diff --git "a/6322.jsonl" "b/6322.jsonl" new file mode 100644--- /dev/null +++ "b/6322.jsonl" @@ -0,0 +1,761 @@ +{"seq_id":"513869100","text":"# Task 1\nANSWERS = {\n 'How are you?': 'Good!',\n 'What time is it?': '11 am',\n 'What are you doing?': 'Coding',\n}\n\n\ndef ask_user():\n while True:\n question = input('Ask me something: ')\n answer = ANSWERS.get(question, 'Ask me another question please.')\n if question == 'Bye!':\n print(answer)\n break\n else:\n print(answer)\n\n\nask_user()\n","sub_path":"lesson_2/while_tasks.py","file_name":"while_tasks.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"38463142","text":"from telegram.ext import *\r\nfrom telegram import *\r\nfrom datetime import timedelta, datetime\r\nimport re\r\n\r\n\r\ndef sleep(update, context):\r\n try:\r\n moment = []\r\n momentime = []\r\n list = update.message.text\r\n time_in = re.split(r'[: ]+', list)\r\n hour = int(time_in[1])\r\n minut = int(time_in[2])\r\n print(hour)\r\n if hour > 24 or minut > 60:\r\n update.message.reply_text('Ошибка, время введено некорректно')\r\n else:\r\n time = timedelta(hours=hour, minutes=minut)\r\n interval = time - timedelta(hours=1, minutes=30)\r\n moment.append(str(interval).replace(\"-1 day,\", \" \"))\r\n for i in range(5):\r\n interval = interval - timedelta(hours=1, minutes=30)\r\n moment.append(str(interval).replace(\"-1 day,\", \" \"))\r\n momentime.append(\"; \".join(moment))\r\n update.message.reply_text(momentime)\r\n update.message.reply_text(\r\n '*обратите внимание: результат рассчитан без учета времени на засыпание. Отнимите еще 10-20 минут,'\r\n ' в течение которых вы засыпаете.')\r\n\r\n except:\r\n update.message.reply_text('Произошла ошибка.')\r\n\r\n\r\ndef start(update, context):\r\n update.message.reply_text(\r\n 'Приветсвую, я бот, который поможет вам определиться с временем, в которое лучше проснуться ')\r\n update.message.reply_text(\r\n 'Введете время, в которое хотите проснуться. Перед временем напишите \"/sleep\", например \"/sleep 7:00\".')\r\n\r\n\r\n\r\ndef creator(update, bot):\r\n update.message.reply_text(\r\n 'Бот создан как проект для яндекс лицея ')\r\n update.message.reply_text(\r\n 'О создателе: '\r\n 'Меня зовут Валерия. '\r\n 'Приятно познакомиться. Рекламная интеграция: '\r\n 'Моя группа в вк: https://vk.com/babyeel слушайте и наслаждайтесь песнями. '\r\n 'Вк: https://vk.com/babyel1 '\r\n 'Тикток: https://vm.tiktok.com/ZSJBuT2ad/ 😈')\r\n #тут должен быть вывод моей фотографии )))\r\n #update.bot.send_photo(chat_id=bot.message.chat.id, photo=open('https://ic.wampi.ru/2021/04/24/creator.png', 'rb'))\r\n\r\n\r\ndef aboutthebot(update, bot):\r\n update.message.reply_text(\r\n 'Когда человек засыпает, он проходит несколько циклов из '\r\n 'чередующихся медленной и быстрой фаз сна. Если вы проснетесь в период '\r\n 'медленной фазы, то будете ощущать тяжесть, разбитость и усталость. '\r\n 'Вам будет сложнее пробудиться и встать с кровати, чего не произойдет, '\r\n 'если проснуться в конце быстрой фазы. Вот почему так важно '\r\n 'соблюдать режим сна.')\r\n update.message.reply_text(\r\n 'В калькуляторе сна онлайн учитывается продолжительность циклов, '\r\n 'которые длятся в среднем 90 минут. Вам необходимо указать время, '\r\n 'когда вы планируете лечь спать, затем калькулятор обозначит благоприятное время, '\r\n 'когда лучше подняться утром, чтобы чувствовать себя полноценно '\r\n 'отдохнувшим. Бот также напоминает о времени на засыпание, ')\r\n\r\n\r\ndef main():\r\n updater = Updater('1702435770:AAEZbU9fC074ZaGrelsMMWQrg6Hbgy1En_M', use_context=True)\r\n\r\n dp = updater.dispatcher\r\n dp.add_handler(CommandHandler(\"start\", start))\r\n dp.add_handler(CommandHandler(\"sleep\", sleep))\r\n dp.add_handler(CommandHandler(\"creator\", creator))\r\n dp.add_handler(CommandHandler(\"aboutthebot\", aboutthebot))\r\n\r\n updater.start_polling()\r\n\r\n updater.idle()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"504380198","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\"\nCENTRO DE INVESTIGACION EN MATEMATICAS\nDOCTORADO EN CIENCIAS DE LA COMPUTACION\nFERNANDO CERVANTES SANCHEZ\n\nFILE NAME: detection.py\n\nPURPOSE: Enhace the coronary arteries in an X-ray angiogram using GMF.\n\nFILE REFERENCES:\nName I/O Description\nNone ---- ----------\n\nABNORMAL TERMINATION CONDITIONS, ERROR AND WARNING MESSAGES:\n\nDEVELOPMENT HISTORY:\nDate Author Change Id Release Description Of Change\n20/Jun/2017 Fernando C. 0 1.0 Creation\n18/Dic/2017 Fernando C. 1 1.0 Functionality equal to the Matlab version\n07/Sep/2018 Fernando C. 2 1.1 Fast convolution using extension implemented in C (requires acml library)\n\"\"\"\n\nimport numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport cv2\nimport compute_FOV_mask as cFOV\nimport sys\nimport time\n\nfrom gmffilter import *\nfrom computeAUCROC import *\nfrom time import perf_counter\n\n\"\"\" \nFUNCTION NAME: fastConvolution\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nsrc numpy.array I Source data\nkernel numpy.array I Convolution kernel\n\nRETURNS:\nThe response to the convolution of 'src' and 'kernel'.\n\"\"\"\ndef fastConvolution(src, kernel):\n kernel_height, kernel_width = kernel.shape\n height, width = src.shape\n \n \"\"\"\n The source and kernel are expanded to the nearest 2-powered dimension\n and zero padding in order to execute a more efficient Fourier Transform.\n \"\"\"\n largest_dim = width\n if (height > width):\n largest_dim = height\n \n current_2p_dim = np.log2(largest_dim)\n remainder_2p_dim = current_2p_dim - np.floor(current_2p_dim)\n power_increase = 1\n if (remainder_2p_dim < 1e-12):\n power_increase = 0\n \n nearest_2p_dim = int(2 ** (np.floor(current_2p_dim) + 1))\n \n kernels_offset_y = int(kernel_height / 2)\n kernels_offset_x = int(kernel_width / 2)\n \n # Zero padding:\n src_zp = cv2.copyMakeBorder(src, 0, nearest_2p_dim - height, 0, nearest_2p_dim - width, 0)\n\n kernel_zp = cv2.copyMakeBorder(kernel, 0, nearest_2p_dim - kernel_height, 0, nearest_2p_dim - kernel_width, 0)\n \n fft_src_zp = np.fft.fft2(src_zp)\n fft_kernel_zp = np.fft.fft2(kernel_zp)\n \n fft_resp = np.multiply(fft_src_zp, fft_kernel_zp)\n \n # Response in the space domain is shifted to move the (0, 0) origin of the image to the center.\n resp = np.fft.ifft2(fft_resp)\n \n # The response is resized to the original image size:\n return resp.real[kernels_offset_y:(kernels_offset_y + height), kernels_offset_x:(kernels_offset_x + width)]\n\n\n\n\"\"\" \nFUNCTION NAME: convolution\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nsrc numpy.array I Source data\nkernel numpy.array I Convolution kernel\n\nRETURNS:\nThe response to the convolution of 'src' and 'kernel'.\n\"\"\"\ndef convolution(src, kernel):\n \"\"\"\n This function is required due to Open CV uses the DFT for kernels\n of dimension greater than (11, 11), and the result differs slightly\n from the expected\n \"\"\"\n kernel_height, kernel_width = kernel.shape\n height, width = src.shape\n \n src_temp = cv2.copyMakeBorder(src, kernel_height-1, kernel_height-1, kernel_width-1, kernel_width-1, 0)\n dst = np.zeros([height + kernel_height - 1, width + kernel_width - 1], dtype=np.float64)\n \n flipped_kernel = cv2.flip(kernel, flipCode = -1)\n \n for i in range(height + kernel_height - 1):\n for j in range(width + kernel_width - 1):\n dst[i, j] = np.sum(np.multiply(kernel, src_temp[i:(i+kernel_height), j:(j+kernel_width)]))\n\n return dst[int(kernel_height/2):int(height + kernel_height/2), int(kernel_width/2):int(width + kernel_width/2)]\n\n\n\n\n\n\"\"\" \nFUNCTION NAME: bicubicInterpolation\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nsrc numpy.array I Source data\nx float I Position in x-axis\ny float I Position in y-axis\n\nRETURNS:\nInterpolates the value in the position (x, y) from the data:\n\"\"\"\ndef bicubicInterpolation(src, x, y):\n x_int = int(np.floor(x))\n y_int = int(np.floor(y))\n\n s0_x = x - float(x_int)\n s_1_x = 1.0 + s0_x\n s1_x = 1.0 - s0_x\n s2_x = 2.0 - s0_x\n\n s0_y = y - float(y_int)\n s_1_y = 1.0 + s0_y\n s1_y = 1.0 - s0_y\n s2_y = 2.0 - s0_y\n\n # Compute coefficients for the x-axis interpolation\n ux = np.array([\n -0.5 * s_1_x*s_1_x*s_1_x + 2.5 * s_1_x * s_1_x - 4.0 * s_1_x + 2.0,\n 1.5 * s0_x*s0_x*s0_x - 2.5 * s0_x*s0_x + 1.0,\n 1.5 * s1_x*s1_x*s1_x - 2.5 * s1_x*s1_x + 1.0,\n -0.5 * s2_x*s2_x*s2_x + 2.5 * s2_x * s2_x - 4.0 * s2_x + 2.0], dtype=np.float64)\n\n # Compute coefficients for the y-axis interpolation\n uy = np.array([\n -0.5 * s_1_y*s_1_y*s_1_y + 2.5 * s_1_y * s_1_y - 4.0 * s_1_y + 2.0,\n 1.5 * s0_y*s0_y*s0_y - 2.5 * s0_y*s0_y + 1.0,\n 1.5 * s1_y*s1_y*s1_y - 2.5 * s1_y*s1_y + 1.0,\n -0.5 * s2_y*s2_y*s2_y + 2.5 * s2_y * s2_y - 4.0 * s2_y + 2.0], dtype=np.float64)\n\n return np.dot(np.dot(src[(y_int-1):(y_int+3), (x_int-1):(x_int+3)], ux), uy)\n\n\n\n\"\"\" \nFUNCTION NAME: rotateBicubic\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nsrc numpy.array I Source data\ntheta float I Degrees of rotation\n\nRETURNS:\nSource data 'src' rotated 'theta' degrees into 'dst', using the bicubic interpolation\n\"\"\"\ndef rotateBicubic(src, theta):\n height, width = src.shape \n dst = np.zeros(src.shape)\n c_theta = 0\n s_theta = 0\n half_height = 0\n half_width = 0\n \n if (theta == 0.0):\n dst = src.copy()\n return dst\n \n if (theta == 90.0):\n c_theta = 0.0\n s_theta = 1.0\n half_height = np.floor(height / 2.0)\n half_width = np.floor(width / 2.0)\n \n elif (theta == 180.0):\n c_theta = -1.0\n s_theta = 0.0\n\n half_height = np.floor(height / 2.0)\n half_width = np.floor(width/ 2.0)\n\n elif (theta == 270.0):\n c_theta = 0.0\n s_theta = -1.0\n\n half_height = np.floor(height / 2.0)\n half_width = np.floor(width/ 2.0)\n\n else:\n c_theta = np.cos(theta / 180.0 * np.pi)\n s_theta = np.sin(theta / 180.0 * np.pi)\n\n half_height = float(height - 1)/ 2.0\n half_width = float(width -1)/ 2.0\n\n img_temp = cv2.copyMakeBorder(src, 6, 6, 6, 6, 0)\n\n for i in range(height):\n for j in range(width):\n src_x = c_theta * (j - half_width) - s_theta * (i - half_height) + half_width\n src_y = s_theta * (j - half_width) + c_theta * (i - half_height) + half_height\n \n if (src_x < -6 or src_x >= (width+6) or src_y < -6 or src_y >= (height+6)):\n dst[i, j] = 0.0\n\n else:\n dst[i, j] = bicubicInterpolation(img_temp, src_x + 6, src_y + 6)\n\n return dst\n\n\n\"\"\"\nFUNCTION NAME: GMF_filtering\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nimg numpy.array I Input image to be filtered\nparameters tuple I Parameters used to form the GMF kernel\n\nRETURNS:\nThe GMF response of the input image to the Gaussian kernel\n\"\"\"\n#@jit(cache=True)\ndef GMF_filtering_old(img, parameters):\n my_par_T, my_par_L, my_par_sigma, my_par_K = parameters\n if np.max(img) > 1.0: \n img_temp = img / 255.