok: a) Follow instructions here to download and install: https://ngrok.com/download b) Run this command to create a secure public URL for port 8080: ./ngrok http 8080 c) Note the HTTP forwarding address shown in the terminal (e.g., http://55e57164.ngrok.io). You will use this address in your applet, below. WARNING: Using ngrok exposes your local web server to the internet. See the ngrok documentation for more information: https://ngrok.com/docs 2) Set up your applet on the "If This Then That" website. a) Sign up and sign into https://ifttt.com b) Create an applet: https://ifttt.com/create c) Set up your trigger. 1. Click "this". 2. Select "Gmail" as your service. If prompted, click "Connect", select your Gmail account, and click “Allow” to provide permissions to IFTTT for your email account. Click "Done". 3. Under "Choose a Trigger", select “Any new email in inbox". d) Set up your action. 1. Click “that". 2. Select “Maker" to set it as your action channel. Connect to the Maker channel if prompted. 3. Click “Make a web request" and fill out the fields as follows. Remember your publicly accessible URL from above (e.g., http://55e57164.ngrok.io) and use it in the URL field, followed by "/iftttGmail" as shown below: URL: http://55e57164.ngrok.io/iftttGmail Method: POST Content Type: application/json Body: {"FromAddress":"{{FromAddress}}"} 5. Click “Create Action" then “Finish". 3) Test your applet. a) Run this script at the command line: ./ifttt_gmail.py b) On ifttt.com, on your applet page, click “Check now”. See that IFTTT confirms that the applet was checked. c) Send an email to the Gmail account in your recipe d) On your IFTTT applet webpage, again click “Check now”. This should cause IFTTT to detect that the email was received and send a web request to the ifttt_gmail.py script. e) In response to the ifttt web request, Cozmo should roll off the charger, raise and lower his lift, announce the email, and then show a mailbox image on his face. ''' import asyncio import re import sys try: from aiohttp import web except ImportError: sys.exit("Cannot import from aiohttp. Do `pip3 install --user aiohttp` to install") import cozmo from common import IFTTTRobot app = web.Application() async def serve_gmail(request): '''Define an HTTP POST handler for receiving requests from If This Then That. You may modify this method to change how Cozmo reacts to the email being received. ''' json_object = await request.json() # Extract the name of the email sender. from_email_address = json_object["FromAddress"] # Use a regular expression to break apart pieces of the email address match_object = re.search(r'([\w.]+)@([\w.]+)', from_email_address) email_local_part = match_object.group(1) robot = request.app['robot'] async def read_name(): try: async with robot.perform_off_charger(): '''If necessary, Move Cozmo's Head and Lift to make it easy to see Cozmo's face.''' await robot.get_in_position() # First, have Cozmo play animation "ID_pokedB", which tells # Cozmo to raise and lower his lift. To change the animation, # you may replace "ID_pokedB" with another animation. Run # remote_control_cozmo.py to see a list of animations. await robot.play_anim(name='ID_pokedB').wait_for_completed() # Next, have Cozmo speak the name of the email sender. await robot.say_text("Email from " + email_local_part).wait_for_completed() # Last, have Cozmo display an email image on his face. robot.display_image_file_on_face("../face_images/ifttt_gmail.png") except cozmo.RobotBusy: cozmo.logger.warning("Robot was busy so didn't read email address: "+ from_email_address) # Perform Cozmo's task in the background so the HTTP server responds immediately. asyncio.ensure_future(read_name()) return web.Response(text="OK") # Attach the function as an HTTP handler. app.router.add_post('/iftttGmail', serve_gmail) if __name__ == '__main__': cozmo.setup_basic_logging() cozmo.robot.Robot.drive_off_charger_on_connect = False # Use our custom robot class with extra helper methods cozmo.conn.CozmoConnection.robot_factory = IFTTTRobot try: sdk_conn = cozmo.connect_on_loop(app.loop) # Wait for the robot to become available and add it to the app object. app['robot'] = app.loop.run_until_complete(sdk_conn.wait_for_robot()) except cozmo.ConnectionError as e: sys.exit("A connection error occurred: %s" % e) web.run_app(app)
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    5,140
    0.773398
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    13,843
    py
    Python
    plotutils.py
    parkus/mypy
    21043c559dca14abe7508e0f6b2f8053bf376bb8
    [ "MIT" ]
    1
    2015-11-06T06:27:59.000Z
    2015-11-06T06:27:59.000Z
    plotutils.py
    parkus/mypy
    21043c559dca14abe7508e0f6b2f8053bf376bb8
    [ "MIT" ]
    null
    null
    null
    plotutils.py
    parkus/mypy
    21043c559dca14abe7508e0f6b2f8053bf376bb8
    [ "MIT" ]
    null
    null
    null
    # -*- coding: utf-8 -*- """ Created on Fri May 30 17:15:27 2014 @author: Parke """ from __future__ import division, print_function, absolute_import import numpy as np import matplotlib as mplot import matplotlib.pyplot as plt import mypy.my_numpy as mnp dpi = 100 fullwidth = 10.0 halfwidth = 5.0 # use these with line.set_dashes and iterate through more linestyles than come with matplotlib # consider ussing a ::2 slice for fewer dashes = [[], [30, 10], [20, 8], [10, 5], [3, 2], [30, 5, 3, 5, 10, 5, 3, 5], [15] + [5, 3]*3 + [5], [15] + [5, 3]*2 + [5], [15] + [5, 3] + [5]] def click_coords(fig=None, timeout=600.): if fig is None: fig = plt.gcf() xy = [] def onclick(event): if not event.inaxes: fig.canvas.stop_event_loop() else: xy.append([event.xdata, event.ydata]) print("Gathering coordinates of mouse clicks. Click outside of the axes " \ "when done.") cid = fig.canvas.mpl_connect('button_press_event', onclick) fig.canvas.start_event_loop(timeout=timeout) fig.canvas.mpl_disconnect(cid) return np.array(xy) def common_axes(fig, pos=None): if pos is None: bigax = fig.add_subplot(111) else: bigax = fig.add_axes(pos) [bigax.spines[s].set_visible(False) for s in ['top', 'bottom', 'left', 'right']] bigax.tick_params(labelleft=False, labelbottom=False, left='off', bottom='off') bigax.set_zorder(-10) return bigax def log_frac(x, frac): l0, l1 = list(map(np.log10, x)) ld = l1 - l0 l = ld*frac + l0 return 10**l def log2linear(x, errneg=None, errpos=None): xl = 10**x result = [xl] if errneg is not None: xn = xl - 10**(x - np.abs(errneg)) result.append(xn) if errpos is not None: xp = 10**(x + errpos) - xl result.append(xp) return result def linear2log(x, errneg=None, errpos=None): xl = np.log10(x) result = [x] if errneg is not None: xn = xl - np.log10(x - np.abs(errneg)) result.append(xn) if errpos is not None: xp = np.log10(x + errpos) - xl result.append(xp) return result def step(*args, **kwargs): edges, values = args[0], args[1] # deal with potentially gappy 2-column bin specifications edges = np.asarray(edges) if edges.ndim == 2: if np.any(edges[1:,0] < edges[:-1,1]): raise ValueError('Some bins overlap') if np.any(edges[1:,0] < edges[:-1,0]): raise ValueError('Bins must be in increasing order.') gaps = edges[1:,0] > edges[:-1,1] edges = np.unique(edges) if np.any(gaps): values = np.insert(values, np.nonzero(gaps), np.nan) edges = mnp.lace(edges[:-1], edges[1:]) values = mnp.lace(values, values) args = list(args) args[0], args[1] = edges, values ax = kwargs.pop('ax', plt.gca()) return ax.plot(*args, **kwargs) def point_along_line(x, y, xfrac=None, xlbl=None, scale='linear'): if scale == 'log': lx, ly = point_along_line(np.log10(x), np.log10(y), xfrac, xlbl, ylbl, scale) return 10 ** lx, 10 ** ly if xfrac is not None: if xfrac == 0: return x[0], y[0] if xfrac == 1: return x[-1], y[-1] else: d = np.cumsum(np.sqrt(np.diff(x)**2 + np.diff(y)**2)) d = np.insert(d, 0, 0) f = d/d[-1] xp, yp = [np.interp(xfrac, f, a) for a in [x,y]] return xp, yp if xlbl is not None: return xlbl, np.interp(xlbl, x, y) def textSize(ax_or_fig=None, coordinate='data'): """ Return x & y scale factors for converting text sizes in points to another coordinate. Useful for properly spacing text labels and such when you need to know sizes before the text is made (otherwise you can use textBoxSize). Coordinate can be 'data', 'axes', or 'figure'. If data coordinates are requested and the data is plotted on a log scale, then the factor will be given in dex. """ if ax_or_fig is None: fig = plt.gcf() ax = fig.gca() else: if isinstance(ax_or_fig, plt.Figure): fig = ax_or_fig ax = fig.gca() elif isinstance(ax_or_fig, plt.Axes): ax = ax_or_fig fig = ax.get_figure() else: raise TypeError('ax_or_fig must be a Figure or Axes instance, if given.') w_fig_in, h_fig_in = ax.get_figure().get_size_inches() if coordinate == 'fig': return 1.0/(w_fig_in*72), 1.0/(h_fig_in*72) w_ax_norm, h_ax_norm = ax.get_position().size w_ax_in = w_ax_norm * w_fig_in h_ax_in = h_ax_norm * h_fig_in w_ax_pts, h_ax_pts = w_ax_in*72, h_ax_in*72 if coordinate == 'axes': return 1.0/w_ax_pts, 1.0/h_ax_pts if coordinate == 'data': xlim = ax.get_xlim() ylim = ax.get_ylim() if ax.get_xscale() == 'log': xlim = np.log10(xlim) if ax.get_yscale() == 'log': ylim = np.log10(ylim) w_ax_data = xlim[1] - xlim[0] h_ax_data = ylim[1] - ylim[0] return w_ax_data/w_ax_pts, h_ax_data/h_ax_pts def tight_axis_limits(ax=None, xory='both', margin=0.05): if ax is None: ax = plt.gca() def newlim(oldlim): delta = abs(oldlim[1] - oldlim[0]) pad = delta*margin if oldlim[1] > oldlim[0]: return (oldlim[0] - pad, oldlim[1] + pad) else: return (oldlim[0] + pad, oldlim[1] - pad) def newlim_log(oldlim): loglim = [np.log10(l) for l in oldlim] newloglim = newlim(loglim) return (10.0**newloglim[0], 10.0**newloglim[1]) def newlim_either(oldlim,axlim,scale): if axlim[1] < axlim [0]: oldlim = oldlim[::-1] if scale == 'linear': return newlim(oldlim) elif scale == 'log': return newlim_log(oldlim) elif scale == 'symlog': raise NotImplementedError('Past Parke to future Parke, you did\'t write an implementation for symlog' 'scaled axes.') if xory == 'x' or xory == 'both': datalim = ax.dataLim.extents[[0,2]] axlim = ax.get_xlim() scale = ax.get_xscale() ax.set_xlim(newlim_either(datalim,axlim,scale)) if xory == 'y' or xory == 'both': datalim = ax.dataLim.extents[[1,3]] axlim = ax.get_ylim() scale = ax.get_yscale() ax.set_ylim(newlim_either(datalim,axlim,scale)) #TODO: discard this function? def standard_figure(app, slideAR=1.6, height=1.0): """Generate a figure of standard size for publishing. implemented values for app (application) are: 'fullslide' height is the fractional height of the figure relative to the "standard" height. For slides the standard is the full height of a slide. returns the figure object and default font size """ if app == 'fullslide': fontsize = 20 figsize = [fullwidth, fullwidth/slideAR*height] fig = mplot.pyplot.figure(figsize=figsize, dpi=dpi) mplot.rcParams.update({'font.size': fontsize}) return fig, fontsize def pcolor_reg(x, y, z, **kw): """ Similar to `pcolor`, but assume that the grid is uniform, and do plotting with the (much faster) `imshow` function. """ x, y, z = np.asarray(x), np.asarray(y), np.asarray(z) if x.ndim != 1 or y.ndim != 1: raise ValueError("x and y should be 1-dimensional") if z.ndim != 2 or z.shape != (y.size, x.size): raise ValueError("z.shape should be (y.size, x.size)") dx = np.diff(x) dy = np.diff(y) if not np.allclose(dx, dx[0], 1e-2) or not np.allclose(dy, dy[0], 1e-2): raise ValueError("The grid must be uniform") if np.issubdtype(z.dtype, np.complexfloating): zp = np.zeros(z.shape, float) zp[...] = z[...] z = zp plt.imshow(z, origin='lower', extent=[x.min(), x.max(), y.min(), y.max()], interpolation='nearest', aspect='auto', **kw) plt.axis('tight') def errorpoly(x, y, yerr, fmt=None, ecolor=None, ealpha=0.5, ax=None, **kw): if ax is None: ax = plt.gca() p = ax.plot(x, y, **kw) if fmt is None else ax.plot(x, y, fmt, **kw) if len(yerr.shape) == 2: ylo = y - yerr[0,:] yhi = y + yerr[1,:] else: ylo, yhi = y - yerr, y + yerr if ecolor is None: ecolor = p[0].get_color() # deal with matplotlib sometimes not showing polygon when it extends beyond plot range xlim = ax.get_xlim() inrange = mnp.inranges(x, xlim) if not np.all(inrange): n = np.sum(inrange) yends = np.interp(xlim, x, y) yloends = np.interp(xlim, x, ylo) yhiends = np.interp(xlim, x, yhi) x = np.insert(x[inrange], [0, n], xlim) y = np.insert(y[inrange], [0, n], yends) ylo = np.insert(ylo[inrange], [0, n], yloends) yhi = np.insert(yhi[inrange], [0, n], yhiends) f = ax.fill_between(x,ylo,yhi,color=ecolor,alpha=ealpha) return p[0],f def onscreen_pres(mpl, screenwidth=1200): """ Set matplotlibrc values so that plots are readable as they are created and maximized for an audience far from a screen. Parameters ---------- mpl : module Current matplotlib module. Use 'import matplotlib as mpl'. screewidth : int Width of the screen in question in pixels. Returns ------- None """ mpl.rcParams['lines.linewidth'] = 2 fontsize = round(14 / (800.0 / screenwidth)) mpl.rcParams['font.size'] = fontsize def textBoxSize(txt, transformation=None, figure=None): """Get the width and height of a text object's bounding box transformed to the desired coordinates. Defaults to figure coordinates if transformation is None.""" fig= txt.get_figure() if figure is None else figure if transformation is None: transformation = fig.transFigure coordConvert = transformation.inverted().transform bboxDisp = txt.get_window_extent(fig.canvas.renderer) bboxConv = coordConvert(bboxDisp) w = bboxConv[1,0] - bboxConv[0,0] h = bboxConv[1,1] - bboxConv[0,1] return w, h def stars3d(ra, dec, dist, T=5000.0, r=1.0, labels='', view=None, size=(800,800), txt_scale=1.0): """ Make a 3D diagram of stars positions relative to the Sun, with semi-accurate colors and distances as desired. Coordinates must be in degrees. Distance is assumed to be in pc (for axes labels). Meant to be used with only a handful of stars. """ from mayavi import mlab from color.maps import true_temp n = len(ra) dec, ra = dec*np.pi/180.0, ra*np.pi/180.0 makearr = lambda v: np.array([v] * n) if np.isscalar(v) else v T, r, labels = list(map(makearr, (T, r, labels))) # add the sun ra, dec, dist = list(map(np.append, (ra, dec, dist), (0.0, 0.0, 0.0))) r, T, labels = list(map(np.append, (r, T, labels), (1.0, 5780.0, 'Sun'))) # get xyz coordinates z = dist * np.sin(dec) h = dist * np.cos(dec) x = h * np.cos(ra) y = h * np.sin(ra) # make figure fig = mlab.figure(bgcolor=(0,0,0), fgcolor=(1,1,1), size=size) # plot lines down to the dec=0 plane for all but the sun lines = [] for x1, y1, z1 in list(zip(x, y, z))[:-1]: xx, yy, zz = [x1, x1], [y1, y1], [0.0, z1] line = mlab.plot3d(xx, yy, zz, color=(0.7,0.7,0.7), line_width=0.5, figure=fig) lines.append(line) # plot spheres r_factor = np.max(dist) / 30.0 pts = mlab.quiver3d(x, y, z, r, r, r, scalars=T, mode='sphere', scale_factor=r_factor, figure=fig, resolution=100) pts.glyph.color_mode = 'color_by_scalar' # center the glyphs on the data point pts.glyph.glyph_source.glyph_source.center = [0, 0, 0] # set a temperature colormap cmap = true_temp(T) pts.module_manager.scalar_lut_manager.lut.table = cmap # set the camera view mlab.view(focalpoint=(0.0, 0.0, 0.0), figure=fig) if view is not None: mlab.view(*view, figure=fig) ## add labels # unit vec to camera view = mlab.view() az, el = view[:2] hc = np.sin(el * np.pi / 180.0) xc = hc * np.cos(az * np.pi / 180.0) yc = hc * np.sin(az * np.pi / 180.0) zc = -np.cos(el * np.pi / 180.0) # unit vec orthoganal to camera if xc**2 + yc**2 == 0.0: xoff = 1.0 yoff = 0.0 zoff = 0.0 else: xoff = yc / np.sqrt(xc**2 + yc**2) yoff = np.sqrt(1.0 - xoff**2) zoff = 0.0 # xoff, yoff, zoff = xc, yc, zc # scale orthogonal vec by sphere size r_label = 1.0 * r_factor xoff, yoff, zoff = [r_label * v for v in [xoff, yoff, zoff]] # plot labels size = r_factor * txt_scale * 0.75 for xx, yy, zz, label in zip(x, y, z, labels): mlab.text3d(xx + xoff, yy + yoff, zz + zoff, label, figure=fig, color=(1,1,1), scale=size) ## add translucent dec=0 surface n = 101 t = np.linspace(0.0, 2*np.pi, n) r = np.max(dist * np.cos(dec)) x, y = r*np.cos(t), r*np.sin(t) z = np.zeros(n+1) x, y = [np.insert(a, 0, 0.0) for a in [x,y]] triangles = [(0, i, i + 1) for i in range(1, n)] mlab.triangular_mesh(x, y, z, triangles, color=(1,1,1), opacity=0.3, figure=fig) ## add ra=0 line line = mlab.plot3d([0, r], [0, 0], [0, 0], color=(1,1,1), line_width=1, figure=fig) rtxt = '{:.1f} pc'.format(r) orientation=np.array([180.0, 180.0, 0.0]) mlab.text3d(r, 0, 0, rtxt, figure=fig, scale=size*1.25, orient_to_camera=False, orientation=orientation) if view is not None: mlab.view(*view, figure=fig) return fig
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    0.223868
    92f10be0db1bfd9a43f5256577f3d2fdfd1c920b
    13,185
    py
    Python
    marvel_world/views.py
    xiaoranppp/si664-final
    f5545c04452fd674ddf1d078444e79ea58385e7e
    [ "MIT" ]
    null
    null
    null
    marvel_world/views.py
    xiaoranppp/si664-final
    f5545c04452fd674ddf1d078444e79ea58385e7e
    [ "MIT" ]
    1
    2018-11-25T21:07:37.000Z
    2018-11-25T21:07:37.000Z
    marvel_world/views.py
    xiaoranppp/si664-final
    f5545c04452fd674ddf1d078444e79ea58385e7e
    [ "MIT" ]
    1
    2018-12-21T12:06:03.000Z
    2018-12-21T12:06:03.000Z
    from django.shortcuts import render,redirect from django.http import HttpResponse,HttpResponseRedirect from django.views import generic from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from .models import Character,Comic,Power,CharacterPower,CharacterComic from django_filters.views import FilterView from .filters import Marvel_worldFilter,Marvel_comicFilter from .forms import CharacterForm,PowerForm,ComicForm from django.urls import reverse,reverse_lazy def index(request): return HttpResponse("Hello, world. You're at the marvel world super hero") class AboutPageView(generic.TemplateView): template_name = 'marvel_world/about.html' class HomePageView(generic.TemplateView): template_name = 'marvel_world/home.html' @method_decorator(login_required, name='dispatch') class CharacterListView(generic.ListView): model = Character context_object_name = 'characters' template_name = 'marvel_world/characters.html' paginate_by = 50 def get_queryset(self): return Character.objects.all().select_related('alignment','eye_color','skin_color','hair_color','race','gender','publisher').order_by('character_name') @method_decorator(login_required, name='dispatch') class CharacterDetailView(generic.DetailView): model = Character context_object_name= 'character' template_name = 'marvel_world/character_information.html' @method_decorator(login_required, name='dispatch') class ComicListView(generic.ListView): model = Comic context_object_name = 'comics' template_name = 'marvel_world/comics.html' paginate_by = 600 def get_queryset(self): return Comic.objects.all().order_by('comic_name') @method_decorator(login_required, name='dispatch') class ComicDetailView(generic.DetailView): model = Comic context_object_name= 'comic' template_name = 'marvel_world/comic_information.html' @method_decorator(login_required, name='dispatch') class PowerListView(generic.ListView): model = Power context_object_name = 'powers' template_name = 'marvel_world/super_power.html' paginate_by = 50 def get_queryset(self): return Power.objects.all().order_by('power_name') @method_decorator(login_required, name='dispatch') class PowerDetailView(generic.DetailView): model = Power context_object_name= 'power' template_name = 'marvel_world/super_power_information.html' @method_decorator(login_required, name='dispatch') class CharacterFilterView(FilterView): filterset_class = Marvel_worldFilter template_name = 'marvel_world/character_filter.html' @method_decorator(login_required, name='dispatch') class ComicFilterView(FilterView): filterset_class = Marvel_comicFilter template_name = 'marvel_world/comic_filter.html' @method_decorator(login_required, name='dispatch') class CharacterCreateView(generic.View): model = Character form_class = CharacterForm success_message = "Character created successfully" template_name = 'marvel_world/character_new.html' # fields = '__all__' <-- superseded by form_class # success_url = reverse_lazy('heritagesites/site_list') def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def post(self, request): form = CharacterForm(request.POST) if form.is_valid(): character = form.save(commit=False) character.save() for power in form.cleaned_data['super_power']: CharacterPower.objects.create(character=character, power=power) for comic in form.cleaned_data['comics']: CharacterComic.objects.create(character=character, comic=comic) return redirect(character) # shortcut to object's get_absolute_url() # return HttpResponseRedirect(site.get_absolute_url()) return render(request, 'marvel_world/character_new.html', {'form': form}) def get(self, request): form = CharacterForm() return render(request, 'marvel_world/character_new.html', {'form': form}) @method_decorator(login_required, name='dispatch') class PowerCreateView(generic.View): model = Power form_class = PowerForm success_message = "Super power created successfully" template_name = 'marvel_world/power_new.html' # fields = '__all__' <-- superseded by form_class # success_url = reverse_lazy('heritagesites/site_list') def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def post(self, request): form = PowerForm(request.POST) if form.is_valid(): power = form.save(commit=False) power.save() for character in form.cleaned_data['character']: CharacterPower.objects.create(character=character, power=power) return redirect(power) # shortcut to object's get_absolute_url() # return HttpResponseRedirect(site.get_absolute_url()) return render(request, 'marvel_world/power_new.html', {'form': form}) def get(self, request): form = PowerForm() return render(request, 'marvel_world/power_new.html', {'form': form}) @method_decorator(login_required, name='dispatch') class ComicCreateView(generic.View): model = Comic form_class = ComicForm success_message = "Comic created successfully" template_name = 'marvel_world/comic_new.html' # fields = '__all__' <-- superseded by form_class # success_url = reverse_lazy('heritagesites/site_list') def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def post(self, request): form = ComicForm(request.POST) if form.is_valid(): comic = form.save(commit=False) comic.save() for character in form.cleaned_data['character']: CharacterComic.objects.create(character=character, comic=comic) return redirect(comic) # shortcut to object's get_absolute_url() # return HttpResponseRedirect(site.get_absolute_url()) return render(request, 'marvel_world/comic_new.html', {'form': form}) def get(self, request): form = ComicForm() return render(request, 'marvel_world/comic_new.html', {'form': form}) #class CharacterDetailView(generic.DetailView):model = Characters context_object_name= 'character'template_name='marvel_world/character_information.html' @method_decorator(login_required, name='dispatch') class CharacterUpdateView(generic.UpdateView): model = Character form_class = CharacterForm # fields = '__all__' <-- superseded by form_class context_object_name = 'character' # pk_url_kwarg = 'site_pk' success_message = "Character updated successfully" template_name = 'marvel_world/character_update.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def form_valid(self, form): character = form.save(commit=False) # site.updated_by = self.request.user # site.date_updated = timezone.now() character.save() # Current country_area_id values linked to site old_ids = CharacterPower.objects\ .values_list('power_id', flat=True)\ .filter(character_id=character.character_id) # New countries list new_powers = form.cleaned_data['super_power'] # TODO can these loops be refactored? # New ids new_ids = [] # Insert new unmatched country entries for power in new_powers: new_id = power.power_id new_ids.append(new_id) if new_id in old_ids: continue else: CharacterPower.objects \ .create(character=character, power=power) # Delete old unmatched country entries for old_id in old_ids: if old_id in new_ids: continue else: CharacterPower.objects \ .filter(character_id=character.character_id, power_id=old_id) \ .delete() old_ids1 = CharacterComic.objects\ .values_list('comic_id', flat=True)\ .filter(character_id=character.character_id) # New countries list new_comics = form.cleaned_data['comics'] # TODO can these loops be refactored? # New ids new_ids1 = [] # Insert new unmatched country entries for comic in new_comics: new_id1 = comic.comic_id new_ids1.append(new_id1) if new_id1 in old_ids1: continue else: CharacterComic.objects \ .create(character=character, comic=comic) # Delete old unmatched country entries for old_id1 in old_ids1: if old_id1 in new_ids1: continue else: CharacterComic.objects \ .filter(character_id=character.character_id, comic_id=old_id1) \ .delete() return HttpResponseRedirect(character.get_absolute_url()) @method_decorator(login_required, name='dispatch') class PowerUpdateView(generic.UpdateView): model = Power form_class = PowerForm # fields = '__all__' <-- superseded by form_class context_object_name = 'power' # pk_url_kwarg = 'site_pk' success_message = "Super power updated successfully" template_name = 'marvel_world/power_update.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def form_valid(self, form): power = form.save(commit=False) # site.updated_by = self.request.user # site.date_updated = timezone.now() power.save() # Current country_area_id values linked to site old_ids = CharacterPower.objects\ .values_list('character_id', flat=True)\ .filter(power_id=power.power_id) # New countries list new_chs = form.cleaned_data['character'] # TODO can these loops be refactored? # New ids new_ids = [] # Insert new unmatched country entries for character in new_chs: new_id = character.character_id new_ids.append(new_id) if new_id in old_ids: continue else: CharacterPower.objects \ .create(character=character, power=power) # Delete old unmatched country entries for old_id in old_ids: if old_id in new_ids: continue else: CharacterPower.objects \ .filter(character_id=old_id, power_id=power.power_id) \ .delete() return HttpResponseRedirect(power.get_absolute_url()) # return redirect('heritagesites/site_detail', pk=site.pk) @method_decorator(login_required, name='dispatch') class ComicUpdateView(generic.UpdateView): model = Comic form_class = ComicForm # fields = '__all__' <-- superseded by form_class context_object_name = 'comic' # pk_url_kwarg = 'site_pk' success_message = "Comic updated successfully" template_name = 'marvel_world/comic_update.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def form_valid(self, form): comic = form.save(commit=False) # site.updated_by = self.request.user # site.date_updated = timezone.now() comic.save() # Current country_area_id values linked to site old_ids = CharacterComic.objects\ .values_list('character_id', flat=True)\ .filter(comic_id=comic.comic_id) # New countries list new_chs = form.cleaned_data['character'] # TODO can these loops be refactored? # New ids new_ids = [] # Insert new unmatched country entries for character in new_chs: new_id = character.character_id new_ids.append(new_id) if new_id in old_ids: continue else: CharacterComic.objects \ .create(character=character, comic=comic) # Delete old unmatched country entries for old_id in old_ids: if old_id in new_ids: continue else: CharacterComic.objects \ .filter(character_id=old_id, comic_id=comic.comic_id) \ .delete() return HttpResponseRedirect(comic.get_absolute_url()) @method_decorator(login_required, name='dispatch') class CharacterDeleteView(generic.DeleteView): model =Character success_message = "Character deleted successfully" success_url = reverse_lazy('characters') context_object_name = 'character' template_name = 'marvel_world/character_delete.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def delete(self, request, *args, **kwargs): self.object = self.get_object() # Delete HeritageSiteJurisdiction entries CharacterPower.objects \ .filter(character_id=self.object.character_id) \ .delete() CharacterComic.objects \ .filter(character_id=self.object.character_id) \ .delete() self.object.delete() return HttpResponseRedirect(self.get_success_url()) @method_decorator(login_required, name='dispatch') class PowerDeleteView(generic.DeleteView): model =Power success_message = "Super power deleted successfully" success_url = reverse_lazy('super_power') context_object_name = 'power' template_name = 'marvel_world/power_delete.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def delete(self, request, *args, **kwargs): self.object = self.get_object() # Delete HeritageSiteJurisdiction entries CharacterPower.objects \ .filter(power_id=self.object.power_id) \ .delete() self.object.delete() return HttpResponseRedirect(self.get_success_url()) @method_decorator(login_required, name='dispatch') class ComicDeleteView(generic.DeleteView): model =Comic success_message = "Comic deleted successfully" success_url = reverse_lazy('comics') context_object_name = 'comic' template_name = 'marvel_world/comic_delete.html' def dispatch(self, *args, **kwargs): return super().dispatch(*args, **kwargs) def delete(self, request, *args, **kwargs): self.object = self.get_object() # Delete HeritageSiteJurisdiction entries CharacterComic.objects \ .filter(comic_id=self.object.comic_id) \ .delete() self.object.delete() return HttpResponseRedirect(self.get_success_url())
    31.618705
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    0.754873
    11,523
    0.873948
    0
    0
    12,222
    0.926962
    0
    0
    3,805
    0.288586
    92f1aa0fa9769c9eeef09b7c084da22948285538
    267
    py
    Python
    src/rpi/fwd.py
    au-chrismor/selfdrive
    31325dd7a173bbb16a13e3de4c9598aab0a50632
    [ "BSD-3-Clause" ]
    null
    null
    null
    src/rpi/fwd.py
    au-chrismor/selfdrive
    31325dd7a173bbb16a13e3de4c9598aab0a50632
    [ "BSD-3-Clause" ]
    6
    2018-03-15T05:23:55.000Z
    2018-10-26T10:28:47.000Z
    src/rpi/fwd.py
    au-chrismor/selfdrive
    31325dd7a173bbb16a13e3de4c9598aab0a50632
    [ "BSD-3-Clause" ]
    null
    null
    null
    """Set-up and execute the main loop""" import RPi.GPIO as GPIO import time GPIO.setmode(GPIO.BCM) GPIO.setwarnings(False) #Right motor input A GPIO.setup(18,GPIO.OUT) #Right motor input B GPIO.setup(23,GPIO.OUT) GPIO.output(18,GPIO.HIGH) GPIO.output(23,GPIO.LOW)
    16.6875
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    0.749064
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    0
    0
    78
    0.292135
    92f3155f2bddf2f3a6336a7c75d33f3d299e7e91
    931
    py
    Python
    util/get_from_db.py
    Abel-Huang/simple-image-classifier
    89d2822c2b06cdec728f734d43d9638f4b601348
    [ "MIT" ]
    4
    2017-05-17T08:01:38.000Z
    2018-07-22T11:13:55.000Z
    util/get_from_db.py
    Abel-Huang/ImageClassifier
    89d2822c2b06cdec728f734d43d9638f4b601348
    [ "MIT" ]
    null
    null
    null
    util/get_from_db.py
    Abel-Huang/ImageClassifier
    89d2822c2b06cdec728f734d43d9638f4b601348
    [ "MIT" ]
    null
    null
    null
    import pymysql # 连接配置信息 config = { 'host': '127.0.0.1', 'port': 3306, 'user': 'root', 'password': '', 'db': 'classdata', 'charset': 'utf8', 'cursorclass': pymysql.cursors.DictCursor, } def get_summary_db(unitag): # 创建连接 conn = pymysql.connect(**config) cur = conn.cursor() # 执行sql语句 try: # 执行sql语句,进行查询 sql = 'SELECT * FROM summary where unitag= %s' cur.execute(sql,unitag) # 获取查询结果 result = cur.fetchall() return result finally: cur.close() conn.close() def get_result_db(unitag): # 创建连接 conn = pymysql.connect(**config) cur = conn.cursor() # 执行sql语句 try: # 执行sql语句,进行查询 sql = 'SELECT * FROM result where unitag= %s' cur.execute(sql,unitag) # 获取查询结果 result = cur.fetchall() return result finally: cur.close() conn.close()
    20.23913
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    0.541353
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    0
    0
    0
    0
    355
    0.342995
    92f361f8ed017247a610edc6823b43b8f1e0fefd
    290
    py
    Python
    registerapp/api.py
    RajapandiR/django-register
    cf20829fe3515bdd3112a88a890d83d852f09bde
    [ "MIT" ]
    null
    null
    null
    registerapp/api.py
    RajapandiR/django-register
    cf20829fe3515bdd3112a88a890d83d852f09bde
    [ "MIT" ]
    6
    2021-03-19T04:17:51.000Z
    2021-09-22T19:07:17.000Z
    registerapp/api.py
    RajapandiR/django-register
    cf20829fe3515bdd3112a88a890d83d852f09bde
    [ "MIT" ]
    null
    null
    null
    from rest_framework import viewsets from rest_framework.views import APIView from registerapp import serializers from registerapp import models class RegisterViewSet(viewsets.ModelViewSet): serializer_class = serializers.RegisterSerializer queryset = models.RegisterPage.objects.all()
    26.363636
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    0.855172
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    0.489655
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    0
    0
    0
    0
    0
    0
    0
    92f638d897dda2bf328a3077b43f492f38f39bb7
    1,412
    py
    Python
    jduck/robot.py
    luutp/jduck
    3c60a79c926bb9452777cddbebe28982273068a6
    [ "Apache-2.0" ]
    null
    null
    null
    jduck/robot.py
    luutp/jduck
    3c60a79c926bb9452777cddbebe28982273068a6
    [ "Apache-2.0" ]
    null
    null
    null
    jduck/robot.py
    luutp/jduck
    3c60a79c926bb9452777cddbebe28982273068a6
    [ "Apache-2.0" ]
    null
    null
    null
    #!/usr/bin/env python # -*- coding: utf-8 -*- """ jduck.py Description: Author: luutp Contact: [email protected] Created on: 2021/02/27 """ # Utilities # %% # ================================IMPORT PACKAGES==================================== # Utilities from traitlets.config.configurable import SingletonConfigurable # Custom Packages from jduck.DCMotor import DCMotor # ================================================================================ class JDuck(SingletonConfigurable): def __init__(self, *args, **kwargs): self.left_motor = DCMotor(32, 36, 38, alpha=1.0) self.right_motor = DCMotor(33, 35, 37, alpha=1.0) self.left_motor.set_speed(50) self.right_motor.set_speed(50) def set_speeds(self, left_speed, right_speed): self.left_motor.set_speed(left_speed) self.right_motor.set_speed(right_speed) def move_forward(self): self.left_motor.rotate_forward() self.right_motor.rotate_forward() def move_backward(self): self.left_motor.rotate_backward() self.right_motor.rotate_backward() def turn_left(self): self.left_motor.rotate_backward() self.right_motor.rotate_forward() def turn_right(self): self.left_motor.rotate_forward() self.right_motor.rotate_backward() def stop(self): self.left_motor.stop() self.right_motor.stop()
    25.214286
    85
    0.61119
    946
    0.669972
    0
    0
    0
    0
    0
    0
    351
    0.248584
    92f8d1944416ddff1cb95f31ed4c8d93f364c956
    5,193
    py
    Python
    src/nebulo/gql/alias.py
    olirice/nebulo
    de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7
    [ "MIT" ]
    76
    2020-04-03T01:21:47.000Z
    2021-12-06T02:54:53.000Z
    src/nebulo/gql/alias.py
    olirice/nebulo
    de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7
    [ "MIT" ]
    7
    2020-04-06T04:44:10.000Z
    2021-05-17T12:38:15.000Z
    src/nebulo/gql/alias.py
    olirice/nebulo
    de9b043fe66d0cb872c5c0f2aca3c5c6f20918a7
    [ "MIT" ]
    2
    2020-10-23T10:25:16.000Z
    2020-10-28T14:16:57.000Z
