{ // 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*$/i)) && linkText !== 'OCR模型免费转Markdown' && linkText !== 'OCR模型免费转Markdown' ) { link.textContent = 'OCR模型免费转Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/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*$/i)) && linkText !== '模型下载攻略' ) { link.textContent = '模型下载攻略'; link.href = '/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/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, 'OCR模型免费转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 }); }); } })(); \r\n\"\"\"\r\n\r\nsoup = BeautifulSoup(html, 'html.parser')\r\n\r\nlinks = soup.find_all(\"a\")\r\n# print('links', type(links))\r\na = soup.find_all(\"a\", string='daum')\r\n# print('a', a)\r\nb = soup.find_all(\"a\", limit=3)\r\n# print('b', b)\r\nc = soup.find_all(string=[\"naver\", \"google\"])\r\nprint(c)\r\nfor link in links:\r\n # print('link', type(link), link)\r\n href = link.attrs['href']\r\n txt = link.string\r\n # print('txt >> ', txt, 'href >> ', href)"},"repo_name":{"kind":"string","value":"lcy8417/Python"},"sub_path":{"kind":"string","value":"download2-5-3.py"},"file_name":{"kind":"string","value":"download2-5-3.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":994,"string":"994"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":1,"string":"1"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"sys.stdout","line_number":5,"usage_type":"attribute"},{"api_name":"io.TextIOWrapper","line_number":5,"usage_type":"call"},{"api_name":"sys.stdout.detach","line_number":5,"usage_type":"call"},{"api_name":"sys.stderr","line_number":6,"usage_type":"attribute"},{"api_name":"io.TextIOWrapper","line_number":6,"usage_type":"call"},{"api_name":"sys.stderr.detach","line_number":6,"usage_type":"call"},{"api_name":"bs4.BeautifulSoup","line_number":20,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"sys.stdout\",\n \"line_number\": 5,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"io.TextIOWrapper\",\n \"line_number\": 5,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sys.stdout.detach\",\n \"line_number\": 5,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sys.stderr\",\n \"line_number\": 6,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"io.TextIOWrapper\",\n \"line_number\": 6,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sys.stderr.detach\",\n \"line_number\": 6,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"bs4.BeautifulSoup\",\n \"line_number\": 20,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":184,"cells":{"seq_id":{"kind":"string","value":"25175601159"},"text":{"kind":"string","value":"import random\nimport json\n\nimport numpy as np\nimport torch\n\n# Custom imports\nimport data\nfrom model_eval import evaluate\n\n\nclass LSTM(torch.nn.Module):\n def __init__(self, embedding: torch.FloatTensor):\n super().__init__()\n\n # Embedding wrapper\n self.__embedding = torch.nn.Embedding.from_pretrained(\n embedding, freeze=True, padding_idx=0)\n\n # RNN layers\n self.__rnn1 = torch.nn.LSTM(300, 150,\n num_layers=2, batch_first=False)\n self.__rnn2 = torch.nn.LSTM(300, 150,\n num_layers=2, batch_first=False)\n\n # FC layers\n self.__fc1 = torch.nn.Linear(150, 150)\n self.__fc2 = torch.nn.Linear(150, 1)\n\n def all_params(self):\n params = []\n\n params.extend(self.__rnn1.parameters())\n params.extend(self.__rnn2.parameters())\n params.extend(self.__fc1.parameters())\n params.extend(self.__fc2.parameters())\n params.extend(self.__embedding.parameters())\n\n return params\n\n def forward(self, x):\n x = self.__embedding(x)\n x = torch.transpose(x, 0, 1)\n\n # Consists of (h, c)\n hidden = None\n\n y, hidden = self.__rnn1(x, hidden)\n y, hidden = self.__rnn2(x, hidden)\n\n # Last output\n y = y[-1]\n\n # Linear layer\n y = self.__fc1(y)\n y = torch.relu(y)\n\n return self.__fc2(y)\n\n def predict(self, x):\n with torch.no_grad():\n y = torch.sigmoid(self.forward(x))\n y = y.round().int().squeeze(-1)\n\n return y\n\n\ndef train(model: torch.nn.Module, data,\n optimizer, criterion):\n # Set state for training\n model.train()\n\n # Go through batches\n losses = list()\n for batch_num, batch in enumerate(data):\n model.zero_grad()\n\n # Calculate loss\n logits = model.forward(batch[0]).squeeze(-1)\n y = batch[1].float()\n loss = criterion(logits, y)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.all_params(), 0.25)\n optimizer.step()\n\n losses.append(float(loss))\n\n # At the end of an epoch print loss\n #print(f\"loss = {np.mean(losses)}\")\n\n return np.mean(losses)\n\n\nif __name__ == \"__main__\":\n # Statistics\n hyperparameters = dict()\n hyperparameters[\"max_size\"] = -1\n hyperparameters[\"min_freq\"] = 1\n hyperparameters[\"train_batch_size\"] = 10\n hyperparameters[\"valid_batch_size\"] = 32\n hyperparameters[\"test_batch_size\"] = 32\n hyperparameters[\"learning_rate\"] = 1e-4\n statistics = dict()\n statistics[\"hyperparameters\"] = hyperparameters\n\n # Frequencies\n frequencies = data.getFrequencies(data.TRAIN_DATASET_PATH)\n labelFrequencies = data.getLabelFrequencies(data.TRAIN_DATASET_PATH)\n\n # Vocabs\n x_vocab = data.Vocab(\n frequencies, max_size=hyperparameters[\"max_size\"], min_freq=hyperparameters[\"min_freq\"])\n y_vocab = data.Vocab(labelFrequencies, labels=True)\n\n # Datasets\n train_dataset = data.NLPDataset.from_file(data.TRAIN_DATASET_PATH)\n valid_dataset = data.NLPDataset.from_file(data.VALID_DATASET_PATH)\n test_dataset = data.NLPDataset.from_file(data.TEST_DATASET_PATH)\n\n # Embedding matrix\n embedding = data.generateEmbeddingMatrix(\n x_vocab, data.VECTOR_REPR_PATH)\n\n # Baseline model\n lstm = LSTM(embedding)\n\n optimizer = torch.optim.Adam(\n lstm.all_params(), lr=hyperparameters[\"learning_rate\"])\n criterion = torch.nn.BCEWithLogitsLoss()\n\n iters = 5\n epochs = 5\n for i in range(iters):\n print(f\"RUN {i+1}\")\n\n # Set seed\n seed = random.randint(0, 7052020)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n statistics[seed] = dict()\n statistics[seed][\"train_loss\"] = None\n statistics[seed][\"valid\"] = list()\n\n for epoch in range(epochs):\n print(f\"Epoch {epoch+1}:\")\n\n dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=hyperparameters[\"train_batch_size\"],\n shuffle=True, collate_fn=data.pad_collate_fn)\n print(\"\\tTraining...\")\n train_loss = train(lstm, dataloader,\n optimizer, criterion)\n statistics[seed][\"train_loss\"] = train_loss\n\n dataloader = torch.utils.data.DataLoader(dataset=valid_dataset, batch_size=hyperparameters[\"valid_batch_size\"],\n shuffle=False, collate_fn=data.pad_collate_fn)\n print(\"\\tValidating...\")\n valid_evals = evaluate(lstm, dataloader, criterion)\n statistics[seed][\"valid\"].append(valid_evals)\n\n # Test dataset\n dataloader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=32,\n shuffle=False, collate_fn=data.pad_collate_fn)\n print(\"Testing...\")\n test_evals = evaluate(lstm, dataloader, criterion)\n statistics[seed][\"test\"] = test_evals\n\n print(\"\\nAll done.\")\n\n # Write to statistics file\n with open(\"c:/workspace/fer-dl/lab03/stats/lstm_stats.json\", \"w\") as stats_file:\n stats_file.write(json.dumps(statistics))\n"},"repo_name":{"kind":"string","value":"ftodoric/fer-du"},"sub_path":{"kind":"string","value":"lab03/rnn.py"},"file_name":{"kind":"string","value":"rnn.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":5265,"string":"5,265"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":1,"string":"1"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"torch.nn","line_number":12,"usage_type":"attribute"},{"api_name":"torch.FloatTensor","line_number":13,"usage_type":"attribute"},{"api_name":"torch.nn.Embedding.from_pretrained","line_number":17,"usage_type":"call"},{"api_name":"torch.nn","line_number":17,"usage_type":"attribute"},{"api_name":"torch.nn.LSTM","line_number":21,"usage_type":"call"},{"api_name":"torch.nn","line_number":21,"usage_type":"attribute"},{"api_name":"torch.nn.LSTM","line_number":23,"usage_type":"call"},{"api_name":"torch.nn","line_number":23,"usage_type":"attribute"},{"api_name":"torch.nn.Linear","line_number":27,"usage_type":"call"},{"api_name":"torch.nn","line_number":27,"usage_type":"attribute"},{"api_name":"torch.nn.Linear","line_number":28,"usage_type":"call"},{"api_name":"torch.nn","line_number":28,"usage_type":"attribute"},{"api_name":"torch.transpose","line_number":43,"usage_type":"call"},{"api_name":"torch.relu","line_number":56,"usage_type":"call"},{"api_name":"torch.no_grad","line_number":61,"usage_type":"call"},{"api_name":"torch.sigmoid","line_number":62,"usage_type":"call"},{"api_name":"torch.nn","line_number":68,"usage_type":"attribute"},{"api_name":"torch.nn.utils.clip_grad_norm_","line_number":83,"usage_type":"call"},{"api_name":"torch.nn","line_number":83,"usage_type":"attribute"},{"api_name":"numpy.mean","line_number":91,"usage_type":"call"},{"api_name":"data.getFrequencies","line_number":107,"usage_type":"call"},{"api_name":"data.TRAIN_DATASET_PATH","line_number":107,"usage_type":"attribute"},{"api_name":"data.getLabelFrequencies","line_number":108,"usage_type":"call"},{"api_name":"data.TRAIN_DATASET_PATH","line_number":108,"usage_type":"attribute"},{"api_name":"data.Vocab","line_number":111,"usage_type":"call"},{"api_name":"data.Vocab","line_number":113,"usage_type":"call"},{"api_name":"data.NLPDataset.from_file","line_number":116,"usage_type":"call"},{"api_name":"data.NLPDataset","line_number":116,"usage_type":"attribute"},{"api_name":"data.TRAIN_DATASET_PATH","line_number":116,"usage_type":"attribute"},{"api_name":"data.NLPDataset.from_file","line_number":117,"usage_type":"call"},{"api_name":"data.NLPDataset","line_number":117,"usage_type":"attribute"},{"api_name":"data.VALID_DATASET_PATH","line_number":117,"usage_type":"attribute"},{"api_name":"data.NLPDataset.from_file","line_number":118,"usage_type":"call"},{"api_name":"data.NLPDataset","line_number":118,"usage_type":"attribute"},{"api_name":"data.TEST_DATASET_PATH","line_number":118,"usage_type":"attribute"},{"api_name":"data.generateEmbeddingMatrix","line_number":121,"usage_type":"call"},{"api_name":"data.VECTOR_REPR_PATH","line_number":122,"usage_type":"attribute"},{"api_name":"torch.optim.Adam","line_number":127,"usage_type":"call"},{"api_name":"torch.optim","line_number":127,"usage_type":"attribute"},{"api_name":"torch.nn.BCEWithLogitsLoss","line_number":129,"usage_type":"call"},{"api_name":"torch.nn","line_number":129,"usage_type":"attribute"},{"api_name":"random.randint","line_number":137,"usage_type":"call"},{"api_name":"numpy.random.seed","line_number":138,"usage_type":"call"},{"api_name":"numpy.random","line_number":138,"usage_type":"attribute"},{"api_name":"torch.manual_seed","line_number":139,"usage_type":"call"},{"api_name":"torch.utils.data.DataLoader","line_number":148,"usage_type":"call"},{"api_name":"torch.utils","line_number":148,"usage_type":"attribute"},{"api_name":"data.pad_collate_fn","line_number":149,"usage_type":"attribute"},{"api_name":"torch.utils.data.DataLoader","line_number":155,"usage_type":"call"},{"api_name":"torch.utils","line_number":155,"usage_type":"attribute"},{"api_name":"data.pad_collate_fn","line_number":156,"usage_type":"attribute"},{"api_name":"model_eval.evaluate","line_number":158,"usage_type":"call"},{"api_name":"torch.utils.data.DataLoader","line_number":162,"usage_type":"call"},{"api_name":"torch.utils","line_number":162,"usage_type":"attribute"},{"api_name":"data.pad_collate_fn","line_number":163,"usage_type":"attribute"},{"api_name":"model_eval.evaluate","line_number":165,"usage_type":"call"},{"api_name":"json.dumps","line_number":172,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 12,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.FloatTensor\",\n \"line_number\": 13,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.Embedding.from_pretrained\",\n \"line_number\": 17,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 17,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.LSTM\",\n \"line_number\": 21,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 21,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.LSTM\",\n \"line_number\": 23,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 23,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.Linear\",\n \"line_number\": 27,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 27,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.Linear\",\n \"line_number\": 28,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 28,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.transpose\",\n \"line_number\": 43,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.relu\",\n \"line_number\": 56,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.no_grad\",\n \"line_number\": 61,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.sigmoid\",\n \"line_number\": 62,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 68,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.utils.clip_grad_norm_\",\n \"line_number\": 83,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 83,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"numpy.mean\",\n \"line_number\": 91,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.getFrequencies\",\n \"line_number\": 107,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.TRAIN_DATASET_PATH\",\n \"line_number\": 107,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.getLabelFrequencies\",\n \"line_number\": 108,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.TRAIN_DATASET_PATH\",\n \"line_number\": 108,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.Vocab\",\n \"line_number\": 111,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.Vocab\",\n \"line_number\": 113,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.NLPDataset.from_file\",\n \"line_number\": 116,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.NLPDataset\",\n \"line_number\": 116,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.TRAIN_DATASET_PATH\",\n \"line_number\": 116,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.NLPDataset.from_file\",\n \"line_number\": 117,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.NLPDataset\",\n \"line_number\": 117,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.VALID_DATASET_PATH\",\n \"line_number\": 117,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.NLPDataset.from_file\",\n \"line_number\": 118,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.NLPDataset\",\n \"line_number\": 118,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.TEST_DATASET_PATH\",\n \"line_number\": 118,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.generateEmbeddingMatrix\",\n \"line_number\": 121,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"data.VECTOR_REPR_PATH\",\n \"line_number\": 122,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.optim.Adam\",\n \"line_number\": 127,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.optim\",\n \"line_number\": 127,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.nn.BCEWithLogitsLoss\",\n \"line_number\": 129,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.nn\",\n \"line_number\": 129,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"random.randint\",\n \"line_number\": 137,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.random.seed\",\n \"line_number\": 138,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.random\",\n \"line_number\": 138,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.manual_seed\",\n \"line_number\": 139,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.utils.data.DataLoader\",\n \"line_number\": 148,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.utils\",\n \"line_number\": 148,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.pad_collate_fn\",\n \"line_number\": 149,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"torch.utils.data.DataLoader\",\n \"line_number\": 155,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.utils\",\n \"line_number\": 155,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.pad_collate_fn\",\n \"line_number\": 156,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"model_eval.evaluate\",\n \"line_number\": 158,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.utils.data.DataLoader\",\n \"line_number\": 162,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"torch.utils\",\n \"line_number\": 162,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"data.pad_collate_fn\",\n \"line_number\": 163,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"model_eval.evaluate\",\n \"line_number\": 165,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"json.dumps\",\n \"line_number\": 172,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":185,"cells":{"seq_id":{"kind":"string","value":"34487423583"},"text":{"kind":"string","value":"import serial\nimport time\nimport struct\nimport logging as trace\nimport threading\n\nclass Communication(object):\n data_chars = [b'!', b'\"', b'#', b'$', b'%', b'&', b\"'\", b'(']\n response_timeout = 2 #second\n _handle = serial.Serial()\n _error_counter = 0\n _done = False\n _thread = None\n\n def __init__(self, address=None):\n # Initialize class parameters\n\n # perform port configuration at start-up\n self._handle.port = \"/dev/ttyUSB0\"\n self._handle.baudrate = 115200\n self._handle.bytesize = serial.EIGHTBITS # number of bits per bytes\n self._handle.parity = serial.PARITY_NONE # set parity check: no parity\n self._handle.stopbits = serial.STOPBITS_ONE # number of stop bits\n\n self._handle.timeout = 0.25 # non-block read\n self._handle.writeTimeout = 0.25 # timeout for write\n\n trace.debug('serial port configuration done')\n\n # self._address = address\n\n # Only one data stream per port\n self.data = []\n self.devices = {}\n self.sync_data_ready = threading.Event()\n self.async_data_ready = threading.Event()\n self.bin_data_ready = threading.Event()\n self._thread = threading.Thread(name='serial_thr', target= self._read_from_device)\n\n def __del__(self):\n self.disconnect()\n\n def connect(self, port_name=\"/dev/ttyUSB0\"):\n \"\"\"\n Connect device\n :param port_name: Specify serial port name if different\n than /dev/ttyUSB0\n :return: True if connected, False if connection failed\n \"\"\"\n\n # if port is different than default use it\n if self._handle.port != port_name:\n self._handle.port = port_name\n\n # start connecting\n trace.debug(\"Trying to connect..\")\n try:\n self._handle.open()\n except Exception as e:\n trace.error(\"error open serial port: \" + str(e))\n return False\n\n if self._handle.isOpen():\n trace.debug('serial port opened')\n else:\n trace.debug('serial port not opened')\n return False\n\n # flush buffers at start-up\n try:\n self._handle.flushInput()\n self._handle.flushOutput()\n except Exception as e:\n trace.error(\"error flushing input \" + str(e))\n\n # at this point device should be connected\n self._thread.start()\n return True\n\n def disconnect(self):\n\n # mark job as done (this flag is for background thread)\n self._done = True\n\n # wait until background thread is done\n # if it is still running\n self._thread.join()\n\n # close serial port\n if self._handle.isOpen():\n self._handle.close()\n trace.debug('serial port closed')\n\n def init_device(self, idn):\n self.devices[idn] = {'sync': [], 'async': []}\n\n def write_command(self, command, idn):\n \"\"\"\n Write command to device\n :param command: self-explanatory\n :return: None\n \"\"\"\n\n # add prefix and CR on the end\n command = str(idn) + \":\" + command + '\\n'\n trace.debug('writing command: ' + command)\n self._handle.write(bytes(command, 'utf8'))\n\n def write_command_ret(self, command, idn):\n \"\"\"\n Writes a command to device and waits for standard response\n :param command: self-explanatory\n :return: None\n \"\"\"\n self.sync_data_ready.clear()\n self.write_command(command,idn)\n self.sync_data_ready.wait(self.response_timeout)\n if not bool(self.devices.get(idn).get('sync')):\n resp=self.devices.get(idn).get('sync').pop()\n trace.debug(\"Command: \\\"\"+str(command)+\"\\\" successfully sent. Response: \\\"\"+str(resp)+\"\\\"\")\n return resp\n else:\n trace.debug(\"No response for command: \\\"\" + str(command) + \"\\\"\")\n return None\n\n\n def write_command_stdr(self, command, idn):\n \"\"\"\n Writes a command to device and waits for standard response\n :param command: self-explanatory\n :return: None\n \"\"\"\n self.sync_data_ready.clear()\n self.write_command(command,idn)\n self.sync_data_ready.wait(self.response_timeout)\n if not bool(self.devices.get(idn).get('sync')):\n resp=self.devices.get(idn).get('sync').pop()\n if resp.rsplit()[0] == command.rsplit()[0]:\n trace.debug(\"Command: \\\"\"+str(command)+\"\\\" successfully sent. Response: \\\"\"+str(resp)+\"\\\"\")\n else:\n trace.error(\"Wrong response for command: \\\"\" + str(command) + \"\\\". Response: \\\"\" + str(resp) + \"\\\" , expected: \\\"\"+str(command.rsplit()[0]))\n if len(resp.rsplit()) > 1:\n return resp.rsplit()[1]\n else:\n return None\n\n def decode_binvalue(self,seq):\n # Data format is: CSHHHHH\\r\n # C - is a prefix that also serves as a 3 bit-long counter (starts with prefix0)\n # S - status byte (6 bits: 1 + negated 5 bits representing input lines)\n # HHHHH - is a 18-bit hex value (should be treated as value with sign)\n # \\r - terminating CR\n # Extended format is: CSHHHHHhH\\r\n # 0 ascii 0/1 sin+- X X 0/1 shutter 0/1 shutter~ X X\n # where CSH are as above and h is H (hex digit) with highest bit set\n # this signals the fact that also fractional part is sent so the bit should\n # be cleared, whole value treated as int and later divided by 256\n flag_count=seq[0]-ord('!')\n c = seq[1]\n flag_al=bool(c & 0b01000000)\n flag_dl=bool(c & 0b00001000)\n c = seq[2]\n value = (-1 if c >= ord('8') else 0) # test for sign bit (in hex digit)\n shift = False\n\n for c in list(seq)[3:]:\n if (c & 0x80):\n c &= 0x7F\n shift = True\n\n if c >= ord('0') and c <= ord('9'):\n nibble = c - ord('0')\n elif c >= ord('A') and c <= ord('F'):\n nibble = c - (ord('A') - 10)\n else:\n break\n value <<= 4\n value |= nibble\n\n return (float(value) / 256 if shift else float(value))* 6.25 / 65536,flag_count,flag_al,flag_dl\n\n def read_line(self, line):\n coms = line.split(b'\\r')\n for com in coms:\n if com[0] >= ord('!') and com[0] <= ord('('):\n value = self.decode_binvalue(com)\n self.data.append(list(value))\n self.bin_data_ready.set()\n trace.debug('Data value:'+ str(value))\n else:\n idn, com_type, message = tuple(com.partition(b'.'))\n # First char after the id number\n if com_type == b'.':\n com_type = 'sync'\n else:\n # if not, try other ordering character\n idn, com_type, message = tuple(com.partition(b';'))\n if com_type == b';':\n com_type = 'async'\n else:\n trace.error('Major parsing fuckup, good luck')\n return -1\n\n idnn = int(idn)\n if idnn not in self.devices.keys():\n self.init_device(idnn)\n message=message.decode('ascii') #convert bytes to string\n self.devices[idnn][com_type].append(message)\n if com_type == 'sync':\n self.sync_data_ready.set()\n elif com_type == 'async':\n self.async_data_ready.set()\n trace.debug('Device ID: %d Communication type: %s Message: %s', idnn, com_type, message)\n\n def _read_from_device(self):\n \"\"\"\n Read from device. This function is executed in separate\n thread. Function also updates necessary parameters for\n this class\n \"\"\"\n self.rawdata = bytearray()\n while not self._done:\n\n # if incoming bytes are waiting to be\n # read from the serial input buffer\n if self._handle.inWaiting():\n # read and remove all whitespaces\n # on the right side, including '\\n'\n self.rawdata.extend( self._handle.read(self._handle.inWaiting()))\n\n while True:\n line,sep,rest=tuple(self.rawdata.partition(b'\\r'))\n if sep != b'\\r':\n break\n trace.debug(\"new data to parse: \" + str(line))\n self.read_line(line.strip())\n self.rawdata=rest\n\n # sleep for a moment (pseudo-yield in python)\n time.sleep(0.0001)\n"},"repo_name":{"kind":"string","value":"ccucumber/verdeta-lockin"},"sub_path":{"kind":"string","value":"fotonowy/komunikacja.py"},"file_name":{"kind":"string","value":"komunikacja.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":8765,"string":"8,765"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"serial.Serial","line_number":10,"usage_type":"call"},{"api_name":"serial.EIGHTBITS","line_number":21,"usage_type":"attribute"},{"api_name":"serial.PARITY_NONE","line_number":22,"usage_type":"attribute"},{"api_name":"serial.STOPBITS_ONE","line_number":23,"usage_type":"attribute"},{"api_name":"logging.debug","line_number":28,"usage_type":"call"},{"api_name":"threading.Event","line_number":35,"usage_type":"call"},{"api_name":"threading.Event","line_number":36,"usage_type":"call"},{"api_name":"threading.Event","line_number":37,"usage_type":"call"},{"api_name":"threading.Thread","line_number":38,"usage_type":"call"},{"api_name":"logging.debug","line_number":56,"usage_type":"call"},{"api_name":"logging.error","line_number":60,"usage_type":"call"},{"api_name":"logging.debug","line_number":64,"usage_type":"call"},{"api_name":"logging.debug","line_number":66,"usage_type":"call"},{"api_name":"logging.error","line_number":74,"usage_type":"call"},{"api_name":"logging.debug","line_number":92,"usage_type":"call"},{"api_name":"logging.debug","line_number":106,"usage_type":"call"},{"api_name":"logging.debug","line_number":120,"usage_type":"call"},{"api_name":"logging.debug","line_number":123,"usage_type":"call"},{"api_name":"logging.debug","line_number":139,"usage_type":"call"},{"api_name":"logging.error","line_number":141,"usage_type":"call"},{"api_name":"logging.debug","line_number":189,"usage_type":"call"},{"api_name":"logging.error","line_number":201,"usage_type":"call"},{"api_name":"logging.debug","line_number":213,"usage_type":"call"},{"api_name":"logging.debug","line_number":235,"usage_type":"call"},{"api_name":"time.sleep","line_number":240,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"serial.Serial\",\n \"line_number\": 10,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"serial.EIGHTBITS\",\n \"line_number\": 21,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"serial.PARITY_NONE\",\n \"line_number\": 22,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"serial.STOPBITS_ONE\",\n \"line_number\": 23,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 28,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"threading.Event\",\n \"line_number\": 35,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"threading.Event\",\n \"line_number\": 36,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"threading.Event\",\n \"line_number\": 37,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"threading.Thread\",\n \"line_number\": 38,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 56,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.error\",\n \"line_number\": 60,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 64,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 66,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.error\",\n \"line_number\": 74,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 92,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 106,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 120,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 123,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 139,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.error\",\n \"line_number\": 141,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 189,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.error\",\n \"line_number\": 201,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 213,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"logging.debug\",\n \"line_number\": 235,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 240,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":186,"cells":{"seq_id":{"kind":"string","value":"41854725692"},"text":{"kind":"string","value":"from absl.testing import parameterized\nimport dataclasses\nimport tensorflow as tf\n\nfrom official.core import config_definitions as cfg\nfrom official.core import input_reader\nfrom official.modeling import hyperparams\nfrom official.vision.beta.dataloaders import tfds_detection_decoders\nfrom official.vision.beta.projects.yolo.dataloaders import yolo_detection_input\n\n\n@dataclasses.dataclass\nclass Parser(hyperparams.Config):\n \"\"\"Dummy configuration for parser.\"\"\"\n output_size: int = (416, 416)\n num_classes: int = 80\n fixed_size: bool = True\n jitter_im: float = 0.1\n jitter_boxes: float = 0.005\n min_process_size: int = 320\n max_process_size: int = 608\n max_num_instances: int = 200\n random_flip: bool = True\n seed: int = 10\n shuffle_buffer_size: int = 10000\n\n\n@dataclasses.dataclass\nclass DataConfig(cfg.DataConfig):\n \"\"\"Input config for training.\"\"\"\n input_path: str = ''\n tfds_name: str = 'coco/2017'\n tfds_split: str = 'train'\n global_batch_size: int = 10\n is_training: bool = True\n dtype: str = 'float16'\n decoder = None\n parser: Parser = Parser()\n shuffle_buffer_size: int = 10\n\n\nclass YoloDetectionInputTest(tf.test.TestCase, parameterized.TestCase):\n\n @parameterized.named_parameters(('training', True), ('testing', False))\n def test_yolo_input(self, is_training):\n params = DataConfig(is_training=is_training)\n\n decoder = tfds_detection_decoders.MSCOCODecoder()\n anchors = [[12.0, 19.0], [31.0, 46.0], [96.0, 54.0], [46.0, 114.0],\n [133.0, 127.0], [79.0, 225.0], [301.0, 150.0], [172.0, 286.0],\n [348.0, 340.0]]\n masks = {'3': [0, 1, 2], '4': [3, 4, 5], '5': [6, 7, 8]}\n\n parser = yolo_detection_input.Parser(\n output_size=params.parser.output_size,\n num_classes=params.parser.num_classes,\n fixed_size=params.parser.fixed_size,\n jitter_im=params.parser.jitter_im,\n jitter_boxes=params.parser.jitter_boxes,\n min_process_size=params.parser.min_process_size,\n max_process_size=params.parser.max_process_size,\n max_num_instances=params.parser.max_num_instances,\n random_flip=params.parser.random_flip,\n seed=params.parser.seed,\n anchors=anchors,\n masks=masks)\n postprocess_fn = parser.postprocess_fn(is_training=is_training)\n\n reader = input_reader.InputReader(params,\n dataset_fn=tf.data.TFRecordDataset,\n decoder_fn=decoder.decode,\n parser_fn=parser.parse_fn(\n params.is_training))\n dataset = reader.read(input_context=None).batch(10).take(1)\n if postprocess_fn:\n image, _ = postprocess_fn(\n *tf.data.experimental.get_single_element(dataset))\n else:\n image, _ = tf.data.experimental.get_single_element(dataset)\n print(image.shape)\n self.assertAllEqual(image.shape, (10, 10, 416, 416, 3))\n self.assertTrue(\n tf.reduce_all(tf.math.logical_and(image >= 0, image <= 1)))\n\n\nif __name__ == '__main__':\n tf.test.main()\n\n"},"repo_name":{"kind":"string","value":"sek788432/Waymo-2D-Object-Detection"},"sub_path":{"kind":"string","value":"input/models/official/vision/beta/projects/yolo/dataloaders/yolo_detection_input_test.py"},"file_name":{"kind":"string","value":"yolo_detection_input_test.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":3074,"string":"3,074"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":79,"string":"79"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"official.modeling.hyperparams.Config","line_number":13,"usage_type":"attribute"},{"api_name":"official.modeling.hyperparams","line_number":13,"usage_type":"name"},{"api_name":"dataclasses.dataclass","line_number":12,"usage_type":"attribute"},{"api_name":"official.core.config_definitions.DataConfig","line_number":29,"usage_type":"attribute"},{"api_name":"official.core.config_definitions","line_number":29,"usage_type":"name"},{"api_name":"dataclasses.dataclass","line_number":28,"usage_type":"attribute"},{"api_name":"tensorflow.test","line_number":42,"usage_type":"attribute"},{"api_name":"absl.testing.parameterized.TestCase","line_number":42,"usage_type":"attribute"},{"api_name":"absl.testing.parameterized","line_number":42,"usage_type":"name"},{"api_name":"official.vision.beta.dataloaders.tfds_detection_decoders.MSCOCODecoder","line_number":48,"usage_type":"call"},{"api_name":"official.vision.beta.dataloaders.tfds_detection_decoders","line_number":48,"usage_type":"name"},{"api_name":"official.vision.beta.projects.yolo.dataloaders.yolo_detection_input.Parser","line_number":54,"usage_type":"call"},{"api_name":"official.vision.beta.projects.yolo.dataloaders.yolo_detection_input","line_number":54,"usage_type":"name"},{"api_name":"official.core.input_reader.InputReader","line_number":69,"usage_type":"call"},{"api_name":"official.core.input_reader","line_number":69,"usage_type":"name"},{"api_name":"tensorflow.data","line_number":70,"usage_type":"attribute"},{"api_name":"tensorflow.data.experimental.get_single_element","line_number":77,"usage_type":"call"},{"api_name":"tensorflow.data","line_number":77,"usage_type":"attribute"},{"api_name":"tensorflow.data.experimental.get_single_element","line_number":79,"usage_type":"call"},{"api_name":"tensorflow.data","line_number":79,"usage_type":"attribute"},{"api_name":"tensorflow.reduce_all","line_number":83,"usage_type":"call"},{"api_name":"tensorflow.math.logical_and","line_number":83,"usage_type":"call"},{"api_name":"tensorflow.math","line_number":83,"usage_type":"attribute"},{"api_name":"absl.testing.parameterized.named_parameters","line_number":44,"usage_type":"call"},{"api_name":"absl.testing.parameterized","line_number":44,"usage_type":"name"},{"api_name":"tensorflow.test.main","line_number":87,"usage_type":"call"},{"api_name":"tensorflow.test","line_number":87,"usage_type":"attribute"}],"string":"[\n {\n \"api_name\": \"official.modeling.hyperparams.Config\",\n \"line_number\": 13,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"official.modeling.hyperparams\",\n \"line_number\": 13,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"dataclasses.dataclass\",\n \"line_number\": 12,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"official.core.config_definitions.DataConfig\",\n \"line_number\": 29,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"official.core.config_definitions\",\n \"line_number\": 29,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"dataclasses.dataclass\",\n \"line_number\": 28,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"tensorflow.test\",\n \"line_number\": 42,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"absl.testing.parameterized.TestCase\",\n \"line_number\": 42,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"absl.testing.parameterized\",\n \"line_number\": 42,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"official.vision.beta.dataloaders.tfds_detection_decoders.MSCOCODecoder\",\n \"line_number\": 48,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"official.vision.beta.dataloaders.tfds_detection_decoders\",\n \"line_number\": 48,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"official.vision.beta.projects.yolo.dataloaders.yolo_detection_input.Parser\",\n \"line_number\": 54,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"official.vision.beta.projects.yolo.dataloaders.yolo_detection_input\",\n \"line_number\": 54,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"official.core.input_reader.InputReader\",\n \"line_number\": 69,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"official.core.input_reader\",\n \"line_number\": 69,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"tensorflow.data\",\n \"line_number\": 70,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"tensorflow.data.experimental.get_single_element\",\n \"line_number\": 77,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tensorflow.data\",\n \"line_number\": 77,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"tensorflow.data.experimental.get_single_element\",\n \"line_number\": 79,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tensorflow.data\",\n \"line_number\": 79,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"tensorflow.reduce_all\",\n \"line_number\": 83,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tensorflow.math.logical_and\",\n \"line_number\": 83,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tensorflow.math\",\n \"line_number\": 83,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"absl.testing.parameterized.named_parameters\",\n \"line_number\": 44,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"absl.testing.parameterized\",\n \"line_number\": 44,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"tensorflow.test.main\",\n \"line_number\": 87,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tensorflow.test\",\n \"line_number\": 87,\n \"usage_type\": \"attribute\"\n }\n]"}}},{"rowIdx":187,"cells":{"seq_id":{"kind":"string","value":"18194693461"},"text":{"kind":"string","value":"from rest_framework.routers import DefaultRouter\n\nfrom messaging import api_view\n\nrouter = DefaultRouter()\nrouter.register('message', api_view.MessageVewSet, base_name='message')\nrouter.register('chat', api_view.GroupBlogViewSet, base_name='chat')\nurlpatterns = [\n]\nurlpatterns += router.urls\n"},"repo_name":{"kind":"string","value":"SivakumarSkr/Movieclub"},"sub_path":{"kind":"string","value":"messaging/api_urls.py"},"file_name":{"kind":"string","value":"api_urls.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":293,"string":"293"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"rest_framework.routers.DefaultRouter","line_number":5,"usage_type":"call"},{"api_name":"messaging.api_view.MessageVewSet","line_number":6,"usage_type":"attribute"},{"api_name":"messaging.api_view","line_number":6,"usage_type":"name"},{"api_name":"messaging.api_view.GroupBlogViewSet","line_number":7,"usage_type":"attribute"},{"api_name":"messaging.api_view","line_number":7,"usage_type":"name"}],"string":"[\n {\n \"api_name\": \"rest_framework.routers.DefaultRouter\",\n \"line_number\": 5,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"messaging.api_view.MessageVewSet\",\n \"line_number\": 6,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"messaging.api_view\",\n \"line_number\": 6,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"messaging.api_view.GroupBlogViewSet\",\n \"line_number\": 7,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"messaging.api_view\",\n \"line_number\": 7,\n \"usage_type\": \"name\"\n }\n]"}}},{"rowIdx":188,"cells":{"seq_id":{"kind":"string","value":"3502830678"},"text":{"kind":"string","value":"\"\"\"Retreive extracts wrt. previous lexicon update cycle. \n\nUpdate extracts.json and output cycle extracts as extracts-{cycle}.txt for easy inspection\n\nExample\n $ python3 get_extracts.py 4 working_folder\n $ python3 get_extracts.py 4 _aroma_NOUN+ADJ\n\nArgs:\n n (int): number of (CPU Threads) processes to use\n working_folder: \n\nRequired in working folder:\n extracts.json: {\"cycle\":[([seeds], extract, parsed_extract),..],..}\n lexicon.json: [\n # cycle 0\n [\n # entry 0 in cycle: all coincident extracts corresponding to a pattern\n (\n [(\"_sharp_ADJ\", \"_lemon_NOUN\"), ... ], #list of coincident vocabulary tuples tagetted by pattern A\n pattern_A,\n ),\n ....\n ],\n ....\n ]\nrequired in datasets:\n harvesting.json: {\"book_code\": [sentence, parsed sentence tuples],.. }\n\"\"\"\n\nimport json\nimport multiprocessing\nimport os\nimport re\nimport sys\n\nimport regex\nfrom tqdm import tqdm\n# add libraries to path\nsys.path.append(os.path.join(sys.path[0], \"libraries\"))\n# add working folder to path\nsys.path.append(os.path.join(sys.path[0], sys.argv[2]))\n\nfrom CHUNKS import chunks\nfrom pattern_abstraction import convert_patterns, expand_chunks, to_chunks\nfrom PATTERNS import extraction_patterns, identification_patterns\n\n\ndef main(argv):\n\n # CL arguments\n folder = argv[1]\n n = int(argv[0])\n\n # get a list of previously seen extracts, from all prior cycles\n extracts_file = folder + \"/extracts.json\"\n previous_extracts, current_cycle = get_extracts(extracts_file)\n # previous_extracts = {\"cycle\":[([seeds], extract, parsed_extract),..],..}\n\n print(f\"current cycle = {current_cycle}\")\n\n seen_extracts = extracts_as_set(previous_extracts)\n # seen_extracts = {set of unparsed extracts previously seen}\n\n # Collect the previous cycle's lexicon entries\n with open(folder + \"/lexicon.json\", \"r\") as f:\n lexicon = json.load(f)\n vocabulary = get_lexicon(lexicon) # [(compiled re, (coincident phrases),..]\n # [(compiled re, (coincident phrases),..]\n\n # compile previously seen abstractions\n seen_abstractions = identification_patterns\n seen_patterns = compile_patterns(seen_abstractions)\n\n # ITERATE THROUGH HARVESTING SET, extracting where\n # * an extract is unseen\n # * and where known patterns do not match\n\n with open(\"./datasets/harvesting.json\", \"r\") as f:\n dataset = json.load(f)\n # dataset = {\"book_code\": [(sentence, parsed sentence),..]}\n\n # iterate through the harvesting set\n for book_index, (book_code, extracts) in enumerate(tqdm(dataset.items())):\n\n # discard extracts already seen\n extracts_trimmed = trim_extracts(extracts, seen_extracts) # [(extract, parsed_extract),...]\n\n # split extracts n chunks, for multi-proccessing\n extract_sets = group_extracts(extracts_trimmed, n) # [[(extract, parsed_extract),...],...]\n\n processes = []\n queue = multiprocessing.Queue()\n # iterate through the extract chunks as separate processes\n for i in range(n):\n\n # run vocabulary pattern matching against trimmed extracts\n process = multiprocessing.Process(\n target=mapped_function, args=(extract_sets[i], vocabulary, seen_patterns, queue,),\n )\n process.start()\n processes.append(process)\n\n # collect process output\n for r in range(n):\n previous_extracts[current_cycle] += queue.get()\n\n # terminate the processes\n for process in processes:\n process.join()\n\n # save to json\n with open(folder + \"/extracts.json\", \"w\") as f:\n json.dump(previous_extracts, f, ensure_ascii=False)\n\n # save ouput to text files for inspection\n with open(folder + f\"/extracts-{current_cycle}.txt\", \"w\") as f:\n for phrases, extract, parsed_extract in previous_extracts[current_cycle]:\n f.write(\"\\n\\n\")\n f.write(f\"{phrases}\")\n f.write(\"\\n\" + extract)\n f.write(\"\\n\" + parsed_extract)\n\n\ndef mapped_function(extract_set, vocabulary, seen_patterns, queue):\n \"\"\"Iterate through the extract_set and return a list of those extracts matching the previous lexicon cycle entries.\n \"\"\"\n\n returned = []\n\n for extract, parsed_extract in extract_set:\n\n for v_pattern, phrases in vocabulary:\n mo_lexicon = regex.search(v_pattern, parsed_extract)\n\n if mo_lexicon:\n # check does not conform to a seen pattern\n mo_seen = None\n for seen_abstraction, seen_compiled in seen_patterns:\n mo_seen = regex.match(seen_compiled, parsed_extract)\n if mo_seen:\n print(\"\\n\\nseen pattern\")\n print(extract)\n print(seen_abstraction) \n break # break seen pattern loop\n\n if mo_lexicon and not mo_seen:\n # if both vocab match and not conforming to seen_patterns\n returned.append((phrases, extract, parsed_extract))\n # print(\"\\n\\naccepted\")\n # print(extract) \n\n queue.put(returned)\n\ndef get_extracts(file):\n \"\"\"Return existing extracts file container or create new.\n \"\"\"\n\n # if file exists, then load\n if os.path.exists(file):\n with open(file, \"r\") as f:\n previous_extracts = json.load(f)\n # save as \"folder/extracts.json\" in case wish to revert\n with open(file, \"w\") as f:\n json.dump(previous_extracts, f, ensure_ascii=False, indent=4)\n # add new cycle\n previous_extracts[str(len(previous_extracts.keys()))] = []\n # if file doesn't exist, create new\n else:\n previous_extracts = {\"0\": []}\n\n # get the current cycle's index key for extracts\n current_cycle = str(list(previous_extracts.keys())[-1])\n\n return previous_extracts, current_cycle\n\n\ndef extracts_as_set(extracts):\n \"\"\"Return the extracts to date as a set\n Args:\n extracts (dict): {\"cycle\":[([seeds], extract, parsed_extract),..],..}\n\n Return:\n set of seen extracts\n \"\"\"\n\n seen_extracts = []\n\n for keys, values in extracts.items():\n for phrase, extract, parsed_extract in values:\n seen_extracts.append(extract)\n seen_extracts = set(seen_extracts)\n\n return seen_extracts\n\ndef get_lexicon(lexicon):\n \"\"\"Return preivious lexicon vocab as a list of (compiled re, (coincident phrases)).\n Args:\n lexicon.json: [\n # cycle 0\n [\n\n # list of entries, each entry corresponds to pattern\n [\n [(phrase0, phrase1), ..], # list of coincidents phrases matched (e.g., adj, noun collection)\n pattern_A\n ],\n [\n [(phrase0, phrase1), ..],\n pattern_B\n ]\n ....\n ],\n ....\n ]\n\"\"\"\n patterns = []\n for entry in lexicon[-1]: # each entry in previous cycle\n for phrases in entry[0]:\n try:\n converted_compounded_phrases = \"\"\n for phrase in phrases:\n converted_compounded_phrases += \".*\" + convert_patterns([phrase],chunks)[0]\n\n patterns.append((regex.compile(converted_compounded_phrases), phrases))\n except:\n print(f\"lexicon error, please correct, token: {phrases}\")\n\n return patterns\n\n\ndef compile_patterns(abstractions):\n \"\"\"Assemble list of (abstracted_pattern, compiled) tuples of abstracted patterns.\n\n Args:\n abstractions: []\n Returns:\n [(abstracted_pattern, compiled),...]\n \"\"\"\n # assemble (new) extraction patterns in python re format\n patterns = [] # patterns = [(abstraction, compiled pattern), ..]\n for abstraction in abstractions:\n print(abstraction)\n patterns.append(\n (\n abstraction,\n regex.compile(\n \"^.*\" + convert_patterns([abstraction], chunks)[0] + \".*\",\n re.MULTILINE,\n ),\n )\n )\n\n return patterns\n\n\ndef trim_extracts(extracts, seen_extracts):\n \"\"\"Return a list of (extract, parsed_extract) for unseen extracts, not conforming to a known pattern.\n Args:\n extracts (list): [(sentence, parsed sentence),..]\n \"\"\"\n # trim extract set, based on seen extracts\n extracts_trimmed = []\n for extract, parsed_extract in extracts:\n if extract not in seen_extracts:\n extracts_trimmed.append((extract, parsed_extract))\n\n\n return extracts_trimmed\n\ndef group_extracts(extracts, n):\n \"\"\"Return extracts as a list of n lists of extracts (for multiprocessing)\n e.g., where n = 4, [[(extract, parsed_extract),...],[],[],[]]\n\n Args:\n extracts: [(sentence, parsed sentence),..]\n \"\"\"\n extract_sets = [[] for i in range(n)]\n for i in range(0, len(extracts), n):\n for j in range(0, n):\n try:\n extract_sets[j].append(extracts[i + j])\n except:\n pass\n\n return extract_sets\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n"},"repo_name":{"kind":"string","value":"ryanbrate/DS_thesis"},"sub_path":{"kind":"string","value":"5_Process/get_extracts.py"},"file_name":{"kind":"string","value":"get_extracts.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":9761,"string":"9,761"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"sys.path.append","line_number":40,"usage_type":"call"},{"api_name":"sys.path","line_number":40,"usage_type":"attribute"},{"api_name":"os.path.join","line_number":40,"usage_type":"call"},{"api_name":"os.path","line_number":40,"usage_type":"attribute"},{"api_name":"sys.path.append","line_number":42,"usage_type":"call"},{"api_name":"sys.path","line_number":42,"usage_type":"attribute"},{"api_name":"os.path.join","line_number":42,"usage_type":"call"},{"api_name":"os.path","line_number":42,"usage_type":"attribute"},{"api_name":"sys.argv","line_number":42,"usage_type":"attribute"},{"api_name":"json.load","line_number":67,"usage_type":"call"},{"api_name":"PATTERNS.identification_patterns","line_number":72,"usage_type":"name"},{"api_name":"json.load","line_number":80,"usage_type":"call"},{"api_name":"tqdm.tqdm","line_number":84,"usage_type":"call"},{"api_name":"multiprocessing.Queue","line_number":93,"usage_type":"call"},{"api_name":"multiprocessing.Process","line_number":98,"usage_type":"call"},{"api_name":"json.dump","line_number":114,"usage_type":"call"},{"api_name":"regex.search","line_number":134,"usage_type":"call"},{"api_name":"regex.match","line_number":140,"usage_type":"call"},{"api_name":"os.path.exists","line_number":160,"usage_type":"call"},{"api_name":"os.path","line_number":160,"usage_type":"attribute"},{"api_name":"json.load","line_number":162,"usage_type":"call"},{"api_name":"json.dump","line_number":165,"usage_type":"call"},{"api_name":"pattern_abstraction.convert_patterns","line_number":223,"usage_type":"call"},{"api_name":"CHUNKS.chunks","line_number":223,"usage_type":"argument"},{"api_name":"regex.compile","line_number":225,"usage_type":"call"},{"api_name":"regex.compile","line_number":247,"usage_type":"call"},{"api_name":"pattern_abstraction.convert_patterns","line_number":248,"usage_type":"call"},{"api_name":"CHUNKS.chunks","line_number":248,"usage_type":"argument"},{"api_name":"re.MULTILINE","line_number":249,"usage_type":"attribute"},{"api_name":"sys.argv","line_number":290,"usage_type":"attribute"}],"string":"[\n {\n \"api_name\": \"sys.path.append\",\n \"line_number\": 40,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sys.path\",\n \"line_number\": 40,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 40,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 40,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"sys.path.append\",\n \"line_number\": 42,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sys.path\",\n \"line_number\": 42,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 42,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 42,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"sys.argv\",\n \"line_number\": 42,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"json.load\",\n \"line_number\": 67,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"PATTERNS.identification_patterns\",\n \"line_number\": 72,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"json.load\",\n \"line_number\": 80,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"tqdm.tqdm\",\n \"line_number\": 84,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"multiprocessing.Queue\",\n \"line_number\": 93,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"multiprocessing.Process\",\n \"line_number\": 98,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"json.dump\",\n \"line_number\": 114,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"regex.search\",\n \"line_number\": 134,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"regex.match\",\n \"line_number\": 140,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path.exists\",\n \"line_number\": 160,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 160,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"json.load\",\n \"line_number\": 162,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"json.dump\",\n \"line_number\": 165,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"pattern_abstraction.convert_patterns\",\n \"line_number\": 223,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"CHUNKS.chunks\",\n \"line_number\": 223,\n \"usage_type\": \"argument\"\n },\n {\n \"api_name\": \"regex.compile\",\n \"line_number\": 225,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"regex.compile\",\n \"line_number\": 247,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"pattern_abstraction.convert_patterns\",\n \"line_number\": 248,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"CHUNKS.chunks\",\n \"line_number\": 248,\n \"usage_type\": \"argument\"\n },\n {\n \"api_name\": \"re.MULTILINE\",\n \"line_number\": 249,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"sys.argv\",\n \"line_number\": 290,\n \"usage_type\": \"attribute\"\n }\n]"}}},{"rowIdx":189,"cells":{"seq_id":{"kind":"string","value":"40466793180"},"text":{"kind":"string","value":"#!/home/gabriel/funcam/venv/bin/python3\n# ONLY TESTED ON LINUX\n# To run using ./run.py [args] on your terminal (without python3)\n# point the first line to some python interpreter containing the requirements\n# or create a venv inside this project.\n# Or delete this to use another method.\n\nfrom cam import Cam\nfrom vcam import VCam\nimport argparse\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument('-v', '--virtual',help='enable virtual cam',action='store_true')\n parser.add_argument('--video', help='choose video input', type=int, default=0)\n parser.add_argument('--maxhands', help='set max hands for detection', type=int, default=1)\n parser.add_argument('-d', help='enable draw for marks and functional areas', action='store_true')\n parser.add_argument('--finger', help='choose the finger for control', type=int, default=8, choices=[4, 8, 12, 16, 20])\n parser.add_argument('-p', help='enable camera to take photos', action='store_true')\n args = parser.parse_args()\n\n if args.virtual:\n # virtual cam\n vc = VCam(video=args.video, mxhand=args.maxhands, du=args.d, f=args.finger)\n vc.start()\n\n else:\n # own cam\n cam = Cam(video=args.video, mxhand=args.maxhands, du=args.d, f=args.finger, p=args.p)\n cam.open()\n"},"repo_name":{"kind":"string","value":"biguelito/funcam"},"sub_path":{"kind":"string","value":"funcam.py"},"file_name":{"kind":"string","value":"funcam.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":1315,"string":"1,315"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"argparse.ArgumentParser","line_number":14,"usage_type":"call"},{"api_name":"vcam.VCam","line_number":26,"usage_type":"call"},{"api_name":"cam.Cam","line_number":31,"usage_type":"call"},{"api_name":"cam.open","line_number":32,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"argparse.ArgumentParser\",\n \"line_number\": 14,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"vcam.VCam\",\n \"line_number\": 26,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"cam.Cam\",\n \"line_number\": 31,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"cam.open\",\n \"line_number\": 32,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":190,"cells":{"seq_id":{"kind":"string","value":"35951961808"},"text":{"kind":"string","value":"# -*- coding: utf-8 -*-\nimport logging\n\nfrom path import Path\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_mipname(fastq_file):\n \"\"\"Takes a demux fastq file and returns a MIP compatible fastq file\n\n Args:\n fastq_file (str): a FQP to a fastq file.\n\n Returns (str): A MIP compatible fastq file.\n \"\"\"\n dirparts = fastq_file.split(\"/\")\n nameparts = dirparts[-1].split(\"_\")\n\n # H3LGFCCXX-l1t21_973470_CGGCTATG_L001_R2_001.fastq.gz\n # H3LGFCCXX-l1t21_Undetermined_CGGCTATG_L001_R1_001.fastq.gz\n # RNA1460A10_dual10_TCCGGAGA-ATAGAGGC_L001_R1_001.fastq.gz\n # RNA1460A10_TCCGGAGA-ATAGAGGC_L001_R1_001.fastq.gz\n\n index = nameparts[-4]\n # no worries, this'll always work, right?\n fc = dirparts[-5].split(\"_\")[-1][1:]\n lane = int(nameparts[-3][-1:])\n readdirection = nameparts[-2][-1:]\n rundir = dirparts[-5]\n date = rundir.split(\"_\")[0]\n sample_id = dirparts[-2].split(\"_\")[1]\n\n # X stuff\n undetermined = ''\n if nameparts[1] == 'Undetermined':\n undetermined = '-Undetermined'\n\n tile = ''\n if '-' in nameparts[0]:\n # H2V2YCCXX-l2t21\n tile = nameparts[0].split('-')[1].split('t')[1]\n tile = '-' + tile\n\n newname = \"{lane}_{date}_{fc}{tile}{undetermined}_{sample}_{index}_{readdirection}.fastq.gz\".format(\n lane=lane,\n date=date,\n fc=fc,\n sample=sample_id,\n index=index,\n readdirection=readdirection,\n undetermined=undetermined,\n tile=tile\n )\n\n return newname\n\n\ndef make_link(source, dest, link_type='hard'):\n Path(dest).remove_p()\n\n try:\n if link_type == 'soft':\n logger.debug(\"ln -s {} {} ...\".format(source, dest))\n Path(source).symlink(dest)\n else:\n real_source = Path(source).realpath()\n logger.debug(\"ln {} {} ...\".format(real_source, dest))\n Path(real_source).link(dest)\n except Exception as error:\n # catch, print, and continue\n logger.error(repr(error))\n return False\n\n return True\n"},"repo_name":{"kind":"string","value":"Clinical-Genomics/deliver"},"sub_path":{"kind":"string","value":"deliver/utils/files.py"},"file_name":{"kind":"string","value":"files.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":2052,"string":"2,052"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":1,"string":"1"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"logging.getLogger","line_number":7,"usage_type":"call"},{"api_name":"path.Path","line_number":61,"usage_type":"call"},{"api_name":"path.Path","line_number":66,"usage_type":"call"},{"api_name":"path.Path","line_number":68,"usage_type":"call"},{"api_name":"path.Path","line_number":70,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"logging.getLogger\",\n \"line_number\": 7,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"path.Path\",\n \"line_number\": 61,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"path.Path\",\n \"line_number\": 66,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"path.Path\",\n \"line_number\": 68,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"path.Path\",\n \"line_number\": 70,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":191,"cells":{"seq_id":{"kind":"string","value":"1397488728"},"text":{"kind":"string","value":"from collections import defaultdict\ndef longestPalindrome(s):\n\tmaxlen, maxp, l, dit = 0, \"\", len(s), defaultdict(list)\n\tfor i in range(l):\n\t\tdit[s[i]].append(i)\n\t\tfor j in dit[s[i][::-1]]:\n\t\t\tif s[j:i+1] == s[j:i+1][::-1]:\n\t\t\t\tif len(s[j:i+1]) > maxlen:\n\t\t\t\t\tmaxlen = len(s[j:i+1])\n\t\t\t\t\tmaxp = s[j:i+1]\n\t\t\t\t\tbreak\n\treturn maxp\n\nst=input()\nprint(longestPalindrome(st))\n"},"repo_name":{"kind":"string","value":"anjaliugale31/placement_preparation"},"sub_path":{"kind":"string","value":"strongest_palindrome.py"},"file_name":{"kind":"string","value":"strongest_palindrome.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":368,"string":"368"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"collections.defaultdict","line_number":3,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"collections.defaultdict\",\n \"line_number\": 3,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":192,"cells":{"seq_id":{"kind":"string","value":"10010702062"},"text":{"kind":"string","value":"import numpy as np\nimport pandas as pd\nfrom flask import Flask, request, jsonify, render_template\nimport pickle\n\napp = Flask(__name__)\nfrom keras.models import load_model\nmodel = load_model('customer-churn\\saved_model (1).pb')\n# Importing the dataset\ndataset = pd.read_csv('customer_churn_large_dataset.csv')\n# Extracting dependent and independent variables:\n# Extracting independent variable:\nX = dataset.iloc[:,3:13].values\n# Extracting dependent variable:\ny = dataset.iloc[:, 5].values\n# Encoding Categorical data:\n# Encoding the Independent Variable\nfrom sklearn.preprocessing import LabelEncoder\nlabelencoder_X = LabelEncoder()\nX[:, 1] = labelencoder_X.fit_transform(X[:, 1])\n# Encoding Categorical data:\n# Encoding the Independent Variable\nfrom sklearn.preprocessing import LabelEncoder\nlabelencoder_X = LabelEncoder()\nX[:, 2] = labelencoder_X.fit_transform(X[:, 2])\n#dummy encoding.\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\ncolumnTransformer = ColumnTransformer([('yograj', OneHotEncoder(), [1])],remainder='passthrough')\nX=columnTransformer.fit_transform(X)\n#dummy encoding.\n\n # Dummy Variable trapping\nX = X[:, 1:] \n# Splitting the Dataset into the Training set and Test set\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n# Feature Scaling\n# Standard Scaling: Standardization = X'=X-mean(X)/standard deviation\n# normal scaling : Normalization= X'=X-min(X)/max(x)-min(X)\n\nfrom sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\n\n\n@app.route('/')\ndef home():\n \n return render_template(\"index.html\")\n \n@app.route('/predict',methods=['GET'])\ndef predict():\n '''\n For rendering results on HTML GUI\n '''\n creditscore = int(request.args.get('CustomerID'))\n geo = int(request.args.get('Name'))\n age = int(request.args.get('Age'))\n tenure = int(request.args.get('Gender')) \n balance = int(request.args.get('Location'))\n numofproducts = int(request.args.get('Subscription_Length_Months')) \n creditcards=int(request.args.get('Monthly_Bill'))\n activemember = int(request.args.get('Total_Usage_GB'))\n \n salary = int(request.args.get('Churn')) \n \n \n y_pred= model.predict(sc_X.transform(np.array([[0,1,CustomerID ,Name,Age,Gender,Location,\n Subscription_Length_Months ,Monthly_Bill,Total_Usage_GB,Churn]])))\n y_pred = (y_pred > 0.5)\n if y_pred>0.5:\n result=\"Customer will not churn\"\n else:\n result=\"Customer will exit to\"\n \n return render_template('index.html', prediction_text='Model has predicted : {}'.format(result))\n"},"repo_name":{"kind":"string","value":"meyograj/churn1"},"sub_path":{"kind":"string","value":"app.py"},"file_name":{"kind":"string","value":"app.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":2806,"string":"2,806"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"flask.Flask","line_number":6,"usage_type":"call"},{"api_name":"keras.models.load_model","line_number":8,"usage_type":"call"},{"api_name":"pandas.read_csv","line_number":10,"usage_type":"call"},{"api_name":"sklearn.preprocessing.LabelEncoder","line_number":19,"usage_type":"call"},{"api_name":"sklearn.preprocessing.LabelEncoder","line_number":24,"usage_type":"call"},{"api_name":"sklearn.compose.ColumnTransformer","line_number":29,"usage_type":"call"},{"api_name":"sklearn.preprocessing.OneHotEncoder","line_number":29,"usage_type":"call"},{"api_name":"sklearn.model_selection.train_test_split","line_number":39,"usage_type":"call"},{"api_name":"sklearn.preprocessing.StandardScaler","line_number":45,"usage_type":"call"},{"api_name":"flask.render_template","line_number":53,"usage_type":"call"},{"api_name":"flask.request.args.get","line_number":60,"usage_type":"call"},{"api_name":"flask.request.args","line_number":60,"usage_type":"attribute"},{"api_name":"flask.request","line_number":60,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":61,"usage_type":"call"},{"api_name":"flask.request.args","line_number":61,"usage_type":"attribute"},{"api_name":"flask.request","line_number":61,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":62,"usage_type":"call"},{"api_name":"flask.request.args","line_number":62,"usage_type":"attribute"},{"api_name":"flask.request","line_number":62,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":63,"usage_type":"call"},{"api_name":"flask.request.args","line_number":63,"usage_type":"attribute"},{"api_name":"flask.request","line_number":63,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":64,"usage_type":"call"},{"api_name":"flask.request.args","line_number":64,"usage_type":"attribute"},{"api_name":"flask.request","line_number":64,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":65,"usage_type":"call"},{"api_name":"flask.request.args","line_number":65,"usage_type":"attribute"},{"api_name":"flask.request","line_number":65,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":66,"usage_type":"call"},{"api_name":"flask.request.args","line_number":66,"usage_type":"attribute"},{"api_name":"flask.request","line_number":66,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":67,"usage_type":"call"},{"api_name":"flask.request.args","line_number":67,"usage_type":"attribute"},{"api_name":"flask.request","line_number":67,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":69,"usage_type":"call"},{"api_name":"flask.request.args","line_number":69,"usage_type":"attribute"},{"api_name":"flask.request","line_number":69,"usage_type":"name"},{"api_name":"numpy.array","line_number":72,"usage_type":"call"},{"api_name":"flask.render_template","line_number":80,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"flask.Flask\",\n \"line_number\": 6,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"keras.models.load_model\",\n \"line_number\": 8,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"pandas.read_csv\",\n \"line_number\": 10,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.preprocessing.LabelEncoder\",\n \"line_number\": 19,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.preprocessing.LabelEncoder\",\n \"line_number\": 24,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.compose.ColumnTransformer\",\n \"line_number\": 29,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.preprocessing.OneHotEncoder\",\n \"line_number\": 29,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.model_selection.train_test_split\",\n \"line_number\": 39,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sklearn.preprocessing.StandardScaler\",\n \"line_number\": 45,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.render_template\",\n \"line_number\": 53,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 60,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 60,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 60,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 61,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 61,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 61,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 62,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 62,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 62,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 63,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 63,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 63,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 64,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 64,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 64,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 65,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 65,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 65,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 66,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 66,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 66,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 67,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 67,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 67,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 69,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 69,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 69,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 72,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.render_template\",\n \"line_number\": 80,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":193,"cells":{"seq_id":{"kind":"string","value":"29205882969"},"text":{"kind":"string","value":"import subprocess\nimport time\nimport os\nimport math\nfrom PIL import Image\nimport psutil\nimport re\nfrom skyfield.api import Star\nimport numpy as np\nimport threading\nimport select\nfrom pathlib import Path\nimport fitsio\nimport Nexus\nimport Coordinates\nimport Display\n\nhome_path = str(Path.home())\nversion = \"21_7\"\n#os.system('pkill -9 -f eFinder.py') # stops the autostart eFinder program running\nx = y = 0 # x, y define what page the display is showing\ndeltaAz = deltaAlt = 0\nexpInc = 1 # sets how much exposure changes when using handpad adjust (seconds)\ngainInc = 5 # ditto for gain\noffset_flag = False\nalign_count = 0\noffset = 640, 480\nstar_name = \"no star\"\nsolve = False\nsync_count = 0\nsDog = True\ngotoFlag = False\n\n\ndef xy2rd(x, y): # returns the RA & Dec equivalent to a camera pixel x,y\n result = subprocess.run(\n [\n \"wcs-xy2rd\",\n \"-w\",\n destPath + \"capture.wcs\",\n \"-x\",\n str(x),\n \"-y\",\n str(y),\n ],\n capture_output=True,\n text=True,\n )\n result = str(result.stdout)\n line = result.split(\"RA,Dec\")[1]\n ra, dec = re.findall(\"[-,+]?\\d+\\.\\d+\", line)\n return (float(ra), float(dec))\n\ndef pixel2dxdy(pix_x, pix_y): # converts a pixel position, into a delta angular offset from the image centre\n deg_x = (float(pix_x) - 640) * pix_scale / 3600 # in degrees\n deg_y = (480 - float(pix_y)) * pix_scale / 3600\n dxstr = \"{: .1f}\".format(float(60 * deg_x)) # +ve if finder is left of Polaris\n dystr = \"{: .1f}\".format(\n float(60 * deg_y)\n ) # +ve if finder is looking below Polaris\n return (deg_x, deg_y, dxstr, dystr)\n\ndef dxdy2pixel(dx, dy):\n pix_x = dx * 3600 / pix_scale + 640\n pix_y = 480 - dy * 3600 / pix_scale\n dxstr = \"{: .1f}\".format(float(60 * dx)) # +ve if finder is left of Polaris\n dystr = \"{: .1f}\".format(float(60 * dy)) # +ve if finder is looking below Polaris\n return (pix_x, pix_y, dxstr, dystr)\n\ndef imgDisplay(): # displays the captured image on the Pi desktop.\n for proc in psutil.process_iter():\n if proc.name() == \"display\":\n proc.kill() # delete any previous image display\n im = Image.open(destPath + \"capture.jpg\")\n #im.show()\n\ndef capture():\n global param\n if param[\"Test mode\"] == \"1\":\n if offset_flag == False:\n m13 = True\n polaris_cap = False\n else:\n m13 = False\n polaris_cap = True\n else:\n m13 = False\n polaris_cap = False\n radec = nexus.get_short()\n camera.capture(\n int(float(param[\"Exposure\"]) * 1000000),\n int(float(param[\"Gain\"])),\n radec,\n m13,\n polaris_cap,\n destPath,\n )\n \ndef solveImage():\n global offset_flag, solve, solvedPos, elapsed_time, star_name, star_name_offset, solved_radec, solved_altaz\n scale_low = str(pix_scale * 0.9)\n scale_high = str(pix_scale * 1.1)\n name_that_star = ([]) if (offset_flag == True) else ([\"--no-plots\"])\n handpad.display(\"Started solving\", \"\", \"\")\n limitOptions = [\n \"--overwrite\", # overwrite any existing files\n \"--skip-solved\", # skip any files we've already solved\n \"--cpulimit\",\n \"10\", # limit to 10 seconds(!). We use a fast timeout here because this code is supposed to be fast\n ]\n optimizedOptions = [\n \"--downsample\",\n \"2\", # downsample 4x. 2 = faster by about 1.0 second; 4 = faster by 1.3 seconds\n \"--no-remove-lines\", # Saves ~1.25 sec. Don't bother trying to remove surious lines from the image\n \"--uniformize\",\n \"0\", # Saves ~1.25 sec. Just process the image as-is\n ]\n scaleOptions = [\n \"--scale-units\",\n \"arcsecperpix\", # next two params are in arcsecs. Supplying this saves ~0.5 sec\n \"--scale-low\",\n scale_low, # See config above\n \"--scale-high\",\n scale_high, # See config above\n ]\n fileOptions = [\n \"--new-fits\",\n \"none\", # Don't create a new fits\n \"--solved\",\n \"none\", # Don't generate the solved output\n \"--match\",\n \"none\", # Don't generate matched output\n \"--corr\",\n \"none\", # Don't generate .corr files\n \"--rdls\",\n \"none\", # Don't generate the point list\n ] \n cmd = [\"solve-field\"]\n captureFile = destPath + \"capture.jpg\"\n options = (\n limitOptions + optimizedOptions + scaleOptions + fileOptions + [captureFile]\n )\n start_time = time.time()\n # next line runs the plate-solve on the captured image file\n result = subprocess.run(\n cmd + name_that_star + options, capture_output=True, text=True\n )\n elapsed_time = time.time() - start_time\n print(\"solve elapsed time \" + str(elapsed_time)[0:4] + \" sec\\n\")\n print(result.stdout) # this line added to help debug.\n result = str(result.stdout)\n if \"solved\" not in result:\n print(\"Bad Luck - Solve Failed\")\n handpad.display(\"Not Solved\", \"\", \"\")\n solve = False\n return\n if (offset_flag == True) and (\"The star\" in result):\n table, h = fitsio.read(destPath + \"capture.axy\", header=True)\n star_name_offset = table[0][0], table[0][1]\n lines = result.split(\"\\n\")\n for line in lines:\n if line.startswith(\" The star \"):\n star_name = line.split(\" \")[4]\n print(\"Solve-field Plot found: \", star_name)\n break\n solvedPos = applyOffset()\n ra, dec, d = solvedPos.apparent().radec(coordinates.get_ts().now())\n solved_radec = ra.hours, dec.degrees\n solved_altaz = coordinates.conv_altaz(nexus, *(solved_radec))\n nexus.set_scope_alt(solved_altaz[0] * math.pi / 180)\n arr[0, 2][0] = \"Sol: RA \" + coordinates.hh2dms(solved_radec[0])\n arr[0, 2][1] = \" Dec \" + coordinates.dd2dms(solved_radec[1])\n arr[0, 2][2] = \"time: \" + str(elapsed_time)[0:4] + \" s\"\n solve = True\n deltaCalc()\n\ndef applyOffset():\n x_offset, y_offset, dxstr, dystr = dxdy2pixel(\n float(param[\"d_x\"]), float(param[\"d_y\"])\n )\n print('applied_offset_pixels x,y',x_offset,y_offset)\n ra, dec = xy2rd(x_offset, y_offset)\n solved = Star(\n ra_hours=ra / 15, dec_degrees=dec\n ) # will set as J2000 as no epoch input\n solvedPos_scope = (\n nexus.get_location().at(coordinates.get_ts().now()).observe(solved)\n ) # now at Jnow and current location\n return solvedPos_scope\n\ndef deltaCalc():\n global deltaAz, deltaAlt, elapsed_time\n deltaAz = solved_altaz[1] - nexus.get_altAz()[1]\n if abs(deltaAz) > 180:\n if deltaAz < 0:\n deltaAz = deltaAz + 360\n else:\n deltaAz = deltaAz - 360\n deltaAz = 60 * (\n deltaAz * math.cos(nexus.get_scope_alt())\n ) # actually this is delta'x' in arcminutes\n deltaAlt = solved_altaz[0] - nexus.get_altAz()[0]\n deltaAlt = 60 * (deltaAlt) # in arcminutes\n deltaXstr = \"{: .2f}\".format(float(deltaAz))\n deltaYstr = \"{: .2f}\".format(float(deltaAlt))\n arr[0, 3][0] = \"Delta: x= \" + deltaXstr\n arr[0, 3][1] = \" y= \" + deltaYstr\n arr[0, 3][2] = \"time: \" + str(elapsed_time)[0:4] + \" s\"\n\ndef align():\n global align_count, solve, sync_count, param, offset_flag, arr, x,y\n new_arr = nexus.read_altAz(arr)\n arr = new_arr\n capture()\n imgDisplay()\n solveImage()\n if solve == False:\n handpad.display(arr[x, y][0], \"Solved Failed\", arr[x, y][2])\n return\n align_ra = \":Sr\" + coordinates.dd2dms((solved_radec)[0]) + \"#\"\n align_dec = \":Sd\" + coordinates.dd2aligndms((solved_radec)[1]) + \"#\"\n valid = nexus.get(align_ra)\n print(align_ra)\n if valid == \"0\":\n print(\"invalid position\")\n handpad.display(arr[x, y][0], \"Invalid position\", arr[x, y][2])\n time.sleep(3)\n return\n valid = nexus.get(align_dec)\n print(align_dec)\n if valid == \"0\":\n print(\"invalid position\")\n handpad.display(arr[x, y][0], \"Invalid position\", arr[x, y][2])\n time.sleep(3)\n return\n reply = nexus.get(\":CM#\")\n nexus.read_altAz(arr)\n deltaCalc()\n print(\"reply: \", reply)\n p = nexus.get(\":GW#\")\n print(\"Align status reply \", p)\n if nexus.is_aligned() == False: # wasnt aligned before this action\n align_count += 1 \n if p[1] != \"T\": # and still not aligned\n arr[0,4][0] = \"'OK' aligns\"\n arr[0,4][1] = \"Align count \" + str(align_count)\n arr[0,4][2] = \"Nexus reply:\" + p[0:3]\n handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2])\n else: \n arr[0,4][0] = \"'OK' now syncs\"\n arr[0,4][1] = \"Sync count \" + str(sync_count)\n arr[0,4][2] = \"Nexus reply:\" + p[0:3]\n arr[2,0][1] = \"Nexus is aligned\"\n handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2]) \n nexus.set_aligned(True)\n else:\n sync_count +=1\n arr[0,4][0] = \"'OK' syncs\"\n arr[0,4][1] = \"Sync count \" + str(sync_count)\n arr[0,4][2] = \"\"\n handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2])\n print(\"Nexus is aligned:\",nexus.is_aligned())\n return\n\ndef measure_offset():\n global offset_str, offset_flag, param, scope_x, scope_y, star_name\n offset_flag = True\n handpad.display(\"started capture\", \"\", \"\")\n capture()\n imgDisplay()\n solveImage()\n if solve == False:\n handpad.display(\"solve failed\", \"\", \"\")\n return\n scope_x = star_name_offset[0]\n scope_y = star_name_offset[1]\n print('pixel_offset x,y',star_name_offset)\n d_x, d_y, dxstr, dystr = pixel2dxdy(scope_x, scope_y)\n param[\"d_x\"] = d_x\n param[\"d_y\"] = d_y\n save_param()\n offset_str = dxstr + \",\" + dystr\n arr[2, 1][1] = \"new \" + offset_str\n arr[2, 2][1] = \"new \" + offset_str\n handpad.display(arr[2, 1][0], arr[2, 1][1], star_name + \" found\")\n offset_flag = False\n\ndef up_down(v):\n global x\n x = x + v\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n\ndef left_right(v):\n global y\n y = y + v\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n\ndef up_down_inc(inc, sign):\n arr[x, y][1] = int(float(arr[x, y][1])) + inc * sign\n param[arr[x, y][0]] = float(arr[x, y][1])\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n update_summary()\n time.sleep(0.1)\n\n\ndef flip():\n global param\n arr[x, y][1] = 1 - int(float(arr[x, y][1]))\n param[arr[x, y][0]] = str((arr[x, y][1]))\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n update_summary()\n time.sleep(0.1)\n\ndef update_summary():\n global param\n arr[1, 0][0] = (\n \"Ex:\" + str(param[\"Exposure\"]) + \" Gn:\" + str(param[\"Gain\"])\n )\n arr[1, 0][1] = \"Test:\" + str(param[\"Test mode\"]) + \" GoTo++:\" + str(param[\"Goto++ mode\"])\n save_param()\n\ndef go_solve():\n global x, y, solve, arr\n new_arr = nexus.read_altAz(arr)\n arr = new_arr\n handpad.display(\"Image capture\", \"\", \"\")\n capture()\n imgDisplay()\n handpad.display(\"Plate solving\", \"\", \"\")\n solveImage()\n if solve == True:\n handpad.display(\"Solved\", \"\", \"\")\n else:\n handpad.display(\"Not Solved\", \"\", \"\")\n return\n x = 0\n y = 3\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n\ndef gotoDistant():\n nexus.read_altAz(arr)\n nexus_radec = nexus.get_radec()\n deltaRa = abs(nexus_radec[0]-goto_radec[0])*15\n if deltaRa > 180:\n deltaRa = abs(deltaRa - 360)\n deltaDec = abs(nexus_radec[1]-goto_radec[1])\n print('goto distance, RA,Dec :',deltaRa,deltaDec)\n if deltaRa+deltaDec > 5:\n return(True)\n else:\n return(False)\n\ndef readTarget():\n global goto_radec,goto_ra,goto_dec\n goto_ra = nexus.get(\":Gr#\")\n if (\n goto_ra[0:2] == \"00\" and goto_ra[3:5] == \"00\"\n ): # not a valid goto target set yet.\n print(\"no GoTo target\")\n handpad.display(\"no GoTo target\",\"set yet\",\"\")\n return\n goto_dec = nexus.get(\":Gd#\")\n ra = goto_ra.split(\":\")\n dec = re.split(r\"[:*]\", goto_dec)\n goto_radec = (float(ra[0]) + float(ra[1]) / 60 + float(ra[2]) / 3600), math.copysign(\n abs(abs(float(dec[0])) + float(dec[1]) / 60 + float(dec[2]) / 3600),\n float(dec[0]),\n )\n print(\"Target goto RA & Dec\", goto_ra, goto_dec)\n\ndef goto():\n global gotoFlag\n handpad.display(\"Attempting\", \"GoTo\", \"\")\n gotoFlag = True\n readTarget()\n if gotoDistant():\n if sDog == True: \n nexus.write(\":Sr\" + goto_ra + \"#\")\n nexus.write(\":Sd\" + goto_dec + \"#\")\n reply = nexus.get(\":MS#\")\n else: \n gotoStr = '%s%06.3f %+06.3f' %(\"g\",goto_radec[0],goto_radec[1])\n print(\"Target goto RA & Dec\", gotoStr)\n servocat.send(gotoStr)\n handpad.display(\"Performing\", \" GoTo\", \"\")\n time.sleep(1)\n gotoStopped()\n handpad.display(\"Finished\", \" GoTo\", \"\")\n go_solve()\n if int(param[\"Goto++ mode\"]) == 0:\n return\n align() # close, so local sync scope to true RA & Dec\n if sDog == True:\n nexus.write(\":Sr\" + goto_ra + \"#\")\n nexus.write(\":Sd\" + goto_dec + \"#\")\n reply = nexus.get(\":MS#\")\n else:\n gotoStr = '%s%06.3f %+06.3f' %(\"g\",goto_radec[0],goto_radec[1])\n print('GoToStr: ',gotoStr)\n servocat.send(gotoStr)\n gotoStopped()\n gotoFlag = False\n handpad.display(\"Finished\", \" GoTo++\", \"\")\n go_solve()\n\ndef getRadec():\n nexus.read_altAz(None)\n return(nexus.get_radec())\n\ndef gotoStopped():\n radecNow = getRadec()\n while True:\n time.sleep(1)\n radec = getRadec()\n print(radec[0],radecNow[0],radec[1],radecNow[1])\n if (abs(radecNow[0] - radec[0])*15 < 0.01) and (abs(radecNow[1] - radec[1]) < 0.01):\n return\n else:\n radecNow = radec\n\ndef reset_offset():\n global param, arr\n param[\"d_x\"] = 0\n param[\"d_y\"] = 0\n offset_str = \"0,0\"\n arr[2,1][1] = \"new \" + offset_str\n arr[2,2][1] = \"new \" + offset_str\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n save_param()\n\ndef get_param():\n global param, offset_str, pix_scale\n if os.path.exists(home_path + \"/Solver/eFinder.config\") == True:\n with open(home_path + \"/Solver/eFinder.config\") as h:\n for line in h:\n line = line.strip(\"\\n\").split(\":\")\n param[line[0]] = str(line[1])\n pix_scale = float(param[\"pixel scale\"])\n pix_x, pix_y, dxstr, dystr = dxdy2pixel(\n float(param[\"d_x\"]), float(param[\"d_y\"])\n )\n offset_str = dxstr + \",\" + dystr\n\n\ndef save_param():\n global param\n with open(home_path + \"/Solver/eFinder.config\", \"w\") as h:\n for key, value in param.items():\n #print(\"%s:%s\\n\" % (key, value))\n h.write(\"%s:%s\\n\" % (key, value))\n\ndef reader():\n global button\n while True:\n if handpad.get_box() in select.select([handpad.get_box()], [], [], 0)[0]:\n button = handpad.get_box().readline().decode(\"ascii\").strip(\"\\r\\n\")\n time.sleep(0.1)\n\ndef home_refresh():\n global x,y\n while True:\n if x == 0 and y == 0:\n time.sleep(1)\n while x ==0 and y==0:\n nexus.read_altAz(arr)\n radec = nexus.get_radec()\n ra = coordinates.hh2dms(radec[0])\n dec = coordinates.dd2dms(radec[1])\n handpad.display('Nexus live',' RA: '+ra, 'Dec: '+dec)\n time.sleep(0.5)\n else:\n handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2])\n time.sleep (0.5)\n \n\n# main code starts here\n\nhandpad = Display.Handpad(version)\ncoordinates = Coordinates.Coordinates()\nnexus = Nexus.Nexus(handpad, coordinates)\nnexus.read()\nparam = dict()\nget_param()\n\n\n# array determines what is displayed, computed and what each button does for each screen.