\n\n else:\n img_temp = img \n \n GMF_resp = -np.inf * np.ones(img.shape)\n GMF_resp_angles = np.zeros(img.shape)\n\n # Generate the GMF kernel:\n x = np.arange(-np.floor(my_par_T/2.0), np.floor(my_par_T/2.0) + 1, 1.0)\n Gauss_line = np.exp(-(x*x) / (2.*my_par_sigma*my_par_sigma))\n Gauss_line = np.tile(Gauss_line, (my_par_L, 1))\n Gauss_line = np.max(Gauss_line) - Gauss_line\n Gauss_sum = np.sum(Gauss_line)\n Gauss_mean = Gauss_sum / float(my_par_T * my_par_L)\n Gauss_line = (Gauss_line - Gauss_mean) / Gauss_sum\n Gauss_line = cv2.copyMakeBorder(Gauss_line, 3, 3, 1, 1, 0)\n\n rot_angle = 180./ float(my_par_K)\n for k in range(my_par_K):\n GMF_kernel = rotateBicubic(Gauss_line, 180.0 * k / float(my_par_K)) \n GMF_kernel_resp = fastConvolution(img_temp, GMF_kernel)\n\n GMF_resp_angles[GMF_kernel_resp > GMF_resp] = k * rot_angle\n GMF_resp[GMF_kernel_resp > GMF_resp] = GMF_kernel_resp[GMF_kernel_resp > GMF_resp]\n \n return GMF_resp, GMF_resp_angles\n\n\n\n\"\"\"\nFUNCTION NAME: GMF_filtering_fast\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nimg numpy.array I Input image to be filtered\nparameters tuple I Parameters used to form the GMF kernel\n\nRETURNS:\nThe GMF response of the input image to the Gaussian kernel\n\"\"\"\n#@jit(cache=True)\ndef GMF_filtering(img, parameters):\n my_par_T, my_par_L, my_par_sigma, my_par_K = parameters\n if np.max(img) > 1.0: \n img_temp = img / 255.\n\n else:\n img_temp = img.copy() \n \n height, width = img_temp.shape\n\n GMF_resp = np.zeros(img.shape)\n GMF_resp_angles = np.zeros(img.shape)\n\n filterGMF(img_temp, height, width, my_par_T, my_par_L, my_par_sigma, my_par_K, GMF_resp, GMF_resp_angles)\n\n return GMF_resp, GMF_resp_angles\n\n\n\n\n\n\"\"\"\nFUNCTION NAME: GMF_filtering_untrimmed\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nimg numpy.array I Input image to be filtered\nparameters tuple I Parameters used to form the GMF kernel\n\nRETURNS:\nThe GMF response of the input image to the Gaussian kernel\n\"\"\"\n#@jit(cache=True)\ndef GMF_filtering_untrimmed(img, parameters):\n my_par_T, my_par_L, my_par_sigma, my_par_K = parameters\n if np.max(img) > 1.0: \n img_temp = img / 255.\n\n else:\n img_temp = img.copy() \n \n height, width = img_temp.shape\n\n GMF_resp = np.zeros(img.shape)\n GMF_resp_angles = np.zeros(img.shape)\n\n filterGMFUntrimmed(img_temp, height, width, my_par_T, my_par_L, my_par_sigma, my_par_K, GMF_resp, GMF_resp_angles)\n\n return GMF_resp, GMF_resp_angles\n\n\n\n\n\"\"\"\nFUNCTION NAME: computeAUCROC\n\nARGUMENTS:\nARGUMENT TYPE I/O DESCRIPTION\nimg numpy.array I Input filtered image\nimg_gt numpy.arrayI I Ground-truth image\n\nRETURNS:\nThe area under the ROC curve of the image 'img' used as multiclass classifier\n\"\"\"\ndef computeAUCROC(img, img_gt, mask = [], roc_curve_filename = ''):\n if np.max(img_gt) > 1.0:\n img_gt_temp = (img_gt / 255.0) * 2.0 - 1.0\n \n else:\n img_gt_temp = img_gt.copy()\n\n if np.max(img) > 1.0:\n img_temp = img / 255.0\n\n else:\n img_temp = img.copy()\n\n if len(roc_curve_filename) and len(mask):\n if np.max(mask) > 1.0:\n mask_tmp = mask / 255.0\n\n else:\n mask_tmp = mask.copy()\n\n auc = aucROCmaskedsavefile(img_temp, img_gt_temp, mask_tmp, roc_curve_filename)\n\n elif len(roc_curve_filename) and not(len(mask)):\n auc = aucROCsavefile(img_temp, img_gt_temp)\n \n elif not(len(roc_curve_filename)) and len(mask):\n if np.max(mask) > 1.0:\n mask_tmp = mask / 255.0\n\n else:\n mask_tmp = mask.copy()\n\n auc = aucROCmasked(img_temp, img_gt_temp, mask_tmp)\n \n else:\n start_time = perf_counter()\n auc = aucROC(img_temp, img_gt_temp)\n elapsed_time = perf_counter() - start_time\n print(\"Az = {}, computed in {} seconds\".format(auc, elapsed_time))\n\n return auc\n\n\nif (__name__ == \"__main__\"):\n print(\"\\tGMF Filtering using C++ extension and fast convolution\")\n # Read arguments:\n if (len(sys.argv) < 2):\n print(\"This program requires:\")\n print('1) Path to an image')\n print('2) Parameter T')\n print('3) Parameter L')\n print('4) Parameter sigma')\n print('5) Parameter K')\n sys.exit()\n\n img = cv2.imread(sys.argv[1])\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n mask = cFOV.compute_FOV_mask(img)\n mask = cFOV.mask_corners(mask)\n\n img = cFOV.fill_blackarea(img / 255.0, mask)\n \n fp = open('input_pre.txt', 'w')\n for ii in range(img.shape[0]):\n for jj in range(img.shape[1]):\n fp.write('{} '.format(img[ii, jj]))\n \n fp.write('\\n')\n \n fp.close()\n\n if (len(sys.argv) > 2):\n par_T = int(sys.argv[2])\n else:\n par_T = 15\n\n if (len(sys.argv) > 3):\n par_L = int(sys.argv[3])\n else:\n par_L = 12\n\n if (len(sys.argv) > 4):\n par_sigma = float(sys.argv[4])\n else:\n par_sigma = 2.4\n\n if (len(sys.argv) > 5):\n par_K = int(sys.argv[5])\n else:\n par_K = 12\n\n parameters = (par_T, par_L, par_sigma, par_K)\n print(parameters)\n \n GMF_resp, GMF_resp_angles = GMF_filtering_untrimmed(img, parameters)\n\n plt.subplot(1, 3, 1)\n plt.imshow(img)\n plt.subplot(1, 3, 2) \n plt.imshow(GMF_resp, 'gray')\n plt.subplot(1, 3, 3)\n plt.imshow(GMF_resp_angles)\n plt.show()\n \n fp = open('GMF_resp.txt', 'w')\n for ii in range(GMF_resp.shape[0]):\n for jj in range(GMF_resp.shape[0]):\n fp.write('{} '.format(GMF_resp[ii, jj]))\n \n fp.write('\\n')\n \n fp.close()\n\n GMF_resp_normalized = (GMF_resp - np.min(GMF_resp)) / (np.max(GMF_resp) - np.min(GMF_resp)) * 255\n cv2.imwrite('GMF_resp.png', GMF_resp_normalized)\n","sub_path":"support_vector_machine_python/gmf_svm_python/src/detection.py","file_name":"detection.py","file_ext":"py","file_size_in_byte":13945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"304754595","text":"import torch\nfrom torch import nn\nfrom torch.nn import utils as nn_utils\nfrom modules.torch_transformer import Encoder, PositionalEncoding\nfrom modules.torch_TextCNNNet import TextCNNNet\nfrom sru import SRU\nfrom time import time\n\n\nclass MCDN(nn.Module):\n def __init__(self, token_embeddings, args, logger):\n super(MCDN, self).__init__()\n start_t = time()\n n_dict, n_emb = token_embeddings.shape\n self.max_len = args.max_len['full']\n self.att_hidden = n_emb\n self.gru_hidden = args.n_hidden\n self.crn_hidden = 4 * args.n_hidden\n self.n_block = args.n_block\n self.n_head = args.n_head\n self.n_layer = args.n_layer\n self.n_filter = args.n_filter\n self.n_kernels = args.n_kernels\n self.is_sinusoid = args.is_sinusoid\n self.is_ffn = args.is_ffn\n self.word_embedding = nn.Embedding(n_dict, n_emb, padding_idx=0)\n if self.is_sinusoid:\n self.pos_embedding = PositionalEncoding(n_emb, max_len=self.max_len)\n # self.position_embedding = PositionEmbedding(n_emb, zeros_pad=False, scale=False)\n # else:\n # self.position_embedding = WordEmbedding(self.max_len, n_emb, zeros_pad=False, scale=False)\n self.emb_dropout = nn.Dropout(args.dropout['emb'])\n self.transformer = Encoder(self.n_head, self.n_block, n_emb, args.dropout['layer'])\n self.seg_encoder = nn.GRU(n_emb, self.gru_hidden, self.n_layer, dropout=args.dropout['layer'], batch_first=True,\n bidirectional=True)\n\n self.word_fc = nn.Linear(self.max_len * self.att_hidden, self.att_hidden)\n\n self.pre_encoder = TextCNNNet(n_emb, args.max_len['pre'], self.n_filter, self.n_kernels)\n self.alt_encoder = TextCNNNet(n_emb, args.max_len['alt'], self.n_filter, self.n_kernels)\n self.cur_encoder = TextCNNNet(n_emb, args.max_len['cur'], self.n_filter, self.n_kernels)\n self.g_fc = nn.Sequential(nn.Linear(6 * self.n_filter + 2 * self.gru_hidden, self.crn_hidden),\n nn.ReLU(),\n nn.Dropout(args.dropout['layer']),\n nn.Linear(self.crn_hidden, self.crn_hidden),\n nn.ReLU())\n self.f_fc = nn.Sequential(nn.Linear(self.crn_hidden, self.crn_hidden),\n nn.ReLU(),\n nn.Dropout(args.dropout['layer']))\n\n self.out_fc = nn.Sequential(nn.Linear(self.att_hidden + self.crn_hidden, self.gru_hidden),\n nn.ReLU(),\n nn.Dropout(args.dropout['layer']),\n nn.Linear(self.gru_hidden, args.n_class))\n\n self._init_weights(token_embeddings)\n logger.info('Time to build graph: {} s'.format(time() - start_t))\n\n def _init_weights(self, embeddings):\n self.word_embedding.weight.data.copy_(torch.from_numpy(embeddings))\n self.word_embedding.weight.requires_grad = False\n\n def forward(self, x, x_pre, x_alt, x_cur, seq_lens):\n batch_size = x.shape[0]\n sorted_seq_lens, indices = torch.sort(seq_lens, dim=0, descending=True)\n _, desorted_indices = torch.sort(indices, descending=False)\n x = x[indices]\n x_mask = (x != 0).unsqueeze(-2)\n x_word_emb = self.word_embedding(x)\n x_pre_word_emb = self.word_embedding(x_pre)\n x_alt_word_emb = self.word_embedding(x_alt)\n x_cur_word_emb = self.word_embedding(x_cur)\n x_pre_word_emb = self.emb_dropout(x_pre_word_emb)\n x_alt_word_emb = self.emb_dropout(x_alt_word_emb)\n x_cur_word_emb = self.emb_dropout(x_cur_word_emb)\n\n if self.is_sinusoid:\n x_word_emb = self.pos_embedding(x_word_emb)\n # x_word_emb += self.position_embedding(x)\n # else:\n # x_word_emb += self.position_embedding(torch.unsqueeze(torch.arange(0, x.size()[1]), 0).repeat(x.size(0), 1).long().cuda())\n x_embeded = self.emb_dropout(x_word_emb)\n y_transformed = self.transformer(x_embeded, x_mask)\n y_word = torch.reshape(y_transformed, [-1, self.max_len * self.att_hidden])\n y_word = self.word_fc(y_word)\n\n x_word_emb = nn_utils.rnn.pack_padded_sequence(x_word_emb, sorted_seq_lens, batch_first=True)\n output, state = self.seg_encoder(x_word_emb)\n state = state.view(self.n_layer, 2, batch_size, self.gru_hidden)\n forward_state, backward_state = state[-1][0], state[-1][1]\n y_state = torch.cat([forward_state, backward_state], dim=1)\n y_pre = self.pre_encoder(x_pre_word_emb)\n y_alt = self.alt_encoder(x_alt_word_emb)\n y_cur = self.cur_encoder(x_cur_word_emb)\n pre_cur = torch.cat((y_pre, y_cur), dim=1)\n cur_pre = torch.cat((y_cur, y_pre), dim=1)\n pre_alt = torch.cat((y_pre, y_alt), dim=1)\n alt_cur = torch.cat((y_alt, y_cur), dim=1)\n y_composed = torch.stack([pre_cur, cur_pre, pre_alt, alt_cur], dim=1)\n y_state = torch.unsqueeze(y_state, 1)\n y_state = y_state.repeat(1, 4, 1)\n y_pair = torch.cat([y_composed, y_state], 2)\n\n y_pair = y_pair.view(batch_size * 4, 6 * self.n_filter + 2 * self.gru_hidden)\n y_pair = self.g_fc(y_pair)\n y_pair = y_pair.view(batch_size, 4, self.crn_hidden)\n y_pair = y_pair.sum(1).squeeze()\n y_segment = self.f_fc(y_pair)\n\n y_word_seg = torch.cat([y_word, y_segment], 1)\n return self.out_fc(y_word_seg)\n","sub_path":"models/torch_MCDN.py","file_name":"torch_MCDN.py","file_ext":"py","file_size_in_byte":5557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"416253469","text":"#!