    # pylint: disable=missing-class-docstring,invalid-name import typing from graphql.language import ( InputObjectTypeDefinitionNode, InputObjectTypeExtensionNode, ObjectTypeDefinitionNode, ObjectTypeExtensionNode, ) from graphql.type import ( GraphQLArgument, GraphQLBoolean, GraphQLEnumType, GraphQLEnumValue, GraphQLField, GraphQLFieldMap, GraphQLFloat, GraphQLID, GraphQLInputFieldMap, GraphQLInputObjectType, GraphQLInt, GraphQLInterfaceType, GraphQLIsTypeOfFn, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLResolveInfo, GraphQLScalarType, GraphQLSchema, GraphQLString, GraphQLType, Thunk, ) from graphql.type.definition import GraphQLInputFieldOutType from nebulo.sql.composite import CompositeType as SQLACompositeType # Handle name changes from graphql-core and graphql-core-next try: from graphql.type import GraphQLInputObjectField as GraphQLInputField except ImportError: from graphql.type import GraphQLInputField Type = GraphQLType List = GraphQLList NonNull = GraphQLNonNull Argument = GraphQLArgument Boolean = GraphQLBoolean String = GraphQLString ScalarType = GraphQLScalarType ID = GraphQLID InterfaceType = GraphQLInterfaceType Int = GraphQLInt InputField = GraphQLInputField ResolveInfo = GraphQLResolveInfo EnumType = GraphQLEnumType EnumValue = GraphQLEnumValue Schema = GraphQLSchema Field = GraphQLField Float = GraphQLFloat EnumType = GraphQLEnumType class HasSQLAModel: # pylint: disable= too-few-public-methods sqla_table = None class HasSQLFunction: # pylint: disable= too-few-public-methods sql_function = None class HasSQLAComposite: # pylint: disable= too-few-public-methods sqla_composite: SQLACompositeType class ObjectType(GraphQLObjectType, HasSQLAModel): def __init__( self, name: str, fields: Thunk[GraphQLFieldMap], interfaces: typing.Optional[Thunk[typing.Collection["GraphQLInterfaceType"]]] = None, is_type_of: typing.Optional[GraphQLIsTypeOfFn] = None, extensions: typing.Optional[typing.Dict[str, typing.Any]] = None, description: typing.Optional[str] = None, ast_node: typing.Optional[ObjectTypeDefinitionNode] = None, extension_ast_nodes: typing.Optional[typing.Collection[ObjectTypeExtensionNode]] = None, sqla_model=None, ) -> None: super().__init__( name=name, fields=fields, interfaces=interfaces, is_type_of=is_type_of, extensions=extensions, description=description, ast_node=ast_node, extension_ast_nodes=extension_ast_nodes, ) self.sqla_model = sqla_model class ConnectionType(ObjectType): pass class EdgeType(ObjectType): pass class TableType(ObjectType): pass class CompositeType(ObjectType, HasSQLAComposite): pass class MutationPayloadType(ObjectType): pass class CreatePayloadType(MutationPayloadType): pass class UpdatePayloadType(MutationPayloadType): pass class DeletePayloadType(MutationPayloadType): pass class FunctionPayloadType(MutationPayloadType, HasSQLFunction): pass class InputObjectType(GraphQLInputObjectType, HasSQLAModel): def __init__( self, name: str, fields: Thunk[GraphQLInputFieldMap], description: typing.Optional[str] = None, out_type: typing.Optional[GraphQLInputFieldOutType] = None, extensions: typing.Optional[typing.Dict[str, typing.Any]] = None, ast_node: typing.Optional[InputObjectTypeDefinitionNode] = None, extension_ast_nodes: typing.Optional[typing.Collection[InputObjectTypeExtensionNode]] = None, sqla_model=None, ) -> None: super().__init__( name=name, fields=fields, description=description, out_type=out_type, extensions=extensions, ast_node=ast_node, extension_ast_nodes=extension_ast_nodes, ) self.sqla_model = sqla_model class CreateInputType(InputObjectType): pass class TableInputType(InputObjectType): pass class UpdateInputType(InputObjectType): pass class DeleteInputType(InputObjectType): pass class FunctionInputType(GraphQLInputObjectType): def __init__( self, name: str, fields: Thunk[GraphQLInputFieldMap], description: typing.Optional[str] = None, out_type: typing.Optional[GraphQLInputFieldOutType] = None, extensions: typing.Optional[typing.Dict[str, typing.Any]] = None, ast_node: typing.Optional[InputObjectTypeDefinitionNode] = None, extension_ast_nodes: typing.Optional[typing.Collection[InputObjectTypeExtensionNode]] = None, sql_function=None, ) -> None: super().__init__( name=name, fields=fields, description=description, out_type=out_type, extensions=extensions, ast_node=ast_node, extension_ast_nodes=extension_ast_nodes, ) self.sql_function = sql_function
    25.965
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    0.700558
    0
    0
    0
    0
    0
    0
    260
    0.050067
    92f9c4373d43c67eefcb0f04052b7d238d59ad11
    2,297
    py
    Python
    integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py
    rise-lang/iree
    46ad3fe392d38ce3df6eff7826cc1ab331a40b72
    [ "Apache-2.0" ]
    1
    2020-08-13T09:25:59.000Z
    2020-08-13T09:25:59.000Z
    integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py
    rise-lang/iree
    46ad3fe392d38ce3df6eff7826cc1ab331a40b72
    [ "Apache-2.0" ]
    null
    null
    null
    integrations/tensorflow/bindings/python/pyiree/tf/compiler/saved_model_test.py
    rise-lang/iree
    46ad3fe392d38ce3df6eff7826cc1ab331a40b72
    [ "Apache-2.0" ]
    null
    null
    null
    # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import importlib import os import sys import tempfile from pyiree.tf import compiler # Dynamically import tensorflow. try: # Use a dynamic import so as to avoid hermetic dependency analysis # (i.e. we only want the tensorflow from the environment). tf = importlib.import_module("tensorflow") # Just in case if linked against a pre-V2 defaulted version. if hasattr(tf, "enable_v2_behavior"): tf.enable_v2_behavior() tf = tf.compat.v2 except ImportError: print("Not running tests because tensorflow is not available") sys.exit(0) class StatelessModule(tf.Module): def __init__(self): pass @tf.function(input_signature=[ tf.TensorSpec([4], tf.float32), tf.TensorSpec([4], tf.float32) ]) def add(self, a, b): return tf.tanh(a + b) class RuntimeTest(tf.test.TestCase): def testLoadSavedModelToXlaPipeline(self): """Tests that a basic saved model to XLA workflow grossly functions. This is largely here to verify that everything is linked in that needs to be and that there are not no-ops, etc. """ with tempfile.TemporaryDirectory() as temp_dir: sm_dir = os.path.join(temp_dir, "simple.sm") print("Saving to:", sm_dir) my_module = StatelessModule() options = tf.saved_model.SaveOptions(save_debug_info=True) tf.saved_model.save(my_module, sm_dir, options=options) # Load it up. input_module = compiler.tf_load_saved_model(sm_dir) xla_asm = input_module.to_asm() print("XLA ASM:", xla_asm) self.assertRegex(xla_asm, "mhlo.tanh") if __name__ == "__main__": tf.test.main()
    29.831169
    80
    0.727035
    1,008
    0.438833
    0
    0
    159
    0.069221
    0
    0
    1,131
    0.492381
    92f9e01e28f69fd9e75ceb00e00af2fc86419292
    1,618
    py
    Python
    api/models/indicator/child_objects/properties.py
    taco-chainalysis/pypulsedive
    e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d
    [ "MIT" ]
    null
    null
    null
    api/models/indicator/child_objects/properties.py
    taco-chainalysis/pypulsedive
    e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d
    [ "MIT" ]
    null
    null
    null
    api/models/indicator/child_objects/properties.py
    taco-chainalysis/pypulsedive
    e89a2651e1ef41a1a51ddbeabc1f914a0d4e467d
    [ "MIT" ]
    1
    2021-11-10T16:50:32.000Z
    2021-11-10T16:50:32.000Z
    from .grandchild_objects import Cookies from .grandchild_objects import Dns from .grandchild_objects import Dom from .grandchild_objects import Geo #from .grandchild_objects import Http #from .grandchild_objects import Meta from .grandchild_objects import Ssl #from .grandchild_objects import WhoIs class Properties(object): FIELD_MAP = { "cookies": "cookies", "dns": "dns", "dom": "dom", "geo": "geo", "http": "http", "meta": "meta", "ssl": "ssl", "whois": "whois" } def __init__(self): self.cookies = "" self.dns = "" self.dom = "" self.geo = "" self.http = "" self.meta = "" self.ssl = "" self.whois = "" @staticmethod def from_dictionary(properties_dict: dict): properties = Properties() field_map = getattr(properties.__class__, "FIELD_MAP") for key_name in field_map: if key_name in properties_dict: setattr(properties, field_map[key_name], properties_dict[key_name]) properties.cookies = Cookies.from_dictionary(properties.cookies) properties.dns = Dns.from_dictionary(properties.dns) properties.dom = Dom.from_dictionary(properties.dom) properties.geo = Geo.from_dictionary(properties.geo) #properties.http = Http.from_dictionary(properties.http) #properties.meta = Meta.from_dictionary(properties.meta) properties.ssl = Ssl.from_dictionary(properties.ssl) #properties.whois = WhoIs.from_dictionary(properties.whois) return properties
    33.708333
    83
    0.642769
    1,318
    0.814586
    0
    0
    862
    0.532756
    0
    0
    406
    0.250927
    92fa506f1dc831d005f72a65f033e46f94fe54e8
    9,084
    py
    Python
    iRep/gc_skew.py
    scottdaniel/iRep
    5d31688eeeab057ce54f39698e3f9cc5738e05ad
    [ "MIT" ]
    55
    2016-06-17T17:31:48.000Z
    2022-01-19T08:24:43.000Z
    iRep/gc_skew.py
    scottdaniel/iRep
    5d31688eeeab057ce54f39698e3f9cc5738e05ad
    [ "MIT" ]
    35
    2016-06-24T17:19:04.000Z
    2021-11-06T16:08:43.000Z
    iRep/gc_skew.py
    scottdaniel/iRep
    5d31688eeeab057ce54f39698e3f9cc5738e05ad
    [ "MIT" ]
    14
    2016-07-21T17:34:16.000Z
    2020-03-18T03:45:55.000Z
    #!/usr/bin/env python3 """ script for calculating gc skew Chris Brown [email protected] """ # python modules import os import sys import argparse import numpy as np from scipy import signal from itertools import cycle, product # plotting modules from matplotlib import use as mplUse mplUse('Agg') import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages plt.rcParams['pdf.fonttype'] = 42 from matplotlib import rc rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) # ctb from ctbBio.fasta import iterate_fasta as parse_fasta def plot_two(title, subtitle, A, B, labels, legend, vert = False): """ plot with differnt y axes title = title for chart A = data for left axis [[x], [y]] B = data for right axis lables = [left label, right label, x label] legend = [[left legend], [right legend]] """ fig, ax1 = plt.subplots() colors = ['0.75', 'b', 'r', 'c', 'y', 'm', 'k', 'g'] a_colors = cycle(colors) b_colors = cycle(colors[::-1]) a_label = cycle(legend[0]) b_label = cycle(legend[1]) # plot left axis and x - axis for a in A: x, y = a ax1.set_ylabel(labels[0], labelpad = 3) ax1.set_xlabel(labels[-1]) ax1.plot(x, y, c = next(a_colors), marker = 'o', ms = 4, label = next(a_label)) # add vertical lines if vert is not False: for i in vert: x, c = i ax1.axvline(x = x, c = c, label = next(a_label), linewidth = 2) # plot right axis ax2 = ax1.twinx() for b in B: x, y = b ax2.set_ylabel(labels[1], labelpad = 8) ax2.plot(x, y, c = next(b_colors), linewidth = 2, label = next(b_label)) xmin = min([min(i[1]) for i in A] + [min(i[0]) for i in B]) xmax = max([max(i[0]) for i in A] + [max(i[0]) for i in B]) ax2.set_xlim(xmin, xmax) # title plt.suptitle(title, fontsize = 16) plt.title(subtitle, fontsize = 10) # legend ax1.legend(loc = 'upper left', \ bbox_to_anchor=(0.55, -0.125), \ prop = {'size':8}, \ framealpha = 0.0 ) plt.legend(loc = 'upper right', \ bbox_to_anchor=(0.45, -0.125), \ prop = {'size':8}, \ framealpha = 0.0\ ) # save pdf = PdfPages('%s.pdf' % title.replace(' ', '_')) pdf.savefig(bbox_inches = 'tight') plt.close() pdf.close() def check_peaks(peaks, length): """ select pair of min and max that are not too close or too far apart and have greatest y distance between one another """ # if ori/ter peaks are too close or too far apart, they are probably wrong closest, farthest = int(length * float(0.45)), int(length * float(0.55)) pairs = [] for pair in list(product(*peaks)): ### added this to make sure gets origin and ter right tr, pk = sorted(list(pair), key = lambda x: x[1], reverse = False) # trough and peak a = (tr[0] - pk[0]) % length b = (pk[0] - tr[0]) % length pt = abs(tr[1] - pk[1]) # distance between values if (a <= farthest and a >= closest) or (b <=farthest and b >= closest): pairs.append([pt, tr, pk]) if len(pairs) == 0: return [False, False] pt, tr, pk = sorted(pairs, reverse = True)[0] return [tr[0], pk[0]] def find_ori_ter(c_skew, length): """ find origin and terminus of replication based on cumulative GC Skew """ # find origin and terminus of replication based on # cumulative gc skew min and max peaks c_skew_min = signal.argrelextrema(np.asarray(c_skew[1]), np.less, order = 1)[0].tolist() c_skew_max = signal.argrelextrema(np.asarray(c_skew[1]), np.greater, order = 1)[0].tolist() # return False if no peaks were detected if len(c_skew_min) == 0 or len(c_skew_min) == 0: return [False, False] else: c_skew_min = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_min] c_skew_max = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_max] ori, ter = check_peaks([c_skew_min, c_skew_max], length) return ori, ter def gc_skew(name, length, seq, window, slide, plot_skew): """ calculate gc skew and cumulative sum of gc skew over sequence windows gc skew = ((G - C) / (G + C)) * window size * genome length """ # convert to G - C replacements = {'G':1, 'C':-1, 'A':0, 'T':0, 'N':0} gmc = [] # G - C for base in seq: try: gmc.append(replacements[base]) except: gmc.append(0) # convert to G + C gpc = [abs(i) for i in gmc] # G + C # calculate sliding windows for (G - C) and (G + C) weights = np.ones(window)/window gmc = [[i, c] for i, c in enumerate(signal.fftconvolve(gmc, weights, 'same').tolist())] gpc = [[i, c] for i, c in enumerate(signal.fftconvolve(gpc, weights, 'same').tolist())] # calculate gc skew and cummulative gc skew sum skew = [[], []] # x and y for gc skew c_skew = [[], []] # x and y for gc skew cummulative sums cs = 0 # cummulative sum # select windows to use based on slide for i, m in gmc[0::slide]: p = gpc[i][1] if p == 0: gcs = 0 else: gcs = m/p cs += gcs skew[0].append(i) c_skew[0].append(i) skew[1].append(gcs) c_skew[1].append(cs) ori, ter = find_ori_ter(c_skew, length) # plot data if plot_skew is True: title = '%s GC Skew' % (name) subtitle = '(window = %s, slide = %s)' % (window, slide) labels = ['GC Skew', 'Cumulative GC Skew', 'Position on Genome (bp)'] # remove some points for plotting (approx. 1,000 datapoints) N = int(len(skew[0])/1000) if N != 0: skew = [skew[0][0::N], skew[1][0::N]] if ori is False: plot_two(title, subtitle, [skew], [c_skew], labels, \ [[labels[0]], [labels[1]]]) else: plot_two(title, subtitle, [skew], [c_skew], labels, \ [[labels[0], 'Ori:%s' % ('{:,}'.format(ori)), \ 'Ter:%s' % ('{:,}'.format(ter))], [labels[1]]], \ vert = [(ori, 'r'), (ter, 'b')]) return ori, ter, skew, c_skew def parse_genomes(fastas, single): """ generator for parsing fastas if single is True, combine sequences in multifasta file """ if single is True: for genome in fastas: sequence = [] for seq in parse_fasta(genome): sequence.extend(list(seq[1].upper())) yield (genome.name.rsplit('.', 1)[0], len(sequence), sequence) else: for genome in fastas: for seq in parse_fasta(genome): ID = seq[0].split('>', 1)[1].split()[0] yield (ID, len(seq[1]), list(seq[1].upper())) def open_files(files): """ open files in list, use stdin if first item in list is '-' """ if files is None: return files if files[0] == '-': return (sys.stdin) return (open(i) for i in files) if __name__ == '__main__': parser = argparse.ArgumentParser(description = \ '# calculate gc skew and find Ori and Ter of replication') parser.add_argument(\ '-f', nargs = '*', action = 'store', required = True, \ help = 'fasta(s)') parser.add_argument(\ '-l', default = False, type = int, \ help = 'minimum contig length (default = 10 x window)') parser.add_argument(\ '-w', default = 1000, type = int, \ help = 'window length (default = 1000)') parser.add_argument(\ '-s', default = 10, type = int, \ help = 'slide length (default = 10)') parser.add_argument(\ '--single', action = 'store_true', \ help = 'combine multi-fasta sequences into single genome') parser.add_argument(\ '--no-plot', action = 'store_false', \ help = 'do not generate plots, print GC Skew to stdout') args = vars(parser.parse_args()) fastas = open_files(args['f']) single, plot_skew = args['single'], args['no_plot'] window, slide = args['w'], args['s'] min_len = args['l'] if min_len is False: min_len = 10 * window for name, length, seq in parse_genomes(fastas, single): if length < min_len: print('%s: Too Short' % (name), file=sys.stderr) continue ori, ter, skew, c_skew = gc_skew(name, length, seq, window, slide, plot_skew) if ori == False: ori, ter = 'n/a', 'n/a' else: ori, ter = '{:,}'.format(ori), '{:,}'.format(ter) print('%s -> Origin: %s Terminus: %s' \ % (name, ori, ter), file=sys.stderr) if plot_skew is False: print('\t'.join(['# Name', 'Position', 'GC Skew', 'Cumulative GC Skew'])) for i, pos in enumerate(skew[0]): out = [name, pos, skew[1][i], c_skew[1][i]] print('\t'.join([str(i) for i in out]))
    36.191235
    95
    0.55295
    0
    0
    598
    0.06583
    0
    0
    0
    0
    2,457
    0.270476
    92fa730397bfd4949cfd5d8aa12c70a6b5cb5576
    2,429
    py
    Python
    examples/send_governance_vote_transaction.py
    Algofiorg/algofi-py-sdk
    6100a6726d36db4d4d3287064f0ad1d0b9a05e03
    [ "MIT" ]
    38
    2021-12-30T02:32:57.000Z
    2022-03-23T22:09:16.000Z
    examples/send_governance_vote_transaction.py
    Algofiorg/algofi-py-sdk
    6100a6726d36db4d4d3287064f0ad1d0b9a05e03
    [ "MIT" ]
    4
    2021-11-03T00:14:46.000Z
    2022-03-28T02:17:33.000Z
    examples/send_governance_vote_transaction.py
    Algofiorg/algofi-py-sdk
    6100a6726d36db4d4d3287064f0ad1d0b9a05e03
    [ "MIT" ]
    8
    2021-12-15T05:29:55.000Z
    2022-02-08T03:45:11.000Z
    # This sample is provided for demonstration purposes only. # It is not intended for production use. # This example does not constitute trading advice. import os from dotenv import dotenv_values from algosdk import mnemonic, account from algofi.v1.asset import Asset from algofi.v1.client import AlgofiTestnetClient, AlgofiMainnetClient from algofi.utils import get_ordered_symbols, prepare_payment_transaction, get_new_account from example_utils import print_market_state, print_user_state ### run setup.py before proceeding. make sure the .env file is set with mnemonic + storage_mnemonic. # Hardcoding account keys is not a great practice. This is for demonstration purposes only. # See the README & Docs for alternative signing methods. my_path = os.path.abspath(os.path.dirname(__file__)) ENV_PATH = os.path.join(my_path, ".env") # load user passphrase user = dotenv_values(ENV_PATH) sender = mnemonic.to_public_key(user['mnemonic']) key = mnemonic.to_private_key(user['mnemonic']) # IS_MAINNET IS_MAINNET = False client = AlgofiMainnetClient(user_address=sender) if IS_MAINNET else AlgofiTestnetClient(user_address=sender) # NOTE: Get the live governance address at https://governance.algorand.foundation/api/periods/ # under "sign_up_address" for the relevant governance period # Specify your vote according to the formats that are permissible in the Algorand Foundation Spec # https://github.com/algorandfoundation/governance/blob/main/af-gov1-spec.md # Get the idx, vote choices based on the relevant voting session from https://governance.algorand.foundation/api/periods/ address = sender governance_address = "" vote_note = b'af/gov1:j[6,"a","c"]' # NOTE: an example, not to be used in live voting necessarily vault_address = client.manager.get_storage_address(address) print("~"*100) print("Processing send_governance_vote_transaction transaction for vault address " + vault_address) print("~"*100) txn = client.prepare_send_governance_vote_transactions(governance_address, note=vote_note, address=address) txn.sign_with_private_key(sender, key) txn.submit(client.algod, wait=True) # After sending, check your vote at # https://governance.algorand.foundation/api/periods/<governance-period-slug>/governors/<vault_address> # to confirm successful vote in voting session # print final state print("~"*100) print("Final State") print("Sent governance transaction with note: " + str(vote_note)) print("~"*100)
    42.614035
    121
    0.799918
    0
    0
    0
    0
    0
    0
    0
    0
    1,334
    0.549197
    92fa7f11780de4e7d336cb67c51c29ac5c8fbc36
    9,059
    py
    Python
    bid/inventoryClient.py
    franklx/SOAPpy-py3
    f25afba322e9300ba4ebdd281118b629ca63ba24
    [ "BSD-3-Clause" ]
    7
    2018-01-03T18:24:43.000Z
    2022-03-07T04:34:01.000Z
    bid/inventoryClient.py
    franklx/SOAPpy-py3
    f25afba322e9300ba4ebdd281118b629ca63ba24
    [ "BSD-3-Clause" ]
    null
    null
    null
    bid/inventoryClient.py
    franklx/SOAPpy-py3
    f25afba322e9300ba4ebdd281118b629ca63ba24
    [ "BSD-3-Clause" ]
    18
    2018-08-06T11:30:16.000Z
    2022-03-09T11:24:24.000Z
    #!/usr/bin/env python import getopt import sys import string import re import time sys.path.insert(1,"..") from SOAPpy import SOAP import traceback DEFAULT_SERVERS_FILE = './inventory.servers' DEFAULT_METHODS = ('SimpleBuy', 'RequestForQuote','Buy','Ping') def usage (error = None): sys.stdout = sys.stderr if error != None: print(error) print("""usage: %s [options] [server ...] If a long option shows an argument is mandatory, it's mandatory for the equivalent short option also. -?, --help display this usage -d, --debug turn on debugging in the SOAP library -i, --invert test servers *not* in the list of servers given -m, --method=METHOD#[,METHOD#...] call only the given methods, specify a METHOD# of ? for the list of method numbers -o, --output=TYPE turn on output, TYPE is one or more of s(uccess), f(ailure), n(ot implemented), F(ailed (as expected)), a(ll) [f] -s, --servers=FILE use FILE as list of servers to test [%s] -t, --stacktrace print a stack trace on each unexpected failure -T, --always-stacktrace print a stack trace on any failure """ % (sys.argv[0], DEFAULT_SERVERS_FILE), end=' ') sys.exit (0) def methodUsage (): sys.stdout = sys.stderr print("Methods are specified by number. Multiple methods can be " \ "specified using a\ncomma-separated list of numbers or ranges. " \ "For example 1,4-6,8 specifies\nmethods 1, 4, 5, 6, and 8.\n") print("The available methods are:\n") half = (len (DEFAULT_METHODS) + 1) / 2 for i in range (half): print("%4d. %-25s" % (i + 1, DEFAULT_METHODS[i]), end=' ') if i + half < len (DEFAULT_METHODS): print("%4d. %-25s" % (i + 1 + half, DEFAULT_METHODS[i + half]), end=' ') print() sys.exit (0) def readServers (file): servers = [] f = open (file, 'r') while 1: line = f.readline () if line == '': break if line[0] in ('#', '\n') or line[0] in string.whitespace: continue cur = {'nonfunctional': {}} tag = None servers.append (cur) while 1: if line[0] in string.whitespace: if tag == 'nonfunctional': value = method + ' ' + cur[tag][method] else: value = cur[tag] value += ' ' + line.strip () else: tag, value = line.split (':', 1) tag = tag.strip ().lower () value = value.strip () if value[0] == '"' and value[-1] == '"': value = value[1:-1] if tag == 'nonfunctional': value = value.split (' ', 1) + [''] method = value[0] cur[tag][method] = value[1] else: cur[tag] = value line = f.readline () if line == '' or line[0] == '\n': break return servers def str2list (s): l = {} for i in s.split (','): if i.find ('-') != -1: i = i.split ('-') for i in range (int (i[0]),int (i[1]) + 1): l[i] = 1 else: l[int (i)] = 1 l = list(l.keys ()) l.sort () return l def SimpleBuy(serv, sa, epname): serv = serv._sa (sa % {'methodname':'SimpleBuy'}) return serv.SimpleBuy(ProductName="widget", Quantity = 50, Address = "this is my address") #JHawk, Phalanx require this order of params def RequestForQuote(serv, sa, epname): serv = serv._sa (sa % {'methodname':'RequestForQuote'}) return serv.RequestForQuote(Quantity=3, ProductName = "thing") # for Phalanx, JHawk def Buy(serv, sa, epname): import copy serv = serv._sa (sa % {'methodname':'Buy'}) billTo_d = {"name":"Buyer One", "address":"1 1st Street", "city":"New York", "state":"NY", "zipCode":"10000"} shipTo_d = {"name":"Buyer One ", "address":"1 1st Street ", "city":"New York ", "state":"NY ", "zipCode":"10000 "} for k,v in list(shipTo_d.items()): shipTo_d[k] = v[:-1] itemd1 = SOAP.structType( {"name":"widg1","quantity":200,"price":SOAP.decimalType(45.99), "_typename":"LineItem"}) itemd2 = SOAP.structType( {"name":"widg2","quantity":400,"price":SOAP.decimalType(33.45), "_typename":"LineItem"}) items_d = SOAP.arrayType( [itemd1, itemd2] ) items_d._ns = "http://www.soapinterop.org/Bid" po_d = SOAP.structType( data = {"poID":"myord","createDate":SOAP.dateTimeType(),"shipTo":shipTo_d, "billTo":billTo_d, "items":items_d}) try: # it's called PO by MST (MS SOAP Toolkit), JHawk (.NET Remoting), # Idoox WASP, Paul (SOAP::Lite), PranishK (ATL), GLUE, Aumsoft, # HP, EasySoap, and Jake (Frontier). [Actzero accepts either] return serv.Buy(PO=po_d) except: # called PurchaseOrder by KeithBa return serv.Buy(PurchaseOrder=po_d) def Ping(serv, sa, epname): serv = serv._sa (sa % {'methodname':'Ping'}) return serv.Ping() def main(): servers = DEFAULT_SERVERS_FILE methodnums = None output = 'f' invert = 0 succeed = 0 printtrace = 0 stats = 1 total = 0 fail = 0 failok = 0 notimp = 0 try: opts,args = getopt.getopt (sys.argv[1:], '?dm:io:s:t', ['help', 'method', 'debug', 'invert', 'output', 'servers=']) for opt, arg in opts: if opt in ('-?', '--help'): usage () elif opt in ('-d', '--debug'): SOAP.Config.debug = 1 elif opt in ('-i', '--invert'): invert = 1 elif opt in ('-m', '--method'): if arg == '?': methodUsage () methodnums = str2list (arg) elif opt in ('-o', '--output'): output = arg elif opt in ('-s', '--servers'): servers = arg else: raise AttributeError("Recognized but unimplemented option `%s'" % opt) except SystemExit: raise except: usage (sys.exc_info ()[1]) if 'a' in output: output = 'fFns' servers = readServers(servers) if methodnums == None: methodnums = list(range(1, len (DEFAULT_METHODS) + 1)) limitre = re.compile ('|'.join (args), re.IGNORECASE) for s in servers: if (not not limitre.match (s['name'])) == invert: continue serv = SOAP.SOAPProxy(s['endpoint'], namespace = s['namespace']) for num in (methodnums): if num > len(DEFAULT_METHODS): break total += 1 name = DEFAULT_METHODS[num - 1] title = '%s: %s (#%d)' % (s['name'], name, num) try: fn = globals ()[name] except KeyboardInterrupt: raise except: if 'n' in output: print(title, "test not yet implemented") notimp += 1 continue try: res = fn (serv, s['soapaction'], s['name']) if name in s['nonfunctional']: print(title, "succeeded despite marked nonfunctional") elif 's' in output: print(title, "succeeded ") succeed += 1 except KeyboardInterrupt: print("fail") raise except: if name in s['nonfunctional']: if 'F' in output: t = 'as expected' if s['nonfunctional'][name] != '': t += ', ' + s['nonfunctional'][name] print(title, "failed (%s) -" %t, sys.exc_info()[1]) failok += 1 else: if 'f' in output: print(title, "failed -", str (sys.exc_info()[1])) fail += 1 if stats: print(" Tests ended at:", time.ctime (time.time())) if stats > 0: print(" Total tests: %d" % total) print(" Successes: %d (%3.2f%%)" % \ (succeed, 100.0 * succeed / total)) if stats > 0 or fail > 0: print("Failed unexpectedly: %d (%3.2f%%)" % \ (fail, 100.0 * fail / total)) if stats > 0: print(" Failed as expected: %d (%3.2f%%)" % \ (failok, 100.0 * failok / total)) if stats > 0 or notimp > 0: print(" Not implemented: %d (%3.2f%%)" % \ (notimp, 100.0 * notimp / total)) return fail + notimp if __name__ == "__main__": main()
    31.130584
    139
    0.486919
    0
    0
    0
    0
    0
    0
    0
    0
    2,769
    0.305663
    92fb1af4be141cb39cbab935a9b9551b1ec5b453
    934
    py
    Python
    src/compile.py
    Pixxeasy/WinTools
    e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d
    [ "MIT" ]
    null
    null
    null
    src/compile.py
    Pixxeasy/WinTools
    e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d
    [ "MIT" ]
    null
    null
    null
    src/compile.py
    Pixxeasy/WinTools
    e67c365cd4a7a47a410c25b7df8eeaeedc05dd8d
    [ "MIT" ]
    null
    null
    null
    import os import json import shutil with open("entry.tp") as entry: entry = json.loads(entry.read()) startcmd = entry['plugin_start_cmd'].split("%TP_PLUGIN_FOLDER%")[1].split("\\") filedirectory = startcmd[0] fileName = startcmd[1] if os.path.exists(filedirectory): os.remove(os.path.join(os.getcwd(), "WinTools")) else: os.makedirs("temp/"+filedirectory) for file in os.listdir("."): if file not in ["compile.py", "utils", "requirements.txt", "build", "dist", "main.py", "main.spec", "__pycache__", "temp"]: print("copying", file) shutil.copy(os.path.join(os.getcwd(), file), os.path.join("temp", filedirectory)) os.rename("dist\Main.exe", "dist\WinTools.exe") shutil.copy(os.path.join(os.getcwd(), r"dist\WinTools.exe"), "temp/"+filedirectory) shutil.make_archive(base_name="WinTools", format='zip', root_dir="temp", base_dir="WinTools") os.rename("WinTools.zip", "WinTools.tpp")
    33.357143
    127
    0.674518
    0
    0
    0
    0
    0
    0
    0
    0
    296
    0.316916
    92fca9c0141bc42e92af9526839fedc292014b9b
    292
    py
    Python
    suda/1121/12.py
    tusikalanse/acm-icpc
    20150f42752b85e286d812e716bb32ae1fa3db70
    [ "MIT" ]
    2
    2021-06-09T12:27:07.000Z
    2021-06-11T12:02:03.000Z
    suda/1121/12.py
    tusikalanse/acm-icpc
    20150f42752b85e286d812e716bb32ae1fa3db70
    [ "MIT" ]
    1
    2021-09-08T12:00:05.000Z
    2021-09-08T14:52:30.000Z
    suda/1121/12.py
    tusikalanse/acm-icpc
    20150f42752b85e286d812e716bb32ae1fa3db70
    [ "MIT" ]
    null
    null
    null
    for _ in range(int(input())): x, y = list(map(int, input().split())) flag = 1 for i in range(x, y + 1): n = i * i + i + 41 for j in range(2, n): if j * j > n: break if n % j == 0: flag = 0 break if flag == 0: break if flag: print("OK") else: print("Sorry")
    17.176471
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    0.037671
    92feee19e193679cb75cdd6152ceb20caad92e8b
    389
    gyp
    Python
    notification/app/node_modules/hiredis/binding.gyp
    c2gconsulting/bulkpay
    224a52427f80a71f66613c367a5596cbd5e97294
    [ "MIT" ]
    208
    2015-01-07T03:50:56.000Z
    2022-03-21T03:34:12.000Z
    binding.gyp
    badboy/hiredis-node-win
    d113945a182ba1d616f8fba06e2d80dc9f09552b
    [ "BSD-3-Clause" ]
    72
    2015-01-11T09:54:16.000Z
    2019-11-21T14:07:43.000Z
    binding.gyp
    badboy/hiredis-node-win
    d113945a182ba1d616f8fba06e2d80dc9f09552b
    [ "BSD-3-Clause" ]
    55
    2015-01-10T20:54:13.000Z
    2022-02-02T14:08:01.000Z
    { 'targets': [ { 'target_name': 'hiredis', 'sources': [ 'src/hiredis.cc' , 'src/reader.cc' ], 'include_dirs': ["<!(node -e \"require('nan')\")"], 'dependencies': [ 'deps/hiredis.gyp:hiredis-c' ], 'defines': [ '_GNU_SOURCE' ], 'cflags': [ '-Wall', '-O3' ] } ] }
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    0.51671
    92ff4f4bfa893dc686e0e12fb0d4936e8c8b259d
    272
    py
    Python
    basic_and.py
    Verkhovskaya/PyDL
    4c3f2d952dd988ff27bf359d2f2cdde65737e062
    [ "MIT" ]
    5
    2018-07-28T18:18:59.000Z
    2022-01-05T19:01:50.000Z
    basic_and.py
    Verkhovskaya/PyDL
    4c3f2d952dd988ff27bf359d2f2cdde65737e062
    [ "MIT" ]
    null
    null
    null
    basic_and.py
    Verkhovskaya/PyDL
    4c3f2d952dd988ff27bf359d2f2cdde65737e062
    [ "MIT" ]
    null
    null
    null
    from pywire import * def invert(signal): if signal: return False else: return True class Inverter: def __init__(self, a, b): b.drive(invert, a) width = 4 a = Signal(width, io="in") b = Signal(width, io="out") Inverter(a, b) build()
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    0.033088
    13003b62c91dfe370f2b6ae3d293c73a5a463179
    4,521
    py
    Python
    network/evaluate_keypoints.py
    mhsung/deep-functional-dictionaries
    8b3d70c3376339cb1b7baacf7753094cd1ffef45
    [ "MIT" ]
    41
    2018-07-10T10:15:02.000Z
    2021-04-20T03:10:16.000Z
    network/evaluate_keypoints.py
    Yajha/deep-functional-dictionaries
    deecf8c6c85e253cfa52be7c6b3c308d5e5aaf81
    [ "MIT" ]
    2
    2018-07-05T06:34:13.000Z
    2019-09-18T08:57:56.000Z
    network/evaluate_keypoints.py
    Yajha/deep-functional-dictionaries
    deecf8c6c85e253cfa52be7c6b3c308d5e5aaf81
    [ "MIT" ]
    7
    2018-07-28T00:00:08.000Z
    2021-06-30T13:39:44.000Z
    # Minhyuk Sung ([email protected]) # April 2018 import os, sys BASE_DIR = os.path.normpath( os.path.join(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(os.path.join(BASE_DIR, '..')) from datasets import * from generate_outputs import * from scipy.optimize import linear_sum_assignment #import matplotlib.pyplot as plt import numpy as np def compute_all_keypoints(sess, net, data): P = data.point_clouds assert(P.shape[0] == data.n_data) assert(P.shape[1] == data.n_points) KP = data.keypoints assert(KP.shape[0] == data.n_data) assert(KP.shape[1] == data.n_labels) A = predict_A(P, sess, net) assert(A.shape[0] == data.n_data) assert(A.shape[1] == data.n_points) assert(A.shape[2] == net.K) pred_KP = np.argmax(A, axis=1) return P, KP, pred_KP def evaluate_PCK(P, KP, pred_KP): n_data = P.shape[0] n_points = P.shape[1] n_labels = KP.shape[1] K = pred_KP.shape[1] # dists_info: (point_cloud_index, label, basis_index, distance) dists_info = [] for k in range(n_data): # NOTE: # Skip if the keypoint does not exist. labels = [i for i in range(n_labels) if KP[k,i] >= 0] # Find the closest prediction (w/o matching). for i, label in enumerate(labels): all_dists = np.zeros(K) idx_i = KP[k,label] assert(idx_i < n_points) p_i = P[k,idx_i] for j in range(K): idx_j = pred_KP[k,j] assert(idx_j < n_points) p_j = P[k,idx_j] all_dists[j] = np.linalg.norm(p_i - p_j) j = np.argmin(all_dists) dists_info.append((k, i, j, all_dists[j])) dists_info = np.array(dists_info) return dists_info def evaluate_PCK_after_label_basis_matching(P, KP, pred_KP): n_data = P.shape[0] n_points = P.shape[1] n_labels = KP.shape[1] K = pred_KP.shape[1] # Find the best mapping from labels to bases. all_dists = np.zeros((n_data, n_labels, K)) label_counts = np.zeros(n_labels) for k in range(n_data): for i in range(n_labels): # NOTE: # Skip if the keypoint does not exist. if KP[k,i] < 0: continue idx_i = KP[k,i] assert(idx_i < n_points) p_i = P[k,idx_i] label_counts[i] += 1. for j in range(K): idx_j = pred_KP[k,j] assert(idx_j < n_points) p_j = P[k,idx_j] all_dists[k,i,j] += np.linalg.norm(p_i - p_j) mean_dists = np.sum(all_dists, axis=0) / \ np.expand_dims(label_counts, axis=-1) row_ind, col_ind = linear_sum_assignment(mean_dists) # dists_info: (point_cloud_index, label, basis_index, distance) dists_info = [] for k in range(n_data): for (i, j) in zip(row_ind, col_ind): if KP[k,i] < 0: continue dists_info.append((k, i, j, all_dists[k,i,j])) dists_info = np.array(dists_info) return dists_info def save_results(dists_info, out_dir, postfix=None): # dists_info: (point_cloud_index, label, basis_index, distance) dists = dists_info[:,3] if postfix is not None: out_file = os.path.join(out_dir, 'distances_{}.npy'.format(postfix)) else: out_file = os.path.join(out_dir, 'distances.npy') np.save(out_file, dists) print("Saved '{}'.".format(out_file)) ''' # Draw plot. n_matches = dists.size x_list = np.linspace(0.0, 0.1, 20 + 1) counts = np.zeros(x_list.size, dtype=int) for i in range(x_list.size): counts[i] = np.sum(dists <= x_list[i]) y_list = counts.astype(x_list.dtype) / float(n_matches) plt.clf() plt.plot(x_list, y_list) plt.ylim(0., 1.) plt.yticks(np.linspace(0., 1., 10 + 1)) if postfix is not None: out_file = os.path.join(out_dir, 'pck_{}.png'.format(postfix)) else: out_file = os.path.join(out_dir, 'pck.png') plt.savefig(out_file) print("Saved '{}'.".format(out_file)) ''' def evaluate(sess, net, data, out_dir): if not os.path.exists(out_dir): os.makedirs(out_dir) P, KP, pred_KP = compute_all_keypoints(sess, net, data) dists = evaluate_PCK(P, KP, pred_KP) save_results(dists, out_dir) dists_after_matching = evaluate_PCK_after_label_basis_matching( P, KP, pred_KP) save_results(dists_after_matching, out_dir, postfix='after_matching')
    26.438596
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    1,145
    0.253263
    13008c4023106e4274d2b92d9aa79a58e4551138
    2,388
    py
    Python
    recipes/cxxopts/all/conanfile.py
    dvirtz/conan-center-index
    2e7a6337804325616f8d97e3a5b6f66cc72699cb
    [ "MIT" ]
    562
    2019-09-04T12:23:43.000Z
    2022-03-29T16:41:43.000Z
    recipes/cxxopts/all/conanfile.py
    dvirtz/conan-center-index
    2e7a6337804325616f8d97e3a5b6f66cc72699cb
    [ "MIT" ]
    9,799
    2019-09-04T12:02:11.000Z
    2022-03-31T23:55:45.000Z
    recipes/cxxopts/all/conanfile.py
    dvirtz/conan-center-index
    2e7a6337804325616f8d97e3a5b6f66cc72699cb
    [ "MIT" ]
    1,126
    2019-09-04T11:57:46.000Z
    2022-03-31T16:43:38.000Z
