diff --git "a/5119.jsonl" "b/5119.jsonl"
new file mode 100644--- /dev/null
+++ "b/5119.jsonl"
@@ -0,0 +1,593 @@
+{"seq_id":"415358582","text":"# Logistic Regression\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass MLP(nn.Module):\n def __init__(self, input_size, num_classes, hidden_dim):\n super(MLP, self).__init__()\n self.linear_input = nn.Linear(input_size, hidden_dim)\n self.linear1 = nn.Linear(hidden_dim, hidden_dim)\n self.linear_output = nn.Linear(hidden_dim, num_classes)\n\n # xavier initialization\n torch.nn.init.xavier_normal(self.linear_input.weight)\n self.linear_input.bias.data.fill_(1e-5)\n torch.nn.init.xavier_normal(self.linear1.weight)\n self.linear1.bias.data.fill_(1e-5)\n torch.nn.init.xavier_normal(self.linear_output.weight)\n self.linear_output.bias.data.fill_(1e-5)\n\n def forward(self, x):\n out = F.tanh(self.linear_input(x))\n out = F.tanh(self.linear1(out))\n out = self.linear_output(out)\n return out\n","sub_path":"models/MLP.py","file_name":"MLP.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"182733541","text":"import numpy as np\nimport cv2\n\ndetector_path = '/mnt/sdb/mspark/data/brain_aneurysm_yonsei/npz/total/total_input_3dce_detector.npz'\nsegmentation_path = '/mnt/sdb/mspark/data/brain_aneurysm_yonsei/npz/total/total_input_3dce_segmentation.npz'\nlabel_path = '/mnt/sdb/mspark/data/brain_aneurysm_yonsei/npz/total/total_label_3dce_detector.npz'\n\ndef masking_rgb(img, color=None, multiply=255):\n if len(np.shape(img)) <= 2:\n _img = np.expand_dims(img, axis=3)\n else:\n _img = img\n rgb_list = [np.zeros(np.shape(_img)) for _ in range(3)]\n\n if color == 'yellow':\n rgb_list[1] = _img\n rgb_list[2] = _img\n B, G, R = rgb_list\n\n elif color != None:\n rgb_dic = {'blue': 0, 'green': 1, 'red': 2}\n rgb_list[rgb_dic[color]] = _img\n B, G, R = rgb_list\n else:\n B = G = R = _img\n\n concat_img = np.concatenate((B, G, R), axis=-1)\n out_img = concat_img * multiply\n\n return out_img\n\ndetector = np.load(detector_path)['all'][0]\nsegmentation = np.load(segmentation_path)['all'][0]\nlabel = np.load(label_path)['all'][0]\n\n# print(detector.shape, np.max(detector))\n# print(segmentation.shape, np.max(segmentation))\n# print(label.shape)\n\n\nfor idx, de, se in zip(range(9), detector, segmentation):\n print(np.max(de))\n print(np.max(se))\n # cv2.imwrite('/home/mspark/deepnoid/data/de{}.png'.format(idx), masking_rgb(de/np.max(de)))\n # cv2.imwrite('/home/mspark/deepnoid/data/se{}.png'.format(idx), masking_rgb(se/np.max(se)))\n#\n# segmentation = segmentation[4].astype(np.uint8)\n# label = np.round(label[0] * 256).astype(np.uint8)\n#\n# cv2.rectangle(segmentation, (label[2] - 5, label[1] - 5), (label[4] + 5, label[3] + 5), (1, 1, 1), 1)\n# cv2.imwrite('/home/mspark/deepnoid/data/label.png', masking_rgb(segmentation))\n\n\n\n","sub_path":"aneurysm_detection/seg_3dce/note.py","file_name":"note.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"407345293","text":"# -*- encoding:utf-8 -*-\n\nimport io\nimport os\nimport sys\nimport struct\nimport hashlib\nimport binascii\n\nfrom ecdsa import BadSignatureError\nfrom ecdsa.der import UnexpectedDER\nfrom ecdsa.keys import SigningKey, VerifyingKey\nfrom ecdsa.util import sigencode_der_canonize, sigdecode_der\nfrom ecdsa.curves import SECP256k1\n\nimport base58\n\nPY3 = True if sys.version_info[0] >= 3 else False\n\nif not PY3:\n\tfrom StringIO import StringIO\nelse:\n\tfrom io import BytesIO as StringIO\n\n# byte as int conversion\nbasint = (lambda e:e) if PY3 else \\\n (lambda e:ord(e))\n# read value as binary data from buffer\nunpack = lambda fmt, fileobj: struct.unpack(fmt, fileobj.read(struct.calcsize(fmt)))\n# write value as binary data into buffer\npack = lambda fmt, fileobj, value: fileobj.write(struct.pack(fmt, *value))\n# read bytes from buffer\nunpack_bytes = lambda f,n: unpack(\"<\"+\"%ss\"%n, f)[0]\n# write bytes into buffer\npack_bytes = (lambda f,v: pack(\"!\"+\"%ss\"%len(v), f, (v,))) if PY3 else \\\n (lambda f,v: pack(\"!\"+\"c\"*len(v), f, v))\n\n\ndef hexlify(data):\n\tresult = binascii.hexlify(data)\n\treturn str(result.decode() if isinstance(result, bytes) else result)\n\n\ndef unhexlify(data):\n\tif len(data)%2: data = \"0\"+data\n\tresult = binascii.unhexlify(data)\n\treturn result if isinstance(result, bytes) else result.encode()\n\n\ndef compressEcdsaPublicKey(pubkey):\n\tfirst, last = pubkey[:32], pubkey[32:]\n\t# check if last digit of second part is even (2%2 = 0, 3%2 = 1)\n\teven = not bool(basint(last[-1]) % 2)\n\treturn (b\"\\x02\" if even else b\"\\x03\") + first\n\n# Uncompressed public key is:\n# 0x04 + x-coordinate + y-coordinate\n#\n# Compressed public key is:\n# 0x02 + x-coordinate if y is even\n# 0x03 + x-coordinate if y is odd\n#\n# y^2 mod p = (x^3 + 7) mod p\n\ndef uncompressEcdsaPublicKey(pubkey):\n\t# read more : https://bitcointalk.org/index.php?topic=644919.msg7205689#msg7205689\n\tp = 0xfffffffffffffffffffffffffffffffffffffffffffffffffffffffefffffc2f\n\ty_parity = int(pubkey[:2]) - 2\n\tx = int(pubkey[2:], 16)\n\ta = (pow(x, 3, p) + 7) % p\n\ty = pow(a, (p + 1) // 4, p)\n\tif y % 2 != y_parity:\n\t\ty = -y % p\n\t# return result as der signature (no 0x04 preffix)\n\treturn '{:x}{:x}'.format(x, y)\n\n\ndef getKeys(secret, seed=None):\n\t\"\"\"\n Generate keyring containing public key, signing and checking keys as\n attribute.\n\n Keyword arguments:\n secret (str or bytes) -- a human pass phrase\n seed (byte) -- a sha256 sequence bytes (private key actualy)\n\n Return dict\n \"\"\"\n\tseed = hashlib.sha256(secret.encode(\"utf8\") if not isinstance(secret, bytes) else secret).digest() if not seed else seed\n\tsigningKey = SigningKey.from_secret_exponent(int(hexlify(seed), 16), SECP256k1, hashlib.sha256)\n\tpublicKey = signingKey.get_verifying_key().to_string()\n\treturn {\n\t\t\"publicKey\": hexlify(compressEcdsaPublicKey(publicKey)),\n\t\t\"privateKey\": hexlify(signingKey.to_string()),\n\t}\n\n\ndef getSignature(tx, privateKey):\n\t\"\"\"\n\tGenerate transaction signature using private key.\n\n\tArguments:\n\ttx (dict) -- a transaction description\n\tprivateKey (str) -- a private key as hex string\n\n\tReturn str\n\t\"\"\"\n\tsigningKey = SigningKey.from_string(unhexlify(privateKey), SECP256k1, hashlib.sha256)\n\treturn hexlify(signingKey.sign_deterministic(getBytes(tx), hashlib.sha256, sigencode=sigencode_der_canonize))\n\n\ndef getSignatureFromBytes(data, privateKey):\n\t\"\"\"\n\tGenerate data signature using private key.\n\n\tArguments:\n\tdata (bytes) -- data in bytes\n\tprivateKey (str) -- a private key as hex string\n\n\tReturn str\n\t\"\"\"\n\tsigningKey = SigningKey.from_string(unhexlify(privateKey), SECP256k1, hashlib.sha256)\n\treturn hexlify(signingKey.sign_deterministic(data, hashlib.sha256, sigencode=sigencode_der_canonize))\n\n\ndef getId(tx):\n\t\"\"\"\n\tGenerate transaction id.\n\n\tArguments:\n\ttx (dict) -- a transaction description\n\n\tReturn str\n\t\"\"\"\n\treturn hexlify(hashlib.sha256(getBytes(tx)).digest())\n\n\ndef getIdFromBytes(data):\n\t\"\"\"\n\tGenerate data id.\n\n\tArguments:\n\tdata (bytes) -- data in bytes\n\n\tReturn str\n\t\"\"\"\n\treturn hexlify(hashlib.sha256(data).digest())\n\n\ndef verifySignatureFromBytes(data, publicKey, signature):\n\t\"\"\"\n\tVerify signature.\n\n\tArguments:\n\tdata (bytes) -- data in bytes\n\tpublicKey (str) -- a public key as hex string\n\tsignature (str) -- a signature as hex string\n\n\tReturn bool\n\t\"\"\"\n\tif len(publicKey) == 66:\n\t\tpublicKey = uncompressEcdsaPublicKey(publicKey)\n\tverifyingKey = VerifyingKey.from_string(unhexlify(publicKey), SECP256k1, hashlib.sha256)\n\ttry:\n\t\tverifyingKey.verify(unhexlify(signature), data, hashlib.sha256, sigdecode_der)\n\texcept (BadSignatureError, UnexpectedDER):\n\t\treturn False\n\treturn True\n\n\ndef getBytes(tx):\n\t\"\"\"\n\tHash transaction object into bytes data.\n\n\tArgument:\n\ttx (dict) -- transaction object\n\n\tReturn bytes sequence\n\t\"\"\"\n\tbuf = StringIO()\n\t# write type and timestamp\n\tpack(\"\"\n str_out += \"成功\"\n return HttpResponse(str_out)\n# ------------------------------------------------------------------\n","sub_path":"src/file_upload/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"140519499","text":"import cv2\nfrom facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization\nimport torch\nfrom torchvision import transforms\nimport numpy as np\nfrom PIL import Image\nimport datetime\nimport openpyxl\n\ndef recognize_faces():\n # testing image path\n frame_size = (640, 480)\n IMG_PATH = 'C:/Users/Gen Bodmas/PycharmProjects/CIFAR10/test_images'\n DATA_PATH = 'C:/Users/Gen Bodmas/PycharmProjects/CIFAR10'\n\n now = datetime.datetime.now()\n today = now.day\n month = now.month\n\n wb = openpyxl.load_workbook(\"attendance.xlsx\")\n ws = wb.active\n\n def trans(img):\n transform = transforms.Compose([\n transforms.ToTensor(),\n fixed_image_standardization\n ])\n return transform(img)\n\n\n # Loading facelist\n def load_faceslist():\n if device == 'cpu':\n embeds = torch.load(DATA_PATH + '/faceslistCPU.pth')\n else:\n embeds = torch.load(DATA_PATH + '/faceslist.pth')\n names = np.load(DATA_PATH + '/usernames.npy')\n return embeds, names\n\n\n def inference(model, face, local_embeds, threshold=3):\n embeds = []\n embeds.append(model(trans(face).to(device).unsqueeze(0)))\n detect_embeds = torch.cat(embeds) # [1,512]\n norm_diff = detect_embeds.unsqueeze(-1) - torch.transpose(local_embeds, 0, 1).unsqueeze(0)\n norm_score = torch.sum(torch.pow(norm_diff, 2), dim=1)\n min_dist, embed_idx = torch.min(norm_score, dim=1)\n print(min_dist * power, names[embed_idx])\n if min_dist * power > threshold:\n return -1, -1\n else:\n return embed_idx, min_dist.double()\n\n\n def extract_face(box, img, margin=20):\n face_size = 160\n img_size = frame_size\n margin = [\n margin * (box[2] - box[0]) / (face_size - margin),\n margin * (box[3] - box[1]) / (face_size - margin),\n ]\n box = [\n int(max(box[0] - margin[0] / 2, 0)),\n int(max(box[1] - margin[1] / 2, 0)),\n int(min(box[2] + margin[0] / 2, img_size[0])),\n int(min(box[3] + margin[1] / 2, img_size[1])),\n ]\n img = img[box[1]:box[3], box[0]:box[2]]\n face = cv2.resize(img, (face_size, face_size), interpolation=cv2.INTER_AREA)\n face = Image.fromarray(face)\n return face\n\n\n if __name__ == \"__main__\":\n power = pow(10, 6)\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n print(device)\n\n model = InceptionResnetV1(\n classify=False,\n pretrained=\"casia-webface\"\n ).to(device)\n model.eval()\n\n # BUG*****\n mtcnn = MTCNN(thresholds=[0.7, 0.7, 0.8], keep_all=False, device=device)\n\n cap = cv2.VideoCapture(0)\n cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n embeddings, names = load_faceslist()\n while cap.isOpened():\n isSuccess, frame = cap.read()\n if isSuccess:\n boxes, _ = mtcnn.detect(frame)\n if boxes is not None:\n for box in boxes:\n bbox = list(map(int, box.tolist()))\n face = extract_face(bbox, frame)\n idx, score = inference(model, face, embeddings)\n if idx != -1:\n frame = cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 6)\n score = torch.Tensor.cpu(score[0]).detach().numpy() * power\n frame = cv2.putText(frame, names[idx] + '_{:.2f}'.format(score), (bbox[0], bbox[1]),\n cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 0), 2, cv2.LINE_8)\n\n else:\n frame = cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 6)\n frame = cv2.putText(frame, 'Unknown', (bbox[0], bbox[1]), cv2.FONT_HERSHEY_DUPLEX, 2,\n (0, 255, 0), 2, cv2.LINE_8)\n\n cv2.imshow('Face Recognition', frame)\n if cv2.waitKey(1) & 0xFF == 27:\n break\n\n cap.release()\n cv2.destroyAllWindows()\n","sub_path":"face_recogntion.py","file_name":"face_recogntion.py","file_ext":"py","file_size_in_byte":4296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"539289659","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.support.ui import WebDriverWait\nimport time\nfrom faker import Faker\n\nfake = Faker()\n\nname = \"Anna\"\nsurname = \"Nowak\"\ncountry = \"United States\"\nphone = \"123456789\"\nemail = fake.email()\npassword = \"12345\"\n\n\nclass WizzairRegistration(unittest.TestCase):\n\n def setUp(self) -> None:\n self.driver = webdriver.Chrome()\n self.driver.get(\"https://wizzair.com/pl-pl#/\")\n self.driver.maximize_window()\n self.driver.implicitly_wait(20)\n\n def testIncorrectEmail(self):\n\n # Kliknij \"Zaloguj się\"\n signin = self.driver.find_element_by_xpath(\"/html/body/div[2]/div/header/div[1]/div/nav/ul/li[11]/button\")\n signin.click()\n\n # Kliknij \"Rejestracja\"\n registration = self.driver.find_element_by_xpath(\"/html/body/div[2]/span/article/div/div/div/form/p/button\")\n registration.click()\n\n # Wpisz imię\n name_input = self.driver.find_element_by_xpath(\"/html/body/div[2]/span/article/div/div/div/form/div[2]/div[1]/label[1]/div[1]/input\")\n name_input.send_keys(name)\n\n # Wpisz nazwisko\n surname_input = self.driver.find_element_by_xpath(\"/html/body/div[2]/span/article/div/div/div/form/div[2]/div[1]/label[2]/div[1]/input\")\n surname_input.send_keys(surname)\n\n # Wybierz płeć\n gender_female = self.driver.find_element_by_xpath(\"/html/body/div[2]/span/article/div/div/div/form/div[3]/div[1]/label[1]/span\")\n gender_female.click()\n\n # Wybierz kod kraju\n\n\n\n # Wpisz numer\n phone_input = self.driver.find_element_by_xpath(\"/html/body/div[2]/span/article/div/div/div/form/div[4]/div[2]/div/div[1]/div/label/input\")\n phone_input.send_keys(phone)\n\n time.sleep(2)\n\n print(\"OK\")\n\n def tearDown(self) -> None:\n self.driver.close()\n\n\nif __name__ == \"__main__\":\n unittest.main(verbosity=2)","sub_path":"PycharmProjects/AutomationPractice/wizzairRegistration.py","file_name":"wizzairRegistration.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"11057259","text":"from pyquery import PyQuery as pq\nimport re\nimport urllib.request\nimport os\nimport shutil\nimport time\nimport multiprocessing\n\nbaseUrl = 'http://manhua.fzdm.com/91/'\ncomicName = \"美食的俘虏\"\n\ndef _writeLog(log, lock):\n with lock:\n with open(\"__log__.txt\", \"a\") as f:\n f.write(log)\n\ndef _parseChapterList(titleList, hrefList, lock):\n for index in range(len(titleList)):\n title = titleList[index]\n href = hrefList[index]\n _parseOneChapter(title, href, lock)\n\ndef _parseOneChapter(title, href, lock):\n rootDirPath = os.getcwd()\n title = comicName + \"\\\\\" + title\n if not os.path.exists(title):\n os.makedirs(title)\n else:\n print(\"%s already exist\" % (title))\n\n\n dirPath = rootDirPath + \"\\\\\" + title\n print(title + \" ----\" + href)\n\n docComic = pq(href)\n navigation = docComic('div.navigation')\n pageUrlList = []\n\n pageIndex = 0\n\n while True:\n pageIndex += 1\n pageHref = \"%sindex_%d.html\" % ( href, pageIndex - 1)\n if not pageHref in pageUrlList:\n pageUrlList.append(pageHref)\n print(pageHref)\n\n 'http://manhua.fzdm.com/17/011/index_0.html'\n\n jsStrContainsMhurl = \"\"\n\n f = None\n try:\n with urllib.request.urlopen(pageHref) as f:\n docxx = pq(f.read())\n for x in docxx(\"script\").items():\n if \"var mhurl\" in x.text():\n jsStrContainsMhurl = x.text()\n ''' print (jsStrContainsMhurl) '''\n break\n except Exception as e:\n if f:\n print(f.code)\n\n\n if not jsStrContainsMhurl:\n break\n\n m = re.search('var mhurl = \\\"(.*)\\\"', jsStrContainsMhurl)\n mhurl = m.group(1)\n\n mhss = \"p1.xiaoshidi.net\"\n\n if mhurl.startswith('2015') or mhurl.startswith('2016') or mhurl.startswith('2017'):\n mhss = \"p1.xiaoshidi.net\"\n else:\n mhss = \"s1.nb-pintai.com\"\n\n imgUrl = \"http://\" + mhss + \"/\" + mhurl;\n print(imgUrl)\n\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36\",\n \"Host\": mhss,\n \"Accept-Language\": \"zh-CN,zh;q=0.8\",\n \"Cache-Control\": \"no-cache\",\n \"Connection\": \"keep-alive\",\n \"Referer\": imgUrl,\n \"Upgrade-Insecure-Requests\": \"1\"\n }\n req = urllib.request.Request(imgUrl, None, headers)\n _downOnePic(dirPath, pageIndex, req, imgUrl, lock)\n '''\n with urllib.request.urlopen(req, None, 10) as f:\n with open(\"%s\\\\%d.jpg\" % (dirPath, pageIndex), \"ab\") as file:\n file.write(f.read())\n '''\n\ndef _downOnePic(dirPath, pageIndex, req, imgUrl, lock):\n destFilePath = \"%s\\\\%d.jpg\" % (dirPath, pageIndex)\n\n if os.path.exists(destFilePath):\n if os.path.getsize(destFilePath) == 0:\n os.remove(destFilePath)\n print('delete empty pic file')\n else:\n print ('picture already exist')\n return;\n\n retryTime = 0\n timeOutTime = 5\n while True:\n f = None\n try:\n print(\"%a----dowing...\" % (imgUrl))\n with urllib.request.urlopen(req, None, timeOutTime) as f:\n with open(destFilePath, \"ab\") as file:\n file.write(f.read())\n print(\"%a----saved\" % (imgUrl))\n '''\n _writeLog(\"%s\\\\%d.jpg ----- successfully saved\\n\" % (dirPath, pageIndex), lock);\n '''\n file.close()\n if os.path.exists(destFilePath):\n if os.path.getsize(destFilePath) == 0:\n _writeLog('download empty pic file---%s---%s' % (destFilePath, imgUrl))\n\n return\n except Exception as e:\n if f:\n print(f.code)\n\n if retryTime == 4:\n _writeLog(\"%s\\\\%d.jpg ----- %s ------ save failed!!!!!!!!!!!!!\\n\" % (dirPath, pageIndex, imgUrl), lock);\n return\n time.sleep(2)\n retryTime += 1\n timeOutTime += 30\n print(\"%a----retry---- %d times----\" % (imgUrl, retryTime))\n\ndef comicSpider():\n doc = pq(baseUrl)\n\n chapterTitleList = []\n chapterUrlList = []\n\n for item in doc('li.pure-u-1-2')('a').items():\n title = item.attr('title')\n href = baseUrl + item.attr('href')\n chapterTitleList.append(title)\n chapterUrlList.append(href)\n\n size = len(chapterTitleList)\n PROCESS_COUNT = 20\n blockSize = int(size / PROCESS_COUNT)\n\n processList = []\n lock = multiprocessing.Lock()\n end = 0\n for blockIndex in range(PROCESS_COUNT):\n subTileList = []\n subUrlList = []\n\n start = blockIndex * blockSize\n if blockIndex < PROCESS_COUNT - 1:\n end = start + blockSize - 1\n else:\n end = size - 1\n\n subTileList = chapterTitleList[start: end+1]\n subUrlList = chapterUrlList[start: end+1]\n '''\n _parseChapterList(subTileList, subUrlList)\n '''\n\n p = multiprocessing.Process(target=_parseChapterList, args=(subTileList, subUrlList, lock))\n p.start()\n processList.append(p)\n\n for pp in processList:\n pp.join()\n print(\"end\")\n\n'''\n _parseOnChapter(title, href)\n'''\n\nif __name__ == \"__main__\":\n comicSpider()\n\n\n\n'''\nprint (doc('div#mhimg0')('a').attr('href'))\n'''\n\n\n\n","sub_path":"myPythonDemos/fengzhidongman/manhua.fzdm.com_spider.py","file_name":"manhua.fzdm.com_spider.py","file_ext":"py","file_size_in_byte":5757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"108330483","text":"from selenium import webdriver\r\nimport time\r\nimport math\r\n\r\n\r\ndef script_1():\r\n try:\r\n browser = webdriver.Chrome()\r\n browser.execute_script(\"document.title='Script executing';alert('Robots at work');\")\r\n finally:\r\n time.sleep(3)\r\n browser.quit()\r\n\r\n\r\ndef script_2():\r\n browser = webdriver.Chrome()\r\n link = \"https://SunInJuly.github.io/execute_script.html\"\r\n browser.get(link)\r\n button = browser.find_element_by_tag_name(\"button\")\r\n browser.execute_script(\"return arguments[0].scrollIntoView(true);\", button)\r\n button.click()\r\n time.sleep(3)\r\n # Эта команда проскроллит страницу на 100 пикселей вниз:\r\n # browser.execute_script(\"window.scrollBy(0, 100);\")\r\n\r\n\r\ndef script_3():\r\n link = \"http://suninjuly.github.io/execute_script.html\"\r\n try:\r\n browser = webdriver.Chrome()\r\n browser.get(link)\r\n x = int(browser.find_element_by_id(\"input_value\").text)\r\n answer = math.log((abs(12*math.sin(x))))\r\n answer_edit = browser.find_element_by_id(\"answer\")\r\n answer_edit.send_keys(str(answer))\r\n checkbox = browser.find_element_by_id(\"robotCheckbox\")\r\n browser.execute_script(\"return arguments[0].scrollIntoView(true);\", checkbox)\r\n checkbox.click()\r\n radio = browser.find_element_by_id(\"robotsRule\")\r\n radio.click()\r\n button = browser.find_element_by_css_selector(\"button.btn\")\r\n button.click()\r\n finally:\r\n time.sleep(5)\r\n browser.quit()","sub_path":"script_execute.py","file_name":"script_execute.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"339703918","text":"'''\nWrite a program to calculate the credit card balance after one year if a person only pays the minimum monthly payment required by the credit card company each month.\nThe following variables contain values as described below:\n\nbalance - the outstanding balance on the credit card\n\nannualInterestRate - annual interest rate as a decimal\n\nmonthlyPaymentRate - minimum monthly payment rate as a decimal\n\nFor each month, calculate statements on the monthly payment and remaining balance. At the end of 12 months, print out the remaining balance.\n'''\ndef balancecalculator(balance,annualInterestRate,monthlyPaymentRate):\n \n \n counter = 0\n while(counter < 12 ):\n \n balance = balance + balance*(annualInterestRate/12)\n balance = balance - balance*monthlyPaymentRate\n balance = round(balance,2)\n \n counter = counter + 1\n print(balance) \n return balance\n\n \nbalancecalculator(42, 0.2, 0.04)\n","sub_path":"payingoffdebtyear.py","file_name":"payingoffdebtyear.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"20863270","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n# OpenERP, Open Source Management Solution\n# Copyright (C) 2004-2010 Tiny SPRL ().\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n##############################################################################\n\nimport time\nfrom openerp.report import report_sxw\nfrom openerp.osv import osv\nfrom openerp import pooler\n\nclass fleet_aditionals(report_sxw.rml_parse):\n _name = 'report.fleet.aditionals'\n\n def __init__(self, cr, uid, name, context):\n super(fleet_aditionals, self).__init__(cr, uid, name, context=context)\n self.localcontext.update({\n 'time': time,\n 'get_adds': self._get_adds,\n })\n self.context = context\n\n def _get_adds(self,vehicle,data):\n res = []\n for line in vehicle.aditional_ids:\n values = {\n 'name' : line.name,\n 'serial' : line.serial,\n 'type': line.type_id.name,\n 'brand': line.brand_id.name,\n 'use': line.use_id.name,\n 'size':line.size,\n 'date_in': line.date_in,\n }\n res.append(values)\n return res\n\nreport_sxw.report_sxw('report.fleet.aditionals','fleet.vehicle', parser=fleet_aditionals, header='header', rml='herrera_fleet/report/fleet_aditionals.rml')\n","sub_path":"herrera_fleet/report/fleet_aditionals.py","file_name":"fleet_aditionals.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"41325339","text":"import scipy.io\nimport scipy\nfrom control import matlab,tf\nimport control\nfrom control import StateSpace\nimport numpy as np\nfrom scipy import signal\nimport matplotlib.pyplot as plt\n\n# Read linearized model as ABCD matrix\n\ndef tf(ss,start,end):\n mat_dict = scipy.io.loadmat(\"./linmod_noctrl.mat\")\n st = mat_dict['linss']\n A,B,C,D,statename,outputname,inputname,operpoint,ts = st[0][0]\n ss = control.matlab.ss(A, B, C, D)\n inputname = np.asarray([i[0][0] for i in inputname])\n outputname = np.asarray([i[0][0] for i in outputname])\n \n idx_from = np.where(inputname==start)[0][0]\n idx_to = np.where(outputname==end)[0][0]\n print('From :',idx_from,inputname[idx_from])\n print('To :',idx_to,outputname[idx_to])\n out = ss.returnScipySignalLTI()\n ss = out[idx_to][idx_from]\n ss_siso = control.ss(ss.A,ss.B,ss.C,ss.D)\n return ss_siso\n\nif __name__ == '__main__':\n mat_dict = scipy.io.loadmat(\"./linmod/noctrl.mat\")\n st = mat_dict['linss']\n A,B,C,D,statename,outputname,inputname,operpoint,ts = st[0][0]\n ss = control.matlab.ss(A, B, C, D)\n inputname = np.asarray([i[0][0] for i in inputname])\n outputname = np.asarray([i[0][0] for i in outputname])\n #print(filter(lambda x: 'accGndL' in x, inputname))\n start = 'controlmodel/accGndL'\n end = 'controlmodel/dispTML'\n idx_from = np.where(inputname==start)[0][0]\n idx_to = np.where(outputname==end)[0][0]\n print('From :',idx_from,inputname[idx_from])\n print('To :',idx_to,outputname[idx_to])\n out = ss.returnScipySignalLTI()\n ss = out[idx_to][idx_from]\n ss_siso = control.ss(ss.A,ss.B,ss.C,ss.D)\n \n # Plot\n wrap = lambda phases : ( phases + np.pi) % (2 * np.pi ) - np.pi\n f = np.logspace(-3,1,1001)\n mag, phase,w = matlab.bode(ss_siso,f*2.0*np.pi,Plot=False)\n mag = mag*(w**2)\n phase = np.rad2deg(wrap(phase-np.pi))\n fig,(ax0,ax1) = plt.subplots(2,1)\n f = w/2.0/np.pi\n ax0.loglog(f,mag)\n ax1.semilogx(f,phase)\n ax0.set_xlim(1e-2,1e1)\n ax1.set_xlim(1e-2,1e1)\n ax0.set_ylim(1e-4,1e1)\n ax1.set_ylim(-181,181)\n ax1.set_yticks(range(-180,181,90))\n ax1.grid(which='major',color='black',linestyle='-')\n ax1.grid(which='minor',color='black',linestyle=':')\n ax0.grid(which='major',color='black',linestyle='-')\n ax0.grid(which='minor',color='black',linestyle=':')\n plt.savefig('TF.png')\n plt.close()\n","sub_path":"model/typeA/example_plottf.py","file_name":"example_plottf.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"535803575","text":"# -*- coding: utf8 -*-\n##################################################################\n## Create 2016.03.07 Holmes.\n##################################################################\nfrom PySide.QtGui import *\nfrom PySide.QtCore import *\n\nclass MysqlAddKeys(QDialog,QWidget):\n\tdef __init__(self):\n\t\tsuper(MysqlAddKeys,self).__init__()\n\t\tself.setWindowTitle(\"Identification Of Harmful Information System\")\n\t\tself.initgui()\n\t\tself.setIcon()\n\n\tdef center(self):\n\t\tself.qRect = self.frameGeometry()\n\t\tself.centerPoint = QDesktopWidget().availableGeometry().center()\n\t\tself.qRect.moveCenter(self.centerPoint)\n\t\tself.move(self.qRect.topLeft())\n\n\tdef setIcon(self):\n\t\t#设置icon\n\t\tappIcon = QIcon(\"res/icon/icon.png\")\n\t\tself.setWindowIcon(appIcon)\n\n\tdef keys(self):\n\t\tfrom core.KeyWordsHandle.AddNewKey import addNewKey\n\t\tword = self.wordEdit.text()\n\t\tspeech = self.speechEdit.text()\n\t\tlevel = self.levelEdit.text()\n\t\tif word == \"\":\n\t\t\tQMessageBox.about(self,\"Enter Error\",\"\\nPlease enter word.\\n[word] Is the key.\")\n\t\telif speech == \"\":\n\t\t\tQMessageBox.about(self,\"Enter Error\",\"\\nPlease enter speech.\\n[speech] Is the key speech.\")\n\t\telif level == \"\":\n\t\t\tQMessageBox.about(self,\"Enter Error\",\"\\nPlease enter level.\\n[level] Is the key word hazard level.\")\n\t\tif (word != \"\") & (speech != \"\") & (level != \"\"):\n\t\t\tresult = addNewKey(word,speech,level)\n\t\t\tif result == \"success\":\n\t\t\t\tself.info.hide()\n\t\t\t\tself.info = QLabel(\"[+] [%s %s %s] write success.\"%(word,speech,level))\n\t\t\t\tself.info.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\t\t\t\tself.layout.addWidget(self.info,4,2,1,1)\n\t\t\telif result == \"exists\":\n\t\t\t\tself.info.hide()\n\t\t\t\tself.info = QLabel(\"[+] [%s %s %s] already exists.\"%(word,speech,level))\n\t\t\t\tself.info.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\t\t\t\tself.layout.addWidget(self.info,4,2,1,1)\n\n\tdef initgui(self):\n\t\t(x, y, w, h) = (300, 300, 1010, 480)\n\t\tself.setGeometry(x, y, w, h)\n\t\tself.center()\n\n\t\tself.style = \"\"\"\n\t\tQPushButton{\n\t\tcolor:#FFFFFF;\n\t\tborder:1px solid #2e6da4;\n\t\tborder-radius:4px;\n\t\tfont:bold;\n\t\tfont-size:24px;\n\t\tfont-family:Time News Romen;\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #2e6da4,stop:1 #437ab7);\n\t\t}\n\n\t\tQPushButton:hover{\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #2e6da4,stop:1 #2e6da4);\n\t\t}\n\t\t\"\"\"\n\n\t\tself.setWindowTitle(\"KeyWords Add\")\n\t\tself.setWindowFlags(Qt.WindowSystemMenuHint | Qt.MSWindowsFixedSizeDialogHint | Qt.FramelessWindowHint)\n\n\t\tself.word = QLabel(\"KeyWord\")\n\t\tself.speech = QLabel(\"speech\")\n\t\tself.level = QLabel(\"Level\")\n\t\tself.info = QLabel()\n\n\t\tself.zkLabel = QLabel(\"\")\n\t\tself.zkLabel2 = QLabel()\n\t\tself.zkLabel.setFixedSize(128,128)\n\n\t\tself.word.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\t\tself.speech.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\t\tself.level.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\t\tself.info.setFont(QFont(\"Times New Roman\",16,QFont.Bold))\n\n\t\tself.wordEdit = QLineEdit(unicode(\"冰毒\",\"utf8\"))\n\t\tself.speechEdit = QLineEdit(\"noun\")\n\t\tself.levelEdit = QLineEdit(\"5\")\n\n\t\tself.wordEdit.setFixedSize(360, 36)\n\t\tself.speechEdit.setFixedSize(360, 36)\n\t\tself.levelEdit.setFixedSize(360, 36)\n\n\t\tself.wordEdit.setFont(QFont(\"Times New Roman\",15))\n\t\tself.speechEdit.setFont(QFont(\"Times New Roman\",15))\n\t\tself.levelEdit.setFont(QFont(\"Times New Roman\",15))\n\n\t\tself.addkey = QPushButton(\"Add Key\")\n\t\tself.addkey.setStyleSheet(self.style)\n\t\tself.addkey.setFixedSize(QSize(240, 128))\n\n\t\tself.layout = QGridLayout()\n\n\t\tself.layout.addWidget(self.zkLabel,0,0,1,6)\n\t\tself.layout.addWidget(self.zkLabel2,0,0,1,6)\n\n\t\tself.layout.addWidget(self.word,1,1,1,1,4)\n\t\tself.layout.addWidget(self.wordEdit,1,2,1,1,4)\n\n\t\tself.layout.addWidget(self.speech,2,1,1,1,4)\n\t\tself.layout.addWidget(self.speechEdit,2,2,1,1,4)\n\n\t\tself.layout.addWidget(self.level,3,1,1,1,4)\n\t\tself.layout.addWidget(self.levelEdit,3,2,1,1,4)\n\n\t\tself.layout.addWidget(self.addkey,1,3,3,1,4)\n\n\t\tself.layout.addWidget(self.info,4,2,1,1)\n\n\n\n\t\tself.setLayout(self.layout)\n\n\t\tself.addkey.clicked.connect(self.keys)\n\n\t\tcloseb = \"\"\"\n\t\tQPushButton{\n\t\tcolor:#FFFFFF;\n\t\tborder:1px solid #d9534f;\n\t\tborder-radius:4px;\n\t\tfont:bold;\n\t\tfont-size:15px;\n\t\tfont-family:Time News Romen;\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #d9534f,stop:1 #d9534f);\n\t\t}\n\n\t\tQPushButton:hover{\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #C0392B,stop:1 #C0392B);\n\t\t}\n\t\t\"\"\"\n\n\t\taboutb = \"\"\"\n\t\tQPushButton{\n\t\tcolor:#FFFFFF;\n\t\tborder:1px solid #f0ad4e;\n\t\tborder-radius:4px;\n\t\tfont:bold;\n\t\tfont-size:15px;\n\t\tfont-family:Time News Romen;\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #f0ad4e,stop:1 #f0ad4e);\n\t\t}\n\n\t\tQPushButton:hover{\n\t\tbackground:qlineargradient(spread:pad,x1:0,y1:0,x2:0,y2:1,stop:0 #E67E22,stop:1 #E67E22);\n\t\t}\n\t\t\"\"\"\n\t\tcloseButton = QPushButton(unicode(\"Exit\",\"utf8\"),self)\n\t\tcloseButton.move(10, 10)\n\t\tcloseButton.setFixedSize(QSize(64, 32))\n\t\tcloseButton.setStyleSheet(closeb)\n\t\tcloseButton.clicked.connect(self.close)\n\n\t\taboutButton = QPushButton(unicode(\"About\",\"utf8\"),self)\n\t\taboutButton.move(84, 10)\n\t\taboutButton.setFixedSize(QSize(64, 32))\n\t\taboutButton.setStyleSheet(aboutb)\n\t\taboutButton.clicked.connect(self.showAbout)\n\t\t\n\tdef showAbout(self):\n\t\tQMessageBox.about(self, \"About Holmes\",\"Version : 1.0.\\nOwner : Holmes.\\nE-mail : vii26@outlook.com\\nIf you have any questions, please send me.\")\n\nif __name__ == '__main__':\n\timport sys\n\ttry:\n\t\tmyApp = QApplication(sys.argv)\n\t\tmyWindow = MysqlAddKeys()\n\t\tmyWindow.show()\n\t\tmyApp.exec_()\n\t\tsys.exit(0)\n\n\texcept NameError:\n\t\tprint(\"NameError:\", sys.exc_info()[1])\n\texcept SystemExit:\n\t\tprint(\"Closing Window...\")\n\texcept Exception:\n\t\tprint(sys.exc_info()[1])","sub_path":"core/gui/MysqlAddKeys.py","file_name":"MysqlAddKeys.py","file_ext":"py","file_size_in_byte":5673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"431641135","text":"from unittest import TestCase\nfrom modules.ms.one_note.one_note import OneNote\n\nclass TestOneNote(TestCase):\n def setUp(self) -> None:\n self.oneNote = OneNote()\n self.notebook = \"TestNotebook\"\n self.section = \"TestSection\"\n self.nameForRead = \"TestPage\"\n self.nameForModification = \"ModificationPage\"\n\n def test_can_get_page(self):\n page = self.oneNote.getPage(self.notebook, self.section, self.nameForRead)\n self.assertIsNotNone(page)\n\n def test_can_get_content_of_page(self):\n validation_text = \"Text for validation\"\n page = self.oneNote.getPage(self.notebook, self.section, self.nameForRead)\n text = page.getText()\n self.assertNotEqual(text.find(validation_text), -1)\n\n def test_can_modify_content_of_page(self):\n page = self.oneNote.getPage(self.notebook, self.section,\n self.nameForModification)\n modification_text = \"TextForModification\"\n text = page.getText()\n self.assertNotEqual(text.find(modification_text), -1)\n\n paragraphs = page.getParagraphs()\n updated_paragraph = paragraphs[0]\n updated_paragraph['text'] = \"New text\"\n page.updateContent(updated_paragraph)\n","sub_path":"tests/test_one_note.py","file_name":"test_one_note.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"559712900","text":"#%%\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import Sequential \n\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport os\nimport shutil\n\nprint('Tensorflow version: ', tf.VERSION)\n\nlearning_rate = 1e-3\nbatch_size = 20\nepochs = 80\nsteps_per_epoch = 1000\nimg_height = 256\nimg_width = 256\nimg_channels = 3\n\n# 为了测试代码，我们先不使用整个数据集，而是从原始数据集中划分出一小部分数据进行测试\nbase_dir = 'C:/Users/Admin/Downloads/dogvscat'\noriginal_dir = os.path.join(base_dir, 'train')\n\ntrain_dir = os.path.join(base_dir, 'small_train')\neval_dir = os.path.join(base_dir, 'small_eval')\ntest_dir = os.path.join(base_dir, 'test')\n\n#%%\n# if not os.path.exists(eval_dir):\n# os.mkdir(train_dir)\n# os.mkdir(eval_dir)\n\n# for i in range(2500, 12500):\n# name = 'cat.{}.jpg'.format(i)\n# src = os.path.join(original_dir, name)\n# if i < 2000:\n# dst = os.path.join(train_dir, name)\n# else:\n# dst = os.path.join(eval_dir, name)\n# shutil.copyfile(src, dst)\n\n# for i in range(2500, 12500):\n# name = 'dog.{}.jpg'.format(i)\n# src = os.path.join(original_dir, name)\n# if i < 2000:\n# dst = os.path.join(train_dir, name)\n# else:\n# dst = os.path.join(eval_dir, name)\n# shutil.copyfile(src, dst)\n\n#%%\ndef unison_shuffled_copies(a, b):\n a = np.array(a)\n b = np.array(b)\n assert len(a) == len(b)\n p = np.random.permutation(len(a))\n return a[p], b[p]\n\nfiles = os.listdir(train_dir)\ntrain_files = [os.path.join(train_dir, name) for name in files]\ntrain_labels = np.array(['dog' in name for name in files]).astype(np.float)\ntrain_files, train_labels = unison_shuffled_copies(train_files, train_labels)\n\nfiles = os.listdir(eval_dir)\neval_files = [os.path.join(eval_dir, name) for name in files]\neval_labels = np.array(['dog' in name for name in files]).astype(np.float)\neval_files, eval_labels = unison_shuffled_copies(eval_files, eval_labels)\n\nfiles = ['{}.jpg'.format(i) for i in range(1, 12501)]\ntest_files = [os.path.join(test_dir, name) for name in files]\ntest_labels = np.array([-1] * len(files)).astype(np.float)\n#%%\ndef image_input_fn(filenames, labels=None, shuffle=False, repeat_count=1, batch_size=1):\n def _read_img(filename, label=None):\n img_raw = tf.read_file(filename)\n img = tf.image.decode_image(img_raw, channels=3)\n img.set_shape([None, None, None])\n img = tf.image.resize_images(img, [img_height, img_width])\n img = tf.divide(img, 255.)\n img.set_shape([img_height, img_width, img_channels])\n if label is None:\n return img\n else:\n return img, label\n if labels is None:\n dataset = tf.data.Dataset.from_tensor_slices(filenames)\n else:\n dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))\n dataset = dataset.map(_read_img)\n if shuffle:\n dataset = dataset.shuffle(2000)\n dataset = dataset.batch(batch_size).repeat(repeat_count)\n return dataset\n\n# vgg16 = VGG16(\n# weights='imagenet',\n# include_top=False,\n# input_shape=(img_height, img_width, img_channels))\n# model = Sequential([\n# vgg16,\n# layers.Flatten(),\n# layers.Dropout(0.5),\n# layers.Dense(1, activation='sigmoid')\n# ])\n# vgg16.trainable = False\n\nmodel = Sequential([\n layers.Conv2D(32, 3, 1, padding='SAME', activation='relu', input_shape=(img_height, img_width, img_channels)),\n layers.MaxPool2D(strides=2, padding='SAME'),\n layers.Dropout(0.5),\n\n layers.Conv2D(64, 3, 1, padding='SAME', activation='relu'),\n layers.MaxPool2D(strides=2, padding='SAME'),\n layers.Dropout(0.5),\n \n layers.Conv2D(128, 3, 1, padding='SAME', activation='relu'),\n layers.MaxPool2D(strides=2, padding='SAME'),\n layers.Dropout(0.5),\n\n layers.Flatten(),\n layers.Dense(512, activation='relu'),\n layers.Dropout(0.5),\n layers.Dense(1, activation='sigmoid')\n])\n\nmodel.summary()\n\nmodel.compile(\n optimizer=tf.train.AdamOptimizer(learning_rate),\n loss='binary_crossentropy',\n metrics=['acc'])\n\nMODEL_DIR = './model/'\ncheckpoint_path = MODEL_DIR + \"cp-{epoch:04d}.ckpt\"\ncp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, \n monitor='val_loss',\n save_best_only=True,\n verbose=1, \n save_weights_only=True, \n period=5)\n\n# model.load_weights('./model/cp-0040.ckpt')\n\nmodel.fit(\n image_input_fn(\n train_files, \n train_labels,\n shuffle=True, \n repeat_count=epochs,\n batch_size=batch_size), \n validation_data=image_input_fn(\n eval_files,\n eval_labels,\n shuffle=False,\n repeat_count=epochs,\n batch_size=50),\n epochs=epochs,\n steps_per_epoch=steps_per_epoch,\n validation_steps=100,\n callbacks=[cp_callback])\n#%%\nresult = model.predict(\n image_input_fn(\n test_files,\n test_labels,\n shuffle=False,\n batch_size=50), \n steps=250)\n\npath = './submission.csv'\ncounter = range(1, len(result) + 1)\nresult = np.array(result, np.float)\nresult = np.squeeze(result)\n\ndef limit(x):\n if x < 0.005:\n return 0.005\n elif x > 0.995:\n return 0.995\n else:\n return x\n\ndf = pd.DataFrame({'id': counter, 'label': result})\ndf['label'] = df['label'].map(limit)\n\nfile = df.to_csv(path_or_buf=None, index=None)\nwith tf.gfile.Open(path, 'w') as f:\n f.write(file)\n\nprint('Mission Accomplished!')","sub_path":"code/dogcat/dogcat.py","file_name":"dogcat.py","file_ext":"py","file_size_in_byte":5868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"172573079","text":"from euler import phi\nfrom time import time\nT = time()\n\n\n# length of the farey sequence F_n\ndef farey_length(n):\n res = 1\n for m in range(1, n+1):\n res += phi(m)\n\n return res-2 # discount the endpoints due to the exercise\n\n\nprint(farey_length(10**6))\nprint(\"time elapsed:\", time() - T)\n","sub_path":"072.py","file_name":"072.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"60962889","text":"\"\"\"\nMIT License\n\nCopyright (c) 2020 Hyeonki Hong \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\nfrom os import path\nimport time\nfrom typing import Union\n\nimport cv2\nimport numpy as np\n\ntry:\n import tensorflow.lite as tflite\nexcept ModuleNotFoundError:\n import tflite_runtime.interpreter as tflite\n\nfrom ..common import media, predict\nfrom ..common.base_class import BaseClass\n\n\nclass YOLOv4(BaseClass):\n def __init__(self, tiny: bool = False, tpu: bool = False):\n \"\"\"\n Default configuration\n \"\"\"\n super(YOLOv4, self).__init__(tiny=tiny, tpu=tpu)\n self.grid_coord = []\n self.input_index = None\n self.interpreter = None\n self.output_index = None\n self.output_size = None\n\n def load_tflite(self, tflite_path):\n if self.tpu:\n self.interpreter = tflite.Interpreter(\n model_path=tflite_path,\n experimental_delegates=[\n tflite.load_delegate(\"libedgetpu.so.1\")\n ],\n )\n else:\n self.interpreter = tflite.Interpreter(model_path=tflite_path)\n self.interpreter.allocate_tensors()\n input_details = self.interpreter.get_input_details()[0]\n self.input_size = input_details[\"shape\"][1]\n self.input_index = input_details[\"index\"]\n output_details = self.interpreter.get_output_details()\n self.output_index = [details[\"index\"] for details in output_details]\n if self.tpu:\n # sig, raw, sig, raw, ...\n self.output_size = [\n output_details[2 * i][\"shape\"][1]\n for i in range(len(output_details) // 2)\n ]\n self.grid_coord = []\n for _size in self.output_size:\n xy_grid = np.meshgrid(np.arange(_size), np.arange(_size))\n xy_grid = np.stack(xy_grid, axis=-1)\n xy_grid = xy_grid[np.newaxis, ...]\n self.grid_coord.append(xy_grid)\n\n #############\n # Inference #\n #############\n\n def tpu_hair(self, x):\n x = [np.split(_x, 3, axis=-1) for _x in x]\n\n for i in range(len(self.output_size)):\n for j in range(3):\n # pylint: disable=unbalanced-tuple-unpacking\n # sig\n txty, _, conf_prob = np.split(x[2 * i][j], (2, 4), axis=-1)\n # raw\n _, twth, _ = np.split(x[2 * i + 1][j], (2, 4), axis=-1)\n txty = (txty - 0.5) * self.xyscales[i] + 0.5\n bxby = (txty + self.grid_coord[i]) / self.output_size[i]\n bwbh = (self.anchors[i][j] / self.input_size) * np.exp(twth)\n x[2 * i][j] = np.concatenate([bxby, bwbh, conf_prob], axis=-1)\n\n pred = [\n np.concatenate(x[2 * i], axis=-1)\n for i in range(len(self.output_size))\n ]\n return pred\n\n def predict(self, frame: np.ndarray):\n \"\"\"\n Predict one frame\n\n @param frame: Dim(height, width, channels)\n\n @return pred_bboxes == Dim(-1, (x, y, w, h, class_id, probability))\n \"\"\"\n # image_data == Dim(1, input_szie, input_size, channels)\n image_data = self.resize_image(frame)\n image_data = image_data / 255\n image_data = image_data[np.newaxis, ...].astype(np.float32)\n\n # s_pred, m_pred, l_pred\n # x_pred == Dim(1, output_size, output_size, anchors, (bbox))\n self.interpreter.set_tensor(self.input_index, image_data)\n self.interpreter.invoke()\n candidates = [\n self.interpreter.get_tensor(index) for index in self.output_index\n ]\n if self.tpu:\n candidates = self.tpu_hair(candidates)\n _candidates = []\n for candidate in candidates:\n grid_size = candidate.shape[1]\n _candidates.append(\n np.reshape(candidate[0], (1, grid_size * grid_size * 3, -1))\n )\n candidates = np.concatenate(_candidates, axis=1)\n\n pred_bboxes = self.candidates_to_pred_bboxes(candidates[0])\n pred_bboxes = self.fit_pred_bboxes_to_original(pred_bboxes, frame.shape)\n return pred_bboxes\n\n def inference(self, media_path, is_image=True, cv_waitKey_delay=10):\n if not path.exists(media_path):\n raise FileNotFoundError(\"{} does not exist\".format(media_path))\n if is_image:\n frame = cv2.imread(media_path)\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n start_time = time.time()\n bboxes = self.predict(frame)\n exec_time = time.time() - start_time\n print(\"time: {:.2f} ms\".format(exec_time * 1000))\n\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n image = self.draw_bboxes(frame, bboxes)\n cv2.namedWindow(\"result\", cv2.WINDOW_AUTOSIZE)\n cv2.imshow(\"result\", image)\n else:\n vid = cv2.VideoCapture(media_path)\n while True:\n return_value, frame = vid.read()\n if return_value:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n else:\n break\n\n start_time = time.time()\n bboxes = self.predict(frame)\n curr_time = time.time()\n exec_time = curr_time - start_time\n info = \"time: %.2f ms\" % (1000 * exec_time)\n print(info)\n\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n image = self.draw_bboxes(frame, bboxes)\n cv2.namedWindow(\"result\", cv2.WINDOW_AUTOSIZE)\n cv2.imshow(\"result\", image)\n if cv2.waitKey(cv_waitKey_delay) & 0xFF == ord(\"q\"):\n break\n\n print(\"YOLOv4: Inference is finished\")\n while cv2.waitKey(10) & 0xFF != ord(\"q\"):\n pass\n cv2.destroyWindow(\"result\")\n","sub_path":"py_src/yolov4/tflite/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"224675803","text":"def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\r\n\ttrie = {}\r\n\t\r\n\tfor word in words:\r\n\t\tnode = trie\r\n\t\tfor char in word:\r\n\t\t\tnode = node.setdefault(char, {})\r\n\t\tnode['#'] = True\r\n\r\n\tdef search(i, j, node, pre, visited): \r\n\t\r\n\t\tif '#' in node: \r\n\t\t\tres.add(pre) \r\n\t\t\t\r\n\t\tfor (di, dj) in ((-1, 0), (1, 0), (0, -1), (0, 1)):\r\n\t\t\t_i, _j = i+di, j+dj\r\n\t\t\tif -1 < _i < h and -1 < _j < w and board[_i][_j] in node and (_i, _j) not in visited:\r\n\t\t\t\tsearch(_i, _j, node[board[_i][_j]], pre+board[_i][_j], visited | {(_i, _j)})\r\n\r\n\tres, h, w = set(), len(board), len(board[0])\r\n\tfor i in range(h):\r\n\t\tfor j in range(w):\r\n\t\t\tif board[i][j] in trie:\r\n\t\t\t\tsearch(i, j, trie[board[i][j]], board[i][j], {(i, j)})\r\n\treturn list(res)\t","sub_path":"Week 6/id_600/LeetCode_33_212.py","file_name":"LeetCode_33_212.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"627859356","text":"'''\n Author: Vidit Maheshwrai\n Date: 20170204 (YYYYMMDD)\n Copyright :\n\n Purpose: This service communicate between DB\n\n History:\n\n'''\n\n# dependencies\n\nimport json\n\n# Local app imports\nimport models as Models\nimport IM_Constants as constants\nimport IM_DAO as dao\n\ndef saveVehical(data):\n vehical = Models.Vehical\n jObject = json.loads(data)\n try:\n\n # TODO:\n # 1: Check if given documnet alsready exists\n # 2: Check if parameters are missing\n # 3: Othr validation\n\n _purchaseDate = jObject['purchanseDate']\n _vn = jObject['vn']\n _chasisNumber = jObject['chasisNumber']\n _tripHistory = {}\n _serviceHistory = {}\n\n #Check if vehical with given vn and chasisNumber already present\n if(dao.getVehicalByVinNumber(_vn) != None):\n return \"Error: Vehical with vn=\" + _vn + \" already exist.\"\n if (dao.getVehicalByChasisNumber(_chasisNumber) != None):\n return \"Error: Vehical with chasisNumber=\" + _chasisNumber + \" already exist.\"\n\n vehical = Models.Vehical();\n vehical.purchaseDate = _purchaseDate\n vehical.vn = _vn\n vehical.chasisNumber = _chasisNumber\n vehical.tripHistory = _tripHistory\n vehical.serviceHistory = _serviceHistory\n\n #Making connection to DB\n\n dao.saveVehicalDAO(vehical)\n\n return \"Success:\" + \"done\"\n except KeyError as e:\n\n return \"Error:\" + constants.MALFORMED_DATA;\n\n except Exception as e:\n print(e)\n return \"[Error] : \"\n\ndef getVehicalData(req):\n resp = \"\"\n try:\n print(req)\n if(req == \"\"):\n jObject = \"\";\n else:\n jObject = json.loads(req)\n vehicals = dao.getVehicalDataDAO(jObject)\n return vehicals[0].toJSON()\n except json.JSONDecoder as e:\n print(e)\n resp = \"Invalid JSON request\"\n\n return resp\n\ndef addTrip(req):\n try:\n print(req)\n jObject = json.loads(req)\n resp = {}\n # Get vehical information\n ## Check if valid vn is given\n vn = \"\";\n if('vn' not in jObject):\n resp['code'] = \"400\"\n resp['message']=\"Missing vn.\"\n return json.dumps(resp)\n else:\n vn = jObject['vn']\n\n ## Check for given vn we have vehical in the database\n _query = {}\n _query[\"vn\"] = vn\n vehical = dao.getVehicalDataDAO(_query)\n if(vehical is not None):\n ## Validate input request\n tripData = getTripDataFromRequest(jObject)\n if(tripData is not None):\n vehical.update(add_to_set__tripHistory=tripData )\n return \"Successfully Added Trip\"\n else:\n resp['code'] = \"203\"\n resp['message'] = \"Illegal Trip Data\"\n return json.dumps(resp)\n else:\n resp['code'] = \"203\"\n resp['message'] = \"Vehical Not In System.\"\n return json.dumps(resp)\n\n # Add new trip data\n ## check for valid trip data\n ## validate trip data from previous trip data\n\n\n except json.JSONDecoder as e:\n print(e)\n resp = \"Invalid JSON request\"\n return resp\n\n return \"Application Error\"\n\ndef addService(req):\n try:\n print(req)\n jObject = json.loads(req)\n resp = {}\n # Get vehical information\n ## Check if valid vn is given\n vn = \"\";\n if('vn' not in jObject):\n resp['code'] = \"400\"\n resp['message']=\"Missing vn.\"\n return json.dumps(resp)\n else:\n vn = jObject['vn']\n\n ## Check for given vn we have vehical in the database\n _query = {}\n _query[\"vn\"] = vn\n vehical = dao.getVehicalDataDAO(_query)\n if(vehical is not None):\n ## Validate input request\n serviceData = getServiceDataFromRequest(jObject)\n if(serviceData is not None):\n vehical.update(add_to_set__serviceHistory=serviceData )\n return \"Successfully Added Service\"\n else:\n resp['code'] = \"203\"\n resp['message'] = \"Illegal Trip Data\"\n return json.dumps(resp)\n else:\n resp['code'] = \"203\"\n resp['message'] = \"Vehical Not In System.\"\n return json.dumps(resp)\n\n # Add new trip data\n ## check for valid trip data\n ## validate trip data from previous trip data\n\n\n except json.JSONDecoder as e:\n print(e)\n resp = \"Invalid JSON request\"\n return resp\n\n return \"Application Error\"\n\ndef getTripDataFromRequest(jObject):\n if(\"trip\" not in jObject):\n return None\n else:\n tripData = jObject[\"trip\"]\n trip = Models.TripDetail();\n trip.setData(tripData[\"tripFrom\"], tripData[\"tripTo\"], tripData[\"revenueGenerated\"], tripData[\"runningCost\"],\n tripData[\"journeyDuration\"],tripData[\"journeyDate\"],tripData[\"odoMeasure\"])\n return trip;\n\ndef getServiceDataFromRequest(jObject):\n if(\"service\" not in jObject):\n return None\n else:\n serviceData = jObject[\"service\"]\n service = Models.ServiceData();\n service.setData(serviceData[\"created\"], serviceData[\"KMstand\"], serviceData[\"itemListReplaced\"], serviceData[\"comment\"],\n serviceData[\"cost\"])\n return service;\n\ndef getVehicalServiceData(req):\n resp = \"\"\n try:\n print(req)\n if(req == \"\"):\n jObject = \"\";\n else:\n jObject = json.loads(req)\n vehicals = dao.getVehicalDataDAO(jObject)\n resp = {}\n resp[\"ServiceHistory\"] = json.loads(vehicals[0].toJSON())[\"serviceHistory\"]\n return json.dumps(resp)\n except json.JSONDecoder as e:\n print(e)\n resp = \"Invalid JSON request\"\n\n return resp\n\ndef getVehicalTripData(req):\n resp = \"\"\n try:\n print(req)\n if(req == \"\"):\n jObject = \"\";\n else:\n jObject = json.loads(req)\n vehicals = dao.getVehicalDataDAO(jObject)\n resp = {}\n resp[\"TripHistory\"] = json.loads(vehicals[0].toJSON())[\"tripHistory\"]\n return json.dumps(resp)\n except json.JSONDecoder as e:\n print(e)\n resp = \"Invalid JSON request\"\n\n return resp\n","sub_path":"inventoryMangement/IM_Service.py","file_name":"IM_Service.py","file_ext":"py","file_size_in_byte":6414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"435454251","text":"import music21 as mc\nimport json\n\n\ndef read_midi_from_archive(archive, path):\n mf = mc.midi.MidiFile()\n mf.readstr(archive.read(path))\n return mc.midi.translate.midiFileToStream(mf)\n\n\ndef read_mode(archive, midi_path):\n symbol_nokey_path = midi_path.replace(\"/pianoroll/\", \"/event/\").replace(\n \"nokey.mid\", \"symbol_nokey.json\"\n )\n read_data = archive.read(symbol_nokey_path)\n json_data = json.loads(read_data)\n return json_data.get(\"metadata\", {}).get(\"mode\", \"?\")\n","sub_path":"src/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"628231032","text":"import datetime\n\nclass Person(object):\n def __init__(self, name):\n # create a person with name name\n self.name = name\n try:\n lastBlank = name.rindex(' ')\n self.lastName = name[lastBlank+1:]\n except:\n self.lastName = name\n self.birthday = None\n def getLastName(self):\n # return self's last name\n return self.lastName\n def setBirthday(self, birthDate):\n # assumes bd is of type datetime.date\n self.birthday = birthDate\n def getAge(self):\n #returns self's current age in days\n if self.birthday == None:\n raise ValueError\n return (datetime.date.today() - self.birthday).days\n def __lt__(self, other):\n # return true if name lexicographically less than other names, false if no\n if self.lastName == other.lastName:\n return self.name < other.name\n return self.lastName < other.lastName\n def __str__(self):\n # return self's name\n return self.name\n\n\nme = Person('Paul Washburn')\nhim = Person('Barack Hussein Obama')\nher = Person('Madonna')\nprint(him.getLastName())\nhim.setBirthday(datetime.date(1961, 8, 4))\nher.setBirthday(datetime.date(1958, 8, 16))\n\n\n# classes can be be used to inherit attributes to others (parent class)\n\nclass MITPerson(Person):\n nextIdNum = 0\n def __init__(self, name):\n Person.__init__(self, name)\n self.idNum = MITPerson.nextIdNum\n MITPerson.nextIdNum += 1\n def getIdNum(self):\n return self.idNum\n def __lt__(self, other):\n return self.idNum < other.idNum\n\nprint('''\nMITPerson is a subclass of Person, and inherits the attributes of its\nsuperclass. You can add new attributes here too. You can also override attributes\nof the superclass.\n\nThe method MITPerson.__init__ first invokes Person.__init__ to initialize inherited instance\nvariable self.name. Then it initializes self.idNum, an instance variable\nthat instances of MITPerson have but instances of Person do not.\n\nThe instances variable self.idNum is initializesd using a class variable,\nnextIdNum, and an instance of MITPerson is created, and a new instance of nextIDNum\nis not created. \n''')\n\np1 = MITPerson('Paul Washburn') #creates an MITPerson\nprint(str(p1) + '\\'s id number is ' + str(p1.getIdNum()))\n \n# Python checks if there is an __str__ method assoc with MITPerson; since\n# there is not, it checks for __str__ method associated with class Person.\n\n\nprint('''\nThere can be multiple levels of inheritance.\n\nReserved word pass will give all attributes from superclass to the inheritor.\n''')\n\nclass Student(MITPerson):\n pass\n\nclass UG(Student):\n def __init__(self, name, classYear):\n MITPerson.__init__(self, name)\n self.year = classYear\n def getClass(self):\n return self.year\n\nclass Grad(Student):\n pass\n\n\np5 = Grad('Vicky Crago')\np6 = UG('Jenny Consuela', 2032)\nprint(p5, 'is a graduate student is', type(p5) == Grad)\nprint(p5, 'is an undergraduate student is', type(p5) == UG)\n\n\n\n\n","sub_path":"Python-Basics/007_classes_object_oriented_programming.py","file_name":"007_classes_object_oriented_programming.py","file_ext":"py","file_size_in_byte":3043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"171069483","text":"#!/usr/bin/python3\n# coding=UTF-8\n\nfrom aiqdoctests.structures import AiqTest\n\n\n@AiqTest.SetStructure(\"health\")\nclass tests(AiqTest):\n def test_health(self):\n r = self.assertOK()\n self.assertEqual(\"Hello World! AiqdocTests-example\", r.json())\n","sub_path":"tests/test_health.py","file_name":"test_health.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"161724521","text":"import socket as s\r\nfrom threading import Thread as th\r\nimport tkinter as gui\r\n\r\nIP = s.gethostname(), 8081\r\n\r\ncs = s.socket(s.AF_INET, s.SOCK_STREAM)\r\ncs.connect(IP)\r\n\r\n\r\ndef receive():\r\n while True:\r\n try:\r\n msg = cs.recv(1024).decode(\"utf8\")\r\n if msg == b'quit':\r\n break\r\n msgList.insert(gui.END, msg)\r\n except OSError: # användare kan ha lämnat\r\n break\r\n\r\n\r\ndef handleMsg(event=None):\r\n msg = msgSend.get()\r\n msgSend.set(\"\") # rensar fältet\r\n cs.send(bytes(msg, \"utf8\"))\r\n if msg == 'quit':\r\n #cs.close()\r\n root.quit()\r\n\r\n\r\ndef close(event=None):\r\n msgSend.set('quit')\r\n handleMsg()\r\n\r\n\r\nroot = gui.Tk()\r\nroot.title(\"SlutUppgift\")\r\n\r\nmsgsFrame = gui.Frame(root)\r\nmsgSend = gui.StringVar()\r\nmsgSend.set(\"Type here\")\r\n# för att kunna gå tillbaka i meddelanden\r\nscroll = gui.Scrollbar(msgsFrame)\r\n\r\n# kommer att innehålla meddelandet\r\nmsgList = gui.Listbox(msgsFrame, height=25, width=60, yscrollcommand=scroll.set)\r\nscroll.pack(side=gui.RIGHT, fill=gui.Y)\r\nmsgList.pack(side=gui.LEFT, fill=gui.BOTH)\r\nmsgList.pack()\r\nmsgsFrame.pack()\r\n\r\nentryField = gui.Entry(root, textvariable=msgSend)\r\nentryField.bind(\"\", handleMsg)\r\nentryField.pack()\r\nsendBtn = gui.Button(root, text=\"Send\", command=handleMsg)\r\nsendBtn.pack()\r\n\r\nroot.protocol(\"WM_DELETE_WINDOW\", close)\r\n\r\n\r\n#######################################################\r\n\r\nif __name__ == \"__main__\":\r\n receiveThread = th(target=receive, daemon=True)\r\n receiveThread.start()\r\n gui.mainloop()\r\n receiveThread.join()\r\n cs.close()","sub_path":"KLNT.py","file_name":"KLNT.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"418368303","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reservedimport unittest\n\nimport unittest\n\nimport torch\nfrom pytext.contrib.pytext_lib.transforms import TruncateTransform\n\n\nclass TestTruncateTranform(unittest.TestCase):\n\n DATA = [[0, 1, 2, 3, 4, 5, 6], [0, 1, 2], []]\n MAX_SEQ_LEN = 4\n\n def test_truncate_transform(self):\n transform = TruncateTransform(max_seq_len=TestTruncateTranform.MAX_SEQ_LEN)\n res = transform(TestTruncateTranform.DATA)\n\n for row in res:\n # Truncate lengths above max_seq_len, smaller lens aren't padded.\n self.assertEqual(len(row), min(TestTruncateTranform.MAX_SEQ_LEN, len(row)))\n\n def test_truncate_transform_torchscript(self):\n transform = TruncateTransform(max_seq_len=TestTruncateTranform.MAX_SEQ_LEN)\n ts_transform = torch.jit.script(transform)\n res = ts_transform(TestTruncateTranform.DATA)\n\n for row in res:\n # Truncate lengths above max_seq_len, smaller lens aren't padded.\n self.assertEqual(len(row), min(TestTruncateTranform.MAX_SEQ_LEN, len(row)))\n","sub_path":"pytext/contrib/pytext_lib/tests/transforms_test.py","file_name":"transforms_test.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"36997325","text":"T = int(input())\nfor test in range(T):\n n = input()\n flag = 0\n no = ['aa','aab','ana','aaaaa']\n for i in range(4):\n if n == no[i]:\n flag = 1\n break\n print(\"#{}\".format(test+1),end=' ')\n if flag == 0:\n count = 0\n n = list(map(str, n))\n j = len(n) - 1\n for i in range(int(len(n)/2)):\n if n[i] == n[j] or n[i] == '?' or n[j] == '?':\n count+=1\n j-=1\n if count == int(len(n)/2):\n print('Exist')\n else:\n print('Not exist')\n else:\n print('Not exist')","sub_path":"4522_world_of_palindrome.py","file_name":"4522_world_of_palindrome.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"327723392","text":"import pygame, sys # import pygame and sys\npygame.init() # initiate pygame\nfrom enemy import Enemy\nfrom player import Player\nfrom projectile import Projectile\nfrom map import Map\nfrom pygame.locals import * # import pygame modules\nfrom helpers import *\nfrom settings import *\n# from map_scripts.map1 import map1\n\nclock = pygame.time.Clock() # set up the clock\npygame.display.set_caption(WINDOW_NAME)# set the window name\nscreen = pygame.display.set_mode(WINDOW_SIZE,0,32) # initiate screen\ndisplay = pygame.Surface((300, 200))\n\nimport sprites;\nmap = Map(\"map1\")\nplayer = Player(pygame.Rect(50, 50, sprites.player.get_width(), sprites.player.get_height()))\nenemy = Enemy(pygame.Rect(50, 50, sprites.yeti_standing.get_width(), sprites.yeti_standing.get_height()))\n\nwhile True: # game loop\n #display stuff\n display.fill((146,244,255))\n map.draw(display, sprites)\n display.blit(sprites.player, (player.rect.x, player.rect.y))\n \n if(enemy.flipped):\n enemy.sprite = pygame.transform.flip(sprites.yeti_running[enemy.sprite_index], True, False)\n else:\n enemy.sprite = sprites.yeti_running[enemy.sprite_index]\n display.blit(enemy.sprite, (enemy.rect.x, enemy.rect.y))\n player.draw_health_bar(display)\n\n #movement\n player.move_projectiles(display)\n for event in pygame.event.get(): # event loop\n if event.type == KEYDOWN and event.key == K_SPACE: \n projectile = Projectile(10, player.rect.x, player.rect.y, 1 if player.moving_right == True else -1, BLACK)\n player.projectiles.append(projectile)\n\n if event.type == QUIT: # check for window quit\n pygame.quit() # stop pygame\n sys.exit() # stop script\n else:\n player.getMovementInput(event)#get WASD input\n player.move(map)\n enemy.moveRoutine(pygame, map, player)\n \n surf = pygame.transform.scale(display, WINDOW_SIZE)\n screen.blit(surf, (0, 0))\n pygame.display.update() # update display\n clock.tick(FPS) # maintain 60 f","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"435217802","text":"\"\"\"\nCopyright 2017 Nikolay Stanchev\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n\n\n# Simple unittests for the ADT BinarySearchTree\nimport unittest\n\nfrom ADTs.AbstractDataStructures import BinarySearchTree\n\n\nclass BinarySearchTreeTest(unittest.TestCase):\n\n def test_size(self):\n binary = BinarySearchTree(12)\n self.assertEqual(binary.get_number_of_elements(), len(binary), \"Len method not working.\")\n self.assertEqual(binary.get_number_of_elements(), 1, \"The binary tree must be with 1 elements when initialised\"\n \"with a root.\")\n binary = BinarySearchTree()\n self.assertEqual(binary.get_number_of_elements(), 0, \"The binary tree must be with 0 elements when initialised\")\n binary.add(25)\n for i in range(1, 11):\n binary.add(i)\n binary.add(i + 25)\n self.assertEqual(binary.get_number_of_elements(), len(binary), \"Len method not working.\")\n self.assertEqual(binary.get_number_of_elements(), 21, \"BinaryTree gives wrong size.\")\n\n def test_contains(self):\n binary = BinarySearchTree(26)\n binary.add(12)\n with self.assertRaises(TypeError):\n binary.contains(12.50)\n binary.add(54)\n self.assertTrue(binary.contains(12), \"BinaryTree contain method doesn't work.\")\n binary.add(32)\n self.assertFalse(binary.contains(104), \"BinaryTree contain method doesn't work.\")\n binary.add(104)\n with self.assertRaises(TypeError):\n binary.contains(\"string\")\n binary.add(0)\n self.assertTrue(binary.contains(26), \"BinaryTree contain method doesn't work.\")\n binary.add(5)\n self.assertTrue(binary.contains(5), \"BinaryTree contain method doesn't work.\")\n self.assertTrue(binary.contains(0), \"BinaryTree contain method doesn't work.\")\n\n def test_add(self):\n binary = BinarySearchTree()\n binary.add(23)\n self.assertEqual(binary.get_root(), 23, \"Add method cannot add the item to the root\")\n self.assertEqual(binary.get_number_of_elements(), 1, \"Add method doesn't increment number of elements\")\n binary.add(12)\n binary.add(54)\n binary.add(1)\n self.assertEqual(binary.get_number_of_elements(), 4, \"Add method doesn't adjust the number of elements\")\n self.assertTrue(binary.contains(54), \"Add method doesn't add the elements properly\")\n self.assertFalse(binary.contains(0), \"Add method adds elements that are not supposed to be there.\")\n with self.assertRaises(TypeError):\n binary.add(\"string\")\n binary.add(23)\n self.assertEqual(binary.get_number_of_elements(), 4, \"Add method adds elements which already exist.\")\n\n def test_delete(self):\n binary = BinarySearchTree(50)\n binary.delete(50)\n self.assertEqual(binary.get_number_of_elements(), 0, \"Delete method cannot delete the root.\")\n self.assertFalse(binary.contains(50), \"Delete method cannot delete the root\")\n binary.add(1)\n binary.add(0)\n binary.add(2)\n binary.add(3)\n binary.delete(2)\n self.assertEqual(binary.get_number_of_elements(), 3, \"Delete method cannot delete properly.\")\n binary.add(12)\n binary.add(9)\n with self.assertRaises(TypeError):\n binary.delete(\"9\")\n binary.add(34)\n binary.delete(1)\n self.assertEqual(binary.get_number_of_elements(), 5, \"Delete method cannot delete the root.\")\n self.assertFalse(binary.contains(1), \"Delete method cannot delete the root\")\n binary.add(-4)\n binary.add(-6)\n binary.delete(-4)\n self.assertEqual(binary.get_number_of_elements(), 6, \"Delete method cannot delete a node with one child.\")\n self.assertFalse(binary.contains(-4), \"Delete method cannot a node with one child.\")\n with self.assertRaises(KeyError):\n binary.delete(11232)\n\n def test_iterator(self):\n numbers = [30, 22, 46, 11, 15, 26, 101, 67, 140, 38]\n binary = BinarySearchTree()\n for number in numbers:\n binary.add(number)\n index = 0\n numbers.sort()\n for number in binary:\n self.assertEqual(number, numbers[index], \"Iterator doesn't return correct values\")\n index += 1\n\n binary = BinarySearchTree()\n count = 0\n for i in binary:\n count += i\n self.assertEqual(count, 0, \"Iterator doesn't work with empty tree\")\n\n def test_type(self):\n with self.assertRaises(TypeError):\n BinarySearchTree(elements_type=None)\n\n with self.assertRaises(TypeError):\n BinarySearchTree(elements_type=2)\n\n binary = BinarySearchTree(elements_type=str)\n with self.assertRaises(TypeError):\n binary.add(4)\n with self.assertRaises(TypeError):\n BinarySearchTree(root=\"hey\", elements_type=int)\n\n binary = BinarySearchTree(root=\"hey\", elements_type=str)\n words = [\"hey\", \"abc\", \"zcd\", \"dfg\", \"cxz\", \"xsa\", \"gfd\", \"word\", \"anotherword\"]\n counter = 0\n for word in words:\n binary.add(word)\n with self.assertRaises(TypeError):\n binary.add(counter)\n counter += 1\n self.assertEqual(len(binary), counter, \"Len method of the binary tree doesn't work\")\n self.assertTrue(binary.contains(\"abc\"), \"Contains method doesn't work with strings\")\n self.assertFalse(binary.contains(\"hbh\"), \"Contains method doesn't work with strings\")\n self.assertEqual(binary.get_minimum(), \"abc\", \"Binary tree doesn't return minimum element.\")\n self.assertEqual(binary.get_maximum(), \"zcd\", \"Binary tree doesn't return maximum element.\")\n\n words.sort()\n index = 0\n for word in binary:\n self.assertEqual(word, words[index], \"Iterator doesn't work with strings\")\n index += 1\n\n with self.assertRaises(TypeError):\n binary.delete(index)\n with self.assertRaises(TypeError):\n binary.delete([])\n\n binary.delete(\"abc\")\n self.assertEqual(binary.get_minimum(), \"anotherword\", \"Binary tree delete method doesn't work properly\")\n\n with self.assertRaises(KeyError):\n binary.delete(\"gfgfg\")\n\n self.assertTrue(binary.get_root() == \"hey\", \"Binary tree get_root method doesn't work properly\")\n binary.delete(\"hey\")\n self.assertTrue(binary.get_root() != \"hey\", \"Binary tree delete method doesn't work properly\")\n\n binary = BinarySearchTree()\n self.assertTrue(binary.get_root() is None, \"Binary tree get_root method doesn't work properly\")\n binary.add(1)\n binary.delete(1)\n self.assertTrue(binary.get_root() is None, \"Binary tree get_root method doesn't work properly after deletion\")\n\n def test_get_min(self):\n binary = BinarySearchTree(elements_type=int)\n self.assertEqual(binary.get_minimum(), None, \"Incorrect implementation of get_min\")\n\n binary = BinarySearchTree(root=10)\n binary.add(11)\n binary.add(5)\n binary.add(223)\n self.assertEqual(binary.get_minimum(), 5, \"Incorrect implementation of get_min\")\n\n binary = BinarySearchTree(root=2.5, elements_type=float)\n l = [2.4, 5.7, 3.3, 7.8, 8.99]\n for i in l:\n binary.add(i)\n self.assertEqual(binary.get_minimum(), min(l))\n\n def test_get_max(self):\n binary = BinarySearchTree(elements_type=int)\n self.assertEqual(binary.get_maximum(), None, \"Incorrect implementation of get_max\")\n\n binary = BinarySearchTree(root=10)\n binary.add(11)\n binary.add(5)\n binary.add(223)\n self.assertEqual(binary.get_maximum(), 223, \"Incorrect implementation of get_max\")\n\n binary = BinarySearchTree(root=2.5, elements_type=float)\n l = [2.4, 5.7, 3.3, 7.8, 8.99]\n for i in l:\n binary.add(i)\n self.assertEqual(binary.get_maximum(), max(l))\n\n def test_str(self):\n binary = BinarySearchTree()\n self.assertEqual(str(binary), \"Binary search tree with root: None\")\n\n binary = BinarySearchTree(5)\n self.assertEqual(str(binary), \"Binary search tree with root: 5\")\n\n binary = BinarySearchTree(\"root\", elements_type=str)\n self.assertEqual(str(binary), \"Binary search tree with root: root\")\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"Tests/testBinarySearchTree.py","file_name":"testBinarySearchTree.py","file_ext":"py","file_size_in_byte":8960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"610519859","text":"\"\"\"This module provides view functions for experiments endpoints.