/usr/bin/env python3\nimport argparse\nimport datetime\nimport re\nfrom collections import defaultdict, Counter\nfrom os import walk\nimport numpy as np\nimport pprint\n\n\nlog_types = ['FATAL', 'RECONF']\n\n\nclass WrongLogLine(Exception):\n pass\n\n\nclass WrongFileName(Exception):\n pass\n\n\nargs = None\nresolution = 1\n\ndef print_in_columns(data, width=15, separator=','):\n print(separator.join([str(d).ljust(width) for d in data]))\n\n\ndef to_unixtime(t):\n \"\"\" note that this does not work if all timestamps are not from the same time zone \"\"\"\n return int((t - datetime.datetime(1970, 1, 1)).total_seconds()*resolution)\n\n\ndef parse_args():\n global args\n global resolution\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', help='the log file', required=False, nargs='*')\n parser.add_argument('-ff', help='Folder with log Folders', required=False)\n parser.add_argument('-v', help='be more verbose', default=False,\n action='store_true')\n parser.add_argument('-c', help='Compute convergence delay', default=False,\n action='store_true')\n parser.add_argument('-t', help='compute the number of updates generated',\n default=False, action='store_true')\n parser.add_argument('-T', help='time resolution (s/decimal/cent/milli)',\n default='SEC',\n choices=['SECS', 'DSEC', 'CSEC', 'MSEC'])\n parser.add_argument('-d', required=False, default=0, action='store', help=\"Use this option to see a negative delta\")\n args = parser.parse_args()\n\n if not (args.f or args.ff):\n parser.error('No folders provided, you have to choose at one type of folder to pass')\n if args.f and args.ff:\n parser.error('Too much folders provided, you have to choose at ONE type of folder to pass')\n\n if args.T == 'DSEC':\n resolution = 10\n elif args.T == 'CSEC':\n resolution = 100\n elif args.T == 'MSEC':\n resolution = 1000\n\n\ndef parse_line(line, verb):\n split_line = line.split()\n try:\n date_time = split_line[0] + \" \" + split_line[1]\n log_time = to_unixtime(datetime.datetime.strptime(date_time, '%Y-%m-%d %H:%M:%S.%f'))\n except (ValueError, IndexError):\n if verb:\n print(\"Ignoring this log line:\", line)\n raise WrongLogLine\n log_type = ''.join(split_line[2][1:-1])\n if log_type not in log_types:\n if verb:\n print(\"Ignoring this log line:\", line)\n raise WrongLogLine\n try:\n log_body = line.split('{')[1].split('}')[0]\n except IndexError:\n if verb:\n print(\"Ignoring this log line:\", line)\n raise WrongLogLine\n log_fields = log_body.split(',')\n log_dict = {}\n for k, v in [x.split(':') for x in log_fields]:\n log_dict[k.strip()] = v.strip()\n if log_dict['type'] == 'RECONF':\n return log_time, log_dict, True\n return log_time, log_dict, False\n\n\ndef parse_file(fname):\n try:\n AS_number = int(fname.split('_')[-1][:-4]) + 1\n except (ValueError, ):\n print('Invalid file name:', fname)\n print('I expect a file name of the form log_h_X.log '\n 'where X is an AS number')\n exit()\n data = []\n reconf_time = None\n last_message_before_reconf = None\n with open(fname, 'r') as f:\n for line in f:\n try:\n t, d, reconf = parse_line(line, args.v)\n if reconf:\n reconf_time = t\n last_message_before_reconf = last_one\n last_one = t\n except WrongLogLine:\n continue\n data.append([t, d])\n return AS_number, data, last_message_before_reconf, reconf_time\n\n\ndef compute_convergence_time(data):\n convergence_time = ''\n best_path = ''\n first_log = None\n for (t, d) in data:\n if not first_log:\n first_log = t\n if 'processing' in d and d['processing'] == 'NEW_BEST_PATH':\n convergence_time = t\n best_path = d['actual_best_path']\n if 'processing' in d and (d['processing'] == 'REMOVED_REPLACED_PATH'\n or d['processing'] == 'NEW_PATH'):\n if d['actual_best_path'] == 'NONE':\n convergence_time = ''\n best_path = ''\n elif d['actual_best_path'] != best_path:\n best_path = d['actual_best_path']\n convergence_time = t\n if args.v and 'processing' in d:\n print(t, d)\n if convergence_time:\n return convergence_time, first_log\n return None, None\n\n\ndef compute_updates_number(data):\n updates = Counter()\n min_secs = data[0][0]\n max_secs = data[-1][0]\n for (t, d) in data:\n if d['type'] == 'UPDATE_TX':\n updates[t] += 1\n return updates, min_secs, max_secs\n\n\ndef main():\n parse_args()\n AS_data = defaultdict(dict)\n reconf_time = None\n last_reconf = None\n last_message_before_reconf = None\n reconf_ASes = list()\n max_key = 0\n key_counter = Counter()\n AS_data_all = defaultdict(dict)\n if args.f and not args.ff:\n for fname in args.f:\n AS_number, data, last_message_before_tmp, reconf_temp = parse_file(fname)\n if reconf_temp:\n reconf_time = reconf_temp\n last_message_before_reconf = last_message_before_tmp\n reconf_ASes.append(AS_number)\n if args.c:\n convergence_time, first_log = compute_convergence_time(data)\n AS_data[AS_number]['convergence_time'] = convergence_time\n AS_data[AS_number]['first_log'] = first_log\n if args.t:\n updates, min_secs, max_secs = compute_updates_number(data)\n AS_data[AS_number]['updates'] = updates\n AS_data[AS_number]['min_secs'] = min_secs\n AS_data[AS_number]['max_secs'] = max_secs\n\n for AS_number in AS_data:\n if AS_data[AS_number]['convergence_time']:\n if reconf_time:\n AS_data[AS_number]['convergence_time'] -= reconf_time\n else:\n AS_data[AS_number]['convergence_time'] -= AS_data[AS_number]['first_log']\n else:\n if AS_number in reconf_ASes:\n AS_data[AS_number]['convergence_time'] = 0\n else:\n AS_data[AS_number]['convergence_time'] = 1000000\n\n if 'updates' in AS_data[AS_number]:\n new_counter = Counter()\n for key in AS_data[AS_number]['updates']:\n new_key = key - reconf_time\n max_key = max(max_key, new_key)\n key_counter[str(AS_number) + str(new_key)] += 1\n value = AS_data[AS_number]['updates'][key]\n new_counter[new_key] = value\n AS_data[AS_number]['updates'] = new_counter\n else:\n dirNames = list()\n for (dir_path, dir_names, filenames) in walk(args.ff):\n dirNames.extend(dir_names)\n break\n for dir in dirNames:\n fileList = list()\n for (dir_path, dir_names, filenames) in walk(args.ff + \"/\" + dir):\n fileList.extend(filenames)\n break\n if dir not in AS_data_all:\n AS_data_all[dir] = defaultdict(dict)\n for fname in fileList:\n AS_number, data, last_message_before_tmp, reconf_temp = parse_file(args.ff + \"/\" + dir + \"/\" + fname)\n if reconf_temp:\n reconf_time = reconf_temp\n last_message_before_reconf = last_message_before_tmp\n reconf_ASes.append(AS_number)\n if args.c:\n convergence_time, first_log = compute_convergence_time(data)\n AS_data_all[dir][AS_number]['convergence_time'] = convergence_time\n AS_data_all[dir][AS_number]['first_log'] = first_log\n if args.t:\n updates, min_secs, max_secs = compute_updates_number(data)\n AS_data_all[dir][AS_number]['updates'] = updates\n AS_data_all[dir][AS_number]['min_secs'] = min_secs\n AS_data_all[dir][AS_number]['max_secs'] = max_secs\n for AS_number in AS_data_all[dir]:\n if AS_data_all[dir][AS_number]['convergence_time']:\n if reconf_time:\n AS_data_all[dir][AS_number]['convergence_time'] -= reconf_time\n else:\n AS_data_all[dir][AS_number]['convergence_time'] -= AS_data_all[dir][AS_number]['first_log']\n else:\n if AS_number in reconf_ASes:\n AS_data_all[dir][AS_number]['convergence_time'] = 0\n else:\n AS_data_all[dir][AS_number]['convergence_time'] = 1000000\n\n if 'convergence_time' not in AS_data[AS_number]:\n AS_data[AS_number]['convergence_time'] = [AS_data_all[dir][AS_number]['convergence_time']]\n else:\n AS_data[AS_number]['convergence_time'].append(AS_data_all[dir][AS_number]['convergence_time'])\n\n if 'updates' in AS_data_all[dir][AS_number]:\n new_counter = Counter()\n for key in AS_data_all[dir][AS_number]['updates']:\n new_key = key - reconf_time\n # if new_key < 0:\n # print(\"ERROR\")\n max_key = max(max_key, new_key)\n key_counter[str(AS_number) + str(new_key)] += 1\n value = AS_data_all[dir][AS_number]['updates'][key]\n new_counter[new_key] = value\n AS_data_all[dir][AS_number]['updates'] = new_counter\n\n if 'updates' not in AS_data[AS_number]:\n AS_data[AS_number]['updates'] = AS_data_all[dir][AS_number]['updates']\n else:\n AS_data[AS_number]['updates'] += AS_data_all[dir][AS_number]['updates']\n\n #for AS_number in AS_data:\n # AS_data[AS_number]['convergence_time'] /= float(len(dirNames))\n # for key in AS_data[AS_number]['updates']:\n # AS_data[AS_number]['updates'][key] /= float(len(AS_data_all.keys()))\n\n delta = reconf_time - last_message_before_reconf\n\n if args.c:\n print_in_columns(['AS', 'convergence_time'])\n for AS_number, c_data in sorted(AS_data.items()):\n print_line = [AS_number, max(c_data['convergence_time'])]\n print_in_columns(print_line)\n print('\\n\\n')\n\n print_in_columns(['time', 'converged_ASes', 'non_converged_ASes', 'total_nodes'])\n if int(args.d) > 0:\n if delta > int(args.d):\n i = int(args.d)\n while i > 0:\n print_in_columns(['-' + str(i), str(len(AS_data)), '0', str(len(AS_data))])\n i -= 1\n convergence_time = []\n never_converged_ASes = 0\n non_reconfigured_ASes = 0\n last_reconf = 0\n for AS_number, c_data in sorted(AS_data.items()):\n if 'convergence_time' in c_data:\n for conv_time in c_data['convergence_time']:\n\n if conv_time >= 0:\n convergence_time.append((AS_number, conv_time))\n if c_data['convergence_time'] > last_reconf:\n last_reconf = min(last_reconf,c_data['convergence_time'])\n else:\n non_reconfigured_ASes += 1\n else:\n if AS_number not in reconf_ASes:\n never_converged_ASes += 1\n else:\n convergence_time.append((AS_number, 0))\n tot_nodes = len(AS_data)\n max_time = max([x[1] for x in convergence_time]) if max_key == 0 else max_key\n for i in range(max_time + 1):\n conv_ASes = 0\n for (AS, t) in convergence_time:\n if i >= t:\n conv_ASes += 1\n print_in_columns([i, conv_ASes + non_reconfigured_ASes,\n tot_nodes - conv_ASes - non_reconfigured_ASes,\n tot_nodes])\n print('\\n\\n')\n\n reconf_time = 0\n\n if args.t:\n # here seconds are in unix time\n if