    import os from conans import ConanFile, tools from conans.errors import ConanInvalidConfiguration class CxxOptsConan(ConanFile): name = "cxxopts" homepage = "https://github.com/jarro2783/cxxopts" url = "https://github.com/conan-io/conan-center-index" description = "Lightweight C++ option parser library, supporting the standard GNU style syntax for options." license = "MIT" topics = ("conan", "option-parser", "positional-arguments ", "header-only") settings = "compiler" options = { "unicode": [True, False] } default_options = { "unicode": False } no_copy_source = True @property def _source_subfolder(self): return "source_subfolder" @property def _minimum_cpp_standard(self): return 11 @property def _minimum_compilers_version(self): return { "Visual Studio": "14", "gcc": "5", "clang": "3.9", "apple-clang": "8", } def configure(self): if self.settings.compiler.get_safe("cppstd"): tools.check_min_cppstd(self, self._minimum_cpp_standard) min_version = self._minimum_compilers_version.get(str(self.settings.compiler)) if not min_version: self.output.warn("{} recipe lacks information about the {} compiler support.".format( self.name, self.settings.compiler)) else: if tools.Version(self.settings.compiler.version) < min_version: raise ConanInvalidConfiguration("{} requires C++{} support. The current compiler {} {} does not support it.".format( self.name, self._minimum_cpp_standard, self.settings.compiler, self.settings.compiler.version)) def requirements(self): if self.options.unicode: self.requires("icu/64.2") def source(self): tools.get(**self.conan_data["sources"][self.version]) os.rename("{}-{}".format(self.name, self.version), self._source_subfolder) def package(self): self.copy("LICENSE", dst="licenses", src=self._source_subfolder) self.copy("{}.hpp".format(self.name), dst="include", src=os.path.join(self._source_subfolder, "include")) def package_id(self): self.info.header_only() def package_info(self): if self.options.unicode: self.cpp_info.defines = ["CXXOPTS_USE_UNICODE"]
    36.738462
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    589
    0.24665
    1300c8abfbfcff2fad07bdd38a7b66244215a15d
    1,868
    py
    Python
    p_030_039/problem31.py
    ericgreveson/projecteuler
    1844bf383fca871b82d88ef1eb3a9b1a0e363054
    [ "Apache-2.0" ]
    null
    null
    null
    p_030_039/problem31.py
    ericgreveson/projecteuler
    1844bf383fca871b82d88ef1eb3a9b1a0e363054
    [ "Apache-2.0" ]
    null
    null
    null
    p_030_039/problem31.py
    ericgreveson/projecteuler
    1844bf383fca871b82d88ef1eb3a9b1a0e363054
    [ "Apache-2.0" ]
    null
    null
    null
    class CoinArray(list): """ Coin list that is hashable for storage in sets The 8 entries are [1p count, 2p count, 5p count, ... , 200p count] """ def __hash__(self): """ Hash this as a string """ return hash(" ".join([str(i) for i in self])) def main(): """ Entry point """ # Important: sorted smallest to largest coins = [1, 2, 5, 10, 20, 50, 100, 200] coin_index = {coin: index for index, coin in enumerate(coins)} # How many ways are there of making each number from 1 to 200 from these values? # Building up from 1 means we can re-use earlier results # e.g.: # 1p: [{1}] # 2p: [{1,1}, {2}] # 3p: [{1,1,1}, {2,1}] # 4p: [{1,1,1,1}, {2,1,1}, {2,2}] # etc way_sets = [None] for i in range(1, 201): way_set_i = set() # Try using 1 of each coin and then all the ways of the remainder, if > 0 for coin in coins: remainder = i - coin if remainder == 0: # We can make this with exactly this coin alone - but no larger coins coin_count = [0 for i in coins] coin_count[coin_index[coin]] = 1 way_set_i.add(CoinArray(coin_count)) break elif remainder > 0: # We can use this coin and whatever the options for the smaller value are for rem_list in way_sets[remainder]: new_coin_count = [c for c in rem_list] new_coin_count[coin_index[coin]] += 1 way_set_i.add(CoinArray(new_coin_count)) else: # Can't use any bigger coins break way_sets.append(way_set_i) print(f"Number of ways of making £2: {len(way_sets[200])}") return if __name__ == "__main__": main()
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    0.420011
    1300e7747076d34572209fef1029da836f1dbf7b
    2,358
    py
    Python
    video/cloud-client/quickstart/quickstart.py
    nasirdec/GCP-AppEngine-Example
    3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f
    [ "Apache-2.0" ]
    1
    2019-11-17T08:59:14.000Z
    2019-11-17T08:59:14.000Z
    video/cloud-client/quickstart/quickstart.py
    nasirdec/GCP-AppEngine-Example
    3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f
    [ "Apache-2.0" ]
    16
    2019-06-15T00:02:56.000Z
    2021-03-25T23:22:38.000Z
    video/cloud-client/quickstart/quickstart.py
    nasirdec/GCP-AppEngine-Example
    3f5ad26ad2c1e3c8deceb5844adfb40cf7c2e53f
    [ "Apache-2.0" ]
    3
    2019-02-11T16:16:11.000Z
    2019-04-19T21:34:37.000Z
    #!/usr/bin/env python # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This application demonstrates label detection on a demo video using the Google Cloud API. Usage: python quickstart.py """ def run_quickstart(): # [START video_quickstart] from google.cloud import videointelligence video_client = videointelligence.VideoIntelligenceServiceClient() features = [videointelligence.enums.Feature.LABEL_DETECTION] operation = video_client.annotate_video( 'gs://demomaker/cat.mp4', features=features) print('\nProcessing video for label annotations:') result = operation.result(timeout=120) print('\nFinished processing.') # first result is retrieved because a single video was processed segment_labels = result.annotation_results[0].segment_label_annotations for i, segment_label in enumerate(segment_labels): print('Video label description: {}'.format( segment_label.entity.description)) for category_entity in segment_label.category_entities: print('\tLabel category description: {}'.format( category_entity.description)) for i, segment in enumerate(segment_label.segments): start_time = (segment.segment.start_time_offset.seconds + segment.segment.start_time_offset.nanos / 1e9) end_time = (segment.segment.end_time_offset.seconds + segment.segment.end_time_offset.nanos / 1e9) positions = '{}s to {}s'.format(start_time, end_time) confidence = segment.confidence print('\tSegment {}: {}'.format(i, positions)) print('\tConfidence: {}'.format(confidence)) print('\n') # [END video_quickstart] if __name__ == '__main__': run_quickstart()
    37.428571
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    1,064
    0.45123
    13027944554eb9d8705c77a8073c891a250b3842
    1,853
    py
    Python
    ally/instrument.py
    platformmaster9/PyAlly
    55400e0835ae3ac5b3cf58e0e8214c6244aeb149
    [ "MIT" ]
    null
    null
    null
    ally/instrument.py
    platformmaster9/PyAlly
    55400e0835ae3ac5b3cf58e0e8214c6244aeb149
    [ "MIT" ]
    null
    null
    null
    ally/instrument.py
    platformmaster9/PyAlly
    55400e0835ae3ac5b3cf58e0e8214c6244aeb149
    [ "MIT" ]
    null
    null
    null
    from . import utils ################################################# """ INSTRUMENT """ ################################################# def Instrument(symbol): symbol = str(symbol).upper() return { '__symbol' : symbol, 'Sym' : symbol, 'SecTyp' : 'CS', '__type' : 'equity' } ################################################# def Equity(symbol): return Instrument(symbol) ################################################# def Option (instrument, maturity_date, strike): return { **{ 'MatDt' : str(maturity_date) + 'T00:00:00.000-05:00', 'StrkPx' : str(int(strike)), 'SecTyp' : 'OPT', '__maturity' : str(maturity_date), '__strike' : str(int(strike)) }, **instrument } ################################################# def Call (instrument, maturity_date, strike): # Let Option do some lifting x = { **{ 'CFI':'OC' }, **Option(instrument, maturity_date, strike) } x['__underlying'] = x['Sym'] x['__type'] = 'call' x['__symbol'] = utils.option_format( symbol = x['Sym'], exp_date = x['__maturity'], strike = x['__strike'], direction = 'C' ) return x ################################################# def Put (instrument, maturity_date, strike): # Let Option do some lifting x = { **{ 'CFI':'OP' }, **Option(instrument, maturity_date, strike) } x['__underlying'] = x['Sym'] x['__type'] = 'put' x['__symbol'] = utils.option_format( symbol = x['Sym'], exp_date = x['__maturity'], strike = x['__strike'], direction = 'P' ) return x
    29.412698
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    0.362115
    1302b2f57e10ec891cc57b121da1cf9b5593731f
    432
    py
    Python
    airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py
    onaio/airbyte
    38302e82a25f1b66742c3febfbff0668556920f2
    [ "MIT" ]
    22
    2020-08-27T00:47:20.000Z
    2020-09-17T15:39:39.000Z
    airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py
    onaio/airbyte
    38302e82a25f1b66742c3febfbff0668556920f2
    [ "MIT" ]
    116
    2020-08-27T01:11:27.000Z
    2020-09-19T02:47:52.000Z
    airbyte-integrations/connectors/source-yahoo-finance-price/integration_tests/acceptance.py
    onaio/airbyte
    38302e82a25f1b66742c3febfbff0668556920f2
    [ "MIT" ]
    1
    2020-09-15T06:10:01.000Z
    2020-09-15T06:10:01.000Z
    # # Copyright (c) 2022 Airbyte, Inc., all rights reserved. # import pytest pytest_plugins = ("source_acceptance_test.plugin",) @pytest.fixture(scope="session", autouse=True) def connector_setup(): """This fixture is a placeholder for external resources that acceptance test might require.""" # TODO: setup test dependencies if needed. otherwise remove the TODO comments yield # TODO: clean up test dependencies
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    299
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    0
    0
    303
    0.701389
    13037ede58d748bec28747c289956464268c2ceb
    118
    py
    Python
    ddt/__init__.py
    GawenChen/test_pytest
    da7a29dc43e8027d3fd1a05054480ed7007131c3
    [ "Apache-2.0" ]
    null
    null
    null
    ddt/__init__.py
    GawenChen/test_pytest
    da7a29dc43e8027d3fd1a05054480ed7007131c3
    [ "Apache-2.0" ]
    null
    null
    null
    ddt/__init__.py
    GawenChen/test_pytest
    da7a29dc43e8027d3fd1a05054480ed7007131c3
    [ "Apache-2.0" ]
    null
    null
    null
    # -*- coding: utf-8 -*- """ @Time : 2021/10/9 17:51 @Auth : 潇湘 @File :__init__.py.py @IDE :PyCharm @QQ : 810400085 """
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    129
    0.992308
    13058e5281c2e6d308c1c802707f6f83b62cdc9b
    1,809
    py
    Python
    darts/models/linear_regression_model.py
    BiancaMT25/darts
    bb550dede6d8927a45aea0d9f3df53de32a6eee2
    [ "Apache-2.0" ]
    1
    2021-07-15T11:12:05.000Z
    2021-07-15T11:12:05.000Z
    darts/models/linear_regression_model.py
    BiancaMT25/darts
    bb550dede6d8927a45aea0d9f3df53de32a6eee2
    [ "Apache-2.0" ]
    null
    null
    null
    darts/models/linear_regression_model.py
    BiancaMT25/darts
    bb550dede6d8927a45aea0d9f3df53de32a6eee2
    [ "Apache-2.0" ]
    null
    null
    null
    """ Standard Regression model ------------------------- """ import numpy as np import pandas as pd from typing import Union from ..logging import get_logger from .regression_model import RegressionModel from sklearn.linear_model import LinearRegression logger = get_logger(__name__) class LinearRegressionModel(RegressionModel): def __init__(self, lags: Union[int, list] = None, lags_exog: Union[int, list, bool] = None, **kwargs): """ Simple wrapper for the linear regression model in scikit-learn, LinearRegression(). Parameters ---------- lags : Union[int, list] Number of lagged target values used to predict the next time step. If an integer is given the last `lags` lags are used (inclusive). Otherwise a list of integers with lags is required. lags_exog : Union[int, list, bool] Number of lagged exogenous values used to predict the next time step. If an integer is given the last `lags_exog` lags are used (inclusive). Otherwise a list of integers with lags is required. If True `lags` will be used to determine lags_exog. If False, the values of all exogenous variables at the current time `t`. This might lead to leakage if for predictions the values of the exogenous variables at time `t` are not known. **kwargs Additional keyword arguments passed to `sklearn.linear_model.LinearRegression`. """ self.kwargs = kwargs super().__init__( lags=lags, lags_exog=lags_exog, model=LinearRegression(**kwargs) ) def __str__(self): return 'LinearRegression(lags={}, lags_exog={})'.format(self.lags, self.lags_exog)
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    0
    0
    0
    0
    1,128
    0.623549
    130613c0dd1daf7edf4aa6e30bb0303b2984e2ce
    4,862
    py
    Python
    hail/python/test/hailtop/utils/test_utils.py
    vrautela/hail
    7db6189b5b1feafa88452b8470e497d9505d9a46
    [ "MIT" ]
    null
    null
    null
    hail/python/test/hailtop/utils/test_utils.py
    vrautela/hail
    7db6189b5b1feafa88452b8470e497d9505d9a46
    [ "MIT" ]
    null
    null
    null
    hail/python/test/hailtop/utils/test_utils.py
    vrautela/hail
    7db6189b5b1feafa88452b8470e497d9505d9a46
    [ "MIT" ]
    null
    null
    null
    from hailtop.utils import (partition, url_basename, url_join, url_scheme, url_and_params, parse_docker_image_reference) def test_partition_zero_empty(): assert list(partition(0, [])) == [] def test_partition_even_small(): assert list(partition(3, range(3))) == [range(0, 1), range(1, 2), range(2, 3)] def test_partition_even_big(): assert list(partition(3, range(9))) == [range(0, 3), range(3, 6), range(6, 9)] def test_partition_uneven_big(): assert list(partition(2, range(9))) == [range(0, 5), range(5, 9)] def test_partition_toofew(): assert list(partition(6, range(3))) == [range(0, 1), range(1, 2), range(2, 3), range(3, 3), range(3, 3), range(3, 3)] def test_url_basename(): assert url_basename('/path/to/file') == 'file' assert url_basename('https://hail.is/path/to/file') == 'file' def test_url_join(): assert url_join('/path/to', 'file') == '/path/to/file' assert url_join('/path/to/', 'file') == '/path/to/file' assert url_join('/path/to/', '/absolute/file') == '/absolute/file' assert url_join('https://hail.is/path/to', 'file') == 'https://hail.is/path/to/file' assert url_join('https://hail.is/path/to/', 'file') == 'https://hail.is/path/to/file' assert url_join('https://hail.is/path/to/', '/absolute/file') == 'https://hail.is/absolute/file' def test_url_scheme(): assert url_scheme('https://hail.is/path/to') == 'https' assert url_scheme('/path/to') == '' def test_url_and_params(): assert url_and_params('https://example.com/') == ('https://example.com/', {}) assert url_and_params('https://example.com/foo?') == ('https://example.com/foo', {}) assert url_and_params('https://example.com/foo?a=b&c=d') == ('https://example.com/foo', {'a': 'b', 'c': 'd'}) def test_parse_docker_image_reference(): x = parse_docker_image_reference('animage') assert x.domain is None assert x.path == 'animage' assert x.tag is None assert x.digest is None assert x.name() == 'animage' assert str(x) == 'animage' x = parse_docker_image_reference('hailgenetics/animage') assert x.domain == 'hailgenetics' assert x.path == 'animage' assert x.tag is None assert x.digest is None assert x.name() == 'hailgenetics/animage' assert str(x) == 'hailgenetics/animage' x = parse_docker_image_reference('localhost:5000/animage') assert x.domain == 'localhost:5000' assert x.path == 'animage' assert x.tag is None assert x.digest is None assert x.name() == 'localhost:5000/animage' assert str(x) == 'localhost:5000/animage' x = parse_docker_image_reference('localhost:5000/a/b/name') assert x.domain == 'localhost:5000' assert x.path == 'a/b/name' assert x.tag is None assert x.digest is None assert x.name() == 'localhost:5000/a/b/name' assert str(x) == 'localhost:5000/a/b/name' x = parse_docker_image_reference('localhost:5000/a/b/name:tag') assert x.domain == 'localhost:5000' assert x.path == 'a/b/name' assert x.tag == 'tag' assert x.digest is None assert x.name() == 'localhost:5000/a/b/name' assert str(x) == 'localhost:5000/a/b/name:tag' x = parse_docker_image_reference('localhost:5000/a/b/name:tag@sha256:abc123') assert x.domain == 'localhost:5000' assert x.path == 'a/b/name' assert x.tag == 'tag' assert x.digest == 'sha256:abc123' assert x.name() == 'localhost:5000/a/b/name' assert str(x) == 'localhost:5000/a/b/name:tag@sha256:abc123' x = parse_docker_image_reference('localhost:5000/a/b/name@sha256:abc123') assert x.domain == 'localhost:5000' assert x.path == 'a/b/name' assert x.tag is None assert x.digest == 'sha256:abc123' assert x.name() == 'localhost:5000/a/b/name' assert str(x) == 'localhost:5000/a/b/name@sha256:abc123' x = parse_docker_image_reference('name@sha256:abc123') assert x.domain is None assert x.path == 'name' assert x.tag is None assert x.digest == 'sha256:abc123' assert x.name() == 'name' assert str(x) == 'name@sha256:abc123' x = parse_docker_image_reference('gcr.io/hail-vdc/batch-worker:123fds312') assert x.domain == 'gcr.io' assert x.path == 'hail-vdc/batch-worker' assert x.tag == '123fds312' assert x.digest is None assert x.name() == 'gcr.io/hail-vdc/batch-worker' assert str(x) == 'gcr.io/hail-vdc/batch-worker:123fds312' x = parse_docker_image_reference('us-docker.pkg.dev/my-project/my-repo/test-image') assert x.domain == 'us-docker.pkg.dev' assert x.path == 'my-project/my-repo/test-image' assert x.tag is None assert x.digest is None assert x.name() == 'us-docker.pkg.dev/my-project/my-repo/test-image' assert str(x) == 'us-docker.pkg.dev/my-project/my-repo/test-image'
    37.689922
    113
    0.644385
    0
    0
    0
    0
    0
    0
    0
    0
    1,722
    0.354175
    13064197a568b4ea0fdb674d3a8685e3b27e92eb
    863
    py
    Python
    hood/urls.py
    wadi-1000/Vicinity
    a41f6ec2c532cb06f7444b55073b6879a1fce63a
    [ "MIT" ]
    null
    null
    null
    hood/urls.py
    wadi-1000/Vicinity
    a41f6ec2c532cb06f7444b55073b6879a1fce63a
    [ "MIT" ]
    null
    null
    null
    hood/urls.py
    wadi-1000/Vicinity
    a41f6ec2c532cb06f7444b55073b6879a1fce63a
    [ "MIT" ]
    null
    null
    null
    from django.urls import path,include from . import views urlpatterns = [ path('home/', views.home, name = 'home'), path('add_hood/',views.uploadNeighbourhood, name = 'add_hood'), path('viewhood/',views.viewHood, name = 'viewhood'), path('hood/<int:pk>/',views.hood, name = 'hood'), path('add_bizna/',views.uploadBuisness, name = 'add_bizna'), path('bizna/',views.viewBizna, name = 'view_bizna'), path('viewbizna/<int:pk>/',views.bizna, name = 'bizna'), path('post/',views.create_post, name = 'post'), path('posts/',views.viewPost, name = 'posts'), path('searchbizna/', views.searchBizna, name="search_results"), path('searchhood/', views.searchHood, name="search_res"), path('join_hood/<id>', views.join_neighbourhood, name='join-hood'), path('leave_hood/<id>', views.leave_neighbourhood, name='leave-hood'), ]
    47.944444
    74
    0.665122
    0
    0
    0
    0
    0
    0
    0
    0
    287
    0.332561
    1306e647595d1f2f64e2d6dd214b9b25580f3ed1
    8,305
    py
    Python
    src/licensedcode/tokenize.py
    chetanya-shrimali/scancode-toolkit
    a1a22fb225cbeb211bd6f92272a46f1351f57d6b
    [ "Apache-2.0", "CC0-1.0" ]
    null
    null
    null
    src/licensedcode/tokenize.py
    chetanya-shrimali/scancode-toolkit
    a1a22fb225cbeb211bd6f92272a46f1351f57d6b
    [ "Apache-2.0", "CC0-1.0" ]
    null
    null
    null
    src/licensedcode/tokenize.py
    chetanya-shrimali/scancode-toolkit
    a1a22fb225cbeb211bd6f92272a46f1351f57d6b
    [ "Apache-2.0", "CC0-1.0" ]
    null
    null
    null
    # -*- coding: utf-8 -*- # # Copyright (c) 2017 nexB Inc. and others. All rights reserved. # http://nexb.com and https://github.com/nexB/scancode-toolkit/ # The ScanCode software is licensed under the Apache License version 2.0. # Data generated with ScanCode require an acknowledgment. # ScanCode is a trademark of nexB Inc. # # You may not use this software except in compliance with the License. # You may obtain a copy of the License at: http://apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software distributed # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR # CONDITIONS OF ANY KIND, either express or implied. See the License for the # specific language governing permissions and limitations under the License. # # When you publish or redistribute any data created with ScanCode or any ScanCode # derivative work, you must accompany this data with the following acknowledgment: # # Generated with ScanCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES # OR CONDITIONS OF ANY KIND, either express or implied. No content created from # ScanCode should be considered or used as legal advice. Consult an Attorney # for any legal advice. # ScanCode is a free software code scanning tool from nexB Inc. and others. # Visit https://github.com/nexB/scancode-toolkit/ for support and download. from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals from itertools import islice from itertools import izip import re from zlib import crc32 from textcode.analysis import text_lines """ Utilities to break texts in lines and tokens (aka. words) with specialized version for queries and rules texts. """ def query_lines(location=None, query_string=None, strip=True): """ Return an iterable of text lines given a file at `location` or a `query string`. Include empty lines. """ # TODO: OPTIMIZE: tokenizing line by line may be rather slow # we could instead get lines and tokens at once in a batch? lines = [] if location: lines = text_lines(location, demarkup=False) elif query_string: if strip: keepends = False else: keepends = True lines = query_string.splitlines(keepends) for line in lines: if strip: yield line.strip() else: yield line # Split on whitespace and punctuations: keep only characters # and + in the middle or end of a word. # Keeping the trailing + is important for licenses name such as GPL2+ query_pattern = '[^\W_]+\+?[^\W_]*' word_splitter = re.compile(query_pattern, re.UNICODE).findall def query_tokenizer(text, lower=True): """ Return an iterable of tokens from a unicode query text. """ if not text: return [] text = lower and text.lower() or text return (token for token in word_splitter(text) if token) # Alternate pattern used for matched text collection not_query_pattern = '[\W_+]+[\W_]?' # collect tokens and non-token texts in two different groups _text_capture_pattern = '(?P<token>' + query_pattern + ')' + '|' + '(?P<punct>' + not_query_pattern + ')' tokens_and_non_tokens = re.compile(_text_capture_pattern, re.UNICODE).finditer def matched_query_text_tokenizer(text): """ Return an iterable of tokens and non-tokens from a unicode query text keeping everything (including punctuations, line endings, etc.) The returned iterable contains 2-tuples of: - True if the string is a text token or False if this is not (such as punctuation, spaces, etc). - the corresponding string This is used to reconstruct the matched query text accurately. """ if not text: return for match in tokens_and_non_tokens(text): if not match: continue mgd = match.groupdict() token = mgd.get('token') punct = mgd.get('punct') if token or punct: yield (True, token) if token else (False, punct) # Template-aware splitter, keeping a templated part {{anything}} as a token. # This splitter yields plain token strings or double braces-enclosed strings # {{something}} for templates. curly barces are otherwise treated as punctuation. # A template part is anything enclosed in double braces template_pattern = '\{\{[^{}]*\}\}' rule_pattern = '%s|%s+' % (query_pattern, template_pattern,) template_splitter = re.compile(rule_pattern , re.UNICODE).findall def rule_tokenizer(text, lower=True): """ Return an iterable of tokens from a unicode rule text, skipping templated parts, including leading and trailing templated parts. For example: >>> list(rule_tokenizer('')) [] >>> list(rule_tokenizer('some Text with spAces! + _ -')) [u'some', u'text', u'with', u'spaces'] Unbalanced templates are handled correctly: >>> list(rule_tokenizer('{{}some }}Text with spAces! + _ -')) [u'some', u'text', u'with', u'spaces'] Templates are handled and skipped for templated sequences: >>> list(rule_tokenizer('{{Hi}}some {{}}Text with{{noth+-_!@ing}} {{junk}}spAces! + _ -{{}}')) [u'some', u'text', u'with', u'spaces'] """ if not text: return [] text = lower and text.lower() or text tokens = template_splitter(text) # skip templates return (token for token in tokens if token and not token.startswith('{{')) def ngrams(iterable, ngram_length): """ Return an iterable of ngrams of length `ngram_length` given an iterable. Each ngram is a tuple of ngram_length items. The returned iterable is empty if the input iterable contains less than `ngram_length` items. Note: this is a fairly arcane but optimized way to compute ngrams. For example: >>> list(ngrams([1,2,3,4,5], 2)) [(1, 2), (2, 3), (3, 4), (4, 5)] >>> list(ngrams([1,2,3,4,5], 4)) [(1, 2, 3, 4), (2, 3, 4, 5)] >>> list(ngrams([1,2,3,4], 2)) [(1, 2), (2, 3), (3, 4)] >>> list(ngrams([1,2,3], 2)) [(1, 2), (2, 3)] >>> list(ngrams([1,2], 2)) [(1, 2)] >>> list(ngrams([1], 2)) [] This also works with arrays or tuples: >>> from array import array >>> list(ngrams(array(b'h', [1,2,3,4,5]), 2)) [(1, 2), (2, 3), (3, 4), (4, 5)] >>> list(ngrams(tuple([1,2,3,4,5]), 2)) [(1, 2), (2, 3), (3, 4), (4, 5)] """ return izip(*(islice(iterable, i, None) for i in range(ngram_length))) def select_ngrams(ngrams, with_pos=False): """ Return an iterable as a subset of a sequence of ngrams using the hailstorm algorithm. If `with_pos` is True also include the starting position for the ngram in the original sequence. Definition from the paper: http://www2009.eprints.org/7/1/p61.pdf The algorithm first fingerprints every token and then selects a shingle s if the minimum fingerprint value of all k tokens in s occurs at the first or the last position of s (and potentially also in between). Due to the probabilistic properties of Rabin fingerprints the probability that a shingle is chosen is 2/k if all tokens in the shingle are different. For example: >>> list(select_ngrams([(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)])) [(2, 1, 3), (1, 1, 3), (2, 6, 1), (7, 3, 4)] Positions can also be included. In this case, tuple of (pos, ngram) are returned: >>> list(select_ngrams([(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)], with_pos=True)) [(0, (2, 1, 3)), (1, (1, 1, 3)), (3, (2, 6, 1)), (4, (7, 3, 4))] This works also from a generator: >>> list(select_ngrams(x for x in [(2, 1, 3), (1, 1, 3), (5, 1, 3), (2, 6, 1), (7, 3, 4)])) [(2, 1, 3), (1, 1, 3), (2, 6, 1), (7, 3, 4)] """ last = None for i, ngram in enumerate(ngrams): # FIXME: use a proper hash nghs = [crc32(str(ng)) for ng in ngram] min_hash = min(nghs) if with_pos: ngram = (i, ngram,) if nghs[0] == min_hash or nghs[-1] == min_hash: yield ngram last = ngram else: # always yield the first or last ngram too. if i == 0: yield ngram last = ngram if last != ngram: yield ngram
    36.425439
    105
    0.646117
    0
    0
    3,247
    0.390969
    0
    0
    0
    0
    5,809
    0.699458
    1308f0dd9c15ba9ea58abe836ba21f50ef3863ef
    679,794
    py
    Python
    homework_05/graficos_3.py
    ufpa-organization-repositories/evolutionary-computing
    e16786f9619e2b357b94ab91ff3a7b352e6a0d92
    [ "MIT" ]
    null
    null
    null
    homework_05/graficos_3.py
    ufpa-organization-repositories/evolutionary-computing
    e16786f9619e2b357b94ab91ff3a7b352e6a0d92
    [ "MIT" ]
    null
    null
    null
    homework_05/graficos_3.py
    ufpa-organization-repositories/evolutionary-computing
    e16786f9619e2b357b94ab91ff3a7b352e6a0d92
    [ "MIT" ]
    null
    null
    null
    # ensaio = [[[[1, 999.4951009067408, 999.495100909791, '1001100.11100010011001100001001', '100011.10010111010000111110100', '1', '1'], [2, 999.5052434400473, 999.5052434497359, '0000100.11100010011001100001001', '111011.10010111010000111110000', '1', '2'], [3, 999.51676448592, 999.516764613072, '0000100.11100010011001100001001', '011011.10010111010000111110100', '1', '3'], [4, 999.5986670278455, 999.5986691897469, '0000100.11100010011001100001001', '001011.10010111010000111110100', '1', '4'], [5, 999.8231043912172, 999.8231154915733, '0000100.11100010011001100001001', '000011.10010111010000111110100', '1', '5'], [6, 999.8507392915436, 999.8507498599146, '0000100.11101010011001100001001', '000011.10010111010000111110100', '1', '6'], [7, 999.8770250807991, 999.8770357110892, '0000100.11101010011001100001001', '000011.10011011010000111110110', '1', '7'], [8, 999.9035511429103, 999.9035563402527, '0000100.11111011011001100001001', '000011.10011011010000111110110', '1', '8'], [9, 999.8985266375895, 999.8985377843169, '0000100.11111011011001100001001', '000011.11011011010000111110110', '1', '9'], [10, 999.9175205241293, 999.9175299982971, '0000100.11111001011001100001001', '000011.11011011010000111110110', '1', '10']]], [[[999.7693956630327, 999.5712824548395, 999.5712938998612, '-1111.1100110000001100100111', '1100.0111101110100011011001101', '2', '1'], [999.772264826825, 999.722709716149, 999.7227150806862, '-1111.1100110000001100000111', '1100.0101101110011011011001101', '2', '2'], [999.772309110711, 999.7568109989896, 999.7568135752206, '-1111.1100110000001100000101', '1100.0101111110011011011001101', '2', '3'], [999.7723098264468, 999.7626773116484, 999.762679240235, '-1111.1100110010001000000111', '1100.0101111110011011011001101', '2', '4'], [999.772309861109, 999.7628189867448, 999.7628208837242, '-1111.1100110010101100000111', '1100.0101111111011011011001101', '2', '5'], [999.7723098614766, 999.7575339099722, 999.7575364450361, '-1111.1100110010011100000111', '1100.0101111111001011011001101', '2', '6'], [999.7723098614781, 999.7330120594124, 999.7330190985555, '-1111.1100110010011100000111', '1100.0101111111001011011101101', '2', '7'], [999.7723098614792, 999.7425919826176, 999.7425978232334, '-1111.1100110010011100000011', '1100.0101111111001011011101101', '2', '8'], [999.7723098614795, 999.7521383886242, 999.7521421922053, '-1111.1100110010011100000011', '1100.0101111111001011011111101', '2', '9'], [999.7723098614797, 999.7479881509024, 999.7479935597561, '-1111.1100110010011100000010', '1100.0101111111001011011111101', '2', '10']]]] ensaio = [[[[999.5055172457077, 999.4950089601775, 999.4950089625081, '1000110.11100010011001100001001', '110011.10010111010000111110100', '1', '1'], [999.5061559955897, 999.5023090273177, 999.502309034424, '1000100.01100010011001100001001', '110111.10010111010000111110110', '1', '2'], [999.5064522811073, 999.503907723421, 999.503907729031, '1000110.11110010001001100001001', '110011.11010111010000111110110', '1', '3'], [999.5121423697065, 999.5035214104588, 999.5035214189329, '1000100.01100010001001100001001', '010111.11010111010000111110110', '1', '4'], [999.5129192569484, 999.5073323118404, 999.5073323302529, '1000100.01100010001001100001001', '010111.01010111010000111110110', '1', '5'], [999.5129217445863, 999.5088300870693, 999.5088303022028, '1000100.01100010001001100001001', '010111.01010011010000111100000', '1', '6'], [999.5129221606866, 999.5109518591257, 999.5109518758048, '1000100.01100010001001100001001', '010111.01010010010000111100000', '1', '7'], [999.5129230124272, 999.512166424112, 999.5121664266934, '1000100.01100000001001100001001', '010111.01010011010000111100000', '1', '8'], [999.5129230131629, 999.5124005902164, 999.5124005938198, '1000100.01100000001001100001001', '010111.01010011010010111100000', '1', '9'], [999.5129230148639, 999.5103974240145, 999.5103976354789, '1000100.01100000001011100001001', '010111.01010011010010111110000', 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'50', '96'], [999.6878968594276, 999.6619993921432, 999.6620030672873, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '97'], [999.6878968594276, 999.6767364740043, 999.6767381465787, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '98'], [999.6878968594276, 999.6674371363, 999.6674397163673, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '99'], [999.6878968594276, 999.6818107105054, 999.6818115173727, '10111.011110001101110111111111', '-1001.0000111100100000111', '50', '100']]]] fitness = [] media = [] desvio = [] experimento_plot = [] from matplotlib import pyplot as plt import numpy as np for index, experimento in enumerate(ensaio): for populacao in experimento: for x, individuo in enumerate(populacao): fitness.append(individuo[0]) media.append(individuo[1]) desvio.append(individuo[2]) x = np.arange(1, fitness.__len__() + 1) plt.plot(x, fitness) experimento_plot.append([fitness, media, desvio]) fitness = [] media = [] desvio = [] plt.show()
    23,441.172414
    676,649
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    346,369
    0.509521
    13099ef7ac061602aaff6bfaa4733bfcc5e92bd8
    225
    py
    Python
    etest_test/fixtures_test/ebuilds_test/__init__.py
    alunduil/etest
    e5f06d7e8c83be369576976f239668545bcbfffd
    [ "MIT" ]
    6
    2015-01-08T13:56:50.000Z
    2018-01-08T00:53:08.000Z
    etest_test/fixtures_test/ebuilds_test/__init__.py
    alunduil/etest
    e5f06d7e8c83be369576976f239668545bcbfffd
    [ "MIT" ]
    64
    2015-01-01T23:10:07.000Z
    2021-06-12T06:55:31.000Z
    etest_test/fixtures_test/ebuilds_test/__init__.py
    alunduil/etest
    e5f06d7e8c83be369576976f239668545bcbfffd
    [ "MIT" ]
    2
    2015-05-08T01:32:48.000Z
    2015-05-30T15:38:31.000Z
    """Ebuild Test Fixtures.""" import os from typing import Any, Dict, List from etest_test import helpers_test EBUILDS: Dict[str, List[Dict[str, Any]]] = {} helpers_test.import_directory(__name__, os.path.dirname(__file__))
    22.5
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    27
    0.12
    130a61bee54706bd995afa354e7681d8726e5d5d
    3,764
    py
    Python
    src/model.py
    palucki/RememberIt
    1d66616d4bb1bca026dda031d876dca226ba71ad
    [ "MIT" ]
    null
    null
    null
    src/model.py
    palucki/RememberIt
    1d66616d4bb1bca026dda031d876dca226ba71ad
    [ "MIT" ]
    null
    null
    null
    src/model.py
    palucki/RememberIt
    1d66616d4bb1bca026dda031d876dca226ba71ad
    [ "MIT" ]
    null
    null
    null
    import random from pymongo import MongoClient from observable import Observable from phrase import Phrase class MongoDbProxy: """Proxy for MongoDB""" def __init__(self, url, dbName, tableName): self.client = MongoClient(url) self.db = self.client[dbName] self.table = tableName self.count = self.db[self.table].find().count() def get_db(self): return self.db def add_phrase(self, phrase): #[{ "english": eng, "polish" : pl}] record = {"english" : phrase.eng, "polish" : phrase.meanings} self.db[self.table].insert(record) self.count = self.db[self.table].find().count() def show_one(self, phrase): print("eng: \'%s\' pol: \'%s\'" % (phrase["english"], phrase["polish"])) def get_all(self): #define your data struct here words = {} for i, phrase in enumerate(self.db[self.table].find()): eng = phrase["english"] #lang = phrase["lang"] meaning = phrase["polish"] words[eng] = meaning return words def show_all(self): if self.count > 0: for i, phrase in enumerate(self.db[self.table].find()): print(i, end=" ") self.show_one(phrase) else: print("Database is empty") def show_random(self): entries = self.db[self.table].find() self.count = entries.count() if self.count > 0: self.show_one(entries[random.randrange(self.count)]) else: print("Database is empty") def record_exists(self, eng): if self.db[self.table].find_one({"english" : eng}): return True else: return False def drop_record(self, eng): self.db[self.table].delete_one({"english":eng}) def drop_db(self): print("Dropping") self.db.self.table.drop() self.count = self.db[self.table].find().count() class Model: """That needs a table of pairs - eng and its meanings""" def __init__(self): self.phrases = Observable({}) self.db = MongoDbProxy("mongodb://localhost:27017/", "RepeatItDb", "phrases") data = self.db.get_all() self.phrases.setData(data) def addWord(self, key, lang, meanings): newData = self.phrases.getData() newData[key] = meanings self.phrases.setData(newData) def getAllWords(self): return self.phrases.getData() def removeWord(self, key): newData = self.phrases.getData() newData.pop(key) self.phrases.setData(newData) def saveWord(self, wordAndMeaning): word = wordAndMeaning[0] meaning = wordAndMeaning[1] self.addWord(word, "pl", meaning) def saveDb(self): dbData = self.db.get_all() modelData = self.getAllWords() #That's for future optimization: update db instead of adding it all dbKeysSet = set(dbData.keys()) dbValuesSet = set(dbData.values()) modelKeysSet = set(modelData.keys()) modelValuesSet = set(modelData.values()) newRecordsKeys = modelKeysSet - dbKeysSet deletedRecordsKeys = dbKeysSet - modelKeysSet if len(newRecordsKeys): for newKey in newRecordsKeys: self.db.add_phrase(Phrase(newKey, "pl", modelData[newKey])) if len(deletedRecordsKeys): for deletedKey in deletedRecordsKeys: self.db.drop_record(deletedKey) #Handle also value update print("Saving database...")