\"\"\"\n\n# pylint: disable=wrong-import-order\n\nfrom fastapi import APIRouter, Form\nfrom http import HTTPStatus\nimport requests\nfrom starlette.responses import JSONResponse, RedirectResponse\nfrom starlette.requests import Request\nfrom typing import Text\n\nfrom common.utils import error_response, get_utc_timestamp\nfrom projects.src.project_management import ProjectManager\nfrom projects.src.utils import log_request\n\nrouter = APIRouter() # pylint: disable=invalid-name\n\n\n@router.get('/experiments', tags=['experiments'])\ndef list_experiments(request: Request, project_id: int) -> JSONResponse:\n \"\"\"Get experiments list.\n Args:\n project_id {int}: project id\n Returns:\n starlette.responses.JSONResponse\n \"\"\"\n log_request(request)\n\n project_manager = ProjectManager()\n url = project_manager.get_internal_tracking_uri(project_id)\n resp = requests.get(\n url=f'{url}/api/2.0/preview/mlflow/experiments/list'\n )\n experiments = []\n\n for exp in resp.json().get('experiments'):\n\n experiment_id = exp.get('experiment_id')\n runs_resp = requests.get(\n f'{url}/api/2.0/preview/mlflow/runs/search?experiment_ids=[{experiment_id}]'\n )\n runs = runs_resp.json().get('runs', [])\n creation_time = ''\n last_update_time = ''\n\n \"\"\"\n if corresponding tags are empty then fields:\n * creation_time = start_time of the first run;\n * last_update_time = end_time of the last run.\n \"\"\"\n if len(runs) > 0:\n creation_time = runs[len(runs) - 1].get('info', {}).get('start_time')\n last_update_time = runs[0].get('info', {}).get('end_time')\n\n tags = {tag['key']: tag['value'] for tag in exp.get('tags', [])}\n experiments.append({\n 'id': experiment_id,\n 'user_id': tags.get('user_id', ''),\n 'name': exp.get('name'),\n 'artifact_location': exp.get('artifact_location'),\n 'lifecycle_stage': exp.get('lifecycle_stage'),\n 'last_update_time': tags.get('last_update_time', last_update_time),\n 'creation_time': tags.get('creation_time', creation_time),\n 'description': tags.get('mlflow.note.content', ''),\n 'project_id': tags.get('project_id', project_id)\n })\n\n return JSONResponse(experiments)\n\n\n@router.post('/experiments', tags=['experiments'])\ndef create_experiment(\n request: Request,\n project_id: int,\n user_id: Text = Form(''),\n name: Text = Form(''),\n description: Text = Form('')\n) -> JSONResponse:\n \"\"\"Create experiment\n Args:\n project_id {int}: project id\n user_id {Text}: user id (name)\n name {Text}: experiment name\n description {Text}: experiment description\n Returns:\n starlette.responses.JSONResponse\n \"\"\"\n\n log_request(request, {\n 'project_id': project_id,\n 'user_id': user_id,\n 'name': name,\n 'description': description\n })\n\n project_manager = ProjectManager()\n url = project_manager.get_internal_tracking_uri(project_id)\n creation_resp = requests.post(\n url=f'{url}/api/2.0/preview/mlflow/experiments/create',\n json={'name': name}\n )\n creation_json = creation_resp.json()\n experiment_id = creation_json.get('experiment_id')\n\n if creation_resp.status_code != HTTPStatus.OK:\n return error_response(\n http_response_code=creation_resp.status_code,\n message=creation_json.get('message')\n )\n\n utc_timestamp = get_utc_timestamp()\n tags = {\n 'mlflow.note.content': description,\n 'user_id': user_id,\n 'project_id': str(project_id),\n 'creation_time': utc_timestamp,\n 'last_update_time': utc_timestamp\n }\n\n for key, value in tags.items():\n requests.post(\n url=f'{url}/api/2.0/preview/mlflow/experiments/set-experiment-tag',\n json={\n 'experiment_id': experiment_id,\n 'key': key,\n 'value': value\n }\n )\n\n experiment_request = requests.get(\n url=f'{url}/api/2.0/preview/mlflow/experiments/get?experiment_id={experiment_id}'\n )\n\n experiment = experiment_request.json().get('experiment')\n tags = {tag['key']: tag['value'] for tag in experiment.pop('tags', [])}\n experiment['description'] = tags.get('mlflow.note.content', '')\n experiment['user_id'] = tags.get('user_id', '')\n experiment['project_id'] = tags.get('project_id', '')\n experiment['creation_time'] = tags.get('creation_time', '')\n experiment['last_update_time'] = tags.get('last_update_time', '')\n\n return JSONResponse(experiment, HTTPStatus.CREATED)\n\n\n@router.get('/experiments/{experiment_id}', tags=['experiments'])\ndef get_experiment(request: Request, experiment_id: Text, project_id: int) -> JSONResponse:\n \"\"\"Get experiment.\n Args:\n experiment_id {Text}: experiment id\n project_id {int}: project id\n Returns:\n starlette.responses.JSONResponse\n \"\"\"\n\n log_request(request)\n\n project_manager = ProjectManager()\n url = project_manager.get_internal_tracking_uri(project_id)\n experiment_resp = requests.get(\n url=f'{url}/api/2.0/preview/mlflow/experiments/get?experiment_id={experiment_id}'\n )\n\n if experiment_resp.status_code != HTTPStatus.OK:\n return error_response(\n http_response_code=experiment_resp.status_code,\n message=experiment_resp.json().get('message')\n )\n\n experiment = experiment_resp.json().get('experiment')\n experiment_id = experiment.get('experiment_id')\n runs_resp = requests.get(\n f'{url}/api/2.0/preview/mlflow/runs/search?experiment_ids=[{experiment_id}]'\n )\n runs = runs_resp.json().get('runs', [])\n creation_time = ''\n last_update_time = ''\n\n \"\"\"\n if corresponding tags are empty then fields:\n * creation_time = start_time of the first run;\n * last_update_time = end_time of the last run.\n \"\"\"\n if len(runs) > 0:\n creation_time = runs[len(runs) - 1].get('info', {}).get('start_time')\n last_update_time = runs[0].get('info', {}).get('end_time')\n\n experiment['id'] = experiment.pop('experiment_id')\n tags = {tag['key']: tag['value'] for tag in experiment.pop('tags', [])}\n experiment['description'] = tags.get('mlflow.note.content', '')\n experiment['user_id'] = tags.get('user_id', '')\n experiment['project_id'] = tags.get('project_id', project_id)\n experiment['creation_time'] = tags.get('creation_time', creation_time)\n experiment['last_update_time'] = tags.get('last_update_time', last_update_time)\n\n return JSONResponse(experiment)\n\n\n@router.delete('/experiments/{experiment_id}', tags=['experiments'])\ndef delete_experiment(request: Request, experiment_id: Text, project_id: int) -> JSONResponse:\n \"\"\"Delete experiment.\n Args:\n experiment_id {Text}: experiment id\n project_id {int}: project id\n Returns:\n starlette.responses.JSONResponse\n \"\"\"\n\n log_request(request, {\n 'project_id': project_id,\n 'experiment_id': experiment_id\n })\n\n project_manager = ProjectManager()\n url = project_manager.get_internal_tracking_uri(project_id)\n experiment_resp = requests.post(\n url=f'{url}/api/2.0/preview/mlflow/experiments/delete',\n json={'experiment_id': experiment_id}\n )\n\n if experiment_resp.status_code != HTTPStatus.OK:\n return error_response(\n http_response_code=experiment_resp.status_code,\n message=experiment_resp.json().get('message')\n )\n\n return JSONResponse({'experiment_id': experiment_id})\n\n\n# TODO(Alex): remove endpoint\n@router.get('/projects/{id}/experiments')\ndef alt_list_experiments(id: int):\n\n return RedirectResponse(f'/experiments?project_id={id}')\n","sub_path":"services/projects/src/routers/experiments.py","file_name":"experiments.py","file_ext":"py","file_size_in_byte":7942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"394743829","text":"\"\"\"\n rfk.api.web\n ~~~~~~~~~~~\n\n A REST interface for PyRfK\n\n\"\"\"\n\nfrom functools import wraps, partial\nfrom datetime import datetime\n\nfrom sqlalchemy import between\n\nfrom flask import jsonify, request, g\n\nfrom rfk.api import api\nfrom rfk import exc as rexc\n\nimport rfk.database\nfrom rfk.database.base import User, ApiKey\nfrom rfk.database.show import Show, UserShow\nfrom rfk.database.track import Track\nfrom rfk.database.streaming import Listener, Relay\nfrom rfk.database.stats import Statistic, StatsistcsData\n\nfrom rfk.liquidsoap import LiquidInterface\nfrom rfk.exc.base import UserNotFoundException\n\n\ndef wrapper(data, ecode=0, emessage=None):\n return {'pyrfk': {'version': '0.1', 'codename': 'Weltklang'}, 'status': {'code': ecode, 'message': emessage}, 'data': data}\n\n\ndef check_auth(f=None, required_permissions=None):\n if f is None:\n return partial(check_auth, required_permissions=required_permissions)\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n\n def raise_error(text):\n response = jsonify(wrapper(None, 403, text))\n response.status_code = 403\n return response\n\n if not 'key' in request.args:\n return raise_error('api key missing')\n\n key = request.args.get('key')\n\n try:\n apikey = ApiKey.check_key(key)\n rfk.database.session.commit()\n except rexc.api.KeyInvalidException:\n return raise_error('api key invalid')\n except rexc.api.KeyDisabledException:\n return raise_error('api key disabled')\n except rexc.api.FastQueryException:\n return raise_error('throttling')\n except:\n return raise_error('unknown error')\n\n if required_permissions != None:\n for required_permission in required_permissions:\n if not apikey.flag & required_permission:\n return raise_error(\n 'Flag %s (%i) required' % (ApiKey.FLAGS.name(required_permission), required_permission))\n\n g.apikey = apikey\n\n return f(*args, **kwargs)\n\n return decorated_function\n\n\n@api.route('/web/')\ndef page_not_found(path):\n response = jsonify(wrapper(None, 404, \"'%s' not found\" % path))\n response.status_code = 404\n return response\n\n\n@api.route('/web/dj')\n@check_auth()\n## DONE\ndef dj():\n \"\"\"Returns complete dj information\n\n Keyword arguments:\n - dj_id -- database id of the requested dj\n - dj_name -- nickname of the requested dj\n\n Returns:\n {'dj': {'dj_id': x, 'dj_name': x }}\n\n At least one argument is required\n \"\"\"\n\n dj_id = request.args.get('dj_id', None)\n dj_name = request.args.get('dj_name', None)\n\n try:\n user = User.get_user(id=dj_id, username=dj_name)\n return jsonify(wrapper({'dj': {'dj_id': user.user, 'dj_name': user.username}}))\n except rexc.base.UserNotFoundException:\n return jsonify(wrapper({'dj': None}))\n except AssertionError:\n return jsonify(wrapper(None, 400, 'missing required query parameter'))\n\n\n@api.route('/web/current_dj')\n@check_auth()\n## DONE (for now) ##\ndef current_dj():\n \"\"\"Returns dj information for the currently streaming dj(s)\n\n Keyword arguments:\n - None\n\n Returns:\n {'current_dj': {'dj_id': x, 'dj_name': x, 'dj_status': x }}\n \"\"\"\n\n result = UserShow.query.filter(UserShow.status == UserShow.STATUS.STREAMING).first()\n if result:\n data = {'current_dj': {'dj_id': result.user.user, 'dj_name': result.user.username, 'dj_status': result.status}}\n else:\n data = {'current_dj': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/kick_dj')\n@check_auth(required_permissions=[ApiKey.FLAGS.KICK])\n## DONE ##\ndef kick_dj():\n \"\"\"Kicks the dj, who's currently connected to the streaming server\n\n Keyword arguments:\n - None\n\n Returns:\n {'kick_dj': {'dj_id': x, 'dj_name': x, 'success': x}}\n \"\"\"\n\n def try_kick():\n try:\n li = LiquidInterface()\n li.connect()\n kicked = li.kick_harbor()\n li.close()\n return kicked\n except:\n return False\n\n result = UserShow.query.filter(UserShow.status == UserShow.STATUS.STREAMING).first()\n if result:\n if try_kick():\n data = {'kick_dj': {'dj_id': result.user.user, 'dj_name': result.user.username, 'success': True}}\n else:\n data = {'kick_dj': {'dj_id': result.user.user, 'dj_name': result.user.username, 'success': False}}\n else:\n data = {'kick_dj': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/current_show')\n@check_auth()\n## DONE ##\ndef current_show():\n \"\"\"Return the currently running show\n\n Keyword arguments:\n - None\n \"\"\"\n\n clauses = [(between(datetime.utcnow(), Show.begin, Show.end)) | (Show.end == None)]\n result = Show.query.filter(*clauses).order_by(Show.begin.desc(), Show.end.asc()).all()\n\n data = {'current_show': {}}\n if result:\n for show in result:\n\n begin = show.begin.isoformat()\n if show.end:\n end = show.end.isoformat()\n else:\n end = None\n\n dj = []\n connected = False\n for usershow in show.users:\n dj.append({'dj_name': usershow.user.username, 'dj_id': usershow.user.user, 'status': usershow.status})\n if usershow.status == UserShow.STATUS.STREAMING:\n connected = True\n\n if (show.flags & Show.FLAGS.UNPLANNED and len(result) == 2) or len(result) == 1:\n target = 'running_show'\n if show.flags & Show.FLAGS.PLANNED and len(result) == 2:\n target = 'planned_show'\n\n data['current_show'][target] = {\n 'show_id': show.show,\n 'show_name': show.name,\n 'show_description': show.description,\n 'show_flags': show.flags,\n 'show_connected': connected,\n 'show_begin': begin,\n 'show_end': end,\n 'dj': dj\n }\n else:\n data = {'current_show': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/next_shows')\n@check_auth\n## DONE ##\ndef next_shows():\n \"\"\"Return the next planned show(s)\n\n Keyword arguments:\n - dj_id -- filter by dj\n - dj_name -- filter by dj\n - limit -- limit the output (default=5)\n \"\"\"\n\n dj_id = request.args.get('dj_id', None)\n dj_name = request.args.get('dj_name', None)\n limit = request.args.get('limit', 5)\n\n clauses = [Show.begin > datetime.utcnow()]\n\n try:\n if dj_id:\n clauses.append(UserShow.user == User.get_user(id=dj_id))\n if dj_name:\n clauses.append(UserShow.user == User.get_user(username=dj_name))\n\n result = Show.query.join(UserShow).filter(*clauses).order_by(Show.begin.asc()).limit(limit).all()\n\n data = {'next_shows': {'shows': []}}\n if result:\n for show in result:\n\n begin = show.begin.isoformat()\n end = show.end.isoformat()\n\n dj = []\n for usershow in show.users:\n dj.append({'dj_name': usershow.user.username, 'dj_id': usershow.user.user, 'status': usershow.status})\n\n data['next_shows']['shows'].append({\n 'show_id': show.show,\n 'show_name': show.name,\n 'show_description': show.description,\n 'show_flags': show.flags,\n 'show_begin': begin,\n 'show_end': end,\n 'dj': dj\n })\n else:\n data = {'next_shows': None}\n return jsonify(wrapper(data))\n except UserNotFoundException:\n return jsonify(wrapper({'next_shows': None}))\n\n\n@api.route('/web/last_shows')\n@check_auth\n## DONE ##\ndef last_shows():\n \"\"\"Return show history\n\n Keyword arguments:\n - dj_id -- filter by dj\n - dj_name -- filter by dj\n - limit -- limit the output (default=5)\n \"\"\"\n\n dj_id = request.args.get('dj_id', None)\n dj_name = request.args.get('dj_name', None)\n limit = request.args.get('limit', 5)\n\n clauses = [Show.end < datetime.utcnow()]\n\n try:\n if dj_id:\n clauses.append(UserShow.user == User.get_user(id=dj_id))\n if dj_name:\n clauses.append(UserShow.user == User.get_user(username=dj_name))\n\n result = Show.query.join(UserShow).filter(*clauses).order_by(Show.begin.desc()).limit(limit).all()\n\n data = {'last_shows': {'shows': []}}\n if result:\n for show in result:\n\n begin = show.begin.isoformat()\n end = show.end.isoformat()\n\n dj = []\n for usershow in show.users:\n dj.append({'dj_name': usershow.user.username, 'dj_id': usershow.user.user, 'status': usershow.status})\n\n data['last_shows']['shows'].append({\n 'show_id': show.show,\n 'show_name': show.name,\n 'show_description': show.description,\n 'show_flags': show.flags,\n 'show_begin': begin,\n 'show_end': end,\n 'dj': dj\n })\n else:\n data = {'last_shows': None}\n return jsonify(wrapper(data))\n except UserNotFoundException:\n return jsonify(wrapper({'last_shows': None}))\n\n\n@api.route('/web/current_track')\n@check_auth\n## DONE ##\ndef current_track():\n \"\"\"Return the currently playing track\n\n Keyword arguments:\n - None\n \"\"\"\n\n result = Track.current_track()\n if result:\n data = {'current_track': {\n 'track_id': result.track,\n 'track_begin': result.begin.isoformat(),\n 'track_title': result.title.name,\n 'track_artist': result.title.artist.name\n }}\n else:\n data = {'current_track': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/last_tracks')\n@check_auth\n## DONE ##\ndef last_tracks():\n \"\"\"Return the last played tracks\n\n Keyword arguments:\n - dj_id -- filter by dj\n - dj_name -- filter by dj\n - limit -- limit the output (default=5)\n \"\"\"\n\n dj_id = request.args.get('dj_id', None)\n dj_name = request.args.get('dj_name', None)\n limit = int(request.args.get('limit', 5))\n limit = limit if limit <= 50 else 50\n\n clauses = [Track.end < datetime.utcnow()]\n\n if dj_id is not None:\n clauses.append(UserShow.user == User.get_user(id=dj_id))\n if dj_name is not None:\n clauses.append(UserShow.user == User.get_user(username=dj_name))\n\n result = Track.query.join(Show).join(UserShow).filter(*clauses).order_by(Track.end.desc()).limit(limit).all()\n\n data = {'last_tracks': {'tracks': []}}\n if result:\n for track in result:\n begin = track.begin.isoformat()\n end = track.end.isoformat()\n\n data['last_tracks']['tracks'].append({\n 'track_id': track.track,\n 'track_begin': begin,\n 'track_end': end,\n 'track_title': track.title.name,\n 'track_artist': track.title.artist.name\n })\n else:\n data = {'last_tracks': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/listener')\n@check_auth\n## DONE ##\ndef listener():\n \"\"\"Return current listener count\n\n Keyword arguments:\n - None\n \"\"\"\n\n clauses = [Listener.disconnect == None]\n result = Listener.query.filter(*clauses).all()\n\n data = {'listener': {'listener': []}}\n temp = {'per_stream': {}, 'per_country': {}, 'total_count': len(result)}\n\n if result:\n\n for listener in result:\n\n stream = listener.stream_relay.stream\n try:\n temp['per_stream'][stream.code]['count'] += 1\n except KeyError:\n temp['per_stream'][stream.code] = {'count': 1, 'name': stream.name}\n\n country = listener.country\n try:\n temp['per_country'][country]['count'] += 1\n except KeyError:\n temp['per_country'][country] = {'count': 1}\n\n data['listener'] = temp\n return jsonify(wrapper(data))\n\n\n@api.route('/web/active_relays')\n@check_auth\n## DONE ##\ndef active_relays():\n \"\"\"Return information about all active relays\n\n Keyword arguments:\n - None\n \"\"\"\n\n result = Relay.query.filter(Relay.status == Relay.STATUS.ONLINE).all()\n\n data = {'active_relays': {'relays': [], 'total_bandwidth': 0}}\n\n if result:\n for relay in result:\n data['active_relays']['relays'].append({\n 'relay_id': relay.relay,\n 'relay_type': relay.type,\n 'relay_address': relay.address,\n 'relay_max_bandwidth': relay.bandwidth,\n 'relay_current_bandwidth': relay.usage\n })\n data['active_relays']['total_bandwidth'] += relay.usage\n else:\n data = {'active_relays': None}\n return jsonify(wrapper(data))\n\n\n@api.route('/web/listener_peak')\n@check_auth\n## DONE ##\ndef listener_peak():\n \"\"\"Return the global listener peak\n\n Keyword arguments:\n - None\n \"\"\"\n\n peak = StatsistcsData.query.join(Statistic).filter(Statistic.identifier == 'lst-total').order_by(StatsistcsData.value.desc()).first()\n if peak:\n data = {'listener_peak': {'peak_value': peak.value, 'peak_time': peak.timestamp.isoformat()}}\n\n show = Show.query.join(UserShow).filter(between(peak.timestamp, Show.begin, Show.end)).first()\n if show:\n dj = []\n for usershow in show.users:\n dj.append({'dj_name': usershow.user.username, 'dj_id': usershow.user.user, 'status': usershow.status})\n\n data['listener_peak']['peak_show'] = {\n 'show_id': show.show,\n 'show_name': show.name,\n 'show_description': show.description,\n 'show_flags': show.flags,\n 'show_begin': show.begin.isoformat(),\n 'show_end': show.end.isoformat(),\n 'dj': dj\n }\n else:\n data['listener_peak']['peak_show'] = None\n else:\n data = {'listener_peak': None}\n return jsonify(wrapper(data))\n","sub_path":"lib/rfk/api/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":14405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"279548465","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator 2.3.33.0\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass OutputOptions(Model):\n \"\"\"Additional options that control how the data should be uploaded.\n\n :param path_on_compute: Which local path on compute the data should be\n uploaded from. This is only applicable to the\n mount and upload mechanisms.\n :type path_on_compute: str\n :param registration_options: If present, whether to register the output as\n a named dataset.\n :type registration_options: ~_restclient.models.RegistrationOptions\n :param upload_options: Options that is specific to upload. This should\n only be set if the mechanism is set to upload.\n :type upload_options: ~_restclient.models.UploadOptions\n \"\"\"\n\n _attribute_map = {\n 'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},\n 'registration_options': {'key': 'registrationOptions', 'type': 'RegistrationOptions'},\n 'upload_options': {'key': 'uploadOptions', 'type': 'UploadOptions'},\n }\n\n def __init__(self, path_on_compute=None, registration_options=None, upload_options=None):\n super(OutputOptions, self).__init__()\n self.path_on_compute = path_on_compute\n self.registration_options = registration_options\n self.upload_options = upload_options\n","sub_path":"venv/Lib/site-packages/azureml/_restclient/models/output_options.py","file_name":"output_options.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"469974187","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 11 14:26:58 2019\n\n@author: CCF\n\"\"\"\n\nfrom numpy import *\nimport pickle\ndef make_dataset(dataset):\n X = []\n Y = []\n\n for video in dataset:\n X.append(video[\"features\"])\n Y.append(video[\"category\"])\n\n return X, Y\n\n\n \nclass NeuralNet(object): \n def __init__(self): \n # Generate random numbers \n random.seed(1) \n \n # Assign random weights to a 3 x 1 matrix, \n self.synaptic_weights = 2 * random.random((3, 1)) - 1\n \n # The Sigmoid function \n def __sigmoid(self, x): \n return 1 / (1 + exp(-x)) \n \n # The derivative of the Sigmoid function. \n # This is the gradient of the Sigmoid curve. \n def __sigmoid_derivative(self, x): \n return x * (1 - x) \n \n # Train the neural network and adjust the weights each time. \n def train(self, inputs, outputs, training_iterations): \n for iteration in xrange(training_iterations): \n \n # Pass the training set through the network. \n output = self.learn(inputs) \n \n # Calculate the error \n error = outputs - output \n \n # Adjust the weights by a factor \n factor = dot(inputs.T, error * self.__sigmoid_derivative(output)) \n self.synaptic_weights += factor \n \n # The neural network thinks. \n def learn(self, inputs): \n return self.__sigmoid(dot(inputs, self.synaptic_weights)) \n \nif __name__ == \"__main__\": \n \n #Initialize \n neural_network = NeuralNet() \n \n # The training set. \n dataset = pickle.load(open(dataset_bow, \"rb\"))\n X, Y = make_dataset(dataset)\n \n # Train the neural network \n neural_network.train(X, Y, 10000) \n \n # Test the neural network with a test example. \n #print neural_network.learn() ","sub_path":"nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"439803386","text":"#! coding: utf-8\nfrom flask import request, url_for, jsonify\nfrom . import app\nfrom .task import parse_msg, format_task_list\nfrom .models import db, User, Task, MessageID\nfrom .message import FanfouClient\nfrom datetime import datetime\nfrom dateutil import parser\n\nclient = FanfouClient(\n {\n 'key': app.config.get('FANFOU_CONSUMER_KEY'),\n 'secret': app.config.get('FANFOU_CONSUMER_SECRET')\n },\n {\n 'key': app.config.get('FANFOU_AUTH_KEY'),\n 'secret': app.config.get('FANFOU_AUTH_SECRET')\n }\n)\n\ndef htmlec(s):\n el = {'<': '<', '>': '>', '"': '\"', '&': '&'}\n for k, v in el.items():\n s = s.replace(k, v)\n return s\n\ndef check_msg(msg_num):\n newest_msg = MessageID.query.filter_by(name='msg').first()\n if newest_msg:\n newest_msg_id = newest_msg.message_id\n newest_msg_ts = newest_msg.message_ts\n else:\n newest_msg_id = None\n newest_msg_ts = datetime.fromtimestamp(0)\n newest_msg = MessageID(name='msg', message_id='',\n message_ts=newest_msg_ts)\n db.session.add(newest_msg)\n count = min(60, msg_num)\n pages = msg_num / count + 2\n processed_msg = 0\n for page in range(1, pages):\n messages = client.get_msg_list(count, page, newest_msg_id)\n for item in messages:\n msg_timestamp = parser.parse(item['created_at']).replace(tzinfo=None)\n if newest_msg_ts < msg_timestamp:\n newest_msg_ts = msg_timestamp\n newest_msg_id = item['id']\n if not item['text'].startswith('@TodoBot'):\n continue\n text = item['text'].replace('@TodoBot ', '').strip()\n text = text.replace('@', '#')\n user_id = item['user']['id']\n user_name = item['user']['name']\n reply = parse_msg(user_id, text, user_name)\n user = User.query.filter_by(user_id=user_id).first()\n if not user:\n reply = u'没有任务'\n if not user or user.msg_type == 'public':\n client.send_msg(reply, user_id, user_name)\n else:\n client.send_dm(reply, user_id)\n processed_msg += 1\n newest_msg.message_id = newest_msg_id\n newest_msg.message_ts = newest_msg_ts\n db.session.commit()\n return processed_msg\n\ndef check_dm(dm_num):\n newest_msg = MessageID.query.filter_by(name='dm').first()\n if newest_msg:\n newest_msg_id = newest_msg.message_id\n newest_msg_ts = newest_msg.message_ts\n else:\n newest_msg_id = None\n newest_msg_ts = datetime.fromtimestamp(0)\n newest_msg = MessageID(name='dm', message_id='',\n message_ts=newest_msg_ts)\n db.session.add(newest_msg)\n count = min(60, dm_num)\n pages = dm_num / count + 2\n processed_dm = 0\n for page in range(1, pages):\n direct_messages = client.get_dm_list(count, page, newest_msg_id)\n for item in direct_messages:\n msg_timestamp = parser.parse(item['created_at']).replace(tzinfo=None)\n if newest_msg_ts < msg_timestamp:\n newest_msg_ts = msg_timestamp\n newest_msg_id = item['id']\n text = htmlec(item['text'].strip())\n text = text.replace('@', '#')\n user_id = item['sender']['id']\n user_name = item['sender']['name']\n client.delete_dm(item['id'])\n reply = parse_msg(user_id, text, user_name, 'private')\n user = User.query.filter_by(user_id=user_id).first()\n if not user:\n reply = u'没有任务'\n if not user or user.msg_type == 'private':\n client.send_dm(reply, user_id)\n else:\n client.send_msg(reply, user_id, user_name)\n processed_dm += 1\n newest_msg.message_id = newest_msg_id\n newest_msg.message_ts = newest_msg_ts\n db.session.commit()\n return processed_dm\n\n@app.route('/check')\ndef check():\n msg_num, dm_num = client.get_notification()\n processed_msg = processed_dm = 0\n if msg_num > 0:\n processed_msg = check_msg(msg_num)\n if dm_num > 0:\n processed_dm = check_dm(dm_num)\n return 'done checking, #msg %d #dm %d' % (processed_msg, processed_dm)\n\n@app.route('/notify')\ndef notify():\n users = User.query.all()\n for user in users:\n tasks = Task.query.filter_by(user=user).filter_by(status='todo').all()\n if not tasks:\n continue\n if user.msg_type == 'private':\n client.send_dm(format_task_list(tasks), user.user_id)\n elif user.msg_type == 'public':\n client.send_msg(format_task_list(tasks), user.user_id, user.user_name)\n return 'finished daily notification for %d users' % len(users)\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"610860026","text":"log_file = 'result-1/log-33000.txt'\n\nwith open(log_file) as f:\n bitrate = 0\n rebuf = 0\n cv = 0\n smooth = 0\n count = 0\n for line in f:\n parse = line.split()\n bitrate += float(parse[8])\n rebuf += float(parse[4])\n cv += float(parse[6])\n smooth += float(parse[10])\n count += 1\n if count == 10000:\n break\n ave_bitrate = bitrate / count\n ave_rebuf = rebuf / count\n ave_cv = cv / count\n ave_smooth = smooth / count\n reward = ave_bitrate - 33 * ave_rebuf / 1000 - 5.3 * ave_cv - ave_smooth\n print(ave_bitrate, ave_rebuf, ave_cv, ave_smooth, reward)\n\n","sub_path":"abr/a3c-final/analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"12551658","text":"#!/usr/bin/python3\n\n\n\nimport sys\nimport os\nimport shutil\nfrom datetime import datetime\n\ndef clean_downloads (dossier):\n \n liste_fichier = os.listdir (dossier)\n \n if len (liste_fichier) == 0:\n print ('No file in directory !')\n else:\n print (\"cleanup old files : (more than 10 days + 50MB)\\n\")\n for fichier in liste_fichier:\n jours_depuis_creation = (datetime.now() - datetime.fromtimestamp(os.stat(dossier+fichier).st_mtime)).days\n taille_fichier = os.stat (dossier+fichier).st_size\n if jours_depuis_creation > 10 and taille_fichier > 6250000:\n print (fichier)\n choix = input (\"[yes/No]?\\n\")\n if choix == \"y\":\n os.remove (dossier+fichier)\n print (fichier+\" delete\\n\")\n else:\n print(\"\")\n else:\n if jours_depuis_creation > 10:\n print (fichier)\n print (\"organizing your files :\")\n annee_mois_du_fichier = str(datetime.fromtimestamp(os.stat(dossier+fichier).st_atime).year)+\"-\"+str(datetime.fromtimestamp(os.stat(dossier+fichier).st_atime).month)\n try:\n os.mkdir (dossier+annee_mois_du_fichier)\n except:\n ligne_bidon = 0\n print (\"moving into \"+annee_mois_du_fichier+\" : \"+fichier+\"\\n\") \n shutil.move (dossier+fichier,dossier+annee_mois_du_fichier)\n \n\n\n \n \n\n return \"end\"\n \nif __name__ == \"__main__\":\n print(clean_downloads(sys.argv[1]))","sub_path":"exam3/clean_downloads.py","file_name":"clean_downloads.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"380956397","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\nimport mysql.connector\r\n\r\n# WINDOW SETUP\r\nroot= Tk()\r\nroot.title(\"TONY TEXTILES\")\r\nroot.geometry('1920x1080')\r\nroot.config(background=\"#ffffff\")\r\n\r\nback_image=PhotoImage(file=\"canvas.png\")\r\nback=Label(root,image=back_image)\r\nback.place(relwidth=1,relheight=1)\r\n\r\nnew_frame= Frame(root,bg=\"#D1F2EB\",bd=5)\r\nnew_frame.place(relx=0.2,rely=0.11,relwidth=0.6,relheight=0.75)\r\n\r\nnew_frame_= Frame(new_frame,bg=\"white\",bd=5)\r\nnew_frame_.place(relx=0.26,rely=0.13,relwidth=0.46,relheight=0.5)\r\n\r\ncon = mysql.connector.connect(host = \"localhost\", user = \"root\", passwd = \"123456\")\r\ncrs = con.cursor()\r\ncrs.execute(\"USE tony_textiles;\")\r\n\r\ncon.commit()\r\n\r\n# CANVAS SETUP\r\n\r\nt= Label(\r\n new_frame,\r\n text= \"BUY DRESS:\",\r\n background= \"#D1F2EB\",\r\n foreground= \"#30110D\",\r\n font= (\"Goudy Old Style\",18),\r\n\r\n)\r\nt.place(relx=0.43,rely=0.05)\r\n\r\nt1= Label(\r\n text= \"ENTER THE DRESS CODE \\n BELOW..\",\r\n background= \"#D1F2EB\",\r\n foreground= \"#30110D\",\r\n font= (\"Goudy Old Style\",17),\r\n\r\n)\r\nt1.place(relx=0.42,rely=0.6)\r\n\r\nt2= Label(\r\n text= \"CODE :\",\r\n background= \"#D1F2EB\",\r\n foreground= \"#30110D\",\r\n font= (\"Goudy Old Style\",17),\r\n\r\n)\r\nt2.place(relx=0.4,rely=0.67)\r\n\r\ncode_entry= Entry(\r\n new_frame,\r\n font= (\"Goudy Old Style\",16),\r\n )\r\ncode_entry.place(relx=0.42,rely=0.75)\r\n\r\n\r\n# FUNCTIONS\r\n\r\ndef cancel_pressed():\r\n quit()\r\n\r\ndef ok_pressed():\r\n d_code=code_entry.get()\r\n \r\n crs.execute(\"SELECT d_code FROM men_wear;\")\r\n rows= crs.fetchall()\r\n ids= []\r\n for id in rows:\r\n ids.append(str(id[0]))\r\n\r\n if d_code in ids:\r\n crs.execute(f\"delete from men_wear where d_code = '{d_code}'\")\r\n con.commit()\r\n messagebox.showinfo(\"TONY TEXTILES\", \"PURCHASE SUCCESSFULL\")\r\n \r\n quit()\r\n else:\r\n messagebox.showinfo(\"TONY TEXTILES\", \"NO ITEM WITH THE ENTERED CODE,\\nPLEASE RE-CHECK THE CODE.\") \r\n\r\n# DECISION BUTTONS\r\n\r\ncancel_but= Button(\r\n new_frame,\r\n text=\"<< CANCEL\",\r\n font=(\"Comic Sans MS\",12),\r\n background= \"RED\",\r\n foreground=\"#D1F2EB\",\r\n command = cancel_pressed,\r\n )\r\ncancel_but.place(relx=0.38,rely=0.85,bordermode=OUTSIDE,relwidth=0.1)\r\n\r\nok_but= Button(\r\n new_frame,\r\n text=\"OK >>\",\r\n font=(\"Comic Sans MS\",12),\r\n background= \"green\",\r\n foreground=\"#D1F2EB\",\r\n command = ok_pressed,\r\n )\r\nok_but.place(relx=0.52,rely=0.85,bordermode=OUTSIDE,relwidth=0.1)\r\n\r\n# DISPLAYING TABLE\r\n\r\nattributes=[\"Dress code \",\" Dress Name \",' colour ',' size ',\" price \"]\r\ncrs.execute(\"SELECT * FROM men_wear ORDER BY d_code;\")\r\n\r\ninfo= crs.fetchall()\r\ndatabase=[]\r\n\r\nfor i in info:\r\n database.append(i)\r\n\r\nfor attr in attributes:\r\n heading= Label(\r\n new_frame_,\r\n text= attr,\r\n bg= \"white\",\r\n font=(\"Arial\",15),\r\n )\r\n heading.grid(\r\n row=2,\r\n column= attributes.index(attr)\r\n )\r\n\r\nfor item in database:\r\n for detail in item:\r\n item_detail= Label(\r\n new_frame_,\r\n text= detail,\r\n font= (\"Arial\",15),\r\n bg= \"white\"\r\n )\r\n item_detail.grid(\r\n row=database.index(item)+3,\r\n column= item.index(detail),\r\n )\r\n\r\nroot.mainloop()","sub_path":"tony tk/buy.py","file_name":"buy.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"517317058","text":"from PIL import Image\n\n\ndef make_preview(size, n_colors):\n img = Image.open('27.1.jpg')\n img = img.resize(size)\n img = img.quantize(colors=n_colors)\n img.save('27.1.bmp', 'BMP')\n\n\nmake_preview((200, 150), 10)\n","sub_path":"Python3/27.1.py","file_name":"27.1.py","file_ext":"py","file_size_in_byte":221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"274095207","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport create_loss as crloss\n\nimport random\nimport numpy as np\nimport itertools as it\n# In[1]:\nimport torch\nimport torchvision.models as models\nfrom torch.utils.data import DataLoader\nimport torch.nn as nn\nfrom torchvision import datasets, transforms\n\nimport os\nimport time\nfrom data.my_dataset import MyDataset\n\nimport fire\n\n\n# In[3]:\n\n\nclass AverageMeter(object):\n \"\"\"\n Computes and stores the average and current value\n Copied from: https://github.com/pytorch/examples/blob/master/imagenet/main.py\n \"\"\"\n def __init__(self):\n self.reset()\n \n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\n# In[ ]:\n\n\ndef train_epoch(model,loader,criterion,optimizer,epoch,n_epochs,print_freq=1):\n '''\n Args:\n model:\n loader:\n optimizer:\n criterion:\n \n \n '''\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n batch_time = AverageMeter()\n losses = AverageMeter()\n \n model.train()\n end = time.time()\n batch_idx = 0\n for inputs,labels in loader:\n \n #print(batch_idx)\n # Create vaiables\n \n inputs = inputs.to(device)\n labels = labels.to(device)\n \n # feed the network\n batch_size = labels.size(0)\n embeddings = model(inputs) # BS* m\n loss = criterion(embeddings, labels)\n #record loss\n losses.update(loss.item(), batch_size)\n \n \n # backpropagation\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n # measure elapsed time\n batch_time.update(time.time() - end)\n end = time.time()\n \n # print stats\n if batch_idx % print_freq == 0:\n res = '\\t'.join([\n 'Epoch: [%d/%d]' % (epoch + 1, n_epochs),\n 'Iter: [%d/%d]' % (batch_idx + 1, len(loader)),\n 'Time %.3f (%.3f)' % (batch_time.val, batch_time.avg),\n 'Loss %.4f (%.4f)' % (losses.val, losses.avg),\n ])\n print(res)\n batch_idx +=1\n \n return losses.avg\n \n \n\n\n# In[ ]:\n\n\ndef train(model,train_set, # model and data\n batch_size ,n_epochs,T, # others\n embed_size,loss,metric,sampler,num_cls,lambda_, # train\n save,result_f,model_f,\n wd=0,momentum=0.9,lr=0.001): # optim\n \n '''\n Args:\n model:\n result_f:\n data_sets:\n \n opti_para:\n scheduler_para:\n \n ''' \n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n freq = 7\n model = model.to(device)\n \n # Wrap model for multi-GPUs, if necessary\n model_wrapper = model\n \n # Optimizer : optim strategy to be completed...\n \n to_optim = [{'params':model_wrapper.parameters(),'lr':lr,'momentum': momentum ,'weight_decay':wd}]\n criterion,to_optim = crloss.select_loss(loss=loss,e_size = embed_size, metric=metric, sampling_method=sampler,num_cls=num_cls,lambda_=lambda_,lr=lr,momentum=momentum,wd=wd,to_optim=to_optim) # check if the new para are in the cuda or not??\n \n criterion = criterion.to(device)\n \n \n optimizer = torch.optim.SGD(to_optim)#model_wrapper.parameters(), lr=lr, momentum=0.9, dampening=0, weight_decay=0, nesterov=False)\n \n # scheduler\n #scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,milestones=[5,9,12,14])\n # ...\n \n # Start log\n with open(os.path.join(save, result_f), 'w') as f:\n f.write('epoch,train_loss,l_r,dif\\n')\n l_rate = lr\n best_loss = 1000\n pre_loss = 10.\n for epoch in range(n_epochs):\n # scheduler.step()\n \n train_loss = train_epoch(\n model=model_wrapper,\n loader=train_set,\n optimizer=optimizer,\n criterion = criterion,\n epoch=epoch,\n n_epochs=n_epochs,\n )\n dif = abs(pre_loss-train_loss)\n if epoch % freq == 0:\n dif = pre_loss-train_loss\n pre_loss = train_loss\n \n if dif< T:\n \n l_rate *= 0.5\n # for p in optimizer.param_groups:\n # p['lr']*=0.5\n print('lr has been update \\n')\n \n \n # Determine if model is the best and save the best model\n if train_loss < best_loss:\n # save the model\n torch.save(model.state_dict(), os.path.join(save, model_f))\n if metric == 'maha' or metric == 'rM':\n cri_f = 'L.dat'\n torch.save(criterion.state_dict(),os.path.join(save,cri_f))\n # update the best_loss\n best_loss = train_loss\n \n # Log results\n with open(os.path.join(save, result_f), 'a') as f:\n f.write('%03d,%0.6f,%0.9f,%05f\\n' % ( #**\n (epoch + 1),\n train_loss,\n l_rate,\n dif\n ))\n \n \n \n \n\n\n# In[4]:\n\n\n\ndef demo_p(data,save,fine_tune,\n metric,loss,sampler='rand',embed_size=16,num_cls = 196, lambda_=0, lr=0.001,T = 0.005,\n model_saved=None,result='r_.csv',model_name='m_.dat',list_file = './cars_train.txt',\n batch_size=100,n_epochs = 10,seed=1):\n '''\n Arg:\n # data\n data: where the data are stored\n dataset: the name of the dataset: CIFAR196,etc.\n train_size:\n test_size:\n \n # log\n save: tr where the logs are stored\n model_saved: trained_model\n result: loss\n model_new : \n \n # trainning para\n embed_size: m : 16,32,64\n \n metric: 'E', 'rE','maha','snr','rM'\n \n sampler:'dist','npair'\n \n loss; 'tripl','contras','npair','lifted',\n \n #margin\n \n # others\n n_epochs :10 tr\n batch_size tr\n seed :1\n \n \n \n '''\n # seed\n torch.backends.cudnn.deterministic=True\n if seed is not None:\n \n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n \n # dataLoader\n train_set = MyDataset(dataroot=data,phase='train',image_list_file =list_file )\n #test_set = MyDataset(dataroot=data,phase='test',image_lise_file = list_file)\n \n #test_loader = DataLoader(dataset=test_set, batch_size=batch_size,shuffle=False, num_workers=4,pin_memory=torch.cuda.is_available())\n train_loader = DataLoader(dataset=train_set, batch_size=batch_size,shuffle=True, num_workers=4,pin_memory=torch.cuda.is_available()) \n # model_setup\n if model_saved:\n model = models.resnet18(pretrained=False)\n for p in model.parameters():\n p.requires_grad=False\n \n inp_fts = model.fc.in_features\n #classifier[6].in_features\n #inp_fts = model.fc.in_features#model.classifier[6] = nn.Linear(inp_fts, embed_size)\n \n model.fc = nn.Linear(inp_fts,embed_size)\n model.load_state_dict(torch.load(os.path.join(save, model_saved),map_location = 'cpu'))\n \n if fine_tune:\n for p in model.parameters():\n p.requires_grad = not p.requires_grad\n else:\n model = models.resnet18(pretrained=True)\n for p in model.parameters():\n p.requires_grad=False\n \n inp_fts = model.fc.in_features#\n #inp_fts=model.classifier[6].in_features\n model.fc = nn.Linear(inp_fts,embed_size)\n #model.classifier[6] = nn.Linear(inp_fts, embed_size)\n if fine_tune:\n for p in model.parameters():\n p.requires_grad = not p.requires_grad\n \n \n print(model)\n # create folder for logging file\n # Make save directory\n if not os.path.exists(save):\n os.makedirs(save)\n if not os.path.isdir(save):\n raise Exception('%s is not a dir' % save)\n \n train(model=model,train_set=train_loader, # model and data\n batch_size = batch_size,n_epochs=n_epochs, lambda_=lambda_,lr=lr,T=T, # others\n embed_size=embed_size,loss = loss,metric = metric,sampler =sampler,num_cls = num_cls, # train\n save=save,result_f=result,model_f=model_name # log\n )\n print('Done')\n \n \nif __name__ == '__main__':\n fire.Fire(demo_p)\n \n\n\n# In[ ]:\n\n\n\n\n","sub_path":"demo_p.py","file_name":"demo_p.py","file_ext":"py","file_size_in_byte":8710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"608317452","text":"#!/usr/bin/python\n\nimport sys\nimport pandas as pd\nimport Select as st\nfrom decimal import *\nimport time\n\n\ndef initial_selection(df_origin, rul='2_3_4'):\n \"\"\"\n Initial selection of STR loci: apply repeat unit length filter\n :param df_origin:\n :param rul:\n :return:\n \"\"\"\n print('========================================================================================')\n print('Processing begins')\n print('Total loci:', len(df_origin))\n df_origin = df_origin[~df_origin.CHROM.isin(['chrX', 'chrY'])] # drop chrX and chrY\n print('========================================================================================')\n print('After drop chrX and chrY, remaining:', len(df_origin))\n df_origin = df_origin[df_origin.RUL.isin([int(i) for i in rul.split('_')])] # select repeat length\n df_origin = df_origin.sort_values(by=['PD', 'GT_FREQ'], ascending=[False, False]).reset_index(drop=True)\n return df_origin\n\n\ndef para_selection(df_origin, called_threshold, n_sample):\n \"\"\"\n Select loci with required HE, PM, PE\n :param called_threshold:\n :param df_origin:\n :param n_sample:\n :return:\n \"\"\"\n # called_threshold = float(called_threshold)\n # isFound = False\n # filter_info = []\n # final_selected_df = None\n\n step_filter_info = Decimal(called_threshold).quantize(Decimal('0.01'), rounding=ROUND_UP), # turple\n # step_filter_info = round(called_threshold, 2)\n print('=======================================================================================')\n print('After selecting repeat length, remaining:', len(df_origin))\n step_filter_info += len(df_origin), # append filter info\n\n if called_threshold < 0:\n print('No eligible loci')\n\n else:\n df = st.filter_called(df_origin, int(n_sample), called_threshold) # filter with called number\n print('=======================================================================================')\n print('After filtering nonentity, remaining:', len(df))\n step_filter_info += len(df),\n\n # select filter type #\n # loose filter\n\n # Default\n\n # df = df[df.EXP_HE >= 0.777]\n df = df[df.EXP_HE >= 0.1]\n print('=======================================================================================')\n print('After filtering with HE, remaining:', len(df))\n step_filter_info += len(df),\n\n # df = df[df.PM <= 0.087]\n df = df[df.PM <= 0.9]\n print('=======================================================================================')\n print('After filtering with PM, remaining:', len(df))\n step_filter_info += len(df),\n\n # df = df[df.PE >= 0.579]\n df = df[df.PE >= 0.1]\n print('=======================================================================================')\n print('After filtering with PE, remaining:', len(df), '\\n')\n step_filter_info += len(df),\n\n return df\n\n\n# HE 0.864\t0.777\n# PD 0.967\t0.913\n# PE 0.729\t0.579\n# PM 0.198\t0.087\n\ndef main():\n\n ################### Phrase Parameters ######################\n import argparse\n Argparser = argparse.ArgumentParser()\n Argparser.add_argument('-f', '--file', action='store', dest='data_in', type=str, help='Input file [.txt]')\n Argparser.add_argument('-v', '--vcf-in', action='store', dest='vcf_in', type=str, default=None)\n Argparser.add_argument('-n', '--n-sample', action='store', dest='n_sample', type=int, default=30)\n Argparser.add_argument('-t', '--thres', action='store', dest='called_threshold', type=float)\n Argparser.add_argument('-p', '--prob-thres', action='store', dest='prob_threshold', type=str, default='0.000001')\n Argparser.add_argument('--tag', action='store', dest='species_name', type=str, default='Unknown')\n Argparser.add_argument('--filter', action='store', dest='filter_type', type=str, default='default')\n Argparser.add_argument('-r', '--rul', action='store', dest='rul', type=str, default='2_3_4')\n Argparser.add_argument('-o', '--out-dir', action='store', dest='output_path', type=str)\n\n para, unknown = Argparser.parse_known_args()\n\n ################### Main Body ######################\n print('Selection info.:')\n print('\\tNumber of sample:', para.n_sample)\n print('\\tCalled threshold:', para.called_threshold)\n print('\\tProb threshold:', para.prob_threshold)\n print('\\tFilter:', para.filter_type)\n print('\\tRUL:', para.rul, '\\n\\n')\n df_origin = pd.read_table(para.data_in, dtype={'ID': str})\n #vcf_canis = vcf.Reader(filename=para.vcf_in)\n\n print('========================================================================================')\n print('Processing begins')\n\n called_threshold = float(para.called_threshold)\n\n df_selected = initial_selection(df_origin, rul='2_3_4')\n print('Current called threshold:', called_threshold)\n df = para_selection(df_selected, para.called_threshold, para.n_sample)\n df.to_csv(para.output_path + para.filter_type + '_' + para.prob_threshold + '_info', sep='\\t', index=False)\n\n return 0\n\n\nif __name__ == '__main__':\n start_time = time.time()\n return_code = main()\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n sys.exit(return_code)\n","sub_path":"filter_loci.py","file_name":"filter_loci.py","file_ext":"py","file_size_in_byte":5182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"81926565","text":"#! /usr/bin/python\n\nfrom __init__ import *\nimport tensorflow as tf\nimport numpy as np\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n\nclass vgg16:\n def __init__(self, param):\n self._param = param;\n\n def inference(self, input):\n \"\"\"Build vgg model\n \"\"\"\n\n def conv_relu(self, input, kernel_shape):\n weights = tf.get_variable(\"weights\",\n kernel_shape,\n initializer = tf.truncated_normal_initializer(0, 0.01),\n dtype = tf.float32)\n biases = tf.get_variable(\"biases\",\n [kernel_shape[-1]],\n initializer = tf.constant_initializer(0.0),\n dtype = tf.float32)\n conv = tf.nn.conv2d(input,\n weights,\n [1,1,1,1],\n padding='SAME',\n use_cudnn_on_gpu=True,\n data_format = 'NCHW')\n out = tf.nn.bias_add(conv, biases, data_format = 'NCHW')\n return tf.nn.relu(out, name = 'relu')\n\n def vgg_conv(self, name, input, n_conv, in_filters, out_filters):\n with tf.variable_scope(name):\n for idx in range(n_conv):\n kernel_shape = [3, 3, 0, 0]\n if idx == 0:\n kernel_shape[2] = in_filters\n if idx == n_conv - 1:\n kernel_shape[3] = out_filters\n with tf.variable_scope(str(idx)):\n conv_relu_layer = conv_relu(input, kernel_shape)\n pool = tf.nn.max_pool(conv_relu_layer,\n ksize = [1, 2, 2, 1],\n strides = [1, 2, 2, 1],\n padding = 'SAME',\n name = 'pool',\n data_format = 'NCHW')\n return pool\n\n def conv_net(self, x):\n pool1 = vgg_conv('conv1', x, 2, 64, 128)\n pool2 = vgg_conv('conv2', pool1, 2, 128, 256)\n pool3 = vgg_conv('conv3', pool2, 3, 256, 512)\n pool4 = vgg_conv('conv4', pool3, 3, 512, 512)\n pool5 = vgg_conv('conv5', pool4, 3, 512, 512)\n return pool5\n\n def inference(self, x):\n x = tf.placeholder(tf.float32)\n y = tf.placeholder(tf.float32)\n pred = conv_net(x)\n cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y) )\n optimizer = tf.train.AdamOptimizer(learning_rate=self._param.lr).minimize(cost)\n init = tf.global_variables_initializer()\n with tf.Session() as sess:\n sess.run(init)\n step = 1\n with step * self._param.batch_size < self._param.training_iters:\n batch_x, batch_y = mnist.traning.net_batch(self._param.batch_size)\n sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n step += 1\n print(\"Optimization Finished!\")\n","sub_path":"wal/model/vgg.py","file_name":"vgg.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"512888136","text":"# https://www.acmicpc.net/problem/10814\n\nfrom sys import stdin\ninput = stdin.readline\n\nmembers = {}\nfor _ in range(int(input())):\n\tage, name = input().rstrip().split()\n\tif members.get(int(age)):\n\t\tmembers[int(age)].append(name)\n\telse :\n\t\tmembers[int(age)] = [name]\n[print(age, name) for age, name_list in sorted(members.items()) for name in name_list]","sub_path":"dojinyou/code_2week/06_10814.py","file_name":"06_10814.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"399152394","text":"import grpc\n\nfrom dataservice_pb2_grpc import BostonHousingStub\nfrom dataservice_pb2 import ParamsRequest\n\nfrom io import BytesIO\nimport numpy as np\n\nchannel = grpc.insecure_channel(\"localhost:50051\")\nclient = BostonHousingStub(channel)\n\nrequest = ParamsRequest(test_size=0.2)\n\nresponse = client.PrepareData(request)\nnorm_train_X_bytes = BytesIO(response.norm_train_x)\nnorm_train_X = np.load(norm_train_X_bytes, allow_pickle=False)\nnorm_test_X_bytes = BytesIO(response.norm_test_x)\nnorm_test_X = np.load(norm_test_X_bytes, allow_pickle=False)\nnorm_val_X_bytes = BytesIO(response.norm_val_x)\nnorm_val_X = np.load(norm_val_X_bytes, allow_pickle=False)\ntrain_Y_bytes = BytesIO(response.train_y)\ntrain_Y = np.load(train_Y_bytes, allow_pickle=False)\ntest_Y_bytes = BytesIO(response.test_y)\ntest_Y = np.load(test_Y_bytes, allow_pickle=False)\nval_Y_bytes = BytesIO(response.val_y)\nval_Y = np.load(val_Y_bytes, allow_pickle=False)\n\nprint('Normalized training X:', norm_train_X.shape)\nprint('Normalized testing X:', norm_test_X.shape)\nprint('Normalized validation X:', norm_val_X.shape)\nprint('Training Y:', train_Y[0].shape)\nprint('Testing Y:', test_Y[0].shape)\nprint('Validation Y:', val_Y[0].shape)\n\n","sub_path":"services/dataservice/app/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"228660255","text":"from datetime import time\nfrom vnpy.app.cta_strategy import (\n CtaTemplate,\n StopOrder,\n TickData,\n BarData,\n TradeData,\n OrderData,\n BarGenerator,\n ArrayManager\n)\nfrom vnpy.app.cta_strategy.base import (\n EngineType,\n STOPORDER_PREFIX,\n StopOrder,\n StopOrderStatus,\n)\nfrom vnpy.app.cta_strategy.TSMtools import TSMArrayManager\n\nclass TSMyoTemplateStrategy(CtaTemplate):\n \"\"\"\"\"\"\n\n author = \"TheSuperMyo\"\n\n # 日内交易\n exit_time = time(hour=14, minute=55)\n\n # 针对不同交易时间的市场\n open_time_night = time(hour=21,minute=0)# 商品夜盘\n open_time_day_1 = time(hour=9,minute=0)# 商品\n open_time_day_2 = time(hour=9,minute=30)# 股指\n\n close_time_day = time(hour=15,minute=0)# 商品/股指（除了利率期货）\n close_time_night_1 = time(hour=23,minute=0)# 其他夜盘商品\n close_time_night_2 = time(hour=1,minute=0)# 工业金属\n close_time_night_3 = time(hour=2,minute=30)# 黄金/白银/原油\n \n break_time_start_1 = time(hour=10,minute=15)# 商品茶歇\n break_time_start_2 = time(hour=11,minute=30)# 全体午休\n break_time_end_1 = time(hour=10,minute=30)# 商品茶歇\n break_time_end_2 = time(hour=13,minute=0)# 股指下午\n break_time_end_3 = time(hour=13,minute=30)# 商品下午\n\n parameters = []\n variables = []\n\n def __init__(self, cta_engine, strategy_name, vt_symbol, setting):\n \"\"\"\"\"\"\n super(TSMyoTemplateStrategy, self).__init__(\n cta_engine, strategy_name, vt_symbol, setting\n )\n self.bg = BarGenerator(self.on_bar)\n self.am = TSMArrayManager()\n # 策略自身订单管理\n self.active_orderids = []\n\n def on_init(self):\n \"\"\"\n Callback when strategy is inited.\n \"\"\"\n self.write_log(\"策略初始化\")\n # 根据需要的历史数据长度设定\n self.load_bar(5)\n \n def on_start(self):\n \"\"\"\n Callback when strategy is started.\n \"\"\"\n self.write_log(\"策略启动\")\n\n def on_stop(self):\n \"\"\"\n Callback when strategy is stopped.\n \"\"\"\n self.write_log(\"策略停止\")\n\n def tick_filter(self, tick: TickData):\n \"\"\"\n 过滤异常时间的tick\n \"\"\"\n pass\n\n def on_tick(self, tick: TickData):\n \"\"\"\n Callback of new tick data update.\n \"\"\"\n self.bg.update_tick(tick)\n\n def on_bar(self, bar: BarData):\n \"\"\"\n Callback of new bar data update.\n \"\"\"\n\n def on_order(self, order: OrderData):\n \"\"\"\n Callback of new order data update.\n \"\"\"\n # 移除已成交或已撤销的订单\n if not order.is_active() and order.vt_orderid in self.active_orderids:\n self.active_orderids.remove(order.vt_orderid)\n\n def on_trade(self, trade: TradeData):\n \"\"\"\n Callback of new trade data update.\n \"\"\"\n # 邮寄提醒\n self.send_email(f\"{trade.vt_symbol}在{trade.time}成交，价格{trade.price}，方向{trade.direction}{trade.offset}，数量{trade.volume}\")\n self.put_event()\n\n def on_stop_order(self, stop_order: StopOrder):\n \"\"\"\n Callback of stop order update.\n \"\"\"\n # 刚刚生成的本地停止单\n if stop_order.status == StopOrderStatus.WAITING:\n return\n # 撤销的本地停止单，从活跃列表移除\n if stop_order.status == StopOrderStatus.CANCELLED:\n if stop_order.stop_orderid in self.active_orderids:\n self.active_orderids.remove(stop_order.stop_orderid)\n # 触发的本地停止单，停止单移除，限价单加入\n if stop_order.status == StopOrderStatus.TRIGGERED:\n if stop_order.stop_orderid in self.active_orderids:\n self.active_orderids.remove(stop_order.stop_orderid)\n self.active_orderids.extend(stop_order.vt_orderids)\n # 撤掉其他停止单\n for other_orderids in self.active_orderids:\n if other_orderids.startswith(STOPORDER_PREFIX):\n self.cancel_order(other_orderids)","sub_path":"vnpy/app/cta_strategy/strategies/tsmyo_template.py","file_name":"tsmyo_template.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"67654317","text":"# coding=utf-8\n\n\"\"\"\nCollect basic metrics from cassandra using nodetool (yuk!)\n\"\"\"\n\nimport subprocess\nimport re\n\nimport diamond.collector\n\n\nclass CassandraCollector(diamond.collector.Collector):\n def get_default_config(self):\n config = super(CassandraCollector, self).get_default_config()\n config.update({\n 'path': 'cassandra',\n })\n return config\n\n def publish_dict(self, prefix, metrics):\n for name, value in metrics.iteritems():\n self.publish('.'.join((prefix, name)), value)\n\n def _nodetool(self, *args):\n command = ['/usr/bin/nodetool']\n command.extend(args)\n self.log.debug('Running %r' % command)\n return subprocess.check_output(command).split('\\n')\n\n def nodetool_gcstats(self):\n output = self._nodetool('gcstats')\n names = ['interval', 'elapsed_max', 'elapsed_total', 'elapsed_stdev',\n 'reclaimed_mb', 'collections']\n values = re.split(r'\\s+', output[1].strip())\n return dict(zip(names, values))\n\n def nodetool_compactionstats(self):\n output = self._nodetool('compactionstats')\n metrics = {}\n for line in output:\n m = re.search('pending tasks: (\\d+)', line)\n if m:\n metrics['pending'] = m.group(1)\n return metrics\n\n def nodetool_status(self):\n output = self._nodetool('status')\n status_map = {\n 'U': 'up', 'D': 'down',\n 'N': 'normal', 'L': 'leaving',\n 'J': 'joining', 'M': 'moving',\n }\n metrics = {}\n in_status = False\n for line in output:\n if line.startswith('-- '):\n in_status = True\n if not in_status:\n continue\n if re.match('^[UD][NLJM] ', line):\n node_updown = status_map[line[0]]\n node_status = status_map[line[1]]\n metrics[node_updown] = metrics.setdefault(node_updown, 0) + 1\n metrics[node_status] = metrics.setdefault(node_status, 0) + 1\n return metrics\n\n def nodetool_tpstats(self):\n output = self._nodetool('tpstats')\n names = ['name', 'active', 'pending', 'completed',\n 'blocked', 'all_time_blocked']\n metrics = {}\n for line in output[1:]:\n parts = re.split('\\s+', line.strip())\n if len(parts) != len(names):\n continue\n tp_stat = dict(zip(names, parts))\n for key, value in tp_stat.iteritems():\n if key == 'name':\n continue\n metric_name = '.'.join((tp_stat['name'], key))\n metrics[metric_name] = value\n return metrics\n\n def collect(self):\n self.publish_dict('status', self.nodetool_status())\n self.publish_dict('compaction', self.nodetool_compactionstats())\n self.publish_dict('gc', self.nodetool_gcstats())\n self.publish_dict('tpstats', self.nodetool_tpstats())\n","sub_path":"modules/diamond/files/collector/cassandra.py","file_name":"cassandra.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"392952337","text":"import torch\nfrom torch import nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport data_helper_2018\nimport numpy as np\nimport time\nfrom sklearn.metrics import classification_report\nimport os\n\nclass TextBiLSTM(nn.Module):\n def __init__(self, config):\n super(TextBiLSTM, self).__init__()\n self.word2id = config['word2id']\n self.pretrain_emb = config['pretrain_emb']\n self.vocabulary_size = len(self.word2id) + 1\n self.embedding_size = config['embedding_size']\n self.hidden_size = config['hidden_size']\n self.layer_num = config['layer_num']\n self.do_BN = config['do_BN']\n\n self.dropout = nn.Dropout(config['dropout'])\n self.embedding = nn.Embedding(num_embeddings=self.vocabulary_size, embedding_dim=self.embedding_size)\n # self.init_embeddings()\n self.embedding.weight.data[0] = 0\n\n if self.pretrain_emb:\n self.load_pretrained_embeddings()\n\n if self.do_BN:\n self.quote_input_BN = nn.BatchNorm1d(num_features=self.embedding_size)\n self.response_input_BN = nn.BatchNorm1d(num_features=self.embedding_size)\n\n self.quote_bilstm = nn.LSTM(\n input_size=self.embedding_size,\n hidden_size=self.hidden_size,\n num_layers=self.layer_num,\n dropout=config['lstm_dropout'],\n bidirectional=True,\n batch_first=True\n )\n\n self.response_bilstm = nn.LSTM(\n input_size=self.embedding_size,\n hidden_size=self.hidden_size,\n num_layers=self.layer_num,\n dropout=config['lstm_dropout'],\n bidirectional=True,\n batch_first=True\n )\n\n def forward(self, X_q_inputs, X_r_inputs, q_init_state, r_init_state):\n\n # quote_inputs = self.dropout(self.embedding(X_q_inputs))\n # response_inputs = self.dropout(self.embedding(X_r_inputs))\n quote_inputs = self.embedding(X_q_inputs)\n response_inputs = self.embedding(X_r_inputs)\n\n if self.do_BN:\n quote_inputs = quote_inputs.transpose(1, 2).contiguous()\n quote_inputs = self.quote_input_BN(quote_inputs)\n quote_inputs = quote_inputs.transpose(1, 2)\n response_inputs = response_inputs.transpose(1, 2).contiguous()\n response_inputs = self.response_input_BN(response_inputs)\n response_inputs = response_inputs.transpose(1, 2)\n\n quote_outputs, _ = self.quote_bilstm(quote_inputs, q_init_state)\n response_outputs, _ = self.response_bilstm(response_inputs, r_init_state)\n\n return quote_outputs, response_outputs\n\n def init_embeddings(self, init_range=0.1):\n self.embedding.weight.data.uniform_(-init_range, init_range)\n\n def init_hidden(self, batch_size):\n weight = next(self.parameters()).data\n return (Variable(weight.new(self.layer_num * 2, batch_size, self.hidden_size).zero_()),\n Variable(weight.new(self.layer_num * 2, batch_size, self.hidden_size).zero_()))\n\n def load_pretrained_embeddings(self, embeddingFileName='../qrPair/data/glove.840B.300d.txt'):\n hit = 0\n with open(embeddingFileName, 'r') as fr:\n lines = fr.readlines()\n for line in lines:\n line = line.strip().split(' ')\n word = line[0]\n embedding = line[1:]\n if word in self.word2id.index:\n wordIndex = self.word2id[word]\n embeddingArray = np.fromstring('\\n'.join(embedding), dtype=np.float32, sep='\\n')\n self.embedding.weight.data[wordIndex].copy_(torch.from_numpy(embeddingArray))\n hit += 1\n hitRate = float(hit) / self.vocabulary_size\n print('PreTrain Embedding hitRate={}'.format(hitRate))\n\nclass AttentionModel(nn.Module):\n def __init__(self, config):\n super(AttentionModel, self).__init__()\n self.bilstm = TextBiLSTM(config)\n self.dropout = nn.Dropout(config['dropout'])\n self.attention_mechanism = config['attention_mechanism']\n self.hidden_size = config['hidden_size']\n self.do_BN = config['do_BN']\n\n if self.do_BN:\n if self.attention_mechanism['Type'] != 'both_concat':\n self.quote_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2)\n self.response_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2)\n else:\n self.quote_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2 * 2)\n self.response_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2 * 2)\n\n if self.attention_mechanism['Type'] != 'None':\n\n if self.attention_mechanism['Type'][:4] == 'both':\n self.quote2quote_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n self.response2quote_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n self.response2response_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n self.quote2response_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n elif self.attention_mechanism['Type'] == 'share':\n self.share_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n else:\n self.quote_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n self.response_attention_layer = nn.Linear(in_features=self.hidden_size * 2, out_features=1)\n # init_range = 0.1\n # self.quote_attention_layer.weight.data.uniform_(-init_range, init_range)\n # self.quote_attention_layer.bias.data.fill_(init_range)\n # self.response_attention_layer.weight.data.uniform_(-init_range, init_range)\n # self.response_attention_layer.bias.data.fill_(init_range)\n\n def forward(self, X_q_inputs, X_r_inputs, q_init_state, r_init_state):\n quote_outputs, response_outputs = self.bilstm.forward(X_q_inputs, X_r_inputs, q_init_state, r_init_state)\n\n if self.attention_mechanism['Type'] != 'None':\n ActFunc = self.attention_mechanism['ActFunc']\n if self.attention_mechanism['Type'] == 'self_attention':\n quote_outputs = self.attention_layer(self.quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=quote_outputs, ActFunc=ActFunc)\n response_outputs = self.attention_layer(self.response_attention_layer, bilstm_outputs=response_outputs, attention_source=response_outputs, ActFunc=ActFunc)\n if self.attention_mechanism['Type'] == 'cross_attention':\n _quote_outputs = self.attention_layer(self.quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=response_outputs, ActFunc=ActFunc)\n _response_outputs = self.attention_layer(self.response_attention_layer, bilstm_outputs=response_outputs, attention_source=quote_outputs, ActFunc=ActFunc)\n quote_outputs = _quote_outputs\n response_outputs = _response_outputs\n if self.attention_mechanism['Type'] == 'both_sum':\n _quote_outputs = self.attention_layer(self.quote2quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=quote_outputs, ActFunc=ActFunc) + \\\n self.attention_layer(self.response2quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=response_outputs, ActFunc=ActFunc)\n _response_outputs = self.attention_layer(self.response2response_attention_layer, bilstm_outputs=response_outputs, attention_source=response_outputs, ActFunc=ActFunc) + \\\n self.attention_layer(self.quote2response_attention_layer, bilstm_outputs=response_outputs, attention_source=quote_outputs, ActFunc=ActFunc)\n quote_outputs = _quote_outputs\n response_outputs = _response_outputs\n if self.attention_mechanism['Type'] == 'both_concat':\n _quote_outputs = torch.cat((self.attention_layer(self.quote2quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=quote_outputs, ActFunc=ActFunc),\n self.attention_layer(self.response2quote_attention_layer, bilstm_outputs=quote_outputs, attention_source=response_outputs, ActFunc=ActFunc)), dim=1)\n _response_outputs = torch.cat((self.attention_layer(self.response2response_attention_layer, bilstm_outputs=response_outputs, attention_source=response_outputs, ActFunc=ActFunc), \n self.attention_layer(self.quote2response_attention_layer, bilstm_outputs=response_outputs, attention_source=quote_outputs, ActFunc=ActFunc)), dim=1)\n quote_outputs = _quote_outputs\n response_outputs = _response_outputs\n if self.attention_mechanism['Type'] == 'share':\n _quote_outputs = self.attention_layer(self.share_attention_layer, bilstm_outputs=quote_outputs, attention_source=response_outputs, ActFunc=ActFunc)\n _response_outputs = self.attention_layer(self.share_attention_layer, bilstm_outputs=response_outputs, attention_source=quote_outputs, ActFunc=ActFunc)\n quote_outputs = _quote_outputs\n response_outputs = _response_outputs\n else:\n quote_outputs = torch.sum(quote_outputs, dim=1)\n response_outputs = torch.sum(response_outputs, dim=1)\n # quote_outputs = torch.mean(quote_outputs, dim=1)\n # response_outputs = torch.mean(response_outputs, dim=1)\n if self.do_BN:\n quote_outputs = self.quote_output_BN(quote_outputs)\n response_outputs = self.response_output_BN(response_outputs)\n return quote_outputs, response_outputs\n\n def attention_layer(self, layer, bilstm_outputs, attention_source, ActFunc):\n\n attention_inputs = attention_source.contiguous().view(-1, self.hidden_size * 2) # [32, 150, 256] -> [32*150, 256]\n # attention_logits = ActFunc(layer(self.dropout(attention_inputs))) # [32*150, 256] -> [32*150, 1]\n attention_logits = ActFunc(layer(attention_inputs)) # [32*150, 256] -> [32*150, 1]\n attention_logits = attention_logits.view(-1, attention_source.size()[1], 1) # [32*150, 1] -> [32, 150, 1]\n attention_logits = attention_logits.transpose(1, 2).contiguous() # [32, 150, 1] -> [32, 1, 150]\n attention_logits = attention_logits.view(-1, attention_source.size()[1]) # [32, 1, 150] -> [32*1, 150]\n attention_signals = F.softmax(attention_logits, dim=1).view(-1, 1, attention_source.size()[1]) # [32*1, 150] -> [32, 1, 150]\n\n return torch.bmm(attention_signals, bilstm_outputs).view(-1, bilstm_outputs.size()[2])\n \n # attention_inputs = attention_source.contiguous().view(-1, self.hidden_size * 2)\n # # attention_logits = layer(self.dropout(attention_inputs)).view(-1, attention_source.size()[1])\n # attention_logits = layer(attention_inputs).view(-1, attention_source.size()[1])\n # attention_signals = F.softmax(ActFunc(attention_logits), dim=1).view(-1, attention_source.size()[1], 1)\n # outputs = attention_signals * bilstm_outputs\n # outputs = torch.sum(outputs, dim=1)\n # # outputs = torch.mean(outputs, dim=1)\n # return outputs\n\n def init_hidden(self, batch_size):\n return self.bilstm.init_hidden(batch_size)\n\nclass Classifier(nn.Module):\n def __init__(self, config):\n super(Classifier, self).__init__()\n self.model = AttentionModel(config)\n self.dropout = nn.Dropout(config['dropout'])\n self.hidden_size = config['hidden_size']\n self.attention_mechanism = config['attention_mechanism']\n self.do_BN = config['do_BN']\n self.class_num = config['class_num']\n \n if self.attention_mechanism['Type'] != 'both_concat':\n self.out = nn.Linear(in_features=self.hidden_size * 2 * 2, out_features=self.class_num)\n else:\n self.out = nn.Linear(in_features=self.hidden_size * 2 * 2 * 2, out_features=self.class_num)\n\n # self.init_weights()\n\n if self.do_BN:\n # self.concat_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2 * 2)\n if self.attention_mechanism['Type'] != 'both_concat':\n self.concat_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2 * 2)\n else:\n self.concat_output_BN = nn.BatchNorm1d(num_features=self.hidden_size * 2 * 2 * 2)\n\n def init_weights(self, init_range=0.1):\n self.out.weight.data.uniform_(-init_range, init_range)\n self.out.bias.data.fill_(init_range)\n\n def forward(self, X_q_inputs, X_r_inputs, q_init_state, r_init_state):\n quote_outputs, response_outputs = self.model.forward(X_q_inputs, X_r_inputs, q_init_state, r_init_state)\n concat_outputs = torch.cat((quote_outputs, response_outputs), dim=1)\n\n # Siamese\n # response_outputs_siamese, quote_outputs_siamese = self.model.forward(X_r_inputs, X_q_inputs, r_init_state, q_init_state)\n # concat_outputs_siamese = torch.cat((quote_outputs_siamese, response_outputs_siamese), dim=1)\n # Siamese\n\n if self.do_BN:\n concat_outputs = self.concat_output_BN(concat_outputs)\n # concat_outputs_siamese = self.concat_output_BN(concat_outputs_siamese)\n output = self.out(self.dropout(concat_outputs))\n # return output, concat_outputs, concat_outputs_siamese\n return output, quote_outputs, response_outputs\n\n def init_hidden(self, batch_size):\n return self.model.init_hidden(batch_size)\n\nclass ContrastiveLoss(nn.Module):\n\n def __init__(self, margin=2.0):\n super(ContrastiveLoss, self).