\n# [first line,second line,third line, up button action,down...,left...,right...,select button short press action, long press action]\n# empty string does nothing.\n# example: left_right(-1) allows left button to scroll to the next left screen\n# button texts are infact def functions\np = \"\"\nhome = [\n \"Nexus live\",\n \" RA:\",\n \"Dec:\",\n \"\",\n \"up_down(1)\",\n \"\",\n \"left_right(1)\",\n \"align()\",\n \"goto()\",\n]\nnex = [\n \"Nex: RA \",\n \" Dec \",\n \"\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\nsol = [\n \"No solution yet\",\n \"'OK' solves\",\n \"\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\ndelta = [\n \"Delta: No solve\",\n \"'OK' solves\",\n \"\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\naligns = [\n \"'OK' aligns\",\n \"not aligned yet\",\n str(p),\n \"\",\n \"\",\n \"left_right(-1)\",\n \"\",\n \"align()\",\n \"\",\n]\npolar = [\n \"'OK' Bright Star\",\n offset_str,\n \"\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"measure_offset()\",\n \"\",\n]\nreset = [\n \"'OK' Resets\",\n offset_str,\n \"\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"reset_offset()\",\n \"\",\n]\nsummary = [\"\", \"\", \"\", \"up_down(-1)\", \"up_down(1)\", \"\", \"left_right(1)\", \"go_solve()\", \"\"]\nexp = [\n \"Exposure\",\n param[\"Exposure\"],\n \"\",\n \"up_down_inc(expInc,1)\",\n \"up_down_inc(expInc,-1)\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\ngn = [\n \"Gain\",\n param[\"Gain\"],\n \"\",\n \"up_down_inc(gainInc,1)\",\n \"up_down_inc(gainInc,-1)\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\ngotoMode = [\n \"Goto++ mode\",\n int(param[\"Goto++ mode\"]),\n \"\",\n \"flip()\",\n \"flip()\",\n \"left_right(-1)\",\n \"\",\n \"go_solve()\",\n \"goto()\",\n]\nmode = [\n \"Test mode\",\n int(param[\"Test mode\"]),\n \"\",\n \"flip()\",\n \"flip()\",\n \"left_right(-1)\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\nstatus = [\n \"Nexus via \" + nexus.get_nexus_link(),\n \"Nex align \" + str(nexus.is_aligned()),\n \"Brightness\",\n \"up_down(-1)\",\n \"\",\n \"\",\n \"left_right(1)\",\n \"go_solve()\",\n \"goto()\",\n]\nbright = [\n \"Handpad\",\n \"Display\",\n \"Bright Adj\",\n \"\",\n \"\",\n \"left_right(-1)\",\n \"\",\n \"go_solve()\",\n \"goto()\",\n]\narr = np.array(\n [\n [home, nex, sol, delta, aligns],\n [summary, exp, gn, mode, gotoMode],\n [status, polar, reset, bright, bright],\n ]\n)\nupdate_summary()\ndeg_x, deg_y, dxstr, dystr = dxdy2pixel(float(param[\"d_x\"]), float(param[\"d_y\"]))\noffset_str = dxstr + \",\" + dystr\nnew_arr = nexus.read_altAz(arr)\narr = new_arr\nif nexus.is_aligned() == True:\n arr[0, 4][1] = \"Nexus is aligned\"\n arr[0, 4][0] = \"'OK' syncs\"\n #arr[2,0][1] = \"Nexus is aligned\"\n\nif param[\"Camera Type ('QHY' or 'ASI')\"]=='ASI':\n import ASICamera2\n camera = ASICamera2.ASICamera(handpad)\nelif param[\"Camera Type ('QHY' or 'ASI')\"]=='QHY':\n import QHYCamera2\n camera = QHYCamera2.QHYCamera(handpad)\n\nif param[\"Drive ('scopedog' or 'servocat')\"].lower()=='servocat':\n import ServoCat\n servocat = ServoCat.ServoCat()\n sDog = False\n print('ServoCat mode')\n arr[2,0][1] = \"ServoCat mode\"\nelse:\n print('ScopeDog mode')\n arr[2,0][1] = \"ScopeDog mode\"\n\nif param[\"Ramdisk\"].lower()=='true':\n destPath = \"/var/tmp/\"\nelse:\n destPath = home_path + \"/Solver/images/\"\nprint('Working folder: '+destPath)\n\nhandpad.display(\"ScopeDog eFinder\", \"ver \" + version, \"Drive: \"+param[\"Drive ('scopedog' or 'servocat')\"])\ntime.sleep(3)\nbutton = \"\"\n\nscan = threading.Thread(target=reader)\nscan.daemon = True\nscan.start()\n\nwhile True: # next loop looks for button press and sets display option x,y\n if button == \"20\":\n exec(arr[x, y][7])\n elif button == \"21\":\n exec(arr[x, y][8])\n elif button == \"18\":\n exec(arr[x, y][4])\n elif button == \"16\":\n exec(arr[x, y][3])\n elif button == \"19\":\n exec(arr[x, y][5])\n elif button == \"17\":\n exec(arr[x, y][6])\n button = \"\"\n if x == 0 and y == 0 and gotoFlag == False:\n nexus.read_altAz(arr)\n radec = nexus.get_radec()\n if nexus.is_aligned() == True:\n tick = \"T\"\n else:\n tick = \"N\"\n ra = coordinates.hh2dms(radec[0])\n dec = coordinates.dd2dms(radec[1])\n handpad.display('Nexus live '+tick,' RA: '+ra, 'Dec: '+dec)\n time.sleep(0.2)\n else:\n time.sleep(0.1)\n\n"},"repo_name":{"kind":"string","value":"WimDeMeester/eFinder"},"sub_path":{"kind":"string","value":"eFinder.py"},"file_name":{"kind":"string","value":"eFinder.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":20522,"string":"20,522"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"pathlib.Path.home","line_number":18,"usage_type":"call"},{"api_name":"pathlib.Path","line_number":18,"usage_type":"name"},{"api_name":"subprocess.run","line_number":36,"usage_type":"call"},{"api_name":"re.findall","line_number":51,"usage_type":"call"},{"api_name":"psutil.process_iter","line_number":71,"usage_type":"call"},{"api_name":"PIL.Image.open","line_number":74,"usage_type":"call"},{"api_name":"PIL.Image","line_number":74,"usage_type":"name"},{"api_name":"time.time","line_number":143,"usage_type":"call"},{"api_name":"subprocess.run","line_number":145,"usage_type":"call"},{"api_name":"time.time","line_number":148,"usage_type":"call"},{"api_name":"fitsio.read","line_number":158,"usage_type":"call"},{"api_name":"math.pi","line_number":170,"usage_type":"attribute"},{"api_name":"skyfield.api.Star","line_number":183,"usage_type":"call"},{"api_name":"math.cos","line_number":200,"usage_type":"call"},{"api_name":"time.sleep","line_number":227,"usage_type":"call"},{"api_name":"time.sleep","line_number":234,"usage_type":"call"},{"api_name":"time.sleep","line_number":303,"usage_type":"call"},{"api_name":"time.sleep","line_number":312,"usage_type":"call"},{"api_name":"re.split","line_number":364,"usage_type":"call"},{"api_name":"math.copysign","line_number":365,"usage_type":"call"},{"api_name":"time.sleep","line_number":386,"usage_type":"call"},{"api_name":"time.sleep","line_number":413,"usage_type":"call"},{"api_name":"os.path.exists","line_number":433,"usage_type":"call"},{"api_name":"os.path","line_number":433,"usage_type":"attribute"},{"api_name":"select.select","line_number":455,"usage_type":"call"},{"api_name":"time.sleep","line_number":457,"usage_type":"call"},{"api_name":"time.sleep","line_number":463,"usage_type":"call"},{"api_name":"time.sleep","line_number":470,"usage_type":"call"},{"api_name":"time.sleep","line_number":473,"usage_type":"call"},{"api_name":"Display.Handpad","line_number":478,"usage_type":"call"},{"api_name":"Coordinates.Coordinates","line_number":479,"usage_type":"call"},{"api_name":"Nexus.Nexus","line_number":480,"usage_type":"call"},{"api_name":"numpy.array","line_number":636,"usage_type":"call"},{"api_name":"ASICamera2.ASICamera","line_number":655,"usage_type":"call"},{"api_name":"QHYCamera2.QHYCamera","line_number":658,"usage_type":"call"},{"api_name":"ServoCat.ServoCat","line_number":662,"usage_type":"call"},{"api_name":"time.sleep","line_number":677,"usage_type":"call"},{"api_name":"threading.Thread","line_number":680,"usage_type":"call"},{"api_name":"time.sleep","line_number":708,"usage_type":"call"},{"api_name":"time.sleep","line_number":710,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"pathlib.Path.home\",\n \"line_number\": 18,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"pathlib.Path\",\n \"line_number\": 18,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"subprocess.run\",\n \"line_number\": 36,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"re.findall\",\n \"line_number\": 51,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"psutil.process_iter\",\n \"line_number\": 71,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"PIL.Image.open\",\n \"line_number\": 74,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"PIL.Image\",\n \"line_number\": 74,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"time.time\",\n \"line_number\": 143,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"subprocess.run\",\n \"line_number\": 145,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.time\",\n \"line_number\": 148,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"fitsio.read\",\n \"line_number\": 158,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"math.pi\",\n \"line_number\": 170,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"skyfield.api.Star\",\n \"line_number\": 183,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"math.cos\",\n \"line_number\": 200,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 227,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 234,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 303,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 312,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"re.split\",\n \"line_number\": 364,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"math.copysign\",\n \"line_number\": 365,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 386,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 413,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path.exists\",\n \"line_number\": 433,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 433,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"select.select\",\n \"line_number\": 455,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 457,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 463,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 470,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 473,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"Display.Handpad\",\n \"line_number\": 478,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"Coordinates.Coordinates\",\n \"line_number\": 479,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"Nexus.Nexus\",\n \"line_number\": 480,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 636,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"ASICamera2.ASICamera\",\n \"line_number\": 655,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"QHYCamera2.QHYCamera\",\n \"line_number\": 658,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"ServoCat.ServoCat\",\n \"line_number\": 662,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 677,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"threading.Thread\",\n \"line_number\": 680,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 708,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"time.sleep\",\n \"line_number\": 710,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":194,"cells":{"seq_id":{"kind":"string","value":"36040524676"},"text":{"kind":"string","value":"import statistics\n\nfrom ParadoxTrading.Indicator.IndicatorAbstract import IndicatorAbstract\nfrom ParadoxTrading.Utils import DataStruct\n\n\nclass AdaBBands(IndicatorAbstract):\n def __init__(\n self, _period: int, _use_key: str,\n _init_n: int = 20, _min_n: int = 20, _max_n: int = 60,\n _rate: float = 2.0, _idx_key: str = 'time'\n ):\n super().__init__()\n\n self.use_key = _use_key\n self.idx_key = _idx_key\n self.keys = [self.idx_key, 'upband', 'midband', 'downband']\n\n self.data = DataStruct(\n self.keys, self.idx_key\n )\n\n self.period = _period\n self.rate = _rate\n self.buf = []\n\n self.prev_std = None\n\n self.dynamic_n = float(_init_n)\n self.min_n = _min_n\n self.max_n = _max_n\n\n def _addOne(self, _data_struct: DataStruct):\n index_value = _data_struct.index()[0]\n self.buf.append(_data_struct.getColumn(self.use_key)[0])\n\n if len(self.data) > self.period:\n const_std = statistics.pstdev(self.buf[-self.period:])\n self.dynamic_n *= const_std / self.prev_std\n self.dynamic_n = max(self.min_n, self.dynamic_n)\n self.dynamic_n = min(self.max_n, self.dynamic_n)\n tmp_n = int(round(self.dynamic_n))\n\n mean = statistics.mean(self.buf[-tmp_n:])\n std = statistics.pstdev(self.buf[-tmp_n:])\n\n self.data.addRow(\n [index_value, mean + self.rate * std,\n mean, mean - self.rate * std],\n self.keys\n )\n\n self.prev_std = const_std\n else:\n if len(self.data) == self.period:\n self.prev_std = statistics.pstdev(self.buf)\n\n self.data.addRow(\n [index_value, None, None, None],\n self.keys\n )\n"},"repo_name":{"kind":"string","value":"ppaanngggg/ParadoxTrading"},"sub_path":{"kind":"string","value":"ParadoxTrading/Indicator/General/AdaBBands.py"},"file_name":{"kind":"string","value":"AdaBBands.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":1870,"string":"1,870"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":51,"string":"51"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"ParadoxTrading.Indicator.IndicatorAbstract.IndicatorAbstract","line_number":7,"usage_type":"name"},{"api_name":"ParadoxTrading.Utils.DataStruct","line_number":19,"usage_type":"call"},{"api_name":"ParadoxTrading.Utils.DataStruct","line_number":33,"usage_type":"name"},{"api_name":"statistics.pstdev","line_number":38,"usage_type":"call"},{"api_name":"statistics.mean","line_number":44,"usage_type":"call"},{"api_name":"statistics.pstdev","line_number":45,"usage_type":"call"},{"api_name":"statistics.pstdev","line_number":56,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"ParadoxTrading.Indicator.IndicatorAbstract.IndicatorAbstract\",\n \"line_number\": 7,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"ParadoxTrading.Utils.DataStruct\",\n \"line_number\": 19,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"ParadoxTrading.Utils.DataStruct\",\n \"line_number\": 33,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"statistics.pstdev\",\n \"line_number\": 38,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"statistics.mean\",\n \"line_number\": 44,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"statistics.pstdev\",\n \"line_number\": 45,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"statistics.pstdev\",\n \"line_number\": 56,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":195,"cells":{"seq_id":{"kind":"string","value":"19780951486"},"text":{"kind":"string","value":"#!/usr/bin/env python3\n# -*- encoding: utf-8 -*-\nimport os\nimport argparse\nimport subprocess\nfrom glob import glob\n\n\ndef translatefolder(src, trg, **kw):\n python = kw.get(\"python\", \"python3\")\n translate = kw.get(\"translate\", \"./translate/translate.py\")\n port = int(kw.get(\"port\", 3035))\n host = kw.get(\"host\", \"127.0.0.1\")\n \n # create directories\n if not os.path.exists(trg):\n os.makedirs(trg)\n \n # collect files\n domains, problems = [], []\n for f in os.listdir(src):\n if \"domain\" in f and f.endswith(\".pddl\"):\n domains.append(os.path.join(src, f))\n elif \"problem\" in f and f.endswith(\".pddl\"):\n problems.append(os.path.join(src, f))\n domains.sort()\n problems.sort()\n \n # assign agents\n agents = []\n for i in range(len(domains)):\n agents.append(\"tcp://{}:{}\".format(host, str(port+i)))\n \n # create command\n tmpl = (\"{} {} {} {} --agent-url \" + \" --agent-url \".join(agents) +\n \" --agent-id {} --output {} --json\")\n cmd = \"\"\n for i, d in enumerate(domains):\n s = tmpl.format(python, translate, d, problems[i], i,\n os.path.join(trg,str(i)+'.json')) + ' & '\n print(s)\n cmd += s\n cmd = cmd[:-2]\n \n os.system(cmd)\n\n\ndef translateall(src='benchmarks/factored/', trg='benchmarks/compiled/', **kw):\n files_src = glob(src + \"*/*/\")\n files_trg = [os.path.join(trg, *f.split('/')[2:]) for f in files_src]\n\n port = 3035\n shift = 100\n errors = []\n for s, t in zip(files_src, files_trg):\n try:\n print(\"translating \" + s + \" to \" + t + \" port: \" + str(port))\n translatefolder(s, t, port=port)\n except Exception as e:\n errors += [e]\n port += shift\n for i, error in enumerate(errors):\n print(\"ERR %d: %s: %s\" % (i, type(error), error))\n\n\ndef on_translate(*args, **kw):\n if kw['all']:\n translateall(**kw)\n else:\n translatefolder(**kw)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Run GOA planner')\n parser.add_argument('src', help='path to folder containing src task')\n parser.add_argument('trg', help='destination path')\n parser.add_argument(\n '--port', \n default=3035,\n help='the port (default: 3035)'\n )\n parser.add_argument(\n '--all', \n help='translate all domains of given folder',\n action='store_true'\n )\n parser.set_defaults(func=on_translate)\n\n args, rest = parser.parse_known_args()\n args.func(*rest, **vars(args))\n"},"repo_name":{"kind":"string","value":"schultet/goa"},"sub_path":{"kind":"string","value":"scripts/translate.py"},"file_name":{"kind":"string","value":"translate.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":2582,"string":"2,582"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":2,"string":"2"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"os.path.exists","line_number":16,"usage_type":"call"},{"api_name":"os.path","line_number":16,"usage_type":"attribute"},{"api_name":"os.makedirs","line_number":17,"usage_type":"call"},{"api_name":"os.listdir","line_number":21,"usage_type":"call"},{"api_name":"os.path.join","line_number":23,"usage_type":"call"},{"api_name":"os.path","line_number":23,"usage_type":"attribute"},{"api_name":"os.path.join","line_number":25,"usage_type":"call"},{"api_name":"os.path","line_number":25,"usage_type":"attribute"},{"api_name":"os.path.join","line_number":40,"usage_type":"call"},{"api_name":"os.path","line_number":40,"usage_type":"attribute"},{"api_name":"os.system","line_number":45,"usage_type":"call"},{"api_name":"glob.glob","line_number":49,"usage_type":"call"},{"api_name":"os.path.join","line_number":50,"usage_type":"call"},{"api_name":"os.path","line_number":50,"usage_type":"attribute"},{"api_name":"argparse.ArgumentParser","line_number":74,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"os.path.exists\",\n \"line_number\": 16,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 16,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.makedirs\",\n \"line_number\": 17,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.listdir\",\n \"line_number\": 21,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 23,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 23,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 25,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 25,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 40,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 40,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"os.system\",\n \"line_number\": 45,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"glob.glob\",\n \"line_number\": 49,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path.join\",\n \"line_number\": 50,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"os.path\",\n \"line_number\": 50,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"argparse.ArgumentParser\",\n \"line_number\": 74,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":196,"cells":{"seq_id":{"kind":"string","value":"35616591437"},"text":{"kind":"string","value":"# https://adventofcode.com/2022/day/22\n\nfrom collections import defaultdict\n\nfrom aoctk.data import Graph, Unbound2DGrid\nfrom aoctk.input import get_groups\n\n\ndef parse(data):\n ps, (ins,) = get_groups(data)\n\n m = Unbound2DGrid(\n (\n (complex(j, i), c)\n for i, r in enumerate(ps)\n for j, c in enumerate(r)\n if c != \" \"\n )\n )\n p = map(complex, ins.replace(\"R\", \" 1j \").replace(\"L\", \" -1j \").split())\n\n return m, p, complex(ps[0].index(ps[0].strip()))\n\n\ndef solve(wrapping, data=\"input.txt\"):\n m, p, z = parse(data)\n\n d = 1\n while True:\n s = next(p)\n for _ in range(int(abs(s))):\n if z + d not in m:\n w, e = wrapping(m, z, d)\n if m[w] != \"#\":\n z, d = w, e\n continue\n elif m[z + d] == \"#\":\n break\n z += d\n try:\n d *= next(p)\n except StopIteration:\n break\n\n return (\n int(z.real + 1) * 4 + int(z.imag + 1) * 1000 + {1: 0, 1j: 1, -1: 2, -1j: 3}[d]\n )\n\n\ndef part_one(data=\"input.txt\"):\n def wrapping(m, z, d):\n w = z\n while w - d in m:\n w -= d\n return w, d\n\n return solve(wrapping, data)\n\n\ndef part_two(data=\"input.txt\"):\n m, _, _ = parse(data)\n\n # Determine the face size\n w, h = (_.hi + 1 for _ in m.bounds())\n l = max(w, h) - min(w, h)\n\n class Faces(Graph):\n def adj(self, n):\n return {\n (n + l * d, d)\n for d in (1j ** k for k in range(4))\n if n + l * d in self.data\n }\n\n def __iter__(self):\n return iter(self.data)\n\n fs = Faces(\n {\n complex(i, j)\n for i in range(0, w, l)\n for j in range(0, h, l)\n if complex(i, j) in m\n }\n )\n\n # Determine the wrapping rules based on how the faces are connected\n # The mapping tells for each face and each direction the destination face\n # and the direction to go in that face.\n wrs, c = defaultdict(dict), 24\n for s in fs:\n for t, d in fs.adj(s):\n wrs[s][d] = (t, d)\n c -= 1\n while c > 0:\n for s in fs:\n r = wrs[s]\n for k in (1j ** _ for _ in range(4)):\n if c <= 0:\n break\n if k in r and k * 1j in r:\n (t, phi), (q, psi) = r[k], r[k * 1j]\n if phi * 1j not in wrs[t]:\n wrs[t][phi * 1j] = (q, psi * 1j)\n c -= 1\n if -psi * 1j not in wrs[q]:\n wrs[q][-psi * 1j] = (t, -phi * 1j)\n c -= 1\n\n def wrapping(m, z, d):\n a = complex(z.real // l, z.imag // l) * l\n b, e = wrs[a][d]\n w = (z - a) - (l - 1) * d + (1 + 1j)\n rot = e / d\n tr = (l + 1) * (1 + 1j) * (1 - rot) / 2\n w = b + w * rot + tr - (1 + 1j)\n return w, e\n\n return solve(wrapping, data)\n\n\ndef test():\n assert part_one(\"test.txt\") == 6032\n assert part_two(\"test.txt\") == 5031\n"},"repo_name":{"kind":"string","value":"P403n1x87/aoc"},"sub_path":{"kind":"string","value":"2022/22/code.py"},"file_name":{"kind":"string","value":"code.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":3157,"string":"3,157"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":2,"string":"2"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"aoctk.input.get_groups","line_number":10,"usage_type":"call"},{"api_name":"aoctk.data.Unbound2DGrid","line_number":12,"usage_type":"call"},{"api_name":"aoctk.data.Graph","line_number":67,"usage_type":"name"},{"api_name":"collections.defaultdict","line_number":90,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"aoctk.input.get_groups\",\n \"line_number\": 10,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"aoctk.data.Unbound2DGrid\",\n \"line_number\": 12,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"aoctk.data.Graph\",\n \"line_number\": 67,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"collections.defaultdict\",\n \"line_number\": 90,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":197,"cells":{"seq_id":{"kind":"string","value":"32177006845"},"text":{"kind":"string","value":"import numpy as np\n\nfrom sentinelhub import BBox, bbox_to_dimensions,CRS\n\n\nclass resolution_image:\n def __init__(self,bbox,resolution):\n self.bbox = bbox\n self.resolution = resolution\n self.size=None\n \n def run(self):\n our_bbox = list(np.round(self.bbox,2))\n our_bbox = BBox(bbox=our_bbox, crs=CRS.WGS84)\n self.size = bbox_to_dimensions(our_bbox, resolution=self.resolution)\n return self.size"},"repo_name":{"kind":"string","value":"VaclavLamich/Cloud-Detection"},"sub_path":{"kind":"string","value":"resolution.py"},"file_name":{"kind":"string","value":"resolution.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":454,"string":"454"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"numpy.round","line_number":13,"usage_type":"call"},{"api_name":"sentinelhub.BBox","line_number":14,"usage_type":"call"},{"api_name":"sentinelhub.CRS.WGS84","line_number":14,"usage_type":"attribute"},{"api_name":"sentinelhub.CRS","line_number":14,"usage_type":"name"},{"api_name":"sentinelhub.bbox_to_dimensions","line_number":15,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"numpy.round\",\n \"line_number\": 13,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sentinelhub.BBox\",\n \"line_number\": 14,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"sentinelhub.CRS.WGS84\",\n \"line_number\": 14,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"sentinelhub.CRS\",\n \"line_number\": 14,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"sentinelhub.bbox_to_dimensions\",\n \"line_number\": 15,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":198,"cells":{"seq_id":{"kind":"string","value":"3647213738"},"text":{"kind":"string","value":"import heapq\nimport numpy as np\nimport itertools\n \nclass PQueue:\n def __init__(self):\n self.pq = [] # list of entries arranged in a heap\n self.entry_finder = {} # mapping of tasks to entries\n self.REMOVED = '' # placeholder for a removed task\n self.counter = itertools.count() # unique sequence count\n\n def add_task(self, task, priority=0):\n 'Add a new task or update the priority of an existing task'\n add_to_q = True\n if task in self.entry_finder:\n add_to_q = self.remove_task_if_lower_priority(task, priority)\n if add_to_q:\n count = next(self.counter)\n entry = [priority, count, task]\n self.entry_finder[task] = entry\n heapq.heappush(self.pq, entry)\n\n def remove_task_if_lower_priority(self, task, priority):\n 'Mark an existing task as self.REMOVED. Raise KeyError if not found.'\n entry = self.entry_finder[task]\n if entry[0] > priority:\n del self.entry_finder[task]\n entry[-1] = self.REMOVED\n return True\n else:\n return False\n\n def pop_task(self):\n 'Remove and return the lowest priority task. Raise KeyError if empty.'\n while self.pq:\n priority, count, task = heapq.heappop(self.pq)\n if task is not self.REMOVED:\n #print(task)\n #print(self.entry_finder)\n del self.entry_finder[task]\n return task\n raise KeyError('pop from an empty priority queue')\n\n def empty(self):\n return len(self.entry_finder) == 0\n\n def qsize(self):\n return len(self.entry_finder)\n\ndef test():\n q = PQueue()\n q.add_task((tuple(np.array([1,2,3])),1),1)\n q.add_task((tuple(np.array([4,5,6])),1),0)\n q.add_task((tuple(np.array([1,2,3])),1),-1)\n print(q.pop_task())\n print(q.pop_task())\n q.add_task((tuple(np.array([1,2,3])),1),0.5)\n\n print(q.pop_task())\n"},"repo_name":{"kind":"string","value":"joedlcolvin/Tugboats"},"sub_path":{"kind":"string","value":"p_queue.py"},"file_name":{"kind":"string","value":"p_queue.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":2032,"string":"2,032"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"itertools.count","line_number":10,"usage_type":"call"},{"api_name":"heapq.heappush","line_number":21,"usage_type":"call"},{"api_name":"heapq.heappop","line_number":36,"usage_type":"call"},{"api_name":"numpy.array","line_number":52,"usage_type":"call"},{"api_name":"numpy.array","line_number":53,"usage_type":"call"},{"api_name":"numpy.array","line_number":54,"usage_type":"call"},{"api_name":"numpy.array","line_number":57,"usage_type":"call"}],"string":"[\n {\n \"api_name\": \"itertools.count\",\n \"line_number\": 10,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"heapq.heappush\",\n \"line_number\": 21,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"heapq.heappop\",\n \"line_number\": 36,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 52,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 53,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 54,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"numpy.array\",\n \"line_number\": 57,\n \"usage_type\": \"call\"\n }\n]"}}},{"rowIdx":199,"cells":{"seq_id":{"kind":"string","value":"27462814126"},"text":{"kind":"string","value":"from app import app\nfrom app import db\nfrom app.models import booking\nfrom flask import jsonify, request\n\n@app.route('/get_booking', methods=['GET'])\ndef get_booking():\n date = request.args.get('date')\n idTable = request.args.get('idTable')\n\n phone = ['','','','','','','','']\n\n users = booking.query.all()\n for u in users:\n if u.date == date and u.table == int(idTable):\n for h in range(8):\n if (u.hour_start <= 12+h) and (12+h <= u.hour_end):\n phone[h] = u.phone\n\n return jsonify({\n \"schedule\":{\n \"table_id\": idTable,\n \"date\": date,\n \"hours\":[\n {\n \"hour\": \"12:00\",\n \"customerPhone\": phone[0]\n },\n {\n \"hour\": \"13:00\",\n \"customerPhone\": phone[1]\n },\n {\n \"hour\": \"14:00\",\n \"customerPhone\": phone[2]\n },\n {\n \"hour\": \"15:00\",\n \"customerPhone\": phone[3]\n },\n {\n \"hour\": \"16:00\",\n \"customerPhone\": phone[4]\n },\n {\n \"hour\": \"17:00\",\n \"customerPhone\": phone[5]\n },\n {\n \"hour\": \"18:00\",\n \"customerPhone\": phone[6]\n },\n {\n \"hour\": \"19:00\",\n \"customerPhone\": phone[7]\n }\n ]\n }\n })\n\n@app.route('/post_new_booking', methods=['POST'])\ndef post_new_booking():\n date = request.json['date']\n table = request.json['table_id']\n name = request.json['name']\n comment = request.json['comment']\n phone = request.json['phone']\n hours_start = request.json['hours_start']\n hours_end = request.json['hours_end']\n\n u = booking(table=table, name=name, phone=phone, info=comment, date=date, hour_start=hours_start, hour_end=hours_end)\n db.session.add(u)\n db.session.commit()\n\n return jsonify({\"status\": \"OK\"})"},"repo_name":{"kind":"string","value":"SevaSob/Na-rogah"},"sub_path":{"kind":"string","value":"routes.py"},"file_name":{"kind":"string","value":"routes.py"},"file_ext":{"kind":"string","value":"py"},"file_size_in_byte":{"kind":"number","value":2216,"string":"2,216"},"program_lang":{"kind":"string","value":"python"},"lang":{"kind":"string","value":"en"},"doc_type":{"kind":"string","value":"code"},"stars":{"kind":"number","value":0,"string":"0"},"dataset":{"kind":"string","value":"github-code"},"pt":{"kind":"string","value":"6"},"api":{"kind":"list like","value":[{"api_name":"flask.request.args.get","line_number":8,"usage_type":"call"},{"api_name":"flask.request.args","line_number":8,"usage_type":"attribute"},{"api_name":"flask.request","line_number":8,"usage_type":"name"},{"api_name":"flask.request.args.get","line_number":9,"usage_type":"call"},{"api_name":"flask.request.args","line_number":9,"usage_type":"attribute"},{"api_name":"flask.request","line_number":9,"usage_type":"name"},{"api_name":"app.models.booking.query.all","line_number":13,"usage_type":"call"},{"api_name":"app.models.booking.query","line_number":13,"usage_type":"attribute"},{"api_name":"app.models.booking","line_number":13,"usage_type":"name"},{"api_name":"flask.jsonify","line_number":20,"usage_type":"call"},{"api_name":"app.app.route","line_number":6,"usage_type":"call"},{"api_name":"app.app","line_number":6,"usage_type":"name"},{"api_name":"flask.request.json","line_number":63,"usage_type":"attribute"},{"api_name":"flask.request","line_number":63,"usage_type":"name"},{"api_name":"flask.request.json","line_number":64,"usage_type":"attribute"},{"api_name":"flask.request","line_number":64,"usage_type":"name"},{"api_name":"flask.request.json","line_number":65,"usage_type":"attribute"},{"api_name":"flask.request","line_number":65,"usage_type":"name"},{"api_name":"flask.request.json","line_number":66,"usage_type":"attribute"},{"api_name":"flask.request","line_number":66,"usage_type":"name"},{"api_name":"flask.request.json","line_number":67,"usage_type":"attribute"},{"api_name":"flask.request","line_number":67,"usage_type":"name"},{"api_name":"flask.request.json","line_number":68,"usage_type":"attribute"},{"api_name":"flask.request","line_number":68,"usage_type":"name"},{"api_name":"flask.request.json","line_number":69,"usage_type":"attribute"},{"api_name":"flask.request","line_number":69,"usage_type":"name"},{"api_name":"app.models.booking","line_number":71,"usage_type":"call"},{"api_name":"app.db.session.add","line_number":72,"usage_type":"call"},{"api_name":"app.db.session","line_number":72,"usage_type":"attribute"},{"api_name":"app.db","line_number":72,"usage_type":"name"},{"api_name":"app.db.session.commit","line_number":73,"usage_type":"call"},{"api_name":"app.db.session","line_number":73,"usage_type":"attribute"},{"api_name":"app.db","line_number":73,"usage_type":"name"},{"api_name":"flask.jsonify","line_number":75,"usage_type":"call"},{"api_name":"app.app.route","line_number":61,"usage_type":"call"},{"api_name":"app.app","line_number":61,"usage_type":"name"}],"string":"[\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 8,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 8,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 8,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.request.args.get\",\n \"line_number\": 9,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"flask.request.args\",\n \"line_number\": 9,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"flask.request\",\n \"line_number\": 9,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"app.models.booking.query.all\",\n \"line_number\": 13,\n \"usage_type\": \"call\"\n },\n {\n \"api_name\": \"app.models.booking.query\",\n \"line_number\": 13,\n \"usage_type\": \"attribute\"\n },\n {\n \"api_name\": \"app.models.booking\",\n \"line_number\": 13,\n \"usage_type\": \"name\"\n },\n {\n \"api_name\": \"flask.jsonify\",\n \"line_number\": 20,\n \"usage_type\": 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string
text
string
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string
sub_path
string
file_name
string
file_ext
string
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api
list
75136417147
import random from tgalice.cascade import Pr from cascade import csc, Turn from datetime import datetime, timedelta from uuid import uuid4 from scenarios.exercising import EXERCISES, Exercise def is_morning_show(turn: Turn) -> bool: if not turn.ctx.yandex or not turn.ctx.yandex.request: return False r = turn.ctx.yandex.request if r.type != 'Show.Pull': return False return r.show_type == 'MORNING' @csc.add_handler(priority=Pr.CRITICAL, checker=is_morning_show) def morning_show(turn: Turn): ex: Exercise = random.choice(list(EXERCISES.values())) turn.response_text = f'А теперь - упражнение из навыка "Шпагат за месяц".\n{ex.text}' now = datetime.utcnow() turn.show_item_meta = dict( content_id=str(uuid4()), title='Упражнение на растяжку', # title_tts='Упражнение на растяжку', publication_date=str(now).replace(' ', 'T') + 'Z', # expiration_date=str(now + timedelta(days=7)) + 'Z', )
avidale/alice-stretching
scenarios/show.py
show.py
py
1,066
python
en
code
1
github-code
6
[ { "api_name": "cascade.Turn", "line_number": 11, "usage_type": "name" }, { "api_name": "cascade.Turn", "line_number": 21, "usage_type": "name" }, { "api_name": "scenarios.exercising.Exercise", "line_number": 22, "usage_type": "name" }, { "api_name": "random.choice", "line_number": 22, "usage_type": "call" }, { "api_name": "scenarios.exercising.EXERCISES.values", "line_number": 22, "usage_type": "call" }, { "api_name": "scenarios.exercising.EXERCISES", "line_number": 22, "usage_type": "name" }, { "api_name": "datetime.datetime.utcnow", "line_number": 25, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 25, "usage_type": "name" }, { "api_name": "uuid.uuid4", "line_number": 27, "usage_type": "call" }, { "api_name": "cascade.csc.add_handler", "line_number": 20, "usage_type": "call" }, { "api_name": "cascade.csc", "line_number": 20, "usage_type": "name" }, { "api_name": "tgalice.cascade.Pr.CRITICAL", "line_number": 20, "usage_type": "attribute" }, { "api_name": "tgalice.cascade.Pr", "line_number": 20, "usage_type": "name" } ]
73894506107
#!/usr/bin/env python # -*- coding:utf-8 -*- from appium import webdriver from selenium.webdriver.support.wait import WebDriverWait import logging, time, os class BaseView: '''二次封装''' def __init__(self, driver: webdriver.Remote): self.driver = driver self.timeout = 2 self.poll_frequency = 0.5 self.x = self.driver.get_window_size()['width'] self.y = self.driver.get_window_size()['height'] def findElement(self, locator): if not isinstance(locator, tuple): logging.error('locator参数类型错误,必须传元组类型:loc=("id","value")') else: logging.info('正在定位元素信息,定位方式——>%s,value值——>%s' % (locator[0], locator[1])) element = WebDriverWait(self.driver, self.timeout, self.poll_frequency).until( lambda x: x.find_element(*locator)) return element def findElements(self, locator): try: if not isinstance(locator, tuple): logging.error('locator参数类型错误,必须传元组类型:loc=("id","value")') else: logging.info('正在定位元素信息,定位方式——>%s,value值——>%s' % (locator[0], locator[1])) elements = WebDriverWait(self.driver, self.timeout, self.poll_frequency).until( lambda x: x.find_elements(*locator)) return elements except: return [] def sendKeys(self, locator, text): element = self.findElement(locator) element.send_keys(text) def click(self, locator): element = self.findElement(locator) element.click() def clear(self, locator): element = self.findElement(locator) element.clear() def swipe_up(self): start_x = self.x * 0.5 start_y = self.y * 0.8 end_x = self.x * 0.5 end_y = self.y * 0.2 self.driver.swipe(start_x, start_y, end_x, end_y) logging.info('==========swipe up==========') def swipe_down(self): start_x = self.x * 0.5 start_y = self.y * 0.2 end_x = self.x * 0.5 end_y = self.y * 0.8 self.driver.swipe(start_x, start_y, end_x, end_y) logging.info('==========swipe down==========') def swipe_left(self): start_x = self.x * 0.8 start_y = self.y * 0.5 end_x = self.x * 0.2 end_y = self.y * 0.5 self.driver.swipe(start_x, start_y, end_x, end_y) logging.info('==========swipe left==========') def swipe_right(self): start_x = self.x * 0.2 start_y = self.y * 0.5 end_x = self.x * 0.8 end_y = self.y * 0.5 self.driver.swipe(start_x, start_y, end_x, end_y) logging.info('==========swipe right==========') def getScreenShot(self, module): # module为模块名称,即保存文件名称,可自定义 now = time.strftime('%Y-%m-%d %H_%M_%S') image_dir = os.path.abspath('../screenshots/%s_%s.png' % (module, now)) self.driver.get_screenshot_as_file(image_dir) logging.info('%s已保存截图,保存地址为:%s' % (module, image_dir)) return image_dir if __name__ == '__main__': from common.desired_caps import appium_desired driver = appium_desired() app = BaseView(driver) time.sleep(2) print(app.getScreenShot('启动页'))
inttcc/MyPro
workcoming/baseView/base.py
base.py
py
3,466
python
en
code
0
github-code
6
[ { "api_name": "appium.webdriver.Remote", "line_number": 11, "usage_type": "attribute" }, { "api_name": "appium.webdriver", "line_number": 11, "usage_type": "name" }, { "api_name": "logging.error", "line_number": 20, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 22, "usage_type": "call" }, { "api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 23, "usage_type": "call" }, { "api_name": "logging.error", "line_number": 30, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 32, "usage_type": "call" }, { "api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 33, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 57, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 65, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 73, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 81, "usage_type": "call" }, { "api_name": "time.strftime", "line_number": 84, "usage_type": "call" }, { "api_name": "os.path.abspath", "line_number": 85, "usage_type": "call" }, { "api_name": "os.path", "line_number": 85, "usage_type": "attribute" }, { "api_name": "logging.info", "line_number": 87, "usage_type": "call" }, { "api_name": "common.desired_caps.appium_desired", "line_number": 94, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 96, "usage_type": "call" } ]
39262377956
from collections import namedtuple from django.conf import settings from elasticsearch import Elasticsearch from elasticsearch.helpers import scan from eums.elasticsearch.delete_records import DeleteRecords from eums.elasticsearch.mappings import setup_mappings from eums.elasticsearch.sync_info import SyncInfo from eums.models import DistributionPlanNode as DeliveryNode, Consignee, Programme, OrderItem, Item, SalesOrder, \ PurchaseOrder, ReleaseOrder, Question, TextAnswer, MultipleChoiceAnswer, NumericAnswer, Option, Run ES_SETTINGS = settings.ELASTIC_SEARCH def list_nodes_to_update(): last_sync = SyncInfo.last_successful_sync() nodes_to_update = _find_nodes_to_update(last_sync) new_nodes = _find_new_nodes(last_sync) return list(nodes_to_update) + list(new_nodes) def list_nodes_to_delete(): delete_records = DeleteRecords.objects.first() return delete_records.nodes_to_delete if delete_records else [] def _find_new_nodes(last_sync): if not last_sync: setup_mappings() return DeliveryNode.objects.all() last_sync_time = last_sync.start_time return DeliveryNode.objects.filter(created__gte=last_sync_time) def _find_nodes_to_update(last_sync): if last_sync: changed_nodes = DeliveryNode.objects.filter(modified__gte=last_sync.start_time) es = Elasticsearch([ES_SETTINGS.HOST]) match_terms = _build_match_terms(last_sync) if not match_terms: return changed_nodes query = { "fields": [], "filter": { "bool": { "should": match_terms } } } scan_results = scan(es, query=query, index=ES_SETTINGS.INDEX, doc_type=ES_SETTINGS.NODE_TYPE) node_ids = [hit['_id'] for hit in list(scan_results)] changed_node_ids = list(changed_nodes.values_list('id', flat=True)) return DeliveryNode.objects.filter(pk__in=node_ids + changed_node_ids) return [] def _build_match_terms(last_sync): last_sync_time = last_sync.start_time consignee_ids = _find_changes_for_model(Consignee, last_sync_time) programme_ids = _find_changes_for_model(Programme, last_sync_time) order_item_ids = _find_changes_for_model(OrderItem, last_sync_time) item_ids = _find_changes_for_model(Item, last_sync_time) sales_order_ids = _find_changes_for_model(SalesOrder, last_sync_time) purchase_order_ids = _find_changes_for_model(PurchaseOrder, last_sync_time) release_order_ids = _find_changes_for_model(ReleaseOrder, last_sync_time) question_ids = _find_changes_for_model(Question, last_sync_time) text_answer_ids = _find_changes_for_model(TextAnswer, last_sync_time) multiple_choice_answer_ids = _find_changes_for_model(MultipleChoiceAnswer, last_sync_time) numeric_answer_ids = _find_changes_for_model(NumericAnswer, last_sync_time) option_ids = _find_changes_for_model(Option, last_sync_time) run_ids = _find_changes_for_model(Run, last_sync_time) match_term = namedtuple('MatchTerm', ['key', 'value']) match_terms = [ match_term("consignee.id", consignee_ids), match_term("ip.id", consignee_ids), match_term("programme.id", programme_ids), match_term("order_item.id", order_item_ids), match_term("order_item.item.id", item_ids), match_term("order_item.order.sales_order.id", sales_order_ids), match_term("order_item.order.id", purchase_order_ids + release_order_ids), match_term("responses.question.id", question_ids), match_term("responses.id", text_answer_ids + multiple_choice_answer_ids + numeric_answer_ids), match_term("responses.value_id", option_ids), match_term("responses.run.id", run_ids), match_term("id", _find_nodes_affected_by_dependency_deletion()), ] non_empty_match_terms = filter(lambda term: len(term.value), match_terms) if non_empty_match_terms: formatted_match_terms = map(lambda term: {'term': {term.key: term.value}}, non_empty_match_terms) return formatted_match_terms return None def _find_changes_for_model(model, last_sync_time): return list(model.objects.filter(modified__gte=last_sync_time).values_list('id', flat=True)) def _find_nodes_affected_by_dependency_deletion(): delete_records = DeleteRecords.objects.first() return delete_records.nodes_with_deleted_dependencies or [] if delete_records else []
unicefuganda/eums
eums/elasticsearch/sync_data_generators.py
sync_data_generators.py
py
4,471
python
en
code
9
github-code
6
[ { "api_name": "django.conf.settings.ELASTIC_SEARCH", "line_number": 12, "usage_type": "attribute" }, { "api_name": "django.conf.settings", "line_number": 12, "usage_type": "name" }, { "api_name": "eums.elasticsearch.sync_info.SyncInfo.last_successful_sync", "line_number": 16, "usage_type": "call" }, { "api_name": "eums.elasticsearch.sync_info.SyncInfo", "line_number": 16, "usage_type": "name" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords.objects.first", "line_number": 23, "usage_type": "call" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords.objects", "line_number": 23, "usage_type": "attribute" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords", "line_number": 23, "usage_type": "name" }, { "api_name": "eums.elasticsearch.mappings.setup_mappings", "line_number": 29, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects.all", "line_number": 30, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects", "line_number": 30, "usage_type": "attribute" }, { "api_name": "eums.models.DistributionPlanNode", "line_number": 30, "usage_type": "name" }, { "api_name": "eums.models.DistributionPlanNode.objects.filter", "line_number": 32, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects", "line_number": 32, "usage_type": "attribute" }, { "api_name": "eums.models.DistributionPlanNode", "line_number": 32, "usage_type": "name" }, { "api_name": "eums.models.DistributionPlanNode.objects.filter", "line_number": 37, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects", "line_number": 37, "usage_type": "attribute" }, { "api_name": "eums.models.DistributionPlanNode", "line_number": 37, "usage_type": "name" }, { "api_name": "elasticsearch.Elasticsearch", "line_number": 39, "usage_type": "call" }, { "api_name": "elasticsearch.helpers.scan", "line_number": 53, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects.filter", "line_number": 56, "usage_type": "call" }, { "api_name": "eums.models.DistributionPlanNode.objects", "line_number": 56, "usage_type": "attribute" }, { "api_name": "eums.models.DistributionPlanNode", "line_number": 56, "usage_type": "name" }, { "api_name": "eums.models.Consignee", "line_number": 62, "usage_type": "argument" }, { "api_name": "eums.models.Programme", "line_number": 63, "usage_type": "argument" }, { "api_name": "eums.models.OrderItem", "line_number": 64, "usage_type": "argument" }, { "api_name": "eums.models.Item", "line_number": 65, "usage_type": "argument" }, { "api_name": "eums.models.SalesOrder", "line_number": 66, "usage_type": "argument" }, { "api_name": "eums.models.PurchaseOrder", "line_number": 67, "usage_type": "argument" }, { "api_name": "eums.models.ReleaseOrder", "line_number": 68, "usage_type": "argument" }, { "api_name": "eums.models.Question", "line_number": 69, "usage_type": "argument" }, { "api_name": "eums.models.TextAnswer", "line_number": 70, "usage_type": "argument" }, { "api_name": "eums.models.MultipleChoiceAnswer", "line_number": 71, "usage_type": "argument" }, { "api_name": "eums.models.NumericAnswer", "line_number": 72, "usage_type": "argument" }, { "api_name": "eums.models.Option", "line_number": 73, "usage_type": "argument" }, { "api_name": "eums.models.Run", "line_number": 74, "usage_type": "argument" }, { "api_name": "collections.namedtuple", "line_number": 76, "usage_type": "call" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords.objects.first", "line_number": 104, "usage_type": "call" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords.objects", "line_number": 104, "usage_type": "attribute" }, { "api_name": "eums.elasticsearch.delete_records.DeleteRecords", "line_number": 104, "usage_type": "name" } ]
27483656200
import logging from flask import abort, request, g, Response, make_response, jsonify, current_app from flask_restplus import Namespace, Resource, fields, marshal_with from battle.db.dbops import dbops from battle.db.models import Battle log = logging.getLogger(__name__) posts_api = Namespace('postmeta', description='post information about bigbang') model = posts_api.model('Model', { "Developer_Issues" : fields.Integer, "Issues_Resolved" : fields.Integer, "Issues_Pending": fields.Integer, "Component_Issues": fields.Integer, "Component_Failures": fields.List(fields.String), "Total_Tickets" : fields.List(fields.String), "Jiras" : fields.List(fields.String), "Faq_Updated": fields.Integer, 'date_updated': fields.DateTime() }) #ns = api.namespace('post', description='Operations related to data post') @posts_api.route('') @posts_api.response("200", "updated successfully") class posts(Resource): @posts_api.expect(model) def post(self): """ Update metadata of bigbang. """ data = request.get_json() dbops.post_meta(data) return None, 204 def get(self): data = dbops.get_meta() return data
mthak/classmojo
battle/api/post.py
post.py
py
1,214
python
en
code
0
github-code
6
[ { "api_name": "logging.getLogger", "line_number": 7, "usage_type": "call" }, { "api_name": "flask_restplus.Namespace", "line_number": 9, "usage_type": "call" }, { "api_name": "flask_restplus.fields.Integer", "line_number": 11, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields", "line_number": 11, "usage_type": "name" }, { "api_name": "flask_restplus.fields.Integer", "line_number": 12, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields", "line_number": 12, "usage_type": "name" }, { "api_name": "flask_restplus.fields.Integer", "line_number": 13, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields", "line_number": 13, "usage_type": "name" }, { "api_name": "flask_restplus.fields.Integer", "line_number": 14, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields", "line_number": 14, "usage_type": "name" }, { "api_name": "flask_restplus.fields.List", "line_number": 15, "usage_type": "call" }, { "api_name": "flask_restplus.fields", "line_number": 15, "usage_type": "name" }, { "api_name": "flask_restplus.fields.String", "line_number": 15, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields.List", "line_number": 16, "usage_type": "call" }, { "api_name": "flask_restplus.fields", "line_number": 16, "usage_type": "name" }, { "api_name": "flask_restplus.fields.String", "line_number": 16, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields.List", "line_number": 17, "usage_type": "call" }, { "api_name": "flask_restplus.fields", "line_number": 17, "usage_type": "name" }, { "api_name": "flask_restplus.fields.String", "line_number": 17, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields.Integer", "line_number": 18, "usage_type": "attribute" }, { "api_name": "flask_restplus.fields", "line_number": 18, "usage_type": "name" }, { "api_name": "flask_restplus.fields.DateTime", "line_number": 19, "usage_type": "call" }, { "api_name": "flask_restplus.fields", "line_number": 19, "usage_type": "name" }, { "api_name": "flask_restplus.Resource", "line_number": 27, "usage_type": "name" }, { "api_name": "flask.request.get_json", "line_number": 35, "usage_type": "call" }, { "api_name": "flask.request", "line_number": 35, "usage_type": "name" }, { "api_name": "battle.db.dbops.dbops.post_meta", "line_number": 36, "usage_type": "call" }, { "api_name": "battle.db.dbops.dbops", "line_number": 36, "usage_type": "name" }, { "api_name": "battle.db.dbops.dbops.get_meta", "line_number": 41, "usage_type": "call" }, { "api_name": "battle.db.dbops.dbops", "line_number": 41, "usage_type": "name" } ]
73354334268
import argparse import json import logging import os import random import math from pprint import pprint logger = logging.getLogger() logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)-7s - [%(funcName)s] %(message)s') # uncomment for submission # logger.disabled = True ACTIONS = { -1: 'DoNothing', 1: 'MoveUp', 2: 'MoveLeft', 3: 'MoveRight', 4: 'MoveDown', 5: 'PlaceBomb', 6: 'TriggerBomb', } def main(player_key, output_path): logger.info('Player key: {}'.format(player_key)) logger.info('Output path: {}'.format(output_path)) with open(os.path.join(output_path, 'state.json'), 'r') as f: state = json.load(f) # logger.info('State: {}'.format(state)) # Constants for json path PLAYER_ENTITY = "RegisteredPlayerEntities" GAME_BLOCKS = "GameBlocks" # Constants for data about map MAP_HEIGHT = state["MapHeight"] MAP_WIDTH = state["MapWidth"] CURRENT_ROUND = state["CurrentRound"] # Constants for entity type WALL = "Domain.Entities.IndestructibleWallEntity, Domain" OBSTACLE = "Domain.Entities.DestructibleWallEntity, Domain" PLAYER = "Domain.Entities.PlayerEntity, Domain" SUPER_POWER_UP = "Domain.Entities.PowerUps.SuperPowerUp, Domain" POWER_UP_BOMBBAG = "Domain.Entities.PowerUps.BombBagPowerUpEntity, Domain" POWER_UP_BOMBRADIUS = "Domain.Entities.PowerUps.BombRaduisPowerUpEntity, Domain" # emang typo dari sananya kok :( TOTAL_PLAYER = len(state[PLAYER_ENTITY]) # Class queue class Queue: def __init__(self): self.items = [] def isEmpty(self): return self.items == [] def enqueue(self, item): self.items.insert(0,item) def dequeue(self): return self.items.pop() def size(self): return len(self.items) # Functions and Procedures def Player_Name(index): # Getter untuk nama bot return(state[PLAYER_ENTITY][index]["Name"]) def Player_Index(key): # Getter untuk indeks bot i = 0 while ((i < TOTAL_PLAYER) and (Player_Key(i) != key)): i += 1 return (i) def Player_Key(index): # Getter untuk key bot return (state[PLAYER_ENTITY][index]["Key"]) def Player_Points(index): # Getter untuk jumlah point yang dimiliki bot return (state[PLAYER_ENTITY][index]["Points"]) def Player_Killed(index): # Getter untuk status nyawa musuh return(state[PLAYER_ENTITY][index]["Killed"]) def Player_BombBag(index): # Getter untuk jumlah bomb bag yang dimiliki bot return(state[PLAYER_ENTITY][index]["BombBag"]) def Player_BombRadius(index): # Getter untuk blast radius yang dimiliki bot return(state[PLAYER_ENTITY][index]["BombRadius"]) def Player_X(index): # Getter untuk absis bot return(state[PLAYER_ENTITY][index]["Location"]["X"]) def Player_Y(index): # Getter untuk ordinat bot return(state[PLAYER_ENTITY][index]["Location"]["Y"]) def Map_Entity(x, y): # Getter untuk entitas yang berada pada petak (x, y) if (state[GAME_BLOCKS][x-1][y-1]["Entity"] == None): return(None) elif (state[GAME_BLOCKS][x-1][y-1]["Entity"]["$type"] == PLAYER): return(state[GAME_BLOCKS][x-1][y-1]["Entity"]["Key"]) else: return(state[GAME_BLOCKS][x-1][y-1]["Entity"]["$type"]) def Map_Bomb(x, y): # Bernilai true apabila ada bom pada petak (x, y) if (state[GAME_BLOCKS][x-1][y-1]["Bomb"] == None): return(False) else: return(True) def Map_Bomb_Key(x, y): # Getter untuk pemilik bom pada petak (x, y) return(state[GAME_BLOCKS][x-1][y-1]["Bomb"]["Owner"]["Key"]) def Map_Bomb_Radius(x, y): # Getter untuk blast radius bom yang terletak pada petak (x, y) return(state[GAME_BLOCKS][x-1][y-1]["Bomb"]["BombRadius"]) def Map_Bomb_Timer(x, y): # Getter untuk timer bom yang terletak pada petak (x, y) return(state[GAME_BLOCKS][x-1][y-1]["Bomb"]["BombTimer"]) def Map_PowerUp(x, y): # Getter untuk power up pada petak (x, y) if (state[GAME_BLOCKS][x-1][y-1]["PowerUp"] == None): return(None) else: return(state[GAME_BLOCKS][x-1][y-1]["PowerUp"]["$type"]) def Map_Exploding(x, y): # Getter untuk status peledakan petak (x, y) return(state[GAME_BLOCKS][x-1][y-1]["Exploding"]) def HasPlacedBomb(): # Memberikan nilai true apabila bot kita telah meletakkan bom dan timernya > 2 found = False y = 0 while ((y < MAP_HEIGHT) and (not found)): x = 0 while ((x < MAP_WIDTH) and (not found)): if ((Map_Bomb(x, y)) and (Map_Bomb_Key(x, y) == player_key) and (Map_Bomb_Timer(x, y) > 2)): found = True x += 1 y += 1 return (found) def InDanger (x, y): # Memberi nilai true apabila bot kita berada dalam zona bahaya # Zona bahaya: dapat terkena ledakan bom danger = False # Left check x_left = x while ((x_left >= 0) and (Map_Entity(x_left, y) != WALL) and (Map_Entity(x_left, y) != OBSTACLE) and (not danger)): if (Map_Bomb(x_left, y)) and (Map_Bomb_Radius(x_left, y) >= abs(x_left - x)): danger = True else: x_left -= 1 # Right check x_right = x + 1 while ((x_right <= MAP_WIDTH) and (Map_Entity(x_right, y) != WALL) and (Map_Entity(x_right, y) != OBSTACLE) and (not danger)): if (Map_Bomb(x_right, y)) and (Map_Bomb_Radius(x_right, y) >= abs(x_right - x)): danger = True else: x_right += 1 # Up check y_up = y - 1 while ((y_up >= 0) and (Map_Entity(x, y_up) != WALL) and (Map_Entity(x, y_up) != OBSTACLE) and (not danger)): if (Map_Bomb(x, y_up)) and (Map_Bomb_Radius(x, y_up) >= abs(y_up - y)): danger = True else: y_up -= 1 # Down check y_down = y + 1 while ((y_down <= MAP_HEIGHT) and (Map_Entity(x, y_down) != WALL) and (Map_Entity(x, y_down) != OBSTACLE) and (not danger)): if (Map_Bomb(x, y_down)) and (Map_Bomb_Radius(x, y_down) >= abs(y_down - y)): danger = True else: y_down += 1 # Return return (danger) def DangerCounter(x, y): # Mengembalikan timer bomb yang paling kecil yang dapat membahayakan bila bot berada di posisi x, y most_urgent_timer = 99 # Left check x_left = x while ((x_left >= 0) and (Map_Entity(x_left, y) != WALL) and (Map_Entity(x_left, y) != OBSTACLE)): if ((Map_Bomb(x_left, y)) and (Map_Bomb_Radius(x_left, y) >= abs(x_left - x)) and (most_urgent_timer > Map_Bomb_Timer(x_left, y))): most_urgent_timer = Map_Bomb_Timer(x_left, y) x_left -= 1 # Right check x_right = x + 1 while ((x_right <= MAP_WIDTH) and (Map_Entity(x_right, y) != WALL) and (Map_Entity(x_right, y) != OBSTACLE)): if ((Map_Bomb(x_right, y)) and (Map_Bomb_Radius(x_right, y) >= abs(x_right - x)) and (most_urgent_timer > Map_Bomb_Timer(x_right, y))): most_urgent_timer = Map_Bomb_Timer(x_right, y) x_right += 1 # Up check y_up = y - 1 while ((y_up >= 0) and (Map_Entity(x, y_up) != WALL) and (Map_Entity(x, y_up) != OBSTACLE)): if ((Map_Bomb(x, y_up)) and (Map_Bomb_Radius(x, y_up) >= abs(y_up - y)) and (most_urgent_timer > Map_Bomb_Timer(x, y_up))): most_urgent_timer = Map_Bomb_Timer(x, y_up) y_up -= 1 # Down check y_down = y + 1 while ((y_down <= MAP_HEIGHT) and (Map_Entity(x, y_down) != WALL) and (Map_Entity(x, y_down) != OBSTACLE)): if ((Map_Bomb(x, y_down)) and (Map_Bomb_Radius(x, y_down) >= abs(y_down - y)) and (most_urgent_timer > Map_Bomb_Timer(x, y_down))): most_urgent_timer = Map_Bomb_Timer(x, y_down) y_down += 1 # Return return(most_urgent_timer) def Distance (x1, y1, x2, y2): # Mengembalikan banyak petak yang harus dilalui apabila ingin berpindah dari (x1, y1) ke (x2, y2) return (abs(x1 - x2) + abs(y1 - y2)) def PythagorasPow (x1, y1, x2, y2): # Mengembalikan kuadrat jarak Euclidean dari (x1, y1) ke (x2, y2) return ((x1-x2)**2 + (y1-y2)**2) def IsPowerUpInRange (x, y, radius): # Mengembalikan nilai true apabila terdapat power up dalam radius tertentu dari titik (x, y) # Mencegah x keluar batas map x_end = x + radius if (x_end > MAP_WIDTH): x_end = MAP_WIDTH # Mencegah y keluar batas map y_start = y - radius if (y_start < 1): y_start = 1 y_end = y + radius if (y_end > MAP_HEIGHT): y_end = MAP_HEIGHT found = False # Inisialisasi awal # Pencarian power up per ordinat while ((y_start <= y_end) and (not found)): # Mencegah x keluar batas map x_start = x - radius if (x_start < 1): x_start = 1 # Melakukan iterasi per absis while ((x_start <= x_end) and (not found)): if (Map_PowerUp(x_start, y_start) != None): found = True else: x_start += 1 y_start += 1 # Return return (found) def IsEnemyInRange (player_index,radius): # Mengembalikan indeks musuh (yang masih hidup) yang berada dalam radius tertentu dari bot # Bernilai -1 apabila tidak ada musuh yang berada dalam radius enemy_index = 0 found = False # Pencarian musuh dalam radius tertentu while ((enemy_index < TOTAL_PLAYER-1) and (not found)): if ((enemy_index != player_index) and (not Player_Killed(enemy_index)) and (radius >= Distance(Player_X(player_index), Player_Y(player_index), Player_X(enemy_index), Player_Y(enemy_index)))): found = True else: enemy_index += 1 if (found): return(enemy_index) else: return(-1) def SOS(index): # Menghasilkan aksi yang harus bot lakukan untuk melarikan diri dari zona bahaya goal = 0 X = Queue() # Queue yang digunakan untuk menyimpan absis (x) Y = Queue() # Queue yang digunakan untuk menyimpan ordinat (y) M = Queue() # Queue yang digunakan untuk menyimpan aksi (move) X.enqueue(Player_X(index)) # Insialisasi awal dengan absis bot saat ini Y.enqueue(Player_Y(index)) # Inisialisasi awal dengan ordinat bot saat ini M.enqueue([]) # Inisialisasi awal dengan list kosong # Melakukan iterasi selama queue absis tidak kosong dan belum menemukan jalan keluar while ((not X.isEmpty()) and (goal == 0)): i = X.dequeue() j = Y.dequeue() move = M.dequeue() valid = False # valid adalah penentu apakah jalan tersebut dapat dilalui atau tidak if ((Map_Entity(i,j) == None) or (Map_Entity(i,j) == player_key)): # Kosong (tidak ada halangan) if (Map_Bomb(i,j)): # Ada bom if ((Player_X(index) == i) and (Player_Y(index) == j)): # Posisi bom = posisi bot valid = True else: # Tidak ada bom valid = True count = DangerCounter(i,j)-len(move) # Menentukan apakah sempat melarikan diri dengan pergerakan tersebut if ((count == 0) or (count == 1)): valid = False if (valid): if (not InDanger(i,j)): goal = move[0] elif (len(move) < 10): temp = TargetPos(i,j,math.floor(MAP_WIDTH/2),1) if (temp == -1): temp = TargetPos(i,j,math.floor(MAP_WIDTH/2),2) x_target = GetTargetX(temp) y_target = GetTargetY(temp) dist = [] dist.append(Distance(i,j-1,x_target,y_target)) # Memasukkan jarak antar tetangga atas (i, j) ke koordinat target dist.append(Distance(i-1,j,x_target,y_target)) # Memasukkan jarak antar tetangga kiri (i, j) ke koordinat target dist.append(Distance(i+1,j,x_target,y_target)) # Memasukkan jarak antar tetangga kanan (i, j) ke koordinat target dist.append(Distance(i,j+1,x_target,y_target)) # Memasukkan jarak antar tetangga bawah (i, j) ke koordinat target X.enqueue(i) Y.enqueue(j) M.enqueue(move + [-1]) for q in range(0,4): shortest = 0 for w in range(1,4): if (dist[w] < dist[shortest]): shortest = w if (shortest == 0): X.enqueue(i) Y.enqueue(j-1) M.enqueue(move + [1]) elif (shortest == 1): X.enqueue(i-1) Y.enqueue(j) M.enqueue(move + [2]) elif (shortest == 2): X.enqueue(i+1) Y.enqueue(j) M.enqueue(move + [3]) elif (shortest == 3): X.enqueue(i) Y.enqueue(j+1) M.enqueue(move + [4]) dist[shortest] = 100000 #big number if (goal == 0): # Tidak ada jalan keluar return (-1) else: return(goal) def TargetPos(x,y,radius,search): # Terdiri dari 2 jenis search # Search 1: Mengembalikan nilai yang mengandung koordinat target # Search 2: Mengembalikan nilai yang mengandung indeks musuh x_end = x + radius # Menjaga agar x tidak keluar batas if (x_end > MAP_WIDTH): x_end = MAP_WIDTH y_start = y - radius # Menjaga agar y tidak keluar batas if (y_start < 1): y_start = 1 y_end = y + radius # Menjaga agar y tidak keluar batas if (y_end > MAP_HEIGHT): y_end = MAP_HEIGHT x_start = x - radius # Menjaga agar x tidak keluar batas if (x_start < 1): x_start = 1 # Insialisasi awal found_x = -1 found_y = -1 # Melakukan pencarian for i in range(x_start, x_end): for j in range(y_start, y_end): # Search 1 if (search == 1): if (Map_PowerUp(i, j) != None): if (found_x == -1): found_x = i found_y = j else: if (Distance(x,y,i,j) < Distance(x,y,found_x,found_y)): found_x = i found_y = j # Search 2 elif (search == 2): player_index = Player_Index(player_key) enemy_index = IsEnemyInRange(player_index,radius) if ((enemy_index != player_index) and (not Player_Killed(enemy_index)) and (i == Player_X(enemy_index)) and (j == Player_Y(enemy_index)) and (Distance(x, y, i, j) <= radius)): if (found_x == -1): found_x = i found_y = j else: if (Distance(x,y,i,j) < Distance(x,y,found_x,found_y)): found_x = i found_y = j if (found_x == -1): # Tidak ketemu return -1 else: if (search == 1): # Search 1 return (found_x*(10**(math.floor(math.log(MAP_HEIGHT,10))+1))+found_y) # Return value adalah koordinat target (data dimanipulasi) elif (search == 2): # Search 2 # return(enemy_index*(10**(2*math.floor(math.log(MAP_HEIGHT,10))+1))+found_x*(10**(math.floor(math.log(MAP_HEIGHT,10))+1))+found_y) # Return value adalah indeks musuh (data dimanipulasi) return(enemy_index) def GetEnemyIndex(val): # Mengekstrak indeks musuh dari manipulasi data yang telah dilakukan # return (math.floor(val/(10**(2*math.floor(math.log(MAP_HEIGHT,10)))))) return(val) def GetTargetX(val): # Mengekstrak absis target dari manipulasi data yang telah dilakukan return (math.floor(val/(10**(math.floor(math.log(MAP_HEIGHT,10))+1)))) def GetTargetY(val): # Mengekstrak ordinat target dari manipulasi data yang telah dilakukan return (val % (10**(math.floor(math.log(MAP_HEIGHT,10))+1))) def GoToTarget(x,y,radius,index,search): # Menghasilkan aksi yang harus dilakukan untuk bergerak mendekati target (Search 1) atau musuh (Search 2) # Menggunakan Greedy Best-First Search if (search == 1): # Search 1: mencari power up temp = TargetPos(x,y,radius,1) # Koordinat target berupa manipulasi data smin = 9999 # Insialisasi awal move = -1 # Inisialisasi awal # Perbandingan nilai heuristik (kuadrat pythagoras) dari keempat tetangganya if (Map_Entity(Player_X(index), Player_Y(index)-1) == None) and (not InDanger(Player_X(index), Player_Y(index)-1)): sup = PythagorasPow(GetTargetX(temp),GetTargetY(temp),x,y-1) # Atas if (smin > sup) : smin = sup move = 1 if (Map_Entity(Player_X(index)-1, Player_Y(index)) == None) and (not InDanger(Player_X(index)-1, Player_Y(index))): sleft = PythagorasPow(GetTargetX(temp),GetTargetY(temp),x-1,y) # Kiri if (smin > sleft) : smin = sleft move = 2 if (Map_Entity(Player_X(index)+1, Player_Y(index)) == None) and (not InDanger(Player_X(index)+1, Player_Y(index))): sright = PythagorasPow(GetTargetX(temp),GetTargetY(temp),x+1,y) # Kanan if (smin > sright) : smin = sright move = 3 if (Map_Entity(Player_X(index), Player_Y(index)+1) == None) and (not InDanger(Player_X(index), Player_Y(index)+1)): sdown = PythagorasPow(GetTargetX(temp),GetTargetY(temp),x,y+1) # Bawah if (smin > sdown) : smin = sdown move = 4 # Mengembalikan aksi terbaik yang didapatkan return move else: # Search 2 dan 3: mencari musuh if (search == 2): # Dalam radius tertentu temp = TargetPos(x,y,radius,2) # Indeks musuh berupa manipulasi data enemy_index = GetEnemyIndex(temp) # Inisialisasi dengan indeks musuh sesungguhnya else: # Mengincar musuh yang masih hidup di manapun mereka berada found = False searchingindex = 0 while (searchingindex <= TOTAL_PLAYER-1) and (not found): if (Player_Key(searchingindex) != player_key) and (not Player_Killed(searchingindex)): found = True enemy_index = searchingindex else: searchingindex += 1 # Apabila jarak musuh <= blast radius bom bot dan musuh masih hidup if ((not Player_Killed(enemy_index)) and Distance(Player_X(index),Player_Y(index),Player_X(enemy_index),Player_Y(enemy_index)),Player_BombRadius(index)): time_to_attack = False # Horizontal check if (Player_X(enemy_index) == Player_X(index)): # Left check x_left = x while ((x_left >= 0) and (Map_Entity(x_left, y) != WALL) and (Map_Entity(x_left, y) != OBSTACLE) and (not time_to_attack)): if (Map_Entity(x_left, y) == Player_Key(enemy_index)): time_to_attack = True else: x_left -= 1 # Right check x_right = x + 1 while ((x_right <= MAP_WIDTH) and (Map_Entity(x_right, y) != WALL) and (Map_Entity(x_right, y) != OBSTACLE) and (not time_to_attack)): if (Map_Entity(x_right, y) == Player_Key(enemy_index)): time_to_attack = True else: x_right += 1 # Vertical check elif (Player_Y(enemy_index) == Player_Y(index)): # Up check y_up = y - 1 while ((y_up >= 0) and (Map_Entity(x, y_up) != WALL) and (Map_Entity(x, y_up) != OBSTACLE) and (not time_to_attack)): if (Map_Entity(x, y_up) == Player_Key(enemy_index)): time_to_attack = True else: y_up -= 1 # Down check y_down = y + 1 while ((y_down <= MAP_HEIGHT) and (Map_Entity(x, y_down) != WALL) and (Map_Entity(x, y_down) != OBSTACLE) and (not time_to_attack)): if (Map_Entity(x, y_down) == Player_Key(enemy_index)): time_to_attack = True else: y_down += 1 # Ada kemungkinan dapat meledakkan musuh if (time_to_attack): return(5) else: # not time_to_attack smin = 9999 # Inisialisasi awal move = -1 # Inisialisasi awal # Perbandingan nilai heuristik (kuadrat pythagoras) dari keempat tetangganya if (Map_Entity(Player_X(index), Player_Y(index)-1) == None) and (not InDanger(Player_X(index), Player_Y(index)-1)): sup = PythagorasPow(Player_X(enemy_index),Player_Y(enemy_index),Player_X(index),Player_Y(index)-1) if (smin > sup) : smin = sup move = 1 if (Map_Entity(Player_X(index)-1, Player_Y(index)) == None) and (not InDanger(Player_X(index)-1, Player_Y(index))): sleft = PythagorasPow(Player_X(enemy_index),Player_Y(enemy_index),Player_X(index)-1,Player_Y(index)) if (smin > sleft) : smin = sleft move = 2 if (Map_Entity(Player_X(index)+1, Player_Y(index)) == None) and (not InDanger(Player_X(index)+1, Player_Y(index))): sright = PythagorasPow(Player_X(enemy_index),Player_Y(enemy_index),Player_X(index)+1,Player_Y(index)) if (smin > sright) : smin = sright move = 3 if (Map_Entity(Player_X(index), Player_Y(index)+1) == None) and (not InDanger(Player_X(index), Player_Y(index)+1)): sdown = PythagorasPow(Player_X(enemy_index),Player_Y(enemy_index),Player_X(index),Player_Y(index)+1) if (smin > sdown) : smin = sdown move = 4 # Mengembalikan aksi terbaik yang didapatkan return move def Choice(index): # Menentukan aksi yang dilakukan dengan berdasarkan urutan prioritas bot # Prioritas pertama: kabur dari zona bahaya if (InDanger(Player_X(index), Player_Y(index))): return(SOS(index)) # Prioritas kedua: memicu ledakan bom yang sudah bot letakkan apabila sudah tidak berada di zona bahaya elif (HasPlacedBomb()): return(6) # Prioritas ketiga: meledakkan obstacle yang ada di sebelah bot elif ((Map_Entity(Player_X(index)-1, Player_Y(index)) == OBSTACLE) or (Map_Entity(Player_X(index)+1, Player_Y(index)) == OBSTACLE) or (Map_Entity(Player_X(index), Player_Y(index)-1) == OBSTACLE) or (Map_Entity(Player_X(index), Player_Y(index)+1) == OBSTACLE)): return(5) # Prioritas keempat: mengejar power up sebagai target elif (IsPowerUpInRange(Player_X(index),Player_Y(index), math.floor(MAP_WIDTH/2))): return(GoToTarget(Player_X(index),Player_Y(index),MAP_WIDTH,index,1)) # Prioritas kelima: mengejar musuh dalam radius tertentu elif (IsEnemyInRange(index,math.floor(MAP_WIDTH)) != -1): enemy_key = Player_Key(IsEnemyInRange(index,math.floor(MAP_WIDTH))) if (((Map_Entity(Player_X(index)-1, Player_Y(index)) == enemy_key) or (Map_Entity(Player_X(index)+1, Player_Y(index)) == enemy_key) or (Map_Entity(Player_X(index), Player_Y(index)-1) == enemy_key) or (Map_Entity(Player_X(index), Player_Y(index)+1) == enemy_key)) and SOS(Player_Index(enemy_key))): return(5) else: return(GoToTarget(Player_X(index),Player_Y(index),MAP_WIDTH,index,2)) # Prioritas keenam: berdiam diri apabila telah terpojok elif ((InDanger(Player_X(index)-1,Player_Y(index))) and (InDanger(Player_X(index),Player_Y(index)+1)) and (InDanger(Player_X(index),Player_Y(index)-1)) and (InDanger(Player_X(index),Player_Y(index)+1))): return(-1) # Prioritas ketujuh: hunting down the enemy else: return(GoToTarget(Player_X(index),Player_Y(index),MAP_WIDTH,index,3)) action = Choice(Player_Index(player_key)) logger.info('Action: {}'.format(ACTIONS[action])) with open(os.path.join(output_path, 'move.txt'), 'w') as f: f.write('{}\n'.format(action)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('player_key', nargs='?') parser.add_argument('output_path', nargs='?', default=os.getcwd()) args = parser.parse_args() assert(os.path.isdir(args.output_path)) main(args.player_key, args.output_path)
luqmanarifin/2016-Bomberman
Kecewa/bot.py
bot.py
py
21,742
python
en
code
1
github-code
6
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33574367695
import utils def build(data): G = dict() for line in data: a, b = line.split('-') if a in G: G[a].append(b) else: G[a] = [b] if b in G: G[b].append(a) else: G[b] = [a] return G def traverse(G, current_cave, current_path=None, can_revisit=False): if current_path == None: current_path = [] current_path.append(current_cave) for neighbor in G[current_cave]: if neighbor != 'start' and (neighbor.isupper() or (can_revisit or neighbor not in current_path)): if neighbor == 'end': PATHS.append(current_path + [neighbor]) else: traverse(G, neighbor, current_path=current_path.copy(), can_revisit=can_revisit and not (neighbor.islower() and neighbor in current_path)) if __name__ == '__main__': timer = utils.Timer() # Part 1 """ timer.start() data = utils.read_str_lines() G = build(data) PATHS = [] traverse(G, 'start') print(len(PATHS)) timer.stop() # 50.94ms """ # Part 2 # """ timer.start() data = utils.read_str_lines() G = build(data) PATHS = [] traverse(G, 'start', can_revisit=True) print(len(PATHS)) timer.stop() # 610.58ms # """
742617000027/advent-of-code-2021
12/12.py
12.py
py
1,386
python
en
code
0
github-code
6
[ { "api_name": "utils.Timer", "line_number": 35, "usage_type": "call" }, { "api_name": "utils.read_str_lines", "line_number": 51, "usage_type": "call" } ]
21115802122