reconf_time is not None:\n reconf_secs = reconf_time\n else:\n reconf_secs = min([AS_data[x]['min_secs'] for x in AS_data])\n\n if last_reconf is not None:\n end_secs = max_key if max_key != 0 else last_reconf\n else:\n end_secs = max([AS_data[x]['max_secs'] for x in AS_data])\n\n integral_on_time = dict()\n if args.ff and not args.f:\n integral_list = list()\n for dir in AS_data_all:\n integral_on_time_dir = dict()\n for i in range(reconf_time, end_secs + 1):\n if i not in integral_on_time:\n integral_on_time_dir[i] = 0\n for as_number in AS_data_all[dir]:\n integral_on_time_dir[i] += AS_data_all[dir][as_number]['updates'][i]\n for i in range(reconf_time + 1, end_secs + 1):\n integral_on_time_dir[i] += integral_on_time_dir[i - 1]\n integral_list.append(integral_on_time_dir)\n for i in range(reconf_time, end_secs + 1):\n for integral in integral_list:\n if i not in integral_on_time:\n integral_on_time[i] = 0\n integral_on_time[i] += integral[i]\n #integral_on_time[i] = integral_on_time[i]/float(len(integral_list))\n\n print_in_columns(['time'] + ['sum'] + sorted(AS_data.keys()), width=4)\n if int(args.d) > 0:\n if delta > int(args.d):\n i = int(args.d)\n while i > 0:\n print_in_columns(['-' + str(i)] + ['0'] + ['0' for x in AS_data.keys()], width=4)\n i -= 1\n # just a check that we are not leving any number behind\n control_total = 0\n for i in range(reconf_time, end_secs+1):\n print_list = []\n tot_udp = 0\n for (AS_number, c_data) in sorted(AS_data.items()):\n upd = c_data['updates'].get(i, 0)\n tot_udp += upd\n print_list.append(str(upd))\n print_list.insert(0, tot_udp)\n print_list.insert(0, str(i-reconf_secs))\n print_in_columns(print_list, width=4)\n control_total += tot_udp\n tot_updates = 0\n for (AS_number, c_data) in sorted(AS_data.items()):\n for k,v in c_data['updates'].items():\n if k >= reconf_time and k < end_secs + 1:\n tot_updates += v\n if (tot_updates != control_total):\n print(\"Error in counting updates\")\n print('\\n\\n')\n\n print_in_columns(['tim','sum'])\n #for i in range(reconf_time-int(args.d), end_secs+1):\n # print_in_columns([str(i), str(tot_updates)])\n if int(args.d) > 0:\n if delta > int(args.d):\n i = int(args.d)\n while i > 0:\n print_in_columns(['-' + str(i)] + ['0'], width=4)\n i -= 1\n total_upd = 0\n if args.f and not args.ff:\n for i in range(reconf_time, end_secs+1):\n for (AS_number, c_data) in sorted(AS_data.items()):\n upd = c_data['updates'][i]\n total_upd += upd\n print_in_columns([str(i), str(total_upd)], width=4)\n else:\n for i in range(reconf_time, end_secs+1):\n \"\"\"counter = 0\n delta = 0\n for (AS_number, c_data) in sorted(AS_data.items()):\n upd = 0\n print(AS_number, c_data['updates'])\n if i in c_data['updates']:\n counter += 1\n upd = c_data['updates'][i]\n delta += upd\n if upd > 0:\n total_upd += upd/counter\"\"\"\n print_in_columns([str(i), str(integral_on_time[i])], width=4)\nmain()\n","sub_path":"logHandlers/parser/log_parser_sum.py","file_name":"log_parser_sum.py","file_ext":"py","file_size_in_byte":16466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"72473689","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport pandas as pd\nimport os,sys\nimport scipy.io as scio\n\ndef mimictrack(ts_dir):\n for i in os.listdir(ts_dir):\n if str(i).endswith('_ts.txt'):\n ts_path = os.path.join(ts_dir,i)\n Time = pd.read_csv(ts_path, sep=\" \",encoding = \"utf-16\",header =None,names=['ts(s)'])\n #matlab tracing results\n \n matfile = os.path.join(ts_dir,('result_'+ i.replace('_ts.txt','.mat')))\n \n if os.path.exists(matfile):\n data = scio.loadmat(matfile)\n X = [i[0] for i in data['result'][0][0][-2]]\n Y = [i[1] for i in data['result'][0][0][-2]]\n if not len(Time) == len(X):\n print(f'matlab tracking lost {abs(len(Time)-len(X))} frames')\n while not len(Time) == len(X):\n X.append(X[-1])\n Y.append(Y[-1])\n else:\n X = Y = [100 for i in range(len(Time))]\n \n Frame_No = np.linspace(1,len(X),len(X),dtype=int)\n \n \n dataframe = pd.DataFrame({'Frame':Frame_No,'time':Time['ts(s)'],'X':X,'Y':Y}) \n if not os.path.exists(ts_path.replace('_ts.txt','_tracking.csv')): \n dataframe.to_csv(ts_path.replace('_ts.txt','_tracking.csv'),index = None)\n print(f'{i} tracking file is created')\n else:\n print(f'{i}tracking file alread exists.')\n \n\ndef detect(Frame_No,Time,X,Y):\n \n distances = (np.diff(X)**2 + np.diff(Y) ** 2)**0.5\n speeds = np.divide(distances,np.diff(Time))\n distances = np.insert(distances,0,0)\n speeds = np.insert(speeds,0,0)\n\n mean_speed = np.mean(speeds)\n std_speed = np.std(speeds,ddof =1)\n\n lost_object = []\n wrong_object = []\n \n for frame_No,x,y,speed in zip(Frame_No,X,Y,speeds):\n \n if x+y < 0 or x*y< 0:\n lost_object.append(frame_No)\n continue\n if abs(speed-mean_speed)/std_speed > 2.33: #z_score = (distance-mean)/std\n wrong_object.append(frame_No)\n \n## print(f'{len(lost_object)},{len(wrong_object)}')\n## print(f'{wrong_object}')\n start = end =0\n for frame in lost_object:\n if frame < end:\n continue\n else:\n def start_frame(frame):\n if X[frame-1]+Y[frame-1]<0 or X[frame-1]*Y[frame-1]<0:\n frame = frame-1\n return start_frame(frame)\n else:\n return frame\n def end_frame(frame):\n if X[frame-1]+Y[frame-1]<0 or X[frame-1]*Y[frame-1]<0:\n frame = frame+1\n return end_frame(frame)\n else:\n return frame\n\n start = start_frame(frame)\n end = end_frame(frame) \n\n X[(start-1):end] = np.linspace(X[start-1],X[end+1],end-start+1)\n Y[(start-1):end] = np.linspace(Y[start-1],Y[end+1],end-start+1)\n return wrong_object,X,Y\n \ndef correct(video_path,scale = 0.11,mannual =True,detection = True):\n videotrack_path = os.path.splitext(video_path)[-2] + '_tracking.csv'\n try:\n track = pd.read_table(videotrack_path,sep=',')\n except:\n print(f\"{videotrack_path} \\n路径中不要含有中文，空格等奇怪符号\")\n sys.exit()\n \n Frame_No = list(track['Frame'])\n Time = list(track['time'])\n X = list(track['X'])\n Y = list(track['Y'])\n if detection == True: \n wrong_frames,X,Y=detect(Frame_No,Time,X,Y)\n else: \n wrong_frames = Frame_No \n \n if mannual == True:\n font = cv2.FONT_HERSHEY_COMPLEX\n cv2.namedWindow('correct_coordination',0)\n cap = cv2.VideoCapture(video_path)\n \n temp_Frame_No = []\n temp_X = []\n temp_Y = []\n\n def choose_frame(event,x,y,flags,param): \n if event == cv2.EVENT_LBUTTONDOWN:\n nonlocal temp_Frame_No,temp_X,temp_Y\n temp_Frame_No.append(frame_No)\n temp_X.append(x*scale)\n temp_Y.append(y*scale)\n cv2.circle(frame,(x,y),5,(255,0,0),2)\n if len(temp_Frame_No) == 1:\n print('it is the 1st poind') \n if len(temp_Frame_No) == 2:\n print(\"it is the 2nd point\")\n if temp_Frame_No[1] 2:\n temp_X[-1] = x\n temp_Y[-1] = y\n pts = np.array([i for i in zip(temp_X,temp_Y)])\n print(pts)\n cv2.fillPoly(img,[pts],255,2)\n img = cv2.addWeighted(frame,1.0,img,0.5,0.)\n cv2.imshow(\"roi\",img)\n\n frame_No = 1\n for wrong_frame in wrong_frames:\n if frame_No >= wrong_frame:\n continue\n else:\n cap.set(cv2.CAP_PROP_POS_FRAMES,wrong_frame-1)\n ret,frame = cap.read()\n frame_No = wrong_frame\n cv2.putText(frame,f'{frame_No}',(20,30), font, 1, (255,255,255))\n x_pos = int(round(X[frame_No-1]/scale))\n y_pos = int(round(Y[frame_No-1]/scale))\n cv2.circle(frame,(x_pos,y_pos),5,(0,0,255),2)\n cv2.imshow('correct_coordination',frame)\n \n while 1:\n key = cv2.waitKey(30) & 0xFF\n\n if key == ord('f'):\n frame_No += 10\n cap.set(cv2.CAP_PROP_POS_FRAMES,frame_No)\n ret,frame=cap.read() \n if ret:\n cv2.putText(frame,f'{frame_No}',(20,30), font, 1, (255,255,255))\n x_pos = int(round(X[frame_No-1]/scale))\n y_pos = int(round(Y[frame_No-1]/scale))\n cv2.circle(frame,(x_pos,y_pos),5,(0,0,255),2)\n cv2.imshow('correct_coordination',frame) \n else:\n break\n if key == ord('a'):\n if frame_No <=1:\n frame_No = 1\n print('it is the first frame!')\n else:\n frame_No = frame_No -1\n \n cap.set(cv2.CAP_PROP_POS_FRAMES,frame_No)\n ret,frame=cap.read()\n if ret:\n cv2.putText(frame,f'{frame_No}',(20,30), font, 1, (255,255,255))\n x_pos = int(round(X[frame_No-1]/scale))\n y_pos = int(round(Y[frame_No-1]/scale))\n cv2.circle(frame,(x_pos,y_pos),5,(0,0,255),2)\n cv2.imshow('correct_coordination',frame)\n else:\n break\n if key == ord('n'):\n break\n if key == ord('q'):\n print(len(X))\n print(len(track['X']))\n try:\n track['X'] = X\n track['Y'] = Y\n except:\n print(\"X or Y is longer or shorter\")\n track.to_csv(videotrack_path,mode = 'w',index = False)\n print('corrected coordinates have been saved')\n cap.release()\n cv2.destroyAllWindows()\n return\n cv2.setMouseCallback(\"correct_coordination\",drow_roi,\n {\"frame\":frame})\n\n cap.release()\n cv2.destroyAllWindows()\n else:\n print(f\"{os.path.basename(videotrack_path)}auto-correction finish\") \n track['X'] = X\n track['Y'] = Y\n track.to_csv(videotrack_path,mode='w',index = False)\n print('corrected coordinates have been saved')\n\n \n \n \nif __name__ == '__main__':\n## ts_dir = r'C:\\Users\\Sabri\\Desktop\\program\\video\\video_analyze\\correct_coord\\matfile\\asf'\n## mimictrack(ts_dir)\n\n## ts_dir = r'Y:\\吴近泥\\# miniscope\\RAW_DATA\\CTX_CFC_6mice\\M181078_DPCA1\\20190404_shock\\H1_M2_S14'\n## mimictrack(ts_dir)\n## video_path = r'C:\\Users\\Sabri\\Desktop\\program\\video\\video_analyze\\correct_coord\\matfile\\asf\\#10186-day1.asf'\n video_path=r'C:\\Users\\Sabri\\Desktop\\program\\video\\video_analyze\\correct_coord\\2019031100002.AVI'\n correct(video_path,scale = 1,mannual = True,detection = True)\n\n","sub_path":"utils/correct_videotrack.py","file_name":"correct_videotrack.py","file_ext":"py","file_size_in_byte":9834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"313320129","text":"# 모듈 ======================================================================================\nimport os\nimport sys\nimport numpy as np\nimport cv2 as cv\nimport time\n# import threading\n\nfrom PyQt5.QtCore import QSize,Qt,QThread,QTimer, pyqtSignal, pyqtSlot\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import uic\nfrom PyQt5.QtGui import *\n# import piexif\n\n# from threading import Thread\n# ===========================================================================================\n\nmainScreen = uic.loadUiType(\"MainScreen.ui\")[0]\n\n'''동영상 재생 스레드'''\nclass VideoThread(QThread):\n change_pixmap_signal = pyqtSignal(QThread,np.ndarray)\n\n def __init__(self):\n super().