    30.852459
    85
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    3,653
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    479
    0.127258
    130ad5b5c3caa22c7668a018ea30cf4d2bc3c2f4
    1,381
    py
    Python
    sampleApplication/clientGenerator.py
    chall68/BlackWatch
    0b95d69e4b7de9213a031557e9aff54ce35b12dd
    [ "MIT" ]
    null
    null
    null
    sampleApplication/clientGenerator.py
    chall68/BlackWatch
    0b95d69e4b7de9213a031557e9aff54ce35b12dd
    [ "MIT" ]
    null
    null
    null
    sampleApplication/clientGenerator.py
    chall68/BlackWatch
    0b95d69e4b7de9213a031557e9aff54ce35b12dd
    [ "MIT" ]
    null
    null
    null
    #!flask/bin/python #from user import User from sampleObjects.User import User from datetime import datetime from sampleObjects.DetectionPoint import DetectionPoint import time, requests, random, atexit def requestGenerator(): userObject = randomUser() detectionPointObject = randomDetectionPoint() req = requests.post('http://localhost:5000/addevent', json = {"User": userObject.__dict__, "DetectionPoint" : detectionPointObject.__dict__, "Time" : str(datetime.now().isoformat())}) print (req.text) checkResp = requests.get('http://localhost:5000/getResponses') print (checkResp.text) def randomUser(): user = random.randint(1,3) attacker=0 if (user==1): attacker = User("Phillipo", "255.255.255.101", "xxxx") elif (user==2): attacker = User("Sergio", "109.123.234.1", "yyyy") elif (user==3): attacker = User("Anonymous", "101.101.101.87", "354343jjk23") return attacker def randomDetectionPoint(): rand = random.randint(1,2) dp=0 if (rand==1): dp = DetectionPoint("HTTP Verb", "GET Request used where POST is expected") elif (rand==2): dp = DetectionPoint("Login Page", "Hidden field altered within the login form") return dp for i in range (50): requestGenerator() time.sleep(1.5) def closingTime(): print ("Exiting") atexit.register(closingTime)
    27.62
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    0.674873
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    355
    0.25706
    130c49099f8aa40a9dd92ff170ecb6c15b43d8f9
    1,873
    py
    Python
    news_collector/collector/consumers.py
    ridwaniyas/channels-examples
    9e6a26c8e6404483695cbd96ebf12fc4ed9956b2
    [ "BSD-3-Clause" ]
    null
    null
    null
    news_collector/collector/consumers.py
    ridwaniyas/channels-examples
    9e6a26c8e6404483695cbd96ebf12fc4ed9956b2
    [ "BSD-3-Clause" ]
    null
    null
    null
    news_collector/collector/consumers.py
    ridwaniyas/channels-examples
    9e6a26c8e6404483695cbd96ebf12fc4ed9956b2
    [ "BSD-3-Clause" ]
    null
    null
    null
    import asyncio import json import datetime from aiohttp import ClientSession from channels.generic.http import AsyncHttpConsumer from .constants import BLOGS class NewsCollectorAsyncConsumer(AsyncHttpConsumer): """ Async HTTP consumer that fetches URLs. """ async def handle(self, body): # Adapted from: # "Making 1 million requests with python-aiohttp" # https://pawelmhm.github.io/asyncio/python/aiohttp/2016/04/22/asyncio-aiohttp.html async def fetch(url, session): async with session.get(url) as response: return await response.read() tasks = [] loop = asyncio.get_event_loop() # aiohttp allows a ClientSession object to link all requests together t0 = datetime.datetime.now() async with ClientSession() as session: for name, url in BLOGS.items(): print('Start downloading "%s"' % name) # Launch a coroutine for each URL fetch task = loop.create_task(fetch(url, session)) tasks.append(task) # Wait on, and then gather, all responses responses = await asyncio.gather(*tasks) dt = (datetime.datetime.now() - t0).total_seconds() print('All downloads completed; elapsed time: {} [s]'.format(dt)) # asyncio.gather returns results in the order of the original sequence, # so we can safely zip these together. data = dict(zip(BLOGS.keys(), [r.decode('utf-8') for r in responses])) text = json.dumps(data) # We have to send a response using send_response rather than returning # it in Channels' async HTTP consumer await self.send_response(200, text.encode(), headers=[ ("Content-Type", "application/json"), ] )
    35.339623
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    0.360918
    130d77d6c796e047f21c43df476be8389b35aecb
    737
    py
    Python
    src/randomcsv/FileUtils.py
    PhilipBuhr/randomCsv
    34b1da62134077dfe4db2682ee0da386ef380c1d
    [ "MIT" ]
    null
    null
    null
    src/randomcsv/FileUtils.py
    PhilipBuhr/randomCsv
    34b1da62134077dfe4db2682ee0da386ef380c1d
    [ "MIT" ]
    null
    null
    null
    src/randomcsv/FileUtils.py
    PhilipBuhr/randomCsv
    34b1da62134077dfe4db2682ee0da386ef380c1d
    [ "MIT" ]
    null
    null
    null
    import os from pathlib import Path def write(file_name, content): Path(os.path.dirname(file_name)).mkdir(parents=True, exist_ok=True) with open(file_name, 'w') as file: file.write(content) def read_line_looping(file_name, count): i = 0 lines = [] file = open(file_name, 'r') line = file.readline() if line == '': raise EmptyFileError(f'Error: Dictionary {file_name} seems to be empty') while i < count: lines.append(line.strip()) i += 1 line = file.readline() if line == '': file.close() file = open(file_name, 'r') line = file.readline() file.close() return lines class EmptyFileError(Exception): pass
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    63
    0.085482
    130e4d54c2deae9e851943dbae96e38707ce57ce
    46
    py
    Python
    stringtoiso/__init__.py
    vats98754/stringtoiso
    985da5efa26111ef1d92b7026b5d5d68f0101ef1
    [ "MIT" ]
    null
    null
    null
    stringtoiso/__init__.py
    vats98754/stringtoiso
    985da5efa26111ef1d92b7026b5d5d68f0101ef1
    [ "MIT" ]
    null
    null
    null
    stringtoiso/__init__.py
    vats98754/stringtoiso
    985da5efa26111ef1d92b7026b5d5d68f0101ef1
    [ "MIT" ]
    null
    null
    null
    from stringtoiso.convert_to_iso import convert
    46
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    130ec5dfef9f34118ca5d16b6a8a1a90a53517da
    5,495
    py
    Python
    aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py
    PNNL-Comp-Mass-Spec/DtaRefinery
    609cc90d0322af69aea43c2fc21d9cf05a06797a
    [ "BSD-2-Clause" ]
    null
    null
    null
    aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py
    PNNL-Comp-Mass-Spec/DtaRefinery
    609cc90d0322af69aea43c2fc21d9cf05a06797a
    [ "BSD-2-Clause" ]
    null
    null
    null
    aux_sys_err_prediction_module/additive/R_runmed_spline/my_R_runmed_spline_analysis.py
    PNNL-Comp-Mass-Spec/DtaRefinery
    609cc90d0322af69aea43c2fc21d9cf05a06797a
    [ "BSD-2-Clause" ]
    null
    null
    null
    from aux_sys_err_prediction_module.additive.R_runmed_spline.my_R_runmed_spline_fit import R_runmed_smooth_spline from numpy import random, array, median, zeros, arange, hstack from win32com.client import Dispatch import math myName = 'R_runmed_spline' useMAD = True # use median absolute deviations instead of sum of squared residues # ----------------------------------------------------------------------- def R_runmed_spline_MAIN(ARG3, Controller): pars = Controller.updatedSettings['refiningPars']['regressionSettings'][myName] # ARG3 x = ARG3[0][0] y = ARG3[0][1] sc = Dispatch("StatConnectorSrv.StatConnector") sc.Init("R") # get the best smoothing parameter bestSpar = R_runmed_spline_KCV_OPTIMIZATION(x, y, sc=sc, **pars) # get the prediction error for this smoothing parameter bestPredErr = R_runmed_spline_KCV_predErr(x, y, spar=bestSpar, sc=sc, **pars) # compare with original SSE # is fit successful? # return isSuccessfulFit, yFit, yEval, runMedData SSE = sum(y ** 2) MAD = 1.4826 * median(abs(y)) if useMAD: SSE = MAD if bestPredErr < SSE: isSuccessfulFit = True # ppmArrs = [[] for i in range(len(ARG3))] for ind in range(len(ARG3)): x = ARG3[ind][0] y = ARG3[ind][1] xEval = ARG3[ind][2] # yFit, runMedData = R_runmed_smooth_spline(x, y, x, spar=bestSpar, sc=sc, **pars) yEval, runMedData = R_runmed_smooth_spline(x, y, xEval, spar=bestSpar, sc=sc, **pars) # ppmArrs[ind] = [yFit, yEval] else: isSuccessfulFit = False # ppmArrs = [[] for i in range(len(ARG3))] for ind in range(len(ARG3)): x = ARG3[ind][0] y = ARG3[ind][1] xEval = ARG3[ind][2] # yFit = zeros(len(x), 'd') yEval = zeros(len(xEval), 'd') # ppmArrs[ind] = [yFit, yEval] sc.Close() return isSuccessfulFit, bestPredErr, ppmArrs # ----------------------------------------------------------------------- # ----------------------------------------------------------------------- def R_runmed_spline_KCV_OPTIMIZATION(x, y, sc, **pars): sparRange = array([float(i) for i in pars['spar range'].split(',')]) sparStepsNum = int(pars['spar steps number']) sparStep = round((sparRange[1] - sparRange[0]) / sparStepsNum, 5) sparSet = arange(sparRange[0], sparRange[1], sparStep) predErrSet = zeros(len(sparSet), 'd') for i in range(len(sparSet)): predErr = R_runmed_spline_KCV_predErr(x, y, spar=sparSet[i], sc=sc, **pars) predErrSet[i] = predErr ## p(zip(sparSet, predErrSet)) spar = sparSet[predErrSet == min(predErrSet)][-1] # take the last one (smoothest) if there are few ## print('spar ', spar) return spar # ----------------------------------------------------------------------- # ----------------------------------------------------------------------- def R_runmed_spline_KCV_predErr(x, y, **kwargs): """ just returns the prediction error """ K = int(kwargs['K']) # --Related to K-fold CV--------------------------- L = len(x) N = L / K ##min length of pieces W = list(range(L)) Z = list(range(1, K + 1)) Z = [N for j in Z] R = L % K Z[0:R] = [j + 1 for j in Z[0:R]] # length of the pieces random.shuffle(W) ind = 0 predErr = 0 allResiduals = array([]) SSE = sum(y ** 2) # VLAD. Why do I need this??? # ---running through K training/testings------------- for val in Z: j = math.floor(val) # ---making training/testing subsets------------- test = W[ind:ind + j] test.sort() train = W[0:ind] + W[ind + j:] train.sort() ind += j # ----------------------------------------------- # ---fit runmed_spline here---------------------- yFit, runMed = R_runmed_smooth_spline(x[train], y[train], x[test], **kwargs) residualsTest = y[test] - yFit predErr += sum(residualsTest ** 2) allResiduals = hstack((allResiduals, residualsTest)) # ----------------------------------------------- if useMAD: predErr = 1.4826 * median(abs(allResiduals)) return predErr # ----------------------------------------------------------------------- if __name__ == '__main__': from numpy import linspace, cos, lexsort, zeros, sin from pylab import plot, show, subplot, savefig, clf, ylim from pprint import pprint as p from time import clock as c x1 = linspace(0, 30, 300) ## y1 = cos(x1) ## y1 = zeros(len(x1),'d') #nice test y1 = x1 * 0.03 y1 += random.normal(scale=0.2, size=y1.shape) ind = lexsort(keys=(y1, x1)) x1 = x1[ind] y1 = y1[ind] t1 = c() isSuccessfulFit, yFit, yEval, runMedData, predErr = \ R_runmed_spline_MAIN(x1, y1, x1, runMedSpan=0.01, K=10, sparRange=[0.6, 1.1, 0.1]) t2 = c() print('done in %s seconds' % (t2 - t1)) subplot(211) plot(x1, y1, 'bo') plot(runMedData[0], runMedData[1], 'y^') plot(x1, yEval, 'r+-') ylim([-1.5, +1.5]) subplot(212) plot(x1, y1 - yEval, 'go') ylim([-1.5, +1.5]) show()
    32.323529
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    1,503
    0.273521
    130f0d527db89218f9714b016db75a6b60750779
    2,721
    py
    Python
    setup.py
    Ms2ger/python-zstandard
    b8ea1f6722a710e252b452554442b84c81049439
    [ "BSD-3-Clause" ]
    null
    null
    null
    setup.py
    Ms2ger/python-zstandard
    b8ea1f6722a710e252b452554442b84c81049439
    [ "BSD-3-Clause" ]
    null
    null
    null
    setup.py
    Ms2ger/python-zstandard
    b8ea1f6722a710e252b452554442b84c81049439
    [ "BSD-3-Clause" ]
    null
    null
    null
    #!/usr/bin/env python # Copyright (c) 2016-present, Gregory Szorc # All rights reserved. # # This software may be modified and distributed under the terms # of the BSD license. See the LICENSE file for details. import os import sys from setuptools import setup try: import cffi except ImportError: cffi = None import setup_zstd SUPPORT_LEGACY = False SYSTEM_ZSTD = False WARNINGS_AS_ERRORS = False if os.environ.get('ZSTD_WARNINGS_AS_ERRORS', ''): WARNINGS_AS_ERRORS = True if '--legacy' in sys.argv: SUPPORT_LEGACY = True sys.argv.remove('--legacy') if '--system-zstd' in sys.argv: SYSTEM_ZSTD = True sys.argv.remove('--system-zstd') if '--warnings-as-errors' in sys.argv: WARNINGS_AS_ERRORS = True sys.argv.remote('--warning-as-errors') # Code for obtaining the Extension instance is in its own module to # facilitate reuse in other projects. extensions = [ setup_zstd.get_c_extension(name='zstd', support_legacy=SUPPORT_LEGACY, system_zstd=SYSTEM_ZSTD, warnings_as_errors=WARNINGS_AS_ERRORS), ] install_requires = [] if cffi: import make_cffi extensions.append(make_cffi.ffi.distutils_extension()) # Need change in 1.10 for ffi.from_buffer() to handle all buffer types # (like memoryview). # Need feature in 1.11 for ffi.gc() to declare size of objects so we avoid # garbage collection pitfalls. install_requires.append('cffi>=1.11') version = None with open('c-ext/python-zstandard.h', 'r') as fh: for line in fh: if not line.startswith('#define PYTHON_ZSTANDARD_VERSION'): continue version = line.split()[2][1:-1] break if not version: raise Exception('could not resolve package version; ' 'this should never happen') setup( name='zstandard', version=version, description='Zstandard bindings for Python', long_description=open('README.rst', 'r').read(), url='https://github.com/indygreg/python-zstandard', author='Gregory Szorc', author_email='[email protected]', license='BSD', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Programming Language :: C', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], keywords='zstandard zstd compression', packages=['zstandard'], ext_modules=extensions, test_suite='tests', install_requires=install_requires, )
    27.765306
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    1,249
    0.459022
    130f1aca25388c2ce8dd308d2bb872e5263f8305
    140
    py
    Python
    Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py
    c4st1lh0/Projetos-de-Aula
    e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302
    [ "MIT" ]
    null
    null
    null
    Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py
    c4st1lh0/Projetos-de-Aula
    e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302
    [ "MIT" ]
    null
    null
    null
    Escolas/Curso em Video/Back-End/Curso de Python/Mundos/Mundo 01/Exercicio_16.py
    c4st1lh0/Projetos-de-Aula
    e8abc9f4bce6cc8dbc6d7fb5da0f549ac8ef5302
    [ "MIT" ]
    null
    null
    null
    import math num = float(input('Digite um numero real qualquer: ')) print('O numero: {} tem a parte inteira {}'.format(num, math.trunc(num)))
    46.666667
    73
    0.7
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    71
    0.507143
    1310f38eea5f3635000289a9e4355d5205ba5668
    551
    py
    Python
    mmdet/ops/orn/functions/__init__.py
    JarvisUSTC/DARDet
    debbf476e9750030db67f030a40cf8d4f03e46ee
    [ "Apache-2.0" ]
    274
    2021-04-06T15:46:06.000Z
    2022-03-31T02:00:10.000Z
    mmdet/ops/orn/functions/__init__.py
    JarvisUSTC/DARDet
    debbf476e9750030db67f030a40cf8d4f03e46ee
    [ "Apache-2.0" ]
    136
    2021-07-11T11:26:54.000Z
    2022-03-31T02:45:34.000Z
    mmdet/ops/orn/functions/__init__.py
    JarvisUSTC/DARDet
    debbf476e9750030db67f030a40cf8d4f03e46ee
    [ "Apache-2.0" ]
    84
    2021-05-29T06:58:14.000Z
    2022-03-31T07:44:10.000Z
    # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch from .active_rotating_filter import active_rotating_filter from .active_rotating_filter import ActiveRotatingFilter from .rotation_invariant_encoding import rotation_invariant_encoding from .rotation_invariant_encoding import RotationInvariantEncoding from .rotation_invariant_pooling import RotationInvariantPooling __all__ = ['ActiveRotatingFilter', 'active_rotating_filter', 'rotation_invariant_encoding', 'RotationInvariantEncoding', 'RotationInvariantPooling']
    61.222222
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    199
    0.361162
    131271d5663df6a63ce374c8c4958678283dd5fc
    218
    py
    Python
    sympy/core/tests/test_cache.py
    eriknw/sympy
    b7544e2bb74c011f6098a7e886fd77f41776c2c4
    [ "BSD-3-Clause" ]
    7
    2015-01-14T06:55:33.000Z
    2018-08-11T14:43:52.000Z
    sympy/core/tests/test_cache.py
    pbeltran/sympy-1
    94f92b36731c2bebe6de1037c063c2a258a8a399
    [ "BSD-3-Clause" ]
    1
    2018-02-19T04:56:04.000Z
    2018-02-19T04:56:04.000Z
    sympy/core/tests/test_cache.py
    pbeltran/sympy-1
    94f92b36731c2bebe6de1037c063c2a258a8a399
    [ "BSD-3-Clause" ]
    1
    2016-04-24T14:39:22.000Z
    2016-04-24T14:39:22.000Z
    from sympy.core.cache import cacheit def test_cacheit_doc(): @cacheit def testfn(): "test docstring" pass assert testfn.__doc__ == "test docstring" assert testfn.__name__ == "testfn"
    18.166667
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    40
    0.183486
    1312f8f0f49eb471bc17c6830c67ae3b593f1370
    8,694
    py
    Python
    mmdet/models/losses/ranking_losses.py
    VietDunghacker/VarifocalNet
    f57917afb3c29ceba1d3c4f824d10b9cc53aaa40
    [ "Apache-2.0" ]
    null
    null
    null
    mmdet/models/losses/ranking_losses.py
    VietDunghacker/VarifocalNet
    f57917afb3c29ceba1d3c4f824d10b9cc53aaa40
    [ "Apache-2.0" ]
    null
    null
    null
    mmdet/models/losses/ranking_losses.py
    VietDunghacker/VarifocalNet
    f57917afb3c29ceba1d3c4f824d10b9cc53aaa40
    [ "Apache-2.0" ]
    null
    null
    null
    import torch class RankSort(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets > 0.) fg_logits = logits[fg_labels] fg_targets = targets[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta_RS relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) relevant_bg_logits = logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() sorting_error=torch.zeros(fg_num).cuda() ranking_error=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: # Difference Transforms (x_ij) fg_relations=fg_logits-fg_logits[ii] bg_relations=relevant_bg_logits-fg_logits[ii] if delta_RS > 0: fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) else: fg_relations = (fg_relations >= 0).float() bg_relations = (bg_relations >= 0).float() # Rank of ii among pos and false positive number (bg with larger scores) rank_pos=torch.sum(fg_relations) FP_num=torch.sum(bg_relations) # Rank of ii among all examples rank=rank_pos+FP_num # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) ranking_error[ii]=FP_num/rank # Current sorting error of example ii. (Eq. 7) current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos #Find examples in the target sorted order for example ii iou_relations = (fg_targets >= fg_targets[ii]) target_sorted_order = iou_relations * fg_relations #The rank of ii among positives in sorted order rank_pos_target = torch.sum(target_sorted_order) #Compute target sorting error. (Eq. 8) #Since target ranking error is 0, this is also total target error target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target #Compute sorting error on example ii sorting_error[ii] = current_sorting_error - target_sorting_error #Identity Update for Ranking Error if FP_num > eps: #For ii the update is the ranking error fg_grad[ii] -= ranking_error[ii] #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) #Find the positives that are misranked (the cause of the error) #These are the ones with smaller IoU but larger logits missorted_examples = (~ iou_relations) * fg_relations #Denominotor of sorting pmf sorting_pmf_denom = torch.sum(missorted_examples) #Identity Update for Sorting Error if sorting_pmf_denom > eps: #For ii the update is the sorting error fg_grad[ii] -= sorting_error[ii] #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) #Normalize gradients by number of positives classification_grads[fg_labels]= (fg_grad/fg_num) classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) ctx.save_for_backward(classification_grads) return ranking_error.mean(), sorting_error.mean() @staticmethod def backward(ctx, out_grad1, out_grad2): g1, =ctx.saved_tensors return g1*out_grad1, None, None, None class aLRPLoss(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets == 1) fg_logits = logits[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta #Get valid bg logits relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) relevant_bg_logits=logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() rank=torch.zeros(fg_num).cuda() prec=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() max_prec=0 #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: #x_ij s as score differences with fgs fg_relations=fg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with fgs fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) #Discard i=j in the summation in rank_pos fg_relations[ii]=0 #x_ij s as score differences with bgs bg_relations=relevant_bg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with bgs bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) #Compute the rank of the example within fgs and number of bgs with larger scores rank_pos=1+torch.sum(fg_relations) FP_num=torch.sum(bg_relations) #Store the total since it is normalizer also for aLRP Regression error rank[ii]=rank_pos+FP_num #Compute precision for this example to compute classification loss prec[ii]=rank_pos/rank[ii] #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads if FP_num > eps: fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) #aLRP with grad formulation fg gradient classification_grads[fg_labels]= fg_grad #aLRP with grad formulation bg gradient classification_grads[relevant_bg_labels]= relevant_bg_grad classification_grads /= (fg_num) cls_loss=1-prec.mean() ctx.save_for_backward(classification_grads) return cls_loss, rank, order @staticmethod def backward(ctx, out_grad1, out_grad2, out_grad3): g1, =ctx.saved_tensors return g1*out_grad1, None, None, None, None class APLoss(torch.autograd.Function): @staticmethod def forward(ctx, logits, targets, delta=1.): classification_grads=torch.zeros(logits.shape).cuda() #Filter fg logits fg_labels = (targets == 1) fg_logits = logits[fg_labels] fg_num = len(fg_logits) #Do not use bg with scores less than minimum fg logit #since changing its score does not have an effect on precision threshold_logit = torch.min(fg_logits)-delta #Get valid bg logits relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) relevant_bg_logits=logits[relevant_bg_labels] relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() rank=torch.zeros(fg_num).cuda() prec=torch.zeros(fg_num).cuda() fg_grad=torch.zeros(fg_num).cuda() max_prec=0 #sort the fg logits order=torch.argsort(fg_logits) #Loops over each positive following the order for ii in order: #x_ij s as score differences with fgs fg_relations=fg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with fgs fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) #Discard i=j in the summation in rank_pos fg_relations[ii]=0 #x_ij s as score differences with bgs bg_relations=relevant_bg_logits-fg_logits[ii] #Apply piecewise linear function and determine relations with bgs bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) #Compute the rank of the example within fgs and number of bgs with larger scores rank_pos=1+torch.sum(fg_relations) FP_num=torch.sum(bg_relations) #Store the total since it is normalizer also for aLRP Regression error rank[ii]=rank_pos+FP_num #Compute precision for this example current_prec=rank_pos/rank[ii] #Compute interpolated AP and store gradients for relevant bg examples if (max_prec<=current_prec): max_prec=current_prec relevant_bg_grad += (bg_relations/rank[ii]) else: relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) #Store fg gradients fg_grad[ii]=-(1-max_prec) prec[ii]=max_prec #aLRP with grad formulation fg gradient classification_grads[fg_labels]= fg_grad #aLRP with grad formulation bg gradient classification_grads[relevant_bg_labels]= relevant_bg_grad classification_grads /= fg_num cls_loss=1-prec.mean() ctx.save_for_backward(classification_grads) return cls_loss @staticmethod def backward(ctx, out_grad1): g1, =ctx.saved_tensors return g1*out_grad1, None, None
    35.198381
    96
    0.743961
    8,672
    0.99747
    0
    0
    8,539
    0.982172
    0
    0
    2,846
    0.327352
    131342de18ae50cff3d8d09f0b5c640ef367d9c5
    997
    py
    Python
    tests/test_dcd_api.py
    sadamek/pyIMX
    52af15e656b400f0812f16cf31d9bf6edbe631ad
    [ "BSD-3-Clause" ]
    null
    null
    null
    tests/test_dcd_api.py
    sadamek/pyIMX
    52af15e656b400f0812f16cf31d9bf6edbe631ad
    [ "BSD-3-Clause" ]
    null
    null
    null
    tests/test_dcd_api.py
    sadamek/pyIMX
    52af15e656b400f0812f16cf31d9bf6edbe631ad
    [ "BSD-3-Clause" ]
    null
    null
    null
    # Copyright (c) 2017-2018 Martin Olejar # # SPDX-License-Identifier: BSD-3-Clause # The BSD-3-Clause license for this file can be found in the LICENSE file included with this distribution # or at https://spdx.org/licenses/BSD-3-Clause.html#licenseText import os import pytest from imx import img # Used Directories DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data') # Test Files DCD_TXT = os.path.join(DATA_DIR, 'dcd_test.txt') DCD_BIN = os.path.join(DATA_DIR, 'dcd_test.bin') def setup_module(module): # Prepare test environment pass def teardown_module(module): # Clean test environment pass def test_txt_parser(): with open(DCD_TXT, 'r') as f: dcd_obj = img.SegDCD.parse_txt(f.read()) assert dcd_obj is not None assert len(dcd_obj) == 12 def test_bin_parser(): with open(DCD_BIN, 'rb') as f: dcd_obj = img.SegDCD.parse(f.read()) assert dcd_obj is not None assert len(dcd_obj) == 12
    22.155556
    105
    0.691073
    0
    0
    0
    0
    0
    0
    0
    0
    368
    0.369107
    1313abf2371aefa93bced19321156374195a59c5
    9,387
    py
    Python
    recentjson.py
    nydailynews/feedutils
    8cb18b26ebf70033df420f3fece8c2cac363f918
    [ "MIT" ]
    null
    null
    null
    recentjson.py
    nydailynews/feedutils
    8cb18b26ebf70033df420f3fece8c2cac363f918
    [ "MIT" ]
    1
    2017-07-11T17:37:50.000Z
    2017-07-11T17:37:50.000Z
    recentjson.py
    nydailynews/feedutils
    8cb18b26ebf70033df420f3fece8c2cac363f918
    [ "MIT" ]
    null
    null
    null
    #!/usr/bin/env python # -*- coding: utf-8 -*- # Return recent items from a json feed. Recent means "In the last X days." import os import doctest import json import urllib2 import argparse import types import gzip from datetime import datetime, timedelta from time import mktime class RecentJson: """ Methods for ingesting and publishing JSON feeds. >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628' >>> parser = build_parser() >>> args = parser.parse_args([url]) >>> rj = RecentJson(args) """ def __init__(self, args={}): self.args = args if not hasattr(self.args, 'days'): self.args.days = 0 self.days = self.args.days self.date_format = '%a, %d %b %Y %X' def get(self, url): """ Wrapper for API requests. Take a URL, return a json array. >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628' >>> parser = build_parser() >>> args = parser.parse_args([url]) >>> rj = RecentJson(args) >>> rj.get(url) True """ response = urllib2.urlopen(url) if int(response.code) >= 400: if 'verbose' in self.args and self.args.verbose: print "URL: %s" % url raise ValueError("URL %s response: %s" % (url, response['status'])) self.xml = response.read() return True def parse(self): """ Turn the xml into an object. >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628' >>> parser = build_parser() >>> args = parser.parse_args([url]) >>> rj = RecentJson(args) >>> rj.get(url) True >>> xml = rj.parse() >>> print len(xml) 50 """ try: p = json.loads(self.xml) except: # Sometimes we download gzipped documents from the web. fh = open('json.gz', 'wb') fh.write(self.xml) fh.close() try: gz = gzip.GzipFile('json.gz', 'r').read() p = json.loads(gzip.GzipFile('json.gz', 'r').read()) except IOError: return None self.p = p return p def recently(self): """ Return a feedparser entry object for the last X days of feed entries. >>> url = 'http://www.nydailynews.com/json/cmlink/aaron-judge-1.3306628' >>> parser = build_parser() >>> args = parser.parse_args([url]) >>> rj = RecentJson(args) >>> rj.get(url) True >>> xml = rj.parse() >>> articles = rj.recently() """ items = [] for item in self.p: # print item.keys() # [u'body', u'tags', u'url', u'contentId', u'abstract', u'author', u'lastUpdated', u'mobileTitle', u'mobileUrl', u'publish_date', u'images', u'title', u'type', u'categories'] # print item['publish_date'] # Fri, 7 Jul 2017 15:16:38 -0400 #dt = datetime.strptime(item['publish_date'], '%a, %d %b %Y %X %z') dt = datetime.strptime(' '.join(item['publish_date'].split(' ')[:5]), self.date_format) delta = datetime.today() - dt if delta.days > int(self.days): continue items.append(item) if 'verbose' in self.args and self.args.verbose: print delta.days, dt self.items = items return items def pretty_date(ago): """ Process a timedelta object. From https://stackoverflow.com/questions/1551382/user-friendly-time-format-in-python """ second_diff = ago.seconds day_diff = ago.days if day_diff < 0: return '' if day_diff == 0: if second_diff < 10: return "just now" if second_diff < 60: return str(second_diff) + " seconds ago" if second_diff < 120: return "a minute ago" if second_diff < 3600: return str(second_diff / 60) + " minutes ago" if second_diff < 7200: return "an hour ago" if second_diff < 86400: return str(second_diff / 3600) + " hours ago" if day_diff == 1: return "Yesterday" if day_diff < 7: return str(day_diff) + " days ago" if day_diff < 31: if day_diff / 7 == 1: return str(day_diff / 7) + " week ago" return str(day_diff / 7) + " weeks ago" if day_diff < 365: if day_diff / 30 == 1: return str(day_diff / 30) + " month ago" return str(day_diff / 30) + " months ago" if day_diff / 365 == 1: return str(day_diff / 365) + " year ago" return str(day_diff / 365) + " years ago" def main(args): """ For command-line use. """ rj = RecentJson(args) if args: articles = [] for arg in args.urls[0]: if args.verbose: print arg rj.get(arg) try: p = rj.parse() except: continue if not p: continue articles.append(rj.recently()) if len(articles) is 0: return None for i, article in enumerate(articles[0]): if i >= args.limit and args.limit > 0: break dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X') ago = datetime.now() - dt # print ago # 2 days, 15:57:48.578638 if args.output == 'html': if type(article['title']) is types.UnicodeType: article['title'] = article['title'].encode('utf-8', 'replace') if args.listitem == True: print '<li><a href="{0}">{1}</a> <span>({2})</span></li>'.format(article['url'], article['title'], pretty_date(ago).lower()) elif args.nostamp == True: print '<li><a href="{0}">{1}</a></li>'.format(article['url'], article['title'], pretty_date(ago).lower()) else: print '<a href="{0}">{1}</a> <span>({2})</span>'.format(article['url'], article['title'], pretty_date(ago).lower()) if args.output == 'js': if type(article['title']) is types.UnicodeType: article['title'] = article['title'].encode('utf-8', 'replace') print 'var hed = "<a href=\'{0}\'>{1}</a> <span>({2})</span>";'.format(article['url'], article['title'].replace('"', '\\\\"'), pretty_date(ago).lower()) elif args.output == 'json': print json.dumps({'title': article['title'], 'id': article['id'], 'description': article['description']}) elif args.output == 'csv': dt = datetime.strptime(' '.join(article['publish_date'].split(' ')[:5]), '%a, %d %b %Y %X') article['datetime'] = '%s-%s-%s' % (dt.year, dt.month, dt.day) if dt.month < 10: article['datetime'] = '%d-0%d-%d' % (dt.year, dt.month, dt.day) if dt.day < 10: article['datetime'] = '%d-0%d-0%d' % (dt.year, dt.month, dt.day) article['slug'] = article['title'].lower().replace(' ', '-').replace('--', '-').replace(':', '') article['iframe_url'] = article['media_player']['url'] article['image_url'] = article['media_thumbnail'][0]['url'] article['image_large_url'] = article['media_thumbnail'][1]['url'] article['description'] = article['description'].replace('"', "'") # date,title,id,slug,player_url,image_url,image_large_url,keywords,description print '%(datetime)s,"%(title)s",%(id)s,%(slug)s,%(iframe_url)s,%(image_url)s,%(image_large_url)s,"%(media_keywords)s","%(description)s"' % article def build_parser(): """ We put the argparse in a method so we can test it outside of the command-line. """ parser = argparse.ArgumentParser(usage='$ python recentjson.py http://domain.com/json/', description='''Takes a list of URLs passed as args. Returns the items published today unless otherwise specified.''', epilog='') parser.add_argument("-v", "--verbose", dest="verbose", default=False, action="store_true") parser.add_argument("--test", dest="test", default=False, action="store_true") parser.add_argument("-d", "--days", dest="days", default=0) parser.add_argument("-l", "--limit", dest="limit", default=0, type=int) parser.add_argument("-o", "--output", dest="output", default="html", type=str) parser.add_argument("--li", dest="listitem", default=False, action="store_true") parser.add_argument("--ns", dest="nostamp", default=False, action="store_true") parser.add_argument("urls", action="append", nargs="*") return parser if __name__ == '__main__': """ """ parser = build_parser() args = parser.parse_args() if args.test: doctest.testmod(verbose=args.verbose) main(args)
    40.813043
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    3,823
    0.407265
    1314a6b3e97ad080ab7cf47017455ad35f9e033a
    34,521
    py
    Python
    maint/MultiStage2.py
    Liastre/pcre2
    ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8
    [ "BSD-3-Clause" ]
    null
    null
    null
    maint/MultiStage2.py
    Liastre/pcre2
    ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8
    [ "BSD-3-Clause" ]
    1
    2020-04-07T10:42:22.000Z
    2020-04-07T10:42:22.000Z
    maint/MultiStage2.py
    Liastre/pcre2
    ca4fd145ee16acbc67b52b8563ab6e25c67ddfc8
    [ "BSD-3-Clause" ]
    null
    null
    null