#!/usr/bin/env python from gi.repository import Gtk, Gdk, GtkSource, GObject, Vte, GLib, Pango from gi.repository.GdkPixbuf import Pixbuf import os import stat import time import jedi class Handler: def onShowCompletion(self, sview): buffer = sview.get_buffer() startiter, enditer = buffer.get_bounds() mark = buffer.get_insert() cpostiter = buffer.get_iter_at_mark(mark).copy() source = buffer.get_text(startiter, enditer, include_hidden_chars=False) script = jedi.Script(source, cpostiter.get_line() + 1, cpostiter.get_line_offset(), 'example.py') completions = script.completions() if completions != []: Handler.openCompletions(completions, sview, cpostiter) def openCompletions(completions, sview, cpostiter): iter_loc = sview.get_iter_location(cpostiter) win_loc = sview.buffer_to_window_coords( Gtk.TextWindowType.WIDGET, iter_loc.x, iter_loc.y) win = sview.get_window(Gtk.TextWindowType.WIDGET) view_pos = win.get_toplevel().get_position() x = win_loc[0] + view_pos[0] + 180 y = win_loc[1] + view_pos[1] + 130 try: ccwin = Gtk.Window() ccwin.set_keep_above(True) ccwin.set_decorated(False) vbox = Gtk.Box(orientation=Gtk.Orientation.VERTICAL) hbox = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL) swin = Gtk.ScrolledWindow() title = Gtk.Label("Title") descr = Gtk.Label("Descr") vbox2 = Gtk.Box(orientation=Gtk.Orientation.VERTICAL) vbox2.pack_start(title, True, True, 0) vbox2.pack_start(descr, True, True, 0) for c in completions: b = Gtk.Button(c.name) b.connect("clicked", Handler.onComplete, c, ccwin, sview.get_buffer()) b.connect("focus-in-event", Handler.onFocusCompletion, c, title, descr) b.connect("focus-out-event", Handler.onUnFocusCompletion) vbox.pack_start(b, True, True, 0) hbox.pack_start(swin, True, True, 0) swin.add(vbox) hbox.pack_start(vbox2, True, True, 0) ccwin.add(hbox) ccwin.set_size_request(800, 400) ccwin.move(x, y) ccwin.connect("focus-out-event", Handler.onCCWinDestroy, ccwin) ccwin.connect("key-release-event", Handler.onCCWinEsc) ccwin.show_all() except Exception as e: print(e) def onFocusCompletion(self, evt, completion, title, descr): title.set_text(completion.description) descr.set_text(completion.doc) def onUnFocusCompletion(self, evt, data=None): print("P") def onCCWinEsc(self, event, data=None): if event.keyval == Gdk.KEY_Escape: self.destroy() def onComplete(self, completion, win, buf): buf.insert_at_cursor(completion.complete) win.destroy() def onCCWinDestroy(self, evt, window): window.destroy() ######################################################## def onCopy(self, *args): Handler.getCurrentBuffer().copy_clipboard(app.clipboard) def onCut(self, *args): Handler.getCurrentBuffer().cut_clipboard(app.clipboard, True) def onPaste(self, *args): Handler.getCurrentBuffer().paste_clipboard(app.clipboard, None, True) def onModified(self, label, buffer): if buffer.get_modified(): label.set_markup("<span foreground='#ff8000'>%s</span>" % label.get_text()) def onDeleteWindow(self, *args): for i in app.openfiles: pos = app.builder.get_object("notebook1").page_num(i[2]) app.builder.get_object("notebook1").set_current_page(pos) isclosed = Handler.onCloseTab(Handler(), i[0], i[1], i[2]) print(isclosed) if not isclosed: return True Gtk.main_quit(*args) def onFullscreen(self, *args): app.builder.get_object("window1").fullscreen() def onWindow(self, *args): app.builder.get_object("window1").unfullscreen() def onOpen(self, *args): dialog = Gtk.FileChooserDialog("open file", app.builder.get_object("window1"), Gtk.FileChooserAction.OPEN, (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL, Gtk.STOCK_OPEN, Gtk.ResponseType.OK)) response = dialog.run() if response == Gtk.ResponseType.OK: Handler.openfile(dialog.get_filename()) dialog.destroy() def onNew(self, *args): buffer = GtkSource.Buffer() lanm = GtkSource.LanguageManager() lan = lanm.get_language('python') buffer.set_language(lan) buffer.set_highlight_syntax(True) buffer.set_highlight_matching_brackets(True) buffer.set_text("#!/usr/bin/env python") buffer.set_modified(False) swindow = Handler.create_tab("unnamed", buffer) swindow.get_children()[0].connect("show-completion", Handler.onShowCompletion, buffer) app.openfiles.append([None, buffer, swindow]) def create_tab(path, buffer): hbox = Gtk.HBox(False, 0) label = Gtk.Label(path) hbox.pack_start(label, True, True, 0) close_image = Gtk.IconTheme.get_default().load_icon("exit", 16, 0) imgw = Gtk.Image() imgw.set_from_pixbuf(close_image) btn = Gtk.Button() btn.set_focus_on_click(False) btn.add(imgw) hbox.pack_start(btn, False, False, 0) hbox.show_all() sview = GtkSource.View() sview.set_buffer(buffer) # make settings sview.set_show_line_numbers(True) sview.set_auto_indent(True) sview.set_tab_width(4) sview.set_indent_width(4) sview.set_insert_spaces_instead_of_tabs(True) sview.set_right_margin_position(80) sview.set_show_right_margin(True) sview.modify_font(Pango.FontDescription('Dejavu Sans Mono')) # try: # bg_color = Gdk.RGBA() # Gdk.RGBA.parse(bg_color, "#111111") # sview.override_background_color(Gtk.StateType.NORMAL, bg_color) # fg_color = Gdk.RGBA() # Gdk.RGBA.parse(fg_color, "#DDDDDD") # sview.override_color(Gtk.StateType.NORMAL, fg_color) # except Exception as e: # print(e) # pass swindow = Gtk.ScrolledWindow() swindow.add(sview) notebook = app.builder.get_object("notebook1") pos = notebook.append_page(swindow, hbox) notebook.show_all() btn.connect("clicked", Handler.onCloseTab, path, buffer, swindow) buffer.connect("modified-changed", Handler.onModified, label, buffer) notebook.set_current_page(pos) return swindow def openfile(path): for of in app.openfiles: if of[0] != None: if path in of[0]: return with open(path, "r") as loadedfile: buffer = GtkSource.Buffer() buffer.set_text(loadedfile.read()) buffer.set_modified(False) # syntax highlighting lman = GtkSource.LanguageManager() lan = lman.guess_language(path) swindow = Handler.create_tab(path, buffer) if lan: buffer.set_highlight_syntax(True) buffer.set_language(lan) if lan.get_name() == 'Python': swindow.get_children()[0].connect("show-completion", Handler.onShowCompletion, swindow.get_children()[0]) else: buffer.set_highlight_syntax(False) buffer.set_highlight_matching_brackets(True) app.openfiles.append([path, buffer, swindow]) def askForSave(buffer): dialog = Gtk.Dialog("ask for save dialog", app.builder.get_object("window1"), 0, (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL, Gtk.STOCK_YES, Gtk.ResponseType.YES, Gtk.STOCK_NO, Gtk.ResponseType.NO)) dialog.get_content_area().add(Gtk.Label("Datei nicht gespeichert. Wollen Sie die datei jetzt speichern?")) dialog.set_default_size(150, 100) dialog.show_all() response = dialog.run() if response == Gtk.ResponseType.YES: Handler.onSaveCurrent(Handler()) dialog.destroy() if not buffer.get_modified(): return True else: return False elif response == Gtk.ResponseType.NO: dialog.destroy() return True else: dialog.destroy() return False def onCloseTab(self, path, buffer, swindow): pos = app.builder.get_object("notebook1").page_num(swindow) window = app.builder.get_object("notebook1").get_nth_page(pos) buffer = window.get_child().get_buffer() if buffer.get_modified(): response = Handler.askForSave(buffer) if response: app.builder.get_object("notebook1").remove_page(pos) for i in app.openfiles: if i[1] == buffer: path = i[0] app.openfiles.remove([path, buffer, swindow]) return True else: return False else: app.builder.get_object("notebook1").remove_page(pos) for i in app.openfiles: if i[1] == buffer: path = i[0] app.openfiles.remove([path, buffer, swindow]) return True def savefile(buffer, path, label): with open(path, 'w') as f: f.write(buffer.get_text(*buffer.get_bounds(), include_hidden_chars=True)) label.set_markup("<span foreground='#000000'>%s</span>" % label.get_text()) buffer.set_modified(False) Handler.updateOpenFiles(path, buffer) def onSaveCurrent(self, *args): buffer, label = Handler.getCurrentBufferAndLabel() path = Handler.getPathFromOpenFiles(buffer) if path == None: path = Handler.saveAs() label.set_text(path) Handler.savefile(buffer, path, label) def updateOpenFiles(path, buffer): for i in app.openfiles: if i[1] == buffer: i[0] = path i[1] = buffer def onSaveAsCurrent(self, *args): buffer, label = Handler.getCurrentBufferAndLabel() path = Handler.saveAs() label.set_text(path) Handler.savefile(buffer, path, label) def saveAs(): dialog = Gtk.FileChooserDialog("save file as", app.builder.get_object("window1"), Gtk.FileChooserAction.SAVE, (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL, Gtk.STOCK_SAVE, Gtk.ResponseType.OK)) response = dialog.run() retval = None if response == Gtk.ResponseType.OK: retval = dialog.get_filename() dialog.destroy() return retval def getPathFromOpenFiles(buffer): for i in app.openfiles: if i[1] == buffer: return i[0] def getCurrentBufferAndLabel(): currentpage = app.builder.get_object("notebook1").get_current_page() window = app.builder.get_object("notebook1").get_nth_page(currentpage) label = app.builder.get_object("notebook1").get_tab_label(window).get_children()[0] view = window.get_child() return view.get_buffer(), label def onRunApp(self, *args): f = "/tmp/%i.py" % int(time.time()) with open (f, "w") as loadedfile: buffer, label = Handler.getCurrentBufferAndLabel() loadedfile.write(buffer.get_text(*buffer.get_bounds(), include_hidden_chars=True)) label.set_markup("<span foreground='#009000'>%s</span>" % label.get_text()) termwin = Gtk.Window() termwin.set_default_size(800, 600) def closeTerm(win, evt, label, buffer): win.destroy() os.remove(f) if buffer.get_modified(): label.set_markup("<span foreground='#FF8000'>%s</span>" % label.get_text()) else: label.set_markup("<span foreground='#000000'>%s</span>" % label.get_text()) termwin.connect("delete-event", closeTerm, label, buffer) terminal = Vte.Terminal() terminal.spawn_sync( Vte.PtyFlags.DEFAULT, os.environ['HOME'], ["/bin/bash"], [], GLib.SpawnFlags.DO_NOT_REAP_CHILD, None, None, ) termwin.add(terminal) termwin.show_all() cmd = "python " + f + "\n" terminal.feed_child(cmd, len(cmd)) class FsTree: def populateFileSystemTreeStore(treeStore, path, parent=None): itemCounter = 0 # iterate over the items in the path for item in os.listdir(path): # Get the absolute path of the item itemFullname = os.path.join(path, item) # Extract metadata from the item try: itemMetaData = os.stat(itemFullname) except: pass # Determine if the item is a folder itemIsFolder = stat.S_ISDIR(itemMetaData.st_mode) # Generate an icon from the default icon theme itemIcon = Gtk.IconTheme.get_default().load_icon("folder" if itemIsFolder else "empty", 22, 0) # Append the item to the TreeStore currentIter = treeStore.append(parent, [item, itemIcon, itemFullname]) # add dummy if current item was a folder if itemIsFolder: try: if not os.listdir(itemFullname) == [] : treeStore.append(currentIter, [None, None, None]) except: pass #increment the item counter itemCounter += 1 # add the dummy node back if nothing was inserted before if itemCounter < 1: treeStore.append(parent, [None, None, None]) def onFSRowExpanded(treeView, treeIter, treePath): # get the associated model treeStore = treeView.get_model() # get the full path of the position newPath = treeStore.get_value(treeIter, 2) # populate the subtree on curent position FsTree.populateFileSystemTreeStore(treeStore, newPath, treeIter) # remove the first child (dummy node) treeStore.remove(treeStore.iter_children(treeIter)) def onFSRowCollapsed(treeView, treeIter, treePath): # get the associated model treeStore = treeView.get_model() # get the iterator of the first child currentChildIter = treeStore.iter_children(treeIter) # loop as long as some childern exist while currentChildIter: # remove the first child treeStore.remove(currentChildIter) # refresh the iterator of the next child currentChildIter = treeStore.iter_children(treeIter) # append dummy node treeStore.append(treeIter, [None, None, None]) def onFSRowActivated(treeView, path, column): model = treeView.get_model() curiter = model.get_iter(path) fspath = model.get_value(curiter, 2) if not os.path.isdir(str(fspath)): Handler.openfile(str(fspath)) class Pyide: openfiles = [] # fs tree store from http://stackoverflow.com/questions/23433819/creating-a-simple-file-browser-using-python-and-gtktreeview def __init__(self, *args): self.builder = Gtk.Builder() self.clipboard = Gtk.Clipboard.get(Gdk.SELECTION_CLIPBOARD) GObject.type_register(GtkSource.View) self.builder.add_from_file("pyide.glade") self.my_accelerators = Gtk.AccelGroup() fileSystemTreeStore = Gtk.TreeStore(str, Pixbuf, str) FsTree.populateFileSystemTreeStore(fileSystemTreeStore, os.path.expanduser("~")) fileSystemTreeView = self.builder.get_object("treeview1") fileSystemTreeView.set_model(fileSystemTreeStore) treeViewCol = Gtk.TreeViewColumn("File") colCellText = Gtk.CellRendererText() colCellImg = Gtk.CellRendererPixbuf() treeViewCol.pack_start(colCellImg, False) treeViewCol.pack_start(colCellText, True) treeViewCol.add_attribute(colCellText, "text", 0) treeViewCol.add_attribute(colCellImg, "pixbuf", 1) fileSystemTreeView.append_column(treeViewCol) fileSystemTreeView.connect("row-expanded", FsTree.onFSRowExpanded) fileSystemTreeView.connect("row-collapsed", FsTree.onFSRowCollapsed) fileSystemTreeView.connect("row-activated", FsTree.onFSRowActivated) self.builder.connect_signals(Handler()) def add_accelerator(self, widget, accelerator, signal="activate"): if accelerator is not None: key, mod = Gtk.accelerator_parse(accelerator) widget.add_accelerator(signal, self.my_accelerators, key, mod, Gtk.AccelFlags.VISIBLE) print("The accelerator is well added with the signal " + signal) def run(self): window = self.builder.get_object("window1") window.add_accel_group(self.my_accelerators) window.show_all() Handler.openfile("./pyide.py") Gtk.main() if __name__ == "__main__": app = Pyide() app.run()
superdachs/pyide
pyide.py
pyide.py
py
18,021
python
en
code
1
github-code
6
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"usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 241, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.ResponseType", "line_number": 248, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 248, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.FileChooserDialog", "line_number": 307, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 307, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.FileChooserAction", "line_number": 308, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 308, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.STOCK_CANCEL", "line_number": 309, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 309, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.ResponseType", "line_number": 309, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk.STOCK_SAVE", "line_number": 309, "usage_type": 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"attribute" }, { "api_name": "gi.repository.GLib", "line_number": 356, "usage_type": "name" }, { "api_name": "os.listdir", "line_number": 372, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 374, "usage_type": "call" }, { "api_name": "os.path", "line_number": 374, "usage_type": "attribute" }, { "api_name": "os.stat", "line_number": 377, "usage_type": "call" }, { "api_name": "stat.S_ISDIR", "line_number": 381, "usage_type": "call" }, { "api_name": "gi.repository.Gtk.IconTheme.get_default", "line_number": 383, "usage_type": "call" }, { "api_name": "gi.repository.Gtk.IconTheme", "line_number": 383, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 383, "usage_type": "name" }, { "api_name": "os.listdir", "line_number": 389, "usage_type": "call" }, { "api_name": "os.path.isdir", "line_number": 426, "usage_type": "call" }, { "api_name": "os.path", "line_number": 426, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk.Builder", "line_number": 434, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 434, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.Clipboard.get", "line_number": 435, "usage_type": "call" }, { "api_name": "gi.repository.Gtk.Clipboard", "line_number": 435, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 435, "usage_type": "name" }, { "api_name": "gi.repository.Gdk.SELECTION_CLIPBOARD", "line_number": 435, "usage_type": "attribute" }, { "api_name": "gi.repository.Gdk", "line_number": 435, "usage_type": "name" }, { "api_name": "gi.repository.GObject.type_register", "line_number": 436, "usage_type": "call" }, { "api_name": "gi.repository.GObject", "line_number": 436, "usage_type": "name" }, { "api_name": "gi.repository.GtkSource.View", "line_number": 436, "usage_type": "attribute" }, { "api_name": "gi.repository.GtkSource", "line_number": 436, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.AccelGroup", "line_number": 439, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 439, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.TreeStore", "line_number": 441, "usage_type": "call" }, { "api_name": "gi.repository.GdkPixbuf.Pixbuf", "line_number": 441, "usage_type": "argument" }, { "api_name": "gi.repository.Gtk", "line_number": 441, "usage_type": "name" }, { "api_name": "os.path.expanduser", "line_number": 442, "usage_type": "call" }, { "api_name": "os.path", "line_number": 442, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk.TreeViewColumn", "line_number": 445, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 445, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.CellRendererText", "line_number": 446, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 446, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.CellRendererPixbuf", "line_number": 447, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 447, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.accelerator_parse", "line_number": 461, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 461, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.AccelFlags", "line_number": 462, "usage_type": "attribute" }, { "api_name": "gi.repository.Gtk", "line_number": 462, "usage_type": "name" }, { "api_name": "gi.repository.Gtk.main", "line_number": 473, "usage_type": "call" }, { "api_name": "gi.repository.Gtk", "line_number": 473, "usage_type": "name" } ]
31249185445
import io import os import torch from torch import nn from tqdm import tqdm from torch.utils.data import Dataset, DataLoader from transformers import (set_seed, TrainingArguments, Trainer, GPT2Config, GPT2Tokenizer, AdamW, get_linear_schedule_with_warmup, GPT2ForSequenceClassification, PreTrainedTokenizerFast) from sklearn.metrics import classification_report, accuracy_score from music_midi_dataset import MidiMusicDataset def start(): set_seed(123) epochs = 4 batch_size = 8 max_length = 60 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model_name_or_path = 'gpt2' labels_ids = {'cheerful': 0, 'tense': 1} n_labels = len(labels_ids) train_midi_data_dir = '../data/music_midi/emotion_midi_text/train' test_midi_data_dir = '../data/music_midi/emotion_midi_text/test' path_tokenizer = 'tokenizer.json' tokenizer = PreTrainedTokenizerFast(tokenizer_file=path_tokenizer) tokenizer.add_special_tokens({'pad_token': '[PAD]'}) classes = ['cheerful', 'tense'] pad_length = 128 learning_rate = 0.001 print('Loading configuraiton...') model_config = GPT2Config.from_pretrained(pretrained_model_name_or_path=model_name_or_path, num_labels=n_labels) print('Loading model...') model = GPT2ForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name_or_path, config=model_config) model.resize_token_embeddings(len(tokenizer)) # fix model padding token id model.config.pad_token_id = model.config.eos_token_id # Load model to defined device. model.to(device) training_data = MidiMusicDataset(midi_data_dir=train_midi_data_dir, classes=classes, tokenizer=tokenizer, block_size=pad_length) test_data = MidiMusicDataset(midi_data_dir=test_midi_data_dir, classes=classes, tokenizer=tokenizer, block_size=pad_length) train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True) valid_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True) optimizer = AdamW(model.parameters(), lr=2e-5, # default is 5e-5, our notebook had 2e-5 eps=1e-8 # default is 1e-8. ) # Total number of training steps is number of batches * number of epochs. # `train_dataloader` contains batched data so `len(train_dataloader)` gives # us the number of batches. total_steps = len(train_dataloader) * epochs # Create the learning rate scheduler. scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, # Default value in run_glue.py num_training_steps=total_steps) # Store the average loss after each epoch so we can plot them. all_loss = {'train_loss': [], 'val_loss': []} all_acc = {'train_acc': [], 'val_acc': []} for epoch in range(epochs): print(f'Epoch {epoch}') print('Training on batches...') # Perform one full pass over the training set. train_labels, train_predict, train_loss = train(train_dataloader, model, optimizer, scheduler, device) train_acc = accuracy_score(train_labels, train_predict) # Get prediction form model on validation data. print('Validation on batches...') valid_labels, valid_predict, val_loss = validation(valid_dataloader, model, device) val_acc = accuracy_score(valid_labels, valid_predict) # Print loss and accuracy values to see how training evolves. print(" train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f" % ( train_loss, val_loss, train_acc, val_acc)) print() # Store the loss value for plotting the learning curve. all_loss['train_loss'].append(train_loss) all_loss['val_loss'].append(val_loss) all_acc['train_acc'].append(train_acc) all_acc['val_acc'].append(val_acc) def train(dataloader, model, optimizer_, scheduler_, device_): predictions_labels = [] true_labels = [] total_loss = 0 model.train() for batch in tqdm(dataloader): # print(batch) true_labels += batch['labels'].numpy().flatten().tolist() batch = {k: v.type(torch.long).to(device_) for k, v in batch.items()} model.zero_grad() outputs = model(**batch) loss, logits = outputs[:2] total_loss += loss.item() loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer_.step() scheduler_.step() logits = logits.detach().cpu().numpy() predictions_labels += logits.argmax(axis=-1).flatten().tolist() avg_epoch_loss = total_loss / len(dataloader) return true_labels, predictions_labels, avg_epoch_loss def validation(dataloader, model, device_): predictions_labels = [] true_labels = [] total_loss = 0 model.eval() for batch in tqdm(dataloader): true_labels += batch['labels'].numpy().flatten().tolist() batch = {k: v.type(torch.long).to(device_) for k, v in batch.items()} with torch.no_grad(): outputs = model(**batch) loss, logits = outputs[:2] logits = logits.detach().cpu().numpy() total_loss += loss.item() predict_content = logits.argmax(axis=-1).flatten().tolist() predictions_labels += predict_content avg_epoch_loss = total_loss / len(dataloader) return true_labels, predictions_labels, avg_epoch_loss if __name__ == '__main__': start()
Vitaliy1234/music_generation
emotion_classification/gpt2_classifier.py
gpt2_classifier.py
py
6,088
python
en
code
0
github-code
6
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74794654268
# import urllib library from urllib.request import urlopen import json import random score = 0 import string NUMBER_OF_ATTEMPTS = 2 ENTER_ANSWER = 'Hit %s for your answer\n' TRY_AGAIN = 'Incorrect!!! Try again.' CORRECT = 'Correct' NO_MORE_ATTEMPTS = 'Incorrect!!! You ran out of your attempts' def question(message, options, correct, attempts=NUMBER_OF_ATTEMPTS): print (message) while attempts > 0: response = input(ENTER_ANSWER % ', '.join(options)) if response == correct: print (CORRECT) return True else: attempts -= 1 print (TRY_AGAIN) print (NO_MORE_ATTEMPTS) return False urlQuestion = "https://d-wwts.ext.hp.com/qna/questions.json" urlAnswers = "https://d-wwts.ext.hp.com/qna/answers.json" responseQuestionsAndAnswers = urlopen(urlQuestion) responseQandA_json = json.loads(responseQuestionsAndAnswers.read()) responseCurrectAnswers = urlopen(urlAnswers) responseUrlCurrectAnswers_json = json.loads(responseCurrectAnswers.read()) random_item = random.choice(responseQandA_json) questionId = random_item['id']; filterAnswer = [f for f in responseUrlCurrectAnswers_json if f["id"] == questionId] ans = filterAnswer[0]['a']; question2 = question(random_item['q'], random_item['a'], ans)
RoiAtias/Devops_test
test/test.py
test.py
py
1,321
python
en
code
0
github-code
6
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64085434
# -*- coding: utf-8 -*- """ Created on Fri Mar 1 20:30:54 2019 @author: Sunanda """ import argparse, re, decimal parser = argparse.ArgumentParser( description='''The purpose of this application is to check the COMP 472/6721 Winter 2019 Projects''' ) parser.add_argument('-c', required = False, action='store_true', help="optional argument to check the format") parser.add_argument( '-m', required = False, action='store_true', help="check the trace files for minimax implementation") parser.add_argument('-a', required = False, action='store_true', help="check the trace files for alpha beta implementation") parser.add_argument("-f", dest="filename", required=True, help="output file from demos", metavar="FILE", type=argparse.FileType('r')) args = parser.parse_args() content = args.filename.read().strip() groups = re.split('(?:\r\n\r\n|\n\n)',content) if args.m or args.a : print("\n\x1b[1;31;mACESS DENIED\x1b[0m ") else: print("Checking format.. ") error = 0 traceNo = 0 for i,bunch in enumerate(groups,1): if bunch.startswith('\r') or bunch.startswith('\n'): error = 5 break rows = bunch.split() if i % 2 == 1: traceNo += 1 if len(rows) > 2: error = 1 break elif len(rows) < 2: error = 2 break for val in rows: try: float(val) except: error = 3 break if decimal.Decimal(val).as_tuple().exponent < -1: error = 4 break if error != 0 : break # print("done") if error == 1: print("\x1b[1;31;mERROR:\x1b[0m Too many values in the beginning (Trace No. "+ str(traceNo) +")") elif error == 2: print("\x1b[1;31;mERROR:\x1b[0m Not enough values in the beginning (Trace No. "+ str(traceNo) +")") elif error == 3: print("\x1b[1;31;mERROR:\x1b[0m Number expected (Trace No. "+ str(traceNo) +")") elif error == 4: print("\x1b[1;31;mERROR:\x1b[0m Upto one decimal point expected (Trace No. "+ str(traceNo) +")") elif error == 5: print("\x1b[1;31;mERROR:\x1b[0m Too many new lines (Trace No. "+ str(traceNo) +")") else: print("\x1b[1;32;mCORRECT FORMAT\x1b[0m")
lqw1111/COMP6721-AI-Project
check.py
check.py
py
2,781
python
en
code
1
github-code
6
[ { "api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call" }, { "api_name": "argparse.FileType", "line_number": 31, "usage_type": "call" }, { "api_name": "re.split", "line_number": 37, "usage_type": "call" }, { "api_name": "decimal.Decimal", "line_number": 64, "usage_type": "call" } ]
41169055403
import argparse import numpy as np import os import torch import torch.nn as nn import datetime import time import matplotlib.pyplot as plt from torchinfo import summary import yaml import json import sys sys.path.append("..") from lib.utils import ( MaskedMAELoss, print_log, seed_everything, set_cpu_num, CustomJSONEncoder, ) from lib.metrics import MAE_RMSE, RMSE_MAE_MAPE from lib.data_prepare import get_dataloaders_from_tvt from models import model_select @torch.no_grad() def eval_model(model, valset_loader, criterion): model.eval() batch_loss_list = [] for x_batch, y_batch in valset_loader: x_batch = x_batch.to(DEVICE) y_batch = y_batch.to(DEVICE) out_batch = model(x_batch) # out_batch = SCALER.inverse_transform(out_batch) loss = criterion(out_batch, y_batch) batch_loss_list.append(loss.item()) return np.mean(batch_loss_list) @torch.no_grad() def predict(model, loader): model.eval() y = [] out = [] for x_batch, y_batch in loader: x_batch = x_batch.to(DEVICE) y_batch = y_batch.to(DEVICE) out_batch = model(x_batch) # out_batch = SCALER.inverse_transform(out_batch) out_batch = out_batch.cpu().numpy() y_batch = y_batch.cpu().numpy() out.append(out_batch) y.append(y_batch) out = np.vstack(out).squeeze() # (samples, out_steps, num_nodes) y = np.vstack(y).squeeze() return y, out def train_one_epoch( model, trainset_loader, optimizer, scheduler, criterion, clip_grad, log=None ): model.train() batch_loss_list = [] for x_batch, y_batch in trainset_loader: x_batch = x_batch.to(DEVICE) y_batch = y_batch.to(DEVICE) out_batch = model(x_batch) # out_batch = SCALER.inverse_transform(out_batch) loss = criterion(out_batch, y_batch) batch_loss_list.append(loss.item()) optimizer.zero_grad() loss.backward() if clip_grad: torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad) optimizer.step() epoch_loss = np.mean(batch_loss_list) scheduler.step() return epoch_loss def train( model, trainset_loader, valset_loader, optimizer, scheduler, criterion, clip_grad=0, max_epochs=200, early_stop=10, compile_model=False, verbose=1, plot=False, log=None, save=None, ): if torch.__version__ >= "2.0.0" and compile_model: model = torch.compile(model) model = model.to(DEVICE) wait = 0 min_val_loss = np.inf train_loss_list = [] val_loss_list = [] for epoch in range(max_epochs): train_loss = train_one_epoch( model, trainset_loader, optimizer, scheduler, criterion, clip_grad, log=log ) train_loss_list.append(train_loss) val_loss = eval_model(model, valset_loader, criterion) val_loss_list.append(val_loss) if (epoch + 1) % verbose == 0: print_log( datetime.datetime.now(), "Epoch", epoch + 1, " \tTrain Loss = %.5f" % train_loss, "Val Loss = %.5f" % val_loss, log=log, ) if val_loss < min_val_loss: wait = 0 min_val_loss = val_loss best_epoch = epoch best_state_dict = model.state_dict() else: wait += 1 if wait >= early_stop: break model.load_state_dict(best_state_dict) train_mae, train_rmse = MAE_RMSE(*predict(model, trainset_loader)) val_mae, val_rmse = MAE_RMSE(*predict(model, valset_loader)) out_str = f"Early stopping at epoch: {epoch+1}\n" out_str += f"Best at epoch {best_epoch+1}:\n" out_str += "Train Loss = %.5f\n" % train_loss_list[best_epoch] out_str += "Train MAE = %.5f, RMSE = %.5f\n" % ( train_mae, train_rmse, ) out_str += "Val Loss = %.5f\n" % val_loss_list[best_epoch] out_str += "Val MAE = %.5f, RMSE = %.5f" % ( val_mae, val_rmse, ) print_log(out_str, log=log) if plot: plt.plot(range(0, epoch + 1), train_loss_list, "-", label="Train Loss") plt.plot(range(0, epoch + 1), val_loss_list, "-", label="Val Loss") plt.title("Epoch-Loss") plt.xlabel("Epoch") plt.ylabel("Loss") plt.legend() plt.show() if save: torch.save(best_state_dict, save) return model @torch.no_grad() def test_model(model, testset_loader, log=None): model.eval() print_log("--------- Test ---------", log=log) start = time.time() y_true, y_pred = predict(model, testset_loader) end = time.time() ( mae_all, rmse_all, ) = MAE_RMSE(y_true, y_pred) out_str = "Test MAE = %.5f, RMSE = %.5f\n" % ( mae_all, rmse_all, ) # (rmse_all, mae_all, mape_all) = RMSE_MAE_MAPE(y_true, y_pred) # out_str = "Test MAE = %.5f, RMSE = %.5f, MAPE = %.5f\n" % ( # rmse_all, # mae_all, # mape_all, # ) print_log(out_str, log=log, end="") print_log("Inference time: %.2f s" % (end - start), log=log) if __name__ == "__main__": # -------------------------- set running environment ------------------------- # parser = argparse.ArgumentParser() parser.add_argument("-n", type=int, default="500") parser.add_argument("-p", type=int, default="20") parser.add_argument("-m", "--model", type=str, default="gridgcn") parser.add_argument("-g", "--gpu_num", type=int, default=0) parser.add_argument("-c", "--compile", action="store_true") parser.add_argument("--seed", type=int, default=233) parser.add_argument("--cpus", type=int, default=1) args = parser.parse_args() seed_everything(args.seed) set_cpu_num(args.cpus) GPU_ID = args.gpu_num os.environ["CUDA_VISIBLE_DEVICES"] = f"{GPU_ID}" DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") n = args.n p = args.p dataset = f"n_{n}_p_{p}" data_path = f"../data/{dataset}" model_name = args.model.upper() model_class = model_select(model_name) model_name = model_class.__name__ with open(f"../configs/{model_name}.yaml", "r") as f: cfg = yaml.safe_load(f) cfg = cfg[dataset] # -------------------------------- load model -------------------------------- # # cfg.get(key, default_value=None): no need to write in the config if not used # cfg[key]: must be assigned in the config, else KeyError if cfg.get("pass_device"): cfg["model_args"]["device"] = DEVICE model = model_class(**cfg["model_args"]) # ------------------------------- make log file ------------------------------ # now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") log_path = f"../logs/{model_name}" if not os.path.exists(log_path): os.makedirs(log_path) log = os.path.join(log_path, f"{model_name}-{dataset}-{now}.log") log = open(log, "a") log.seek(0) log.truncate() # ------------------------------- load dataset ------------------------------- # print_log(dataset, log=log) ( trainset_loader, valset_loader, testset_loader, ) = get_dataloaders_from_tvt( n, p, batch_size=cfg.get("batch_size", 32), log=log, ) print_log(log=log) # --------------------------- set model saving path -------------------------- # save_path = f"../saved_models/{model_name}" if not os.path.exists(save_path): os.makedirs(save_path) save = os.path.join(save_path, f"{model_name}-{dataset}-{now}.pt") # ---------------------- set loss, optimizer, scheduler ---------------------- # # criterion = nn.SmoothL1Loss() # criterion = MaskedMAELoss() criterion = nn.MSELoss() optimizer = torch.optim.Adam( model.parameters(), lr=cfg["lr"], weight_decay=cfg.get("weight_decay", 0), eps=cfg.get("eps", 1e-8), ) scheduler = torch.optim.lr_scheduler.MultiStepLR( optimizer, milestones=cfg.get("milestones", []), gamma=cfg.get("lr_decay_rate", 0.1), verbose=False, ) # --------------------------- print model structure -------------------------- # print_log("---------", model_name, "---------", log=log) print_log( json.dumps(cfg, ensure_ascii=False, indent=4, cls=CustomJSONEncoder), log=log ) print_log( summary( model, [ cfg["batch_size"], cfg["num_grids_width"], cfg["num_grids_height"], cfg["model_args"]["input_dim"], ], verbose=0, ), log=log, ) print_log(log=log) # --------------------------- train and test model --------------------------- # print_log(f"Loss: {criterion._get_name()}", log=log) print_log(log=log) model = train( model, trainset_loader, valset_loader, optimizer, scheduler, criterion, clip_grad=cfg.get("clip_grad"), max_epochs=cfg.get("max_epochs", 200), early_stop=cfg.get("early_stop", 10), compile_model=args.compile, verbose=1, log=log, save=save, ) test_model(model, testset_loader, log=log) log.close()
XDZhelheim/GN-RRT
scripts/train.py
train.py
py
9,502
python
en
code
0
github-code
6
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12019045259
from PyQt5.QtWidgets import QTableView, QPushButton, QHeaderView, QDialog, QVBoxLayout from PyQt5.QtCore import Qt from PyQt5.QtSql import QSqlDatabase, QSqlTableModel, QSqlQuery class HistoryWindow(QDialog): def __init__(self): super().__init__() self.setWindowTitle("History") self.setWindowFlags(self.windowFlags() & ~Qt.WindowContextHelpButtonHint) self.db = None self.table_model = None self.table_view = QTableView() self.table_view.horizontalHeader().setSectionResizeMode(QHeaderView.ResizeToContents) self.table_view.horizontalHeader().setStretchLastSection(1) self.table_view.verticalHeader().setSectionResizeMode(QHeaderView.ResizeToContents) self.load_data() self.btn_clear_history = QPushButton("Clear") self.layout = QVBoxLayout() self.layout.addWidget(self.table_view) self.layout.addWidget(self.btn_clear_history) self.setLayout(self.layout) self.setGeometry(400, 200, 400, 600) self.btn_clear_history.clicked.connect(self.clear_history) def load_data(self): self.db = QSqlDatabase.addDatabase('QSQLITE') self.db.setDatabaseName('history.db') self.db.open() self.table_model = QSqlTableModel() self.table_model.setTable("history") self.table_model.select() self.table_view.setModel(self.table_model) def clear_history(self): query = QSqlQuery() query.exec_("DELETE FROM history") if query.isActive(): print("Records deleted successfully") else: print("Error deleting records: ", query.lastError().text()) self.load_data()
umraan-xm/Image-Based-Equation-Solver
HistoryWindow.py
HistoryWindow.py
py
1,767
python
en
code
4
github-code
6
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17399515937
from sys import argv from special.mode import Mode from special.settings import Settings class BadMode(Exception): pass class BadCommandLineArguments(Exception): pass class Run: def __init__(self, mode: Mode): if len(argv) > 1: self.parse_arguments(argv[1:]) else: self.parse_mode(mode) def parse_arguments(self, args): nicks_path = 'nicks_test' play_path = 'play_test' games_path = 'games_test' chat_path = 'chat_test' testing_data_path = 'testing' backup_testing_path = 'backup testing' network_name = 'neural network' if args[0] == '--unit-tests': Settings.game_mode = Mode.UnitTest self.start_unit_tests() elif args[0] == '--evolution-tests': from holdem.name_manager import NameManager from holdem.play.play_manager import PlayManager Settings.game_mode = Mode.Evolution NameManager.NicksPath = nicks_path PlayManager.PlayPath = play_path PlayManager.GenCount = 30 self.start_evolution(100, 9, 27, 1000) NameManager.remove_folder() elif args[0] == '--parsing-tests': from data.game_parser import GameParser, PokerGame Settings.game_mode = Mode.Parse PokerGame.converted_games_folder = games_path PokerGame.converted_chat_folder = chat_path games = GameParser.parse_dir(testing_data_path, True, True) assert len(games) == 6 GameParser.copy_dir(backup_testing_path, testing_data_path) PokerGame.load_dir(testing_data_path) elif args[0] == '--learning-tests': from learning.learning import Learning from learning.data_sets.decision_model.poker_decision import PokerDecision Settings.game_mode = Mode.Learning learn = Learning() learn.create_data_set(PokerDecision) learn.add_data_set(testing_data_path) learn.save_data_set(network_name) learn.load_data_set(network_name) learn.learning(network_name) elif args[0] == '--network-play-tests': from holdem.game.game import Game from holdem.play.play_manager import PlayManager from holdem.player.neural_network.net1_net2_player import Net1Net2Player Settings.game_mode = Mode.Testing PlayManager.PlayPath = play_path game = Game() for _ in range(8): game.add_bot_player() game.add_nn_player(network_name, Net1Net2Player) PlayManager.remove_folder() else: raise BadCommandLineArguments(str(args)) def parse_mode(self, mode): Settings.game_mode = mode if mode == Mode.GameEngine: from holdem.game.game_manager import GameManager # PlayManager.standings() GameManager().run() elif mode == Mode.Parse: from data.game_parser import GameParser, PokerGame # GameParser.parse_dir('pack0') GameParser.parse_dir('pack1', False, False) # game.save() # game.convert() # print(game) # PokerGame.load('hh.txt') elif mode == Mode.Evolution: self.start_evolution(100000, 9, 999, 10000) elif mode == Mode.Testing: # from learning.neural_network import NeuralNetwork # NeuralNetwork.PokerDecision.Bubble(100, 9).show() from time import sleep from datetime import datetime from pickle import load from statistics import mean from holdem.game.game import Game from holdem.player.neural_network.net1_net2_player import Net1Net2Player from holdem.player.neural_network.net3_player import Net3Player from holdem.player.neural_network.net4_player import Net4Player from holdem.player.neural_network.net5_player import Net5Player from holdem.player.neural_network.net6_player import Net6Player from holdem.player.neural_network.net7_player import Net7Player from holdem.player.neural_network.net8_player import Net8Player from holdem.player.neural_network.net9_player import Net9Player from holdem.play.play_manager import PlayManager start_time = datetime.now() if 1: for _id in range(400): game = Game(players=100) for _ in range(92): game.add_bot_player() game.add_nn_player('nn2', Net1Net2Player) game.add_nn_player('nn3', Net3Player) game.add_nn_player('nn4', Net4Player) game.add_nn_player('nn5', Net5Player) game.add_nn_player('nn6', Net6Player) game.add_nn_player('nn7', Net7Player) game.add_nn_player('nn8', Net8Player) game.add_nn_player('nn9', Net9Player) print('Start game #', _id + 1) while not game.game_finished: sleep(0.01) plays = load(open('networks/plays', 'rb')) plays = sorted([(k, v) for k, v in plays.items()], key=lambda k: mean(k[1])) for i, play in enumerate(plays): pl = PlayManager.get_play_by_name(play[0]) print(f'{i+1:>4}. {round(mean(play[1]), 2):>6} {play[0]:>10} ' f'(ex {pl.exemplar:>6}) {"*" * pl.wins}') print('It took', datetime.now() - start_time) elif mode == Mode.UnitTest: self.start_unit_tests() elif mode == Mode.Learning: from learning.learning import Learning from learning.data_sets.decision_model.poker_decision_10 import PokerDecision10 from data.game_parser import GameParser from datetime import datetime learn = Learning() learn.create_data_set(PokerDecision10) start = datetime.now() # GameParser.parse_dir('pack1', False, False) # learn.add_data_set('pack1') # learn.save_data_set('nn11 common cards.txt') learn.load_data_set('nn11 common cards.txt') learn.learning('nn11 200x100x100') end = datetime.now() print('Learning took', end - start) elif mode == Mode.Search: from data.game_parser import GameParser GameParser.search_in_dir('pack1', 'Seat 10') else: raise BadMode('Bad mode') @staticmethod def start_unit_tests(): from unit_tests.testing import UnitTesting UnitTesting.test_all() @staticmethod def start_evolution(games: int, seats_on_table: int, players: int, start_money: int): from holdem.play.play_manager import PlayManager from learning.evolution import Evolution from core.blinds.scheme.schemes import Schemes PlayManager.standings() Evolution(games, seats_on_table, players, start_money, Schemes.Rapid.value).run()
aaaaaa2493/poker-engine
src/special/run.py
run.py
py
7,276
python
en
code
0
github-code
6
[ { "api_name": "special.mode.Mode", "line_number": 16, "usage_type": "name" }, { "api_name": "sys.argv", "line_number": 17, "usage_type": "argument" }, { "api_name": "sys.argv", "line_number": 18, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 33, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 33, "usage_type": "name" }, { "api_name": "special.mode.Mode.UnitTest", "line_number": 33, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 33, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 39, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 39, "usage_type": "name" }, { "api_name": "special.mode.Mode.Evolution", "line_number": 39, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 39, "usage_type": "name" }, { "api_name": "holdem.name_manager.NameManager.NicksPath", "line_number": 40, "usage_type": "attribute" }, { "api_name": "holdem.name_manager.NameManager", "line_number": 40, "usage_type": "name" }, { "api_name": "holdem.play.play_manager.PlayManager.PlayPath", "line_number": 41, "usage_type": "attribute" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 41, "usage_type": "name" }, { "api_name": "holdem.play.play_manager.PlayManager.GenCount", "line_number": 42, "usage_type": "attribute" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 42, "usage_type": "name" }, { "api_name": "holdem.name_manager.NameManager.remove_folder", "line_number": 44, "usage_type": "call" }, { "api_name": "holdem.name_manager.NameManager", "line_number": 44, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 48, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 48, "usage_type": "name" }, { "api_name": "special.mode.Mode.Parse", "line_number": 48, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 48, "usage_type": "name" }, { "api_name": "data.game_parser.PokerGame.converted_games_folder", "line_number": 49, "usage_type": "attribute" }, { "api_name": "data.game_parser.PokerGame", "line_number": 49, "usage_type": "name" }, { "api_name": "data.game_parser.PokerGame.converted_chat_folder", "line_number": 50, "usage_type": "attribute" }, { "api_name": "data.game_parser.PokerGame", "line_number": 50, "usage_type": "name" }, { "api_name": "data.game_parser.GameParser.parse_dir", "line_number": 51, "usage_type": "call" }, { "api_name": "data.game_parser.GameParser", "line_number": 51, "usage_type": "name" }, { "api_name": "data.game_parser.GameParser.copy_dir", "line_number": 53, "usage_type": "call" }, { "api_name": "data.game_parser.GameParser", "line_number": 53, "usage_type": "name" }, { "api_name": "data.game_parser.PokerGame.load_dir", "line_number": 54, "usage_type": "call" }, { "api_name": "data.game_parser.PokerGame", "line_number": 54, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 59, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 59, "usage_type": "name" }, { "api_name": "special.mode.Mode.Learning", "line_number": 59, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 59, "usage_type": "name" }, { "api_name": "learning.learning.Learning", "line_number": 60, "usage_type": "call" }, { "api_name": "learning.data_sets.decision_model.poker_decision.PokerDecision", "line_number": 61, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 71, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 71, "usage_type": "name" }, { "api_name": "special.mode.Mode.Testing", "line_number": 71, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 71, "usage_type": "name" }, { "api_name": "holdem.play.play_manager.PlayManager.PlayPath", "line_number": 72, "usage_type": "attribute" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 72, "usage_type": "name" }, { "api_name": "holdem.game.game.Game", "line_number": 73, "usage_type": "call" }, { "api_name": "holdem.player.neural_network.net1_net2_player.Net1Net2Player", "line_number": 76, "usage_type": "name" }, { "api_name": "holdem.play.play_manager.PlayManager.remove_folder", "line_number": 77, "usage_type": "call" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 77, "usage_type": "name" }, { "api_name": "special.settings.Settings.game_mode", "line_number": 83, "usage_type": "attribute" }, { "api_name": "special.settings.Settings", "line_number": 83, "usage_type": "name" }, { "api_name": "special.mode.Mode.GameEngine", "line_number": 84, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 84, "usage_type": "name" }, { "api_name": "holdem.game.game_manager.GameManager", "line_number": 87, "usage_type": "call" }, { "api_name": "special.mode.Mode.Parse", "line_number": 89, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 89, "usage_type": "name" }, { "api_name": "data.game_parser.GameParser.parse_dir", "line_number": 92, "usage_type": "call" }, { "api_name": "data.game_parser.GameParser", "line_number": 92, "usage_type": "name" }, { "api_name": "special.mode.Mode.Evolution", "line_number": 98, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 98, "usage_type": "name" }, { "api_name": "special.mode.Mode.Testing", "line_number": 101, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 101, "usage_type": "name" }, { "api_name": "datetime.datetime.now", "line_number": 118, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 118, "usage_type": "name" }, { "api_name": "holdem.game.game.Game", "line_number": 122, "usage_type": "call" }, { "api_name": "holdem.player.neural_network.net1_net2_player.Net1Net2Player", "line_number": 125, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net3_player.Net3Player", "line_number": 126, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net4_player.Net4Player", "line_number": 127, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net5_player.Net5Player", "line_number": 128, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net6_player.Net6Player", "line_number": 129, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net7_player.Net7Player", "line_number": 130, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net8_player.Net8Player", "line_number": 131, "usage_type": "name" }, { "api_name": "holdem.player.neural_network.net9_player.Net9Player", "line_number": 132, "usage_type": "name" }, { "api_name": "time.sleep", "line_number": 135, "usage_type": "call" }, { "api_name": "pickle.load", "line_number": 137, "usage_type": "call" }, { "api_name": "statistics.mean", "line_number": 138, "usage_type": "call" }, { "api_name": "holdem.play.play_manager.PlayManager.get_play_by_name", "line_number": 140, "usage_type": "call" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 140, "usage_type": "name" }, { "api_name": "statistics.mean", "line_number": 141, "usage_type": "call" }, { "api_name": "datetime.datetime.now", "line_number": 143, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 143, "usage_type": "name" }, { "api_name": "special.mode.Mode.UnitTest", "line_number": 145, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 145, "usage_type": "name" }, { "api_name": "special.mode.Mode.Learning", "line_number": 148, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 148, "usage_type": "name" }, { "api_name": "learning.learning.Learning", "line_number": 153, "usage_type": "call" }, { "api_name": "learning.data_sets.decision_model.poker_decision_10.PokerDecision10", "line_number": 154, "usage_type": "name" }, { "api_name": "datetime.datetime.now", "line_number": 155, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 155, "usage_type": "name" }, { "api_name": "datetime.datetime.now", "line_number": 161, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 161, "usage_type": "name" }, { "api_name": "special.mode.Mode.Search", "line_number": 164, "usage_type": "attribute" }, { "api_name": "special.mode.Mode", "line_number": 164, "usage_type": "name" }, { "api_name": "data.game_parser.GameParser.search_in_dir", "line_number": 166, "usage_type": "call" }, { "api_name": "data.game_parser.GameParser", "line_number": 166, "usage_type": "name" }, { "api_name": "unit_tests.testing.UnitTesting.test_all", "line_number": 174, "usage_type": "call" }, { "api_name": "unit_tests.testing.UnitTesting", "line_number": 174, "usage_type": "name" }, { "api_name": "holdem.play.play_manager.PlayManager.standings", "line_number": 181, "usage_type": "call" }, { "api_name": "holdem.play.play_manager.PlayManager", "line_number": 181, "usage_type": "name" }, { "api_name": "learning.evolution.Evolution", "line_number": 182, "usage_type": "call" }, { "api_name": "core.blinds.scheme.schemes.Schemes.Rapid", "line_number": 182, "usage_type": "attribute" }, { "api_name": "core.blinds.scheme.schemes.Schemes", "line_number": 182, "usage_type": "name" } ]
71000611389
import mob import room from time import sleep import pygame pygame.init() size = (800, 600) screen = pygame.display.set_mode(size) pygame.display.set_caption("Bran's Cool Game") # Define some colors BLACK = ( 0, 0, 0) WHITE = ( 255, 255, 255) GREEN = ( 0, 255, 0) RED = ( 255, 0, 0) BLUE = ( 0, 0, 255) # Player Initialization player_state = 0 # none (0), inv (1), menu (2), event (3), movement (4), choice (5) mc = mob.Mob("Bran") # Initialize Player character img = pygame.image.load("base.bmp").convert() #Import image img.set_colorkey(WHITE) # bg transparency mc_rect = img.get_rect() # Use this rect to do collision detection! mc_rect.x = mc.x mc_rect.y = mc.y inv_open = 0 menu_wait = 0 # Move Rect first, check for collision, then move if safe #Room Initialization curr_room = 0 # Index of current room rooms = [] rooms.append(room.Room("test_room")) # Loop until the user clicks the close button. done = False # Used to manage how fast the screen updates clock = pygame.time.Clock() # -------- Main Program Loop ----------- pygame.key.set_repeat(1, 1) while not done: # --- Main event loop if pygame.event.get(pygame.QUIT): done = True blocked = [0, 0, 0, 0] # Down, Up, Left, and Right block states keys = pygame.key.get_pressed() # Returns a list of key statuses if keys[pygame.K_SPACE]: print("Rect", mc_rect.x, " ", mc_rect.y) print("MC", mc.x, " ", mc.y) if keys[pygame.K_x]: if inv_open == 1: inv_open = 0 sleep(1) else: inv_open = 1 sleep(1) if keys[pygame.K_DOWN]: # Loop through bounds, comparing y + 5 coord to list and change blocked to 1 if # a match is found. Then do an if to check if blocked[x] is 1 before continuing on. # After that, revert blocked mc_rect = mc_rect.move(0, 5) if mc_rect.collidelist(rooms[curr_room].rects) != -1: print("COLLISION D") mc_rect = mc_rect.move(0, -5) else: mc.y += 5 if keys[pygame.K_UP]: mc_rect = mc_rect.move(0, -5) if mc_rect.collidelist(rooms[curr_room].rects) != -1: print("COLLISION U") mc_rect = mc_rect.move(0, 5) else: mc.y -= 5 if keys[pygame.K_LEFT]: mc_rect = mc_rect.move(-5, 0) if mc_rect.collidelist(rooms[curr_room].rects) != -1: print("COLLISION L") mc_rect = mc_rect.move(5, 0) else: mc.x -= 5 if keys[pygame.K_RIGHT]: mc_rect = mc_rect.move(5, 0) if mc_rect.collidelist(rooms[curr_room].rects) != -1: print("COLLISION R") mc_rect = mc_rect.move(-5, 0) else: mc.x += 5 # --- Game logic should go here # Wall collision test # --- Drawing code should go here # bottom layer screen.fill(WHITE) rooms[curr_room].build_walls(screen) '''Draw room function''' '''Mobs and items draw functions''' '''MC draw function''' screen.blit(img, [mc.x, mc.y], [0, 0, 100, 100]) # x1, y1, w, h (of image) if inv_open == 1: pygame.draw.rect(screen, BLACK, [0, 400, 800, 750]) # Dialog/Inventory BlkBox pygame.draw.rect(screen, WHITE, [25, 425, 125, 150]) # Dialog/Inventory Pic pygame.draw.rect(screen, WHITE, [400, 450, 100, 100]) # Dialog/Inventory Box1 pygame.draw.rect(screen, WHITE, [525, 450, 100, 100]) # Dialog/Inventory Box2 pygame.draw.rect(screen, WHITE, [650, 450, 100, 100]) # Dialog/Inventory Box3 pygame.draw.rect(screen, WHITE, [275, 450, 100, 100]) # Dialog/Inventory Box4 # topmost layer # --- Go ahead and update the screen with what we've drawn. pygame.display.flip() # --- Limit to 60 frames per second clock.tick(60) pygame.quit()
heroicbran/games
Bran_s Pygame Engine/main.py
main.py
py
4,050
python
en
code
0
github-code
6
[ { "api_name": "pygame.init", "line_number": 6, "usage_type": "call" }, { "api_name": "pygame.display.set_mode", "line_number": 8, "usage_type": "call" }, { "api_name": "pygame.display", "line_number": 8, "usage_type": "attribute" }, { "api_name": "pygame.display.set_caption", "line_number": 9, "usage_type": "call" }, { "api_name": "pygame.display", "line_number": 9, "usage_type": "attribute" }, { "api_name": "mob.Mob", "line_number": 21, "usage_type": "call" }, { "api_name": "pygame.image.load", "line_number": 22, "usage_type": "call" }, { "api_name": "pygame.image", "line_number": 22, "usage_type": "attribute" }, { "api_name": "room.Room", "line_number": 35, "usage_type": "call" }, { "api_name": "pygame.time.Clock", "line_number": 41, "usage_type": "call" }, { "api_name": "pygame.time", "line_number": 41, "usage_type": "attribute" }, { "api_name": "pygame.key.set_repeat", "line_number": 44, "usage_type": "call" }, { "api_name": "pygame.key", "line_number": 44, "usage_type": "attribute" }, { "api_name": "pygame.event.get", "line_number": 47, "usage_type": "call" }, { "api_name": "pygame.event", "line_number": 47, "usage_type": "attribute" }, { "api_name": "pygame.QUIT", "line_number": 47, "usage_type": "attribute" }, { "api_name": "pygame.key.get_pressed", "line_number": 51, "usage_type": "call" }, { "api_name": "pygame.key", "line_number": 51, "usage_type": "attribute" }, { "api_name": "pygame.K_SPACE", "line_number": 53, "usage_type": "attribute" }, { "api_name": "pygame.K_x", "line_number": 57, "usage_type": "attribute" }, { "api_name": "time.sleep", "line_number": 60, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 64, "usage_type": "call" }, { "api_name": "pygame.K_DOWN", "line_number": 67, "usage_type": "attribute" }, { "api_name": "pygame.K_UP", "line_number": 78, "usage_type": "attribute" }, { "api_name": "pygame.K_LEFT", "line_number": 86, "usage_type": "attribute" }, { "api_name": "pygame.K_RIGHT", "line_number": 94, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 120, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 120, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 121, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 121, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 123, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 123, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 124, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 124, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 125, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 125, "usage_type": "attribute" }, { "api_name": "pygame.draw.rect", "line_number": 126, "usage_type": "call" }, { "api_name": "pygame.draw", "line_number": 126, "usage_type": "attribute" }, { "api_name": "pygame.display.flip", "line_number": 132, "usage_type": "call" }, { "api_name": "pygame.display", "line_number": 132, "usage_type": "attribute" }, { "api_name": "pygame.quit", "line_number": 137, "usage_type": "call" } ]
69958425789
"""Test delete model @Author: NguyenKhacThanh """ import pytest from voluptuous import Schema, All, Required from tests.api import APITestCase @pytest.mark.usefixtures("inject_client", "inject_params_model_regression") class DeleteModelTestCase(APITestCase): def url(self): return "/regression" def method(self): return "DELETE" def test_success(self): # push data res = self.client.post( "/api/v1/regression/huber", json=self.params["huber"] ) code, body = self.call_api( url=f"/regression/{res.get_json()['id']}", ) schema = Schema({ Required("message"): str }) schema(body) assert 200 == code, body["message"] def test_id_model_not_found(self): code, body = self.call_api( url="/regression/123456781234567812345678", ) schema = Schema({ Required("message"): str }) schema(body) assert 400 == code, body["message"]
magiskboy/wipm
tests/api/test_delete_model_regression.py
test_delete_model_regression.py
py
1,047
python
en
code
0
github-code
6
[ { "api_name": "tests.api.APITestCase", "line_number": 10, "usage_type": "name" }, { "api_name": "voluptuous.Schema", "line_number": 26, "usage_type": "call" }, { "api_name": "voluptuous.Required", "line_number": 27, "usage_type": "call" }, { "api_name": "voluptuous.Schema", "line_number": 37, "usage_type": "call" }, { "api_name": "voluptuous.Required", "line_number": 38, "usage_type": "call" }, { "api_name": "pytest.mark.usefixtures", "line_number": 9, "usage_type": "call" }, { "api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute" } ]
41168902007
from utils import get_input_lines from numpy import median from collections import Counter lines = get_input_lines("input7.txt") input_list = [int(x) for x in lines[0].split(",")] # pt1 print(sum(abs(x - median(input_list)) for x in input_list)) # pt2 mi, mx = min(input_list), max(input_list) fuel_required = Counter({ elem:0 for elem in range(mi, mx+1) }) sum_one_to_n = lambda n: (n * (n + 1)) // 2 for i in range(mi, mx+1): for e in input_list: fuel_required[i] += sum_one_to_n(abs(e - i)) print(fuel_required.most_common()[-1])
sakshaat/aoc
solution7.py
solution7.py
py
551
python
en
code
0
github-code
6
[ { "api_name": "utils.get_input_lines", "line_number": 5, "usage_type": "call" }, { "api_name": "numpy.median", "line_number": 9, "usage_type": "call" }, { "api_name": "collections.Counter", "line_number": 13, "usage_type": "call" } ]
6117900800
import os import sys import numpy as np import pandas as pd import argparse import pybedtools import re import pyBigWig as pbw import time import urllib.request def main(): start = time.time() print("Generating consensus peak file...") args = parse_args() #for Input_peaks=pd.read_csv(args.Peaks, sep="\t") # Current peaks = for i in range(0, len(Input_peaks.index)): urllib.request.urlretrieve(Input_peaks.iloc[i]["URL"], "../tmp/tmp_peak.bed.gz") Current_peaks=pybedtools.BedTool("../tmp/tmp_peak.bed.gz") Current_peaks_pd=pd.read_table(Current_peaks.fn) Current_peaks_pd.to_csv("../tmp/Concat_peaks.bed", mode='a', header=False, sep="\t", index=False) # Formatted peaks = print(str(len(Input_peaks.index))+" peak files read in "+str(time.time()-start)+" seconds...") Concat_peaks=pybedtools.BedTool("../tmp/Concat_peaks.bed") Concat_peaks_sorted=Concat_peaks.sort() Concat_peaks_merged=Concat_peaks_sorted.merge(d=0) Concat_peaks_merged.saveas(args.outdir+args.prefix+"_Consensus_peaks.bed") def parse_args(): """ Load command line args """ parser = argparse.ArgumentParser() parser.add_argument('--prefix', metavar="<str>", help=("Output prefix"), type=str, required=True) parser.add_argument('--Peaks', metavar="<str>", help=("Input peak URLs"), type=str, required=True) parser.add_argument('--outdir', metavar="<str>", help=("Output directory"), type=str, required=True) args = parser.parse_args() return args if __name__ == '__main__': main() # python Generate_consensus_peaks.py --prefix BLUEPRINT --Peaks ~/BLUEPRINT_peak_URLs.tsv --outdir ~/BLUEPRINT_peaks/ #
xyg123/SNP_enrich_preprocess
scripts/CHEERS_preprocessing/Generate_consensus_peaks.py
Generate_consensus_peaks.py
py
1,717
python
en
code
1
github-code
6
[ { "api_name": "time.time", "line_number": 14, "usage_type": "call" }, { "api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call" }, { "api_name": "urllib.request.request.urlretrieve", "line_number": 22, "usage_type": "call" }, { "api_name": "urllib.request.request", "line_number": 22, "usage_type": "attribute" }, { "api_name": "urllib.request", "line_number": 22, "usage_type": "name" }, { "api_name": "pybedtools.BedTool", "line_number": 23, "usage_type": "call" }, { "api_name": "pandas.read_table", "line_number": 24, "usage_type": "call" }, { "api_name": "time.time", "line_number": 29, "usage_type": "call" }, { "api_name": "pybedtools.BedTool", "line_number": 31, "usage_type": "call" }, { "api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call" } ]
10420803323
from __future__ import annotations from typing import TYPE_CHECKING from randovania.game_description.pickup import pickup_category from randovania.game_description.pickup.pickup_entry import PickupEntry, PickupGeneratorParams, PickupModel from randovania.game_description.resources.location_category import LocationCategory from randovania.games.prime3.patcher import corruption_items from randovania.generator.pickup_pool import PoolResults if TYPE_CHECKING: from randovania.game_description.resources.resource_database import ResourceDatabase ENERGY_CELL_CATEGORY = pickup_category.PickupCategory( name="energy_cell", long_name="Energy Cell", hint_details=("an ", "energy cell"), hinted_as_major=True, is_key=True ) def add_energy_cells( resource_database: ResourceDatabase, ) -> PoolResults: """ :param resource_database: :return: """ item_pool: list[PickupEntry] = [] for i in range(9): item_pool.append(create_energy_cell(i, resource_database)) return PoolResults(item_pool, {}, []) def create_energy_cell( cell_index: int, resource_database: ResourceDatabase, ) -> PickupEntry: return PickupEntry( name=f"Energy Cell {cell_index + 1}", progression=((resource_database.get_item(corruption_items.ENERGY_CELL_ITEMS[cell_index]), 1),), extra_resources=( (resource_database.get_item(corruption_items.ENERGY_CELL_TOTAL_ITEM), 1), (resource_database.get_item(corruption_items.PERCENTAGE), 1), ), model=PickupModel( game=resource_database.game_enum, name=corruption_items.ENERGY_CELL_MODEL, ), pickup_category=ENERGY_CELL_CATEGORY, broad_category=pickup_category.GENERIC_KEY_CATEGORY, generator_params=PickupGeneratorParams( preferred_location_category=LocationCategory.MAJOR, probability_offset=0.25, ), )
randovania/randovania
randovania/games/prime3/generator/pickup_pool/energy_cells.py
energy_cells.py
py
1,931
python
en
code
165
github-code
6
[ { "api_name": "typing.TYPE_CHECKING", "line_number": 11, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_category.PickupCategory", "line_number": 14, "usage_type": "call" }, { "api_name": "randovania.game_description.pickup.pickup_category", "line_number": 14, "usage_type": "name" }, { "api_name": "randovania.game_description.resources.resource_database.ResourceDatabase", "line_number": 20, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_entry.PickupEntry", "line_number": 26, "usage_type": "name" }, { "api_name": "randovania.generator.pickup_pool.PoolResults", "line_number": 31, "usage_type": "call" }, { "api_name": "randovania.generator.pickup_pool.PoolResults", "line_number": 21, "usage_type": "name" }, { "api_name": "randovania.game_description.resources.resource_database.ResourceDatabase", "line_number": 36, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_entry.PickupEntry", "line_number": 38, "usage_type": "call" }, { "api_name": "randovania.games.prime3.patcher.corruption_items.ENERGY_CELL_ITEMS", "line_number": 40, "usage_type": "attribute" }, { "api_name": "randovania.games.prime3.patcher.corruption_items", "line_number": 40, "usage_type": "name" }, { "api_name": "randovania.games.prime3.patcher.corruption_items.ENERGY_CELL_TOTAL_ITEM", "line_number": 42, "usage_type": "attribute" }, { "api_name": "randovania.games.prime3.patcher.corruption_items", "line_number": 42, "usage_type": "name" }, { "api_name": "randovania.games.prime3.patcher.corruption_items.PERCENTAGE", "line_number": 43, "usage_type": "attribute" }, { "api_name": "randovania.games.prime3.patcher.corruption_items", "line_number": 43, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_entry.PickupModel", "line_number": 45, "usage_type": "call" }, { "api_name": "randovania.games.prime3.patcher.corruption_items.ENERGY_CELL_MODEL", "line_number": 47, "usage_type": "attribute" }, { "api_name": "randovania.games.prime3.patcher.corruption_items", "line_number": 47, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_category.GENERIC_KEY_CATEGORY", "line_number": 50, "usage_type": "attribute" }, { "api_name": "randovania.game_description.pickup.pickup_category", "line_number": 50, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_entry.PickupGeneratorParams", "line_number": 51, "usage_type": "call" }, { "api_name": "randovania.game_description.resources.location_category.LocationCategory.MAJOR", "line_number": 52, "usage_type": "attribute" }, { "api_name": "randovania.game_description.resources.location_category.LocationCategory", "line_number": 52, "usage_type": "name" }, { "api_name": "randovania.game_description.pickup.pickup_entry.PickupEntry", "line_number": 37, "usage_type": "name" } ]
19716184286
import os, subprocess, datetime, re, shlex class TortoiseSVNManager: def __init__(self, tortoisesvn=None): if tortoisesvn == None: print("\n\n None Path - TortoiseProc.exe") os.system("Pause") sys.exit() else: self.tortoisesvn = tortoisesvn def makecommitmsg(self, buildversion, commitmsg): # Make Commit Message try: with open(r'./commitmsg.txt', "w") as f: buildversion = re.sub(",", ", ", buildversion) f.write(commitmsg + "\n\n" + buildversion) except FileNotFoundError: return False return True def commit(self, projectlist): # Ensure TortoiseProc exists if not os.path.isfile(self.tortoisesvn + '\\TortoiseProc.exe'): raise Exception('TortoiseProc.exe not found. path=' + self.tortoisesvn + '\\TortoiseProc.exe') commitmsgpath = os.getcwd() os.chdir(self.tortoisesvn) for project in projectlist: if project["isuse"] == "1": print("PROGRESSING COMMIT - " + project["project_path"] + "\n") command = 'TortoiseProc.exe' command += ' /command:commit' command += (' /path:' + project["project_path"]) command += (' /logmsgfile:"' + commitmsgpath + '\\commitmsg.txt"') command += ' /closeonend:0' os.system(command) print("\n") return True def run(self, buildversion=None, projectlist=None, commitmsg=None): summary = '' # File header start = datetime.datetime.now() print('\n' * 3) summary += self.log('STARTED SVN COMMIT - ' + start.strftime("%Y-%m-%d %H:%M:%S")) # Make Commit Message if (buildversion is not None) and (commitmsg is not None): makeOk = self.makecommitmsg(buildversion, commitmsg) if not makeOk: self.log('COMMIT: FAILED - FILE NOT FOUND', start) sys.exit(100) summary += self.log('COMMIT: SUCCEEDED - MAKE COMMIT MESSAGE', start) else: summary += self.log('COMMIT: NOT SPECIFIED') # Commit if projectlist is not None: commitOK = self.commit(projectlist) if not commitOK: self.log('COMMIT: FAILED', start) sys.exit(100) summary += self.log('COMMIT: SUCCEEDED', start) else: summary += self.log('COMMIT: NOT SPECIFIED - PROJECT LIST') summary += self.log('COMMIT: *** FINISH ***', start) # Build summary print('\n\n' + '-' * 80) print(summary) print('-' * 80) def log(self, message, start=None): timestamp = '' numsecs = '' if start is not None: split = datetime.datetime.now() diff = split - start timestamp = split.strftime("%Y-%m-%d %H:%M:%S") + '\t' numsecs = ' (' + str(diff.seconds) + ' seconds)' msg = timestamp + message + numsecs + '\n\n' print('=' * 10 + '> ' + msg) return msg
Nohhhhhh/PUBLIC
Project/AutoDeployment/AutoDeployment/tortoisesvnmanager.py
tortoisesvnmanager.py
py
3,153
python
en
code
1
github-code
6
[ { "api_name": "os.system", "line_number": 7, "usage_type": "call" }, { "api_name": "re.sub", "line_number": 16, "usage_type": "call" }, { "api_name": "os.path.isfile", "line_number": 25, "usage_type": "call" }, { "api_name": "os.path", "line_number": 25, "usage_type": "attribute" }, { "api_name": "os.getcwd", "line_number": 28, "usage_type": "call" }, { "api_name": "os.chdir", "line_number": 29, "usage_type": "call" }, { "api_name": "os.system", "line_number": 40, "usage_type": "call" }, { "api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute" }, { "api_name": "datetime.datetime.now", "line_number": 87, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 87, "usage_type": "attribute" } ]
29216462786
from enum import Enum, unique from sqlalchemy import ( Column, Table, MetaData, Integer, String, ForeignKey, Enum as PgEnum, DateTime, PrimaryKeyConstraint, UniqueConstraint ) convention = { 'all_column_names': lambda constraint, table: '_'.join([ column.name for column in constraint.columns.values() ]), 'ix': 'ix__%(table_name)s__%(all_column_names)s', 'uq': 'uq__%(table_name)s__%(all_column_names)s', 'ck': 'ck__%(table_name)s__%(constraint_name)s', 'fk': 'fk__%(table_name)s__%(all_column_names)s__%(referred_table_name)s', 'pk': 'pk__%(table_name)s' } metadata = MetaData(naming_convention=convention) @unique class ShopUnitType(Enum): OFFER = 'OFFER' CATEGORY = 'CATEGORY' shop_unit_ids_table = Table( 'shop_unit_ids', metadata, Column('id', String, primary_key=True), ) shop_unit_revisions_table = Table( 'shop_unit_revisions', metadata, Column('id', Integer, primary_key=True, autoincrement=True), Column('date', DateTime, nullable=False), Column('shop_unit_id', String, ForeignKey('shop_unit_ids.id', ondelete='CASCADE', onupdate='RESTRICT'), nullable=False), Column('name', String, nullable=False), Column('price', Integer, nullable=True), Column('type', PgEnum(ShopUnitType, name='shop_unit_type'), nullable=False), UniqueConstraint('shop_unit_id', 'date', name='uq__shop_unit_revisions__shop_unit_id_date'), ) relations_table = Table( 'relations', metadata, Column('child_revision_id', Integer, ForeignKey('shop_unit_revisions.id', ondelete='CASCADE', onupdate='RESTRICT'), nullable=False), Column('parent_id', String, ForeignKey('shop_unit_ids.id', ondelete='RESTRICT', onupdate='CASCADE'), nullable=False), UniqueConstraint('child_revision_id', 'parent_id'), PrimaryKeyConstraint('child_revision_id', name='pk__relations'), )
Dest0re/backend-school2022
megamarket/db/schema.py
schema.py
py
1,911
python
en
code
0
github-code
6
[ { "api_name": "sqlalchemy.MetaData", "line_number": 20, "usage_type": "call" }, { "api_name": "enum.Enum", "line_number": 24, "usage_type": "name" }, { "api_name": "enum.unique", "line_number": 23, "usage_type": "name" }, { "api_name": "sqlalchemy.Table", "line_number": 29, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "argument" }, { "api_name": "sqlalchemy.Table", "line_number": 34, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call" }, { "api_name": "sqlalchemy.Integer", "line_number": 36, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call" }, { "api_name": "sqlalchemy.DateTime", "line_number": 37, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "argument" }, { "api_name": "sqlalchemy.ForeignKey", "line_number": 39, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call" }, { "api_name": "sqlalchemy.Integer", "line_number": 41, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call" }, { "api_name": "sqlalchemy.Enum", "line_number": 42, "usage_type": "call" }, { "api_name": "sqlalchemy.UniqueConstraint", "line_number": 43, "usage_type": "call" }, { "api_name": "sqlalchemy.Table", "line_number": 46, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call" }, { "api_name": "sqlalchemy.Integer", "line_number": 48, "usage_type": "argument" }, { "api_name": "sqlalchemy.ForeignKey", "line_number": 49, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 51, "usage_type": "argument" }, { "api_name": "sqlalchemy.ForeignKey", "line_number": 52, "usage_type": "call" }, { "api_name": "sqlalchemy.UniqueConstraint", "line_number": 53, "usage_type": "call" }, { "api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 54, "usage_type": "call" } ]
22079658709
import numpy as np from termcolor import colored import matplotlib.pyplot as plt filename = 'scan1.txt' file = open(filename, 'r') lines = file.readlines() inittime=lines[0] endtime=lines[1] print('# of lines',len(lines)) ADCout=[] ADCoutstepsMean=[] ADCoutstepsStd=[] i=2 data=len(lines) while i<data: ADCout.append(float(lines[i])) i+=1 xaxis=range(data-2) plt.plot(xaxis, ADCout, marker='.', linestyle='') plt.grid(True) plt.title(filename) plt.xlabel('sample') plt.ylabel('ADC output (V)') plt.tight_layout() plt.savefig(filename[:-4]+'.png') plt.show()
gpapad14/RPy_CROC
18bit_ADC_data/analysis.py
analysis.py
py
568
python
en
code
0
github-code
6
[ { "api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.grid", "line_number": 26, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.title", "line_number": 27, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.ylabel", "line_number": 29, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.tight_layout", "line_number": 30, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.savefig", "line_number": 31, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name" } ]
5051482507
import csv import sys import getopt import numpy as np import pandas as pd import nltk def get_dataframe(filename): return pd.read_table(filename) def get_hfw(): word_file = open('./picked/pacifier.txt', 'r') res = list() for word in word_file.readlines(): word = word.split(" ")[0] res.append(word) return res def get_hfw_weight(): word_file = open('./picked/pacifier.txt', 'r') res = list() for word in word_file.readlines(): for weigth in word.split(" "): try: res.append(float(weigth)) except ValueError: pass return res def get_adj(): word_file = open('./picked/pacifier_a.txt', 'r') res_p = list() res_n = list() for word in word_file.readlines(): is_positive = False if "1" in word.split(" ") or "1\n" in word.split(" "): is_positive = True word = word.split(" ")[0] if is_positive: res_p.append(word) else: res_n.append(word) return (res_p, res_n) def get_brands(): table = get_dataframe("/source_file_path/Problem_C_Data/pacifier.tsv") product_titles = table[table['helpful_votes']!=0].product_title.tolist() count = {} for t in product_titles: count[t] = count.get(t, 0) + 1 res = list() for title in count: if count[title] > 5: res.append(title) return res def get_staratings(product_title): table = get_dataframe("/source_file_path/Problem_C_Data/pacifier.tsv") product_stars = table[table['product_title']==product_title].star_rating.tolist() product_votes = table[table['product_title']==product_title].helpful_votes.tolist() res = 0.0 count = 0 for i in range(len(product_stars)): res += product_stars[i] * product_votes[i] count += product_votes[i] return res/count def get_sentence(product_title): table = get_dataframe("/source_file_path/Problem_C_Data/pacifier.tsv") product_reviews = table[table['product_title']==product_title].review_body.tolist() product_review_titles = table[table['product_title']==product_title].review_headline.tolist() ''' product_reviews = table.review_body.tolist() product_review_titles = table.review_headline.tolist() ''' product_reviews.extend(product_review_titles) tokenizer = nltk.data.load('tokenizers/punkt/english.pickle') sentences = list() for paragraph in product_reviews: try: sentences.extend(tokenizer.tokenize(paragraph)) except: continue finally: pass return sentences def get_pairs(product_title): # print("----------"+product_title+"----------") hfw = get_hfw() product_reviews = get_sentence(product_title) counts = {} for rw in product_reviews: tokens = nltk.word_tokenize(rw) for hf_word in hfw: if hf_word in tokens: pos_tags = nltk.pos_tag(tokens) last_token = "" for token, pos in pos_tags: if pos == "JJ" or pos == "JJS" or pos == "JJR": tmp_pair=(hf_word.lower(), token.lower()) if last_token != "not" and last_token != "barely" and last_token != "hardly": counts[tmp_pair] = counts.get(tmp_pair, 0) + 1 last_token = token return counts def compute_vector(brandname): adjs = get_adj() positive_adj = adjs[0] negative_adj = adjs[1] dimension = get_hfw() pair_counts = get_pairs(brandname) items = list(pair_counts.items()) items.sort(key=lambda x:x[1], reverse=True) vector = [] # each dimension for d in dimension: val = 0 adj_count = 0 dimension_score = 0 # iteration in pairs to for pairs_ct in items: pairs, count = pairs_ct count = int(count) if pairs[0] == d: if pairs[1] in positive_adj: val += 1 * count elif pairs[1] in negative_adj: val -= 1 * count adj_count += count if adj_count != 0: dimension_score = val / adj_count dimension_res = (d, dimension_score) vector.append(dimension_res) return vector def compute_value(brandname): vector = compute_vector(brandname) value = 0.0 weights = get_hfw_weight() total = 0.0 for w in weights: total += w st_weight = list() for w in weights: st_weight.append(w/total) for i in range(len(vector)): value += vector[i][1] * st_weight[i] return value def main(): items = get_brands() score_line = "" star_line = "" for i in items: score = compute_value(i) star = get_staratings(i) if True: #star < (20*score+2.5) and star > (1.25*16 / 3)*(score+0.125): score_line += str(score) + ", " star_line += str(star) + ", " print(score_line) print(star_line) # main() # compute_vector() # get_sentence("samsung smh1816s 1.8 cu. ft. stainless steel over-the-range pacifier") main()
WXM99/DataMiningProject
python_scripts/project/script/text_based_analysis/product_analysis/stat_product_score.py
stat_product_score.py
py
5,220
python
en
code
0
github-code
6
[ { "api_name": "pandas.read_table", "line_number": 9, "usage_type": "call" }, { "api_name": "nltk.data.load", "line_number": 77, "usage_type": "call" }, { "api_name": "nltk.data", "line_number": 77, "usage_type": "attribute" }, { "api_name": "nltk.word_tokenize", "line_number": 94, "usage_type": "call" }, { "api_name": "nltk.pos_tag", "line_number": 97, "usage_type": "call" } ]
23937560939
import torch import torch.nn as nn # Tuple is structured by (filters, kernel_size, stride) ''' Information about architecture config: Tuple is structured by (filters, kernel_size, stride) Every conv is a same convolution. List is structured by "B" indicating a residual block followed by the number of repeats "S" is for scale prediction block and computing the yolo loss "U" is for upsampling the feature map and concatenating with a previous layer ''' # ['B',重复次数] config = [ (32, 3, 1), (64, 3, 2), ["B", 1], (128, 3, 2), ["B", 2], (256, 3, 2), ["B", 8], (512, 3, 2), ["B", 8], (1024, 3, 2), ["B", 4], # To this point is Darknet-53 (512, 1, 1), (1024, 3, 1), "S", (256, 1, 1), "U", (256, 1, 1), (512, 3, 1), "S", (128, 1, 1), "U", (128, 1, 1), (256, 3, 1), "S", ] class CNNBlock(nn.Module): def __init__(self,in_channels,out_channels,bn_act=True,**kwargs): super().__init__() self.conv = nn.Conv2d(in_channels,out_channels,bias=not bn_act,**kwargs) self.bn = nn.BatchNorm2d(out_channels) self.leaky = nn.LeakyReLU(0.1) self.use_bn_act = bn_act def forward(self,x): if self.use_bn_act: self.leaky(self.bn(self.conv)) else : return self.conv(x) class ResidualBlock(nn.Module): def __init__(self,channels,use_residual=True,num_repeats = 1): super().__init__() self.layers = nn.ModuleList() for _ in num_repeats: self.layers +=[ CNNBlock(channels,channels//2,kernel_size=1), CNNBlock(channels//2,channels,kernel_size=3,padding=1) ] self.use_residual = use_residual self.num_repeats = num_repeats def forward(self): for layers in self.layers: x = layers(x)+x if self.use_residual else layers(x) return x class ScalePrediction(nn.Module): pass class YOLOv3(nn.Module): pass
1zzc/yolov3_achieve
model.py
model.py
py
1,917
python
en
code
0
github-code
6
[ { "api_name": "torch.nn.Module", "line_number": 44, "usage_type": "attribute" }, { "api_name": "torch.nn", "line_number": 44, "usage_type": "name" }, { "api_name": "torch.nn.Conv2d", "line_number": 47, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 47, "usage_type": "name" }, { "api_name": "torch.nn.BatchNorm2d", "line_number": 48, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 48, "usage_type": "name" }, { "api_name": "torch.nn.LeakyReLU", "line_number": 50, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 50, "usage_type": "name" }, { "api_name": "torch.nn.Module", "line_number": 64, "usage_type": "attribute" }, { "api_name": "torch.nn", "line_number": 64, "usage_type": "name" }, { "api_name": "torch.nn.ModuleList", "line_number": 67, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 67, "usage_type": "name" }, { "api_name": "torch.nn.Module", "line_number": 84, "usage_type": "attribute" }, { "api_name": "torch.nn", "line_number": 84, "usage_type": "name" }, { "api_name": "torch.nn.Module", "line_number": 88, "usage_type": "attribute" }, { "api_name": "torch.nn", "line_number": 88, "usage_type": "name" } ]
38165912553