__init__()\n self._run_flag = True\n \n\n def run(self):\n cap = cv.VideoCapture(self.fname) # 파일에서 영상을 불러와 캡쳐\n FPS = cap.get(cv.CAP_PROP_FPS) # 초당 프레임 수\n # 영상 재생\n # _run_flag에 따라 재생/멈춤 결정\n while True:\n if self._run_flag: # 재생\n ret, cv_img = cap.read()\n if ret: # 동영상 모두 재생 전\n self.change_pixmap_signal.emit(self,cv_img) # (스레드 본인,이미지)\n # 동영상 속도 조절 (정확하지는 않지만 가장 일반적임)\n time.sleep(1/FPS)\n else: # 동영상 모두 재생 시 종료\n break\n else: # 멈춤\n time.sleep(0.5)\n # capture 종료\n cap.release()\n\n # 재생 버튼 클릭 메소드\n def play(self):\n self._run_flag = True\n \n # 멈춤 버튼 클릭 메소드\n def pause(self):\n self._run_flag = False\n\n\n def stop(self):\n # run flag를 False로 하고 스레드 종료를 기다림\n self._run_flag = False\n self.wait()\n\n def setFilename(self,fname):\n # 파일명 설정\n self.fname = fname\n\n\n\n'''사용자 설정 스크린'''\nclass OptionScreen(QDialog):\n def __init__(self):\n super(OptionScreen,self).__init__()\n self.ui = uic.loadUi('OptionScreen.ui',self)\n\n # modelSelcCombobox에 아이템 추가\n self.setup_modelSelcCombobox(self.modelSelcCombobox)\n # IOUCombobox에 아이템 추가\n self.setup_IOUCombobox(self.IOUCombobox)\n\n '''콤보박스에 함수�� 연결하는 코드'''\n self.modelSelcCombobox.currentIndexChanged.connect(self.modelSelcComboboxChanged)\n self.IOUCombobox.currentIndexChanged.connect(self.IOUComboboxChanged)\n\n\n\n '''콤보박스 셋업'''\n def setup_modelSelcCombobox(self,combobox):\n combobox.addItem(\"Model 1\")\n\n def setup_IOUCombobox(self,combobox):\n combobox.addItem(\"0.5\")\n\n '''콤보박스 선택 아이템 변경 메서드'''\n def modelSelcComboboxChanged(self):\n # TODO: 카메라 선택 콤보박스 아이템 변경 시의 동작\n pass\n\n def IOUComboboxChanged(self):\n # TODO: 모드 선택 콤보박스 아이템 변경 시의 동작\n pass\n\n'''메인 스크린'''\nclass MainScreen(QMainWindow, mainScreen):\n def __init__(self) :\n super().__init__()\n self.setupUi(self)\n\n # camSelcCombobox에 아이템 추가\n self.setup_camSelcCombobox(self.camSelcCombobox)\n # modeSelcCombobox에 아이템 추가\n self.setup_modeSelcCombobox(self.modeSelcCombobox)\n\n '''콤보박스에 함수를 연결하는 코드'''\n self.camSelcCombobox.currentIndexChanged.connect(self.camSelcComboboxChanged)\n self.modeSelcCombobox.currentIndexChanged.connect(self.modeSelcComboboxChanged)\n\n '''버튼에 함수를 연결하는 코드'''\n # 재생/멈춤 버튼\n self.playerBtn.clicked.connect(self.playerBtnClicked)\n self.playerBtnFlag = True\n # 사운드 버튼\n self.soundBtn.clicked.connect(self.soundBtnClicked)\n self.soundBtnFlag = True\n # 설정 버튼\n self.optionBtn.clicked.connect(self.optionBtnClicked)\n\n self.cam1Btn.clicked.connect(self.cam1BtnClicked)\n self.cam2Btn.clicked.connect(self.cam2BtnClicked)\n self.cam3Btn.clicked.connect(self.cam3BtnClicked)\n self.cam4Btn.clicked.connect(self.cam4BtnClicked)\n self.zoomBtn.clicked.connect(self.zoomBtnClicked)\n self.cam1Btn.setFlat(True)\n self.cam2Btn.setFlat(True)\n self.cam3Btn.setFlat(True)\n self.cam4Btn.setFlat(True)\n\n self.zoomFrame.hide()\n \n# 170 50 901 541\n \n '''스레드 관련 코드'''\n # 캠 스레드 4개 생성\n self.cam = [0,0,0,0]\n for i in range(4):\n self.cam[i] = VideoThread()\n # 스레드에 이미지뷰 할당\n self.frame = {self.cam[0]:self.camView1,\n self.cam[1]:self.camView2,\n self.cam[2]:self.camView3,\n self.cam[3]:self.camView4}\n\n # 캠 스레드에서 출력될 비디오 설정\n self.setVideo2Thread()\n # 캠 스레드 시작 (캠 출력 시작)\n self.startThread()\n \n # 비디오 출력 화면 크기(모든 화면의 크기가 동일하다고 가정)\n self.width1 = 900\n self.height1 = 600\n\n self.width2 = 426\n self.height2 = 240\n self.display_width = self.width1\n self.display_height = self.height1\n \n self.prev = self.camView1\n self.now = None\n '''콤보박스 셋업'''\n def setup_camSelcCombobox(self,combobox):\n combobox.addItem(\"Main\")\n combobox.addItem(\"Camera 1\")\n combobox.addItem(\"Camera 2\")\n combobox.addItem(\"Camera 3\")\n combobox.addItem(\"Camera 4\")\n\n def setup_modeSelcCombobox(self,combobox):\n combobox.addItem(\"day mode\")\n combobox.addItem(\"night mode\")\n combobox.addItem(\"auto mode\")\n\n '''콤보박스 선택 아이템 변경 메서드'''\n def camSelcComboboxChanged(self):\n # TODO: 카메라 선택 콤보박스 아이템 변경 시의 동작\n pass\n\n def modeSelcComboboxChanged(self):\n # TODO: 모드 선택 콤보박스 아이템 변경 시의 동작\n pass\n\n '''버튼 클릭 메서드'''\n def cam1BtnClicked(self):\n self.camView1.raise_()\n self.camView1.setGeometry(170,3,self.width1,self.height1)\n self.zoomFrame.show()\n self.zoomBtn.setFlat(True)\n self.zoomFrame.raise_()\n \n def cam2BtnClicked(self):\n self.camView2.raise_()\n self.camView2.setGeometry(170,3,self.width1,self.height1)\n self.zoomFrame.show()\n self.zoomBtn.setFlat(True)\n self.zoomFrame.raise_()\n \n def cam3BtnClicked(self):\n self.camView3.raise_()\n self.camView3.setGeometry(170,3,self.width1,self.height1)\n self.zoomFrame.show()\n self.zoomBtn.setFlat(True)\n self.zoomFrame.raise_()\n\n def cam4BtnClicked(self):\n self.camView4.raise_()\n self.camView4.setGeometry(170,3,self.width1,self.height1)\n self.zoomFrame.show()\n self.zoomBtn.setFlat(True)\n self.zoomFrame.raise_()\n \n def zoomBtnClicked(self):\n self.zoomFrame.hide()\n self.camView1.setGeometry(170,50,self.width2,self.height2)\n self.camView2.setGeometry(640,50,self.width2,self.height2)\n self.camView3.setGeometry(170,350,self.width2,self.height2)\n self.camView4.setGeometry(640,350,self.width2,self.height2)\n self.cam1Btn.raise_()\n self.cam2Btn.raise_()\n self.cam3Btn.raise_()\n self.cam4Btn.raise_()\n \n def playerBtnClicked(self):\n # TODO: 재생 버튼 클릭 시의 동작\n if self.playerBtnFlag == True:\n self.playerBtn.setText(\"||\")\n self.playerBtnFlag = False\n else:\n self.playerBtn.setText(\"▶\")\n self.playerBtnFlag = True\n\n def soundBtnClicked(self):\n # TODO: 사운드 버튼 클릭 시의 동작\n if self.soundBtnFlag == True:\n self.soundBtn.setText(\"Sound OFF\")\n self.soundBtnFlag = False\n else:\n self.soundBtn.setText(\"Sound ON\")\n self.soundBtnFlag = True\n\n def optionBtnClicked(self) :\n self.optionScreen = OptionScreen()\n self.optionScreen.exec_()\n\n \n\n \n # '''스레드 관련 메서드'''\n # 각 캠 스레드에서 출력될 비디오 할당\n def setVideo2Thread(self):\n # TODO: 캠 연결\n self.cam[0].setFilename(\"sample_video/1_Thunder.mp4\")\n self.cam[1].setFilename(\"sample_video/1_Vibration.mp4\")\n self.cam[2].setFilename(\"sample_video/2_Wind.mp4\")\n self.cam[3].setFilename(\"sample_video/2_Vibration.mp4\")\n\n def startThread(self):\n for i in range(4):\n self.cam[i].change_pixmap_signal.connect(self.update_image)\n self.cam[i].start()\n\n @pyqtSlot(QThread,np.ndarray)\n # 이미지 뷰어를 새로운 이미지로 업데이트\n def update_image(self,thread,cv_img):\n qt_img = self.convert_cv_qt(cv_img)\n self.frame[thread].setPixmap(qt_img)\n\n # Opencv 이미지에서 QPixmap 이미지로 변환\n def convert_cv_qt(self,cvImg):\n height, width, channel = cvImg.shape\n convert_to_Qt_format = QImage(cvImg.data, width, height, QImage.Format_RGB888)\n qImg = convert_to_Qt_format.scaled(self.display_width,self.display_height,Qt.KeepAspectRatio)\n return QPixmap.fromImage(qImg)\n # gray_image = cv.cvtColor(cv_img,cv.COLOR_BGR2GRAY)\n # # print(rgb_image.shape)\n # # 높이, 너비 값 가져오기\n # h,w = gray_image.shape\n # #bytes_per_line = ch*w\n # # opencv 이미지를 QImage로 변환\n # convert_to_Qt_format = QImage(gray_image,w,h,QImage.Format_Grayscale8)\n # # 동영상 스케일 조정\n # p = convert_to_Qt_format.scaled(self.display_width,self.display_height,Qt.KeepAspectRatio)\n\n # return QPixmap.fromImage(p)\n\n def closeEvent(self,event):\n for i in range(4):\n self.cam[i].terminate()\n\n event.accept()\n\n\n\n\n\nif __name__ == \"__main__\" :\n app = QApplication(sys.argv)\n myWindow = MainScreen()\n myWindow.show()\n app.exec_()","sub_path":"main_screen.py","file_name":"main_screen.py","file_ext":"py","file_size_in_byte":10300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"20776022","text":"'''\nCreated on Jun 23, 2010\n\n@author: folz\n'''\n\nfrom engine import *\nfrom engine.misc import *\n\nimport multiplayer\nimport managers\nimport entities\n\nclass CalculusBlasters:\n\tTITLE = \"Calculus Blasters\"\n\t\n\tdef __init__( self, id, svrip ):\n\t\tpygame.init()\n\n\t\tself.id = id\n\t\tself.svrip = svrip\n\t\tself.client = multiplayer.Client( self.svrip, self.use_data )\n\t\tself.player2 = None\n\n\t\t# set the dimensions of the display window\n\t\tself.SIZE = self.WIDTH, self.HEIGHT = 800, 600\n\n\t\t# set the FPS\n\t\tself.FPS = 30\n\n\t\t# the actual FPS value refers to the delay, so let's update it properly\n\t\t# 1000ms/self.FPS = delay (but we're still calling it FPS)\n\t\tself.FPS = 1000 / self.FPS\n\n\t\t# create the window and display it\n\t\tself.window = gamewindow.GameWindow( self.SIZE )\n\t\tself.window.set_title( CalculusBlasters.TITLE )\n\t\tself.window.set_flags( pygame.HWSURFACE | pygame.DOUBLEBUF )# | pygame.FULLSCREEN )\n\t\tself.window.display()\n\t\t\n\t\tself.helveticaFnt = pygame.font.SysFont( \"Arial\", 16, True, False )\n\n\t\tself.clock = pygame.time.Clock()\n\n\t\tself.keys = {\n\t\t\tpygame.K_ESCAPE : False,\n\t\t\tpygame.K_LEFT : False,\n\t\t\tpygame.K_RIGHT : False,\n\t\t\tpygame.K_z : False,\n\t\t\tpygame.K_x : False\n\t\t}\n\n\t\tself.world = world.World( ( 2000, 2000 ) )\n\t\tself.world.set_background( \"giantbg.png\" )\n\t\tself.world.set_gravity( geometry.Vector( 0, 9.8 ) )\n\t\tself.world.debug = True\n\n\t\tself.networkBullets = managers.NetworkBulletManager( self.world )\n\n\t\t# create the viewport that will view the world\n\t\tself.viewport = viewport.Viewport( self.window, self.world )\n\n\t\t# create a player1 character\n\t\tif self.id == 1:\n\t\t\tself.player1 = entities.PlayerEntity( \"blue\", ( 1940, 1940 ) )\n\t\t\tself.player2 = entities.PlayerEntity( \"red\", ( 100, 100 ), \"rocketplok.gif\" )\n\t\telif self.id == 2:\n\t\t\tself.player1 = entities.PlayerEntity( \"red\", ( 100, 100 ) )\n\t\t\tself.player2 = entities.PlayerEntity( \"blue\", ( 1940, 1940 ), \"rocketplok.gif\" )\n\n\t\tself.world.add_entity( self.player1, attrs=\"player\" )\n\t\tself.world.set_entity_name( self.player1, \"player1\" )\n\t\tself.player1.add_gun( managers.BulletManager( self.player1 ) )\n\n\t\tself.world.add_entity( self.player2, attrs=\"player\" )\n\t\tself.world.set_entity_name( self.player2, \"player2\" )\n\t\tself.player2.add_gun( managers.BulletManager( self.player2 ) )\n\n\t\tself.flag1 = entities.FlagEntity( \"red\", ( 50, 175 ) )\n\t\tself.flag2 = entities.FlagEntity( \"blue\", ( 1950, 1975 ) )\n\t\tself.world.add_entity( self.flag1 )\n\t\tself.world.add_entity( self.flag2 )\n\n\t\tself.polygons = ()\n\n\t\tself.viewport.follow( self.player1 )\n\n\t\tself.running = True\n\n\t\tself.delta = 0.0\n\t\tself.delta_count = 0.0\n\n\t\tself.make_terrain()\n\n\t\twhile self.running:\n\t\t\tself.game_loop()\n\n\tdef use_data( self, data ):\n\t\tif self.player2 is None or data is None:\n\t\t\treturn\n\t\tif data.pId != self.id:\n\t\t\tself.player2.location = geometry.Vector( data.px, data.py )\n\t\t\tself.player2.set_facing( data.pf )\n\t\t\tif self.id == 2:\n\t\t\t\tself.flag1.set_facing( data.ff )\n\t\t\t\tself.flag2.updateScore( data.s )\n\t\t\t\tif data.fc:\n\t\t\t\t\tself.flag1.was_captured_by( self.player2 )\n\t\t\t\telse:\n\t\t\t\t\tself.flag1.release()\n\t\t\telse:\n\t\t\t\tself.flag2.set_facing( data.ff )\n\t\t\t\tself.flag1.updateScore( data.s )\n\t\t\t\tif data.fc:\n\t\t\t\t\tself.flag2.was_captured_by( self.player2 )\n\t\t\t\telse:\n\t\t\t\t\tself.flag2.release()\n\t\t\t#networkBullets.fromNetwork(data.bullets)\n\t\t\tfor b in data.bullets:\n\t\t\t\tself.player2.gun.addBullet( ( b[0], b[1] ), ( b[2], b[3] ) )\n\t\t\tif data.hit:\n\t\t\t\tself.player1.was_hit()\n\n\tdef create_block( self, x, y, w, h ):\n\t\tp = geometry.Terrain( [( x, y ), ( x + w, y ), ( x + w, y + h ), ( x, y + h )], self.world )\n\t\tself.world.add_terrain( p )\n\n\tdef make_terrain( self ):\n\n\t\t# create a boundary around the world\n\t\tleft_wall = geometry.Slope( [( 0, 0 ), ( 0, self.world.get_height() )] )\n\t\tself.world.add_terrain( left_wall )\n\n\t\tright_wall = geometry.Slope( [( self.world.get_width(), 0 ), ( self.world.get_width(), self.world.get_height() )] )\n\t\tself.world.add_terrain( right_wall )\n\n\t\ttop_wall = geometry.Slope( [( 0, 0 ), ( self.world.get_width(), 0 )] )\n\t\tself.world.add_terrain( top_wall )\n\n\t\tbottom_wall = geometry.Slope( [( 0, self.world.get_height() ), ( self.world.get_width(), self.world.get_height() )] )\n\t\tself.world.add_terrain( bottom_wall )\n\n\tdef send_data( self ):\n\t\tbullets = self.player1.gun.bullets\n\t\tbs = []\n\t\tfor b in bullets:\n\t\t\tif not b.sent:\n\t\t\t\tbs.append( ( b.location.x, b.location.y, b.velocity.x, b.velocity.y ) )\n\t\t\t\tb.sent = True\n\n\t\tif self.id == 1:\n\t\t\tflagFace = self.flag1.facing\n\t\t\tscore = self.flag2.score\n\t\t\tcap = self.flag1.captured\n\t\telse:\n\t\t\tflagFace = self.flag2.facing\n\t\t\tscore = self.flag1.score\n\t\t\tcap = self.flag2.captured\n\t\tdata = multiplayer.Data( self.player1.location.x, self.player1.location.y,\n\t\t\t\t\t\t\t\tbs, id, self.player2.hit, self.player1.facing,\n\t\t\t\t\t\t\tflagFace, score, cap )\n\t\tself.client.send_data( data )\n\t\tself.player2.hit = False\n\n\tdef handle_events( self ):\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\tself.client.kill()\n\t\t\t\tself.running = False\n\n\t\t\telif event.type in ( pygame.KEYDOWN, pygame.KEYUP ):\n\t\t\t\tself.handle_keys( event )\n\n\tdef handle_keys( self, keyEvent ):\n\t\tkey = keyEvent.key\n\n\t\tif keyEvent.type == pygame.KEYDOWN:\n\t\t\tself.keys[key] = True\n\t\tif keyEvent.type == pygame.KEYUP:\n\t\t\tself.keys[key] = False\n\n\tdef do_logic( self ):\n\t\tif self.keys[pygame.K_ESCAPE]:\n\t\t\tpygame.event.post( pygame.event.Event( pygame.QUIT ) )\n\n\t\tif self.keys[pygame.K_LEFT]:\n\t\t\tself.player1.move_left()\n\n\t\tif self.keys[pygame.K_RIGHT]:\n\t\t\tself.player1.move_right()\n\n\t\tif self.keys[pygame.K_z]:\n\t\t\tself.player1.fly()\n\n\t\tif self.keys[pygame.K_x]:\n\t\t\tself.player1.shoot()\n\t\t\tself.keys[pygame.K_x] = False\n\n\tdef game_loop( self ):\n\t\t# Timing controls\n\t\tself.delta = self.clock.tick()\n\t\tself.delta_count += self.delta\n\t\t\n\t\tself.window.set_title(\"{0} (FPS: {1})\".format( CalculusBlasters.TITLE, 1000 / self.delta_count ) )\n\n\t\tself.handle_events()\n\t\tself.do_logic()\n\n\t\tself.viewport.update( self.delta )\n\t\tself.send_data()\n\n\t\tif self.delta_count > self.FPS:\n\t\t\tself.delta_count -= self.FPS\n\t\t\tself.viewport.render( self.delta )\n\n\t\tself.networkBullets.draw()\n\t\tself.window.screen.blit( self.helveticaFnt.render( \"Blue Team Score: \" + str( self.flag2.score ), True, ( 0, 0, 255 ), ( 0, 0, 0 ) ), ( 0, 0 ) )\n\t\tself.window.screen.blit( self.helveticaFnt.render( \"Red Team Score: \" + str( self.flag1.score ), True, ( 255, 0, 0 ), ( 0, 0, 0 ) ), ( 0, 18 ) )\n\t\tpygame.display.flip()\n\nif __name__ == \"__main__\":\n\tid = int ( input ( \"id? \" ) )\n\tsvrip = input ( \"server ip? \" )\n\tCalculusBlasters( id, svrip )\n","sub_path":"calculus-blasters.py","file_name":"calculus-blasters.py","file_ext":"py","file_size_in_byte":6493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"331950893","text":"import subprocess, logging, time, sys\n\n\ndef setlogger(logdir, log_name):\n subprocess.call(['mkdir', '-p', logdir])\n\n logger = logging.getLogger()\n logger.setLevel(logging.INFO)\n fmt = logging.Formatter('%(asctime)s: [ %(message)s ]',\n '%m/%d/%Y %I:%M:%S %p')\n console = logging.StreamHandler()\n console.setFormatter(fmt)\n logger.addHandler(console)\n\n timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n\n logfile = logging.FileHandler(logdir + \"/\" + log_name + timestr, 'w')\n logfile.setFormatter(fmt)\n logger.addHandler(logfile)\n\n try:\n GIT_BRANCH = subprocess.check_output(['git', 'symbolic-ref', '--short', 'HEAD'])\n except:\n GIT_BRANCH = \"Unknown\"\n try:\n GIT_REVISION = subprocess.check_output(['git', 'rev-parse', 'HEAD'])\n except:\n GIT_REVISION = \"Unknown\"\n\n logger.info('COMMAND: [ nohup python %s & ], GIT_REVISION: [%s] [%s]'\n % (' '.join(sys.argv), GIT_BRANCH.strip(), GIT_REVISION.strip()))\n return logger","sub_path":"code/dkvmn_mod/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"525190282","text":"import warnings\nwarnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\n\n\nimport pandas as pd \nimport numpy as np\nfrom gensim.models import Word2Vec, KeyedVectors\nfrom pattern import en\nimport pickle\nimport config\nfrom nltk import pos_tag, word_tokenize\nfrom pymongo import MongoClient\n\n\nnp.random.seed(0)\n\ndef preprocess_training_data(df, dir_model_name):\n\t\"\"\"\n\t param df: dataframe\n\t param dir_model_name: Name of the model\n\n\t\"\"\"\n\tall_text = ' '\n\tfor i in df['text']:\n\t\ti = str(i)\n\t\tall_text+= i+\" \"\n\n\tall_text = all_text.lower()\n\t# print(all_text)\n\n\tvector = []\n\n\ttagged_text = pos_tag(word_tokenize(all_text))\n\n\t# for i in tagged_text:\n\t# \tif 'NN' in i[1] or 'JJ' in i[1] or 'VBG' in i[1]:\n\t# \t\tvector.append(i[0])\n\n\tvector = []\n\tfor sentence in en.parsetree(all_text, tokenize=True, lemmata=True, tags=True):\n\t\ttemp = []\n\t\tfor chunk in sentence.chunks:\n\t\t\tfor word in chunk.words:\n\t\t\t\tif word.tag == 'NN': # or word.tag == 'VB':\n\t\t\t\t\ttemp.append(word.lemma)\n\t\tvector.append(temp)\n\n\t# print(vector)\n\n\tglobal model\n\tmodel = Word2Vec(vector, size=100, window=5, min_count=3, seed=42)\n\tmodel.save(config.output_path+dir_model_name)\n\n\nif __name__ == '__main__':\n\n\t# client = MongoClient(config.mongo_host, 27017)\n\t# db = client.Resparse \n\t# coll = db['ResumeText']\n\t# df = pd.DataFrame(list(coll.find()))\n\n\tdf = pd.read_csv(config.output_path+\"tech.csv\")\n\tdf.dropna(inplace=True)\n\n\tpreprocess_training_data(df, \"model_tech\")\n\n\n","sub_path":"code/word_embedding_model.py","file_name":"word_embedding_model.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"100110766","text":"\n\n#calss header\nclass _IBIS():\n\tdef __init__(self,): \n\t\tself.name = \"IBIS\"\n\t\tself.definitions = [u'a large bird with long legs and a long, downward-curving beak, that walks around in water to find its food ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_ibis.py","file_name":"_ibis.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"42186642","text":"# Course Schedule II\n\nclass Solution:\n white = 1\n gray = 2\n black = 3\n \n def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\n \n # Create Adjacency List\n adjacency_list = defaultdict(list)\n \n # Add a pair of source -> dest in list\n for dest, src in prerequisites:\n adjacency_list[src].append(dest)\n \n # Stack\n stack = []\n is_possible = True\n \n # By default, all vertices are white\n color = {k: Solution.white for k in range(numCourses)}\n \n # Depth First Search\n def dfs(node):\n nonlocal is_possible\n \n # If cycle found, return\n if not is_possible:\n return\n \n # Start Recursion using DFS\n # The node which is being considered changes from white to gray\n color[node] = Solution.gray\n \n # For all nodes in adjacency list\n if node in adjacency_list:\n # Check for all neighbors to the node\n for neighbor in adjacency_list[node]:\n # If the neighbor is unvisited, visit them\n if color[neighbor] == Solution.white:\n dfs(neighbor)\n # else there exists a cycle\n elif color[neighbor] == Solution.gray:\n is_possible = False\n \n # Once dfs is done, mark the visited nodes as black\n color[node] = Solution.black\n \n # Append Node to Stack\n stack.append(node)\n \n \n # For vertices in numCourses, perform DFS\n for vertex in range(numCourses):\n # If node is unprocessed, perform dfs on it\n if color[vertex] == Solution.white:\n dfs(vertex)\n \n # Return all elements from the stack from top to bottom order, if no cycle found\n return stack[::-1] if is_possible else []\n","sub_path":"Course_Schedule_II.py","file_name":"Course_Schedule_II.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"387666833","text":"# -*- coding: utf-8 -*-\n#\n# Copyright © Spyder Project Contributors\n# Licensed under the terms of the MIT License\n# (see spyder/__init__.py for details)\n\n\"\"\"\nCreate a stand-alone macOS app using py2app\n\nTo be used like this:\n$ python setup.py\n\"\"\"\n\nimport sys\nimport shutil\nfrom logging import getLogger, StreamHandler, Formatter\nfrom pathlib import Path\nfrom setuptools import setup\n\nfrom spyder import get_versions\n\n# Setup logger\nfmt = Formatter('%(asctime)s [%(levelname)s] [%(name)s] -> %(message)s')\nh = StreamHandler()\nh.setFormatter(fmt)\nlogger = getLogger('macOS-installer')\nlogger.addHandler(h)\nlogger.setLevel('INFO')\n\n# Define paths\nTHISDIR = Path(__file__).resolve().parent\nSPYREPO = (THISDIR / '..' / '..').resolve()\nICONFILE = SPYREPO / 'img_src' / 'spyder.icns'\nAPPSCRIPT = SPYREPO / 'scripts' / 'spyder'\n\nMAC_APP_NAME = 'Spyder.app'\nAPP_BASE_NAME = MAC_APP_NAME[:-4]\n\n# Python version\nPYVER = [sys.version_info.major, sys.version_info.minor,\n sys.version_info.micro]\n\nversion = get_versions()\nSPYVER = version['spyder']\nSPYCOM = version['revision']\nSPYBRA = version['branch']\n\n\ndef make_app_bundle(dist_dir, make_lite=False):\n \"\"\"\n Make macOS application bundle.\n\n Parameters\n ----------\n dist_dir : pathlib.Path\n Directory in which to put the application bundle.\n make_lite : bool, optional\n Whether to create the application bundle with minimal packages.