__init__()\n self.margin = margin\n\n def forward(self, output1, output2, label):\n label = label.float().view(-1, 1)\n euclidean_distance = F.pairwise_distance(output1, output2)\n loss_contrastive = torch.mean(label * torch.pow(euclidean_distance, 2) + (1-label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n return loss_contrastive\n\ndef train_epoch(model, data_train):\n model.train()\n \n tr_batch_num = int(data_train.y.shape[0] / tr_batch_size)\n display_batch = int(tr_batch_num / display_num)\n\n start_time = time.time()\n _accs = 0.0\n _costs = 0.0\n show_accs = 0.0\n show_costs = 0.0\n\n for batch in range(tr_batch_num):\n model.train()\n\n X_q_batch, X_r_batch, y_batch = data_train.next_batch(tr_batch_size)\n\n X_q_batch = Variable(torch.LongTensor(X_q_batch)).cuda() if USE_GPU else Variable(torch.LongTensor(X_q_batch))\n X_r_batch = Variable(torch.LongTensor(X_r_batch)).cuda() if USE_GPU else Variable(torch.LongTensor(X_r_batch))\n\n y_batch = Variable(torch.LongTensor(y_batch)).cuda() if USE_GPU else Variable(torch.LongTensor(y_batch))\n\n q_init_state = model.init_hidden(y_batch.size()[0])\n r_init_state = model.init_hidden(y_batch.size()[0])\n\n output, quote_outputs, response_outputs = model(X_q_batch, X_r_batch, q_init_state, r_init_state)\n _cost = loss_func(output, y_batch) + 0.1 * siamese_loss(quote_outputs, response_outputs, y_batch)\n # _cost = loss_func(output, y_batch) + siamese_loss(quote_outputs, response_outputs, y_batch)\n y_pred = torch.max(output, 1)[1]\n\n _acc = sum(y_pred.data.cpu() == y_batch.data.cpu()) / float(y_batch.size(0)) if USE_GPU else sum(y_pred.data == y_batch.data) / float(y_batch.size(0))\n\n _accs += _acc\n _costs += _cost.data[0]\n show_accs += _acc\n show_costs += _cost.data[0]\n\n optimizer.zero_grad()\n _cost.backward()\n nn.utils.clip_grad_norm(model.parameters(), max_norm=max_grad_norm)\n optimizer.step()\n\n if (batch + 1) % display_batch == 0:\n valid_acc, valid_cost, _ = test_epoch(model, data_test)\n print('\\ttraining acc={:.6f}, cost={:.6f}; valid acc={:.6f}, cost={:.6f}'.format(show_accs / display_batch, show_costs / display_batch, valid_acc, valid_cost))\n show_accs = 0.0\n show_costs = 0.0\n\n mean_acc = _accs / tr_batch_num\n mean_cost = _costs / tr_batch_num\n\n print('Epoch training {}, acc={:.6f}, cost={:.6f}, speed={:.6f} s/epoch'.format(data_train.y.shape[0], mean_acc, mean_cost, time.time() - start_time))\n test_acc, test_cost, test_cr = test_epoch(model, data_test)\n print('**Test {}, acc={:.6f}, cost={:.6f}\\n'.format(data_test.y.shape[0], test_acc, test_cost))\n print(test_cr)\n\ndef test_epoch(model, dataset):\n model.eval()\n data_size = len(dataset.y)\n _batch_size = data_size\n # _batch_size = int(data_size / 2)\n batch_num = int(data_size / _batch_size)\n _accs = 0.0\n _costs = 0.0\n _pred = []\n _true = []\n for i in range(batch_num):\n\n X_q_batch, X_r_batch, y_batch = dataset.next_batch(_batch_size)\n\n X_q_batch = Variable(torch.LongTensor(X_q_batch), volatile=True).cuda() if USE_GPU else Variable(torch.LongTensor(X_q_batch), volatile=True)\n X_r_batch = Variable(torch.LongTensor(X_r_batch), volatile=True).cuda() if USE_GPU else Variable(torch.LongTensor(X_r_batch), volatile=True)\n\n y_batch = Variable(torch.LongTensor(y_batch), volatile=True).cuda() if USE_GPU else Variable(torch.LongTensor(y_batch), volatile=True)\n\n q_init_state = model.init_hidden(y_batch.size()[0])\n r_init_state = model.init_hidden(y_batch.size()[0])\n\n test_output, quote_outputs, response_outputs = model(X_q_batch, X_r_batch, q_init_state, r_init_state)\n _cost = loss_func(test_output, y_batch) + 0.1 * siamese_loss(quote_outputs, response_outputs, y_batch)\n # _cost = loss_func(test_output, y_batch) + siamese_loss(quote_outputs, response_outputs, y_batch)\n\n y_pred = torch.max(test_output, 1)[1]\n _acc = sum(y_pred.data.cpu() == y_batch.data.cpu()) / float(y_batch.size(0)) if USE_GPU else sum(y_pred.data == y_batch.data) / float(y_batch.size(0))\n\n if i == 0:\n _pred = y_pred.data.cpu().numpy() if USE_GPU else y_pred.data.numpy()\n _true = y_batch.data.cpu().numpy() if USE_GPU else y_batch.data.numpy()\n else:\n _pred = np.concatenate((_pred, y_pred.data.cpu().numpy())) if USE_GPU else np.concatenate((_pred, y_pred.data.numpy()))\n _true = np.concatenate((_true, y_batch.data.cpu().numpy())) if USE_GPU else np.concatenate((_true, y_batch.data.numpy()))\n _accs += _acc\n _costs += _cost.data[0]\n mean_acc = _accs / batch_num\n mean_cost = _costs / batch_num\n cr = classification_report(y_true=_true, y_pred=_pred, target_names=['disagree', 'agree'], digits=4)\n return mean_acc, mean_cost, cr\n\nif __name__ == '__main__':\n\n USE_GPU = True\n if USE_GPU:\n os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n print('Using GPU: {}...'.format(os.environ['CUDA_VISIBLE_DEVICES']))\n \n # attention_mechanism_config = {'Type': 'None', 'ActFunc': F.tanh} # gpu: 0\n # attention_mechanism_config = {'Type': 'self_attention', 'ActFunc': F.tanh} # gpu: 1\n attention_mechanism_config = {'Type': 'cross_attention', 'ActFunc': F.tanh} # gpu: 2\n # attention_mechanism_config = {'Type': 'both_sum', 'ActFunc': F.tanh} # gpu: 2\n # attention_mechanism_config = {'Type': 'both_concat', 'ActFunc': F.tanh} # gpu: 2\n # attention_mechanism_config = {'Type': 'share', 'ActFunc': F.tanh} # gpu: 2\n\n # data_train, data_test, word2id = data_helper_2018.getDataSet(task='disagree_agree', topic='evolution', max_len=64, resampleFlag=False)\n data_train, data_test, word2id = data_helper_2018.getDataSet(task='disagree_agree', topic=None, max_len=64, resampleFlag=False)\n # data_train, data_test, word2id = data_helper_2018.getDataSet(task='debatepedia', topic=None, max_len=64, resampleFlag=False)\n\n # import data_helper_2018_cd\n # data_train, data_test, word2id = data_helper_2018_cd.getDataSet(task='create_debate', max_len=64)\n # data_train, data_test, word2id = data_helper_2018_cd.getDataSet(task='create_debate_noise', max_len=64)\n # data_train, data_test, word2id = data_helper_2018_cd.getDataSet(task='create_debate', max_len=80)\n\n config = {\n 'word2id': word2id, 'pretrain_emb': True, 'class_num': 2,\n 'embedding_size': 300, 'hidden_size': 128, 'layer_num': 2, 'dropout': 0.3, 'lstm_dropout': 0.5,\n 'do_BN': True, 'attention_mechanism': attention_mechanism_config}\n \n model = Classifier(config)\n\n max_grad_norm = 0.1\n\n for name, para in model.named_parameters():\n if para.requires_grad == True:\n print(name)\n if USE_GPU:\n model.cuda()\n\n lr = 1e-3\n optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n loss_func = nn.CrossEntropyLoss()\n siamese_loss = ContrastiveLoss()\n\n tr_batch_size = 32\n max_epoch = 30\n max_max_epoch = 100\n display_num = 5\n\n for epoch in range(1, max_max_epoch+1):\n if epoch > max_epoch:\n for param_group in optimizer.param_groups:\n param_group['lr'] *= 0.97\n lr = optimizer.param_groups[0]['lr']\n print('EPOCH {}, lr={}'.format(epoch, lr))\n train_epoch(model, data_train)\n\n\n\n\n\n\n\n","sub_path":"qrPair_2018/pytorch_bilstm_2018_siamese.py","file_name":"pytorch_bilstm_2018_siamese.py","file_ext":"py","file_size_in_byte":21470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"392995848","text":"from enum import Enum\n\nfrom common.sc_keynodes import ScKeynodes\nfrom common.sc_set import ScSet\nfrom common.sc_event import ScEventParams\n\nfrom sc import *\nfrom scb import *\n\n\nclass ScAgent:\n\n # keynodes\n kCmdInitiated = 'command_initiated'\n kCmdFinished = 'command_finished'\n kNrelResult = 'nrel_result'\n\n kResultNo = 'sc_result_no'\n kResultUnknown = 'sc_result_unknown'\n kResultError = 'sc_result_error'\n kResultErrorInvalidParams = 'sc_result_error_invalid_params'\n kResultErrorInvalidType = 'sc_result_error_invalid_type'\n kResultErrorIO = 'sc_result_error_io'\n kResultInvalidState = 'sc_result_invalid_state'\n kResultErrorNotFound = 'sc_result_error_not_found'\n kResultErrorNoWriteRights = 'sc_result_error_no_write_rights'\n kResultErrorNoReadRights = 'sc_result_error_no_read_rights'\n\n def __init__(self, module):\n self.module = module\n self.evt = None\n self.keynodes = ScKeynodes(module.ctx)\n\n def Register(self, addr, evt_type):\n \"\"\"Register this agent to a specified event\n addr - ScAddr of sc-element to subscribe event\n evt_type - on of ScPythonEventType values (type of event)\n \"\"\"\n assert self.evt == None\n self.evt = self.module.events.CreateEventInternal(\n addr, evt_type, self._run)\n \n self.module.log.info(self.__class__.__name__ + ' registered')\n\n def Unregister(self):\n self.module.events.DestroyEvent(self.evt)\n self.evt = None\n\n self.module.log.info(self.__class__.__name__ + ' unregistered')\n\n def RunImpl(self, evt: ScEventParams) -> ScResult:\n \"\"\"Should be override and do agent logic.\n It should return one of ScAgent.Status values\n\n evt - ScEventParams instance\n \"\"\"\n return ScResult.Error\n\n def CheckImpl(self, evt: ScEventParams):\n \"\"\"This function can be overrided to check any conditions before run.\n If this function returns True, then RunImpl should be called; \n otherwise it woudn't be run\n \"\"\"\n return True\n\n # --- Internal usage methods ---\n def _run(self, evt: ScEventParams):\n \"\"\"Just for internal usage\n \"\"\"\n if self.CheckImpl(evt):\n self.module.log.info(self.__class__.__name__ + ' emited')\n result = self.RunImpl(evt)\n if result != ScResult.Ok:\n self.module.log.warning(self.__class__.__name__ + ' finished with error')\n else:\n self.module.log.info(self.__class__.__name__ + ' finished')\n\n\nclass ScAgentCommand(ScAgent):\n \"\"\"This type of agents initiated with command_initiated set.\n It check if initiated command class is equal to specified one,\n then run it. You doesn't need to call register function for this\n type of agents\n \"\"\"\n\n def __init__(self, module, cmd_class_addr):\n ScAgent.__init__(self, module)\n self.cmd_class = cmd_class_addr\n self.cmd_addr = ScAddr()\n self.result_set = None\n\n self.Register(\n self.keynodes[ScAgent.kCmdInitiated],\n ScPythonEventType.AddOutputEdge)\n\n def CheckImpl(self, evt):\n \"\"\"Check if type of initiated command is equal to specified one\n \"\"\"\n return self.module.ctx.HelperCheckEdge(\n self.cmd_class,\n evt.other_addr,\n ScType.EdgeAccessConstPosPerm)\n\n def RunImpl(self, evt):\n self.cmd_addr = evt.other_addr\n assert self.cmd_addr.IsValid()\n\n # change state to a progress\n progress_edge = evt.edge_addr\n\n def change_progress(state: ScAddr):\n self.module.ctx.DeleteElement(progress_edge)\n self.module.ctx.CreateEdge(ScType.EdgeAccessConstPosPerm, state, self.cmd_addr)\n\n change_progress(ScKeynodes.kCommandProgressdAddr())\n\n # create result structure\n templ = ScTemplate()\n templ.TripleWithRelation(\n self.cmd_addr,\n ScType.EdgeDCommonVar,\n ScType.NodeVarStruct >> '_result',\n ScType.EdgeAccessVarPosPerm,\n self.keynodes[ScAgent.kNrelResult])\n\n gen_res = self.module.ctx.HelperGenTemplate(templ, ScTemplateGenParams())\n assert gen_res.Size() > 0\n\n res_addr = gen_res['_result']\n self.result_set = ScSet(self.module.ctx, res_addr)\n\n # run implementation of command\n result = self.DoCommand()\n\n # generate result type\n self.module.ctx.CreateEdge(ScType.EdgeAccessConstPosPerm, ScKeynodes.GetResultCodeAddr(result), res_addr)\n change_progress(ScKeynodes.kCommandFinishedAddr())\n\n return result\n\n def DoCommand(self) -> ScResult:\n \"\"\"Should be overrided.\n This method calls to run command implementation\n Should return value like RunImpl\n \"\"\"\n return ScResult.No\n\n def GetParam(self, index):\n \"\"\"Return parameter of command by specified index.\n Index value starts from 1. This function trying to find\n sc-element in command structure with attribute `rrel_`\n \"\"\"\n templ = ScTemplate()\n templ.TripleWithRelation(\n self.cmd_addr,\n ScType.EdgeAccessVarPosPerm,\n ScType.Unknown >> '_el',\n ScType.EdgeAccessVarPosPerm,\n self.keynodes['rrel_{}'.format(index)])\n\n search_res = self.module.ctx.HelperSearchTemplate(templ)\n if search_res.Size() == 0:\n return ScAddr()\n\n return search_res[0]['_el']\n\n @staticmethod\n def CreateCommand(ctx: ScMemoryContext, cmd_class_addr: ScAddr, params: [ScAddr]) -> ScAddr:\n return ScAgentCommandImpl.CreateCommand(ctx, cmd_class_addr, params)\n\n @staticmethod\n def RunCommand(ctx: ScMemoryContext, cmd_addr: ScAddr) -> bool:\n return ScAgentCommandImpl.RunCommand(ctx, cmd_addr)\n\n @staticmethod\n def RunCommandWait(ctx: ScMemoryContext, cmd_addr: ScAddr, wait_timeout_ms: int) -> bool:\n return ScAgentCommandImpl.RunCommandWait(ctx, cmd_addr, wait_timeout_ms)\n\n @staticmethod\n def GetCommandResultAddr(ctx: ScMemoryContext, cmd_addr: ScAddr) -> ScAddr:\n return ScAgentCommandImpl.GetCommandResultAddr(ctx, cmd_addr)\n\n # --- internal functions ---\n def _kb_resolve_status_addr(self, status):\n if status == ScAgent.Status.SC_RESULT_ERROR:\n sc_result_ok\n\n return ScAddr()\n\n def _kb_generate_status(self, status):\n \"\"\"Append result structure to a special set depending on a status\n \"\"\"\n pass\n","sub_path":"sc-kpm/sc-python/services/common/sc_agent.py","file_name":"sc_agent.py","file_ext":"py","file_size_in_byte":6032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"120736415","text":"import _plotly_utils.basevalidators\n\n\nclass InsidetextorientationValidator(_plotly_utils.basevalidators.EnumeratedValidator):\n def __init__(\n self, plotly_name=\"insidetextorientation\", parent_name=\"sunburst\", **kwargs\n ):\n super(InsidetextorientationValidator, self).__init__(\n plotly_name=plotly_name,\n parent_name=parent_name,\n edit_type=kwargs.pop(\"edit_type\", \"plot\"),\n values=kwargs.pop(\"values\", [\"horizontal\", \"radial\", \"tangential\", \"auto\"]),\n **kwargs,\n )\n","sub_path":"packages/python/plotly/plotly/validators/sunburst/_insidetextorientation.py","file_name":"_insidetextorientation.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"649661224","text":"from permission import registry\nfrom permission import PermissionHandler\n\nfrom models import YourModel\n\nclass FilizverPermissionHandler(PermissionHandler):\n \"\"\"Permission handler class for ``YourModel``. Similar with AdminSite\"\"\"\n def has_perm(self, user_obj, perm, obj=None):\n \"\"\"this is called for checking permission of the model.\"\"\"\n if user_obj.is_authenticated():\n if perm == 'yourapp.add_yourmodel':\n # Authenticated user has add permissions of this model\n return True\n elif obj and obj.author == user_obj:\n # Otherwise (change/delete) user must be an author\n return True\n # User doesn't have permission of ``perm``\n return False\n\n# register this ``YourModelPermissionHandler`` with ``YourModel``\n#registry.register(YourModel, YourModelPermissionHandler)","sub_path":"filizver/_apps/core/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"639593478","text":"#!/usr/local/bin/python3\n# coding: utf-8\nfrom copy import deepcopy\n\nfrom .lib.util import LOG_FILE_PATH\n\nTEMPLATE = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {\n \"default\": {\n \"format\": \"[%(asctime)s] %(levelname)s %(message)s\",\n \"datefmt\": \"%Y-%m-%d %H:%M:%S\",\n },\n \"long\": {\n \"format\": \"[%(asctime)s] %(levelname)s - \" +\n \"%(filename)s#%(funcName)s:%(lineno)d: %(message)s\",\n \"datefmt\": \"%Y-%m-%d %H:%M:%S\",\n },\n },\n \"handlers\": {\n \"console\": {\n \"level\": \"INFO\",\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"default\",\n \"stream\": \"ext://sys.stderr\"\n },\n \"file\": {\n \"level\": \"INFO\",\n \"class\": \"logging.FileHandler\",\n \"formatter\": \"default\",\n \"filename\": str(LOG_FILE_PATH),\n },\n },\n \"root\": {\n \"handlers\": [\"console\", \"file\"],\n \"level\": \"INFO\",\n },\n}\n\nwsgi_info = deepcopy(TEMPLATE)\nwsgi_info[\"root\"][\"handlers\"] = [\"file\"]\nwsgi_debug = deepcopy(TEMPLATE)\nwsgi_debug[\"handlers\"][\"file\"][\"level\"] = \"DEBUG\"\nwsgi_debug[\"handlers\"][\"file\"][\"formatter\"] = \"long\"\nwsgi_debug[\"root\"][\"handlers\"] = [\"file\"]\nwsgi_debug[\"root\"][\"level\"] = \"DEBUG\"\n\nlocal_info = deepcopy(TEMPLATE)\nlocal_debug = deepcopy(TEMPLATE)\nlocal_debug[\"handlers\"][\"console\"][\"level\"] = \"DEBUG\"\nlocal_debug[\"handlers\"][\"console\"][\"formatter\"] = \"long\"\nlocal_debug[\"handlers\"][\"file\"][\"level\"] = \"DEBUG\"\nlocal_debug[\"handlers\"][\"file\"][\"formatter\"] = \"long\"\nlocal_debug[\"root\"][\"level\"] = \"DEBUG\"\n","sub_path":"src/app/logging_config.py","file_name":"logging_config.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"285531510","text":"import re\n\nurl_param_re = re.compile(\"/{([0-9a-zA-Z_]+)}/?\")\n\n\nclass UrlParamParser:\n def __init__(self, route):\n self.route = route\n self.fields = []\n\n self._build()\n\n def _build(self):\n url_matcher = self.route\n\n match = url_param_re.search(self.route)\n if match:\n for grp in match.groups():\n self.fields.append(grp)\n\n url_matcher = url_matcher.replace(\"{\" + grp + \"}\", \"(.+)\")\n\n self.matcher = re.compile(\"^\" + url_matcher + \"/?$\")\n\n def match(self, route):\n return self.matcher.match(route) is not None\n\n def resolve_params(self, route):\n params = {}\n\n match = self.matcher.search(route)\n if not match:\n return {}\n\n groups = list(match.groups())\n\n if len(groups) != len(self.fields):\n raise Exception(\n f\"Field and group counts do not match while resolving params. \"\n \"Groups: {len(groups)} Fields: {len(self.fields)}\"\n )\n\n for grp, field in zip(groups, self.fields):\n params[field] = grp\n\n return params\n","sub_path":"sydney/server/url_param_parser.py","file_name":"url_param_parser.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"651909051","text":"import sys\nimport random\nimport time\nimport cv2\nimport operator\nimport numpy as np\nfrom sim import *\nimport tflearn\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.estimator import regression\nfrom statistics import mean, median\nfrom collections import Counter\n\ns = Simulator(512, 512)\n\ndef neural_network_model(input_size):\n\n network = input_data(shape=[None, input_size, 1], name=\"input\")\n\n network = fully_connected(network, 128, activation=\"relu\")\n network = dropout(network, 0.8)\n\n network = fully_connected(network, 256, activation=\"relu\")\n network = dropout(network, 0.8)\n\n network = fully_connected(network, 512, activation=\"relu\")\n network = dropout(network, 0.8)\n\n network = fully_connected(network, 256, activation=\"relu\")\n network = dropout(network, 0.8)\n\n network = fully_connected(network, 128, activation=\"relu\")\n network = dropout(network, 0.8)\n\n network = fully_connected(network, 2, activation=\"softmax\")\n network = regression(network, optimizer=\"adam\", learning_rate = 1e-3, loss=\"categorical_crossentropy\", name=\"targets\")\n model = tflearn.DNN(network, tensorboard_dir=\"j_log\")\n\n model.load('jm.model')\n\n return model\n\n# try:\n# for i in range(256):\n# model = neural_network_model(i)\n# print(i)\n# except:\n# pass\n\nfor i in range(256):\n try:\n model = neural_network_model(i)\n except:\n continue\n\n\nscores = []\nchoices = []\n\nfor each_game in range(5):\n score = 0\n game_memory = []\n prev_obs = []\n steps = 0\n s.reset(view=True)\n for i in range(1000):\n \n if len(prev_obs) == 0: # prev_obs\n action = s.randomActionSampler()\n else:\n action = np.argmax(model.predict(prev_obs.reshape(-1,len(prev_obs),1))[0])\n \n choices.append(action)\n steps += 1\n\n if action == 0:\n s.emulateKeyPress(\"w\", view=True)\n print(\"w\")\n elif action == 1:\n s.emulateKeyPress(\"a\", view=True)\n print(\"a\")\n elif action == 2:\n s.emulateKeyPress(\"s\", view=True)\n print(\"s\")\n elif action == 3:\n s.emulateKeyPress(\"d\", view=True)\n print(\"d\")\n\n new_observation = s.getObservation()\n prev_obs = new_observation\n game_memory.append([new_observation, action])\n score += s.getScore()\n # cv2.waitKey(0)\n # time.sleep(.1)\n if steps > 150:\n s.reset()\n break\n \n if s.getDoneStatus():\n break\n \n scores.append(score)\n\nprint('Average Score:',sum(scores)/len(scores))\n# print('choice 1:{} choice 0:{}'.format(choices.count(1)/len(choices),choices.count(0)/len(choices)))\nprint(\"choices: {}\".format(choices))\nprint(\"choice 0 (W): {}\".format(choices.count(0)))\nprint(\"choice 1 (A): {}\".format(choices.count(1)))\nprint(\"choice 2 (S): {}\".format(choices.count(2)))\nprint(\"choice 3 (D): {}\".format(choices.count(3)))\nprint(score_requirement)\n","sub_path":"testModel.py","file_name":"testModel.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"472596601","text":"#!/usr/bin/env python\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\n\nimport sys\nfrom datetime import datetime\nfrom humanize import naturalsize\nfrom collections import OrderedDict, deque\nfrom pprint import pformat\nfrom semantic_version import Version as semver\n\ntry:\n import ConfigParser as configparser\nexcept ImportError:\n import configparser\n\nif sys.version_info[0] == 2:\n reload(sys)\n sys.setdefaultencoding('utf-8')\n\ndef info(*objs):\n print(\"INFO:\", *objs, file=sys.stderr)\n\ndef warning(*objs):\n print(\"WARNING:\", *objs, file=sys.stderr)\n\n\ndef debug(*objs):\n print(\"DEBUG:\\n\", *objs, file=sys.stderr)\n\n\ndef get_date(date_string, uts=False):\n if not uts:\n return datetime.strptime(date_string, \"%a %b %d %H:%M:%S %Y\")\n else:\n return datetime.fromtimestamp(float(date_string))\n\n\ndef get_str(s):\n if sys.version_info[0] == 2 and s is not None:\n return s.decode('ISO-8859-1')\n else:\n return s\n\n\n\nclass ConfigLoader(object):\n\n def __init__(self, config_file):\n self.settings = {}\n self.vpns = OrderedDict()\n config = configparser.RawConfigParser()\n contents = config.read(config_file)\n\n if not contents and config_file == './openvpn-monitor.conf':\n warning('Config file does not exist or is unreadable: {0!s}'.format(config_file))\n if sys.prefix == '/usr':\n conf_path = '/etc/'\n else:\n conf_path = sys.prefix + '/etc/'\n config_file = conf_path + 'openvpn-monitor.conf'\n contents = config.read(config_file)\n\n if contents:\n info('Using config file: {0!s}'.format(config_file))\n fp = open(\"port_info.txt\",\"w\")\n fp.close()\n else:\n warning('Config file does not exist or is unreadable: {0!s}'.format(config_file))\n self.load_default_settings()\n\n for section in config.sections():\n if section == 'OpenVPN-Monitor':\n self.parse_global_section(config)\n else:\n self.parse_vpn_section(config, section)\n\n def load_default_settings(self):\n info('Using default settings => localhost:5555')\n self.settings = {'site': 'Default Site',\n 'geoip_data': '/usr/share/GeoIP/GeoIPCity.dat',\n 'datetime_format': '%d/%m/%Y %H:%M:%S'}\n self.vpns['Default VPN'] = {'name': 'default',\n 'host': 'localhost',\n 'port': '5555',\n 'show_disconnect': False}\n\n def parse_global_section(self, config):\n global_vars = ['site', 'logo', 'latitude', 'longitude', 'maps', 'geoip_data', 'datetime_format']\n for var in global_vars:\n try:\n self.settings[var] = config.get('OpenVPN-Monitor', var)\n except configparser.NoOptionError:\n pass\n\n def parse_vpn_section(self, config, section):\n self.vpns[section] = {}\n vpn = self.vpns[section]\n options = config.options(section)\n for option in options:\n try:\n vpn[option] = config.get(section, option)\n if vpn[option] == -1:\n warning('CONFIG: skipping {0!s}'.format(option))\n except configparser.Error as e:\n warning('CONFIG: {0!s} on option {1!s}: '.format(e, option))\n vpn[option] = None\n if 'show_disconnect' in vpn and vpn['show_disconnect'] == 'True':\n vpn['show_disconnect'] = True\n else:\n vpn['show_disconnect'] = False\n\n\n\n","sub_path":"config_loader.py","file_name":"config_loader.py","file_ext":"py","file_size_in_byte":3757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"629962462","text":"#!/usr/bin/env/python\n\n'''\nModule :mod: 'hmtk.plotting.seismicity.completeness.plot_stepp_1971' \ncreates plot to illustrate outcome of Stepp (1972) method for completeness \nanalysis\n'''\nimport os.path\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nvalid_markers = ['*', '+', '1', '2', '3', '4', '8', '<', '>', 'D', 'H', '^',\n '_', 'd', 'h', 'o', 'p', 's', 'v', 'x', '|']\n\n\ndef create_stepp_plot(model, filename, filetype='png', \n filedpi=300):\n '''\n Creates the classic Stepp (1972) plots for a completed Stepp analysis, \n and exports the figure to a file.\n :param model:\n Completed Stepp (1972) analysis as instance of :class:\n 'hmtk.seismicity.completeness.comp_stepp_1971.Stepp1971'\n :param string filename: \n Name of output file\n :param string filetype: \n Type of file (from list supported by matplotlib)\n :param int filedpi:\n Resolution (dots per inch) of output file\n '''\n if os.path.exists(filename):\n raise IOError('File already exists!')\n \n legend_list = [(str(model.magnitude_bin[iloc] + 0.01) + ' - ' + \n str(model.magnitude_bin[iloc + 1])) for iloc in range(0, \n len(model.magnitude_bin) - 1)]\n \n rgb_list = []\n marker_vals = []\n # Get marker from valid list\n while len(valid_markers) < len(model.magnitude_bin):\n valid_markers.append(valid_markers)\n \n marker_sampler = np.arange(0, len(valid_markers),1)\n np.random.shuffle(marker_sampler)\n # Get colour for each bin\n for value in range(0, len(model.magnitude_bin) - 1):\n rgb_samp = np.random.uniform(0., 1., 3)\n rgb_list.append((rgb_samp[0], rgb_samp[1], rgb_samp[2]))\n marker_vals.append(valid_markers[marker_sampler[value]])\n # Plot observed Sigma lambda\n for iloc in range(0, len(model.magnitude_bin) - 1):\n plt.loglog(model.time_values, \n model.sigma[:, iloc],\n linestyle='None',\n marker=marker_vals[iloc], \n color=rgb_list[iloc])\n \n plt.legend(legend_list)\n # Plot expected Poisson rate\n for iloc in range(0, len(model.magnitude_bin) - 1):\n plt.loglog(model.time_values, \n model.model_line[:,iloc], \n linestyle='-',\n marker='None',\n color=rgb_list[iloc])\n xmarker = model.end_year - model.completeness_table[iloc, 0]\n id0 = model.model_line[:, iloc] > 0.\n ymarker = 10.0 ** np.interp(np.log10(xmarker),\n np.log10(model.time_values[id0]),\n np.log10(model.model_line[id0, iloc]))\n plt.loglog(xmarker, ymarker, 'ks')\n plt.xlabel('Time (years)', fontsize=15)\n plt.ylabel('$\\sigma_{\\lambda} = \\sqrt{\\lambda} / \\sqrt{T}$', \n fontsize=15)\n # Save figure to file\n plt.savefig(filename, dpi=filedpi, format=filetype)\n","sub_path":"hmtk/plotting/seismicity/completeness/plot_stepp_1972.py","file_name":"plot_stepp_1972.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"257019482","text":"import logging\n\nfrom src.api_rest.model.entity.EntityRoute import EntityRoute\nfrom src.api_rest.model.planning_strategies.StochasticVRPMultiDepotStrategy import StochasticVRPMultiDepotStrategy\nfrom src.api_rest.service.ProductService import modifyProductsStock\nfrom src.api_rest.utils import toRoutes, toProducts, ProdutOperation\nfrom src.commons import MongoCollection, MongoRouteFields, RouteState\nfrom src.mongo_adapter.MongoClientSingleton import MongoClientSingleton\n\n\n### function: getRoutes ###\n\ndef getRoutes (filters = None) :\n try :\n query = {}\n if filters is not None : query = filters\n\n logging.info (\"RouteService: getRoutes: filters: [\" + str (filters) + \"]\")\n\n res = MongoClientSingleton ().getCollection (MongoCollection.ROUTE).find (query)\n\n return toRoutes (res)\n\n except Exception as exc :\n logging.error (\"RouteService: getRoutes: Error getting routes, filters: \" + str (filters))\n logging.error (\"[Exception: \" + str (exc) + \"]\")\n\n\n### function: addRoute ###\n\ndef addRoute (origin, destiny, departure, arrival, productsJson, strategy) :\n try :\n if strategy == StochasticVRPMultiDepotStrategy.STRATEGY_NAME :\n strategy = StochasticVRPMultiDepotStrategy ()\n else :\n raise Exception (\"Unknow strategy '\" + strategy + \"'\")\n\n route = EntityRoute (origin, destiny, departure, arrival, toProducts (productsJson), strategy)\n\n MongoClientSingleton ().getCollection (MongoCollection.ROUTE).insertOne (route.toJson ())\n\n modifyProductsStock (route.products, ProdutOperation.DECREMENT)\n\n return route\n\n except Exception as exc :\n logging.error (\"RouteService: addRoute: Error addind route. Params: \" + origin + \", \" + destiny + \", \" + \\\n departure + \", \" + arrival + \", \" + productsJson)\n logging.error (\"[Exception: \" + str (exc) + \"]\")\n\n\n### function: checkEditRoute ###\n\ndef checkEditRoute (route) :\n if (route.state == RouteState.PENDING): return True\n else : return False\n\n\n### function: updateRoute ###\n\ndef updateRoute (originalRoute, fieldsToUpdate) :\n MongoClientSingleton ().getCollection (MongoCollection.ROUTE).updateOneById (originalRoute.id, fieldsToUpdate)\n\n return getRoutes ({MongoRouteFields.ID : originalRoute.id}) [0]\n\n\n### function: cancelRoute ###\n\ndef cancelRoute (route) :\n try :\n route.state = RouteState.CANCELED\n\n MongoClientSingleton ().getCollection (MongoCollection.ROUTE)\\\n .updateOneFieldById (route.id, MongoRouteFields.STATE, route.state)\n\n modifyProductsStock (route.products, ProdutOperation.INCREMENT)\n\n return route\n\n except Exception as exc :\n logging.error (\"RouteService: cancelRoute: Error canceling route: \" + str (route))\n logging.error (\"[Exception: \" + str (exc) + \"]\")","sub_path":"src/api_rest/service/RouteService.py","file_name":"RouteService.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"377118952","text":"# !/usr/bin/env python\nimport os\n\ndef main():\n path = os.getcwd()\n # print(path)\n for root, dirs, files in os.walk(path):\n # print(root, dirs, files)\n for f in files:\n fname = os.path.splitext(f)\n if fname[1] == \".wav\":\n # print(f)\n os.remove(os.path.join(root,f))\n print(\"remove file \"+os.path.join(root,f)+\" success!\")\n os.system(\"pause\")\n\nif __name__ == '__main__':\n main()","sub_path":"delWAV.py","file_name":"delWAV.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"533433115","text":"#! usr/bin/python3\nimport os\n\nos.chdir(os.getenv(\"HOME\"))\n\nprint(\" Open Shell !!!\\n\")\n\n# Running the terminal\nwhile True:\n command = input(\">>>\")\n if command.split()[0] == \"cd\":\n try:\n os.chdir(command[3:])\n except FileNotFoundError:\n print(\"bash: cd: No such file or directory\")\n elif command == 'exit':\n break\n else:\n os.system(command)","sub_path":"7.shell.py","file_name":"7.shell.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"206421456","text":"from rest_framework import generics, serializers\nfrom rest_framework.response import Response\n\nfrom sundaycodelab.models import CodeLabExternal\n\n\nclass CodelabListSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = CodeLabExternal\n fields = '__all__'\n\n\nclass CodeLabExternalListView(generics.ListAPIView):\n queryset = CodeLabExternal.objects.all()\n serializer_class = CodelabListSerializer\n\n def list(self, request):\n queryset = self.get_queryset()\n serializer_class = self.get_serializer_class()\n serializer = serializer_class(queryset, many=True)\n\n page = self.paginate_queryset(queryset)\n if page is not None:\n serializer = self.get_serializer(page, many=True)\n return self.get_paginated_response(serializer.data)\n\n return Response(serializer.data)\n\n\nclass CodelabExternalDetailSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = CodeLabExternal\n exclude = ()\n depth = 1\n fields = '__all__'\n\n\nclass CodeLabExternalDetailView(generics.RetrieveAPIView):\n queryset = CodeLabExternal.objects.all()\n serializer_class = CodelabExternalDetailSerializer\n lookup_field = 'pk'\n","sub_path":"api/views/codelab.py","file_name":"codelab.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"175842728","text":"\"\"\"Test nsjail.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport subprocess\nimport unittest\nimport nsjail\n\n\nclass NsjailTest(unittest.TestCase):\n\n def setUp(self):\n nsjail.__file__ = '/'\n\n def testMinimalParameters(self):\n commands = nsjail.run(\n nsjail_bin='/bin/true',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name')\n self.assertEqual(\n commands,\n [\n [\n '/bin/true',\n '--bindmount', '/source_dir:/src',\n '--chroot', '/chroot',\n '--env', 'USER=android-build',\n '--config', '/nsjail.cfg',\n '--', '/bin/bash'\n ]\n ]\n )\n\n def testFailingJailedCommand(self):\n with self.assertRaises(subprocess.CalledProcessError):\n nsjail.run(\n nsjail_bin='/bin/false',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name')\n\n def testDist(self):\n commands = nsjail.run(\n nsjail_bin='/bin/true',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name',\n dist_dir='/dist_dir')\n self.assertEqual(\n commands,\n [\n [\n '/bin/true',\n '--bindmount', '/source_dir:/src',\n '--chroot', '/chroot',\n '--env', 'USER=android-build',\n '--config', '/nsjail.cfg',\n '--bindmount', '/dist_dir:/dist',\n '--env', 'DIST_DIR=/dist',\n '--', '/bin/bash'\n ]\n ]\n )\n\n def testBuildID(self):\n commands = nsjail.run(\n nsjail_bin='/bin/true',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name',\n build_id='0')\n self.assertEqual(\n commands,\n [\n [\n '/bin/true',\n '--bindmount', '/source_dir:/src',\n '--chroot', '/chroot',\n '--env', 'USER=android-build',\n '--config', '/nsjail.cfg',\n '--env', 'BUILD_NUMBER=0',\n '--', '/bin/bash'\n ]\n ]\n )\n\n def testMaxCPU(self):\n commands = nsjail.run(\n nsjail_bin='/bin/true',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name',\n max_cpus=1)\n self.assertEqual(\n commands,\n [\n [\n '/bin/true',\n '--bindmount', '/source_dir:/src',\n '--chroot', '/chroot',\n '--env', 'USER=android-build',\n '--config', '/nsjail.cfg',\n '--max_cpus=1',\n '--', '/bin/bash'\n ]\n ]\n )\n\n def testUserGroupID(self):\n nsjail.GROUPADD_COMMAND = '/bin/true'\n nsjail.USERADD_COMMAND = '/bin/true'\n commands = nsjail.run(\n nsjail_bin='/bin/true',\n chroot='/chroot',\n source_dir='/source_dir',\n command=['/bin/bash'],\n android_target='target_name',\n user_id=os.getuid(),\n group_id=os.getgid())\n self.assertEqual(\n commands,\n [\n [\n '/bin/true',\n 'android-build',\n '--gid', str(os.getgid()),\n ],\n [\n '/bin/true',\n '--gid', 'android-build',\n '--groups', 'sudo',\n '--uid', str(os.getuid()),\n '--create-home',\n 'android-build'\n ],\n [\n '/bin/true',\n '--bindmount', '/source_dir:/src',\n '--chroot', '/chroot',\n '--env', 'USER=android-build',\n '--config', '/nsjail.cfg',\n '--', '/bin/bash'\n ]\n ]\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"keystone/nsjail_test.py","file_name":"nsjail_test.py","file_ext":"py","file_size_in_byte":4133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"266327220","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport sys\nimport re\nimport os\n\ndef __read_file_lines(file_name):\n try:\n with open(file_name, 'r') as file:\n lines = (file.read())\n lines = lines.split('\\n') \n except:\n print('file not exist or corupted')\n sys.exit()\n return lines \n \ndef __get_input_file_name(dir_name,sub_dir):\n file_list = []\n try:\n file_dir = os.getcwd() + '\\\\' + dir_name + '\\\\' + sub_dir\n for root, dirs, files in os.walk(file_dir):\n for file in files:\n if os.path.splitext(file)[1] == '.h': \n file_list.append(os.path.join(root,file))\n return file_list\n except:\n print('file not exist or corupted')\n sys.exit()\n \ndef __mkdir_out_filefolder():\n try:\n myfile = os.getcwd()+'\\\\out_dir'\n if not os.path.exists(myfile):\n os.mkdir(myfile)\n except:\n print('file not exist or corupted')\n sys.exit()\n \ndef __demo_file_line_process_module(line,demo_lines,oldstr):\n demo_lines_out = [] \n try:\n modulestr = line.split('\\\"')[1]\n modulestr = modulestr.split('[')[0]\n modulestr = modulestr.upper()\n for demo_line in demo_lines: \n demo_line = demo_line.replace(oldstr,modulestr) \n demo_lines_out.append(demo_line) \n except:\n print('len of line is 1 ')\n sys.exit() \n return demo_lines_out \n\ndef __demo_file_line_process_bit(line,demo_lines,oldstr):\n demo_lines_out = [] \n modulestr = line.split(' ')\n modulestr = modulestr[0]\n modulestr = modulestr.upper()\n try:\n for demo_line in demo_lines: \n demo_line = demo_line.replace(oldstr,modulestr)\n demo_lines_out.append(demo_line) \n except:\n print('len of line is 1 ')\n sys.exit() \n return demo_lines_out \n\ndef __get_output_file_name(out_file_name):\n try: \n out_file_name = out_file_name.lower()\n out_file_name = 'out_dir\\\\' + out_file_name + '_'+ 'struct_interface_bit.h' \n return out_file_name \n except:\n print('len of line is 1 ')\n sys.exit() \n \ndef __file_line_process(lines,demo_lines): \n demo_lines_out = demo_lines\n try: \n demo_lines_out = __demo_file_line_process_module(lines[0],demo_lines_out,'AAAAA')\n demo_lines_out = __demo_file_line_process_module(lines[1],demo_lines_out,'BBBBB')\n oldstr = 'NULL'\n counter = 0\n for line in lines[2:]: \n line_check = line[0:1] \n if line_check !='' and line_check !='\\n' and line_check !=' ' : \n oldstr1 = oldstr + str(counter)\n demo_lines_out = __demo_file_line_process_bit(line,demo_lines_out,oldstr1) \n counter = counter+1 \n return demo_lines_out \n except:\n print('len of line is 22 ')\n sys.exit() \n \ndef __wirte_list_to_file(list_out,file_name):\n try:\n with open(file_name, 'w+') as file:\n file_name = file_name.split('\\\\')\n file_name = file_name[-1]\n file_name = file_name.replace('.','_')\n file_name1 = '#ifndef ' + file_name.upper()+'\\n'\n file_name2 = '#define ' + file_name.upper()+'\\n'\n file.write(file_name1)\n file.write(file_name2)\n file.write('\\n'.join(list_out)) \n file.write('\\n#endif\\n')\n except:\n print('file not exist or corupted')\n sys.exit()\n \ndef __file_read_process(file_all,demo_lines,sub_dir):\n demo_lines_out_all = []\n for file_name in file_all: \n lines = __read_file_lines(file_name)\n demo_lines_out = __file_line_process(lines,demo_lines)\n demo_lines_out_all = demo_lines_out_all + demo_lines_out\n out_file_name = __get_output_file_name(sub_dir)\n __wirte_list_to_file(demo_lines_out_all,out_file_name)\n \n \ndef get_struct_bit(dir_name,sub_dir):\n file_list = __get_input_file_name(dir_name,sub_dir)\n demo_lines = __read_file_lines('demo.h')\n __mkdir_out_filefolder() \n __file_read_process(file_list,demo_lines,sub_dir)\n \ndef main(argv=None):\n if argv is None:\n argv = sys.argv\n if len(argv) < 2:\n print('please set argv as log file')\n else: \n get_struct_bit(argv[1]) \n print('generate file end'); \n\nif __name__ == \"__main__\":\n sys.exit(main())\n ","sub_path":"struct_bit.py","file_name":"struct_bit.py","file_ext":"py","file_size_in_byte":4681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"500436656","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Project: QuestionFromProfWang\n# File name: query_church\n# Author: Mark Wang\n# Date: 24/7/2016\n\nimport os\n\nfrom ..google_maps.query_us_place_information import query_information_from_google_maps\n\npath = '/'.join(__file__.split('/')[:-1])\n# print path\n\n# raise Exception('hahah')\n\nboundary = {'west': -88.73352838270105,\n 'east': -88.38255513960547,\n 'north': 47.26982759633027,\n 'south': 43.16892014678899}\n\nquery_information_from_google_maps(query_type='church', country_code='usa', radius=5000.0, save_path=path,\n boundary=boundary, require_detail=False,\n # previous_file=os.path.join(path, 'usa_church_information.csv')\n )\n","sub_path":"GeoInfoQuery/church/query_church.py","file_name":"query_church.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"526902888","text":"t=int(input())\n\nwhile(t!=0):\n\tn=int(input())\n\tls=[]\n\tfor i in range(n):\n\t\tls.append(list(input()))\n\t\t\n\tarr=[]\n\tfor i in range(n):\n\t\tarr.append(int (ls[i][-1]))\n\t# ~ print(arr)\n\tones=arr.count(1)\n\tzeroes=arr.count(0)\n\tans=max(ones,zeroes)\n\tif(ans==ones):\n\t\tprint(ans)\n\telse:\n\t\t\n\t\tif(ans%2==0):\n\t\t\tprint(ans)\n\t\telse:\n\t\t\tprint(ans+1)\n\tt-=1\n","sub_path":"TRAINSET.py","file_name":"TRAINSET.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"641631534","text":"import json\nimport os\nimport secrets\n\nfrom functools import wraps\n\nfrom flask import Flask, request, Response, abort, redirect, make_response, session\n\nfrom jupyterhub.services.auth import HubOAuth\nfrom models import JobsResponse\nfrom services import SlurmAPI\n\nprefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')\n\nauth = HubOAuth(api_token=os.environ['JUPYTERHUB_API_TOKEN'], cache_max_age=60)\n\napp = Flask('slurm_proxy')\napp.secret_key = secrets.token_bytes(32)\n\ndef authenticated(f):\n \"\"\"Decorator for authenticating with the Hub\"\"\"\n @wraps(f)\n def decorated(*args, **kwargs):\n token = session.get(\"token\")\n if token:\n user = auth.user_for_token(token)\n else:\n user = None\n if user:\n return f(user, *args, **kwargs)\n else:\n state = auth.generate_state(next_url=request.host_url)\n response = make_response(redirect(auth.login_url + '&state=%s' % state))\n response.set_cookie(auth.state_cookie_name, state)\n return response\n return decorated\n\n\n@app.route(prefix)\n@authenticated\ndef home(user):\n return Response(\n json.dumps(user, indent=1, sort_keys=True), mimetype='application/json')\n\n@app.route(prefix + 'oauth_callback')\ndef oauth_callback():\n code = request.args.get('code', None)\n if code is None:\n return 403\n # validate state field\n arg_state = request.args.get('state', None)\n cookie_state = request.cookies.get(auth.state_cookie_name)\n if arg_state is None or arg_state != cookie_state:\n # state doesn't match\n return 403\n\n token = auth.token_for_code(code)\n # store token in session cookie\n session[\"token\"] = token\n next_url = auth.get_next_url(cookie_state) or prefix\n response = make_response(redirect(next_url))\n return response\n\n@app.route(prefix + '/api/jobs/')\n@authenticated\ndef get_jobs(user):\n slurm_api = SlurmAPI(user)\n jobs = slurm_api.get_slurm_jobs_list()\n response = JobsResponse(jobs=jobs)\n return response.dict()\n\n\n@app.route(prefix + '/api/jobs//log/')\n@authenticated\ndef get_job_logs(user, job_id):\n slurm_api = SlurmAPI(user)\n job = slurm_api.get_job_stdout_log(job_id)\n return job\n\n\n@app.route(prefix + '/api/jobs//', methods=['GET', 'DELETE'])\n@authenticated\ndef get_job(user, job_id):\n slurm_api = SlurmAPI(user)\n if request.method == 'DELETE':\n return slurm_api.delete_job(job_id)\n job = slurm_api.get_job_or_404(job_id)\n return job.dict()\n\n\nif __name__ == '__main__':\n print('Starting SlurmProxy service..........')\n from waitress import serve\n serve(app, host=\"0.0.0.0\", port=5000)\n","sub_path":"jupyterhub/files/etc/jupyterhub/services/slurm_proxy/slurm_proxy.py","file_name":"slurm_proxy.py","file_ext":"py","file_size_in_byte":2682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"605070914","text":"from src import randomvideo\nimport re\n\n\ndef response_random_video(update):\n \"\"\"\n response_random_video(request, bot) - sends random video to telegram chat\n\n :param update: web request from telegram bot\n :return: link to YouTube Video\n \"\"\"\n regex = 'bm[\\.\\?\\!]?'\n\n if update.message is None:\n message = update.inline_query.query\n else:\n message = update.message.text\n user = update.message.chat.username\n print('Received message %s from %s' % (message, user))\n\n if re.match(regex, message, re.IGNORECASE):\n\n video_link = randomvideo.random_video()\n\n return video_link\n return None\n","sub_path":"src/random_video_request_handler.py","file_name":"random_video_request_handler.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"145629660","text":"import os\nimport sys\nfrom .rules.rules import Artefact\nimport json\nimport logging\n\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\n\ndef classify(identifier_result_file):\n result_file = os.path.join(\n os.path.dirname(identifier_result_file), \"classifier_results.json\"\n )\n with open(identifier_result_file) as f:\n links = json.load(f)\n\n for fname, artefacts in links.items():\n for artefact in artefacts:\n a = Artefact(artefact)\n a.set_categories()\n artefact[\"possible_categories\"] = a.categories\n artefact[\"categorie\"] = a.categorie\n\n # save to result file\n with open(result_file, \"w\") as f:\n json.dump(links, f, indent=4, sort_keys=False)\n print(\"Finished classification.\", f\"results saved in {result_file}\")\n return result_file\n\n\ndef main(links):\n pass\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"classifier/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"280994304","text":"import os\n\ndef rm_subtitles(path):\n \"\"\" delete all subtitles in path recursively\n \"\"\"\n sub_exts = ['ass', 'srt', 'sub']\n for root, dirs, files in os.walk(path):\n for f in files:\n _, ext = os.path.splitext(f)\n ext = ext[1:]\n if ext in sub_exts:\n os.remove(os.path.join(root, f))\n\n\n","sub_path":"subfinder/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"644966598","text":"from typing import Dict\n\nimport httpx\nimport pytest\nfrom starlette import status\n\n\n@pytest.mark.asyncio\nasync def test_get(test_client_all_sensitive: httpx.AsyncClient):\n response = await test_client_all_sensitive.get(\"/get\")\n\n assert response.status_code == status.HTTP_200_OK\n\n assert \"csrftoken\" in response.cookies\n\n set_cookie_header = response.headers[\"set-cookie\"]\n assert \"Path=/;\" in set_cookie_header\n assert \"HttpOnly\" not in set_cookie_header\n assert \"Secure\" not in set_cookie_header\n\n\n@pytest.mark.asyncio\n@pytest.mark.parametrize(\n \"cookies,headers\",\n [\n ({}, {}),\n ({}, {\"x-csrftoken\": \"aaa\"}),\n ({\"csrftoken\": \"aaa\"}, {}),\n ({\"csrftoken\": \"aaa\"}, {\"x-csrftoken\": \"aaa\"}),\n ],\n)\nasync def test_post_invalid_csrf(\n test_client_all_sensitive: httpx.AsyncClient,\n cookies: Dict[str, str],\n headers: Dict[str, str],\n):\n response = await test_client_all_sensitive.post(\n \"/post\", cookies=cookies, headers=headers\n )\n\n assert response.status_code == status.HTTP_403_FORBIDDEN\n\n\n@pytest.mark.asyncio\nasync def test_valid_csrf(test_client_all_sensitive: httpx.AsyncClient):\n response_get = await test_client_all_sensitive.get(\"/get\")\n csrf_cookie = response_get.cookies[\"csrftoken\"]\n\n response_post = await test_client_all_sensitive.post(\n \"/post\",\n headers={\"x-csrftoken\": csrf_cookie},\n json={\"hello\": \"world\"},\n )\n\n assert response_post.status_code == status.HTTP_200_OK\n\n\n@pytest.mark.asyncio\nasync def test_some_sensitive_csrf(test_client_some_sensitive: httpx.AsyncClient):\n response_get = await test_client_some_sensitive.get(\"/get\")\n csrf_cookie = response_get.cookies[\"csrftoken\"]\n\n response_post_not_sensitive = await test_client_some_sensitive.post(\n \"/post\",\n cookies={\"foo\": \"bar\"},\n json={\"hello\": \"world\"},\n )\n\n assert response_post_not_sensitive.status_code == status.HTTP_200_OK\n\n response_post_sensitive_no_csrf_token = await test_client_some_sensitive.post(\n \"/post\",\n cookies={\"sensitive\": \"bar\"},\n json={\"hello\": \"world\"},\n )\n\n assert (\n response_post_sensitive_no_csrf_token.status_code == status.HTTP_403_FORBIDDEN\n )\n\n response_post_sensitive_csrf_token = await test_client_some_sensitive.post(\n \"/post\",\n cookies={\"sensitive\": \"bar\"},\n headers={\"x-csrftoken\": csrf_cookie},\n json={\"hello\": \"world\"},\n )\n\n assert response_post_sensitive_csrf_token.status_code == status.HTTP_200_OK\n","sub_path":"tests/test_middleware.py","file_name":"test_middleware.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"544757511","text":"# centroids_helper_test.py - Tests for crf_helper.py\n# Written by: Ben Lengerich\n# Created at: Sat May 30 18:19:59 EDT 2015\n# Last Modified at: Sat May 30 18:19:59 EDT 2015\n\n\nfrom __future__ import print_function\n\nimport centroids_helper\nfrom nose.tools import eq_\nfrom nose.tools import with_setup\nimport os\n\ntest_file_name = \"centroids_helper_test_file.txt\"\ntest_centroids = [\"AGCTTAGCT\", \"TGACTGATC\", \"GTACGTAC\"]\n\n# Setup functions\ndef write_file():\n with open(test_file_name, 'w') as test_file:\n for centroid in test_centroids:\n print(centroid, file=test_file)\n\n# Cleanup functions\ndef cleanup_file():\n os.remove(test_file_name)\n\n# Test functions\n@with_setup(write_file, cleanup_file)\ndef read_all_test():\n with open(test_file_name, 'r') as test_file:\n eq_(centroids_helper.read_all(test_file), test_centroids)\n","sub_path":"src/common/data_gen/centroids_helper_test.py","file_name":"centroids_helper_test.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"286832706","text":"'''\nCopyright (C) 2017-2021 Bryant Moscon - bmoscon@gmail.com\n\nPlease see the LICENSE file for the terms and conditions\nassociated with this software.\n'''\nimport asyncio\nfrom decimal import Decimal\nimport hashlib\nimport hmac\nimport logging\nimport time\n\nfrom yapic import json\n\nfrom cryptofeed.defines import BUY, SELL, TRADES\nfrom cryptofeed.exchange import RestExchange\n\n\nLOG = logging.getLogger('cryptofeed.rest')\n\n\nclass BinanceRestMixin(RestExchange):\n api = \"https://api.binance.com/api/v3/\"\n rest_channels = (\n TRADES\n )\n\n def _nonce(self):\n return str(int(round(time.time() * 1000)))\n\n def _generate_signature(self, url: str, body=json.dumps({})):\n nonce = self._nonce()\n signature = \"/api/\" + url + nonce + body\n h = hmac.new(self.config.key_secret.encode('utf8'), signature.encode('utf8'), hashlib.sha384)\n signature = h.hexdigest()\n\n return {\n \"X-MBX-APIKEY\": self.config.key_id,\n \"signature\": signature,\n \"content-type\": \"application/json\"\n }\n\n async def trades(self, symbol: str, start=None, end=None, retry_count=1, retry_delay=60):\n symbol = self.std_symbol_to_exchange_symbol(symbol)\n start, end = self._interval_normalize(start, end)\n if start and end:\n start = int(start * 1000)\n end = int(end * 1000)\n\n while True:\n if start and end:\n endpoint = f\"{self.api}aggTrades?symbol={symbol}&limit=1000&startTime={start}&endTime={end}\"\n else:\n endpoint = f\"{self.api}aggTrades?symbol={symbol}&limit=1000\"\n\n r = await self.http_conn.read(endpoint, retry_count=retry_count, retry_delay=retry_delay)\n data = json.loads(r, parse_float=Decimal)\n\n if data:\n if data[-1]['T'] == start:\n LOG.warning(\"%s: number of trades exceeds exchange time window, some data will not be retrieved for time %d\", self.id, start)\n start += 1\n else:\n start = data[-1]['T']\n\n yield [self._trade_normalization(symbol, d) for d in data]\n\n if len(data) < 1000 or end is None:\n break\n await asyncio.sleep(1 / self.request_limit)\n\n def _trade_normalization(self, symbol: str, trade: list) -> dict:\n ret = {\n 'timestamp': self.timestamp_normalize(trade['T']),\n 'symbol': self.exchange_symbol_to_std_symbol(symbol),\n 'id': trade['a'],\n 'feed': self.id,\n 'side': BUY if trade['m'] else SELL,\n 'amount': abs(Decimal(trade['q'])),\n 'price': Decimal(trade['p']),\n }\n return ret\n\n\nclass BinanceFuturesRestMixin(BinanceRestMixin):\n api = \"https://fapi.binance.com/fapi/v1/\"\n rest_channels = (\n TRADES\n )\n\n\nclass BinanceDeliveryRestMixin(BinanceRestMixin):\n api = \"https://dapi.binance.com/dapi/v1/\"\n rest_channels = (\n TRADES\n )\n","sub_path":"cryptofeed/exchanges/mixins/binance_rest.py","file_name":"binance_rest.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"278974560","text":"import unittest\nimport tempfile\nimport os\nimport shutil\nimport numpy as np\n\nfrom coremltools.libmilstoragepython import _BlobStorageWriter as BlobWriter\nfrom coremltools.libmilstoragepython import _BlobStorageReader as BlobReader\n\nclass WeightTest(unittest.TestCase):\n def setUp(self):\n self.working_dir = tempfile.mkdtemp()\n\n def tearDown(self):\n if os.path.exists(self.working_dir):\n shutil.rmtree(self.working_dir)\n\n def test_weight_blob_uint8(self):\n writer = BlobWriter(self.working_dir + \"/net.wt\")\n input_arr = np.array([1.0, 2, 3, 4, 5], dtype=np.uint8)\n offset = writer.write_uint8_data(input_arr)\n writer = None\n\n reader = BlobReader(self.working_dir + \"/net.wt\")\n output_arr = np.array(reader.read_uint8_data(offset))\n np.testing.assert_almost_equal(input_arr, output_arr)\n\n def test_weight_blob_fp16(self):\n writer = BlobWriter(self.working_dir + \"/net.wt\")\n input_arr = np.array([1.0, 2, 3, 4, 5], dtype=np.float16)\n offset = writer.write_fp16_data(input_arr)\n writer = None\n\n reader = BlobReader(self.working_dir + \"/net.wt\")\n output_arr = np.array(reader.read_fp16_data(offset))\n np.testing.assert_almost_equal(input_arr, output_arr)\n\n def test_weight_blob_fp32(self):\n writer = BlobWriter(self.working_dir + \"/net.wt\")\n input_arr = np.array([1.0, 2, 3, 4, 5], dtype=np.float32)\n offset = writer.write_float_data(input_arr)\n writer = None\n\n reader = BlobReader(self.working_dir + \"/net.wt\")\n output_arr = np.array(reader.read_float_data(offset))\n np.testing.assert_almost_equal(input_arr, output_arr)\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"coremltools/test/blob/test_weights.py","file_name":"test_weights.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"517063015","text":"'''\npretrain_fb_kbeam\n'''\nimport sys, time, os, itertools\nsys.path.insert(0, '..')\n \nimport input_data \nfrom modules.degradlNet import residualNet\nfrom modules.budgetNet import budgetNet_kbeam\nfrom loss import *\nfrom utils import *\nfrom validation import run_validation\n\nfrom common_flags import COMMON_FLAGS\nfrom pretrain_flags import FLAGS\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=FLAGS.GPU_id\nprint('Using GPU:', FLAGS.GPU_id)\nprint('GPU_NUM:', FLAGS.GPU_NUM)\n\n_K = 8\n\nMAX_STEPS = 150\nSAVE_STEP = 50\nVAL_STEP = 10\nPRINT_STEP = 10\nTRAIN_BATCH_SIZE = 2\n\ndef create_architecture_adversarial(scope, videos, budget_labels, istraining_placeholder):\n\t'''\n\tCreate the architecture of the adversarial model in the graph\n\tis_training: whether it is in the adversarial training part. (include testing, not two-fold)\n\t'''\n\t# fd part:\n\tdegrad_videos = residualNet(videos, is_video=True)\n\tdegrad_videos = avg_replicate(degrad_videos) if FLAGS.use_avg_replicate else degrad_videos\n\t# fd part ends\n\n\t# fb part:\n\tloss_budget, logits_budget = [0.0]*_K, [tf.zeros([TRAIN_BATCH_SIZE, COMMON_FLAGS.NUM_CLASSES_BUDGET])]*_K\n\tfor i in range(_K): # each element in loss_budget and logits_budget have the same graph structure.\n\t\twith tf.name_scope('%d' % _K) as scope:\n\t\t\tlogits = budgetNet_kbeam(degrad_videos, K_id=FLAGS.base_idx+i, is_training=istraining_placeholder, depth_multiplier=0.6)\n\t\t\tloss = tower_loss_xentropy_sparse(logits, budget_labels, use_weight_decay=False)\n\t\t\tlogits_budget[i] += logits\n\t\t\tloss_budget[i] += loss\n\t# fd part ends.\n\t\n\treturn loss_budget, logits_budget\n\n# Training set for traning, validation set for validation.\n# lambda: weight for L1 loss (degrade model L1 loss)\ndef pretrain_fb_kbeam(start_from_trained_model):\n\t'''\n\tpretrain_fb_kbeam\n\tArgs:\n start_from_trained_model: boolean. If False, use random initialized fb. If true, use pretrained fb.\n\t'''\n\t# mkdir:\n\tdegradation_ckpt_dir = os.path.join(COMMON_FLAGS.pretrain_dir, 'degradation_models')\n\tbudget_ckpt_dir = ['']*_K\n\tfor i in range(_K):\n\t\tbudget_ckpt_dir[i] = os.path.join(COMMON_FLAGS.pretrain_dir, 'budget_k%d_new' % (FLAGS.base_idx+i))\n\t\tif not os.path.isdir(budget_ckpt_dir[i]):\n\t\t\tos.mkdir(budget_ckpt_dir[i])\n\n\t# define graph\n\tgraph = tf.Graph()\n\twith graph.as_default():\n\t\t# global step:\n\t\tglobal_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)\n\t\t# placeholder inputs:\n\t\tvideos_placeholder, utility_labels_placeholder, budget_labels_placeholder, dropout_placeholder, istraining_placeholder = \\\n\t\t\tplaceholder_inputs(TRAIN_BATCH_SIZE * FLAGS.GPU_NUM)\n\n\t\t# initialize some lists, each element coresponds to one gpu:\n\t\ttower_grads_budget, logits_budget_lst, loss_budget_lst = [[] for i in range(_K)], [[] for i in range(_K)], [[] for i in range(_K)] # budget\n\n\t\t# Optimizer for the 3 components respectively\n\t\topt_budget = tf.train.AdamOptimizer(FLAGS.budget_lr)\n\n\t\twith tf.variable_scope(tf.get_variable_scope()):\n\t\t\tfor gpu_index in range(0, FLAGS.GPU_NUM):\n\t\t\t\twith tf.device('/gpu:%d' % gpu_index):\n\t\t\t\t\tprint('/gpu:%d' % gpu_index)\n\t\t\t\t\twith tf.name_scope('%s_%d' % ('gpu', gpu_index)) as scope:\n\t\t\t\t\t\t# placeholder inputs:\n\t\t\t\t\t\tvideos = videos_placeholder[gpu_index * TRAIN_BATCH_SIZE:(gpu_index + 1) * TRAIN_BATCH_SIZE]\n\t\t\t\t\t\tbudget_labels = budget_labels_placeholder[gpu_index * TRAIN_BATCH_SIZE:(gpu_index + 1) * TRAIN_BATCH_SIZE]\n\t\t\t\t\t\t# output of the graph:\n\t\t\t\t\t\tloss_budget, logits_budget = \\\n\t\t\t\t\t\t\t\t\tcreate_architecture_adversarial(scope, videos, budget_labels, istraining_placeholder)\n\t\t\t\t\t\t# Reuse variables for the next tower.\n\t\t\t\t\t\ttf.get_variable_scope().reuse_variables()\n\t\t\t\t\n\t\t\t\t# degrade:\n\t\t\t\tvarlist_degrad = [v for v in tf.trainable_variables() if \"DegradationModule\" in v.name]\n\t\t\t\t# bn varlist\n\t\t\t\tvarlist_bn = [g for g in tf.global_variables() if 'moving_mean' in g.name]\n\t\t\t\tvarlist_bn += [g for g in tf.global_variables() if 'moving_variance' in g.name]\n\t\t\t\t# budget:\n\t\t\t\tvarlist_budget = [[] for i in range(_K)]\n\t\t\t\tvarlist_budget_bn = [[] for i in range(_K)]\n\t\t\t\t### Append elements on each GPU to lists:\n\t\t\t\tfor i in range(_K):\n\t\t\t\t\t# loss and logits:\n\t\t\t\t\tloss_budget_lst[i].append(loss_budget[i])\n\t\t\t\t\tlogits_budget_lst[i].append(logits_budget[i])\n\t\t\t\t\t# gradients:\n\t\t\t\t\tvarlist_budget[i] = [v for v in tf.trainable_variables() if \"BudgetModule_%d\" % (FLAGS.base_idx+i) in v.name]\n\t\t\t\t\tvarlist_budget_bn[i] = [v for v in varlist_bn if \"BudgetModule_%d\" % (FLAGS.base_idx+i) in v.name]\n\t\t\t\t\tgrads_budget = opt_budget.compute_gradients(loss_budget[i], varlist_budget[i])\n\t\t\t\t\ttower_grads_budget[i].append(grads_budget)\n\t\t\t\t### End appending elements on each GPU to lists.\n\t\t\n\t\t### Average or concat Operations/Tnesors in a list to a single Operation/Tensor:\n\t\t## L_b\n\t\t# budget:\n\t\tloss_budget_op, accuracy_budget_op, right_count_budget_op = [None,]*_K, [None,]*_K, [None,]*_K\n\t\tzero_ops_budget, accum_ops_budget, apply_gradient_op_budget = [None,]*_K, [None,]*_K, [None,]*_K\n\t\tfor i in range(_K):\n\t\t\tloss_budget_op[i] = tf.reduce_mean(loss_budget_lst[i], name='softmax') # Lb\n\t\t\t_logits_budget = tf.concat(logits_budget_lst[i], 0)\n\t\t\taccuracy_budget_op[i] = accuracy(_logits_budget, budget_labels_placeholder)\n\t\t\tright_count_budget_op[i] = correct_num(_logits_budget, budget_labels_placeholder)\n\t\t\tzero_ops_budget[i], accum_ops_budget[i], apply_gradient_op_budget[i] = create_grad_accum_for_late_update(\n\t\t\t\topt_budget, tower_grads_budget[i], varlist_budget[i], FLAGS.n_minibatches, global_step, decay_with_global_step=False)\n\t\t### End averaging or concatenating Operations/Tnesors in a list to a single Operation/Tensor.\n\t\t\n\t\t# operations for placeholder inputs:\n\t\ttr_videos_op, _, tr_actor_labels_op = create_videos_reading_ops(is_train=True, is_val=False, GPU_NUM=FLAGS.GPU_NUM, BATCH_SIZE=TRAIN_BATCH_SIZE)\n\t\t# saver and summary files:\n\t\tsaver_kb = [None]*_K\n\t\tfor i in range(_K): # saver used for saving pretrained fb.\n\t\t\tsaver_kb[i] = tf.train.Saver(var_list=varlist_budget[i]+varlist_budget_bn[i], max_to_keep=1)\n\t\t\n\t\tinit_op = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())\n\n\t# session config:\n\tconfig = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)\n\t# run session:\n\twith tf.Session(graph=graph, config=config) as sess:\n\t\t# initialize:\n\t\tsess.run(init_op)\n\t\t# multi-threads:\n\t\tcoord = tf.train.Coordinator()\n\t\tthreads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\t\t# restore d net:\n\t\trestore_model_ckpt(sess=sess, ckpt_dir=degradation_ckpt_dir, varlist=varlist_degrad)\n\t\t# restore b net:\n\t\tif start_from_trained_model:\n\t\t\tfor i in range(_K):\n\t\t\t\trestore_model_ckpt(sess, budget_ckpt_dir[i], varlist_budget[i]+varlist_budget_bn[i])\n\t\t\t\t\n\t\t# train\n\t\tfor step in range(0, MAX_STEPS):\n\t\t\tstart_time = time.time()\n\t\t\tsess.run(zero_ops_budget)\n\t\t\tacc_budget_lst, loss_budget_lst = [[] for i in range(_K)], [[] for i in range(_K)]\n\t\t\tacc_budget_lst_mean, loss_budget_lst_mean = [None]*_K, [None]*_K\n\t\t\t# accumulating gradient for late update:\n\t\t\tfor _ in itertools.repeat(None, FLAGS.n_minibatches):\n\t\t\t\t# placeholder inputs:\n\t\t\t\ttr_videos, tr_actor_labels = sess.run([tr_videos_op, tr_actor_labels_op])\n\t\t\t\t# run operations:\n\t\t\t\ttemp_sess_run_return_list = sess.run(accum_ops_budget + accuracy_budget_op + loss_budget_op,\n\t\t\t\t\t\t\tfeed_dict={videos_placeholder: tr_videos,\n\t\t\t\t\t\t\t\t\t\tbudget_labels_placeholder: tr_actor_labels,\n\t\t\t\t\t\t\t\t\t\tistraining_placeholder: True})\n\t\t\t\tacc_budget_value = temp_sess_run_return_list[_K : 2*_K]\n\t\t\t\tloss_budget_value = temp_sess_run_return_list[2*_K : 3*_K]\n\t\t\t\t# append loss and acc for budget model:\n\t\t\t\tfor i in range(_K):\n\t\t\t\t\tacc_budget_lst[i].append(acc_budget_value[i])\n\t\t\t\t\tloss_budget_lst[i].append(loss_budget_value[i])\n\t\t\t# finish accumulating gradient for late update\n\t\t\t# find acc and loss mean across all gpus:\n\t\t\tfor i in range(_K):\n\t\t\t\tacc_budget_lst_mean[i] = np.mean(acc_budget_lst[i])\n\t\t\t\tloss_budget_lst_mean[i] = np.mean(loss_budget_lst[i])\n\t\t\t\n\t\t\tsess.run([apply_gradient_op_budget]) # update all k wb's\n\t\t\t# finish update on fb\n\n\t\t\t# loss summary:\n\t\t\tif step % PRINT_STEP == 0:\n\t\t\t\tloss_summary = 'step: %4d, time: %.4f, ' \\\n\t\t\t\t\t\t\t'training budget accuracy: %s, budget loss: %s' % (\n\t\t\t\t\t\t\tstep, time.time() - start_time, \n\t\t\t\t\t\t\tacc_budget_lst_mean, loss_budget_lst_mean)\n\t\t\t\tprint(loss_summary)\n\t\t\t# end loss summary\t\n\n\t\t\tif step % VAL_STEP == 0:\n\t\t\t\ttest_correct_num_lst, test_acc_lst, total_v = run_validation(sess=sess, \n\t\t\t\t\tright_count_op_list=right_count_budget_op,\n\t\t\t\t\tplaceholder_list=[videos_placeholder, utility_labels_placeholder, budget_labels_placeholder, dropout_placeholder, istraining_placeholder],\n\t\t\t\t\tbatch_size=TRAIN_BATCH_SIZE*FLAGS.GPU_NUM, dataset='val', istraining=True)\n\t\t\t\ttest_summary = \"Step: %d, validation budget correct num: %s, accuracy: %s\" % (\n step, test_correct_num_lst, test_acc_lst)\n\t\t\t\tprint(test_summary)\n\t\t\t\t# bn_temp_value = sess.run(varlist_bn[-1])\n\t\t\t\t# print('bn_temp_value:', bn_temp_value.shape, bn_temp_value[0:5])\n\n\t\t\t# save model:\n\t\t\tif step % SAVE_STEP == 0 or (step + 1) == MAX_STEPS:\n\t\t\t\tfor i in range(_K):\n\t\t\t\t\tsaver_kb[i].save(sess, os.path.join(budget_ckpt_dir[i], 'pretrained_fb_k%d.ckpt' % i), global_step=step) \n\t\t# End min step\n\t\t# End part 3\n\n\t\tcoord.request_stop()\n\t\tcoord.join(threads)\n\tprint(\"done\")\n\n\nif __name__ == '__main__':\n\tstart_from_trained_model = False\n\tpretrain_fb_kbeam(start_from_trained_model)","sub_path":"SBU-exp/adversarial_training/pretraining/pretrain_fb_kbeam.py","file_name":"pretrain_fb_kbeam.py","file_ext":"py","file_size_in_byte":9383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"263304934","text":"\"\"\"\ndef samnefnari(n, m):\n if n%m == 0:\n return m\n elif n%m == 1:\n return 1\n return samnefnari(m, n%m)\n\nprint(samnefnari(25,85))\n\ndef thversumma(n):\n if not n:\n return 0\n else:\n return int(str(n)[0]) + thversumma(str(n)[1:])\n\nprint(thversumma(10))\n\ndef runa(m):\n if m == 1:\n print(1, end=\" \")\n else:\n runa(m-1)\n print(str((m**2 + m)//2), end=\" \")\n\n\"\"\"\ndef summa(m):\n if m == 0:\n return 0\n return m**2 + summa(m-1)\nprint(summa(5))\n\"\"\"\ndef decimalToBinary(num):\n if num > 1:\n decimalToBinary(num // 2)\n print(num % 2, end='')\n\n\nnumber = int(input(\"Enter any decimal number: \"))\n\ndecimalToBinary(number)\n\"\"\"\n","sub_path":"Skilaverkefni_2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"136643003","text":"# Overrides\nfrom .settings import * # noqa: F401\n\nSECRET_KEY = 'za#q^j+$6frru&3*)b0yl=#9wmue%rf38akqux(fjvl-&zy@_l'\n\nDEBUG = True\n\nALLOWED_HOSTS = ['0.0.0.0', '118.201.195.130']\n\nRUNSERVERPLUS_SERVER_ADDRESS_PORT = '0.0.0.0:8080'\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': 'local.sqlite3',\n }\n}\n\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\nEMAIL_FILE_PATH = os.path.join(BASE_DIR, 'sent_emails')\nprint('EMAIL_FILE_PATH', EMAIL_FILE_PATH)\n\n# TICKET-specific settings\nTICKET_STAFF_ONLY = True\nTICKET_DEFAULT_ASSIGNEE = None\nTICKET_PUBLIC_SUBMIT_REDIRECT = 'ticket:mine'\n# TICKET_ALLOW_FILE_ATTACHMENTS = True\n# TICKET_LIMIT_FILE_ATTACHMENTS = [\".jpg\", \".gif\", \".png\", \".csv\", \".pdf\"]\n# TICKET_MAXIMUM_ATTACHMENT_SIZE = 5000000 # In bytes\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': True,\n 'formatters': {\n 'verbose': {\n 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s',\n 'datefmt' : '%Y/%b/%d %H:%M:%S'\n },\n 'simple': {\n 'format': '%(levelname)s %(message)s'\n },\n },\n 'handlers': {\n 'null': {\n 'level': 'DEBUG',\n 'class': 'logging.NullHandler',\n },\n 'console': {\n 'level': 'DEBUG',\n 'class': 'logging.StreamHandler',\n },\n 'file': {\n 'level': 'DEBUG',\n 'class': 'logging.handlers.TimedRotatingFileHandler',\n 'filename': '/home/it/cs-ticketing/debug.log',\n 'backupCount': 30,\n 'when': 'midnight',\n 'formatter': 'verbose'\n },\n },\n 'loggers': {\n 'django': {\n 'handlers': ['console', 'file'],\n 'propagate': True,\n 'level': 'INFO',\n },\n 'werkzeug': {\n 'handlers': ['console', 'file'],\n 'level': 'DEBUG',\n 'propagate': True,\n },\n },\n}","sub_path":"project/deployment.py","file_name":"deployment.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"62174714","text":"import cv2\r\nimport numpy as np\r\nimg1 =cv2.imread(\"ex.png\")#reads your image\r\nrows,cols,z=img1.shape\r\nx=rows/2#sets coordinate about which image is to be rotated\r\ny=cols/2\r\nimg3=cv2.getRotationMatrix2D((x,y),90,1)#rotation angle is 90 degree\r\nimg2=cv2.warpAffine(img1,img3,(cols,rows))\r\nprint(img2.shape)#print the resolution of rotated image\r\ncv2.imshow(\"abc\",img2)\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()\r\n","sub_path":"rotateimg.py","file_name":"rotateimg.