import warnings from typing import List import numpy as np from scipy import linalg class Tensor(np.ndarray): def __new__(cls, num_modes: int, modes: tuple[int], data: np.ndarray): obj = np.asarray(data).view(cls) obj.num_modes = num_modes obj.modes = np.asarray(modes) if np.any(obj.modes) <= 0: raise ValueError( "'modes' must contain strictly positive values; if np.any mode is 1, consider a smaller num_modes") return obj # Pour pouvoir accéder aux données via .data et mimiquer rTensor. Mais inutile, à déprécier @property def data(self): return self[...] def is_zero_tensor(self): if np.sum(self.data == 0) == np.prod(np.asarray(self.modes)): return True return False def astensor(array: np.ndarray) -> Tensor: modes = array.shape num_modes = len(modes) return Tensor(num_modes=num_modes, modes=modes, data=array) def unfold(tensor: Tensor, row_idx: List[int], col_idx: List[int], order='F') -> Tensor: rs = np.asarray(row_idx) cs = np.asarray(col_idx) # if not rs or not cs: raise ValueError("row and column indices must be specified") num_modes = tensor.num_modes # pour démarrer à zero if len(rs) + len(cs) != num_modes: raise ValueError("Incorrect number of indices. Number of modes not reached.") if np.any(rs < 0) or np.any(rs > num_modes - 1) or np.any(cs < 0) or np.any(cs > num_modes - 1): raise ValueError( "Illegal indices specified. 'row_idx' and 'col_idx' must be positive and strictly less than 'num_modes'.") perm = np.array(list(rs) + list(cs)) if np.any(np.sort(perm) != np.arange(num_modes)): raise ValueError("Missing and/or repeated indices") modes = tensor.modes mat = tensor.data new_modes = np.array([np.prod(modes[rs]), np.prod(modes[cs])]) mat = np.transpose(tensor.data, perm) # probablement soustraire -1 à perm pour les indices mat = mat.reshape(new_modes, order=order) # rearrangement style fortran comme pour dim() <- dim en R: # https://rstudio.github.io/reticulate/reference/array_reshape.html return astensor(mat) def rs_unfold(tensor: Tensor, m: int, order='F') -> Tensor: assert 0 <= m < tensor.num_modes, f"'m' must be a valid mode of the tensor, not {m}." rs = np.asarray([m]) cs = np.asarray([i for i in range(tensor.num_modes) if i != m]) return unfold(tensor, row_idx=rs, col_idx=cs, order=order) # Validé par essai manuel def superdiagonal_tensor2(num_modes, length, elements=1): modes = [length] * num_modes arr = np.zeros(modes, dtype=np.float32) if isinstance(elements, int): elements = [elements] * length for i in range(length): indices = [i] * num_modes arr[tuple(indices)] = elements[i] return astensor(arr) # L'implémentation originale pernd une liste en argument, et les multiplie entre elles, "element-wise". # L'opération est largement simplifiée avec un ndarray # Vérifiée à la main # def hadamard_list(L: np.ndarray) -> np.ndarray: # # TODO: Verif forme des tableaux, et de la nature de L`` # # return np.prod(L, axis=-1) # typiquement axis=2 dans notre cas # # retmat = L[0] # for matrice in L[1:]: # retmat = np.multiply(retmat, matrice) # return retmat def hadamard_list(L): retmat = L[0] for matrice in L[1:]: retmat *= matrice return retmat def kronecker_list(L): result = L[0] for matrix in L[1:]: result = np.kron(result, matrix) return result def superdiagonal_tensor(num_modes, length, elements=1): modes = np.repeat(length, num_modes) arr = np.zeros(modes) if isinstance(elements, int) == 1: elements = np.repeat(elements, length) for i in range(length): txt = "arr[" + ",".join([str(i)] * num_modes) + "]=" + str(elements[i]) txt = txt.replace(" ", ", ") print(txt) exec(txt) return arr def khatri_rao_list_2(L, reverse=False): if reverse: L = L[::-1] retmat = L[0] for matrice in L[1:]: retmat = linalg.khatri_rao(retmat, matrice) return retmat def khatri_rao_list(L, reverse=False): assert all([isinstance(x, np.ndarray) for x in L]), "All elements in L must be matrices" ncols = [x.shape[1] for x in L] assert len(set(ncols)) == 1, "All matrices in L must have the same number of columns" ncols = ncols[0] nrows = [x.shape[0] for x in L] retmat = np.zeros((np.prod(nrows), ncols)) if reverse: L = L[::-1] for j in range(ncols): Lj = [x[:, j] for x in L] retmat[:, j] = kronecker_list(Lj) return retmat def khatri_rao_list_bis(L, reverse=False): # Vérifie que tous les éléments de L sont des matrices assert all(isinstance(matrix, np.ndarray) for matrix in L), "Tous les éléments de L doivent être des matrices" # Vérifie que toutes les matrices ont le même nombre de colonnes ncols = [matrix.shape[1] for matrix in L] assert len(set(ncols)) == 1, "Toutes les matrices doivent avoir le même nombre de colonnes" ncols = ncols[0] # Initialise la matrice résultante nrows = [matrix.shape[0] for matrix in L] retmat = np.zeros((np.prod(nrows), ncols)) # Inverse l'ordre des matrices si reverse=True if reverse: L = L[::-1] # Remplit la matrice résultante en utilisant le produit de Kronecker for j in range(ncols): # Lj = [matrix[:, j] for matrix in L] # retmat[:, j] = kronecker_list(Lj) retmat = linalg.khatri_rao(a, b) return retmat def ttl(tnsr, list_mat, ms=None): if ms is None or not isinstance(ms, (list, np.ndarray)): raise ValueError("m modes must be specified as a vector") if len(ms) != len(list_mat): raise ValueError("m modes length does not match list_mat length") num_mats = len(list_mat) if len(set(ms)) != num_mats: print("Consider pre-multiplying matrices for the same m for speed") mat_nrows = [mat.shape[0] for mat in list_mat] mat_ncols = [mat.shape[1] for mat in list_mat] for i in range(num_mats): mat = list_mat[i] m = ms[i] mat_dims = mat.shape modes_in = tnsr.modes if modes_in[m] != mat_dims[1]: raise ValueError(f"Modes mismatch: tnsr.modes[{m}] != mat.shape[1]") modes_out = modes_in.copy() modes_out[m] = mat_dims[0] tnsr_m = rs_unfold(tnsr, m=m).data retarr_m = np.dot(mat, tnsr_m) tnsr = rs_fold(retarr_m, m=m, modes=modes_out) return tnsr def fold(mat: Tensor | np.ndarray, row_idx: List[int], col_idx: List[int], modes: List[int], order='F'): rs = row_idx cs = col_idx if not isinstance(mat, np.ndarray): raise ValueError("mat must be of type 'numpy.ndarray'") if mat.ndim != 2: raise ValueError("mat must be a 2D matrix") num_modes = len(modes) if num_modes != len(rs) + len(cs): raise ValueError("Number of modes does not match the sum of row and column space indices") mat_modes = mat.shape if mat_modes[0] != np.prod([modes[i] for i in rs]) or mat_modes[1] != np.prod([modes[i] for i in cs]): raise ValueError("Matrix dimensions do not match Tensor modes") # iperm = [modes.index(mode) + 1 for mode in rs + cs] modes = list(modes) iperm = rs + cs # iperm = [modes.index(x) + 1 if x in modes else None for x in rs + cs] # iperm = [np.where(np.array(modes) == mode)[0][0] if mode in modes else None for mode in rs + cs] modes = np.asarray(modes) mat = mat.reshape([modes[i] for i in rs] + [modes[i] for i in cs], order=order) # folded_tensor = np.transpose(mat, iperm) folded_tensor = np.moveaxis(mat, range(len(rs) + len(cs)), rs + cs) # mat = mat.reshape(new_modes, order='F') # rearrangement style fortran comme pour dim() <- dim en R: # https://rstudio.github.io/reticulate/reference/array_reshape.html return astensor(mat) return astensor(folded_tensor) def k_fold(mat: Tensor | np.ndarray, m: int, modes: List[int], order='F') -> Tensor: num_modes = len(modes) rs = [m] cs = [i for i in range(num_modes) if i != m] # vérifier si on bouge m, ou l'indice lié à m return fold(mat, row_idx=rs, col_idx=cs, modes=modes, order=order) def rs_fold(mat: Tensor | np.ndarray, m: int, modes: List[int], order='F') -> Tensor: return k_fold(mat, m, modes, order)
lukbrb/pyTensor
pyTensor/tensorclass.py
tensorclass.py
py
8,507
python
en
code
0
github-code
6
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14012082445
import re import numpy as np import pickle import spacy_udpipe import spacy import pandas as pd from nltk.stem.porter import PorterStemmer from nltk.tokenize import RegexpTokenizer from tqdm import tqdm from bs4 import BeautifulSoup from collections import Counter class VectorSpaceModel(): ''' Vector space information retrieval system Baseline has: - terms: word forms - case normalization: no - removing stopwords: no - query construction: all word forms from "title" - term weighting: natural - document frequency weighting: none - vector normalization: cosine - similarity measurement: cosine - relevance feedback: none - query expansion: none Options include: stopwords, lemmas, stemming, lower-case, pivoted document length, tf/df weighting ''' def __init__(self, args, index): self.run = args.run self.output = args.output self.stopwords = args.stopwords self.lemmas = args.lemmas self.stemming = args.stemming self.lowercase = args.lowercase self.pivoted = args.pivoted self.tf_weighting = args.tf_weighting self.df_weighting = args.df_weighting self.index = index self.lang = args.documents.split(".")[0][-2:] self.query_terms = self.get_topic_terms(args.queries) self.docs = self.get_docs(args.documents) self.save_results() def get_topic_terms(self, queries): ''' return dictionary of topic_num: [term for term in title] ''' with open(queries, 'r') as f: topics = f.read() soup = BeautifulSoup(topics, 'html.parser') head = soup.contents[2] topics = [item for item in head.children][1::2] nums = [doc.num.contents[0] for doc in topics] if self.lang == "en": nlp = spacy.load("en_core_web_sm", exclude=['parser', 'ner']) else: nlp = spacy_udpipe.load("cs") stopword_list = nlp.Defaults.stop_words tokenizer = RegexpTokenizer(r'\w+') if self.lemmas: titles = [nlp(str(doc.title.contents[0])) for doc in topics] else: titles = [tokenizer.tokenize(doc.title.contents[0]) for doc in topics] if self.lemmas: titles = [[k.lemma_.lower() for k in doc] for doc in titles] elif self.lowercase: titles = [[k.lower() for k in doc] for doc in titles] else: titles = [[k for k in doc] for doc in titles] if self.stopwords: titles = [[k for k in doc if not k in stopword_list and k.isalpha()] for doc in titles] # only for English - Czech is pre-stemmed if self.stemming: stemmer = PorterStemmer() titles = [[stemmer.stem(str(k)) for k in doc] for doc in titles] query_terms = {num: title for num, title in zip(nums, titles)} return query_terms def get_tf_weight(self, tf, d, weighting='natural'): ''' weighting options are as below natural (default): tf_{t,d} logarithm: 1 + log(tf_{t,d}) augmented: 0.5 + (0.5*tf_{t,d})/max_t(tf_{t,d}) ''' if self.tf_weighting: weighting = self.tf_weighting if weighting == 'natural': tf_weight = tf elif weighting == 'logarithm': tf_weight = 1 + np.log2(tf) elif weighting == 'augmented': tf_weight = 0.5 + ((0.5 * tf)/ max(Counter(d).values())) return tf_weight def get_df_weight(self, df, tf, weighting='no'): ''' weighting options are as below no (default): 1 idf: log(N/df_{t}) prob_idf: max(0, log((N-df)/df)) ''' if self.df_weighting: weighting = self.df_weighting if weighting == 'no': df_weight = 1 elif weighting == 'idf': df_weight = np.log2(len(self.docs)/df) elif weighting == 'prob_idf': df_weight = max(0, np.log2((len(self.docs) - df)/df)) return df_weight def get_docs(self, docs_file): ''' returns list of tuples of (doc_id, [terms]) ''' docs_folder = docs_file.split(".")[0]+self.run+"/" with open(docs_file, "r") as f: filenames = [line.split(".")[0] for line in f.readlines()] docs = [] for fn in filenames: with open(docs_folder+fn+"_docs.bin", 'rb') as f: collection = pickle.load(f) for doc in collection: docs.append((doc[0], doc[1])) return docs def similarity(self, query, length, k=1000): ''' fast cosine score (IIR fig 7.1) returns top k docs for query ''' docs = self.docs doc_dict = {doc[0]: doc[1] for doc in docs} scores = pd.DataFrame(np.zeros((1,len(docs))), columns=[doc[0] for doc in docs]) for t in query: try: postings_list = self.index[t] for d, tf in postings_list.items(): # just storing natural term frequency scores[d] += self.get_tf_weight(tf, doc_dict[d]) \ * self.get_df_weight(len(postings_list), tf) except KeyError: pass query_length = np.linalg.norm(list(Counter(query).values())) scores = scores/(query_length*length) scores = scores.to_numpy().reshape((len(docs),)) inds = np.argpartition(scores, -k)[-k:] sorted_inds = inds[np.argsort(scores[inds])] doc_nos = [docs[docid][0] for docid in sorted_inds] return scores[sorted_inds][::-1], doc_nos[::-1] def save_results(self): ''' save results in TREC format, as described in section 5.3 (qid, iter, docno, rank, sim, run_id) ''' iteration = "0" run_id = self.run print("Processing queries") doc_dict = {doc[0]: list(Counter(doc[1]).values()) for doc in self.docs} # cosine normalization length = np.array([np.linalg.norm(counts) for counts in doc_dict.values()]) if self.pivoted: # value of a if self.lang == "cs": # values are computed as described in report piv = 24.6788 else: piv = 40.7795 length = self.pivoted*length + (1-self.pivoted)*piv for (qid, query) in tqdm(self.query_terms.items()): sim_scores, doc_nos = self.similarity(query, length) results = [qid+"\t"+iteration+"\t"+doc_no+"\t"+str(i)+"\t"+str(sim) +"\t"+run_id+"\n" for i, (doc_no, sim) in enumerate(zip(doc_nos, sim_scores))] with open(self.output, "a+") as f: f.writelines(results)
awmcisaac/charles
winter/npfl103/A2/A1/model.py
model.py
py
6,816
python
en
code
0
github-code
6
[ { "api_name": "bs4.BeautifulSoup", "line_number": 55, "usage_type": "call" }, { "api_name": "spacy.load", "line_number": 61, "usage_type": "call" }, { "api_name": "spacy_udpipe.load", "line_number": 63, "usage_type": "call" }, { "api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 65, "usage_type": "call" }, { "api_name": "nltk.stem.porter.PorterStemmer", "line_number": 83, "usage_type": "call" }, { "api_name": "numpy.log2", "line_number": 100, "usage_type": "call" }, { "api_name": "collections.Counter", "line_number": 102, "usage_type": "call" }, { "api_name": "numpy.log2", "line_number": 115, "usage_type": "call" }, { "api_name": "numpy.log2", "line_number": 117, "usage_type": "call" }, { "api_name": "pickle.load", "line_number": 128, "usage_type": "call" }, { "api_name": "pandas.DataFrame", "line_number": 141, "usage_type": "call" }, { "api_name": "numpy.zeros", "line_number": 141, "usage_type": "call" }, { "api_name": "numpy.linalg.norm", "line_number": 152, "usage_type": "call" }, { "api_name": "numpy.linalg", "line_number": 152, "usage_type": "attribute" }, { "api_name": "collections.Counter", "line_number": 152, "usage_type": "call" }, { "api_name": "numpy.argpartition", "line_number": 157, "usage_type": "call" }, { "api_name": "numpy.argsort", "line_number": 159, "usage_type": "call" }, { "api_name": "collections.Counter", "line_number": 173, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 175, "usage_type": "call" }, { "api_name": "numpy.linalg.norm", "line_number": 175, "usage_type": "call" }, { "api_name": "numpy.linalg", "line_number": 175, "usage_type": "attribute" }, { "api_name": "tqdm.tqdm", "line_number": 183, "usage_type": "call" } ]
71927421627
import pymdicator.timeseries as ts from pymdicator.indicators import Momentum, MACD, RSI import numpy as np import datetime import pytest import os from pandas import read_csv @pytest.fixture(params=[False, True]) def test_data(request, datadir): if request.param: pd = read_csv(datadir.join('stock_data.txt')) dts = pd['Date'].tolist() vals = pd["Close"].tolist() else: dts = [datetime.date(2018, 1, 1)] for ii in range(1, 20): dts.append(dts[0] + datetime.timedelta(ii)) vals = [100.0, 102.0, 99.0, 101.0, 103.0, 101.5, 103.0, 104.0, 103.5, 105, 106.0, 105.5, 108.0, 109.0, 111.0, 109.5, 112.0, 114.0, 113.5, 115] pd = None test_ts = ts.Timeseries(dts, vals, ts.TimeseriesType.PRICE, ts.TimeseriesSubType.ABSOLUTE, 1) return { 'dts': dts, 'vals': vals, 'test_ts': test_ts, 'is_csv': request.param, 'pd' : pd} @pytest.fixture def dts(test_data): return test_data['dts'] @pytest.fixture def vals(test_data): return test_data['vals'] @pytest.fixture def test_ts(test_data): return test_data['test_ts'] @pytest.fixture def is_csv(test_data): return test_data['is_csv'] @pytest.fixture def pd(test_data): return test_data['pd'] def test_latest_momentum(test_ts, vals, dts, is_csv, pd): momIndicator = Momentum(12) mom = momIndicator.calculate_current_ts(test_ts) if not is_csv: assert np.isclose(mom, 100.0 * 115.0 / 104.0) else: assert np.isclose(mom, 100.0*vals[-1] / vals[-13]) mom = momIndicator.calculate_current_df(pd) assert np.isclose(mom, 100.0 * vals[-1] / vals[-13]) def test_latest_momentum_overlong(test_ts, vals): momIndicator = Momentum(len(test_ts)) mom = momIndicator.calculate_current_ts(test_ts) assert np.isnan(mom) momIndicator = Momentum(len(test_ts) - 1) mom = momIndicator.calculate_current_ts(test_ts) assert np.isclose(mom, 100.0 * vals[-1] / vals[0]) def test_momentum_timeseries(test_ts, vals, dts, is_csv, pd): momIndicator = Momentum(10) mom_ts = momIndicator.calculate_timeseries_ts(test_ts) if not is_csv: assert np.isclose(mom_ts.values[0], 106.0) assert np.isclose(mom_ts.values[1], 105.5/1.02) assert np.isclose(mom_ts.values[2], 108.0/0.99) assert np.isclose(mom_ts.values[3], 109.0/1.01) else: n_checks = min(20, len(vals)-10) mom_ts_pd = momIndicator.calculate_timeseries_df(pd) for ii in range(n_checks): assert np.isclose(mom_ts.values[ii], 100.0 * vals[ii + 10] / vals[ii]) assert np.isclose(mom_ts_pd.values[ii], 100.0 * vals[ii + 10] / vals[ii]) def test_latest_momentum_outer(test_ts, vals, dts, is_csv, pd): momIndicator = Momentum(12) mom = momIndicator.calculate_current(test_ts) if not is_csv: assert np.isclose(mom, 100.0 * 115.0 / 104.0) else: assert np.isclose(mom, 100.0*vals[-1] / vals[-13]) mom_pd = momIndicator.calculate_current(pd) assert np.isclose(mom_pd, 100.0*vals[-1] / vals[-13]) mom_dict = momIndicator.calculate_current({"a": pd}) assert np.isclose(mom_dict["a"], 100.0*vals[-1] / vals[-13]) def test_momentum_timeseries_outer(test_ts, vals, dts, is_csv, pd): momIndicator = Momentum(10) mom_ts = momIndicator.calculate_timeseries(test_ts) if not is_csv: assert np.isclose(mom_ts.values[0], 106.0) assert np.isclose(mom_ts.values[1], 105.5/1.02) assert np.isclose(mom_ts.values[2], 108.0/0.99) assert np.isclose(mom_ts.values[3], 109.0/1.01) else: n_checks = min(20, len(vals)-10) mom_pd = momIndicator.calculate_timeseries(pd) mom_dict = momIndicator.calculate_timeseries({"a": pd}) for ii in range(n_checks): assert np.isclose(mom_ts.values[ii], 100.0 * vals[ii + 10] / vals[ii]) assert np.isclose(mom_pd.values[ii], 100.0 * vals[ii + 10] / vals[ii]) assert np.isclose(mom_dict["a"].values[ii], 100.0 * vals[ii + 10] / vals[ii]) def test_latest_macd(test_ts, vals, is_csv, pd): macd_calc = MACD() (macd, signal) = macd_calc.calculate_current_ts(test_ts) if not is_csv: assert np.isnan(macd) and np.isnan(signal) else: # I can't be bothered to do full calculation, # so make sure the values are sensible slow_average = sum(vals[-26:]) / 26.0 fast_average = sum(vals[-12:]) / 12.0 assert abs(macd) <= abs(2*(fast_average - slow_average)) assert abs(signal) <= abs(2*(fast_average - slow_average)) (macd_df, signal_df) = macd_calc.calculate_current_df(pd) assert np.isclose(macd_df, macd) assert np.isclose(signal_df, signal) def test_latest_macd_outer(test_ts, vals, is_csv, pd): macd_calc = MACD() (macd, signal) = macd_calc.calculate_current(test_ts) if not is_csv: assert np.isnan(macd) and np.isnan(signal) else: # I can't be bothered to do full calculation, # so make sure the values are sensible slow_average = sum(vals[-26:]) / 26.0 fast_average = sum(vals[-12:]) / 12.0 assert abs(macd) <= abs(2*(fast_average - slow_average)) assert abs(signal) <= abs(2*(fast_average - slow_average)) (macd_df, signal_df) = macd_calc.calculate_current(pd) assert np.isclose(macd_df, macd) assert np.isclose(signal_df, signal) (macd_dict, signal_dict) = macd_calc.calculate_current({"a":pd})["a"] assert np.isclose(macd_dict, macd) assert np.isclose(signal_dict, signal) def test_latest_rsi(test_ts, vals, is_csv, pd): rsi_calc = RSI(10) rsi = rsi_calc.calculate_current_ts(test_ts) if not is_csv: assert np.isclose(100.0 - 100.0 / (1 + 12.5/2.5), rsi) else: rsi_df = rsi_calc.calculate_current_df(pd) assert np.isclose(rsi, rsi_df) def test_timeseries_rsi(test_ts, vals, is_csv, pd): rsi_calc = RSI(10) rsi = rsi_calc.calculate_current_ts(test_ts) rsi_ts = rsi_calc.calculate_timeseries_ts(test_ts) if not is_csv: assert np.isclose(100.0 - 100.0 / (1 + 12.5/2.5), rsi_ts.values[-1]) assert np.isclose(100.0 - 100.0 / (1 + 12.5/2.5), rsi_ts.values[-2]) assert np.isclose(100.0 - 100.0 / (1 + 12.5/2.5), rsi_ts.values[-3]) assert np.isclose(100.0 - 100.0 / (1 + 11.5/2.5), rsi_ts.values[-4]) assert np.isclose(100.0 - 100.0 / (1 + 10.5/2.5), rsi_ts.values[-5]) assert np.isclose(100.0 - 100.0 / (1 + 10.5/2.5), rsi_ts.values[-6]) else: assert np.isclose(rsi_ts.values[-1], rsi)
Cronan/pymdicator
tests/test_indicators.py
test_indicators.py
py
6,858
python
en
code
0
github-code
6
[ { "api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call" }, { "api_name": "datetime.date", "line_number": 17, "usage_type": "call" }, { "api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call" }, { "api_name": "pymdicator.timeseries.Timeseries", "line_number": 23, "usage_type": "call" }, { "api_name": "pymdicator.timeseries", "line_number": 23, "usage_type": "name" }, { "api_name": "pymdicator.timeseries.TimeseriesType", "line_number": 23, "usage_type": "attribute" }, { "api_name": "pymdicator.timeseries.TimeseriesSubType", "line_number": 23, "usage_type": "attribute" }, { "api_name": "pytest.fixture", "line_number": 10, "usage_type": "call" }, { "api_name": "pytest.fixture", "line_number": 32, "usage_type": "attribute" }, { "api_name": "pytest.fixture", "line_number": 37, "usage_type": "attribute" }, { "api_name": "pytest.fixture", "line_number": 42, "usage_type": "attribute" }, { "api_name": "pytest.fixture", "line_number": 47, "usage_type": "attribute" }, { "api_name": "pytest.fixture", "line_number": 52, "usage_type": "attribute" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 58, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 62, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 64, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 66, "usage_type": "call" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 70, "usage_type": "call" }, { "api_name": "numpy.isnan", "line_number": 72, "usage_type": "call" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 74, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 76, "usage_type": "call" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 80, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 83, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 84, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 85, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 86, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 91, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 93, "usage_type": "call" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 98, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 102, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 104, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 106, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 108, "usage_type": "call" }, { "api_name": "pymdicator.indicators.Momentum", "line_number": 112, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 115, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 116, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 117, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 118, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 125, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 127, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 129, "usage_type": "call" }, { "api_name": "pymdicator.indicators.MACD", "line_number": 134, "usage_type": "call" }, { "api_name": "numpy.isnan", "line_number": 137, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 146, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 147, "usage_type": "call" }, { "api_name": "pymdicator.indicators.MACD", "line_number": 151, "usage_type": "call" }, { "api_name": "numpy.isnan", "line_number": 154, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 163, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 164, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 166, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 167, "usage_type": "call" }, { "api_name": "pymdicator.indicators.RSI", "line_number": 171, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 174, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 177, "usage_type": "call" }, { "api_name": "pymdicator.indicators.RSI", "line_number": 180, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 184, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 185, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 186, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 187, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 188, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 189, "usage_type": "call" }, { "api_name": "numpy.isclose", "line_number": 191, "usage_type": "call" } ]
13708970511
#AREA from database.config import Conexion from helper import helper #TABLA AREA class Area: def __init__(self, tipoArea=None): self.tipoArea = tipoArea def add_Area(self, area, app): try: conn = Conexion() query = f''' INSERT INTO area(tipoArea) VALUES('{area.tipoArea}') ''' conn.ejecutar_sentencia(query) conn.commit() message = f'''Se agrego el area: {area.tipoA}''' return helper.handler_response(app, 201, message) except Exception as e: raise print(e) finally: conn.cerrar_conexion() def listar_Area(self, app): listado_area = [] diccionario={} try: conn = Conexion() query = f''' SELECT * FROM area ''' cursor = conn.ejecutar_sentencia(query) filas = cursor.fetchall() for fila in filas: listado_area.append({'id ':str(fila[0]), 'Cargo ': fila[1]}) diccionario['Area'] = listado_area print(diccionario) return helper.handler_response(app, 201, diccionario) except Exception as e: raise print(e) finally: conn.cerrar_conexion() def obtener_Area(self, app, id_area): listado_area = [] diccionario={} try: conn = Conexion() query = f''' SELECT * FROM area WHERE id={id_area} ''' cursor = conn.ejecutar_sentencia(query) fila = cursor.fetchone() area = Area(fila[1]) listado_area.append({'id ':str(fila[0]), 'Cargo ': area.tipoArea}) diccionario['Area'] = listado_area return helper.handler_response(app, 201, diccionario) except Exception as e: raise print(e) finally: conn.cerrar_conexion() def actualizar_Area(self, app, id_area, area): try: conn = Conexion() query = f''' UPDATE area SET tipoArea = '{id_area.tipoArea}' WHERE id = {id_area} ''' conn.ejecutar_sentencia(query) conn.commit() proces = 'Procesado' return helper.handler_response(app, 201, proces) except Exception as e: raise print(e) finally: conn.cerrar_conexion() def eliminar_Area(self, app, id_area): try: conn = Conexion() query = f''' DELETE FROM area WHERE id={id_area} ''' cursor = conn.ejecutar_sentencia(query) conn.commit() eliminado = 'Eliminado...' return helper.handler_response(app, 201, eliminado) except Exception as e: raise print(e) finally: conn.cerrar_conexion()
jesustr20/Reto10_PythonFLASK_Mysql_Empresa
apps/classes/area.py
area.py
py
3,061
python
en
code
0
github-code
6
[ { "api_name": "database.config.Conexion", "line_number": 13, "usage_type": "call" }, { "api_name": "helper.helper.handler_response", "line_number": 21, "usage_type": "call" }, { "api_name": "helper.helper", "line_number": 21, "usage_type": "name" }, { "api_name": "database.config.Conexion", "line_number": 32, "usage_type": "call" }, { "api_name": "helper.helper.handler_response", "line_number": 42, "usage_type": "call" }, { "api_name": "helper.helper", "line_number": 42, "usage_type": "name" }, { "api_name": "database.config.Conexion", "line_number": 53, "usage_type": "call" }, { "api_name": "helper.helper.handler_response", "line_number": 62, "usage_type": "call" }, { "api_name": "helper.helper", "line_number": 62, "usage_type": "name" }, { "api_name": "database.config.Conexion", "line_number": 71, "usage_type": "call" }, { "api_name": "helper.helper.handler_response", "line_number": 80, "usage_type": "call" }, { "api_name": "helper.helper", "line_number": 80, "usage_type": "name" }, { "api_name": "database.config.Conexion", "line_number": 89, "usage_type": "call" }, { "api_name": "helper.helper.handler_response", "line_number": 96, "usage_type": "call" }, { "api_name": "helper.helper", "line_number": 96, "usage_type": "name" } ]
70316000189
from lib.get_endpoint import call_endpoint, endpoints from lib.utils import format_response, trace endpoint_inputs = { "headers": { "Content-Type": "application/json" } } @trace(text="Delete company tasks") def delete_tasks(command_args: dict): """ Function compiles a list of tasks associated with a companyid that is supplied to the the deleteCompanyTasks endpoint in order to delete all tasks associated with a client :return: endpoint call response object """ # Check required argument assert command_args.get('companyid'), "Required argument 'companyid' is missing" # Find count search_args = {"companyid": command_args.get('companyid')} endpoint_inputs["args"] = search_args endpoint_inputs['json'] = search_args count_response = call_endpoint(endpoint_config=endpoints.company_tasks_svc.countCompanyTasks, command_args=command_args, **endpoint_inputs) actual_count = count_response.data.get('content') if actual_count == 0: print("Client has no tasks.") return # Search task Ids search_args["pageRequest"] = {"size": actual_count} search_response = call_endpoint(endpoint_config=endpoints.company_tasks_svc.getCompanyTasks, command_args=command_args, **endpoint_inputs) if search_response.status == 404: print(search_response.data['errors'][0]['description']) return # Delete all task Ids ids = [company_task['id'] for company_task in search_response.data['content']] if ids: endpoint_inputs['json'] = ids delete_response = call_endpoint(endpoint_config=endpoints.company_tasks_svc.deleteCompanyTasks, command_args=command_args, **endpoint_inputs) assert search_response.status == 200, f"Delete operation failed! Error: {format_response(delete_response)}" print(f"{actual_count} Company tasks deleted successfully! Now, Client has no tasks.")
dattatembare/pytaf
utilities/delete_all_company_tasks.py
delete_all_company_tasks.py
py
2,136
python
en
code
0
github-code
6
[ { "api_name": "lib.get_endpoint.call_endpoint", "line_number": 25, "usage_type": "call" }, { "api_name": "lib.get_endpoint.endpoints.company_tasks_svc", "line_number": 25, "usage_type": "attribute" }, { "api_name": "lib.get_endpoint.endpoints", "line_number": 25, "usage_type": "name" }, { "api_name": "lib.get_endpoint.call_endpoint", "line_number": 34, "usage_type": "call" }, { "api_name": "lib.get_endpoint.endpoints.company_tasks_svc", "line_number": 34, "usage_type": "attribute" }, { "api_name": "lib.get_endpoint.endpoints", "line_number": 34, "usage_type": "name" }, { "api_name": "lib.get_endpoint.call_endpoint", "line_number": 44, "usage_type": "call" }, { "api_name": "lib.get_endpoint.endpoints.company_tasks_svc", "line_number": 44, "usage_type": "attribute" }, { "api_name": "lib.get_endpoint.endpoints", "line_number": 44, "usage_type": "name" }, { "api_name": "lib.utils.format_response", "line_number": 47, "usage_type": "call" }, { "api_name": "lib.utils.trace", "line_number": 11, "usage_type": "call" } ]
14162889179
import pyaudio import wave import pydub import numpy as np import os import threading import random import time import sys freq = 60 #60秒おきに集中力を計算 excert_range = 5 #その5倍の時間の姿勢データを計算に使う global position position = [4] * freq*excert_range #####集中力を計算するために姿勢を格納する配列(最初は非集中なので姿勢4を入れてる) def get_position_seq(): global position n = len(position) i = 0 while True: position[i] = 1 ##############ここに姿勢を入れる#################### print("姿勢は",position[i]) i += 1 if(i == n): i = 0 time.sleep(1) ##1秒ディレイを入れてる(多分いらない) def concentration_rate(sequence): ###集中力を計算する(関数は適当) counts = [0, 0, 0, 0] for num in sequence: if num == 1: counts[0] += 1 elif num == 2: counts[1] += 1 elif num == 3: counts[2] += 1 elif num == 4: counts[3] += 1 concentrate_raw = (counts[0]+counts[1]*0.2)/(len(sequence)) if concentrate_raw >= 0.7: ##集中力はせいぜい0.7が最大と仮定 concentrate = 1 else: concentrate = concentrate_raw/0.7 print("集中力は",concentrate) return concentrate def choose_music(concentration,threshold): ##集中力に応じて音楽を選ぶ folder_path = os.path.dirname(os.path.abspath(sys.argv[0])) #上がる方 if concentration < threshold: mp3_folder_path = os.path.join(folder_path, "no_concentrate_music") mp3_files = [file for file in os.listdir(mp3_folder_path) if file.endswith(".mp3")] random_file = random.choice(mp3_files) file_path = os.path.join(mp3_folder_path, random_file) print("上がる音楽",file_path,"を再生します") #集中できる方 else: mp3_folder_path = os.path.join(folder_path, "concentrate_music") mp3_files = [file for file in os.listdir(mp3_folder_path) if file.endswith(".mp3")] random_file = random.choice(mp3_files) file_path = os.path.join(mp3_folder_path, random_file) print("集中できる音楽",file_path,"を再生します") return file_path def volume(raw_volume): ##dBに基づいて適切な音量に変える min_volume = 0.1 return (10**(raw_volume*-0.5)-10**-0.5+min_volume)/(1-10**-0.5+min_volume) def play_audio(freq): ##音楽を再生する、音量は1秒おきに少しずつ滑らかに変わるようになってる(中断ボタンに合わせて再生を終了するとかは未実装) global position global event decay = int(freq/2) threshold_0 = 0.1 ##これを下回ったら非集中と仮定 threshold_1 = 0.5 ##これを上回ったら集中と仮定 n = 0 concentration_1 = 0 while True: if event == "end": break file_path = choose_music(concentration_1,threshold_0) # WAV形式に変換 wav_file = file_path[:-4] + ".wav" sound = pydub.AudioSegment.from_mp3(file_path) sound.export(wav_file, format="wav") # WAVファイルを再生 wf = wave.open(wav_file, 'rb') chunk = wf.getframerate() # PyAudioの初期化 p = pyaudio.PyAudio() stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate = chunk, output=True) # 音声のストリームを再生 data = wf.readframes(chunk) concentration_origin = concentration_1 print("最初の集中力は",concentration_origin) while data: # 入力値に基づいて音量を調整 concentration_0 = concentration_1 concentration_1 = concentration_rate(position) n += freq if concentration_origin < threshold_0 and concentration_1 > threshold_1: break elif concentration_origin > threshold_1 and concentration_1 < threshold_0: break concentration_step = (concentration_1 - concentration_0)/decay raw_volume = concentration_0 raw_volume += concentration_step for i in range(decay): # バイナリデータをnumpy配列に変換 audio_array = np.frombuffer(data, dtype=np.int16) volume_factor = volume(raw_volume) print("音量は",volume_factor) adjusted_array = (audio_array * volume_factor).astype(np.int16) # 音声データをバイナリに戻す adjusted_data = adjusted_array.tobytes() # 調整済みの音声を再生 stream.write(adjusted_data) # 次のデータを読み込む data = wf.readframes(chunk) raw_volume += concentration_step #########ここに中断ボタンを押されたらループを抜けるコード?? if event == "end": break if event == "end": break volume_factor = volume(raw_volume) for i in range(freq - decay): # バイナリデータをnumpy配列に変換 audio_array = np.frombuffer(data, dtype=np.int16) print("音量は",volume_factor) adjusted_array = (audio_array * volume_factor).astype(np.int16) # 音声データをバイナリに戻す adjusted_data = adjusted_array.tobytes() # 調整済みの音声を再生 stream.write(adjusted_data) # 次のデータを読み込む data = wf.readframes(chunk) #########ここに中断ボタンを押されたらループを抜けるコード?? if event == "end": break if event == "end": break # ストリームを閉じる stream.stop_stream() stream.close() # PyAudioを終了する p.terminate() # 一時的に作成したWAVファイルを削除 os.remove(wav_file) #########ここに中断ボタンを押されたらループを抜けるコード?? if event == "end": break # メインの処理 if __name__ == "__main__": # ロックオブジェクトを作成 lock = threading.Lock() # スレッドを作成 t1 = threading.Thread(target=get_position_seq) t2 = threading.Thread(target=play_audio, args=(freq,)) # スレッドを開始 t1.start() t2.start()
agridrama/system-project-1
volume_control.py
volume_control.py
py
6,822
python
ja
code
0
github-code
6
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41431741975
from django.forms import ModelForm, TextInput from django import forms from .models import List class ListForm(ModelForm): class Meta: model = List fields = ['list'] widgets = {'task': TextInput(attrs={ 'class': 'form-control', 'name': 'list', 'id': 'list', 'placeholder': 'List' }), }
awpogodin/py-CustomField
django/listvalid/forms.py
forms.py
py
402
python
en
code
0
github-code
6
[ { "api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "name" }, { "api_name": "models.List", "line_number": 7, "usage_type": "name" }, { "api_name": "django.forms.TextInput", "line_number": 9, "usage_type": "call" } ]
74911410428
import sys from PyQt5.QtWidgets import * from PyQt5.QtGui import QIcon from qtasync import AsyncTask, coroutine from PyQt5.QtCore import QCoreApplication, Qt,QThread from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure import matplotlib.pyplot as plt from core import fmaker import random from planmaker import make_plan import numpy as np import asyncio class App(QWidget): fmaker_ =0 def __init__(self): super().__init__() self.left = 400 self.top = 400 self.title = 'PyQt5 matplotlib example - pythonspot.com' self.width = 800 self.height = 600 self.fmaker_ = fmaker() self.plan=[[],[]] self.initUI() def center(self): qr = self.frameGeometry() cp = QDesktopWidget().availableGeometry().center() qr.moveCenter(cp) self.move(qr.topLeft()) def initUI(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height) self.center() hbox = QHBoxLayout(self) topleft = QSplitter(Qt.Vertical) topleft.setFrameShape(QFrame.StyledPanel) splitbutton = QSplitter(Qt.Horizontal) splitbutton.setFrameShape(QFrame.StyledPanel) splitter1 = QSplitter(Qt.Horizontal) splitter1.addWidget(topleft) topleft.addWidget(splitbutton) parts= [] for i in range(3): parts.append(QSplitter(Qt.Horizontal)) parts[i].setFrameShape(QFrame.StyledPanel) topleft.addWidget(parts[i]) hbox.addWidget(splitter1) self.setLayout(hbox) self.setWindowTitle('Синтез непрерывных D-планов для нечетких моделей с тремя подобластями') self.m = PlotCanvas(splitter1, width=5, height=4) self.m.move(0,0) self.radiobutton = [] dic = ["в первом","во втором", "в третьем"] for i in range(3): grid = QGridLayout() group_box = QGroupBox("Модель "+dic[i]+" нечетком множестве") group_box.setLayout(grid) self.radiobutton.append(QRadioButton("Квадратичная")) self.radiobutton[i * 2].setChecked(True) self.radiobutton[i * 2].type = "quad" self.radiobutton[i * 2].toggled.connect(self.on_radio_button_toggled1) self.radiobutton.append(QRadioButton("Линейная")) self.radiobutton[i * 2 + 1].type = "lin" self.radiobutton[i * 2 + 1].toggled.connect(self.on_radio_button_toggled1) parts[i].addWidget(group_box) grid.addWidget(self.radiobutton[i * 2],1,1) grid.addWidget(self.radiobutton[i * 2 + 1],2,1) button = QPushButton('Сформировать план') button.clicked.connect(self.start_calculations) splitbutton.addWidget(button) self.show() def on_radio_button_toggled1(self): radiobutton = self.sender() if radiobutton.isChecked(): self.fmaker_.change_model((self.radiobutton.index(radiobutton)+1)//3, radiobutton.type) @coroutine def start_calculations(self,arg): button = self.sender() button.setText('Производятся вычисления') button.setEnabled(False) for rb in self.radiobutton: rb.setEnabled(False) self.plan = yield AsyncTask(make_plan,self.fmaker_,button) self.m.plot(self.plan) file = open('plan.txt','w') for i in range(len(self.plan[0])): file.write(str(self.plan[0][i]) + '\t' + str(self.plan[1][i]) + '\n') file.close() button.setText('Сформировать план') button.setEnabled(True) for rb in self.radiobutton: rb.setEnabled(True) class PlotCanvas(FigureCanvas): def __init__(self, parent=None, width=5, height=4, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) self.axes = fig.add_subplot(111) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding) FigureCanvas.updateGeometry(self) def plot(self, plan): data = [random.random() for i in range(25)] self.axes.cla() self.axes.scatter( x = plan[0], y = [0 for _ in plan[0]], s = 5e3 * np.array(plan[1]), c = np.random.rand(len(plan[0])), alpha = 0.5, label = 'Веса точек в плане') self.axes.scatter( x = [0], y = [0], s = 0, alpha = 0.0, label = '|M| = ' + str(plan[2])) plt.ylim(-1,1) self.axes.legend() for i, num in enumerate(plan[1]): self.axes.annotate(round(num,3), (plan[0][i]-0.05,plan[1][i]/40)) self.axes.set_title('План эксперимента') self.axes.grid() self.draw() if __name__ == '__main__': app = QApplication(sys.argv) ex = App() sys.exit(app.exec_())
lupusomniator/paae_kurs
main v2.0.py
main v2.0.py
py
5,611
python
en
code
0
github-code
6
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5259672483
import csv import io import pickle import proper_noun import os import subprocess import common_nouns import location import re from stop_words import get_stop_words from nltk.tokenize import TweetTokenizer from collections import defaultdict import pickle from nltk.corpus import wordnet as wn from itertools import product import spacy from spacy.symbols import * from nltk import Tree import nltk from nltk.stem import * import spacy nlp=spacy.load('en') import math import sys from gensim.models import * import numpy as np import sys from nltk.stem import * import spacy import time import CMUTweetTagger stop_words=get_stop_words('en') stop_words_2=['i','me','we','us','you','u','she','her','his','he','him','it','they','them','who','which','whom','whose','that','this','these','those','anyone','someone','some','all','most','himself','herself','myself','itself','hers','ours','yours','theirs','to','in','at','for','from','etc',' ',','] for i in stop_words_2: if i not in stop_words: stop_words.append(i) nepal_stop_list=['nepal','earthquake','quake','nepalese','italy'] nepal_re="(nepal|quake|earthquake|nepalese|Earthquake|Nepal|NEPAL|Quake|Earth|Italy|italy)+" web_url="http[s]?:[a-zA-Z._0-9/]+[a-zA-Z0-9]" replacables="RT\s|-\s|\s-|#|@" prop_name="([A-Z][a-z]+)" num="([0-9]+)" name="([A-Za-z]+)" and_rate="([&][a][m][p][;])" ellipses="([A-Za-z0-9]+[…])" mentions="([a-zA-z\s0-9]+[:])" nlp=spacy.load('en') model=KeyedVectors.load_word2vec_format('/media/hdd/hdd/crisisNLP_word2vec_model/crisisNLP_word_vector.bin',binary=True) dict_file=open('built_dict_italy.txt','r') prannay_dict={} for line in dict_file: line=line.rstrip().split(',') prannay_dict[line[0]]=line[1] from nltk.stem.lancaster import LancasterStemmer stem2=LancasterStemmer() import numpy as np wc2_vector_array=np.load('/media/hdd/hdd/data_backup/results/nepal/Need/wc2_nepal_2_word_embeddings.npy') global_offer_resource_list=[] global_need_resource_list=[] id_need_list=[] offer_text=[] need_text=[] id_offer_list=[] import pickle with open('nepal_global_offer_resource_list.p','rb') as handle: global_offer_resource_list=pickle.load(handle) with open('nepal_global_need_resource_list.p','rb') as handle: global_need_resource_list= pickle.load(handle) with open('nepal_need_text.p','rb') as handle: need_text=pickle.load(handle) with open('nepal_offer_text.p','rb') as handle: offer_text= pickle.load(handle) with open('nepal_id_need_list.p','rb') as handle: id_need_list= pickle.load(handle) with open('nepal_id_offer_list.p','rb')as handle: id_offer_list= pickle.load(handle) # print(len(global_need_resource_list)) # print(len(global_offer_resource_list)) # print(len(offer_text)) # print(len(need_text)) # print(len(nepal_id_need_list)) # print(len(nepal_id_offer_list)) need_send_verb_list=['need','require','want','lack','send','give','donate','transfer','distribute','aid','help','earthquake','victims'] stemmer=PorterStemmer() out_stem_list=[stemmer.stem(i.lower()) for i in need_send_verb_list] lanc_stem_list=[stem2.stem(i.lower()) for i in need_send_verb_list] def euclidean_norm(u): prod=0 for i in range(0,len(u)): prod=prod+u[i]*u[i] return math.sqrt(prod) def cosine_similarity(u,v): if len(u)==len(v): e1=euclidean_norm(u) e2=euclidean_norm(v) if e1==0 or e2==0: return 0 length=len(u) scalar_product=0 for i in range(length): scalar_product=scalar_product+u[i]*v[i] return scalar_product/(e1*e2) def get_list_1(need_tweet_list): need_res_set=[] for i in need_tweet_list: for j in i.split(): if stemmer.stem(j.lower()) not in out_stem_list: need_res_set.append(j.lower()) return list(set(need_res_set)) def get_list_2(need_tweet_list): need_res_set=[] for i in need_tweet_list: for j in i.split(): if stem2.stem(j.lower()) not in lanc_stem_list: need_res_set.append(j.lower()) return list(set(need_res_set)) def get_set_1(need_tweet_list): need_res_set=set() for i in need_tweet_list: for j in i.split(): if stemmer.stem(j.lower()) not in out_stem_list: need_res_set.add(stemmer.stem(j.lower())) return need_res_set def resource_similarity_score_via_exact_word_match_1(need_res_set,offer_tweet_list): if len(need_res_set)==0: return 0 offer_res_set=set() for i in offer_tweet_list: for j in i.split(): if j not in out_stem_list: offer_res_set.add(stemmer.stem(j.lower())) return(len(offer_res_set&need_res_set)/len(need_res_set)) def get_similarity_score_1(word,given_list): max_similarity=0 if word.lower() in given_list: max_similarity=1 else: current_verb_list=wn.synsets(word.lower()) for verb in given_list: related_verbs=wn.synsets(verb) for a,b in product(related_verbs,current_verb_list): d=wn.wup_similarity(a,b) try: if d> max_similarity: max_similarity=d except: continue return max_similarity def get_similarity_score_2(word,given_list): max_similarity=0 flag1=0 flag2=0 if word.lower() in given_list: max_similarity=1 else: try: u=model[word] except: u=model['unk'] flag1=1 for item in given_list: try: v=model[item] except: v=model['unk'] flag2=1 if flag1==1 and flag2==1: d=0 else: d=cosine_similarity(u,v) if d >max_similarity: max_similarity=d return max_similarity def get_similarity_score_3(word,given_list): max_similarity=0 flag1=0 flag2=0 if word.lower() in given_list: max_similarity=1 else: try: u=wc2_vector_array[int(prannay_dict[word])] except: u=wc2_vector_array[0] flag1=1 for item in given_list: try: v=wc2_vector_array[int(prannay_dict[item])] except: v=wc2_vector_array[0] flag2=1 if flag1==1 and flag2==1: d=0 else: d=cosine_similarity(u,v) if d>max_similarity: max_similarity=d return max_similarity def resource_similarity_score_via_wc2_2(input_need_res_list,offer_tweet_list): offer_tweet_list=get_list_2(offer_tweet_list) l1=len(input_need_res_list) value=0 for item in input_need_res_list: temp=get_similarity_score_3(item,offer_tweet_list) value=value+temp return value/l1 def resource_similarity_score_via_wc2_1(need_vector,offer_tweet_list): offer_tweet_list_2=get_list_2(offer_tweet_list) l2=len(offer_tweet_list) offer_vector=np.zeros(256) if l2 ==0: return 0 for i in offer_tweet_list_2: try: v2=wc2_vector_array[int(prannay_dict[i.lower()])] except: v2=wc2_vector_array[0] for j in range(len(offer_vector)): offer_vector[j]+=v2[j] offer_vector=[i/l2 for i in offer_vector] return cosine_similarity(need_vector,offer_vector) def resource_similarity_score_via_word_net_1(need_res_set,offer_tweet_list): if len(need_res_set)==0: return 0 value=0 offer_res_list=[] for i in offer_tweet_list: for j in i.split(): if stemmer.stem(j.lower()) not in out_stem_list: offer_res_list.append(stemmer.stem(j.lower())) for word in need_res_set: temp= get_similarity_score_1(word,offer_res_list) if temp > 0.6: value=value+temp return value/len(need_res_set) def resource_similarity_score_via_word_vec_1(need_vector,offer_tweet_list): offer_tweet_list_2=get_list_1(offer_tweet_list) l2=len(offer_tweet_list) offer_vector=np.zeros(300) if l2 ==0: return 0 for i in offer_tweet_list_2: try: v2=model[i.lower()] except: v2=model['unk'] for j in range(len(offer_vector)): offer_vector[j]+=v2[j] offer_vector=[i/l2 for i in offer_vector] return cosine_similarity(need_vector,offer_vector) def resource_similarity_score_via_word_vec_2(input_need_res_list,offer_tweet_list): offer_tweet_list=get_list_1(offer_tweet_list) l1=len(input_need_res_list) #print(offer_tweet_list) value=0 for item in input_need_res_list: temp=get_similarity_score_2(item,offer_tweet_list) value=value+temp return value/l1 def get_top_k_searches_1(input_id,k,method,outfile,idfile): outfile.write('\n'+need_text[id_need_list.index(input_id)]+'\n') #print(need_text[id_need_list.index(input_id)]) input_need_res_set=get_set_1(global_need_resource_list[id_need_list.index(input_id)]) score_array={} if method==1: for item in id_offer_list: score_array[item]=resource_similarity_score_via_exact_word_match_1(input_need_res_set,global_offer_resource_list[id_offer_list.index(item)]) if method==2: for item in id_offer_list: score_array[item]=resource_similarity_score_via_word_net_1(input_need_res_set,global_offer_resource_list[id_offer_list.index(item)]) if method==3: input_need_res_list=get_list_1(global_need_resource_list[id_need_list.index(input_id)]) l1=len(input_need_res_list) if l1==0: for item in id_offer_list: score_array[item]=0 else: need_vector=np.zeros(300) for i in input_need_res_list: try: v1=model[i.lower()] except: v1=model['unk'] for j in range(300): need_vector[j]+=v1[j] need_vector=[i/l1 for i in need_vector] for item in id_offer_list: score_array[item]=resource_similarity_score_via_word_vec_1(need_vector,global_offer_resource_list[id_offer_list.index(item)]) if method ==4: input_need_res_list=get_list_1(global_need_resource_list[id_need_list.index(input_id)]) l1=len(input_need_res_list) if l1==0: for item in id_offer_list: score_array[item]=0 else: for item in id_offer_list: score_array[item]=resource_similarity_score_via_word_vec_2(input_need_res_list,global_offer_resource_list[id_offer_list.index(item)]) if method==5: input_need_res_list=get_list_2(global_need_resource_list[id_need_list.index(input_id)]) l1=len(input_need_res_list) if l1==0: for item in id_offer_list: score_array[item]=0 else: need_vector=np.zeros(256) for i in input_need_res_list: try: v1=wc2_vector_array[int(prannay_dict[i])] except: v1=wc2_vector_array[0] for j in range(256): need_vector[j]+=v1[j] need_vector=[i/l1 for i in need_vector] for item in id_offer_list: score_array[item]=resource_similarity_score_via_wc2_1(need_vector,global_offer_resource_list[id_offer_list.index(item)]) if method==6: input_need_res_list=get_list_2(global_need_resource_list[id_need_list.index(input_id)]) l1=len(input_need_res_list) if l1==0: for item in id_offer_list: score_array[item]=0 else: for item in id_offer_list: score_array[item]=resource_similarity_score_via_wc2_2(input_need_res_list,global_offer_resource_list[id_offer_list.index(item)]) score_array_sorted_keys=sorted(score_array,key=score_array.get,reverse=True) count=0 for r in score_array_sorted_keys: outfile.write(str(score_array[r])+'\t'+offer_text[id_offer_list.index(r)]+'\n') # if method==5 or method ==6: print(str(score_array[r])+'\t'+offer_text[id_offer_list.index(r)]) idfile.write(str(input_id)+'\t'+str(r)+'\n') if count==k: return count+=1 def get_top_k_searches_2(resource_list,k,method,need_offer_flag): print('HERE I AM IN TOP SEARCHES') input_need_res_set=get_set_1(resource_list) score_array={} print(need_offer_flag) print(k) print(method) if need_offer_flag==1: id_need_offer_list=id_offer_list global_need_offer_resource_list=global_offer_resource_list need_offer_text=offer_text else: id_need_offer_list=id_need_list global_need_offer_resource_list=global_need_resource_list need_offer_text=need_text if method==1: for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_exact_word_match_1(input_need_res_set,global_need_offer_resource_list[id_need_offer_list.index(item)]) if method==2: for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_word_net_1(input_need_res_set,global_need_offer_resource_list[id_need_offer_list.index(item)]) if method==3: input_need_res_list=get_list_1(resource_list) l1=len(input_need_res_list) if l1==0: for item in id_need_offer_list: score_array[item]=0 else: need_vector=np.zeros(300) for i in input_need_res_list: try: v1=model[i.lower()] except: v1=model['unk'] for j in range(300): need_vector[j]+=v1[j] need_vector=[i/l1 for i in need_vector] for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_word_vec_1(need_vector,global_need_offer_resource_list[id_need_offer_list.index(item)]) if method ==4: input_need_res_list=get_list_1(resource_list) l1=len(input_need_res_list) if l1==0: for item in id_need_offer_list: score_array[item]=0 else: for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_word_vec_2(input_need_res_list,global_need_offer_resource_list[id_need_offer_list.index(item)]) if method==5: input_need_res_list=get_list_2(resource_list) l1=len(input_need_res_list) if l1==0: for item in id_need_offer_list: score_array[item]=0 else: need_vector=np.zeros(256) for i in input_need_res_list: try: v1=wc2_vector_array[int(prannay_dict[i])] except: v1=wc2_vector_array[0] for j in range(256): need_vector[j]+=v1[j] need_vector=[i/l1 for i in need_vector] for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_wc2_1(need_vector,global_need_offer_resource_list[id_need_offer_list.index(item)]) if method==6: input_need_res_list=get_list_2(resource_list) l1=len(input_need_res_list) if l1==0: for item in id_need_offer_list: score_array[item]=0 else: for item in id_need_offer_list: score_array[item]=resource_similarity_score_via_wc2_2(input_need_res_list,global_need_offer_resource_list[id_need_offer_list.index(item)]) score_array_sorted_keys=sorted(score_array,key=score_array.get,reverse=True) count=0 for r in score_array_sorted_keys: print(str(score_array[r])+'\t'+need_offer_text[id_need_offer_list.index(r)]) if count==k: return count+=1 tknzr=TweetTokenizer(strip_handles=True,reduce_len=True) def tweet_preprocess(text): #text=" ".join(tknzr.tokenize(text)) text=re.sub(web_url,'',text) text=re.sub(mentions,'',text) text=re.sub(ellipses,'',text) text=re.sub(and_rate,'and',text) text=re.sub(str(num)+''+name,"\\1 \\2",text) text=re.sub(name+''+str(num),"\\1 \\2",text) text=re.sub(prop_name+''+prop_name,"\\1 \\2",text) return text.lstrip().rstrip() def tweet_preprocess2(text): #text=" ".join(tknzr.tokenize(text)) text=re.sub(web_url,'',text) text=re.sub(mentions,'',text) text=re.sub(ellipses,'',text) text=re.sub(and_rate,'and',text) text=re.sub(replacables,'',text) #text=re.sub(mentions,'',text) text=" ".join(tknzr.tokenize(text)) text=re.sub(str(num)+''+name,"\\1 \\2",text) text=re.sub(name+''+str(num),"\\1 \\2",text) text=re.sub(prop_name+''+prop_name,"\\1 \\2",text) return text.lstrip().rstrip() verb_dict={} common_resource=['food','water','medicine','tent','clothes','communication','transport','infrastructure','shelter','internet','sanitation','hospital','donations','blood'] def post_preprocess(text,final_resource_keys,quantity_dict,loc_list,source_list,which_k,which_method,need_offer_flag): ########## Remove the nepal stop list terns ############### final_resource_keys_2=[] for i in final_resource_keys: final_resource_keys_2.append(re.sub(nepal_re,'',i)) source_list_2=[] for i in source_list: source_list_2.append(re.sub(nepal_re,'',i)) loc_list_2=[] for i in loc_list: loc_list_2.append(re.sub(nepal_re,'',i)) source_list=list(source_list_2) loc_list=list(loc_list_2) final_resource_keys=list(final_resource_keys_2) ######################################################### for i in source_list_2: if i.lower() in stop_words: try: source_list.remove(i) except: continue for j in loc_list: for i in source_list_2: if i in j: try: source_list.remove(i) except: continue ######### Remove the terms duplicates ############# source_list_2=list(source_list) for i in final_resource_keys_2: length=len(final_resource_keys) for j in range(length): if i in final_resource_keys[j] and len(i) < len(final_resource_keys[j]): try: final_resource_keys.remove(i) break except: continue final_resource_keys_2=list(final_resource_keys) for i in source_list_2: length=len(source_list) for j in range(length): if i in source_list[j] and len(i) < len(source_list[j]): try: source_list.remove(i) break except: continue source_list_2=list(source_list) for i in loc_list_2: length=len(loc_list) for j in range(length): if i in loc_list[j] and len(i)< len(loc_list[j]): try: loc_list.remove(i) break except: continue loc_list_2=list(loc_list) ###################################################### source_list_2=list(source_list) for j in loc_list: for i in source_list_2: if j in i: try: source_list.remove(j) except: continue for i in final_resource_keys_2: for j in loc_list: if i in j: try: final_resource_keys.remove(i) except: continue final_resource_keys_2=list(final_resource_keys) loc_list_2=list(loc_list) source_list_2=list(source_list) ################################################## for i in final_resource_keys_2: if i.lower().rstrip().lstrip() in stop_words: try: final_resource_keys.remove(i) except: continue for i in loc_list_2: i=re.sub('#','',i) if i.lower().rstrip().lstrip() in stop_words: try: loc_list.remove(i) except: continue for i in source_list_2: if i.lower().rstrip().lstrip() in stop_words: try: source_list.remove(i) except: continue if len(final_resource_keys)==0: doc=nlp(text) for word in doc: if word.pos_=='NOUN': final_resource_keys.append(word.orth_) #global_need_resource_list.append(final_resource_keys) print("Resource_list") print(final_resource_keys) print() print("Quantity dictionary") print(quantity_dict) print() print("Location") print(loc_list) print() common_nouns.get_contact(text) print() print("Source list") print(source_list) get_top_k_searches_2(final_resource_keys,which_k,which_method,need_offer_flag) def create_resource_list(need_text_2,which_k,which_method,need_offer_flag): count=0 start_time=time.time() for text in need_text_2: source_list_3=[] urls=re.findall(web_url,text) for i in urls: if len(i)>len('http://t.co'): source_list_3.append(i) text2=tweet_preprocess(text) need_cmu_tags=CMUTweetTagger.runtagger_parse([text2]) text=tweet_preprocess2(text) quantity_dict={} final_resource_keys=[] source_list=[] loc_list=[] poss_places=[] org_person_list=[] quantity_dict,final_resource_keys,source_list,poss_places,org_person_list= common_nouns.get_resource(text) for i in source_list_3: source_list.append(i) # print(count) print(text) doc=nlp(text) #need_tag.append(CMUTweetTagger.runtagger_parse([text])) loc_list=proper_noun.give_location(need_cmu_tags) for i in org_person_list: if i in loc_list: try: loc_list.remove(i) except: continue if i not in source_list: source_list.append(i) for i in loc_list: if i in source_list: try: source_list.remove(i) except: continue for i in poss_places: if i not in loc_list and location.is_inside_Nepal(i)==1: loc_list.append(i) for i in org_person_list: if i in final_resource_keys: try: final_resource_keys.remove(i) except: continue count=count+1 final_resource_lists=[] for key in final_resource_keys: if key in quantity_dict: final_resource_lists.append(key.split(' ')[-1]) continue if key in text: final_resource_lists.append(key) post_preprocess(text,final_resource_lists,quantity_dict,loc_list,source_list,which_k,which_method,need_offer_flag) print(time.time()-start_time) start_time=time.time() need_text_2=[] need_text_2.append('There are many people stranded in Kathmandu') which_k=4 which_method=3 need_offer_flag=1 create_resource_list(need_text_2,which_k,which_method,need_offer_flag)
varun-manjunath/disaster-mitigation
matching/process_both.py
process_both.py
py
20,459
python
en
code
2
github-code
6
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6398739491
from dash import Dash from dash.dependencies import Input, Output from dash_core_components import Dropdown, Graph from dash_html_components import H1, Div, P from peewee import fn from src.database import LastPackage, Package, PackageHistory dash_app = Dash(__name__) server = dash_app.server dash_app.layout = Div( children=[ Div( className='header', children=[ P(children='📈', className='header-emoji'), H1(children='COCOMO-PYTHON', className='header-title'), P( children=''' A Cocomo analysis of the packages available on Pypi. ''', className='header-description', ), P( children='https://github.com/dunossauro/cocomo-python', className='header-description', ), ], ), Div( className='menu', children=[ Div( className='dropdown', children=[ Div(children='Select Group', className='menu-title'), Dropdown( id='group', className='dropdown', ), ], ), Div( className='dropdown', children=[ Div( children='Select packages', className='menu-title' ), Dropdown( id='package', className='dropdown', multi=True, ), ], ), ], ), Div( className='wrapper', children=[ Graph( id='graph_lines_value', config={'displayModeBar': False}, ) ], ), Div( className='wrapper', children=[ Graph( id='graph_license', config={'displayModeBar': False}, ) ], ), H1(children='Package History', className='header-title2'), Div( className='graph-header', children=[ Div( className='menu2', children=[ Div( className='dropdown', children=[ Div( children='Select package', className='menu-title', ), Dropdown( id='package_history', className='dropdown', ), ], ), ], ), Div( className='wrapper', children=[ Graph( id='graph_package_history', config={'displayModeBar': False}, ) ], ), H1(children='Python versions', className='header-title'), Div( className='wrapper', children=[ Graph( id='python_history', config={'displayModeBar': False}, ) ], ), ], ), ] ) @dash_app.callback( Output('group', 'options'), Input('group', 'search_value'), ) def update_groups(search_value): return [ {'label': p.group.capitalize(), 'value': p.group} for p in LastPackage.select().group_by(LastPackage.group).execute() ] @dash_app.callback( Output('package', 'options'), Input('group', 'value'), ) def update_packages(search_value): return [ {'label': p.name.name.capitalize(), 'value': p.name.name} for p in LastPackage.select().where(LastPackage.group == search_value) ] @dash_app.callback( Output('graph_lines_value', 'figure'), Input('group', 'value'), Input('package', 'value'), ) def lines_price(group, package): if not package: query = LastPackage.select().where(LastPackage.group == group) else: query = ( LastPackage.select().join(Package).where(Package.name.in_(package)) ) return { 'data': [ { 'y': [d.name.name for d in query], 'x': [d.total_lines for d in query], 'name': 'Code Lines', 'type': 'bar', 'orientation': 'h', 'marker': { 'color': ['#71134C' for x in query], }, }, { 'y': [d.name.name for d in query], 'x': [d.total_cost for d in query], 'name': 'Cocomo', 'type': 'bar', 'orientation': 'h', 'marker': { 'color': ['#0D7040' for x in query], }, }, ], 'layout': { 'title': { 'text': f'SLOC-package x Cocomo-Value (110.140) - {group}', 'x': 0.05, 'xanchor': 'left', } }, } @dash_app.callback( Output('package_history', 'options'), Input('package_history', 'value'), ) def history(package_history): return [ {'label': p.name, 'value': p.name} for p in Package.select().order_by(Package.name) ] @dash_app.callback( Output('graph_package_history', 'figure'), Input('package_history', 'value'), ) def package_history(package): query = ( PackageHistory.select() .join(Package) .where(Package.name == package) .order_by(PackageHistory.date) ) wheel_query = query.where(PackageHistory.package_type == 'wheel') tar_query = query.where(PackageHistory.package_type == 'tar') return { 'data': [ { 'y': [d.total_lines for d in wheel_query], 'x': [d.date for d in wheel_query], 'name': 'Wheel', }, { 'y': [d.total_cost for d in wheel_query], 'x': [d.date for d in wheel_query], 'name': 'Cocomo wheel', }, { 'y': [d.total_lines for d in tar_query], 'x': [d.date for d in tar_query], 'name': 'Tar', }, { 'y': [d.total_cost for d in tar_query], 'x': [d.date for d in tar_query], 'name': 'Cocomo tar', }, ], 'layout': { 'title': { 'text': f'Package history - {package}', 'x': 0.05, 'xanchor': 'left', } }, } @dash_app.callback( Output('graph_license', 'figure'), Input('group', 'value'), ) def license(value): query = ( Package.select( Package.license, fn.COUNT(Package.id).alias("license_count") ) .join(LastPackage) .where(LastPackage.group == value) .group_by(Package.license) ) return { 'data': [ { 'y': [x.license_count for x in query], 'x': [x.license for x in query], 'type': 'bar', 'marker': { 'color': ['#71134C' for x in query], }, }, ], 'layout': { 'title': { 'text': 'License type', 'x': 0.05, 'xanchor': 'left', } }, } @dash_app.callback( Output('python_history', 'figure'), Input('package_history', 'value'), ) def python(value): query = ( PackageHistory.select().join(Package).where(Package.name == 'python') ) return { 'data': [ { 'x': [x.version for x in query], 'y': [x.total_lines for x in query], 'type': 'bar', 'marker': { 'color': ['#71134C' for x in query], }, 'name': 'code lines', }, { 'x': [x.version for x in query], 'y': [x.total_cost for x in query], 'type': 'bar', 'name': 'cocomo', 'marker': { 'color': ['#0D7040' for x in query], }, }, ], 'layout': { 'title': { 'text': 'Python versions', 'x': 0.05, 'xanchor': 'left', } }, } # dash_app.run_server(debug=True)
dunossauro/cocomo-python
dashboard.py
dashboard.py
py
9,281
python
en
code
5
github-code
6
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}, { "api_name": "dash_html_components.Div", "line_number": 47, "usage_type": "call" }, { "api_name": "dash_core_components.Dropdown", "line_number": 50, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 59, "usage_type": "call" }, { "api_name": "dash_core_components.Graph", "line_number": 62, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 68, "usage_type": "call" }, { "api_name": "dash_core_components.Graph", "line_number": 71, "usage_type": "call" }, { "api_name": "dash_html_components.H1", "line_number": 77, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 78, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 81, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 84, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 87, "usage_type": "call" }, { "api_name": "dash_core_components.Dropdown", "line_number": 91, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 99, "usage_type": "call" }, { "api_name": "dash_core_components.Graph", "line_number": 102, "usage_type": "call" }, { "api_name": "dash_html_components.H1", "line_number": 108, "usage_type": "call" }, { "api_name": "dash_html_components.Div", "line_number": 109, "usage_type": "call" }, { "api_name": "dash_core_components.Graph", "line_number": 112, "usage_type": "call" }, { "api_name": "src.database.LastPackage.select", "line_number": 131, "usage_type": "call" }, { "api_name": "src.database.LastPackage", "line_number": 131, "usage_type": "name" }, { "api_name": "src.database.LastPackage.group", "line_number": 131, "usage_type": "attribute" }, { "api_name": "dash.dependencies.Output", "line_number": 125, "usage_type": "call" }, { "api_name": "dash.dependencies.Input", "line_number": 126, "usage_type": "call" }, { "api_name": "src.database.LastPackage.select", "line_number": 142, 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"src.database.PackageHistory", "line_number": 208, "usage_type": "name" }, { "api_name": "src.database.Package.name", "line_number": 210, "usage_type": "attribute" }, { "api_name": "src.database.Package", "line_number": 210, "usage_type": "name" }, { "api_name": "src.database.PackageHistory.date", "line_number": 211, "usage_type": "attribute" }, { "api_name": "src.database.PackageHistory", "line_number": 211, "usage_type": "name" }, { "api_name": "src.database.PackageHistory.package_type", "line_number": 214, "usage_type": "attribute" }, { "api_name": "src.database.PackageHistory", "line_number": 214, "usage_type": "name" }, { "api_name": "src.database.PackageHistory.package_type", "line_number": 215, "usage_type": "attribute" }, { "api_name": "src.database.PackageHistory", "line_number": 215, "usage_type": "name" }, { "api_name": "dash.dependencies.Output", "line_number": 203, "usage_type": "call" }, { "api_name": "dash.dependencies.Input", "line_number": 204, "usage_type": "call" }, { "api_name": "src.database.LastPackage", "line_number": 258, "usage_type": "argument" }, { "api_name": "src.database.Package.select", "line_number": 255, "usage_type": "call" }, { "api_name": "src.database.Package", "line_number": 255, "usage_type": "name" }, { "api_name": "src.database.Package.license", "line_number": 256, "usage_type": "attribute" }, { "api_name": "src.database.Package", "line_number": 256, "usage_type": "name" }, { "api_name": "peewee.fn.COUNT", "line_number": 256, "usage_type": "call" }, { "api_name": "peewee.fn", "line_number": 256, "usage_type": "name" }, { "api_name": "src.database.Package.id", "line_number": 256, "usage_type": "attribute" }, { "api_name": "src.database.LastPackage.group", "line_number": 259, "usage_type": "attribute" }, { "api_name": "src.database.LastPackage", "line_number": 259, "usage_type": "name" }, { "api_name": "src.database.Package.license", "line_number": 260, "usage_type": "attribute" }, { "api_name": "src.database.Package", "line_number": 260, "usage_type": "name" }, { "api_name": "dash.dependencies.Output", "line_number": 250, "usage_type": "call" }, { "api_name": "dash.dependencies.Input", "line_number": 251, "usage_type": "call" }, { "api_name": "src.database.Package", "line_number": 289, "usage_type": "argument" }, { "api_name": "src.database.PackageHistory.select", "line_number": 289, "usage_type": "call" }, { "api_name": "src.database.PackageHistory", "line_number": 289, "usage_type": "name" }, { "api_name": "src.database.Package.name", "line_number": 289, "usage_type": "attribute" }, { "api_name": "dash.dependencies.Output", "line_number": 284, "usage_type": "call" }, { "api_name": "dash.dependencies.Input", "line_number": 285, "usage_type": "call" } ]
41603415015
from phi.flow import * from phi.geom import Phi import matplotlib.pyplot as plt import time, os, sys, argparse sys.path.append('../') from functions import * parser = argparse.ArgumentParser() parser.add_argument("-res", "--resolution", type = int, default = 128, choices=[64,128,256,512], help = "set resolution") parser.add_argument("-v", "--velocity", type=float, required = True, help="set velocity at center line") parser.add_argument("-dt", "--time_step", type=float, help="set time step") def main(): ################ set parameters ################ args = parser.parse_args() res = args.resolution inflow_velocity = args.velocity DT = 0.5/inflow_velocity*0.01 if args.time_step == None else args.time_step radius = 0.3 diffusivity = 0.001 t_end = 10 ep = res/128 #used for force calculation substeps = 20 if res == 512 else 4 #used for pressure solve ################ set up phiflow domain ################ #set up domain and inflow DOMAIN = dict(x = 2*res, y = res, bounds=Box[-1:3,-1:1], extrapolation = extrapolation.combine_sides(x = extrapolation.BOUNDARY, y = extrapolation.ZERO)) INFLOW = StaggeredGrid(HardGeometryMask(Box[:-0.98, :]), **DOMAIN) #define poiseuille inflow velocity profile def poiseuille_flow(field): x = field.staggered_direction['y'].vector['x'] y = field.staggered_direction['x'].vector['y'] x_values = inflow_velocity*(1 - y**2) y_values = 0*x return math.stack([x_values,y_values], channel('staggered_direction')) INFLOW_VELO = StaggeredGrid(poiseuille_flow, **DOMAIN) #set up domain for phi DOMAIN_PHI = dict(x = 2*res, y = res, bounds=Box[-1:3,-1:1], extrapolation = extrapolation.ZERO) def phi_func(field): x,y = field.unstack(dimension = 'vector') return x**2 + y**2 - radius**2 #instantiate initial phi field phi_field = CenteredGrid(phi_func, **DOMAIN_PHI) phi_geom = Phi(phi_field) phi_obs = Obstacle(phi_geom) #regularize phi (|gradient of phi|= 1) phi_field = make_sdf(phi_field) #initialize field value pressure = None velocity = INFLOW_VELO + 10*StaggeredGrid(Noise(vector=2), **DOMAIN) * INFLOW #add noise to accelerate flow evolution ################ create path ################ path = '../prestored_data/unsteady/res{res}/dt{dt:03d}/poiseuille/'.format(res=res, dt=int(DT*1e2)) try: os.makedirs(path) except: print('Data file already exists.') sys.exit() ################ prepare storage ################ pressure_record = np.zeros((int(t_end/DT),2)) viscous_record = np.zeros((int(t_end/DT),2)) velocity_record = np.zeros(int(t_end/DT)) ################ start simulation ################ t_start = time.time() for i, t in enumerate(np.arange(0, t_end, DT)): velocity = advect.semi_lagrangian(velocity, velocity, DT) velocity = velocity * (1- INFLOW) + INFLOW * INFLOW_VELO velocity = diffuse.explicit(velocity, diffusivity, DT, substeps = substeps) velocity, pressure = fluid.make_incompressible(velocity, obstacles = (phi_obs,), solve=math.Solve('auto', 1e-3, 0, x0 = pressure, max_iterations=1e4, gradient_solve=math.Solve('auto', 1e-5, 1e-5))) velocity_record[i] = np.mean(velocity.at_centers().values.numpy('y,x,vector')[:,10,0]) pressure_force, viscous_force = evaluate_force(phi_field, pressure/DT, velocity, diffusivity, epsilon_factor = ep) pressure_record[i,:] = pressure_force viscous_record[i,:] = viscous_force if i % 100 == 0: print('Iteration {} finished --- time spent: {}min'.format(i, (time.time() - t_start)/60)) t_start = time.time() with cwd(path): with open('velocity_x_rad030_t200_vel{:04d}.txt'.format(int(inflow_velocity*1e3)), 'w') as f: for elem in velocity.vector[0].values.numpy('x,y'): np.savetxt(f, elem) with open('velocity_y_rad030_t200_vel{:04d}.txt'.format(int(inflow_velocity*1e3)), 'w') as f: for elem in velocity.vector[1].values.numpy('x,y'): np.savetxt(f, elem) with open('pressure_rad030_t200_vel{:04d}.txt'.format(int(inflow_velocity*1e3)), 'w') as f: for elem in pressure.values.numpy('x,y'): np.savetxt(f, elem) with open('velocity_record.txt', 'w') as f: np.savetxt(f, velocity_record) with open('pressure_record.txt', 'w') as f: np.savetxt(f, pressure_record) with open('viscous_record.txt', 'w') as f: np.savetxt(f, viscous_record) plt.figure() plt.plot(pressure_record[:,0]) plt.grid() plt.savefig('pressure drag evolution') if __name__ == '__main__': main()
Brian-Hsieh/shapeOptim
code/generate_data.py
generate_data.py
py
4,942
python
en
code
0
github-code
6
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34094783054
#!/usr/bin/env python3 from plotter import collection, dataset from plotter import histo, atlas, presets import ROOT import logging logging.basicConfig( level=logging.INFO, format="%(levelname)s (%(name)s): %(message)s" ) log = logging.getLogger(__name__) atlas.SetAtlasStyle() cData = collection("Data") cData.add_dataset(dataset("Data", "test/Nominal2/data.root")) cBkg = collection("Bkg") cBkg.add_dataset(dataset("Bkg", "test/Nominal2/background.root")) cHis = collection("Hists") cHis.add_dataset(dataset("Hists", "test/Nominal2/hists.root")) def plot_dm(var: str = "ptll", varTitle: str = "p_{T}^{ll}", suffix: str = ""): hD = histo("Data", cData.get_th(var+"_data"+suffix), configPath="configs/data.json") hS = histo("Z", cHis.get_th(var+"_Z"+suffix), fillColor=ROOT.kBlue, configPath="configs/mc.json") hNF = histo("nonFid", cHis.get_th(var+"_nonFid"+suffix), fillColor=ROOT.kRed, configPath="configs/mc.json") hB = histo("Top+EW", cBkg.get_th(var+"_topew"+suffix), fillColor=ROOT.kGreen, configPath="configs/mc.json") dm = presets.dataMC("test"+suffix, xTitle=varTitle) dm.ratioPad.set_yrange(0.701, 1.199) dm.add_and_plot(hD, [hS, hNF, hB]) # dm.mainPad.basis.th.GetXaxis().SetRangeUser(0, 100) # dm.ratioPad.basis.th.GetXaxis().SetRangeUser(0, 100) dm.canvas.tcan.cd() atlas.ATLASLabel(0.22, 0.9, "Internal") extraTitles = [] if extraTitles != []: yPosition = 0.85 for title in extraTitles: dm.canvas.add_text(title, 0.22, yPosition) yPosition -= 0.05 plotName = var+"_"+suffix dm.save("AI/dm/"+plotName+".png") dm.mainPad.logy() dm.save("AI/dm/"+plotName+"_log.png") def plot_frac(var: str = "ptll", varTitle: str = "p_{T}^{ll}", suffix: str = ""): hS = histo("Z", cHis.get_th(var+"_Z"+suffix), lineColor=ROOT.kBlue, configPath="configs/mc.json") hNF = histo("nonFid", cHis.get_th(var+"_nonFid"+suffix), lineColor=ROOT.kRed, configPath="configs/mc.json") hB = histo("Top+EW", cBkg.get_th(var+"_topew"+suffix), lineColor=ROOT.kGreen, configPath="configs/mc.json") frac = presets.fraction("frac"+suffix, xTitle=varTitle) frac.add_and_plot([hS, hNF, hB], [hNF, hB]) # frac.mainPad.basis.th.GetXaxis().SetRangeUser(0, 100) frac.canvas.tcan.cd() atlas.ATLASLabel(0.22, 0.9, "Internal") extraTitles = [] if extraTitles != []: yPosition = 0.85 for title in extraTitles: frac.canvas.add_text(title, 0.22, yPosition) yPosition -= 0.05 plotName = var+"_"+suffix frac.save("AI/frac/"+plotName+".png") frac.mainPad.logy() frac.save("AI/frac/"+plotName+"_log.png") plot_dm() plot_frac() nPt = 25 nY = 8 for y in range(nY): suf = f"_M0_Y{y}" log.info(f"Working on {suf}") plot_dm(suffix=suf) plot_frac(suffix=suf) for pt in range(nPt): suf = f"_PT{pt}_M0" log.info(f"Working on {suf}") plot_dm("yll", "y_{ll}", suffix=suf) plot_frac("yll", "y_{ll}", suffix=suf)
fnechans/plotter
test.py
test.py
py
3,117
python
en
code
0
github-code
6
[ { "api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call" }, { "api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute" }, { "api_name": "logging.getLogger", "line_number": 11, "usage_type": "call" }, { "api_name": "plotter.atlas.SetAtlasStyle", "line_number": 13, "usage_type": "call" }, { "api_name": "plotter.atlas", "line_number": 13, "usage_type": "name" }, { "api_name": "plotter.collection", "line_number": 15, "usage_type": "call" }, { "api_name": "plotter.dataset", "line_number": 16, "usage_type": "call" }, { "api_name": "plotter.collection", "line_number": 18, "usage_type": "call" }, { "api_name": "plotter.dataset", "line_number": 19, "usage_type": "call" }, { "api_name": "plotter.collection", "line_number": 21, "usage_type": "call" }, { "api_name": "plotter.dataset", "line_number": 22, "usage_type": "call" }, { "api_name": "plotter.histo", "line_number": 27, "usage_type": "call" }, { "api_name": "plotter.histo", "line_number": 29, "usage_type": "call" }, { "api_name": "ROOT.kBlue", "line_number": 29, "usage_type": "attribute" }, { "api_name": "plotter.histo", "line_number": 32, "usage_type": "call" }, { "api_name": "ROOT.kRed", "line_number": 32, "usage_type": "attribute" }, { "api_name": "plotter.histo", "line_number": 35, "usage_type": "call" }, { "api_name": "ROOT.kGreen", "line_number": 35, "usage_type": "attribute" }, { "api_name": "plotter.presets.dataMC", "line_number": 38, "usage_type": "call" }, { "api_name": "plotter.presets", "line_number": 38, "usage_type": "name" }, { "api_name": "plotter.atlas.ATLASLabel", "line_number": 45, "usage_type": "call" }, { "api_name": "plotter.atlas", "line_number": 45, "usage_type": "name" }, { "api_name": "plotter.histo", "line_number": 60, "usage_type": "call" }, { "api_name": "ROOT.kBlue", "line_number": 60, "usage_type": "attribute" }, { "api_name": "plotter.histo", "line_number": 63, "usage_type": "call" }, { "api_name": "ROOT.kRed", "line_number": 63, "usage_type": "attribute" }, { "api_name": "plotter.histo", "line_number": 66, "usage_type": "call" }, { "api_name": "ROOT.kGreen", "line_number": 66, "usage_type": "attribute" }, { "api_name": "plotter.presets.fraction", "line_number": 69, "usage_type": "call" }, { "api_name": "plotter.presets", "line_number": 69, "usage_type": "name" }, { "api_name": "plotter.atlas.ATLASLabel", "line_number": 74, "usage_type": "call" }, { "api_name": "plotter.atlas", "line_number": 74, "usage_type": "name" } ]
4467004756
import requests urls = dict() urls['http'] = ['gilgil.net'] urls['https'] = [ 'google.com', 'naver.com', 'daum.net', 'github.com', 'gitlab.com', 'portal.korea.ac.kr', 'yonsei.ac.kr', 'snu.ac.kr', 'kaist.ac.kr', 'kisa.or.kr', 'kitribob.kr', 'twitter.com', 'youtube.com', 'instagram.com', 'netflix.com', 'facebook.com', 'qt.io', 'programmers.co.kr', 'tistory.com', 'arxiv.org', ] for url in urls['http']: r = requests.get(f'http://{url}') print(f'{r.url} status code={r.status_code}') for url in urls['https']: r = requests.get(f'https://{url}') print(f'{r.url} status code={r.status_code}')
ugonfor/suricata-rule
request_url.py
request_url.py
py
694
python
en
code
0
github-code
6
[ { "api_name": "requests.get", "line_number": 30, "usage_type": "call" }, { "api_name": "requests.get", "line_number": 34, "usage_type": "call" } ]
17789517633
from pygost.gost3412 import GOST3412Kuznechik as Kuz from pygost.utils import hexdec, hexenc from rich import print REPLACES = { ",": "ЗПТ", ".": "ТЧК", "-": "ТИРЕ", ";": "ТЧКИЗПТ", } def print_header(text): print(header(text)) def print_kv(k, v): print(kv(k, v)) def header(text): return f"[bold black on bright_white] { text } [/bold black on bright_white]" def kv(k, v): return f"[bold cyan] { k } :[/bold cyan] { v } " default_alph = "абвгдеёжзийклмнопрстуфхцчшщъыьэюя" key = "ffeeddccbbaa99887766554433221100f0f1f2f3f4f5f6f7f8f9fafbfcfdfeff" def to_indexes(text, alph=default_alph): return [alph.index(symbol) for symbol in text] def to_symbols(nums, alph=default_alph): return "".join([alph[num] for num in nums]) def clear_text(text, alph=default_alph): import re text = replace_special(text) text = text.lower() text = re.sub(f"[^{alph}]", "", text) return text def replace_special(text, replaces=REPLACES): for key, value in replaces.items(): text = text.replace(key, value) return text def is_hex(s): import string try: return all(c in string.hexdigits for c in s) except: return False def get_key(key: str) -> bytes: if is_hex(key): key = hexdec(key) else: key = bytes(key, "utf-8") return key def get_text(text: str) -> bytes: if type(text) == str: if is_hex(text): text = hexdec(text) else: text = bytes(text, "utf-8") return text def get_chipher(key: str) -> Kuz: key = get_key(key) return Kuz(key) def enc(text: str, key: str = key): chipher = get_chipher(key) byte_text = get_text(text) enc_bytes = chipher.encrypt(byte_text) enc_text = hexenc(enc_bytes) return enc_text def dec(text: str, key: str = key, t: str = "str"): chipher = get_chipher(key) byte_text = get_text(text) dec_bytes = chipher.decrypt(byte_text) dec_text = "" if t == "hex": dec_text = hexenc(dec_bytes) else: dec_text = dec_bytes.decode("utf-8") return dec_text def main(): print_header("Пример из GOST_R_34_12-2015") text = input("Введите текст-бит: ") #"1122334455667700ffeeddccbbaa9988" , деш_кл = 7f679d90bebc24305a468d42b9d4edcd key = input("Введите ключ-бит: ") #8899aabbccddeeff0011223344556677fedcba98765432100123456789abcdef question = input("Выполнить действие (шифровать/дешифоровать): ") if question == "шифровать": print_kv("Открытый текст", text) enc_text = enc(text, key) print_kv("Результат", enc_text) elif question == "дешифоровать": print_kv("Шифр", text) dec_text = dec(text, key, t="hex") print_kv("Расшифр.", dec_text) if __name__ == "__main__": main()
VasiliiSkrypnik/PKA_2023
files/new_lab/lab7/Kuznyechik.py
Kuznyechik.py
py
3,152
python
en
code
0
github-code
6
[ { "api_name": "rich.print", "line_number": 13, "usage_type": "call" }, { "api_name": "rich.print", "line_number": 17, "usage_type": "call" }, { "api_name": "re.sub", "line_number": 43, "usage_type": "call" }, { "api_name": "string.hexdigits", "line_number": 60, "usage_type": "attribute" }, { "api_name": "pygost.utils.hexdec", "line_number": 67, "usage_type": "call" }, { "api_name": "pygost.utils.hexdec", "line_number": 76, "usage_type": "call" }, { "api_name": "pygost.gost3412.GOST3412Kuznechik", "line_number": 84, "usage_type": "call" }, { "api_name": "pygost.gost3412.GOST3412Kuznechik", "line_number": 82, "usage_type": "name" }, { "api_name": "pygost.utils.hexenc", "line_number": 94, "usage_type": "call" }, { "api_name": "pygost.utils.hexenc", "line_number": 107, "usage_type": "call" } ]