    #! /usr/bin/python # Multistage table builder # (c) Peter Kankowski, 2008 ############################################################################## # This script was submitted to the PCRE project by Peter Kankowski as part of # the upgrading of Unicode property support. The new code speeds up property # matching many times. The script is for the use of PCRE maintainers, to # generate the pcre2_ucd.c file that contains a digested form of the Unicode # data tables. A number of extensions have been added to the original script. # # The script has now been upgraded to Python 3 for PCRE2, and should be run in # the maint subdirectory, using the command # # [python3] ./MultiStage2.py >../src/pcre2_ucd.c # # It requires six Unicode data tables: DerivedGeneralCategory.txt, # GraphemeBreakProperty.txt, Scripts.txt, ScriptExtensions.txt, # CaseFolding.txt, and emoji-data.txt. These must be in the # maint/Unicode.tables subdirectory. # # DerivedGeneralCategory.txt is found in the "extracted" subdirectory of the # Unicode database (UCD) on the Unicode web site; GraphemeBreakProperty.txt is # in the "auxiliary" subdirectory. Scripts.txt, ScriptExtensions.txt, and # CaseFolding.txt are directly in the UCD directory. The emoji-data.txt file is # in files associated with Unicode Technical Standard #51 ("Unicode Emoji"), # for example: # # http://unicode.org/Public/emoji/11.0/emoji-data.txt # # ----------------------------------------------------------------------------- # Minor modifications made to this script: # Added #! line at start # Removed tabs # Made it work with Python 2.4 by rewriting two statements that needed 2.5 # Consequent code tidy # Adjusted data file names to take from the Unicode.tables directory # Adjusted global table names by prefixing _pcre_. # Commented out stuff relating to the casefolding table, which isn't used; # removed completely in 2012. # Corrected size calculation # Add #ifndef SUPPORT_UCP to use dummy tables when no UCP support is needed. # Update for PCRE2: name changes, and SUPPORT_UCP is abolished. # # Major modifications made to this script: # Added code to add a grapheme break property field to records. # # Added code to search for sets of more than two characters that must match # each other caselessly. A new table is output containing these sets, and # offsets into the table are added to the main output records. This new # code scans CaseFolding.txt instead of UnicodeData.txt, which is no longer # used. # # Update for Python3: # . Processed with 2to3, but that didn't fix everything # . Changed string.strip to str.strip # . Added encoding='utf-8' to the open() call # . Inserted 'int' before blocksize/ELEMS_PER_LINE because an int is # required and the result of the division is a float # # Added code to scan the emoji-data.txt file to find the Extended Pictographic # property, which is used by PCRE2 as a grapheme breaking property. This was # done when updating to Unicode 11.0.0 (July 2018). # # Added code to add a Script Extensions field to records. This has increased # their size from 8 to 12 bytes, only 10 of which are currently used. # # 01-March-2010: Updated list of scripts for Unicode 5.2.0 # 30-April-2011: Updated list of scripts for Unicode 6.0.0 # July-2012: Updated list of scripts for Unicode 6.1.0 # 20-August-2012: Added scan of GraphemeBreakProperty.txt and added a new # field in the record to hold the value. Luckily, the # structure had a hole in it, so the resulting table is # not much bigger than before. # 18-September-2012: Added code for multiple caseless sets. This uses the # final hole in the structure. # 30-September-2012: Added RegionalIndicator break property from Unicode 6.2.0 # 13-May-2014: Updated for PCRE2 # 03-June-2014: Updated for Python 3 # 20-June-2014: Updated for Unicode 7.0.0 # 12-August-2014: Updated to put Unicode version into the file # 19-June-2015: Updated for Unicode 8.0.0 # 02-July-2017: Updated for Unicode 10.0.0 # 03-July-2018: Updated for Unicode 11.0.0 # 07-July-2018: Added code to scan emoji-data.txt for the Extended # Pictographic property. # 01-October-2018: Added the 'Unknown' script name # 03-October-2018: Added new field for Script Extensions # 27-July-2019: Updated for Unicode 12.1.0 # ---------------------------------------------------------------------------- # # # The main tables generated by this script are used by macros defined in # pcre2_internal.h. They look up Unicode character properties using short # sequences of code that contains no branches, which makes for greater speed. # # Conceptually, there is a table of records (of type ucd_record), containing a # script number, script extension value, character type, grapheme break type, # offset to caseless matching set, offset to the character's other case, for # every Unicode character. However, a real table covering all Unicode # characters would be far too big. It can be efficiently compressed by # observing that many characters have the same record, and many blocks of # characters (taking 128 characters in a block) have the same set of records as # other blocks. This leads to a 2-stage lookup process. # # This script constructs six tables. The ucd_caseless_sets table contains # lists of characters that all match each other caselessly. Each list is # in order, and is terminated by NOTACHAR (0xffffffff), which is larger than # any valid character. The first list is empty; this is used for characters # that are not part of any list. # # The ucd_digit_sets table contains the code points of the '9' characters in # each set of 10 decimal digits in Unicode. This is used to ensure that digits # in script runs all come from the same set. The first element in the vector # contains the number of subsequent elements, which are in ascending order. # # The ucd_script_sets vector contains lists of script numbers that are the # Script Extensions properties of certain characters. Each list is terminated # by zero (ucp_Unknown). A character with more than one script listed for its # Script Extension property has a negative value in its record. This is the # negated offset to the start of the relevant list in the ucd_script_sets # vector. # # The ucd_records table contains one instance of every unique record that is # required. The ucd_stage1 table is indexed by a character's block number, # which is the character's code point divided by 128, since 128 is the size # of each block. The result of a lookup in ucd_stage1 a "virtual" block number. # # The ucd_stage2 table is a table of "virtual" blocks; each block is indexed by # the offset of a character within its own block, and the result is the index # number of the required record in the ucd_records vector. # # The following examples are correct for the Unicode 11.0.0 database. Future # updates may make change the actual lookup values. # # Example: lowercase "a" (U+0061) is in block 0 # lookup 0 in stage1 table yields 0 # lookup 97 (0x61) in the first table in stage2 yields 17 # record 17 is { 34, 5, 12, 0, -32, 34, 0 } # 34 = ucp_Latin => Latin script # 5 = ucp_Ll => Lower case letter # 12 = ucp_gbOther => Grapheme break property "Other" # 0 => Not part of a caseless set # -32 (-0x20) => Other case is U+0041 # 34 = ucp_Latin => No special Script Extension property # 0 => Dummy value, unused at present # # Almost all lowercase latin characters resolve to the same record. One or two # are different because they are part of a multi-character caseless set (for # example, k, K and the Kelvin symbol are such a set). # # Example: hiragana letter A (U+3042) is in block 96 (0x60) # lookup 96 in stage1 table yields 90 # lookup 66 (0x42) in table 90 in stage2 yields 564 # record 564 is { 27, 7, 12, 0, 0, 27, 0 } # 27 = ucp_Hiragana => Hiragana script # 7 = ucp_Lo => Other letter # 12 = ucp_gbOther => Grapheme break property "Other" # 0 => Not part of a caseless set # 0 => No other case # 27 = ucp_Hiragana => No special Script Extension property # 0 => Dummy value, unused at present # # Example: vedic tone karshana (U+1CD0) is in block 57 (0x39) # lookup 57 in stage1 table yields 55 # lookup 80 (0x50) in table 55 in stage2 yields 458 # record 458 is { 28, 12, 3, 0, 0, -101, 0 } # 28 = ucp_Inherited => Script inherited from predecessor # 12 = ucp_Mn => Non-spacing mark # 3 = ucp_gbExtend => Grapheme break property "Extend" # 0 => Not part of a caseless set # 0 => No other case # -101 => Script Extension list offset = 101 # 0 => Dummy value, unused at present # # At offset 101 in the ucd_script_sets vector we find the list 3, 15, 107, 29, # and terminator 0. This means that this character is expected to be used with # any of those scripts, which are Bengali, Devanagari, Grantha, and Kannada. # # Philip Hazel, 03 July 2008 # Last Updated: 07 October 2018 ############################################################################## import re import string import sys MAX_UNICODE = 0x110000 NOTACHAR = 0xffffffff # Parse a line of Scripts.txt, GraphemeBreakProperty.txt or DerivedGeneralCategory.txt def make_get_names(enum): return lambda chardata: enum.index(chardata[1]) # Parse a line of CaseFolding.txt def get_other_case(chardata): if chardata[1] == 'C' or chardata[1] == 'S': return int(chardata[2], 16) - int(chardata[0], 16) return 0 # Parse a line of ScriptExtensions.txt def get_script_extension(chardata): this_script_list = list(chardata[1].split(' ')) if len(this_script_list) == 1: return script_abbrevs.index(this_script_list[0]) script_numbers = [] for d in this_script_list: script_numbers.append(script_abbrevs.index(d)) script_numbers.append(0) script_numbers_length = len(script_numbers) for i in range(1, len(script_lists) - script_numbers_length + 1): for j in range(0, script_numbers_length): found = True if script_lists[i+j] != script_numbers[j]: found = False break if found: return -i # Not found in existing lists return_value = len(script_lists) script_lists.extend(script_numbers) return -return_value # Read the whole table in memory, setting/checking the Unicode version def read_table(file_name, get_value, default_value): global unicode_version f = re.match(r'^[^/]+/([^.]+)\.txt$', file_name) file_base = f.group(1) version_pat = r"^# " + re.escape(file_base) + r"-(\d+\.\d+\.\d+)\.txt$" file = open(file_name, 'r', encoding='utf-8') f = re.match(version_pat, file.readline()) version = f.group(1) if unicode_version == "": unicode_version = version elif unicode_version != version: print("WARNING: Unicode version differs in %s", file_name, file=sys.stderr) table = [default_value] * MAX_UNICODE for line in file: line = re.sub(r'#.*', '', line) chardata = list(map(str.strip, line.split(';'))) if len(chardata) <= 1: continue value = get_value(chardata) m = re.match(r'([0-9a-fA-F]+)(\.\.([0-9a-fA-F]+))?$', chardata[0]) char = int(m.group(1), 16) if m.group(3) is None: last = char else: last = int(m.group(3), 16) for i in range(char, last + 1): # It is important not to overwrite a previously set # value because in the CaseFolding file there are lines # to be ignored (returning the default value of 0) # which often come after a line which has already set # data. if table[i] == default_value: table[i] = value file.close() return table # Get the smallest possible C language type for the values def get_type_size(table): type_size = [("uint8_t", 1), ("uint16_t", 2), ("uint32_t", 4), ("signed char", 1), ("pcre_int16", 2), ("pcre_int32", 4)] limits = [(0, 255), (0, 65535), (0, 4294967295), (-128, 127), (-32768, 32767), (-2147483648, 2147483647)] minval = min(table) maxval = max(table) for num, (minlimit, maxlimit) in enumerate(limits): if minlimit <= minval and maxval <= maxlimit: return type_size[num] else: raise OverflowError("Too large to fit into C types") def get_tables_size(*tables): total_size = 0 for table in tables: type, size = get_type_size(table) total_size += size * len(table) return total_size # Compress the table into the two stages def compress_table(table, block_size): blocks = {} # Dictionary for finding identical blocks stage1 = [] # Stage 1 table contains block numbers (indices into stage 2 table) stage2 = [] # Stage 2 table contains the blocks with property values table = tuple(table) for i in range(0, len(table), block_size): block = table[i:i+block_size] start = blocks.get(block) if start is None: # Allocate a new block start = len(stage2) / block_size stage2 += block blocks[block] = start stage1.append(start) return stage1, stage2 # Print a table def print_table(table, table_name, block_size = None): type, size = get_type_size(table) ELEMS_PER_LINE = 16 s = "const %s %s[] = { /* %d bytes" % (type, table_name, size * len(table)) if block_size: s += ", block = %d" % block_size print(s + " */") table = tuple(table) if block_size is None: fmt = "%3d," * ELEMS_PER_LINE + " /* U+%04X */" mult = MAX_UNICODE / len(table) for i in range(0, len(table), ELEMS_PER_LINE): print(fmt % (table[i:i+ELEMS_PER_LINE] + (int(i * mult),))) else: if block_size > ELEMS_PER_LINE: el = ELEMS_PER_LINE else: el = block_size fmt = "%3d," * el + "\n" if block_size > ELEMS_PER_LINE: fmt = fmt * int(block_size / ELEMS_PER_LINE) for i in range(0, len(table), block_size): print(("/* block %d */\n" + fmt) % ((i / block_size,) + table[i:i+block_size])) print("};\n") # Extract the unique combinations of properties into records def combine_tables(*tables): records = {} index = [] for t in zip(*tables): i = records.get(t) if i is None: i = records[t] = len(records) index.append(i) return index, records def get_record_size_struct(records): size = 0 structure = '/* When recompiling tables with a new Unicode version, please check the\n' + \ 'types in this structure definition from pcre2_internal.h (the actual\n' + \ 'field names will be different):\n\ntypedef struct {\n' for i in range(len(records[0])): record_slice = [record[i] for record in records] slice_type, slice_size = get_type_size(record_slice) # add padding: round up to the nearest power of slice_size size = (size + slice_size - 1) & -slice_size size += slice_size structure += '%s property_%d;\n' % (slice_type, i) # round up to the first item of the next structure in array record_slice = [record[0] for record in records] slice_type, slice_size = get_type_size(record_slice) size = (size + slice_size - 1) & -slice_size structure += '} ucd_record;\n*/\n' return size, structure def test_record_size(): tests = [ \ ( [(3,), (6,), (6,), (1,)], 1 ), \ ( [(300,), (600,), (600,), (100,)], 2 ), \ ( [(25, 3), (6, 6), (34, 6), (68, 1)], 2 ), \ ( [(300, 3), (6, 6), (340, 6), (690, 1)], 4 ), \ ( [(3, 300), (6, 6), (6, 340), (1, 690)], 4 ), \ ( [(300, 300), (6, 6), (6, 340), (1, 690)], 4 ), \ ( [(3, 100000), (6, 6), (6, 123456), (1, 690)], 8 ), \ ( [(100000, 300), (6, 6), (123456, 6), (1, 690)], 8 ), \ ] for test in tests: size, struct = get_record_size_struct(test[0]) assert(size == test[1]) #print struct def print_records(records, record_size): print('const ucd_record PRIV(ucd_records)[] = { ' + \ '/* %d bytes, record size %d */' % (len(records) * record_size, record_size)) records = list(zip(list(records.keys()), list(records.values()))) records.sort(key = lambda x: x[1]) for i, record in enumerate(records): print((' {' + '%6d, ' * len(record[0]) + '}, /* %3d */') % (record[0] + (i,))) print('};\n') script_names = ['Unknown', 'Arabic', 'Armenian', 'Bengali', 'Bopomofo', 'Braille', 'Buginese', 'Buhid', 'Canadian_Aboriginal', 'Cherokee', 'Common', 'Coptic', 'Cypriot', 'Cyrillic', 'Deseret', 'Devanagari', 'Ethiopic', 'Georgian', 'Glagolitic', 'Gothic', 'Greek', 'Gujarati', 'Gurmukhi', 'Han', 'Hangul', 'Hanunoo', 'Hebrew', 'Hiragana', 'Inherited', 'Kannada', 'Katakana', 'Kharoshthi', 'Khmer', 'Lao', 'Latin', 'Limbu', 'Linear_B', 'Malayalam', 'Mongolian', 'Myanmar', 'New_Tai_Lue', 'Ogham', 'Old_Italic', 'Old_Persian', 'Oriya', 'Osmanya', 'Runic', 'Shavian', 'Sinhala', 'Syloti_Nagri', 'Syriac', 'Tagalog', 'Tagbanwa', 'Tai_Le', 'Tamil', 'Telugu', 'Thaana', 'Thai', 'Tibetan', 'Tifinagh', 'Ugaritic', 'Yi', # New for Unicode 5.0 'Balinese', 'Cuneiform', 'Nko', 'Phags_Pa', 'Phoenician', # New for Unicode 5.1 'Carian', 'Cham', 'Kayah_Li', 'Lepcha', 'Lycian', 'Lydian', 'Ol_Chiki', 'Rejang', 'Saurashtra', 'Sundanese', 'Vai', # New for Unicode 5.2 'Avestan', 'Bamum', 'Egyptian_Hieroglyphs', 'Imperial_Aramaic', 'Inscriptional_Pahlavi', 'Inscriptional_Parthian', 'Javanese', 'Kaithi', 'Lisu', 'Meetei_Mayek', 'Old_South_Arabian', 'Old_Turkic', 'Samaritan', 'Tai_Tham', 'Tai_Viet', # New for Unicode 6.0.0 'Batak', 'Brahmi', 'Mandaic', # New for Unicode 6.1.0 'Chakma', 'Meroitic_Cursive', 'Meroitic_Hieroglyphs', 'Miao', 'Sharada', 'Sora_Sompeng', 'Takri', # New for Unicode 7.0.0 'Bassa_Vah', 'Caucasian_Albanian', 'Duployan', 'Elbasan', 'Grantha', 'Khojki', 'Khudawadi', 'Linear_A', 'Mahajani', 'Manichaean', 'Mende_Kikakui', 'Modi', 'Mro', 'Nabataean', 'Old_North_Arabian', 'Old_Permic', 'Pahawh_Hmong', 'Palmyrene', 'Psalter_Pahlavi', 'Pau_Cin_Hau', 'Siddham', 'Tirhuta', 'Warang_Citi', # New for Unicode 8.0.0 'Ahom', 'Anatolian_Hieroglyphs', 'Hatran', 'Multani', 'Old_Hungarian', 'SignWriting', # New for Unicode 10.0.0 'Adlam', 'Bhaiksuki', 'Marchen', 'Newa', 'Osage', 'Tangut', 'Masaram_Gondi', 'Nushu', 'Soyombo', 'Zanabazar_Square', # New for Unicode 11.0.0 'Dogra', 'Gunjala_Gondi', 'Hanifi_Rohingya', 'Makasar', 'Medefaidrin', 'Old_Sogdian', 'Sogdian', # New for Unicode 12.0.0 'Elymaic', 'Nandinagari', 'Nyiakeng_Puachue_Hmong', 'Wancho' ] script_abbrevs = [ 'Zzzz', 'Arab', 'Armn', 'Beng', 'Bopo', 'Brai', 'Bugi', 'Buhd', 'Cans', 'Cher', 'Zyyy', 'Copt', 'Cprt', 'Cyrl', 'Dsrt', 'Deva', 'Ethi', 'Geor', 'Glag', 'Goth', 'Grek', 'Gujr', 'Guru', 'Hani', 'Hang', 'Hano', 'Hebr', 'Hira', 'Zinh', 'Knda', 'Kana', 'Khar', 'Khmr', 'Laoo', 'Latn', 'Limb', 'Linb', 'Mlym', 'Mong', 'Mymr', 'Talu', 'Ogam', 'Ital', 'Xpeo', 'Orya', 'Osma', 'Runr', 'Shaw', 'Sinh', 'Sylo', 'Syrc', 'Tglg', 'Tagb', 'Tale', 'Taml', 'Telu', 'Thaa', 'Thai', 'Tibt', 'Tfng', 'Ugar', 'Yiii', #New for Unicode 5.0 'Bali', 'Xsux', 'Nkoo', 'Phag', 'Phnx', #New for Unicode 5.1 'Cari', 'Cham', 'Kali', 'Lepc', 'Lyci', 'Lydi', 'Olck', 'Rjng', 'Saur', 'Sund', 'Vaii', #New for Unicode 5.2 'Avst', 'Bamu', 'Egyp', 'Armi', 'Phli', 'Prti', 'Java', 'Kthi', 'Lisu', 'Mtei', 'Sarb', 'Orkh', 'Samr', 'Lana', 'Tavt', #New for Unicode 6.0.0 'Batk', 'Brah', 'Mand', #New for Unicode 6.1.0 'Cakm', 'Merc', 'Mero', 'Plrd', 'Shrd', 'Sora', 'Takr', #New for Unicode 7.0.0 'Bass', 'Aghb', 'Dupl', 'Elba', 'Gran', 'Khoj', 'Sind', 'Lina', 'Mahj', 'Mani', 'Mend', 'Modi', 'Mroo', 'Nbat', 'Narb', 'Perm', 'Hmng', 'Palm', 'Phlp', 'Pauc', 'Sidd', 'Tirh', 'Wara', #New for Unicode 8.0.0 'Ahom', 'Hluw', 'Hatr', 'Mult', 'Hung', 'Sgnw', #New for Unicode 10.0.0 'Adlm', 'Bhks', 'Marc', 'Newa', 'Osge', 'Tang', 'Gonm', 'Nshu', 'Soyo', 'Zanb', #New for Unicode 11.0.0 'Dogr', 'Gong', 'Rohg', 'Maka', 'Medf', 'Sogo', 'Sogd', #New for Unicode 12.0.0 'Elym', 'Nand', 'Hmnp', 'Wcho' ] category_names = ['Cc', 'Cf', 'Cn', 'Co', 'Cs', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu', 'Mc', 'Me', 'Mn', 'Nd', 'Nl', 'No', 'Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps', 'Sc', 'Sk', 'Sm', 'So', 'Zl', 'Zp', 'Zs' ] # The Extended_Pictographic property is not found in the file where all the # others are (GraphemeBreakProperty.txt). It comes from the emoji-data.txt # file, but we list it here so that the name has the correct index value. break_property_names = ['CR', 'LF', 'Control', 'Extend', 'Prepend', 'SpacingMark', 'L', 'V', 'T', 'LV', 'LVT', 'Regional_Indicator', 'Other', 'ZWJ', 'Extended_Pictographic' ] test_record_size() unicode_version = "" script = read_table('Unicode.tables/Scripts.txt', make_get_names(script_names), script_names.index('Unknown')) category = read_table('Unicode.tables/DerivedGeneralCategory.txt', make_get_names(category_names), category_names.index('Cn')) break_props = read_table('Unicode.tables/GraphemeBreakProperty.txt', make_get_names(break_property_names), break_property_names.index('Other')) other_case = read_table('Unicode.tables/CaseFolding.txt', get_other_case, 0) # The grapheme breaking rules were changed for Unicode 11.0.0 (June 2018). Now # we need to find the Extended_Pictographic property for emoji characters. This # can be set as an additional grapheme break property, because the default for # all the emojis is "other". We scan the emoji-data.txt file and modify the # break-props table. file = open('Unicode.tables/emoji-data.txt', 'r', encoding='utf-8') for line in file: line = re.sub(r'#.*', '', line) chardata = list(map(str.strip, line.split(';'))) if len(chardata) <= 1: continue if chardata[1] != "Extended_Pictographic": continue m = re.match(r'([0-9a-fA-F]+)(\.\.([0-9a-fA-F]+))?$', chardata[0]) char = int(m.group(1), 16) if m.group(3) is None: last = char else: last = int(m.group(3), 16) for i in range(char, last + 1): if break_props[i] != break_property_names.index('Other'): print("WARNING: Emoji 0x%x has break property %s, not 'Other'", i, break_property_names[break_props[i]], file=sys.stderr) break_props[i] = break_property_names.index('Extended_Pictographic') file.close() # The Script Extensions property default value is the Script value. Parse the # file, setting 'Unknown' as the default (this will never be a Script Extension # value), then scan it and fill in the default from Scripts. Code added by PH # in October 2018. Positive values are used for just a single script for a # code point. Negative values are negated offsets in a list of lists of # multiple scripts. Initialize this list with a single entry, as the zeroth # element is never used. script_lists = [0] script_abbrevs_default = script_abbrevs.index('Zzzz') scriptx = read_table('Unicode.tables/ScriptExtensions.txt', get_script_extension, script_abbrevs_default) for i in range(0, MAX_UNICODE): if scriptx[i] == script_abbrevs_default: scriptx[i] = script[i] # With the addition of the new Script Extensions field, we need some padding # to get the Unicode records up to 12 bytes (multiple of 4). Set a value # greater than 255 to make the field 16 bits. padding_dummy = [0] * MAX_UNICODE padding_dummy[0] = 256 # This block of code was added by PH in September 2012. I am not a Python # programmer, so the style is probably dreadful, but it does the job. It scans # the other_case table to find sets of more than two characters that must all # match each other caselessly. Later in this script a table of these sets is # written out. However, we have to do this work here in order to compute the # offsets in the table that are inserted into the main table. # The CaseFolding.txt file lists pairs, but the common logic for reading data # sets only one value, so first we go through the table and set "return" # offsets for those that are not already set. for c in range(MAX_UNICODE): if other_case[c] != 0 and other_case[c + other_case[c]] == 0: other_case[c + other_case[c]] = -other_case[c] # Now scan again and create equivalence sets. sets = [] for c in range(MAX_UNICODE): o = c + other_case[c] # Trigger when this character's other case does not point back here. We # now have three characters that are case-equivalent. if other_case[o] != -other_case[c]: t = o + other_case[o] # Scan the existing sets to see if any of the three characters are already # part of a set. If so, unite the existing set with the new set. appended = 0 for s in sets: found = 0 for x in s: if x == c or x == o or x == t: found = 1 # Add new characters to an existing set if found: found = 0 for y in [c, o, t]: for x in s: if x == y: found = 1 if not found: s.append(y) appended = 1 # If we have not added to an existing set, create a new one. if not appended: sets.append([c, o, t]) # End of loop looking for caseless sets. # Now scan the sets and set appropriate offsets for the characters. caseless_offsets = [0] * MAX_UNICODE offset = 1; for s in sets: for x in s: caseless_offsets[x] = offset offset += len(s) + 1 # End of block of code for creating offsets for caseless matching sets. # Combine the tables table, records = combine_tables(script, category, break_props, caseless_offsets, other_case, scriptx, padding_dummy) record_size, record_struct = get_record_size_struct(list(records.keys())) # Find the optimum block size for the two-stage table min_size = sys.maxsize for block_size in [2 ** i for i in range(5,10)]: size = len(records) * record_size stage1, stage2 = compress_table(table, block_size) size += get_tables_size(stage1, stage2) #print "/* block size %5d => %5d bytes */" % (block_size, size) if size < min_size: min_size = size min_stage1, min_stage2 = stage1, stage2 min_block_size = block_size print("/* This module is generated by the maint/MultiStage2.py script.") print("Do not modify it by hand. Instead modify the script and run it") print("to regenerate this code.") print() print("As well as being part of the PCRE2 library, this module is #included") print("by the pcre2test program, which redefines the PRIV macro to change") print("table names from _pcre2_xxx to xxxx, thereby avoiding name clashes") print("with the library. At present, just one of these tables is actually") print("needed. */") print() print("#ifndef PCRE2_PCRE2TEST") print() print("#ifdef HAVE_CONFIG_H") print("#include \"config.h\"") print("#endif") print() print("#include \"pcre2_internal.h\"") print() print("#endif /* PCRE2_PCRE2TEST */") print() print("/* Unicode character database. */") print("/* This file was autogenerated by the MultiStage2.py script. */") print("/* Total size: %d bytes, block size: %d. */" % (min_size, min_block_size)) print() print("/* The tables herein are needed only when UCP support is built,") print("and in PCRE2 that happens automatically with UTF support.") print("This module should not be referenced otherwise, so") print("it should not matter whether it is compiled or not. However") print("a comment was received about space saving - maybe the guy linked") print("all the modules rather than using a library - so we include a") print("condition to cut out the tables when not needed. But don't leave") print("a totally empty module because some compilers barf at that.") print("Instead, just supply some small dummy tables. */") print() print("#ifndef SUPPORT_UNICODE") print("const ucd_record PRIV(ucd_records)[] = {{0,0,0,0,0,0,0 }};") print("const uint16_t PRIV(ucd_stage1)[] = {0};") print("const uint16_t PRIV(ucd_stage2)[] = {0};") print("const uint32_t PRIV(ucd_caseless_sets)[] = {0};") print("#else") print() print("const char *PRIV(unicode_version) = \"{}\";".format(unicode_version)) print() print("/* If the 32-bit library is run in non-32-bit mode, character values") print("greater than 0x10ffff may be encountered. For these we set up a") print("special record. */") print() print("#if PCRE2_CODE_UNIT_WIDTH == 32") print("const ucd_record PRIV(dummy_ucd_record)[] = {{") print(" ucp_Unknown, /* script */") print(" ucp_Cn, /* type unassigned */") print(" ucp_gbOther, /* grapheme break property */") print(" 0, /* case set */") print(" 0, /* other case */") print(" ucp_Unknown, /* script extension */") print(" 0, /* dummy filler */") print(" }};") print("#endif") print() print(record_struct) # --- Added by PH: output the table of caseless character sets --- print("/* This table contains lists of characters that are caseless sets of") print("more than one character. Each list is terminated by NOTACHAR. */\n") print("const uint32_t PRIV(ucd_caseless_sets)[] = {") print(" NOTACHAR,") for s in sets: s = sorted(s) for x in s: print(' 0x%04x,' % x, end=' ') print(' NOTACHAR,') print('};') print() # ------ print("/* When #included in pcre2test, we don't need the table of digit") print("sets, nor the the large main UCD tables. */") print() print("#ifndef PCRE2_PCRE2TEST") print() # --- Added by PH: read Scripts.txt again for the sets of 10 digits. --- digitsets = [] file = open('Unicode.tables/Scripts.txt', 'r', encoding='utf-8') for line in file: m = re.match(r'([0-9a-fA-F]+)\.\.([0-9a-fA-F]+)\s+;\s+\S+\s+#\s+Nd\s+', line) if m is None: continue first = int(m.group(1),16) last = int(m.group(2),16) if ((last - first + 1) % 10) != 0: print("ERROR: %04x..%04x does not contain a multiple of 10 characters" % (first, last), file=sys.stderr) while first < last: digitsets.append(first + 9) first += 10 file.close() digitsets.sort() print("/* This table lists the code points for the '9' characters in each") print("set of decimal digits. It is used to ensure that all the digits in") print("a script run come from the same set. */\n") print("const uint32_t PRIV(ucd_digit_sets)[] = {") print(" %d, /* Number of subsequent values */" % len(digitsets), end='') count = 8 for d in digitsets: if count == 8: print("\n ", end='') count = 0 print(" 0x%05x," % d, end='') count += 1 print("\n};\n") print("/* This vector is a list of lists of scripts for the Script Extension") print("property. Each sublist is zero-terminated. */\n") print("const uint8_t PRIV(ucd_script_sets)[] = {") count = 0 print(" /* 0 */", end='') for d in script_lists: print(" %3d," % d, end='') count += 1 if d == 0: print("\n /* %3d */" % count, end='') print("\n};\n") # Output the main UCD tables. print("/* These are the main two-stage UCD tables. The fields in each record are:") print("script (8 bits), character type (8 bits), grapheme break property (8 bits),") print("offset to multichar other cases or zero (8 bits), offset to other case") print("or zero (32 bits, signed), script extension (16 bits, signed), and a dummy") print("16-bit field to make the whole thing a multiple of 4 bytes. */\n") print_records(records, record_size) print_table(min_stage1, 'PRIV(ucd_stage1)') print_table(min_stage2, 'PRIV(ucd_stage2)', min_block_size) print("#if UCD_BLOCK_SIZE != %d" % min_block_size) print("#error Please correct UCD_BLOCK_SIZE in pcre2_internal.h") print("#endif") print("#endif /* SUPPORT_UNICODE */") print() print("#endif /* PCRE2_PCRE2TEST */") # This code was part of the original contribution, but is commented out as it # was never used. A two-stage table has sufficed. """ # Three-stage tables: # Find the optimum block size for 3-stage table min_size = sys.maxint for stage3_block in [2 ** i for i in range(2,6)]: stage_i, stage3 = compress_table(table, stage3_block) for stage2_block in [2 ** i for i in range(5,10)]: size = len(records) * 4 stage1, stage2 = compress_table(stage_i, stage2_block) size += get_tables_size(stage1, stage2, stage3) # print "/* %5d / %3d => %5d bytes */" % (stage2_block, stage3_block, size) if size < min_size: min_size = size min_stage1, min_stage2, min_stage3 = stage1, stage2, stage3 min_stage2_block, min_stage3_block = stage2_block, stage3_block print "/* Total size: %d bytes" % min_size */ print_records(records) print_table(min_stage1, 'ucd_stage1') print_table(min_stage2, 'ucd_stage2', min_stage2_block) print_table(min_stage3, 'ucd_stage3', min_stage3_block) """
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    Python
    setup.py
    ihayhurst/RetroBioCat
    d674897459c0ab65faad5ed3017c55cf51bcc020
    [ "MIT" ]
    9
    2020-12-01T16:33:02.000Z
    2022-01-19T20:02:42.000Z
    setup.py
    ihayhurst/RetroBioCat
    d674897459c0ab65faad5ed3017c55cf51bcc020
    [ "MIT" ]
    4
    2020-10-02T14:38:32.000Z
    2021-08-02T09:23:58.000Z
    setup.py
    ihayhurst/RetroBioCat
    d674897459c0ab65faad5ed3017c55cf51bcc020
    [ "MIT" ]
    6
    2021-01-14T07:48:36.000Z
    2022-03-20T17:34:27.000Z
    from setuptools import setup, find_packages from retrobiocat_web import __version__ with open('requirements.txt') as f: requirements = f.read().splitlines() setup( name = 'retrobiocat_web', packages = find_packages(), include_package_data=True, version = __version__, license='', description = 'Retrosynthesis', author = 'William Finnigan', author_email = '[email protected]', url = '', download_url = '', keywords = ['enzyme'], install_requires=requirements, classifiers=[ 'Development Status :: 3 - Alpha', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3'], )
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    1,748
    py
    Python
    rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py
    Lung-Yi/rxn_yield_context
    116d6f21a1b6dc39016d87c001dc5b142cfb697a
    [ "MIT" ]
    null
    null
    null
    rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py
    Lung-Yi/rxn_yield_context
    116d6f21a1b6dc39016d87c001dc5b142cfb697a
    [ "MIT" ]
    null
    null
    null
    rxn_yield_context/preprocess_data/preprocess/augmentation_utils.py
    Lung-Yi/rxn_yield_context
    116d6f21a1b6dc39016d87c001dc5b142cfb697a
    [ "MIT" ]
    null
    null
    null
    # -*- coding: utf-8 -*- import pickle import numpy as np from rdkit import Chem from rdkit.Chem import AllChem,DataStructs def get_classes(path): f = open(path, 'rb') dict_ = pickle.load(f) f.close() classes = sorted(dict_.items(), key=lambda d: d[1],reverse=True) classes = [(x,y) for x,y in classes] return classes def create_rxn_Morgan2FP_concatenate(rsmi, psmi, rxnfpsize=16384, pfpsize=16384, useFeatures=False, calculate_rfp=True, useChirality=True): # Similar as the above function but takes smiles separately and returns pfp and rfp separately rsmi = rsmi.encode('utf-8') psmi = psmi.encode('utf-8') try: mol = Chem.MolFromSmiles(rsmi) except Exception as e: print(e) return try: fp_bit = AllChem.GetMorganFingerprintAsBitVect( mol=mol, radius=2, nBits=rxnfpsize, useFeatures=useFeatures, useChirality=useChirality) fp = np.empty(rxnfpsize, dtype='float32') DataStructs.ConvertToNumpyArray(fp_bit, fp) except Exception as e: print("Cannot build reactant fp due to {}".format(e)) return rfp = fp try: mol = Chem.MolFromSmiles(psmi) except Exception as e: return try: fp_bit = AllChem.GetMorganFingerprintAsBitVect( mol=mol, radius=2, nBits=pfpsize, useFeatures=useFeatures, useChirality=useChirality) fp = np.empty(pfpsize, dtype='float32') DataStructs.ConvertToNumpyArray(fp_bit, fp) except Exception as e: print("Cannot build product fp due to {}".format(e)) return pfp = fp rxn_fp = pfp - rfp final_fp = np.concatenate((pfp, rxn_fp)) return final_fp
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    131665ba7b9465c31b9a3f7865c4b018c27a3aec
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    py
    Python
    src/webstruct-demo/__init__.py
    zanachka/webstruct-demo
    f5b5081760d9a2b7924704041cd74748a5c98664
    [ "MIT" ]
    5
    2019-04-15T14:54:23.000Z
    2020-10-03T04:47:12.000Z
    src/webstruct-demo/__init__.py
    zanachka/webstruct-demo
    f5b5081760d9a2b7924704041cd74748a5c98664
    [ "MIT" ]
    2
    2021-06-01T22:49:44.000Z
    2021-12-13T19:51:11.000Z
    src/webstruct-demo/__init__.py
    zanachka/webstruct-demo
    f5b5081760d9a2b7924704041cd74748a5c98664
    [ "MIT" ]
    3
    2019-06-25T10:31:30.000Z
    2020-10-03T04:49:01.000Z