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"123769476","text":"# SPDX-License-Identifier: Apache-2.0\n#\n# The OpenSearch Contributors require contributions made to\n# this file be licensed under the Apache-2.0 license or a\n# compatible open source license.\n\nimport os\nimport stat\nimport unittest\n\nfrom system.temporary_directory import TemporaryDirectory\n\n\nclass TestTemporaryDirectory(unittest.TestCase):\n def test_keep_true(self):\n with TemporaryDirectory(keep=True) as work_dir:\n self.assertTrue(os.path.exists(work_dir.name))\n self.assertTrue(os.path.exists(work_dir.name))\n\n def test_keep_false(self):\n with TemporaryDirectory() as work_dir:\n self.assertTrue(os.path.exists(work_dir.name))\n self.assertFalse(os.path.exists(work_dir.name))\n\n def test_remove_readonly(self):\n with TemporaryDirectory() as work_dir:\n filename = os.path.join(work_dir.name, \"test.txt\")\n with open(filename, \"w+\") as f:\n f.write(\"This is intentionally left blank.\")\n mode = os.stat(filename)[stat.ST_MODE]\n os.chmod(filename, mode & ~stat.S_IWUSR & ~stat.S_IWGRP & ~stat.S_IWOTH)\n self.assertTrue(os.path.exists(filename))\n self.assertFalse(os.path.exists(work_dir.name))\n self.assertFalse(os.path.exists(filename))\n\n def test_chdir_true(self):\n before_dir = os.getcwd()\n with TemporaryDirectory(chdir=True):\n self.assertNotEqual(before_dir, os.getcwd())\n self.assertEqual(before_dir, os.getcwd())\n\n def test_chdir_false(self):\n before_dir = os.getcwd()\n with TemporaryDirectory():\n self.assertEqual(before_dir, os.getcwd())\n self.assertEqual(before_dir, os.getcwd())\n","sub_path":"tests/tests_system/test_temporary_directory.py","file_name":"test_temporary_directory.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"14443768","text":"import contextlib\nimport glfw # type: ignore\nimport skia # type: ignore\nimport traceback\nimport random\nimport math\nimport ast\nimport importlib\nfrom dataclasses import dataclass\nfrom OpenGL import GL # type: ignore\nfrom typing import List\nfrom functools import lru_cache, wraps\nimport numpy as np # type: ignore\nimport matplotlib # type: ignore\nfrom buffer import Buffer\n\nfont = skia.Font(skia.Typeface('Liberation Mono'), 14)\nline_height = font.getSpacing()\ncol_width = font.getWidths([ord('x')])[0]\n\n\n@dataclass\nclass Cell:\n input: Buffer\n output: object\n\n\ntarget_scroll = [0, 0]\n\n\ndef clamp(l, u, v):\n return max(min(v, u), l)\n\n\ndef scroll(x, y):\n lines = 100 # FIXME\n target_scroll[0] = x\n target_scroll[1] = y\n\n # clamp along y axis\n if target_scroll[1] > 0:\n target_scroll[1] = 0\n if target_scroll[1] - HEIGHT < -(line_height * lines):\n target_scroll[1] = -line_height * lines + HEIGHT\n\n\ndef scroll_to_line(i):\n scroll(target_scroll[0], -line_height * i)\n\n\ninput_paint = skia.Paint(AntiAlias=True, Color=skia.ColorBLACK)\noutput_paint = skia.Paint(AntiAlias=True, Color=skia.ColorGRAY)\n\nWIDTH, HEIGHT = 800, 600\n\n\n@contextlib.contextmanager\ndef glfw_window():\n if not glfw.init():\n raise RuntimeError('glfw.init() failed')\n glfw.window_hint(glfw.STENCIL_BITS, 8)\n window = glfw.create_window(WIDTH, HEIGHT, '', None, None)\n glfw.make_context_current(window)\n yield window\n glfw.terminate()\n\n\n@contextlib.contextmanager\ndef skia_surface(window):\n context = skia.GrDirectContext.MakeGL()\n backend_render_target = skia.GrBackendRenderTarget(\n WIDTH,\n HEIGHT,\n 0, # sampleCnt\n 0, # stencilBits\n skia.GrGLFramebufferInfo(0, GL.GL_RGBA8))\n surface = skia.Surface.MakeFromBackendRenderTarget(\n context, backend_render_target, skia.kBottomLeft_GrSurfaceOrigin,\n skia.kRGBA_8888_ColorType, skia.ColorSpace.MakeSRGB())\n assert surface is not None\n yield surface\n context.abandonContext()\n\n\n@dataclass\nclass Cursor:\n _buf: Buffer\n _pos: int = 0\n\n @property\n def column(self):\n return self._buf.col(self._pos)\n\n @column.setter\n def column(self, v):\n line_start = self._buf.pos_for_line(self.line)\n line_len = self._buf.line_length(self.line) - 1\n self._pos = clamp(line_start, line_start + line_len, line_start + v)\n\n @property\n def line(self):\n return self._buf.line(self._pos)\n\n @line.setter\n def line(self, n):\n if 0 <= n < self._buf.nlines():\n c = self.column\n self._pos = self._buf.pos_for_line(n)\n self.column = c\n\n\ndef exec_block(block, context_globals=None):\n if context_globals is None:\n context_globals = {}\n\n if isinstance(block.body[-1], ast.Expr):\n last = ast.Expression(block.body.pop().value)\n\n exec(compile(block, '', mode='exec'),\n context_globals)\n return eval(compile(last, '', mode='eval'),\n context_globals)\n else:\n exec(compile(block, '', mode='exec'),\n context_globals)\n return None\n\n\ncells: List[Cell] = []\ncells.append(Cell(Buffer(''), 'some output'))\ncells.append(Cell(Buffer('some\\nmore\\ninput\\nhere'), 'some output'))\n\ncur_cell = 0\ncursor = Cursor(cells[cur_cell].input)\n\nevent_pipe = []\n\nwith glfw_window() as window:\n GL.glClear(GL.GL_COLOR_BUFFER_BIT)\n\n def char_callback(_win, c):\n event_pipe.append(('key_press', chr(c)))\n\n def key_callback(_win, k, scancode, action, mods):\n key_map = {glfw.KEY_BACKSPACE: 'backspace',\n glfw.KEY_ENTER: 'enter',\n glfw.KEY_UP: 'up',\n glfw.KEY_DOWN: 'down',\n glfw.KEY_LEFT: 'left',\n glfw.KEY_RIGHT: 'right'}\n\n if k in key_map and action in (glfw.PRESS, glfw.REPEAT):\n key = key_map[k]\n else: # not a key we handle in the key callback\n return\n\n if mods & glfw.MOD_CONTROL:\n event_pipe.append(('key_combo', 'ctrl', key))\n else:\n event_pipe.append(('key_press', key))\n\n def scroll_callback(_win, dx, dy):\n target_scroll[0] += dx * line_height * 2\n target_scroll[1] += dy * line_height * 2\n scroll(target_scroll[0] + dx * 20, target_scroll[1] + dy * 20)\n\n glfw.set_scroll_callback(window, scroll_callback)\n glfw.set_char_callback(window, char_callback)\n glfw.set_key_callback(window, key_callback)\n\n globs = {}\n globs['plt'] = importlib.import_module(\n 'matplotlib.pyplot')\n\n # wrap plt.plot\n _orig_plot = globs['plt'].plot\n @wraps(_orig_plot)\n def wrapped_plot(*args, **kwargs):\n wrapped_plot.was_called = True\n return _orig_plot(*args, **kwargs)\n\n\n last_frame = 0\n with skia_surface(window) as surface:\n while (glfw.get_key(window, glfw.KEY_ESCAPE) != glfw.PRESS\n and not glfw.window_should_close(window)):\n\n current_frame = glfw.get_time()\n dt = current_frame - last_frame\n last_frame = current_frame\n\n glfw.poll_events()\n\n while event_pipe:\n event_type, *args = event_pipe.pop(0)\n if event_type == 'key_press':\n if args[0] == 'enter':\n cells[cur_cell].input.insert('\\n', cursor._pos)\n cursor._pos += 1\n elif args[0] == 'down':\n cursor.line += 1\n elif args[0] == 'up':\n cursor.line -= 1\n elif args[0] == 'right':\n cursor.column += 1\n elif args[0] == 'left':\n cursor.column -= 1\n elif args[0] == 'backspace':\n if cursor._pos - 1 >= 0:\n cells[cur_cell].input.delete(cursor._pos-1, 1)\n cursor._pos -= 1\n else:\n cells[cur_cell].input.insert(args[0], cursor._pos)\n cursor._pos += 1\n elif event_type == 'key_combo':\n mod, key = args\n if mod == 'ctrl' and key == 'up':\n if cur_cell > 0:\n cur_cell -= 1\n cursor = Cursor(cells[cur_cell].input)\n if mod == 'ctrl' and key == 'down':\n if cur_cell + 1 < len(cells):\n cur_cell += 1\n cursor = Cursor(cells[cur_cell].input)\n if mod == 'ctrl' and key == 'enter':\n tree = ast.parse(cells[cur_cell].input.as_str(),\n mode='exec')\n try:\n globs['plt'].clf() # clear out plot\n globs['plt'].plot = wrapped_plot\n wrapped_plot.was_called = False\n globs['np'] = importlib.import_module('numpy')\n output = exec_block(tree, globs)\n\n if wrapped_plot.was_called:\n output = globs['plt'].gcf()\n output.canvas.draw()\n data = np.fromstring(\n output.canvas.tostring_rgb(), dtype=np.uint8, sep='')\n data = data.reshape(\n output.canvas.get_width_height()[::-1] + (3,))\n data = np.dstack(\n (data, np.ones((data.shape[0], data.shape[1]), dtype=np.uint8) * 255))\n cells[cur_cell].output = skia.Image.fromarray(data)\n else:\n cells[cur_cell].output = str(output)\n except:\n cells[cur_cell].output = traceback.format_exc()\n\n with surface as canvas:\n # ensure cursor is visible\n target_scroll[1] = clamp(\n cursor.line * -line_height, (cursor.line + 2) * -line_height + HEIGHT, target_scroll[1])\n\n M = canvas.getTotalMatrix()\n if abs(target_scroll[1] - M.getTranslateY()) > 2:\n canvas.translate(\n 0, (target_scroll[1] - M.getTranslateY()) * 0.2)\n elif abs(target_scroll[1] - M.getTranslateY()) > 1: # snap\n canvas.translate(0, target_scroll[1] - M.getTranslateY())\n\n canvas.clear(skia.Color(255, 255, 255))\n line = 1\n for c in cells:\n if cursor._buf == c.input:\n # draw cursor\n canvas.drawRect(skia.Rect.MakeXYWH(\n cursor.column * col_width, line_height * (line - 1) + cursor.line * line_height + 4, 2, line_height), input_paint)\n\n # display input\n for l in c.input.as_str().split('\\n'):\n canvas.drawString(\n l, 0, line_height * line, font, input_paint)\n line += 1\n\n # display output\n if isinstance(c.output, str):\n for l in c.output.split('\\n'):\n canvas.drawString(\n l, 0, line_height * line, font, output_paint)\n line += 1\n elif isinstance(c.output, skia.Image):\n canvas.drawImage(c.output, 0, line_height * line, None)\n line += np.ceil(c.output.height() / line_height)\n surface.flushAndSubmit()\n glfw.swap_buffers(window)\n\n\n'''\nMain TODO items\n\n== UI\n* wordwise movement, cell movement\n* text selection (with keyboard *and* mouse)\n* copy/paste\n* tab layout for different outputs of the same object\n\n== Serialization and deserialization\n\n== Track inputs and allow more input types\n* Resource -- watched for changes to file/socket/etc (mark dirty, reload and\n recompute)\n* VariableValue -- an input value that the user can change (e.g. slider,\n mouse pos)\n* RecordedVariableValue -- record a value (potentially into a circular\n buffer) and replay it\n\n== Add additional widgets\n* OpenGL output\n* Buttons/sliders/dropdowns\n'''\n","sub_path":"workbench.py","file_name":"workbench.py","file_ext":"py","file_size_in_byte":10560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"653855656","text":"#1. Получить common words со страниц, на которые есть ссылки\n#Получить ссылки на соседние страницы.\n#Спарсить отдельно соседние страницы.\n#Сложить все в один список.\n\nimport wiki_requests as wiki\nimport re\n\ndef get_topic_words (link):\n html_content = wiki.get_topic_page (link)\n words = re.findall(r\"[а-яА-Я\\-\\']{3,}\", html_content)\n return words\n\ndef get_common_words (words_list): \n rate = {}\n for word in words_list:\n if word in rate:\n rate[word] +=1\n else:\n rate[word] = 1\n rate_list = list (rate.items())\n rate_list.sort(key = lambda x:-x[1])\n return rate_list\n\ndef visualize_common_words(topic):\n link = wiki.get_link (topic)\n words = get_topic_words (link)\n list_links = wiki.get_list_of_links (topic)\n\n for link in list_links:\n words += get_topic_words (link)\n\n rate_list = get_common_words (words)\n print (\"TOP 10:\")\n for i in rate_list[0:9]:\n print (i[0])\n\ndef main():\n topic = input(\"topic?: \")\n visualize_common_words (topic)\n\nmain()","sub_path":"GeekBrains OOP/lesson05/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"383747490","text":"# 王泽昊\n# 1700010718\n# 4 min\n# Computing 8.3\n\ndef squeeze(list1,list2):\n for x in list1:\n assert isinstance(x,int)\n for x in list2:\n assert isinstance(x,int) \n l = []\n for x in list1:\n d = 0\n for y in list2:\n if x - y == 1:\n d = 1\n if d == 0:\n l.append(x)\n return l\n\nprint([1,2,3],[1],':',squeeze([1,2,3],[1]))\nprint([1,2,3],[1,2],':',squeeze([1,2,3],[1,2]))\nprint([1,10],[1,2,3],':',squeeze([1,10],[1,2,3]))\n","sub_path":"计算概论/作业/0510/8.3.py","file_name":"8.3.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"306089610","text":"\"\"\"\nTratamiento de archivos CSV\n\"\"\"\n\"\"\"\nHay que modificar el programa para que admita csv de columnas desconocidas \nque hay que comprobar con el header\n\"\"\"\n# import sys\nimport csv\n\nfilename = \"estadistica_audio_PC.csv\"\nseparador = ';'\n\ndata = []\nmi_dic = {}\ntry:\n with open(filename) as f:\n reader = csv.reader(f, delimiter = separador)\n c = 0\n for row in reader:\n k = row[0]\n v = row[1]\n w = row[2]\n mi_dic[k] = v, w\n if c == 0:\n header = row\n n_cols = len(header)\n else:\n data.append(row)\n c += 1\nexcept csv.Error as e:\n print(\"csv.error at line %s : %s\" , (reader.line_enum, e))\n\n\nif header:\n print('header tamaño', len(header), header)\n print(\"===================================\")\nfor datarow in data:\n print(datarow)\n\nprint(mi_dic)\nprint(type(header))\nprint(type(row))\nprint(type(reader))\n","sub_path":"utilidades/csv/ejemplos_csv.py","file_name":"ejemplos_csv.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"487035516","text":"#!/usr/bin/python3\n# -*- coding: utf8\n#\n#####################################################\n#\nimport os\nimport sys\nimport argparse\nimport re\nimport requests # sudo dnf install python3-requests\nfrom urllib.request import urlopen, ProxyHandler # sudo dnf install python3-urllib\nimport socket\nfrom bs4 import BeautifulSoup # sudo dnf install python3-beautifulsoup4\nimport chardet # detection encodage fichier (installé d'origine ???)\nfrom time import sleep\n# classes locales\n# fichier des regexp\nfrom lib import ApplyRegex\nfrom lib import rulesKikourou as kikourou\nfrom lib import rulesBaseFFA as ffa\nfrom lib import Tools\n#\n#####################################################\n# globales\n\n####################### a modifier en fonction de son environement ####################\n#FICDIR = '{}{}'.format(os.environ['HOME'], '/nextcloud/kikourou/fichiers/source/')\nREPWORK = '{}{}'.format(os.environ['HOME'], '/nextcloud/kikourou/fichiers/source/')\nREPDEST = '{}{}'.format(os.environ['HOME'], '/nextcloud/kikourou/fichiers/csv/')\n#######################################################################################\n#\n\nCSV = 'class;temps;nom;club;f1;cat\\n'\nBOLD = \"\\033[1m\"\nRESET = \"\\033[0;0m\"\n#\n#####################################################\n# classes\nclass Factory(object):\n \"\"\"docstring for Factory\"\"\"\n def __init__(self):\n super().__init__()\n print('objets : ', self.objets)\n\n @staticmethod\n def get_factory(which):\n if which == 'pdf':\n return PdfFactory()\n elif which == 'all':\n return AllFactory()\n elif which == 'csv':\n return CsvFactory()\n elif which == 'htm':\n return HtmFactory()\n elif which == 'txt':\n return TxtFactory()\n elif which == 'xlsx':\n return XlsxFactory()\n elif which == 'course':\n return CourseFactory()\n else:\n return CalendrierFactory()\n\n def create_file(self):\n raise NotImplementedError()\n\n\nclass AllFactory(object):\n def genClass(self, options):\n return All()\n\n\nclass CalendrierFactory(object):\n def genClass(self, options):\n return Calendrier()\n\n\nclass PdfFactory(object):\n def genClass(self, options):\n return Pdf()\n\n\nclass CsvFactory(object):\n def genClass(self, options):\n return Csv()\n\n\nclass HtmFactory(object):\n def genClass(self, options):\n return Htm()\n\n\nclass TxtFactory(object):\n def genClass(self):\n return Txt()\n\n\nclass XlsxFactory(object):\n def genClass(self, options):\n return Xlsx()\n\n\nclass CourseFactory(object):\n def genClass(self, options):\n return Course()\n\n\nclass All(Tools):\n \"\"\"Traitement de tous les fichiers du repertoire:\n - on parcours le repertoire et on extrait les noms de fichiers\n - creation d'un dico avec nom, extensions\n \"\"\"\n\n def start(self):\n if self.ext == '*':\n print('traitement de tous les fichiers')\n else:\n print('{}Traitement des fichiers {} :{}'.format(BOLD, self.ext, RESET))\n # self.lst_fic = glob('fichiers/source/*{}'.format(self.ext))\n self.lst_fic = [fic for fic in os.listdir('fichiers/source/') if fic.endswith('.{}'.format(self.ext))]\n for fic in self.lst_fic:\n print('{} ---> {}{}'.format(BOLD, fic, RESET))\n f = Factory.get_factory(self.ext)\n c = f.genClass()\n c.options = self.params(fic)\n c.start()\n\n def params(self, fic):\n fic, ext = os.path.splitext(self.lst_fic[0])\n fic = fic.split('/')[-1]\n _dic = {}\n _dic['ficpdf'] = '{}{}.pdf'.format(FICDIR, fic)\n _dic['ficcsv'] = '{}{}.csv'.format(FICDIR, fic)\n _dic['fictxt'] = '{}{}.txt'.format(FICDIR, fic)\n _dic['fichtm'] = '{}{}.htm'.format(FICDIR, fic)\n return _dic\n\n\nclass Pdf(Tools):\n \"\"\"Gestion d'un fichier pdf :\n - on le convertit avec pdftotext\n - creation d'un fichier csv (avec application des regex)\n \"\"\"\n def start(self):\n self.coureurs = self.pdftoliste()\n if self.checkCoureurs(self.coureurs):\n self.writeCsv(self.coureurs, self.fickikou)\n print(BOLD, \" <{}> : correct\".format(self.ficpdf), RESET)\n print(BOLD, \" : a transférer sur KiKourou\".format(self.fickikou), RESET)\n # sauvegarde des fichiers de source vers sv_fic_source\n fic = 'mv {}* {}sv_fic_source'.format(self.ficcsv[:-4], REPWORK)\n sv = os.system(fic)\n else:\n print(BOLD, '\\nFichier {} pourri'.format(self.ficpdf), RESET)\n print(BOLD, 'Modifier le fic {} et relancer'.format(self.ficcsv), RESET)\n self.writeCsv(self.coureurs, self.ficcsv)\n for cle, valeur in self.anos.items():\n print(BOLD, \"\\nAnomalie de type : \", cle, RESET)\n for ano in valeur:\n print(' {}'.format(ano)) \n\n #if self.pdftocsv():\n # print('- Fichier {} traité.\\n- Fichier {} créé.'.format(self.ficpdf, self.ficcsv))\n # if self.encodage_fichier(self.ficcsv) and self.csvtotxt():\n # print('- Fichier {} créé.'.format(self.fictxt))\n # self.csvdef()\n # return True\n\n\nclass Xlsx(Tools):\n \"\"\"Gestion d'un fichier xlsx :\n - utilisation du script xls2csv.py\n - chargement/formatage des coureurs en table\n - ecriture fichier si OK\"\"\"\n def start(self):\n self.coureurs = self.xlsxtoliste()\n if self.checkCoureurs(self.coureurs):\n self.writeCsv(self.coureurs, self.fickikou)\n print(BOLD, \" <{}> : correct\".format(self.ficxlsx), RESET)\n print(BOLD, \" : a transférer sur KiKourou\".format(self.fickikou), RESET)\n # sauvegarde des fichiers de source vers sv_fic_source\n fic = 'mv {}* {}sv_fic_source'.format(self.ficxlsx[:-4], REPWORK)\n sv = os.system(fic)\n else:\n print(BOLD, 'Fichier {} pourri'.format(self.ficxlsx), RESET)\n self.writeCsv(self.coureurs, self.ficcsv)\n for cle, valeur in self.anos.items():\n print(BOLD, \"\\nAnomalie de type : \", cle, RESET)\n for ano in valeur:\n print(' {}'.format(ano)) \n print(' {}'.format(ano)) \n\nclass Csv(Tools):\n \"\"\"Gestion d'un fichier csv :\n - lecture du csv\n - chargement/formatage des coureurs en table\n - ecriture fichier si OK\"\"\"\n def start(self):\n self.coureurs = self.csvtoliste()\n if self.checkCoureurs(self.coureurs):\n self.writeCsv(self.coureurs, self.fickikou)\n print(BOLD, \" <{}> : correct\".format(self.ficcsv), RESET)\n print(BOLD, \" : a transférer sur KiKourou\".format(self.fickikou), RESET)\n # sauvegarde des fichiers de source vers sv_fic_source\n fic = 'mv {}* {}sv_fic_source'.format(self.ficcsv[:-4], REPWORK)\n sv = os.system(fic)\n else:\n print(BOLD, 'Fichier {} pourri'.format(self.ficcsv), RESET)\n self.writeCsv(self.coureurs, self.ficcsv)\n for cle, valeur in self.anos.items():\n print(BOLD, \"\\nAnomalie de type : \", cle, RESET)\n for ano in valeur:\n print(' {}'.format(ano)) \n print(' {}'.format(ano)) \n\n\nclass Htm(Tools):\n \"\"\"Gestion d'un fichier htm :\n - lecture fichier html\n - creation d'un fichier csv (avec application des regex)\n - enfin, creation d'un fichier txt\n \"\"\"\n def start(self):\n if self.encodage_fichier(self.fichtm) and self.htmtocsv():\n print('- Fichier {} traité.\\n- Fichier {} créé.'.format(self.fichtm, self.ficcsv))\n if self.csvtotxt():\n print('- Fichier {} créé.'.format(self.fictxt))\n return True\n\n\nclass Txt(Tools):\n \"\"\"on passe d'un fichier txt a un fichier csv dans Fichiers/csv :\n - lecture txt : fictoliste\n - creation dictionnaire\n - ecriture fichier csv si aucune anomalie\n \"\"\"\n def start(self):\n print (self.options)\n return 'Objet Txt - Fichier : {}'.format(self.fichier)\n\n\nclass Course(Tools):\n \"\"\"a partir du numero de course :\n - calcul du nombre de participants\n - calcul du nombre de page à extraire\n - si option -f, ecriture d'un fichier\n \"\"\"\n def start(self):\n for epreuve in self.liste_epreuves():\n # url = 'http://bases.athle.com/asp.net/liste.aspx?frmbase=resultats&frmmode=1&frmespace=0&frmcompetition={}&frmepreuve={}'\n url = 'http://bases.athle.com/asp.net/liste.aspx?frmbase=resultats&frmmode=1&frmespace=0&frmcompetition={}&frmepreuve={}&frmposition={}'\n suite = True\n self.coureurs = []\n self.url_epreuve = url.format(self.numcourse, epreuve.lower(), 0)\n self.participants = self.nb_participants()\n self.nb_page = int(self.participants / 250) + 1\n self.fic_epreuve = '{}_{}.csv'.format(self.ficcsv[:-4], epreuve.lower().replace('+', '_'))\n print(BOLD, '\\nTraitement epreuve {} :'.format(self.fic_epreuve.split('/')[-1]), RESET)\n for page in range(0, self.nb_page):\n # sleep(2)\n # print('page {}/{}'.format( page, self.nb_page))\n self.url_epreuve = url.format(self.numcourse, epreuve.lower(), page)\n if self.nb_participants() > 0:\n self.coureurs += self.lec_html()\n else:\n print('- aucun participant trouvé pour la course n° {}'.format(self.numcourse))\n # tentative pour ne garder que le bon nombre de participants (probleme de la derniere page)\n self.coureurs = self.coureurs[ :self.participants +1 ]\n self.entete = 'class;temps;nom;club;cat'\n self.coureurs = [self.regFFA(';'.join(coureur)) for coureur in self.coureurs]\n self.coureurs = self.entete.split() + self.coureurs\n if self.check_format(): \n self.coureurs = self.listetodic()\n else:\n with open(self.fic_epreuve, 'w')as out:\n for c in self.coureurs:\n out.write('{}\\n'.format(c))\n print(BOLD + ' - probleme avec le fichier {} \\\n \\n - Corriger le fichier et relancer \\'./kikourou.py {}'.format(self.fic_epreuve, self.fic_epreuve) + RESET)\n for ano in self.anos_format:\n print('--> ', ano)\n print('')\n suite = False\n ###\n if suite:\n if self.checkCoureurs(self.coureurs):\n dest = self.fic_epreuve.replace('/source/', '/csv/')\n print(BOLD, ' - <{}> : correct'.format(self.fic_epreuve), RESET)\n print(BOLD, ' - : a transférer sur KiKourou'.format(dest), RESET)\n self.writeCsv(self.coureurs, dest)\n else:\n print(BOLD, ' - fichier {} pourri'.format(self.fic_epreuve), RESET)\n self.writeCsv(self.coureurs, self.fic_epreuve)\n for cle, valeur in self.anos.items():\n print(BOLD, ' - anomalie de type : ', cle, RESET)\n for ano in valeur:\n print(' {}'.format(ano)) \n\n def nb_participants(self):\n # html = urlopen('file:///home/paulo/nextcloud/kikourou/testCourse0.htm')\n html = urlopen(self.url_epreuve)\n soup = BeautifulSoup(html, 'lxml')\n nb = [i for i in soup.find(True, {'class': ['barCount']})]\n return int(re.sub(r'([0-9]+)( .*)', r'\\1', ''.join(nb)))\n\n def lec_html(self):\n coureur = []\n coureurs = []\n html = urlopen(self.url_epreuve)\n soup = BeautifulSoup(html, 'lxml')\n for tr in soup.findAll('tr'):\n coureur = [ td.text for td in tr.findAll('td', class_=[\"datas0\", \"datas1\"]) if '\\xa0' not in td]\n if coureur != []:\n coureurs.append(coureur)\n return coureurs\n\n def liste_epreuves(self):\n URL = 'http://bases.athle.com/asp.net/liste.aspx?frmbase=resultats&frmmode=1&frmespace=0&frmcompetition={}'\n html = urlopen(URL.format(self.numcourse))\n soup = BeautifulSoup(html, 'lxml')\n td = [ td for td in soup.findAll('td',{ \"class\" : \"barInputs\" }) ]\n options = td[3].findAll('option')\n ### si une seule épreuve à rechercher : \n ### http://bases.athle.com/asp.net/liste.aspx?frmbase=resultats&frmmode=1&frmespace=0&frmcompetition=192341&frmepreuve=13+km\n ### return ['13+km']\n return [ option.get('value') for option in options if option.get('value') ]\n\n def display(self):\n retour = ''\n for i in self.coureurs:\n if len(i) > 7:\n if retour == '':\n retour = u\"class: %-5s - Nom: %-30s - Temps: %-12s\" % (i[0], i[2], i[1])\n else:\n retour += u\"\\nclass: %-5s - Nom: %-30s - Temps: %-12s\" % (i[0], i[2], i[1])\n return retour\n\n\nclass Calendrier(Tools):\n \"\"\"docstring for Calendrier\"\"\"\n\n def nb_jour_mois(self):\n '''renvoi le nombre de jour du mois'''\n nj = (0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)[self.mois]\n if self.mois == 2 and ((self.annee % 4 == 0 and self.annee % 100 != 0) or self.annee % 400 == 0):\n return nj + 1\n return nj\n\n def start(self):\n self.liste_courses = self.consult_calendrier()\n if len(self.liste_courses) > 0:\n print(self.resultat())\n else:\n print('Aucune compétition trouvée avec ces paramètres:\\n- année : {} mois : {}\\n- departement : {}'.format(self.annee, self.mois, self.dept))\n\n def consult_calendrier(self):\n URL = 'http://bases.athle.com/asp.net/liste.aspx?frmpostback=true&frmbase=resultats&frmmode=2&frmespace=0&frmsaison={}&frmtype1=Hors+Stade&frmtype2=&frmtype3=&frmtype4=&frmniveau=&frmniveaulab=&frmligue=&frmdepartement={}&frmeprrch=&frmclub=&frmdate_j1={}&frmdate_m1={}&frmdate_a1={}&frmdate_j2={}&frmdate_m2={}&frmdate_a2={}'\n self.deb = 1\n url = URL.format(self.annee, self.dept, self.deb, self.mois, self.annee, self.nb_jour_mois(), self.mois, self.annee)\n ip = socket.gethostbyname(socket.getfqdn())\n html = urlopen(url)\n course = courses = []\n soup = BeautifulSoup(html, 'lxml')\n for tr in soup.findAll('tr'):\n course = []\n num = ''\n for td in tr.findAll('td', class_=[\"datasCMP0\", \"datasCMP1\"]):\n if td.text != '':\n course.append(td.text)\n if td.a:\n num = td.a.get('href')[-6:]\n participants = re.sub(r'(^.*- )([0-9]+)(.*)', r'\\2', td.a.get('title'))\n num = u'%s (%s coureurs)' % (num, participants)\n course.append(num)\n if len(course) > 6:\n courses.append(course)\n return courses\n\n def resultat(self):\n retour = ''\n for i in self.liste_courses:\n if len(i) > 8:\n if retour == '':\n retour = u\"date: %-9s - Course: %-45s - Numero : %-25s - Lieu: %-30s - Departement: %3s\" % (i[0], i[2], i[-1], i[3], i[5])\n else:\n retour += u\"\\ndate: %-9s - Course: %-45s - Numero : %-25s - Lieu: %-30s - Departement: %3s\" % (i[0], i[2], i[-1], i[3], i[5])\n return retour\n\n\n#\n# fonctions\ndef opt(argv):\n '''dictionnaire qui sera passé à la création des objets de la fabrique'''\n parser = argparse.ArgumentParser(prog='./parser.py', usage='%(prog)s [options]', description='Generation csv...')\n #parser.add_argument('fic', nargs='*', default='')\n subparsers = parser.add_subparsers(help='sub-command help')\n # gestion du numerotage automagique\n auto = False\n if 'auto' in argv:\n auto = True\n argv.remove('auto')\n\n #uniquement un nom de fichier\n if len(argv) == 1:\n #on recupere uniquement le nom du fichier\n dic = {}\n fic, _ = os.path.splitext(argv[0])\n fic = fic.split('/')[-1]\n ext = argv[0].split('.')[-1]\n dic['which'] = '{}'.format(ext)\n dic['ficpdf'] = '{}{}.pdf'.format(REPWORK, fic)\n dic['ficcsv'] = '{}{}.csv'.format(REPWORK, fic)\n dic['ficxlsx'] = '{}{}.xlsx'.format(REPWORK, fic)\n dic['fictxt'] = '{}{}.txt'.format(REPWORK, fic)\n dic['fichtm'] = '{}{}.htm'.format(REPWORK, fic)\n dic['fickikou'] = '{}{}.csv'.format(REPDEST, fic)\n if auto:\n dic['auto'] = True\n return dic\n elif len(argv) > 1:\n # recherche dans le calendrier\n parser_a = subparsers.add_parser('calendrier', help='Recherche dans le calendrier Base Athlé')\n parser_a.set_defaults(which='calendrier')\n parser_a.add_argument(\"-d\", \"--dept\", action=\"store\", dest=\"dept\", default='069', help=\"Departement a rechercher\")\n parser_a.add_argument(\"-m\", \"--mois\", action=\"store\", dest=\"mois\", default='6', type=int, help=\"Mois recherché en numerique\")\n parser_a.add_argument(\"-a\", \"--annee\", action=\"store\", dest=\"annee\", default='2017', type=int, help=\"Année recherchée\")\n \n # recherche resultat d'une course\n parser_b = subparsers.add_parser('course', help='Consultation d\\'une course')\n parser_b.set_defaults(which='course')\n parser_b.add_argument(\"-n\", \"--num\", action=\"store\", dest=\"numcourse\", help=\"Course a rechercher\")\n parser_b.add_argument(\"-f\", \"--fichier\", action=\"store\", dest=\"fichier\", default=None, help=\"Nom du fichier en sortie\")\n #traitage des options \n options = parser.parse_args()\n dic = dict(options._get_kwargs())\n if (dic['which'] == 'course' and not dic['fichier']) or (dic['which'] == 'calendrier') or (dic['which'] == 'all'):\n dic['ficcsv'] = dic['fictxt'] = dic['fichtm'] = None\n else:\n fic, ext = os.path.splitext(dic['fichier'])\n fic = fic.split('/')[-1]\n dic['ficpdf'] = '{}{}.pdf'.format(REPWORK, fic)\n dic['fictxt'] = '{}{}.txt'.format(REPWORK, fic)\n dic['fichtm'] = '{}{}.htm'.format(REPWORK, fic)\n dic['ficcsv'] = '{}{}.csv'.format(REPWORK, fic)\n dic['fickikou'] = '{}{}.csv'.format(REPDEST, fic)\n return dic\n else:\n parser.error('./parser.py -h [--help] pour obtenir la liste des options')\n#\n#####################################################\n#\n\n\ndef main(argv):\n #dictionnaire des options\n options = opt(argv)\n \n #utilisation de la fabrique : retourne un objet en fonction de l'extension du fichier transmis\n f = Factory.get_factory(options['which'])\n \n #creation/lancement objet\n c = f.genClass(options)\n c.options = options\n c.start()\n#\n#####################################################\n#\nif __name__ == '__main__':\n main(sys.argv[1:])\n#\n#####################################################\n","sub_path":"kikourou.py","file_name":"kikourou.py","file_ext":"py","file_size_in_byte":19435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"183792635","text":"import os\narchivo=open(\"index.html\",\"r\")\narchread=archivo.read()\nerrormensage=\"Building Instructions [Lego]-images\"\nthegoodone=\"./BuildingInstructions_images\"\nos.system(\"sudo cp -r Building\\ Instructions\\ \\[Lego\\]-images/ BuildingInstructions_images\")\nr=archread.replace(errormensage,thegoodone)\narchivo.close()\narchivo=open(\"index.html\",\"w\")\narchivo.write(r)\narchivo.close()","sub_path":"Rpi/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"67310519","text":"def C(n, k):\n result = 1\n K = k\n i = 0\n while i < K:\n result *= n\n n -= 1\n i += 1\n\n i = 1\n while i < K:\n result /= k\n k -= 1\n i += 1\n\n return (int)(result)\n\nwith open(\"input.txt\") as inputFile:\n Args = inputFile.read().split(\" \")\n N = (int)(Args[0])\n A = (int)(Args[1])\n B = (int)(Args[2])\n with open(\"output.txt\", \"w\") as outputFile:\n outputFile.write((str)(C(N + A, A) * C(N + B, B)))\n outputFile.close()\n inputFile.close()\n","sub_path":"401.py","file_name":"401.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"311766340","text":"# Data Setup\n# -----------------\n# Players\nplayers = [\"Player 1\", \"Player 2\", \"Player 3\"]\n\n# AFC Teams\nafc_east = {\"Buffalo Bills\": players, \"Miami Dolphins\": players, \"New England Patriots\": players,\n \"New York Jets\": players}\nafc_north = {\"Baltimore Ravens\": players, \"Cincinnati Bengals\": players, \"Cleveland Browns\": players,\n \"Pittsburgh Steelers\": players}\nafc_south = {\"Houston Texans\": players, \"Indianapolis Colts\": players, \"Jacksonville Jaguars\": players,\n \"Tennessee Titans\": players}\nafc_west = {\"Denver Broncos\": players, \"Kansas City Chiefs\": players, \"Oakland Raiders\": players,\n \"San Diego Chargers\": players}\n\n# NFC Teams\nnfc_east = {\"Dallas Cowboys\": players, \"New York Giants\": players, \"Philadelphia Eagles\": players,\n \"Washington Redskins\": players}\nnfc_north = {\"Chicago Bears\": players, \"Detroit Lions\": players, \"Green Bay Packers\": players,\n \"Minnesota Vikings\": players}\nnfc_south = {\"Atlanta Falcons\": players, \"Carolina Panthers\": players, \"New Orleans Saints\": players,\n \"Tampa Bay Buccaneers\": players}\nnfc_west = {\"Arizona Cardinals\": players, \"Los Angeles Rams\": players, \"San Francisco 49ers\": players,\n \"Seattle Seahawks\": players}\n\n# Conferences\nafc = {\"East\": afc_east, \"North\": afc_north, \"South\": afc_south, \"West\": afc_west}\nnfc = {\"East\": nfc_east, \"North\": nfc_north, \"South\": nfc_south, \"West\": nfc_west}\n\n# League\nnfl = {\"AFC\": afc, \"NFC\": nfc}\n\n\n# Setup\n# -----------------\n\ndef is_conference_of(string, data):\n \"\"\"Given a text string, test to see if a conference.\n\n >>> is_conference_of(\"test\",nfl)\n False\n\n >>> is_conference_of(\"AFC\",nfl)\n True\n\n >>> is_conference_of(\"NFC\",nfl)\n True\n\n \"\"\"\n\n for key in data.keys():\n if string == key:\n return True\n return False\n\n\ndef is_team(team_input, dataset):\n \"\"\"Given a text input test against list of teams.\n\n >>> is_team(\"Test\",nfl)\n False\n\n >>> is_team(\"Philadelphia Eagles\",nfl)\n True\n\n >>> is_team(\"Arizona Cardinals\",nfl)\n True\n\n \"\"\"\n\n for conference in dataset.values():\n for division in conference.values():\n for team in division.keys():\n if team_input == team:\n return True\n return False\n\n\ndef get_team_info(team_input, dataset):\n \"\"\"Given a team, return conference, division, and players.\n\n >>> get_team_info(\"Arizona Cardinals\",nfl)\n ('NFC', 'West', ['Player 1', 'Player 2', 'Player 3'])\n\n >>> get_team_info(\"Philadelphia Eagles\",nfl)\n ('NFC', 'East', ['Player 1', 'Player 2', 'Player 3'])\n\n >>> get_team_info(\"San Francisco 49ers\",nfl)\n ('NFC', 'West', ['Player 1', 'Player 2', 'Player 3'])\n\n >>> get_team_info(\"Pittsburgh Steelers\",nfl)\n ('AFC', 'North', ['Player 1', 'Player 2', 'Player 3'])\n\n \"\"\"\n\n # Setup\n team_conference = \"\"\n team_division = \"\"\n team_members = \"\"\n\n # Transform\n for conference_name, conference in dataset.items():\n for division_name, division in conference.items():\n for team_name, team_roster in division.items():\n if team_input == team_name:\n team_conference = conference_name\n team_division = division_name\n team_members = team_roster\n\n # Output\n return (team_conference, team_division, team_members)\n\n\ndef get_divisions(conference, dataset):\n \"\"\"Given a conference and dataset, return list of division_request\n\n >>> get_divisions(\"NFC\", nfl)\n ['East', 'North', 'South', 'West']\n\n >>> get_divisions(\"AFC\", nfl)\n ['East', 'North', 'South', 'West']\n\n \"\"\"\n\n # Transform\n results = list(dataset[conference].keys())\n results.sort()\n\n # Output\n return results # returns type sorted list\n\n\ndef get_teams(conference, division, dataset):\n \"\"\"Given a conference, division, and dataset return an ordered list of teams.\n\n >>> get_teams(\"NFC\",\"East\", nfl)\n ['Dallas Cowboys', 'New York Giants', 'Philadelphia Eagles', 'Washington Redskins']\n\n >>> get_teams(\"AFC\",\"East\", nfl)\n ['Buffalo Bills', 'Miami Dolphins', 'New England Patriots', 'New York Jets']\n \"\"\"\n\n # Transform\n team_list = list(dataset[conference][division].keys())\n team_list.sort()\n\n # Output\n return team_list\n\n\ndef main(dataset):\n # Setup\n team = False\n conference = False\n\n # Input\n while not (team or conference):\n user_input = input(\"Please Enter a team or conference: \\n\")\n try:\n if is_conference_of(user_input, dataset):\n conference = True\n elif is_team(user_input, dataset):\n team = True\n else:\n print(\"Not found, please try again.\")\n except TypeError:\n print(\"Team or conference not found please check your input and try again.\\n\")\n\n # Transform\n if team == True:\n results = get_team_info(user_input, dataset)\n unpack_conference, unpack_division, unpack_team_members = results\n format_team_members = \", \".join(unpack_team_members)\n string = \"{team} is in the {conference} {division} and has the players: {team_members}\".format(team=user_input,\n conference=unpack_conference,\n division=unpack_division,\n team_members=format_team_members)\n else:\n # Select division\n division_list = get_divisions(user_input, dataset)\n division_list_string = \", \".join(division_list)\n is_division = False\n while not is_division:\n division = input(\"Please select a division from {string}\\n\".format(string=division_list_string))\n try:\n # Return Teams\n list_of_teams = \", \".join(get_teams(user_input, division, dataset))\n is_division = True\n except KeyError:\n print(\"Error: Division not on list, try again.\")\n\n # Format string for output\n string = \"The teams in the {conference} {division} are the {list_of_teams}\".format(conference=user_input,\n division=division,\n list_of_teams=list_of_teams)\n\n # Output\n print(string)\n\n\nmain(nfl)\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n is_conference_of(\"NFC\", nfl)\n is_team(\"Test\", nfl)\n get_team_info(\"team\", nfl)\n\n# -----------------------------------------------------------------------------\n# Old Code Fragments Below\n# -----------------------------------------------------------------------------\n#\n# # Input\n# # -----------------\n#\n# # Transform\n# # -----------------\n#\n# team = False\n# conference = False\n#\n#\n# # Find Teams\n# def find_info(conference, division):\n# return nfl[conference][division]\n#\n#\n# # Find Conference and Division\n# def find_division(team):\n# for key, value in nfl.items():\n# for sub_key, sub_value in value.items():\n# for team_name in sub_value:\n# if team_name == team:\n# return (sub_key, key) # division,conference\n#\n#\n# # Output\n# # -----------------\n# if team:\n# return_valuesA, return_valuesB = find_division(user_input)\n# print(\"The conference is {conference} and the division is {division}\".format(conference = return_valuesA, division = return_valuesB))\n# # return find_division(user_input)\n# # return_conf_and_division\n# # Return Conference and Division\n# else:\n# conference = user_input\n# division = get_division()\n# print(\"Teams in division:\", \", \".join(find_info(conference, division)))\n# # Ask for division, return team list\n# # Determine Input Type: Team, Division, Conference\n#\n# if __name__ == \"__main__\":\n# import doctest\n#\n# doctest.testmod()\n# is_team(\"text\")\n","sub_path":"_assignments/python/nflteams/nflteams.py","file_name":"nflteams.py","file_ext":"py","file_size_in_byte":8192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"268083217","text":"import math\r\n\r\ndef Lcm(num1):\r\n lcm=num1[0]\r\n for i in range(1,len(num1)):\r\n lcm=lcm*num1[i]//math.gcd(lcm,num1[i])\r\n return lcm\r\n\r\ndef lcmgcd():\r\n while True:\r\n print(\"Enter 1 for LCM and GCD of Two numbers\\nEnter 2 for LCM and GCD of Three numbers\\nEnter 3 for back\")\r\n k=input(\"Enter here:\")\r\n try: \r\n key=int(k)\r\n except:\r\n print(\"Invalid key!\")\r\n \r\n if key==1:\r\n A=input(\"Enter first number:\")\r\n B=input(\"Enter second number:\")\r\n try:\r\n a=int(A)\r\n b=int(B)\r\n except:\r\n print(\"Error!\")\r\n break\r\n if a==0 or b==0:\r\n print(\"Zero not allowed!\")\r\n break\r\n gcd=math.gcd(a,b)\r\n print(\"GCD is:\",gcd)\r\n print(\"LCM is:\",int((int(A)*int(B))/gcd))\r\n if key==2:\r\n num=[]\r\n A=input(\"Enter first number:\")\r\n B=input(\"Enter second number:\")\r\n C=input(\"Enter third number:\")\r\n try:\r\n a=int(A)\r\n b=int(B)\r\n c=int(C)\r\n except:\r\n print(\"Error!\")\r\n break\r\n if a==0 or b==0 or c==0:\r\n print(\"Zero not allowed!\")\r\n break\r\n num.append(a)\r\n num.append(b)\r\n num.append(c)\r\n \r\n g=math.gcd(a,b)\r\n gcd=math.gcd(g,c)\r\n print(\"GCD is:\",gcd)\r\n print(\"LCM is:\",Lcm(num))\r\n if key==3:\r\n break\r\n else:\r\n print(\"Enter correct key!\")\r\n break\r\n\r\nlcmgcd()\r\n","sub_path":"LCM_GCD.py","file_name":"LCM_GCD.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"156576142","text":"import pymongo\n\nclient = pymongo.MongoClient(host='localhost', port=27017)\n\n# client = MongoClient('mongodb://localhost:27017/')\n\ndb = client.test\n# db = client['test']\n\ncollection = db.students\n\nresult = collection.remove({'name': 'Kevin'})\nprint(result)\n\nresult = collection.delete_one({'name': 'Kevin'})\nprint(result)\nprint(result.deleted_count)\nresult = collection.delete_many({'age': {'$lt': 25}})\nprint(result.deleted_count)\n","sub_path":"demo7.py","file_name":"demo7.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"348799028","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom odoo import api, fields, models\r\nfrom random import randint\r\n\r\nclass Fuel(models.Model):\r\n _name = 'fuel.fuel'\r\n\r\n\r\n def _get_default_color(self):\r\n return randint(1, 11)\r\n\r\n name = fields.Char('Fuel')\r\n color = fields.Integer(string='Color Index', default=_get_default_color)\r\n active = fields.Boolean(default=True)\r\n\r\nclass FleetVehicleModel(models.Model):\r\n _inherit = 'fleet.vehicle.model'\r\n \r\n vehicle_type = fields.Selection([('cover_van', 'Covered Van'), ('car', 'Car'), ('bike', 'Motor Bike')], default='car', required=True)\r\n \r\n @api.depends('name', 'brand_id', 'vehicle_type')\r\n def name_get(self):\r\n ret_list = []\r\n for record in self:\r\n name = record.name\r\n if record.brand_id.name:\r\n name = record.brand_id.name + '/' + name\r\n if record.vehicle_type:\r\n name = name + '(' + record.vehicle_type + ')'\r\n ret_list.append((record.id, name))\r\n return ret_list\r\n\r\n\r\nclass FleetVehicle(models.Model): \r\n _inherit = 'fleet.vehicle'\r\n\r\n @api.onchange('driver_id')\r\n def on_change_driver_id(self):\r\n for i in self:\r\n if i.driver_id:\r\n i.driver_number = i.driver_id.mobile\r\n\r\n @api.onchange('helper_id')\r\n def on_change_future_driver_id(self):\r\n for i in self:\r\n if i.helper_id:\r\n i.helper_number = i.helper_id.mobile\r\n\r\n @api.onchange('department_id')\r\n def on_change_department_id(self):\r\n\r\n if self.department_id:\r\n return {'domain': {'vehicle_asign_id': [('department_id', '=', self.department_id.id)]}}\r\n \r\n registration_nbr = fields.Text('Vehicle Registration No')\r\n date_purchase = fields.Date('Vehicle Purchase Date')\r\n cubic_centimeter = fields.Float(\"Vehicle Capacity (CBM)\")\r\n vehicle_capacity = fields.Float(\"Vehicle Capacity (Ton)\")\r\n kanban_state = fields.Selection([\r\n ('done', 'USED'),\r\n ('blocked', 'IDLE'),\r\n ], string='Vehicle Status',\r\n copy=False, default='blocked', tracking=True\r\n )\r\n\r\n doc1 = fields.Binary('Registration Certificate')\r\n file_name = fields.Char(\"File Name\")\r\n doc2 = fields.Binary('Route permit')\r\n file_name2 = fields.Char(\"File Name\")\r\n doc3 = fields.Binary('Tax token')\r\n file_name3 = fields.Char(\"File Name\")\r\n doc4 = fields.Binary('Fitness')\r\n file_name4 = fields.Char(\"File Name\")\r\n doc5 = fields.Binary('Insurance')\r\n file_name5 = fields.Char(\"File Name\")\r\n\r\n tyres_count = fields.Integer(string='Tyres', compute='get_tyres_count')\r\n # fuel_type = fields.Selection([('gasoline', 'Petrol'),('lpg', 'CNG'),('diesel', 'Diesel'),('electric','Electric'),('hybrid','Hybrid')], string=\"Fuel Type\")\r\n fuel_type_ids = fields.Many2many('fuel.fuel', string=\"Fuel Type\")\r\n fuel_log_count = fields.Integer(string='Fuel Logs', compute='get_fuel_log_count')\r\n vehicle_seat = fields.Float('Seat/Capacity')\r\n # components_ids = fields.One2many('vehicle.components', 'vehicle_id', string='Components')\r\n vehicle_asign_id = fields.Many2one('hr.employee', string=\"Vehicle Assigned To\")\r\n#added fuel_type and rootes_count \r\n routes_count = fields.Integer(string='Tyres', compute='get_routes_count')\r\n component_count = fields.Integer(string='Component', compute='get_component_count')\r\n average_mileage = fields.Float(string='Average Mileage')\r\n driver_number = fields.Char('Driver Number')\r\n helper_id = fields.Many2one('res.partner', string='Helper Name')\r\n helper_number = fields.Char('Helper Number')\r\n department_id = fields.Many2one('hr.department', string='Department')\r\n driver_nid = fields.Char('Driver ID')\r\n vehicle_history_count = fields.Integer(string='Fuel Logs', compute='get_vehicle_history_count')\r\n tyres_amount = fields.Integer('No of Tyres')\r\n depot_id = fields.Many2one('stock.warehouse', string='Depot')\r\n progress = fields.Float(\"Docs Upload\", compute='_compute_progress', store=True, group_operator=\"avg\", help=\"Display Docs Attached.\")\r\n\r\n @api.depends('doc1', 'doc2', 'doc3','doc4','doc5')\r\n def _compute_progress(self):\r\n for task in self:\r\n task.progress = 0.0\r\n if task.doc1:\r\n task.progress += 20\r\n if task.doc2:\r\n task.progress += 20\r\n if task.doc3:\r\n task.progress += 20\r\n if task.doc4:\r\n task.progress += 20\r\n if task.doc5:\r\n task.progress += 20\r\n \r\n\r\n \r\n def open_vehicle_tyres(self):\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Tyres',\r\n 'view_mode': 'tree,form',\r\n 'res_model': 'vehicle.tyre.detail',\r\n 'domain': [('vehicle_id', '=', self.id)],\r\n 'context': {'default_vehicle_id': self.id} \r\n \r\n }\r\n\r\n def open_vehicle_component(self):\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Components',\r\n 'view_mode': 'tree,form',\r\n 'res_model': 'vehicle.components',\r\n 'domain': [('vehicle_id', '=', self.id)],\r\n 'context': {'default_vehicle_id': self.id} \r\n \r\n }\r\n\r\n def get_component_count(self):\r\n count = self.env['vehicle.components'].search_count([('vehicle_id', '=', self.id)])\r\n self.component_count = count\r\n \r\n \r\n def get_tyres_count(self):\r\n count = self.env['vehicle.tyre.detail'].search_count([('vehicle_id', '=', self.id)])\r\n self.tyres_count = count\r\n \r\n\r\n def open_vehicle_fuel_logs(self):\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Fuel Issue',\r\n 'view_mode': 'tree,form',\r\n 'res_model': 'fleet.vehicle.log.fuel',\r\n 'domain': [('vehicle_id', '=', self.id)], \r\n 'context': {'default_vehicle_id': self.id} \r\n\r\n }\r\n \r\n def get_fuel_log_count(self):\r\n count = self.env['fleet.vehicle.log.fuel'].search_count([('vehicle_id', '=', self.id)])\r\n self.fuel_log_count = count\r\n \r\n# Smart button for Routes \r\n def open_vehicle_routes(self):\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Routes',\r\n 'view_mode': 'tree,form',\r\n 'res_model': 'fleet.route.plan',\r\n 'domain': [('vehicle_id', '=', self.id)],\r\n 'context': {'default_vehicle_id': self.id} \r\n \r\n }\r\n \r\n# Routes Count \r\n def get_routes_count(self):\r\n count = self.env['fleet.route.plan'].search_count([('vehicle_id', '=', self.id)])\r\n self.routes_count = count\r\n\r\n def open_vehicle_history(self):\r\n return {\r\n 'type': 'ir.actions.act_window',\r\n 'name': 'Routes',\r\n 'view_mode': 'tree,form',\r\n 'res_model': 'vehicle.history',\r\n 'domain': [('vehicle_id', '=', self.id)],\r\n 'context': {'default_vehicle_id': self.id}\r\n\r\n }\r\n\r\n def get_vehicle_history_count(self):\r\n count = self.env['vehicle.history'].search_count([('vehicle_id', '=', self.id)])\r\n self.vehicle_history_count = count\r\n \r\n# Document Upload in Vehicle Service \r\n \r\nclass FleetVehicleLogServices(models.Model):\r\n _inherit = 'fleet.vehicle.log.services'\r\n \r\n doc = fields.Binary('Bill Upload')\r\n file_name = fields.Char(\"File Name\") \r\n\r\n ","sub_path":"olila_fleet/models/fleet.py","file_name":"fleet.py","file_ext":"py","file_size_in_byte":7572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"278230272","text":"class Solution:\n def minWindow(self, s1: str, s2: str) -> str:\n req = collections.Counter(s2)\n s1_c = collections.defaultdict(int)\n l, r, cur = 0, 0, 0\n res = [float('inf'), 0, 0]\n while r < len(s1):\n added = s1[r]\n s1_c[added] += 1\n if added in req and s1_c[added] == req[added]:\n cur += 1\n \n while l <= r and cur == len(req):\n if (r - l + 1) < res[0]:\n res = [r - l + 1, l, r]\n prev = s1[l]\n s1_c[prev] -= 1\n if prev in req and s1_c[prev] == req[prev] - 1:\n cur -= 1\n l += 1\n r += 1\n return s1[res[1]: res[2]+1] if res[0] != float('inf') else ''\n","sub_path":"Facebook/76.py","file_name":"76.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"179680249","text":"import matplotlib.pyplot as plt\nimport code.thinkdsp as thinkdsp\n\ncos_sig = thinkdsp.CosSignal(freq=440, amp=1.0, offset=0)\nsin_sig = thinkdsp.SinSignal(freq=880, amp=0.5, offset=0)\n\nmix = cos_sig+sin_sig\n\nwave = mix.make_wave(duration=0.5, start=0, framerate=11025)\n\nperiod = mix.period\nsegment = wave.segment(start=0, duration=period*1)\n\nsegment.plot()\nplt.show()","sub_path":"practice_11_27_2020.py","file_name":"practice_11_27_2020.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"564930910","text":"'''\nFunction:\n Implementation of Inferencer\nAuthor:\n Zhenchao Jin\n'''\nimport os\nimport cv2\nimport copy\nimport torch\nimport warnings\nimport argparse\nimport numpy as np\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom configs import BuildConfig\nfrom modules import (\n BuildDataset, BuildDistributedDataloader, BuildDistributedModel, BuildLoss, BuildBackbone, BuildSegmentor, BuildPixelSampler, \n Logger, initslurm, setrandomseed, touchdir, loadckpts, saveckpts, BuildOptimizer, BuildScheduler\n)\nwarnings.filterwarnings('ignore')\n\n\n'''parse arguments in command line'''\ndef parsecmdargs():\n parser = argparse.ArgumentParser(description='SSSegmentation is an open source supervised semantic segmentation toolbox based on PyTorch')\n parser.add_argument('--imagedir', dest='imagedir', help='images dir for testing multi images', type=str)\n parser.add_argument('--imagepath', dest='imagepath', help='imagepath for testing single image', type=str)\n parser.add_argument('--outputfilename', dest='outputfilename', help='name to save output image(s)', type=str, default='')\n parser.add_argument('--cfgfilepath', dest='cfgfilepath', help='config file path you want to use', type=str, required=True)\n parser.add_argument('--ckptspath', dest='ckptspath', help='checkpoints you want to resume from', type=str, required=True)\n args = parser.parse_args()\n return args\n\n\n'''Inferencer'''\nclass Inferencer():\n def __init__(self):\n self.cmd_args = parsecmdargs()\n self.cfg, self.cfg_file_path = BuildConfig(self.cmd_args.cfgfilepath)\n assert self.cmd_args.imagepath or self.cmd_args.imagedir, 'imagepath or imagedir should be specified'\n # open full fp32\n torch.backends.cuda.matmul.allow_tf32 = False\n torch.backends.cudnn.allow_tf32 = False\n '''start'''\n def start(self):\n cmd_args, cfg, cfg_file_path = self.cmd_args, self.cfg, self.cfg_file_path\n # touch work dir\n touchdir(cfg.SEGMENTOR_CFG['work_dir'])\n # cuda detect\n use_cuda = torch.cuda.is_available()\n # initialize logger_handle\n logger_handle = Logger(cfg.SEGMENTOR_CFG['logfilepath'])\n # build segmentor\n cfg.SEGMENTOR_CFG['backbone']['pretrained'] = False\n segmentor = BuildSegmentor(segmentor_cfg=cfg.SEGMENTOR_CFG, mode='TEST')\n if use_cuda: segmentor = segmentor.cuda()\n # build dataset\n dataset = BuildDataset(mode='TEST', logger_handle=logger_handle, dataset_cfg=cfg.SEGMENTOR_CFG['dataset'])\n # build palette\n palette = dataset.palette\n # load ckpts\n cmd_args.local_rank = 0\n ckpts = loadckpts(cmd_args.ckptspath)\n try:\n segmentor.load_state_dict(ckpts['model'])\n except Exception as e:\n logger_handle.warning(str(e) + '\\n' + 'Try to load ckpts by using strict=False')\n segmentor.load_state_dict(ckpts['model'], strict=False)\n # set eval\n segmentor.eval()\n # start to test\n inference_cfg = copy.deepcopy(cfg.SEGMENTOR_CFG['inference'])\n FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor\n if not cmd_args.imagedir:\n imagepaths = [cmd_args.imagepath]\n else:\n imagenames = os.listdir(cmd_args.imagedir)\n imagepaths = [os.path.join(cmd_args.imagedir, name) for name in imagenames]\n pbar = tqdm(range(len(imagepaths)))\n for idx in pbar:\n imagepath = imagepaths[idx]\n if imagepath.split('.')[-1] not in ['jpg', 'jpeg', 'png']: \n continue\n pbar.set_description('Processing %s' % imagepath)\n infer_tricks = inference_cfg['tricks']\n cascade_cfg = infer_tricks.get('cascade', {'key_for_pre_output': 'memory_gather_logits', 'times': 1, 'forward_default_args': None})\n sample_meta = dataset.read(imagepath, 'none.png')\n image = sample_meta['image']\n sample_meta = dataset.synctransforms(sample_meta)\n image_tensor = sample_meta['image'].unsqueeze(0).type(FloatTensor)\n for idx in range(cascade_cfg['times']):\n forward_args = None\n if idx > 0: \n output_list = [\n F.interpolate(outputs, size=output_list[-1].shape[2:], mode='bilinear', align_corners=segmentor.align_corners) for outputs in output_list\n ]\n forward_args = {cascade_cfg['key_for_pre_output']: sum(output_list) / len(output_list)}\n if cascade_cfg['forward_default_args'] is not None: \n forward_args.update(cascade_cfg['forward_default_args'])\n output_list = self.auginference(\n segmentor=segmentor,\n images=image_tensor,\n inference_cfg=inference_cfg,\n num_classes=cfg.SEGMENTOR_CFG['num_classes'],\n FloatTensor=FloatTensor,\n align_corners=segmentor.align_corners,\n forward_args=forward_args,\n )\n output_list = [\n F.interpolate(output, size=(sample_meta['height'], sample_meta['width']), mode='bilinear', align_corners=segmentor.align_corners) for output in output_list\n ]\n output = sum(output_list) / len(output_list)\n pred = (torch.argmax(output[0], dim=0)).cpu().numpy().astype(np.int32)\n mask = np.zeros((pred.shape[0], pred.shape[1], 3), dtype=np.uint8)\n for clsid, color in enumerate(palette):\n mask[pred == clsid, :] = np.array(color)[::-1]\n image = image * 0.5 + mask * 0.5\n image = image.astype(np.uint8)\n if cmd_args.outputfilename:\n cv2.imwrite(os.path.join(cfg.SEGMENTOR_CFG['work_dir'], cmd_args.outputfilename + '_%d' % idx + '.png'), image)\n else:\n cv2.imwrite(os.path.join(cfg.SEGMENTOR_CFG['work_dir'], imagepath.split('/')[-1].split('.')[0] + '.png'), image)\n '''inference with augmentations'''\n def auginference(self, segmentor, images, inference_cfg, num_classes, FloatTensor, align_corners, forward_args=None):\n infer_tricks, outputs_list = inference_cfg['tricks'], []\n for scale_factor in infer_tricks['multiscale']:\n images_scale = F.interpolate(images, scale_factor=scale_factor, mode='bilinear', align_corners=align_corners)\n outputs = self.inference(\n segmentor=segmentor, \n images=images_scale.type(FloatTensor), \n inference_cfg=inference_cfg, \n num_classes=num_classes, \n forward_args=forward_args,\n ).cpu()\n outputs_list.append(outputs)\n if infer_tricks['flip']:\n images_flip = torch.from_numpy(np.flip(images_scale.cpu().numpy(), axis=3).copy())\n outputs_flip = self.inference(\n segmentor=segmentor, \n images=images_flip.type(FloatTensor), \n inference_cfg=inference_cfg, \n num_classes=num_classes, \n forward_args=forward_args,\n )\n fixed_seg_target_pairs = inference_cfg.get('fixed_seg_target_pairs', None)\n if fixed_seg_target_pairs is None:\n for data_pipeline in self.cfg.SEGMENTOR_CFG['dataset']['train']['data_pipelines']:\n if 'RandomFlip' in data_pipeline: \n fixed_seg_target_pairs = data_pipeline[-1].get('fixed_seg_target_pairs', None)\n if fixed_seg_target_pairs is not None:\n outputs_flip_clone = outputs_flip.data.clone()\n for (pair_a, pair_b) in fixed_seg_target_pairs:\n outputs_flip[:, pair_a, :, :] = outputs_flip_clone[:, pair_b, :, :]\n outputs_flip[:, pair_b, :, :] = outputs_flip_clone[:, pair_a, :, :]\n outputs_flip = torch.from_numpy(np.flip(outputs_flip.cpu().numpy(), axis=3).copy()).type_as(outputs)\n outputs_list.append(outputs_flip)\n return outputs_list\n '''inference'''\n def inference(self, segmentor, images, inference_cfg, num_classes, forward_args=None):\n assert inference_cfg['mode'] in ['whole', 'slide']\n use_probs_before_resize = inference_cfg['tricks']['use_probs_before_resize']\n if inference_cfg['mode'] == 'whole':\n if forward_args is None:\n outputs = segmentor(images)\n else:\n outputs = segmentor(images, **forward_args)\n if use_probs_before_resize: \n outputs = F.softmax(outputs, dim=1)\n else:\n align_corners = segmentor.align_corners\n opts = inference_cfg['opts']\n stride_h, stride_w = opts['stride']\n cropsize_h, cropsize_w = opts['cropsize']\n batch_size, _, image_h, image_w = images.size()\n num_grids_h = max(image_h - cropsize_h + stride_h - 1, 0) // stride_h + 1\n num_grids_w = max(image_w - cropsize_w + stride_w - 1, 0) // stride_w + 1\n outputs = images.new_zeros((batch_size, num_classes, image_h, image_w))\n count_mat = images.new_zeros((batch_size, 1, image_h, image_w))\n for h_idx in range(num_grids_h):\n for w_idx in range(num_grids_w):\n x1, y1 = w_idx * stride_w, h_idx * stride_h\n x2, y2 = min(x1 + cropsize_w, image_w), min(y1 + cropsize_h, image_h)\n x1, y1 = max(x2 - cropsize_w, 0), max(y2 - cropsize_h, 0)\n crop_images = images[:, :, y1:y2, x1:x2]\n if forward_args is None:\n outputs_crop = segmentor(crop_images)\n else:\n outputs_crop = segmentor(crop_images, **forward_args)\n outputs_crop = F.interpolate(outputs_crop, size=crop_images.size()[2:], mode='bilinear', align_corners=align_corners)\n if use_probs_before_resize: \n outputs_crop = F.softmax(outputs_crop, dim=1)\n outputs += F.pad(outputs_crop, (int(x1), int(outputs.shape[3] - x2), int(y1), int(outputs.shape[2] - y2)))\n count_mat[:, :, y1:y2, x1:x2] += 1\n assert (count_mat == 0).sum() == 0\n outputs = outputs / count_mat\n return outputs\n\n\n'''debug'''\nif __name__ == '__main__':\n with torch.no_grad():\n client = Inferencer()\n client.start()","sub_path":"ssseg/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":10647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"609543450","text":"__author__ = 'Administrator'\n\nentity_information_file_path = \"../../data/resource/entity_information.txt\"\noutput_file_path = \"../../data/resource/wikipage_title_entity_id\"\n\ncoding = \"utf8\"\n\ntitle_entity_id_dic = {}\n\nfr = open(entity_information_file_path,'r',encoding = coding)\nfw = open(output_file_path,'w',encoding = coding)\n\nline = fr.readline()\nwhile(line):\n line = line.strip()\n if(line != ''):\n line_list = line.split(\"\\t\")\n entity_id = line_list[0].strip(\"\\\"\")\n title = line_list[3].strip(\"\\\"\")\n\n fw.write(\"{0}\\t{1}\\n\".format(title,entity_id))\n\n line = fr.readline()\n\nfr.close()\nfw.close()\n\n\n\n","sub_path":"src/tools/convert_title_id_to_title_entityid.py","file_name":"convert_title_id_to_title_entityid.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"573815340","text":"# -*- coding: utf-8 -*-\n\"\"\"\nfunction：\n@author: qinxunhui\ntime:\n\"\"\"\n\nimport xgboost as xgb\nimport json\nimport numpy as np\n\nfrom load_q_xianghai import load_q_info\nimport extract_feature\n\npath = 'D:/evaluation/data/测试数据/finance8000_q_extend_info_ex_with_type.txt'\nq_info_dic = load_q_info(path)\n\n\ndef gen_feature_label(json_path):\n with open(json_path, 'r') as file:\n data = json.load(file)\n label = [row['pass'] for row in data]\n\n feature = []\n for row in data:\n feature_one = extract_feature.get_all_feature(row, q_info_dic)\n feature.append(feature_one)\n\n label = np.array(label)\n feature = np.array(feature)\n return feature, label\n\n\ndef train_test():\n\n # 训练样本路径和测试样本路径\n json_path_train = \"D:\\evaluation\\data\\测试数据\\\\test_8_topN_22859_train.json\"\n json_path_test = \"D:\\evaluation\\data\\测试数据\\\\test_8_topN_22859_test.json\"\n\n model_path = \"finance8000_xgboost_scores_wmdr_type.model\"\n\n # xgboost param\n param = {\n 'booster': 'gbtree', #gbtree使用基于树的模型进行提升计算，gblinear使用线性模型进行提升计算\n 'learning_rate': 0.01, #学习率\n 'max_depth': 10, #树的最大深度。缺省值为6。通常取值：3-10。树的深度越大，则对数据的拟合程度越高\n 'min_child_weight': 1, #孩子节点中最小的样本权重和。如果一个叶子节点的样本权重和小于min_child_weight则拆分过程结束。即调大这个参数能够控制过拟合。\n 'gamma': 0.1, #gamma值【0-】值越大算法算法更conservation（保守），且其值依赖于loss function ，在模型中应该进行调参\n 'subsample': 0.7, #用于训练模型的子样本占整个样本集合的比例。如果设置为0.5则意味着XGBoost将随机的从整个样本集合中抽取出50%的子样本建立树模型，这能够防止过拟合。\n 'objective': 'binary:logistic', #目标函数类型，'binary:logistic'二分类的逻辑回归问题，输出为概率。\n 'scale_pos_weight': 1, #大于0的取值可以处理类别不平衡的情况。帮助模型更快收敛\n 'seed': 1000 #随机数的种子。缺省值为0。可以用于产生可重复的结果\n }\n\n plst = param.items()\n\n # 训练样本\n # feature_train, label_train = gen_feature_label(json_path_train)\n feature_train, label_train = gen_feature_label(json_path_train)\n\n # 测试样本\n # feature_test, label_test = gen_feature_label(json_path_test)\n feature_test, label_test = gen_feature_label(json_path_test)\n\n dtrain = xgb.DMatrix(feature_train, label=label_train)\n dtest = xgb.DMatrix(feature_test, label=label_test)\n evallist = [(dtest,'eval'), (dtrain,'train')]\n\n num_round = 20\n bst = xgb.train(plst, dtrain, num_round, evallist)\n bst.save_model(model_path)\n\n\nif __name__ == \"__main__\":\n # json_path_train = \"D:\\svn_algorithm\\standard\\测试平台算法评测\\code\\\\test_13_topN_22859_train.json\"\n # intent_path = \"../data/测试数据/finance8000_intent.txt\"\n # feature, label = gen_feature_label(json_path_train, intent_path)\n # print(feature, label)\n\n train_test()\n\n\n\n\n","sub_path":"train_xgboost.py","file_name":"train_xgboost.py","file_ext":"py","file_size_in_byte":3346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"310060673","text":"defaults = {\n \"Movement\": 5,\n \"max_bleeding_tokens\": 5,\n}\n\n\n#\n# these are the 'macro' options used when creating new settlements. See the\n# new_settlement_special() method of the settlements.Settlement model/class\n#\n\nspecials = {\n \"create_first_story_survivors\": {\n \"name\": \"First Story\",\n \"title\": 'Four \"First Story\" Survivors',\n \"desc\": 'Two male and two female survivors will be randomly generated and automatically added to the Departing Survivors group. Starting gear is added to Settlement Storage.',\n \"current_quarry\": \"White Lion (First Story)\",\n 'showdown_type': 'normal',\n \"random_survivors\": [\n {\"sex\": \"M\", \"Waist\": 1, \"departing\": True},\n {\"sex\": \"M\", \"Waist\": 1, \"departing\": True},\n {\"sex\": \"F\", \"Waist\": 1, \"departing\": True},\n {\"sex\": \"F\", \"Waist\": 1, \"departing\": True},\n ],\n \"storage\": [\n {\"name\": \"founding_stone\", \"quantity\": 4},\n {\"name\": \"cloth\", \"quantity\": 4},\n ],\n \"timeline_events\": [\n {\"ly\": 0, \"sub_type\": \"showdown_event\", \"name\": \"White Lion (First Story)\"},\n ],\n },\n\n \"create_seven_swordsmen\": {\n \"name\": \"Seven Swordsmen\",\n \"title\": \"Seven Swordsmen\",\n \"desc\": 'Seven survivors with the \"Ageless\" ability and Sword mastery will be randomly generated.',\n \"random_survivors\": [\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n {\"sex\": \"R\", 'abilities_and_impairments': ['ageless','mastery_sword']},\n ],\n },\n\n}\n\n\n\n#\n# the survivors in the BCS PDF\n#\n\nbeta_challenge_scenarios = {\n \"adam\": {\n \"name\": \"Adam\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Adam\",\n \"sex\": \"M\",\n \"survival\": 4,\n \"Insanity\": 4,\n \"Courage\": 6,\n \"Understanding\": 3,\n \"Weapon Proficiency\": 3,\n \"Strength\": 1,\n \"weapon_proficiency_type\": \"sword\",\n \"fighting_arts\": [\"timeless_eye\"],\n \"abilities_and_impairments\": [\"partner\", \"sword_specialization\"],\n },\n# \"storage\": [\n# \"Leather Mask\",\n# \"Leather Cuirass\",\n# \"Leather Bracers\",\n# \"Leather Skirt\",\n# \"Leather Boots\",\n# \"Scrap Sword\",\n# ],\n },\n \"anna\": {\n \"name\": \"Anna\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Anna\",\n \"sex\": \"F\",\n \"survival\": 4,\n \"Insanity\": 4,\n \"Courage\": 3,\n \"Understanding\": 6,\n \"Weapon Proficiency\": 3,\n \"Evasion\": 1,\n \"weapon_proficiency_type\": \"spear\",\n \"fighting_arts\": [\"leader\"],\n \"abilities_and_impairments\": [\"partner\", \"spear_specialization\"],\n },\n# \"storage\": [\n# \"Leather Mask\",\n# \"Leather Cuirass\",\n# \"Leather Bracers\",\n# \"Leather Skirt\",\n# \"Leather Boots\",\n# \"King Spear\",\n# ],\n },\n \"paul_the_survivor\": {\n \"name\": \"Paul the Survivor\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Paul\",\n \"sex\": \"M\",\n \"survival\": 5,\n \"Insanity\": 9,\n \"Courage\": 3,\n \"Understanding\": 3,\n \"fighting_arts\": [\"thrill_seeker\",\"clutch_fighter\",\"extra_sense\"],\n \"disorders\": [\"rageholic\",\"sworn_enemy\"],\n },\n \"storage\": [\n# \"Rawhide Pants\",\n# \"Rawhide Gloves\",\n# \"Rawhide Vest\",\n# \"Rawhide Boots\",\n# \"Bone Dagger\",\n# \"Scrap Sword\",\n# \"Piranha Helm\",\n# \"Petal Lantern\",\n ],\n },\n \"candy_and_cola\":{\n \"name\": \"Candy & Cola\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\"name\": \"Candy & Cola\", \"Movement\": 6, \"disorders\": [\"hyperactive\"], \"sex\": \"F\"},\n },\n \"kara_black\": {\n \"name\": \"Kara Black\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\"name\": \"Kara Black\", \"survival\": 3, \"Strength\": 1, \"fighting_arts\": [\"leader\",\"tough\"], \"sex\": \"F\"},\n# \"storage\": [\"Founding Stone\", \"Cloth\", \"Giant Stone Face\"],\n },\n \"messenger_of_the_first_story\": {\n \"name\": \"Messenger of the First Story\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Messenger of the First Story\",\n \"sex\": \"F\",\n \"survival\": 6,\n \"Insanity\": 6,\n \"Courage\": 6,\n \"Strength\": 1,\n \"Evasion\": 1,\n \"Speed\": 1,\n \"fighting_arts\": [\"last_man_standing\"],\n },\n# \"storage\": [\n# \"Screaming Horns\",\n# \"Screaming Coat\",\n# \"Screaming Skirt\",\n# \"Screaming Bracers\",\n# \"Screaming Leg Warmers\",\n# \"Monster Grease\",\n# \"Cat Eye Circlet\",\n# \"Dried Acanthus\",\n# \"Arm of the First Tree\",\n# ],\n },\n \"messenger_of_courage\":{\n \"name\": \"Messenger of Courage\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Messenger of Courage\",\n \"sex\": \"F\",\n \"survival\": 6,\n \"Insanity\": 9,\n \"Courage\": 9,\n \"Understanding\": 5,\n \"Strength\": 1,\n \"Evasion\": 2,\n \"Speed\": 2,\n \"hunt_xp\": 2,\n \"Weapon Proficiency\": 5,\n \"weapon_proficiency_type\": \"twilight_sword\",\n \"fighting_arts\": [\"last_man_standing\"],\n \"cursed_items\": [\"twilight_sword\"],\n \"abilities_and_impairments\": [\"twilight_sword_specialization\"],\n \"epithets\": [\"twilight_sword\"],\n },\n# \"storage\": [\n# \"Scout's Tunic\",\n# \"Fairy Bottle\",\n# \"Leather Skirt\",\n# \"Leather Boots\",\n# \"Leather Bracers\",\n# \"Feather Mantle\",\n# \"Twilight Sword\"\n# ],\n },\n \"messenger_of_humanity\":{\n \"name\": \"Messenger of Humanity\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Messenger of Humanity\",\n \"sex\": \"M\",\n \"survival\": 10,\n \"abilities_and_impairments\": [\"bitter_frenzy\", \"solid\", \"grand_weapon_specialization\"],\n \"fighting_arts\": [\"berserker\", \"crossarm_block\", \"unconscious_fighter\"],\n \"disorders\": [\"rageholic\"],\n },\n# \"storage\": [\n# \"Lantern Helm\",\n# \"Lantern Cuirass\",\n# \"Lantern Greaves\",\n# \"Lantern Mail\",\n# \"Lantern Gauntlets\",\n# \"Beacon Shield\",\n# \"Dragon Slayer\"\n# \"Stone Arm\"\n# ],\n },\n \"snow_the_savior\":{\n \"name\": \"Snow the Savior\",\n 'expansion': 'beta_challenge_scenarios',\n \"attribs\": {\n \"name\": \"Snow\",\n \"sex\": \"F\",\n \"survival\": 6,\n \"Insanity\": 8,\n \"Courage\": 5,\n \"Understanding\": 5,\n \"fighting_arts\": [\"unconscious_fighter\"],\n \"abilities_and_impairments\": [\"red_glow\", \"blue_glow\", \"green_glow\"]\n },\n },\n\n}\n","sub_path":"v2/api/assets/survivors.py","file_name":"survivors.py","file_ext":"py","file_size_in_byte":7798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"287771470","text":"import aiohttp\nimport asyncio\nfrom pprint import pprint\nfrom parsel import Selector\nfrom concurrent.futures import ThreadPoolExecutor\n\n# py4seo variant\n# async def main():\n# session = aiohttp.ClientSession()\n# response = await session.get('http://python.org')\n# html = await response.text()\n# sel = Selector(text=html)\n# print(dir(sel))\n# await session.close()\n\ndomain = 'kartochki-domana.com.ua'\nqueue = asyncio.Queue()\nadded_prod_urls = set()\n\nloop = asyncio.get_event_loop()\nexecutor = ThreadPoolExecutor(max_workers=10)\n\n\ndef scrape_data(html):\n sel = Selector(text=html)\n title = sel.xpath('//h1[contains(@class, \"product_title\")]/text()').get()\n price = sel.xpath('//p[@class=\"price\"]/span[contains(@class, \"woocommerce-Price-amount\")]/text()').get()\n return title, price\n\n# My variant\nasync def worker(session):\n while not queue.empty():\n target_url = await queue.get()\n async with session.get(target_url) as response:\n html = await response.text()\n \n title, price = await loop.run_in_executor(executor, scrape_data, html)\n print(f'{title}: {price}')\n \n # [\n # (await queue.put(prod_url), added_prod_urls.add(prod_url))\n # for prod_url in prod_urls\n # if prod_url not in added_prod_urls\n # ]\n\n\nasync def main(): \n async with aiohttp.ClientSession() as session:\n response = await session.get('https://kartochki-domana.com.ua/ru/product-category/podarochnie-nabori/')\n html = await response.text()\n \n sel = Selector(text=html)\n prod_urls = sel.xpath('//h3[@class=\"product_title\"]/a/@href').getall()\n\n [\n (await queue.put(prod_url), added_prod_urls.add(prod_url))\n for prod_url in prod_urls\n if prod_url not in added_prod_urls\n ]\n \n print(queue.qsize())\n # pprint(added_prod_urls)\n \n tasks = []\n for _ in range(50):\n task = asyncio.Task(worker(session))\n tasks.append(task)\n await asyncio.gather(*tasks)\n\n\nif __name__ == '__main__':\n\n\n # task1 = asyncio.Task(coro())\n # task2 = asyncio.Task(main())\n\n # runer = asyncio.gather(task1, task2)\n\n # loop = asyncio.get_event_loop()\n # loop.run_until_complete(runer)\n\n\n # Run just 1 coroutine main()\n \n loop.run_until_complete(main())\n","sub_path":"17_1_Asyncron/6_asyncio_aiohttp_with_threads.py","file_name":"6_asyncio_aiohttp_with_threads.