26331405503
import cv2 import glob class FrameIterator: """ An iterator to iterate over multiple files containing either images other videos. The files are gathered using pattern matching and read with the universal cv2.VideoCapture(). ... Attributes ---------- pathpattern : str input path pattern to be matched verbose : int verbosity (0,1) for logging into standard output Methods ------- current_frame(): Returns the path and position of the current frame. """ def __init__(self, pathpattern, verbose=0): self.pathpattern = pathpattern # list all files matching the pathpattern self.files = glob.glob(self.pathpattern) if verbose >= 1: print(f'filenames: {self.files}') # check that at least one file exists self.n_files = len(self.files) if self.n_files < 1: raise ValueError(f'Could not find any filename matching the path pattern: \'{pathpattern}\'') def __iter__(self): # initialize counter and current VideoCapture self.index = 0 try: self.cap = cv2.VideoCapture(self.files[self.index]) except: raise RuntimeError('Opening VideoCapture from \'{self.files[self.index]}\' failed.') return self def __next__(self): # try to read next frame try: ret, frame = self.cap.read() except: raise RuntimeError('Reading frame from \'{self.files[self.index]}\' failed.') # return frame if read was sucessfull if ret: return frame # try to open next VideoCapture if read was unsucessful self.index = self.index + 1 # stop iterating if there are no more files if self.index >= self.n_files: raise StopIteration # initiallize next VideoCapture try: self.cap = cv2.VideoCapture(self.files[self.index]) except: raise RuntimeError('Opening VideoCapture from \'{self.files[self.index]}\' failed.') # return first frame of next VideoCapture return self.__next__() def current_frame(self): '''Return path and position of the current frame.''' path = self.files[self.index] pos = int(self.cap.get(cv2.CAP_PROP_POS_FRAMES)) - 1 return f'{path}::{pos}' def first(self): '''Return first frame from first file''' # open new video capture try: cap = cv2.VideoCapture(self.files[0]) except: raise RuntimeError('Opening VideoCapture from \'{self.files[0]}\' failed.') # read next frame try: ret, frame = cap.read() except: raise RuntimeError('Reading frame from \'{self.files[0]}\' failed.') # if stream is empty if not ret: raise RuntimeError('Reading frame from \'{self.files[0]}\' failed.') return frame
bunjj/Catadioptric-Stereo
FrameIterator.py
FrameIterator.py
py
2,894
python
en
code
1
github-code
6
[ { "api_name": "glob.glob", "line_number": 28, "usage_type": "call" }, { "api_name": "cv2.VideoCapture", "line_number": 40, "usage_type": "call" }, { "api_name": "cv2.VideoCapture", "line_number": 61, "usage_type": "call" }, { "api_name": "cv2.CAP_PROP_POS_FRAMES", "line_number": 71, "usage_type": "attribute" }, { "api_name": "cv2.VideoCapture", "line_number": 78, "usage_type": "call" } ]
27550279361
import random import urllib.request import os import openai openai.api_key = os.getenv("OPENAI_API_KEY") res = openai.Image.create_variation( image=open("838.png", "rb"), # kindly replace "838.png" with an image on your local computer n=2, # no of variations to generate size="1024x1024", response_format="url" ) resp = res["data"] resp_list = [resp[x]["url"] for x in range(len(resp))] # list of all generated image variations def download_image(url_list: list): """ this method will loop through the url_list, a list,containing URLS download the image from the URL, and save it locally :param url_list: """ for url in url_list: name = random.randrange(1, 100) full_name = str(name) + '-variations.png' urllib.request.urlretrieve(url, full_name) print(f'image {full_name} download succssfully...') download_image(resp_list)
Afeez1131/openAI-image-generation
image_variations.py
image_variations.py
py
910
python
en
code
0
github-code
6
[ { "api_name": "openai.api_key", "line_number": 6, "usage_type": "attribute" }, { "api_name": "os.getenv", "line_number": 6, "usage_type": "call" }, { "api_name": "openai.Image.create_variation", "line_number": 7, "usage_type": "call" }, { "api_name": "openai.Image", "line_number": 7, "usage_type": "attribute" }, { "api_name": "random.randrange", "line_number": 26, "usage_type": "call" }, { "api_name": "urllib.request.request.urlretrieve", "line_number": 28, "usage_type": "call" }, { "api_name": "urllib.request.request", "line_number": 28, "usage_type": "attribute" }, { "api_name": "urllib.request", "line_number": 28, "usage_type": "name" } ]
11951628523
import plotly.express as px import streamlit as st from functions import * st.set_page_config( page_title="Time series annotations", page_icon="⬇" ) # @st.cache(allow_output_mutation=True) @st.cache_data def load_data(op_data): # df_despesa = pd.read_csv('https://raw.githubusercontent.com/jsaj/st_forecasting/master/datasets/despesa.csv') # df_receita = pd.read_csv('https://raw.githubusercontent.com/jsaj/st_forecasting/master/datasets/receita.csv') df = pd.read_excel( 'https://onedrive.live.com/download?resid=71AA33284B297464%21422&authkey=!ABm-ikjLePrrS74&excel=2.xslx', sheet_name='{}'.format(op_data)) return df # Criar uma barra deslizante (slider) para selecionar qual será a previsão: receitas ou despesas op_data = st.sidebar.selectbox('O que deseja prever?', ['Receitas', 'Despesas']) # Carrega os dados ou utiliza os dados em cache df = load_data(op_data) df_filtrado = processing_columns_values(df, op_data) # df_receita_despesa = df_receita.merge(df_despesa, how='left', on=['ds', 'Unidade Gestora']).fillna(0) # df_receita_despesa['ds'] = pd.to_datetime(df_receita_despesa['ds']) if op_data == 'Despesas': # Sidebar com o filtro list_to_filter = ['TODOS'] + list(df['NATUREZA'].drop_duplicates()) filtro_type_data = st.sidebar.selectbox('Elemento: ', list_to_filter, index=list_to_filter.index( 'TODOS')) else: list_to_filter = ['TODAS'] + list(df['ESPÉCIE DA RECEITA'].drop_duplicates()) filtro_type_data = st.sidebar.selectbox('Espécie da Receita:', list_to_filter, index=list_to_filter.index('TODAS')) df_filtrado = processing_data(df, op_data, filtro_type_data) st.write(df.head()) st.write(df_filtrado.head()) type_periods = st.sidebar.selectbox('Qual o intervalo da previsão? ', ['Mensal', 'Semestral']) if type_periods == 'Mensal': # Sidebar com o filtro dos meses de previsão n_periods = st.sidebar.selectbox('Quantos meses?', list(range(1, 13))) else: # Sidebar com o filtro dos semestres de previsão n_periods = st.sidebar.selectbox('Quantos semestres? ', list(range(1, 13))) # Renomear as colunas para que o modelo possa reconhecê-las df_filtrado.columns = ['ds', 'y'] # Criar uma barra deslizante (slider) para selecionar a variável exógerna model_name = st.sidebar.selectbox('Modelo preditivo:', ['ARIMA', 'Prophet']) if filtro_type_data in ['TODAS', 'TODOS']: # Criar uma barra deslizante (slider) para selecionar a variável exógerna op_exog = st.sidebar.selectbox('Usar variável exógena?:', ['Sim', 'Não'], index=list(['Sim', 'Não']).index('Não')) if op_exog == 'Sim': # Criar uma barra deslizante (slider) para selecionar a variável exógerna if op_data == 'Receitas': exog_var = st.sidebar.selectbox('Variável exógena:', list(df['ESPÉCIE DA RECEITA'].drop_duplicates())) else: exog_var = st.sidebar.selectbox('Variável exógena:', list(df['NATUREZA'].drop_duplicates())) df_to_predict = create_exog_table(df, df_filtrado, op_data, exog_var) # Criar uma barra deslizante (slider) para selecionar a porcentagem porcentagem = st.sidebar.slider('% vs. Var. Exógena:', min_value=0, max_value=100, value=100, step=1) # Aplicar a função ao DataFrame para criar uma nova coluna com os valores multiplicados df_to_predict[exog_var] = df_to_predict[exog_var] * (porcentagem / 100) st.write(df_to_predict) # Criar o modelo de previsão if model_name == 'ARIMA': predictions = predict_ARIMA(df=df_to_predict, n_periods=n_periods, type_periods=type_periods, exog_var=exog_var) df_to_predict = df_to_predict.reset_index() elif model_name == 'Prophet': predictions = predict_prophet(df=df_to_predict, n_periods=n_periods, type_periods=type_periods, exog_var=exog_var) else: # Criar o modelo de previsão if model_name == 'ARIMA': predictions = predict_ARIMA(df=df_filtrado, n_periods=n_periods, type_periods=type_periods, exog_var=None) df_filtrado = df_filtrado.reset_index() elif model_name == 'Prophet': predictions = predict_prophet(df=df_filtrado, n_periods=n_periods, type_periods=type_periods, exog_var=None) else: # Criar o modelo de previsão if model_name == 'ARIMA': predictions = predict_ARIMA(df=df_filtrado, n_periods=n_periods, type_periods=type_periods, exog_var=None) df_filtrado = df_filtrado.reset_index() elif model_name == 'Prophet': predictions = predict_prophet(df=df_filtrado, n_periods=n_periods, type_periods=type_periods, exog_var=None) # st.write(df_filtrado) # Converter valores para milhões (M) ou milhares (K) def format_value(value): if abs(value) >= 1e6: return '{:.2f}M'.format(value / 1e6) elif abs(value) >= 1e3: return '{:.2f}K'.format(value / 1e3) else: return '{:.2f}'.format(value) # Criar o gráfico de linhas usando Plotly Express fig = px.line(df_filtrado, x='ds', y='y', text=[format_value(val) for val in df_filtrado['y']], labels={'y': '{} atuais'.format(op_data)}, title='{} atuais vs. {} preditas'.format(op_data, op_data)) # Adicionar a série de previsão de receita fig.add_scatter(x=predictions['ds'], y=predictions['yhat'], mode='lines+text', text=[format_value(val) for val in predictions['yhat']], name='{} preditas'.format(op_data)) # Personalizar layout do gráfico fig.update_traces(textposition='top center') fig.update_layout(xaxis_title='Mês-Ano', yaxis_title='{}'.format(op_data), showlegend=True) # Exibir o gráfico usando Streamlit st.plotly_chart(fig) # Calcular a média da previsão mean_prediction = predictions['yhat'].mean() df_filtrado = df_filtrado.loc[df_filtrado['ds'] >= '2023-01-01'] # Criar o gráfico de barras usando Plotly Express fig = px.bar(df_filtrado, x='ds', y='y', text=[format_value(val) for val in df_filtrado['y']], labels={'y': '{} atuais'.format(op_data)}, title='{} atuais vs. {} preditas - Média de previsão: {}'.format(op_data, op_data, format_value(mean_prediction))) # Adicionar a série de previsão de receita fig.add_bar(x=predictions['ds'], y=predictions['yhat'], text=[format_value(val) for val in predictions['yhat']], name='{} preditas'.format(op_data)) # Personalizar layout do gráfico fig.update_traces(textposition='outside') fig.update_layout(xaxis_title='Mês-Ano', yaxis_title='{}'.format(op_data), showlegend=True) # Exibir o gráfico usando Streamlit st.plotly_chart(fig) # m = Prophet() # # future = m.make_future_dataframe(periods=periods_input) # @st.experimental_memo(ttl=60 * 60 * 24) # def get_chart(data): # hover = alt.selection_single( # fields=["date"], # nearest=True, # on="mouseover", # empty="none", # ) # # lines = ( # alt.Chart(data, height=500, title="Evolution of stock prices") # .mark_line() # .encode( # x=alt.X("date", title="Date"), # y=alt.Y("price", title="Price"), # color="symbol", # ) # ) # # # Draw points on the line, and highlight based on selection # points = lines.transform_filter(hover).mark_circle(size=65) # # # Draw a rule at the location of the selection # tooltips = ( # alt.Chart(data) # .mark_rule() # .encode( # x="yearmonthdate(date)", # y="price", # opacity=alt.condition(hover, alt.value(0.3), alt.value(0)), # tooltip=[ # alt.Tooltip("date", title="Date"), # alt.Tooltip("price", title="Price (USD)"), # ], # ) # .add_selection(hover) # ) # # return (lines + points + tooltips).interactive() # # # st.title("⬇ Time series annotations") # # st.write("Give more context to your time series using annotations!") # # col1, col2, col3 = st.columns(3) # with col1: # ticker = st.text_input("Choose a ticker (⬇💬👇ℹ️ ...)", value="⬇") # with col2: # ticker_dx = st.slider( # "Horizontal offset", min_value=-30, max_value=30, step=1, value=0 # ) # with col3: # ticker_dy = st.slider( # "Vertical offset", min_value=-30, max_value=30, step=1, value=-10 # ) # # # Original time series chart. Omitted `get_chart` for clarity # source = get_data() # chart = get_chart(source) # # # Input annotations # ANNOTATIONS = [ # ("Mar 01, 2008", "Pretty good day for GOOG"), # ("Dec 01, 2007", "Something's going wrong for GOOG & AAPL"), # ("Nov 01, 2008", "Market starts again thanks to..."), # ("Dec 01, 2009", "Small crash for GOOG after..."), # ] # # # Create a chart with annotations # annotations_df = pd.DataFrame(ANNOTATIONS, columns=["date", "event"]) # annotations_df.date = pd.to_datetime(annotations_df.date) # annotations_df["y"] = 0 # annotation_layer = ( # alt.Chart(annotations_df) # .mark_text(size=15, text=ticker, dx=ticker_dx, dy=ticker_dy, align="center") # .encode( # x="date:T", # y=alt.Y("y:Q"), # tooltip=["event"], # ) # .interactive() # ) # # # Display both charts together # st.altair_chart((chart + annotation_layer).interactive(), use_container_width=True) # # st.write("## Code") # # st.write( # "See more in our public [GitHub" # " repository](https://github.com/streamlit/example-app-time-series-annotation)" # ) # # st.code( # f""" # import altair as alt # import pandas as pd # import streamlit as st # from vega_datasets import data # # @st.experimental_memo # def get_data(): # source = data.stocks() # source = source[source.date.gt("2004-01-01")] # return source # # source = get_data() # # # Original time series chart. Omitted `get_chart` for clarity # chart = get_chart(source) # # # Input annotations # ANNOTATIONS = [ # ("Mar 01, 2008", "Pretty good day for GOOG"), # ("Dec 01, 2007", "Something's going wrong for GOOG & AAPL"), # ("Nov 01, 2008", "Market starts again thanks to..."), # ("Dec 01, 2009", "Small crash for GOOG after..."), # ] # # # Create a chart with annotations # annotations_df = pd.DataFrame(ANNOTATIONS, columns=["date", "event"]) # annotations_df.date = pd.to_datetime(annotations_df.date) # annotations_df["y"] = 0 # annotation_layer = ( # alt.Chart(annotations_df) # .mark_text(size=15, text="{ticker}", dx={ticker_dx}, dy={ticker_dy}, align="center") # .encode( # x="date:T", # y=alt.Y("y:Q"), # tooltip=["event"], # ) # .interactive() # ) # # # Display both charts together # st.altair_chart((chart + annotation_layer).interactive(), use_container_width=True) # # """, # "python", # )
jsaj/st_forecasting
st_forecasting.py
st_forecasting.py
py
10,989
python
en
code
0
github-code
6
[ { "api_name": "streamlit.set_page_config", "line_number": 6, "usage_type": "call" }, { "api_name": "streamlit.cache_data", "line_number": 12, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 23, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 23, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 36, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 36, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 42, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 42, "usage_type": "attribute" }, { "api_name": "streamlit.write", "line_number": 46, "usage_type": "call" }, { "api_name": "streamlit.write", "line_number": 47, "usage_type": "call" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 49, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 49, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 52, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 52, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 55, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 55, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 61, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 61, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 65, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 65, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 70, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 70, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.selectbox", "line_number": 72, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 72, "usage_type": "attribute" }, { "api_name": "streamlit.sidebar.slider", "line_number": 77, "usage_type": "call" }, { "api_name": "streamlit.sidebar", "line_number": 77, "usage_type": "attribute" }, { "api_name": "streamlit.write", "line_number": 82, "usage_type": "call" }, { "api_name": "plotly.express.line", "line_number": 119, "usage_type": "call" }, { "api_name": "plotly.express", "line_number": 119, "usage_type": "name" }, { "api_name": "streamlit.plotly_chart", "line_number": 132, "usage_type": "call" }, { "api_name": "plotly.express.bar", "line_number": 139, "usage_type": "call" }, { "api_name": "plotly.express", "line_number": 139, "usage_type": "name" }, { "api_name": "streamlit.plotly_chart", "line_number": 152, "usage_type": "call" } ]
3864065470
from collections import OrderedDict """ https://www.scribbr.fr/elements-linguistiques/determinant/ Le déterminant permet de présenter le nom. Il le précède et compose avec lui le groupe nominal. Un adjectif ou un autre déterminant peuvent se placer entre le déterminant et le nom. """ déterminants = OrderedDict({ "articles": { "indéfinis": ["un", "une", "des"], "définis": ["le", "l’", "la", "les"], "définis contractés": ["au", "du", "à la", "de la", "aux", "des"], "partitifs": ["du", "de l’", "de la", "des"], }, "démonstratifs": ["ce", "cet", "cette", "ces"], "possessifs": ["mon", "ton", "son", "ma", "ta", "sa", "mes", "tes", "ses", "notre", "votre", "leur", "nos", "vos", "leurs"], "exclamatifs et interrogatifs": ["quel", "quelle", "quels", "quelles"], "numéraux": ["un", "deux", "trois", "quatre", "premier", "deuxième", "troisième", "quatrième"], "relatifs": ["lequel", "laquelle", "lesquels", "lesquelles", "duquel", "de laquelle", "desquels", "desquelles", "auquel", "à laquelle", "auxquels", "auxquelles"], "indéfinis": ["certain", "quelque", "aucun", "nul", "chaque", "différent", "plusieurs"], }) """ https://www.scribbr.fr/elements-linguistiques/les-adjectifs/ Les adjectifs en français sont qualifiés de « qualificatifs », car ils permettent de donner des informations sur le nom auquel ils se rapportent. Ils s’accordent en genre et en nombre avec le nom qu’ils qualifient. """
Fushy/PythonLib
Francais.py
Francais.py
py
1,495
python
fr
code
0
github-code
6
[ { "api_name": "collections.OrderedDict", "line_number": 8, "usage_type": "call" } ]
38254722790
from django.shortcuts import render from django.contrib.auth.models import User from hknweb.utils import login_and_permission from hknweb.candidate.utils_candportal import CandidatePortalData @login_and_permission("candidate.change_offchallenge") def summary(request): cands = User.objects.filter(groups__name="candidate") headers, rows = [], [] for cand in cands: data = CandidatePortalData(cand).get_user_cand_data() if not headers: headers = [ "Name", "Forms", "Payments", "Project", "BitByte", "Hangouts", ] for event in data["events"]: event_title = event["title"] if len(event_title) > 15: event_title = event_title.split()[0] headers.append(event_title) headers.append("Overall") status = [ data["candidate_forms"]["all_done"], data["due_payments"]["all_done"], data["committee_project"]["all_done"], data["bitbyte"]["status"], data["interactivities"]["status"], *(e["status"] for e in data["events"]), ] status.append(all(status)) row = { "name": f"{cand.first_name} {cand.last_name} ({cand.username})", "status": status, "link": f"/cand/portal/{cand.username}", } rows.append(row) context = { "headers": headers, "rows": rows, } return render(request, "candidate/summary.html", context=context)
Gabe-Mitnick/hknweb
hknweb/candidate/views/summary.py
summary.py
py
1,635
python
en
code
null
github-code
6
[ { "api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 10, "usage_type": "call" }, { "api_name": "django.contrib.auth.models.User.objects", "line_number": 10, "usage_type": "attribute" }, { "api_name": "django.contrib.auth.models.User", "line_number": 10, "usage_type": "name" }, { "api_name": "hknweb.candidate.utils_candportal.CandidatePortalData", "line_number": 13, "usage_type": "call" }, { "api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call" }, { "api_name": "hknweb.utils.login_and_permission", "line_number": 8, "usage_type": "call" } ]
25228010428
import numpy as np import cv2 as cv from tkinter import * from tkinter.filedialog import * #button1 = Button(window,text="Upload",fg="black",bg="gray",command=upload).pack() img = [] class eye: def __init__(self,master): frame = Frame(master) frame.pack() button1 = Button(frame,text="Upload",fg="green",bg="gray",command=self.upload).pack() #quit_button = Button(frame, text ="Quit",bg="Red",fg="green",command=frame.destroy).pack() def upload(self): imgTemp = askopenfilename() img = cv.imread(imgTemp) img = np.array(img, dtype=np.uint8) face_cascade = cv.CascadeClassifier('data/haarcascade_frontalface_default.xml') eye_cascade = cv.CascadeClassifier('data/haarcascade_eye.xml') gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) roi_gray = gray[y:y+h, x:x+w] roi_color = img[y:y+h, x:x+w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex,ey,ew,eh) in eyes: cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2) cv.imshow('img',img) cv.waitKey(0) cv.destroyAllWindow() #def exit(self): #button1 = Button(window,text="Upload",fg="black",bg="gray",command=obj.upload).pack() #button1 = Button(window,text="Upload",fg="black",bg="green",command=disp).pack() def quit(root): root.destroy() root = Tk() root.title("FaceIt___") root.geometry("500x500") button2 = Button(root,text="Exit",fg="green",bg="red",command=lambda root=root:quit(root)).pack() root.configure(background="black") obj = eye(root) #button1 = Button(root,text="Exit",fg="green",bg="red",command=lambda root=root:quit(root)).pack() root.mainloop()
gods-mack/face-Detection_project
eye.py
eye.py
py
1,816
python
en
code
5
github-code
6
[ { "api_name": "cv2.imread", "line_number": 23, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 24, "usage_type": "call" }, { "api_name": "numpy.uint8", "line_number": 24, "usage_type": "attribute" }, { "api_name": "cv2.CascadeClassifier", "line_number": 25, "usage_type": "call" }, { "api_name": "cv2.CascadeClassifier", "line_number": 26, "usage_type": "call" }, { "api_name": "cv2.cvtColor", "line_number": 27, "usage_type": "call" }, { "api_name": "cv2.COLOR_BGR2GRAY", "line_number": 27, "usage_type": "attribute" }, { "api_name": "cv2.rectangle", "line_number": 31, "usage_type": "call" }, { "api_name": "cv2.rectangle", "line_number": 36, "usage_type": "call" }, { "api_name": "cv2.imshow", "line_number": 38, "usage_type": "call" }, { "api_name": "cv2.waitKey", "line_number": 39, "usage_type": "call" }, { "api_name": "cv2.destroyAllWindow", "line_number": 40, "usage_type": "call" } ]
72627145467
import inspect import os from typing import Any, Dict, List, Optional, Union import yaml import pmd import pmd.core # These have to be written explicitly for typing from pmd.core.Builder import Builder from pmd.core.Job import Job from pmd.core.Lammps import Lammps from pmd.core.Procedure import Procedure from pmd.core.System import System from pmd.util import Pmdlogging, build_dir PRIMITIVE_TYPES = (str, int, float, bool) SUPPORTED_YAML_EXTS = (".yml", ".yaml") OBJECT_PRFIX = 'pmd.' # Custom yaml config file dictionary constructor def to_yaml_dict(cls: Union[System, Builder, Lammps, Procedure, Job]) -> Dict: return { # strip off the front underscore and only add to dict # if value is not None k.lstrip('_'): custom_class_yaml_dumper(v) for k, v in cls.__dict__.items() if v is not None } # Custom method to dump values of non-primitive type to the yaml config file def custom_class_yaml_dumper(v: Any) -> Any: return_value = v # If value is a list, recursively go through each item in the list # Specifically, this is for the Lammps procedure list if isinstance(v, list): return_value = [custom_class_yaml_dumper(i) for i in v] # If value is a non-primitive type, expand it to a dict elif not isinstance(v, PRIMITIVE_TYPES): return_value = {f"{OBJECT_PRFIX}{v}": to_yaml_dict(v)} return return_value def instantiate_from_cls_name(class_name: str, prop_dict: dict): # first obtain a list of all classes in this module class_list = inspect.getmembers(pmd.core, inspect.isclass) class_dict = {k: v for k, v in class_list} # find the matching class filtered_class_name = class_name.lstrip(OBJECT_PRFIX).split('-')[0] the_class = class_dict.get(filtered_class_name, None) if the_class is None: raise NameError( f'{class_name} type is not found in {pmd.core.__name__} module') # get the constructor parameter list of the class sig = inspect.signature(the_class.__init__) param_keys = list(sig.parameters.keys()) if param_keys[0] == 'self': param_keys = param_keys[1:] # remove props not in the parameter list of the class filtered_prop_dict = { k: custom_class_yaml_loader(v) for k, v in prop_dict.items() if k in param_keys } Pmdlogging.info( f'{class_name} object successfully loaded from the YAML file.') return the_class(**filtered_prop_dict) # Custom method to load values from the yaml config file def custom_class_yaml_loader(v: Any) -> Any: return_value = v # If value is a list, recursively go through each item in the list # Specifically, this is for the Lammps procedure list if isinstance(v, list): return_value = [custom_class_yaml_loader(i) for i in v] # If value is a dict, instantiate it to an object elif isinstance(v, dict): class_name, props_dict = next(iter(v.items())) return_value = instantiate_from_cls_name(class_name, props_dict) # If value is starts with pmd., instantiate it to an object with # default params elif isinstance(v, str) and v.startswith(OBJECT_PRFIX): return_value = instantiate_from_cls_name(v, {}) return return_value class Pmd: '''Template object to perform tasks for Systems, Lammps, and Jobs altogether (e.g. create data files, lammps input files, job scheduler files, or config files) Attributes: system (System): a System object lammps (Lammps or list[Lammps]): one or a list of Lammps objects job (Job or list[Job]): one or a list of Job objects ''' def __init__( self, system: Optional[System] = None, lammps: Optional[Union[Lammps, List[Lammps]]] = None, job: Optional[Union[Job, List[Job]]] = None, ): if lammps and not isinstance(lammps, list): lammps = [lammps] if job and not isinstance(job, list): job = [job] self._system = system self._lammps = lammps self._job = job @build_dir def create(self, output_dir: str = '.', save_config: bool = False, config_fname: str = 'config.yaml') -> None: '''Method to create files from all the pmd objects. This method can can also automatically generate a config file if `save_config` input argument is set to True. Parameters: output_dir (str): Directory for all the generated files; default: `"."` save_config (bool): Whether to save a config file; default: `False` config_fname (str): Name of the config file; default: `"config.yaml"` Returns: None ''' if self._system: self._system.write_data(output_dir) if self._lammps: for lmp in self._lammps: lmp.write_lammps(output_dir) if self._job: for job in self._job: job.write_job(output_dir) if save_config: self.save_config(output_dir, config_fname) @build_dir def save_config(self, output_dir: str, config_fname: str = 'config.yaml'): '''Method to create a config file with all the details of the System, Lammps, or Job settings. This method only creates the config file. Parameters: output_dir (str): Directory for all the generated files; default: `"."` config_fname (str): Name of the config file; default: `"config.yaml"` Returns: None ''' config_dict = {'pmd.version': pmd.__version__} if self._system: config_dict[f'{OBJECT_PRFIX}{self._system}'] = to_yaml_dict( self._system) if self._lammps and len(self._lammps) > 1: for i, lmp in enumerate(self._lammps): config_dict[f'{OBJECT_PRFIX}{lmp}-{i}'] = to_yaml_dict(lmp) elif self._lammps and len(self._lammps) == 1: config_dict[f'{OBJECT_PRFIX}{self._lammps[0]}'] = to_yaml_dict( self._lammps[0]) if self._job and len(self._job) > 1: for i, job in enumerate(self._job): config_dict[f'{OBJECT_PRFIX}{job}-{i}'] = to_yaml_dict(job) elif self._job and len(self._job) == 1: config_dict[f'{OBJECT_PRFIX}{self._job[0]}'] = to_yaml_dict( self._job[0]) with open(os.path.join(output_dir, config_fname), 'w') as yaml_file: yaml.safe_dump(config_dict, yaml_file, sort_keys=False) Pmdlogging.info(f'Config file - {config_fname} successfully ' f'saved to {output_dir}') @staticmethod def load_config(config_file: str, output_dir: str = '.'): '''Method to load a config file and create all the objects listed in the config file Parameters: config_file (str): Config file to load output_dir (str): Directory for all the generated files; default: `"."` Returns: None ''' if os.path.splitext(config_file)[1] not in SUPPORTED_YAML_EXTS: raise ValueError( f'The file you are loading does not seem to be a yaml file' f'(file must end with {" ,".join(SUPPORTED_YAML_EXTS)})') with open(config_file) as yaml_file: yaml_dict = yaml.safe_load(yaml_file) for k, v in yaml_dict.items(): # do not instantiate an object if it is the version item if k == 'pmd.version': if v != pmd.__version__: Pmdlogging.warning('Config file version does not ' 'match your current PMD version') continue obj = instantiate_from_cls_name(k, v) if isinstance(obj, System): obj.write_data(output_dir) elif isinstance(obj, Lammps): obj.write_lammps(output_dir) elif isinstance(obj, Job): obj.write_job(output_dir)
ritesh001/Polymer-Molecular-Dynamics
pmd/core/Pmd.py
Pmd.py
py
8,334
python
en
code
0
github-code
6
[ { "api_name": "typing.Union", "line_number": 23, "usage_type": "name" }, { "api_name": "pmd.core.System.System", "line_number": 23, "usage_type": "name" }, { "api_name": "pmd.core.Builder.Builder", "line_number": 23, "usage_type": "name" }, { "api_name": "pmd.core.Lammps.Lammps", "line_number": 23, "usage_type": "name" }, { "api_name": "pmd.core.Procedure.Procedure", "line_number": 23, "usage_type": "name" }, { "api_name": "pmd.core.Job.Job", "line_number": 23, "usage_type": "name" }, { "api_name": "typing.Dict", "line_number": 23, "usage_type": "name" }, { "api_name": "typing.Any", "line_number": 33, "usage_type": "name" }, { "api_name": "inspect.getmembers", "line_number": 50, "usage_type": "call" }, { "api_name": "pmd.core", "line_number": 50, "usage_type": "attribute" }, { "api_name": "inspect.isclass", "line_number": 50, "usage_type": "attribute" }, { "api_name": "pmd.core", "line_number": 58, "usage_type": "attribute" }, { "api_name": "inspect.signature", "line_number": 61, "usage_type": "call" }, { "api_name": "pmd.util.Pmdlogging.info", "line_number": 72, "usage_type": "call" }, { "api_name": "pmd.util.Pmdlogging", "line_number": 72, "usage_type": "name" }, { "api_name": "typing.Any", "line_number": 78, "usage_type": "name" }, { "api_name": "typing.Optional", "line_number": 114, "usage_type": "name" }, { "api_name": "pmd.core.System.System", "line_number": 114, "usage_type": "name" }, { "api_name": "typing.Optional", "line_number": 115, "usage_type": "name" }, { "api_name": "typing.Union", "line_number": 115, "usage_type": "name" }, { "api_name": "pmd.core.Lammps.Lammps", "line_number": 115, "usage_type": "name" }, { "api_name": "typing.List", "line_number": 115, "usage_type": "name" }, { "api_name": "typing.Optional", "line_number": 116, "usage_type": "name" }, { "api_name": "typing.Union", "line_number": 116, "usage_type": "name" }, { "api_name": "pmd.core.Job.Job", "line_number": 116, "usage_type": "name" }, { "api_name": "typing.List", "line_number": 116, "usage_type": "name" }, { "api_name": "pmd.util.build_dir", "line_number": 127, "usage_type": "name" }, { "api_name": "pmd.__version__", "line_number": 176, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 196, "usage_type": "call" }, { "api_name": "os.path", "line_number": 196, "usage_type": "attribute" }, { "api_name": "yaml.safe_dump", "line_number": 197, "usage_type": "call" }, { "api_name": "pmd.util.Pmdlogging.info", "line_number": 199, "usage_type": "call" }, { "api_name": "pmd.util.Pmdlogging", "line_number": 199, "usage_type": "name" }, { "api_name": "pmd.util.build_dir", "line_number": 160, "usage_type": "name" }, { "api_name": "os.path.splitext", "line_number": 217, "usage_type": "call" }, { "api_name": "os.path", "line_number": 217, "usage_type": "attribute" }, { "api_name": "yaml.safe_load", "line_number": 223, "usage_type": "call" }, { "api_name": "pmd.__version__", "line_number": 227, "usage_type": "attribute" }, { "api_name": "pmd.util.Pmdlogging.warning", "line_number": 228, "usage_type": "call" }, { "api_name": "pmd.util.Pmdlogging", "line_number": 228, "usage_type": "name" }, { "api_name": "pmd.core.System.System", "line_number": 232, "usage_type": "argument" }, { "api_name": "pmd.core.Lammps.Lammps", "line_number": 234, "usage_type": "argument" }, { "api_name": "pmd.core.Job.Job", "line_number": 236, "usage_type": "argument" } ]
73706490427
"""Image tools interfaces.""" from nilearn.image import resample_to_img import numpy as np import nibabel as nb from nipype.utils.filemanip import fname_presuffix from nipype import logging from nipype.interfaces.base import (traits, TraitedSpec, BaseInterfaceInputSpec, SimpleInterface, File) LOGGER = logging.getLogger('nipype.interface') class _ResampleTPMInputSpec(BaseInterfaceInputSpec): moving_file = File(exists=True, mandatory=True, desc='Eroded Tissues probability map file in T1 space') fixed_file = File(exists=True, mandatory=True, desc=' timeseries mask in BOLD space') class _ResampleTPMOutputSpec(TraitedSpec): out_file = File(exists=True, desc='output Resampled WM file') class ResampleTPM(SimpleInterface): """ Resample all white matter tissue prob mask to BOLD space. """ input_spec = _ResampleTPMInputSpec output_spec = _ResampleTPMOutputSpec # def _run_interface(self,runtime): # self._results['out_file'] = resample_WM( # self.inputs.moving_file, # self.inputs.fixed_file, # newpath=runtime.cwd # ) # return runtime def _run_interface(self, runtime): out_file = _TPM_2_BOLD( self.inputs.moving_file, self.inputs.fixed_file, newpath=runtime.cwd, ) self._results['out_file'] = out_file return runtime def _TPM_2_BOLD(moving_file, fixed_file, newpath=None): """ Resample the input white matter tissues probability using resample_to_img from nilearn. """ out_file = fname_presuffix(moving_file, suffix='_resampled', newpath=newpath) resample_wm = resample_to_img(source_img=moving_file, target_img=fixed_file, interpolation='nearest') resample_wm.to_filename(out_file) return out_file
jerdra/TIGR_PURR
bin/resample.py
resample.py
py
2,104
python
en
code
0
github-code
6
[ { "api_name": "nipype.logging.getLogger", "line_number": 11, "usage_type": "call" }, { "api_name": "nipype.logging", "line_number": 11, "usage_type": "name" }, { "api_name": "nipype.interfaces.base.BaseInterfaceInputSpec", "line_number": 14, "usage_type": "name" }, { "api_name": "nipype.interfaces.base.File", "line_number": 15, "usage_type": "call" }, { "api_name": "nipype.interfaces.base.File", "line_number": 18, "usage_type": "call" }, { "api_name": "nipype.interfaces.base.TraitedSpec", "line_number": 23, "usage_type": "name" }, { "api_name": "nipype.interfaces.base.File", "line_number": 24, "usage_type": "call" }, { "api_name": "nipype.interfaces.base.SimpleInterface", "line_number": 27, "usage_type": "name" }, { "api_name": "nipype.utils.filemanip.fname_presuffix", "line_number": 57, "usage_type": "call" }, { "api_name": "nilearn.image.resample_to_img", "line_number": 61, "usage_type": "call" } ]
11485777017
import random import subprocess from difflib import SequenceMatcher from typing import cast from smellybot.access_control import Everyone, ModPlus from smellybot.bot_command import BotCommand, SuperCommand from smellybot.bot_module import BotModule from smellybot.config.definition import ListConfigDefinition from smellybot.config.element import ListConfigElement from smellybot.config.secure_config import Config from smellybot.config.types.string import CUsername, CString from smellybot.context import MessageContext class Owoifier(BotModule): def __init__(self, config: Config, bot_channel): super().__init__(config, bot_channel) self.command_list() self.auto_targets: ListConfigElement = cast(ListConfigElement, config.register( ListConfigDefinition("owoifier.auto_targets", ctype=CUsername(), unique=True), read_access_control=ModPlus() )) self.endings: ListConfigElement = cast(ListConfigElement, config.register( ListConfigDefinition("owoifier.endings", ctype=CString(), unique=True), read_access_control=ModPlus() )) @classmethod def name(cls): return "owoifier" def command_list(self): self.add_command(BotCommand(Config("owoify", self.config), self, self.owoify, name="owoify", access_control=Everyone())) owoifier_command = SuperCommand( Config("owoifier", self.config), self, access_control=ModPlus(), name="owoifier" ) target_command = BotCommand( Config("target", owoifier_command.config), self, self.target, access_control=ModPlus(), name="target" ) untarget_command = BotCommand( Config("untarget", owoifier_command.config), self, self.untarget, access_control=ModPlus(), name="untarget" ) owoifier_command.add_subcommand(target_command) owoifier_command.add_subcommand(untarget_command) self.add_command(owoifier_command) async def _handle_message(self, context: MessageContext): if context.author.username.lower() not in self.auto_targets.get(): return if context.message.startswith("!"): return self.logger.info(f"{context.author.username}: {context.message}") owo_message = self.owoify_message(context.message) if not self.message_differs_significantly(context.message, owo_message): return owo_message = self.add_ending(owo_message) await self.bot_channel.send(owo_message) async def owoify(self, _context: MessageContext, arguments: str, _command: str, _head: str, **_kwargs): if arguments: await self.send_owo_message(arguments) elif self.bot_channel.context.previous_context.message: await self.send_owo_message(self.bot_channel.context.previous_context.message) async def target(self, _context: MessageContext, arguments: str, _command: str, _head: str, **_kwargs): self.auto_targets.add(arguments) await self.bot_channel.send("Target acquired") async def untarget(self, _context: MessageContext, arguments: str, _command: str, _head: str, **_kwargs): self.auto_targets.remove(arguments) await self.bot_channel.send("We'll get 'em next time") async def send_owo_message(self, message: str): owo_message = self.owoify_message(message) owo_message = self.add_ending(owo_message) await self.bot_channel.send(owo_message) def message_differs_significantly(self, original_message: str, owo_message: str): difference = 1 - SequenceMatcher(None, original_message.strip(), owo_message.strip()).ratio() return difference > 0.04 def owoify_message(self, message: str): result = subprocess.run(['owoifier', "-t", message], capture_output=True) return result.stdout.decode("utf-8") def add_ending(self, message: str): separators = [", ", " "] endings = self.endings.get() if not endings: return message separator = random.choice(separators) ending = random.choice(endings) return message.rstrip() + separator + ending
schmarcel02/smellybot
smellybot/modules/owoifier.py
owoifier.py
py
4,337
python
en
code
0
github-code
6
[ { "api_name": "smellybot.bot_module.BotModule", "line_number": 16, "usage_type": "name" }, { "api_name": "smellybot.config.secure_config.Config", "line_number": 18, "usage_type": "name" }, { "api_name": "smellybot.config.element.ListConfigElement", "line_number": 22, "usage_type": "name" }, { "api_name": "typing.cast", "line_number": 22, "usage_type": "call" }, { "api_name": "smellybot.config.definition.ListConfigDefinition", "line_number": 23, "usage_type": "call" }, { "api_name": "smellybot.config.types.string.CUsername", "line_number": 23, "usage_type": "call" }, { "api_name": "smellybot.access_control.ModPlus", "line_number": 24, "usage_type": "call" }, { "api_name": "smellybot.config.element.ListConfigElement", "line_number": 27, "usage_type": "name" }, { "api_name": "typing.cast", "line_number": 27, "usage_type": "call" }, { "api_name": "smellybot.config.definition.ListConfigDefinition", "line_number": 28, "usage_type": "call" }, { "api_name": "smellybot.config.types.string.CString", "line_number": 28, "usage_type": "call" }, { "api_name": "smellybot.access_control.ModPlus", "line_number": 29, "usage_type": "call" }, { "api_name": "smellybot.bot_command.BotCommand", "line_number": 37, "usage_type": "call" }, { "api_name": "smellybot.config.secure_config.Config", "line_number": 37, "usage_type": "call" }, { "api_name": "smellybot.access_control.Everyone", "line_number": 37, "usage_type": "call" }, { "api_name": "smellybot.bot_command.SuperCommand", "line_number": 39, "usage_type": "call" }, { "api_name": "smellybot.config.secure_config.Config", "line_number": 40, "usage_type": "call" }, { "api_name": "smellybot.access_control.ModPlus", "line_number": 42, "usage_type": "call" }, { "api_name": "smellybot.bot_command.BotCommand", "line_number": 46, "usage_type": "call" }, { "api_name": "smellybot.config.secure_config.Config", "line_number": 47, "usage_type": "call" }, { "api_name": "smellybot.access_control.ModPlus", "line_number": 50, "usage_type": "call" }, { "api_name": "smellybot.bot_command.BotCommand", "line_number": 54, "usage_type": "call" }, { "api_name": "smellybot.config.secure_config.Config", "line_number": 55, "usage_type": "call" }, { "api_name": "smellybot.access_control.ModPlus", "line_number": 58, "usage_type": "call" }, { "api_name": "smellybot.context.MessageContext", "line_number": 67, "usage_type": "name" }, { "api_name": "smellybot.context.MessageContext", "line_number": 79, "usage_type": "name" }, { "api_name": "smellybot.context.MessageContext", "line_number": 85, "usage_type": "name" }, { "api_name": "smellybot.context.MessageContext", "line_number": 89, "usage_type": "name" }, { "api_name": "difflib.SequenceMatcher", "line_number": 99, "usage_type": "call" }, { "api_name": "subprocess.run", "line_number": 103, "usage_type": "call" }, { "api_name": "random.choice", "line_number": 113, "usage_type": "call" }, { "api_name": "random.choice", "line_number": 114, "usage_type": "call" } ]
9259374186
from pca import PCA import numpy as np import matplotlib.pyplot as plt from skimage.transform import resize import scipy.io as scio from kmeans import KMeans mat = scio.loadmat('./ExtYaleB10.mat') Y_test = mat['test'] Y_train = mat['train'] def imageResizing(data): resizedDatasET = [] for img in data: resizedDatasET.append(resize(img, (20, 17), mode='constant')) resizedDatasET = np.array(resizedDatasET) return resizedDatasET def imageReshaping(data): dimension = data.shape[1] * data.shape[2] return data.reshape(data.shape[0], dimension) def inputProcessing(data): X = [];Y = [] for i in range(len(data[0])): people_count = data[0][i].T for j in range(len(people_count)): X.append(people_count[j].T);Y.append(i) X = np.array(X);Y = np.array(Y) fig, axis = plt.subplots(figsize=(12,8)) axis.imshow(X[1], cmap='gray') X = imageResizing(X) X = imageReshaping(X) return X, Y X,Y = inputProcessing(Y_train) Xtst,Ytst = inputProcessing(Y_test) # apply KMeans with k = 10 centers, predictedLabels = KMeans(X.T, 10, 10) # Error err = 0 for i in range(len(predictedLabels)): if predictedLabels[i] != Y[i]: err += 1 print("Clustering Error ratio with Kmeans: ", float(err) / len(predictedLabels)) # PCA with d = 2 and d = 100 pcaarray = [2,100] for i in pcaarray: print("For pca with dimensions = " , i) X = PCA(X.T, i)[-1].T # Plotting the graph plt.style.use("classic") colors = ['b', 'lime', 'c', 'r', 'y', 'm', 'k', 'teal', 'silver', 'aqua'] figure, axis = plt.subplots() for i in range(10): nodes = np.array([X[j] for j in range(len(X)) if predictedLabels[j] == i]) axis.scatter(nodes[:, 0], nodes[:, 1], s=16, c=colors[i]) plt.show()
nancyagrwal/Machine-Learning
Feed FOrward NN/testG.py
testG.py
py
1,799
python
en
code
0
github-code
6
[ { "api_name": "scipy.io.loadmat", "line_number": 8, "usage_type": "call" }, { "api_name": "scipy.io", "line_number": 8, "usage_type": "name" }, { "api_name": "skimage.transform.resize", "line_number": 15, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 16, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 29, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.subplots", "line_number": 30, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name" }, { "api_name": "kmeans.KMeans", "line_number": 40, "usage_type": "call" }, { "api_name": "pca.PCA", "line_number": 52, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.style.use", "line_number": 55, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.style", "line_number": 55, "usage_type": "attribute" }, { "api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.subplots", "line_number": 58, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name" }, { "api_name": "numpy.array", "line_number": 60, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name" } ]
21992599596
import collections import numpy as np from scipy.signal import butter class ButterFilter(object): """ Implements butterworth low-pass filter. Based on https://github.com/google-research/motion_imitation/blob/master/motion_imitation/robots/action_filter.py """ def __init__(self, sampling_rate, action_size, highcut = [4.0]): self.action_size = action_size self.sampling_rate = sampling_rate self.highcut = highcut self.lowcut = [0.0] self.order = 2 a_coeffs = [] b_coeffs = [] for i, h in enumerate(self.highcut): b, a = self.butter_filter_coefficients(h, sampling_rate, self.order) b_coeffs.append(b) a_coeffs.append(a) if isinstance(a, list): self.a = a self.b = b else: self.a = [a] self.b = [b] # Normalize by a[0] for i in range(len(self.a)): self.b[i] /= self.a[i][0] self.a[i] /= self.a[i][0] # Convert single filter to same format as filter per joint if len(self.a) == 1: self.a *= action_size self.b *= action_size self.a = np.stack(self.a) self.b = np.stack(self.b) assert len(self.b[0]) == len(self.a[0]) == self.order + 1 self.hist_len = self.order self.yhist = collections.deque(maxlen=self.hist_len) self.xhist = collections.deque(maxlen=self.hist_len) self.reset() def reset(self): self.yhist.clear() self.xhist.clear() for _ in range(self.hist_len): self.yhist.appendleft(np.zeros((self.action_size, 1))) self.xhist.appendleft(np.zeros((self.action_size, 1))) def filter(self, x): xs = np.concatenate(list(self.xhist), axis=-1) ys = np.concatenate(list(self.yhist), axis=-1) y = np.multiply(x, self.b[:, 0]) + np.sum( np.multiply(xs, self.b[:, 1:]), axis=-1) - np.sum( np.multiply(ys, self.a[:, 1:]), axis=-1) self.xhist.appendleft(x.reshape((self.action_size, 1)).copy()) self.yhist.appendleft(y.reshape((self.action_size, 1)).copy()) return y def init_history(self, x): x = np.expand_dims(x, axis=-1) for i in range(self.hist_len): self.xhist[i] = x self.yhist[i] = x def butter_filter_coefficients(self, highcut, fs, order=5): nyq = 0.5 * fs high = highcut / nyq b, a = butter(order, [high], btype='low') return b, a
bit-bots/deep_quintic
deep_quintic/butter_filter.py
butter_filter.py
py
2,574
python
en
code
0
github-code
6
[ { "api_name": "numpy.stack", "line_number": 42, "usage_type": "call" }, { "api_name": "numpy.stack", "line_number": 43, "usage_type": "call" }, { "api_name": "collections.deque", "line_number": 48, "usage_type": "call" }, { "api_name": "collections.deque", "line_number": 49, "usage_type": "call" }, { "api_name": "numpy.zeros", "line_number": 56, "usage_type": "call" }, { "api_name": "numpy.zeros", "line_number": 57, "usage_type": "call" }, { "api_name": "numpy.concatenate", "line_number": 60, "usage_type": "call" }, { "api_name": "numpy.concatenate", "line_number": 61, "usage_type": "call" }, { "api_name": "numpy.multiply", "line_number": 62, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 62, "usage_type": "call" }, { "api_name": "numpy.multiply", "line_number": 63, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 63, "usage_type": "call" }, { "api_name": "numpy.multiply", "line_number": 64, "usage_type": "call" }, { "api_name": "numpy.expand_dims", "line_number": 70, "usage_type": "call" }, { "api_name": "scipy.signal.butter", "line_number": 78, "usage_type": "call" } ]
9180027330
import argparse import base64 try: from http.server import BaseHTTPRequestHandler except ImportError: # Python 2.x compatibility hack. from BaseHTTPServer import BaseHTTPRequestHandler import os import os.path try: from socketserver import TCPServer if os.name != 'nt': from socketserver import UnixStreamServer except ImportError: # Python 2.x compatibility hack. from SocketServer import TCPServer if os.name != 'nt': from SocketServer import UnixStreamServer import random import socket import sys import time class Handler(BaseHTTPRequestHandler): """Handlers for testing HTTP server.""" auth = False not_found = False simulate_timeout = False filename = None redirect = None valid_headers = [ b'Basic ' + base64.b64encode('foo:bar'.encode('ascii')), b'Bearer TOKEN' ] def do_HEAD(self): # pylint: disable=invalid-name self.send_response(200) self.send_header('Content-type', 'text/html') self.end_headers() def do_AUTHHEAD(self): # pylint: disable=invalid-name self.send_response(401) self.send_header('WWW-Authenticate', 'Basic realm=\"Bazel\"') self.send_header('Content-type', 'text/html') self.end_headers() def do_GET(self): # pylint: disable=invalid-name if not self.client_address: # Needed for Unix domain connections as the response functions # fail without this being set. self.client_address = 'localhost' if self.simulate_timeout: while True: time.sleep(1) if self.not_found: self.send_response(404) self.end_headers() return if self.redirect is not None: self.send_response(301) self.send_header('Location', self.redirect) self.end_headers() return if not self.auth: self.do_HEAD() self.serve_file() return auth_header = self.headers.get('Authorization', '').encode('ascii') if auth_header in self.valid_headers: self.do_HEAD() self.serve_file() else: self.do_AUTHHEAD() self.wfile.write( 'Bad authorization header: {}'.format(auth_header).encode('ascii') ) def serve_file(self): path_to_serve = self.path[1:] if self.filename is not None: path_to_serve = self.filename to_serve = os.path.join(os.getcwd(), path_to_serve) with open(to_serve, 'rb') as file_to_serve: self.wfile.write(file_to_serve.read()) def main(argv): parser = argparse.ArgumentParser() parser.add_argument('--unix_socket', action='store') parser.add_argument('mode', type=str, nargs='?') parser.add_argument('target', type=str, nargs='?') args = parser.parse_args(argv) if args.mode: if args.mode == 'always' and args.target: Handler.filename = args.target elif args.mode == 'redirect' and args.target: Handler.redirect = args.target elif args.mode == '404': Handler.not_found = True elif args.mode == 'timeout': Handler.simulate_timeout = True elif args.mode == 'auth': Handler.auth = True if args.target: Handler.filename = args.target httpd = None if args.unix_socket: httpd = UnixStreamServer(args.unix_socket, Handler) sys.stderr.write('Serving forever on %s.\n' % args.unix_socket) else: port = None while port is None: try: port = random.randrange(32760, 59760) httpd = TCPServer(('', port), Handler) except socket.error: port = None sys.stdout.write('%d\nstarted\n' % (port,)) sys.stdout.flush() sys.stdout.close() sys.stderr.write('Serving forever on %d.\n' % port) try: httpd.serve_forever() finally: sys.stderr.write('Goodbye.\n') if __name__ == '__main__': sys.exit(main(sys.argv[1:]))
bazelbuild/bazel
src/test/shell/bazel/testing_server.py
testing_server.py
py
3,746
python
en
code
21,632
github-code
6
[ { "api_name": "os.name", "line_number": 12, "usage_type": "attribute" }, { "api_name": "os.name", "line_number": 17, "usage_type": "attribute" }, { "api_name": "BaseHTTPServer.BaseHTTPRequestHandler", "line_number": 25, "usage_type": "name" }, { "api_name": "base64.b64encode", "line_number": 33, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 55, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 88, "usage_type": "call" }, { "api_name": "os.path", "line_number": 88, "usage_type": "attribute" }, { "api_name": "os.getcwd", "line_number": 88, "usage_type": "call" }, { "api_name": "argparse.ArgumentParser", "line_number": 94, "usage_type": "call" }, { "api_name": "SocketServer.UnixStreamServer", "line_number": 116, "usage_type": "call" }, { "api_name": "sys.stderr.write", "line_number": 117, "usage_type": "call" }, { "api_name": "sys.stderr", "line_number": 117, "usage_type": "attribute" }, { "api_name": "random.randrange", "line_number": 122, "usage_type": "call" }, { "api_name": "SocketServer.TCPServer", "line_number": 123, "usage_type": "call" }, { "api_name": "socket.error", "line_number": 124, "usage_type": "attribute" }, { "api_name": "sys.stdout.write", "line_number": 126, "usage_type": "call" }, { "api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute" }, { "api_name": "sys.stdout.flush", "line_number": 127, "usage_type": "call" }, { "api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute" }, { "api_name": "sys.stdout.close", "line_number": 128, "usage_type": "call" }, { "api_name": "sys.stdout", "line_number": 128, "usage_type": "attribute" }, { "api_name": "sys.stderr.write", "line_number": 129, "usage_type": "call" }, { "api_name": "sys.stderr", "line_number": 129, "usage_type": "attribute" }, { "api_name": "sys.stderr.write", "line_number": 134, "usage_type": "call" }, { "api_name": "sys.stderr", "line_number": 134, "usage_type": "attribute" }, { "api_name": "sys.exit", "line_number": 138, "usage_type": "call" }, { "api_name": "sys.argv", "line_number": 138, "usage_type": "attribute" } ]
17378765276
import cv2 import numpy as np import argparse import os from PIL import Image import matplotlib.pyplot as plt from scipy.ndimage.filters import gaussian_filter # condition1dls pixel 검은색 확인 def pixel_is_black(arr,x,y): if arr[x,y] ==1: return True return False #condtion2 2개에서 6개의 검은 픽셀 가짐? def pixel_has_2_to_6_black_neighbors(arr,x,y): if(2<=arr[x, y-1] + arr[x+1, y-1] + arr[x+1, y] + arr[x+1, y+1] + arr[x, y+1] + arr[x-1, y+1] + arr[x-1, y] + arr[x-1, y-1] <= 6): return True return False #condition3 transition확인 def pixel_has_1_white_to_black_neighbor_transition(arr,x,y): neighbors = [arr[x, y - 1], arr[x + 1, y - 1], arr[x + 1, y], arr[x + 1, y + 1], arr[x, y + 1], arr[x, y + 1], arr[x - 1, y], arr[x - 1, y - 1], arr[x, y - 1]] transitions = sum((a, b) == (0, 1) for a, b in zip(neighbors, neighbors[1:])) if transitions == 1: return True return False #condition4 p2, def at_least_one_of_P2_P4_P6_is_white(arr, x, y): if (arr[x, y - 1] and arr[x + 1, y] and arr[x, y + 1]) == False: return True return False #condition5 def at_least_one_of_P4_P6_P8_is_white(arr, x, y): if (arr[x + 1, y] and arr[x, y + 1] and arr[x - 1, y]) == False: return True return False #condition4 for step two def at_least_one_of_P2_P4_P8_is_white(arr, x, y): if (arr[x, y - 1] and arr[x + 1, y] and arr[x - 1, y]) == False: return True return False def at_least_one_of_P2_P6_P8_is_white(arr, x, y): if (arr[x, y - 1] and arr[x, y + 1] and arr[x - 1, y]) == False: return True return False def main(): dirname = 'C:/Users/oeunju/Downloads/1500-1700' filenames = os.listdir(dirname) for i in range(486,1000, 1): dirname2= dirname +'/'+ str(i) if not os.path.exists(dirname2): exit() filenames =os.listdir(dirname2) for filename in filenames: full_filename =os.path.join(dirname2, filename) print(filename) img = cv2.imread(full_filename, 0) retval, orig_thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY) bin_thresh = (orig_thresh == 0).astype(int) thinned_thresh = bin_thresh.copy() while 1: # make a copy of the thinned threshold array to check for changes thresh_copy = thinned_thresh.copy() # step one pixels_meeting_criteria = [] # check all pixels except for border and corner pixels # if a pixel meets all criteria, add it to pixels_meeting_criteria list for i in range(1, thinned_thresh.shape[0] - 1): for j in range(1, thinned_thresh.shape[1] - 1): if (pixel_is_black(thinned_thresh, i, j) and pixel_has_2_to_6_black_neighbors(thinned_thresh, i, j) and pixel_has_1_white_to_black_neighbor_transition(thinned_thresh, i, j) and at_least_one_of_P2_P4_P6_is_white(thinned_thresh, i, j) and at_least_one_of_P4_P6_P8_is_white(thinned_thresh, i, j)): pixels_meeting_criteria.append((i, j)) # change noted pixels in thinned threshold array to 0 (white) for pixel in pixels_meeting_criteria: thinned_thresh[pixel] = 0 # step two pixels_meeting_criteria = [] # check all pixels except for border and corner pixels # if a pixel meets all criteria, add it to pixels_meeting_criteria list for i in range(1, thinned_thresh.shape[0] - 1): for j in range(1, thinned_thresh.shape[1] - 1): if (pixel_is_black(thinned_thresh, i, j) and pixel_has_2_to_6_black_neighbors(thinned_thresh, i, j) and pixel_has_1_white_to_black_neighbor_transition(thinned_thresh, i, j) and at_least_one_of_P2_P4_P8_is_white(thinned_thresh, i, j) and at_least_one_of_P2_P6_P8_is_white(thinned_thresh, i, j)): pixels_meeting_criteria.append((i, j)) # change noted pixels in thinned threshold array to 0 (white) for pixel in pixels_meeting_criteria: thinned_thresh[pixel] = 0 # if the latest iteration didn't make any difference, exit loop if np.all(thresh_copy == thinned_thresh) == True: break # convert all ones (black pixels) to zeroes, and all zeroes (white pixels) to ones thresh = (thinned_thresh == 0).astype(np.uint8) # convert ones to 255 (white) thresh *= 255 dirname_simple = dirname2[-3:] # display original and thinned images # cv2.imshow('original image', orig_thresh) # cv2.imshow('thinned image', thresh) # cv2.waitKey(0) # cv2.destroyAllWindows() cv2.imwrite('C:/Users/oeunju/Desktop/1500-1700/'+dirname_simple+filename, thresh) if __name__ == '__main__': main()
Leegunmin/RecognizeKorean
zaung_shen.py
zaung_shen.py
py
5,571
python
en
code
0
github-code
6
[ { "api_name": "os.listdir", "line_number": 58, "usage_type": "call" }, { "api_name": "os.path.exists", "line_number": 62, "usage_type": "call" }, { "api_name": "os.path", "line_number": 62, "usage_type": "attribute" }, { "api_name": "os.listdir", "line_number": 64, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 66, "usage_type": "call" }, { "api_name": "os.path", "line_number": 66, "usage_type": "attribute" }, { "api_name": "cv2.imread", "line_number": 68, "usage_type": "call" }, { "api_name": "cv2.threshold", "line_number": 69, "usage_type": "call" }, { "api_name": "cv2.THRESH_BINARY", "line_number": 69, "usage_type": "attribute" }, { "api_name": "numpy.all", "line_number": 110, "usage_type": "call" }, { "api_name": "numpy.uint8", "line_number": 114, "usage_type": "attribute" }, { "api_name": "cv2.imwrite", "line_number": 124, "usage_type": "call" } ]
73036280829
import pandas as pd from numpy.random import RandomState from sklearn import preprocessing # Read Data data = pd.read_csv("/Users/yazen/Desktop/datasets/PimaDiabetes/pima.csv") # Label columns data.columns = ["pregnancy", "plasma/glucose concentration", "blood pressure","tricep skin fold thickness", "serum insulin", "body mass index", "diabetes pedigree function", "age", "label"] # Remove rows with missing data data = data.loc[data["plasma/glucose concentration"] > 20] data = data.loc[data["blood pressure"] > 60] data = data.loc[data["body mass index"] > 20] # Under sample negative rows negative = data.loc[data["label"] < 1].sample(frac=0.5, random_state=RandomState()) positive = data.loc[data["label"] > 0] neutral = positive.append(negative) # Normalize data min_max_scaler = preprocessing.MinMaxScaler() neutral = min_max_scaler.fit_transform(neutral) neutral = pd.DataFrame(neutral,columns = data.columns) # Create test and training set train = neutral.sample(frac = .7, random_state=RandomState()) test = neutral.loc[~neutral.index.isin(train.index)] train.to_csv('/Users/yazen/Desktop/datasets/PimaDiabetes/train.csv') test.to_csv('/Users/yazen/Desktop/datasets/PimaDiabetes/test.csv')
yazsh/PimaDiabetesPrediction
PimaDataCleaning.py
PimaDataCleaning.py
py
1,208
python
en
code
0
github-code
6
[ { "api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call" }, { "api_name": "numpy.random.RandomState", "line_number": 17, "usage_type": "call" }, { "api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 22, "usage_type": "call" }, { "api_name": "sklearn.preprocessing", "line_number": 22, "usage_type": "name" }, { "api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call" }, { "api_name": "numpy.random.RandomState", "line_number": 27, "usage_type": "call" } ]
16376172760
""" Basic commands and practice of Selenium library.""" import os import time from dotenv import load_dotenv from selenium import webdriver from selenium.webdriver.common.keys import Keys load_dotenv() chrome_driver_path = os.environ.get("DRIVER_PATH") driver = webdriver.Chrome(executable_path=chrome_driver_path) # # Scrapping data from wikipedia and searching something # driver.get("https://en.wikipedia.org/wiki/Main_Page") # # Getting article count # article_count = driver.find_element_by_css_selector("#articlecount a") # print(article_count.text) # # Visit the link # article_count.click() # # Locate search bar and send data # search_bar = driver.find_element_by_name("search") # search_bar.send_keys("Python") # search_bar.send_keys(Keys.ENTER) # Web page Automatic registration driver.get("http://secure-retreat-92358.herokuapp.com/") # Get the input boxes first_box = driver.find_element_by_name("fName") second_box = driver.find_element_by_name("lName") third_box = driver.find_element_by_name("email") # Populate data first_box.send_keys("Agapito") second_box.send_keys("Ramirez") third_box.send_keys("[email protected]") time.sleep(3) # Find button and send data send_button = driver.find_element_by_css_selector(".form-signin button") send_button.click() time.sleep(4) driver.quit()
FstRms/selenium-basics
example_automation.py
example_automation.py
py
1,316
python
en
code
1
github-code
6
[ { "api_name": "dotenv.load_dotenv", "line_number": 9, "usage_type": "call" }, { "api_name": "os.environ.get", "line_number": 11, "usage_type": "call" }, { "api_name": "os.environ", "line_number": 11, "usage_type": "attribute" }, { "api_name": "selenium.webdriver.Chrome", "line_number": 12, "usage_type": "call" }, { "api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name" }, { "api_name": "time.sleep", "line_number": 43, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 47, "usage_type": "call" } ]
37048825490
#!/usr/bin/env python # coding: utf-8 import itertools import os import re from io import open import yaml from jinja2 import Template HERE = os.path.abspath(os.path.dirname(__file__)) def read(fname): with open(os.path.join(HERE, "..", fname), "r") as fd: return fd.read() def write(content, fname): with open(os.path.join(HERE, "..", fname), "w") as fd: fd.write(content) def generate_pipeline_name(env_value): images = re.findall(r"\.*IMAGE=(.*?)(?!\S)", env_value, re.DOTALL) return "_".join(image.replace(":", "") for image in images) def generate_pipeline_variables(env_value): variables = {} for key_value in env_value.split(): key, value = key_value.split("=") variables[key] = value return variables def generate_pipelines(): """Parse command-line arguments and execute bumpversion command.""" env = yaml.safe_load(read(".ci/env.yml")) iterables = [[f"{key}={value}" for value in values] for key, values in env.items()] env_list = [" ".join(t) for t in itertools.product(*iterables)] pipelines = [ { "env": env_value, "name": generate_pipeline_name(env_value), "variables": generate_pipeline_variables(env_value), } for env_value in env_list ] write( Template(read(".ci/travis.yml.j2")).render(pipelines=pipelines), ".travis.yml" ) if __name__ == "__main__": generate_pipelines()
itsolutionsfactory/dbcut
scripts/generate-ci-pipelines.py
generate-ci-pipelines.py
py
1,469
python
en
code
20
github-code
6
[ { "api_name": "os.path.abspath", "line_number": 11, "usage_type": "call" }, { "api_name": "os.path", "line_number": 11, "usage_type": "attribute" }, { "api_name": "os.path.dirname", "line_number": 11, "usage_type": "call" }, { "api_name": "io.open", "line_number": 15, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 15, "usage_type": "call" }, { "api_name": "os.path", "line_number": 15, "usage_type": "attribute" }, { "api_name": "io.open", "line_number": 20, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 20, "usage_type": "call" }, { "api_name": "os.path", "line_number": 20, "usage_type": "attribute" }, { "api_name": "re.findall", "line_number": 25, "usage_type": "call" }, { "api_name": "re.DOTALL", "line_number": 25, "usage_type": "attribute" }, { "api_name": "yaml.safe_load", "line_number": 39, "usage_type": "call" }, { "api_name": "itertools.product", "line_number": 42, "usage_type": "call" }, { "api_name": "jinja2.Template", "line_number": 53, "usage_type": "call" } ]
34407258832
import torch.nn as nn import torch.nn.functional as F class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Conv3d(in_channels=1, out_channels=64, kernel_size=(3, 5, 5), stride=(1, 1, 1), bias=False) self.max1 = nn.MaxPool3d(kernel_size=(2,2,2), stride=(2,2,2)) self.conv2 = nn.Conv3d(in_channels=64, out_channels=64, kernel_size=(3, 3, 3), stride=(1, 1, 1), bias=False) self.conv3 = nn.Conv3d(in_channels=64, out_channels=64, kernel_size=(1, 3, 3), stride=(1, 1, 1), bias=False) #self.fc1 = nn.Linear(64,150) self.fc1 = nn.Conv3d(in_channels=64, out_channels=150, kernel_size=(2, 2, 2), stride=(1, 1, 1), bias=False) #self.fc2 = nn.Linear(150, 2) self.fc2 = nn.Conv3d(in_channels=150, out_channels=2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) def forward(self, x): #print('Input: ',x.shape) x = self.conv1(x) x = F.relu(x) print('After conv 1: ',x.shape) x = self.max1(x) print('After max pool 1: ', x.shape) x = self.conv2(x) x = F.relu(x) print('After conv 2: ',x.shape) x = self.conv3(x) x = F.relu(x) print('After conv 3: ', x.shape) # x = x.reshape(x.size(0), -1) # #print('After reshape :', x.shape) x = self.fc1(x) x = F.relu(x) print('After full conv 1: ', x.shape) x = self.fc2(x) x = F.relu(x) print('After full conv 2: ', x.shape) return x import train_val_split import torch.optim as optim from torch.autograd import Variable train_dset, val_dset, test_dset = train_val_split.train_test_split() model = CNN().cuda() model.train() for i, (images, labels) in enumerate(train_dset): images = Variable(images.cuda()) labels = Variable(labels.cuda()) outputs = model(images) break
kishanbala/BrainLesionSegmentation
cmb_3dcnn/build_screening_stage.py
build_screening_stage.py
py
2,008
python
en
code
1
github-code
6
[ { "api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute" }, { "api_name": "torch.nn", "line_number": 5, "usage_type": "name" }, { "api_name": "torch.nn.Conv3d", "line_number": 8, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 8, "usage_type": "name" }, { "api_name": "torch.nn.MaxPool3d", "line_number": 10, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 10, "usage_type": "name" }, { "api_name": "torch.nn.Conv3d", "line_number": 11, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 11, "usage_type": "name" }, { "api_name": "torch.nn.Conv3d", "line_number": 12, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 12, "usage_type": "name" }, { "api_name": "torch.nn.Conv3d", "line_number": 15, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 15, "usage_type": "name" }, { "api_name": "torch.nn.Conv3d", "line_number": 17, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 17, "usage_type": "name" }, { "api_name": "torch.nn.functional.relu", "line_number": 22, "usage_type": "call" }, { "api_name": "torch.nn.functional", "line_number": 22, "usage_type": "name" }, { "api_name": "torch.nn.functional.relu", "line_number": 30, "usage_type": "call" }, { "api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name" }, { "api_name": "torch.nn.functional.relu", "line_number": 35, "usage_type": "call" }, { "api_name": "torch.nn.functional", "line_number": 35, "usage_type": "name" }, { "api_name": "torch.nn.functional.relu", "line_number": 41, "usage_type": "call" }, { "api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name" }, { "api_name": "torch.nn.functional.relu", "line_number": 45, "usage_type": "call" }, { "api_name": "torch.nn.functional", "line_number": 45, "usage_type": "name" }, { "api_name": "train_val_split.train_test_split", "line_number": 55, "usage_type": "call" }, { "api_name": "torch.autograd.Variable", "line_number": 61, "usage_type": "call" }, { "api_name": "torch.autograd.Variable", "line_number": 62, "usage_type": "call" } ]
37126840757
import matplotlib.pyplot as plt import numpy as np import time def plot_voxel(voxels, filename): start = time.time() colors = np.where(voxels, "blue", "red") fig = plt.figure() ax = fig.gca(projection='3d') template = np.ones(voxels.shape, dtype=object) ax.voxels(template, facecolors=colors, edgecolor='k') ax.set(xlabel='x', ylabel='y', zlabel='z') # plt.show() plt.savefig(f'processed/mesh_image/{filename}.png') fig = plt.figure() ax = fig.gca(projection='3d') temp = np.where(voxels, False, True) ax.voxels(temp, facecolors=colors, edgecolor='k') ax.set(xlabel='x', ylabel='y', zlabel='z') plt.savefig(f'processed/mesh_image/{filename}_pole.png') print("ploting time:", time.time()-start)
born9507/Prediction-of-E-using-CNN
src/data/plot.py
plot.py
py
782
python
en
code
1
github-code
6
[ { "api_name": "time.time", "line_number": 6, "usage_type": "call" }, { "api_name": "numpy.where", "line_number": 8, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name" }, { "api_name": "numpy.ones", "line_number": 12, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.savefig", "line_number": 17, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 19, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name" }, { "api_name": "numpy.where", "line_number": 21, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.savefig", "line_number": 24, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name" }, { "api_name": "time.time", "line_number": 25, "usage_type": "call" } ]
36062246358
from django.urls import path from .views import addtask, mark_as_done, mark_as_undone, edit, delete_task urlpatterns = [ # adding a task path('addtask/', addtask, name='addtask'), # mark as done task path('mark_as_done/<int:pk>/', mark_as_done, name='mark_as_done'), # mark as undone task path('mark_as_undone/<int:pk>/', mark_as_undone, name='mark_as_undone'), # edit task path('edit/<int:pk>/', edit, name='edit'), # delete task path('delete/<int:pk>/', delete_task, name='delete'), ]
shaikmoinuddin/todo_django
todo_app/urls.py
urls.py
py
530
python
en
code
0
github-code
6
[ { "api_name": "django.urls.path", "line_number": 7, "usage_type": "call" }, { "api_name": "views.addtask", "line_number": 7, "usage_type": "argument" }, { "api_name": "django.urls.path", "line_number": 9, "usage_type": "call" }, { "api_name": "views.mark_as_done", "line_number": 9, "usage_type": "argument" }, { "api_name": "django.urls.path", "line_number": 11, "usage_type": "call" }, { "api_name": "views.mark_as_undone", "line_number": 11, "usage_type": "argument" }, { "api_name": "django.urls.path", "line_number": 13, "usage_type": "call" }, { "api_name": "views.edit", "line_number": 13, "usage_type": "argument" }, { "api_name": "django.urls.path", "line_number": 15, "usage_type": "call" }, { "api_name": "views.delete_task", "line_number": 15, "usage_type": "argument" } ]
40428611431
#!/usr/bin/env python3 """ Name: cluster_create_update_all.py Description: Create/update clusters defined in ``--yaml` """ import argparse from netbox_tools.common import netbox, load_yaml from netbox_tools.cluster import Cluster OUR_VERSION = 101 def get_parser(): """ return an argparse parser object """ help_yaml = "YAML file containing cluster type information." ex_prefix = "Example: " ex_yaml = f"{ex_prefix} --yaml ./clusters.yml" parser = argparse.ArgumentParser( description="DESCRIPTION: Create/update Netbox clusters defined in ``--yaml``" ) mandatory = parser.add_argument_group(title="MANDATORY SCRIPT ARGS") mandatory.add_argument( "--yaml", dest="yaml", required=True, help=help_yaml + ex_yaml ) parser.add_argument( "--version", action="version", version=f"%(prog)s {OUR_VERSION}" ) return parser.parse_args() cfg = get_parser() netbox_obj = netbox() info = load_yaml(cfg.yaml) for key in info["clusters"]: c = Cluster(netbox_obj, info["clusters"][key]) c.create_or_update()
allenrobel/netbox-tools
scripts/cluster_create_update_all.py
cluster_create_update_all.py
py
1,091
python
en
code
6
github-code
6
[ { "api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call" }, { "api_name": "netbox_tools.common.netbox", "line_number": 40, "usage_type": "call" }, { "api_name": "netbox_tools.common.load_yaml", "line_number": 41, "usage_type": "call" }, { "api_name": "netbox_tools.cluster.Cluster", "line_number": 43, "usage_type": "call" } ]
8097009121
""" Image utility functions. """ import PIL.Image import PIL.ImageChops import numpy def equalize(image, levels=256, grayscale=False): """ Equalizes an image such that the darkest pixels become black, the lightest become white, and others are based on their percentile. If a pixel is brighter than 25% of the other pixels, it will be 25% grey in the output. If the image has multiple channels, they will be processed separately and merged into a new image. If the image only has one color, the return image will be 50% gray. :param image: Source image :param levels: Number of grayscale levels. If this is less than 256, there will be different discrete bands in the output image. :param grayscale: If True, the image is forced to grayscale rather than splitting bands. :return: A new equalized image. """ if image.mode != 'L': if not grayscale: # merge requires a list (not a generator), so this comprehension produces a list instead of a generator. return PIL.Image.merge(image.mode, [equalize(band, levels) for band in image.split()]) image = image.convert('L') histogram = image.histogram() # Compute divisor divisor = ( (image.width * image.height) # Total number of pixels - next(filter(None, reversed(histogram))) # Minus the last nonzero amount, otherwise it won't turn out white ) / (levels - 1) # Divided by levels, which effectively multiplies them in the rounding phase. if not divisor: return PIL.Image.new('L', image.size, 127) # Multiplier to scale back up after dividing. multiplier = 255 / (levels - 1) # Generate remap table remap = [] pixels = 0 for count in histogram: remap.append(max(0, min(255, round(round(pixels / divisor) * multiplier)))) pixels += count # Apply to image. return PIL.Image.eval(image, remap.__getitem__) # lambda x: remap[x] but faster def convert(image, mode): """ Equivalent to image.convert(mode), except returns the source image if already in that mode. :param image: Source image :param mode: Desired mode :return: Image in desired mode """ if image.mode != mode: image = image.convert(mode) return image def score(composite, image, exponent=1): """ Determines how a particular image scores against a composite. Lower scores indicate a closer match. :param composite: The composite reference :param image: The image being scored. :param exponent: Arbitrary exponent to make a large difference in a small area more significant than a small difference in a large one. :return: An arbitrary score value where 0 is a perfect match and (255**exponent)*numchannels is the theoretical upper bound. """ diff = PIL.ImageChops.difference(composite, image) if composite.mode != 'L': return sum(sum(c**exponent for c in x) for x in diff.getdata()) / (diff.width * diff.height) # return return sum(x**exponent for x in diff.getdata(0)) / (diff.width * diff.height) def numpify(image): # result = numpy.frombuffer(image.tobytes(), dtype=numpy.uint8) # return result.reshape((*image.size, 3)) # return ( # numpy.array(image, dtype=numpy.uint8).reshape((image)) # # .frombuffer(image.tobytes(), dtype=numpy.uint8) # # .reshape((image.size[0], image.size[1], -1)) # # .transpose((1, 0, 2)) # ) return numpy.array(image, dtype=numpy.uint8)
dewiniaid/sigsolve
sigsolve/imageutil.py
imageutil.py
py
3,598
python
en
code
3
github-code
6
[ { "api_name": "PIL.Image.Image.merge", "line_number": 28, "usage_type": "call" }, { "api_name": "PIL.Image.Image", "line_number": 28, "usage_type": "attribute" }, { "api_name": "PIL.Image", "line_number": 28, "usage_type": "name" }, { "api_name": "PIL.Image.Image.new", "line_number": 41, "usage_type": "call" }, { "api_name": "PIL.Image.Image", "line_number": 41, "usage_type": "attribute" }, { "api_name": "PIL.Image", "line_number": 41, "usage_type": "name" }, { "api_name": "PIL.Image.Image.eval", "line_number": 54, "usage_type": "call" }, { "api_name": "PIL.Image.Image", "line_number": 54, "usage_type": "attribute" }, { "api_name": "PIL.Image", "line_number": 54, "usage_type": "name" }, { "api_name": "PIL.Image.ImageChops.difference", "line_number": 81, "usage_type": "call" }, { "api_name": "PIL.Image.ImageChops", "line_number": 81, "usage_type": "attribute" }, { "api_name": "PIL.Image", "line_number": 81, "usage_type": "name" }, { "api_name": "numpy.array", "line_number": 99, "usage_type": "call" }, { "api_name": "numpy.uint8", "line_number": 99, "usage_type": "attribute" } ]
21367680266
import serial import serial.tools.list_ports as lp class serialPort(): def __init__(self) -> None: self.ser = serial.Serial() self.timeout = None # specify timeout when using readline() self.ports = lp.comports() def connectPort(self, port_name, baudrate=115200): self.ser.port = port_name # "/dev/cu.usbmodem14101" # 'COM3' # Arduino serial port self.ser.baudrate = baudrate self.ser.timeout = self.timeout # specify timeout when using readline() self.ser.parity = serial.PARITY_NONE self.ser.stopbits = serial.STOPBITS_ONE # self.ser.bytesize = serial.EIGHTBITS try: self.ser.open() return self.ser.is_open except serial.serialutil.SerialException: return False # self.ser.reset_input_buffer() # self.ser.write(str.encode('1\r\n', 'UTF-8')) def disconnectPort(self): self.ser.close() return
PhysiologicAILab/PhysioKit
utils/devices.py
devices.py
py
1,003
python
en
code
5
github-code
6
[ { "api_name": "serial.Serial", "line_number": 6, "usage_type": "call" }, { "api_name": "serial.tools.list_ports.comports", "line_number": 8, "usage_type": "call" }, { "api_name": "serial.tools.list_ports", "line_number": 8, "usage_type": "name" }, { "api_name": "serial.PARITY_NONE", "line_number": 14, "usage_type": "attribute" }, { "api_name": "serial.STOPBITS_ONE", "line_number": 15, "usage_type": "attribute" }, { "api_name": "serial.serialutil", "line_number": 20, "usage_type": "attribute" } ]
73939813946