\n The default is False.\n \"\"\"\n from spyder.config.utils import EDIT_FILETYPES, _get_extensions\n\n build_type = 'lite' if make_lite else 'full'\n logger.info('Creating %s app bundle...', build_type)\n\n from packages import PACKAGES, INCLUDES, EXCLUDES, SCIENTIFIC\n\n if make_lite:\n EXCLUDES.extend(SCIENTIFIC)\n else:\n INCLUDES.extend(SCIENTIFIC)\n\n EDIT_EXT = [ext[1:] for ext in _get_extensions(EDIT_FILETYPES)]\n\n OPTIONS = {\n 'optimize': 0,\n 'packages': PACKAGES,\n 'includes': INCLUDES,\n 'excludes': EXCLUDES,\n 'iconfile': ICONFILE.as_posix(),\n 'dist_dir': dist_dir.as_posix(),\n 'emulate_shell_environment': True,\n 'plist': {\n 'CFBundleDocumentTypes': [{'CFBundleTypeExtensions': EDIT_EXT,\n 'CFBundleTypeName': 'Text File',\n 'CFBundleTypeRole': 'Editor'}],\n 'CFBundleIdentifier': 'org.spyder-ide.Spyder',\n 'CFBundleShortVersionString': SPYVER,\n 'NSRequiresAquaSystemAppearance': False, # Darkmode support\n 'LSEnvironment': {'SPY_COMMIT': SPYCOM, 'SPY_BRANCH': SPYBRA}\n }\n }\n\n # Build the application\n setup(name=APP_BASE_NAME, app=[APPSCRIPT.as_posix()],\n options={'py2app': OPTIONS})\n\n return\n\n\ndef make_disk_image(dist_dir, make_lite=False):\n \"\"\"\n Make macOS disk image containing Spyder.app application bundle.\n\n Parameters\n ----------\n dist_dir : pathlib.Path\n Directory in which to put the disk image.\n make_lite : bool, optional\n Whether to append the disk image file and volume name with 'Lite'.\n The default is False.\n\n \"\"\"\n logger.info('Creating disk image...')\n\n from dmgbuild import build_dmg\n from dmgbuild.core import DMGError\n\n volume_name = '{}-{} Py-{}.{}.{}'.format(APP_BASE_NAME, SPYVER, *PYVER)\n dmg_name = 'Spyder'\n if make_lite:\n volume_name += ' Lite'\n dmg_name += '-Lite'\n dmg_name += '.dmg'\n dmgfile = (dist_dir / dmg_name).as_posix()\n\n settings_file = (THISDIR / 'dmg_settings.py').as_posix()\n settings = {\n 'files': [(dist_dir / MAC_APP_NAME).as_posix()],\n 'badge_icon': ICONFILE.as_posix(),\n 'icon_locations': {MAC_APP_NAME: (140, 120),\n 'Applications': (500, 120)}\n }\n\n try:\n build_dmg(dmgfile, volume_name, settings_file=settings_file,\n settings=settings, detach_retries=30)\n logger.info('Building disk image complete.')\n except DMGError as exc:\n if exc.args[0] == 'Unable to detach device cleanly':\n # don't raise this error since the dmg is forced to detach\n logger.warning(exc.args[0])\n else:\n raise exc\n\n return\n\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('-n', '--no-app', dest='make_app',\n action='store_false', default=True,\n help='Do not create application bundle')\n parser.add_argument('-l', '--lite', dest='make_lite', action='store_true',\n default=False,\n help='Build with minimal internal packages')\n parser.add_argument('-i', '--dmg', dest='make_dmg', action='store_true',\n default=False, help='Create disk image')\n parser.add_argument('-d', '--dist-dir', dest='dist_dir', default='dist',\n help='Distribution directory; passed to py2app')\n parser.add_argument('-b', '--bdist-base', dest='build_dir',\n default='build',\n help='Build directory; passed to py2app')\n\n args, rem = parser.parse_known_args()\n\n # Groom sys.argv for py2app\n sys.argv = sys.argv[:1] + ['py2app'] + rem\n\n dist_dir = Path(args.dist_dir).resolve()\n build_dir = Path(args.build_dir).resolve()\n\n if args.make_app:\n shutil.rmtree(build_dir, ignore_errors=True)\n make_app_bundle(dist_dir, make_lite=args.make_lite)\n else:\n logger.info('Skipping app bundle.')\n\n if args.make_dmg:\n make_disk_image(dist_dir, make_lite=args.make_lite)\n else:\n logger.info('Skipping disk image.')\n","sub_path":"installers/macOS/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":5705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"60"} +{"seq_id":"257099525","text":"import os\nimport argparse\n\n# Top of C++ file with includes and namespaces\ntop_source_string = \"\"\"\n#include \n#include \n#include \nusing namespace std;\nusing namespace std::chrono;\n\n\"\"\"\n\n# The reference function creates a loop that simply adds together all floating point constants in a loop\ndef reference_loop_source(chain_length):\n\n # function header\n function = \"void reference_loop(float *b, int size) {\"\n\n # loop header\n loop = \" for (int i = 0; i < size; i++) {\"\n\n # read the original value from memory\n init = \" float tmp = b[i];\"\n\n # create the dependency chain\n chain = []\n for i in range(0,chain_length):\n chain.append(\" tmp += \"+ str(i+1)+\".0f;\")\n\n # store the final value to memory\n close = \" b[i] = tmp;\"\n\n # close the loop\n loop_close = \" }\"\n\n # close the function\n function_close = \"}\"\n\n # join together all the parts to make a complete function\n return \"\\n\".join([function, loop, init, \"\\n\".join(chain), close, loop_close, function_close])\n\n# First hoomework function here! Implement the reference loop unrolled\n# *sequentially*, That is, create dependency chains of length\n# *chain_length*. Unroll the loop by a factor of *unroll_factor*. Do\n# the unrolled loop iterations sequentially: i.e. do not start the\n# chain of one iteration before the previous one is finished.\n\n# to view the reference loop for a dependency chain of N, just\n# run: python3 skeleton.py N 1.\n# Your code will initially fail the assertion check, but you\n# should be able to view the reference loop in homework.cpp.\n\n# The reference for this loop is slide number 75 and 76 of the\n# April 8 lecture. Those slides show the example of going from\n# unroll factor of 1 to 2. You need to generalize this.\n\n# You can assume that the unroll factor evenly divides the\n# array length. That is, you should be able to do this all\n# in one loop without any extra clean-up loops.\n\n# Don't forget! Floating point constants must have 'f' after them!\n# that is, you would write 2 in floating point as '2.0f'\n#\n# You can gain confidence that implemented this correctly by executing\n# skeleton.py with several power-of-two options for the unroll factor\n# for example, try 1,2,4,8, etc.\ndef homework_loop_sequential_source(chain_length, unroll_factor):\n function = \"void homework_loop_sequential(float *b, int size) {\"\n\n #implement me!\n function_body = []\n\n #header\n function_body.append(\" for (int i = 0; i < size; i+=\"+str(unroll_factor)+\") {\")\n\n #var init\n for i in range(0,unroll_factor):\n function_body.append(\" float tmp\"+str(i)+\" = b[i+\"+str(i)+\"];\")\n\n #sequential ordering\n for i in range(0,unroll_factor):\n for j in range(0,chain_length):\n function_body.append(\" tmp\"+str(i)+\" += \"+ str(j+1)+\".0f;\")\n\n #saving back to memory\n for i in range(0,unroll_factor):\n function_body.append(\" b[i+\"+str(i)+\"] = tmp\"+str(i)+\";\")\n\n #closer for header\n function_body.append(\"}\")\n function_close = \"}\"\n return \"\\n\".join([function, \"\\n\".join(function_body), function_close])\n\n# Second homework function here! The specification for this\n# function is the same as the first homework function, except\n# this time you will interleave the instructions of the unrolled\n# dependency chains.\n\n# The reference slides for this loop are slide 77 - 80 on the April 8\n# lecture. That example shows a dependency chain of length N with an\n# unroll factor of 2. You need to generalize the unroll factor and\n# place dependent instructions as far apart as possible.\n\n# You can assume the unroll factor is a power of 2 and that the\n# the dependency chain also a power of two. \ndef homework_loop_interleaved_source(chain_length, unroll_factor):\n function = \"void homework_loop_interleaved(float *b, int size) {\"\n #implement me!\n function_body = []\n\n #header\n function_body.append(\" for (int i = 0; i < size; i+=\"+str(unroll_factor)+\") {\")\n\n #var init\n for i in range(0,unroll_factor):\n function_body.append(\" float tmp\"+str(i)+\" = b[i+\"+str(i)+\"];\")\n\n #interleaving of dependencies\n #very similar to seqential, but with different ordering\n for i in range(0,chain_length):\n for j in range(0,unroll_factor):\n function_body.append(\" tmp\"+str(j)+\" += \"+ str(i+1)+\".0f;\")\n\n #saving back to memory\n for i in range(0,unroll_factor):\n function_body.append(\" b[i+\"+str(i)+\"] = tmp\"+str(i)+\";\")\n\n #closer for header\n function_body.append(\"}\")\n function_close = \"}\"\n return \"\\n\".join([function, \"\\n\".join(function_body), function_close])\n\n# String for the main function, including timings and\n# reference checks.\nmain_source_string = \"\"\"\n#define SIZE 1024 * 1024 * 8\n\nint main() {\n float *a;\n a = (float *) malloc(SIZE * sizeof(float));\n\n float *b;\n b = (float *) malloc(SIZE * sizeof(float));\n\n float *c;\n c = (float *) malloc(SIZE * sizeof(float));\n\n for (int i = 0; i < SIZE; i++) {\n a[i] = i;\n b[i] = i;\n c[i] = i;\n }\n\n auto sequential_start = high_resolution_clock::now();\n homework_loop_sequential(a,SIZE);\n auto sequential_end = high_resolution_clock::now();\n auto sequential_duration = duration_cast(sequential_end - sequential_start);\n double sequential_seconds = sequential_duration.count()/1000000000.0;\n\n auto interleaved_start = high_resolution_clock::now();\n homework_loop_interleaved(c,SIZE);\n auto interleaved_end = high_resolution_clock::now();\n auto interleaved_duration = duration_cast(interleaved_end - interleaved_start);\n double interleaved_seconds = interleaved_duration.count()/1000000000.0;\n\n\n auto ref_start = high_resolution_clock::now();\n reference_loop(b,SIZE);\n auto ref_stop = high_resolution_clock::now();\n auto ref_duration = duration_cast(ref_stop - ref_start);\n double ref_seconds = ref_duration.count()/1000000000.0;\n //added for correctness check\n int j = 0;\n for(int i = 0; i < SIZE; i++){\n assert(a[i] == c[i]);\n assert(b[i] == c[i]);\n }\n\n cout << \"sequential loop time: \" << sequential_seconds << endl; \n cout << \"interleaved loop time: \" << interleaved_seconds << endl; \n cout << \"reference loop time: \" << ref_seconds << endl;\n cout << \"----\" << endl;\n cout << \"speedups:\" << endl;\n cout << \"sequential speedup over reference: \" << ref_seconds / sequential_seconds << endl << endl;\n cout << \"interleaved speedup over reference: \" << ref_seconds / interleaved_seconds << endl << endl;\n\n return 0;\n}\n\"\"\"\n\n# Create the program source code\ndef pp_program(chain_length, unroll_factor):\n\n # Your two functions are called here\n homework_source_string_sequential = homework_loop_sequential_source(chain_length, unroll_factor)\n homework_source_string_interleaved = homework_loop_interleaved_source(chain_length, unroll_factor)\n\n # join together all the other parts to make a complete C++ program\n return \"\\n\".join([top_source_string, reference_loop_source(chain_length), homework_source_string_sequential, homework_source_string_interleaved, main_source_string])\n\n# Write a string to a file (helper function)\ndef write_str_to_file(st, fname):\n f = open(fname, 'w')\n f.write(st)\n f.close()\n\n# Compile the program. Don't change the options here for the official\n# assignment submission. Feel free to change them for your own curiosity.