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"612931044","text":"# -*- coding: cp1254 -*-\n#sansa bağlı olmayan caprazlama anneden ve babadan eşit sayıda dna gelir\ndef Caprazla2(indes1,indes2,popilasyon):\n #print dizi\n #print dizi2\n #dizi=[1,2,3,4,5,6,7,8,9,12,3,4,5,6]\n #dizi2=[44,33,11,77,55,44,33,88,77,99,445,654,323,54]\n\n import random\n\n dizi3=[]\n dizi4=[]\n\n #dizi3 için\n secim=0\n for i in range(0,len(popilasyon[indes1])):\n if (i/2== i/2.0):\n dizi3.append(popilasyon[indes1][i])\n \n else:\n dizi3.append(popilasyon[indes2][i])\n\n #dizi4 için #sadece bir çaprazlamadan bir çocuk olduğunu varsayıyoruz\n## for i in range(0,len(dizi2)):\n## secim=random.randrange(2)\n## if (secim == 0):\n## dizi4.append(dizi[i])\n## else:\n## dizi4.append(dizi2[i])\n\n return dizi3\n#dizi1 ve dizi2 kendiyle bir seçilimde en fazla 4 kere çaprazlanmalı\n#yeni jenerasyon oluştuktan sonra dizi1 ve dizi2 silinmeli\n\n","sub_path":"caprazlama2.py","file_name":"caprazlama2.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"559442471","text":"from flask import Flask, jsonify, request\n\nfrom flask_swaggervalidator import decorators\n\n\napp = Flask(__name__)\n\n\n@app.route('/')\n@decorators.validate_swagger_request('api.say_hello')\n@decorators.validate_swagger_response('api.say_hello')\ndef say_hello():\n name = request.args.get('name', 'Unknown')\n data = {\n 'weekend_greeting': 'Welcome to the weekend, {}!'.format(name),\n 'pizza_party': 1\n }\n return jsonify(data)\n\n\nif __name__ == '__main__':\n app.debug = True\n app.run()\n","sub_path":"docs/examples/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"504501975","text":"from glue import custom_viewer\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\ncolor_points = custom_viewer('Color Plot', x='att', y='att', c='att')\n\ncmap = plt.get_cmap('spectral')\nnorm = mpl.colors.Normalize(vmin=0., vmax=1.)\n\n@color_points.plot_data\ndef show_scatter(axes, x, y, c, cmap=cmap, norm=norm):\n axes.scatter(x, y, marker='o', ms=5, c=c, cmap=cmap, norm=norm)\n\n@color_points.plot_subset\ndef show_points(axes, x, y, c, style):\n axes.scatter(x, y, marker='o', ms=6, mew=2, c=c, mec=style.color, cmap=cmap, norm=norm)\n\n","sub_path":"CfAHackDay/custom_plots.py","file_name":"custom_plots.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"9553182","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 11 10:50:28 2013\n\n@author: Mahmoudreza Aghighi\n\nmodule __FicksLawDiffusion__: Fick's Law Diffusion\n========================================================================\n\n.. warning:: The classes of this module should be loaded through the 'ALG.__init__.py' file.\n\n\"\"\"\n\nimport OpenPNM\nimport numpy as np\nimport scipy.sparse as sprs\nimport scipy.sparse.linalg as splin\nfrom __GenericAlgorithm__ import GenericAlgorithm\n\nclass FicksLawDiffusion(GenericAlgorithm):\n r\"\"\" \n \n FicksLawDiffusion - Class to run Fick's law mass transfer diffusion on constructed networks\n \n It returns conecentration gradient inside the network.\n An invasion algorithm should be used before running diffusion calculations.\n \n \n Parameters\n ----------\n - Alg:\n Algorithm for Non wetting phase configuration\n 'OP' , 'IP' and 'None' are possible options. \n - Pressure for 'OP':\n Applied pressure on the network which causes some pores and throats be invaded by non-wetting phase.\n It can be a single value or a list.\n The class will consider uninvaded conduits based on this pressure, as possible voids for gas diffusion.\n Every pore with Pc_invade greater than Pressure, will be considered as open pores \n - Psequence for 'IP':\n The desired sequence of invaded pores based on IP algorithm.\n The class will consider uninvaded conduits based on this sequence number, as possible voids for gas diffusion.\n It can be a single value or a list.\n Every pore with sequence number greater than Psequence, will be considered as open pores.\n - Pinvaded for 'None':\n It is a list which contains invaded pores and based on user's definition.\n \n -loglevel : int\n Level of the logger (10=Debug, 20=INFO, 30=Warning, 40=Error, 50=Critical) \n - Total_Conc: \n Total gas concentration\n - Diff_Coefficient:\n Diffision coefficient for diffusion of A through stagnant B(e.g. Oxygen through Nitrogen and Water vapor)\n \n Examples\n --------\n\n All of the variables in this class have default values, but users can define them too.\n >>>\n \n To Do:\n - Instead of sending (Pressure in OP) or (Psequence in IP) as inputs, this class should only accept \n a list of invaded voids.\n - loglevel and documentation is not complete yet.\n - For Applying Neumann boundary condition, a function is needed to consider mass conservation, sink or\n source terms and warn user about the applied values.\n \n \"\"\"\n \n def __init__(self,net=OpenPNM.NET.GenericNetwork(),loglevel=10,Alg='None',Pressure=[0],Psequence=[0],Pinvaded=[],**kwargs):\n r\"\"\"\n Initializing the class\n \"\"\"\n super(FicksLawDiffusion,self).__init__(net = net,**kwargs)\n self._logger.info(\"Create Fick's Diffusion Algorithm Object\")\n self.Alg = Alg\n self.Pressure = Pressure\n self.Psequence = Psequence\n self.Pinvaded = Pinvaded\n\n \n def _setup_for_FicksDiffusion(self):\n r\"\"\"\n Main features: Applying Boundary Conditions & Creating Transport Conductivities \n\n This function executes the essential mathods for building matrices in Linear solution \n \"\"\"\n self._logger.info(\"Create Diffusion Conductivities\") \n self._fill_Conductivity()\n self._logger.info(\"Applying Boundary Conditions\")\n self._Boundary_Pores_Conditions() \n\n def _Boundary_Pores_Conditions(self,FaceTypes=[4,1,4,4,1,4],FaceValues=[0,0.2,0,0,0.5,0],BList=[]):\n r\"\"\" \n - Assigning Type and Value for Boundary Pores.\n - Types of Boundary Values in FaceTypes:\n internal=0, Dirichlet=1, flux=2, periodic=3, insulated=4 mass_rate=5\n - In FaceValues, each item represents the value of that face according\n to the boundary condition type. Except for mass_rate condition which \n the value represents the total mass rate applied to the entire face,\n not just a single pore. \n For instance: if we have Facetypes[2]=1 and FaceValues[2]=0.75,it means\n that for face 3, concentration=0.75\n but if we have FaceTypes[4]=2 and FaceValues[4]=0.8, it means that\n for face 5, flux=0.8\n - Instead of applying boundary conditions to entire face, by using \n BLists, it is possible to apply BC to arbitrary pores. For instance:\n BLists = [(459,2,0.097),(2043,1,0.5)]\n It means that for Pore 459: flux=0.097 & for Pore 2043: Concentration=0.5\n \"\"\"\n self._net.pore_properties['BCtype'] = np.zeros(self._net.get_num_pores())\n self._net.pore_properties['BCvalue'] = np.zeros(self._net.get_num_pores())\n for i in range(1,7):\n self._net.pore_properties['BCtype'][self._net.pore_properties['type']==i] = FaceTypes[i-1]\n self._net.pore_properties['BCvalue'][self._net.pore_properties['type']==i] = FaceValues[i-1]\n if BList:\n for item in BList:\n pore = item[0]\n self._net.pore_properties['BCtype'][pore] = item[1]\n self._net.pore_properties['BCvalue'][pore] = item[2]\n \n def _Inv_Conduit(self,P_val): \n r\"\"\"\n creates a tlist containing which is useful to indicate the conduit is\n filled with invading fluid or not.\n \n \"\"\" \n \n if self.Alg=='None':\n self._logger.info(\"Invaded pores have already been determined\") \n else:\n if self.Alg=='OP':\n val_name = 'Pc_invaded'\n B_condition = self._net.pore_properties[val_name]P_val)\n \n self._net.set_pore_property(name=\"Concentration_at_\"+str(P_val),ndarray=Concentration)\n \n\n def _do_one_inner_iteration(self,P_val):\n \n if hasattr(self._net,'dry_coefficient_matrix'):\n self.logger.info('Dry matrix has already been created') \n else: \n setattr(self._net,\"dry_coefficient_matrix\",self._Coefficient_Matrix(P_val=0))\n \n if ((P_val==0)and(len(self.Pinvaded)==0)):\n A = self._net.dry_coefficient_matrix\n else:\n A = self._Coefficient_Matrix(P_val) \n \n if (self._net.pore_properties['BCtype']==2).any():\n B = np.zeros([self.A_dim,1])\n Dir_pores = np.array(range(self._net.get_num_pores()))[self._net.pore_properties['BCtype']==1]\n B[Dir_pores] = np.reshape(self._net.pore_properties['BCvalue'][Dir_pores],[len(Dir_pores),1])\n for item in len(self.extera_neu):\n F_type = np.unique(self._net.pore_properties['BCtype'][self._net.pore_properties['BCvalue']==self.extera_neu[item]])\n if F_type==1:\n Area = self._net.divisions[1]*self._net.divisions[2]*self._net.lattice_spacing\n elif F_type==2:\n Area = self._net.divisions[0]*self._net.divisions[1]*self._net.lattice_spacing\n elif F_type==3:\n Area = self._net.divisions[0]*self._net.divisions[2]*self._net.lattice_spacing\n elif F_type==4:\n Area = self._net.divisions[0]*self._net.divisions[2]*self._net.lattice_spacing\n elif F_type==5:\n Area = self._net.divisions[0]*self._net.divisions[1]*self._net.lattice_spacing\n elif F_type==6:\n Area = self._net.divisions[1]*self._net.divisions[2]*self._net.lattice_spacing \n B[self.A_dim-item,0] = - self.extera_neu[item]*Area\n else:\n A_dim = self._net.get_num_pores() \n B = np.reshape(self._net.pore_properties['BCvalue'],[A_dim,1])\n\n\n for i in range(0,self._net.get_num_pores()):\n if self._net.pore_properties['BCvalue'][i]!=0:\n neighbors = self._net.get_neighbor_pores(i)\n if np.in1d(neighbors,self.Pinvaded).all():\n B[i,0] = 0\n\n x = splin.spsolve(A,B) \n return(x)\n \n \n def _fill_Conductivity(self):\n r\"\"\"\n\n \"\"\"\n self._logger.info(\"Calculating Diffusion Conductivity for all of the Voids \")\n C = self._net.Total_Conc\n D = self._net.Diff_Coefficient\n self._net.set_throat_property(name=\"Cdiff\",ndarray=np.zeros(self._net.get_num_throats()))\n tcond = C*D*np.abs(( self._net.throat_properties['diameter'])**2/self._net.throat_properties['length'])\n pcond = C*D*np.abs((self._net.pore_properties['diameter'])**2/(self._net.pore_properties['diameter']/2))\n for i in range(self._net.get_num_throats()):\n PNeighbors = self._net.get_connected_pores(i)\n if (self._net.pore_properties['type'][PNeighbors[0]]!=0):\n self._net.throat_properties['Cdiff'][i] = (1/tcond[i]+1/pcond[PNeighbors[1]]+1/pcond[PNeighbors[1]])**(-1)\n elif (self._net.pore_properties['type'][PNeighbors[1]]!=0):\n self._net.throat_properties['Cdiff'][i] = (1/pcond[PNeighbors[0]]+1/pcond[PNeighbors[0]]+1/tcond[i])**(-1)\n else:\n self._net.throat_properties['Cdiff'][i] = (1/pcond[PNeighbors[0]]+1/tcond[i]+1/pcond[PNeighbors[1]])**(-1)\n \n \n def _Coefficient_Matrix(self,P_val):\n \n # Determine open and closed conduits for mass transfer \n if hasattr(self._net,'dry_coefficient_matrix'):\n \n if ((len(self.Pinvaded)>0)or(P_val>0)):\n self._Inv_Conduit(P_val)\n list_name = 'CdiffWet'\n# if P_val>0:\n# if self.Alg=='OP':\n# val_name = 'Pc_invaded' \n# elif self.Alg=='IP':\n# val_name = 'IP_Pseq'\n# self.Pinvaded = np.array(range(self._net.get_num_pores()))[self._net.pore_properties[val_name]P_val)) \n# else:\n temp_list = np.multiply(self._net.throat_properties['Cdiff'],self._net.throat_properties['UninvadedConduits']) \n self._net.set_throat_property(name=list_name,ndarray=temp_list)\n else:\n list_name = 'Cdiff'\n # Filling coefficient matrix\n\n nodes = self._net.throat_properties[list_name]>0 \n tpore1 = self._net.throat_properties['connections'][:,0]\n tpore2 = self._net.throat_properties['connections'][:,1]\n row = np.append(tpore1[nodes],tpore1[-nodes])\n col = np.append(tpore2[nodes],tpore2[-nodes])\n data= np.append(self._net.throat_properties[list_name][nodes],np.ones([len(tpore1[-nodes])])*1e-30)\n \n\n pnum = np.array(range(self._net.get_num_pores()))\n pdry = tpore2[nodes] \n loc1 = np.in1d(tpore2,pdry[np.in1d(pdry,pnum[self._net.pore_properties['BCtype']!=1])])\n modified_tpore2_dry = tpore2[loc1]\n modified_tpore1_dry = tpore1[loc1] \n row = np.append(row,modified_tpore2_dry) \n col = np.append(col,modified_tpore1_dry)\n data = np.append(data,self._net.throat_properties[list_name][loc1])\n \n pwet = tpore2[-nodes]\n loc2 = np.in1d(tpore2,pwet[np.in1d(pwet,pnum[self._net.pore_properties['BCtype']!=1])])\n modified_tpore2_wet = tpore2[loc2]\n modified_tpore1_wet = tpore1[loc2] \n row = np.append(row,modified_tpore2_wet) \n col = np.append(col,modified_tpore1_wet)\n data = np.append(data,np.ones([len(modified_tpore2_wet)])*1e-30)\n \n if (self._net.pore_properties['BCtype']==2).any():\n self.extera_neu = np.unique(self._net.pore_properties['BCvalue'][self._net.pore_properties['BCtype']==2])\n self.A_dim = self._net.get_num_pores()+ len(self.extera_neu)\n extera_neu = self.extera_neu\n A_dim = self.A_dim\n\n for item in len(extera_neu):\n loc_neu = np.in1d(tpore2,pnum[self._net.pore_properties['BCvalue']==extera_neu[item]])\n neu_tpore2 = tpore2[loc_neu] \n row = np.append(row,neu_tpore2) \n col = np.append(col,np.ones([len(neu_tpore2)])*(A_dim-item))\n data = np.append(data,np.ones([len(neu_tpore2)])*1e-18)\n \n row = np.append(row,np.ones([len(neu_tpore2)])*(A_dim-item)) \n col = np.append(col,neu_tpore2)\n data = np.append(data,np.ones([len(neu_tpore2)])*1e-18)\n \n else:\n A_dim = self._net.get_num_pores()\n \n # Adding positions for diagonal\n \n row = np.append(row,range(0,A_dim))\n col = np.append(col,range(0,A_dim))\n data = np.append(data,np.zeros((A_dim,1)))\n\n a = sprs.coo.coo_matrix((data,(row,col)),(A_dim,A_dim))\n A = a.tocsr() \n \n for i in range(0,A_dim): \n if self._net.pore_properties['BCtype'][i]==1:\n A[i,i] = 1 \n else:\n A[i,i] = -np.sum(A[i,:][np.nonzero(A[i,:])])\n \n return(A)","sub_path":"OpenPNM/ALG/__FicksLawDiffusion__.py","file_name":"__FicksLawDiffusion__.py","file_ext":"py","file_size_in_byte":15827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"254219617","text":"# performs geometric functions\n\ndef menu():\n\t\"\"\"displays program options\"\"\"\n\tprint(\"\\tGeometry Calculator\\n\")\n\tprint(\"Welcome! This program makes simple geometry calculations.\")\n\tprint(\"\\nSelect one of the following:\")\n\t# these can probably be rewritten as a dict\n\tprint(\"1: Area\")\n\tprint(\"2: Perimeter\")\n\tprint(\"3: Volume\")\n\t\n\t# string is default input type--no need to convert to int later\n\tmenu_list = ['1','2','3']\n\n\tmenu_choice = input()\n\t# input validation\n\twhile menu_choice not in menu_list:\n\t\tmenu_choice = input(\"Enter a valid selection: \")\n\tif menu_choice == '1':\n\t\tarea()\n\telif menu_choice == '2':\n\t\tperimeter()\n\telif menu_choice == '3':\n\t\tvolume()\n\t\t\ndef area():\n\t\"\"\"displays shape selection for area calculations\"\"\"\n\t# rewrite as dict\n\tprint(\"\\nSelect one of the following shapes: \")\n\tprint(\"1: Square\")\n\tprint(\"2: Rectangle\")\n\tprint(\"3: Parallelogram\")\n\tprint(\"4: Trapezoid\")\n\tprint(\"5: Circle\")\n\n\tarea_list = ['1','2','3','4','5','6']\n\n\tarea_choice = input()\n\t# input validation\n\twhile area_choice not in area_list:\n\t\tarea_choice = input(\"Enter a valid selection: \")\n\tif area_choice == '1':\n\t\tprint(\"The area of the square is:\", square_area())\n\telif area_choice == '2':\n\t\tprint(\"The area of the rectangle is:\", rectangle())\n\telif area_choice == '3':\n\t\tprint(\"The area of the parallelogram is:\", parallelogram())\n\telif area_choice == '4':\n\t\tprint(\"The area of the trapezoid is:\", trapezoid())\n\telif area_choice == '5':\n\t\tprint(\"The area of the circle is:\", circle())\n\ndef perimeter():\n\t\"\"\"calculates the perimter of a shape given side lengths\"\"\"\n\t# validate input for number of sides\n\t# loops until int received\n\twhile True:\n\t\ttry:\n\t\t\tsides = int(input(\"How many sides does the shape have? \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Enter a valid integer.\")\n\t\t\tcontinue\n\n\t# incrementer for number sides\n\tcounter = 0\n\n\t# counter for perimeter\n\tperimeter = 0\n\n\t# loop for n in number sides\n\tfor num in range(sides):\n\t\t\n\t\twhile True:\n\t\t\t# loop input for side length until int received\n\t\t\ttry:\n\t\t\t\tprint(\"Enter the length of side number \", counter + 1, \": \", sep='',end='')\n\t\t\t\tside_length = float(input())\n\t\t\t\tbreak\n\t\t\texcept ValueError:\n\t\t\t\tprint(\"Please enter a valid integer.\")\n\t\t\t\tcontinue\n\t\t# add side length to perimeter, increment counter\n\t\tperimeter += side_length\n\t\tcounter += 1\n\n\tprint(\"The perimeter is:\", perimeter)\n\ndef volume():\n\t# rewrite as dict--the whole list below can iterate over the dict\n\tprint(\"\\n\\tVolume Calculator\")\n\tprint(\"\\nSelect one of the following shapes: \")\n\tprint(\"1: Cube\")\n\tprint(\"2: Rectangular prism\")\n\tprint(\"3: Cylinder\")\n\tprint(\"4: Pyramid\")\n\tprint(\"5: Cone\")\n\tprint(\"6: Sphere\")\n\tprint(\"7: Elipsoid\")\n\n\tvol_list = ['1','2','3','4','5','6']\n\n\tvol_choice = input()\n\t# input validation\n\twhile vol_choice not in vol_list:\n\t\tvol_choice = input(\"Enter a valid selection: \")\n\tif vol_choice == '1':\n\t\tprint(\"The volume of the cube is:\", cube())\n\telif vol_choice == '2':\n\t\tprint(\"The volume of the rectangular prism is:\", rect_prism())\n\telif vol_choice == '3':\n\t\tprint(\"The volume of the cylinder is:\", cylinder())\n\telif vol_choice == '4':\n\t\tprint(\"The volume of the pyramid is:\", pyramid())\n\telif vol_choice == '5':\n\t\tprint(\"The volume of the cone is:\", cone()())\n\telif vol_choice == '6':\n\t\tprint(\"The volume of the sphere is:\", sphere())\n\telif vol_choice == '7':\n\t\tprint(\"The volume of the elipsoid is:\", elipsoid())\n\ndef square_area():\n\t\"\"\"calculates the area of a square\"\"\"\n\t# get length--these while True loops/exceptions disallow all but int/float input\n\twhile True:\n\t\ttry:\n\t\t\tlength = float(input(\"Enter the side length: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn length ** 2\n\ndef rectangle():\n\t\"\"\"calculates the area of a rectangle\"\"\"\n\t# get length\n\twhile True:\n\t\ttry:\n\t\t\tlength = float(input(\"Enter the length: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\t# get width\n\twhile True:\n\t\ttry:\n\t\t\twidth = float(input(\"Enter the width: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn length * width\n\ndef parallelogram():\n\t\"\"\"calcluates the area of a parallelogram\"\"\"\n\twhile True:\n\t\t# get base\n\t\ttry:\n\t\t\tbase = float(input(\"Enter the length of the base: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\twhile True:\n\t\t# get height\n\t\ttry:\n\t\t\theight = float(input(\"Enter the height: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn base * height\n\ndef trapezoid():\n\t\"\"\"calculates the area of a trapezoid\"\"\"\n\t# get base1\n\twhile True:\n\t\ttry:\n\t\t\tbase1 = float(input(\"Enter the length of the first base: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\t# get base2\n\twhile True:\n\t\ttry:\n\t\t\tbase2 = float(input(\"Enter the length of the second base: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\t# get height\n\twhile True:\n\t\ttry:\n\t\t\theight = float(input(\"Enter the height: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn height / 2 * (base1 + base2)\n\ndef cube():\n\t\"\"\"calculates the area of a cube\"\"\"\n\t# get and cube length\n\twhile True:\n\t\ttry:\n\t\t\tlength = float(input(\"Enter the side length: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn length ** 3\n\ndef circle():\n\t\"\"\"calculates the area of a circle\"\"\"\n\t# eventually improve accuracy with a better representation of pi\n\twhile True:\n\t\ttry:\n\t\t\tradius = float(input(\"Enter the radius: \"))\n\t\t\tbreak\n\t\texcept ValueError:\n\t\t\tprint(\"Please enter a valid number.\")\n\t\t\tcontinue\n\treturn 3.14 * radius ** 2\n\ndef circumference(radius):\n\t\"\"\"calculates the circumference of a circle\"\"\"\n\treturn radius * 2 * 3.14\n\n","sub_path":"geometry.py","file_name":"geometry.py","file_ext":"py","file_size_in_byte":5787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"492037917","text":"__author__ = 'sw'\nfrom flask import Blueprint,request\nfrom flask.ext.mongoengine import mongoengine\nfrom iotServer.models import Device,TokenRef,Sensor,DeviceData\nfrom iotServer.api1.helper import succ,erro,succ_doc\n\napi_sensor=Blueprint('api_sensor',__name__)\n\n@api_sensor.route('/sensor/',methods=['GET'])\ndef datapoint_crtate(sensor_symbol=None):\n if not sensor_symbol:\n return erro('no sensor_symbol provided. ')\n\n (_type,instance)=TokenRef.get_ref(request.headers.get('token'))\n\n if _type is not 'device':\n return erro('only support device token . ')\n\n sensors = instance.sensors\n args = request.args\n ticks={}\n\n for arg in args:\n if arg in sensors:\n ticks[arg]=args.get(arg)\n\n if len(ticks) > 0:\n try:\n DeviceData(device = instance,ticks=ticks).save()\n return succ('succ.')\n except Exception as err:\n return erro(err)\n else:\n return erro('nothing saved.')\n\n@api_sensor.route('/sensor/',methods=['POST'])\ndef sensor_crtate(sensor_symbol=None):\n if not sensor_symbol:\n return erro('no sensor_symbol provided. ')\n\n arg=request.args\n sensor_name=arg.get('name')\n sensor_type=arg.get('type')\n if not (sensor_type and sensor_type):\n return erro('no sensor_type sensor_name provided. ')\n\n sensor_type = sensor_type.upper()\n\n (_type,instance)=TokenRef.get_ref(request.headers.get('token'))\n if _type is not 'device':\n return erro('only support device token . ')\n\n sensors = instance.sensors or {}\n sensor = Sensor(name=sensor_name,type=sensor_type)\n sensors[sensor_symbol]=sensor\n\n try:\n instance.update(sensors=sensors)\n return succ_doc(sensor)\n except Exception as err:\n return erro(err)\n\n","sub_path":"iotServer/api1/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"483838146","text":"import tcod as tc\nfrom logic.logger import OperationLog\nfrom logic.move import MoveType\nfrom logic.patterns.command import Command\nfrom logic.states import State\n\n\nclass KeysHandler:\n @staticmethod\n def inventory_keys(engine, key):\n if key in [tc.event.K_ESCAPE, tc.event.K_i, tc.event.K_d]:\n if engine.prev_state.value:\n engine.curr_state.value = engine.prev_state.value\n else:\n engine.curr_state.value = State.PLAYER_TURN\n return OperationLog()\n\n if key == tc.event.K_1:\n return OperationLog([{'inv_index': 0}])\n if key == tc.event.K_2:\n return OperationLog([{'inv_index': 1}])\n if key == tc.event.K_3:\n return OperationLog([{'inv_index': 2}])\n\n return OperationLog()\n\n @staticmethod\n def user_input(engine):\n for event in tc.event.get():\n # debug\n if engine.info.DEBUG:\n if event.type != \"MOUSEMOTION\":\n print(event)\n # quit\n if event.type == \"QUIT\":\n engine.IS_GAME = False\n # нажатие клавивиши\n if event.type == \"KEYDOWN\":\n # передача управления в инвентарь\n if engine.curr_state.value != State.PLAYER_DEAD and \\\n engine.curr_state.value in [State.SHOWING_MENU, State.DROP_ITEM]:\n return Command(KeysHandler.inventory_keys, engine, event.sym)\n\n if event.sym == tc.event.K_ESCAPE:\n # выход из инвентаря\n if engine.curr_state.value == (State.SHOWING_MENU, State.DROP_ITEM):\n engine.curr_state.value = engine.prev_state.value\n else:\n # выход из игры\n engine.IS_GAME = False\n if engine.curr_state.value != State.PLAYER_DEAD:\n if event.sym == tc.event.K_UP:\n return KeysHandler.create_command(engine, MoveType.UP)\n if event.sym == tc.event.K_DOWN:\n return KeysHandler.create_command(engine, MoveType.DOWN)\n if event.sym == tc.event.K_LEFT:\n return KeysHandler.create_command(engine, MoveType.LEFT)\n if event.sym == tc.event.K_RIGHT:\n return KeysHandler.create_command(engine, MoveType.RIGHT)\n if event.sym == tc.event.K_g:\n return Command(engine.player.get_item, engine.get_entities())\n if event.sym == tc.event.K_i:\n # выход из меню инвентаря\n if engine.curr_state.value == State.SHOWING_MENU:\n engine.curr_state.value = engine.prev_state.value\n return Command(lambda arg: arg, OperationLog())\n # вход в меню инвентаря\n return Command(lambda arg: arg, OperationLog([{'show_menu': True}]))\n if event.sym == tc.event.K_d:\n # выход из drop меню инвентаря\n if engine.curr_state.value == State.DROP_ITEM:\n engine.curr_state.value = engine.prev_state.value\n return Command(lambda arg: arg, OperationLog())\n # вход в drop меню инвентаря\n return Command(lambda arg: arg, OperationLog([{'drop_menu': True}]))\n\n return Command(lambda arg: arg, OperationLog())\n\n @staticmethod\n def create_command(engine, move_type):\n return Command(engine.player.mv_handler.move, move_type,\n engine.map, engine.get_entities(), engine.curr_state)\n","sub_path":"test_data/projects/roguelike/project/engine/keys_handler.py","file_name":"keys_handler.py","file_ext":"py","file_size_in_byte":3942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"634810813","text":"from ..common.config import MARKET_NUMBER_DICT\nfrom jsonpath import jsonpath\nfrom retry import retry\nfrom typing import Dict, List, Union\nfrom tqdm import tqdm\nimport multitasking\nimport pandas as pd\nfrom ..utils import to_numeric\nfrom ..shared import session\nfrom .config import (EASTMONEY_QUOTE_FIELDS,\n EASTMONEY_REQUEST_HEADERS,\n EASTMONEY_KLINE_FIELDS,\n EASTMONEY_HISTORY_BILL_FIELDS)\nfrom ..utils import get_quote_id\n\n\n@to_numeric\ndef get_realtime_quotes_by_fs(fs: str) -> pd.DataFrame:\n \"\"\"\n 获取沪深市场最新行情总体情况\n\n Returns\n -------\n DataFrame\n 沪深市场最新行情信息（涨跌幅、换手率等信息）\n\n \"\"\"\n\n fields = \",\".join(EASTMONEY_QUOTE_FIELDS.keys())\n params = (\n ('pn', '1'),\n ('pz', '1000000'),\n ('po', '1'),\n ('np', '1'),\n ('fltt', '2'),\n ('invt', '2'),\n ('fid', 'f3'),\n ('fs', fs),\n ('fields', fields)\n )\n url = 'http://push2.eastmoney.com/api/qt/clist/get'\n json_response = session.get(url,\n headers=EASTMONEY_REQUEST_HEADERS,\n params=params).json()\n df = pd.DataFrame(json_response['data']['diff'])\n df = df.rename(columns=EASTMONEY_QUOTE_FIELDS)\n df = df[EASTMONEY_QUOTE_FIELDS.values()]\n df['行情ID'] = df['市场编号'].astype(str)+'.'+df['代码'].astype('str')\n df['市场类型'] = df['市场编号'].astype(str).apply(\n lambda x: MARKET_NUMBER_DICT.get(x))\n\n return df\n\n\n@to_numeric\ndef get_quote_history_single(code: str,\n beg: str = '19000101',\n end: str = '20500101',\n klt: int = 101,\n fqt: int = 1,\n **kwargs) -> pd.DataFrame:\n \"\"\"\n 获取单只股票、债券 K 线数据\n\n \"\"\"\n\n fields = list(EASTMONEY_KLINE_FIELDS.keys())\n columns = list(EASTMONEY_KLINE_FIELDS.values())\n fields2 = \",\".join(fields)\n if kwargs.get('quote_id_mode'):\n quote_id = code\n else:\n quote_id = get_quote_id(code)\n params = (\n ('fields1', 'f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13'),\n ('fields2', fields2),\n ('beg', beg),\n ('end', end),\n ('rtntype', '6'),\n ('secid', quote_id),\n ('klt', f'{klt}'),\n ('fqt', f'{fqt}'),\n )\n\n url = 'https://push2his.eastmoney.com/api/qt/stock/kline/get'\n\n json_response = session.get(\n url, headers=EASTMONEY_REQUEST_HEADERS, params=params).json()\n klines: List[str] = jsonpath(json_response, '$..klines[:]')\n if not klines:\n columns.insert(0, '代码')\n columns.insert(0, '名称')\n return pd.DataFrame(columns=columns)\n\n rows = [kline.split(',') for kline in klines]\n name = json_response['data']['name']\n code = quote_id.split('.')[-1]\n df = pd.DataFrame(rows, columns=columns)\n df.insert(0, '代码', code)\n df.insert(0, '名称', name)\n\n return df\n\n\ndef get_quote_history_multi(codes: List[str],\n beg: str = '19000101',\n end: str = '20500101',\n klt: int = 101,\n fqt: int = 1,\n tries: int = 3,\n **kwargs\n ) -> Dict[str, pd.DataFrame]:\n \"\"\"\n 获取多只股票、债券历史行情信息\n\n \"\"\"\n\n dfs: Dict[str, pd.DataFrame] = {}\n total = len(codes)\n\n @multitasking.task\n @retry(tries=tries, delay=1)\n def start(code: str):\n _df = get_quote_history_single(\n code,\n beg=beg,\n end=end,\n klt=klt,\n fqt=fqt,\n **kwargs)\n dfs[code] = _df\n pbar.update(1)\n pbar.set_description_str(f'Processing => {code}')\n\n pbar = tqdm(total=total)\n for code in codes:\n start(code)\n multitasking.wait_for_tasks()\n pbar.close()\n return dfs\n\n\ndef get_quote_history(codes: Union[str, List[str]],\n beg: str = '19000101',\n end: str = '20500101',\n klt: int = 101,\n fqt: int = 1,\n **kwargs) -> Union[pd.DataFrame, Dict[str, pd.DataFrame]]:\n \"\"\"\n 获取股票、ETF、债券的 K 线数据\n\n Parameters\n ----------\n codes : Union[str,List[str]]\n 股票、债券代码 或者 代码构成的列表\n beg : str, optional\n 开始日期，默认为 ``'19000101'`` ，表示 1900年1月1日\n end : str, optional\n 结束日期，默认为 ``'20500101'`` ，表示 2050年1月1日\n klt : int, optional\n 行情之间的时间间隔，默认为 ``101`` ，可选示例如下\n\n - ``1`` : 分钟\n - ``5`` : 5 分钟\n - ``15`` : 15 分钟\n - ``30`` : 30 分钟\n - ``60`` : 60 分钟\n - ``101`` : 日\n - ``102`` : 周\n - ``103`` : 月\n\n fqt : int, optional\n 复权方式，默认为 ``1`` ，可选示例如下\n\n - ``0`` : 不复权\n - ``1`` : 前复权\n - ``2`` : 后复权\n\n Returns\n -------\n Union[DataFrame, Dict[str, DataFrame]]\n 股票、债券的 K 线数据\n\n - ``DataFrame`` : 当 ``codes`` 是 ``str`` 时\n - ``Dict[str, DataFrame]`` : 当 ``codes`` 是 ``List[str]`` 时\n\n \"\"\"\n\n if isinstance(codes, str):\n return get_quote_history_single(codes,\n beg=beg,\n end=end,\n klt=klt,\n fqt=fqt,\n **kwargs)\n\n elif hasattr(codes, '__iter__'):\n codes = list(codes)\n return get_quote_history_multi(codes,\n beg=beg,\n end=end,\n klt=klt,\n fqt=fqt,\n **kwargs)\n raise TypeError(\n '代码数据类型输入不正确！'\n )\n\n\n@to_numeric\ndef get_history_bill(code: str) -> pd.DataFrame:\n \"\"\"\n 获取单支股票、债券的历史单子流入流出数据\n\n Parameters\n ----------\n code : str\n 股票、债券代码\n\n Returns\n -------\n DataFrame\n 沪深市场单只股票、债券历史单子流入流出数据\n\n \"\"\"\n\n fields = list(EASTMONEY_HISTORY_BILL_FIELDS.keys())\n columns = list(EASTMONEY_HISTORY_BILL_FIELDS.values())\n fields2 = \",\".join(fields)\n quote_id = get_quote_id(code)\n params = (\n ('lmt', '100000'),\n ('klt', '101'),\n ('secid', quote_id),\n ('fields1', 'f1,f2,f3,f7'),\n ('fields2', fields2),\n\n )\n url = 'http://push2his.eastmoney.com/api/qt/stock/fflow/daykline/get'\n json_response = session.get(url,\n headers=EASTMONEY_REQUEST_HEADERS,\n params=params).json()\n\n klines: List[str] = jsonpath(json_response, '$..klines[:]')\n if not klines:\n columns.insert(0, '代码')\n columns.insert(0, '名称')\n return pd.DataFrame(columns=columns)\n rows = [kline.split(',') for kline in klines]\n name = jsonpath(json_response, '$..name')[0]\n code = quote_id.split('.')[-1]\n df = pd.DataFrame(rows, columns=columns)\n df.insert(0, '代码', code)\n df.insert(0, '名称', name)\n\n return df\n\n\n@to_numeric\ndef get_today_bill(code: str) -> pd.DataFrame:\n \"\"\"\n 获取单只股票最新交易日的日内分钟级单子流入流出数据\n\n Parameters\n ----------\n code : str\n 股票、债券代码\n\n Returns\n -------\n DataFrame\n 单只股票、债券最新交易日的日内分钟级单子流入流出数据\n\n\n \"\"\"\n quote_id = get_quote_id(code)\n params = (\n ('lmt', '0'),\n ('klt', '1'),\n ('secid', quote_id),\n ('fields1', 'f1,f2,f3,f7'),\n ('fields2', 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63'),\n )\n url = 'http://push2.eastmoney.com/api/qt/stock/fflow/kline/get'\n json_response = session.get(url,\n headers=EASTMONEY_REQUEST_HEADERS,\n params=params).json()\n columns = ['时间', '主力净流入', '小单净流入', '中单净流入', '大单净流入', '超大单净流入']\n name = jsonpath(json_response, '$..name')[0]\n code = quote_id.split('.')[-1]\n klines: List[str] = jsonpath(json_response, '$..klines[:]')\n if not klines:\n columns.insert(0, '代码')\n columns.insert(0, '名称')\n return pd.DataFrame(columns=columns)\n rows = [kline.split(',') for kline in klines]\n df = pd.DataFrame(rows, columns=columns)\n df.insert(0, '代码', code)\n df.insert(0, '名称', name)\n return df\n","sub_path":"efinance/common/getter.py","file_name":"getter.py","file_ext":"py","file_size_in_byte":9014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"244334454","text":"#!/usr/bin/env python\n# \n\nimport os, sys, numpy\n\nimport astropy\nimport astropy.io.ascii as asciitable\n\nimport scipy\nfrom scipy import interpolate\n\nfrom copy import copy\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import ScalarFormatter, FuncFormatter, LogLocator, FormatStrFormatter\n\n\n# define my_axis_formatter\ndef format_axis_tick_values(x_array):\n str_array = []\n for x in x_array:\n if x >= 100 or x <= -100:\n str_item = '%1.0f'%(x)\n else:\n str_item = '%1.1f'%(x)\n str_array.append(str_item)\n return str_array\n\n\n# set up color\ncmap = matplotlib.cm.get_cmap('plasma_r') # 'jet' #. 'gist_heat_r' (same as Eric J.-A. paper)\nnormalize = matplotlib.colors.Normalize(vmin=0.0, vmax=1.0)\n\n\n# Read data\n#differential_curve_1_2 = asciitable.read('Completeness_sim_Sbeam_1.0_2.0/datatable_MC_sim_completeness_differential.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\n#differential_curve_2_3 = asciitable.read('Completeness_sim_Sbeam_2.0_3.0/datatable_MC_sim_completeness_differential.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\n#differential_curve_3_4 = asciitable.read('Completeness_sim_Sbeam_3.0_4.0/datatable_MC_sim_completeness_differential.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\n#differential_curve_4_5 = asciitable.read('Completeness_sim_Sbeam_4.0_5.0/datatable_MC_sim_completeness_differential.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_1p0_1p5 = asciitable.read('Completeness_sim_Sbeam_1.0_1.5/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_1p5_2p0 = asciitable.read('Completeness_sim_Sbeam_1.5_2.0/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_2p0_2p5 = asciitable.read('Completeness_sim_Sbeam_2.0_2.5/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_2p5_3p0 = asciitable.read('Completeness_sim_Sbeam_2.5_3.0/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_3p0_3p5 = asciitable.read('Completeness_sim_Sbeam_3.0_3.5/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_3p5_4p0 = asciitable.read('Completeness_sim_Sbeam_3.5_4.0/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_4p0_4p5 = asciitable.read('Completeness_sim_Sbeam_4.0_4.5/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\ndifferential_curve_4p5_5p0 = asciitable.read('Completeness_sim_Sbeam_4.5_5.0/datatable_MC_sim_completeness_differential.S_total_sim_to_rms_noise.txt', names=['snr','incomp','aa','bb'], fill_values=[('-99',numpy.nan)])\n#print(differential_curve_1_2)\n\n#differential_cube = numpy.column_stack((\n# 1.0-differential_curve_1_2['incomp'].data,\n# 1.0-differential_curve_2_3['incomp'].data,\n# 1.0-differential_curve_3_4['incomp'].data,\n# 1.0-differential_curve_4_5['incomp'].data))\ndifferential_cube = numpy.column_stack((\n (differential_curve_1p0_1p5['incomp']).data,\n (differential_curve_1p5_2p0['incomp']).data,\n (differential_curve_2p0_2p5['incomp']).data,\n (differential_curve_2p5_3p0['incomp']).data,\n (differential_curve_3p0_3p5['incomp']).data,\n (differential_curve_3p5_4p0['incomp']).data,\n (differential_curve_4p0_4p5['incomp']).data,\n (differential_curve_4p5_5p0['incomp']).data))\nprint((1.0-differential_curve_1p0_1p5['incomp']).data)\nprint(differential_cube.T)\nprint(differential_cube.shape)\nprint(len(differential_curve_1p0_1p5['snr'].data))\nx_array = numpy.power(10, numpy.linspace(numpy.log10(1.0),numpy.log10(1000.0),16)) # differential_curve_1p0_1p5['snr'].data\ny_array = numpy.linspace(1.0,5.0,9)\nprint(x_array)\nprint(y_array)\n\n\n# transpose so that each column is a different SNRpeak and each row is a different Theta_beam\ndifferential_cube = differential_cube.T\n\n\n\n# interpolate\nnx = differential_cube.shape[1]\nny = differential_cube.shape[0]\nxx, yy = numpy.meshgrid(numpy.arange(nx), numpy.arange(ny))\npts = numpy.array([[i,j] for i in range(nx) for j in range(ny)])\nvals = differential_cube.flatten()\nmask1 = ~numpy.isnan(vals) # valid values to interpolate with\npts1 = pts[mask1]\nvals1 = vals[mask1]\nprint('pts.shape', pts.shape)\nprint('vals.shape', vals.shape)\n#mask2 = numpy.logical_and(numpy.isnan(vals), numpy.logical_and(xx.flatten()>=7, yy.flatten()>=2)) # invalid values to interpolate for\n#pts2 = pts[mask2]\n#vals2 = vals[mask2]\nmask2 = numpy.logical_or(numpy.logical_or(numpy.logical_or(\\\n numpy.logical_and(xx.flatten()==9, yy.flatten()==2), \n numpy.logical_and(xx.flatten()==10, yy.flatten()==5)), \n numpy.logical_and(xx.flatten()==10, yy.flatten()==6)), \n numpy.logical_and(xx.flatten()==12, yy.flatten()==6)) # invalid values to interpolate for\npts2 = pts[mask2]\ndifferential_cube_original = copy(differential_cube)\ndifferential_cube_interpolated = differential_cube.flatten()\ndifferential_cube_interpolated[mask2] = interpolate.griddata(pts1, vals1, pts2, method='nearest')\ndifferential_cube = differential_cube_interpolated.reshape(differential_cube.shape)\nprint('differential_cube.shape', differential_cube.shape)\n# \n#nx = differential_cube.shape[1]\n#ny = differential_cube.shape[0]\n#x = numpy.arange(nx)\n#y = numpy.arange(ny)\n#xx, yy = numpy.meshgrid(x, y)\n#zz = differential_cube\n#print('xx.shape', xx.shape)\n#print('yy.shape', yy.shape)\n#print('zz.shape', zz.shape)\n#mask = (~numpy.isnan(zz))\n#xxx = xx[mask]\n#yyy = yy[mask]\n#zzz = zz[mask]\n#print('xxx.shape', xxx.shape)\n#print('yyy.shape', yyy.shape)\n#print('zzz.shape', zzz.shape)\n#fff = interpolate.interp2d(xxx, yyy, zzz, kind='linear')\n#differential_cube_interpolated = fff(x, y)\n#print('differential_cube_interpolated.shape', differential_cube_interpolated.shape)\n#differential_cube_original = differential_cube\n#differential_cube = differential_cube_interpolated.reshape(differential_cube.shape)\n#print('differential_cube.shape', differential_cube.shape)\n\n\n\n# make plot\nfig = plt.figure(figsize=(5.5,3.0))\nax = fig.add_subplot(1,1,1)\nax.imshow(differential_cube, cmap=cmap, norm=normalize, aspect=1.0, origin='lower') # , extent=[-0.5, len(x_array)-0.5, 1.0, 5.0]\nplt.xticks(numpy.arange(-0.5,len(x_array)-0.5,1), rotation=45)\nplt.yticks(numpy.arange(-0.5,len(y_array)-0.5,1))\nax.set_xticklabels(format_axis_tick_values(x_array))\nax.set_yticklabels(format_axis_tick_values(y_array))\nax.set_xlabel(r'$S_{\\mathrm{total,sim.}}\\,/\\,\\mathrm{rms\\,noise}$', fontsize=15)\nax.set_ylabel(r'$\\Theta_{\\mathrm{beam,sim.,convol.}}$', fontsize=15)\nax.yaxis.labelpad = 10\nax.tick_params(axis='x', direction='in')\nax.tick_params(axis='y', direction='in')\n\n\n\n# Show more label\nax.text(1.00, 1.04, r'FULL$\\,-\\,$PYBDSF', transform=ax.transAxes, va='center', ha='right', fontsize=13.5)\n\n\n# Now adding the colorbar\ncax = fig.add_axes([0.85, 0.27, 0.03, 0.65]) # l,b,w,h\ncbar = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=normalize, ticks=[1.0, 0.8, 0.6, 0.4, 0.2, 0.0])\ncbar.ax.set_yticklabels(['0%','20%','40%','60%','80%','100%'])\ncbar.ax.set_ylim(1.0,0.0)\ncbar.ax.tick_params(axis='y', direction='in')\ncbar.ax.text(4.3, 0.48, 'Completeness', transform=cbar.ax.transAxes, rotation=90, va='center', ha='center', fontsize=14)\n\n\n# Save figure\nfig.subplots_adjust(bottom=0.20, left=0.15, right=0.84, top=0.97)\nfig.savefig('Plot_Completeness_blocks_for_various_source_sizes.S_total_sim_to_rms_noise.pdf')\n\nos.system('open Plot_Completeness_blocks_for_various_source_sizes.S_total_sim_to_rms_noise.pdf')\n\n\n\n\n","sub_path":"Pipeline/a3cosmos-MC-simulation-completeness-analysis-tools/backups/a_dzliu_code_plot_completeness_blocks_for_various_source_sizes_versus_S_total_sim_to_rms_noise_obsolete_20190506.py","file_name":"a_dzliu_code_plot_completeness_blocks_for_various_source_sizes_versus_S_total_sim_to_rms_noise_obsolete_20190506.py","file_ext":"py","file_size_in_byte":8129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"173449557","text":"import requests, sys\n\nif len(sys.argv) != 2:\n print(\"data not val, help: \\npython3 sms.py (Phone Number)\")\n sys.exit()\nelse:\n phone = sys.argv[1].strip()\n phone_shad = phone.replace(\"0\",\"98\")\n while True:\n try:\n hamyarzaban_data = {'phone':phone}\n hamyarzaban = requests.post(\"https://hamyarzaban.com/wp-content/themes/hamyarzaban/sms.php\")\n print(f\"ham status: {hamyarzaban}\")\n except:\n print(\"ham not\")\n try:\n snaptrip_data = {\"lang\":\"fa\",\"country_id\":\"860\",\"password\":\"fhj5tvT6D9jWkPw\",\"mobile_phone\":phone,\"country_code\":\"+98\"}\n snaptrip = requests.post(\"https://www.snapptrip.com/register\", json=snaptrip_data)\n print(f\"snap trip status: {snaptrip}\")\n except:\n print(\"snap trip not\")\n try:\n alibaba_data = {\"phoneNumber\":phone}\n alibaba = requests.post(\"https://ws.alibaba.ir/api/v3/account/mobile/otp\", json=alibaba_data)\n print(f\"alibaba status: {alibaba}\")\n except:\n print(\"alibaba not\")\n try:\n data_snap_food = {'cellphone':phone,'captcha':'111'}\n snap_food = requests.post(\"https://snappfood.ir/auth/login_with_no_pass\", data=data_snap_food)\n print(f\"snapfood status: {snap_food}\")\n except:\n print(\"snap food not\")\n try:\n data_snap = {\"type\":2,\"phone\":phone}\n snap = requests.post(\"https://api.snapp.ir/api/v1/sms/link\", data=data_snap)\n print(f\"snap status: {snap}\")\n except:\n print(\"snap not\")\n try:\n data_shad = {\"api_version\":\"3\",\"method\":\"sendCode\",\"data\":{\"phone_number\":phone_shad,\"send_type\":\"SMS\"}}\n shad = requests.post(\"https://shadmessenger103.iranlms.ir/\", json=data_shad)\n print(f\"shad status: {shad}\")\n except:\n print(\"shad not\")\n try:\n data_tp30 = {\"credential\":{\"phoneNumber\":phone,\"role\":\"PASSENGER\"}}\n tp30 = requests.post(\"https://tap33.me/api/v2/user\", json=data_tp30)\n print(f\"tp30 status: {tp30}\")\n except:\n print(\"tp30 not\")\n try:\n devar_data = {\"phone\":phone}\n devar = requests.post(\"https://api.divar.ir/v5/auth/authenticate\", json=devar_data)\n print(f\"devar status: {devar}\")\n except:\n print(\"devar not\")\n try:\n data_snapfood2 = {'cellphone':phone}\n snapfood2 = requests.post(\"https://snappfood.ir/customer/app-dl/send\", data=data_snapfood2)\n print(f\"snap food 2 status: {snapfood2}\")\n except:\n print(\"snap food 2 not\")","sub_path":"sms.py","file_name":"sms.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"385000239","text":"from Login import Ui_Form\nfrom Common_Function import *\n\n\nclass Login_class(Ui_Form,Farther,Windows_Move,QMainWindow):\n\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.center()\n # 透明处理，移动需要拖动数字\n self.setWindowFlags(QtCore.Qt.FramelessWindowHint | QtCore.Qt.SubWindow | QtCore.Qt.WindowStaysOnTopHint)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground, True)\n\n self.lineEdit.setFocus()\n\n self.set_gif(':/images/dou5.gif',self.lab_background)\n #self.set_gif(':/images/yunye.gif', self.lab_background)\n\n # my_list = ['1','2','3']\n # my_str,ok = QInputDialog.getItem(self,\"下拉框\",'提示',my_list)\n # print(my_str,ok)\n\n #界面的退出按钮\n def Exit(self):\n os._exit(0)\n # exit(1)\n # return\n\n\n #一个确认的按钮的函数\n def Login_Reslute(self):\n\n text, ok = QInputDialog.getText(self, \"title\", \"User name:\", QLineEdit.Normal, '>>>:')\n\n # if self.lineEdit.text()=='123':\n # return '管理员账号'\n # else:\n # image_file, _ = QFileDialog.getOpenFileName(self, 'Open file', 'C:\\\\',\n # 'Image files (*.jpg *.gif *.png *.jpeg)')\n\n\nif __name__==\"__main__\":\n app = QtWidgets.QApplication(argv)\n MainWindow = QtWidgets.QMainWindow()\n\n Login_son = Login_class()\n Login_son.show()\n\n exit(app.exec_())","sub_path":"Login_monther.py","file_name":"Login_monther.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"337445105","text":"import numpy as np\nimport tensorflow as tf\nfrom progressbar import ProgressBar\nimport train_dataset\nimport config\nfrom embedding_graph_dataset import dataset\n\n# load data\npath = r'D:\\data\\new_data'\np = ProgressBar(max_value=9713)\ndata = dataset(path)\n\n# start session\n\nwith tf.Graph().as_default():\n config_ = tf.ConfigProto()\n config_.gpu_options.allow_growth = True\n sess = tf.Session(config=config_)\n with sess.as_default():\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(update_ops):\n model = train_dataset.model_tf(data.num_vocab, data.num_nodes)\n global_step = tf.Variable(0, trainable=False)\n # c = tf.train.exponential_decay(config.lr, global_step, 30, 0.9, staircase=False)\n opt = tf.train.AdamOptimizer(learning_rate=0.01)\n train_op = opt.minimize(model.loss, global_step)\n sess.run(tf.global_variables_initializer())\n # summary_writer = tf.summary.FileWriter(\"./logs\", )\n\n # training\n print('start training.......')\n\n for epoch in range(config.num_epoch):\n loss_epoch = 0\n p.init()\n for _c in p(range(9713)):\n node_a, node_b, contents, time, polarity = next(data.generate_batches())\n\n feed_dict = {\n model.Text_a: contents,\n model.Node_a: node_a,\n model.Node_b: node_b,\n model.Time_a: time,\n model.Polarity_a: polarity\n }\n\n # run the graph\n # merge = tf.summary.merge_all()\n _, loss_batch = sess.run([train_op, model.loss], feed_dict=feed_dict)\n\n loss_epoch += loss_batch\n\n # def variable_summaries(var, name):\n # with tf.name_scope('summaries'):\n # mean = tf.reduce_mean(var)\n # tf.summary.scalar('mean/' + name, mean)\n # variable_summaries(loss_epoch, \"loss\")\n print('epoch: ', epoch + 1, ' loss: ', loss_epoch)\n\n # file = open('embed.txt', 'wb')\n # batches = data.generate_batches(mode='add')\n # num_batch = len(batches)\n # embed = [[] for _ in range(data.num_nodes)]\n # for i in range(num_batch):\n # batch = batches[i]\n #\n # node1, node2, node3 = zip(*batch)\n # node1, node2, node3 = np.array(node1), np.array(node2), np.array(node3)\n # text1, text2, text3 = data.text[node1], data.text[node2], data.text[node3]\n #\n # feed_dict = {\n # model.Text_a: text1,\n # model.Text_b: text2,\n # # model.Text_neg: text3,\n # model.Node_a: node1,\n # model.Node_b: node2,\n # # model.Node_neg: node3\n # }\n\n # run the graph\n # convA, convB, TA, TB = sess.run([model.gruA, model.gruB, model.N_A, model.N_B], feed_dict=feed_dict)\n # for i in range(config.batch_size):\n # em = list(convA[i]) + list(TA[i])\n # embed[node1[i]].append(em)\n # em = list(convB[i]) + list(TB[i])\n # embed[node2[i]].append(em)\n # for i in range(data.num_nodes):\n # if embed[i]:\n # # print embed[i]\n # tmp = np.sum(embed[i], axis=0) / len(embed[i])\n # str_ = ' '.join(map(str, tmp)) + '\\n'\n # file.write(bytes(str_, encoding='utf-8'))\n # else:\n # file.write(bytes(\"\\n\", encoding='utf-8'))\n","sub_path":"new_train.py","file_name":"new_train.py","file_ext":"py","file_size_in_byte":3759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"147991997","text":"#! /usr/bin/env python\n\n# $Id$\n# Author: David Goodger \n# Copyright: This module has been placed in the public domain.\n\n\"\"\"\nTests for docutils/readers/python/moduleparser.py.\n\"\"\"\n\nfrom __init__ import DocutilsTestSupport\n\n\ndef suite():\n s = DocutilsTestSupport.PythonModuleParserTestSuite()\n s.generateTests(totest, testmethod='test_token_parser_rhs')\n return s\n\ntotest = {}\n\ntotest['expressions'] = [\n['''a = 1''', '''1'''],\n['''a = b = 1''', '''1'''],\n['''\\\na = (\n 1 + 2\n + 3\n )\n''',\n'''(1 + 2 + 3)'''],\n['''\\\na = \"\"\"\\\\\nline one\nline two\"\"\"\n''',\n'''\"\"\"\\\\\\nline one\\nline two\"\"\"'''],\n['''a = `1`''', '''`1`'''],\n['''a = `1`+`2`''', '''`1` + `2`'''],\n]\n\n\nif __name__ == '__main__':\n import unittest\n unittest.main(defaultTest='suite')\n","sub_path":"sandbox/davidg/test/test_readers/test_python/test_token_parser.py","file_name":"test_token_parser.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"5135117","text":"import database\nimport random\nimport csv\nfrom pathlib import Path\n\nfrom helpers.hashing import EmbedImage\nfrom discord.ext import commands\nfrom discord.ext.commands import Bot, Cog\nfrom discord.ext.tasks import loop\n\nmons = []\n\n\n# pylint: disable=no-self-use\nclass Fieldwork(Cog):\n def __init__(self, bot: Bot):\n self.time = ''\n self.bot = bot\n if bot is not None:\n self.db = bot.get_cog('database')\n if not self.db:\n self.db = database.Database(None)\n self.observe_random_mon.start()\n\n @loop(seconds=90)\n async def observe_random_mon(self) -> None:\n if not mons:\n load()\n self.time = 'night' if self.time == 'day' else 'day'\n dex, name = mons.pop()\n url = f\"https://server.poketwo.net/image?species={dex}&time={self.time}\"\n img = EmbedImage(url)\n phash = img.phash\n print(f'{phash}={name}')\n pkmn = self.db.get_pokemon_image_by_phash(phash)\n if not pkmn.pokemon:\n pkmn.pokemon = self.db.get_pokemon_by_name(name)\n pkmn.save()\n print(f'Learned that {phash} is {name} from fieldwork')\n\ndef isnumber(v):\n try:\n int(v)\n except ValueError:\n return False\n return True\n\ndef load() -> None:\n path = Path.cwd() / \"data\" / \"pokemon.csv\"\n\n with open(path, encoding='utf-8') as f:\n reader = csv.DictReader(f)\n data = list(\n {k: int(v) if isnumber(v) else v for k, v in row.items() if v != \"\"}\n for row in reader\n )\n mons.extend([(p['id'], p.get('name.en', p.get('slug'))) for p in data])\n random.shuffle(mons)\n\ndef setup(bot: commands.Bot) -> None:\n bot.add_cog(Fieldwork(bot))\n","sub_path":"fieldwork/seeker.py","file_name":"seeker.py","file_ext":"py","file_size_in_byte":1725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"401808547","text":"'''\nGiven an array of 2n integers, your task is to group these integers into n pairs of integer, say (a1, b1), (a2, b2), ..., (an, bn) which makes sum of min(ai, bi) for all i from 1 to n as large as possible.\nExample 1:\nInput: [1,4,3,2]\n\nOutput: 4\nExplanation: n is 2, and the maximum sum of pairs is 4.\nNote:\n1.n is a positive integer, which is in the range of [1, 10000].\n2.All the integers in the array will be in the range of [-10000, 10000].\n'''\n# May 27, 2017\nclass Solution(object):\n def arrayPairSum(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n def partition(a, left, right, pivotIndex):\n temp = a[right];\n a[right] = a[pivotIndex];\n a[pivotIndex] = temp;\n storeIndex = left;\n for i in range(left, right):\n if a[i] <= a[right]:\n temp = a[i];\n a[i] = a[storeIndex];\n a[storeIndex] = temp;\n storeIndex += 1;\n temp = a[storeIndex];\n a[storeIndex] = a[right];\n a[right] = temp;\n return storeIndex;\n \n def quickSort(a, left, right):\n if right > left:\n pivotNewIndex = partition(a, left, right, (right+left)/2);\n quickSort(a, left, pivotNewIndex-1);\n quickSort(a, pivotNewIndex+1, right);\n return; \n \n quickSort(nums, 0, len(nums)-1);\n \n sum = 0;\n for i in range(0, len(nums)-1, 2):\n sum += nums[i];\n \n return sum;\n\n# Self-review, May 28, 2017\n# Why do I implement quick sort myself rather than use sort function?\n# sorted is a built-in function, return a new container\n# sort is a property of container, sort within the container\n'''\nclass Solution(object):\n def arrayPairSum(self, nums):\n \"\"\"\n\t:type nums: List[int]\n\t:rtype: int\n\t\"\"\"\n\treturn sum(sorted(nums)[::2]);\n'''\n","sub_path":"array_partition_I.py","file_name":"array_partition_I.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"11756476","text":"\nimport sys\nimport argparse\n\nimport math\nimport numpy as np\nfrom scipy import interpolate\n\nfrom util import date, arcinfo\nfrom util.arg_parsing import add_argument\n\n\n# Some physical constants\nyear_in_sec = 365.25 * 24 * 60 * 60\nrho = 917 * 1.0e-6 / year_in_sec**2\ng = 9.81 * year_in_sec**2\nA0 = 3.985e-13 * year_in_sec * 1.0e18\nQ = 6.0e4\nT = 273 - 13\nR = 8.3144\nA = A0 * math.exp(-Q / (R * T))\n\n\n# ------------------------\ndef surface_slope(x, y, s):\n \"\"\"\n \"\"\"\n\n ny, nx = np.shape(s)\n dx = x[1] - x[0]\n dy = x[1] - y[0]\n\n dsdx = np.zeros((ny, nx), dtype = np.float64)\n dsdy = np.zeros((ny, nx), dtype = np.float64)\n\n for i in range(1, ny - 1):\n for j in range(1, nx - 1):\n dsdx[i, j] = 0.5 * (s[i, j+1] - s[i, j-1]) / dx\n dsdy[i, j] = 0.5 * (s[i+1, j] - s[i-1, j]) / dx\n\n ds = np.sqrt(dsdx**2 + dsdy**2)\n\n avg = np.average(ds)\n stddev = np.std(ds)\n\n # Cutoff for the surface slope\n mva = avg + 0.25 * stddev\n\n for i in range(ny):\n for j in range(nx):\n ds[i, j] = min(ds[i, j], mva)\n\n return ds\n\n\n# --------------------------------------------\ndef guess_basal_fields(x, y, s, b, u, v, frac):\n \"\"\"\n Inputs:\n x : list of horizontal coordinates of the grid\n y : list of vertical coordinates of the grid\n s : array of ice surface elevations\n b : array of ice bed elevations\n u : array of ice velocities in the x-direction\n v : array of ice velocities in the y-direction\n frac : optional parameter; the fraction of the driving stress that the\n basal shear stress is assumed to support\n\n Outputs:\n beta : basal sliding coefficient, under the shallow ice approximation\n ub : basal sliding velocity in the x-direction\n vb : basal sliding velocity in the y-direction\n \"\"\"\n\n ny, nx = np.shape(u)\n ds = surface_slope(x, y, s)\n\n #---------------------------------------------------------\n # Compute the bed sliding velocities & friction parameter\n ub = np.zeros((ny, nx), dtype = np.float64)\n vb = np.zeros((ny, nx), dtype = np.float64)\n beta = np.zeros((ny, nx), dtype = np.float64)\n\n q = 0.0\n speed = 0.0\n\n for i in range(1, ny - 1):\n for j in range(1, nx - 1):\n if u[i, j] != -2.0e+9:\n alpha = frac\n h = max(s[i, j] - b[i, j], 0.0)\n q = A * (rho * g * h)**3 * ds[i, j]**3 / 2\n speed = np.sqrt(u[i, j]**2 + v[i, j]**2)\n\n basal_speed = speed - alpha**3*h*q\n if basal_speed <= 0.0:\n basal_speed = min(10.0, 0.1 * speed)\n alpha = ((speed - basal_speed) / (h*q))**(1.0/3)\n\n # The basal sliding velocities are assumed to have the same\n # direction as the surface velocities, only with lower speed\n # according to a rough SIA-like approximation.\n ub[i, j] = basal_speed/speed * u[i, j]\n vb[i, j] = basal_speed/speed * v[i, j]\n\n # Since we've already guessed the sliding speed and the\n # x-z strain rate from the SIA, the boundary condition\n # tau_xz = -beta**2 * u (resp. tau_yz, v)\n # gives us the value of beta consistent with the guesses\n # we've already made.\n beta[i, j] = np.nanmin([(2*alpha**3*q / (A*basal_speed**3))**(1.0/6), 0.0])\n else:\n ub[i, j] = -2.0e+9\n vb[i, j] = -2.0e+9\n beta[i, j] = -2.0e+9\n\n\n def fill_to_boundary(phi):\n phi[0, :] = phi[1, :]\n phi[-1, :] = phi[-2, :]\n phi[:, 0] = phi[:, 1]\n phi[:, -1] = phi[:, -2]\n\n fill_to_boundary(beta)\n fill_to_boundary(ub)\n fill_to_boundary(vb)\n\n return beta, ub, vb\n\n\n# -----------------------\ndef main(day, year, frac):\n directory = \"data/processed/\"\n\n d = date.closest_date(year + day / 365.25,\n directory,\n file_prefix = \"u-\")\n\n x, y, u, _ = arcinfo.read(directory + \"u-\" + d + \".txt\")\n _, _, v, _ = arcinfo.read(directory + \"v-\" + d + \".txt\")\n\n ny, nx = np.shape(u)\n\n xs, ys, ss, _ = arcinfo.read(directory + \"zsDEM-\" + d + \".txt\")\n xb, yb, bb, _ = arcinfo.read(directory + \"zbDEM.txt\")\n\n # Smooth the ice surface elevation\n for i in range(1, len(ys) - 1):\n for j in range(1, len(xs) - 1):\n ss[i, j] = (4 * ss[i, j] +\n ss[i+1, j] + ss[i-1, j] +\n ss[i, j+1] + ss[i, j-1]) / 8.0\n\n sf = interpolate.RectBivariateSpline(xs, ys, ss.T)\n bf = interpolate.RectBivariateSpline(xb, yb, bb.T)\n\n s = np.zeros((ny, nx), dtype = np.float64)\n b = np.zeros((ny, nx), dtype = np.float64)\n\n for i in range(ny):\n for j in range(nx):\n s[i, j] = sf(x[j], y[i])\n b[i, j] = bf(x[j], y[i])\n\n del sf, bf, xs, ys, ss, xb, yb, bb\n\n\n beta, ub, vb = guess_basal_fields(x, y, s, b, u, v, frac)\n\n for i in range(ny):\n for j in range(nx):\n if beta[i, j] != -2.0e+9:\n beta[i, j] = max(beta[i, j], 0.015)\n\n arcinfo.write(directory + \"beta-\" + d + \".txt\", x, y, beta, -2.0e+9)\n arcinfo.write(directory + \"ub-\" + d + \".txt\", x, y, ub, -2.0e+9)\n arcinfo.write(directory + \"vb-\" + d + \".txt\", x, y, vb, -2.0e+9)\n\n\n# --------------------------\ndef add_cmdline_args(parser):\n add_argument(parser,\n \"-y\", \"--year\", required = True,\n help = \"Year for inversion\")\n add_argument(parser,\n \"-d\", \"--day\", required = True,\n help = \"day of year for inversion\")\n add_argument(parser,\n \"-f\", \"--frac\", required = True,\n help = \"% of driving stress to guess basal shear\")\n\n\n# -----------------------\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n add_cmdline_args(parser)\n args, _ = parser.parse_known_args(sys.argv[1:])\n\n main(float(args.day), float(args.year), float(args.frac))\n","sub_path":"data/initial_guesses.py","file_name":"initial_guesses.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"207804280","text":"import sys, os, glob\n\n# Find JPEG images in every folder and append list of images to next index\ndef findImages(image_paths, owd):\n file_list = []\n for path in image_paths:\n print(path)\n os.chdir(path)\n print(glob.glob(\"*.JPEG\"))\n file_list.append(glob.glob(path + \"*.JPEG\"))\n os.chdir(owd)\n return file_list\n\ndef writeFile(output_file, image_list):\n with open(output_file, 'w') as file:\n for index, images in enumerate(image_list):\n for image in images:\n file.write(os.path.basename(image) + \" \" + str(index) + \"\\n\")\n\ndef main():\n if (len(sys.argv) < 3):\n return\n\n owd = os.getcwd()\n output_file = str(sys.argv[1])\n print(sys.argv[2:])\n image_paths = sys.argv[2:]\n image_list = findImages(image_paths, owd)\n writeFile(output_file, image_list)\n\n# py preprocess.py outputfile /path/to/images/\nif __name__ == \"__main__\":\n main()\n","sub_path":"colorization/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"604051983","text":"#!/usr/bin/env python\r\n# This file is part of tcollector.\r\n# Copyright (C) 2010 The tcollector Authors.\r\n#\r\n# This program is free software: you can redistribute it and/or modify it\r\n# under the terms of the GNU Lesser General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or (at your\r\n# option) any later version. This program is distributed in the hope that it\r\n# will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty\r\n# of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser\r\n# General Public License for more details. You should have received a copy\r\n# of the GNU Lesser General Public License along with this program. If not,\r\n# see .\r\n\r\nimport os\r\nimport sys\r\nimport time\r\n\r\ntry:\r\n import json\r\nexcept ImportError:\r\n json = None\r\n\r\nimport utils\r\nfrom hadoop_http import HadoopHttp\r\n\r\n\r\nREPLACEMENTS = {\r\n \"datanodeactivity-\": [\"activity\"],\r\n \"fsdatasetstate-ds-\": [\"fs_data_set_state\"],\r\n \"rpcdetailedactivityforport\": [\"rpc_activity\"],\r\n \"rpcactivityforport\": [\"rpc_activity\"]\r\n}\r\n\r\n\r\nclass HadoopDataNode(HadoopHttp):\r\n \"\"\"\r\n Class that will retrieve metrics from an Apache Hadoop DataNode's jmx page.\r\n\r\n This requires Apache Hadoop 1.0+ or Hadoop 2.0+.\r\n Anything that has the jmx page will work but the best results will com from Hadoop 2.1.0+\r\n \"\"\"\r\n\r\n def __init__(self):\r\n super(HadoopDataNode, self).__init__('hadoop', 'datanode', 'localhost', 50075)\r\n\r\n def emit(self):\r\n step = os.path.realpath(__file__).split(\"/\")[-1].split(\"_\", 2)[0]\r\n current_time = int(time.time())\r\n metrics = self.poll()\r\n for context, metric_name, value in metrics:\r\n for k, v in REPLACEMENTS.iteritems():\r\n if any(c.startswith(k) for c in context):\r\n context = v\r\n self.emit_metric(context, current_time, metric_name, value, step)\r\n self.print_metric()\r\n\r\n\r\ndef main(args):\r\n utils.drop_privileges()\r\n if json is None:\r\n utils.err(\"This collector requires the `json' Python module.\")\r\n return 13 # Ask tcollector not to respawn us\r\n datanode_service = HadoopDataNode()\r\n datanode_service.emit()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n sys.exit(main(sys.argv))","sub_path":"chapter8/60_hadoop_datanode.py","file_name":"60_hadoop_datanode.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"222137809","text":"S = input()\nK = int(input())\nN = len(S)\n\nbef = \"\"\nstart = 0\nl = []\nfor i, s in enumerate(S):\n if bef==\"\":\n start = i\n elif bef!=s:\n l.append(i-start)\n start = i\n bef = s\nl.append(i-start+1)\n\nif len(set(S))==1:\n print((N*K)//2)\nelse:\n if S[0]==S[-1]:\n ans = (l[0]//2)+(l[-1]//2)+((l[0]+l[-1])//2)*(K-1)\n else:\n ans = (l[0]//2)*K+(l[-1]//2)*K\n for i in l[1:-1]:\n ans += (i//2)*K\n print(ans)\n","sub_path":"problems/AGC039/agc039_a.py","file_name":"agc039_a.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"419967192","text":"# 최댓값 구하기\n# 입력: 숫자가 n개 들어 있는 리스트\n# 출력: 숫자가 n개중 최댓값\n\n\ndef find_max(a):\n n = len(a)\n max_v = a[0]\n\n for i in range(1, n): # 인덱스를 생각해서 n-1까지 비교해야 한다.\n if a[i] > max_v:\n max_v = a[i]\n return max_v\n\nv = [17, 92, 18, 33, 58, 7, 33, 42]\n\nprint (find_max(v))\n","sub_path":"ch01_basic_algorithm/02.find-the-max/p02-1-findmax-jin.py","file_name":"p02-1-findmax-jin.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"119952334","text":"#!/usr/bin/env python\n# /etc/init.d/sample.py\n### BEGIN INIT INFO\n# Provides: watcherAuto.py\n# Required-Start: $remote_fs $syslog\n# Required-Stop: $remote_fs $syslog\n# Default-Start: 2 3 4 5\n# Default-Stop: 0 1 6\n# Short-Description: Start daemon at boot time\n# Description: Enable service provided by daemon.\n### END INIT INFO\n\nimport requests, json, ast\nfrom picamera import PiCamera\nfrom time import sleep\nfrom playsound import playsound\nfrom datetime import datetime\n\ncamera = PiCamera()\n\nwhile True:\n \n # file name\n dateStamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n file_name = '/home/pi/picsCatDetector/picture_{}.jpg'.format(dateStamp) \n\n # Take picture\n camera.start_preview()\n camera.capture(file_name)\n camera.stop_preview()\n\n url = 'http://0.0.0.0:5000/api'\n url = 'http://35.200.240.45:80/api'\n files = {'image': open(file_name, 'rb')}\n r = requests.post(url, files=files)\n print(r.text)\n\n dicOut = ast.literal_eval(r.text)\n proba = dicOut['probaCat']\n\n if proba > 0.95:\n playsound('vaccum1.mp3')\n","sub_path":"watcher/watcherAuto.bak.py","file_name":"watcherAuto.bak.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"630044312","text":"#!/usr/bin/env python\n\nfrom collections import defaultdict, Counter\nfrom random import randint, choice\n\nwith open('log2_512') as f:\n x = [list(map(int, line.strip().split(','))) for line in f.readlines()]\n\n# base, hash value\n\nbase_to_hash = defaultdict(set)\nhash_to_base = defaultdict(set)\n\nbest_result = 512\nwhile best_result > 40:\n result = []\n for r in x:\n base_to_hash[r[0]].add(r[1])\n hash_to_base[r[1]].add(r[0])\n\n while any([len(base_to_hash[i]) for i in base_to_hash]):\n best_size = 0\n best_base = -1\n if randint(0, 100) > 80:\n c = Counter()\n for i in base_to_hash:\n c.update(list(base_to_hash[i]))\n least_common = c.most_common()[-1]\n for i in hash_to_base[least_common[0]]:\n if len(base_to_hash[i]) > best_size and randint(0, 100) > 10:\n best_size = len(base_to_hash[i])\n best_base = i\n #if len(base_to_hash[i]) == best_size and i < best_base and randint(0, 100) > 10:\n # best_base = i\n else:\n for i in base_to_hash:\n if len(base_to_hash[i]) > best_size and randint(0, 100) > 10:\n best_size = len(base_to_hash[i])\n best_base = i\n #if len(base_to_hash[i]) == best_size and i < best_base and randint(0, 100) > 10:\n # best_base = i\n if best_base == -1:\n continue\n vals = set(list(base_to_hash[best_base]))\n result.append((best_base, vals))\n for i in base_to_hash:\n base_to_hash[i] -= vals\n\n if len(result) < best_result:\n best_result = len(result)\n arr = [-1 for _ in range(512)]\n bases = [0 for _ in range(best_result)]\n i = 0\n for r in result:\n best_base, vals = r\n for v in vals:\n arr[v] = i\n bases[i] = best_base\n i += 1\n count = sum([x == -1 for x in arr])\n print(arr)\n print(bases)\n print(len(result), best_result)\n","sub_path":"bench/hash2/512/512.py","file_name":"512.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"443984226","text":"def transform_scalars(dataset):\n \"\"\"3D Reconstruct from a tilt series using Direct Fourier Method\"\"\"\n\n from tomviz import utils\n import numpy as np\n\n # Get Tilt angles\n tilt_angles = utils.get_tilt_angles(dataset)\n\n data_py = utils.get_array(dataset)\n if data_py is None:\n raise RuntimeError(\"No scalars found!\")\n\n recon = dfm3(data_py,tilt_angles,np.size(data_py,0)*2)\n print('Reconsruction Complete')\n\n # Set the result as the new scalars.\n utils.set_array(dataset, recon)\n\n # Mark dataset as volume\n utils.mark_as_volume(dataset)\n\nimport pyfftw\nimport numpy as np\n\ndef dfm3(input,angles,Npad):\n # input: aligned data\n # angles: projection angles\n # N_pad: size of padded projection.