import mira assert mira.__file__ == '/liulab/alynch/projects/multiomics/BatchEffect/MIRA/mira/__init__.py' from scipy import sparse import shutil import frankencell as fc import scanpy as sc from .utils import read_and_process, plot_umaps import os import optuna def run_mira(dynframe, out_h5, plot_file, threads = 1): shutil.copy(dynframe, out_h5) data = read_and_process(out_h5) data.layers['sparse_counts'] = sparse.csr_matrix(data.layers['counts']) model = mira.topics.TopicModel( *data.shape, feature_type='expression', exogenous_key='highly_variable', counts_layer='sparse_counts', categorical_covariates='batch', cost_beta = 2. ) model.set_learning_rates(3e-3, 0.25) def faux_print(*x): return 'Trial completed.' mira.topic_model.trainer._print_study = faux_print train_data = dynframe+'_train' test_data = dynframe+'_test' if os.path.isdir(train_data): shutil.rmtree(train_data) shutil.rmtree(test_data) train, test = mira.topics.SpeedyTuner.train_test_split(data, train_size=0.8, stratify=data.obs_vector('batch'), seed = 0 ) model.write_ondisk_dataset(train, dirname= train_data) model.write_ondisk_dataset(test, dirname= test_data) del train, test try: optuna.delete_study( storage = 'sqlite:///mira-BENCHMARKING.db', study_name = dynframe ) except KeyError: pass tuner = mira.topics.SpeedyTuner( model = model, save_name = dynframe, min_topics = 3, max_topics = 10, seed = 2556, min_trials = 32, max_trials = 64, n_jobs = threads, stop_condition = 8, storage = 'sqlite:///mira-BENCHMARKING.db', ) tuner.fit(train_data, test_data) model = tuner.fetch_best_weights() model.predict(data) model.get_umap_features(data, box_cox=0.33) sc.pp.neighbors(data, use_rep='X_umap_features', metric='manhattan') sc.tl.umap(data, min_dist=0.1) plot_umaps(data, plot_file) fc.add_dimred_prior(out_h5, data.obsm['X_umap_features']) def main(args): run_mira( args.dynframe, args.outh5, args.plotfile, threads = args.threads, ) def add_arguments(parser): parser.add_argument('dynframe', type = str) parser.add_argument('outh5', type = str) parser.add_argument('plotfile', type = str) parser.add_argument('--threads','-t', type = int, default = 1)
AllenWLynch/CODA-reproduction
disentangler/frankencell/dimred_methods/disentangler.py
disentangler.py
py
2,566
python
en
code
2
github-code
6
[ { "api_name": "mira.__file__", "line_number": 3, "usage_type": "attribute" }, { "api_name": "shutil.copy", "line_number": 15, "usage_type": "call" }, { "api_name": "utils.read_and_process", "line_number": 17, "usage_type": "call" }, { "api_name": "scipy.sparse.csr_matrix", "line_number": 18, "usage_type": "call" }, { "api_name": "scipy.sparse", "line_number": 18, "usage_type": "name" }, { "api_name": "mira.topics.TopicModel", "line_number": 20, "usage_type": "call" }, { "api_name": "mira.topics", "line_number": 20, "usage_type": "attribute" }, { "api_name": "mira.topic_model", "line_number": 34, "usage_type": "attribute" }, { "api_name": "os.path.isdir", "line_number": 39, "usage_type": "call" }, { "api_name": "os.path", "line_number": 39, "usage_type": "attribute" }, { "api_name": "shutil.rmtree", "line_number": 40, "usage_type": "call" }, { "api_name": "shutil.rmtree", "line_number": 41, "usage_type": "call" }, { "api_name": "mira.topics.SpeedyTuner.train_test_split", "line_number": 43, "usage_type": "call" }, { "api_name": "mira.topics", "line_number": 43, "usage_type": "attribute" }, { "api_name": "optuna.delete_study", "line_number": 54, "usage_type": "call" }, { "api_name": "mira.topics.SpeedyTuner", "line_number": 61, "usage_type": "call" }, { "api_name": "mira.topics", "line_number": 61, "usage_type": "attribute" }, { "api_name": "scanpy.pp.neighbors", "line_number": 81, "usage_type": "call" }, { "api_name": "scanpy.pp", "line_number": 81, "usage_type": "attribute" }, { "api_name": "scanpy.tl.umap", "line_number": 82, "usage_type": "call" }, { "api_name": "scanpy.tl", "line_number": 82, "usage_type": "attribute" }, { "api_name": "utils.plot_umaps", "line_number": 84, "usage_type": "call" }, { "api_name": "frankencell.add_dimred_prior", "line_number": 86, "usage_type": "call" } ]
28672716901
from trello import TrelloClient, util from atlassian import Confluence from os import listdir, path import pystache import datetime import json from traceback import print_exc from time import sleep from re import sub try: keys = {} if path.exists('.keys'): with open('.keys') as f: keys = json.load(f) url = keys.get('url') or input('Confluence URL:').strip() email = keys.get('email') or input('Email address:').strip() api_key = keys.get('api_key') or input('API Key for Atlassian (https://id.atlassian.com/manage/api-tokens):').strip() confluence = Confluence( url=url, username=email, password=api_key ) parent = keys.get('parent') or int(input("Parent page ID:").strip()) parent_page = confluence.get_page_by_id(parent) while not isinstance(parent_page, dict): email = input('Email address:').strip() api_key = input('API Key for Atlassian (https://id.atlassian.com/manage/api-tokens):').strip() confluence = Confluence( url=url, username=email, password=api_key ) parent_page = confluence.get_page_by_id(parent) while not input(f"Create page under {parent_page['title']}? [y/n]:").strip().lower().startswith('y'): space = input("Confluence Space ID:").strip() parent = input("Parent page ID:").strip() parent_page = confluence.get_page_by_id(parent) boards = None while not boards: trello_api_key = keys.get('trello_api_key') or input("Trello API Key (https://trello.com/app-key):").strip() trello_api_secret = keys.get('trello_api_secret') or input("Trello API Secret (https://trello.com/app-key):").strip() if 'oauth_token' not in keys or 'oauth_token_secret' not in keys: try: oauth_result = util.create_oauth_token('never', 'read,write', trello_api_key, trello_api_secret) keys['oauth_token'] = oauth_result['oauth_token'] keys['oauth_token_secret'] = oauth_result['oauth_token_secret'] except: try: del keys['trello_api_key'] del keys['trello_api_secret'] except: pass oauth_token = keys.get('oauth_token') oauth_token_secret = keys.get('oauth_token_secret') trello = TrelloClient( api_key=trello_api_key, api_secret=trello_api_secret, token=oauth_token, token_secret=oauth_token_secret ) try: boards = trello.list_boards() with open('.keys', 'w') as f: json.dump({ "url": url, "email": email, "api_key": api_key, "trello_api_key": trello_api_key, "trello_api_secret": trello_api_secret, "parent": parent, "oauth_token": oauth_token, "oauth_token_secret": oauth_token_secret }, f) except: del keys['oauth_token'] del keys['oauth_token_secret'] print("\n\nPlease select a board:") for i, board in enumerate(boards): print(f"{board.name} - {i+1}") board_index = int(input("id [1]: ").strip() or 1) board = boards[board_index - 1] print(f"\nSelected board {board.name}") columns = board.get_lists(None) templates = listdir('templates') print("\n\nPlease select the template for the page") for i, template in enumerate(templates): print(f"{template} - {i+1}") template_index = int(input("\nSelect template to use [1]:").strip() or 1) template_filename = path.join("templates", templates[template_index - 1]) print("\n\nPlease select relevant columns") for i, column in enumerate(columns): print(f"{column.name} - {i+1}") config = {} if path.exists('columns.json'): with open('columns.json') as f: config = json.load(f) column_config = config.get(template_filename, {}) done = False column_index = 0 if column_config: print("\n\nCurrent column configuration is:") for name, col in column_config.items(): print(f"{columns[col].name} => {name}") done = (input("\nKeep this configuration? [y]:").strip() or 'y').lower().startswith('y') if not done: column_config = {} if not done: print("\n\n") while not done and column_index < len(columns): column_or_done = input(f'Select a column or type n to stop [{column_index + 1}]:').strip() if column_or_done.startswith('n'): break column_index = int(column_or_done or (column_index + 1)) if column_index > len(columns): print(f"Column {column_index} does not exist!") continue column_name = sub('[^a-z0-9]+', '_', columns[column_index - 1].name.lower()) column_name = input(f"Select a name for the column [{column_name}]:").strip() or column_name column_config[column_name] = column_index - 1 config[template_filename] = column_config with open('columns.json', 'w') as f: json.dump(config, f) data = {k: columns[i].list_cards() for k, i in column_config.items()} with open(template_filename) as f: body = pystache.render(f.read(), data) print("\n\n####################################################################\n\n") print(body) ok = input("\nDoes this look good? y/n [n]:").strip() or 'n' if not ok.lower().startswith('y'): print("\n\nPlease start again") else: all_cards = [c for v in data.values() for c in v] if all_cards: today = datetime.date.today() title = f"{today.strftime('%d %B %Y')} - {today.strftime('%A')} Retrospective" #TODO: Make this more generic title = input(f"\n\nSelect a page title [{title}]: ").strip() or title confluence.create_page( space=parent_page['space']['key'], title=title, parent_id=parent, body=body, representation='wiki' ) else: print("\n\nNo cards to add to page") for card in all_cards: card.set_closed(True) except: print_exc() sleep(2) input("\n\nPress enter to close")
iain-neirfeno/trello-to-confluence
create_confluence_page.py
create_confluence_page.py
py
6,643
python
en
code
0
github-code
6
[ { "api_name": "os.path.exists", "line_number": 13, "usage_type": "call" }, { "api_name": "os.path", "line_number": 13, "usage_type": "name" }, { "api_name": "json.load", "line_number": 15, "usage_type": "call" }, { "api_name": "atlassian.Confluence", "line_number": 22, "usage_type": "call" }, { "api_name": "atlassian.Confluence", "line_number": 33, "usage_type": "call" }, { "api_name": "trello.util.create_oauth_token", "line_number": 53, "usage_type": "call" }, { "api_name": "trello.util", "line_number": 53, "usage_type": "name" }, { "api_name": "trello.TrelloClient", "line_number": 66, "usage_type": "call" }, { "api_name": "trello.list_boards", "line_number": 73, "usage_type": "call" }, { "api_name": "json.dump", "line_number": 75, "usage_type": "call" }, { "api_name": "os.listdir", "line_number": 100, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 106, "usage_type": "call" }, { "api_name": "os.path", "line_number": 106, "usage_type": "name" }, { "api_name": "os.path.exists", "line_number": 112, "usage_type": "call" }, { "api_name": "os.path", "line_number": 112, "usage_type": "name" }, { "api_name": "json.load", "line_number": 114, "usage_type": "call" }, { "api_name": "re.sub", "line_number": 136, "usage_type": "call" }, { "api_name": "json.dump", "line_number": 143, "usage_type": "call" }, { "api_name": "pystache.render", "line_number": 147, "usage_type": "call" }, { "api_name": "datetime.date.today", "line_number": 161, "usage_type": "call" }, { "api_name": "datetime.date", "line_number": 161, "usage_type": "attribute" }, { "api_name": "traceback.print_exc", "line_number": 179, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 180, "usage_type": "call" } ]
2235060581
import xml.etree.ElementTree as ET import subprocess import os import glob import time def clonerep(url): name = url.split("/")[-1].split(".")[0] os.system("git"+ " clone " + "https://github.com/" + url + " repos/" + name + "/" ) def insertIntoPom(repdir): # ET.register_namespace("", "http://maven.apache.org/POM/4.0.0") # tree = ET.parse("apollo/pom.xml") # plugs = tree.findall("./{http://maven.apache.org/POM/4.0.0}build/{http://maven.apache.org/POM/4.0.0}plugins") # cloverplug = ET.fromstring("<plugin> <groupId>org.openclover</groupId> <artifactId>clover-maven-plugin</artifactId> <version>4.2.0</version> <configuration> <generateXml>true</generateXml> </configuration> </plugin>") # if len(plugs) != 0: # plugs[0].insert(0, cloverplug) # tree.write("pom.xml") # stre = "<plugin> <groupId>org.openclover</groupId> <artifactId>clover-maven-plugin</artifactId> <version>4.2.0</version> <configuration> <generateXml>true</generateXml> </configuration> </plugin>" stre = "<plugin> <groupId>org.codehaus.mojo</groupId> <artifactId>cobertura-maven-plugin</artifactId> <version>2.7</version> <configuration> <formats> <format>html</format> <format>xml</format> </formats><aggregate>true</aggregate> </configuration> </plugin>" fileHandle = open ( repdir + '/pom.xml',"r") lines = fileHandle.readlines() fileHandle.close() lastlineind = len(lines) - 1 idd = 0 i = 0 alreadyHas = False alreadyI = 0 alreadyIndex = 0 for line in lines: if (line.strip() == "<artifactId>cobertura-maven-plugin</artifactId>"): alreadyIndex = alreadyI break alreadyI += 1 for line in lines: if (line.strip() == "<plugin>"): idd = i break i += 1 if alreadyIndex: lines.insert(alreadyIndex, "<configuration> <formats> <format>html</format> <format>xml</format> </formats><aggregate>true</aggregate> </configuration>") fileHandle = open(repdir + "/pom.xml", "w") contents = "".join(lines) fileHandle.write(contents) fileHandle.close() elif idd != 0: lines.insert(idd, stre) fileHandle = open(repdir + "/pom.xml", "w") contents = "".join(lines) fileHandle.write(contents) fileHandle.close() else: projend = 0 j = 0 #plugins tag not found so append to end for line in lines: if (line.strip() == "</project>"): projend = j break j += 1 #projend -= 1 # lines.insert(projend, "<build><plugins><plugin> <groupId>org.openclover</groupId> <artifactId>clover-maven-plugin</artifactId> <version>4.2.0</version> <configuration> <generateXml>true</generateXml> </configuration> </plugin> </plugins> </build>") lines.insert(projend, "<build><plugins><plugin> <groupId>org.codehaus.mojo</groupId> <artifactId>cobertura-maven-plugin</artifactId> <version>2.7</version> <configuration> <formats> <format>html</format> <format>xml</format> </formats> <aggregate>true</aggregate></configuration> </plugin> </plugins> </build>") fileHandle = open(repdir + "/pom.xml", "w") contents = "".join(lines) fileHandle.write(contents) fileHandle.close() # print(contents) # runs cobertura def runcov(repdir): os.chdir("repos/" + repdir + "/") subprocess.call(["mvn", "cobertura:cobertura", "-Dlicense.skip=true"]) # subprocess.run(["mvn", "clean" ,"clover:setup" ,"test" ,"clover:aggregate" ,"clover:clover"]) os.chdir("../..") def getAllCovXML(repdir): covXMLs = [] for dirpath, dirnames, files in os.walk('repos/' + repdir + '/'): for name in files: if name == "coverage.xml": covXMLs.append(os.path.join(dirpath, name)) # print(covXMLs) return covXMLs def getTotalCodeCov(covList): linesCovered = 0 totalLines = 0 for covFile in covList: root = ET.parse(covFile) c = root.find(".") percent = c.attrib["line-rate"] print(percent) linesCovered += int(c.attrib["lines-covered"]) totalLines += int(c.attrib["lines-valid"]) return float(linesCovered/totalLines) def main(): # repoURL = "https://github.com/ctripcorp/apollo.git" # repoURL = "https://github.com/shuzheng/zheng.git" # repoURL = "https://github.com/alibaba/arthas.git" # repoURL = "https://github.com/openzipkin/zipkin" """ 'ctripcorp/apollo' 'google/auto' 'low perc dbeaver/dbeaver' 'dropwizard/dropwizard' 'low perc google/guava' 'google/guice' 'failed build hankcs/HanLP' 'apache/incubator-druid' 'apache/incubator-shardingsphere' 'xetorthio/jedis' 'mybatis/mybatis-3' 'naver/pinpoint' 'broken builds proxyee-down-org/proxyee-down' 'broken builds redisson/redisson' 'broken build spring-projects/spring-boot' 'b3log/symphony' 'code4craft/webmagic' 'xuxueli/xxl-job' 'openzipkin/zipkin' """ # hardcodedList = ['ctripcorp/apollo', 'google/auto', 'dbeaver/dbeaver', 'dropwizard/dropwizard', 'google/guava', 'google/guice', 'hankcs/HanLP', 'apache/incubator-druid', 'apache/incubator-shardingsphere', 'xetorthio/jedis'] hardcodedList = ['openzipkin/zipkin'] for hardcoded in hardcodedList: clonerep(hardcoded) repdir = hardcoded.split("/")[-1].split(".")[0] # for a single repo... coms = open("commits/" + repdir + ".csv") lines = coms.readlines() csv = open("codecov/" + repdir + ".csv", "w") csv.write("id,tag_name,covpercent,dayDifference, dayDifferenceHours\n") for line in lines: llist = line.split(",") print(llist) os.chdir("repos/" + repdir) subprocess.run(["git", "checkout", "--", "."]) subprocess.run(["git", "checkout", llist[2]]) subprocess.run(["git", "checkout", "--", "."]) os.chdir("../..") insertIntoPom("repos/" + repdir) #codecov lines runcov(repdir) codeCovFiles = getAllCovXML(repdir) if (len(codeCovFiles) == 0): print("NO COV FILES FOUND SKIP") continue totalCoveragePercent = getTotalCodeCov(codeCovFiles) id = llist[0] tag = llist[1] daydiff = llist[3].strip() toWrite = id + "," + tag + "," + str(totalCoveragePercent)+ "," + daydiff if len(llist) == 5: daydiffhr = llist[4].strip() toWrite += "," + daydiffhr toWrite += "\n" csv.write(toWrite) csv.close main() # codeCovFiles = getAllCovXML("auto") # totalCoveragePercent = getTotalCodeCov(codeCovFiles) # print(totalCoveragePercent)
tyheise/402-Course-Project
codecov.py
codecov.py
py
6,907
python
en
code
0
github-code
6
[ { "api_name": "os.system", "line_number": 10, "usage_type": "call" }, { "api_name": "os.chdir", "line_number": 91, "usage_type": "call" }, { "api_name": "subprocess.call", "line_number": 92, "usage_type": "call" }, { "api_name": "os.chdir", "line_number": 94, "usage_type": "call" }, { "api_name": "os.walk", "line_number": 101, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 104, "usage_type": "call" }, { "api_name": "os.path", "line_number": 104, "usage_type": "attribute" }, { "api_name": "xml.etree.ElementTree.parse", "line_number": 114, "usage_type": "call" }, { "api_name": "xml.etree.ElementTree", "line_number": 114, "usage_type": "name" }, { "api_name": "os.chdir", "line_number": 169, "usage_type": "call" }, { "api_name": "subprocess.run", "line_number": 170, "usage_type": "call" }, { "api_name": "subprocess.run", "line_number": 171, "usage_type": "call" }, { "api_name": "subprocess.run", "line_number": 172, "usage_type": "call" }, { "api_name": "os.chdir", "line_number": 173, "usage_type": "call" } ]
35704005613
from django.contrib import admin from django.urls import path, include from home import views urlpatterns = [ path('', views.index, name='home'), path('gallery', views.gallery, name='gallery'), path('login', views.login_view, name='login'), path('pricing', views.price, name='pricing'), path('signup', views.handleSignup, name='signup'), path('contact', views.contact, name='contact'), path('about', views.about, name='about'), path('logout', views.logout_view, name='logout') ]
Shivam-08/gymdesign
home/urls.py
urls.py
py
511
python
en
code
0
github-code
6
[ { "api_name": "django.urls.path", "line_number": 6, "usage_type": "call" }, { "api_name": "home.views.index", "line_number": 6, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 6, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 7, "usage_type": "call" }, { "api_name": "home.views.gallery", "line_number": 7, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 7, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 8, "usage_type": "call" }, { "api_name": "home.views.login_view", "line_number": 8, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 8, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 9, "usage_type": "call" }, { "api_name": "home.views.price", "line_number": 9, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 9, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 10, "usage_type": "call" }, { "api_name": "home.views.handleSignup", "line_number": 10, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 10, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 11, "usage_type": "call" }, { "api_name": "home.views.contact", "line_number": 11, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 11, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 12, "usage_type": "call" }, { "api_name": "home.views.about", "line_number": 12, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 12, "usage_type": "name" }, { "api_name": "django.urls.path", "line_number": 13, "usage_type": "call" }, { "api_name": "home.views.logout_view", "line_number": 13, "usage_type": "attribute" }, { "api_name": "home.views", "line_number": 13, "usage_type": "name" } ]
71576276027
import os import numpy as np import torch from matplotlib import pyplot as plt from ..experiments.attention.attention import AttentionHookModule, group_by_type, interpolate, low_mem, sum_over_dim, stack_attentions from .. import util from ..StableDiffuser import StableDiffuser def edit_output(activation, name): activation = interpolate(activation, name) activation = low_mem(activation, name) return activation def to_image(att, title, vmax, vmin): plt.figure(figsize=(5,5), dpi=200) plt.imshow(att, cmap='inferno', interpolation='nearest', vmin=vmin, vmax=vmax) plt.title(title) plt.axis('off') plt.tight_layout(pad=0) image = util.figure_to_image(plt.gcf()) plt.close() return image def main(prompt, outpath): os.makedirs(outpath, exist_ok=True) diffuser = StableDiffuser(scheduler='EA').to(torch.device('cuda:0')).half() layers = set([module_name for module_name, module in diffuser.named_modules() if 'attnprobshook' in module_name and 'attn2' in module_name]) images, trace_steps = diffuser(prompt, generator=torch.manual_seed(50), n_steps=50, trace_args={'layers' : layers, 'edit_output' : edit_output} ) images[0][-1].save(os.path.join(outpath, 'image.png')) attentions = stack_attentions(trace_steps) self_attentions, cross_attentions = group_by_type(attentions) tokens = diffuser.text_tokenize([prompt])['input_ids'][0][1:] tokens = diffuser.text_detokenize(tokens) layers = cross_attentions.keys() cross_attentions = np.stack(list(cross_attentions.values())) attention_over_time = cross_attentions.mean(axis=0) attention_over_time = attention_over_time.mean(axis=1) vmin = attention_over_time[:,1:(len(tokens)+1)].min() vmax = attention_over_time[:,1:(len(tokens)+1)].max() aot_images = [] for timestep in range(attention_over_time.shape[0]): token_images = [] for token_idx in range(len(tokens)): token_images.append(to_image(attention_over_time[timestep, token_idx+1], tokens[token_idx], vmax, vmin)) aot_images.append(util.image_grid([token_images])) util.to_gif(aot_images, os.path.join(outpath, 'aot.gif')) os.makedirs(outpath, exist_ok=True) for layer_idx, layer in enumerate(layers): attention_over_time = cross_attentions[layer_idx].mean(axis=1) vmin = attention_over_time[:,1:(len(tokens)+1)].min() vmax = attention_over_time[:,1:(len(tokens)+1)].max() aot_images = [] for timestep in range(attention_over_time.shape[0]): token_images = [] for token_idx in range(len(tokens)): token_images.append(to_image(attention_over_time[timestep, token_idx+1], tokens[token_idx], vmax, vmin)) aot_images.append(util.image_grid([token_images])) util.to_gif(aot_images, os.path.join(outpath, f'{layer}.gif')) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('prompt') parser.add_argument('outpath') main(**vars(parser.parse_args()))
JadenFiotto-Kaufman/thesis
thesis/final/cross_attention.py
cross_attention.py
py
3,146
python
en
code
0
github-code
6
[ { "api_name": "experiments.attention.attention.interpolate", "line_number": 11, "usage_type": "call" }, { "api_name": "experiments.attention.attention.low_mem", "line_number": 12, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.imshow", "line_number": 19, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.axis", "line_number": 21, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.tight_layout", "line_number": 22, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.gcf", "line_number": 24, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.close", "line_number": 26, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name" }, { "api_name": "os.makedirs", "line_number": 33, "usage_type": "call" }, { "api_name": "StableDiffuser.StableDiffuser", "line_number": 35, "usage_type": "call" }, { "api_name": "torch.device", "line_number": 35, "usage_type": "call" }, { "api_name": "torch.manual_seed", "line_number": 40, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 45, "usage_type": "call" }, { "api_name": "os.path", "line_number": 45, "usage_type": "attribute" }, { "api_name": "experiments.attention.attention.stack_attentions", "line_number": 47, "usage_type": "call" }, { "api_name": "experiments.attention.attention.group_by_type", "line_number": 49, "usage_type": "call" }, { "api_name": "numpy.stack", "line_number": 55, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 77, "usage_type": "call" }, { "api_name": "os.path", "line_number": 77, "usage_type": "attribute" }, { "api_name": "os.makedirs", "line_number": 79, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 101, "usage_type": "call" }, { "api_name": "os.path", "line_number": 101, "usage_type": "attribute" }, { "api_name": "argparse.ArgumentParser", "line_number": 108, "usage_type": "call" } ]
34038250008
import logging from datetime import timedelta from aio_proxy.request.search_type import SearchType from aio_proxy.search.es_index import StructureMapping from aio_proxy.search.geo_search import build_es_search_geo_query from aio_proxy.search.helpers.helpers import ( execute_and_agg_total_results_by_identifiant, extract_ul_and_etab_from_es_response, page_through_results, ) from aio_proxy.search.text_search import build_es_search_text_query from aio_proxy.utils.cache import cache_strategy TIME_TO_LIVE = timedelta(days=31) MIN_EXECUTION_TIME = 400 MAX_TOTAL_RESULTS = 10000 class ElasticSearchRunner: def __init__(self, search_params, search_type): self.es_search_client = StructureMapping.search() self.search_type = search_type self.search_params = search_params self.has_full_text_query = False self.es_search_results = None self.total_results = None self.execution_time = None self.run() def sort_es_search_query(self): # Sorting is very heavy on performance if there are no # search terms (only filters). As there is no search terms, we can # exclude this sorting because score is the same for all results # documents. Beware, nom and prenoms are search fields. if self.has_full_text_query: self.es_search_client = self.es_search_client.sort( {"_score": {"order": "desc"}}, {"unite_legale.etat_administratif_unite_legale": {"order": "asc"}}, {"unite_legale.nombre_etablissements_ouverts": {"order": "desc"}}, ) # If only filters are used, use nombre établissements ouverts to sort the # results else: self.es_search_client = self.es_search_client.sort( {"unite_legale.nombre_etablissements_ouverts": {"order": "desc"}}, ) def execute_and_format_es_search(self): self.es_search_client = page_through_results(self) es_response = self.es_search_client.execute() self.total_results = es_response.hits.total.value self.execution_time = es_response.took # Due to performance issues when aggregating on filter queries, we use # aggregation on total_results only when total_results is lower than # 10 000 results. If total_results is higher than 10 000 results, # the aggregation causes timeouts on API. We return by default 10 000 results. max_results_exceeded = self.total_results >= MAX_TOTAL_RESULTS if not max_results_exceeded: execute_and_agg_total_results_by_identifiant(self) self.es_search_results = [] for matching_structure in es_response.hits: matching_structure_dict = extract_ul_and_etab_from_es_response( matching_structure ) self.es_search_results.append(matching_structure_dict) def sort_and_execute_es_search_query(self): self.es_search_client = self.es_search_client.extra( track_scores=True, explain=True ) # Collapse is used to aggregate the results by siren. It is the consequence of # separating large documents into smaller ones self.es_search_client = self.es_search_client.update_from_dict( {"collapse": {"field": "identifiant"}} ) # Sort results self.sort_es_search_query() # Execute search, only called if key not found in cache # (see cache strategy below) def get_es_search_response(): self.execute_and_format_es_search() es_results_to_cache = { "total_results": self.total_results, "response": self.es_search_results, "execution_time": self.execution_time, } return es_results_to_cache # To make sure the page and page size are part of the cache key cache_key = page_through_results(self) cached_search_results = cache_strategy( cache_key, get_es_search_response, self.should_cache_search_response, TIME_TO_LIVE, ) self.total_results = cached_search_results["total_results"] self.es_search_results = cached_search_results["response"] self.execution_time = cached_search_results["execution_time"] def should_cache_search_response(self): """Cache search response if execution time is higher than 400 ms""" try: if self.execution_time > MIN_EXECUTION_TIME: return True return False except KeyError as error: logging.info(f"Error getting search execution time: {error}") return False def run(self): if self.search_type == SearchType.TEXT: build_es_search_text_query(self) elif self.search_type == SearchType.GEO: build_es_search_geo_query(self) self.sort_and_execute_es_search_query()
etalab/annuaire-entreprises-search-api
aio/aio-proxy/aio_proxy/search/es_search_runner.py
es_search_runner.py
py
5,014
python
en
code
13
github-code
6
[ { "api_name": "datetime.timedelta", "line_number": 15, "usage_type": "call" }, { "api_name": "aio_proxy.search.es_index.StructureMapping.search", "line_number": 22, "usage_type": "call" }, { "api_name": "aio_proxy.search.es_index.StructureMapping", "line_number": 22, "usage_type": "name" }, { "api_name": "aio_proxy.search.helpers.helpers.page_through_results", "line_number": 50, "usage_type": "call" }, { "api_name": "aio_proxy.search.helpers.helpers.execute_and_agg_total_results_by_identifiant", "line_number": 61, "usage_type": "call" }, { "api_name": "aio_proxy.search.helpers.helpers.extract_ul_and_etab_from_es_response", "line_number": 65, "usage_type": "call" }, { "api_name": "aio_proxy.search.helpers.helpers.page_through_results", "line_number": 96, "usage_type": "call" }, { "api_name": "aio_proxy.utils.cache.cache_strategy", "line_number": 98, "usage_type": "call" }, { "api_name": "logging.info", "line_number": 116, "usage_type": "call" }, { "api_name": "aio_proxy.request.search_type.SearchType.TEXT", "line_number": 120, "usage_type": "attribute" }, { "api_name": "aio_proxy.request.search_type.SearchType", "line_number": 120, "usage_type": "name" }, { "api_name": "aio_proxy.search.text_search.build_es_search_text_query", "line_number": 121, "usage_type": "call" }, { "api_name": "aio_proxy.request.search_type.SearchType.GEO", "line_number": 122, "usage_type": "attribute" }, { "api_name": "aio_proxy.request.search_type.SearchType", "line_number": 122, "usage_type": "name" }, { "api_name": "aio_proxy.search.geo_search.build_es_search_geo_query", "line_number": 123, "usage_type": "call" } ]
28521533515
from sqlalchemy import Column, Float, ForeignKey, Integer, \ String, Text, and_ from sqlalchemy.orm import contains_eager, load_only, relationship from sqlalchemy.orm.exc import NoResultFound from Sugar import Dictifiable from extensions import celery, db class SixteenPReport(Dictifiable, db.Model): __tablename__ = 'sixteen_p_report' test_attempt_id = Column(Integer, ForeignKey('test_attempt.id', ondelete="CASCADE"), primary_key=True) personality_type = Column(String(512)) role = Column(String(512)) strategy = Column(String(512)) mind_value = Column(Float) mind_text = Column(Text) energy_value = Column(Float) energy_text = Column(Text) nature_value = Column(Float) nature_text = Column(Text) tactics_value = Column(Float) tactics_text = Column(Text) identity_value = Column(Float) identity_text = Column(Text) test_attempt = relationship("TestAttempt", back_populates="sixteen_p_report", uselist=False) @staticmethod @celery.task() def generate_report(test_attempt_id): from models import Question from models import QuestionAttempt from models import SectionAttempt from models import Choice from Algos.SixteenP import scraping question_attempts = (QuestionAttempt.query .join(QuestionAttempt.question) .outerjoin(Question.choices) .join(SectionAttempt, and_( SectionAttempt.id == QuestionAttempt.section_attempt_id, SectionAttempt.test_attempt_id == test_attempt_id)) .options(load_only(QuestionAttempt.choice_id)) .options(contains_eager(QuestionAttempt.question) .load_only(Question.id) .contains_eager(Question.choices) .load_only(Choice.id)) .all() ) scrapped_info = scraping.scrape(question_attempts) if scrapped_info is None: return try: report = SixteenPReport.query.filter( SixteenPReport.test_attempt_id == test_attempt_id).one() db.session.delete(report) except NoResultFound: pass report = SixteenPReport(test_attempt_id=test_attempt_id, **scrapped_info) db.session.add(report) db.session.commit() return question_attempts
harveyslash/backend-cleaned
beatest/models/SixteenPReport.py
SixteenPReport.py
py
2,816
python
en
code
0
github-code
6
[ { "api_name": "Sugar.Dictifiable", "line_number": 10, "usage_type": "name" }, { "api_name": "extensions.db.Model", "line_number": 10, "usage_type": "attribute" }, { "api_name": "extensions.db", "line_number": 10, "usage_type": "name" }, { "api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call" }, { "api_name": "sqlalchemy.Integer", "line_number": 13, "usage_type": "argument" }, { "api_name": "sqlalchemy.ForeignKey", "line_number": 14, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 17, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 17, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 18, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call" }, { "api_name": "sqlalchemy.String", "line_number": 19, "usage_type": "call" }, { "api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call" }, { "api_name": "sqlalchemy.Float", "line_number": 21, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call" }, { "api_name": "sqlalchemy.Text", "line_number": 22, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call" }, { "api_name": "sqlalchemy.Float", "line_number": 24, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call" }, { "api_name": "sqlalchemy.Text", "line_number": 25, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call" }, { "api_name": "sqlalchemy.Float", "line_number": 27, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call" }, { "api_name": "sqlalchemy.Text", "line_number": 28, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call" }, { "api_name": "sqlalchemy.Float", "line_number": 30, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call" }, { "api_name": "sqlalchemy.Text", "line_number": 31, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call" }, { "api_name": "sqlalchemy.Float", "line_number": 33, "usage_type": "argument" }, { "api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call" }, { "api_name": "sqlalchemy.Text", "line_number": 34, "usage_type": "argument" }, { "api_name": "sqlalchemy.orm.relationship", "line_number": 36, "usage_type": "call" }, { "api_name": "models.QuestionAttempt.query.join", "line_number": 49, "usage_type": "call" }, { "api_name": "models.QuestionAttempt.query", "line_number": 49, "usage_type": "attribute" }, { "api_name": "models.QuestionAttempt", "line_number": 49, "usage_type": "name" }, { "api_name": "models.QuestionAttempt.question", "line_number": 50, "usage_type": "attribute" }, { "api_name": "models.QuestionAttempt", "line_number": 50, "usage_type": "name" }, { "api_name": "models.Question.choices", "line_number": 51, "usage_type": "attribute" }, { "api_name": "models.Question", "line_number": 51, "usage_type": "name" }, { "api_name": "models.SectionAttempt", "line_number": 52, "usage_type": "name" }, { "api_name": "sqlalchemy.and_", "line_number": 53, "usage_type": "call" }, { "api_name": "models.SectionAttempt.id", "line_number": 54, "usage_type": "attribute" }, { "api_name": "models.SectionAttempt", "line_number": 54, "usage_type": "name" }, { "api_name": "models.QuestionAttempt.section_attempt_id", "line_number": 54, "usage_type": "attribute" }, { "api_name": "models.QuestionAttempt", "line_number": 54, "usage_type": "name" }, { "api_name": "models.SectionAttempt.test_attempt_id", "line_number": 55, "usage_type": "attribute" }, { "api_name": "models.SectionAttempt", "line_number": 55, "usage_type": "name" }, { "api_name": "sqlalchemy.orm.load_only", "line_number": 57, "usage_type": "call" }, { "api_name": "models.QuestionAttempt.choice_id", "line_number": 57, "usage_type": "attribute" }, { "api_name": "models.QuestionAttempt", "line_number": 57, "usage_type": "name" }, { "api_name": "sqlalchemy.orm.contains_eager", "line_number": 58, "usage_type": "call" }, { "api_name": "models.QuestionAttempt.question", "line_number": 58, "usage_type": "attribute" }, { "api_name": "models.QuestionAttempt", "line_number": 58, "usage_type": "name" }, { "api_name": "models.Question.id", "line_number": 59, "usage_type": "attribute" }, { "api_name": "models.Question", "line_number": 59, "usage_type": "name" }, { "api_name": "models.Question.choices", "line_number": 60, "usage_type": "attribute" }, { "api_name": "models.Question", "line_number": 60, "usage_type": "name" }, { "api_name": "models.Choice.id", "line_number": 61, "usage_type": "attribute" }, { "api_name": "models.Choice", "line_number": 61, "usage_type": "name" }, { "api_name": "Algos.SixteenP.scraping.scrape", "line_number": 65, "usage_type": "call" }, { "api_name": "Algos.SixteenP.scraping", "line_number": 65, "usage_type": "name" }, { "api_name": "{'Question': 'models.Question', 'QuestionAttempt': 'models.QuestionAttempt', 'SectionAttempt': 'models.SectionAttempt', 'Choice': 'models.Choice', 'scraping': 'Algos.SixteenP.scraping'}.query.filter", "line_number": 71, "usage_type": "call" }, { "api_name": "{'Question': 'models.Question', 'QuestionAttempt': 'models.QuestionAttempt', 'SectionAttempt': 'models.SectionAttempt', 'Choice': 'models.Choice', 'scraping': 'Algos.SixteenP.scraping'}.query", "line_number": 71, "usage_type": "attribute" }, { "api_name": "{'Question': 'models.Question', 'QuestionAttempt': 'models.QuestionAttempt', 'SectionAttempt': 'models.SectionAttempt', 'Choice': 'models.Choice', 'scraping': 'Algos.SixteenP.scraping'}.test_attempt_id", "line_number": 72, "usage_type": "attribute" }, { "api_name": "extensions.db.session.delete", "line_number": 73, "usage_type": "call" }, { "api_name": "extensions.db.session", "line_number": 73, "usage_type": "attribute" }, { "api_name": "extensions.db", "line_number": 73, "usage_type": "name" }, { "api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 76, "usage_type": "name" }, { "api_name": "{'Question': 'models.Question', 'QuestionAttempt': 'models.QuestionAttempt', 'SectionAttempt': 'models.SectionAttempt', 'Choice': 'models.Choice', 'scraping': 'Algos.SixteenP.scraping'}", "line_number": 78, "usage_type": "call" }, { "api_name": "extensions.db.session.add", "line_number": 80, "usage_type": "call" }, { "api_name": "extensions.db.session", "line_number": 80, "usage_type": "attribute" }, { "api_name": "extensions.db", "line_number": 80, "usage_type": "name" }, { "api_name": "extensions.db.session.commit", "line_number": 81, "usage_type": "call" }, { "api_name": "extensions.db.session", "line_number": 81, "usage_type": "attribute" }, { "api_name": "extensions.db", "line_number": 81, "usage_type": "name" }, { "api_name": "extensions.celery.task", "line_number": 41, "usage_type": "call" }, { "api_name": "extensions.celery", "line_number": 41, "usage_type": "name" } ]
12570928349
from .strings import Strings from .actions import Action from dataclasses import dataclass, field from telegram import User, InlineKeyboardButton, InlineKeyboardMarkup import random from datetime import datetime REMOVE_ID_INDEX = 13 # в коллбеке для удаления человека передаём айди, начинается с 13 индекса @dataclass class Tasting: chat_id: int name: str | None = None tasting_message_id: int | None = None people: int = 0 users: dict[int, User] = field(default_factory=lambda: {}) initiated_user: User | None = None shuffled_ids: list[int] = field(default_factory=list) def __post_init__(self): self.name = f'{self.chat_id} {datetime.now().strftime("%m/%d/%Y, %H:%M:%S")}' def clear(self): self.tasting_message_id = None self.people = 0 self.users.clear() def add(self, user: User) -> bool: if user.id not in self.users.keys(): self.users[user.id] = user return True return False def remove(self, user: User) -> bool: if user.id in self.users.keys(): del self.users[user.id] return True return False def generate_keyboard(self) -> InlineKeyboardMarkup: keyboard = [ [InlineKeyboardButton(Strings.KEYBOARD_TITLE, callback_data=Action.ROLL.value)], [ InlineKeyboardButton(Strings.KEYBOARD_MINUS, callback_data=Action.MINUS.value), InlineKeyboardButton(Strings.KEYBOARD_PEOPLE.format(self.people), callback_data=Action.NUM.value), InlineKeyboardButton(Strings.KEYBOARD_PLUS, callback_data=Action.PLUS.value) ], [InlineKeyboardButton(Strings.KEYBOARD_ADD, callback_data=Action.ADD_ME.value)] ] if len(self.users) > 0: for user_id, user in self.users.items(): # use last_name if username is not present last = f'(@{user.username})' if user.username else user.last_name single_user = [ InlineKeyboardButton(f'{user.first_name} {last}', callback_data=Action.NAME.value), InlineKeyboardButton(Strings.KEYBOARD_REMOVE, callback_data=f'{Action.REMOVE_ME.value} id:{user_id}'), ] keyboard.append(single_user) return InlineKeyboardMarkup(keyboard) def roll(self, initiated_user: User): self.initiated_user = initiated_user all_ids = list(self.users.keys()) random.shuffle(all_ids) self.shuffled_ids = all_ids def winners_message(self) -> str: def get_user_info(num: int, user_id: int) -> str: user = self.users.get(user_id) user_string = f'{num + 1}) {user.full_name}' if user.username: user_string += f' (@{user.username})' user_string += "\n" return user_string winners = f'{Strings.TITLE}\n\n' winners += f'{Strings.WINNERS}\n' for counter, shuffle_id in enumerate(self.shuffled_ids): if counter < self.people: winners += get_user_info(counter, shuffle_id) elif counter == self.people: winners += f'{Strings.WAITING_LIST}\n' winners += get_user_info(counter, shuffle_id) else: winners += get_user_info(counter, shuffle_id) winners += f'\n@{self.initiated_user.username}' return winners
maxkupetskii/kurwabotV2
kurwa_bot/tasting.py
tasting.py
py
3,575
python
en
code
0
github-code
6
[ { "api_name": "telegram.User", "line_number": 18, "usage_type": "name" }, { "api_name": "dataclasses.field", "line_number": 18, "usage_type": "call" }, { "api_name": "telegram.User", "line_number": 19, "usage_type": "name" }, { "api_name": "dataclasses.field", "line_number": 20, "usage_type": "call" }, { "api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call" }, { "api_name": "datetime.datetime", "line_number": 23, "usage_type": "name" }, { "api_name": "telegram.User", "line_number": 30, "usage_type": "name" }, { "api_name": "telegram.User", "line_number": 36, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 44, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_TITLE", "line_number": 44, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 44, "usage_type": "name" }, { "api_name": "actions.Action.ROLL", "line_number": 44, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 44, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 46, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_MINUS", "line_number": 46, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 46, "usage_type": "name" }, { "api_name": "actions.Action.MINUS", "line_number": 46, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 46, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 47, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_PEOPLE.format", "line_number": 47, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_PEOPLE", "line_number": 47, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 47, "usage_type": "name" }, { "api_name": "actions.Action.NUM", "line_number": 47, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 47, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 48, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_PLUS", "line_number": 48, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 48, "usage_type": "name" }, { "api_name": "actions.Action.PLUS", "line_number": 48, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 48, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 50, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_ADD", "line_number": 50, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 50, "usage_type": "name" }, { "api_name": "actions.Action.ADD_ME", "line_number": 50, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 50, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 57, "usage_type": "call" }, { "api_name": "actions.Action.NAME", "line_number": 57, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 57, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardButton", "line_number": 58, "usage_type": "call" }, { "api_name": "strings.Strings.KEYBOARD_REMOVE", "line_number": 58, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 58, "usage_type": "name" }, { "api_name": "actions.Action.REMOVE_ME", "line_number": 59, "usage_type": "attribute" }, { "api_name": "actions.Action", "line_number": 59, "usage_type": "name" }, { "api_name": "telegram.InlineKeyboardMarkup", "line_number": 62, "usage_type": "call" }, { "api_name": "telegram.InlineKeyboardMarkup", "line_number": 42, "usage_type": "name" }, { "api_name": "telegram.User", "line_number": 64, "usage_type": "name" }, { "api_name": "random.shuffle", "line_number": 67, "usage_type": "call" }, { "api_name": "strings.Strings.TITLE", "line_number": 79, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 79, "usage_type": "name" }, { "api_name": "strings.Strings.WINNERS", "line_number": 80, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 80, "usage_type": "name" }, { "api_name": "strings.Strings.WAITING_LIST", "line_number": 85, "usage_type": "attribute" }, { "api_name": "strings.Strings", "line_number": 85, "usage_type": "name" }, { "api_name": "dataclasses.dataclass", "line_number": 12, "usage_type": "name" } ]
22762975242
import cv2 import time import PoseModule as pm cap = cv2.VideoCapture('Videos/14.mp4') ok_flag = False if cap.isOpened(): ok_flag = cap.isOpened() else: print("Cannot open camera") exit() pTime = 0 detector = pm.poseDetector() while ok_flag: success, img = cap.read() # if frame is read correctly ret is True if not success: print("Can't receive frame (stream end?). Exiting ...") break img = detector.findPose(img) lmList = detector.findPosition(img) if len(lmList)!=0: print(lmList[14]) cv2.circle(img, (lmList[14][1], lmList[14][2]), 10, (0, 0, 100), cv2.FILLED) cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTime cv2.putText(img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) cv2.imshow("Image", img) cv2.waitKey(10) if cv2.waitKey(10) == ord('q'): cap.release() cv2.destroyAllWindows() break if cv2.getWindowProperty("Image", cv2.WND_PROP_VISIBLE) < 1: cap.release() cv2.destroyAllWindows() break cTime = time.time() fps = 1 / (cTime - pTime) pTime = cTime cv2.putText(img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) cv2.imshow("Image", img) cv2.waitKey(10) if cv2.waitKey(1) == ord('q'): cap.release() cv2.destroyAllWindows() break if cv2.getWindowProperty("Image", cv2.WND_PROP_VISIBLE) < 1: cap.release() cv2.destroyAllWindows() break
GabrielaVasileva/ComputerVision
pose_estimation/PoseProject.py
PoseProject.py
py
1,538
python
en
code
0
github-code
6
[ { "api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call" }, { "api_name": "PoseModule.poseDetector", "line_number": 13, "usage_type": "call" }, { "api_name": "cv2.circle", "line_number": 25, "usage_type": "call" }, { "api_name": "cv2.FILLED", "line_number": 25, "usage_type": "attribute" }, { "api_name": "time.time", "line_number": 27, "usage_type": "call" }, { "api_name": "cv2.putText", "line_number": 31, "usage_type": "call" }, { "api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 31, "usage_type": "attribute" }, { "api_name": "cv2.imshow", "line_number": 32, "usage_type": "call" }, { "api_name": "cv2.waitKey", "line_number": 34, "usage_type": "call" }, { "api_name": "cv2.waitKey", "line_number": 36, "usage_type": "call" }, { "api_name": "cv2.destroyAllWindows", "line_number": 38, "usage_type": "call" }, { "api_name": "cv2.getWindowProperty", "line_number": 41, "usage_type": "call" }, { "api_name": "cv2.WND_PROP_VISIBLE", "line_number": 41, "usage_type": "attribute" }, { "api_name": "cv2.destroyAllWindows", "line_number": 43, "usage_type": "call" }, { "api_name": "time.time", "line_number": 45, "usage_type": "call" }, { "api_name": "cv2.putText", "line_number": 49, "usage_type": "call" }, { "api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 49, "usage_type": "attribute" }, { "api_name": "cv2.imshow", "line_number": 50, "usage_type": "call" }, { "api_name": "cv2.waitKey", "line_number": 52, "usage_type": "call" }, { "api_name": "cv2.waitKey", "line_number": 54, "usage_type": "call" }, { "api_name": "cv2.destroyAllWindows", "line_number": 56, "usage_type": "call" }, { "api_name": "cv2.getWindowProperty", "line_number": 59, "usage_type": "call" }, { "api_name": "cv2.WND_PROP_VISIBLE", "line_number": 59, "usage_type": "attribute" }, { "api_name": "cv2.destroyAllWindows", "line_number": 61, "usage_type": "call" } ]
40694907934
# coding: utf-8 from fabric.api import local, sudo, lcd, put, cd from fabric.context_managers import settings from fabric.contrib.files import exists from fabric.operations import local as lrun, run from fabric.api import task from fabric.state import env import os env.user = 'adminbuy' proj_dir = '/home/user/www' root_folder = '/adminbuy' proj_fullpath = proj_dir + root_folder local_config_dir = proj_fullpath + '/config' local_config_dir_super = local_config_dir + "/supervisor" remote_nginx_dir = '/etc/nginx/sites-enabled' remote_supervisor_dir = '/etc/supervisor/conf.d' super_flikiss = "flikiss.conf" remote_wiki_dir = '/home/www/wiki' wiki_conf_file = '.flikissrc' @task def localhost(): global proj_dir, local_config_dir, local_config_dir_super proj_dir = "/home/user/bitbucket" local_config_dir = proj_dir + root_folder + '/config' local_config_dir_super = local_config_dir + "/supervisor" env.run = lrun env.hosts = ['localhost'] env.port = '22' env.user = 'user' @task def remote(): env.run = run env.hosts = ['46.101.216.62'] env.port = '22' @task def deploy(): create_user() with settings(user='user'): install_env() clone_proj() install_dependency() install_rabbitmq() install_redis() prepare_db() configure_nginx() configure_supervisor_proj() configure_supervisor_socket() configure_supervisor_celery() reload_nginx() reload_super() create_superuser() @task def update(): install_dependency() with settings(user='user'): with cd(proj_dir + root_folder): run('git pull origin master') migration() reload_super() reload_nginx() @task def migration(): with cd(proj_dir + root_folder): run("python manage.py db upgrade") @task def prepare_db(): with cd(proj_dir + root_folder): user = run('python -c "from config import USER; print USER"') password = run('python -c "from config import PASSWORD; print PASSWORD"') db = run('python -c "from config import DB; print DB"') run('sudo -u postgres psql -c "CREATE ROLE {0} WITH PASSWORD \'{1}\' NOSUPERUSER CREATEDB NOCREATEROLE LOGIN;"'.format(user, password)) run('sudo -u postgres psql -c "CREATE DATABASE {0} WITH OWNER={1} TEMPLATE=template0 ENCODING=\'utf-8\';"'.format(db, user)) migration() @task def clone_proj(): run('mkdir ' + proj_dir + ' -p') with cd(proj_dir): run('git clone https://github.com/StasEvseev/adminbuy.git') put("config_local.py", proj_fullpath) @task def create_user(): sudo('adduser user') sudo('gpasswd -a user sudo') @task def install_env(): sudo('add-apt-repository ppa:chris-lea/nginx-devel -y') sudo('apt-get update') sudo('apt-get install -y python') sudo('apt-get install python-setuptools') sudo('easy_install pip') sudo('apt-get install -y python-virtualenv') sudo('apt-get install -y nginx') sudo('apt-get install -y supervisor') sudo('apt-get install -y git') sudo('apt-get install build-essential gcc libxml2-dev libxslt1-dev -y') sudo('apt-get install libpq-dev python-dev -y') sudo('apt-get install postgresql-9.3 -y') sudo('apt-get install libjpeg-dev') @task def install_dependency(): with cd(proj_dir + root_folder): sudo('pip install -r REQUIREMENTS') @task def create_superuser(): with settings(user='user'): with cd(proj_dir + root_folder): run('python manage.py create_superuser') @task def install_rabbitmq(): try: sudo("dpkg -l | grep rabbitmq-server") except: sudo("echo 'deb http://www.rabbitmq.com/debian/ testing main' | tee -a /etc/apt/sources.list") sudo("wget https://www.rabbitmq.com/rabbitmq-signing-key-public.asc") sudo("apt-key add rabbitmq-signing-key-public.asc") sudo("apt-get update") sudo("apt-get install rabbitmq-server -y") sudo("rabbitmqctl add_user myuser mypassword") sudo("rabbitmqctl add_vhost myvhost") sudo("rabbitmqctl set_permissions -p myvhost myuser \".*\" \".*\" \".*\"") @task def install_redis(): sudo("apt-get install redis-server -y") @task def configure_wiki(): local("pip install flikiss") if os.path.exists(remote_wiki_dir + "/" + wiki_conf_file) is False: local("sudo mkdir %s -p" % remote_wiki_dir) local("sudo cp %s/%s %s/%s " % ( local_config_dir, wiki_conf_file, remote_wiki_dir, wiki_conf_file)) if os.path.exists(remote_supervisor_dir + "/" + super_flikiss) is False: local("sudo cp %s/%s %s/%s" % ( local_config_dir_super, super_flikiss, remote_supervisor_dir, super_flikiss)) @task def reload_nginx(): sudo('/etc/init.d/nginx restart') @task def reload_super(): try: sudo('service supervisor start') except: pass sudo('supervisorctl reread') sudo('supervisorctl reload') @task def configure_nginx(): """ """ with settings(user='user'): sudo('/etc/init.d/nginx start') if exists('/etc/nginx/sites-enabled/default'): sudo('rm /etc/nginx/sites-enabled/default') put("./config/buyapi", remote_nginx_dir, use_sudo=True) put("private.key", '/etc/nginx/', use_sudo=True) put("ssl.crt", '/etc/nginx/', use_sudo=True) if exists("/etc/nginx/sites-enabled/buyapi") is False: sudo('ln -s /etc/nginx/sites-available/buyapi' + ' /etc/nginx/sites-enabled/buyapi') @task def configure_supervisor_proj(): """ """ if exists(remote_supervisor_dir + '/buyapi.conf') is False: sudo('cp ' + local_config_dir_super + '/buyapi.conf ' + remote_supervisor_dir + '/buyapi.conf') @task def configure_supervisor_socket(): """ """ if exists(remote_supervisor_dir + '/socket.conf') is False: sudo('cp ' + local_config_dir_super + '/socket.conf ' + remote_supervisor_dir + '/socket.conf') @task def configure_supervisor_celery(): if exists(remote_supervisor_dir + "/celery.conf") is False: sudo('cp ' + local_config_dir_super + '/celery.conf ' + remote_supervisor_dir + '/celery.conf') sudo('mkdir /var/log/celery') if exists(remote_supervisor_dir + "/celerybeats.conf") is False: sudo('cp ' + local_config_dir_super + '/celerybeats.conf ' + remote_supervisor_dir + '/celerybeats.conf') sudo('mkdir /var/log/celerybeats')
StasEvseev/adminbuy
fabfile.py
fabfile.py
py
6,566
python
en
code
0
github-code
6
[ { "api_name": "fabric.state.env.user", "line_number": 14, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 14, "usage_type": "name" }, { "api_name": "fabric.state.env.run", "line_number": 40, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 40, "usage_type": "name" }, { "api_name": "fabric.operations.local", "line_number": 40, "usage_type": "name" }, { "api_name": "fabric.state.env.hosts", "line_number": 41, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 41, "usage_type": "name" }, { "api_name": "fabric.state.env.port", "line_number": 42, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 42, "usage_type": "name" }, { "api_name": "fabric.state.env.user", "line_number": 43, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 43, "usage_type": "name" }, { "api_name": "fabric.api.task", "line_number": 34, "usage_type": "name" }, { "api_name": "fabric.state.env.run", "line_number": 48, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 48, "usage_type": "name" }, { "api_name": "fabric.operations.run", "line_number": 48, "usage_type": "name" }, { "api_name": "fabric.state.env.hosts", "line_number": 49, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 49, "usage_type": "name" }, { "api_name": "fabric.state.env.port", "line_number": 50, "usage_type": "attribute" }, { "api_name": "fabric.state.env", "line_number": 50, "usage_type": "name" }, { "api_name": "fabric.api.task", "line_number": 46, "usage_type": "name" }, { "api_name": "fabric.context_managers.settings", "line_number": 56, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 53, "usage_type": "name" }, { "api_name": "fabric.context_managers.settings", "line_number": 75, "usage_type": "call" }, { "api_name": "fabric.api.cd", "line_number": 76, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 77, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 72, "usage_type": "name" }, { "api_name": "fabric.api.cd", "line_number": 85, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 86, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 83, "usage_type": "name" }, { "api_name": "fabric.api.cd", "line_number": 91, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 92, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 93, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 94, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 96, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 97, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 89, "usage_type": "name" }, { "api_name": "fabric.operations.run", "line_number": 104, "usage_type": "call" }, { "api_name": "fabric.api.cd", "line_number": 105, "usage_type": "call" }, { "api_name": "fabric.operations.run", "line_number": 106, "usage_type": "call" }, { "api_name": "fabric.api.put", "line_number": 107, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 102, "usage_type": "name" }, { "api_name": "fabric.api.sudo", "line_number": 112, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 113, "usage_type": "call" }, { "api_name": "fabric.api.task", "line_number": 110, "usage_type": "name" }, { "api_name": "fabric.api.sudo", "line_number": 118, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 119, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 120, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 121, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 122, "usage_type": "call" }, { "api_name": "fabric.api.sudo", "line_number": 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73746980669
import warnings import time import os import joblib import json import pandas as pd from src.training import hyper_param_tuning, test_models from src.utils import parse_terminal_arguments, get_repr_model, dict_cartesian_product warnings.simplefilter(action='ignore', category=FutureWarning) #%% start = time.time() dataset, model, save_name = parse_terminal_arguments() model_save_path = f'./results/{dataset}/{save_name}/' if not os.path.exists(model_save_path): os.makedirs(model_save_path) with open('configs.json') as f: configs = json.load(f) with open('xgb_params.json') as f: xgb_params = json.load(f) #%% sb_threshold = configs[f'{dataset}_sb_threshold'] train_folds_path = configs[f'{dataset}_folds'] train_folds = [pd.read_csv(f'{train_folds_path}fold_{fold_idx}.csv') for fold_idx in range(5)] for fold in train_folds: fold['ligand_id'] = fold['ligand_id'].astype(str) test = pd.read_csv(configs[f'{dataset}_test']) test['ligand_id'] = test['ligand_id'].astype(str) print('Read the training/test data') representation_model = get_repr_model(dataset, model, configs) n_phases = len(xgb_params['search_params']) fixed_params = xgb_params['fixed_params'] best_params = {} for phase in range(n_phases): print(f'Fine-tuning. Phase: {phase+1}') param_combinations = dict_cartesian_product(xgb_params['search_params'][phase]) fixed_params = {**fixed_params, **best_params} best_models, best_params, cv_scores = hyper_param_tuning(fixed_params, param_combinations, representation_model, train_folds, sb_threshold, model_save_path) cv_scores.to_csv(f'{model_save_path}cv_scores_p{phase+1}.csv', index=None) #%% joblib.dump(best_models, model_save_path + 'models.pkl ', compress=3) with open(model_save_path + 'best_params.json', 'w') as f: json.dump({**fixed_params, **best_params}, f) print('Done tuning. Testing...') test_models(best_models, representation_model, train_folds, test, sb_threshold, model_save_path) print('DONE!') elapsed_total_time = time.time() - start total_time = time.strftime('%H:%M:%S', time.gmtime(elapsed_total_time)) print(f'Whole program took {total_time}')
boun-tabi/chemboost
src/runner.py
runner.py
py
2,510
python
en
code
7
github-code
6
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32402735389
#!/usr/bin/env python # coding: utf-8 #Project Information # We will create a classifier that can distinguish spam (junk or commercial or bulk) emails from ham (non-spam) emails. # Spam/Ham Classification # EDA, Feature Engineering, Classifier # Dataset Information # In email classification, our goal is to classify emails as spam or not spam (referred to as "ham") using features generated from the text in the email. # The dataset consists of email messages and their labels (0 for ham, 1 for spam). # Your labeled training dataset contains 8348 labeled examples, and the test set contains 1000 unlabeled examples. # Run the following cells to load in the data into DataFrames. # The `train` DataFrame contains labeled data that we will use to train your model. It contains four columns: # 1. `id`: An identifier for the training example # 1. `subject`: The subject of the email # 1. `email`: The text of the email # 1. `spam`: 1 if the email is spam, 0 if the email is ham (not spam) # The `test` DataFrame contains 1000 unlabeled emails. # We will predict labels for these emails and submit your predictions to Kaggle for evaluation. #Importing libraries from client.api.notebook import Notebook import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from utils import fetch_and_cache_gdrive from sklearn.model_selection import train_test_split from IPython.display import display, Markdown from sklearn.linear_model import LogisticRegression from datetime import datetime import re get_ipython().system('pip install wordcloud') from wordcloud import WordCloud, STOPWORDS get_ipython().run_line_magic('matplotlib', 'inline') sns.set(style = "whitegrid", color_codes = True, font_scale = 1.5) # 1. Load the dataset fetch_and_cache_gdrive('1SCASpLZFKCp2zek-toR3xeKX3DZnBSyp', 'train.csv') fetch_and_cache_gdrive('1ZDFo9OTF96B5GP2Nzn8P8-AL7CTQXmC0', 'test.csv') original_training_data = pd.read_csv('data/train.csv') test = pd.read_csv('data/test.csv') # 2. Preprocessing ## a) Convert the emails to lower case as a first step to processing the text original_training_data['email'] = original_training_data['email'].str.lower() test['email'] = test['email'].str.lower() original_training_data.head() ## b) We will check if our data contains any missing values and replace them with appropriate filler values. ## (i.e., NaN values in the `subject` or `email` columns should be replaced with empty strings). ## Note that while there are no NaN values in the `spam` column, we should be careful when replacing NaN labels. # Doing so without consideration may introduce significant bias into our model when fitting. original_training_data['subject'].fillna("",inplace = True) original_training_data['email'].fillna("",inplace = True) original_training_data.isnull().sum() ## c) Print the text of first ham and first spam email in the original training set to see the difference between the two emails that might relate to the identification of spam. first_ham = original_training_data[original_training_data['spam'] == 0]['email'].iloc[0] first_spam = original_training_data[original_training_data['spam'] == 1]['email'].iloc[0] print(first_ham) print(first_spam) ## We notice that spam email contains a lot of tags like head, body, html, br, href etc as compared to the ham email. ## These tags could be used to differentiate between two emails and determine if an email is spam or ham. ## d) Training Validation Split # The training data we downloaded is all the data we have available for both training models and testing the models that we train. We therefore need to split the training data into separate training and testing datsets. Note that we set the seed (random_state) to 42. This will produce a pseudo-random sequence of random numbers that is the same for every student. train, test = train_test_split(original_training_data, test_size=0.1, random_state=42) ### Basic Feature Engineering ''' We would like to take the text of an email and predict whether the email is ham or spam. This is a classification problem, so we can use logistic regression to train a classifier. Recall that to train an logistic regression model we need a numeric feature matrix $X$ and a vector of corresponding binary labels $y$. Unfortunately, our data are text, not numbers. To address this, we can create numeric features derived from the email text and use those features for logistic regression. Each row of $X$ is an email. Each column of $X$ contains one feature for all the emails. ''' # Create a 2-dimensional NumPy array containing one row for each email text and that row should contain either a 0 or 1 for each word in the list. def words_in_texts(words, texts): ''' Args: words (list-like): words to find texts (Series): strings to search in Returns: NumPy array of 0s and 1s with shape (n, p) where n is the number of texts and p is the number of words. ''' indicator_array = [] for text in texts: list = [] for word in words: val = [1 if (word in text) else 0] list += val indicator_array.append(list) return indicator_array # 3. BASIC EDA # We need to identify some features that allow us to distinguish spam emails from ham emails. # One idea is to compare the distribution of a single feature in spam emails to the distribution of the same feature in ham emails. # If the feature is itself a binary indicator, such as whether a certain word occurs in the text, # Then this amounts to comparing the proportion of spam emails with the word to the proportion of ham emails with the word. # The following plot (which was created using `sns.barplot`) compares the proportion of emails in each class containing a particular set of words. # ![training conditional proportions](./images/training_conditional_proportions.png "Class Conditional Proportions") df = pd.DataFrame({ 'word_1': [1, 0, 1, 0], 'word_2': [0, 1, 0, 1], 'type': ['spam', 'ham', 'ham', 'ham'] }) display(Markdown("> Our Original DataFrame has some words column and a type column. You can think of each row as a sentence, and the value of 1 or 0 indicates the number of occurances of the word in this sentence.")) display(df); display(Markdown("> `melt` will turn columns into variale, notice how `word_1` and `word_2` become `variable`, their values are stored in the value column")) display(df.melt("type")) ## Create a bar chart like the one above comparing the proportion of spam and ham emails containing certain words. ## Choose a set of words that have different proportions for the two classes. ## Make sure to only consider emails from `train`. train=train.reset_index(drop=True) # We must do this in order to preserve the ordering of emails to labels for words_in_texts set_of_words=['head','href','br'] matrix = np.matrix(words_in_texts(set_of_words, train['email'])) new_df = pd.DataFrame(matrix).rename(columns={0:'head',1:'href',2:'br'}) new_df['type'] = train['spam'] new_df = new_df.melt('type') new_df['type'] = new_df['type'].map({0:'ham',1:'spam'}) sns.barplot(x='variable', y='value', hue='type', data=new_df, ci=None); ## When the feature is binary, it makes sense to compare its proportions across classes (as in the previous question). ## Otherwise, if the feature can take on numeric values, we can compare the distributions of these values for different classes. ## ![training conditional densities](./images/training_conditional_densities2.png "Class Conditional Densities") ## Create a class conditional density plot to compare the distribution of the length of spam emails to the distribution of the length of ham emails in the training set. df = pd.DataFrame({'length': train['email'].apply(len),'spam': train['spam']}) df = df.melt('spam') df['spam'] = df['spam'].map({0:'ham',1:'spam'}) x=df[df['spam']=='ham'] y=df[df['spam']=='spam'] plt.figure() plt.xlim(0,50000) a=sns.distplot(x['value'], label='ham', hist=False) b=sns.distplot(y['value'], label='spam', hist=False) a.set(xlabel='Length of email', ylabel='Distribution') plt.legend(); ## We notice in general, the length of spam emails is more than the length of ham emails. # 4. Basic Classification ## Notice that the output of `words_in_texts(words, train['email'])` is a numeric matrix containing features for each email. ## This means we can use it directly to train a classifier! ## `X_train` should be a matrix of 0s and 1s created by using your `words_in_texts` function on all the emails in the training set. ## `Y_train` should be a vector of the correct labels for each email in the training set. some_words = ['drug', 'bank', 'prescription', 'memo', 'private'] X_train = np.array(words_in_texts(some_words, train['email'])) Y_train = train['spam'] X_train[:5], Y_train[:5] # Now that we have matrices, we can use to scikit-learn! # Using the [`LogisticRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) classifier. # Train a logistic regression model using `X_train` and `Y_train`. # Then, output the accuracy of the model (on the training data) in the cell below. model = LogisticRegression() model.fit(X_train, Y_train) training_accuracy = model.score(X_train, Y_train) print("Training Accuracy: ", training_accuracy) # We have trained our first logistic regression model and it can correctly classify around 76% of the training data! # We can definitely do better than this by selecting more and better features. # 5. Evaluating Classifiers ''' The model we trained doesn't seem too shabby! But the classifier you made above isn't as good as this might lead us to believe. First, we are evaluating accuracy on the training set, which may provide a misleading accuracy measure, especially if we used the training set to identify discriminative features. In future parts of this analysis, it will be safer to hold out some of our data for model validation and comparison. Presumably, our classifier will be used for filtering, i.e. preventing messages labeled `spam` from reaching someone's inbox. There are two kinds of errors we can make: - False positive (FP): a ham email gets flagged as spam and filtered out of the inbox. - False negative (FN): a spam email gets mislabeled as ham and ends up in the inbox. These definitions depend both on the true labels and the predicted labels. False positives and false negatives may be of differing importance, leading us to consider more ways of evaluating a classifier, in addition to overall accuracy. ''' ''' Precision measures the proportion $\frac{\text{TP}}{\text{TP} + \text{FP}}$ of emails flagged as spam that are actually spam. Recall measures the proportion $\frac{\text{TP}}{\text{TP} + \text{FN}}$ of spam emails that were correctly flagged as spam. False-alarm rate measures the proportion $\frac{\text{FP}}{\text{FP} + \text{TN}}$ of ham emails that were incorrectly flagged as spam. ''' # The following image might help: # # <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/700px-Precisionrecall.svg.png" width="500px"> # ''' Note that a true positive (TP) is a spam email that is classified as spam, and a true negative (TN) is a ham email that is classified as ham. ''' Y_train_hat = model.predict(X_train) true_pos = np.sum(Y_train_hat & Y_train) total_pos = np.sum(Y_train_hat) false_neg = np.sum(Y_train) - true_pos false_pos = total_pos - true_pos true_neg = np.sum(Y_train==0) - false_pos logistic_predictor_precision = true_pos/ total_pos logistic_predictor_recall = true_pos/ (total_pos + false_neg) logistic_predictor_far = false_pos/ (false_pos + true_neg) print(logistic_predictor_precision, logistic_predictor_recall,logistic_predictor_far ) ## ham and spam emails ham_emails = train[train['spam'] == 0] spam_emails = train[train['spam'] == 1] ''' Finding better features based on the email text: - Number of characters in the subject / body - Number of words in the subject / body - Use of punctuation (e.g., how many '!' were there?) - Number / percentage of capital letters - Whether the email is a reply to an earlier email or a forwarded email - Number of html tags ''' # Number of characters in the subject def subject_char(df): return df['subject'].str.findall('\w').str.len() # Number of words in the subject def subject_words(df): return df['subject'].str.findall("\w+").str.len().fillna(0) # Use of punctuation (e.g., how many '!' were there?) def punc_exclamation(df): return df['email'].str.findall("!").str.len() def punc(df): return df['email'].str.findall('[^A-Za-z0-9]').str.len() / df['email'].str.findall('\w+').str.len() # Number / percentage of capital letters def capital_letters_percentage(df): return (df['subject'].str.findall(r'[A-Z]').str.len() / df['subject'].str.len()) # Whether the email is a reply to an earlier email or a forwarded email def reply_email(df): return df['subject'].apply(lambda x: 1 if "Re:" in x else 0) def forward_email(df): return df['subject'].apply(lambda x: 1 if "Fw:" in x else 0) # Number of html tags def html_tag(df): return df['email'].str.findall("/>").str.len() # Number of characters in the subject sns.distplot(subject_char(spam_emails), label = 'spam', hist=False) sns.distplot(subject_char(ham_emails), label = 'ham', hist=False) plt.xlabel('Number of characters in Subject'); # *We can notice that both that both the spam and ham emails have a similar amount of number of characters in the subject/body.* # Number of words in the subject sns.distplot(subject_words(spam_emails), label = 'spam', hist=False) sns.distplot(subject_words(ham_emails), label = 'ham', hist=False) plt.xlabel('Number of words in Subject'); # *We can notice that both that both the spam and ham emails have a similar amount of number of words in the subject/body.* # Number of ! punctuations in the email sns.distplot(punc_exclamation(spam_emails), label = 'spam', hist=False) sns.distplot(punc_exclamation(ham_emails), label = 'ham', hist=False) plt.xlabel('Number of punctuations (!) in emails'); # *We can notice here that spam emails have a higher use of exclamation marks as compared to the ham emails.* # Number of punctuations in the email sns.distplot(punc(spam_emails), label = 'spam', hist=False) sns.distplot(punc(ham_emails), label = 'ham', hist=False) plt.xlabel('Number of punctuations in email per word'); # *We can notice here that spam emails have a higher use of punctuations per word as compared to the ham emails.* # Number / percentage of capital letters sns.distplot(capital_letters_percentage(spam_emails), label = 'spam', hist=False) sns.distplot(capital_letters_percentage(ham_emails), label = 'ham', hist=False) plt.xlabel('percentage of capital letters in Subject'); # *Again, we find that the percentage of capital letters in the subject for both the emails are similar.* # 2. Improving word features : # Top words in spam and ham emails to help us find better word features. def word_bags(df): wordList = {} for email in df['email']: words = re.findall('\w+', email) for w in words: if (w in wordList): wordList[w] += 1 else: wordList[w] = 1 return wordList spam_bag = (pd.Series(word_bags(spam_emails)) / spam_emails.shape[0]).sort_values(ascending=False).iloc[:20] ham_bag = (pd.Series(word_bags(ham_emails)) / ham_emails.shape[0]).sort_values(ascending=False).iloc[:20] fig, axs = plt.subplots(ncols=2) fig.set_size_inches(8,10) spam_bar = sns.barplot(x=spam_bag.values, y=spam_bag.index, ax=axs[0]) spam_bar.set_title("Top words in spam emails") hams_bar = sns.barplot(x=ham_bag.values, y=ham_bag.index, ax=axs[1]) hams_bar.set_title("Top words in ham emails") train_word_bag = (pd.Series(word_bags(train)) / train.shape[0]).sort_values(ascending=False)[:300] train_word_bag ## Adding new words from sklearn.linear_model import LogisticRegressionCV def process_data_set(df): some_words = ['$', '!', 'body', 'html', '/>'] + train_word_bag.index.tolist() X_train = np.array(words_in_texts(some_words, df['email'])).astype(int) feature = pd.concat([subject_words(df), punc_exclamation(df), punc(df)], axis = 1).values X_train = np.concatenate((X_train, feature), axis=1) return X_train X_train = process_data_set(train) Y_train = train['spam'] model = LogisticRegressionCV(Cs=4, fit_intercept=True, cv=10, verbose =1, random_state=42) model.fit(X_train, Y_train) training_accuracy = model.score(X_train, Y_train) print("Training Accuracy: ", training_accuracy) # 5. Feature/Model Selection Process ''' - I used the idea mentioned in the section moving forward. I visualised these features like (Number of characters in the subject, Number of words in the subject, use of punctuation, percentage of capital letters, etc. I also digged into the email text itself to find words which could be used to distinguish between the emails. I have shown the process in the previous part. - While plotting, I compared the distribution of the feature in ham and spam emails. A lot of the features had similar distributions. For example, features inlcuding number of words in subjects, number of characters in the subject and number of capital letters had similar distribution for both the ham and spam emails. While distribution of features like punctuation (!) and general punctuations were different for the ham and spam emails which means these features were good features. I also found better words to distinguish between the emails using word bag method and inquiring the emails. Some of these words include '$', '!', 'body', 'html', '/>', 'http', 'com', etc. - It is suprising to see opposite distribution of general use of punctuation in the emails and specific use of exclamation marks in the emails. Basically, we notice that ham emails use more punctuations in the emails as compared to spam emails. We notice the opposite effect where significantly higher exclamation marks are utilised by spam emails as compared to the ham emails. ''' # I have used wordCloud library on spam and ham emails to visualise which words are used more. # We can notice that spam emails use words like font, html, td, tr, etc while the ham emails use words like https, com, etc. # We can use this visualisation to choose better word features to distinguish between the spam and ham emails. ham_emails = train[train['spam'] == 0] spam_emails = train[train['spam'] == 1] spam_text = spam_emails['email'].values ham_text = ham_emails['email'].values wordcloud = WordCloud( width = 3000, height = 2000, background_color = 'black', stopwords = STOPWORDS).generate(str(spam_text)) print("SPAM EMAILS") fig = plt.figure( figsize = (40, 30), facecolor = 'k', edgecolor = 'k') plt.imshow(wordcloud) wordcloud1 = WordCloud( width = 3000, height = 2000, background_color = 'black', stopwords = STOPWORDS).generate(str(ham_text)) print("HAM EMAILS") fig1 = plt.figure( figsize = (40, 30), facecolor = 'k', edgecolor = 'k') plt.imshow(wordcloud1) plt.axis('off') plt.tight_layout(pad=0) plt.show() ## 5. Submitting to Kaggle test_predictions = model.predict(process_data_set(test)) # The following saves a file to submit to Kaggle. submission_df = pd.DataFrame({ "Id": test['id'], "Class": test_predictions, }, columns=['Id', 'Class']) timestamp = datetime.isoformat(datetime.now()).split(".")[0] submission_df.to_csv("submission_{}.csv".format(timestamp), index=False) print('Created a CSV file: {}.'.format("submission_{}.csv".format(timestamp))) print('You may now upload this CSV file to Kaggle for scoring.') ## We got a 99.7% accuracy.