    import functools import logging import random from flask import Flask, render_template, request import joblib from lxml.html import html5parser import lxml.html import requests import yarl import webstruct.model import webstruct.sequence_encoding import webstruct.webannotator webstruct_demo = Flask(__name__, instance_relative_config=True) webstruct_demo.config.from_pyfile('config.py') def absolutize_link(link, base_url): if link.startswith('#'): return link try: target_url = yarl.URL(link) except: return link if target_url.is_absolute() and target_url.scheme: return link if target_url.is_absolute() and not target_url.scheme: target_url = target_url.with_scheme(base_url.scheme) return str(target_url) try: target_url = base_url.join(target_url) except: return link return str(target_url) def absolute_links(tree, url): _LINK_SOURCES = ['src', 'href'] try: base_url = yarl.URL(url) except: return tree for _, element in lxml.html.etree.iterwalk(tree, events=('start', )): if not isinstance(element.tag, str): continue for attr in _LINK_SOURCES: if attr not in element.attrib: continue element.attrib[attr] = absolutize_link(element.attrib[attr], base_url) return tree def parent_links(tree, base_url): base_url = yarl.URL(base_url) for _, element in lxml.html.etree.iterwalk(tree, events=('start', )): if not isinstance(element.tag, str): continue if element.tag != 'a': continue if 'href' not in element.attrib: continue url = element.attrib['href'] if url.startswith('#'): continue element.attrib['target'] = '_parent' element.attrib['href'] = str(base_url.update_query(url=url)) return tree def remove_namespace(tree): _NS="{http://www.w3.org/1999/xhtml}" for _, element in lxml.html.etree.iterwalk(tree, events=('start', )): if not isinstance(element.tag, str): continue if not element.tag.startswith(_NS): continue element.tag = element.tag[len(_NS):] return tree _TOKENS_PER_PART = 2000 def run_model(tree, model): html_tokens, _ = model.html_tokenizer.tokenize_single(tree) if not html_tokens: return tree, list(), list() tree = html_tokens[0].elem.getroottree().getroot() tags = model.model.predict([html_tokens[i:i+_TOKENS_PER_PART] for i in range(0, len(html_tokens), _TOKENS_PER_PART)]) tags = [i for t in tags for i in t] return tree, html_tokens, tags def download(url): splash_url = webstruct_demo.config.get('SPLASH_URL', None) splash_user = webstruct_demo.config.get('SPLASH_USER', None) splash_pass = webstruct_demo.config.get('SPLASH_PASS', None) is_splash = functools.reduce(lambda x,y: x and y is not None, [splash_url, splash_user, splash_pass], True) if not is_splash: response = requests.get(url) return response.content, response.url load = {'url': url, 'images': 0, 'base_url': url} response = requests.post(splash_url + '/render.html', json=load, auth=requests.auth.HTTPBasicAuth(splash_user, splash_pass)) return response.content, url def extract_ner(response_content, response_url, base_url): url = response_url tree = html5parser.document_fromstring(response_content) tree = remove_namespace(tree) tree = absolute_links(tree, url) tree = parent_links(tree, base_url) title = tree.xpath('//title')[0].text model = joblib.load(webstruct_demo.config['MODEL_PATH']) tree, tokens, tags = run_model(tree, model) tree = model.html_tokenizer.detokenize_single(tokens, tags) tree = webstruct.webannotator.to_webannotator( tree, entity_colors=model.entity_colors, url=url ) content = lxml.html.tostring(tree, encoding='utf-8').decode('utf-8') entities = webstruct.sequence_encoding.IobEncoder.group(zip(tokens, tags)) entities = webstruct.model._drop_empty( (model.build_entity(tokens), tag) for (tokens, tag) in entities if tag != 'O' ) groups = webstruct.model.extract_entitiy_groups( tokens, tags, dont_penalize=None, join_tokens=model.build_entity ) return content, title, entities, groups def sample_entities(entities): unique = list(set(entities)) random.shuffle(unique) sampled = unique[:5] sampled = sorted(sampled, key=lambda e:(e[1], e[0])) return sampled def sample_groups(groups): groups = [tuple(sorted(g)) for g in groups] sampled = sorted(list(set(groups)), key=lambda g:-len(g)) return sampled[:2] @webstruct_demo.route('/') def index(): url = request.args.get('url', 'http://en.wikipedia.org/') output = request.args.get('output', 'html') try: response_content, response_url = download(url) content, title, entities, groups = extract_ner(response_content, response_url, request.url) except: logging.exception('Got exception') content = None title = 'Error during obtaining %s' % (url, ) entities = [] groups = [] _TEMPLATE_MAPPING = {'html': 'main.html', 'entities': 'entities.html', 'groups': 'groups.html'} template = _TEMPLATE_MAPPING.get(output, _TEMPLATE_MAPPING['html']) sampled_entities = sample_entities(entities) sampled_groups = sample_groups(groups) base_url = yarl.URL(request.url) routing = {t: str(base_url.update_query(output=t)) for t in ['html', 'entities', 'groups']} values = {'url': url, 'title': title, 'entities': entities, 'sampled_entities': sampled_entities, 'sampled_groups': sampled_groups, 'routing': routing, 'srcdoc': content, 'groups': groups, 'output': output} return render_template(template, **values)
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    py
    Python
    setup.py
    Liang813/einops
    9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c
    [ "MIT" ]
    4,738
    2018-10-30T08:38:50.000Z
    2022-03-31T17:35:50.000Z
    setup.py
    Liang813/einops
    9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c
    [ "MIT" ]
    120
    2018-10-30T09:04:01.000Z
    2022-03-27T11:27:30.000Z
    setup.py
    Liang813/einops
    9edce3d9a2d0a2abc51a6aaf86678eac43ffac0c
    [ "MIT" ]
    216
    2018-11-09T02:50:30.000Z
    2022-03-30T05:46:44.000Z
    __author__ = 'Alex Rogozhnikov' from setuptools import setup setup( name="einops", version='0.3.2', description="A new flavour of deep learning operations", long_description=open('README.md', encoding='utf-8').read(), long_description_content_type='text/markdown', url='https://github.com/arogozhnikov/einops', author='Alex Rogozhnikov', packages=['einops', 'einops.layers'], classifiers=[ 'Intended Audience :: Science/Research', 'Programming Language :: Python :: 3 ', ], keywords='deep learning, neural networks, tensor manipulation, machine learning, ' 'scientific computations, einops', install_requires=[ # no run-time or installation-time dependencies ], )
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    0.554822
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    1,146
    py
    Python
    website/migrations/0084_auto_20210215_1401.py
    czhu1217/cmimc-online
    5ef49ceec0bb86d8ae120a6ecfd723532e277821
    [ "MIT" ]
    null
    null
    null
    website/migrations/0084_auto_20210215_1401.py
    czhu1217/cmimc-online
    5ef49ceec0bb86d8ae120a6ecfd723532e277821
    [ "MIT" ]
    1
    2022-01-23T21:08:12.000Z
    2022-01-23T21:08:12.000Z
    website/migrations/0084_auto_20210215_1401.py
    czhu1217/cmimc-online
    5ef49ceec0bb86d8ae120a6ecfd723532e277821
    [ "MIT" ]
    1
    2021-10-17T17:11:42.000Z
    2021-10-17T17:11:42.000Z
    # Generated by Django 3.1.6 on 2021-02-15 19:01 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('website', '0083_remove_aisubmission_code'), ] operations = [ migrations.AddField( model_name='exam', name='division', field=models.IntegerField(default=1), preserve_default=False, ), migrations.CreateModel( name='ExamPair', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, unique=True)), ('contest', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='exampairs', to='website.contest')), ], ), migrations.AddField( model_name='exam', name='exampair', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='exams', to='website.exampair'), ), ]
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    0.178883
    1317fa461eecfb928fe1d73e0f3c19ec9defb396
    14,667
    py
    Python
    ldp/tasks/dlp.py
    evandez/low-dimensional-probing
    3e4af6644a4db7fdf48bc40c5de4815f9db52a6e
    [ "MIT" ]
    1
    2022-03-06T06:59:42.000Z
    2022-03-06T06:59:42.000Z
    ldp/tasks/dlp.py
    evandez/low-dimensional-probing
    3e4af6644a4db7fdf48bc40c5de4815f9db52a6e
    [ "MIT" ]
    null
    null
    null
    ldp/tasks/dlp.py
    evandez/low-dimensional-probing
    3e4af6644a4db7fdf48bc40c5de4815f9db52a6e
    [ "MIT" ]
    null
    null
    null
    """Core experiments for the dependency label prediction task.""" import collections import copy import logging from typing import (Any, Dict, Iterator, Optional, Sequence, Set, Tuple, Type, Union) from ldp import datasets, learning from ldp.models import probes, projections from ldp.parse import ptb from ldp.parse import representations as reps from ldp.utils.typing import Device import numpy import torch import wandb UNK = 'unk' class DLPIndexer: """Map pairs of words to their syntactic relationship, if any.""" def __init__(self, samples: Sequence[ptb.Sample], unk: str = UNK): """Map each relation label to an integer. Args: samples (Sequence[ptb.Sample]): The samples from which to determine possible relations. unk (str): Label to use when un-indexed dependency label is encountered. """ labels = {rel for sample in samples for rel in sample.relations} self.indexer = {unk: 0} for label in sorted(labels): self.indexer[label] = len(self.indexer) self.unk = unk def __call__(self, sample: ptb.Sample) -> torch.Tensor: """Map all possible (word, word) pairs to labels. Args: sample (ptb.Sample): The sample to label. Returns: torch.Tensor: For length W sentence, returns shape (W, W) matrix where element (v, w) is the index of the label describing the relationship between word v and w, if any. Defaults to the "unk" label, even if there is no relationship between v and w. """ heads, relations = sample.heads, sample.relations labels = torch.empty(len(heads), len(heads), dtype=torch.long) labels.fill_(self.indexer[self.unk]) for word, (head, rel) in enumerate(zip(heads, relations)): if head == -1: labels[word, word] = self.indexer[rel] else: label = self.indexer.get(rel, self.indexer[self.unk]) labels[word, head] = label return labels def __len__(self) -> int: """Return the number of unique labels for this task.""" return len(self.indexer) class ControlDLPIndexer: """Map pairs of words to arbitrary syntactic relationships.""" def __init__(self, samples: Sequence[ptb.Sample], dist: Optional[Union[numpy.ndarray, Sequence[float]]] = None): """Map each relation label to an arbitrary (integer) label. We only do this for pairs of words which have a head-dependent relationship in the original dataset. Args: samples (Sequence[ptb.Samples]): The samples from which to pull possible word pairs. dist (Optional[Union[numpy.ndarray, Sequence[float]]], optional): A distribution to use when sampling tags per word type. By default, is computed from the list of samples. """ if dist is None: counts: Dict[str, int] = collections.defaultdict(lambda: 0) for sample in samples: for relation in sample.relations: counts[relation] += 1 dist = numpy.array([float(count) for count in counts.values()]) dist /= numpy.sum(dist) assert dist is not None, 'uninitialized distribution?' self.dist = dist self.rels: Dict[Tuple[str, str], int] = {} for sample in samples: sentence = sample.sentence heads = sample.heads for dep, head in enumerate(heads): if head == -1: head = dep words = (sentence[dep], sentence[head]) if words not in self.rels: # Add one so that 0 is reserved for "no relationship" tag. rel = numpy.random.choice(len(dist), p=dist) + 1 self.rels[words] = rel def __call__(self, sample: ptb.Sample) -> torch.Tensor: """Map all possible (word, word) pairs to labels. Args: sample (ptb.Sample): The sample to label. Returns: torch.Tensor: For length W sentence, returns shape (W, W) matrix where element (v, w) is the index of the label describing the relationship between word v and w, if any. Defaults to the "unk" label, even if there is no relationship between v and w. """ heads = sample.heads labels = torch.zeros(len(heads), len(heads), dtype=torch.long) for dep, head in enumerate(heads): if head == -1: head = dep words = (sample.sentence[dep], sample.sentence[head]) labels[dep, head] = self.rels.get(words, 0) return labels def __len__(self) -> int: """Return the number of relationships, including the null one.""" return len(self.dist) + 1 class DLPTaskDataset(datasets.TaskDataset): """Iterate over (word representation pair, dependency label) pairs.""" def __init__( self, representations: reps.RepresentationLayerDataset, annotations: Sequence[ptb.Sample], indexer: Type[Union[DLPIndexer, ControlDLPIndexer]] = DLPIndexer, **kwargs: Any, ): """Initialize dataset by mapping each dependency label to an index. The kwargs are forwarded to indexer when it is instantiated. Args: representations (representations.RepresentationsLayerDataset): Word representations corresponding to the words to be paired and labeled. annotations (Sequence[ptb.PTBSample]): The PTB annotations from which to pull dependency labels. indexer (Union[DLPIndexer, ControlDLPIndexer]): Type of the indexer to use for mapping PTB dependency label annotations to integer tensors. Instantiated with given annotations unless the samples keyword is set in kwargs. Raises: ValueError: If number of representations/annotations do not match. """ if len(representations) != len(annotations): raise ValueError(f'got {len(representations)} representations ' f'but {len(annotations)} annotations') self.representations = representations self.annotations = annotations kwargs = kwargs.copy() kwargs.setdefault('samples', annotations) self.indexer = indexer(**kwargs) def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]: """Return (representations, integral POS tags) for index'th sentence. Args: index (int): Index of the sentence in the dataset. Returns: Tuple[torch.Tensor, torch.Tensor]: First tensor is shape (sentence_length, representation_dimension) containing word representations, and second is shape (sentence_length,) containing integral POS tags. """ representations = self.representations[index] annotations = self.annotations[index] assert len(representations) == len( annotations.sentence), 'diff sentence lengths?' rels = self.indexer(annotations) # Find all pairs of words sharing an edge. indexes = set(range(len(representations))) pairs = [(i, j) for i in indexes for j in indexes if rels[i, j]] assert pairs and len(pairs) == len(representations), 'missing edges?' # Stack everything before returning it. bigrams = torch.stack([ torch.stack((representations[i], representations[j])) for i, j in pairs ]) labels = torch.stack([rels[i, j] for i, j in pairs]) return bigrams, labels def __iter__(self) -> Iterator[Tuple[torch.Tensor, torch.Tensor]]: """Yield all (sentence representations, sentence POS tags) samples.""" for index in range(len(self)): yield self[index] def __len__(self) -> int: """Return the number of sentences (batches) in the dataset.""" return len(self.annotations) @property def sample_representations_shape(self) -> Sequence[int]: """Return the dimensionality of the representation pairs.""" return (2, self.representations.dataset.dimension) @property def sample_features_shape(self) -> Sequence[int]: """Return the shape of each individual POS tag. Since POS tags are integral scalars, there is no such shape! """ return () def count_samples(self) -> int: """Return the number of words in the dataset.""" return sum( self.representations.dataset.length(index) for index in range(len(self.representations))) def count_unique_features(self) -> int: """Return number of unique POS seen in data.""" return len(self.indexer) # Define the valid probe types for this task. Probe = Union[probes.Linear, probes.MLP] def train(train_dataset: datasets.TaskDataset, dev_dataset: datasets.TaskDataset, test_dataset: datasets.TaskDataset, probe_t: Type[Probe] = probes.Linear, project_to: Optional[int] = None, share_projection: bool = False, epochs: int = 25, patience: int = 4, lr: float = 1e-3, device: Optional[Device] = None, also_log_to_wandb: bool = False) -> Tuple[Probe, float]: """Train a probe on dependency label prediction. Args: train_dataset (TaskDataset): Training data for probe. dev_dataset (TaskDataset): Validation data for probe, used for early stopping. test_dataset (TaskDataset): Test data for probe, used to compute final accuracy after training. probe_t (Type[Probe], optional): Probe type to train. Defaults to probes.Linear. project_to (Optional[int], optional): Project representations to this dimensionality. Defaults to no projection. share_projection (bool): If set, project the left and right components of pairwise probes with the same projection. E.g. if the probe is bilinear of the form xAy, we will always compute (Px)A(Py) as opposed to (Px)A(Qy) for distinct projections P, Q. Defaults to NOT shared. epochs (int, optional): Maximum passes through the training dataset. Defaults to 25. patience (int, optional): Allow dev loss to not improve for this many epochs, then stop training. Defaults to 4. lr (float, optional): Learning rate for optimizer. Defaults to 1e-3. device (Optional[Device], optional): Torch device on which to train probe. Defaults to CPU. also_log_to_wandb (Optional[pathlib.Path], optional): If set, log training data to wandb. By default, wandb is not used. Returns: Tuple[Probe, float]: The trained probe and its test accuracy. """ log = logging.getLogger(__name__) device = device or 'cpu' ndims = train_dataset.sample_representations_shape[-1] log.info('representations have dimension %d', ndims) ntags = train_dataset.count_unique_features() assert ntags is not None, 'no label count, is dataset for different task?' log.info('dependency labeling task has %d tags', ntags) if project_to is None or ndims == project_to: logging.info('projection dim = reps dim, not projecting') projection = None elif share_projection: projection = projections.Projection(ndims, project_to) else: projection = projections.Projection(2 * ndims, 2 * project_to) probe = probe_t(2 * (project_to or ndims), ntags, project=projection) learning.train(probe, train_dataset, dev_dataset=dev_dataset, stopper=learning.EarlyStopping(patience=patience), epochs=epochs, lr=lr, device=device, also_log_to_wandb=also_log_to_wandb) accuracy = learning.test(probe, test_dataset, device=device) return probe, accuracy # TODO(evandez): May as well commonize this, since it's shared with POS. def axis_alignment( probe: Probe, dev_dataset: datasets.TaskDataset, test_dataset: datasets.TaskDataset, device: Optional[Device] = None, also_log_to_wandb: bool = False) -> Sequence[Tuple[int, float]]: """Measure whether the given probe is axis aligned. Args: probe (Probe): The probe to evaluate. dev_dataset (datasets.TaskDataset): Data used to determine which axes to cut. test_dataset (datasets.TaskDataset): Data used to determine the effect of cutting an axis. device (Optional[Device], optional): Torch device on which to train probe. Defaults to CPU. also_log_to_wandb (bool, optional): If set, log results to wandb. Returns: Sequence[Tuple[int, float]]: The ablated axes paired with optimal probe accuracy after that axis is zeroed. """ log = logging.getLogger(__name__) projection = probe.project assert projection is not None, 'no projection?' axes = set(range(projection.project.in_features)) ablated: Set[int] = set() accuracies = [] while axes: best_model, best_axis, best_accuracy = probe, -1, -1. for axis in axes: model = copy.deepcopy(best_model).eval() assert model.project is not None, 'no projection?' model.project.project.weight.data[:, sorted(ablated | {axis})] = 0 accuracy = learning.test(model, dev_dataset, device=device) if accuracy > best_accuracy: best_model = model best_axis = axis best_accuracy = accuracy accuracy = learning.test(best_model, test_dataset, device=device) log.info('ablating axis %d, test accuracy %f', best_axis, accuracy) if also_log_to_wandb: wandb.log({ 'axis': best_axis, 'dev accuracy': best_accuracy, 'test accuracy': accuracy, }) axes.remove(best_axis) ablated.add(best_axis) accuracies.append((best_axis, accuracy)) return tuple(accuracies)
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    Python
    pycquery_krb/common/ccache.py
    naver/PyCQuery
    a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9
    [ "Apache-2.0" ]
    2
    2021-11-17T03:13:16.000Z
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    pycquery_krb/common/ccache.py
    naver/PyCQuery
    a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9
    [ "Apache-2.0" ]
    1
    2021-05-04T06:02:40.000Z
    2021-05-04T06:02:40.000Z
    pycquery_krb/common/ccache.py
    naver/PyCQuery
    a72f74f9b7c208a263fc7cdb14a30d0fe21e63b9
    [ "Apache-2.0" ]
    null
    null
    null
    #!/usr/bin/env python3 # # Author: # Tamas Jos (@skelsec) # import os import io import datetime import glob import hashlib from pycquery_krb.protocol.asn1_structs import Ticket, EncryptedData, \ krb5_pvno, KrbCredInfo, EncryptionKey, KRBCRED, TicketFlags, EncKrbCredPart from pycquery_krb.common.utils import dt_to_kerbtime, TGSTicket2hashcat from pycquery_krb.protocol.constants import EncryptionType, MESSAGE_TYPE from pycquery_krb import logger from asn1crypto import core # http://repo.or.cz/w/krb5dissect.git/blob_plain/HEAD:/ccache.txt class Header: def __init__(self): self.tag = None self.taglen = None self.tagdata = None @staticmethod def parse(data): """ returns a list of header tags """ reader = io.BytesIO(data) headers = [] while reader.tell() < len(data): h = Header() h.tag = int.from_bytes(reader.read(2), byteorder='big', signed=False) h.taglen = int.from_bytes(reader.read(2), byteorder='big', signed=False) h.tagdata = reader.read(h.taglen) headers.append(h) return headers def to_bytes(self): t = self.tag.to_bytes(2, byteorder='big', signed=False) t += len(self.tagdata).to_bytes(2, byteorder='big', signed=False) t += self.tagdata return t def __str__(self): t = 'tag: %s\n' % self.tag t += 'taglen: %s\n' % self.taglen t += 'tagdata: %s\n' % self.tagdata return t class DateTime: def __init__(self): self.time_offset = None self.usec_offset = None @staticmethod def parse(reader): d = DateTime() d.time_offset = int.from_bytes(reader.read(4), byteorder='big', signed=False) d.usec_offset = int.from_bytes(reader.read(4), byteorder='big', signed=False) return d def to_bytes(self): t = self.time_offset.to_bytes(4, byteorder='big', signed=False) t += self.usec_offset.to_bytes(4, byteorder='big', signed=False) return t class Credential: def __init__(self): self.client = None self.server = None self.key = None self.time = None self.is_skey = None self.tktflags = None self.num_address = None self.addrs = [] self.num_authdata = None self.authdata = [] self.ticket = None self.second_ticket = None def to_hash(self): res = Ticket.load(self.ticket.to_asn1()).native tgs_encryption_type = int(res['enc-part']['etype']) t = len(res['sname']['name-string']) if t == 1: tgs_name_string = res['sname']['name-string'][0] else: tgs_name_string = res['sname']['name-string'][1] tgs_realm = res['realm'] if tgs_encryption_type == EncryptionType.AES256_CTS_HMAC_SHA1_96.value: tgs_checksum = res['enc-part']['cipher'][-12:] tgs_encrypted_data2 = res['enc-part']['cipher'][:-12] return '$krb5tgs$%s$%s$%s$%s$%s' % (tgs_encryption_type,tgs_name_string,tgs_realm, tgs_checksum.hex(), tgs_encrypted_data2.hex() ) else: tgs_checksum = res['enc-part']['cipher'][:16] tgs_encrypted_data2 = res['enc-part']['cipher'][16:] return '$krb5tgs$%s$*%s$%s$spn*$%s$%s' % (tgs_encryption_type,tgs_name_string,tgs_realm, tgs_checksum.hex(), tgs_encrypted_data2.hex() ) def to_tgt(self): """ Returns the native format of an AS_REP message and the sessionkey in EncryptionKey native format """ enc_part = EncryptedData({'etype': 1, 'cipher': b''}) tgt_rep = {} tgt_rep['pvno'] = krb5_pvno tgt_rep['msg-type'] = MESSAGE_TYPE.KRB_AS_REP.value tgt_rep['crealm'] = self.server.realm.to_string() tgt_rep['cname'] = self.client.to_asn1()[0] tgt_rep['ticket'] = Ticket.load(self.ticket.to_asn1()).native tgt_rep['enc-part'] = enc_part.native t = EncryptionKey(self.key.to_asn1()).native return tgt_rep, t def to_tgs(self): """ Returns the native format of an AS_REP message and the sessionkey in EncryptionKey native format """ enc_part = EncryptedData({'etype': 1, 'cipher': b''}) tgt_rep = {} tgt_rep['pvno'] = krb5_pvno tgt_rep['msg-type'] = MESSAGE_TYPE.KRB_AS_REP.value tgt_rep['crealm'] = self.server.realm.to_string() tgt_rep['cname'] = self.client.to_asn1()[0] tgt_rep['ticket'] = Ticket.load(self.ticket.to_asn1()).native tgt_rep['enc-part'] = enc_part.native t = EncryptionKey(self.key.to_asn1()).native return tgt_rep, t def to_kirbi(self): filename = '%s@%s_%s' % (self.client.to_string() , self.server.to_string(), hashlib.sha1(self.ticket.to_asn1()).hexdigest()[:8]) krbcredinfo = {} krbcredinfo['key'] = EncryptionKey(self.key.to_asn1()) krbcredinfo['prealm'] = self.client.realm.to_string() krbcredinfo['pname'] = self.client.to_asn1()[0] krbcredinfo['flags'] = core.IntegerBitString(self.tktflags).cast(TicketFlags) if self.time.authtime != 0: #this parameter is not mandatory, and most of the time not present krbcredinfo['authtime'] = datetime.datetime.fromtimestamp(self.time.authtime, datetime.timezone.utc) if self.time.starttime != 0: krbcredinfo['starttime'] = datetime.datetime.fromtimestamp(self.time.starttime, datetime.timezone.utc) if self.time.endtime != 0: krbcredinfo['endtime'] = datetime.datetime.fromtimestamp(self.time.endtime, datetime.timezone.utc) if self.time.renew_till != 0: #this parameter is not mandatory, and sometimes it's not present krbcredinfo['renew-till'] = datetime.datetime.fromtimestamp(self.time.authtime, datetime.timezone.utc) krbcredinfo['srealm'] = self.server.realm.to_string() krbcredinfo['sname'] = self.server.to_asn1()[0] enc_krbcred = {} enc_krbcred['ticket-info'] = [KrbCredInfo(krbcredinfo)] krbcred = {} krbcred['pvno'] = krb5_pvno krbcred['msg-type'] = MESSAGE_TYPE.KRB_CRED.value krbcred['tickets'] = [Ticket.load(self.ticket.to_asn1())] krbcred['enc-part'] = EncryptedData({'etype': EncryptionType.NULL.value, 'cipher': EncKrbCredPart(enc_krbcred).dump()}) kirbi = KRBCRED(krbcred) return kirbi, filename @staticmethod def from_asn1(ticket, data): ### # data = KrbCredInfo ### c = Credential() c.client = CCACHEPrincipal.from_asn1(data['pname'], data['prealm']) c.server = CCACHEPrincipal.from_asn1(data['sname'], data['srealm']) c.key = Keyblock.from_asn1(data['key']) c.is_skey = 0 #not sure! c.tktflags = TicketFlags(data['flags']).cast(core.IntegerBitString).native c.num_address = 0 c.num_authdata = 0 c.ticket = CCACHEOctetString.from_asn1(ticket['enc-part']['cipher']) c.second_ticket = CCACHEOctetString.empty() return c @staticmethod def parse(reader): c = Credential() c.client = CCACHEPrincipal.parse(reader) c.server = CCACHEPrincipal.parse(reader) c.key = Keyblock.parse(reader) c.time = Times.parse(reader) c.is_skey = int.from_bytes(reader.read(1), byteorder='big', signed=False) c.tktflags = int.from_bytes(reader.read(4), byteorder='little', signed=False) c.num_address = int.from_bytes(reader.read(4), byteorder='big', signed=False) for _ in range(c.num_address): c.addrs.append(Address.parse(reader)) c.num_authdata = int.from_bytes(reader.read(4), byteorder='big', signed=False) for _ in range(c.num_authdata): c.authdata.append(Authdata.parse(reader)) c.ticket = CCACHEOctetString.parse(reader) c.second_ticket = CCACHEOctetString.parse(reader) return c @staticmethod def summary_header(): return ['client','server','starttime','endtime','renew-till'] def summary(self): return [ '%s@%s' % (self.client.to_string(separator='/'), self.client.realm.to_string()), '%s@%s' % (self.server.to_string(separator='/'), self.server.realm.to_string()), datetime.datetime.fromtimestamp(self.time.starttime).isoformat() if self.time.starttime != 0 else 'N/A', datetime.datetime.fromtimestamp(self.time.endtime).isoformat() if self.time.endtime != 0 else 'N/A', datetime.datetime.fromtimestamp(self.time.renew_till).isoformat() if self.time.renew_till != 0 else 'N/A', ] def to_bytes(self): t = self.client.to_bytes() t += self.server.to_bytes() t += self.key.to_bytes() t += self.time.to_bytes() t += self.is_skey.to_bytes(1, byteorder='big', signed=False) t += self.tktflags.to_bytes(4, byteorder='little', signed=False) t += self.num_address.to_bytes(4, byteorder='big', signed=False) for addr in self.addrs: t += addr.to_bytes() t += self.num_authdata.to_bytes(4, byteorder='big', signed=False) for ad in self.authdata: t += ad.to_bytes() t += self.ticket.to_bytes() t += self.second_ticket.to_bytes() return t class Keyblock: def __init__(self): self.keytype = None self.etype = None self.keylen = None self.keyvalue = None @staticmethod def from_asn1(data): k = Keyblock() k.keytype = data['keytype'] k.etype = 0 # not sure k.keylen = len(data['keyvalue']) k.keyvalue = data['keyvalue'] return k def to_asn1(self): t = {} t['keytype'] = self.keytype t['keyvalue'] = self.keyvalue return t @staticmethod def parse(reader): k = Keyblock() k.keytype = int.from_bytes(reader.read(2), byteorder='big', signed=False) k.etype = int.from_bytes(reader.read(2), byteorder='big', signed=False) k.keylen = int.from_bytes(reader.read(2), byteorder='big', signed=False) k.keyvalue = reader.read(k.keylen) return k def to_bytes(self): t = self.keytype.to_bytes(2, byteorder='big', signed=False) t += self.etype.to_bytes(2, byteorder='big', signed=False) t += self.keylen.to_bytes(2, byteorder='big', signed=False) t += self.keyvalue return t class Times: def __init__(self): self.authtime = None self.starttime = None self.endtime = None self.renew_till = None @staticmethod def from_asn1(enc_as_rep_part): t = Times() t.authtime = dt_to_kerbtime(enc_as_rep_part['authtime']) \ if 'authtime' in enc_as_rep_part and enc_as_rep_part['authtime'] else 0 t.starttime = dt_to_kerbtime(enc_as_rep_part['starttime']) \ if 'starttime' in enc_as_rep_part and enc_as_rep_part['starttime'] else 0 t.endtime = dt_to_kerbtime(enc_as_rep_part['endtime']) \ if 'endtime' in enc_as_rep_part and enc_as_rep_part['endtime'] else 0 t.renew_till = dt_to_kerbtime(enc_as_rep_part['renew_till']) \ if 'renew_till' in enc_as_rep_part and enc_as_rep_part['renew_till'] else 0 return t @staticmethod def dummy_time(start= datetime.datetime.now(datetime.timezone.utc)): t = Times() t.authtime = dt_to_kerbtime(start) t.starttime = dt_to_kerbtime(start ) t.endtime = dt_to_kerbtime(start + datetime.timedelta(days=1)) t.renew_till = dt_to_kerbtime(start + datetime.timedelta(days=2)) return t @staticmethod def parse(reader): t = Times() t.authtime = int.from_bytes(reader.read(4), byteorder='big', signed=False) t.starttime = int.from_bytes(reader.read(4), byteorder='big', signed=False) t.endtime = int.from_bytes(reader.read(4), byteorder='big', signed=False) t.renew_till = int.from_bytes(reader.read(4), byteorder='big', signed=False) return t def to_bytes(self): t = self.authtime.to_bytes(4, byteorder='big', signed=False) t += self.starttime.to_bytes(4, byteorder='big', signed=False) t += self.endtime.to_bytes(4, byteorder='big', signed=False) t += self.renew_till.to_bytes(4, byteorder='big', signed=False) return t class Address: def __init__(self): self.addrtype = None self.addrdata = None @staticmethod def parse(reader): a = Address() a.addrtype = int.from_bytes(reader.read(2), byteorder='big', signed=False) a.addrdata = CCACHEOctetString.parse(reader) return a def to_bytes(self): t = self.addrtype.to_bytes(2, byteorder='big', signed=False) t += self.addrdata.to_bytes() return t class Authdata: def __init__(self): self.authtype = None self.authdata = None @staticmethod def parse(reader): a = Authdata() a.authtype = int.from_bytes(reader.read(2), byteorder='big', signed=False) a.authdata = CCACHEOctetString.parse(reader) return a def to_bytes(self): t = self.authtype.to_bytes(2, byteorder='big', signed=False) t += self.authdata.to_bytes() return t class CCACHEPrincipal: def __init__(self): self.name_type = None self.num_components = None self.realm = None self.components = [] @staticmethod def from_asn1(principal, realm): p = CCACHEPrincipal() p.name_type = principal['name-type'] p.num_components = len(principal['name-string']) p.realm = CCACHEOctetString.from_string(realm) for comp in principal['name-string']: p.components.append(CCACHEOctetString.from_asn1(comp)) return p @staticmethod def dummy(): p = CCACHEPrincipal() p.name_type = 1 p.num_components = 1 p.realm = CCACHEOctetString.from_string('kerbi.corp') for _ in range(1): p.components.append(CCACHEOctetString.from_string('kerbi')) return p def to_string(self, separator='-'): return separator.join([c.to_string() for c in self.components]) def to_asn1(self): t = {'name-type': self.name_type, 'name-string': [name.to_string() for name in self.components]} return t, self.realm.to_string() @staticmethod def parse(reader): p = CCACHEPrincipal() p.name_type = int.from_bytes(reader.read(4), byteorder='big', signed=False) p.num_components = int.from_bytes(reader.read(4), byteorder='big', signed=False) p.realm = CCACHEOctetString.parse(reader) for _ in range(p.num_components): p.components.append(CCACHEOctetString.parse(reader)) return p def to_bytes(self): t = self.name_type.to_bytes(4, byteorder='big', signed=False) t += len(self.components).to_bytes(4, byteorder='big', signed=False) t += self.realm.to_bytes() for com in self.components: t += com.to_bytes() return t class CCACHEOctetString: def __init__(self): self.length = None self.data = None @staticmethod def empty(): o = CCACHEOctetString() o.length = 0 o.data = b'' return o def to_asn1(self): return self.data def to_string(self): return self.data.decode() @staticmethod def from_string(data): o = CCACHEOctetString() o.data = data.encode() o.length = len(o.data) return o @staticmethod def from_asn1(data): o = CCACHEOctetString() o.length = len(data) if isinstance(data,str): o.data = data.encode() else: o.data = data return o @staticmethod def parse(reader): o = CCACHEOctetString() o.length = int.from_bytes(reader.read(4), byteorder='big', signed=False) o.data = reader.read(o.length) return o def to_bytes(self): if isinstance(self.data,str): self.data = self.data.encode() self.length = len(self.data) t = len(self.data).to_bytes(4, byteorder='big', signed=False) t += self.data return t class CCACHE: """ As the header is rarely used -mostly static- you'd need to init this object with empty = True to get an object without header already present """ def __init__(self, empty = False): self.file_format_version = None #0x0504 self.headers = [] self.primary_principal = None self.credentials = [] if empty == False: self.