\n\n input = np.double(input)\n (Nx,Ny,Nproj) = input.shape\n angles = np.double(angles)\n cen = np.floor(Ny/2.0)\n cen_pad = np.floor(Npad/2.0)\n pad_pre = np.ceil((Npad-Ny)/2.0); pad_post = np.floor((Npad-Ny)/2.0)\n \n # Initialization\n Nz = Ny\n w = np.zeros((Nx,Ny,np.int(Nz/2+1))) #store weighting factors\n v = pyfftw.n_byte_align_empty((Nx,Ny,Nz/2+1),16,dtype='complex128')\n v = np.zeros(v.shape) + 1j*np.zeros(v.shape)\n recon = pyfftw.n_byte_align_empty((Nx,Ny,Nz),16,dtype='float64')\n recon_fftw_object = pyfftw.FFTW(v,recon,direction='FFTW_BACKWARD',axes=(0,1,2))\n\n p = pyfftw.n_byte_align_empty((Nx,Npad),16,dtype='float64')\n pF = pyfftw.n_byte_align_empty((Nx,Npad/2+1),16,dtype='complex128')\n p_fftw_object = pyfftw.FFTW(p,pF,axes=(0,1))\n\n dk = np.double(Ny)/np.double(Npad)\n\n for a in range(0, Nproj):\n #print angles[a]\n ang = angles[a]*np.pi/180\n projection = input[:,:,a] #2D projection image\n p = np.lib.pad(projection,((0,0),(pad_pre,pad_post)),'constant',constant_values=(0,0)) #pad zeros\n p = np.fft.ifftshift(p) \n p_fftw_object.update_arrays(p,pF)\n p_fftw_object()\n\n probjection_f = pF.copy()\n if ang<0:\n probjection_f = np.conj(pF.copy())\n probjection_f[1:,:] = np.flipud(probjection_f[1:,:])\n ang = np.pi+ang\n\n # Bilinear extrapolation\n for i in range(0, np.int(np.ceil(Npad/2))+1):\n ky = i*dk; #kz = 0;\n ky_new = np.cos(ang)*ky #new coord. after rotation\n kz_new = np.sin(ang)*ky \n sy = abs(np.floor(ky_new) - ky_new) #calculate weights\n sz = abs(np.floor(kz_new) - kz_new)\n for b in range(1,5): #bilinear extrapolation\n pz,py,weight = bilinear(kz_new,ky_new,sz,sy,Ny,b)\n if (py>=0 and py=0 and pz None: ...\n\nclass H2Server:\n sock: Any = ...\n conn: Any = ...\n root: Any = ...\n flow_control_events: Any = ...\n def __init__(self, sock: Any, root: Any) -> None: ...\n async def run(self) -> None: ...\n async def request_received(self, headers: Any, stream_id: Any) -> None: ...\n async def send_file(self, file_path: Any, stream_id: Any) -> None: ...\n async def _send_file_data(self, fileobj: Any, stream_id: Any) -> None: ...\n async def wait_for_flow_control(self, stream_id: Any) -> None: ...\n async def window_updated(self, event: Any) -> None: ...\n","sub_path":"Result/4079files/uninferred/2408-Uninferred.pyi","file_name":"2408-Uninferred.pyi","file_ext":"pyi","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"350561872","text":"from aiohttp import web\nfrom aiopg.sa import create_engine\nfrom os import path\nimport logging\nimport sys\nimport argparse\n\nfrom paysys.settings import Settings\nfrom paysys.db_utils import apply_migrations, make_uri\n\nROOT = path.abspath(path.join(path.dirname(__file__), '..'))\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--config', dest='config', default=path.join(ROOT, 'config.json'))\nargs = parser.parse_args()\n\n\nasync def on_startup(app):\n config = Settings()\n config.load_json(path.join(ROOT, 'default_config.json'))\n config.load_json(args.config)\n\n conn_uri = make_uri(config.section('postgres'))\n print(conn_uri)\n apply_migrations(conn_uri)\n\n app['config'] = config\n app['engine'] = await create_engine(conn_uri)\n app['logger'] = init_logger()\n\n\nasync def close_pg(app):\n app['engine'].close()\n await app['engine'].wait_closed()\n\n\ndef init_logger():\n logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s: %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.INFO,\n stream=sys.stdout)\n return logging.getLogger('')\n\n\napp = web.Application()\napp.on_startup.append(on_startup)\napp.on_cleanup.append(close_pg)\n","sub_path":"paysys/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"417067168","text":"print(\"Today we will be finding the median of a list.\")\nprint(\"User enter a length for your list:\")\n\n# users list length and list\nlist_length = int(input())\nnumber_list = []\n\n# asking the user to input the amount of numbers they wanted and adding them the list\nfor i in range(list_length):\n print(\"User enter a number:\")\n users_num = float(input())\n number_list.append(users_num)\n\n# creating a function to find median\n\ndef median():\n remainder = list_length % 2 # finds remainder\n print(remainder)\n sorted_list = sorted(number_list) # sorts list\n print(sorted_list, \"is your list sorted in ascending order\")\n if remainder == 1: # if remainder = 1, slices at halfway point, and prints\n slice_position = (list_length + 1)//2 - 1\n print(sorted_list[slice_position], \"is the median of your list\")\n if remainder == 0: # if remainder = 0, slices at 2 positions - half and half + 1\n sorted_list = sorted(number_list) # sorts list\n slice_position2 = list_length/2\n median1 = sorted_list(slice_position2)\n median2 = sorted_list(slice_position2 - 1)\n print((median1 + median2)/2)\n\nmedian()","sub_path":"Median.py","file_name":"Median.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"562011674","text":"#!/usr/bin/env checkio --domain=py run the-lancers\n\n# \n# END_DESC\n\nclass Warrior(object):\n def __init__(self, *args, **kwargs):\n self.health = 50\n self.attack = 5\n self.defense = 0\n\n def damage(self, opponent):\n dealt_damage = self.attack - opponent.defense\n if opponent.defense < self.attack:\n opponent.health -= dealt_damage\n\n @property\n def is_alive(self):\n if self.health > 0:\n return True\n else:\n return False\n\n def __str__(self):\n return \"health: \" + str(self.health) + \"attack: \" + str(self.attack)\n\n\nclass Rookie(Warrior):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.health = 50\n self.attack = 1\n\n\nclass Knight(Warrior):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.health = 50\n self.attack = 7\n\n\nclass Defender(Warrior):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.health = 60\n self.attack = 3\n self.defense = 2\n\n\nclass Vampire(Warrior):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.health = 40\n self.attack = 4\n self.defense = 0\n self.vampirism = 0.5\n\n # special damage - curing self\n def damage(self, opponent):\n dealt_damage = self.attack - opponent.defense\n if opponent.defense < self.attack:\n opponent.health -= dealt_damage\n self.health += dealt_damage * self.vampirism\n\n\nclass Lancer(Warrior):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.health = 50\n self.attack = 6\n self.defense = 0\n\n # special damage - damage one opponent behind\n def damage(self, opponent1, opponent2=None):\n dealt_damage = self.attack - opponent1.defense\n if opponent1.defense < self.attack:\n opponent1.health -= dealt_damage\n if opponent2:\n if opponent2.defense < self.attack:\n opponent2.health -= dealt_damage * 0.5\n\n\ndef fight(unit1, unit2):\n # if fight is a duet only\n while unit1.is_alive and unit2.is_alive:\n unit1.damage(unit2)\n if unit2.is_alive:\n unit2.damage(unit1)\n if not unit1.is_alive:\n # print(\"u1 is dead\")\n # print(\"u1: \" + str(unit_1.health) + \", u2: \" + str(unit_2.health))\n return False\n else:\n # print(\"u2 is dead\")\n # print(\"u1: \" + str(unit_1.health) + \", u2: \" + str(unit_2.health))\n return True\n\n\nclass Army:\n def __init__(self):\n self._members = []\n self._it = iter(self._members)\n\n def __iter__(self):\n return self\n\n def __next__(self):\n try:\n return next(self._it)\n except StopIteration:\n raise\n\n def add_units(self, cls, units: int):\n for _ in range(0, units):\n self._members.append(cls())\n\n @property\n def members(self):\n return self._members\n\n @property\n def anyalive(self):\n for x in self._members:\n if x.is_alive:\n return True\n else:\n return False\n\n\nclass Battle:\n def __init__(self):\n pass\n\n def fight(self, army1: Army, army2: Army):\n\n try:\n team1f = next(army1)\n except StopIteration:\n return False\n else:\n try:\n team2f = next(army2)\n except StopIteration:\n return True\n\n try:\n team1b = next(army1)\n except StopIteration:\n team1b = None\n\n try:\n team2b = next(army2)\n except StopIteration:\n team2b = None\n\n while team1f.is_alive and team2f.is_alive:\n # team 1 forward is attacking\n if team1f is Lancer:\n team1f.damage(team2f, team2b)\n else:\n team1f.damage(team2f)\n\n # check team 2 backup is exist and still alive\n if team2b:\n if not team2b.is_alive:\n try:\n team2b = next(army2)\n except StopIteration:\n team2b = None\n\n # check team 2 forward is still alive\n if team2f.is_alive:\n # then, attack team 1 and see if he's alive\n if team2f is Lancer:\n team2f.damage(team1f, team1b)\n else:\n team2f.damage(team1f)\n\n # check team 1 backup is exist and still alive\n if team1b:\n if not team1b.is_alive:\n try:\n team1b = next(army1)\n except StopIteration:\n team1b = None\n\n # check team 1 forward is still alive\n if not team1f.is_alive:\n # print(\"team1f is dead: \" + str(type(team1f)))\n if team1b:\n team1f = team1b\n try:\n team1b = next(army1)\n except StopIteration:\n team1b = None\n else:\n continue\n\n else:\n # print(\"team2f is dead: \" + str(type(team2f)))\n if team2b:\n team2f = team2b\n try:\n team2b = next(army2)\n except StopIteration:\n team2b = None\n\n if army1.anyalive and not army2.anyalive:\n # print(\"army1 wins\")\n return True\n else:\n # print(\"army2 wins\")\n return False\n\n\nif __name__ == '__main__':\n # These \"asserts\" using only for self-checking and not necessary for auto-testing\n\n # fight tests\n chuck = Warrior()\n bruce = Warrior()\n carl = Knight()\n dave = Warrior()\n mark = Warrior()\n bob = Defender()\n mike = Knight()\n rog = Warrior()\n lancelot = Defender()\n eric = Vampire()\n adam = Vampire()\n richard = Defender()\n ogre = Warrior()\n freelancer = Lancer()\n vampire = Vampire()\n\n assert fight(chuck, bruce) == True\n assert fight(dave, carl) == False\n assert chuck.is_alive == True\n assert bruce.is_alive == False\n assert carl.is_alive == True\n assert dave.is_alive == False\n assert fight(carl, mark) == False\n assert carl.is_alive == False\n assert fight(bob, mike) == False\n assert fight(lancelot, rog) == True\n assert fight(eric, richard) == False\n assert fight(ogre, adam) == True\n assert fight(freelancer, vampire) == True\n assert freelancer.is_alive == True\n\n # battle tests\n my_army = Army()\n my_army.add_units(Defender, 2)\n my_army.add_units(Vampire, 2)\n my_army.add_units(Lancer, 4)\n my_army.add_units(Warrior, 1)\n\n enemy_army = Army()\n enemy_army.add_units(Warrior, 2)\n enemy_army.add_units(Lancer, 2)\n enemy_army.add_units(Defender, 2)\n enemy_army.add_units(Vampire, 3)\n\n army_3 = Army()\n army_3.add_units(Warrior, 1)\n army_3.add_units(Lancer, 1)\n army_3.add_units(Defender, 2)\n\n army_4 = Army()\n army_4.add_units(Vampire, 3)\n army_4.add_units(Warrior, 1)\n army_4.add_units(Lancer, 2)\n\n battle = Battle()\n\n assert battle.fight(my_army, enemy_army) == True\n assert battle.fight(army_3, army_4) == False\n print(\"Coding complete? Let's try tests!\")","sub_path":"py_checkio_solutions/Incinerator/the_lancers.py","file_name":"the_lancers.py","file_ext":"py","file_size_in_byte":7597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"134044195","text":"from typing import Dict, Tuple, Sequence, List, NoReturn\n\nfrom api_analyzer.analyzer.softwares.software_info_interface import SoftwareInfoInterface\nfrom api_analyzer.analyzer.analyzer_base import AnalyzerBase\nfrom api_analyzer.analyzer.softwares.imagemagick import ImageMagick\nfrom common.text_parser import TextParser\nfrom common.common import Common\n\n\nclass AnalyzerFacade:\n\n def __init__(self, software_info:SoftwareInfoInterface):\n self.analyzer = AnalyzerBase(software_info)\n\n def save_to_file(self, fileoutput, sorted_by_version):\n filename = fileoutput.split(\".\")[0] + \"_all.\" + fileoutput.split(\".\")[1]\n self.analyzer.save_to_file(filename, sorted_by_version)\n\n def version_data_from_list(self, list_cves):\n summary = self.analyzer.get_summary(list_cves)\n sorted_by_version = self.analyzer.convert_to_version_cve_dict(summary)\n return sorted_by_version\n\n def version_data_from_file(self, filename, files_save=True, fileoutput=\"sorted.json\"):\n list_cves = TextParser().get_cve_list_from_file(filename)\n sorted_by_version = self.version_data_from_list(list_cves)\n\n if files_save:\n self.save_to_file(fileoutput, sorted_by_version)\n\n return sorted_by_version\n\n\nif __name__ == \"__main__\":\n file_with_cves = \"/backend/api_analyzer/analyzer\\\\imagemagic_cve.txt\"\n image_magick = ImageMagick()\n analyzer_facade = AnalyzerFacade(image_magick)\n sorted_by_version = analyzer_facade.version_data_from_file(filename=file_with_cves)\n","sub_path":"backend/api_analyzer/analyzer/analyzer_facade.py","file_name":"analyzer_facade.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"628275142","text":"import math\n\nfrom flask import (\n Blueprint, render_template, request\n)\nfrom sqlalchemy import or_\n\nfrom .models import Book\nfrom .utils import paginate\nfrom .settings import ABANDONED, SINGLE_CP\n\n\nPAGE_SIZE = 10\n\nbp = Blueprint('web_app', __name__)\n\n\n# 主页\n@bp.route('/')\n@bp.route('/index')\n# @bp.route('/')\ndef index(page=1):\n query = Book.query.filter(Book.book_type < SINGLE_CP)\n query = query.filter(Book.status < ABANDONED)\n query = query.order_by(Book.pub_date.desc())\n total = math.ceil(query.count() / PAGE_SIZE)\n books = query.slice((page - 1) * PAGE_SIZE, page * PAGE_SIZE)\n return render_template('index.html',\n books=books,\n pagination=paginate(page, total))\n\n\n@bp.route('/search')\ndef search():\n kw = request.args.get('kw')\n books = Book.query.filter(or_(Book.book_name == kw,\n Book.author == kw,\n Book.hero == kw,\n Book.heroine == kw)).all()\n return render_template('search.html',\n books=books)\n\n\n@bp.route('/contact')\ndef contact():\n return render_template('contact.html')\n\n\n@bp.route('/help')\ndef help():\n return render_template('help.html')\n\n\ndef page_not_found(e):\n return render_template('404.html', data='页面'), 404\n\n\ndef server_error(e):\n return render_template('500.html'), 500\n","sub_path":"novel/web_app.py","file_name":"web_app.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"350474886","text":"class Solution:\n def insert(self, intervals: List[List[int]], newInterval: List[int]) -> List[List[int]]:\n \n result = []\n idx = 0\n \n while idx COMPRAS - DETALLES\")\n #\n # for detalle in DetalleFactura.objects.all():\n # if detalle.detalles.exists():\n # detalle.factura = detalle.detalles.all()[0]\n # detalle.save()\n # print(\"Detalle Venta: {}\".format(detalle.id))\n #\n # print(\"COMPLETED -> VENTAS - DETALLES\")\n\n # for detalle in DetalleSolicitudCompra.objects.all():\n # if detalle.solicitudcompra_set.exists():\n # detalle.solicitud = detalle.solicitudcompra_set.all()[0]\n # detalle.save()\n # print(\"Detalle Solicitud Compra: {}\".format(detalle.id))\n #\n # print(\"COMPLETED -> SOLICITUD COMPRAS - DETALLES\")\n\n # Analizar despues (si eliminar estos detalles de factura sin factura asociada)\n # DetalleFactura.objects.filter(factura__isnull=True).count()\n\n # Actualizar Facturas (campo Usuario) a partir de sus sesiones de caja para luego eliminar models SesionCaja y Caja\n # for factura in Factura.objects.order_by('-id'):\n # if factura.sesioncaja and factura.sesioncaja.caja and factura.sesioncaja.caja.usuario:\n # factura.usuario = factura.sesioncaja.caja.usuario\n # print(\"Updated user -> fact: {}\".format(factura.id))\n # else:\n # factura.usuario = User.objects.get(pk=7) # user andres (admin group)\n # print(\"Andres user -> fact: {}\".format(factura.id))\n # factura.save()\n # print(\"FINISHED UPDATE FACTURAS - USUARIOS\")\n\n # # Actualizar Inventario Real para todas las bodegas existentes (si no existe se crea con cantidad en cero)\n # for producto in Producto.objects.filter(ventas_por_mayor=True):\n #\n # for bodega in Bodega.objects.all():\n #\n # if not InventarioReal.objects.filter(producto=producto, bodega=bodega).exists():\n #\n # # get last Costo CIF\n # ultimo_costo_cif = 0\n # if HistorialCifProducto.objects.filter(producto=producto).exists():\n # ultimo_costo_cif = HistorialCifProducto.objects.filter(producto=producto).order_by('-id')[0].cif\n #\n # # get last Costo FOB\n # ultimo_costo_fob = 0\n # if HistorialFobProducto.objects.filter(producto=producto).exists():\n # ultimo_costo_fob = HistorialFobProducto.objects.filter(producto=producto).order_by('-id')[0].fob\n #\n # inventarioreal = InventarioReal(producto=producto,\n # cantidad=0,\n # costo=ultimo_costo_cif,\n # costofob=ultimo_costo_fob,\n # valor=0,\n # bodega=bodega)\n # inventarioreal.save()\n #\n # print(\"Updated Bodega {} en Inv {}\".format(bodega.nombre, inventarioreal.id))\n #\n # # Actualizar Inventarios de costos cif basados las ultimas transacciones cif registradas para los productos\n # for inventario in InventarioReal.objects.filter(producto__ventas_por_mayor=True).order_by('-id'):\n # change_cif, change_fob = inventario.actualizar_costos_valor()\n # if change_cif:\n # print('Updated CIF ({})'.format(inventario.producto.codigo))\n # if change_fob:\n # print('Updated FOB ({})'.format(inventario.producto.codigo))\n\n for traslado in TrasladoInventario.objects.order_by('-id'):\n usuario = \"andresg\"\n if traslado.usuario:\n usuario = traslado.usuario.username\n\n fecha = datetime.now()\n if traslado.fecha:\n fecha = traslado.fecha\n\n traslado.solicitado = True\n traslado.aprobado = True\n traslado.recibido = True\n\n traslado.solicitado_observaciones = traslado.observaciones\n\n traslado.solicitado_fecha = fecha\n traslado.solicitado_usuario = usuario\n\n traslado.aprobado_fecha = fecha\n traslado.aprobado_usuario = usuario\n\n traslado.recibido_fecha = fecha\n traslado.recibido_usuario = usuario\n\n traslado.save()\n print(traslado.id)\n","sub_path":"scripts.py","file_name":"scripts.py","file_ext":"py","file_size_in_byte":7686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"422741378","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]\n# Embedded file name: T:\\InGame\\Gameplay\\Scripts\\Server\\whims\\whim_set.py\n# Compiled at: 2019-02-20 22:33:37\n# Size of source mod 2**32: 14884 bytes\nfrom aspirations.aspiration_tuning import AspirationBasic\nfrom aspirations.aspiration_types import AspriationType\nfrom event_testing import objective_tuning\nfrom event_testing.resolver import DoubleSimResolver\nfrom interactions import ParticipantType\nfrom relationships.relationship_tests import TunableRelationshipTest\nfrom sims import genealogy_tracker\nfrom sims4.localization import TunableLocalizedStringFactory\nfrom sims4.tuning.instances import lock_instance_tunables\nfrom sims4.tuning.tunable import TunableEnumEntry, TunableReference, TunableVariant, OptionalTunable, TunableRange\nfrom sims4.utils import classproperty, constproperty\nfrom situations.situation_goal import TunableWeightedSituationGoalReference\nfrom statistics.commodity import RuntimeCommodity, CommodityTimePassageFixupType\nimport services, sims4.tuning.tunable\nlogger = sims4.log.Logger('Whimset', default_owner='jjacobson')\n\nclass GeneTargetFactory(sims4.tuning.tunable.TunableFactory):\n\n @staticmethod\n def factory(sim_info, relationship):\n family_member_sim_id = sim_info.get_relation(relationship)\n if family_member_sim_id is None:\n return\n family_member_sim_info = services.sim_info_manager().get(family_member_sim_id)\n if family_member_sim_info is not None:\n if family_member_sim_info.is_baby or family_member_sim_info.is_instanced():\n return family_member_sim_info\n\n FACTORY_TYPE = factory\n\n def __init__(self, **kwargs):\n (super().__init__)(description='\\n This option tests for completion of a tuned Achievement.\\n ', \n relationship=TunableEnumEntry(genealogy_tracker.FamilyRelationshipIndex, genealogy_tracker.FamilyRelationshipIndex.FATHER), **kwargs)\n\n\nclass RelationTargetFactory(sims4.tuning.tunable.TunableFactory):\n\n @staticmethod\n def factory(sim_info, relationship_test):\n relationship_match = None\n for relation in sim_info.relationship_tracker:\n relation_sim_info = services.sim_info_manager().get(relation.get_other_sim_id(sim_info.sim_id))\n if not relation_sim_info is not None or relation_sim_info.is_baby or relation_sim_info.is_instanced():\n resolver = DoubleSimResolver(sim_info, relation_sim_info)\n relationship_match = resolver(relationship_test)\n if relationship_match:\n return relation_sim_info\n\n FACTORY_TYPE = factory\n\n def __init__(self, **kwargs):\n (super().__init__)(description='\\n This option tests for completion of a tuned Achievement.\\n ', \n relationship_test=TunableRelationshipTest(description='\\n The relationship state that this goal will complete when\\n obtained.\\n ',\n locked_args={'subject':ParticipantType.Actor, \n 'tooltip':None, \n 'target_sim':ParticipantType.TargetSim, \n 'num_relations':0}), **kwargs)\n\n\nclass TunableWhimSetTargetVariant(TunableVariant):\n\n def __init__(self, *args, **kwargs):\n (super().__init__)(args, genealogy_target=GeneTargetFactory(), \n relationship_target=RelationTargetFactory(), \n default='genealogy_target', **kwargs)\n\n\nclass WhimSetBaseMixin:\n INSTANCE_TUNABLES = {'force_target':OptionalTunable(description='\\n Upon WhimSet activation, use this option to seek out and set a\\n specific target for this set. If the desired target does not exist\\n or is not instanced on the lot, WhimSet will not activate.\\n ',\n tunable=TunableWhimSetTargetVariant()), \n 'secondary_target':OptionalTunable(description='\\n Upon WhimSet activation, define a Sim that is used as a flavor\\n target, such that text can reference it. For example, a Dare whim\\n might use this field such that a \"Flirt with Bobby\" whim has a\\n \"(From Being Dared by Frank)\" origin.\\n ',\n tunable=TunableWhimSetTargetVariant()), \n 'whims':sims4.tuning.tunable.TunableList(description='\\n List of weighted goals.',\n tunable=TunableWeightedSituationGoalReference(pack_safe=True)), \n 'connected_whims':sims4.tuning.tunable.TunableMapping(description='\\n A tunable list of whims that upon a goal from this list succeeding will activate.',\n key_type=TunableReference((services.get_instance_manager(sims4.resources.Types.SITUATION_GOAL)), description='The goal to map.'),\n value_type=sims4.tuning.tunable.TunableList(description='\\n A tunable list of whim sets that upon this whim goal completing will activate',\n tunable=sims4.tuning.tunable.TunableReference(description='\\n These Aspiration Whim Sets become active automatically upon completion of this whim.',\n manager=(services.get_instance_manager(sims4.resources.Types.ASPIRATION)),\n class_restrictions='AspirationWhimSet'))), \n 'connected_whim_sets':sims4.tuning.tunable.TunableList(description='\\n A tunable list of whim sets that upon a goal from this list succeeding will activate',\n tunable=sims4.tuning.tunable.TunableReference(description='\\n These Aspiration Whim Sets become active automatically upon completion of a whim from this set.',\n manager=(services.get_instance_manager(sims4.resources.Types.ASPIRATION)),\n class_restrictions='AspirationWhimSet')), \n 'whim_reason':TunableLocalizedStringFactory(description=\"\\n The reason that shows in the whim tooltip for the reason that this\\n whim was chosen for the sim.\\n \\n 0 (Number): The most relevant numerical value pertaining to the\\n completion of this goal. This is usually the number of iterations\\n required to complete it although it could also be other values such as\\n the price of the item that the user is required to purchase.\\n \\n 1 (Sim): The Sim who owns the goal.\\n \\n 2 (Sim): The Sim the goal is directed at.\\n \\n 3 (Sim): The goal's secondary SimInfo, if one exists.\\n \"), \n 'cooldown_timer':sims4.tuning.tunable.TunableRange(description='\\n Number of Sim minutes this set of Whims is de-prioritized after de-activation.',\n tunable_type=float,\n minimum=0,\n maximum=3600,\n default=60)}\n\n @constproperty\n def aspiration_type():\n return AspriationType.WHIM_SET\n\n @classmethod\n def get_priority(cls, sim_info):\n raise NotImplementedError\n\n @classmethod\n def activate(cls, whims_tracker, chained, target):\n pass\n\n @classproperty\n def deactivate_on_completion(cls):\n return cls in cls.connected_whim_sets\n\n\nclass AspirationWhimSet(WhimSetBaseMixin, AspirationBasic):\n INSTANCE_TUNABLES = {'objectives':sims4.tuning.tunable.TunableList(description='\\n A Set of objectives for completing an aspiration.',\n tunable=sims4.tuning.tunable.TunableReference(description='\\n One objective for an aspiration',\n manager=(services.get_instance_manager(sims4.resources.Types.OBJECTIVE))),\n unique_entries=True), \n 'activated_priority':sims4.tuning.tunable.TunableRange(description='\\n Priority for this set to be chosen if triggered by contextual events.',\n tunable_type=int,\n minimum=0,\n maximum=10,\n default=6), \n 'priority_decay_rate':sims4.tuning.tunable.TunableRange(description=\"\\n The decay rate of a whimset's priority. A whimset's priority will\\n only decay when a whim of that whimset is active. A whimset's\\n priority will converge to the whimset's base priority.\\n \",\n tunable_type=float,\n default=0.01,\n minimum=0.0), \n 'timeout_retest':sims4.tuning.tunable.TunableReference(description='\\n Tuning an objective here will re-test the WhimSet for contextual\\n relevance upon active timer timeout; If the objective test passes,\\n the active timer will be refreshed. Note you can only use tests\\n without data passed in, other types will result in an assert on\\n load.\\n ',\n manager=services.get_instance_manager(sims4.resources.Types.OBJECTIVE),\n allow_none=True), \n 'chained_priority':sims4.tuning.tunable.TunableRange(description='\\n Priority for this set to be chosen if triggered by a previous whim set.',\n tunable_type=int,\n minimum=0,\n maximum=15,\n default=11)}\n priority_commodity = None\n\n @classmethod\n def _tuning_loaded_callback(cls):\n commodity = RuntimeCommodity.generate(cls.__name__)\n commodity.decay_rate = cls.priority_decay_rate\n commodity.convergence_value = 0\n commodity.remove_on_convergence = False\n commodity.visible = False\n if cls.activated_priority > cls.chained_priority:\n commodity.max_value_tuning = cls.activated_priority\n else:\n commodity.max_value_tuning = cls.chained_priority\n commodity.min_value_tuning = 0\n commodity.initial_value = 0\n commodity._time_passage_fixup_type = CommodityTimePassageFixupType.DO_NOT_FIXUP\n cls.priority_commodity = commodity\n\n @classmethod\n def _verify_tuning_callback(cls):\n if cls.activated_priority == 0:\n if cls.chained_priority == 0:\n logger.error('No priority tuned for value greater than 0 in {}', cls)\n for objective in cls.objectives:\n if objective.objective_completion_type == objective_tuning.SimInfoStatisticObjectiveTrack:\n logger.error(\"{} Objective in {} Whim Set tuned with incorrect Objective test type; use 'iterations', 'unique_locations', or 'unique targets'.\", objective, cls)\n if not objective.resettable:\n logger.error('{} Objective in {} Whim Set tuned as a Whim Aspiration Objective but not tuned as resettable. All Aspriation Whim Set objectives must be resettable.', objective, cls)\n\n if cls.timeout_retest is not None:\n if cls.timeout_retest.objective_test.USES_EVENT_DATA or cls.timeout_retest.objective_test.USES_DATA_OBJECT:\n logger.error('Bad Tuning! {} Objective Test {} in Whim Set being used as a timeout_retest cannot use event or object data.', cls.timeout_retest.objective_test, cls)\n\n @classmethod\n def get_priority(cls, sim_info):\n whimset_priority_stat = sim_info.get_statistic((cls.priority_commodity), add=False)\n if whimset_priority_stat is None:\n return 0\n return whimset_priority_stat.get_user_value()\n\n\nlock_instance_tunables(AspirationWhimSet, do_not_register_events_on_load=False, screen_slam=None)\n\nclass ObjectivelessWhimSet(WhimSetBaseMixin, AspirationBasic):\n INSTANCE_TUNABLES = {'priority': TunableRange(description='\\n The priority of this whim set.\\n ',\n tunable_type=float,\n minimum=0,\n default=5)}\n REMOVE_INSTANCE_TUNABLES = ('objective_completion_type', )\n\n @classmethod\n def get_priority(cls, sim_info):\n return cls.priority\n\n @constproperty\n def update_on_load():\n return False\n\n @classproperty\n def deactivate_on_completion(cls):\n return False\n\n\nlock_instance_tunables(ObjectivelessWhimSet, do_not_register_events_on_load=True, objectives=(), screen_slam=None)","sub_path":"Scripts/simulation/whims/whim_set.py","file_name":"whim_set.py","file_ext":"py","file_size_in_byte":11856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"627826807","text":"#!/usr/bin/env python3\n\"\"\"Run TriFinger back-end using multi-process robot data.\"\"\"\nimport argparse\nimport logging\nimport pathlib\nimport sys\n\nimport robot_interfaces\nimport robot_fingers\n\n\ndef main():\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--max-number-of-actions\",\n \"-a\",\n type=int,\n required=True,\n help=\"\"\"Maximum numbers of actions that are processed. After this the\n backend shuts down automatically.\n \"\"\",\n )\n parser.add_argument(\n \"--cameras\", \"-c\", action=\"store_true\", help=\"Run camera backend.\",\n )\n parser.add_argument(\n \"--fake-object-tracker\",\n action=\"store_true\",\n help=\"Run fake object tracker backend\",\n )\n parser.add_argument(\n \"--robot-logfile\",\n type=str,\n help=\"\"\"Path to a file to which the robot data log is written. If not\n specified, no log is generated.\n \"\"\",\n )\n parser.add_argument(\n \"--camera-logfile\",\n type=str,\n help=\"\"\"Path to a file to which the camera data is written. If not\n specified, no log is generated.\n \"\"\",\n )\n parser.add_argument(\n \"--ready-indicator\",\n type=str,\n metavar=\"READY_INDICATOR_FILE\",\n help=\"\"\"Path to a file that will be created once the backend is ready\n and will be deleted again when it stops (before storing the logs).\n \"\"\",\n )\n args = parser.parse_args()\n\n log_handler = logging.StreamHandler(sys.stdout)\n logging.basicConfig(\n format=\"[TRIFINGER_BACKEND %(levelname)s %(asctime)s] %(message)s\",\n level=logging.DEBUG,\n handlers=[log_handler]\n )\n\n if args.cameras:\n logging.info(\"Start camera backend\")\n import trifinger_cameras\n\n CAMERA_TIME_SERIES_LENGTH = 100\n\n camera_data = trifinger_cameras.tricamera.MultiProcessData(\n \"tricamera\", True, CAMERA_TIME_SERIES_LENGTH\n )\n camera_driver = trifinger_cameras.tricamera.TriCameraDriver(\n \"camera60\", \"camera180\", \"camera300\"\n )\n camera_backend = trifinger_cameras.tricamera.Backend( # noqa\n camera_driver, camera_data\n )\n\n logging.info(\"Camera backend ready.\")\n\n logging.info(\"Start robot backend\")\n\n # Use robot-dependent config file\n config_file_path = \"/etc/trifingerpro/trifingerpro.yml\"\n\n # Storage for all observations, actions, etc.\n history_size = args.max_number_of_actions + 1\n robot_data = robot_interfaces.trifinger.MultiProcessData(\n \"trifinger\", True, history_size=history_size\n )\n\n if args.robot_logfile:\n robot_logger = robot_interfaces.trifinger.Logger(robot_data)\n\n # The backend sends actions from the data to the robot and writes\n # observations from the robot to the data.\n backend = robot_fingers.create_trifinger_backend(\n robot_data,\n config_file_path,\n max_number_of_actions=args.max_number_of_actions,\n )\n\n # Initializes the robot (e.g. performs homing).\n backend.initialize()\n\n logging.info(\"Robot backend is ready\")\n\n if args.fake_object_tracker:\n import trifinger_object_tracking.py_object_tracker as object_tracker\n\n object_tracker_data = object_tracker.Data(\"object_tracker\", True)\n object_tracker_backend = object_tracker.FakeBackend( # noqa\n object_tracker_data\n )\n\n if args.cameras and args.camera_logfile:\n camera_fps = 100\n robot_rate_hz = 1000\n episode_length_s = args.max_number_of_actions / robot_rate_hz\n # Compute camera log size based on number of robot actions plus a\n # 10% buffer\n log_size = int(camera_fps * episode_length_s * 1.1)\n\n logging.info(\"Initialize camera logger with buffer size %d\", log_size)\n camera_logger = trifinger_cameras.tricamera.Logger(\n camera_data, log_size\n )\n\n # if specified, create the \"ready indicator\" file to indicate that the\n # backend is ready\n if args.ready_indicator:\n pathlib.Path(args.ready_indicator).touch()\n\n if args.cameras and args.camera_logfile:\n backend.wait_until_first_action()\n camera_logger.start()\n logging.info(\"Start camera logging\")\n\n backend.wait_until_terminated()\n\n # delete the ready indicator file to indicate that the backend has shut\n # down\n if args.ready_indicator:\n pathlib.Path(args.ready_indicator).unlink()\n\n if args.cameras and args.camera_logfile:\n logging.info(\n \"Save recorded camera data to file %s\", args.camera_logfile\n )\n camera_logger.stop_and_save(args.camera_logfile)\n\n if args.robot_logfile:\n logging.info(\"Save robot data to file %s\", args.robot_logfile)\n if args.max_number_of_actions:\n end_index = args.max_number_of_actions\n else:\n end_index = -1\n robot_logger.write_current_buffer(\n args.robot_logfile, start_index=0, end_index=end_index\n )\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/trifinger_backend.py","file_name":"trifinger_backend.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"416444983","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 4 17:34:57 2020\n\n@author: omen\n\"\"\"\n\nfrom tkinter import *\nfrom tkinter import filedialog\n \nfrom PIL import Image,ImageTk\nimport os\n\nimport IPython.display as ipd\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nimport seaborn as sns\nimport sklearn as skl\nfrom sklearn.preprocessing import MinMaxScaler\nimport sklearn.utils, sklearn.preprocessing, sklearn.decomposition, sklearn.svm\nimport librosa\nimport librosa.display\nimport pandas as pd\nimport numpy as np\n\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.utils import shuffle\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.decomposition import PCA\n\nimport matplotlib.pyplot as plt\n\npd.set_option('max_info_columns', 999)\npd.options.display.max_rows = 200\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, Dropout\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras.utils.generic_utils import get_custom_objects\nfrom keras.layers import Activation\nfrom keras.utils.np_utils import to_categorical\nimport tensorflow as tf\nfrom sklearn.preprocessing import StandardScaler\n\n\n\nimport utils\n\n\ndef columns():\n feature_sizes = dict(chroma_stft=12, chroma_cqt=12, chroma_cens=12,\n tonnetz=6, mfcc=20, rms=1, zcr=1,\n spectral_centroid=1, spectral_bandwidth=1,\n spectral_contrast=7, spectral_rolloff=1)\n moments = ('mean', 'std', 'skew', 'kurtosis', 'median', 'min', 'max')\n\n columns = []\n for name, size in feature_sizes.items():\n for moment in moments:\n it = ((name, moment, '{:02d}'.format(i+1)) for i in range(size))\n columns.extend(it)\n\n names = ('feature', 'statistics', 'number')\n columns = pd.MultiIndex.from_tuples(columns, names=names)\n\n # More efficient to slice if indexes are sorted.\n return columns.sort_values()\n\n\ndef compute_features(DIR):\n\n features = pd.Series(index=columns(), dtype=np.float32)\n\n # Catch warnings as exceptions (audioread leaks file descriptors).\n \n\n def feature_stats(name, values):\n features[name, 'mean'] = np.mean(values, axis=1)\n features[name, 'std'] = np.std(values, axis=1)\n features[name, 'skew'] = stats.skew(values, axis=1)\n features[name, 'kurtosis'] = stats.kurtosis(values, axis=1)\n features[name, 'median'] = np.median(values, axis=1)\n features[name, 'min'] = np.min(values, axis=1)\n features[name, 'max'] = np.max(values, axis=1)\n\n try:\n \n \n x, sr = librosa.load(DIR, sr=None, mono=True) # kaiser_fast\n\n f = librosa.feature.zero_crossing_rate(x, frame_length=2048, hop_length=512)\n feature_stats('zcr', f)\n\n cqt = np.abs(librosa.cqt(x, sr=sr, hop_length=512, bins_per_octave=12,\n n_bins=7*12, tuning=None))\n assert cqt.shape[0] == 7 * 12\n assert np.ceil(len(x)/512) <= cqt.shape[1] <= np.ceil(len(x)/512)+1\n\n f = librosa.feature.chroma_cqt(C=cqt, n_chroma=12, n_octaves=7)\n feature_stats('chroma_cqt', f)\n f = librosa.feature.chroma_cens(C=cqt, n_chroma=12, n_octaves=7)\n feature_stats('chroma_cens', f)\n f = librosa.feature.tonnetz(chroma=f)\n feature_stats('tonnetz', f)\n\n del cqt\n stft = np.abs(librosa.stft(x, n_fft=2048, hop_length=512))\n assert stft.shape[0] == 1 + 2048 // 2\n assert np.ceil(len(x)/512) <= stft.shape[1] <= np.ceil(len(x)/512)+1\n del x\n\n f = librosa.feature.chroma_stft(S=stft**2, n_chroma=12)\n feature_stats('chroma_stft', f)\n\n f = librosa.feature.rms(S=stft)\n feature_stats('rms', f)\n\n f = librosa.feature.spectral_centroid(S=stft)\n feature_stats('spectral_centroid', f)\n f = librosa.feature.spectral_bandwidth(S=stft)\n feature_stats('spectral_bandwidth', f)\n f = librosa.feature.spectral_contrast(S=stft, n_bands=6)\n feature_stats('spectral_contrast', f)\n f = librosa.feature.spectral_rolloff(S=stft)\n feature_stats('spectral_rolloff', f)\n\n mel = librosa.feature.melspectrogram(sr=sr, S=stft**2)\n del stft\n f = librosa.feature.mfcc(S=librosa.power_to_db(mel), n_mfcc=20)\n feature_stats('mfcc', f)\n\n except Exception as e:\n print('{}'.format(repr(e)))\n\n return features\n\ndef generate_matrix(DIR):\n X = compute_features(DIR)\n Y = compute_features('predict_samples/bourrage.mp3')\n X = X.to_frame().T\n X2 = Y.to_frame().T\n\n X = pd.concat([X,X2])\n print(X.head())\n X = StandardScaler().fit_transform(X)\n np.save(\"03.npy\", X)\n \ndef load_matrix(loaded_model):\n Xtesst1 =np.load(\"03.npy\",allow_pickle=True)\n Xtest2 = MinMaxScaler().fit_transform(Xtesst1)\n y = loaded_model.predict(Xtest2)\n return y\ndef show_plot(y_X):\n dict_genres = {'Electronic':0, 'Experimental':1, 'Folk':2, 'Hip-Hop':3, \n 'Instrumental':4,'International':5, 'Pop' :6, 'Rock': 7 }\n key_list = list(dict_genres.keys()) \n val_list = list(dict_genres.values()) \n j = 0\n a = max(y_X[0])\n print(a)\n for i in range(len(y_X[0])-1):\n if y_X[0][i] == a:\n j = i \n df = pd.DataFrame({'genre':['Electronic', 'Experimental', 'Folk', 'Hip-Hop', \n 'Instrumental','International', 'Pop' , 'Rock'], '%':y_X[0]*100})\n ax = df.plot.bar(x='genre', y='%', rot=0)\n ax.get_figure().savefig('4.png')\n \n \n \n#------------------------------------------------------------------------------------------------------------------------\n\ndef popup():\n win = Toplevel()\n win.wm_title(\"Plot\")\n image = PhotoImage(file=\"4.png\")\n label = Label(win,image=image)\n label.pack()\n win.mainloop()\n\n\nclass Interface(Frame):\n\n def __init__(self, fenetre, **kwargs):\n Frame.__init__(self, fenetre, width=1200, height=800, **kwargs)\n self.pack(fill=BOTH)\n \n self.message = Label(self, text=\"UPLOAD AN MP3 TRACK\")\n self.message.pack()\n \n self.bouton_Generate = Button(self, text=\"Generate\", fg=\"red\",command=self.Generate)\n self.bouton_Generate.pack(side=\"right\")\n \n self.bouton_showPlot = Button(self, text=\"showPlot\", fg=\"red\",command=popup)\n self.bouton_showPlot.pack(side=\"left\")\n \n self.bouton_browse = Button(self, text=\"Browse\",command=self.load_file)\n self.bouton_browse.pack(side=\"right\")\n \n self.bouton_clear = Button(self, text=\"Clear\",command=self.clear)\n self.bouton_clear.pack(side=\"left\")\n \n \n def load_file(self):\n \n self.file_name = filedialog.askopenfilename(filetypes = [(\"mp3 files\",\"*.mp3\"),(\"all files\",\"*.*\")])\n \n def Generate(self):\n d = self.file_name\n \n generate_matrix(d)\n model = loaded_model = tf.keras.models.load_model('final_model.h5')\n Y = load_matrix(model)\n show_plot(Y)\n print(\"end generating\")\n \n def clear(self):\n self.label.destroyè()\n \n \n \n \n\n \n \nfenetre = Tk()\nfenetre.geometry(\"200x100\")\ninterface = Interface(fenetre)\ninterface.mainloop()\ninterface.destroy()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"518832465","text":"from itertools import product\n\nn = int(input())\nn_len = len(str(n))\nans = 0\nfor i in range(3,n_len):\n ite = product(range(3),repeat=i)\n for it in ite:\n if 0 in it and 1 in it and 2 in it:\n ans += 1\n\n\nnum_d = {0:7,1:5,2:3}\nite = product(range(3),repeat=n_len)\nfor it in ite:\n if 0 in it and 1 in it and 2 in it:\n curr_num = 0\n for i,j in enumerate(it):\n curr_num += num_d[j]*(10**(n_len-1-i))\n if curr_num <= n: \n ans+=1\n\nprint(ans)","sub_path":"2_kakomon/abc042-125/abc114_c2.py","file_name":"abc114_c2.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"634477896","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom myTorch.utils import my_variable\nfrom myTorch.memory import RNNCell, GRUCell, LSTMCell, TARDISCell\n\n\nclass RecurrentBlocksWorldMatrix(nn.Module):\n\n\tdef __init__(self, obs_dim, action_dim, use_gpu=False, rnn_type=\"LSTM\"):\n\t\tsuper(self.__class__, self).__init__()\n\n\t\tself._obs_dim = obs_dim\n\t\tself._action_dim = action_dim\n\t\tself._use_gpu = use_gpu\n\t\tself._rnn_input_size = 512\n\t\tself._rnn_hidden_size = 1024\n\t\tself._rnn_type = rnn_type\n\t\tself._is_rnn_policy = True\n\t\tif self._rnn_type == \"LSTM\": \n\t\t\tself._rnn = LSTMCell(input_size=self._rnn_input_size, hidden_size=self._rnn_hidden_size, use_gpu=use_gpu)\n\t\telif self._rnn_type == \"GRU\":\n\t\t\tself._rnn = GRUCell(input_size=self._rnn_input_size, hidden_size=self._rnn_hidden_size, use_gpu=use_gpu)\n\t\tself._hidden = None\n\n\t\tself._conv1 = nn.Conv2d(self._obs_dim[0], 16, kernel_size=2, stride=1)\n\t\tself._bn1 = nn.BatchNorm2d(16)\n\t\tself._conv2 = nn.Conv2d(16, 32, kernel_size=2, stride=1)\n\t\tself._bn2 = nn.BatchNorm2d(32)\n\t\tself._conv3 = nn.Conv2d(32, 32, kernel_size=2, stride=1)\n\t\tself._bn3 = nn.BatchNorm2d(32)\n\t\tself._conv4 = nn.Conv2d(32, 32, kernel_size=4, stride=1)\n\t\tself._bn4 = nn.BatchNorm2d(32)\n\t\tself._fc1 = nn.Linear(512, 256)\n\t\tself._fc2 = nn.Linear(256, 256)\n\t\tself._fc3 = nn.Linear(256, self._rnn_input_size)\n\n\t\tself._fcv = nn.Linear(self._rnn_hidden_size, 1)\n\t\tself._fcp = nn.Linear(self._rnn_hidden_size, self._action_dim)\n\n\tdef _conv_to_linear(self, obs):\n\t\tif len(obs.shape) < 4:\n\t\t\tobs = obs.unsqueeze(0)\n\t\tx = F.relu(self._bn1(self._conv1(obs)))\n\t\tx = F.relu(self._bn2(self._conv2(x)))\n\t\tx = F.relu(self._bn3(self._conv3(x)))\n\t\tx = F.relu(self._bn4(self._conv4(x)))\n\t\tx = F.relu(self._fc1(x.view(x.size(0), -1)))\n\t\tx = F.relu(self._fc2(x))\n\t\tx = F.relu(self._fc3(x))\n\t\treturn x\n\n\tdef reset_hidden(self, batch_size):\n\t\tself._hidden = {}\n\t\tself._hidden[\"h\"] = my_variable(torch.zeros(batch_size, self._rnn_hidden_size), use_gpu=self._use_gpu)\n\t\t\n\t\tif self._rnn_type == \"LSTM\":\n\t\t\tself._hidden[\"c\"] = my_variable(torch.zeros(batch_size, self._rnn_hidden_size), use_gpu=self._use_gpu)\n\n\tdef forward(self, obs, update_hidden_state):\n\t\tif self._hidden is None:\n\t\t\tself.reset_hidden(obs.shape[0])\n\n\t\tx = self._conv_to_linear(obs)\n\t\thidden_next = self._rnn(x, self._hidden)\n\t\tif update_hidden_state:\n\t\t\tself._hidden = hidden_next\n\t\tp = self._fcp(hidden_next[\"h\"])\n\t\tv = self._fcv(hidden_next[\"h\"])\n\t\treturn p, v\n\n\tdef update_hidden(self, mask):\n\t\tfor k in self._hidden:\n\t\t\tself._hidden[k] = mask.expand(-1,self._rnn_hidden_size) * self._hidden[k]\n\n\tdef detach_hidden(self):\n\t\tfor k in self._hidden:\n\t\t\thidden_value = self._hidden[k].data.cpu().numpy()\n\t\t\tself._hidden[k] = my_variable(torch.from_numpy(hidden_value), use_gpu=self._use_gpu)\n\t\t\n\t@property\n\tdef action_dim(self):\n\t\treturn self._action_dim\n\n\t@property\n\tdef obs_dim(self):\n\t\treturn self._obs_dim\n\n\t@property\n\tdef use_gpu(self):\n\t\treturn self._use_gpu\n\n\t@property\n\tdef is_rnn_policy(self):\n\t\treturn self._is_rnn_policy\n\n\tdef get_attributes(self):\n\t\treturn (self._obs_dim, self._action_dim, self._use_gpu, self._rnn_type)\n\n\tdef get_params(self):\n\t\treturn self.state_dict()\n\n\tdef set_params(self, state_dict):\n\t\tself.load_state_dict(state_dict)\n\n\tdef make_inference_net(self):\n\t\tinference_net = self.__class__(*self.get_attributes())\n\t\tif self._use_gpu == True:\n\t\t\t\tinference_net.cuda()\n\t\tinference_net.set_params(self.get_params())\n\t\treturn inference_net\n\n\nif __name__==\"__main__\":\n\tp, v = RecurrentCartPole(50,10)\n\timport pdb; pdb.set_trace()\n\n","sub_path":"myTorch/rllib/a2c/a2c_networks/recurrent_blocksworld_matrix.py","file_name":"recurrent_blocksworld_matrix.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"187993071","text":"'''\n@Author: your name\n@Date: 2019-11-27 17:23:40\n@LastEditTime: 2019-11-27 17:49:56\n@LastEditors: Please set LastEditors\n@Description: In User Settings Edit\n@FilePath: \\leetcode\\104.二叉树的最大深度.py\n'''\n#\n# @lc app=leetcode.cn id=104 lang=python3\n#\n# [104] 二叉树的最大深度\n#递归 \n\n# @lc code=start\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def maxDepth(self, root: TreeNode):\n if root == None :#结束递归的条件\n return 0\n else:\n left_height = self.maxDepth(root.left)\n right_height = self.maxDepth(root.right)\n return max(left_height,right_height)+1\n\n\n# @lc code=end\n\n","sub_path":"easy/104.二叉树的最大深度.py","file_name":"104.二叉树的最大深度.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"565437976","text":"import datetime\n\nclass Conta:\n \n __slots__ = ['_numero','_cliente','_saldo','_limite','_historico','_data_abertura']\n\n _numero_conta = 0\n\n def __init__(self, cliente, saldo, limite):\n #self._numero = numero\n self._cliente = cliente\n self._saldo = saldo\n self._limite = limite\n self._historico = Historico()\n self._data_abertura = datetime.datetime.now()\n Conta._numero_conta += 1\n\n def deposita(self, valor,transferencia = False,conta = None):\n self._saldo += valor\n if not transferencia:\n self._historico.insere(f\"Deposito de {valor}\")\n else:\n self._historico.insere(f\"Recebida transferencia de {conta._numero} no valor de {valor}\")\n\n \n def saca(self, valor):\n if(self._saldo < valor):\n return False\n else: \n self._saldo -= valor\n self._historico.insere(f\"Saque: {valor}\")\n return True\n \n def transfere_para(self, destino,valor):\n retirou = self.saca(valor)\n\n if(retirou):\n destino.deposita(valor,True,self)\n self._historico.insere(f\"Transferido para {destino.numero} o valor {valor}\")\n \n return retirou\n\n\n def extrato(self):\n print(\"numero: {} \\nsaldo:{}\".format(self._numero,self._saldo))\n self._historico.mostra()\n\nclass Cliente:\n\n def __init__(self, nome, sobrenome, cpf):\n self.nome = nome\n self.sobrenome = sobrenome\n self.cpf = cpf\n\nclass Historico:\n\n def __init__(self):\n self.movimentacoes = []\n \n def mostra(self):\n for movimentacao in self.movimentacoes:\n print(movimentacao)\n\n def insere(self,mensagem):\n self.movimentacoes.append(mensagem)\n\n\n\n\n ","sub_path":"conta.py","file_name":"conta.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"84043478","text":"import csv\nimport os\n\n\ncontent = os.listdir()\n\nbyte_str = b'\\x88A\\xde\\x87\\x00H`\\xb9V7\\xb0\\xfe?\\xda\\x0e\\xd7\\xb0\\xf3\\xac\\x8c'\ncon = byte_str\nwith open('list.csv','w', encoding='UTF-8') as f:\n # for x in con:\n f.write(byte_str)\n f.close()","sub_path":"test_file.py","file_name":"test_file.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"574424890","text":"import numpy as np\n\nSEG_DURATION = 1000.0\nSERVER_START_UP_TH = 2000.0\t\t\t\t# <========= TO BE MODIFIED. TEST WITH DIFFERENT VALUES\n# ADD_DELAY = 3000.0\nRANDOM_SEED = 10\nMS_IN_S = 1000.0\nKB_IN_MB = 1000.0\n# New bitrate setting, 6 actions, correspongding to 240p, 360p, 480p, 720p, 1080p and 1440p(2k)\nBITRATE = [300.0, 500.0, 1000.0, 2000.0, 3000.0, 6000.0]\n# BITRATE = [300.0, 6000.0]\n# BITRATE = [500.0, 2000.0, 5000.0, 8000.0, 16000.0]\t# 5 actions\n\nBITRATE_LOW_NOISE = 0.7\nBITRATE_HIGH_NOISE = 1.3\nRATIO_LOW_2 = 2.0\t\t\t\t# This is the lowest ratio between first chunk and the sum of all others\nRATIO_HIGH_2 = 10.0\t\t\t# This is the highest ratio between first chunk and the sum of all others\nRATIO_LOW_5 = 0.75\t\t\t\t# This is the lowest ratio between first chunk and the sum of all others\nRATIO_HIGH_5 = 1.0\t\t\t# This is the highest ratio between first chunk and the sum of all others\nEST_LOW_NOISE = 0.98\nEST_HIGH_NOISE = 1.02\n\n\nclass Live_Server(object):\n\tdef __init__(self, seg_duration, start_up_th, randomSeed = RANDOM_SEED):\n\t\tnp.random.seed(randomSeed)\n\t\tself.seg_duration = seg_duration\n\t\tself.time = start_up_th + np.random.randint(1,seg_duration)\n\t\tself.start_up_th = start_up_th\n\t\tself.current_seg_idx = -1\n\t\tself.segs = []\t\n\t\tself.current_seg_size = [[] for i in range(len(BITRATE))]\n\t\tself.init_encoding()\n\t\n\n\tdef init_encoding(self):\n\t\t# assert self.ratio\n\t\tself.encoding_update(0.0, self.time)\n\t\tself.next_delivery = []\n\t\tself.generate_next_delivery()\n\n\tdef get_next_delivery(self):\n\t\treturn self.next_delivery\n\n\tdef clean_next_delivery(self):\n\t\tself.next_delivery = []\n\n\tdef get_time(self):\n\t\treturn self.time\n\n\tdef generate_next_delivery(self):\n\t\tself.next_delivery = self.segs.pop(0)\n\t\t\n\tdef encoding_update(self, starting_time, end_time):\n\t\ttemp_time = starting_time\n\t\twhile True:\n\t\t\tnext_time = (int(temp_time/self.seg_duration) + 1) * self.seg_duration\n\t\t\tif next_time > end_time:\n\t\t\t\tbreak\n\t\t\t# Generate segs and insert to encoding buffer\n\t\t\ttemp_time = next_time\n\t\t\tself.current_seg_idx += 1\n\t\t\tself.generate_seg_size()\n\t\t\t\t# print self.current_seg_size\n\t\t\tself.segs.append([self.current_seg_idx, \\\n\t\t\t\t\t\t\t\tself.current_seg_size])\t# for 1s segment\n\t\t\t\n\tdef update(self, downloadig_time):\n\t\tpre_time = self.time\n\t\tself.time += downloadig_time\n\t\tself.encoding_update(pre_time, self.time)\n\t\treturn self.time\n\n\tdef sync_encoding_buffer(self):\n\t\ttarget_encoding_len = 0\n\t\tnew_heading_time = 0.0\n\t\tmissing_count = 0\n\t\ttarget_encoding_len = self.start_up_th/self.seg_duration\n\t\twhile not len(self.segs) == target_encoding_len:\n\t\t\tself.segs.pop(0)\n\t\t\tmissing_count += 1\n\t\tnew_heading_time = self.segs[0][0] * self.seg_duration\n\t\treturn new_heading_time, missing_count\n\n\tdef generate_seg_size(self):\n\t\tself.current_seg_size = [[] for i in range(len(BITRATE))]\n\t\tencoding_coef = 1.0\n\t\testimate_seg_size = [x * encoding_coef for x in BITRATE]\n\t\tself.current_seg_size = estimate_seg_size \n\n\n\tdef wait(self):\n\t\tnext_available_time = (int(self.time/self.seg_duration) + 1) * self.seg_duration\n\t\tself.encoding_update(self.time, next_available_time)\n\t\tassert len(self.segs) == 1\n\t\ttime_interval = next_available_time - self.time\n\t\tself.time = next_available_time\n\t\treturn time_interval \n\n\tdef test_reset(self, start_up_th):\n\t\tself.time = start_up_th + np.random.randint(1,self.seg_duration)\t\t# start from 2000ms\t\n\t\tself.start_up_th = start_up_th\n\t\tself.current_seg_idx = -1\n\t\tself.segs = []\t\n\t\tself.current_seg_size = [[] for i in range(len(BITRATE))]\n\t\tself.encoding_update(0.0, self.time)\n\t\tself.next_delivery = []\n\t\t# self.delay_tol = start_up_th\n\tdef check_segs_empty(self):\n\t\tif len(self.segs) == 0:\n\t\t\treturn True\n\t\telse: return False\n\ndef main():\n\tserver = Live_Server(seg_duration=SEG_DURATION, start_up_th=SERVER_START_UP_TH)\n\tserver.init_encoding()\n\tprint(server.segs, server.time)\n\tprint(server.next_delivery)\n\n\nif __name__ == '__main__':\n\tmain()","sub_path":"mpc_seg_LQR/live_server_seg.py","file_name":"live_server_seg.py","file_ext":"py","file_size_in_byte":3857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"341489049","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\nT = int(input())\n\nfor a in range(T):\n sudoku = []\n for b in range(9):\n sudoku.append(list(map(int, input().split())))\n\n result = 1 # 처음에 스도쿠가 올바른 검증이 되었���고 가정하고 시작\n \n # 가로줄 검사(자료들의 길이로 판별하는 방법) => set()은 중복 요소를 하나로 줄여준다.\n for c in range(9):\n if len(sudoku[c]) != len(set(sudoku[c])):\n result = 0\n break\n\n if result: # 앞에서 0이 나오면 나머지 검사 할 필요 없음\n # 세로줄 검사(0~9까지의 총합으로 판별하는 방법)\n for d in range(9):\n col_list = []\n for e in range(9):\n col_list.append(sudoku[e][d])\n if sum(col_list) != 45:\n result = 0\n break\n\n if result: # 앞에서 0이 나오면 나머지 검사 할 필요 없음\n compare_list = [num for num in range(1, 10)]\n # 3 X 3 형태 검사 (0~9가 모두 있는지 체크하는 방법)\n for f in range(0, 9, 3):\n for g in range(0, 9, 3):\n tri_tri_list = []\n for h in range(f, f+3):\n for i in range(g, g+3):\n tri_tri_list.append(sudoku[h][i])\n if compare_list != sorted(tri_tri_list):\n result = 0\n break\n if not result:\n break\n \n # 끝까지 검사했을 때 오류 없으면 result = 1 그대로 유지됨\n \n print('#{} {}'.format(a + 1, result))","sub_path":"02_algorithm/sw_expert_academy/code_problem/D2/1974.스도쿠 검증/1974.py","file_name":"1974.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"480158061","text":"#!/usr/local/bin/python\r\n# -*- coding: utf-8 -*-\r\nfrom django.http import HttpResponse, HttpResponseRedirect\r\nfrom django.shortcuts import render_to_response\r\nfrom django.template import RequestContext\r\nfrom models import *\r\n\r\nimport calendars\r\nfrom datetime import datetime, date, timedelta\r\n\r\ndef display(request, year, template='calendar.html', extra_context={}):\r\n if not year:\r\n year = datetime.now().year\r\n else:\r\n year = int(year)\r\n \r\n yeartable = calendars.Year(year)\r\n \r\n yeartable.fill_bookings(Item.objects.filter_authorized(request.user))\r\n \r\n context = {'yeartable':yeartable,\r\n 'date_update':date_update(),\r\n 'bookables_by_family':prepare_families(request.session, user=request.user),\r\n 'can_edit':True, 'filter':request.session['filter']}\r\n context.update(extra_context)\r\n response = render_to_response(template, context,\r\n context_instance=RequestContext(request))\r\n return response\r\n\r\ndef edit(request, year, template='calendar.html', extra_context={}):\r\n yeartable = calendars.Year(int(year))\r\n \r\n yeartable.fill_bookings((Item.objects.get(name='Zone A'),))\r\n \r\n context = {'yeartable':yeartable,\r\n 'date_update':date_update(),\r\n 'bookables_by_family':prepare_families(request.session, user=request.user, update_checkboxes=False, edit=True),\r\n 'can_edit':True, 'filter':request.session['filter']}\r\n context.update(extra_context)\r\n response = render_to_response(template, context,\r\n context_instance=RequestContext(request))\r\n return response\r\n\r\ndef add_booking(request, days):\r\n return None\r\n\r\ndef display_filter(request, year, id, value):\r\n if not request.session.has_key('filter'):\r\n request.session['filter'] = {}\r\n if value=='false':\r\n request.session['filter'][id] = False\r\n else:\r\n del request.session['filter'][id]\r\n request.session.modified = True\r\n\r\n return HttpResponseRedirect('../../../')\r\n #return HttpResponse(str(request.session['filter']))\r\n\r\ndef edit_set(request, year, id, days):\r\n datelist = []\r\n for day in days.split(','):\r\n y,m,d = day.split('-')\r\n datelist.append( date(int(y), int(m), int(d)) )\r\n \r\n ranges = get_date_ranges(datelist)\r\n data_object = Item.objects.get(pk=int(id))\r\n for dmin, dmax in ranges:\r\n Booking.objects.create(item=data_object,\r\n user=request.user,\r\n start=dmin, end=dmax)\r\n return HttpResponseRedirect('../../../../')\r\n #return HttpResponse(str(ranges))\r\n\r\ndef prepare_families(session, user=None, update_checkboxes=True, edit=False):\r\n if not session.has_key('filter'):\r\n session['filter'] = {}\r\n flist = []\r\n filter = session['filter']\r\n \r\n for f in GroupItem.objects.all().order_by('sort_order'):\r\n if user:\r\n if edit: permission_name='add'\r\n else: permission_name='read'\r\n oblist = list(Item.objects.filter_authorized(user, permission_name=permission_name).filter(group=f))\r\n else:\r\n oblist = list(f.item_set.all())\r\n \r\n flist.append({'name':f.name, 'items':oblist})\r\n if edit and user:\r\n for ob in oblist:\r\n ob.editable = ob.is_editable_by(user)\r\n if update_checkboxes:\r\n for ob in oblist:\r\n if filter.has_key(u'%d' % ob.pk):\r\n ob.checked = \"\"\r\n else:\r\n ob.checked = \"checked\"\r\n return flist\r\n\r\ndef get_date_ranges(datelist):\r\n dmin = datelist[0]\r\n dmax = dmin\r\n ranges = []\r\n j1 = timedelta(days=1)\r\n for day in datelist[1:]:\r\n if day == dmax + j1:\r\n dmax = day\r\n else:\r\n ranges.append((dmin, dmax))\r\n dmin, dmax = day, day\r\n ranges.append((dmin, dmax))\r\n return ranges\r\n\r\ndef date_update():\r\n return Booking.objects.all().order_by('-date')[0].date","sub_path":"django_booking/booking/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"392339306","text":"import os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.utils.data as data\nfrom scipy.misc import imread\nfrom skimage.transform import AffineTransform, warp\n\nfrom bluntools.image_ops import normalize, adjust_intensity\n\n\ndef _transform(*inputs):\n \"\"\"Affine Transform\"\"\"\n\n *images, lb = *inputs,\n lb = lb.astype(np.float)\n\n bounds = np.deg2rad(3)\n radian = np.random.uniform(low=-bounds, high=bounds)\n factor = np.random.uniform(low=0.9, high=1.1, size=2)\n _trans = np.random.uniform(low=-7, high=7, size=2)\n\n tform = AffineTransform(scale=factor, rotation=radian, shear=radian, translation=_trans)\n *images, lb = [warp(item, tform, mode='reflect', preserve_range=True) for item in [*images, lb]]\n\n return (*images, lb.astype(np.uint8))\n\n\nclass DatasetFromFolder(data.Dataset):\n def __init__(self, mode='train', data_dir='./data/base/', split_index=[40, 47]):\n \"\"\"\n 1. Read one data from file (e.g. using scipy.misc.imread).\n 2. Preprocess the data (e.g. customized functions or torchvision.Transform).\n 3. Return a data pair (e.g. image and label).\n \n Arguments:\n data_dir: path to images and labels, (e.g. 'data_dir/train_image', 'data_dir/train_label')\n split_index: split dataset into train, var, and test subsets\n mode: determine read in which subsets\n \n Assume:\n Filename format: 'X.bmp'\n \"\"\"\n super(DatasetFromFolder, self).__init__()\n assert len(split_index) == 2, \"3 subsets (train, val, test) should have 2 split points.\"\n\n data_dir = [os.path.join(p, 'train_image/') for p in data_dir]\n\n train_image_names = []\n for d in list(data_dir):\n train_image_names += [os.path.join(d, name) for name in os.listdir(d)]\n\n # Set random seed for fixed split\n np.random.seed(7)\n np.random.shuffle(train_image_names)\n\n self.train, self.val, self.test = np.split(train_image_names, split_index)\n\n # self.train, self.val, self.test = np.split(sorted(train_image_names,\n # key=lambda name: int(name[: -4])), split_index)\n\n self.image_path = eval('self.' + mode)\n self.label_path = [p.replace('image', 'label') for p in self.image_path]\n\n def __getitem__(self, index):\n # IMPORTANT, set random seed for ramdom augment\n np.random.seed(int(time.time()))\n\n # Load gray image\n im, lb = [imread(p[index]).squeeze() for p in [self.image_path, self.label_path]]\n\n # Pre-process\n im = normalize(im)\n lb = (lb > 0).astype(np.float32)\n\n # Transform\n im = adjust_intensity(im, low=0.8, high=1.2)\n im, lb = _transform(im, lb)\n\n # inputs: (N, C, H, W)\n im, lb = [item[np.newaxis, ...] for item in [im, lb]]\n\n # Convert to Tensor\n return [torch.from_numpy(item) for item in [im, lb]]\n\n def __len__(self):\n return len(self.image_path)\n\n# --------------------------------------------------------------------------------------------------------------\n# Test for inputs\n# --------------------------------------------------------------------------------------------------------------\n#\n# training_data_loader = data.DataLoader(dataset=DatasetFromFolder(), num_workers=4, batch_size=4, shuffle=True)\n# im, lb = next(iter(training_data_loader))\n# print(im.size())\n\n\n# --------------------------------------------------------------------------------------------------------------\n# Visualize\n# --------------------------------------------------------------------------------------------------------------\n#\n# import visdom\n# vis = visdom.Visdom()\n# vis.images(im.numpy(), opts=dict(title='Random selected images', caption='Shape: {}'.format(im.size())))\n# vis.images(lb.numpy(), opts=dict(title='Random selected labels', caption='Shape: {}'.format(im.size())))\n","sub_path":"Example-Rectus-Femoris-Segmentation/inputs.py","file_name":"inputs.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"614748196","text":"# Copyright 2015, Kay Hayen, mailto:kay.hayen@gmail.com\n#\n# Part of \"Nuitka\", an optimizing Python compiler that is compatible and\n# integrates with CPython, but also works on its own.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\"\"\" XML node tree handling\n\nMeans to create XML elements from Nuitka tree nodes and to convert the\nXML tree to ASCII or output it.\n\"\"\"\n\nfrom nuitka.utils import Utils\n\nfrom . import Tracing\n\ntry:\n import lxml.etree\n\n Element = lxml.etree.Element\nexcept ImportError:\n lxml = None\n Element = None\n\ndef toString(xml):\n return lxml.etree.tostring(xml, pretty_print = True)\n\ndef dump(xml):\n value = toString(xml).rstrip()\n\n if Utils.python_version >= 300:\n value = value.decode(\"utf-8\")\n\n Tracing.printLine(value)\n","sub_path":"nuitka/TreeXML.py","file_name":"TreeXML.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"566398108","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2015 FingerApp Studio, Inc.\n\nimport errno\nimport logging\nimport logging.handlers\nimport os\n\nfrom memory.shared.controller import SharedManager\nfrom logger.formatter import LogFormatter\n\n\nclass LogManager:\n access = None\n application = None\n general = None\n dblog = None\n\n options = dict(\n log_file_max_size=1024 * 1024,\n log_file_num_backups=100,\n log_to_stderr=False\n )\n\n @staticmethod\n def _enable_pretty_logging(logger=None, options=None, task_id=0):\n if options is None:\n return\n if options[\"logging\"] == \"none\":\n return\n\n if logger is None:\n logger = logging.getLogger()\n\n logger.setLevel(\n getattr(logging, options[\"logging\"].upper()))\n\n if options[\"log_file_prefix\"]:\n channel = logging.handlers.RotatingFileHandler(\n filename=options[\"log_file_prefix\"],\n maxBytes=options[\"log_file_max_size\"],\n backupCount=options[\"log_file_num_backups\"])\n channel.setFormatter(\n LogFormatter(task_id=task_id, color=False))\n logger.addHandler(channel)\n\n # Not used with our LogManager, just keep for possible laster use\n if (LogManager.options[\"log_to_stderr\"] or\n (LogManager.options[\"log_to_stderr\"] is None and\n not logger.handlers)):\n channel = logging.StreamHandler()\n channel.setFormatter(\n LogFormatter(task_id=task_id, color=True))\n logger.addHandler(channel)\n\n @staticmethod\n def _build_canonical_file_path(base_path, filename):\n try:\n if not os.path.isdir(base_path):\n os.makedirs(base_path, 0o755)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(base_path):\n pass\n else:\n raise\n\n canonical_file_path = base_path\n if not canonical_file_path.endswith(\"/\"):\n canonical_file_path += \"/\"\n canonical_file_path += filename\n\n return canonical_file_path\n\n @staticmethod\n def enable_access_log():\n # load config\n config_m = SharedManager.get_config()\n config = config_m.config\n task_id = config_m.task_id\n\n LogManager.access = logging.getLogger(\"tornado.access\")\n canonical_file_path = LogManager._build_canonical_file_path(\n base_path=config.logging.access.path,\n filename=config.logging.access.filename\n )\n LogManager.options[\"logging\"] = config.logging.access.level\n LogManager.options[\"log_file_prefix\"] = canonical_file_path\n LogManager._enable_pretty_logging(\n logger=LogManager.access,\n options=LogManager.options,\n task_id=task_id\n )\n\n @staticmethod\n def enable_application_log():\n # load config\n config_m = SharedManager.get_config()\n config = config_m.config\n task_id = config_m.task_id\n\n LogManager.application = logging.getLogger(\"tornado.application\")\n canonical_file_path = LogManager._build_canonical_file_path(\n base_path=config.logging.application.path,\n filename=config.logging.application.filename\n )\n LogManager.options[\"logging\"] = config.logging.application.level\n LogManager.options[\"log_file_prefix\"] = canonical_file_path\n LogManager._enable_pretty_logging(\n logger=LogManager.application,\n options=LogManager.options,\n task_id=task_id\n )\n\n @staticmethod\n def enable_general_log():\n # load config\n config_m = SharedManager.get_config()\n config = config_m.config\n task_id = config_m.task_id\n\n LogManager.general = logging.getLogger(\"tornado.general\")\n canonical_file_path = LogManager._build_canonical_file_path(\n base_path=config.logging.general.path,\n filename=config.logging.general.filename\n )\n LogManager.options[\"logging\"] = config.logging.general.level\n LogManager.options[\"log_file_prefix\"] = canonical_file_path\n LogManager._enable_pretty_logging(\n logger=LogManager.general,\n options=LogManager.options,\n task_id=task_id\n )\n\n @staticmethod\n def enable_http_log():\n # access log\n LogManager.enable_access_log()\n\n # application log\n LogManager.enable_application_log()\n\n # general log\n LogManager.enable_general_log()\n\n @staticmethod\n def enable_db_log():\n # load config\n config_m = SharedManager.get_config()\n config = config_m.config\n task_id = config_m.task_id\n\n LogManager.dblog = logging.getLogger(\"pushgram.mysql\")\n canonical_file_path = LogManager._build_canonical_file_path(\n base_path=config.logging.mysql.path,\n filename=config.logging.mysql.filename\n )\n LogManager.options[\"logging\"] = config.logging.mysql.level\n LogManager.options[\"log_file_prefix\"] = canonical_file_path\n LogManager._enable_pretty_logging(\n logger=LogManager.dblog,\n options=LogManager.options,\n task_id=task_id\n )\n\n @staticmethod\n def _get_access():\n if not LogManager.access:\n LogManager.enable_access_log()\n return LogManager.access\n\n @staticmethod\n def _get_application():\n if not LogManager.application:\n LogManager.enable_application_log()\n return LogManager.application\n\n @staticmethod\n def _get_general():\n if not LogManager.general:\n LogManager.enable_general_log()\n return LogManager.general\n\n @staticmethod\n def _get_dblog():\n if not LogManager.dblog:\n LogManager.enable_db_log()\n return LogManager.dblog\n\n @staticmethod\n def get_instance(logger_name):\n if logger_name is None:\n return None\n\n logger_map = {\n \"access\": LogManager._get_access,\n \"application\": LogManager._get_application,\n \"general\": LogManager._get_general,\n \"dblog\": LogManager._get_dblog\n }\n if logger_name in logger_map:\n return logger_map[logger_name]()\n else:\n return logging.getLogger()\n","sub_path":"logger/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":6431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"614179037","text":"from dsbox.template.template import DSBoxTemplate \nfrom d3m.metadata.problem import TaskKeyword \nfrom dsbox.template.template_steps import TemplateSteps \nfrom dsbox.schema import SpecializedProblem \nimport typing \nimport numpy as np # type: ignore \nclass DataAugmentRegressionTemplate(DSBoxTemplate):\n def __init__(self):\n DSBoxTemplate.