muskaangoyal/data-science-portfolio
spam-ham-master/proj.py
proj.py
py
19,773
python
en
code
0
github-code
6
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"call" }, { "api_name": "IPython.display.display", "line_number": 127, "usage_type": "call" }, { "api_name": "numpy.matrix", "line_number": 135, "usage_type": "call" }, { "api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "call" }, { "api_name": "seaborn.barplot", "line_number": 140, "usage_type": "call" }, { "api_name": "pandas.DataFrame", "line_number": 147, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 152, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.xlim", "line_number": 153, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name" }, { "api_name": "seaborn.distplot", "line_number": 154, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 155, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.legend", "line_number": 157, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name" }, { "api_name": "numpy.array", "line_number": 168, "usage_type": "call" }, { "api_name": "sklearn.linear_model.LogisticRegression", "line_number": 177, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 212, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 213, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 214, "usage_type": "call" }, { "api_name": "numpy.sum", "line_number": 216, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 267, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 268, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 269, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name" }, { "api_name": "seaborn.distplot", "line_number": 273, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 274, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 275, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name" }, { "api_name": "seaborn.distplot", "line_number": 280, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 281, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 282, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name" }, { "api_name": "seaborn.distplot", "line_number": 286, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 287, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 288, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name" }, { "api_name": "seaborn.distplot", "line_number": 292, "usage_type": "call" }, { "api_name": "seaborn.distplot", "line_number": 293, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.xlabel", "line_number": 294, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name" }, { "api_name": "re.findall", "line_number": 303, "usage_type": "call" }, { "api_name": "pandas.Series", "line_number": 311, "usage_type": "call" }, { "api_name": "pandas.Series", "line_number": 312, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.subplots", "line_number": 314, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 314, "usage_type": "name" }, { "api_name": "seaborn.barplot", "line_number": 316, "usage_type": "call" }, { "api_name": "seaborn.barplot", "line_number": 318, "usage_type": "call" }, { "api_name": "pandas.Series", "line_number": 321, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 328, "usage_type": "call" }, { "api_name": "pandas.concat", "line_number": 329, "usage_type": "call" }, { "api_name": "numpy.concatenate", "line_number": 330, "usage_type": "call" }, { "api_name": "sklearn.linear_model.LogisticRegressionCV", "line_number": 335, "usage_type": "call" }, { "api_name": "wordcloud.WordCloud", "line_number": 366, "usage_type": "call" }, { "api_name": "wordcloud.STOPWORDS", "line_number": 370, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 372, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.imshow", "line_number": 376, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name" }, { "api_name": "wordcloud.WordCloud", "line_number": 378, "usage_type": "call" }, { "api_name": "wordcloud.STOPWORDS", "line_number": 382, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.figure", "line_number": 384, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 384, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.imshow", "line_number": 388, "usage_type": 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73338806587
import gc from contextlib import contextmanager from dataclasses import dataclass from typing import Any, ContextManager, Iterator import pytest from typing_extensions import Protocol, runtime_checkable from antidote._internal import enforce_subclass_if_possible, Singleton from antidote._internal.utils import CachedMeta from tests.utils import Box @contextmanager def does_not_raise() -> Iterator[None]: yield does_raise = pytest.raises(TypeError, match="(?i).*(isinstance|subclass|implement).*") class DummyProtocol(Protocol): def dummy(self) -> None: ... @runtime_checkable class DummyRuntimeProtocol(Protocol): def dummy(self) -> None: ... class ValidDummy: def dummy(self) -> None: ... class InvalidDummy: pass class SubDummy(ValidDummy): pass @pytest.mark.parametrize( "expectation, sub, tpe", [ (does_not_raise(), ValidDummy, DummyProtocol), (does_not_raise(), ValidDummy, DummyRuntimeProtocol), (does_not_raise(), InvalidDummy, DummyProtocol), (does_raise, InvalidDummy, DummyRuntimeProtocol), (does_raise, InvalidDummy, ValidDummy), (does_not_raise(), SubDummy, ValidDummy), (does_not_raise(), 1, 1), (does_not_raise(), 1, int), (does_not_raise(), int, 1), ], ) def test_enforce_subtype(expectation: ContextManager[Any], sub: type, tpe: type) -> None: with expectation: enforce_subclass_if_possible(sub, tpe) def test_singleton() -> None: class Dummy(Singleton): pass assert Dummy() is Dummy() def test_cached_instances() -> None: @dataclass(eq=True, unsafe_hash=True) class Dummy(metaclass=CachedMeta): __slots__ = ("value", "__weakref__") value: Box[str] def __init__(self, value: Box[str]) -> None: self.value = value def __repr__(self) -> str: return "Dummy" hello = Box("hello") a = Dummy(hello) assert a.value is hello assert Dummy(hello) is a assert Dummy(Box("hello")) is a assert Dummy(Box("Different")) is not a john = Box("John") def f() -> None: Dummy(john) # create instance without keeping a reference to it f() gc.collect() b = Dummy(Box("John")) assert b.value is not john
Finistere/antidote
tests/internal/test_utils.py
test_utils.py
py
2,310
python
en
code
88
github-code
6
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43755872386
''' Measures the square area of colonies in several image files and exports data as an excel sheet. Written by George Walters-Marrah Last updated: 6/26/2019 ''' # import needed packages import colSizeMeasurer as cm import numpy as np import pandas as pd import os.path from os import path import imageio # analyzes several images at processively class analyzer: # initializes the analyzer with all the information it needs def __init__(self, imFolders, imVectors, imStrains, imPlates, imRepNums, imType, firstMask, secondMaskLow, secondMaskHigh, smallSize, largeSize, stdThreshold, control): self._imFolders = imFolders self._imVectors = imVectors self._imStrains = imStrains self._imPlates = imPlates self._imRepNums = imRepNums self._imType = imType self._control = control self._firstMask = firstMask self._secondMaskLow = secondMaskLow self._secondMaskHigh = secondMaskHigh self._smallSize = smallSize self._largeSize = largeSize self._stdThreshold = stdThreshold self._control = control def checkFiletype(self): fileList = [] for folder in range(len(self._imFolders)): imFolder = self._imFolders[folder] for vector in range(len(self._imVectors)): imVector = self._imVectors[vector] for strain in range(len(self._imStrains)): imStrain = self._imStrains[strain] for plate in range(len(self._imPlates)): imPlate = self._imPlates[plate] for repNum in range(len(self._imRepNums)): imRepNum = self._imRepNums[repNum] # Check if the PATH exists filePath = imFolder + '/' + imVector + '_' + imStrain + '_' + imPlate + '_' + imRepNum + self._imType if path.exists(filePath): imCheck = imageio.imread(filePath) dtypeCheck = imCheck.dtype if dtypeCheck != 'uint8': fileList.append(filePath) if len(fileList) == 0: print('Files in folder(s) ' + str(self._imFolders) + ' checked.') else: raise ValueError(str(fileList) + ' must be uint8. Change image file(s) to uint8 then try again.') # defines and gets the size of the control where it will be def getControl(self): data = [] for Folder in range(len(self._imFolders)): imFolder = self._imFolders[Folder] for repNum in range(len(self._imRepNums)): imRepNum = self._imRepNums[repNum] # Check if the PATH exists controlPath = imFolder + '/' + self._control[0] + '_' + self._control[1] + '_' + self._control[2] + '_' + imRepNum + self._imType if path.exists(controlPath): # Analyze data if the PATH exists controlData = cm.measure(imFolder, self._control[0], self._control[1], self._control[2], imRepNum, self._imType, self._firstMask, self._secondMaskLow, self._secondMaskHigh, self._smallSize, self._largeSize, self._stdThreshold, False, True) data.append(controlData[1]) # Decide what to do if PATH does not exist else: check = input('The PATH "' + controlPath + '" does not exist. Do you want to continue? If yes, type Y. If no, type N:') if check == 'Y' or check == 'y': print('PATH ignored.') elif check == 'N' or check == 'n': raise ValueError('Program stopped. Change PATH and try again.') else: doubleCheck = input('Did you mean to put N?:') if doubleCheck == 'Y' or doubleCheck == 'y': raise ValueError('Program stopped. Change PATH and try again.') else: print('PATH ignored.') np_data = np.array(data) print('') print('||| Control created using', self._control[0] + '_' + self._control[1] + '_' + self._control[2], '|||') return np.around(np.mean(np_data),2) # analyzes the data images in a processive manner def analyze(self, control): fin_data = [] colName = [] colMean = [] colMedian = [] colStd = [] colFolder = [] colVector = [] colStrain = [] colPlate = [] colRepNum = [] colData = [] colRatio = [] for folder in range(len(self._imFolders)): imFolder = self._imFolders[folder] for vector in range(len(self._imVectors)): imVector = self._imVectors[vector] for strain in range(len(self._imStrains)): imStrain = self._imStrains[strain] for plate in range(len(self._imPlates)): imPlate = self._imPlates[plate] for repNum in range(len(self._imRepNums)): imRepNum = self._imRepNums[repNum] # Check if the PATH exists dataPath = imFolder + '/' + imVector + '_' + imStrain + '_' + imPlate + '_' + imRepNum + self._imType if path.exists(dataPath): # Analyze data if the PATH exists initial_data = cm.measure(imFolder, imVector, imStrain, imPlate, imRepNum, self._imType, self._firstMask, self._secondMaskLow, self._secondMaskHigh, self._smallSize, self._largeSize, self._stdThreshold, False, True) ratio = np.around(initial_data[1]/control,3) initial_data.append(ratio) fin_data.append(initial_data) # Decide what to do if PATH does not exist else: check = input('The PATH "' + dataPath + '" does not exist. Do you want to continue? If yes, type Y. If no, type N:') if check == 'Y' or check == 'y': print('PATH ignored.') elif check == 'N' or check == 'n': raise ValueError('Program stopped. Change PATH and try again.') else: doubleCheck = input('Did you mean to put N?:') if doubleCheck == 'Y' or doubleCheck == 'y': raise ValueError('Program stopped. Change PATH and try again.') else: print('PATH ignored.') for l in fin_data: colName.append(l[0]) colMean.append(l[1]) colMedian.append(l[2]) colStd.append(l[3]) colFolder.append(l[4]) colVector.append(l[5]) colStrain.append(l[6]) colPlate.append(l[7]) colRepNum.append(l[8]) colData.append(l[9]) colRatio.append(l[10]) all_data = [colName, colMean, colMedian, colStd, colFolder, colVector, colStrain, colPlate, colRepNum, colData, colRatio] return all_data # makes and returns the data as an excel sheet. Can also combine data if you choose def makeData(exportNameSum, exportNameRaw, listRawData): # combines data if there is more than one dataset if len(listRawData) > 1: rawData = [[],[],[],[],[],[],[],[],[],[],[]] for data in listRawData: for index in range(len(rawData)): rawData[index] = rawData[index] + data[index] else: rawData = listRawData[0] # Make the dataframe of summary data dicSum = {'imName': rawData[0], 'ratio': rawData[10], 'mean': rawData[1], 'median': rawData[2], 'standardDeviation': rawData[3], 'folder': rawData[4], 'vector': rawData[5], 'strain': rawData[6], 'plate': rawData[7], 'repetitionNumber': rawData[8], 'rawData': rawData[9]} finalDataSum = pd.DataFrame(dicSum) colsSum = ['imName', 'ratio', 'mean', 'median', 'standardDeviation', 'folder', 'vector', 'strain', 'plate', 'repetitionNumber', 'rawData'] finalDataSum = finalDataSum[colsSum] print('Summary Data') print(finalDataSum.iloc[:, 0:5]) # folders where raw data(size of every individual colony) will be stored imNameRaw = [] measRaw = [] amountRaw = [] folderRaw = [] # creates the raw data for data in range(len(rawData[9])): for value in rawData[9][data]: imNameRaw.append(rawData[0][data]) measRaw.append(value) amountRaw.append(len(rawData[9][data])) folderRaw.append(rawData[4][data]) dicRaw = {'imName': imNameRaw, 'area': measRaw, 'dataPointNum': amountRaw, 'folder': folderRaw} finalDataRaw = pd.DataFrame(dicRaw) colsRaw = ['imName', 'area', 'dataPointNum', 'folder'] finalDataRaw = finalDataRaw[colsRaw] # Write the data to the excel sheet excelFileSum = exportNameSum + '.xlsx' excelFileRaw = exportNameRaw + '.xlsx' finalDataSum.to_excel(excelFileSum) finalDataRaw.to_excel(excelFileRaw) print('') print('Check folder to see new ' + exportNameSum + ' and ' + exportNameRaw + ' file.') def main(): # Input info here Folders = [] imVectors = [] imStrains = [] imPlates = [] imRepNums = [] imType = '' control = [] col = analyzer(Folders, imVectors, imStrains, imPlates, imRepNums, imType, 190, 50, 185, 2, 235, 1.5, control) control_size = col.getControl() data = col.analyze(control_size) makeData('', '', [data]) if __name__ == '__main__': main()
gwmarrah/colony-measurer
colSizeAnalyzer.py
colSizeAnalyzer.py
py
10,361
python
en
code
1
github-code
6
[ { "api_name": "os.path.exists", "line_number": 52, "usage_type": "call" }, { "api_name": "os.path", "line_number": 52, "usage_type": "name" }, { "api_name": "imageio.imread", "line_number": 53, "usage_type": "call" }, { "api_name": "os.path.exists", "line_number": 73, "usage_type": "call" }, { "api_name": "os.path", "line_number": 73, "usage_type": "name" }, { "api_name": "colSizeMeasurer.measure", "line_number": 75, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 90, "usage_type": "call" }, { "api_name": "numpy.around", "line_number": 93, "usage_type": "call" }, { "api_name": "numpy.mean", "line_number": 93, "usage_type": "call" }, { "api_name": "os.path.exists", "line_number": 124, "usage_type": "call" }, { "api_name": "os.path", "line_number": 124, "usage_type": "name" }, { "api_name": "colSizeMeasurer.measure", "line_number": 126, "usage_type": "call" }, { "api_name": "numpy.around", "line_number": 127, "usage_type": "call" }, { "api_name": "pandas.DataFrame", "line_number": 182, "usage_type": "call" }, { "api_name": "pandas.DataFrame", "line_number": 206, "usage_type": "call" } ]
23699044088
from datetime import date, timedelta from bs4 import BeautifulSoup import pandas as pd import requests import os import warnings warnings.filterwarnings('ignore') def is_workday(day: date) -> bool: """Функция определяет рабочий день или выходной согласно рабочему календарю. True - рабочий False - выходной""" res = requests.get(f"https://isdayoff.ru/{day.strftime('%Y%m%d')}") return not bool(int(res.text)) def get_rate_df(start_date: date, end_date: date) -> (pd.DataFrame, int): """Функция для формирования датафрейма со ставками ЦБ в указанном временном диапазоне """ # Выполняем запрос на сайт ЦБ и вытягиваем данные о ключевых ставках в указанный период url = f"https://www.cbr.ru/hd_base/KeyRate/?UniDbQuery.Posted=True&UniDbQuery.From={start_date.strftime('%d.%m.%Y')}&UniDbQuery.To={end_date.strftime('%d.%m.%Y')}" full_page = requests.get(url) soup = BeautifulSoup(full_page.content, 'html.parser') res = soup.find_all("td") date_list = [] rate_list = [] [rate_list.append(float(res[i].text.replace(',', '.'))) if i % 2 != 0 else date_list.append(res[i].text) for i in range(len(res))] # Для удобства работы формируем датафрейм df = pd.DataFrame() df['date'] = date_list df['rate'] = rate_list df['date'] = pd.to_datetime(df['date'], dayfirst=True) # Данные с сайта ЦБ имеют пропуски в выходные дни. Нам необходимо добавить пропущенные даты, а пустые ячейки # со ставками заполняем последним актуальным значением df_date = pd.DataFrame(pd.date_range(start=df['date'].min(), end=df['date'].max()), columns=['date']) comm_df = pd.merge(df_date, df, on='date', how='left') comm_df['rate'] = comm_df['rate'].ffill() comm_df['is_work_day'] = comm_df['date'].map(is_workday) return comm_df, full_page.status_code def rate_bd_update(first_date: date, last_date: date) -> int or None: """Функция для обновления базы ставок ЦБ, если запрошенный диапазон дат отсутствует""" status_code = None # Если файла с базой ставок нет, то берем весь диапазон и результат записываем в файл if not os.path.exists('tables'): os.mkdir('tables') if not os.path.exists('tables/rate_db.csv'): df, status_code = get_rate_df(start_date=first_date, end_date=last_date) df.to_csv('tables/rate_db.csv', index=False) return status_code # Если файла с базой ставок - есть, подгружаем только необходимый диапазон df_rate = pd.read_csv('tables/rate_db.csv', parse_dates=['date']) max_date = df_rate['date'].max() min_date = df_rate['date'].min() if first_date < min_date: df, status_code = get_rate_df(start_date=first_date, end_date=min_date - timedelta(days=1)) df_rate = pd.concat((df_rate, df), axis=0, ignore_index=True) df_rate = df_rate.sort_values('date') df_rate = df_rate.reset_index(drop=True) df_rate.to_csv('tables/rate_db.csv', index=False) if last_date > max_date: df, status_code = get_rate_df(start_date=max_date + timedelta(days=1), end_date=last_date) df_rate = pd.concat((df_rate, df), axis=0, ignore_index=True) df_rate = df_rate.sort_values('date') df_rate = df_rate.reset_index(drop=True) df_rate.to_csv('tables/rate_db.csv', index=False) return status_code def calc_pay_before_day(sale_date: date, days_for_pay: str): """ Функция - агрегатор. Определяет тип дней по договору(рабочие/календарные) и вызывает соответсвующую функцию. :param sale_date: дата продажи или оказания услуги. :param days_for_pay: количество и тип дней в виде строки (Например: 20 календарных). :return: - дату последнего дня отсрочки или - строку 'Дата не определена' в случае некорректных входных данных """ count_days, type_days = days_for_pay.strip().split() if type_days == 'рабочих': return pay_before_for_workdays(sale_date=sale_date, count_days=int(count_days)) elif type_days == 'календарных': return pay_before_for_cal_days(sale_date=sale_date, count_days=int(count_days)) else: return 'Дата не определена' def pay_before_for_cal_days(sale_date: date, count_days: int) -> date: """ Функция расчета последнего дня отсрочки платежа с учетом календарных дней. :param sale_date: дата продажи или оказания услуги. :param count_days: количество дней по договору. :return: дата последнего дня отсрочки. """ rate_df = pd.read_csv('tables/rate_db.csv', parse_dates=['date']) temp_df = rate_df[rate_df['date'] > sale_date].reset_index(drop=True) day_index = count_days - 1 while not temp_df['is_work_day'][day_index]: day_index += 1 return temp_df['date'][day_index] def pay_before_for_workdays(sale_date: date, count_days: int) -> date: """ Функция расчета последнего дня отсрочки платежа с учетом только рабочих дней. :param sale_date: дата продажи или оказания услуги. :param count_days: количество дней по договору. :return: дата последнего дня отсрочки. """ rate_df = pd.read_csv('tables/rate_db.csv', parse_dates=['date']) return rate_df[(rate_df['date'] > sale_date) & (rate_df['is_work_day'])].reset_index(drop=True)['date'][count_days - 1] def is_leap(date: pd.Timestamp) -> int: year = date.year if year % 4 == 0 and (year % 100 != 0 or year % 400 == 0): return 366 else: return 365 def calc_penalty(row): return round((row['sum'] * (row['rate']/100) * row['delay_period']) / row['day_in_year'], 2) def date2str(date): return date.strftime('%d.%m.%Y') def bild_and_save_final(df: pd.DataFrame, name: str): """Функция выполнят преобразование итогового датафрейма для получения формата в соответствии с требованиями заказчика""" name = name.split('.')[0] final_col = ['document', 'sum', 'sale_date', 'pay_before', 'payment_date', 'delay_period', 'rate', 'penalty'] col_with_dubl = ['document', 'sum', 'sale_date', 'pay_before', 'payment_date'] # Отбираем только необходимые колонки final_df = df.copy()[final_col] # Переводим формат даты в строку for col in ['sale_date', 'pay_before', 'payment_date']: final_df[col] = final_df[col].map(date2str) # Меняем дубликаты на пустые ячейки final_df[col_with_dubl] = final_df[col_with_dubl].mask(final_df[col_with_dubl].duplicated(), "") final_df = final_df.reset_index().rename(columns={'index': 'num_row'}) final_df.loc[len(final_df)] = ['', '', 'Итого:', '', '', '', '', '', final_df['penalty'].sum()] final_df = final_df.rename(columns={'num_row': '№ строки', 'document': 'док-ты о реализации(акт, накладная, УПД)', 'sum': 'Сумма долга', 'sale_date': 'Дата реализации', 'pay_before': 'Оплатить до', 'payment_date': 'Дата оплаты', 'delay_period': 'Срок просрочки', 'rate': 'Ставка ЦБ', 'penalty': 'Неустойка'}) final_df.to_excel(f'tables/{name}_result.xlsx', index=False) return os.path.abspath(f'tables/{name}_result.xlsx')
garick161/penalty_calculator
functions.py
functions.py
py
8,809
python
ru
code
1
github-code
6
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70281340028
import argparse from pathlib import Path import pdb from bert_score import BERTScorer import numpy as np from util import read_test_data, read_generations, match_data if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pred-path", type=str, required=True) parser.add_argument("--ann-path", type=str, required=True) args = parser.parse_args() questions, annotations = read_test_data(Path(args.ann_path)) pred_data = read_generations(Path(args.pred_path)) pairs = match_data(questions, annotations, pred_data, tokenize=False) scorer = BERTScorer(lang='en', device='cuda:0') refs, preds = zip(*pairs) bs = scorer.score(refs, preds) scores = [] for batch in bs: for score in batch: scores.append(score) avg_score = np.mean(scores) print(f"prediction file: {args.pred_path}") print(f"BERT Score: {avg_score:.2f}")
esteng/ambiguous_vqa
models/eval/my_bert_score.py
my_bert_score.py
py
932
python
en
code
5
github-code
6
[ { "api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call" }, { "api_name": "util.read_test_data", "line_number": 18, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 18, "usage_type": "call" }, { "api_name": "util.read_generations", "line_number": 19, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 19, "usage_type": "call" }, { "api_name": "util.match_data", "line_number": 21, "usage_type": "call" }, { "api_name": "bert_score.BERTScorer", "line_number": 23, "usage_type": "call" }, { "api_name": "numpy.mean", "line_number": 30, "usage_type": "call" } ]
8967390830
#!/opt/anaconda3/envs/PECANS-env/bin/python import argparse import os import numpy as np from pecans.ensembles.api import EnsembleRunner from pecans.utilities.config import load_config_file _mydir = os.path.abspath(os.path.realpath(os.path.dirname(__file__))) config_file = os.path.join(_mydir, 'pecans_config.cfg') def name_first_order_output_files(index, **config_opts): lifetime_hours = config_opts['CHEMISTRY/mechanism_opts/lifetime_seconds'] / 3600 emissions_width_km = config_opts['EMISSIONS/emission_opts/width_x'] / 1000 return 'pecans_ens_tau-{}h_emwidth-{}km'.format(lifetime_hours, emissions_width_km) def name_first_order_winds_output_files(index, **config_opts): winds = config_opts['TRANSPORT/wind_speeds/x'] return 'pecans_ens_windspeed_{}m_s'.format(winds) def name_two_phases_first_order_output_files(index, **config_opts): first_lifetime_hours = config_opts['CHEMISTRY/mechanism_opts/first_lifetime_seconds'] / 3600 second_lifetime_horus = config_opts['CHEMISTRY/mechanism_opts/second_lifetime_seconds'] / 3600 first_phase_width = config_opts['CHEMISTRY/mechanism_opts/first_phase_width'] / 1000 emissions_width_km = config_opts['EMISSIONS/emission_opts/width_x'] / 1000 return 'pecans_ens_first_tau-{}h_second_tau-{}h_fpwidth-{}km_emwidth-{}km'.format(first_lifetime_hours, second_lifetime_horus, first_phase_width, emissions_width_km) def sims_first_order_run_winds(): # We want lifetimes that vary from 1-9 hours. This covers about the most extreme values we'd expect for summer NOx # lifetime winds= np.arange(3, 11, 1) ens = EnsembleRunner(config_file, ensemble_variables={'TRANSPORT/wind_speeds/x': winds}, ensemble_mode='combinations', save_in_individual_dirs=False, save_final_output_only=True, member_naming_fxn=name_first_order_winds_output_files, root_output_dir=os.path.join(_mydir, '../../MATLAB/PAN_Data', 'Workspaces', 'PECANS', 'lifetime-ensemble')) ens.run() def sims_first_order_run(): # We want lifetimes that vary from 1-9 hours. This covers about the most extreme values we'd expect for summer NOx # lifetime taus = np.arange(3600, 9*3600+1, 3600) # We also want to test what happens when emissions widths are similar or greater than lifetimes. So we'll calculate # emissions widths equal to each expected lifetime config = load_config_file(config_file) winds = config.get('TRANSPORT', 'wind_speeds') x_wind = winds['x'] widths = taus * x_wind widths = np.concatenate(([3000], widths)) # add a smaller width as an extra test ens = EnsembleRunner(config_file, ensemble_variables={'CHEMISTRY/mechanism_opts/lifetime_seconds': taus, 'EMISSIONS/emission_opts/width_x': widths}, ensemble_mode='combinations', save_in_individual_dirs=False, save_final_output_only=True, member_naming_fxn=name_first_order_output_files, root_output_dir=os.path.join(_mydir, '../../MATLAB/PAN_Data', 'Workspaces', 'PECANS', 'lifetime-ensemble')) ens.run() def sims_two_phases_first_order_run(): first_tau = np.arange(3600, 9*3600, 3600) second_tau = np.arange(3600, 9*3600, 3600) first_phase_width = np.arange(20*1000, 100*1000, 10*1000) config = load_config_file(config_file) winds = config.get('TRANSPORT', 'wind_speeds') x_wind = winds['x'] widths = first_tau * x_wind widths = np.concatenate(([3000], widths)) # add a smaller width as an extra test #widths = [20000, 30000] ens = EnsembleRunner(config_file, ensemble_variables={'CHEMISTRY/mechanism_opts/first_lifetime_seconds': first_tau, 'CHEMISTRY/mechanism_opts/second_lifetime_seconds': second_tau, 'CHEMISTRY/mechanism_opts/first_phase_width': first_phase_width, 'EMISSIONS/emission_opts/width_x': widths}, ensemble_mode='combinations', save_in_individual_dirs=False, save_final_output_only=True, member_naming_fxn=name_two_phases_first_order_output_files, root_output_dir=os.path.join(_mydir, '../../MATLAB/PAN_Data', 'Workspaces', 'PECANS', 'lifetime-ensemble-twophases')) ens.run() def main(): parser = argparse.ArgumentParser(description='Choose one of the chemical solvers') parser.add_argument('solver', type=str, help='What the chemical solver is. Default is "first_order"') args = parser.parse_args() if args.solver == 'first_order': sims_first_order_run_winds() elif args.solver == 'two_phases_first_order': sims_two_phases_first_order_run() else: print("The chemical solver is not implemented.") quit() if __name__ == '__main__': main()
ChiLi90/PECANS-PMOx
run_pecans_sims.py
run_pecans_sims.py
py
5,605
python
en
code
0
github-code
6
[ { "api_name": "os.path.abspath", "line_number": 10, "usage_type": "call" }, { "api_name": "os.path", "line_number": 10, "usage_type": "attribute" }, { "api_name": "os.path.realpath", "line_number": 10, "usage_type": "call" }, { "api_name": "os.path.dirname", "line_number": 10, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 11, "usage_type": "call" }, { "api_name": "os.path", "line_number": 11, "usage_type": "attribute" }, { "api_name": "numpy.arange", "line_number": 37, "usage_type": "call" }, { "api_name": "pecans.ensembles.api.EnsembleRunner", "line_number": 39, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 45, "usage_type": "call" }, { "api_name": "os.path", "line_number": 45, "usage_type": "attribute" }, { "api_name": "numpy.arange", "line_number": 54, "usage_type": "call" }, { "api_name": "pecans.utilities.config.load_config_file", "line_number": 58, "usage_type": "call" }, { "api_name": "numpy.concatenate", "line_number": 62, "usage_type": "call" }, { "api_name": "pecans.ensembles.api.EnsembleRunner", "line_number": 64, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 71, "usage_type": "call" }, { "api_name": "os.path", "line_number": 71, "usage_type": "attribute" }, { "api_name": "numpy.arange", "line_number": 77, "usage_type": "call" }, { "api_name": "numpy.arange", "line_number": 78, "usage_type": "call" }, { "api_name": "numpy.arange", "line_number": 79, "usage_type": "call" }, { "api_name": "pecans.utilities.config.load_config_file", "line_number": 80, "usage_type": "call" }, { "api_name": "numpy.concatenate", "line_number": 84, "usage_type": "call" }, { "api_name": "pecans.ensembles.api.EnsembleRunner", "line_number": 87, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 96, "usage_type": "call" }, { "api_name": "os.path", "line_number": 96, "usage_type": "attribute" }, { "api_name": "argparse.ArgumentParser", "line_number": 102, "usage_type": "call" } ]
30162915076
from django import forms from .models import publishing class CreateAdForm(forms.ModelForm): class Meta: model = publishing fields = ( 'title', 'type', 'brand', 'model', 'category', 'year', 'transmission', 'milage', 'fuel', 'engine', 'image1', 'image2', 'image3', 'image4', 'image5', 'description', 'condition', 'price', 'tel', 'city', 'address', )
DenukaSandeepa/Avehiz-Project
publishing/forms.py
forms.py
py
556
python
en
code
0
github-code
6
[ { "api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute" }, { "api_name": "django.forms", "line_number": 4, "usage_type": "name" }, { "api_name": "models.publishing", "line_number": 6, "usage_type": "name" } ]
8731736072
import numpy as np import matplotlib.pyplot as plt a = np.array([1, 2, 3]) print(a) plt.plot([1, 2, 3], [2, 4, 6]) plt.show() for num in [1, 2, 3, 4]: print(num) def sqaure(x): return x**2
SMaC-3/GitProject_test
hello.py
hello.py
py
204
python
en
code
0
github-code
6
[ { "api_name": "numpy.array", "line_number": 5, "usage_type": "call" }, { "api_name": "matplotlib.pyplot.plot", "line_number": 8, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name" }, { "api_name": "matplotlib.pyplot.show", "line_number": 9, "usage_type": "call" }, { "api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name" } ]
17748417007
from django.test import TestCase from .forms import MakeBooking # Create a test class for the MakeBooking form class MakeBookingFormTest(TestCase): # Test when the form is valid def test_make_booking_form_is_valid(self): form = MakeBooking(data={ 'date': '2022-10-25', 'time': '14:00', 'party_of': 4 }) self.assertTrue(form.is_valid()) # Check that the form is valid # Test when the form has no data (empty form) def test_make_booking_form_no_data(self): form = MakeBooking(data={}) # Create an empty form self.assertFalse(form.is_valid()) # Check that the form is invalid # Check that it has three errors self.assertEquals(len(form.errors), 3) # Test when the form has invalid 'party_of' data (party size is 0) def test_make_booking_form_invalid_party_of_data(self): form = MakeBooking(data={ 'date': '2022-10-25', 'time': '14:00', 'party_of': 0 }) self.assertFalse(form.is_valid()) # Check that the form is invalid # Check that 'party_of' is in the list of errors self.assertIn('party_of', form.errors)
JustinFourie1993/tables
website/test_forms.py
test_forms.py
py
1,209
python
en
code
0
github-code
6
[ { "api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name" }, { "api_name": "forms.MakeBooking", "line_number": 11, "usage_type": "call" }, { "api_name": "forms.MakeBooking", "line_number": 20, "usage_type": "call" }, { "api_name": "forms.MakeBooking", "line_number": 27, "usage_type": "call" } ]
38473764222
from pathlib import Path import pickle import pandas as pd import numpy as np import json import torch import random import torch from util.tasksim_args import TaskSimArgs BASE_RESULTS_PATH = './results' def get_full_results_dir(args: TaskSimArgs): run_id = args.get_run_id() if args.results_dir == None: results_dir = Path(run_id) else: results_dir = Path(args.results_dir) / run_id return Path(BASE_RESULTS_PATH) / results_dir def get_model_state_dict(args, task_id): results_dir = get_full_results_dir(args) path = results_dir / f'fe_ckpt_task_{task_id}.pt' path = Path(str(path).replace('nmc', 'linear')) if path.exists(): return torch.load(path) else: return None def save_model(args, state_dict, task_id): results_dir = get_full_results_dir(args) if not results_dir.exists(): results_dir.mkdir(parents=True) torch.save(state_dict, results_dir / f'fe_ckpt_task_{task_id}.pt') def save_results(args: TaskSimArgs, results, embeddings): results_dir = get_full_results_dir(args) if not results_dir.exists(): results_dir.mkdir(parents=True) with open(results_dir / 'config.txt', 'w') as config: json.dump(vars(args), config, indent=2) if results is not None: results.to_csv(results_dir / 'results.csv', float_format='%.5f') if args.save_embeddings and embeddings is not None and len(embeddings) > 0: torch.save(embeddings, results_dir / 'embeddings.pt') def set_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(seed) random.seed(seed)
salemohamedo/tasksim
util/utils.py
utils.py
py
1,757
python
en
code
1
github-code
6
[ { "api_name": "util.tasksim_args.TaskSimArgs", "line_number": 13, "usage_type": "name" }, { "api_name": "pathlib.Path", "line_number": 16, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 18, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 19, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 24, "usage_type": "call" }, { "api_name": "torch.load", "line_number": 26, "usage_type": "call" }, { "api_name": "torch.save", "line_number": 34, "usage_type": "call" }, { "api_name": "util.tasksim_args.TaskSimArgs", "line_number": 36, "usage_type": "name" }, { "api_name": "json.dump", "line_number": 42, "usage_type": "call" }, { "api_name": "torch.save", "line_number": 46, "usage_type": "call" }, { "api_name": "torch.manual_seed", "line_number": 51, "usage_type": "call" }, { "api_name": "torch.cuda.manual_seed", "line_number": 52, "usage_type": "call" }, { "api_name": "torch.cuda", "line_number": 52, "usage_type": "attribute" }, { "api_name": "torch.cuda.manual_seed_all", "line_number": 53, "usage_type": "call" }, { "api_name": "torch.cuda", "line_number": 53, "usage_type": "attribute" }, { "api_name": "torch.backends", "line_number": 54, "usage_type": "attribute" }, { "api_name": "torch.backends", "line_number": 55, "usage_type": "attribute" }, { "api_name": "numpy.random.seed", "line_number": 56, "usage_type": "call" }, { "api_name": "numpy.random", "line_number": 56, "usage_type": "attribute" }, { "api_name": "random.seed", "line_number": 57, "usage_type": "call" } ]
5693104002
#!/usr/bin/env python # coding: utf-8 # In[1]: import requests city = input("City name: ") key = 'http://api.openweathermap.org/data/2.5/weather?q={}&appid=2e535070ac9219e3c58f19ac7227c197&q='.format(city) res = requests.get(key) data = res.json() print(res) print(data) # In[ ]:
tkeady/Software-Engineering
weather api.py
weather api.py
py
293
python
en
code
0
github-code
6
[ { "api_name": "requests.get", "line_number": 13, "usage_type": "call" } ]
5356434475
import numpy as np from scipy.spatial import distance_matrix from scipy.sparse import csr_matrix from scipy.sparse.csgraph import connected_components def knn_dist(d_matrix, k): D_knn = np.zeros(d_matrix.shape) #get the indices of the k lowest values and set these indices in D_knn to the same values as in D #the rest stays 0 for i, row in enumerate(d_matrix): index = row.argsort()[:k] D_knn[i][index] = row[index] return D_knn # create 0,1 graph from k_nearest neighbour matrix def create_graph(knn_matrix): graph = knn_matrix > 0 graph = graph*1 return graph #compute the k_nearest neighbour matrix with the new connections def tuned_knn(d_matrix, n_components, labels, knn_d): #get individual combinations comb = [(i,j) for i in range(n_components) for j in range(i,n_components) if i != j] tuned_knn = np.copy(knn_d) dist = [] for c in comb: dist.append(component_dist(labels, d_matrix, c[0], c[1])) dist = sorted(dist, key=lambda x: x[0]) for i in range(n_components-1): l,j = dist[i][1] tuned_knn[l,j] = dist[i][0] return tuned_knn #calculate the shortest distance between the components c1 and c2 def component_dist(labels, d_matrix, c1, c2): l1 = [i for i,j in enumerate(labels) if j==c1] l2 = [i for i,j in enumerate(labels) if j==c2] n,n = d_matrix.shape temp_d = d_matrix + np.eye(n)*10**20 #avoid that the diagonal is measured as shortest distance dist = 100000 lab = 0 for i in l1: temp_dist = min(temp_d[i][l2]) ind = np.argmin(temp_d[i][l2]) if temp_dist < dist: dist = temp_dist lab = [i,l2[ind]] return dist, lab #check for components in the given graph according to the k_nearest neighbour matrix def check_components(knn_matrix): graph = create_graph(knn_matrix) graph = csr_matrix(graph) n_components, labels = connected_components(csgraph=graph, directed=False, return_labels=True) return n_components, labels
Tobi-r9/assignment1
isomap.py
isomap.py
py
2,055
python
en
code
0
github-code
6
[ { "api_name": "numpy.zeros", "line_number": 8, "usage_type": "call" }, { "api_name": "numpy.copy", "line_number": 27, "usage_type": "call" }, { "api_name": "numpy.eye", "line_number": 43, "usage_type": "call" }, { "api_name": "numpy.argmin", "line_number": 48, "usage_type": "call" }, { "api_name": "scipy.sparse.csr_matrix", "line_number": 58, "usage_type": "call" }, { "api_name": "scipy.sparse.csgraph.connected_components", "line_number": 59, "usage_type": "call" } ]
16543455717
import ast import fnmatch import os from nuitka.__past__ import iter_modules from nuitka.importing.Importing import locateModule from nuitka.importing.Recursion import decideRecursion from nuitka.plugins.PluginBase import NuitkaPluginBase from nuitka.utils.ModuleNames import ModuleName from nuitka.utils.Utils import isMacOS, isWin32Windows from nuitka.utils.Yaml import getYamlPackageConfiguration class NuitkaPluginImplicitImports(NuitkaPluginBase): plugin_name = "implicit-imports" plugin_desc = ( "Provide implicit imports of package as per package configuration files." ) def __init__(self): self.config = getYamlPackageConfiguration() self.lazy_loader_usages = {} @staticmethod def isAlwaysEnabled(): return True def _resolveModulePattern(self, pattern): parts = pattern.split(".") current = None for count, part in enumerate(parts): if not part: self.sysexit( "Error, invalid pattern with empty parts used '%s'." % pattern ) # TODO: Checking for shell pattern should be done in more places and shared code. if "?" in part or "*" in part or "[" in part: if current is None: self.sysexit( "Error, cannot use pattern for first part '%s'." % pattern ) module_filename = self.locateModule( module_name=ModuleName(current), ) for sub_module in iter_modules([module_filename]): if not fnmatch.fnmatch(sub_module.name, part): continue if count == len(parts) - 1: yield current.getChildNamed(sub_module.name) else: child_name = current.getChildNamed(sub_module.name).asString() for value in self._resolveModulePattern( child_name + "." + ".".join(parts[count + 1 :]) ): yield value return else: if current is None: current = ModuleName(part) else: current = current.getChildNamed(part) yield current def _handleImplicitImportsConfig(self, module, config): full_name = module.getFullName() for dependency in config.get("depends", ()): if dependency.startswith("."): if ( module.isUncompiledPythonPackage() or module.isCompiledPythonPackage() ): dependency = full_name.getChildNamed(dependency[1:]).asString() elif full_name.getPackageName() is None: # Not a package, potentially a naming conflict, when # compiling with "--module" something that matches a PyPI # name. continue else: dependency = full_name.getSiblingNamed(dependency[1:]).asString() if "*" in dependency or "?" in dependency: for resolved in self._resolveModulePattern(dependency): yield resolved else: yield dependency def _getImportsByFullname(self, module, full_name): """Provides names of modules to imported implicitly.""" # Many variables, branches, due to the many cases, pylint: disable=too-many-branches,too-many-statements # Checking for config, but also allowing fall through. for entry in self.config.get(full_name, section="implicit-imports"): if self.evaluateCondition( full_name=full_name, condition=entry.get("when", "True") ): for dependency in self._handleImplicitImportsConfig( config=entry, module=module ): yield dependency # Support for both pycryotodome (module name Crypto) and pycyptodomex (module name Cryptodome) if full_name.hasOneOfNamespaces("Crypto", "Cryptodome"): crypto_module_name = full_name.getTopLevelPackageName() if full_name == crypto_module_name + ".Cipher._mode_ofb": yield crypto_module_name + ".Cipher._raw_ofb" elif full_name == crypto_module_name + ".Cipher.CAST": yield crypto_module_name + ".Cipher._raw_cast" elif full_name == crypto_module_name + ".Cipher.DES3": yield crypto_module_name + ".Cipher._raw_des3" elif full_name == crypto_module_name + ".Cipher.DES": yield crypto_module_name + ".Cipher._raw_des" elif full_name == crypto_module_name + ".Cipher._mode_ecb": yield crypto_module_name + ".Cipher._raw_ecb" elif full_name == crypto_module_name + ".Cipher.AES": yield crypto_module_name + ".Cipher._raw_aes" yield crypto_module_name + ".Cipher._raw_aesni" yield crypto_module_name + ".Util._cpuid" elif full_name == crypto_module_name + ".Cipher._mode_cfb": yield crypto_module_name + ".Cipher._raw_cfb" elif full_name == crypto_module_name + ".Cipher.ARC2": yield crypto_module_name + ".Cipher._raw_arc2" elif full_name == crypto_module_name + ".Cipher.DES3": yield crypto_module_name + ".Cipher._raw_des3" elif full_name == crypto_module_name + ".Cipher._mode_ocb": yield crypto_module_name + ".Cipher._raw_ocb" elif full_name == crypto_module_name + ".Cipher._EKSBlowfish": yield crypto_module_name + ".Cipher._raw_eksblowfish" elif full_name == crypto_module_name + ".Cipher.Blowfish": yield crypto_module_name + ".Cipher._raw_blowfish" elif full_name == crypto_module_name + ".Cipher._mode_ctr": yield crypto_module_name + ".Cipher._raw_ctr" elif full_name == crypto_module_name + ".Cipher._mode_cbc": yield crypto_module_name + ".Cipher._raw_cbc" elif full_name == crypto_module_name + ".Util.strxor": yield crypto_module_name + ".Util._strxor" elif full_name == crypto_module_name + ".Util._cpu_features": yield crypto_module_name + ".Util._cpuid_c" elif full_name == crypto_module_name + ".Hash.BLAKE2s": yield crypto_module_name + ".Hash._BLAKE2s" elif full_name == crypto_module_name + ".Hash.BLAKE2b": yield crypto_module_name + ".Hash._BLAKE2b" elif full_name == crypto_module_name + ".Hash.SHA1": yield crypto_module_name + ".Hash._SHA1" elif full_name == crypto_module_name + ".Hash.SHA224": yield crypto_module_name + ".Hash._SHA224" elif full_name == crypto_module_name + ".Hash.SHA256": yield crypto_module_name + ".Hash._SHA256" elif full_name == crypto_module_name + ".Hash.SHA384": yield crypto_module_name + ".Hash._SHA384" elif full_name == crypto_module_name + ".Hash.SHA512": yield crypto_module_name + ".Hash._SHA512" elif full_name == crypto_module_name + ".Hash.MD2": yield crypto_module_name + ".Hash._MD2" elif full_name == crypto_module_name + ".Hash.MD4": yield crypto_module_name + ".Hash._MD4" elif full_name == crypto_module_name + ".Hash.MD5": yield crypto_module_name + ".Hash._MD5" elif full_name == crypto_module_name + ".Hash.keccak": yield crypto_module_name + ".Hash._keccak" elif full_name == crypto_module_name + ".Hash.RIPEMD160": yield crypto_module_name + ".Hash._RIPEMD160" elif full_name == crypto_module_name + ".Hash.Poly1305": yield crypto_module_name + ".Hash._poly1305" elif full_name == crypto_module_name + ".Protocol.KDF": yield crypto_module_name + ".Cipher._Salsa20" yield crypto_module_name + ".Protocol._scrypt" elif full_name == crypto_module_name + ".Cipher._mode_gcm": yield crypto_module_name + ".Hash._ghash_clmul" yield crypto_module_name + ".Hash._ghash_portable" yield crypto_module_name + ".Util._galois" elif full_name == crypto_module_name + ".Cipher.Salsa20": yield crypto_module_name + ".Cipher._Salsa20" elif full_name == crypto_module_name + ".Cipher.ChaCha20": yield crypto_module_name + ".Cipher._chacha20" elif full_name == crypto_module_name + ".PublicKey.ECC": yield crypto_module_name + ".PublicKey._ec_ws" yield crypto_module_name + ".PublicKey._ed25519" yield crypto_module_name + ".PublicKey._ed448" elif full_name == crypto_module_name + ".Cipher.ARC4": yield crypto_module_name + ".Cipher._ARC4" elif full_name == crypto_module_name + ".Cipher.PKCS1_v1_5": yield crypto_module_name + ".Cipher._pkcs1_decode" elif full_name == crypto_module_name + ".Math._IntegerCustom": yield crypto_module_name + ".Math._modexp" elif full_name in ("pynput.keyboard", "pynput.mouse"): if isMacOS(): yield full_name.getChildNamed("_darwin") elif isWin32Windows(): yield full_name.getChildNamed("_win32") else: yield full_name.getChildNamed("_xorg") elif full_name == "cryptography": yield "_cffi_backend" elif full_name == "bcrypt._bcrypt": yield "_cffi_backend" def getImplicitImports(self, module): full_name = module.getFullName() # TODO: This code absolutely doesn't belong here. # Read the .pyi file, and provide as implicit dependency. if module.isPythonExtensionModule(): for used_module_name in module.getPyIModuleImportedNames(): yield used_module_name if full_name == "pkg_resources.extern": # TODO: A package specific lookup of compile time "pkg_resources.extern" could # be done here, but this might be simpler to hardcode for now. Once we have # the infrastructure to ask a module that after optimization, we should do # that instead, as it will not use a separate process. for part in ( "packaging", "pyparsing", "appdirs", "jaraco", "importlib_resources", "more_itertools", "six", "platformdirs", ): yield "pkg_resources._vendor." + part for item in self._getImportsByFullname(module=module, full_name=full_name): yield item def _getPackageExtraScanPaths(self, package_dir, config): for config_package_dir in config.get("package-dirs", ()): yield os.path.normpath(os.path.join(package_dir, "..", config_package_dir)) yield package_dir for config_package_name in config.get("package-paths", ()): module_filename = self.locateModule(config_package_name) if module_filename is not None: if os.path.isfile(module_filename): yield os.path.dirname(module_filename) else: yield module_filename def getPackageExtraScanPaths(self, package_name, package_dir): for entry in self.config.get(package_name, section="import-hacks"): if self.evaluateCondition( full_name=package_name, condition=entry.get("when", "True") ): for item in self._getPackageExtraScanPaths( package_dir=package_dir, config=entry ): yield item def _getModuleSysPathAdditions(self, module_name, config): module_filename = self.locateModule(module_name) if os.path.isfile(module_filename): module_filename = yield os.path.dirname(module_filename) for relative_path in config.get("global-sys-path", ()): candidate = os.path.abspath(os.path.join(module_filename, relative_path)) if os.path.isdir(candidate): yield candidate def getModuleSysPathAdditions(self, module_name): for entry in self.config.get(module_name, section="import-hacks"): if self.evaluateCondition( full_name=module_name, condition=entry.get("when", "True") ): for item in self._getModuleSysPathAdditions( module_name=module_name, config=entry ): yield item def onModuleSourceCode(self, module_name, source_filename, source_code): if module_name == "numexpr.cpuinfo": # We cannot intercept "is" tests, but need it to be "isinstance", # so we patch it on the file. TODO: This is only temporary, in # the future, we may use optimization that understands the right # hand size of the "is" argument well enough to allow for our # type too. source_code = source_code.replace( "type(attr) is types.MethodType", "isinstance(attr, types.MethodType)" ) if module_name == "site": if source_code.startswith("def ") or source_code.startswith("class "): source_code = "\n" + source_code source_code = """\ __file__ = (__nuitka_binary_dir + '%ssite.py') if '__nuitka_binary_dir' in dict(__builtins__ ) else '<frozen>';%s""" % ( os.path.sep, source_code, ) # Debian stretch site.py source_code = source_code.replace( "PREFIXES = [sys.prefix, sys.exec_prefix]", "PREFIXES = []" ) # Source code should use lazy_loader, this may not be good enough # for all things yet. attach_call_replacements = ( ( "lazy.attach_stub(__name__, __file__)", "lazy.attach('%(module_name)s', %(submodules)s, %(attrs)s)", ), ) for attach_call, attach_call_replacement in attach_call_replacements: if attach_call in source_code: result = self._handleLazyLoad( module_name=module_name, source_filename=source_filename, ) # Inline the values, to avoid the data files. if result is not None: source_code = source_code.replace( attach_call, attach_call_replacement % { "module_name": module_name.asString(), "submodules": result[0], "attrs": result[1], }, ) if module_name == "huggingface_hub": if ( "__getattr__, __dir__, __all__ = _attach(__name__, submodules=[], submod_attrs=_SUBMOD_ATTRS)" in source_code ): huggingface_hub_lazy_loader_info = ( self.queryRuntimeInformationSingle( setup_codes="import huggingface_hub", value="huggingface_hub._SUBMOD_ATTRS", info_name="huggingface_hub_lazy_loader", ) ) self.lazy_loader_usages[module_name] = ( [], huggingface_hub_lazy_loader_info, ) return source_code def _handleLazyLoad(self, module_name, source_filename): pyi_filename = source_filename + "i" if os.path.exists(pyi_filename): try: import lazy_loader except ImportError: pass else: with open(pyi_filename, "rb") as f: stub_node = ast.parse(f.read()) # We are using private code here, to avoid use duplicating, # pylint: disable=protected-access visitor = lazy_loader._StubVisitor() visitor.visit(stub_node) self.lazy_loader_usages[module_name] = ( visitor._submodules, visitor._submod_attrs, ) return self.lazy_loader_usages[module_name] def createPreModuleLoadCode(self, module): full_name = module.getFullName() for entry in self.config.get(full_name, section="implicit-imports"): if "pre-import-code" in entry: if self.evaluateCondition( full_name=full_name, condition=entry.get("when", "True") ): code = "\n".join(entry.get("pre-import-code")) # TODO: Add a description to the Yaml file. yield code, "According to Yaml configuration." def createPostModuleLoadCode(self, module): full_name = module.getFullName() for entry in self.config.get(full_name, section="implicit-imports"): if "post-import-code" in entry: if self.evaluateCondition( full_name=full_name, condition=entry.get("when", "True") ): code = "\n".join(entry.get("post-import-code")) # TODO: Add a description to the Yaml file. yield code, "According to Yaml configuration." unworthy_namespaces = ( "setuptools", # Not performance relevant. "distutils", # Not performance relevant. "wheel", # Not performance relevant. "pkg_resources", # Not performance relevant. "pycparser", # Not performance relevant. # "cffi", # Not performance relevant. "numpy.distutils", # Largely unused, and a lot of modules. "numpy.f2py", # Mostly unused, only numpy.distutils import it. "numpy.testing", # Useless. "nose", # Not performance relevant. "coverage", # Not performance relevant. "docutils", # Not performance relevant. "pytest", # Not performance relevant. "_pytest", # Not performance relevant. "unittest", # Not performance relevant. "pexpect", # Not performance relevant. "Cython", # Mostly unused, and a lot of modules. "cython", "pyximport", "IPython", # Mostly unused, and a lot of modules. "wx._core", # Too large generated code "pyVmomi.ServerObjects", # Too large generated code "pyglet.gl", # Too large generated code "telethon.tl.types", # Not performance relevant and slow C compile "importlib_metadata", # Not performance relevant and slow C compile "comtypes.gen", # Not performance relevant and slow C compile "win32com.gen_py", # Not performance relevant and slow C compile "phonenumbers.geodata", # Not performance relevant and slow C compile "site", # Not performance relevant and problems with .pth files "packaging", # Not performance relevant. "appdirs", # Not performance relevant. "dropbox.team_log", # Too large generated code "asyncua.ua.object_ids", # Too large generated code "asyncua.ua.uaerrors._auto", # Too large generated code "asyncua.server.standard_address_space.standard_address_space_services", # Too large generated code "azure.mgmt.network", # Too large generated code "azure.mgmt.compute", # Too large generated code "transformers.utils.dummy_pt_objects", # Not performance relevant. "transformers.utils.dummy_flax_objects", # Not performance relevant. "transformers.utils.dummy_tf_objects", # Not performance relevant. ) def decideCompilation(self, module_name): if module_name.hasOneOfNamespaces(self.unworthy_namespaces): return "bytecode" def onModuleUsageLookAhead( self, module_name, module_filename, module_kind, get_module_source ): # Getting the source code will also trigger our modification # and potentially tell us if any lazy loading applies. if get_module_source() is None: return if module_name in self.lazy_loader_usages: from nuitka.HardImportRegistry import ( addModuleAttributeFactory, addModuleDynamicHard, addModuleTrust, trust_module, trust_node, ) addModuleDynamicHard(module_name) sub_module_names, sub_module_attr = self.lazy_loader_usages[module_name] for sub_module_name in sub_module_names: addModuleTrust(module_name, sub_module_name, trust_module) sub_module_name = module_name.getChildNamed(sub_module_name) addModuleDynamicHard(sub_module_name) _lookAhead(using_module_name=module_name, module_name=sub_module_name) for ( sub_module_name, attribute_names, ) in sub_module_attr.items(): sub_module_name = module_name.getChildNamed(sub_module_name) addModuleDynamicHard(sub_module_name) _lookAhead(using_module_name=module_name, module_name=sub_module_name) for attribute_name in attribute_names: addModuleTrust(module_name, attribute_name, trust_node) addModuleAttributeFactory( module_name, attribute_name, makeExpressionImportModuleNameHardExistsAfterImportFactory( sub_module_name=sub_module_name, attribute_name=attribute_name, ), ) def makeExpressionImportModuleNameHardExistsAfterImportFactory( sub_module_name, attribute_name, ): from nuitka.HardImportRegistry import trust_node_factory from nuitka.nodes.ImportHardNodes import ( ExpressionImportModuleNameHardExists, ) key = (sub_module_name, attribute_name) if key in trust_node_factory: return lambda source_ref: trust_node_factory[key](source_ref=source_ref) return lambda source_ref: ExpressionImportModuleNameHardExists( module_name=sub_module_name, import_name=attribute_name, module_guaranteed=False, source_ref=source_ref, ) def _lookAhead(using_module_name, module_name): ( _module_name, package_filename, package_module_kind, finding, ) = locateModule( module_name=module_name, parent_package=None, level=0, ) assert module_name == _module_name if finding != "not-found": decideRecursion( using_module_name=using_module_name, module_filename=package_filename, module_name=module_name, module_kind=package_module_kind, )
Nuitka/Nuitka
nuitka/plugins/standard/ImplicitImports.py
ImplicitImports.py
py
23,781
python
en
code
10,019
github-code
6
[ { "api_name": "nuitka.plugins.PluginBase.NuitkaPluginBase", "line_number": 14, "usage_type": "name" }, { "api_name": "nuitka.utils.Yaml.getYamlPackageConfiguration", "line_number": 22, "usage_type": "call" }, { "api_name": "nuitka.utils.ModuleNames.ModuleName", "line_number": 49, "usage_type": "call" }, { "api_name": "nuitka.__past__.iter_modules", "line_number": 52, "usage_type": "call" }, { "api_name": "fnmatch.fnmatch", "line_number": 53, "usage_type": "call" }, { "api_name": "nuitka.utils.ModuleNames.ModuleName", "line_number": 69, "usage_type": "call" }, { "api_name": "nuitka.utils.Utils.isMacOS", "line_number": 236, "usage_type": "call" }, { "api_name": "nuitka.utils.Utils.isWin32Windows", "line_number": 238, "usage_type": "call" }, { "api_name": "os.path.normpath", "line_number": 278, "usage_type": "call" }, { "api_name": "os.path", "line_number": 278, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 278, "usage_type": "call" }, { "api_name": "os.path.isfile", "line_number": 286, "usage_type": "call" }, { "api_name": "os.path", "line_number": 286, "usage_type": "attribute" }, { "api_name": "os.path.dirname", "line_number": 287, "usage_type": "call" }, { "api_name": "os.path", "line_number": 287, "usage_type": "attribute" }, { "api_name": "os.path.isfile", "line_number": 304, "usage_type": "call" }, { "api_name": "os.path", "line_number": 304, "usage_type": "attribute" }, { "api_name": "os.path.dirname", "line_number": 305, "usage_type": "call" }, { "api_name": "os.path", "line_number": 305, "usage_type": "attribute" }, { "api_name": "os.path.abspath", "line_number": 308, "usage_type": "call" }, { "api_name": "os.path", "line_number": 308, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 308, "usage_type": "call" }, { "api_name": "os.path.isdir", "line_number": 310, "usage_type": "call" }, { "api_name": "os.path", "line_number": 310, "usage_type": "attribute" }, { "api_name": "os.path", "line_number": 340, "usage_type": "attribute" }, { "api_name": "os.path.exists", "line_number": 400, "usage_type": "call" }, { "api_name": "os.path", "line_number": 400, "usage_type": "attribute" }, { "api_name": "ast.parse", "line_number": 407, "usage_type": "call" }, { "api_name": "lazy_loader._StubVisitor", "line_number": 411, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.addModuleDynamicHard", "line_number": 511, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.addModuleTrust", "line_number": 516, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.trust_module", "line_number": 516, "usage_type": "name" }, { "api_name": "nuitka.HardImportRegistry.addModuleDynamicHard", "line_number": 519, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.addModuleDynamicHard", "line_number": 528, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.addModuleTrust", "line_number": 533, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.trust_node", "line_number": 533, "usage_type": "name" }, { "api_name": "nuitka.HardImportRegistry.addModuleAttributeFactory", "line_number": 534, "usage_type": "call" }, { "api_name": "nuitka.HardImportRegistry.trust_node_factory", "line_number": 554, "usage_type": "name" }, { "api_name": "nuitka.HardImportRegistry.trust_node_factory", "line_number": 555, "usage_type": "name" }, { "api_name": "nuitka.nodes.ImportHardNodes.ExpressionImportModuleNameHardExists", "line_number": 557, "usage_type": "call" }, { "api_name": "nuitka.importing.Importing.locateModule", "line_number": 571, "usage_type": "call" }, { "api_name": "nuitka.importing.Recursion.decideRecursion", "line_number": 580, "usage_type": "call" } ]
74920129787
from bs4 import BeautifulSoup import sys import io sys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding = 'utf-8') sys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding = 'utf-8') html = """ <html><body> <ul> <li><a href="http://www.naver.com">naver</a></li> <li><a href="http://www.daun.net">daum</a></li> <li><a href="http://www.daun.com">daum</a></li> <li><a href="http://www.google.com">google</a></li> <li><a href="http://www.tistory.com">tistory</a></li> </ul> </body></html> """ soup = BeautifulSoup(html, 'html.parser') links = soup.find_all("a") # print('links', type(links)) a = soup.find_all("a", string='daum') # print('a', a) b = soup.find_all("a", limit=3) # print('b', b) c = soup.find_all(string=["naver", "google"]) print(c) for link in links: # print('link', type(link), link) href = link.attrs['href'] txt = link.string # print('txt >> ', txt, 'href >> ', href)
lcy8417/Python
download2-5-3.py
download2-5-3.py
py
994
python
en
code
1
github-code
6
[ { "api_name": "sys.stdout", "line_number": 5, "usage_type": "attribute" }, { "api_name": "io.TextIOWrapper", "line_number": 5, "usage_type": "call" }, { "api_name": "sys.stdout.detach", "line_number": 5, "usage_type": "call" }, { "api_name": "sys.stderr", "line_number": 6, "usage_type": "attribute" }, { "api_name": "io.TextIOWrapper", "line_number": 6, "usage_type": "call" }, { "api_name": "sys.stderr.detach", "line_number": 6, "usage_type": "call" }, { "api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call" } ]
25175601159
import random import json import numpy as np import torch # Custom imports import data from model_eval import evaluate class LSTM(torch.nn.Module): def __init__(self, embedding: torch.FloatTensor): super().__init__() # Embedding wrapper self.__embedding = torch.nn.Embedding.from_pretrained( embedding, freeze=True, padding_idx=0) # RNN layers self.__rnn1 = torch.nn.LSTM(300, 150, num_layers=2, batch_first=False) self.__rnn2 = torch.nn.LSTM(300, 150, num_layers=2, batch_first=False) # FC layers self.__fc1 = torch.nn.Linear(150, 150) self.__fc2 = torch.nn.Linear(150, 1) def all_params(self): params = [] params.extend(self.__rnn1.parameters()) params.extend(self.__rnn2.parameters()) params.extend(self.__fc1.parameters()) params.extend(self.__fc2.parameters()) params.extend(self.__embedding.parameters()) return params def forward(self, x): x = self.__embedding(x) x = torch.transpose(x, 0, 1) # Consists of (h, c) hidden = None y, hidden = self.__rnn1(x, hidden) y, hidden = self.__rnn2(x, hidden) # Last output y = y[-1] # Linear layer y = self.__fc1(y) y = torch.relu(y) return self.__fc2(y) def predict(self, x): with torch.no_grad(): y = torch.sigmoid(self.forward(x)) y = y.round().int().squeeze(-1) return y def train(model: torch.nn.Module, data, optimizer, criterion): # Set state for training model.train() # Go through batches losses = list() for batch_num, batch in enumerate(data): model.zero_grad() # Calculate loss logits = model.forward(batch[0]).squeeze(-1) y = batch[1].float() loss = criterion(logits, y) loss.backward() torch.nn.utils.clip_grad_norm_(model.all_params(), 0.25) optimizer.step() losses.append(float(loss)) # At the end of an epoch print loss #print(f"loss = {np.mean(losses)}") return np.mean(losses) if __name__ == "__main__": # Statistics hyperparameters = dict() hyperparameters["max_size"] = -1 hyperparameters["min_freq"] = 1 hyperparameters["train_batch_size"] = 10 hyperparameters["valid_batch_size"] = 32 hyperparameters["test_batch_size"] = 32 hyperparameters["learning_rate"] = 1e-4 statistics = dict() statistics["hyperparameters"] = hyperparameters # Frequencies frequencies = data.getFrequencies(data.TRAIN_DATASET_PATH) labelFrequencies = data.getLabelFrequencies(data.TRAIN_DATASET_PATH) # Vocabs x_vocab = data.Vocab( frequencies, max_size=hyperparameters["max_size"], min_freq=hyperparameters["min_freq"]) y_vocab = data.Vocab(labelFrequencies, labels=True) # Datasets train_dataset = data.NLPDataset.from_file(data.TRAIN_DATASET_PATH) valid_dataset = data.NLPDataset.from_file(data.VALID_DATASET_PATH) test_dataset = data.NLPDataset.from_file(data.TEST_DATASET_PATH) # Embedding matrix embedding = data.generateEmbeddingMatrix( x_vocab, data.VECTOR_REPR_PATH) # Baseline model lstm = LSTM(embedding) optimizer = torch.optim.Adam( lstm.all_params(), lr=hyperparameters["learning_rate"]) criterion = torch.nn.BCEWithLogitsLoss() iters = 5 epochs = 5 for i in range(iters): print(f"RUN {i+1}") # Set seed seed = random.randint(0, 7052020) np.random.seed(seed) torch.manual_seed(seed) statistics[seed] = dict() statistics[seed]["train_loss"] = None statistics[seed]["valid"] = list() for epoch in range(epochs): print(f"Epoch {epoch+1}:") dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=hyperparameters["train_batch_size"], shuffle=True, collate_fn=data.pad_collate_fn) print("\tTraining...") train_loss = train(lstm, dataloader, optimizer, criterion) statistics[seed]["train_loss"] = train_loss dataloader = torch.utils.data.DataLoader(dataset=valid_dataset, batch_size=hyperparameters["valid_batch_size"], shuffle=False, collate_fn=data.pad_collate_fn) print("\tValidating...") valid_evals = evaluate(lstm, dataloader, criterion) statistics[seed]["valid"].append(valid_evals) # Test dataset dataloader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=32, shuffle=False, collate_fn=data.pad_collate_fn) print("Testing...") test_evals = evaluate(lstm, dataloader, criterion) statistics[seed]["test"] = test_evals print("\nAll done.") # Write to statistics file with open("c:/workspace/fer-dl/lab03/stats/lstm_stats.json", "w") as stats_file: stats_file.write(json.dumps(statistics))
ftodoric/fer-du
lab03/rnn.py
rnn.py
py
5,265
python
en
code
1
github-code
6
[ { "api_name": "torch.nn", "line_number": 12, "usage_type": "attribute" }, { "api_name": "torch.FloatTensor", "line_number": 13, "usage_type": "attribute" }, { "api_name": "torch.nn.Embedding.from_pretrained", "line_number": 17, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 17, "usage_type": "attribute" }, { "api_name": "torch.nn.LSTM", "line_number": 21, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 21, "usage_type": "attribute" }, { "api_name": "torch.nn.LSTM", "line_number": 23, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 23, "usage_type": "attribute" }, { "api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 27, "usage_type": "attribute" }, { "api_name": "torch.nn.Linear", "line_number": 28, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 28, "usage_type": "attribute" }, { "api_name": "torch.transpose", "line_number": 43, "usage_type": "call" }, { "api_name": "torch.relu", "line_number": 56, "usage_type": "call" }, { "api_name": "torch.no_grad", "line_number": 61, "usage_type": "call" }, { "api_name": "torch.sigmoid", "line_number": 62, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 68, "usage_type": "attribute" }, { "api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 83, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 83, "usage_type": "attribute" }, { "api_name": "numpy.mean", "line_number": 91, "usage_type": "call" }, { "api_name": "data.getFrequencies", "line_number": 107, "usage_type": "call" }, { "api_name": "data.TRAIN_DATASET_PATH", "line_number": 107, "usage_type": "attribute" }, { "api_name": "data.getLabelFrequencies", "line_number": 108, "usage_type": "call" }, { "api_name": "data.TRAIN_DATASET_PATH", "line_number": 108, "usage_type": "attribute" }, { "api_name": "data.Vocab", "line_number": 111, "usage_type": "call" }, { "api_name": "data.Vocab", "line_number": 113, "usage_type": "call" }, { "api_name": "data.NLPDataset.from_file", "line_number": 116, "usage_type": "call" }, { "api_name": "data.NLPDataset", "line_number": 116, "usage_type": "attribute" }, { "api_name": "data.TRAIN_DATASET_PATH", "line_number": 116, "usage_type": "attribute" }, { "api_name": "data.NLPDataset.from_file", "line_number": 117, "usage_type": "call" }, { "api_name": "data.NLPDataset", "line_number": 117, "usage_type": "attribute" }, { "api_name": "data.VALID_DATASET_PATH", "line_number": 117, "usage_type": "attribute" }, { "api_name": "data.NLPDataset.from_file", "line_number": 118, "usage_type": "call" }, { "api_name": "data.NLPDataset", "line_number": 118, "usage_type": "attribute" }, { "api_name": "data.TEST_DATASET_PATH", "line_number": 118, "usage_type": "attribute" }, { "api_name": "data.generateEmbeddingMatrix", "line_number": 121, "usage_type": "call" }, { "api_name": "data.VECTOR_REPR_PATH", "line_number": 122, "usage_type": "attribute" }, { "api_name": "torch.optim.Adam", "line_number": 127, "usage_type": "call" }, { "api_name": "torch.optim", "line_number": 127, "usage_type": "attribute" }, { "api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 129, "usage_type": "call" }, { "api_name": "torch.nn", "line_number": 129, "usage_type": "attribute" }, { "api_name": "random.randint", "line_number": 137, "usage_type": "call" }, { "api_name": "numpy.random.seed", "line_number": 138, "usage_type": "call" }, { "api_name": "numpy.random", "line_number": 138, "usage_type": "attribute" }, { "api_name": "torch.manual_seed", "line_number": 139, "usage_type": "call" }, { "api_name": "torch.utils.data.DataLoader", "line_number": 148, "usage_type": "call" }, { "api_name": "torch.utils", "line_number": 148, "usage_type": "attribute" }, { "api_name": "data.pad_collate_fn", "line_number": 149, "usage_type": "attribute" }, { "api_name": "torch.utils.data.DataLoader", "line_number": 155, "usage_type": "call" }, { "api_name": "torch.utils", "line_number": 155, "usage_type": "attribute" }, { "api_name": "data.pad_collate_fn", "line_number": 156, "usage_type": "attribute" }, { "api_name": "model_eval.evaluate", "line_number": 158, "usage_type": "call" }, { "api_name": "torch.utils.data.DataLoader", "line_number": 162, "usage_type": "call" }, { "api_name": "torch.utils", "line_number": 162, "usage_type": "attribute" }, { "api_name": "data.pad_collate_fn", "line_number": 163, "usage_type": "attribute" }, { "api_name": "model_eval.evaluate", "line_number": 165, "usage_type": "call" }, { "api_name": "json.dumps", "line_number": 172, "usage_type": "call" } ]