__setup() def __setup(self): self.file_format_version = 0x0504 header = Header() header.tag = 1 header.taglen = 8 #header.tagdata = b'\xff\xff\xff\xff\x00\x00\x00\x00' header.tagdata = b'\x00\x00\x00\x00\x00\x00\x00\x00' self.headers.append(header) #t_hdr = b'' #for header in self.headers: # t_hdr += header.to_bytes() #self.headerlen = 1 #size of the entire header in bytes, encoded in 2 byte big-endian unsigned int self.primary_principal = CCACHEPrincipal.dummy() def __str__(self): t = '== CCACHE ==\n' t+= 'file_format_version : %s\n' % self.file_format_version for header in self.headers: t+= '%s\n' % header t+= 'primary_principal : %s\n' % self.primary_principal return t def add_tgt(self, as_rep, enc_as_rep_part, override_pp = True): #from AS_REP """ Creates credential object from the TGT and adds to the ccache file The TGT is basically the native representation of the asn1 encoded AS_REP data that the AD sends upon a succsessful TGT request. This function doesn't do decryption of the encrypted part of the as_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part override_pp: bool to determine if client principal should be used as the primary principal for the ccache file """ c = Credential() c.client = CCACHEPrincipal.from_asn1(as_rep['cname'], as_rep['crealm']) if override_pp == True: self.primary_principal = c.client c.server = CCACHEPrincipal.from_asn1(enc_as_rep_part['sname'], enc_as_rep_part['srealm']) c.time = Times.from_asn1(enc_as_rep_part) c.key = Keyblock.from_asn1(enc_as_rep_part['key']) c.is_skey = 0 #not sure! c.tktflags = TicketFlags(enc_as_rep_part['flags']).cast(core.IntegerBitString).native c.num_address = 0 c.num_authdata = 0 c.ticket = CCACHEOctetString.from_asn1(Ticket(as_rep['ticket']).dump()) c.second_ticket = CCACHEOctetString.empty() self.credentials.append(c) def add_tgs(self, tgs_rep, enc_tgs_rep_part, override_pp = False): #from AS_REP """ Creates credential object from the TGS and adds to the ccache file The TGS is the native representation of the asn1 encoded TGS_REP data when the user requests a tgs to a specific service principal with a valid TGT This function doesn't do decryption of the encrypted part of the tgs_rep object, it is expected that the decrypted XXX is supplied in enc_as_rep_part override_pp: bool to determine if client principal should be used as the primary principal for the ccache file """ c = Credential() c.client = CCACHEPrincipal.from_asn1(tgs_rep['cname'], tgs_rep['crealm']) if override_pp == True: self.primary_principal = c.client c.server = CCACHEPrincipal.from_asn1(enc_tgs_rep_part['sname'], enc_tgs_rep_part['srealm']) c.time = Times.from_asn1(enc_tgs_rep_part) c.key = Keyblock.from_asn1(enc_tgs_rep_part['key']) c.is_skey = 0 #not sure! c.tktflags = TicketFlags(enc_tgs_rep_part['flags']).cast(core.IntegerBitString).native c.num_address = 0 c.num_authdata = 0 c.ticket = CCACHEOctetString.from_asn1(Ticket(tgs_rep['ticket']).dump()) c.second_ticket = CCACHEOctetString.empty() self.credentials.append(c) def add_kirbi(self, krbcred, override_pp = True, include_expired = False): c = Credential() enc_credinfo = EncKrbCredPart.load(krbcred['enc-part']['cipher']).native ticket_info = enc_credinfo['ticket-info'][0] """ if ticket_info['endtime'] < datetime.datetime.now(datetime.timezone.utc): if include_expired == True: logging.debug('This ticket has most likely expired, but include_expired is forcing me to add it to cache! This can cause problems!') else: logging.debug('This ticket has most likely expired, skipping') return """ c.client = CCACHEPrincipal.from_asn1(ticket_info['pname'], ticket_info['prealm']) if override_pp == True: self.primary_principal = c.client #yaaaaay 4 additional weirdness!!!! #if sname name-string contains a realm as well htne impacket will crash miserably :( if len(ticket_info['sname']['name-string']) > 2 and ticket_info['sname']['name-string'][-1].upper() == ticket_info['srealm'].upper(): logger.debug('SNAME contains the realm as well, trimming it') t = ticket_info['sname'] t['name-string'] = t['name-string'][:-1] c.server = CCACHEPrincipal.from_asn1(t, ticket_info['srealm']) else: c.server = CCACHEPrincipal.from_asn1(ticket_info['sname'], ticket_info['srealm']) c.time = Times.from_asn1(ticket_info) c.key = Keyblock.from_asn1(ticket_info['key']) c.is_skey = 0 #not sure! c.tktflags = TicketFlags(ticket_info['flags']).cast(core.IntegerBitString).native c.num_address = 0 c.num_authdata = 0 c.ticket = CCACHEOctetString.from_asn1(Ticket(krbcred['tickets'][0]).dump()) #kirbi only stores one ticket per file c.second_ticket = CCACHEOctetString.empty() self.credentials.append(c) @staticmethod def from_kirbi(kirbidata): kirbi = KRBCRED.load(kirbidata).native cc = CCACHE() cc.add_kirbi(kirbi) return cc def get_all_tgt(self): """ Returns a list of AS_REP tickets in native format (dict). To determine which ticket are AP_REP we check for the server principal to be the kerberos service """ tgts = [] for cred in self.credentials: if cred.server.to_string(separator='/').lower().find('krbtgt') != -1: tgt = [cred.to_tgt(), cred.time] tgts.append(tgt) return tgts def get_all_tgs(self): tgss = [] for cred in self.credentials: if cred.server.to_string(separator = '/').lower().find('krbtgt') == -1: tgss.append(cred.to_tgs()) return tgss def get_hashes(self, all_hashes = False): """ Returns a list of hashes in hashcat-firendly format for tickets with encryption type 23 (which is RC4) all_hashes: overrides the encryption type filtering and returns hash for all tickets """ hashes = [] for cred in self.credentials: res = Ticket.load(cred.ticket.to_asn1()).native if int(res['enc-part']['etype']) == 23 or all_hashes == True: hashes.append(cred.to_hash()) return hashes @staticmethod def parse(reader): c = CCACHE(True) c.file_format_version = int.from_bytes(reader.read(2), byteorder='big', signed=False) hdr_size = int.from_bytes(reader.read(2), byteorder='big', signed=False) c.headers = Header.parse(reader.read(hdr_size)) #c.headerlen = #for i in range(c.headerlen): # c.headers.append(Header.parse(reader)) c.primary_principal = CCACHEPrincipal.parse(reader) pos = reader.tell() reader.seek(-1,2) eof = reader.tell() reader.seek(pos,0) while reader.tell() < eof: cred = Credential.parse(reader) if not (len(cred.server.components) > 0 and cred.server.components[0].to_string() == 'krb5_ccache_conf_data' and cred.server.realm.to_string() == 'X-CACHECONF:'): c.credentials.append(cred) return c def to_bytes(self): t = self.file_format_version.to_bytes(2, byteorder='big', signed=False) t_hdr = b'' for header in self.headers: t_hdr += header.to_bytes() t += len(t_hdr).to_bytes(2, byteorder='big', signed=False) t += t_hdr t += self.primary_principal.to_bytes() for cred in self.credentials: t += cred.to_bytes() return t @staticmethod def from_kirbifile(kirbi_filename): kf_abs = os.path.abspath(kirbi_filename) kirbidata = None with open(kf_abs, 'rb') as f: kirbidata = f.read() return CCACHE.from_kirbi(kirbidata) @staticmethod def from_kirbidir(directory_path): """ Iterates trough all .kirbi files in a given directory and converts all of them into one CCACHE object """ cc = CCACHE() dir_path = os.path.join(os.path.abspath(directory_path), '*.kirbi') for filename in glob.glob(dir_path): with open(filename, 'rb') as f: kirbidata = f.read() kirbi = KRBCRED.load(kirbidata).native cc.add_kirbi(kirbi) return cc def to_kirbidir(self, directory_path): """ Converts all credential object in the CCACHE object to the kirbi file format used by mimikatz. The kirbi file format supports one credential per file, so prepare for a lot of files being generated. directory_path: str the directory to write the kirbi files to """ kf_abs = os.path.abspath(directory_path) for cred in self.credentials: kirbi, filename = cred.to_kirbi() filename = '%s.kirbi' % filename.replace('..','!') filepath = os.path.join(kf_abs, filename) with open(filepath, 'wb') as o: o.write(kirbi.dump()) @staticmethod def from_file(filename): """ Parses the ccache file and returns a CCACHE object """ with open(filename, 'rb') as f: return CCACHE.parse(f) def to_file(self, filename): """ Writes the contents of the CCACHE object to a file """ with open(filename, 'wb') as f: f.write(self.to_bytes()) @staticmethod def from_bytes(data): return CCACHE.parse(io.BytesIO(data))
    31.909459
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    0.707915
    23,041
    0.975776
    0
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    7,518
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    0
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    0.217931
    131a0e5ecdfc260b3827d4c50aa8606f1a20747d
    3,054
    py
    Python
    getUniformSmiles.py
    OpenEye-Contrib/Molecular-List-Logic
    82caf41f7d8b94e7448d8e839bdbc0620a8666d7
    [ "BSD-3-Clause" ]
    2
    2016-01-21T19:50:12.000Z
    2021-04-10T13:27:40.000Z
    getUniformSmiles.py
    OpenEye-Contrib/Molecular-List-Logic
    82caf41f7d8b94e7448d8e839bdbc0620a8666d7
    [ "BSD-3-Clause" ]
    null
    null
    null
    getUniformSmiles.py
    OpenEye-Contrib/Molecular-List-Logic
    82caf41f7d8b94e7448d8e839bdbc0620a8666d7
    [ "BSD-3-Clause" ]
    null
    null
    null
    #!/opt/az/psf/python/2.7/bin/python from openeye.oechem import * import cgi #creates a list of smiles of the syntax [smiles|molId,smiles|molId] def process_smiles(smiles): smiles = smiles.split('\n') mol = OEGraphMol() smiles_list=[] for line in smiles: if len(line.rstrip())>0: line = line.split() smi = line[0] molId = "" if len(line)>1: molId = line[1].replace(" ","|").rstrip() if(OEParseSmiles(mol,smi)): smi = OECreateSmiString(mol) mol.Clear() smiles_list.append(smi + "|" + molId) #can't send spaces or new lines return smiles_list #takes a list of smiles and writes it as sdf using a memory buffer def write_sdf(smiles_list): sdfs = [] ofs = oemolostream() ofs.SetFormat(OEFormat_SDF) ofs.openstring() mol = OEGraphMol() for smiles in smiles_list: if(OEParseSmiles(mol,smiles.replace("|"," "))): OEWriteMolecule(ofs,mol) sdfs.append(ofs.GetString()) mol.Clear() ofs.SetString("") return sdfs #creates a list of smiles of the syntax [smiles|molId,smiles|molId] def read_sdf(sdf_data): ifs = oemolistream() ifs.SetFormat(OEFormat_SDF) ifs.openstring(sdf_data) smiles_list = [] for mol in ifs.GetOEGraphMols(): smiles = OECreateSmiString(mol) smiles_list.append(smiles + "|" + mol.GetTitle()) return smiles_list if __name__ == "__main__": print "Content-Type: text/html\r\n\r\n" form = cgi.FieldStorage() extension = form.getvalue("extension") dataA = form.getvalue("dataA") operator = form.getvalue("smiles_operator") sdf_output = form.getvalue("sdf_output") if(extension=="smi"): list_A = process_smiles(dataA) else: list_A = read_sdf(dataA) outputString = "" if(operator=="UNI"): #if only one file is supplied outputString = "*".join(set(list_A)) #removes all doubles using the set() function else: dataB = form.getvalue("dataB") #if two files are supplied if(extension=="smi"): list_B = process_smiles(dataB) else: list_B = read_sdf(dataB) if(operator=="AND"): outputString = "*".join(set(list_A) & set(list_B)) elif(operator=="OR"): outputString = "*".join(set(list_A) | set(list_B)) elif(operator=="NOT"): outputString = "*".join(set(list_A) - set(list_B)) if(sdf_output=="on"): #if we want the output as sdf sdfs = write_sdf(outputString.replace("|"," ").split("*")) outputString = "*".join(sdfs) outputString = outputString.replace("\n","!").replace(" ","|") #sends the output to index.html using javascript print """ <html> <head> <input type="text" id="data" value=""" + outputString + """> <script type="text/javascript"> parent.postMessage(data.value,"*"); </script> </head> </html> """
    30.237624
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    0.586444
    0
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    0.272757
    131a15a3a8feaf503c53fb7ce15a6a9a4850f413
    37,988
    py
    Python
    mfem/_par/gridfunc.py
    mfem/PyMFEM
    b7b7c3d3de1082eac1015e3a313cf513db06fd7b
    [ "BSD-3-Clause" ]
    93
    2017-03-01T16:45:33.000Z
    2022-03-27T22:10:33.000Z
    mfem/_par/gridfunc.py
    mfem/PyMFEM
    b7b7c3d3de1082eac1015e3a313cf513db06fd7b
    [ "BSD-3-Clause" ]
    64
    2017-03-15T21:47:31.000Z
    2022-03-31T23:59:00.000Z
    mfem/_par/gridfunc.py
    mfem/PyMFEM
    b7b7c3d3de1082eac1015e3a313cf513db06fd7b
    [ "BSD-3-Clause" ]
    32
    2017-03-02T22:13:38.000Z
    2022-03-26T13:09:31.000Z
    # This file was automatically generated by SWIG (http://www.swig.org). # Version 4.0.2 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info as _swig_python_version_info if _swig_python_version_info < (2, 7, 0): raise RuntimeError("Python 2.7 or later required") # Import the low-level C/C++ module if __package__ or "." in __name__: from . import _gridfunc else: import _gridfunc try: import builtins as __builtin__ except ImportError: import __builtin__ _swig_new_instance_method = _gridfunc.SWIG_PyInstanceMethod_New _swig_new_static_method = _gridfunc.SWIG_PyStaticMethod_New def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except __builtin__.Exception: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) def _swig_setattr_nondynamic_instance_variable(set): def set_instance_attr(self, name, value): if name == "thisown": self.this.own(value) elif name == "this": set(self, name, value) elif hasattr(self, name) and isinstance(getattr(type(self), name), property): set(self, name, value) else: raise AttributeError("You cannot add instance attributes to %s" % self) return set_instance_attr def _swig_setattr_nondynamic_class_variable(set): def set_class_attr(cls, name, value): if hasattr(cls, name) and not isinstance(getattr(cls, name), property): set(cls, name, value) else: raise AttributeError("You cannot add class attributes to %s" % cls) return set_class_attr def _swig_add_metaclass(metaclass): """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" def wrapper(cls): return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) return wrapper class _SwigNonDynamicMeta(type): """Meta class to enforce nondynamic attributes (no new attributes) for a class""" __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) import weakref import mfem._par.array import mfem._par.mem_manager import mfem._par.vector import mfem._par.coefficient import mfem._par.globals import mfem._par.matrix import mfem._par.operators import mfem._par.intrules import mfem._par.sparsemat import mfem._par.densemat import mfem._par.eltrans import mfem._par.fe import mfem._par.geom import mfem._par.fespace import mfem._par.mesh import mfem._par.sort_pairs import mfem._par.ncmesh import mfem._par.vtk import mfem._par.element import mfem._par.table import mfem._par.hash import mfem._par.vertex import mfem._par.fe_coll import mfem._par.lininteg import mfem._par.handle import mfem._par.hypre import mfem._par.restriction import mfem._par.bilininteg import mfem._par.linearform import mfem._par.nonlininteg class GridFunction(mfem._par.vector.Vector): r"""Proxy of C++ mfem::GridFunction class.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def MakeOwner(self, fec_): r"""MakeOwner(GridFunction self, FiniteElementCollection fec_)""" return _gridfunc.GridFunction_MakeOwner(self, fec_) MakeOwner = _swig_new_instance_method(_gridfunc.GridFunction_MakeOwner) def OwnFEC(self): r"""OwnFEC(GridFunction self) -> FiniteElementCollection""" return _gridfunc.GridFunction_OwnFEC(self) OwnFEC = _swig_new_instance_method(_gridfunc.GridFunction_OwnFEC) def VectorDim(self): r"""VectorDim(GridFunction self) -> int""" return _gridfunc.GridFunction_VectorDim(self) VectorDim = _swig_new_instance_method(_gridfunc.GridFunction_VectorDim) def GetTrueVector(self, *args): r""" GetTrueVector(GridFunction self) -> Vector GetTrueVector(GridFunction self) -> Vector """ return _gridfunc.GridFunction_GetTrueVector(self, *args) GetTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_GetTrueVector) def GetTrueDofs(self, tv): r"""GetTrueDofs(GridFunction self, Vector tv)""" return _gridfunc.GridFunction_GetTrueDofs(self, tv) GetTrueDofs = _swig_new_instance_method(_gridfunc.GridFunction_GetTrueDofs) def SetTrueVector(self): r"""SetTrueVector(GridFunction self)""" return _gridfunc.GridFunction_SetTrueVector(self) SetTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_SetTrueVector) def SetFromTrueDofs(self, tv): r"""SetFromTrueDofs(GridFunction self, Vector tv)""" return _gridfunc.GridFunction_SetFromTrueDofs(self, tv) SetFromTrueDofs = _swig_new_instance_method(_gridfunc.GridFunction_SetFromTrueDofs) def SetFromTrueVector(self): r"""SetFromTrueVector(GridFunction self)""" return _gridfunc.GridFunction_SetFromTrueVector(self) SetFromTrueVector = _swig_new_instance_method(_gridfunc.GridFunction_SetFromTrueVector) def GetValue(self, *args): r""" GetValue(GridFunction self, int i, IntegrationPoint ip, int vdim=1) -> double GetValue(GridFunction self, ElementTransformation T, IntegrationPoint ip, int comp=0, Vector tr=None) -> double """ return _gridfunc.GridFunction_GetValue(self, *args) GetValue = _swig_new_instance_method(_gridfunc.GridFunction_GetValue) def GetVectorValue(self, *args): r""" GetVectorValue(GridFunction self, int i, IntegrationPoint ip, Vector val) GetVectorValue(GridFunction self, ElementTransformation T, IntegrationPoint ip, Vector val, Vector tr=None) """ return _gridfunc.GridFunction_GetVectorValue(self, *args) GetVectorValue = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorValue) def GetValues(self, *args): r""" GetValues(GridFunction self, int i, IntegrationRule ir, Vector vals, int vdim=1) GetValues(GridFunction self, int i, IntegrationRule ir, Vector vals, DenseMatrix tr, int vdim=1) GetValues(GridFunction self, ElementTransformation T, IntegrationRule ir, Vector vals, int comp=0, DenseMatrix tr=None) """ return _gridfunc.GridFunction_GetValues(self, *args) GetValues = _swig_new_instance_method(_gridfunc.GridFunction_GetValues) def GetVectorValues(self, *args): r""" GetVectorValues(GridFunction self, int i, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr) GetVectorValues(GridFunction self, ElementTransformation T, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr=None) """ return _gridfunc.GridFunction_GetVectorValues(self, *args) GetVectorValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorValues) def GetFaceValues(self, i, side, ir, vals, tr, vdim=1): r"""GetFaceValues(GridFunction self, int i, int side, IntegrationRule ir, Vector vals, DenseMatrix tr, int vdim=1) -> int""" return _gridfunc.GridFunction_GetFaceValues(self, i, side, ir, vals, tr, vdim) GetFaceValues = _swig_new_instance_method(_gridfunc.GridFunction_GetFaceValues) def GetFaceVectorValues(self, i, side, ir, vals, tr): r"""GetFaceVectorValues(GridFunction self, int i, int side, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr) -> int""" return _gridfunc.GridFunction_GetFaceVectorValues(self, i, side, ir, vals, tr) GetFaceVectorValues = _swig_new_instance_method(_gridfunc.GridFunction_GetFaceVectorValues) def GetLaplacians(self, *args): r""" GetLaplacians(GridFunction self, int i, IntegrationRule ir, Vector laps, int vdim=1) GetLaplacians(GridFunction self, int i, IntegrationRule ir, Vector laps, DenseMatrix tr, int vdim=1) """ return _gridfunc.GridFunction_GetLaplacians(self, *args) GetLaplacians = _swig_new_instance_method(_gridfunc.GridFunction_GetLaplacians) def GetHessians(self, *args): r""" GetHessians(GridFunction self, int i, IntegrationRule ir, DenseMatrix hess, int vdim=1) GetHessians(GridFunction self, int i, IntegrationRule ir, DenseMatrix hess, DenseMatrix tr, int vdim=1) """ return _gridfunc.GridFunction_GetHessians(self, *args) GetHessians = _swig_new_instance_method(_gridfunc.GridFunction_GetHessians) def GetValuesFrom(self, orig_func): r"""GetValuesFrom(GridFunction self, GridFunction orig_func)""" return _gridfunc.GridFunction_GetValuesFrom(self, orig_func) GetValuesFrom = _swig_new_instance_method(_gridfunc.GridFunction_GetValuesFrom) def GetBdrValuesFrom(self, orig_func): r"""GetBdrValuesFrom(GridFunction self, GridFunction orig_func)""" return _gridfunc.GridFunction_GetBdrValuesFrom(self, orig_func) GetBdrValuesFrom = _swig_new_instance_method(_gridfunc.GridFunction_GetBdrValuesFrom) def GetVectorFieldValues(self, i, ir, vals, tr, comp=0): r"""GetVectorFieldValues(GridFunction self, int i, IntegrationRule ir, DenseMatrix vals, DenseMatrix tr, int comp=0)""" return _gridfunc.GridFunction_GetVectorFieldValues(self, i, ir, vals, tr, comp) GetVectorFieldValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorFieldValues) def ReorderByNodes(self): r"""ReorderByNodes(GridFunction self)""" return _gridfunc.GridFunction_ReorderByNodes(self) ReorderByNodes = _swig_new_instance_method(_gridfunc.GridFunction_ReorderByNodes) def GetNodalValues(self, *args): ''' GetNodalValues(i) -> GetNodalValues(vector, vdim) GetNodalValues(i, array<dobule>, vdim) ''' from .vector import Vector if len(args) == 1: vec = Vector() _gridfunc.GridFunction_GetNodalValues(self, vec, args[0]) vec.thisown = 0 return vec.GetDataArray() else: return _gridfunc.GridFunction_GetNodalValues(self, *args) def GetVectorFieldNodalValues(self, val, comp): r"""GetVectorFieldNodalValues(GridFunction self, Vector val, int comp)""" return _gridfunc.GridFunction_GetVectorFieldNodalValues(self, val, comp) GetVectorFieldNodalValues = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorFieldNodalValues) def ProjectVectorFieldOn(self, vec_field, comp=0): r"""ProjectVectorFieldOn(GridFunction self, GridFunction vec_field, int comp=0)""" return _gridfunc.GridFunction_ProjectVectorFieldOn(self, vec_field, comp) ProjectVectorFieldOn = _swig_new_instance_method(_gridfunc.GridFunction_ProjectVectorFieldOn) def GetDerivative(self, comp, der_comp, der): r"""GetDerivative(GridFunction self, int comp, int der_comp, GridFunction der)""" return _gridfunc.GridFunction_GetDerivative(self, comp, der_comp, der) GetDerivative = _swig_new_instance_method(_gridfunc.GridFunction_GetDerivative) def GetDivergence(self, tr): r"""GetDivergence(GridFunction self, ElementTransformation tr) -> double""" return _gridfunc.GridFunction_GetDivergence(self, tr) GetDivergence = _swig_new_instance_method(_gridfunc.GridFunction_GetDivergence) def GetCurl(self, tr, curl): r"""GetCurl(GridFunction self, ElementTransformation tr, Vector curl)""" return _gridfunc.GridFunction_GetCurl(self, tr, curl) GetCurl = _swig_new_instance_method(_gridfunc.GridFunction_GetCurl) def GetGradient(self, tr, grad): r"""GetGradient(GridFunction self, ElementTransformation tr, Vector grad)""" return _gridfunc.GridFunction_GetGradient(self, tr, grad) GetGradient = _swig_new_instance_method(_gridfunc.GridFunction_GetGradient) def GetGradients(self, *args): r""" GetGradients(GridFunction self, ElementTransformation tr, IntegrationRule ir, DenseMatrix grad) GetGradients(GridFunction self, int const elem, IntegrationRule ir, DenseMatrix grad) """ return _gridfunc.GridFunction_GetGradients(self, *args) GetGradients = _swig_new_instance_method(_gridfunc.GridFunction_GetGradients) def GetVectorGradient(self, tr, grad): r"""GetVectorGradient(GridFunction self, ElementTransformation tr, DenseMatrix grad)""" return _gridfunc.GridFunction_GetVectorGradient(self, tr, grad) GetVectorGradient = _swig_new_instance_method(_gridfunc.GridFunction_GetVectorGradient) def GetElementAverages(self, avgs): r"""GetElementAverages(GridFunction self, GridFunction avgs)""" return _gridfunc.GridFunction_GetElementAverages(self, avgs) GetElementAverages = _swig_new_instance_method(_gridfunc.GridFunction_GetElementAverages) def GetElementDofValues(self, el, dof_vals): r"""GetElementDofValues(GridFunction self, int el, Vector dof_vals)""" return _gridfunc.GridFunction_GetElementDofValues(self, el, dof_vals) GetElementDofValues = _swig_new_instance_method(_gridfunc.GridFunction_GetElementDofValues) def ImposeBounds(self, *args): r""" ImposeBounds(GridFunction self, int i, Vector weights, Vector lo_, Vector hi_) ImposeBounds(GridFunction self, int i, Vector weights, double min_=0.0, double max_=mfem::infinity()) """ return _gridfunc.GridFunction_ImposeBounds(self, *args) ImposeBounds = _swig_new_instance_method(_gridfunc.GridFunction_ImposeBounds) def RestrictConforming(self): r"""RestrictConforming(GridFunction self)""" return _gridfunc.GridFunction_RestrictConforming(self) RestrictConforming = _swig_new_instance_method(_gridfunc.GridFunction_RestrictConforming) def ProjectGridFunction(self, src): r"""ProjectGridFunction(GridFunction self, GridFunction src)""" return _gridfunc.GridFunction_ProjectGridFunction(self, src) ProjectGridFunction = _swig_new_instance_method(_gridfunc.GridFunction_ProjectGridFunction) def ProjectCoefficient(self, *args): r""" ProjectCoefficient(GridFunction self, Coefficient coeff) ProjectCoefficient(GridFunction self, Coefficient coeff, intArray dofs, int vd=0) ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff) ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff, intArray dofs) ProjectCoefficient(GridFunction self, VectorCoefficient vcoeff, int attribute) ProjectCoefficient(GridFunction self, mfem::Coefficient *[] coeff) """ return _gridfunc.GridFunction_ProjectCoefficient(self, *args) ProjectCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectCoefficient) ARITHMETIC = _gridfunc.GridFunction_ARITHMETIC HARMONIC = _gridfunc.GridFunction_HARMONIC def ProjectDiscCoefficient(self, *args): r""" ProjectDiscCoefficient(GridFunction self, VectorCoefficient coeff) ProjectDiscCoefficient(GridFunction self, Coefficient coeff, mfem::GridFunction::AvgType type) ProjectDiscCoefficient(GridFunction self, VectorCoefficient coeff, mfem::GridFunction::AvgType type) """ return _gridfunc.GridFunction_ProjectDiscCoefficient(self, *args) ProjectDiscCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectDiscCoefficient) def ProjectBdrCoefficient(self, *args): r""" ProjectBdrCoefficient(GridFunction self, Coefficient coeff, intArray attr) ProjectBdrCoefficient(GridFunction self, VectorCoefficient vcoeff, intArray attr) ProjectBdrCoefficient(GridFunction self, mfem::Coefficient *[] coeff, intArray attr) """ return _gridfunc.GridFunction_ProjectBdrCoefficient(self, *args) ProjectBdrCoefficient = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficient) def ProjectBdrCoefficientNormal(self, vcoeff, bdr_attr): r"""ProjectBdrCoefficientNormal(GridFunction self, VectorCoefficient vcoeff, intArray bdr_attr)""" return _gridfunc.GridFunction_ProjectBdrCoefficientNormal(self, vcoeff, bdr_attr) ProjectBdrCoefficientNormal = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficientNormal) def ProjectBdrCoefficientTangent(self, vcoeff, bdr_attr): r"""ProjectBdrCoefficientTangent(GridFunction self, VectorCoefficient vcoeff, intArray bdr_attr)""" return _gridfunc.GridFunction_ProjectBdrCoefficientTangent(self, vcoeff, bdr_attr) ProjectBdrCoefficientTangent = _swig_new_instance_method(_gridfunc.GridFunction_ProjectBdrCoefficientTangent) def ComputeL2Error(self, *args): r""" ComputeL2Error(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double ComputeL2Error(GridFunction self, mfem::Coefficient *[] exsol, mfem::IntegrationRule const *[] irs=0) -> double ComputeL2Error(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0, intArray elems=None) -> double """ return _gridfunc.GridFunction_ComputeL2Error(self, *args) ComputeL2Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeL2Error) def ComputeGradError(self, exgrad, irs=0): r"""ComputeGradError(GridFunction self, VectorCoefficient exgrad, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeGradError(self, exgrad, irs) ComputeGradError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeGradError) def ComputeCurlError(self, excurl, irs=0): r"""ComputeCurlError(GridFunction self, VectorCoefficient excurl, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeCurlError(self, excurl, irs) ComputeCurlError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeCurlError) def ComputeDivError(self, exdiv, irs=0): r"""ComputeDivError(GridFunction self, Coefficient exdiv, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeDivError(self, exdiv, irs) ComputeDivError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeDivError) def ComputeDGFaceJumpError(self, *args): r""" ComputeDGFaceJumpError(GridFunction self, Coefficient exsol, Coefficient ell_coeff, JumpScaling jump_scaling, mfem::IntegrationRule const *[] irs=0) -> double ComputeDGFaceJumpError(GridFunction self, Coefficient exsol, Coefficient ell_coeff, double Nu, mfem::IntegrationRule const *[] irs=0) -> double """ return _gridfunc.GridFunction_ComputeDGFaceJumpError(self, *args) ComputeDGFaceJumpError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeDGFaceJumpError) def ComputeH1Error(self, *args): r""" ComputeH1Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, Coefficient ell_coef, double Nu, int norm_type) -> double ComputeH1Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, mfem::IntegrationRule const *[] irs=0) -> double """ return _gridfunc.GridFunction_ComputeH1Error(self, *args) ComputeH1Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeH1Error) def ComputeHDivError(self, exsol, exdiv, irs=0): r"""ComputeHDivError(GridFunction self, VectorCoefficient exsol, Coefficient exdiv, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeHDivError(self, exsol, exdiv, irs) ComputeHDivError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeHDivError) def ComputeHCurlError(self, exsol, excurl, irs=0): r"""ComputeHCurlError(GridFunction self, VectorCoefficient exsol, VectorCoefficient excurl, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeHCurlError(self, exsol, excurl, irs) ComputeHCurlError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeHCurlError) def ComputeMaxError(self, *args): r""" ComputeMaxError(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double ComputeMaxError(GridFunction self, mfem::Coefficient *[] exsol, mfem::IntegrationRule const *[] irs=0) -> double ComputeMaxError(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double """ return _gridfunc.GridFunction_ComputeMaxError(self, *args) ComputeMaxError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeMaxError) def ComputeW11Error(self, exsol, exgrad, norm_type, elems=None, irs=0): r"""ComputeW11Error(GridFunction self, Coefficient exsol, VectorCoefficient exgrad, int norm_type, intArray elems=None, mfem::IntegrationRule const *[] irs=0) -> double""" return _gridfunc.GridFunction_ComputeW11Error(self, exsol, exgrad, norm_type, elems, irs) ComputeW11Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeW11Error) def ComputeL1Error(self, *args): r""" ComputeL1Error(GridFunction self, Coefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double ComputeL1Error(GridFunction self, VectorCoefficient exsol, mfem::IntegrationRule const *[] irs=0) -> double """ return _gridfunc.GridFunction_ComputeL1Error(self, *args) ComputeL1Error = _swig_new_instance_method(_gridfunc.GridFunction_ComputeL1Error) def ComputeLpError(self, *args): r""" ComputeLpError(GridFunction self, double const p, Coefficient exsol, Coefficient weight=None, mfem::IntegrationRule const *[] irs=0) -> double ComputeLpError(GridFunction self, double const p, VectorCoefficient exsol, Coefficient weight=None, VectorCoefficient v_weight=None, mfem::IntegrationRule const *[] irs=0) -> double """ return _gridfunc.GridFunction_ComputeLpError(self, *args) ComputeLpError = _swig_new_instance_method(_gridfunc.GridFunction_ComputeLpError) def ComputeElementLpErrors(self, *args): r""" ComputeElementLpErrors(GridFunction self, double const p, Coefficient exsol, Vector error, Coefficient weight=None, mfem::IntegrationRule const *[] irs=0) ComputeElementLpErrors(GridFunction self, double const p, VectorCoefficient exsol, Vector error, Coefficient weight=None, VectorCoefficient v_weight=None, mfem::IntegrationRule const *[] irs=0) """ return _gridfunc.GridFunction_ComputeElementLpErrors(self, *args) ComputeElementLpErrors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementLpErrors) def ComputeElementL1Errors(self, *args): r""" ComputeElementL1Errors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) ComputeElementL1Errors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) """ return _gridfunc.GridFunction_ComputeElementL1Errors(self, *args) ComputeElementL1Errors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementL1Errors) def ComputeElementL2Errors(self, *args): r""" ComputeElementL2Errors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) ComputeElementL2Errors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) """ return _gridfunc.GridFunction_ComputeElementL2Errors(self, *args) ComputeElementL2Errors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementL2Errors) def ComputeElementMaxErrors(self, *args): r""" ComputeElementMaxErrors(GridFunction self, Coefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) ComputeElementMaxErrors(GridFunction self, VectorCoefficient exsol, Vector error, mfem::IntegrationRule const *[] irs=0) """ return _gridfunc.GridFunction_ComputeElementMaxErrors(self, *args) ComputeElementMaxErrors = _swig_new_instance_method(_gridfunc.GridFunction_ComputeElementMaxErrors) def ComputeFlux(self, blfi, flux, wcoef=True, subdomain=-1): r"""ComputeFlux(GridFunction self, BilinearFormIntegrator blfi, GridFunction flux, bool wcoef=True, int subdomain=-1)""" return _gridfunc.GridFunction_ComputeFlux(self, blfi, flux, wcoef, subdomain) ComputeFlux = _swig_new_instance_method(_gridfunc.GridFunction_ComputeFlux) def Assign(self, *args): r""" Assign(GridFunction self, GridFunction rhs) -> GridFunction Assign(GridFunction self, double value) -> GridFunction Assign(GridFunction self, Vector v) -> GridFunction """ return _gridfunc.GridFunction_Assign(self, *args) Assign = _swig_new_instance_method(_gridfunc.GridFunction_Assign) def Update(self): r"""Update(GridFunction self)""" return _gridfunc.GridFunction_Update(self) Update = _swig_new_instance_method(_gridfunc.GridFunction_Update) def FESpace(self, *args): r""" FESpace(GridFunction self) -> FiniteElementSpace FESpace(GridFunction self) -> FiniteElementSpace """ return _gridfunc.GridFunction_FESpace(self, *args) FESpace = _swig_new_instance_method(_gridfunc.GridFunction_FESpace) def SetSpace(self, f): r"""SetSpace(GridFunction self, FiniteElementSpace f)""" return _gridfunc.GridFunction_SetSpace(self, f) SetSpace = _swig_new_instance_method(_gridfunc.GridFunction_SetSpace) def MakeRef(self, *args): r""" MakeRef(GridFunction self, Vector base, int offset, int size) MakeRef(GridFunction self, Vector base, int offset) MakeRef(GridFunction self, FiniteElementSpace f, double * v) MakeRef(GridFunction self, FiniteElementSpace f, Vector v, int v_offset) """ return _gridfunc.GridFunction_MakeRef(self, *args) MakeRef = _swig_new_instance_method(_gridfunc.GridFunction_MakeRef) def MakeTRef(self, *args): r""" MakeTRef(GridFunction self, FiniteElementSpace f, double * tv) MakeTRef(GridFunction self, FiniteElementSpace f, Vector tv, int tv_offset) """ return _gridfunc.GridFunction_MakeTRef(self, *args) MakeTRef = _swig_new_instance_method(_gridfunc.GridFunction_MakeTRef) def SaveVTK(self, out, field_name, ref): r"""SaveVTK(GridFunction self, std::ostream & out, std::string const & field_name, int ref)""" return _gridfunc.GridFunction_SaveVTK(self, out, field_name, ref) SaveVTK = _swig_new_instance_method(_gridfunc.GridFunction_SaveVTK) def SaveSTL(self, out, TimesToRefine=1): r"""SaveSTL(GridFunction self, std::ostream & out, int TimesToRefine=1)""" return _gridfunc.GridFunction_SaveSTL(self, out, TimesToRefine) SaveSTL = _swig_new_instance_method(_gridfunc.GridFunction_SaveSTL) __swig_destroy__ = _gridfunc.delete_GridFunction def __init__(self, *args): r""" __init__(GridFunction self) -> GridFunction __init__(GridFunction self, GridFunction orig) -> GridFunction __init__(GridFunction self, FiniteElementSpace f) -> GridFunction __init__(GridFunction self, FiniteElementSpace f, double * data) -> GridFunction __init__(GridFunction self, Mesh m, std::istream & input) -> GridFunction __init__(GridFunction self, Mesh m, mfem::GridFunction *[] gf_array, int num_pieces) -> GridFunction __init__(GridFunction self, FiniteElementSpace fes, Vector v, int offset) -> GridFunction """ _gridfunc.GridFunction_swiginit(self, _gridfunc.new_GridFunction(*args)) def SaveToFile(self, gf_file, precision): r"""SaveToFile(GridFunction self, char const * gf_file, int const precision)""" return _gridfunc.GridFunction_SaveToFile(self, gf_file, precision) SaveToFile = _swig_new_instance_method(_gridfunc.GridFunction_SaveToFile) def WriteToStream(self, StringIO): r"""WriteToStream(GridFunction self, PyObject * StringIO) -> PyObject *""" return _gridfunc.GridFunction_WriteToStream(self, StringIO) WriteToStream = _swig_new_instance_method(_gridfunc.GridFunction_WriteToStream) def iadd(self, c): r"""iadd(GridFunction self, GridFunction c) -> GridFunction""" return _gridfunc.GridFunction_iadd(self, c) iadd = _swig_new_instance_method(_gridfunc.GridFunction_iadd) def isub(self, *args): r""" isub(GridFunction self, GridFunction c) -> GridFunction isub(GridFunction self, double c) -> GridFunction """ return _gridfunc.GridFunction_isub(self, *args) isub = _swig_new_instance_method(_gridfunc.GridFunction_isub) def imul(self, c): r"""imul(GridFunction self, double c) -> GridFunction""" return _gridfunc.GridFunction_imul(self, c) imul = _swig_new_instance_method(_gridfunc.GridFunction_imul) def idiv(self, c): r"""idiv(GridFunction self, double c) -> GridFunction""" return _gridfunc.GridFunction_idiv(self, c) idiv = _swig_new_instance_method(_gridfunc.GridFunction_idiv) def Save(self, *args): r""" Save(GridFunction self, std::ostream & out) Save(GridFunction self, char const * fname, int precision=16) Save(GridFunction self, char const * file, int precision=16) """ return _gridfunc.GridFunction_Save(self, *args) Save = _swig_new_instance_method(_gridfunc.GridFunction_Save) def SaveGZ(self, file, precision=16): r"""SaveGZ(GridFunction self, char const * file, int precision=16)""" return _gridfunc.GridFunction_SaveGZ(self, file, precision) SaveGZ = _swig_new_instance_method(_gridfunc.GridFunction_SaveGZ) # Register GridFunction in _gridfunc: _gridfunc.GridFunction_swigregister(GridFunction) class JumpScaling(object): r"""Proxy of C++ mfem::JumpScaling class.