__init__(self)\n\n self.template = {\n \"name\": \"data_augment_regression_template\",\n \"taskType\": TaskKeyword.REGRESSION.name,\n \"taskSubtype\": {TaskKeyword.UNIVARIATE.name, TaskKeyword.MULTIVARIATE.name, \"NONE\"},\n \"inputType\": \"table\", # See SEMANTIC_TYPES.keys() for range of values\n \"output\": \"model_step\", # Name of the final step generating the prediction\n \"target\": \"extract_target_step\", # Name of the step generating the ground truth\n \"steps\": [\n {\n \"name\": \"to_dataframe_step\",\n \"primitives\": [\"d3m.primitives.data_transformation.dataset_to_dataframe.Common\"],\n \"inputs\": [\"template_input\"]\n },\n {\n \"name\": \"common_profiler_step\",\n \"primitives\": [\"d3m.primitives.schema_discovery.profiler.Common\"],\n \"inputs\": [\"to_dataframe_step\"]\n },\n {\n \"name\": \"extract_attribute_step\",\n \"primitives\": [{\n \"primitive\": \"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common\",\n \"hyperparameters\":\n {\n 'semantic_types': (\n 'https://metadata.datadrivendiscovery.org/types/PrimaryKey',\n 'https://metadata.datadrivendiscovery.org/types/Attribute',),\n 'use_columns': (),\n 'exclude_columns': ()\n }\n }],\n \"inputs\": [\"common_profiler_step\"]\n },\n {\n \"name\": \"extract_target_step\",\n \"primitives\": [{\n \"primitive\": \"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common\",\n \"hyperparameters\":\n {\n 'semantic_types': (\n 'https://metadata.datadrivendiscovery.org/types/TrueTarget',),\n 'use_columns': (),\n 'exclude_columns': ()\n }\n }],\n \"inputs\": [\"to_dataframe_step\"]\n },\n {\n \"name\": \"target_process_step\",\n \"primitives\": [{\n \"primitive\": \"d3m.primitives.data_transformation.to_numeric.DSBOX\",\n \"hyperparameters\": {\n \"drop_non_numeric_columns\": [False]\n }\n }],\n \"inputs\": [\"extract_target_step\"]\n },\n {\n \"name\": \"profile_step\",\n \"primitives\": [\"d3m.primitives.schema_discovery.profiler.DSBOX\"],\n \"inputs\": [\"extract_attribute_step\"]\n },\n {\n \"name\": \"clean_step\",\n \"primitives\": [\n \"d3m.primitives.data_preprocessing.do_nothing.DSBOX\"\n ],\n \"inputs\": [\"profile_step\"]\n },\n {\n \"name\": \"encode_text_step\",\n \"primitives\": [\n {\n \"primitive\": \"d3m.primitives.feature_construction.corex_text.DSBOX\",\n \"hyperparameters\":\n {\n 'n_hidden': [(10)],\n 'threshold': [(500)],\n 'n_grams': [(1)],\n 'max_df': [(.9)],\n 'min_df': [(.02)],\n }\n },\n ],\n \"inputs\": [\"clean_step\"]\n },\n {\n \"name\": \"encoder_step\",\n \"primitives\": [\n \"d3m.primitives.data_cleaning.label_encoder.DSBOX\"\n ],\n \"inputs\": [\"encode_text_step\"]\n },\n {\n \"name\": \"impute_step\",\n \"primitives\": [\"d3m.primitives.data_preprocessing.mean_imputation.DSBOX\"],\n \"inputs\": [\"encode_text_step\"]\n },\n {\n \"name\": \"model_step\",\n \"primitives\": [\n {\n \"primitive\" : \"d3m.primitives.regression.gradient_boosting.SKlearn\",\n \"hyperparameters\":\n {\n 'use_semantic_types': [True],\n 'add_index_columns': [True],\n }\n },\n ],\n \"inputs\": [\"encode_text_step\", \"target_process_step\"]\n },\n\n\n ]\n }\n\n\n","sub_path":"python/dsbox/template/template_files/not_loaded/DataAugmentRegressionTemplate.py","file_name":"DataAugmentRegressionTemplate.py","file_ext":"py","file_size_in_byte":5161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"381099858","text":"print('Exercício 5.4')\r\n\r\ndef maximo():\r\n p=0 \r\n x=[]\r\n t=[]\r\n while p<5:\r\n y=(input('Digite um número: '))\r\n x.append(y)\r\n p+=1\r\n print(x)\r\n pro=input('Digite um valor que deve ser encontrado na lista: ')\r\n if pro in x :\r\n print('Sim,este número está na sua lista.')\r\n else:\r\n print('Este número não está em sua lista.')\r\n\r\n## for k in x: \r\n## if k==pro:\r\n## print('Temos esse número na Lista')\r\n## else:\r\n## print('Não temos esse número na lista')\r\n## \r\n## print(pro) \r\nmaximo()\r\n","sub_path":"Ex5.4.py","file_name":"Ex5.4.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"367467212","text":"import requests\nfrom bs4 import BeautifulSoup\n\nimport os\nimport sys\nfrom googleapiclient import discovery as d\nfrom googleapiclient.errors import HttpError\nfrom oauth2client.tools import argparser\n#https://open.spotify.com/playlist/18rXN0wAkX2q3A6ynu8aZ5?si=9BoYLhuZQXOJzEqtrO32rQ\n\ndef getSongs(url):\n if(\"spotify.com/playlist\" not in url): \n raise Exception(\"Must be a spotify playlist url\")\n \n try:\n spl = BeautifulSoup(requests.get(url).text, 'html.parser').prettify().split(\"\\n\")\n\n songs = []\n index = 0\n for j in spl:\n if(\"music:song\\\"\" in j):\n songLink = j.split(\"\\\"\")[1]\n song = BeautifulSoup(requests.get(songLink).text, 'html.parser').prettify().split('\\n')\n for i in song:\n if(\"twitter:title\" in i):\n title = i.split(\"\\\"\")[1]\n if(\"twitter:audio:artist_name\" in i):\n artist = i.split(\"\\\"\")[1]\n if(\"twitter:image\" in i):\n image = i.split(\"\\\"\")[1]\n songs.append([title, artist, image])\n index += 1\n return songs\n except requests.exceptions.RequestException as e:\n print(e)\n \n\ndef getYoutubeLinks(songs):\n songUrls = []\n SERVICE = \"youtube\"\n API = \"v3\"\n\n songLinks = []\n with open(os.path.join(sys.path[0], \"key.txt\")) as f:\n keys = []\n for i in f:\n keys.append(i.strip(\"\\n\"))\n\n currKey = 0\n for i in songs:\n while(currKey < len(keys)):\n try:\n youtube = d.build(SERVICE, API, developerKey=keys[currKey])\n response = youtube.search().list(\n q = i[0] + \" \" + i[1],\n type = \"video\",\n part = \"id, snippet\",\n maxResults = 1\n ).execute()\n songLinks.append([response[\"items\"][0][\"snippet\"][\"title\"], \"https://www.youtube.com/watch?v=\" + response[\"items\"][0][\"id\"][\"videoId\"], i[2]])\n break\n except HttpError as e:\n currKey += 1\n pass \n return songLinks\n \ndef main():\n print(\"Enter spotify playlist\")\n # url = input()\n url = \"https://open.spotify.com/playlist/18rXN0wAkX2q3A6ynu8aZ5?si=9BoYLhuZQXOJzEqtrO32rQ\"\n songs = getSongs(url)\n if(not songs):\n print(\"Error: Invalid playlist link\") \n else:\n links = getYoutubeLinks(songs)\n if(not links):\n print(\"Error: quota reached, try again tomorrow\")\n else:\n for i in links:\n print(i[0] + \" \" + i[1] + \" \" + i[2])\n\nif __name__ == \"__main__\":\n main()","sub_path":"spotbot.py","file_name":"spotbot.py","file_ext":"py","file_size_in_byte":2799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"548778395","text":"\n\n#calss header\nclass _CORUSCATING():\n\tdef __init__(self,): \n\t\tself.name = \"CORUSCATING\"\n\t\tself.definitions = [u'flashing brightly', u'extremely intelligent and exciting or humorous: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_coruscating.py","file_name":"_coruscating.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"596083190","text":"\"\"\"Contains tools to read kpoints and bands from EIGENVALUE files.\"\"\"\n\nimport re\n\nimport numpy as np\n\nfrom .parser import BaseParser\n\n\nclass EigParser(BaseParser):\n \"\"\"Contains regex and functions to find grammar elements in EIGENVALUE files.\"\"\"\n\n def __init__(self, filename):\n res = self.parse_eigenval(filename)\n self.header = res[0]\n self.kpoints = res[1]\n self.bands = res[2]\n\n @classmethod\n # pylint: disable=too-many-locals\n def parse_eigenval(cls, filename):\n \"\"\"Parse a VASP EIGENVAL file and extract metadata and a band structure data array\"\"\"\n with open(filename) as eig:\n line_0 = cls.line(eig, int) # read header\n line_1 = cls.line(eig, float) # \"\n line_2 = cls.line(eig, float) # \"\n coord_type = cls.line(eig) # \"\n name = cls.line(eig) # read name line (can be empty)\n param_0, num_kp, num_bands = cls.line(eig, int) # read: ? #kp #bands\n data = eig.read() # rest is data\n num_ions, num_atoms, p00, num_spins = line_0\n data = re.split(cls.empty_line, data) # list of data blocks\n data = [[line.split() for line in block.splitlines()] for block in data]\n kpoints = np.zeros((num_kp, 4))\n bands = np.zeros((num_spins, num_kp, num_bands))\n for k, field in enumerate(data): # iterate over data blocks\n kpbs = filter(None, field) # throw away empty lines\n kpi = map(float, kpbs.pop(0)) # first line of block is kpoints -> pop\n kpoints[k] = kpi\n for point in kpbs: # rest are band energies\n bands[:, k, int(point[0]) - 1] = point[1:num_spins + 1] # place energy value in bands[kp, nb] (BandstrucureData format)\n header = {} # build header dict\n header[0] = line_0\n header[1] = line_1\n header[2] = line_2\n header['n_ions'] = num_ions\n header['n_atoms'] = num_atoms\n header['p00'] = p00\n header['nspin'] = num_spins\n header['cartesian'] = coord_type.startswith(('c', 'C'))\n header['name'] = name\n header['some_num'] = param_0\n header['n_bands'] = num_bands\n header['n_kp'] = num_kp\n\n return header, kpoints, bands\n","sub_path":"aiida_vasp/io/eigenval.py","file_name":"eigenval.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"233401248","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport logging\n\nlogger = logging.getLogger('TEST-LOG') #定义一个输出日志的函数名字，自定义，默认输出到当前屏幕\nlogger.setLevel(logging.DEBUG) #设置最低输出等级\nlogger.warning('这是测试')\n\npm = logging.StreamHandler() #向当前屏幕输出信息\npm.setLevel(logging.WARNING) #设定屏幕最低输出等级，低于这个等级的就不会向屏幕输出\n\nwj = logging.FileHandler('测试.log',encoding='utf-8') #向文件输出信息，即是写入文件，默认方式为a+\nwj.setLevel(logging.WARNING) #设定错误级别以上的才写入文件\n\nformatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s %(lineno)d','%m/%d/%Y %I:%M:%S %p') #设定输出格式，时间 信息等级 信息 第几行代码\n\npm.setFormatter(formatter) #设置屏幕的输出格式为上面formatter设置的格式\nwj.setFormatter(formatter) #设置文件的输出格式为上面formatter设置的格式\n\nlogger.addHandler(pm) #添加向屏幕输出信息\nlogger.addHandler(wj) #添加向文件写入信息\n\nlogger.info('测试这个会不会输出-info')\nlogger.warning('测试这个会不会输出-warning')\nlogger.error('测试这个会不会输出-error')","sub_path":"Modular_two/log多输出.py","file_name":"log多输出.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"425400292","text":"from BaseLayers import *\n\n\nclass MultiGpuModel(nn.DataParallel):\n \"\"\"\n Wrapper to model class for using multi-gpu model.\n \"\"\"\n def __getattr__(self, name):\n try:\n return super().__getattr__(name)\n except AttributeError:\n return getattr(self.module, name)\n\n\nclass DDNet(nn.Module):\n\n def __init__(self, params):\n super().__init__()\n self.inputs_channels = params['inputs_channels_list']\n self.kernels = params['kernels_list']\n self.num_levels = params['num_levels']\n self.base_num_filters = params['base_num_filters']\n self.bottleneck_layers = params['bottleneck_layers']\n self.out_channels = params['out_channels']\n self.attention = params['attention']\n self.attention_heads = params['attention_heads']\n\n assert len(self.kernels) == len(self.inputs_channels), 'Kernels list and inputs channels list must be the ' \\\n 'same length '\n\n # Encoder\n self.encoders = nn.ModuleList([])\n for c, k in zip(self.inputs_channels, self.kernels):\n axes = {(1, 9): 'x', (9, 1): 'y', (3, 3): 'xy'}\n self.encoders.append(Encoder(c, k, self.num_levels, self.base_num_filters, self.attention, self.attention_heads, axes=axes[k]))\n\n # Bottleneck\n self.bottleneck = Bottleneck(self.bottleneck_layers, 2**(self.num_levels - 1)*self.base_num_filters * len(self.inputs_channels),\n 2**self.num_levels*self.base_num_filters)\n\n # Decoder\n self.decoder = Decoder((2**self.num_levels)*self.base_num_filters, len(self.inputs_channels), self.num_levels, 3)\n self.final_layer = FinalLayer(self.base_num_filters, self.out_channels)\n\n def forward(self, x):\n enc_out = None\n skips = [[]] * self.num_levels\n for i, enc in enumerate(self.encoders):\n s, e = enc(x[i])\n\n for j, level in enumerate(s):\n skips[j] = level if not len(skips[j]) else torch.cat([skips[j], level], dim=1)\n\n enc_out = e if enc_out is None else torch.cat([enc_out, e], dim=1)\n\n bottleneck_out = self.bottleneck(enc_out)\n dec_out = self.decoder(bottleneck_out, skips)\n out_cube = self.final_layer(dec_out)\n\n return out_cube\n\n\n\n","sub_path":"Models.py","file_name":"Models.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"628309896","text":"from functools import reduce\ndef is_valid_email(s):\n address = s[:s.find('@')]\n domain = s[s.find('@')+1:s.find('.')]\n extension = s[s.find('.')+1:]\n #print(address)\n #print(domain)\n #print(extension)\n #print(reduce(And,[element.isalnum() or element == '-' or element =='_' for element in address]) and domain.isalnum() and len(extension) == 3 and extension.isalnum())\n return reduce(And,[element.isalnum() or element == '-' or element =='_' for element in address]) and domain.isalnum() and len(extension) == 3 and extension.isalnum()\ndef And(a,b):\n return a and b\n\nN = int(input())\nL = []\nfor i in range(N):\n L.append(input())\n #print()\n #print(L)\nnew_L = list(filter(is_valid_email,L))\nnew_L.sort()\nprint(new_L)\n","sub_path":"Python/valid_email.py","file_name":"valid_email.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"345553673","text":"# -*- coding: utf-8 -*-\n\nimport pygame\n\nCOLOR_DEFAULT_BUTTON = (150, 150, 150)\nCOLOR_DEFAULT_BUTTON_TEXT = (255, 255, 255)\nCOLOR_PRESSED_BUTTON_TEXT = (255, 0, 0)\nCOLOR_MOD_BUTTON_TEXT = (0, 255, 0)\nBUTTON_TEXT_MARGIN_LEFT = 4\n\nclass Command:\n \"\"\"\n A command.\n \"\"\"\n def __init__(self, cmd_name: str, arguments: dict):\n self.cmd_name = cmd_name\n self.arguments = arguments\n\n def execute(self, bid: int, context: dict):\n \"\"\"Execute the command.\"\"\"\n pass\n\n @classmethod\n def cmd_nop(cls):\n return cls(cmd_name=\"nop\", arguments={})\n\n\nclass GuiRctElement:\n def __init__(self, pos_x: int, pos_y: int, width: int, height: str):\n self.pos_x = pos_x\n self.pos_y = pos_y\n self.width = width\n self.height = height\n\n def draw(self):\n raise NotImplementedError()\n\n\nclass ModeButton(GuiRctElement):\n def __init__(\n self,\n pos_x: int,\n pos_y: int,\n width: int,\n height: int,\n bid: int,\n title: str,\n cmd_push: Command,\n cmd_release: Command\n ):\n self.bid = bid\n self.title = title\n self.default_cmd_push = cmd_push\n self.default_cmd_release = cmd_release\n self.down = False\n self.reset()\n super().__init__(pos_x, pos_y, width, height)\n\n def draw(self, context):\n text_color = COLOR_DEFAULT_BUTTON_TEXT if not self.down else COLOR_PRESSED_BUTTON_TEXT\n if self.cmd_push is not self.default_cmd_push:\n text_color = COLOR_MOD_BUTTON_TEXT\n text = context[\"font_main\"].render(self.title, True, text_color)\n pygame.draw.rect(\n context[\"screen\"],\n COLOR_DEFAULT_BUTTON,\n (self.pos_x, self.pos_y, self.width, self.height),\n )\n context[\"screen\"].blit(text, (self.pos_x + BUTTON_TEXT_MARGIN_LEFT, self.pos_y))\n\n def push(self, context: dict):\n self.down = True\n self.cmd_push.execute(self.bid, context)\n\n def release(self, context):\n self.down = False\n self.cmd_release.execute(self.bid, context)\n\n def reset(self):\n self.cmd_push = self.default_cmd_push\n self.cmd_release = self.default_cmd_release\n\n def set_commands(self, cmd_push, cmd_release):\n self.cmd_push = cmd_push\n self.cmd_release = cmd_release\n\n def set_cmd_push(self, cmd_push):\n self.cmd_push = cmd_push\n\n\ndef construct_test_button(pos_x: int, pos_y: int, bid: int, title: str):\n btn = ModeButton(\n pos_x,\n pos_y,\n width=50,\n height=20,\n bid=bid,\n title=title,\n cmd_push=Command.cmd_nop(),\n cmd_release=Command.cmd_nop(),\n )\n return btn\n","sub_path":"hsampler/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"27376469","text":"import os\nimport csv\n\n#filepath = os.path.join(\"Resources\", \"employees.csv\")\nfilepath = \"employee_data1.csv\"\nfilename = \"new_employee_data.csv\"\n#filepath2 = \"C:\\\\Users\\\\Hannah\\\\Desktop\\\\UT Data 2018 HW\\May 5 Python\\\\employee_data2.csv\"\n#filename2 = \"employee_data2\"\n\ndef reformat_date(yyyymmdd):\n date_parts = yyyymmdd.split(\"-\")\n yyyy = date_parts[0]\n mm = date_parts[1]\n dd = date_parts[2]\n return mm + \"/\" + dd + \"/\" + yyyy \n\ndef mask_ssn(social):\n return \"***-**-\" + social[-4:]\n\ndef get_abbreviation(full_state):\n us_state_abbrev = {\n 'Alabama': 'AL',\n 'Alaska': 'AK',\n 'Arizona': 'AZ',\n 'Arkansas': 'AR',\n 'California': 'CA',\n 'Colorado': 'CO',\n 'Connecticut': 'CT',\n 'Delaware': 'DE',\n 'Florida': 'FL',\n 'Georgia': 'GA',\n 'Hawaii': 'HI',\n 'Idaho': 'ID',\n 'Illinois': 'IL',\n 'Indiana': 'IN',\n 'Iowa': 'IA',\n 'Kansas': 'KS',\n 'Kentucky': 'KY',\n 'Louisiana': 'LA',\n 'Maine': 'ME',\n 'Maryland': 'MD',\n 'Massachusetts': 'MA',\n 'Michigan': 'MI',\n 'Minnesota': 'MN',\n 'Mississippi': 'MS',\n 'Missouri': 'MO',\n 'Montana': 'MT',\n 'Nebraska': 'NE',\n 'Nevada': 'NV',\n 'New Hampshire': 'NH',\n 'New Jersey': 'NJ',\n 'New Mexico': 'NM',\n 'New York': 'NY',\n 'North Carolina': 'NC',\n 'North Dakota': 'ND',\n 'Ohio': 'OH',\n 'Oklahoma': 'OK',\n 'Oregon': 'OR',\n 'Pennsylvania': 'PA',\n 'Rhode Island': 'RI',\n 'South Carolina': 'SC',\n 'South Dakota': 'SD',\n 'Tennessee': 'TN',\n 'Texas': 'TX',\n 'Utah': 'UT',\n 'Vermont': 'VT',\n 'Virginia': 'VA',\n 'Washington': 'WA',\n 'West Virginia': 'WV',\n 'Wisconsin': 'WI',\n 'Wyoming': 'WY',\n }\n return us_state_abbrev[full_state]\n\nnew_employee_data = []\n\n\nwith open(filepath) as csvfile:\n reader = csv.DictReader(csvfile)\n\n for row in reader:\n print(row)\n full_name = row[\"Name\"]\n name_parts = full_name.split(\" \")\n first_name = name_parts[0]\n last_name = name_parts[1]\n new_employee_data.append({\n \"Emp ID\": row[\"Emp ID\"],\n \"DOB\": reformat_date(row[\"DOB\"]),\n \"First Name\": first_name,\n \"Last Name\": last_name,\n \"SSN\": mask_ssn(row[\"SSN\"]),\n \"State\": get_abbreviation(row[\"State\"])\n })\n print(new_employee_data)\n\n# Grab the filename from the original path\n\n\n# Write updated data to csv file\ncsvpath = os.path.join(\"output\", filename)\nwith open(csvpath, \"w\") as csvfile:\n fieldnames = [\"Emp ID\", \"First Name\", \"Last Name\", \"SSN\", \"DOB\", \"State\"]\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n writer.writerows(new_employee_data)\n","sub_path":"PyBoss/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"524313930","text":"\"\"\"\nGiven an array nums with n objects colored red, white, or blue, sort them in-place so that objects of the same color are adjacent, with the colors in the order red, white, and blue.\n\nHere, we will use the integers 0, 1, and 2 to represent the color red, white, and blue respectively.\n\nFollow up:\n\nCould you solve this problem without using the library's sort function?\nCould you come up with a one-pass algorithm using only O(1) constant space?\n\nInput: nums = [2,0,2,1,1,0]\nOutput: [0,0,1,1,2,2]\n\n\"\"\"\n\nclass Solution:\n def sortColors(self, nums: List[int]) -> None:\n \"\"\"\n Do not return anything, modify nums in-place instead.\n \"\"\"\n low=mid=0\n high=len(nums)-1\n while(mid<=high):\n if(nums[mid]==0):\n nums[low],nums[mid]=nums[mid],nums[low]\n low+=1\n mid+=1\n elif(nums[mid]==1):\n mid+=1\n else:\n nums[mid],nums[high]=nums[high],nums[mid]\n high-=1\n return nums\n\n# Time Complexity = O(n)\n# Space Complexity = O(1)\n","sub_path":"sortColors.py","file_name":"sortColors.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"254070355","text":"import math\r\n\r\nclass IFLeg:\r\n def __init__(self, wpt):\r\n self.type = 'IF'\r\n self.seq = 0\r\n self.waypoint = wpt\r\n self.routeSeq = 0\r\n\r\n\r\n def message(self):\r\n message = \"SEQ=\" + str(self.seq) + \" TYPE=\" + self.type + \" ID=\" + self.waypoint.id + \" LAT=\" + self.waypoint.latitude + \" LONG=\" + self.waypoint.longitude\r\n return message\r\n\r\nclass TFLeg:\r\n def __init__(self, Wpt1, Wpt2, seq, minaltitude, maxaltitude, RouteSeq):\r\n self.type = 'TF'\r\n self.seq = seq\r\n self.routeSeq = RouteSeq\r\n self.waypointStart = Wpt1\r\n self.waypoint = Wpt2\r\n self.course = int(self.calcul_course())\r\n self.distance = int(self.calcul_distance())\r\n\r\n if minaltitude != \"\":\r\n self.minaltitude = minaltitude\r\n else:\r\n self.minaltitude = \"FL000\"\r\n\r\n if maxaltitude != \"\":\r\n self.maxaltitude = maxaltitude\r\n else:\r\n self.maxaltitude = \"FL460\"\r\n\r\n def setSeq(self, newSeq):\r\n self.seq = newSeq\r\n pass\r\n\r\n def calcul_course(self):\r\n latA, longA = math.radians(self.waypointStart.norm_latitude), math.radians(self.waypointStart.norm_longitude)\r\n latB, longB = math.radians(self.waypoint.norm_latitude), math.radians(self.waypoint.norm_longitude)\r\n X = math.cos(latB)*math.sin(longB-longA)\r\n Y = math.cos(latA)*math.sin(latB) - math.sin(latA)*math.cos(latB)*math.cos(longB-longA)\r\n course = math.atan2(X, Y)\r\n return math.degrees(course)%360\r\n\r\n def calcul_distance(self):\r\n latA, longA = math.radians(self.waypointStart.norm_latitude), math.radians(self.waypointStart.norm_longitude)\r\n latB, longB = math.radians(self.waypoint.norm_latitude), math.radians(self.waypoint.norm_longitude)\r\n distance = 60 * math.degrees(math.acos(math.cos(latA)*math.cos(latB)*math.cos(longB-longA) + math.sin(latA)*math.sin(latB)))\r\n return distance\r\n\r\n def message(self):\r\n message = \"SEQ=\" + str(self.seq) + \" TYPE=\" + self.type + \" ID=\" + self.waypoint.id + \" WPT_TYPE=\" + self.waypoint.type + \" LAT=\" + self.waypoint.latitude + \" LONG=\" + self.waypoint.longitude + \" COURSE=\" + str(self.course) + \" DISTANCE=\" + str(self.distance) + \" FLmin=\" + self.minaltitude + \" FLmax=\" + self.maxaltitude\r\n #message = \"ID=\" + self.waypoint.id + \" SEQ=\" + str(self.seq) + \" LAT=\" + self.waypoint.latitude + \" LON=\" + self.waypoint.longitude + \" COURSE=\" + str(self.course) + \" FLY=\" + self.waypoint.type + \" FLmin=\" + self.minaltitude + \" FLmax=\" + self.maxaltitude + \" SPEEDmax=000\"\r\n return message","sub_path":"FPLN_LEGS/Leg.py","file_name":"Leg.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"202589783","text":"from sheetfu.client import SpreadsheetApp\nfrom sheetfu.model import Spreadsheet, Sheet, Range\nfrom tests.utils import mock_range_instance, mock_spreadsheet_instance, mock_google_sheets_responses\nfrom tests.utils import open_fixture\nimport json\n\n\nclass TestModelClients:\n\n http_sheets_mocks = mock_range_instance()\n\n client = SpreadsheetApp(http=http_sheets_mocks)\n spreadsheet = client.open_by_id('some_id')\n sheet = spreadsheet.get_sheet_by_name(\"people\")\n my_range = sheet.get_range(1, 1)\n\n def test_instances(self):\n assert isinstance(self.client, SpreadsheetApp)\n assert isinstance(self.spreadsheet, Spreadsheet)\n assert isinstance(self.sheet, Sheet)\n assert isinstance(self.my_range, Range)\n\n def test_client_is_in_every_object(self):\n assert self.client is self.spreadsheet.client\n assert self.client is self.sheet.client\n assert self.client is self.my_range.client\n\n\nclass TestSpreadsheet:\n\n http_sheets_mocks = mock_spreadsheet_instance([\"add_sheets.json\"])\n spreadsheet = SpreadsheetApp(http=http_sheets_mocks).open_by_id('some_id')\n\n def test_get_sheets(self):\n sheets = self.spreadsheet.sheets\n assert len(sheets) == 2\n for sheet in sheets:\n assert isinstance(sheet, Sheet)\n\n def test_create_sheets(self):\n self.spreadsheet.create_sheets([\"test_sheet\", \"test_sheet_2\"])\n assert len(self.spreadsheet.sheets) == 4\n\n def test_add_sheets_from_response(self):\n dummy_response_create = json.loads(open_fixture(\"add_sheets.json\"))\n self.spreadsheet._add_sheets_from_response(response=dummy_response_create, reply_type=\"addSheet\")\n assert len(self.spreadsheet.sheets) == 6\n dummy_response_duplicate = json.loads(open_fixture(\"duplicate_sheets.json\"))\n self.spreadsheet._add_sheets_from_response(response=dummy_response_duplicate, reply_type=\"duplicateSheet\")\n assert len(self.spreadsheet.sheets) == 7\n\n\nclass TestGettersFromDataRange:\n\n fixtures = ['get_backgrounds.json', 'get_notes.json', 'get_fonts.json']\n http_sheets_mocks = mock_range_instance(fixtures)\n client = SpreadsheetApp(http=http_sheets_mocks)\n data_range = client.open_by_id('spreadsheet id').get_sheet_by_name('people').get_data_range()\n\n def test_a1_notation_is_right(self):\n assert self.data_range.a1 == \"A1:D21\"\n\n def test_get_backgrounds(self):\n backgrounds = self.data_range.get_backgrounds()\n assert type(backgrounds) == list\n assert len(backgrounds) == self.data_range.coordinates.number_of_rows\n for row in backgrounds:\n assert len(row) == self.data_range.coordinates.number_of_columns\n\n\nclass TestCellRange:\n\n http_sheets_mocks = mock_range_instance()\n client = SpreadsheetApp(http=http_sheets_mocks)\n data_range = client.open_by_id('spreadsheet id').get_sheet_by_name('people').get_data_range()\n\n def test_a1(self):\n assert self.data_range.a1 == 'A1:D21'\n\n def test_get_cell(self):\n assert self.data_range.get_cell(1, 1).a1 == 'A1'\n assert self.data_range.get_cell(1, 2).a1 == 'B1'\n assert self.data_range.get_cell(2, 1).a1 == 'A2'\n\n\nclass TestGridRange:\n\n http_sheets_mocks = mock_spreadsheet_instance()\n spreadsheet = SpreadsheetApp(http=http_sheets_mocks).open_by_id('some_id')\n sheet = spreadsheet.sheets[0]\n\n def test_grid_range_one_cell(self):\n cell = self.sheet.get_range_from_a1('A1')\n assert cell.a1 == 'A1'\n cell_grid_range = cell.get_grid_range()\n assert cell_grid_range['startRowIndex'] == 0\n assert cell_grid_range['endRowIndex'] == 1\n assert cell_grid_range['startColumnIndex'] == 0\n assert cell_grid_range['endColumnIndex'] == 1\n\n def test_grid_range_multiple_cells(self):\n range_ = self.sheet.get_range_from_a1('A3:B4')\n assert range_.a1 == 'A3:B4'\n grid_range = range_.get_grid_range()\n assert grid_range['startRowIndex'] == 2\n assert grid_range['endRowIndex'] == 4\n assert grid_range['startColumnIndex'] == 0\n assert grid_range['endColumnIndex'] == 2\n\n","sub_path":"tests/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":4143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"88011025","text":"#!/usr/bin/python\n\nfrom __future__ import absolute_import, division, print_function\nimport PyU4V\nfrom ansible.module_utils.basic import *\nfrom ansible.module_utils.urls import *\n__metaclass__ = type\n#TODO Add checks to make sure SRDF is not already in requested state\n\nANSIBLE_METADATA = {'metadata_version': '1.0',\n 'status': ['preview'],\n 'supported_by': 'VMAX REST API Community '\n 'https://community.emc.com/docs/DOC-56447'\n }\nDOCUMENTATION = r'''\n---\nmodule: dellpmax_manage_srdf\n\ncontributors: Paul Martin, Rob Mortell\n\nsoftware versions=ansible 2.6.3\n python version = 2.7.15rc1 (default, Apr 15 2018,\n\nshort_description: \n - Module to control SRDF state of storage group, supported actions are \n failover, failback, split, suspend, establish. Requires that storage \n group must exist and have RDF replication already configured and \n running. Single Site SRDF support only in module. \n \n\n\n\nnotes:\n - This module has been tested against UNI 9.0. Every effort has been\n made to verify the scripts run with valid input. These modules\n are a tech preview. Additional error handling will be added at a later\n date, base functionality only right now.\n\nRequirements:\n - Ansible, Python 2.7, Unisphere for PowerMax version 9.0 or higher.\n VMAX All Flash, VMAX3, or PowerMax storage Array\n\nplaybook options:\n Note:- Some Options are repeated across modules, we will look at\n reducing these in future work, however you can use variables in your\n playbook at the outset and reference in the task to reduce error,\n this also allows flexibility in versioning within a single playbook.\n\n unispherehost:\n description:\n - Full Qualified Domain Name or IP address of Unisphere for\n PowerMax host.\n required:True\n\n universion:\n -description:\n - Integer, version of unipshere software\n https://{HostName|IP}:8443/univmax/restapi/{version}/{resource}\n 90 is the release at time of writing module.\n -required:True\n verifycert:\n description:\n -Boolean, securitly check on ssl certificates\n required:True\n\n required: True\n sgname:\n description:\n - Storage Group name\n required:True\n array_id:\n description:\n - Integer 12 Digit Serial Number of PowerMAX or VMAX array.\n required:True\n \n'''\n\nEXAMPLES = r'''\n- name: Create SnapVX SnapShot of existing Storage Group\n hosts: localhost\n connection: local\n no_log: True\n vars:\n unispherehost: '192.168.156.63'\n universion: \"90\"\n verifycert: False\n user: 'smc'\n password: 'smc'\n\n\n tasks:\n - name: Split SRDF\n dellpmax_manage_srdf:\n unispherehost: \"{{unispherehost}}\"\n universion: \"{{universion}}\"\n verifycert: \"{{verifycert}}\"\n user: \"{{user}}\"\n password: \"{{password}}\"\n array_id: '000197600156'\n sgname: 'Ansible_SG'\n action: 'Split'\n'\n\nRETURN = r'''\n\n\n\ndef main():\n changed = False\n module = AnsibleModule(\n argument_spec=dict(\n unispherehost=dict(required=True),\n universion=dict(type='int', required=False),\n verifycert=dict(type='bool', required=True),\n user=dict(type='str', required=True),\n password=dict(type='str', required=True, no_log=True),\n array_id=dict(type='str', required=True),\n sgname=dict(type='str', required=True),\n action=dict(type='str',choices=['Establish','Suspend','Split',\n 'Failover','Failback'],\n required=True)\n )\n )\n # Make REST call to Unisphere Server and execute SRDF control operation\n\n conn = PyU4V.U4VConn(server_ip=module.params['unispherehost'], port=8443,\n array_id=module.params['array_id'],\n u4v_version=module.params['universion'],\n verify=module.params['verifycert'],\n username=module.params['user'],\n password=module.params['password'])\n\n rep=conn.replication\n rdf_sglist = rep.get_storage_group_rep_list(has_srdf=True)\n\n if module.params['sgname'] in rdf_sglist:\n rdfg_list = rep.get_storagegroup_srdfg_list(module.params['sgname'])\n if len(rdfg_list)<=1:\n rdfg = rdfg_list[0]\n rep.modify_storagegroup_srdf(storagegroup_id=module.params['sgname']\n , action=module.params['action'], rdfg=rdfg)\n changed = True\n else:\n module.fail_json(changed=changed,\n msg='Specified Storage Group has mult-site RDF Protection '\n 'Ansible Module currently supports single Site SRDF '\n 'please use Unishpere for PowerMax UI for SRDF group '\n 'managment')\n\n else:\n\n module.fail_json(msg='Specified Storage Group is not currently SRDF '\n 'Protected')\n\n\n module.exit_json(changed=changed)\n\nif __name__ == '__main__':\n main()\n","sub_path":"library/dellpmax_manage_srdf.py","file_name":"dellpmax_manage_srdf.py","file_ext":"py","file_size_in_byte":5231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"428557101","text":"# -*- coding: utf-8 -*-\n\nimport sys, docx, os, re\n\nimport functools\nfrom PyQt5 import QtGui\n\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTextEdit, QPushButton, \\\n QAction, QLineEdit, QFileDialog, QTabWidget, QVBoxLayout, QHBoxLayout, QLabel, QCheckBox, QShortcut\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtCore import pyqtSlot, Qt\n\n\nclass App(QMainWindow):\n def __init__(self):\n super().__init__()\n self.title = 'Parse'\n self.left = 10\n self.top = 10\n self.width = 1280\n self.height = 720\n self.table_widget = MyTableWidget(self)\n self.setCentralWidget(self.table_widget)\n self.initUI()\n\n def initUI(self):\n self.setWindowTitle(self.title)\n self.setWindowIcon(QIcon('image.png'))\n self.setGeometry(self.left, self.top, self.width, self.height)\n # Initialize menu\n main_menu = self.menuBar()\n open_menu = main_menu.addMenu('Open')\n save_menu = main_menu.addMenu('Save as..')\n # Initialize buttons\n self.shortcut_o = QShortcut(QtGui.QKeySequence('Ctrl+O'), self)\n self.shortcut_o.activated.connect(self.open_file)\n open_button = QAction('Open', self)\n open_button.setShortcut('Ctrl+O')\n open_button.triggered.connect(self.open_file)\n\n self.shortcut_s = QShortcut(QtGui.QKeySequence('Ctrl+S'), self)\n self.shortcut_s.activated.connect(self.save_as)\n save_button = QAction('Save as...', self)\n save_button.setShortcut('Ctrl+S')\n save_button.triggered.connect(self.save_as)\n\n open_menu.addAction(open_button)\n save_menu.addAction(save_button)\n self.show()\n\n def keyPressEvent(self, e):\n if e.key() == Qt.Key_Escape:\n self.close()\n\n def open_file(self):\n file_path = QFileDialog.getOpenFileName(self, 'Open file', filter='Word Files *.docx;;Text files (*.txt)')\n # file_path = ('/home/anton/Downloads/Telegram Desktop/51st State Master Set.docx', 'Word Files *.docx')\n if file_path[0]:\n if file_path[1] == 'Text files (*.txt)':\n with open(file_path[0], 'r') as file:\n data = file.read()\n self.table_widget.set_main_text(data)\n else:\n doc = docx.Document(file_path[0])\n fullText = []\n for para in doc.paragraphs:\n fullText.append(para.text)\n self.table_widget.set_main_text('\\n'.join(fullText))\n self.table_widget.clean_textboxs()\n\n def save_as(self):\n file_path = QFileDialog.getSaveFileName(self, 'Save file', filter='Word Files *.docx;;Text files (*.txt)')\n if file_path[0]:\n if file_path[1] == 'Text files (*.txt)':\n if os.path.exists(file_path[0]):\n with open(file_path[0], 'w') as file:\n file.write(self.table_widget.get_main_text())\n else:\n with open(file_path[0] + '.txt', 'w') as file:\n file.write(self.table_widget.get_main_text())\n else:\n doc = docx.Document()\n doc.add_paragraph(self.table_widget.get_main_text())\n path = re.findall(r'.*(?:\\\\|\\/)(.*?)(?:\\.docx|$)', file_path[0])[0]\n doc.save(path + '.docx')\n\n\nclass MyTableWidget(QWidget):\n def __init__(self, parent):\n super(QWidget, self).__init__(parent)\n self.layout = QVBoxLayout(self)\n # Initialize tab screen\n self.tabs = QTabWidget()\n self.tab1 = QWidget()\n self.tab2 = QWidget()\n self.tab3 = QWidget()\n self.tab4 = QWidget()\n # Add tabs\n self.tabs.addTab(self.tab1, \"Overview\")\n self.tabs.addTab(self.tab2, \"Rus\")\n self.tabs.addTab(self.tab3, \"Ukr\")\n self.tabs.addTab(self.tab4, \"Photos\")\n # Create first tab\n self.tab1.layout = QVBoxLayout(self)\n self.textbox_main = QTextEdit(self)\n self.tab1.layout.addWidget(self.textbox_main)\n self.tab1.setLayout(self.tab1.layout)\n # Create second tab\n self.tab2.layout = QVBoxLayout(self)\n self.textbox_rus2 = QTextEdit(self)\n self.tab2.layout.addWidget(self.textbox_rus2)\n self.textbox_rus1 = QTextEdit(self)\n self.tab2.layout.addWidget(self.textbox_rus1)\n self.tab2.setLayout(self.tab2.layout)\n # Create third tab\n self.tab3.layout = QVBoxLayout(self)\n self.textbox_ukr2 = QTextEdit(self)\n self.tab3.layout.addWidget(self.textbox_ukr2)\n self.textbox_ukr1 = QTextEdit(self)\n self.tab3.layout.addWidget(self.textbox_ukr1)\n self.tab3.setLayout(self.tab3.layout)\n # Create fourth tab\n self.tab4.layout = QVBoxLayout(self)\n self.tab4.setLayout(self.tab4.layout)\n self.lbl_g_name = QLabel('Game name: ', self)\n self.lbl_p_name = QLabel('\\tPhoto name: ', self)\n self.lbl_quantity = QLabel('\\tQuantity: ', self)\n self.lbl_folder = QLabel('\\tFolder: ', self)\n self.tab4_line1 = QLineEdit(self)\n self.tab4_line2 = QLineEdit(self)\n self.tab4_line3 = QLineEdit(self)\n self.tab4_line4 = QLineEdit(self)\n\n self.photo_layout = QHBoxLayout(self)\n self.tab4.layout.addLayout(self.photo_layout)\n self.btn_create = QPushButton('Create', self)\n self.btn_create.setStyleSheet(\"background-color: red; color: black\")\n self.btn_create.clicked.connect(self.photos)\n\n self.photo_layout.addWidget(self.lbl_g_name)\n self.photo_layout.addWidget(self.tab4_line1)\n self.photo_layout.addWidget(self.lbl_p_name)\n self.photo_layout.addWidget(self.tab4_line2)\n self.photo_layout.addWidget(self.lbl_quantity)\n self.photo_layout.addWidget(self.tab4_line3)\n self.photo_layout.addWidget(self.lbl_folder)\n self.photo_layout.addWidget(self.tab4_line4)\n self.photo_layout.addWidget(self.btn_create)\n\n self.textbox_photos = QTextEdit(self)\n self.tab4.layout.addWidget(self.textbox_photos)\n # Create fields\n self.lbl_number = QLabel('Number: ', self)\n self.lbl_photo = QLabel('\\tPhoto name: ', self)\n self.lbl_rules = QLabel('\\tRules name: ', self)\n self.line1 = QLineEdit(self)\n self.line2 = QLineEdit(self)\n self.line3 = QLineEdit(self)\n self.line1.setSizePolicy(40, 10)\n\n self.cb = QCheckBox('Rules', self)\n self.cb.stateChanged.connect(self.add_rules)\n self.btn_parse = QPushButton('Parse', self)\n self.btn_parse.setStyleSheet(\"background-color: red; color: black\")\n self.btn_parse.clicked.connect(self.add_data)\n # Add Wingets to HLayout\n self.h_layout = QHBoxLayout(self)\n self.h_layout.addWidget(self.lbl_number)\n self.h_layout.addWidget(self.line1)\n self.h_layout.addWidget(self.lbl_photo)\n self.h_layout.addWidget(self.line2)\n self.h_layout.addWidget(self.cb)\n self.h_layout.addWidget(self.lbl_rules)\n self.h_layout.addWidget(self.line3)\n self.h_layout.addWidget(self.btn_parse)\n self.line3.hide()\n self.lbl_rules.hide()\n self.layout.addLayout(self.h_layout)\n self.layout.addWidget(self.tabs)\n\n self.setLayout(self.layout)\n\n def clean_textboxs(self):\n self.textbox_rus2.clear()\n self.textbox_ukr1.clear()\n self.textbox_ukr2.clear()\n self.textbox_rus1.clear()\n\n def add_rules(self, state):\n if state == Qt.Checked:\n self.lbl_rules.setHidden(False)\n self.line3.setHidden(False)\n else:\n self.lbl_rules.setHidden(True)\n self.line3.setHidden(True)\n\n def get_main_text(self):\n return self.textbox_main.toPlainText()\n\n def set_main_text(self, text):\n self.textbox_main.setText(text)\n\n def add_data(self):\n self.clean_textboxs()\n number = self.line1.text()\n imgname = self.line2.text()\n rules_name = self.line3.text()\n overviews = self.textbox_main.toPlainText().split('&&')\n dict_rus = self.parse_rus(overviews[0])\n rus_html_code = ' ' \\\n '{1}
Тип: {2} ' \\\n 'Издатель: {3} Время игры: {4} ' \\\n 'Кол-во игроков: {5} Возраст: {6} Язык: {7} ' \\\n ' '.format(number, dict_rus['Art text'], dict_rus['Type'], dict_rus['Publisher'],\n dict_rus['Time'], dict_rus['Quantity'], dict_rus['Age'], dict_rus['Language'],\n imgname, dict_rus['Name'])\n\n if self.cb.checkState() == Qt.Checked:\n rus_html_code += 'Скачать правила к настольной игре %s
' % (\n number, rules_name, dict_rus['Name'])\n\n rus_html_code += dict_rus['Main text'] + ' '\n rus_html_code += ' Содержание настольной игры «%s»: ' % dict_rus['Name']\n for x in dict_rus['Components']:\n rus_html_code += ' – %s ' % x\n\n rus_html_code += ' Купить настольную игру «%s» в Киеве или в Украине можно,' \\\n ' оформив заказ у нас на сайте, добавив товар "В Корзину", или позвонив ' \\\n 'по телефону (044) 383-63-67.' % dict_rus['Name']\n\n self.textbox_rus1.insertPlainText(rus_html_code)\n self.textbox_rus2.insertHtml(rus_html_code)\n\n dict_ukr = self.parse_ukr(overviews[1])\n ukr_html_code = '' \\\n '{1}
Тип: {2} ' \\\n 'Видавництво: {3} Час гри: {4} ' \\\n 'Кiлькiсть гравцiв: {5} Вiк: {6} Мова: {7} ' \\\n ' '.format(number, dict_ukr['Art text'], dict_ukr['Type'], dict_ukr['Publisher'],\n dict_ukr['Time'], dict_ukr['Quantity'],\n dict_ukr['Age'], dict_ukr['Language'], imgname, dict_ukr['Name'])\n\n if self.cb.checkState() == Qt.Checked:\n ukr_html_code += 'Скачати правила до настiльної гри %s
' % (\n number, rules_name, dict_ukr['Name'])\n\n ukr_html_code += dict_ukr['Main text'] + ' '\n ukr_html_code += ' До складу настільної гри «%s» входить: ' % dict_ukr['Name']\n for x in dict_ukr['Components']:\n ukr_html_code += ' – %s ' % x\n\n ukr_html_code += ' Придбати настільну гру «%s» ' \\\n 'у Києві чи в Україні досить просто. Достатньо натиснути кнопочку' \\\n ' "В Корзину" та оформити замовлення на нашому сайті, ' \\\n 'або зателефонувати нам за номером (044) 383-63-67.' % dict_ukr['Name']\n\n self.textbox_ukr1.insertPlainText(ukr_html_code)\n self.textbox_ukr2.insertHtml(ukr_html_code)\n\n def parse_ukr(self, data):\n parsed = dict()\n parsed['Name'] = re.search(r'^\\s*(.*)', data).groups()[0]\n parsed['Art text'] = re.search(r'^\\s*.*\\s*([\\s\\S]*)\\sТип:', data).groups()[0]\n t = re.search(r'Тип:\\s*(.*)', data).groups()[0]\n parsed['Type'] = self.links(t)\n parsed['Publisher'] = re.search(r'(?:Видавництво:|Видавець:)\\s*(.*)', data).groups()[0]\n parsed['Time'] = re.search(r'Час гри:\\s*(.*)', data).groups()[0]\n parsed['Quantity'] = re.search(r'Кількість гравців:\\s*(.*)', data).groups()[0]\n parsed['Age'] = re.search(r'Вік:\\s*(.*)', data).groups()[0]\n parsed['Language'] = re.search(r'Мова:\\s*(.*)', data).groups()[0]\n text = \\\n re.search(r'Мова:.*\\s*([\\s\\S]*?)\\s*(?:Компоненти:|До складу.*:|Комплектація:|Вміст.*:)', data).groups()[0]\n parsed['Main text'] = self.manipulations(text, parsed['Name'], 'Увага!')\n components = \\\n re.search(r'(?:Компоненти:|До складу.*:|Комплектація:|Вміст.*:)([\\s\\S]*?)\\s*(?:Купить|$)', data).groups()[0]\n components = self.links(components)\n parsed['Components'] = self.del_extra(components)\n return parsed\n\n def parse_rus(self, data):\n parsed = dict()\n parsed['Name'] = re.search(r'^(.*)', data).groups()[0]\n parsed['Art text'] = re.search(r'^.*\\s*([\\s\\S]*)\\sТип:', data).groups()[0]\n t = re.search(r'Тип:\\s*(.*)', data).groups()[0]\n parsed['Type'] = self.links(t)\n parsed['Publisher'] = re.search(r'Издатель:\\s*(.*)', data).groups()[0]\n parsed['Time'] = re.search(r'Время игры:\\s*(.*)', data).groups()[0]\n parsed['Quantity'] = re.search(r'Кол-во игроков:\\s*(.*)', data).groups()[0]\n parsed['Age'] = re.search(r'Возраст:\\s*(.*)', data).groups()[0]\n parsed['Language'] = re.search(r'Язык:\\s*(.*)', data).groups()[0]\n text = re.search(r'Язык:.*\\s*([\\S\\s]*?)\\s*(?:Компоненты:|Содержание.*:|Комплектация:)', data).groups()[0]\n parsed['Main text'] = self.manipulations(text, parsed['Name'])\n components = \\\n re.search(r'(?:Компоненты:|Содержание настольной.*:|Комплектация:)([\\s\\S]*?)(?:Купить|$)', data).groups()[0]\n components = self.links(components)\n parsed['Components'] = self.del_extra(components)\n return parsed\n\n def manipulations(self, data, name, string='Внимание!'):\n data = self.links(data)\n data = data.replace(name, '%s' % name)\n data = data.replace(string, '%s' % string)\n return data\n\n def photos(self):\n self.textbox_photos.setText('')\n game_name = self.tab4_line1.text()\n imgname = self.tab4_line2.text()\n quantity = list(map(int, self.tab4_line3.text().split('-')))\n folder = self.tab4_line4.text()\n data = ''\n num = 2\n for x in quantity:\n temp, num = self.photo_text1(x, num)\n data += temp.format(folder, imgname, game_name)\n\n data.format()\n self.textbox_photos.insertPlainText(data)\n\n @staticmethod\n def photo_text1(quantity, num):\n data = '
'\n if num == 2:\n data += ' ' % (num, num)\n num += 1\n quantity -= 1\n for x in range(quantity):\n n = str(num).zfill(2)\n data += ' ' % (n, n)\n num += 1\n data += '
\\n\\n\\n'\n return data, num\n\n @staticmethod\n def del_extra(data):\n data = data.split('\\n')\n for x in list(data):\n if x is '':\n data.remove(x)\n for x, y in enumerate(data):\n data[x] = y.replace(';', '')\n for x, y in enumerate(data):\n data[x] = y.lstrip(' -')\n return data\n\n @staticmethod\n def links(data):\n link = re.findall(r'\\[(.*?) (\\/.*?)\\]', data)\n for x in link:\n data = data.replace('[%s %s]' % (x[0], x[1]),\n '%s' % (x[1], x[0], x[0]))\n return data\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = App()\n sys.exit(app.exec_())\n","sub_path":"Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":17043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"649017386","text":"from typing import List, Tuple, Dict\n\nclass Solution:\n def combine(self, n: int, k: int) -> List[List[int]]: \n \"\"\"\n 77. 组合\n 给定两个整数 n 和 k，返回 1 ... n 中所有可能的 k 个数的组合。\n \"\"\" \n nums = list(range(1, k + 1)) + [n + 1] # init first combination\n result, j = [], 0\n while j < k:\n # add current combination\n result.append(nums[:k])\n # increase first nums[j] by one\n # if nums[j] + 1 != nums[j + 1]\n j = 0\n while j < k and nums[j + 1] == nums[j] + 1:\n nums[j] = j + 1\n j += 1\n nums[j] += 1 \n return result\n \n def combine2(self, n: int, k: int) -> List[List[int]]:\n def backtrack(first = 1, curr = []):\n # if the combination is done\n if len(curr) == k: \n result.append(curr[:])\n for i in range(first, n + 1):\n # add i into the current combination\n curr.append(i)\n # use next integers to complete the combination\n backtrack(i + 1, curr)\n # backtrack\n curr.pop()\n \n result = []\n backtrack()\n return result\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n n=4\n k=2\n print(solution.combine2(n,k))","sub_path":"Week_03/combinations.py","file_name":"combinations.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"225707123","text":"import requests\n\n\nclass Test_CloseProject:\n url = \"https://airaapps.evalueserve.com/api/v1/automl/close/project/?token=apikey\"\n\n def test11(self):\n payload = {'ProjectID': 'PID100137',\n 'ProjectName': 'XYZ',\n 'apikey': 'd0f4ac5d0ddf6f4a7bcedce7904f054f',\n 'Keywords': 'XYZ'}\n files = []\n headers = {}\n\n response = requests.request(\"POST\", Test_CloseProject.url, headers=headers, data=payload, files=files)\n\n print(response.text)\n","sub_path":"APITesting/Test_CloseProject.py","file_name":"Test_CloseProject.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"163912661","text":"from .base import *\n\n\n\"\"\"\nSTATIC FILE CONFIGURATION\n\"\"\"\n\n# See: https://warehouse.python.org/project/whitenoise/\nSTATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'\n\n\n\"\"\"\nDJANGO STORAGES\n\"\"\"\n# See: http://django-storages.readthedocs.org/en/latest/backends/amazon-S3.html\nINSTALLED_APPS += (\n 'storages',\n)\n\nDEFAULT_FILE_STORAGE = 'popsseabar.MediaRootS3BotoStorage'\n\nAWS_ACCESS_KEY_ID = environ.get('AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = environ.get('AWS_SECRET_ACCESS_KEY')\nAWS_STORAGE_BUCKET_NAME = environ.get('AWS_STORAGE_BUCKET_NAME')\nAWS_S3_CUSTOM_DOMAIN = environ.get('AWS_S3_CUSTOM_DOMAIN')\nAWS_QUERYSTRING_AUTH = False\nAWS_HEADERS = {\n 'Cache-Control': 'max-age=86400',\n}\n\n\"\"\"\nEMAIL CONFIGURATION\n\"\"\"\n# See: https://github.com/django-ses/django-ses\nEMAIL_BACKEND = 'django_ses.SESBackend'\n\n# AWS_SES_REGION_NAME = environ.get('AWS_SES_REGION_NAME')\n# AWS_SES_REGION_ENDPOINT = environ.get('AWS_SES_REGION_ENDPOINT')\nAWS_SES_RETURN_PATH = environ.get('AWS_SES_RETURN_PATH')\n","sub_path":"popsseabar/settings/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"111187410","text":"# Licensed Materials - Property of IBM\n# Copyright IBM Corp. 2016\nimport unittest\nimport sys\nimport itertools\nimport os\nimport shutil\n\nfrom streamsx.topology.topology import *\nfrom streamsx.topology.tester import Tester\n\nimport test_vers\n\ndef s4():\n return ['one', 'two', 'three', 'four']\n\ndef removeArtifacts(submissionResult):\n if 'bundlePath' in submissionResult:\n os.remove(submissionResult['bundlePath'])\n if 'toolkitRoot' in submissionResult:\n shutil.rmtree(submissionResult['toolkitRoot'])\n if 'archivePath' in submissionResult:\n os.remove(submissionResult['archivePath'])\n\ndef assertBundlePath(test, submissionResult):\n test.assertIn('bundlePath', submissionResult)\n test.assertTrue(os.path.isfile(submissionResult['bundlePath']))\n\ndef assertToolkitRoot(test, submissionResult):\n test.assertIn('toolkitRoot', submissionResult)\n test.assertTrue(os.path.isdir(submissionResult['toolkitRoot']))\n\ndef assertArchivePath(test, submissionResult):\n test.assertIn('archivePath', submissionResult)\n test.assertTrue(os.path.isfile(submissionResult['archivePath']))\n\ndef verifyArtifacts(test):\n if test.test_config.get('topology.keepArtifacts', False):\n # KeepArtifacts is True\n assertToolkitRoot(test, test.result)\n if (test.test_config.get('topology.forceRemoteBuild', False) or\n 'STREAMS_INSTALL' not in os.environ or\n 'BUILD_ARCHIVE' == test.test_ctxtype):\n assertArchivePath(test, test.result)\n test.assertNotIn('bundlePath', test.result)\n elif 'TOOLKIT' == test.test_ctxtype:\n test.assertNotIn('bundlePath', test.result)\n test.assertNotIn('archivePath', test.result)\n else:\n assertBundlePath(test, test.result)\n test.assertNotIn('archivePath', test.result)\n else:\n # KeepArtifacts is False\n if 'TOOLKIT' == test.test_ctxtype:\n assertToolkitRoot(test, test.result)\n else:\n test.assertNotIn('toolkitRoot', test.result)\n if 'BUNDLE' == test.test_ctxtype:\n assertBundlePath(test, test.result)\n test.assertNotIn('archivePath', test.result)\n elif 'BUILD_ARCHIVE' == test.test_ctxtype:\n assertArchivePath(test, test.result)\n test.assertNotIn('bundlePath', test.result)\n else:\n test.assertNotIn('bundlePath', test.result)\n test.assertNotIn('archivePath', test.result)\n\n@unittest.skipIf(not test_vers.tester_supported(), \"Tester not supported\")\nclass TestToolkitMethodsNew(unittest.TestCase):\n\n def setUp(self):\n self.topo = Topology('test_ToolkitSource')\n self.topo.source(['Hello', 'Toolkit'])\n self.test_ctxtype = 'TOOLKIT'\n self.test_config = {}\n self.result = {}\n\n def tearDown(self):\n removeArtifacts(self.result)\n\n def test_NoKeepArtifacts(self):\n self.result = streamsx.topology.context.submit(self.test_ctxtype, self.topo, self.test_config)\n verifyArtifacts(self)\n\n def test_KeepArtifacts(self):\n self.test_config = {'topology.keepArtifacts': True}\n self.result = streamsx.topology.context.submit(self.test_ctxtype, self.topo, self.test_config)\n verifyArtifacts(self)\n\n@unittest.skipIf(not test_vers.tester_supported(), \"Tester not supported\")\nclass TestBuildArchiveMethodsNew(TestToolkitMethodsNew):\n\n def setUp(self):\n self.topo = Topology('test_BuildArchiveSource')\n self.topo.source(['Hello', 'BuildArchive'])\n self.test_ctxtype = 'BUILD_ARCHIVE'\n self.test_config = {}\n self.result = {}\n\n@unittest.skipIf(not test_vers.tester_supported(), \"Tester not supported\")\n@unittest.skipUnless('STREAMS_INSTALL' in os.environ, \"requires STREAMS_INSTALL\")\nclass TestBundleMethodsNew(TestToolkitMethodsNew):\n\n def setUp(self):\n self.topo = Topology('test_BundleSource')\n self.topo.source(['Hello', 'Bundle'])\n self.test_ctxtype = 'BUNDLE'\n self.test_config = {}\n self.result = {}\n\n@unittest.skipIf(not test_vers.tester_supported() , \"Tester not supported\")\nclass TestTopologyMethodsNew(unittest.TestCase):\n\n def setUp(self):\n Tester.setup_standalone(self)\n self.result = {}\n\n def tearDown(self):\n removeArtifacts(self.result)\n\n def test_TopologySourceList(self):\n topo = Topology('test_TopologySourceList')\n hw = topo.source(['Hello', 'Tester'])\n tester = Tester(topo)\n tester.contents(hw, ['Hello', 'Tester'])\n tester.test(self.test_ctxtype, self.test_config)\n self.result = tester.result['submission_result']\n verifyArtifacts(self)\n\n def test_TopologySourceFn(self):\n topo = Topology('test_TopologySourceFn')\n hw = topo.source(s4)\n tester = Tester(topo)\n tester.contents(hw, s4())\n tester.tuple_count(hw, len(s4()))\n self.test_config['topology.keepArtifacts'] = True\n tester.test(self.test_ctxtype, self.test_config)\n self.result = tester.result['submission_result']\n verifyArtifacts(self)\n\n def test_TopologySourceItertools(self):\n topo = Topology('test_TopologySourceItertools')\n hw = topo.source(itertools.repeat(9, 3))\n tester = Tester(topo)\n tester.contents(hw, [9, 9, 9])\n tester.test(self.test_ctxtype, self.test_config)\n\n@unittest.skipIf(not test_vers.tester_supported() , \"Tester not supported\")\nclass TestDistributedTopologyMethodsNew(TestTopologyMethodsNew):\n def setUp(self):\n Tester.setup_distributed(self)\n self.result = {}\n\n@unittest.skipIf(not test_vers.tester_supported() , \"Tester not supported\")\nclass TestBluemixTopologyMethodsNew(TestTopologyMethodsNew):\n def setUp(self):\n Tester.setup_streaming_analytics(self, force_remote_build=True)\n self.result = {}\n","sub_path":"test/python/topology/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":5882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"447852984","text":"#Rolling of a dice\n#Name=Kanav Gupta\n#Division=M\n#Roll no.=25\nimport random\nfrom random import randint as rt\ndef game(dice,faces):\n result=0\n for roll in range(0,dice):\n result+=rt(1,faces)\n return result\nresult=game(1,6)\nprint(result)\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"485450824","text":"import requests\nimport datetime\n\nstickers = [\n \"BIG\",\n \"Ninjas in Pyjamas\",\n \"Astralis\",\n \"Team Liquid\",\n \"Team Spirit\",\n \"FACEIT\",\n \"HellRaisers\",\n \"Cloud9\",\n \"Winstrike Team\",\n \"Rogue\",\n \"FaZe Clan\"\n]\n\n\ndef make_url(stick: str):\n \"\"\"\n gibt den Link der Steamapi zurück\n\n :param stick: der Sticker name\n :return: gibt den Link zurück\n \"\"\"\n url = \"https://steamcommunity.com/market/priceoverview/?currency=1&appid=730&market_hash_name=Sticker%20|%20\" + \\\n stick + \"%20|%20London%202018\"\n return url\n\n\ndef return_dir(url: str):\n \"\"\"\n gibt das dir mit den Daten der Website zurück\n\n :param url: url der API (+Info)\n :return: dir der Daten\n \"\"\"\n\n return requests.get(url).json()\n\n\nprint(datetime.datetime.now().strftime(\"%H:%S:%M\") + \": \")\nfor stick in stickers:\n myDir = return_dir(make_url(stick))\n if myDir and myDir.get(\"success\"):\n print(stick.title() + \": \" + myDir[\"lowest_price\"])\n else:\n print(stick.title() + \": NICHT GEFUNDEN!\")\n\n","sub_path":"goodcrawler.py","file_name":"goodcrawler.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"167117778","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 8 13:20:53 2018\n\n@author: yihangao\n\"\"\"\n\n#Import modules\nfrom __future__ import division\nimport random;\nimport numpy;\nimport math;\n\n#Global Variables\nPi = math.pi;\nE_Crit = 45; #Cirital energy in MeV\nT_P = 0.9525; #Thickness of the plate in cm \nT_G = 0.635; #Thickness of gaps in cm\nR_Det = 7.62; #Radius of detector in cm\nR_Fid = R_Det/2; #Maxium radius of electrons in cm\nX_0 = 8.9; #Radiation length in cm\nTheta_Max = Pi/6; #Maxium Polar angle\n\n\nN_Run = 100; #Total Number of runs persimulation\n\n\n#Function cacluates length particle will travel given an energy in MeV\ndef StoppingLen(E_Val):\n L_Stop = X_0*math.log(1 + E_Val/E_Crit);\n return L_Stop; #Stoping length in cm\n\n#Number of sparks\ndef N_Spark(L,Theta,Z_0):\n Num_Spark = 1 + math.floor((L*math.cos(Theta) - Z_0)/(T_P));\n return Num_Spark;\n\n#Escape length function (calculates escape length for given theta, r and phi)\ndef Escape_L(r,phi,theta):\n L_escp = -r*math.cos(phi) + math.sqrt(R_Det**2 - r**2)/(math.sin(theta)*(T_P/(T_P + T_G)));\n return L_escp;\n \n\n#Generate array of muon masses in MeV\nLowMuonMass = 90; #Lowest Muon mass\nHighMuonMass = 105; #Highest muon mass\nMassRuns = (HighMuonMass - LowMuonMass) + 1; #Number of different masses to be tested \nMuonMass_Array = numpy.linspace(LowMuonMass,HighMuonMass,MassRuns); #Muon mass array increments by 1 MeV (All masses are in MeV)\n\n#These matricies will keep track of the data from the simulations, each column corresponds to a different muon mass and each row corresponds to \n#run number\nSpark_Matrix = numpy.zeros((N_Run,MassRuns)); #Initialize an empty matrix corresponding to number of sparks\nEnergy_Matrix = numpy.zeros((N_Run,MassRuns)); #Initialize an empty matrix corresponding to electron energies\nRadius_Matrix = numpy.zeros((N_Run,MassRuns)); #Initialize an empty matrix corresponding to electron radius\n\n#Simulation loop\nfor i1 in range(0,MassRuns): #Loop over every muon mass\n for i2 in range(0,N_Run): #Run the simulation with a given muon of mass N times\n #Generate Electron Energy\n Electron_En = random.uniform(0,0.5*MuonMass_Array[i1]); #Energy is in MeV\n Energy_Matrix[i2,i1] = Electron_En; #Add electron energy to matrix\n \n #Generate electron radius, polar angle, azimuthal angle and thickness\n r_Val = random.uniform(0,R_Fid);\n Radius_Matrix[i2,i1] = r_Val; #Add radius to matrix\n Theta_Val = random.uniform(0,Theta_Max); #Generate polar angle\n Phi_Val = random.uniform(0,2*Pi); #Generate azimuthal angle\n Z_Val = random.uniform(0,T_P); #Generate a thickness in cm\n \n #Calculate length and escape length\n Len_Val = StoppingLen(Electron_En);\n Escp_Len = Escape_L(r_Val,Theta_Val,Phi_Val);\n \n #Compare escape length and Length\n if (Len_Val > Escp_Len):\n Len_Val = Escp_Len; #Set length to escape length\n \n #Calculate the number of sparks produced and add to data matrix\n Spark_Num = N_Spark(Len_Val,Theta_Val,Z_Val);\n Spark_Matrix[i2,i1] = Spark_Num;\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"301044626","text":"import pandas as pd\r\nfrom datetime import datetime, timedelta\r\nimport boto3\r\nimport s3fs\r\n\r\ns3client = boto3.client('s3', region_name='us-east-1')\r\n\r\n# Processing data from S3 Bucket\r\nsource_bucket = 'cannaspyglass-datalake-raw'\r\ndest_bucket = 'cannaspyglass-datalake-processed-dev'\r\n\r\n# Processing the data from Raw format to processed\r\ndef p_data(s_bucket, file_path, d_bucket, d_file):\r\n s3 = s3fs.S3FileSystem(anon=False)\r\n raw_df = pd.read_excel(f's3://{s_bucket}/{file_path}/', header=1, skipfooter=0)\r\n raw_df.columns = ['status', 'licenseNumber','entityName','city','state','postalCode','firstName', 'lastName', 'phone']\r\n raw_df[['status']] = raw_df[['status']].fillna('No')\r\n raw_df = raw_df.fillna('NA')\r\n raw_df[['status']] = raw_df[['status']].replace('ü', 'Yes')\r\n print(raw_df)\r\n with s3.open(f's3://{d_bucket}/{d_file}/','w') as f:\r\n raw_df.to_csv(f, index=False)\r\n\r\n\r\ndate = (datetime.today() - timedelta(0)).strftime('%Y%m%d')\r\nfilenames = ['cultivation_facility', 'dispensary_facility', 'infused_product_manufacturing_facility',\r\n 'laboratory_testing_facility', 'transportation_facility']\r\nfor file in filenames:\r\n try:\r\n source_filename = 'US/MO/CannabisLifecycle/' + file + '_' + date + '.xlsx'\r\n dest_filename = 'US/MO/CannabisLifecycle/' + file + '_' + date + '.csv'\r\n p_data(source_bucket, source_filename, dest_bucket, dest_filename)\r\n print(\"Processing has been successfully completed\")\r\n except:\r\n print(\"Processing Failed! Please check the source.\")\r\n","sub_path":"processing_draft2.py","file_name":"processing_draft2.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"612773516","text":"\nfrom functools import reduce # forward compatibility for Python 3\nimport operator\nfrom itertools import groupby\nfrom operator import add, itemgetter\n\ndef list_2_str(input_list=[], separator=\",\", log=False):\n \"\"\"\n * convert list to string\n ------------------------\n @ parameters: \n * input list contaning strings\n * separator used to convert list to string\n -----------------------------------------------\n @ outputs:\n * string (Joined if list not empty or \"\")\n \"\"\"\n if input_list:\n result_str = f\"{separator}\".join(map(str, input_list))\n if log:\n logger.debug(\n f'list {input_list} converted to string \"{result_str}\"'\n )\n return result_str\n else:\n logger.error(f'Empty list detected while converting to string ...')\n return \"\"\n\n\ndef merge_records_by(key, combine):\n \"\"\"Returns a function that merges two records rec_a and rec_b.\n The records are assumed to have the same value for rec_a[key]\n and rec_b[key]. For all other keys, the values are combined\n using the specified binary operator.\n \"\"\"\n return lambda rec_a, rec_b: {\n k: rec_a[k] if k == key else combine(rec_a[k], rec_b[k])\n for k in rec_a\n }\n\ndef merge_list_of_records_by(key, combine):\n \"\"\"\n Returns a function that merges a list of records, grouped by\n the specified key, with values combined using the specified\n binary operator.\n \n usage:\n a=[{'time': '25 APR', 'total': 10, 'high': 10}, \n {'time': '26 APR', 'total': 5, 'high': 5}]\n \n b=[{'time': '24 APR', 'total': 10, 'high': 10}, \n {'time': '26 APR', 'total': 15, 'high': 5}]\n\n merger = merge_list_of_records_by('time', add)\n \n print(merger(a + b))\n will print ..\n \n [\n {'time': '24 APR', 'total': 10, 'high': 10},\n {'time': '25 APR', 'total': 10, 'high': 10}, \n {'time': '26 APR', 'total': 20, 'high': 10}\n ]\n \"\"\"\n keyprop = itemgetter(key)\n return lambda lst: [\n reduce(merge_records_by(key, combine), records)\n for _, records in groupby(sorted(lst, key=keyprop), keyprop)\n ]\n\ndef count_frequency(my_list): \n \"\"\"\n count the frequency of elements in a list using a dictionary \n \n Args:\n my_list (list): List\n \n Returns:\n dict: Dictionary containing unique item and frequency\n Example::\n >> my_list = [\"happy\", \"neutral\", \"happy\"]\n >> count_frequency(my_list)\n {'happy': 2, 'neutral': 1}\n \"\"\"\n # Creating an empty dictionary \n freq = {} \n for items in my_list: \n freq[items] = my_list.count(items) \n return freq\n\n\ndef break_list2chunks(lst, n):\n \"\"\"\n Break a list into chunks of size N using list comprehension\n\n Args:\n lst (list): 1d list\n n (int): How many elements each output list should have \n Returns:\n list: 2D list\n \"\"\"\n return [\n lst[i * n:(i + 1) * n] for i in range((len(lst) + n - 1) // n )\n ]","sub_path":"nb_utils/list_manipulation.py","file_name":"list_manipulation.py","file_ext":"py","file_size_in_byte":3087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"526030029","text":"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\n\nfrom pages import Page, SideBar, ResourceView, NewResourceAssistant\nimport skGui\n\n#import lib.scene\n\nclass ScenePage(Page):\n def __init__(self):\n super().__init__(orientation=Gtk.Orientation.HORIZONTAL)\n\n def initGUI(self):\n super().initGUI()\n\n self.sideBar.newItem.connect('clicked', createNewScene)\n\n\nclass AssistantDescriptionPage(Gtk.Box):\n def __init__(self, assistant):\n super().__init__(orientation=Gtk.Orientation.VERTICAL)\n\n self._assistant = assistant\n\n self.initGUI()\n\n def initGUI(self):\n row = Gtk.HBox()\n self.lbl_sceneN = Gtk.Label('Scene Number:')\n row.pack_start(self.lbl_sceneN, False, False, 5)\n self.edit_sceneN = Gtk.Entry()\n self.edit_sceneN.connect('changed', self.check_page_complete)\n row.pack_start(self.edit_sceneN, False, False, 5)\n self.lbl_sceneType = Gtk.Label('Scene Type:')\n row.pack_start(self.lbl_sceneType, False, False, 5)\n self.combo_sceneType = Gtk.ComboBoxText()\n self.combo_sceneType.append('internal', 'Internal')\n self.combo_sceneType.append('external', 'External')\n self.combo_sceneType.append('title', 'Title')\n self.combo_sceneType.set_active_id('external')\n row.pack_start(self.combo_sceneType, False, False, 5)\n\n self.lbl_sceneLocation = Gtk.Label('Location')\n row.pack_start(self.lbl_sceneLocation, False, False, 5)\n self.locationStore = Gtk.ListStore(str, str)\n self.locationStore.append(['none', 'None'])\n self.combo_sceneLocation = Gtk.ComboBox.new_with_model_and_entry(self.locationStore)\n self.combo_sceneLocation.set_id_column(0)\n self.combo_sceneLocation.set_entry_text_column(1)\n locationCompleter = Gtk.EntryCompletion(inline_completion=True)\n locationCompleter.set_model(self.locationStore)\n locationCompleter.set_text_column(1)\n self.combo_sceneLocation.get_child().set_completion(locationCompleter)\n self.combo_sceneLocation.get_child().connect('changed', self.check_page_complete)\n row.pack_start(self.combo_sceneLocation, False, False, 5)\n\n self.pack_start(row, False, False, 5)\n\n row = Gtk.HBox()\n self.lbl_description = Gtk.Label('Description:')\n row.pack_start(self.lbl_description, False, False, 5)\n\n self.pack_start(row, False, False, 5)\n\n self.edit_sceneDescription = Gtk.TextView()\n self.edit_sceneDescription.get_buffer().connect('changed', self.check_page_complete)\n self.edit_sceneDescription.set_left_margin(5)\n self.edit_sceneDescription.set_right_margin(5)\n self.edit_sceneDescription.set_top_margin(5)\n self.edit_sceneDescription.set_bottom_margin(5)\n\n self.pack_start(self.edit_sceneDescription, True, True, 5)\n\n def get_assistant(self):\n return self._assistant\n\n def check_page_complete(self, data):\n state = True\n if self.edit_sceneN.get_text() == '':\n state = False\n if self.edit_sceneDescription.get_buffer().get_char_count() == 0:\n state = False\n if self.combo_sceneLocation.get_child().get_text() == '':\n state = False\n\n self.set_page_complete(state)\n\n def set_page_complete(self, state):\n self.get_assistant().set_page_complete(self, state)\n\n\ndef createNewScene(data):\n assistant = NewResourceAssistant()\n\n index = assistant.add_page(AssistantDescriptionPage(assistant), 'Scene Description')\n #assistant.set_page_complete(assistant.get_nth_page(index), True)\n\n index = assistant.add_page(Gtk.Box(), 'Scene Settings')\n #assistant.set_page_complete(assistant.get_nth_page(index), True)\n\n assistant.show_all()\n","sub_path":"src/pages/scenes.py","file_name":"scenes.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"204690939","text":"l=['a','b','c','d']\nprint \n(l[1])\nfor i in l:\n\tprint (i)\nprint(\"end\")\nlimit=10\nlimit = int (input(\"enter value\"))\n\nfor i in range(1, limit+1):\n\ts = \"\"\n\tfor j in range(0,i):\n\t\ts = s + \"*\"\n\tprint(s)\ndef hw(i):\n\tprint(i)\nhw(i=\"helloo\")\nhw(i=\"hey\")\nhw (i=\"hi!\")\nhw (i=\"seeya!\")\n\n\n","sub_path":"Programming Intro/loob.py","file_name":"loob.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"301063945","text":"from ibm_watson import ToneAnalyzerV3\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\nfrom dotenv import load_dotenv\nimport os\nimport numpy as np\nimport time\n\nload_dotenv()\n\nIBM_API_KEY = os.environ['IBM_API_KEY']\n\ndef get(raw_text):\n \"\"\"\n Fetches tone information from IBM tone API for given text\n\n Args:\n raw_text (str): Text to analyze\n\n Returns:\n Raw API response object on success. Error on failure.\n \"\"\"\n authenticator = IAMAuthenticator(IBM_API_KEY)\n tone_analyzer = ToneAnalyzerV3(\n version='2020-02-25',\n authenticator=authenticator\n )\n tone_analyzer.set_service_url('https://api.us-south.tone-analyzer.watson.cloud.ibm.com/instances/39cb10a6-c500-45b2-8481-053a17155502')\n try:\n resp = tone_analyzer.tone(\n {'text': raw_text},\n content_type='application/json',\n sentences=False\n )\n if resp.status_code == 200:\n return resp.result\n else:\n return {'error': resp.status_code}\n except Exception as e:\n return {'error': e}\n\ndef extract_score_from_tones(tones, tone_id):\n \"\"\"\n Extracts specific tone score from array of tone values\n\n Args:\n tones (list): List of tone objects from IBM tone API\n tone_id (str): specific tone id\n\n Returns:\n Score as float if it exists. NaN if not.\n \"\"\"\n matching_tones = [t for t in tones if t['tone_id'] == tone_id]\n\n if not matching_tones:\n return np.nan\n\n return matching_tones[0]['score']\n\ndef extract_score(raw_tone, tone_id):\n \"\"\"\n Extracts specific tone score from raw tone API response\n\n Args:\n raw_tone (obj): Raw response object from IBM tone API\n tone_id (str): specific tone id\n\n Returns:\n Score as float if it exists. NaN if not.\n \"\"\"\n if not raw_tone.get('document_tone'):\n return np.nan\n\n tones = raw_tone.get('document_tone').get('tones')\n return extract_score_from_tones(tones, tone_id)\n","sub_path":"src/data/tone_analyzer.py","file_name":"tone_analyzer.