34487423583
import serial import time import struct import logging as trace import threading class Communication(object): data_chars = [b'!', b'"', b'#', b'$', b'%', b'&', b"'", b'('] response_timeout = 2 #second _handle = serial.Serial() _error_counter = 0 _done = False _thread = None def __init__(self, address=None): # Initialize class parameters # perform port configuration at start-up self._handle.port = "/dev/ttyUSB0" self._handle.baudrate = 115200 self._handle.bytesize = serial.EIGHTBITS # number of bits per bytes self._handle.parity = serial.PARITY_NONE # set parity check: no parity self._handle.stopbits = serial.STOPBITS_ONE # number of stop bits self._handle.timeout = 0.25 # non-block read self._handle.writeTimeout = 0.25 # timeout for write trace.debug('serial port configuration done') # self._address = address # Only one data stream per port self.data = [] self.devices = {} self.sync_data_ready = threading.Event() self.async_data_ready = threading.Event() self.bin_data_ready = threading.Event() self._thread = threading.Thread(name='serial_thr', target= self._read_from_device) def __del__(self): self.disconnect() def connect(self, port_name="/dev/ttyUSB0"): """ Connect device :param port_name: Specify serial port name if different than /dev/ttyUSB0 :return: True if connected, False if connection failed """ # if port is different than default use it if self._handle.port != port_name: self._handle.port = port_name # start connecting trace.debug("Trying to connect..") try: self._handle.open() except Exception as e: trace.error("error open serial port: " + str(e)) return False if self._handle.isOpen(): trace.debug('serial port opened') else: trace.debug('serial port not opened') return False # flush buffers at start-up try: self._handle.flushInput() self._handle.flushOutput() except Exception as e: trace.error("error flushing input " + str(e)) # at this point device should be connected self._thread.start() return True def disconnect(self): # mark job as done (this flag is for background thread) self._done = True # wait until background thread is done # if it is still running self._thread.join() # close serial port if self._handle.isOpen(): self._handle.close() trace.debug('serial port closed') def init_device(self, idn): self.devices[idn] = {'sync': [], 'async': []} def write_command(self, command, idn): """ Write command to device :param command: self-explanatory :return: None """ # add prefix and CR on the end command = str(idn) + ":" + command + '\n' trace.debug('writing command: ' + command) self._handle.write(bytes(command, 'utf8')) def write_command_ret(self, command, idn): """ Writes a command to device and waits for standard response :param command: self-explanatory :return: None """ self.sync_data_ready.clear() self.write_command(command,idn) self.sync_data_ready.wait(self.response_timeout) if not bool(self.devices.get(idn).get('sync')): resp=self.devices.get(idn).get('sync').pop() trace.debug("Command: \""+str(command)+"\" successfully sent. Response: \""+str(resp)+"\"") return resp else: trace.debug("No response for command: \"" + str(command) + "\"") return None def write_command_stdr(self, command, idn): """ Writes a command to device and waits for standard response :param command: self-explanatory :return: None """ self.sync_data_ready.clear() self.write_command(command,idn) self.sync_data_ready.wait(self.response_timeout) if not bool(self.devices.get(idn).get('sync')): resp=self.devices.get(idn).get('sync').pop() if resp.rsplit()[0] == command.rsplit()[0]: trace.debug("Command: \""+str(command)+"\" successfully sent. Response: \""+str(resp)+"\"") else: trace.error("Wrong response for command: \"" + str(command) + "\". Response: \"" + str(resp) + "\" , expected: \""+str(command.rsplit()[0])) if len(resp.rsplit()) > 1: return resp.rsplit()[1] else: return None def decode_binvalue(self,seq): # Data format is: CSHHHHH\r # C - is a prefix that also serves as a 3 bit-long counter (starts with prefix0) # S - status byte (6 bits: 1 + negated 5 bits representing input lines) # HHHHH - is a 18-bit hex value (should be treated as value with sign) # \r - terminating CR # Extended format is: CSHHHHHhH\r # 0 ascii 0/1 sin+- X X 0/1 shutter 0/1 shutter~ X X # where CSH are as above and h is H (hex digit) with highest bit set # this signals the fact that also fractional part is sent so the bit should # be cleared, whole value treated as int and later divided by 256 flag_count=seq[0]-ord('!') c = seq[1] flag_al=bool(c & 0b01000000) flag_dl=bool(c & 0b00001000) c = seq[2] value = (-1 if c >= ord('8') else 0) # test for sign bit (in hex digit) shift = False for c in list(seq)[3:]: if (c & 0x80): c &= 0x7F shift = True if c >= ord('0') and c <= ord('9'): nibble = c - ord('0') elif c >= ord('A') and c <= ord('F'): nibble = c - (ord('A') - 10) else: break value <<= 4 value |= nibble return (float(value) / 256 if shift else float(value))* 6.25 / 65536,flag_count,flag_al,flag_dl def read_line(self, line): coms = line.split(b'\r') for com in coms: if com[0] >= ord('!') and com[0] <= ord('('): value = self.decode_binvalue(com) self.data.append(list(value)) self.bin_data_ready.set() trace.debug('Data value:'+ str(value)) else: idn, com_type, message = tuple(com.partition(b'.')) # First char after the id number if com_type == b'.': com_type = 'sync' else: # if not, try other ordering character idn, com_type, message = tuple(com.partition(b';')) if com_type == b';': com_type = 'async' else: trace.error('Major parsing fuckup, good luck') return -1 idnn = int(idn) if idnn not in self.devices.keys(): self.init_device(idnn) message=message.decode('ascii') #convert bytes to string self.devices[idnn][com_type].append(message) if com_type == 'sync': self.sync_data_ready.set() elif com_type == 'async': self.async_data_ready.set() trace.debug('Device ID: %d Communication type: %s Message: %s', idnn, com_type, message) def _read_from_device(self): """ Read from device. This function is executed in separate thread. Function also updates necessary parameters for this class """ self.rawdata = bytearray() while not self._done: # if incoming bytes are waiting to be # read from the serial input buffer if self._handle.inWaiting(): # read and remove all whitespaces # on the right side, including '\n' self.rawdata.extend( self._handle.read(self._handle.inWaiting())) while True: line,sep,rest=tuple(self.rawdata.partition(b'\r')) if sep != b'\r': break trace.debug("new data to parse: " + str(line)) self.read_line(line.strip()) self.rawdata=rest # sleep for a moment (pseudo-yield in python) time.sleep(0.0001)
ccucumber/verdeta-lockin
fotonowy/komunikacja.py
komunikacja.py
py
8,765
python
en
code
0
github-code
6
[ { "api_name": "serial.Serial", "line_number": 10, "usage_type": "call" }, { "api_name": "serial.EIGHTBITS", "line_number": 21, "usage_type": "attribute" }, { "api_name": "serial.PARITY_NONE", "line_number": 22, "usage_type": "attribute" }, { "api_name": "serial.STOPBITS_ONE", "line_number": 23, "usage_type": "attribute" }, { "api_name": "logging.debug", "line_number": 28, "usage_type": "call" }, { "api_name": "threading.Event", "line_number": 35, "usage_type": "call" }, { "api_name": "threading.Event", "line_number": 36, "usage_type": "call" }, { "api_name": "threading.Event", "line_number": 37, "usage_type": "call" }, { "api_name": "threading.Thread", "line_number": 38, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 56, "usage_type": "call" }, { "api_name": "logging.error", "line_number": 60, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 64, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 66, "usage_type": "call" }, { "api_name": "logging.error", "line_number": 74, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 92, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 106, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 120, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 123, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 139, "usage_type": "call" }, { "api_name": "logging.error", "line_number": 141, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 189, "usage_type": "call" }, { "api_name": "logging.error", "line_number": 201, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 213, "usage_type": "call" }, { "api_name": "logging.debug", "line_number": 235, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 240, "usage_type": "call" } ]
41854725692
from absl.testing import parameterized import dataclasses import tensorflow as tf from official.core import config_definitions as cfg from official.core import input_reader from official.modeling import hyperparams from official.vision.beta.dataloaders import tfds_detection_decoders from official.vision.beta.projects.yolo.dataloaders import yolo_detection_input @dataclasses.dataclass class Parser(hyperparams.Config): """Dummy configuration for parser.""" output_size: int = (416, 416) num_classes: int = 80 fixed_size: bool = True jitter_im: float = 0.1 jitter_boxes: float = 0.005 min_process_size: int = 320 max_process_size: int = 608 max_num_instances: int = 200 random_flip: bool = True seed: int = 10 shuffle_buffer_size: int = 10000 @dataclasses.dataclass class DataConfig(cfg.DataConfig): """Input config for training.""" input_path: str = '' tfds_name: str = 'coco/2017' tfds_split: str = 'train' global_batch_size: int = 10 is_training: bool = True dtype: str = 'float16' decoder = None parser: Parser = Parser() shuffle_buffer_size: int = 10 class YoloDetectionInputTest(tf.test.TestCase, parameterized.TestCase): @parameterized.named_parameters(('training', True), ('testing', False)) def test_yolo_input(self, is_training): params = DataConfig(is_training=is_training) decoder = tfds_detection_decoders.MSCOCODecoder() anchors = [[12.0, 19.0], [31.0, 46.0], [96.0, 54.0], [46.0, 114.0], [133.0, 127.0], [79.0, 225.0], [301.0, 150.0], [172.0, 286.0], [348.0, 340.0]] masks = {'3': [0, 1, 2], '4': [3, 4, 5], '5': [6, 7, 8]} parser = yolo_detection_input.Parser( output_size=params.parser.output_size, num_classes=params.parser.num_classes, fixed_size=params.parser.fixed_size, jitter_im=params.parser.jitter_im, jitter_boxes=params.parser.jitter_boxes, min_process_size=params.parser.min_process_size, max_process_size=params.parser.max_process_size, max_num_instances=params.parser.max_num_instances, random_flip=params.parser.random_flip, seed=params.parser.seed, anchors=anchors, masks=masks) postprocess_fn = parser.postprocess_fn(is_training=is_training) reader = input_reader.InputReader(params, dataset_fn=tf.data.TFRecordDataset, decoder_fn=decoder.decode, parser_fn=parser.parse_fn( params.is_training)) dataset = reader.read(input_context=None).batch(10).take(1) if postprocess_fn: image, _ = postprocess_fn( *tf.data.experimental.get_single_element(dataset)) else: image, _ = tf.data.experimental.get_single_element(dataset) print(image.shape) self.assertAllEqual(image.shape, (10, 10, 416, 416, 3)) self.assertTrue( tf.reduce_all(tf.math.logical_and(image >= 0, image <= 1))) if __name__ == '__main__': tf.test.main()
sek788432/Waymo-2D-Object-Detection
input/models/official/vision/beta/projects/yolo/dataloaders/yolo_detection_input_test.py
yolo_detection_input_test.py
py
3,074
python
en
code
79
github-code
6
[ { "api_name": "official.modeling.hyperparams.Config", "line_number": 13, "usage_type": "attribute" }, { "api_name": "official.modeling.hyperparams", "line_number": 13, "usage_type": "name" }, { "api_name": "dataclasses.dataclass", "line_number": 12, "usage_type": "attribute" }, { "api_name": "official.core.config_definitions.DataConfig", "line_number": 29, "usage_type": "attribute" }, { "api_name": "official.core.config_definitions", "line_number": 29, "usage_type": "name" }, { "api_name": "dataclasses.dataclass", "line_number": 28, "usage_type": "attribute" }, { "api_name": "tensorflow.test", "line_number": 42, "usage_type": "attribute" }, { "api_name": "absl.testing.parameterized.TestCase", "line_number": 42, "usage_type": "attribute" }, { "api_name": "absl.testing.parameterized", "line_number": 42, "usage_type": "name" }, { "api_name": "official.vision.beta.dataloaders.tfds_detection_decoders.MSCOCODecoder", "line_number": 48, "usage_type": "call" }, { "api_name": "official.vision.beta.dataloaders.tfds_detection_decoders", "line_number": 48, "usage_type": "name" }, { "api_name": "official.vision.beta.projects.yolo.dataloaders.yolo_detection_input.Parser", "line_number": 54, "usage_type": "call" }, { "api_name": "official.vision.beta.projects.yolo.dataloaders.yolo_detection_input", "line_number": 54, "usage_type": "name" }, { "api_name": "official.core.input_reader.InputReader", "line_number": 69, "usage_type": "call" }, { "api_name": "official.core.input_reader", "line_number": 69, "usage_type": "name" }, { "api_name": "tensorflow.data", "line_number": 70, "usage_type": "attribute" }, { "api_name": "tensorflow.data.experimental.get_single_element", "line_number": 77, "usage_type": "call" }, { "api_name": "tensorflow.data", "line_number": 77, "usage_type": "attribute" }, { "api_name": "tensorflow.data.experimental.get_single_element", "line_number": 79, "usage_type": "call" }, { "api_name": "tensorflow.data", "line_number": 79, "usage_type": "attribute" }, { "api_name": "tensorflow.reduce_all", "line_number": 83, "usage_type": "call" }, { "api_name": "tensorflow.math.logical_and", "line_number": 83, "usage_type": "call" }, { "api_name": "tensorflow.math", "line_number": 83, "usage_type": "attribute" }, { "api_name": "absl.testing.parameterized.named_parameters", "line_number": 44, "usage_type": "call" }, { "api_name": "absl.testing.parameterized", "line_number": 44, "usage_type": "name" }, { "api_name": "tensorflow.test.main", "line_number": 87, "usage_type": "call" }, { "api_name": "tensorflow.test", "line_number": 87, "usage_type": "attribute" } ]
18194693461
from rest_framework.routers import DefaultRouter from messaging import api_view router = DefaultRouter() router.register('message', api_view.MessageVewSet, base_name='message') router.register('chat', api_view.GroupBlogViewSet, base_name='chat') urlpatterns = [ ] urlpatterns += router.urls
SivakumarSkr/Movieclub
messaging/api_urls.py
api_urls.py
py
293
python
en
code
0
github-code
6
[ { "api_name": "rest_framework.routers.DefaultRouter", "line_number": 5, "usage_type": "call" }, { "api_name": "messaging.api_view.MessageVewSet", "line_number": 6, "usage_type": "attribute" }, { "api_name": "messaging.api_view", "line_number": 6, "usage_type": "name" }, { "api_name": "messaging.api_view.GroupBlogViewSet", "line_number": 7, "usage_type": "attribute" }, { "api_name": "messaging.api_view", "line_number": 7, "usage_type": "name" } ]
3502830678
"""Retreive extracts wrt. previous lexicon update cycle. Update extracts.json and output cycle extracts as extracts-{cycle}.txt for easy inspection Example $ python3 get_extracts.py 4 working_folder $ python3 get_extracts.py 4 _aroma_NOUN+ADJ Args: n (int): number of (CPU Threads) processes to use working_folder: Required in working folder: extracts.json: {"cycle":[([seeds], extract, parsed_extract),..],..} lexicon.json: [ # cycle 0 [ # entry 0 in cycle: all coincident extracts corresponding to a pattern ( [("_sharp_ADJ", "_lemon_NOUN"), ... ], #list of coincident vocabulary tuples tagetted by pattern A pattern_A, ), .... ], .... ] required in datasets: harvesting.json: {"book_code": [sentence, parsed sentence tuples],.. } """ import json import multiprocessing import os import re import sys import regex from tqdm import tqdm # add libraries to path sys.path.append(os.path.join(sys.path[0], "libraries")) # add working folder to path sys.path.append(os.path.join(sys.path[0], sys.argv[2])) from CHUNKS import chunks from pattern_abstraction import convert_patterns, expand_chunks, to_chunks from PATTERNS import extraction_patterns, identification_patterns def main(argv): # CL arguments folder = argv[1] n = int(argv[0]) # get a list of previously seen extracts, from all prior cycles extracts_file = folder + "/extracts.json" previous_extracts, current_cycle = get_extracts(extracts_file) # previous_extracts = {"cycle":[([seeds], extract, parsed_extract),..],..} print(f"current cycle = {current_cycle}") seen_extracts = extracts_as_set(previous_extracts) # seen_extracts = {set of unparsed extracts previously seen} # Collect the previous cycle's lexicon entries with open(folder + "/lexicon.json", "r") as f: lexicon = json.load(f) vocabulary = get_lexicon(lexicon) # [(compiled re, (coincident phrases),..] # [(compiled re, (coincident phrases),..] # compile previously seen abstractions seen_abstractions = identification_patterns seen_patterns = compile_patterns(seen_abstractions) # ITERATE THROUGH HARVESTING SET, extracting where # * an extract is unseen # * and where known patterns do not match with open("./datasets/harvesting.json", "r") as f: dataset = json.load(f) # dataset = {"book_code": [(sentence, parsed sentence),..]} # iterate through the harvesting set for book_index, (book_code, extracts) in enumerate(tqdm(dataset.items())): # discard extracts already seen extracts_trimmed = trim_extracts(extracts, seen_extracts) # [(extract, parsed_extract),...] # split extracts n chunks, for multi-proccessing extract_sets = group_extracts(extracts_trimmed, n) # [[(extract, parsed_extract),...],...] processes = [] queue = multiprocessing.Queue() # iterate through the extract chunks as separate processes for i in range(n): # run vocabulary pattern matching against trimmed extracts process = multiprocessing.Process( target=mapped_function, args=(extract_sets[i], vocabulary, seen_patterns, queue,), ) process.start() processes.append(process) # collect process output for r in range(n): previous_extracts[current_cycle] += queue.get() # terminate the processes for process in processes: process.join() # save to json with open(folder + "/extracts.json", "w") as f: json.dump(previous_extracts, f, ensure_ascii=False) # save ouput to text files for inspection with open(folder + f"/extracts-{current_cycle}.txt", "w") as f: for phrases, extract, parsed_extract in previous_extracts[current_cycle]: f.write("\n\n") f.write(f"{phrases}") f.write("\n" + extract) f.write("\n" + parsed_extract) def mapped_function(extract_set, vocabulary, seen_patterns, queue): """Iterate through the extract_set and return a list of those extracts matching the previous lexicon cycle entries. """ returned = [] for extract, parsed_extract in extract_set: for v_pattern, phrases in vocabulary: mo_lexicon = regex.search(v_pattern, parsed_extract) if mo_lexicon: # check does not conform to a seen pattern mo_seen = None for seen_abstraction, seen_compiled in seen_patterns: mo_seen = regex.match(seen_compiled, parsed_extract) if mo_seen: print("\n\nseen pattern") print(extract) print(seen_abstraction) break # break seen pattern loop if mo_lexicon and not mo_seen: # if both vocab match and not conforming to seen_patterns returned.append((phrases, extract, parsed_extract)) # print("\n\naccepted") # print(extract) queue.put(returned) def get_extracts(file): """Return existing extracts file container or create new. """ # if file exists, then load if os.path.exists(file): with open(file, "r") as f: previous_extracts = json.load(f) # save as "folder/extracts.json" in case wish to revert with open(file, "w") as f: json.dump(previous_extracts, f, ensure_ascii=False, indent=4) # add new cycle previous_extracts[str(len(previous_extracts.keys()))] = [] # if file doesn't exist, create new else: previous_extracts = {"0": []} # get the current cycle's index key for extracts current_cycle = str(list(previous_extracts.keys())[-1]) return previous_extracts, current_cycle def extracts_as_set(extracts): """Return the extracts to date as a set Args: extracts (dict): {"cycle":[([seeds], extract, parsed_extract),..],..} Return: set of seen extracts """ seen_extracts = [] for keys, values in extracts.items(): for phrase, extract, parsed_extract in values: seen_extracts.append(extract) seen_extracts = set(seen_extracts) return seen_extracts def get_lexicon(lexicon): """Return preivious lexicon vocab as a list of (compiled re, (coincident phrases)). Args: lexicon.json: [ # cycle 0 [ # list of entries, each entry corresponds to pattern [ [(phrase0, phrase1), ..], # list of coincidents phrases matched (e.g., adj, noun collection) pattern_A ], [ [(phrase0, phrase1), ..], pattern_B ] .... ], .... ] """ patterns = [] for entry in lexicon[-1]: # each entry in previous cycle for phrases in entry[0]: try: converted_compounded_phrases = "" for phrase in phrases: converted_compounded_phrases += ".*" + convert_patterns([phrase],chunks)[0] patterns.append((regex.compile(converted_compounded_phrases), phrases)) except: print(f"lexicon error, please correct, token: {phrases}") return patterns def compile_patterns(abstractions): """Assemble list of (abstracted_pattern, compiled) tuples of abstracted patterns. Args: abstractions: [] Returns: [(abstracted_pattern, compiled),...] """ # assemble (new) extraction patterns in python re format patterns = [] # patterns = [(abstraction, compiled pattern), ..] for abstraction in abstractions: print(abstraction) patterns.append( ( abstraction, regex.compile( "^.*" + convert_patterns([abstraction], chunks)[0] + ".*", re.MULTILINE, ), ) ) return patterns def trim_extracts(extracts, seen_extracts): """Return a list of (extract, parsed_extract) for unseen extracts, not conforming to a known pattern. Args: extracts (list): [(sentence, parsed sentence),..] """ # trim extract set, based on seen extracts extracts_trimmed = [] for extract, parsed_extract in extracts: if extract not in seen_extracts: extracts_trimmed.append((extract, parsed_extract)) return extracts_trimmed def group_extracts(extracts, n): """Return extracts as a list of n lists of extracts (for multiprocessing) e.g., where n = 4, [[(extract, parsed_extract),...],[],[],[]] Args: extracts: [(sentence, parsed sentence),..] """ extract_sets = [[] for i in range(n)] for i in range(0, len(extracts), n): for j in range(0, n): try: extract_sets[j].append(extracts[i + j]) except: pass return extract_sets if __name__ == "__main__": main(sys.argv[1:])
ryanbrate/DS_thesis
5_Process/get_extracts.py
get_extracts.py
py
9,761
python
en
code
0
github-code
6
[ { "api_name": "sys.path.append", "line_number": 40, "usage_type": "call" }, { "api_name": "sys.path", "line_number": 40, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 40, "usage_type": "call" }, { "api_name": "os.path", "line_number": 40, "usage_type": "attribute" }, { "api_name": "sys.path.append", "line_number": 42, "usage_type": "call" }, { "api_name": "sys.path", "line_number": 42, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 42, "usage_type": "call" }, { "api_name": "os.path", "line_number": 42, "usage_type": "attribute" }, { "api_name": "sys.argv", "line_number": 42, "usage_type": "attribute" }, { "api_name": "json.load", "line_number": 67, "usage_type": "call" }, { "api_name": "PATTERNS.identification_patterns", "line_number": 72, "usage_type": "name" }, { "api_name": "json.load", "line_number": 80, "usage_type": "call" }, { "api_name": "tqdm.tqdm", "line_number": 84, "usage_type": "call" }, { "api_name": "multiprocessing.Queue", "line_number": 93, "usage_type": "call" }, { "api_name": "multiprocessing.Process", "line_number": 98, "usage_type": "call" }, { "api_name": "json.dump", "line_number": 114, "usage_type": "call" }, { "api_name": "regex.search", "line_number": 134, "usage_type": "call" }, { "api_name": "regex.match", "line_number": 140, "usage_type": "call" }, { "api_name": "os.path.exists", "line_number": 160, "usage_type": "call" }, { "api_name": "os.path", "line_number": 160, "usage_type": "attribute" }, { "api_name": "json.load", "line_number": 162, "usage_type": "call" }, { "api_name": "json.dump", "line_number": 165, "usage_type": "call" }, { "api_name": "pattern_abstraction.convert_patterns", "line_number": 223, "usage_type": "call" }, { "api_name": "CHUNKS.chunks", "line_number": 223, "usage_type": "argument" }, { "api_name": "regex.compile", "line_number": 225, "usage_type": "call" }, { "api_name": "regex.compile", "line_number": 247, "usage_type": "call" }, { "api_name": "pattern_abstraction.convert_patterns", "line_number": 248, "usage_type": "call" }, { "api_name": "CHUNKS.chunks", "line_number": 248, "usage_type": "argument" }, { "api_name": "re.MULTILINE", "line_number": 249, "usage_type": "attribute" }, { "api_name": "sys.argv", "line_number": 290, "usage_type": "attribute" } ]
40466793180
#!/home/gabriel/funcam/venv/bin/python3 # ONLY TESTED ON LINUX # To run using ./run.py [args] on your terminal (without python3) # point the first line to some python interpreter containing the requirements # or create a venv inside this project. # Or delete this to use another method. from cam import Cam from vcam import VCam import argparse if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-v', '--virtual',help='enable virtual cam',action='store_true') parser.add_argument('--video', help='choose video input', type=int, default=0) parser.add_argument('--maxhands', help='set max hands for detection', type=int, default=1) parser.add_argument('-d', help='enable draw for marks and functional areas', action='store_true') parser.add_argument('--finger', help='choose the finger for control', type=int, default=8, choices=[4, 8, 12, 16, 20]) parser.add_argument('-p', help='enable camera to take photos', action='store_true') args = parser.parse_args() if args.virtual: # virtual cam vc = VCam(video=args.video, mxhand=args.maxhands, du=args.d, f=args.finger) vc.start() else: # own cam cam = Cam(video=args.video, mxhand=args.maxhands, du=args.d, f=args.finger, p=args.p) cam.open()
biguelito/funcam
funcam.py
funcam.py
py
1,315
python
en
code
0
github-code
6
[ { "api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call" }, { "api_name": "vcam.VCam", "line_number": 26, "usage_type": "call" }, { "api_name": "cam.Cam", "line_number": 31, "usage_type": "call" }, { "api_name": "cam.open", "line_number": 32, "usage_type": "call" } ]
35951961808
# -*- coding: utf-8 -*- import logging from path import Path logger = logging.getLogger(__name__) def get_mipname(fastq_file): """Takes a demux fastq file and returns a MIP compatible fastq file Args: fastq_file (str): a FQP to a fastq file. Returns (str): A MIP compatible fastq file. """ dirparts = fastq_file.split("/") nameparts = dirparts[-1].split("_") # H3LGFCCXX-l1t21_973470_CGGCTATG_L001_R2_001.fastq.gz # H3LGFCCXX-l1t21_Undetermined_CGGCTATG_L001_R1_001.fastq.gz # RNA1460A10_dual10_TCCGGAGA-ATAGAGGC_L001_R1_001.fastq.gz # RNA1460A10_TCCGGAGA-ATAGAGGC_L001_R1_001.fastq.gz index = nameparts[-4] # no worries, this'll always work, right? fc = dirparts[-5].split("_")[-1][1:] lane = int(nameparts[-3][-1:]) readdirection = nameparts[-2][-1:] rundir = dirparts[-5] date = rundir.split("_")[0] sample_id = dirparts[-2].split("_")[1] # X stuff undetermined = '' if nameparts[1] == 'Undetermined': undetermined = '-Undetermined' tile = '' if '-' in nameparts[0]: # H2V2YCCXX-l2t21 tile = nameparts[0].split('-')[1].split('t')[1] tile = '-' + tile newname = "{lane}_{date}_{fc}{tile}{undetermined}_{sample}_{index}_{readdirection}.fastq.gz".format( lane=lane, date=date, fc=fc, sample=sample_id, index=index, readdirection=readdirection, undetermined=undetermined, tile=tile ) return newname def make_link(source, dest, link_type='hard'): Path(dest).remove_p() try: if link_type == 'soft': logger.debug("ln -s {} {} ...".format(source, dest)) Path(source).symlink(dest) else: real_source = Path(source).realpath() logger.debug("ln {} {} ...".format(real_source, dest)) Path(real_source).link(dest) except Exception as error: # catch, print, and continue logger.error(repr(error)) return False return True
Clinical-Genomics/deliver
deliver/utils/files.py
files.py
py
2,052
python
en
code
1
github-code
6
[ { "api_name": "logging.getLogger", "line_number": 7, "usage_type": "call" }, { "api_name": "path.Path", "line_number": 61, "usage_type": "call" }, { "api_name": "path.Path", "line_number": 66, "usage_type": "call" }, { "api_name": "path.Path", "line_number": 68, "usage_type": "call" }, { "api_name": "path.Path", "line_number": 70, "usage_type": "call" } ]
1397488728
from collections import defaultdict def longestPalindrome(s): maxlen, maxp, l, dit = 0, "", len(s), defaultdict(list) for i in range(l): dit[s[i]].append(i) for j in dit[s[i][::-1]]: if s[j:i+1] == s[j:i+1][::-1]: if len(s[j:i+1]) > maxlen: maxlen = len(s[j:i+1]) maxp = s[j:i+1] break return maxp st=input() print(longestPalindrome(st))
anjaliugale31/placement_preparation
strongest_palindrome.py
strongest_palindrome.py
py
368
python
en
code
0
github-code
6
[ { "api_name": "collections.defaultdict", "line_number": 3, "usage_type": "call" } ]
10010702062
import numpy as np import pandas as pd from flask import Flask, request, jsonify, render_template import pickle app = Flask(__name__) from keras.models import load_model model = load_model('customer-churn\saved_model (1).pb') # Importing the dataset dataset = pd.read_csv('customer_churn_large_dataset.csv') # Extracting dependent and independent variables: # Extracting independent variable: X = dataset.iloc[:,3:13].values # Extracting dependent variable: y = dataset.iloc[:, 5].values # Encoding Categorical data: # Encoding the Independent Variable from sklearn.preprocessing import LabelEncoder labelencoder_X = LabelEncoder() X[:, 1] = labelencoder_X.fit_transform(X[:, 1]) # Encoding Categorical data: # Encoding the Independent Variable from sklearn.preprocessing import LabelEncoder labelencoder_X = LabelEncoder() X[:, 2] = labelencoder_X.fit_transform(X[:, 2]) #dummy encoding. from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer columnTransformer = ColumnTransformer([('yograj', OneHotEncoder(), [1])],remainder='passthrough') X=columnTransformer.fit_transform(X) #dummy encoding. # Dummy Variable trapping X = X[:, 1:] # Splitting the Dataset into the Training set and Test set # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42) # Feature Scaling # Standard Scaling: Standardization = X'=X-mean(X)/standard deviation # normal scaling : Normalization= X'=X-min(X)/max(x)-min(X) from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) @app.route('/') def home(): return render_template("index.html") @app.route('/predict',methods=['GET']) def predict(): ''' For rendering results on HTML GUI ''' creditscore = int(request.args.get('CustomerID')) geo = int(request.args.get('Name')) age = int(request.args.get('Age')) tenure = int(request.args.get('Gender')) balance = int(request.args.get('Location')) numofproducts = int(request.args.get('Subscription_Length_Months')) creditcards=int(request.args.get('Monthly_Bill')) activemember = int(request.args.get('Total_Usage_GB')) salary = int(request.args.get('Churn')) y_pred= model.predict(sc_X.transform(np.array([[0,1,CustomerID ,Name,Age,Gender,Location, Subscription_Length_Months ,Monthly_Bill,Total_Usage_GB,Churn]]))) y_pred = (y_pred > 0.5) if y_pred>0.5: result="Customer will not churn" else: result="Customer will exit to" return render_template('index.html', prediction_text='Model has predicted : {}'.format(result))
meyograj/churn1
app.py
app.py
py
2,806
python
en
code
0
github-code
6
[ { "api_name": "flask.Flask", "line_number": 6, "usage_type": "call" }, { "api_name": "keras.models.load_model", "line_number": 8, "usage_type": "call" }, { "api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call" }, { "api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 19, "usage_type": "call" }, { "api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 24, "usage_type": "call" }, { "api_name": "sklearn.compose.ColumnTransformer", "line_number": 29, "usage_type": "call" }, { "api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 29, "usage_type": "call" }, { "api_name": "sklearn.model_selection.train_test_split", "line_number": 39, "usage_type": "call" }, { "api_name": "sklearn.preprocessing.StandardScaler", "line_number": 45, "usage_type": "call" }, { "api_name": "flask.render_template", "line_number": 53, "usage_type": "call" }, { "api_name": "flask.request.args.get", "line_number": 60, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 60, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 60, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 61, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 61, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 61, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 62, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 62, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 62, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 63, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 63, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 63, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 64, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 64, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 64, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 65, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 65, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 65, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 66, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 66, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 66, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 67, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 67, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 67, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 69, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 69, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 69, "usage_type": "name" }, { "api_name": "numpy.array", "line_number": 72, "usage_type": "call" }, { "api_name": "flask.render_template", "line_number": 80, "usage_type": "call" } ]
29205882969
import subprocess import time import os import math from PIL import Image import psutil import re from skyfield.api import Star import numpy as np import threading import select from pathlib import Path import fitsio import Nexus import Coordinates import Display home_path = str(Path.home()) version = "21_7" #os.system('pkill -9 -f eFinder.py') # stops the autostart eFinder program running x = y = 0 # x, y define what page the display is showing deltaAz = deltaAlt = 0 expInc = 1 # sets how much exposure changes when using handpad adjust (seconds) gainInc = 5 # ditto for gain offset_flag = False align_count = 0 offset = 640, 480 star_name = "no star" solve = False sync_count = 0 sDog = True gotoFlag = False def xy2rd(x, y): # returns the RA & Dec equivalent to a camera pixel x,y result = subprocess.run( [ "wcs-xy2rd", "-w", destPath + "capture.wcs", "-x", str(x), "-y", str(y), ], capture_output=True, text=True, ) result = str(result.stdout) line = result.split("RA,Dec")[1] ra, dec = re.findall("[-,+]?\d+\.\d+", line) return (float(ra), float(dec)) def pixel2dxdy(pix_x, pix_y): # converts a pixel position, into a delta angular offset from the image centre deg_x = (float(pix_x) - 640) * pix_scale / 3600 # in degrees deg_y = (480 - float(pix_y)) * pix_scale / 3600 dxstr = "{: .1f}".format(float(60 * deg_x)) # +ve if finder is left of Polaris dystr = "{: .1f}".format( float(60 * deg_y) ) # +ve if finder is looking below Polaris return (deg_x, deg_y, dxstr, dystr) def dxdy2pixel(dx, dy): pix_x = dx * 3600 / pix_scale + 640 pix_y = 480 - dy * 3600 / pix_scale dxstr = "{: .1f}".format(float(60 * dx)) # +ve if finder is left of Polaris dystr = "{: .1f}".format(float(60 * dy)) # +ve if finder is looking below Polaris return (pix_x, pix_y, dxstr, dystr) def imgDisplay(): # displays the captured image on the Pi desktop. for proc in psutil.process_iter(): if proc.name() == "display": proc.kill() # delete any previous image display im = Image.open(destPath + "capture.jpg") #im.show() def capture(): global param if param["Test mode"] == "1": if offset_flag == False: m13 = True polaris_cap = False else: m13 = False polaris_cap = True else: m13 = False polaris_cap = False radec = nexus.get_short() camera.capture( int(float(param["Exposure"]) * 1000000), int(float(param["Gain"])), radec, m13, polaris_cap, destPath, ) def solveImage(): global offset_flag, solve, solvedPos, elapsed_time, star_name, star_name_offset, solved_radec, solved_altaz scale_low = str(pix_scale * 0.9) scale_high = str(pix_scale * 1.1) name_that_star = ([]) if (offset_flag == True) else (["--no-plots"]) handpad.display("Started solving", "", "") limitOptions = [ "--overwrite", # overwrite any existing files "--skip-solved", # skip any files we've already solved "--cpulimit", "10", # limit to 10 seconds(!). We use a fast timeout here because this code is supposed to be fast ] optimizedOptions = [ "--downsample", "2", # downsample 4x. 2 = faster by about 1.0 second; 4 = faster by 1.3 seconds "--no-remove-lines", # Saves ~1.25 sec. Don't bother trying to remove surious lines from the image "--uniformize", "0", # Saves ~1.25 sec. Just process the image as-is ] scaleOptions = [ "--scale-units", "arcsecperpix", # next two params are in arcsecs. Supplying this saves ~0.5 sec "--scale-low", scale_low, # See config above "--scale-high", scale_high, # See config above ] fileOptions = [ "--new-fits", "none", # Don't create a new fits "--solved", "none", # Don't generate the solved output "--match", "none", # Don't generate matched output "--corr", "none", # Don't generate .corr files "--rdls", "none", # Don't generate the point list ] cmd = ["solve-field"] captureFile = destPath + "capture.jpg" options = ( limitOptions + optimizedOptions + scaleOptions + fileOptions + [captureFile] ) start_time = time.time() # next line runs the plate-solve on the captured image file result = subprocess.run( cmd + name_that_star + options, capture_output=True, text=True ) elapsed_time = time.time() - start_time print("solve elapsed time " + str(elapsed_time)[0:4] + " sec\n") print(result.stdout) # this line added to help debug. result = str(result.stdout) if "solved" not in result: print("Bad Luck - Solve Failed") handpad.display("Not Solved", "", "") solve = False return if (offset_flag == True) and ("The star" in result): table, h = fitsio.read(destPath + "capture.axy", header=True) star_name_offset = table[0][0], table[0][1] lines = result.split("\n") for line in lines: if line.startswith(" The star "): star_name = line.split(" ")[4] print("Solve-field Plot found: ", star_name) break solvedPos = applyOffset() ra, dec, d = solvedPos.apparent().radec(coordinates.get_ts().now()) solved_radec = ra.hours, dec.degrees solved_altaz = coordinates.conv_altaz(nexus, *(solved_radec)) nexus.set_scope_alt(solved_altaz[0] * math.pi / 180) arr[0, 2][0] = "Sol: RA " + coordinates.hh2dms(solved_radec[0]) arr[0, 2][1] = " Dec " + coordinates.dd2dms(solved_radec[1]) arr[0, 2][2] = "time: " + str(elapsed_time)[0:4] + " s" solve = True deltaCalc() def applyOffset(): x_offset, y_offset, dxstr, dystr = dxdy2pixel( float(param["d_x"]), float(param["d_y"]) ) print('applied_offset_pixels x,y',x_offset,y_offset) ra, dec = xy2rd(x_offset, y_offset) solved = Star( ra_hours=ra / 15, dec_degrees=dec ) # will set as J2000 as no epoch input solvedPos_scope = ( nexus.get_location().at(coordinates.get_ts().now()).observe(solved) ) # now at Jnow and current location return solvedPos_scope def deltaCalc(): global deltaAz, deltaAlt, elapsed_time deltaAz = solved_altaz[1] - nexus.get_altAz()[1] if abs(deltaAz) > 180: if deltaAz < 0: deltaAz = deltaAz + 360 else: deltaAz = deltaAz - 360 deltaAz = 60 * ( deltaAz * math.cos(nexus.get_scope_alt()) ) # actually this is delta'x' in arcminutes deltaAlt = solved_altaz[0] - nexus.get_altAz()[0] deltaAlt = 60 * (deltaAlt) # in arcminutes deltaXstr = "{: .2f}".format(float(deltaAz)) deltaYstr = "{: .2f}".format(float(deltaAlt)) arr[0, 3][0] = "Delta: x= " + deltaXstr arr[0, 3][1] = " y= " + deltaYstr arr[0, 3][2] = "time: " + str(elapsed_time)[0:4] + " s" def align(): global align_count, solve, sync_count, param, offset_flag, arr, x,y new_arr = nexus.read_altAz(arr) arr = new_arr capture() imgDisplay() solveImage() if solve == False: handpad.display(arr[x, y][0], "Solved Failed", arr[x, y][2]) return align_ra = ":Sr" + coordinates.dd2dms((solved_radec)[0]) + "#" align_dec = ":Sd" + coordinates.dd2aligndms((solved_radec)[1]) + "#" valid = nexus.get(align_ra) print(align_ra) if valid == "0": print("invalid position") handpad.display(arr[x, y][0], "Invalid position", arr[x, y][2]) time.sleep(3) return valid = nexus.get(align_dec) print(align_dec) if valid == "0": print("invalid position") handpad.display(arr[x, y][0], "Invalid position", arr[x, y][2]) time.sleep(3) return reply = nexus.get(":CM#") nexus.read_altAz(arr) deltaCalc() print("reply: ", reply) p = nexus.get(":GW#") print("Align status reply ", p) if nexus.is_aligned() == False: # wasnt aligned before this action align_count += 1 if p[1] != "T": # and still not aligned arr[0,4][0] = "'OK' aligns" arr[0,4][1] = "Align count " + str(align_count) arr[0,4][2] = "Nexus reply:" + p[0:3] handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2]) else: arr[0,4][0] = "'OK' now syncs" arr[0,4][1] = "Sync count " + str(sync_count) arr[0,4][2] = "Nexus reply:" + p[0:3] arr[2,0][1] = "Nexus is aligned" handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2]) nexus.set_aligned(True) else: sync_count +=1 arr[0,4][0] = "'OK' syncs" arr[0,4][1] = "Sync count " + str(sync_count) arr[0,4][2] = "" handpad.display(arr[0,4][0],arr[0,4][1],arr[0,4][2]) print("Nexus is aligned:",nexus.is_aligned()) return def measure_offset(): global offset_str, offset_flag, param, scope_x, scope_y, star_name offset_flag = True handpad.display("started capture", "", "") capture() imgDisplay() solveImage() if solve == False: handpad.display("solve failed", "", "") return scope_x = star_name_offset[0] scope_y = star_name_offset[1] print('pixel_offset x,y',star_name_offset) d_x, d_y, dxstr, dystr = pixel2dxdy(scope_x, scope_y) param["d_x"] = d_x param["d_y"] = d_y save_param() offset_str = dxstr + "," + dystr arr[2, 1][1] = "new " + offset_str arr[2, 2][1] = "new " + offset_str handpad.display(arr[2, 1][0], arr[2, 1][1], star_name + " found") offset_flag = False def up_down(v): global x x = x + v handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) def left_right(v): global y y = y + v handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) def up_down_inc(inc, sign): arr[x, y][1] = int(float(arr[x, y][1])) + inc * sign param[arr[x, y][0]] = float(arr[x, y][1]) handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) update_summary() time.sleep(0.1) def flip(): global param arr[x, y][1] = 1 - int(float(arr[x, y][1])) param[arr[x, y][0]] = str((arr[x, y][1])) handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) update_summary() time.sleep(0.1) def update_summary(): global param arr[1, 0][0] = ( "Ex:" + str(param["Exposure"]) + " Gn:" + str(param["Gain"]) ) arr[1, 0][1] = "Test:" + str(param["Test mode"]) + " GoTo++:" + str(param["Goto++ mode"]) save_param() def go_solve(): global x, y, solve, arr new_arr = nexus.read_altAz(arr) arr = new_arr handpad.display("Image capture", "", "") capture() imgDisplay() handpad.display("Plate solving", "", "") solveImage() if solve == True: handpad.display("Solved", "", "") else: handpad.display("Not Solved", "", "") return x = 0 y = 3 handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) def gotoDistant(): nexus.read_altAz(arr) nexus_radec = nexus.get_radec() deltaRa = abs(nexus_radec[0]-goto_radec[0])*15 if deltaRa > 180: deltaRa = abs(deltaRa - 360) deltaDec = abs(nexus_radec[1]-goto_radec[1]) print('goto distance, RA,Dec :',deltaRa,deltaDec) if deltaRa+deltaDec > 5: return(True) else: return(False) def readTarget(): global goto_radec,goto_ra,goto_dec goto_ra = nexus.get(":Gr#") if ( goto_ra[0:2] == "00" and goto_ra[3:5] == "00" ): # not a valid goto target set yet. print("no GoTo target") handpad.display("no GoTo target","set yet","") return goto_dec = nexus.get(":Gd#") ra = goto_ra.split(":") dec = re.split(r"[:*]", goto_dec) goto_radec = (float(ra[0]) + float(ra[1]) / 60 + float(ra[2]) / 3600), math.copysign( abs(abs(float(dec[0])) + float(dec[1]) / 60 + float(dec[2]) / 3600), float(dec[0]), ) print("Target goto RA & Dec", goto_ra, goto_dec) def goto(): global gotoFlag handpad.display("Attempting", "GoTo", "") gotoFlag = True readTarget() if gotoDistant(): if sDog == True: nexus.write(":Sr" + goto_ra + "#") nexus.write(":Sd" + goto_dec + "#") reply = nexus.get(":MS#") else: gotoStr = '%s%06.3f %+06.3f' %("g",goto_radec[0],goto_radec[1]) print("Target goto RA & Dec", gotoStr) servocat.send(gotoStr) handpad.display("Performing", " GoTo", "") time.sleep(1) gotoStopped() handpad.display("Finished", " GoTo", "") go_solve() if int(param["Goto++ mode"]) == 0: return align() # close, so local sync scope to true RA & Dec if sDog == True: nexus.write(":Sr" + goto_ra + "#") nexus.write(":Sd" + goto_dec + "#") reply = nexus.get(":MS#") else: gotoStr = '%s%06.3f %+06.3f' %("g",goto_radec[0],goto_radec[1]) print('GoToStr: ',gotoStr) servocat.send(gotoStr) gotoStopped() gotoFlag = False handpad.display("Finished", " GoTo++", "") go_solve() def getRadec(): nexus.read_altAz(None) return(nexus.get_radec()) def gotoStopped(): radecNow = getRadec() while True: time.sleep(1) radec = getRadec() print(radec[0],radecNow[0],radec[1],radecNow[1]) if (abs(radecNow[0] - radec[0])*15 < 0.01) and (abs(radecNow[1] - radec[1]) < 0.01): return else: radecNow = radec def reset_offset(): global param, arr param["d_x"] = 0 param["d_y"] = 0 offset_str = "0,0" arr[2,1][1] = "new " + offset_str arr[2,2][1] = "new " + offset_str handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) save_param() def get_param(): global param, offset_str, pix_scale if os.path.exists(home_path + "/Solver/eFinder.config") == True: with open(home_path + "/Solver/eFinder.config") as h: for line in h: line = line.strip("\n").split(":") param[line[0]] = str(line[1]) pix_scale = float(param["pixel scale"]) pix_x, pix_y, dxstr, dystr = dxdy2pixel( float(param["d_x"]), float(param["d_y"]) ) offset_str = dxstr + "," + dystr def save_param(): global param with open(home_path + "/Solver/eFinder.config", "w") as h: for key, value in param.items(): #print("%s:%s\n" % (key, value)) h.write("%s:%s\n" % (key, value)) def reader(): global button while True: if handpad.get_box() in select.select([handpad.get_box()], [], [], 0)[0]: button = handpad.get_box().readline().decode("ascii").strip("\r\n") time.sleep(0.1) def home_refresh(): global x,y while True: if x == 0 and y == 0: time.sleep(1) while x ==0 and y==0: nexus.read_altAz(arr) radec = nexus.get_radec() ra = coordinates.hh2dms(radec[0]) dec = coordinates.dd2dms(radec[1]) handpad.display('Nexus live',' RA: '+ra, 'Dec: '+dec) time.sleep(0.5) else: handpad.display(arr[x, y][0], arr[x, y][1], arr[x, y][2]) time.sleep (0.5) # main code starts here handpad = Display.Handpad(version) coordinates = Coordinates.Coordinates() nexus = Nexus.Nexus(handpad, coordinates) nexus.read() param = dict() get_param() # array determines what is displayed, computed and what each button does for each screen. # [first line,second line,third line, up button action,down...,left...,right...,select button short press action, long press action] # empty string does nothing. # example: left_right(-1) allows left button to scroll to the next left screen # button texts are infact def functions p = "" home = [ "Nexus live", " RA:", "Dec:", "", "up_down(1)", "", "left_right(1)", "align()", "goto()", ] nex = [ "Nex: RA ", " Dec ", "", "", "", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] sol = [ "No solution yet", "'OK' solves", "", "", "", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] delta = [ "Delta: No solve", "'OK' solves", "", "", "", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] aligns = [ "'OK' aligns", "not aligned yet", str(p), "", "", "left_right(-1)", "", "align()", "", ] polar = [ "'OK' Bright Star", offset_str, "", "", "", "left_right(-1)", "left_right(1)", "measure_offset()", "", ] reset = [ "'OK' Resets", offset_str, "", "", "", "left_right(-1)", "left_right(1)", "reset_offset()", "", ] summary = ["", "", "", "up_down(-1)", "up_down(1)", "", "left_right(1)", "go_solve()", ""] exp = [ "Exposure", param["Exposure"], "", "up_down_inc(expInc,1)", "up_down_inc(expInc,-1)", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] gn = [ "Gain", param["Gain"], "", "up_down_inc(gainInc,1)", "up_down_inc(gainInc,-1)", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] gotoMode = [ "Goto++ mode", int(param["Goto++ mode"]), "", "flip()", "flip()", "left_right(-1)", "", "go_solve()", "goto()", ] mode = [ "Test mode", int(param["Test mode"]), "", "flip()", "flip()", "left_right(-1)", "left_right(1)", "go_solve()", "goto()", ] status = [ "Nexus via " + nexus.get_nexus_link(), "Nex align " + str(nexus.is_aligned()), "Brightness", "up_down(-1)", "", "", "left_right(1)", "go_solve()", "goto()", ] bright = [ "Handpad", "Display", "Bright Adj", "", "", "left_right(-1)", "", "go_solve()", "goto()", ] arr = np.array( [ [home, nex, sol, delta, aligns], [summary, exp, gn, mode, gotoMode], [status, polar, reset, bright, bright], ] ) update_summary() deg_x, deg_y, dxstr, dystr = dxdy2pixel(float(param["d_x"]), float(param["d_y"])) offset_str = dxstr + "," + dystr new_arr = nexus.read_altAz(arr) arr = new_arr if nexus.is_aligned() == True: arr[0, 4][1] = "Nexus is aligned" arr[0, 4][0] = "'OK' syncs" #arr[2,0][1] = "Nexus is aligned" if param["Camera Type ('QHY' or 'ASI')"]=='ASI': import ASICamera2 camera = ASICamera2.ASICamera(handpad) elif param["Camera Type ('QHY' or 'ASI')"]=='QHY': import QHYCamera2 camera = QHYCamera2.QHYCamera(handpad) if param["Drive ('scopedog' or 'servocat')"].lower()=='servocat': import ServoCat servocat = ServoCat.ServoCat() sDog = False print('ServoCat mode') arr[2,0][1] = "ServoCat mode" else: print('ScopeDog mode') arr[2,0][1] = "ScopeDog mode" if param["Ramdisk"].lower()=='true': destPath = "/var/tmp/" else: destPath = home_path + "/Solver/images/" print('Working folder: '+destPath) handpad.display("ScopeDog eFinder", "ver " + version, "Drive: "+param["Drive ('scopedog' or 'servocat')"]) time.sleep(3) button = "" scan = threading.Thread(target=reader) scan.daemon = True scan.start() while True: # next loop looks for button press and sets display option x,y if button == "20": exec(arr[x, y][7]) elif button == "21": exec(arr[x, y][8]) elif button == "18": exec(arr[x, y][4]) elif button == "16": exec(arr[x, y][3]) elif button == "19": exec(arr[x, y][5]) elif button == "17": exec(arr[x, y][6]) button = "" if x == 0 and y == 0 and gotoFlag == False: nexus.read_altAz(arr) radec = nexus.get_radec() if nexus.is_aligned() == True: tick = "T" else: tick = "N" ra = coordinates.hh2dms(radec[0]) dec = coordinates.dd2dms(radec[1]) handpad.display('Nexus live '+tick,' RA: '+ra, 'Dec: '+dec) time.sleep(0.2) else: time.sleep(0.1)
WimDeMeester/eFinder
eFinder.py
eFinder.py
py
20,522
python
en
code
0
github-code
6
[ { "api_name": "pathlib.Path.home", "line_number": 18, "usage_type": "call" }, { "api_name": "pathlib.Path", "line_number": 18, "usage_type": "name" }, { "api_name": "subprocess.run", "line_number": 36, "usage_type": "call" }, { "api_name": "re.findall", "line_number": 51, "usage_type": "call" }, { "api_name": "psutil.process_iter", "line_number": 71, "usage_type": "call" }, { "api_name": "PIL.Image.open", "line_number": 74, "usage_type": "call" }, { "api_name": "PIL.Image", "line_number": 74, "usage_type": "name" }, { "api_name": "time.time", "line_number": 143, "usage_type": "call" }, { "api_name": "subprocess.run", "line_number": 145, "usage_type": "call" }, { "api_name": "time.time", "line_number": 148, "usage_type": "call" }, { "api_name": "fitsio.read", "line_number": 158, "usage_type": "call" }, { "api_name": "math.pi", "line_number": 170, "usage_type": "attribute" }, { "api_name": "skyfield.api.Star", "line_number": 183, "usage_type": "call" }, { "api_name": "math.cos", "line_number": 200, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 227, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 234, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 303, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 312, "usage_type": "call" }, { "api_name": "re.split", "line_number": 364, "usage_type": "call" }, { "api_name": "math.copysign", "line_number": 365, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 386, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 413, "usage_type": "call" }, { "api_name": "os.path.exists", "line_number": 433, "usage_type": "call" }, { "api_name": "os.path", "line_number": 433, "usage_type": "attribute" }, { "api_name": "select.select", "line_number": 455, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 457, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 463, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 470, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 473, "usage_type": "call" }, { "api_name": "Display.Handpad", "line_number": 478, "usage_type": "call" }, { "api_name": "Coordinates.Coordinates", "line_number": 479, "usage_type": "call" }, { "api_name": "Nexus.Nexus", "line_number": 480, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 636, "usage_type": "call" }, { "api_name": "ASICamera2.ASICamera", "line_number": 655, "usage_type": "call" }, { "api_name": "QHYCamera2.QHYCamera", "line_number": 658, "usage_type": "call" }, { "api_name": "ServoCat.ServoCat", "line_number": 662, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 677, "usage_type": "call" }, { "api_name": "threading.Thread", "line_number": 680, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 708, "usage_type": "call" }, { "api_name": "time.sleep", "line_number": 710, "usage_type": "call" } ]
36040524676
import statistics from ParadoxTrading.Indicator.IndicatorAbstract import IndicatorAbstract from ParadoxTrading.Utils import DataStruct class AdaBBands(IndicatorAbstract): def __init__( self, _period: int, _use_key: str, _init_n: int = 20, _min_n: int = 20, _max_n: int = 60, _rate: float = 2.0, _idx_key: str = 'time' ): super().__init__() self.use_key = _use_key self.idx_key = _idx_key self.keys = [self.idx_key, 'upband', 'midband', 'downband'] self.data = DataStruct( self.keys, self.idx_key ) self.period = _period self.rate = _rate self.buf = [] self.prev_std = None self.dynamic_n = float(_init_n) self.min_n = _min_n self.max_n = _max_n def _addOne(self, _data_struct: DataStruct): index_value = _data_struct.index()[0] self.buf.append(_data_struct.getColumn(self.use_key)[0]) if len(self.data) > self.period: const_std = statistics.pstdev(self.buf[-self.period:]) self.dynamic_n *= const_std / self.prev_std self.dynamic_n = max(self.min_n, self.dynamic_n) self.dynamic_n = min(self.max_n, self.dynamic_n) tmp_n = int(round(self.dynamic_n)) mean = statistics.mean(self.buf[-tmp_n:]) std = statistics.pstdev(self.buf[-tmp_n:]) self.data.addRow( [index_value, mean + self.rate * std, mean, mean - self.rate * std], self.keys ) self.prev_std = const_std else: if len(self.data) == self.period: self.prev_std = statistics.pstdev(self.buf) self.data.addRow( [index_value, None, None, None], self.keys )
ppaanngggg/ParadoxTrading
ParadoxTrading/Indicator/General/AdaBBands.py
AdaBBands.py
py
1,870
python
en
code
51
github-code
6
[ { "api_name": "ParadoxTrading.Indicator.IndicatorAbstract.IndicatorAbstract", "line_number": 7, "usage_type": "name" }, { "api_name": "ParadoxTrading.Utils.DataStruct", "line_number": 19, "usage_type": "call" }, { "api_name": "ParadoxTrading.Utils.DataStruct", "line_number": 33, "usage_type": "name" }, { "api_name": "statistics.pstdev", "line_number": 38, "usage_type": "call" }, { "api_name": "statistics.mean", "line_number": 44, "usage_type": "call" }, { "api_name": "statistics.pstdev", "line_number": 45, "usage_type": "call" }, { "api_name": "statistics.pstdev", "line_number": 56, "usage_type": "call" } ]
19780951486
#!/usr/bin/env python3 # -*- encoding: utf-8 -*- import os import argparse import subprocess from glob import glob def translatefolder(src, trg, **kw): python = kw.get("python", "python3") translate = kw.get("translate", "./translate/translate.py") port = int(kw.get("port", 3035)) host = kw.get("host", "127.0.0.1") # create directories if not os.path.exists(trg): os.makedirs(trg) # collect files domains, problems = [], [] for f in os.listdir(src): if "domain" in f and f.endswith(".pddl"): domains.append(os.path.join(src, f)) elif "problem" in f and f.endswith(".pddl"): problems.append(os.path.join(src, f)) domains.sort() problems.sort() # assign agents agents = [] for i in range(len(domains)): agents.append("tcp://{}:{}".format(host, str(port+i))) # create command tmpl = ("{} {} {} {} --agent-url " + " --agent-url ".join(agents) + " --agent-id {} --output {} --json") cmd = "" for i, d in enumerate(domains): s = tmpl.format(python, translate, d, problems[i], i, os.path.join(trg,str(i)+'.json')) + ' & ' print(s) cmd += s cmd = cmd[:-2] os.system(cmd) def translateall(src='benchmarks/factored/', trg='benchmarks/compiled/', **kw): files_src = glob(src + "*/*/") files_trg = [os.path.join(trg, *f.split('/')[2:]) for f in files_src] port = 3035 shift = 100 errors = [] for s, t in zip(files_src, files_trg): try: print("translating " + s + " to " + t + " port: " + str(port)) translatefolder(s, t, port=port) except Exception as e: errors += [e] port += shift for i, error in enumerate(errors): print("ERR %d: %s: %s" % (i, type(error), error)) def on_translate(*args, **kw): if kw['all']: translateall(**kw) else: translatefolder(**kw) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Run GOA planner') parser.add_argument('src', help='path to folder containing src task') parser.add_argument('trg', help='destination path') parser.add_argument( '--port', default=3035, help='the port (default: 3035)' ) parser.add_argument( '--all', help='translate all domains of given folder', action='store_true' ) parser.set_defaults(func=on_translate) args, rest = parser.parse_known_args() args.func(*rest, **vars(args))
schultet/goa
scripts/translate.py
translate.py
py
2,582
python
en
code
2
github-code
6
[ { "api_name": "os.path.exists", "line_number": 16, "usage_type": "call" }, { "api_name": "os.path", "line_number": 16, "usage_type": "attribute" }, { "api_name": "os.makedirs", "line_number": 17, "usage_type": "call" }, { "api_name": "os.listdir", "line_number": 21, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 23, "usage_type": "call" }, { "api_name": "os.path", "line_number": 23, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 25, "usage_type": "call" }, { "api_name": "os.path", "line_number": 25, "usage_type": "attribute" }, { "api_name": "os.path.join", "line_number": 40, "usage_type": "call" }, { "api_name": "os.path", "line_number": 40, "usage_type": "attribute" }, { "api_name": "os.system", "line_number": 45, "usage_type": "call" }, { "api_name": "glob.glob", "line_number": 49, "usage_type": "call" }, { "api_name": "os.path.join", "line_number": 50, "usage_type": "call" }, { "api_name": "os.path", "line_number": 50, "usage_type": "attribute" }, { "api_name": "argparse.ArgumentParser", "line_number": 74, "usage_type": "call" } ]
35616591437
# https://adventofcode.com/2022/day/22 from collections import defaultdict from aoctk.data import Graph, Unbound2DGrid from aoctk.input import get_groups def parse(data): ps, (ins,) = get_groups(data) m = Unbound2DGrid( ( (complex(j, i), c) for i, r in enumerate(ps) for j, c in enumerate(r) if c != " " ) ) p = map(complex, ins.replace("R", " 1j ").replace("L", " -1j ").split()) return m, p, complex(ps[0].index(ps[0].strip())) def solve(wrapping, data="input.txt"): m, p, z = parse(data) d = 1 while True: s = next(p) for _ in range(int(abs(s))): if z + d not in m: w, e = wrapping(m, z, d) if m[w] != "#": z, d = w, e continue elif m[z + d] == "#": break z += d try: d *= next(p) except StopIteration: break return ( int(z.real + 1) * 4 + int(z.imag + 1) * 1000 + {1: 0, 1j: 1, -1: 2, -1j: 3}[d] ) def part_one(data="input.txt"): def wrapping(m, z, d): w = z while w - d in m: w -= d return w, d return solve(wrapping, data) def part_two(data="input.txt"): m, _, _ = parse(data) # Determine the face size w, h = (_.hi + 1 for _ in m.bounds()) l = max(w, h) - min(w, h) class Faces(Graph): def adj(self, n): return { (n + l * d, d) for d in (1j ** k for k in range(4)) if n + l * d in self.data } def __iter__(self): return iter(self.data) fs = Faces( { complex(i, j) for i in range(0, w, l) for j in range(0, h, l) if complex(i, j) in m } ) # Determine the wrapping rules based on how the faces are connected # The mapping tells for each face and each direction the destination face # and the direction to go in that face. wrs, c = defaultdict(dict), 24 for s in fs: for t, d in fs.adj(s): wrs[s][d] = (t, d) c -= 1 while c > 0: for s in fs: r = wrs[s] for k in (1j ** _ for _ in range(4)): if c <= 0: break if k in r and k * 1j in r: (t, phi), (q, psi) = r[k], r[k * 1j] if phi * 1j not in wrs[t]: wrs[t][phi * 1j] = (q, psi * 1j) c -= 1 if -psi * 1j not in wrs[q]: wrs[q][-psi * 1j] = (t, -phi * 1j) c -= 1 def wrapping(m, z, d): a = complex(z.real // l, z.imag // l) * l b, e = wrs[a][d] w = (z - a) - (l - 1) * d + (1 + 1j) rot = e / d tr = (l + 1) * (1 + 1j) * (1 - rot) / 2 w = b + w * rot + tr - (1 + 1j) return w, e return solve(wrapping, data) def test(): assert part_one("test.txt") == 6032 assert part_two("test.txt") == 5031
P403n1x87/aoc
2022/22/code.py
code.py
py
3,157
python
en
code
2
github-code
6
[ { "api_name": "aoctk.input.get_groups", "line_number": 10, "usage_type": "call" }, { "api_name": "aoctk.data.Unbound2DGrid", "line_number": 12, "usage_type": "call" }, { "api_name": "aoctk.data.Graph", "line_number": 67, "usage_type": "name" }, { "api_name": "collections.defaultdict", "line_number": 90, "usage_type": "call" } ]
32177006845
import numpy as np from sentinelhub import BBox, bbox_to_dimensions,CRS class resolution_image: def __init__(self,bbox,resolution): self.bbox = bbox self.resolution = resolution self.size=None def run(self): our_bbox = list(np.round(self.bbox,2)) our_bbox = BBox(bbox=our_bbox, crs=CRS.WGS84) self.size = bbox_to_dimensions(our_bbox, resolution=self.resolution) return self.size
VaclavLamich/Cloud-Detection
resolution.py
resolution.py
py
454
python
en
code
0
github-code
6
[ { "api_name": "numpy.round", "line_number": 13, "usage_type": "call" }, { "api_name": "sentinelhub.BBox", "line_number": 14, "usage_type": "call" }, { "api_name": "sentinelhub.CRS.WGS84", "line_number": 14, "usage_type": "attribute" }, { "api_name": "sentinelhub.CRS", "line_number": 14, "usage_type": "name" }, { "api_name": "sentinelhub.bbox_to_dimensions", "line_number": 15, "usage_type": "call" } ]
3647213738
import heapq import numpy as np import itertools class PQueue: def __init__(self): self.pq = [] # list of entries arranged in a heap self.entry_finder = {} # mapping of tasks to entries self.REMOVED = '<removed-task>' # placeholder for a removed task self.counter = itertools.count() # unique sequence count def add_task(self, task, priority=0): 'Add a new task or update the priority of an existing task' add_to_q = True if task in self.entry_finder: add_to_q = self.remove_task_if_lower_priority(task, priority) if add_to_q: count = next(self.counter) entry = [priority, count, task] self.entry_finder[task] = entry heapq.heappush(self.pq, entry) def remove_task_if_lower_priority(self, task, priority): 'Mark an existing task as self.REMOVED. Raise KeyError if not found.' entry = self.entry_finder[task] if entry[0] > priority: del self.entry_finder[task] entry[-1] = self.REMOVED return True else: return False def pop_task(self): 'Remove and return the lowest priority task. Raise KeyError if empty.' while self.pq: priority, count, task = heapq.heappop(self.pq) if task is not self.REMOVED: #print(task) #print(self.entry_finder) del self.entry_finder[task] return task raise KeyError('pop from an empty priority queue') def empty(self): return len(self.entry_finder) == 0 def qsize(self): return len(self.entry_finder) def test(): q = PQueue() q.add_task((tuple(np.array([1,2,3])),1),1) q.add_task((tuple(np.array([4,5,6])),1),0) q.add_task((tuple(np.array([1,2,3])),1),-1) print(q.pop_task()) print(q.pop_task()) q.add_task((tuple(np.array([1,2,3])),1),0.5) print(q.pop_task())
joedlcolvin/Tugboats
p_queue.py
p_queue.py
py
2,032
python
en
code
0
github-code
6
[ { "api_name": "itertools.count", "line_number": 10, "usage_type": "call" }, { "api_name": "heapq.heappush", "line_number": 21, "usage_type": "call" }, { "api_name": "heapq.heappop", "line_number": 36, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 52, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 53, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 54, "usage_type": "call" }, { "api_name": "numpy.array", "line_number": 57, "usage_type": "call" } ]
27462814126
from app import app from app import db from app.models import booking from flask import jsonify, request @app.route('/get_booking', methods=['GET']) def get_booking(): date = request.args.get('date') idTable = request.args.get('idTable') phone = ['','','','','','','',''] users = booking.query.all() for u in users: if u.date == date and u.table == int(idTable): for h in range(8): if (u.hour_start <= 12+h) and (12+h <= u.hour_end): phone[h] = u.phone return jsonify({ "schedule":{ "table_id": idTable, "date": date, "hours":[ { "hour": "12:00", "customerPhone": phone[0] }, { "hour": "13:00", "customerPhone": phone[1] }, { "hour": "14:00", "customerPhone": phone[2] }, { "hour": "15:00", "customerPhone": phone[3] }, { "hour": "16:00", "customerPhone": phone[4] }, { "hour": "17:00", "customerPhone": phone[5] }, { "hour": "18:00", "customerPhone": phone[6] }, { "hour": "19:00", "customerPhone": phone[7] } ] } }) @app.route('/post_new_booking', methods=['POST']) def post_new_booking(): date = request.json['date'] table = request.json['table_id'] name = request.json['name'] comment = request.json['comment'] phone = request.json['phone'] hours_start = request.json['hours_start'] hours_end = request.json['hours_end'] u = booking(table=table, name=name, phone=phone, info=comment, date=date, hour_start=hours_start, hour_end=hours_end) db.session.add(u) db.session.commit() return jsonify({"status": "OK"})
SevaSob/Na-rogah
routes.py
routes.py
py
2,216
python
en
code
0
github-code
6
[ { "api_name": "flask.request.args.get", "line_number": 8, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 8, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 8, "usage_type": "name" }, { "api_name": "flask.request.args.get", "line_number": 9, "usage_type": "call" }, { "api_name": "flask.request.args", "line_number": 9, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 9, "usage_type": "name" }, { "api_name": "app.models.booking.query.all", "line_number": 13, "usage_type": "call" }, { "api_name": "app.models.booking.query", "line_number": 13, "usage_type": "attribute" }, { "api_name": "app.models.booking", "line_number": 13, "usage_type": "name" }, { "api_name": "flask.jsonify", "line_number": 20, "usage_type": "call" }, { "api_name": "app.app.route", "line_number": 6, "usage_type": "call" }, { "api_name": "app.app", "line_number": 6, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 63, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 63, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 64, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 64, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 65, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 65, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 66, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 66, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 67, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 67, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 68, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 68, "usage_type": "name" }, { "api_name": "flask.request.json", "line_number": 69, "usage_type": "attribute" }, { "api_name": "flask.request", "line_number": 69, "usage_type": "name" }, { "api_name": "app.models.booking", "line_number": 71, "usage_type": "call" }, { "api_name": "app.db.session.add", "line_number": 72, "usage_type": "call" }, { "api_name": "app.db.session", "line_number": 72, "usage_type": "attribute" }, { "api_name": "app.db", "line_number": 72, "usage_type": "name" }, { "api_name": "app.db.session.commit", "line_number": 73, "usage_type": "call" }, { "api_name": "app.db.session", "line_number": 73, "usage_type": "attribute" }, { "api_name": "app.db", "line_number": 73, "usage_type": "name" }, { "api_name": "flask.jsonify", "line_number": 75, "usage_type": "call" }, { "api_name": "app.app.route", "line_number": 61, "usage_type": "call" }, { "api_name": "app.app", "line_number": 61, "usage_type": "name" } ]