\n# Some notes:\n#\n# I am using a recent version of C++ to use the chrono library.\n#\n# I am disabling the compiler's loop unrolling so we can ensure the\n# reference loop and the homework loops are not unrolled \"behind our backs\"\n#\n# I am using the lowest optimization level here (-O0) to disable most\n# optimizations. The compiler does some really ticky things even at\n# (-O1) here. \ndef compile_program():\n cmd = \"clang++ -std=c++14 -fno-unroll-loops -O0 -o homework homework.cpp\"\n print(\"running: \" + cmd)\n assert(os.system(cmd) == 0)\n\n# Execute the program\ndef run_program():\n cmd = \"./homework\"\n print(\"running: \" + cmd)\n print(\"\")\n assert(os.system(cmd) == 0)\n\n# The high-level function: generate the C code, compile it, and execute it.\ndef generate_and_run(chain_length, unroll_factor):\n print(\"\")\n print(\"----------\")\n print(\"generating and running for:\")\n print(\"chain length = \" + str(chain_length))\n print(\"unroll factor = \" + str(unroll_factor))\n print(\"-----\")\n print(\"\")\n \n program_str = pp_program(chain_length, unroll_factor)\n write_str_to_file(program_str, \"homework.cpp\")\n compile_program()\n run_program()\n\n# gets two command line args, chain length (CL) and unroll factor (UF)\ndef main():\n parser = argparse.ArgumentParser(description='Part 1 of Homework 1: exploiting ILP by unrolling independent loops.')\n parser.add_argument('chain_length', metavar='CL', type=int,\n help='the length of dependent instructions per loop iteration to generate')\n parser.add_argument('unroll_factor', metavar='UF', type=int,\n help='how many loop iterations to unroll')\n args = parser.parse_args()\n CL = args.chain_length\n UF = args.unroll_factor\n generate_and_run(CL, UF)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"hw1/part1/homework1_part1_skeleton.py","file_name":"homework1_part1_skeleton.py","file_ext":"py","file_size_in_byte":9210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"438477150","text":"# title: minimum-depth-of-binary-tree\n# detail: https://leetcode.com/submissions/detail/189474143/\n# datetime: Wed Nov 14 13:50:20 2018\n# runtime: 44 ms\n# memory: N/A\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def minDepth(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: int\n \"\"\"\n if root is None:\n return 0\n d1 = self.minDepth(root.left)\n d2 = self.minDepth(root.right)\n if d1 == 0:\n if d2 == 0:\n return 1\n else:\n return d2 + 1\n else:\n if d2 == 0:\n return d1 + 1\n else:\n return min(d1, d2) + 1\n ","sub_path":"leetcode/minimum-depth-of-binary-tree/189474143.py","file_name":"189474143.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"15353089","text":"#!/usr/bin/python\nimport json\nfrom bson import json_util\nimport bottle\nfrom bottle import route, run, request, abort, response, post, get, put, delete\nfrom pymongo import MongoClient\nimport pprint\n\n# Set target db & collection\nconnection = MongoClient('localhost', 27017)\ndb = connection['market']\ncollection = db['stocks']\n\n#############################################\n# PyMongo CRUD Methods #\n#############################################\n\n\n# Inserts a new document into Collection.\n# \n# @param document\n# - JSON-type document to be inserted into the collection\n# \n# @var result\n# - Indicates success/failure of inserting document\n# \n# @throws TypeError\n# - Thrown if document is incorrect format\n# \n# @return result\n# - Created document on successful creation\ndef insert_document(document):\n try:\n collection.save(document)\n except TypeError as te:\n abor(400, str(te))\n else:\n result = collection.find(document)\n return result\n \n \n# Searches Collection for documents matching passed criteria\n# \n# @param document\n# - Key, value pair used as search criteria\n# \n# @var result\n# - Returned cursor from find()\n# \n# @throws TypeError\n# - Thrown if document is incorrect format\n# \n# @return result\n# - Cursor from find(), undefined if search fails\ndef find_document(document):\n result = []\n\n try:\n data = collection.find(document)\n \n for document in data:\n result.append(document)\n except TypeError as te:\n abort(400, str(te))\n else:\n return result\n \n \n# Updates documents based on search criteria.\n# Updated documents are then returned.\n# \n# @param criteria\n# - Key,value pair used to identify documents to be updated\n# \n# @param document\n# - Set of key,value pairs for updating documents\n# \n# @var result\n# - Cursor containing updated documents\n# \n# @throws TypeError\n# - Thrown if criteria is incorrect format\n# \n# @throws Exception\n# - Thrown if document is incorrect format\n# \n# @return result\n# - Cursor containing updated documents, undefined if update fails\ndef edit_document(criteria, document):\n try:\n collection.update_one(criteria,{\"$set\" : document})\n result = collection.find(criteria)\n except TypeError as te:\n abort(400, str(te))\n except Exception as we:\n abort(400, str(we))\n except:\n abort(400, \"Bad Request\")\n else:\n return result\n \n \n# Searches for documents based on passed criteria.\n# If documents are found, they are printed.\n# Documents are then deleted.\n# \n# @param documen\n# - Key,value pair used to identify documents to be deleted\n# \n# \n# @var result\n# - Cursor containing documents pending deletion\n# - If exception, result is set to False\n# - Otherwise, contents are printed then result is set to True\n# \n# @throws TypeError\n# - Thrown if document is incorrect format\n# \n# @return result\n# - True/False based on if documents were successfully deleted\n# - Undefined if deletion fails\ndef remove_document(document):\n try:\n collection.delete_one(document)\n result = \"True\"\n except TypeError as te:\n abort(400, str(te))\n \n return result\n\n\n#############################################\n# Set up URI paths for REST service #\n#############################################\n\n \n# URI path for creating a new document in db\n# Accepts a JSON object and adds to collection\n# by calling insert_document()\n# \n# @param data\n# - Request JSON object\n# \n# @param result\n# - Cursor containing new document\n# \n# @throws NameError\n# - Thrown if document creation fails\n# \n# @return\n# - Returns list conversion of result, parsed into a JSON object\n@post('/create')\ndef create_document():\n try:\n data = request.json\n except:\n abort(404, \"Bad data\")\n \n try:\n result = insert_document(data)\n \n response.headers['Content-Type'] = 'application/json'\n return json.dumps(list(result), indent=4, default=json_util.default)\n except NameError as ne:\n abort(400, str(ne))\n\n\n# URI path for searching for a document\n# Uses the business_name query parameter\n# to search MongoDb using find_document()\n# \n# @param name\n# - Ticker request query parameter\n# \n# @param cursor\n# - Cursor returned from find_document()\n# \n# @throws NameError\n# - Thrown if search fails\n# \n# @return\n# - Returns list conversion of cursor, parsed into a JSON object\n@get('/read')\ndef read_document():\n try:\n name = request.query.Company\n cursor = find_document({\"Company\" : name})\n \n if cursor:\n response.content_type = 'application/json'\n return json.dumps(list(cursor), indent=4, default=json_util.default)\n else:\n abort(404, \"No documents found\")\n except NameError as ne:\n abort(404, str(ne))\n \n# URI path for updating an existing documents\n# Uses the id query parameter to search MongoDb using edit_document(),\n# then updates the returned documents with the result query parameter\n# \n# @param stock_ticker\n# - id request query parameter\n# \n# @param stock_volume\n# - result request query parameter\n# \n# @param criteria\n# - Key, value pair used to query MongoDb\n# \n# @param change\n# - Key, value pair of data to update returned documents\n# \n# @param cursor\n# - Updated documents returned from edit_document()\n@get('/update')\ndef update_document():\n try:\n stock_ticker = request.query.Ticker\n stock_volume = request.query.Volume\n criteria = {\"Ticker\" : stock_ticker}\n change = {\"Volume\" : stock_volume}\n cursor = edit_document(criteria, change)\n \n if cursor:\n response.content_type = 'application/json'\n return json.dumps(list(cursor), indent=4, default=json_util.default)\n else:\n abort(404, \"No documents found\")\n except NameError as ne:\n abort(404, str(ne))\n \n# URI path for deleting a documents\n# Uses the id query parameter to search MongoDb using remove_document(),\n# matching documents are deleted\n# \n# @param stock_ticker\n# - id request query parameter\n# \n# @param result\n# - Return from remove_document(), returns True if successful\n# \n# @throws exception\n# - Thrown if remove_document() fails\n@get('/delete')\ndef delete_document():\n \n try:\n stock_ticker = request.query.Ticker\n result = remove_document({\"Ticker\" : stock_ticker})\n \n if result == \"True\":\n return \"delete success\\n\"\n else:\n abort(404, \"File not found\")\n except:\n abort(400, \"Bad Request\")\n \n\n# Application entry point\n# Starts REST service\nif __name__ == '__main__':\n #app.run(debug=True)\n run(host='localhost',port=8080, debug=True)\n","sub_path":"RESTfulAPI.py","file_name":"RESTfulAPI.py","file_ext":"py","file_size_in_byte":6573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"407549176","text":"import os\nimport time\n\nfrom glob import glob\nimport matplotlib.pyplot as plt\nfrom matplotlib import figure\n\n\nfrom nipype.interfaces.base import (\n TraitedSpec, BaseInterfaceInputSpec,\n File, Directory, InputMultiObject, Str, isdefined,\n SimpleInterface)\nfrom nilearn.plotting import plot_anat, find_cut_slices\nimport nibabel as nb\n\n\nSUBJECT_TEMPLATE = \"\"\"\\\n\\t