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr CONSTANT = _gridfunc.JumpScaling_CONSTANT ONE_OVER_H = _gridfunc.JumpScaling_ONE_OVER_H P_SQUARED_OVER_H = _gridfunc.JumpScaling_P_SQUARED_OVER_H def __init__(self, *args, **kwargs): r"""__init__(JumpScaling self, double nu_=1.0, mfem::JumpScaling::JumpScalingType type_=CONSTANT) -> JumpScaling""" _gridfunc.JumpScaling_swiginit(self, _gridfunc.new_JumpScaling(*args, **kwargs)) def Eval(self, h, p): r"""Eval(JumpScaling self, double h, int p) -> double""" return _gridfunc.JumpScaling_Eval(self, h, p) Eval = _swig_new_instance_method(_gridfunc.JumpScaling_Eval) __swig_destroy__ = _gridfunc.delete_JumpScaling # Register JumpScaling in _gridfunc: _gridfunc.JumpScaling_swigregister(JumpScaling) class QuadratureFunction(mfem._par.vector.Vector): r"""Proxy of C++ mfem::QuadratureFunction class.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): r""" __init__(QuadratureFunction self) -> QuadratureFunction __init__(QuadratureFunction self, QuadratureFunction orig) -> QuadratureFunction __init__(QuadratureFunction self, QuadratureSpace qspace_, int vdim_=1) -> QuadratureFunction __init__(QuadratureFunction self, QuadratureSpace qspace_, double * qf_data, int vdim_=1) -> QuadratureFunction __init__(QuadratureFunction self, Mesh mesh, std::istream & _in) -> QuadratureFunction """ _gridfunc.QuadratureFunction_swiginit(self, _gridfunc.new_QuadratureFunction(*args)) __swig_destroy__ = _gridfunc.delete_QuadratureFunction def GetSpace(self): r"""GetSpace(QuadratureFunction self) -> QuadratureSpace""" return _gridfunc.QuadratureFunction_GetSpace(self) GetSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetSpace) def SetSpace(self, *args): r""" SetSpace(QuadratureFunction self, QuadratureSpace qspace_, int vdim_=-1) SetSpace(QuadratureFunction self, QuadratureSpace qspace_, double * qf_data, int vdim_=-1) """ return _gridfunc.QuadratureFunction_SetSpace(self, *args) SetSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetSpace) def GetVDim(self): r"""GetVDim(QuadratureFunction self) -> int""" return _gridfunc.QuadratureFunction_GetVDim(self) GetVDim = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetVDim) def SetVDim(self, vdim_): r"""SetVDim(QuadratureFunction self, int vdim_)""" return _gridfunc.QuadratureFunction_SetVDim(self, vdim_) SetVDim = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetVDim) def OwnsSpace(self): r"""OwnsSpace(QuadratureFunction self) -> bool""" return _gridfunc.QuadratureFunction_OwnsSpace(self) OwnsSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_OwnsSpace) def SetOwnsSpace(self, own): r"""SetOwnsSpace(QuadratureFunction self, bool own)""" return _gridfunc.QuadratureFunction_SetOwnsSpace(self, own) SetOwnsSpace = _swig_new_instance_method(_gridfunc.QuadratureFunction_SetOwnsSpace) def GetElementIntRule(self, idx): r"""GetElementIntRule(QuadratureFunction self, int idx) -> IntegrationRule""" return _gridfunc.QuadratureFunction_GetElementIntRule(self, idx) GetElementIntRule = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetElementIntRule) def GetElementValues(self, *args): r""" GetElementValues(QuadratureFunction self, int idx, Vector values) GetElementValues(QuadratureFunction self, int idx, Vector values) GetElementValues(QuadratureFunction self, int idx, int const ip_num, Vector values) GetElementValues(QuadratureFunction self, int idx, int const ip_num, Vector values) GetElementValues(QuadratureFunction self, int idx, DenseMatrix values) GetElementValues(QuadratureFunction self, int idx, DenseMatrix values) """ return _gridfunc.QuadratureFunction_GetElementValues(self, *args) GetElementValues = _swig_new_instance_method(_gridfunc.QuadratureFunction_GetElementValues) def Save(self, *args): r""" Save(QuadratureFunction self, std::ostream & out) Save(QuadratureFunction self, char const * file, int precision=16) """ return _gridfunc.QuadratureFunction_Save(self, *args) Save = _swig_new_instance_method(_gridfunc.QuadratureFunction_Save) def SaveGZ(self, file, precision=16): r"""SaveGZ(QuadratureFunction self, char const * file, int precision=16)""" return _gridfunc.QuadratureFunction_SaveGZ(self, file, precision) SaveGZ = _swig_new_instance_method(_gridfunc.QuadratureFunction_SaveGZ) # Register QuadratureFunction in _gridfunc: _gridfunc.QuadratureFunction_swigregister(QuadratureFunction) def __lshift__(*args): r""" __lshift__(std::ostream & os, SparseMatrix mat) -> std::ostream __lshift__(std::ostream & out, Mesh mesh) -> std::ostream __lshift__(std::ostream & out, GridFunction sol) -> std::ostream __lshift__(std::ostream & out, QuadratureFunction qf) -> std::ostream & """ return _gridfunc.__lshift__(*args) __lshift__ = _gridfunc.__lshift__ def ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags=None, with_subdomains=1, with_coeff=False): r"""ZZErrorEstimator(BilinearFormIntegrator blfi, GridFunction u, GridFunction flux, Vector error_estimates, intArray aniso_flags=None, int with_subdomains=1, bool with_coeff=False) -> double""" return _gridfunc.ZZErrorEstimator(blfi, u, flux, error_estimates, aniso_flags, with_subdomains, with_coeff) ZZErrorEstimator = _gridfunc.ZZErrorEstimator def ComputeElementLpDistance(p, i, gf1, gf2): r"""ComputeElementLpDistance(double p, int i, GridFunction gf1, GridFunction gf2) -> double""" return _gridfunc.ComputeElementLpDistance(p, i, gf1, gf2) ComputeElementLpDistance = _gridfunc.ComputeElementLpDistance class ExtrudeCoefficient(mfem._par.coefficient.Coefficient): r"""Proxy of C++ mfem::ExtrudeCoefficient class.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, m, s, n_): r"""__init__(ExtrudeCoefficient self, Mesh m, Coefficient s, int n_) -> ExtrudeCoefficient""" _gridfunc.ExtrudeCoefficient_swiginit(self, _gridfunc.new_ExtrudeCoefficient(m, s, n_)) def Eval(self, T, ip): r"""Eval(ExtrudeCoefficient self, ElementTransformation T, IntegrationPoint ip) -> double""" return _gridfunc.ExtrudeCoefficient_Eval(self, T, ip) Eval = _swig_new_instance_method(_gridfunc.ExtrudeCoefficient_Eval) __swig_destroy__ = _gridfunc.delete_ExtrudeCoefficient # Register ExtrudeCoefficient in _gridfunc: _gridfunc.ExtrudeCoefficient_swigregister(ExtrudeCoefficient) def Extrude1DGridFunction(mesh, mesh2d, sol, ny): r"""Extrude1DGridFunction(Mesh mesh, Mesh mesh2d, GridFunction sol, int const ny) -> GridFunction""" return _gridfunc.Extrude1DGridFunction(mesh, mesh2d, sol, ny) Extrude1DGridFunction = _gridfunc.Extrude1DGridFunction def __iadd__(self, v): ret = _gridfunc.GridFunction_iadd(self, v) ret.thisown = 0 return self def __isub__(self, v): ret = _gridfunc.GridFunction_isub(self, v) ret.thisown = 0 return self def __idiv__(self, v): ret = _gridfunc.GridFunction_idiv(self, v) ret.thisown = 0 return self def __imul__(self, v): ret = _gridfunc.GridFunction_imul(self, v) ret.thisown = 0 return self GridFunction.__iadd__ = __iadd__ GridFunction.__idiv__ = __idiv__ GridFunction.__isub__ = __isub__ GridFunction.__imul__ = __imul__
    49.335065
    201
    0.740076
    32,840
    0.864484
    0
    0
    0
    0
    0
    0
    15,335
    0.40368
    131ada8dd58eaa29a8303d1a7138ffe5d3485877
    6,861
    py
    Python
    src/tracks/settings.py
    adcarmichael/tracks
    04108bbdaf8554e57e278c1556efa9c5b9603973
    [ "Apache-2.0" ]
    null
    null
    null
    src/tracks/settings.py
    adcarmichael/tracks
    04108bbdaf8554e57e278c1556efa9c5b9603973
    [ "Apache-2.0" ]
    41
    2019-06-14T21:19:31.000Z
    2022-02-10T14:41:00.000Z
    src/tracks/settings.py
    adcarmichael/tracks
    04108bbdaf8554e57e278c1556efa9c5b9603973
    [ "Apache-2.0" ]
    null
    null
    null
    import os import sentry_sdk from sentry_sdk.integrations.django import DjangoIntegration # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) PWA_SERVICE_WORKER_PATH = os.path.join( BASE_DIR, 'routes/static/routes/js', 'serviceworker.js') print(os.path.join( BASE_DIR, 'routes/static/routes/js', 'serviceworker.js')) DEBUG = int(os.environ.get("DEBUG", default=0)) SECRET_KEY = os.environ.get("SECRET_KEY", 'asdfkhbsadgui87gjsbdfui') # 'DJANGO_ALLOWED_HOSTS' should be a single string of hosts with a space between each. # For example: 'DJANGO_ALLOWED_HOSTS=localhost 127.0.0.1 [::1]' ALLOWED_HOSTS = os.environ.get("DJANGO_ALLOWED_HOSTS", 'localhost').split(" ") # Application definition INSTALLED_APPS = [ 'routes', 'accounts', 'dashboard.apps.DashboardConfig', 'api.apps.ApiConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'widget_tweaks', 'rest_framework', 'pwa', ] # 'celery', MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'tracks.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'tracks.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { "default": { "ENGINE": os.environ.get("SQL_ENGINE", "django.db.backends.sqlite3"), "NAME": os.environ.get("SQL_DATABASE", os.path.join(BASE_DIR, "db.sqlite3")), "USER": os.environ.get("SQL_USER", "user"), "PASSWORD": os.environ.get("SQL_PASSWORD", "password"), "HOST": os.environ.get("SQL_HOST", "localhost"), "PORT": os.environ.get("SQL_PORT", "5432"), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/media/' STATIC_ROOT = './static/' MEDIA_ROOT = './media/' LOGIN_REDIRECT_URL = 'home' LOGOUT_REDIRECT_URL = 'home' # no email for localhost or staging EMAIL_USE_TLS = os.environ.get("EMAIL_USE_TLS") EMAIL_HOST = os.environ.get("EMAIL_HOST") EMAIL_HOST_USER = os.environ.get("EMAIL_HOST_USER") EMAIL_HOST_PASSWORD = os.environ.get("EMAIL_HOST_PASSWORD") EMAIL_PORT = os.environ.get("EMAIL_PORT") EMAIL_BACKEND = os.environ.get("EMAIL_BACKEND") DEFAULT_FROM_EMAIL = '[email protected]' # CELERY # CELERY_BROKER_URL = 'redis://redis:6379/0' # CELERY_RESULT_BACKEND = 'redis://redis:6379/0' # BROKER_URL = 'redis://localhost:6379/0' # CELERY_RESULT_BACKEND = 'redis://localhost:6379/' # CELERY_ACCEPT_CONTENT = ['application/json'] # CELERY_TASK_SERIALIZER = 'json' # CELERY_RESULT_SERIALIZER = 'json' REST_FRAMEWORK = { # Use Django's standard `django.contrib.auth` permissions, # or allow read-only access for unauthenticated users. 'DEFAULT_PERMISSION_CLASSES': [ 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly' ], 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.TokenAuthentication', 'rest_framework.authentication.SessionAuthentication', ), 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination', 'PAGE_SIZE': 10 } LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'formatters': { 'console': { 'format': '%(levelname)s %(asctime)s %(module)s: %(message)s' }, }, 'handlers': { 'console': { 'class': 'logging.StreamHandler', 'formatter': 'console' }, }, 'loggers': { '': { 'handlers': ['console'], 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'), }, 'django': { 'handlers': ['console'], 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'), }, 'django.request': { 'level': 'INFO', 'handlers': ['console'] } # 'celery': { # 'handlers': ['console'], # 'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'), # }, }, } # STATICFILES_DIRS = [ # os.path.join(BASE_DIR, 'static'), # ] PWA_APP_NAME = 'ChalkTracks' PWA_APP_DESCRIPTION = "Indoor Climbing Tracker" PWA_APP_THEME_COLOR = '#000000' PWA_APP_BACKGROUND_COLOR = '#000000' PWA_APP_DISPLAY = 'standalone' PWA_APP_SCOPE = '/' PWA_APP_ORIENTATION = 'portrait' PWA_APP_START_URL = '/' PWA_APP_ICONS = [ { 'src': '/static/routes/favicon_io/favicon-32x32.png', 'sizes': '32x32', "type": "image/png", "purpose": "any maskable" }, { "src": "/static/routes/favicon_io/android-chrome-192x192.png", "sizes": "192x192", "type": "image/png", "purpose": "any maskable" }, { "src": "/static/routes/favicon_io/android-chrome-512x512.png", "sizes": "512x512", "type": "image/png", "purpose": "any maskable" } ] PWA_APP_DIR = 'ltr' PWA_APP_LANG = 'en-US' sentry_sdk.init( dsn="https://[email protected]/1878812", integrations=[DjangoIntegration()], # If you wish to associate users to errors (assuming you are using # django.contrib.auth) you may enable sending PII data. send_default_pii=True )
    28.5875
    91
    0.655735
    0
    0
    0
    0
    0
    0
    0
    0
    4,261
    0.621046
    131b0afb1746ef9363aae186aba698e6731a895a
    2,647
    py
    Python
    examples/04-lights/plotter_builtins.py
    akeshavan/pyvista
    45fe8b1c38712776f9b628a60a8662d0716dd52b
    [ "MIT" ]
    null
    null
    null
    examples/04-lights/plotter_builtins.py
    akeshavan/pyvista
    45fe8b1c38712776f9b628a60a8662d0716dd52b
    [ "MIT" ]
    6
    2022-03-11T23:21:22.000Z
    2022-03-25T03:32:21.000Z
    examples/04-lights/plotter_builtins.py
    akeshavan/pyvista
    45fe8b1c38712776f9b628a60a8662d0716dd52b
    [ "MIT" ]
    null
    null
    null
    """ Plotter Lighting Systems ~~~~~~~~~~~~~~~~~~~~~~~~ The :class:`pyvista.Plotter` class comes with three options for the default lighting system: * a light kit consisting of a headlight and four camera lights, * an illumination system containing three lights arranged around the camera, * no lighting. With meshes that don't have depth information encoded in their color the importance of an appropriate lighting setup becomes paramount for accurate visualization. Light kit ========= The default ``lighting='light kit'`` option recreates a lighting setup that corresponds to a ``vtk.vtkLightKit``. We can check what type of lights this lighting comprises: """ # sphinx_gallery_thumbnail_number = 3 import pyvista as pv from pyvista import examples # default: light kit plotter = pv.Plotter() light_types = [light.light_type for light in plotter.renderer.lights] # Remove from plotters so output is not produced in docs pv.plotting._ALL_PLOTTERS.clear() light_types ############################################################################### # Add a white terrain to the scene: mesh = examples.download_st_helens().warp_by_scalar() plotter = pv.Plotter() plotter.add_mesh(mesh, color='white') plotter.show() ############################################################################### # Three-lights illumination # ========================= # # Switching to three-lights illumination gives a different character to the # figure, in this case showing less contrast when viewing the mountain from # the top, but having more contrast with views closer to the side. This becomes # especially clear when exploring the figures interactively. plotter = pv.Plotter(lighting='three lights') plotter.add_mesh(mesh, color='white') plotter.show() ############################################################################### # Again we can check what kind of lights this setting uses: plotter = pv.Plotter(lighting='three lights') light_types = [light.light_type for light in plotter.renderer.lights] # Remove from plotters so output is not produced in docs pv.plotting._ALL_PLOTTERS.clear() light_types ############################################################################### # Custom lighting # =============== # # We can introduce our own lighting from scratch by disabling any lighting # on plotter initialization. Adding a single scene light to a scene will # often result in ominous visuals due to objects having larger regions in # shadow: plotter = pv.Plotter(lighting='none') plotter.add_mesh(mesh, color='white') light = pv.Light() light.set_direction_angle(30, 0) plotter.add_light(light) plotter.show()
    30.77907
    79
    0.66377
    0
    0
    0
    0
    0
    0
    0
    0
    1,913
    0.722705
    131c13cd6c6c6b833141fea96f58ed4c3b53dc38
    272
    py
    Python
    src/swimport/tests/15_char_arrays/main.py
    talos-gis/swimport
    e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3
    [ "MIT" ]
    1
    2019-03-07T20:43:42.000Z
    2019-03-07T20:43:42.000Z
    src/swimport/tests/15_char_arrays/main.py
    talos-gis/swimport
    e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3
    [ "MIT" ]
    null
    null
    null
    src/swimport/tests/15_char_arrays/main.py
    talos-gis/swimport
    e8f0fcf02b0c9751b199f750f1f8bc57c8ff54b3
    [ "MIT" ]
    null
    null
    null
    from swimport.all import * src = FileSource('src.h') swim = Swim('example') swim(pools.c_string) swim(pools.numpy_arrays(r"../resources", allow_char_arrays=True)) swim(pools.include(src)) assert swim(Function.Behaviour()(src)) > 0 swim.write('example.i') print('ok!')
    19.428571
    65
    0.720588
    0
    0
    0
    0
    0
    0
    0
    0
    47
    0.172794
    131c14184c1fb810c136590d143d0fdf3f83e6df
    4,523
    py
    Python
    ipyvolume/astro.py
    larsoner/ipyvolume
    8603a47aff4531df69ace44efdcf6b85d6e51e51
    [ "MIT" ]
    1
    2019-04-09T11:57:07.000Z
    2019-04-09T11:57:07.000Z
    ipyvolume/astro.py
    larsoner/ipyvolume
    8603a47aff4531df69ace44efdcf6b85d6e51e51
    [ "MIT" ]
    null
    null
    null
    ipyvolume/astro.py
    larsoner/ipyvolume
    8603a47aff4531df69ace44efdcf6b85d6e51e51
    [ "MIT" ]
    null
    null
    null
    import numpy as np import PIL.Image import pythreejs import ipyvolume as ipv from .datasets import UrlCached def _randomSO3(): """return random rotatation matrix, algo by James Arvo""" u1 = np.random.random() u2 = np.random.random() u3 = np.random.random() R = np.array([[np.cos(2*np.pi*u1), np.sin(2*np.pi*u1), 0], [-np.sin(2*np.pi*u1), np.cos(2*np.pi*u1), 0], [0, 0, 1]]) v = np.array([np.cos(2*np.pi*u2)*np.sqrt(u3), np.sin(2*np.pi*u2)*np.sqrt(u3), np.sqrt(1-u3)]) H = np.identity(3)-2*v*np.transpose([v]) return - np.dot(H, R) def spherical_galaxy_orbit(orbit_x, orbit_y, orbit_z, N_stars=100, sigma_r=1, orbit_visible=False, orbit_line_interpolate=5, N_star_orbits=10, color=[255, 220, 200], size_star=1, scatter_kwargs={}): """Create a fake galaxy around the points orbit_x/y/z with N_stars around it""" if orbit_line_interpolate > 1: import scipy.interpolate x = np.linspace(0, 1, len(orbit_x)) x_smooth = np.linspace(0, 1, len(orbit_x)*orbit_line_interpolate) kind = 'quadratic' orbit_x_line = scipy.interpolate.interp1d(x, orbit_x, kind)(x_smooth) orbit_y_line = scipy.interpolate.interp1d(x, orbit_y, kind)(x_smooth) orbit_z_line = scipy.interpolate.interp1d(x, orbit_z, kind)(x_smooth) else: orbit_x_line = orbit_x orbit_y_line = orbit_y orbit_z_line = orbit_z line = ipv.plot(orbit_x_line, orbit_y_line, orbit_z_line, visible=orbit_visible) x = np.repeat(orbit_x, N_stars).reshape((-1, N_stars)) y = np.repeat(orbit_y, N_stars).reshape((-1, N_stars)) z = np.repeat(orbit_z, N_stars).reshape((-1, N_stars)) xr, yr, zr = np.random.normal(0, scale=sigma_r, size=(3, N_stars))# + r = np.sqrt(xr**2 + yr**2 + zr**2) for i in range(N_stars): a = np.linspace(0, 1, x.shape[0]) * 2 * np.pi * N_star_orbits xo = r[i] * np.sin(a) yo = r[i] * np.cos(a) zo = a * 0 xo, yo, zo = np.dot(_randomSO3(), [xo, yo, zo]) #print(x.shape, xo.shape) x[:, i] += xo y[:, i] += yo z[:, i] += zo sprite = ipv.scatter(x, y, z, texture=radial_sprite((64, 64), color), marker='square_2d', size=size_star, **scatter_kwargs) with sprite.material.hold_sync(): sprite.material.blending = pythreejs.BlendingMode.CustomBlending sprite.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor sprite.material.blendDst = pythreejs.BlendFactors.OneFactor sprite.material.blendEquation = 'AddEquation' sprite.material.transparent = True sprite.material.depthWrite = False sprite.material.alphaTest = 0.1 return sprite, line def radial_sprite(shape, color): color = np.array(color) ara = np.zeros(shape[:2] + (4,), dtype=np.uint8) x = np.linspace(-1, 1, shape[0]) y = np.linspace(-1, 1, shape[1]) x, y = np.meshgrid(x, y) s = 0.5 radius = np.sqrt(x**2+y**2) amplitude = np.maximum(0, np.exp(-radius**2/s**2)).T ara[...,3] = (amplitude * 255) ara[...,:3] = color * amplitude.reshape(shape + (1,)) im = PIL.Image.fromarray(ara, 'RGBA') return im def stars(N=1000, radius=100000, thickness=3, seed=42, color=[255, 240, 240]): import ipyvolume as ipv rng = np.random.RandomState(seed) x, y, z = rng.normal(size=(3, N)) r = np.sqrt(x**2 + y**2 + z**2)/(radius + thickness * radius * np.random.random(N)) x /= r y /= r z /= r return ipv.scatter(x, y, z, texture=radial_sprite((64, 64), color), marker='square_2d', grow_limits=False, size=radius*0.7/100) milkyway_url = 'https://www.nasa.gov/sites/default/files/images/620057main_milkyway_full.jpg' milkyway_image = UrlCached(milkyway_url) def plot_milkyway(R_sun=8, size=100): mw_image = PIL.Image.open(milkyway_image.fetch()) rescale = 40 t = np.linspace(0, 1, 100) xmw = np.linspace(0, 1, 10) ymw = np.linspace(0, 1, 10) xmw, ymw = np.meshgrid(xmw, ymw) zmw = xmw * 0 + 0.01 mw = mesh = ipv.plot_mesh((xmw-0.5)*rescale, (ymw-0.5)*rescale+R_sun, zmw, u=xmw, v=ymw, texture=mw_image, wireframe=False) mw.material.blending = pythreejs.BlendingMode.CustomBlending mw.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor mw.material.blendDst = pythreejs.BlendFactors.OneFactor mw.material.blendEquation = 'AddEquation' mw.material.transparent = True mw.material.depthWrite = False mw.material.alphaTest = 0.1 ipv.xyzlim(size) return mesh
    41.118182
    198
    0.640946
    0
    0
    0
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    0
    0
    0
    0
    308
    0.068096
    131e1d61812e10d7ea42b3ca418199cd229845a3
    1,157
    py
    Python
    deepfunning/function.py
    Zrealshadow/DeepFunning
    5c44210a6b30ea57a0be5f930da4ada540e7e3d0
    [ "MIT" ]
    null
    null
    null
    deepfunning/function.py
    Zrealshadow/DeepFunning
    5c44210a6b30ea57a0be5f930da4ada540e7e3d0
    [ "MIT" ]
    null
    null
    null
    deepfunning/function.py
    Zrealshadow/DeepFunning
    5c44210a6b30ea57a0be5f930da4ada540e7e3d0
    [ "MIT" ]
    null
    null
    null
    ''' * @author Waldinsamkeit * @email [email protected] * @create date 2020-09-25 14:33:38 * @desc ''' import torch '''--------------------- Weighted Binary cross Entropy ----------------------''' ''' In Torch BCELoss, weight is set to every element of input instead of to every class ''' def weighted_binary_cross_entropy(output, target, weights=None): if weights is not None: assert len(weights) == 2 loss = weights[1] * (target * torch.log(output)) + \ weights[0] * ((1 - target) * torch.log(1 - output)) else: loss = target * torch.log(output) + (1 - target) * torch.log(1 - output) return torch.neg(torch.mean(loss)) ''' ---------------------- Binary focal loss function -------------------------- ''' ''' In some degree, it can reduce the influence of imbalanced dataset ''' def focal_loss(y_true,y_pred,device): alpha,gamma = torch.tensor(0.25).to(device) , torch.tensor(2.0).to(device) y_pred=torch.clamp(y_pred,1e-7,1-1e-7) return - alpha * y_true * torch.log(y_pred) * (1 - y_pred) ** gamma\ - (1 - alpha) * (1 - y_true) * torch.log(1 - y_pred) * y_pred
    28.925
    84
    0.577355
    0
    0
    0
    0
    0
    0
    0
    0
    435
    0.375972
    131e36d011ba94f7784c802143deb17326553c0e
    7,055
    py
    Python
    dlms_cosem/hdlc/address.py
    pwitab/dlms-cosem
    aa9e18e6ef8a4fee30da8b797dad03b0b7847780
    [ "MIT" ]
    35
    2018-05-24T08:16:35.000Z
    2022-02-24T16:07:07.000Z
    dlms_cosem/hdlc/address.py
    Layty/dlms-cosem
    95b67054a1dfb928e960547b0246b7b6794f0594
    [ "MIT" ]
    41
    2020-12-18T16:31:40.000Z
    2021-12-13T20:59:42.000Z
    dlms_cosem/hdlc/address.py
    Layty/dlms-cosem
    95b67054a1dfb928e960547b0246b7b6794f0594
    [ "MIT" ]
    19
    2019-04-02T14:32:01.000Z
    2021-12-14T13:24:29.000Z
    from typing import * import attr from dlms_cosem.hdlc import validators @attr.s(auto_attribs=True) class HdlcAddress: """ A client address shall always be expressed on one byte. To enable addressing more than one logical device within a single physical device and to support the multi-drop configuration the server address may be divided in two parts– may be divided into two parts: The logical address to address a logical device (separate addressable entity within a physical device) makes up the upper HDLC address The logical address must always be present. The physical address is used to address a physical device ( a physical device on a multi-drop) The physical address can be omitted it not used. """ logical_address: int = attr.ib(validator=[validators.validate_hdlc_address]) physical_address: Optional[int] = attr.ib( default=None, validator=[validators.validate_hdlc_address] ) address_type: str = attr.ib( default="client", validator=[validators.validate_hdlc_address_type] ) @property def length(self): """ The number of bytes the address makes up. :return: """ return len(self.to_bytes()) def to_bytes(self): out: List[Optional[int]] = list() if self.address_type == "client": # shift left 1 bit and set the lsb to mark end of address. out.append(((self.logical_address << 1) | 0b00000001)) else: # server address type logical_higher, logical_lower = self._split_address(self.logical_address) if self.physical_address: physical_higher, physical_lower = self._split_address( self.physical_address ) # mark physical lower as end physical_lower = physical_lower | 0b00000001 out.extend( [logical_higher, logical_lower, physical_higher, physical_lower] ) else: # no physical address so mark the logial as end. logical_lower = logical_lower | 0b00000001 out.extend([logical_higher, logical_lower]) out_bytes = list() for address in out: if address: out_bytes.append(address.to_bytes(1, "big")) return b"".join(out_bytes) @staticmethod def _split_address(address: int) -> Tuple[Optional[int], int]: higher: Optional[int] lower: int if address > 0b01111111: lower = (address & 0b0000000001111111) << 1 higher = (address & 0b0011111110000000) >> 6 else: lower = address << 1 higher = None return higher, lower @staticmethod def _address_to_byte(address: int) -> bytes: return address.to_bytes(1, "big") @classmethod def destination_from_bytes(cls, frame_bytes: bytes, address_type: str): destination_address_data, _ = HdlcAddress.find_address_in_frame_bytes( frame_bytes ) ( destination_logical, destination_physical, destination_length, ) = destination_address_data return cls(destination_logical, destination_physical, address_type) @classmethod def source_from_bytes(cls, frame_bytes: bytes, address_type: str): _, source_address_data = HdlcAddress.find_address_in_frame_bytes(frame_bytes) source_logical, source_physical, source_length = source_address_data return cls(source_logical, source_physical, address_type) @staticmethod def find_address_in_frame_bytes( hdlc_frame_bytes: bytes, ) -> Tuple[Tuple[int, Optional[int], int], Tuple[int, Optional[int], int]]: """ address can be 1, 2 or 4 bytes long. the end byte is indicated by the of the last byte LSB being 1 The first address is the destination address and the seconds is the source address. :param frame_bytes: :return: """ # Find destination address. destination_length: int = 1 destination_logical: int = 0 destination_physical: Optional[int] = 0 destination_positions_list: List[Tuple[int, int]] = [(3, 1), (4, 2), (6, 4)] address_bytes: bytes for pos, _length in destination_positions_list: end_byte = hdlc_frame_bytes[pos] if bool(end_byte & 0b00000001): # Found end byte: destination_length = _length break continue if destination_length == 1: address_bytes = hdlc_frame_bytes[3].to_bytes(1, "big") destination_logical = address_bytes[0] >> 1 destination_physical = None elif destination_length == 2: address_bytes = hdlc_frame_bytes[3:5] destination_logical = address_bytes[0] >> 1 destination_physical = address_bytes[1] >> 1 elif destination_length == 4: address_bytes = hdlc_frame_bytes[3:7] destination_logical = HdlcAddress.parse_two_byte_address(address_bytes[:2]) destination_physical = HdlcAddress.parse_two_byte_address(address_bytes[3:]) # Find source address source_length: int = 1 source_logical: int = 0 source_physical: Optional[int] = 0 source_position_list: List[Tuple[int, int]] = [ (item[0] + destination_length, item[1]) for item in destination_positions_list ] for pos, _length in source_position_list: end_byte = hdlc_frame_bytes[pos] if bool(end_byte & 0b00000001): # Found end byte: source_length = _length break continue if source_length == 1: address_bytes = hdlc_frame_bytes[3 + destination_length].to_bytes(1, "big") source_logical = address_bytes[0] >> 1 source_physical = None elif source_length == 2: address_bytes = hdlc_frame_bytes[3 + destination_length : 5 + source_length] source_logical = address_bytes[0] >> 1 source_physical = address_bytes[1] >> 1 elif destination_length == 4: address_bytes = hdlc_frame_bytes[3 + destination_length : 7 + source_length] source_logical = HdlcAddress.parse_two_byte_address(address_bytes[:2]) source_physical = HdlcAddress.parse_two_byte_address(address_bytes[3:]) return ( (destination_logical, destination_physical, destination_length), (source_logical, source_physical, source_length), ) @staticmethod def parse_two_byte_address(address_bytes: bytes): if address_bytes != 2: raise ValueError(f"Can only parse 2 bytes for address") upper = address_bytes[0] >> 1 lower = address_bytes[1] >> 1 return lower + (upper << 7)
    36.554404
    88
    0.624522
    6,953
    0.985263
    0
    0
    6,980
    0.989089
    0
    0
    1,307
    0.185206
    131e68c02091db60b313cb5f13708b590b55dc83
    3,676
    py
    Python
    benchmarks/benchmarks/stats.py
    RasmusSemmle/scipy
    4ffeafe269597e6d41b3335549102cd5611b12cb
    [ "FSFAP" ]
    1
    2019-04-13T01:41:50.000Z
    2019-04-13T01:41:50.000Z
    benchmarks/benchmarks/stats.py
    RasmusSemmle/scipy
    4ffeafe269597e6d41b3335549102cd5611b12cb
    [ "FSFAP" ]
    1
    2018-10-16T01:50:18.000Z
    2018-10-16T01:50:18.000Z
    benchmarks/benchmarks/stats.py
    RasmusSemmle/scipy
    4ffeafe269597e6d41b3335549102cd5611b12cb
    [ "FSFAP" ]
    null
    null
    null
    from __future__ import division, absolute_import, print_function import warnings import numpy as np try: import scipy.stats as stats except ImportError: pass from .common import Benchmark class Anderson_KSamp(Benchmark): def setup(self, *args): self.rand = [np.random.normal(loc=i, size=1000) for i in range(3)] def time_anderson_ksamp(self): with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) stats.anderson_ksamp(self.rand) class CorrelationFunctions(Benchmark): param_names = ['alternative'] params = [ ['two-sided', 'less', 'greater'] ] def setup(self, mode): a = np.random.rand(2,2) * 10 self.a = a def time_fisher_exact(self, alternative): oddsratio, pvalue = stats.fisher_exact(self.a, alternative=alternative) class InferentialStats(Benchmark): def setup(self): np.random.seed(12345678) self.a = stats.norm.rvs(loc=5, scale=10, size=500) self.b = stats.norm.rvs(loc=8, scale=10, size=20) self.c = stats.norm.rvs(loc=8, scale=20, size=20) def time_ttest_ind_same_var(self): # test different sized sample with variances stats.ttest_ind(self.a, self.b) stats.ttest_ind(self.a, self.b, equal_var=False) def time_ttest_ind_diff_var(self): # test different sized sample with different variances stats.ttest_ind(self.a, self.c) stats.ttest_ind(self.a, self.c, equal_var=False) class Distribution(Benchmark): param_names = ['distribution', 'properties'] params = [ ['cauchy', 'gamma', 'beta'], ['pdf', 'cdf', 'rvs', 'fit'] ] def setup(self, distribution, properties): np.random.seed(12345678) self.x = np.random.rand(100) def time_distribution(self, distribution, properties): if distribution == 'gamma': if properties == 'pdf': stats.gamma.pdf(self.x, a=5, loc=4, scale=10) elif properties == 'cdf': stats.gamma.cdf(self.x, a=5, loc=4, scale=10) elif properties == 'rvs': stats.gamma.rvs(size=1000, a=5, loc=4, scale=10) elif properties == 'fit': stats.gamma.fit(self.x, loc=4, scale=10) elif distribution == 'cauchy': if properties == 'pdf': stats.cauchy.pdf(self.x, loc=4, scale=10) elif properties == 'cdf': stats.cauchy.cdf(self.x, loc=4, scale=10) elif properties == 'rvs': stats.cauchy.rvs(size=1000, loc=4, scale=10) elif properties == 'fit': stats.cauchy.fit(self.x, loc=4, scale=10) elif distribution == 'beta': if properties == 'pdf': stats.beta.pdf(self.x, a=5, b=3, loc=4, scale=10) elif properties == 'cdf': stats.beta.cdf(self.x, a=5, b=3, loc=4, scale=10) elif properties == 'rvs': stats.beta.rvs(size=1000, a=5, b=3, loc=4, scale=10) elif properties == 'fit': stats.beta.fit(self.x, loc=4, scale=10) # Retain old benchmark results (remove this if changing the benchmark) time_distribution.version = "fb22ae5386501008d945783921fe44aef3f82c1dafc40cddfaccaeec38b792b0" class DescriptiveStats(Benchmark): param_names = ['n_levels'] params = [ [10, 1000] ] def setup(self, n_levels): np.random.seed(12345678) self.levels = np.random.randint(n_levels, size=(1000, 10)) def time_mode(self, n_levels): stats.mode(self.levels, axis=0)
    32.530973
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    0.596572
    3,461
    0.941513
    0
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    439
    0.119423