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"149323238","text":"# -*- coding: utf-8 -*-\n# @Time : 18-10-31 下午9:08\n# @Author : Pugu\n# @FileName: plotting.py\n# @Software: PyCharm\n\nimport torch\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.axes import Axes\nfrom typing import Dict, Optional, Tuple, List\n\nfrom bindsnet.analysis.plotting import plot_input, plot_spikes, plot_weights, plot_assignments, plot_performance\n\n\nclass Keys:\n ALL = \"all\"\n PROP = \"proportion\"\n NG = \"ngram\"\n\n\ndef plot_comparison(curves_list: List[Dict[str, List[float]]], names: List[str] = None,\n axes: Optional[Axes] = None,\n figsize: Tuple[int, int] = (7, 4), dx: int = 250):\n if len(curves_list) > 0:\n num_curves = len(curves_list)\n if isinstance(curves_list[0], dict):\n def check(keys, i):\n if i == 0:\n return check(list(curves_list[0].keys()), i + 1)\n if i >= num_curves:\n return True\n if isinstance(curves_list[i], dict):\n ks = list(curves_list[i].keys())\n if len(list(set(ks).difference(set(keys)))) == 0 and len(list(set(keys).difference(set(ks)))) == 0:\n return check(keys, i + 1)\n else:\n return False\n else:\n return False\n \n if not check([], 0):\n assert \"The list of curves is not correct\"\n return None\n pass\n else:\n assert \"The list of curves is not correct\"\n return None\n pass\n else:\n assert \"The list of curves is empty\"\n return None\n names = [] if names is None else names\n while len(names) < num_curves:\n names.append(\"curve\" + str(len(names) + 1))\n keys = list(curves_list[0].keys())\n num_keys = len(keys)\n xticks = len(curves_list[0][keys[0]]) * dx\n xstep = int(xticks // 10)\n figsize = (figsize[0], figsize[1] * num_keys)\n if not axes:\n _, axes = plt.subplots(nrows=num_keys, figsize=figsize)\n else:\n axes.clear()\n for i, key in enumerate(keys):\n for j, curves in enumerate(curves_list):\n axes[i].plot(np.arange(0, xticks, step=dx), [p for p in curves[key]], label=names[j])\n axes[i].set_ylim(bottom=0, top=100)\n axes[i].set_title(key)\n axes[i].set_xlabel(\"No. of examples\")\n axes[i].set_ylabel(\"Accuracy\")\n axes[i].set_xticks(range(0, xticks + xstep, xstep))\n axes[i].set_yticks(range(0, 110, 10))\n axes[i].legend()\n pass\n \n return axes\n","sub_path":"analyse/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"240774069","text":"'''\n An Utils module for all common and frequent tasks such as file handling etc..\n'''\nimport json\nimport requests\n\nclass Py_Utils:\n \n @staticmethod\n def get_api_result(api_url,*args):\n '''\n Given an API URL do a GET request call and return the result\n :param api_url: URL on which GET will be applied\n :param args: Contains username and password\n '''\n try:\n return requests.get(api_url,auth=(args[0],args[1]))\n except Exception as error:\n return {'data':'ERROR'}\n\n @staticmethod\n def validate_users_keys(user_inputs,required_inputs,user_names_array,check_user_exists=False):\n '''\n Check if the User supplies all the required inputs.\n If any of the user keys are not passed, return False.\n If the username is already available in the database ,then do not process.\n :param user_inputs: Inputs given by the User\n :param required_inputs: Expected inputs, mentioned as part of configs\n :param user_names_array: Array of usernames available in the database\n :param check_user_exists:\n Default Value: True\n When set as True \n -> Apply a check to verify the users attributes against required config attributes and \n Apply a check to verify if user is already part of the system.\n else\n -> Apply a check to verify the users attributes against required config attributes\n '''\n try:\n user_inputs_keys = sorted(list(user_inputs.keys()))\n required_inputs_keys = sorted(required_inputs)\n return user_inputs_keys == required_inputs_keys and str(user_inputs['user_name']).lower() not in user_names_array if bool(check_user_exists)\\\n else user_inputs_keys == required_inputs_keys\n except Exception as error:\n return False\n\n @staticmethod\n def post_api_result(api_url,*args):\n '''\n Given an API URL do a GET request call and return the result\n :param api_url: URL on which GET will be applied\n :param args: Contains username and password\n '''\n try:\n return requests.post(api_url,auth=(args[0],args[1]))\n except Exception as error:\n return {'data':'ERROR'}\n\n\n @staticmethod\n def read_json(json_file):\n ''' \n Read a json file and return the contents\n :param json_file: Full Path of the json file to be read\n @return: \n Success -> Json file contents as a dictionary. \n Exception -> Empty dictionary\n '''\n try:\n with open(json_file,'r') as reader:\n return json.load(reader)\n except Exception as error:\n return {}\n\n @staticmethod\n def write_json(json_file,contents):\n ''' \n Write the contents to the *json* file\n :param json_file: Full Path of the json file to be written to\n :param contents: Contents to be written to json file\n @return: \n Success -> Json file contents as a dictionary. \n Exception -> Empty dictionary\n '''\n try:\n with open(json_file,'w') as writer:\n return json.dump(contents,writer)\n except Exception as error:\n return {}\n\n @staticmethod\n def read_file(file_path):\n ''' \n Read any file and return the contents\n :param file_path: Full Path of the file to be read\n @return: \n Success -> File contents as an Array. \n Exception -> Empty dictionary\n '''\n try:\n with open(file_path,'r') as reader:\n for each_line in reader.readlines():\n yield each_line.split(',')\n except Exception as error:\n return {}\n\n @classmethod\n def populate_json_records(cls,csv_file_path,json_file_path,json_columns):\n '''\n Read the given csv file and populate the json file\n :param csv_file_path: Full path of the csv file\n :param json_file_path: Full path of the json file\n :param json_columns: Array of json columns\n @return:\n Success -> Write the contents to the final json file\n Exception ->Write empty array to the final json file\n '''\n try:\n csv_contents = list(cls.read_file(csv_file_path))\n json_contents = {\"records\":[{str(json_column):str(record) for record,json_column in zip(records,json_columns)} for records in csv_contents]}\n return cls.write_json(json_file=json_file_path,contents=json_contents)\n except Exception as error:\n return cls.write_json(json_file=json_file_path,contents={\"error\":[]})","sub_path":"core/utilities/pyutils.py","file_name":"pyutils.py","file_ext":"py","file_size_in_byte":4933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"383762305","text":"import re ##### For regular expression\nimport ast ######## The ast module helps Python applications to process trees of the Python abstract syntax grammar #############\n\ndef parse_aSHIIP_topology_with_size(topology_file_name, adjacent_matrix):\n '''Parse aSHIIP topology ::: arg1: Name of the topology file, arg2: adjacent_matrix'''\n topology_pointer = open(topology_file_name,'r+')\n size_found = False\n #################### Parser symbols ##############################\n number_of_node_symbol = 'Size :'\n\n for line in topology_pointer:\n line = line.replace('\\n','')\n if not size_found:\n if number_of_node_symbol in line:\n number_of_node = int(line.replace(' ','').split(':')[1])\n del adjacent_matrix[:]\n for i in range(number_of_node):\n adjacent_matrix.append([])\n print(\"Size of Topology : %s\"%(len(adjacent_matrix)))\n size_found = True\n else:\n start_with_digit = re.match('^\\d',line) ##################### Check if the string starts with a digit #####################################\n if start_with_digit is not None:\n line = line.replace(' ','')\n node_id = int(line[0:line.index('(')])\n adjacent_nodes = ast.literal_eval(line[line.index(')')+1:])\n for node_index in range(len(adjacent_nodes)):\n adjacent_nodes[node_index] -= 1\n adjacent_matrix[node_id-1] = adjacent_nodes\n\ndef parse_aSHIIP_topology(topology_file_name, adjacent_matrix):\n '''Parse aSHIIP topology ::: arg1: Name of the topology file, arg2: adjacent_matrix'''\n topology_pointer = open(topology_file_name, 'r+')\n del adjacent_matrix[:]\n for line in topology_pointer:\n line = line.replace('\\n', '')\n start_with_digit = re.match('^\\d',line) ##################### Check if the string starts with a digit #####################################\n if start_with_digit is not None:\n line = line.replace(' ', '')\n node_id = int(line[0:line.index('(')])\n adjacent_nodes = ast.literal_eval(line[line.index(')') + 1:])\n for node_index in range(len(adjacent_nodes)):\n adjacent_nodes[node_index] -= 1\n adjacent_matrix.append(adjacent_nodes)\n topology_pointer.close()\n # print('Adjacent %s'%(adjacent_matrix))\n\ndef parse_aSHIIP_topology_bi_directional(topology_file_name, adjacent_matrix,sort_node=False):\n '''Parse aSHIIP topology ::: arg1: Name of the topology file, arg2: adjacent_matrix'''\n topology_pointer = open(topology_file_name, 'r+')\n del adjacent_matrix[:]\n for line in topology_pointer:\n line = line.replace('\\n', '')\n start_with_digit = re.match('^\\d',line) ##################### Check if the string starts with a digit #####################################\n if start_with_digit is not None:\n line = line.replace(' ', '')\n node_id = int(line[0:line.index('(')])\n adjacent_nodes = ast.literal_eval(line[line.index(')') + 1:])\n for node_index in range(len(adjacent_nodes)):\n adjacent_nodes[node_index] -= 1\n adjacent_matrix.append(list(set(adjacent_nodes)))\n topology_pointer.close()\n\n for node_id in range(len(adjacent_matrix)):\n for adj_node in adjacent_matrix[node_id]:\n if node_id not in adjacent_matrix[adj_node]:\n adjacent_matrix[adj_node].append(node_id)\n\n if sort_node:\n for node_id in range(len(adjacent_matrix)):\n adjacent_matrix[node_id] = sorted(adjacent_matrix[node_id])\n\n # print(\"Adjacency Matrix %s\"%(adjacent_matrix))","sub_path":"ParseTopologyFile.py","file_name":"ParseTopologyFile.py","file_ext":"py","file_size_in_byte":3734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"266821317","text":"'''\r\nCreated on 27 janv. 2020\r\n\r\n@author: gresset6u\r\n'''\r\n\r\nimport tkinter as tk\r\n\r\nclass Application(tk.Frame):\r\n def __init__(self, master=None):\r\n tk.Frame.__init__(self, master)\r\n self.pack()\r\n self.createWidgets()\r\n def createWidgets(self):\r\n self.QUIT = tk.Button(self, text=\"QUIT\", fg=\"red\", command=root.destroy)\r\n self.QUIT.pack(side=\"left\")\r\n hi_there = tk.Button(self, text=\"Hello\", command=self.say_hi)\r\n hi_there.pack(side=\"left\")\r\n def say_hi(self):\r\n print(\"hi there, everyone!\")\r\n\r\nroot = tk.Tk()\r\napp = Application(master=root)\r\napp.mainloop()\r\n","sub_path":"PILS/Python/Tkinter2.py","file_name":"Tkinter2.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"134167061","text":"from BaseCmd import BaseCmd\n\nclass Bissness(BaseCmd):\n\t\"\"\"公司中常用地址\"\"\"\n\tdef __init__(self):\n\t\tself.cmds={\n\t\t\t\"41\":\"http://jira.up366.com:8090\",\n\t\t\t\"42\":\"http://jira.up366.com:8080\",\n\t\t\t\"43\":\"http://192.168.1.203:8998/jenkins/\",\n\t\t\t\"44\":\"http://www.ismartin.com\",\n\t\t\t\"45\":\"http://lsp.ismartin.com/manage/displayLesson.up?lessonId=47977628554756096\"\n\n\t\t}\n\t\tself.menu = \"\"\"\n公司常用\t\n|\t41. wiki\n|\t42. jira\n|\t43. jenkins\n|\t44. ismart地址\n|\t45. 直播地址\n-----------------------------------------------------------\n\t\t \"\"\"\n\n\tdef hanldeCmd(self,key):\n\t\tif key not in self.cmds.keys():\n\t\t\treturn False;\n\t\tself.openChrome(self.cmds[key]);","sub_path":"androidUtil/Bissness.py","file_name":"Bissness.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"425579189","text":"import json\nimport os\n\n\nclass Config:\n def __init__(self, file, default={}, auto_init=True,\n indent=4, sort_keys=True):\n self.file = file\n self.default = default\n self.data = {}\n self.indent = indent\n self.sort_keys = sort_keys\n if auto_init:\n self.init()\n\n def init(self):\n if os.path.isfile(self.file):\n self.load()\n else:\n self.data = self.default\n self.save()\n\n def get(self, key):\n if key in self.data.keys():\n return self.data[key]\n if key in self.default.keys():\n return self.default[key]\n\n def set(self, key, value):\n self.data[key] = value\n\n def save(self):\n open(self.file, 'w').write(json.dumps(\n self.data, indent=self.indent, sort_keys=self.sort_keys\n ))\n\n def load(self):\n if not os.path.isfile(self.file):\n return False\n try:\n self.data = json.load(open(self.file))\n except json.decoder.JSONDecodeError:\n return False\n","sub_path":"src/utils/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"473058408","text":"from bottle import static_file, get, route, run, template\nimport os\nimport json\nimport re\nimport sys\nimport argparse\n\n@route('/static/')\ndef static(filepath):\n return static_file(filepath,root='./static')\n\n@route('/')\ndef index():\n return template('index')\n\n@get('/img')\ndef getImg():\n directory = os.path.join(os.getcwd() + '/static/picture')\n files = os.listdir(directory)\n result = { 'img': [] }\n matcher = re.compile('.+(\\.jpg|\\.JPG|\\.JPEG|\\.jpeg)')\n for f in files:\n if matcher.match(f):\n #result['img'].append({ 'fname': f })\n result['img'].append(f)\n return json.dumps(result)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Image Slide Show')\n parser.add_argument('-H', '--host',\n action='store',\n nargs='?',\n const=None,\n default='localhost',\n type=str,\n choices=None,\n help='IP address or hostname that you want to open locate for web server (default: localhost)',\n metavar=None)\n parser.add_argument('-p', '--port',\n action='store',\n nargs='?',\n const=None,\n default='8080',\n type=str,\n choices=None,\n help='IP address or hostname that you want to open locate for web server (default: localhost)',\n metavar=None)\n args = parser.parse_args()\n\n run(host=args.host, port=args.port)\n","sub_path":"slick/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"194811128","text":"import os\n\n\ndef populate():\n print('Populating database')\n print('-------------------\\n')\n add_transductor_model()\n add_transductor()\n print('Finished database seed')\n\n\ndef add_transductor_model():\n print('Creating TR4020 transductor model')\n\n name = 'TR4020'\n model_code = '123456789'\n transport_protocol = 'UdpProtocol'\n serial_protocol = 'ModbusRTU'\n minutely_register_addresses = [\n [10, 1], [11, 1], [14, 1], [15, 1], [16, 1], [17, 1], [66, 2],\n [68, 2], [70, 2], [72, 2], [74, 2], [76, 2], [78, 2], [80, 2],\n [82, 2], [84, 2], [86, 2], [88, 2], [90, 2], [92, 2], [94, 2],\n [96, 2], [98, 2], [100, 2], [102, 2], [104, 2], [106, 2], [108, 2],\n [110, 2], [112, 2], [114, 2], [116, 2], [118, 2], [120, 2], [122, 2],\n [132, 2], [134, 2], [136, 2], [138, 2]\n ]\n quarterly_register_addresses = [\n [10, 1], [11, 1], [14, 1], [15, 1], [16, 1], [17, 1], [264, 2],\n [266, 2], [270, 2], [272, 2], [276, 2], [278, 2], [282, 2], [284, 2]\n ]\n monthly_register_addresses = [\n [10, 1], [11, 1], [14, 1], [15, 1], [16, 1], [17, 1], [156, 2],\n [158, 2], [162, 2], [164, 2], [168, 2], [170, 2], [174, 2], [176, 2],\n [180, 2], [182, 2], [186, 2], [188, 2], [420, 2], [422, 2], [424, 2],\n [426, 2], [428, 2], [430, 2], [432, 2], [434, 2], [444, 2], [446, 2],\n [448, 2], [450, 2], [452, 2], [454, 2], [456, 2], [458, 2], [516, 4],\n [520, 4], [524, 4], [528, 4], [540, 4], [544, 4], [548, 4], [552, 4]\n ]\n\n model, created = TransductorModel.objects.get_or_create(\n name=name,\n model_code=model_code,\n transport_protocol=transport_protocol,\n serial_protocol=serial_protocol,\n minutely_register_addresses=minutely_register_addresses,\n quarterly_register_addresses=quarterly_register_addresses,\n monthly_register_addresses=monthly_register_addresses\n )\n\n if created:\n print('Created TR4020')\n else:\n print('Transductor Model already existed')\n\n\ndef add_transductor():\n print('Creating transductor #1')\n transductor, created = EnergyTransductor.objects.get_or_create(\n serial_number='12345',\n ip_address='164.41.86.42',\n broken=False,\n active=True,\n model=TransductorModel.objects.get(name='TR4020'),\n firmware_version='12.1.3215',\n physical_location='Faculdade do gama prédio UED',\n geolocation_longitude=-15.989753,\n geolocation_latitude=-48.04542\n )\n\n if created:\n print('Created transductor #1')\n else:\n print('Transductor #1 already existed')\n\n print('Creating transductor #2')\n transductor2, created2 = EnergyTransductor.objects.get_or_create(\n serial_number='54321',\n ip_address='164.41.86.43',\n broken=False,\n active=True,\n model=TransductorModel.objects.get(name='TR4020'),\n firmware_version='12.1.3215',\n physical_location='Faculdade do gama prédio UED',\n geolocation_longitude=15.989533,\n geolocation_latitude=-48.04567\n )\n\n if created2:\n print('Created transductor #2')\n else:\n print('Transductor #2 already existed')\n\n\nif __name__ == '__main__':\n import django\n\n os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'smi.settings')\n django.setup()\n\n from transductor.models import EnergyTransductor\n from transductor_model.models import TransductorModel\n\n populate()\n\n print('Finished populating DB. YAY')\n print('-------------------\\n')\n","sub_path":"seed_db.py","file_name":"seed_db.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"622348397","text":"import unittest\nimport os\nimport openfoodfacts\nimport requests\nimport requests_mock\n\n\nclass TestProducts(unittest.TestCase):\n\n def test_get_product(self):\n with requests_mock.mock() as mock:\n mock.get(\n 'https://world.openfoodfacts.org/api/v0/product/1223435.json',\n text='{\"name\":\"product_test\"}')\n res = openfoodfacts.get_product('1223435')\n self.assertEqual(res, {'name': 'product_test'})\n\n def test_get_by_trace(self):\n with requests_mock.mock() as mock:\n mock.get('https://world.openfoodfacts.org/trace/egg/1.json',\n text='{\"products\":[\"omelet\"]}')\n res = openfoodfacts.products.get_by_trace('egg')\n self.assertEqual(res, [\"omelet\"])\n\n def test_get_by_trace_pagination(self):\n with requests_mock.mock() as mock:\n mock.get('https://world.openfoodfacts.org/trace/egg/2.json',\n text='{\"products\":[\"omelet\"]}')\n res = openfoodfacts.products.get_by_trace('egg', 2)\n self.assertEqual(res, [\"omelet\"])\n\n def test_get_by_country(self):\n with requests_mock.mock() as mock:\n mock.get('https://world.openfoodfacts.org/country/france/1.json',\n text='{\"products\":[\"omelet\"]}')\n res = openfoodfacts.products.get_by_country('france')\n self.assertEqual(res, [\"omelet\"])\n\n def test_get_by_country_and_trace(self):\n res = openfoodfacts.products.get_by_facets({})\n self.assertEqual(res, [])\n\n with requests_mock.mock() as mock:\n mock.get(\n 'https://world.openfoodfacts.org/country/'\n 'france/trace/egg/1.json',\n text='{\"products\":[\"omelet\"]}')\n res = openfoodfacts.products.get_by_facets(\n {'trace': 'egg', 'country': 'france'})\n self.assertEqual(res, [\"omelet\"])\n\n def test_search(self):\n with requests_mock.mock() as mock:\n mock.get(\n 'https://world.openfoodfacts.org/cgi/search.pl?' +\n 'search_terms=kinder bueno&json=1&page=' +\n '1&page_size=20&sort_by=unique_scans',\n text='{\"products\":[\"kinder bueno\"], \"count\": 1}')\n res = openfoodfacts.products.search('kinder bueno')\n self.assertEqual(res[\"products\"], [\"kinder bueno\"])\n mock.get(\n 'https://world.openfoodfacts.org/cgi/search.pl?' +\n 'search_terms=banania&json=1&page=' +\n '2&page_size=10&sort_by=unique_scans',\n text='{\"products\":[\"banania\", \"banania big\"], \"count\": 2}')\n res = openfoodfacts.products.search(\n 'banania', page=2, page_size=10)\n self.assertEqual(res[\"products\"], [\"banania\", \"banania big\"])\n\n def test_advanced_search(self):\n with requests_mock.mock() as mock:\n mock.get(\n 'https://world.openfoodfacts.org/cgi/search.pl?' +\n 'search_terms=coke&tagtype_0=packaging&' +\n 'tag_contains_0=contains&tag_0=plastic&' +\n 'nutriment_0=energy&nutriment_compare_0=gt&' +\n 'nutriment_value_0=0&sort_by=unique_scans&' +\n 'page_size=20',\n text= '{\"products\":[\"Diet Coke\"], \"count\": 1}')\n res = openfoodfacts.products.advanced_search({\n \"search_terms\":\"coke\",\n \"tagtype_0\":\"packaging\",\n \"tag_contains_0\":\"contains\",\n \"tag_0\":\"plastic\",\n \"nutriment_0\":\"energy\",\n \"nutriment_compare_0\":\"gt\",\n \"nutriment_value_0\":\"0\",\n \"sort_by\":\"unique_scans\",\n \"page_size\":\"20\"\n })\n self.assertEqual(res[\"products\"],[\"Diet Coke\"])\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/products_test.py","file_name":"products_test.py","file_ext":"py","file_size_in_byte":3921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"77632724","text":"\n\n#calss header\nclass _BAGEL():\n\tdef __init__(self,): \n\t\tself.name = \"BAGEL\"\n\t\tself.definitions = [u'a type of bread that is small, hard, and in the shape of a ring: ', u'in tennis, a situation in which a player has won a set (= part of a match) by 6 games to 0.']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_bagel.py","file_name":"_bagel.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"153584453","text":"from django.http import request\nfrom base.forms.invoice_form import InvoiceForm\nfrom decimal import Decimal\n\nfrom django.http.response import HttpResponse\nfrom base.models.Invoice import Event\nfrom django.core.checks import messages\nfrom base.models import product\nfrom base.models.product import Product\nfrom django.contrib.auth import logout\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom base.models.Invoice import Invoice\nfrom base.models.order import Order, OrderItem\nfrom django.contrib import messages\nfrom django.contrib.auth.models import User\nfrom django.http import JsonResponse\nfrom base.forms.order_form import UpdateOrderForm\nfrom django.contrib.auth.decorators import login_required\n\n\ndef create_order(request):\n context = {\n 'products': Product.objects.all(),\n 'events': Event.objects.all()\n }\n return render(request, 'orders/add_order.html', context)\n\n\ndef get_product(request):\n product_id = request.GET.get('product')\n # Getting the data of that specific Product\n product = Product.objects.get(id=product_id)\n data = {\n 'rate': product.Price,\n }\n return JsonResponse(data)\n\n\ndef ajax_order_create(request):\n if request.method == 'POST':\n # Getting the data from form inputs from ajax.\n product = request.POST.get('product')\n quantity = request.POST.get('quantity')\n sub_total = request.POST.get('sub_total')\n # The String coming from ajax is e.g \"[\"40\", \"42\"]\", converting this to \"40\", \"42\"\n products = product[1:-1]\n quantities = quantity[1:-1]\n sub_totals = sub_total[1:-1]\n\n # Converting string to array using python split() method\n products_list = products.split(',')\n quantities_list = quantities.split(',')\n sub_totals_list = sub_totals.split(',')\n # Slicing the double quotes (\") from array indexes and converting them to decimals\n sub_totals_list = [x.strip(' \"') for x in sub_totals_list]\n sub_totals_list = [Decimal(i) for i in sub_totals_list]\n # Slicing the double quotes (\") from array indexes and converting them to int\n quantities_list = [x.strip(' \"') for x in quantities_list]\n quantities_list = [int(i) for i in quantities_list]\n # product_list contains the id's of products/items selected in form\n products_list = [x.strip(' \"') for x in products_list]\n products_list = [int(i) for i in products_list]\n # Creating the order object\n order = Order.objects.create(\n full_name='Abu Bakar',\n phn_number='03006890011',\n email='abubakar@gmail.com',\n address='Model Town Lahore'\n )\n for i in range(len(products_list)):\n OrderItem.objects.create(\n orderItem=order,\n products_id=products_list[i],\n price=sub_totals_list[i],\n quantity=quantities_list[i],\n )\n data = {'name': 'Abubakar'}\n return JsonResponse(data)\n\n# def add_order(request):\n# form = InvoiceForm()\n# if request.method == \"POST\":\n# form = InvoiceForm(request.POST or None)\n# if form.is_valid:\n# form.save()\n# #name = Product.objects.only('product_name').get(baseentity_ptr = request.POST.get('Product_id'))\n# sale = Product.objects.only('stock').get(baseentity_ptr = request.POST.get('Product_id'))\n# qty = int(request.POST.get(\"sold_Quantity\"))\n# sale.count_sold = sale.count_sold + qty\n# sale.save()\n# sale = Product.objects.only('stock').get(baseentity_ptr = request.POST.get('Product_id'))\n#\n# else:\n# HttpResponse(\"Error Occured\")\n#\n# context = [{\n# 'Product_id':Product.objects.only('product_name').get(baseentity_ptr = request.POST.get('Product_id')),\n# 'qty' : request.POST.get(\"sold_Quantity\"),\n# 'price' : request.POST.get(\"price\"),\n# 'total_price' : request.POST.get(\"total_price\"),\n# 'date' : request.POST.get('date_time'),\n# 'namount' : request.POST.get('namount'),\n# 'event' : Event.objects.only('event_name').get(id = request.POST.get('Event_Name'))\n# }]\n#\n# return render(request,'invoice/invoice.html',{'context':context})\n# return render(request,'orders/add_order.html',{'form':form,'data':Product.objects.all()})\n\ndef invoice(request):\n return render(request,'invoice/invoice.html',{'context':Invoice.objects.all()})\n@login_required(login_url='login')\ndef manageOrders(request):\n return render(request,'invoice/invoice.html',{'context':Invoice.objects.all()})\ndef viewOrder(request):\n return render(request,'orders/list_of_order.html',{'context':OrderItem.objects.all()})\n\ndef load_price(request):\n product_id = request.GET.get('product_id')\n price = Product.objects.only('Price').filter(baseentity_ptr=product_id).get()\n return render(request, 'orders/price.html', {'prices': price})\n # return JsonResponse(list(cities.values('id', 'name')), safe=False)\n\n@login_required(login_url='login')\ndef updateOrder(request,pk):\n order_item = get_object_or_404(OrderItem, pk=pk) # baseentity_ptr\n form = UpdateOrderForm(instance=order_item)\n if request.method == 'POST':\n form = UpdateOrderForm(request.POST, instance=order_item)\n if form.is_valid():\n form.save()\n return redirect('view-order')\n return render(request,'orders/update_order.html',{'form':form})\n \n@login_required(login_url='login')\ndef deleteOrder(request,pk):\n dell = OrderItem.objects.filter(baseentity_ptr=pk).delete()\n messages.success(request,\"Order Deleted Successfully\")\n return render(request, 'orders/list_of_order.html', {'context': OrderItem.objects.all(), 'dell': dell})\n","sub_path":"base/views/orderView.py","file_name":"orderView.py","file_ext":"py","file_size_in_byte":5838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"550533906","text":"\"\"\"\nPlotting routines\n\"\"\"\nfrom __future__ import absolute_import\nfrom .utility import *\nfrom . import colorconversion\n\n\ndef plot_points(points):\n \"\"\"\n Plots points in (x,y) space\n \"\"\"\n fig, ax = plt.subplots()\n ax.plot(points[:, 0], points[:, 1], 'ko')\n ax.axis('equal')\n ax.set_xlim([-1.1, 1.1])\n ax.set_ylim([-1.1, 1.1])\n plt.show()\n\n\ndef plot_lab_points(lab):\n \"\"\"\n Plots sequence of Lab colors in (a,b) plane\n \"\"\"\n rgb = colorconversion.lab_to_rgb(lab)\n fig, ax = plt.subplots()\n ax.scatter(lab[:, 1], lab[:, 2], s=60, c=rgb, edgecolors='none')\n ax.axis('equal')\n ax.set_xlim([-105., 105.])\n ax.set_ylim([-105., 105.])\n ax.set_xlabel('a')\n ax.set_ylabel('b')\n plt.show()\n\n\ndef plot_colorsequence_lines(rgb_colors):\n \"\"\"\n Plots a series of lines with given colors\n \"\"\"\n fig, ax = plt.subplots()\n ncolors = rgb_colors.shape[0]\n for i, c in enumerate(rgb_colors):\n y = float(i)/ncolors + 0.5/ncolors\n ax.plot([0, 1], [y, y], color=c, lw=4)\n plt.show()\n\n\ndef plot_cmap(cmap, ax=None, aspect=10.):\n if ax is None:\n fig, ax = plt.subplots()\n\n gradient = numpy.linspace(0, 1, 256)\n gradient = numpy.vstack((gradient, gradient))\n\n ax.imshow(gradient, aspect=aspect, cmap=cmap)\n pos = list(ax.get_position().bounds)\n x_text = pos[0] - 0.01\n y_text = pos[1] + pos[3]/2.\n fig.text(x_text, y_text, cmap.name, va='center', ha='right', fontsize=10)\n ax.set_axis_off()\n\n\ndef plot_cmap_list(cmap_list):\n nrows = len(cmap_list)\n fig, axes = plt.subplots(nrows=nrows)\n fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)\n\n for ax, cmap in zip(axes, cmap_list):\n if isinstance(cmap, str):\n c = plt.get_cmap(c)\n else:\n c = cmap\n plot_cmap(cmap, ax)\n","sub_path":"amphora/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"123284972","text":"from time import sleep\n\ndef add_json_node(tx, label='Generic', properties=None):\n if properties is None:\n properties = dict()\n prop_set = '{' + ','.join('{key}:{{{key}}}'.format(key=k) for k in properties) + '}'\n query = 'MERGE (n:{label} '.format(label=label) + prop_set + ') RETURN n'\n return tx.run(query, **properties)\n\ndef get_count(tx, finder):\n # For instance:\n # finder = 'MATCH (n:Article) WHERE n.journal CONTAINS 'Experimental Biology'\n count_query = finder + ' WITH COUNT (n) AS count RETURN count LIMIT 1'\n count = tx.run(count_query)\n count = list(count.records())[0]['count']\n return count\n\ndef get_page_queries(query, count, page_size=1000, rate_limit=0.25):\n properties = dict()\n for i in range(count // page_size):\n properties['skip'] = i * page_size\n properties['limit'] = page_size\n page_query = query + ' SKIP {skip} LIMIT {limit}'\n yield page_query, properties\n sleep(rate_limit)\n\ndef page(tx, finder, query, properties=None, page_size=1000, rate_limit=0.25):\n if properties is None:\n properties = dict()\n # For instance:\n # query = MATCH (n:Article) WHERE n.abstract CONTAINS 'Aerodynamics'\n # finder = 'MATCH (n:Article) WHERE n.journal CONTAINS 'Experimental Biology'\n # WITH COUNT (n) AS count RETURN count LIMIT 1'\n count = get_count(tx, finder)\n for query, q_properties in get_page_queries(query, count, page_size=page_size, rate_limit=rate_limit):\n properties.update(q_properties)\n yield tx.run(query, **properties)\n","sub_path":"legacy/petal/mining/utils/neo.py","file_name":"neo.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"530163356","text":"from django.contrib import admin\n\nfrom .models import *\nfrom .forms import ReviewAdminForm\n\n\nclass CarAdmin(admin.ModelAdmin):\n list_display = ('brand', 'model', 'get_reviews')\n filter = ('brand', 'model')\n search_fields = ('brand', 'model')\n ordering = ('pk',)\n\n\nclass ReviewAdmin(admin.ModelAdmin):\n form = ReviewAdminForm\n list_display = ('car', 'title',)\n filter = ('title', 'car',)\n search_fields = ('car__model',)\n\n\nadmin.site.register(Car, CarAdmin)\nadmin.site.register(Review, ReviewAdmin)\n","sub_path":"site-form-works/car_admin/app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"540431475","text":"# imports\nfrom flask import Flask,jsonify\nfrom flask_mysqldb import MySQL\nfrom blueprints.location import location\nfrom blueprints.buses import buses\n\n\n# Initialize Application\napp = Flask(__name__)\n\n# Registering Blueprint\napp.register_blueprint(location,url_prefix = \"/location\")\napp.register_blueprint(buses,url_prefix = \"/buses\")\n\n# Database Configuration\napp.config['MYSQL_HOST'] = \"localhost\"\napp.config[\"MYSQL_USER\"] = \"root\"\napp.config[\"MYSQL_PASSWORD\"] = \"Krishna@7860\"\napp.config[\"MYSQL_DB\"] = \"busroute\"\napp.config[\"MYSQL_CURSORCLASS\"] = \"DictCursor\"\n\nmysql = MySQL(app)\n\n# @desc : Home Route\n# route: \"/\"\n# @param : null\n# return : JSON\n@app.route(\"/\")\ndef home():\n try:\n conn = mysql.connection.cursor()\n conn.execute(\"\"\"SELECT DATABASE()\"\"\")\n row = conn.fetchone()\n return jsonify(row),200\n except Exception as e:\n return jsonify({\"error\" : str(e)})\n finally:\n conn.close()\n\n\nif __name__ == \"__main__\" and __package__ is None:\n app.run(debug=True)","sub_path":"submissions/sm_108_krishna-kant/week_23/server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"153736925","text":"def readFile(file):\n name = file[:file.index('.')]\n ##print(name)\n f = open(file)\n fout = open(name+'.out','w')\n cases = int(f.readline().strip())\n for i in range(cases):\n case = f.readline().strip().split()\n n = int(case[0])\n s = int(case[1])\n p = int(case[2])\n scores = [int(x) for x in case[3:]]\n result = execute(i,n,s,p,scores)\n print(result)\n fout.write(result)\n\ndef execute(index,n,s,p,scores):\n print(index,n,s,p,scores)\n count=0\n tempS = s\n triplets = []\n\n for j in range(n):\n mean = int(scores[j]/3)\n remainder = scores[j]%3\n\n triplet = [mean for x in range(3)]\n\n c = 0\n while remainder > 0 :\n triplet[c] = triplet[c]+1\n remainder-=1\n c+=1\n\n triplets.append(triplet)\n\n ##add suprising\n for t in triplets:\n if t[0] >= p:\n count+=1\n continue\n elif (tempS > 0 and (p-t[0]) == 1 and t[0] == t[1] and t[1] > 0):\n #surprise case\n t[0]=t[0]+1\n t[1]=t[1]-1\n count+=1\n tempS-=1\n continue\n\n print(tempS)\n print(triplets)\n \n return ''.join(['Case #',str(index+1),': ',str(count),'\\n'])\n \n\n\nif __name__ == \"__main__\":\n readFile('B-small-attempt1.in')\n","sub_path":"solutions_1595491_0/Python/ToR/dancing_with_the_googlers.py","file_name":"dancing_with_the_googlers.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"1610786","text":"#!/usr/bin/env python3\n\nimport os, os.path\nimport sys\nimport socket\nimport time\n\n# Used to gracefully aborting stuff.. like a hippo exiting a stage\nclass AbandonShip(BaseException):\n\tpass\n\n# Default config values.\n_config = {\n\t'host': 'api.anidb.net',\n\t'port': '9000',\n\t'session': '~/.kiara.session',\n\t'database': '~/.kiara.db',\n}\n\ndef _config_items(file):\n\tfor line in map(lambda s: s.strip(), file.readlines()):\n\t\tif line.startswith('#') or not line:\n\t\t\tcontinue\n\t\tyield line.split(None, 1)\n\ndef load_config_file(file_name):\n\tglobal _config\n\ttry:\n\t\twith open(file_name, 'r') as fp:\n\t\t\t_config.update(_config_items(fp))\n\texcept: pass\nload_config_file('/etc/kiararc')\nload_config_file(os.path.expanduser('~/.kiararc'))\n\ndef check_config():\n\tconfig_ok = True\n\tfor key in 'host port user pass database session ' \\\n\t\t'basepath_movie basepath_series'.split():\n\t\tif not key in _config:\n\t\t\tprint('ERROR: Missing config variable:', key, file=sys.stderr)\n\t\t\tconfig_ok = False\n\treturn config_ok\n\ndef _send(msg):\n\tprint()\n\tdef inner():\n\t\tclient = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n\t\tclient.connect(os.path.expanduser(_config['session']))\n\t\tclient.sendall(bytes(msg, 'UTF-8'))\n\t\tdata = ''\n\t\twhile True:\n\t\t\tdata += str(client.recv(1024), 'UTF-8')\n\t\t\tif data == '---end---':\n\t\t\t\tclient.close()\n\t\t\t\treturn\n\t\t\tif '\\n' in data:\n\t\t\t\titem, data = data.split('\\n', 1)\n\t\t\t\tyield item\n\ttry:\n\t\tfor i in inner():\n\t\t\tyield i\n\texcept socket.error:\n\t\tprint('Unable to contact the backend. Will try to start one...')\n\t\tif os.fork():\n\t\t\t# Wait for it...\n\t\t\ttime.sleep(2)\n\t\t\t# Then try the command again. If it fails again, something we\n\t\t\t# cannot fix is wrong\n\t\t\tfor i in inner():\n\t\t\t\tyield i\n\t\t\treturn\n\t\t\tprint('Unable to start a new backend, sorry :(')\n\t\telse:\n\t\t\tfrom libkiara import backend\n\t\t\tbackend.serve(_config)\n\ndef ping():\n\twah = False\n\tfor l in _send('- ping'):\n\t\tprint(l)\n\t\twah = l == 'pong'\n\treturn wah\n\ndef process(file, watch=False, organize=False):\n\tq = 'a'\n\tif watch:\n\t\tq += 'w'\n\tif organize:\n\t\tq += 'o'\n\t\n\tfor line in _send(q + ' ' + file):\n\t\tprint(line)\n\ndef find_duplicates():\n\tfor line in _send('- dups'):\n\t\tprint(line)\n\ndef forget(*fids):\n\tfor line in _send('- forget ' + ' '.join(list(map(str, fids)))):\n\t\tprint(line)\n","sub_path":"libkiara/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"180912846","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# 3rd party imports\nimport numpy as np\n\nfrom cdflib import CDF, cdfepoch\n\n# Local imports\nfrom ..pyrf import ts_skymap, iso86012datetime64, datetime642ttns\n\n__author__ = \"Louis Richard\"\n__email__ = \"louisr@irfu.se\"\n__copyright__ = \"Copyright 2020-2021\"\n__license__ = \"MIT\"\n__version__ = \"2.3.7\"\n__status__ = \"Prototype\"\n\n\ndef get_dist(file_path, cdf_name, tint):\n r\"\"\"Read field named cdf_name in file and convert to velocity distribution\n function.\n\n Parameters\n ----------\n file_path : str\n Path of the cdf file.\n cdf_name : str\n Name of the target variable in the cdf file.\n tint : list of str\n Time interval.\n\n Returns\n -------\n out : xarray.Dataset\n Time series of the velocity distribution function if the target\n specie in the selected time interval.\n\n \"\"\"\n\n tmmode = cdf_name.split(\"_\")[-1]\n\n tint = list(datetime642ttns(iso86012datetime64(np.array(tint))))\n\n with CDF(file_path) as f:\n if tmmode == \"brst\":\n depend0_key = f.varattsget(cdf_name)[\"DEPEND_0\"]\n depend1_key = f.varattsget(cdf_name)[\"DEPEND_1\"]\n depend2_key = f.varattsget(cdf_name)[\"DEPEND_2\"]\n depend3_key = f.varattsget(cdf_name)[\"DEPEND_3\"]\n\n t = f.varget(depend0_key, starttime=tint[0], endtime=tint[1])\n t = cdfepoch.to_datetime(t, to_np=True)\n\n if not t.size:\n return None\n\n dist = f.varget(cdf_name, starttime=tint[0], endtime=tint[1])\n dist = np.transpose(dist, [0, 3, 1, 2])\n ph = f.varget(depend1_key, starttime=tint[0], endtime=tint[1])\n th = f.varget(depend2_key)\n en = f.varget(depend3_key, starttime=tint[0], endtime=tint[1])\n\n en0_name = \"_\".join([cdf_name.split(\"_\")[0],\n cdf_name.split(\"_\")[1], \"energy0\",\n cdf_name.split(\"_\")[-1]])\n en1_name = \"_\".join([cdf_name.split(\"_\")[0],\n cdf_name.split(\"_\")[1], \"energy1\",\n cdf_name.split(\"_\")[-1]])\n d_en_name = \"_\".join([cdf_name.split(\"_\")[0],\n cdf_name.split(\"_\")[1], \"energy_delta\",\n cdf_name.split(\"_\")[-1]])\n e_step_table_name = \"_\".join([cdf_name.split(\"_\")[0],\n cdf_name.split(\"_\")[1],\n \"steptable_parity\",\n cdf_name.split(\"_\")[-1]])\n\n step_table = f.varget(e_step_table_name,\n starttime=tint[0], endtime=tint[1])\n if d_en_name in f.cdf_info()[\"zVariables\"]:\n delta_plus_var = f.varget(d_en_name,\n starttime=tint[0], endtime=tint[1])\n delta_minus_var = f.varget(d_en_name,\n starttime=tint[0], endtime=tint[1])\n\n if en0_name not in f.cdf_info()[\"zVariables\"]:\n energy0 = en[1, :]\n energy1 = en[0, :]\n else:\n energy0 = f.varget(en0_name)\n energy1 = f.varget(en1_name)\n\n res = ts_skymap(t, dist, None, ph, th, energy0=energy0,\n energy1=energy1, esteptable=step_table)\n\n if \"delta_plus_var\" in locals() and \"delta_minus_var\" in locals():\n res.attrs[\"delta_energy_minus\"] = delta_minus_var\n res.attrs[\"delta_energy_plus\"] = delta_plus_var\n\n res.attrs = {**res.attrs, **f.varattsget(cdf_name)}\n\n elif tmmode == \"fast\":\n depend0_key = f.varattsget(cdf_name)[\"DEPEND_0\"]\n depend1_key = f.varattsget(cdf_name)[\"DEPEND_1\"]\n depend2_key = f.varattsget(cdf_name)[\"DEPEND_2\"]\n depend3_key = f.varattsget(cdf_name)[\"DEPEND_3\"]\n\n t = f.varget(depend0_key, starttime=tint[0], endtime=tint[1])\n\n dist = f.varget(cdf_name, starttime=tint[0], endtime=tint[1])\n dist = np.transpose(dist, [0, 3, 1, 2])\n ph = f.varget(depend1_key)\n th = f.varget(depend2_key)\n en = f.varget(depend3_key, starttime=tint[0], endtime=tint[1])\n res = ts_skymap(t, dist, en, ph, th)\n\n for k in f.varattsget(cdf_name):\n res.attrs[k] = f.varattsget(cdf_name)[k]\n\n for k in f.cdf_info():\n res.attrs[k] = f.cdf_info()[k]\n\n res.attrs[\"tmmode\"] = tmmode\n if \"_dis_\" in cdf_name:\n res.attrs[\"species\"] = \"ions\"\n else:\n res.attrs[\"species\"] = \"electrons\"\n return res\n","sub_path":"pyrfu/mms/get_dist.py","file_name":"get_dist.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"329714925","text":"\nclass tree():\n def __init__(self):\n self.root=None\n\n def insert(self,node):\n end=False\n if self.root==None:\n self.root=node\n end=True\n present=self.root\n while end==False:\n if node.id>present.id and present.right_child==None:\n present.right_child=node\n end=True\n elif node.id>present.id and present.right_child!=None:\n present=present.right_child\n elif node.idcurrent.id:\n current=current.right_child\n else:\n current=current.left_child\n return current\n def change(self,node1,node2):\n node1.id=node2.id\n node1.name=node2.name\n node1.interest=node2.interest\n node1.gender=node2.gender\n node1.age=node2.age\n def delete(self, root, user):\n \n # Find the node to be deleted and remove it\n if not root:\n return root\n elif user.id < root.id:\n root.left_child = self.delete_node(root.left_child, user.id)\n elif user.id > root.id:\n root.right_child = self.delete_node(root.right_child, user.id)\n else:\n if root.left_child is None:\n temp = root.right_child\n root = None\n return temp\n elif root.right_child is None:\n temp = root.left_child\n root = None\n return temp\n temp = self.getMinValueNode(root.right_child)\n root.id = temp.id\n root.right_child = self.delete(root.right_child,\n temp.id)\n if root is None:\n return root\n def getMinValueNode(self, root):\n if root is None or root.left_child is None:\n return root\n return self.getMinValueNode(root.left_child)","sub_path":"server/btree.py","file_name":"btree.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"23588508","text":"#!/usr/bin/python\n\nimport ftplib\nimport re\nimport os\n\nHOST=r'alvspxw02'\nDIRN=r'8.5.4/'\n\nfile_filter=r'^sp_setup-\\d\\.\\d\\.\\d\\.\\d{3}\\.exe$'\n\n#ftp=ftplib.FTP(HOST)\nwith ftplib.FTP(HOST) as ftp:\n ftp.login('###\\###','###')\n ftp.cwd(DIRN)\n\n available_builds=[]\n for i in ftp.nlst():\n if re.match(file_filter,i):\n available_builds.append(i)\n\n latest_build=max(available_builds)\n\n if not os.path.exists(latest_build):\n ftp.retrbinary('RETR '+(latest_build),open(latest_build, 'wb').write)\n#ftp.close()\n\n","sub_path":"daily_get_build_windows.py","file_name":"daily_get_build_windows.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"645180743","text":"# Copyright 2018 the rules_flex authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# SPDX-License-Identifier: Apache-2.0\n\nTOOLCHAIN_TYPE = \"@rules_flex//flex:toolchain_type\"\n\nToolchainInfo = provider(fields = [\"files\", \"vars\", \"flex_executable\", \"flex_lexer_h\"])\n\ndef _flex_toolchain_info(ctx):\n toolchain = ToolchainInfo(\n flex_executable = ctx.executable.flex,\n flex_lexer_h = ctx.file.flex_lexer_h,\n files = depset(direct = [ctx.executable.flex]),\n vars = {\"FLEX\": ctx.executable.flex.path},\n )\n return [\n platform_common.ToolchainInfo(flex_toolchain = toolchain),\n platform_common.TemplateVariableInfo(toolchain.vars),\n ]\n\nflex_toolchain_info = rule(\n _flex_toolchain_info,\n attrs = {\n \"flex\": attr.label(\n mandatory = True,\n executable = True,\n cfg = \"host\",\n ),\n \"flex_lexer_h\": attr.label(\n mandatory = True,\n allow_single_file = [\".h\"],\n ),\n },\n provides = [\n platform_common.ToolchainInfo,\n platform_common.TemplateVariableInfo,\n ],\n)\n\ndef _flex_toolchain_alias(ctx):\n toolchain = ctx.toolchains[TOOLCHAIN_TYPE].flex_toolchain\n return [\n DefaultInfo(files = toolchain.files),\n toolchain,\n platform_common.TemplateVariableInfo(toolchain.vars),\n ]\n\nflex_toolchain_alias = rule(\n _flex_toolchain_alias,\n toolchains = [TOOLCHAIN_TYPE],\n provides = [\n DefaultInfo,\n ToolchainInfo,\n platform_common.TemplateVariableInfo,\n ],\n)\n","sub_path":"flex/internal/toolchain.bzl","file_name":"toolchain.bzl","file_ext":"bzl","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"428698540","text":"\"\"\"\nQuest:\n You are given coins of different denominations and a total amount of money amount.\n Write a function to compute the fewest number of coins that you need to make up that amount.\n If that amount of money cannot be made up by any combination of the coins, return -1.\n\n Example 1:\n Input: coins = [1, 2, 5], amount = 11\n Output: 3\n Explanation: 11 = 5 + 5 + 1\n\n Example 2:\n Input: coins = [2], amount = 3\n Output: -1\n Note:\n You may assume that you have an infinite number of each kind of coin.\n\nSolution:\n - sort the coin array, then start from the largest?\n - no, because this is not real coins [1, 5, 10, 50, 100] like this, so we cannot use greedy algorithm\n - for a list [] of length = amount, each list[i] represent the least coins we need to reach the total i amount\n then for the next list[i+1], we could use dp to search 0 - i and get the result\n\"\"\"\n\n\nclass Solution:\n def coinChange(self, coins: 'List[int]', amount: 'int') -> 'int':\n MAX = float('inf') # here MAX means no solution to reach the amount i\n dp = [0] + [MAX] * amount\n\n for i in range(1, amount+1):\n dp[i] = min([dp[i-c] if i - c >= 0 else MAX for c in coins]) + 1\n\n return dp[amount] if dp[amount] != MAX else -1\n\n\ntest = Solution()\ncoins = [2]\namount = 3\nprint(test.coinChange(coins, amount))","sub_path":"coinChange_re.py","file_name":"coinChange_re.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"397274575","text":"import itertools\nimport json\nimport logging\nimport os\nimport numpy as np\nimport pandas as pd\nfrom scipy.special import expit, softmax\n\nimport torch\nfrom transformers.modeling_bert import BertForPreTraining, BertLayerNorm, ACT2FN, BertForQuestionAnswering\n\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss\n\nfrom farm.data_handler.utils import is_json\nfrom farm.utils import convert_iob_to_simple_tags\n\nlogger = logging.getLogger(__name__)\n\n\nclass PredictionHead(nn.Module):\n \"\"\" Takes word embeddings from a language model and generates logits for a given task. Can also convert logits\n to loss and and logits to predictions. \"\"\"\n\n subclasses = {}\n\n def __init_subclass__(cls, **kwargs):\n \"\"\" This automatically keeps track of all available subclasses.\n Enables generic load() for all specific PredictionHead implementation.\n \"\"\"\n super().__init_subclass__(**kwargs)\n cls.subclasses[cls.__name__] = cls\n\n @classmethod\n def create(cls, prediction_head_name, layer_dims, class_weights=None):\n \"\"\"\n Create subclass of Prediction Head.\n\n :param prediction_head_name: Classname (exact string!) of prediction head we want to create\n :type prediction_head_name: str\n :param layer_dims: describing the feed forward block structure, e.g. [768,2]\n :type layer_dims: List[Int]\n :param class_weights: The loss weighting to be assigned to certain label classes during training.\n Used to correct cases where there is a strong class imbalance.\n :type class_weights: list[Float]\n :return: Prediction Head of class prediction_head_name\n \"\"\"\n # TODO make we want to make this more generic.\n # 1. Class weights is not relevant for all heads.\n # 2. Layer weights impose FF structure, maybe we want sth else later\n # Solution: We could again use **kwargs\n return cls.subclasses[prediction_head_name](\n layer_dims=layer_dims, class_weights=class_weights\n )\n\n def save_config(self, save_dir, head_num=0):\n \"\"\"\n Saves the config as a json file.\n\n :param save_dir: Path to save config to\n :type save_dir: str\n :param head_num: Which head to save\n :type head_num: int\n \"\"\"\n output_config_file = os.path.join(\n save_dir, f\"prediction_head_{head_num}_config.json\"\n )\n with open(output_config_file, \"w\") as file:\n json.dump(self.config, file)\n\n def save(self, save_dir, head_num=0):\n \"\"\"\n Saves the prediction head state dict.\n\n :param save_dir: path to save prediction head to\n :type save_dir: str\n :param head_num: which head to save\n :type head_num: int\n \"\"\"\n output_model_file = os.path.join(save_dir, f\"prediction_head_{head_num}.bin\")\n torch.save(self.state_dict(), output_model_file)\n self.save_config(save_dir, head_num)\n\n def generate_config(self):\n \"\"\"\n Generates config file from Class parameters (only for sensible config parameters).\n \"\"\"\n config = {}\n for key, value in self.__dict__.items():\n if is_json(value) and key[0] != \"_\":\n config[key] = value\n config[\"name\"] = self.__class__.__name__\n self.config = config\n\n @classmethod\n def load(cls, config_file):\n \"\"\"\n Loads a Prediction Head. Infers the class of prediction head from config_file.\n\n :param config_file: location where corresponding config is stored\n :type config_file: str\n :return: PredictionHead\n :rtype: PredictionHead[T]\n \"\"\"\n config = json.load(open(config_file))\n prediction_head = cls.subclasses[config[\"name\"]](**config)\n model_file = cls._get_model_file(config_file=config_file)\n logger.info(\"Loading prediction head from {}\".format(model_file))\n prediction_head.load_state_dict(torch.load(model_file, map_location=torch.device(\"cpu\")))\n return prediction_head\n\n def logits_to_loss(self, logits, labels):\n \"\"\"\n Implement this function in your special Prediction Head.\n Should combine logits and labels with a loss fct to a per sample loss.\n\n :param logits: logits, can vary in shape and type, depending on task\n :type logits: object\n :param labels: labels, can vary in shape and type, depending on task\n :type labels: object\n :return: per sample loss as a torch.tensor of shape [batch_size]\n \"\"\"\n raise NotImplementedError()\n\n def logits_to_preds(self, logits):\n \"\"\"\n Implement this function in your special Prediction Head.\n Should combine turn logits into predictions.\n\n :param logits: logits, can vary in shape and type, depending on task\n :type logits: object\n :return: predictions as a torch.tensor of shape [batch_size]\n \"\"\"\n raise NotImplementedError()\n\n def prepare_labels(self, **kwargs):\n \"\"\"\n Some prediction heads need additional label conversion.\n E.g. NER needs word level labels turned into subword token level labels.\n\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n :return: labels in the right format\n :rtype: object\n \"\"\"\n # TODO maybe just return **kwargs to not force people to implement this\n raise NotImplementedError()\n\n @classmethod\n def _get_model_file(cls, config_file):\n if \"config.json\" in config_file and \"prediction_head\" in config_file:\n head_num = int(\"\".join([char for char in os.path.basename(config_file) if char.isdigit()]))\n model_file = os.path.join(os.path.dirname(config_file), f\"prediction_head_{head_num}.bin\")\n else:\n raise ValueError(f\"This doesn't seem to be a proper prediction_head config file: '{config_file}'\")\n return model_file\n\n def _set_name(self, name):\n self.task_name = name\n\n\nclass RegressionHead(PredictionHead):\n def __init__(\n self,\n layer_dims,\n task_name=\"regression\",\n **kwargs,\n ):\n super(RegressionHead, self).__init__()\n # num_labels could in most cases also be automatically retrieved from the data processor\n self.layer_dims = layer_dims\n self.feed_forward = FeedForwardBlock(self.layer_dims)\n self.num_labels = 2\n self.ph_output_type = \"per_sequence_continuous\"\n self.model_type = \"regression\"\n self.loss_fct = MSELoss(reduction=\"none\")\n self.task_name = task_name\n self.generate_config()\n\n def forward(self, x):\n logits = self.feed_forward(x)\n return logits\n\n def logits_to_loss(self, logits, **kwargs):\n # Squeeze the logits to obtain a coherent output size\n label_ids = kwargs.get(self.label_tensor_name)\n return self.loss_fct(logits.squeeze(), label_ids.float())\n\n def logits_to_preds(self, logits, **kwargs):\n preds = logits.cpu().numpy()\n #rescale predictions to actual label distribution\n preds = [x * self.label_list[1] + self.label_list[0] for x in preds]\n return preds\n\n def prepare_labels(self, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n label_ids = label_ids.cpu().numpy()\n label_ids = [x * self.label_list[1] + self.label_list[0] for x in label_ids]\n return label_ids\n\n def formatted_preds(self, logits, samples, **kwargs):\n preds = self.logits_to_preds(logits)\n contexts = [sample.clear_text[\"text\"] for sample in samples]\n\n assert len(preds) == len(contexts)\n\n res = {\"task\": \"regression\", \"predictions\": []}\n for pred, context in zip(preds, contexts):\n res[\"predictions\"].append(\n {\n \"context\": f\"{context}\",\n \"pred\": pred[0]\n }\n )\n return res\n\n\nclass TextClassificationHead(PredictionHead):\n def __init__(\n self,\n layer_dims,\n class_weights=None,\n loss_ignore_index=-100,\n loss_reduction=\"none\",\n task_name=\"text_classification\",\n **kwargs,\n ):\n super(TextClassificationHead, self).__init__()\n # num_labels could in most cases also be automatically retrieved from the data processor\n self.layer_dims = layer_dims\n self.feed_forward = FeedForwardBlock(self.layer_dims)\n self.num_labels = self.layer_dims[-1]\n self.ph_output_type = \"per_sequence\"\n self.model_type = \"text_classification\"\n self.task_name = task_name #used for connecting with the right output of the processor\n self.class_weights = class_weights\n\n if class_weights:\n logger.info(f\"Using class weights for task '{self.task_name}': {self.class_weights}\")\n balanced_weights = nn.Parameter(torch.tensor(class_weights), requires_grad=False)\n else:\n balanced_weights = None\n\n self.loss_fct = CrossEntropyLoss(\n weight=balanced_weights,\n reduction=loss_reduction,\n ignore_index=loss_ignore_index,\n )\n\n self.generate_config()\n\n def forward(self, X):\n logits = self.feed_forward(X)\n return logits\n\n def logits_to_loss(self, logits, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n return self.loss_fct(logits, label_ids.view(-1))\n\n def logits_to_probs(self, logits, return_class_probs, **kwargs):\n softmax = torch.nn.Softmax(dim=1)\n probs = softmax(logits)\n if return_class_probs:\n probs = probs\n else:\n probs = torch.max(probs, dim=1)[0]\n probs = probs.cpu().numpy()\n return probs\n\n def logits_to_preds(self, logits, **kwargs):\n logits = logits.cpu().numpy()\n pred_ids = logits.argmax(1)\n preds = [self.label_list[int(x)] for x in pred_ids]\n return preds\n\n def prepare_labels(self, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n label_ids = label_ids.cpu().numpy()\n labels = [self.label_list[int(x)] for x in label_ids]\n return labels\n\n def formatted_preds(self, logits, samples, return_class_probs=False, **kwargs):\n preds = self.logits_to_preds(logits)\n probs = self.logits_to_probs(logits, return_class_probs)\n contexts = [sample.clear_text[\"text\"] for sample in samples]\n\n assert len(preds) == len(probs) == len(contexts)\n\n res = {\"task\": \"text_classification\", \"predictions\": []}\n for pred, prob, context in zip(preds, probs, contexts):\n if not return_class_probs:\n pred_dict = {\n \"start\": None,\n \"end\": None,\n \"context\": f\"{context}\",\n \"label\": f\"{pred}\",\n \"probability\": prob,\n }\n else:\n pred_dict = {\n \"start\": None,\n \"end\": None,\n \"context\": f\"{context}\",\n \"label\": \"class_probabilities\",\n \"probability\": prob,\n }\n\n res[\"predictions\"].append(pred_dict)\n return res\n\n\nclass MultiLabelTextClassificationHead(PredictionHead):\n def __init__(\n self,\n layer_dims,\n class_weights=None,\n loss_reduction=\"none\",\n task_name=\"text_classification\",\n pred_threshold=0.5,\n **kwargs,\n ):\n super(MultiLabelTextClassificationHead, self).__init__()\n # num_labels could in most cases also be automatically retrieved from the data processor\n self.layer_dims = layer_dims\n self.feed_forward = FeedForwardBlock(self.layer_dims)\n self.num_labels = self.layer_dims[-1]\n self.ph_output_type = \"per_sequence\"\n self.model_type = \"multilabel_text_classification\"\n self.task_name = task_name #used for connecting with the right output of the processor\n self.class_weights = class_weights\n self.pred_threshold = pred_threshold\n\n if class_weights:\n logger.info(f\"Using class weights for task '{self.task_name}': {self.class_weights}\")\n #TODO must balanced weight really be a instance attribute?\n self.balanced_weights = nn.Parameter(\n torch.tensor(class_weights), requires_grad=False\n )\n else:\n self.balanced_weights = None\n\n self.loss_fct = BCEWithLogitsLoss(pos_weight=self.balanced_weights,\n reduction=loss_reduction)\n\n self.generate_config()\n\n def forward(self, X):\n logits = self.feed_forward(X)\n return logits\n\n def logits_to_loss(self, logits, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name).to(dtype=torch.float)\n loss = self.loss_fct(logits.view(-1, self.num_labels), label_ids.view(-1, self.num_labels))\n per_sample_loss = loss.mean(1)\n return per_sample_loss\n\n def logits_to_probs(self, logits, **kwargs):\n sigmoid = torch.nn.Sigmoid()\n probs = sigmoid(logits)\n probs = probs.cpu().numpy()\n return probs\n\n def logits_to_preds(self, logits, **kwargs):\n probs = self.logits_to_probs(logits)\n #TODO we could potentially move this to GPU to speed it up\n pred_ids = [np.where(row > self.pred_threshold)[0] for row in probs]\n preds = []\n for row in pred_ids:\n preds.append([self.label_list[int(x)] for x in row])\n return preds\n\n def prepare_labels(self, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n label_ids = label_ids.cpu().numpy()\n label_ids = [np.where(row == 1)[0] for row in label_ids]\n labels = []\n for row in label_ids:\n labels.append([self.label_list[int(x)] for x in row])\n return labels\n\n def formatted_preds(self, logits, samples, **kwargs):\n preds = self.logits_to_preds(logits)\n probs = self.logits_to_probs(logits)\n contexts = [sample.clear_text[\"text\"] for sample in samples]\n\n assert len(preds) == len(probs) == len(contexts)\n\n res = {\"task\": \"text_classification\", \"predictions\": []}\n for pred, prob, context in zip(preds, probs, contexts):\n res[\"predictions\"].append(\n {\n \"start\": None,\n \"end\": None,\n \"context\": f\"{context}\",\n \"label\": f\"{pred}\",\n \"probability\": prob,\n }\n )\n return res\n\n\nclass TokenClassificationHead(PredictionHead):\n def __init__(self, layer_dims, task_name=\"ner\", **kwargs):\n super(TokenClassificationHead, self).__init__()\n\n self.layer_dims = layer_dims\n self.feed_forward = FeedForwardBlock(self.layer_dims)\n self.num_labels = self.layer_dims[-1]\n self.loss_fct = CrossEntropyLoss(reduction=\"none\")\n self.ph_output_type = \"per_token\"\n self.model_type = \"token_classification\"\n self.task_name = task_name\n self.generate_config()\n\n def forward(self, X):\n logits = self.feed_forward(X)\n return logits\n\n def logits_to_loss(\n self, logits, initial_mask, padding_mask=None, **kwargs\n ):\n label_ids = kwargs.get(self.label_tensor_name)\n\n # Todo: should we be applying initial mask here? Loss is currently calculated even on non initial tokens\n active_loss = padding_mask.view(-1) == 1\n active_logits = logits.view(-1, self.num_labels)[active_loss]\n active_labels = label_ids.view(-1)[active_loss]\n\n loss = self.loss_fct(\n active_logits, active_labels\n ) # loss is a 1 dimemnsional (active) token loss\n return loss\n\n def logits_to_preds(self, logits, initial_mask, **kwargs):\n preds_word_all = []\n preds_tokens = torch.argmax(logits, dim=2)\n preds_token = preds_tokens.detach().cpu().numpy()\n # used to be: padding_mask = padding_mask.detach().cpu().numpy()\n initial_mask = initial_mask.detach().cpu().numpy()\n\n for idx, im in enumerate(initial_mask):\n preds_t = preds_token[idx]\n # Get labels and predictions for just the word initial tokens\n preds_word_id = self.initial_token_only(preds_t, initial_mask=im)\n preds_word = [self.label_list[pwi] for pwi in preds_word_id]\n preds_word_all.append(preds_word)\n return preds_word_all\n\n def logits_to_probs(self, logits, initial_mask, return_class_probs, **kwargs):\n # get per token probs\n softmax = torch.nn.Softmax(dim=2)\n token_probs = softmax(logits)\n if return_class_probs:\n token_probs = token_probs\n else:\n token_probs = torch.max(token_probs, dim=2)[0]\n token_probs = token_probs.cpu().numpy()\n\n # convert to per word probs\n all_probs = []\n initial_mask = initial_mask.detach().cpu().numpy()\n for idx, im in enumerate(initial_mask):\n probs_t = token_probs[idx]\n probs_words = self.initial_token_only(probs_t, initial_mask=im)\n all_probs.append(probs_words)\n return all_probs\n\n def prepare_labels(self, initial_mask, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n labels_all = []\n label_ids = label_ids.cpu().numpy()\n for label_ids_one_sample, initial_mask_one_sample in zip(\n label_ids, initial_mask\n ):\n label_ids = self.initial_token_only(\n label_ids_one_sample, initial_mask_one_sample\n )\n labels = [self.label_list[l] for l in label_ids]\n labels_all.append(labels)\n return labels_all\n\n @staticmethod\n def initial_token_only(seq, initial_mask):\n ret = []\n for init, s in zip(initial_mask, seq):\n if init:\n ret.append(s)\n return ret\n\n def formatted_preds(self, logits, initial_mask, samples, return_class_probs=False, **kwargs):\n preds = self.logits_to_preds(logits, initial_mask)\n probs = self.logits_to_probs(logits, initial_mask,return_class_probs)\n\n # align back with original input by getting the original word spans\n spans = []\n for sample, sample_preds in zip(samples, preds):\n word_spans = []\n span = None\n for token, offset, start_of_word in zip(\n sample.tokenized[\"tokens\"],\n sample.tokenized[\"offsets\"],\n sample.tokenized[\"start_of_word\"],\n ):\n if start_of_word:\n # previous word has ended unless it's the very first word\n if span is not None:\n word_spans.append(span)\n span = {\"start\": offset, \"end\": offset + len(token)}\n else:\n # expand the span to include the subword-token\n span[\"end\"] = offset + len(token.replace(\"##\", \"\"))\n word_spans.append(span)\n spans.append(word_spans)\n\n assert len(preds) == len(probs) == len(spans)\n\n res = {\"task\": \"ner\", \"predictions\": []}\n for preds_seq, probs_seq, sample, spans_seq in zip(\n preds, probs, samples, spans\n ):\n tags, spans_seq = convert_iob_to_simple_tags(preds_seq, spans_seq)\n seq_res = []\n for tag, prob, span in zip(tags, probs_seq, spans_seq):\n context = sample.clear_text[\"text\"][span[\"start\"] : span[\"end\"]]\n seq_res.append(\n {\n \"start\": span[\"start\"],\n \"end\": span[\"end\"],\n \"context\": f\"{context}\",\n \"label\": f\"{tag}\",\n \"probability\": prob,\n }\n )\n res[\"predictions\"].extend(seq_res)\n return res\n\n\nclass BertLMHead(PredictionHead):\n def __init__(self, hidden_size, vocab_size, hidden_act=\"gelu\", task_name=\"lm\", **kwargs):\n super(BertLMHead, self).__init__()\n\n self.hidden_size = hidden_size\n self.hidden_act = hidden_act\n self.vocab_size = vocab_size\n self.loss_fct = CrossEntropyLoss(reduction=\"none\", ignore_index=-1)\n self.num_labels = vocab_size # vocab size\n # TODO Check if weight init needed!\n # self.apply(self.init_bert_weights)\n self.ph_output_type = \"per_token\"\n\n self.model_type = \"language_modelling\"\n self.task_name = task_name\n self.generate_config()\n\n # NN Layers\n # this is the \"transform\" module in the pytorch-transformers repo\n self.dense = nn.Linear(self.hidden_size, self.hidden_size)\n self.transform_act_fn = ACT2FN[self.hidden_act]\n self.LayerNorm = BertLayerNorm(self.hidden_size, eps=1e-12)\n\n # this is the \"decoder\" in the pytorch-transformers repo\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(hidden_size,\n vocab_size,\n bias=False)\n self.bias = nn.Parameter(torch.zeros(vocab_size))\n\n @classmethod\n def load(cls, pretrained_model_name_or_path):\n\n if os.path.exists(pretrained_model_name_or_path) \\\n and \"config.json\" in pretrained_model_name_or_path \\\n and \"prediction_head\" in pretrained_model_name_or_path:\n config_file = os.path.exists(pretrained_model_name_or_path)\n # a) FARM style\n model_file = cls._get_model_file(config_file)\n config = json.load(open(config_file))\n prediction_head = cls(**config)\n logger.info(\"Loading prediction head from {}\".format(model_file))\n prediction_head.load_state_dict(torch.load(model_file, map_location=torch.device(\"cpu\")))\n else:\n # b) pytorch-transformers style\n # load weights from bert model\n # (we might change this later to load directly from a state_dict to generalize for other language models)\n bert_with_lm = BertForPreTraining.from_pretrained(pretrained_model_name_or_path)\n\n # init empty head\n head = cls(hidden_size=bert_with_lm.config.hidden_size,\n vocab_size=bert_with_lm.config.vocab_size,\n hidden_act=bert_with_lm.config.hidden_act)\n\n # load weights\n head.dense.load_state_dict(bert_with_lm.cls.predictions.transform.dense.state_dict())\n head.LayerNorm.load_state_dict(bert_with_lm.cls.predictions.transform.LayerNorm.state_dict())\n\n head.decoder.load_state_dict(bert_with_lm.cls.predictions.decoder.state_dict())\n head.bias.data.copy_(bert_with_lm.cls.predictions.bias)\n del bert_with_lm\n\n return head\n\n def set_shared_weights(self, shared_embedding_weights):\n self.decoder.weight = shared_embedding_weights\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.transform_act_fn(hidden_states)\n hidden_states = self.LayerNorm(hidden_states)\n lm_logits = self.decoder(hidden_states) + self.bias\n return lm_logits\n\n def logits_to_loss(self, logits, **kwargs):\n lm_label_ids = kwargs.get(self.label_tensor_name)\n batch_size = lm_label_ids.shape[0]\n masked_lm_loss = self.loss_fct(\n logits.view(-1, self.num_labels), lm_label_ids.view(-1)\n )\n per_sample_loss = masked_lm_loss.view(-1, batch_size).mean(dim=0)\n return per_sample_loss\n\n def logits_to_preds(self, logits, **kwargs):\n logits = logits.cpu().numpy()\n lm_label_ids = kwargs.get(self.label_tensor_name).cpu().numpy()\n lm_preds_ids = logits.argmax(2)\n # apply mask to get rid of predictions for non-masked tokens\n assert lm_preds_ids.shape == lm_label_ids.shape\n lm_preds_ids[lm_label_ids == -1] = -1\n lm_preds_ids = lm_preds_ids.tolist()\n preds = []\n # we have a batch of sequences here. we need to convert for each token in each sequence.\n for pred_ids_for_sequence in lm_preds_ids:\n preds.append(\n [self.label_list[int(x)] for x in pred_ids_for_sequence if int(x) != -1]\n )\n return preds\n\n def prepare_labels(self, **kwargs):\n label_ids = kwargs.get(self.label_tensor_name)\n label_ids = label_ids.cpu().numpy().tolist()\n labels = []\n # we have a batch of sequences here. we need to convert for each token in each sequence.\n for ids_for_sequence in label_ids:\n labels.append([self.label_list[int(x)] for x in ids_for_sequence if int(x) != -1])\n return labels\n\n\nclass NextSentenceHead(TextClassificationHead):\n \"\"\"\n Almost identical to a TextClassificationHead. Only difference: we can load the weights from\n a pretrained language model that was saved in the pytorch-transformers style (all in one model).\n \"\"\"\n @classmethod\n def load(cls, pretrained_model_name_or_path):\n\n if os.path.exists(pretrained_model_name_or_path) \\\n and \"config.json\" in pretrained_model_name_or_path \\\n and \"prediction_head\" in pretrained_model_name_or_path:\n config_file = os.path.exists(pretrained_model_name_or_path)\n # a) FARM style\n #TODO validate saving/loading after switching to processor.tasks\n model_file = cls._get_model_file(config_file)\n config = json.load(open(config_file))\n prediction_head = cls(**config)\n logger.info(\"Loading prediction head from {}\".format(model_file))\n prediction_head.load_state_dict(torch.load(model_file, map_location=torch.device(\"cpu\")))\n else:\n # b) pytorch-transformers style\n # load weights from bert model\n # (we might change this later to load directly from a state_dict to generalize for other language models)\n bert_with_lm = BertForPreTraining.from_pretrained(pretrained_model_name_or_path)\n\n # init empty head\n head = cls(layer_dims=[bert_with_lm.config.hidden_size, 2], loss_ignore_index=-1, task_name=\"nextsentence\")\n\n # load weights\n head.feed_forward.feed_forward[0].load_state_dict(bert_with_lm.cls.seq_relationship.state_dict())\n del bert_with_lm\n\n return head\n\nclass FeedForwardBlock(nn.Module):\n \"\"\" A feed forward neural network of variable depth and width. \"\"\"\n\n def __init__(self, layer_dims, **kwargs):\n # Todo: Consider having just one input argument\n super(FeedForwardBlock, self).__init__()\n\n # If read from config the input will be string\n n_layers = len(layer_dims) - 1\n layers_all = []\n # TODO: IS this needed?\n self.output_size = layer_dims[-1]\n\n for i in range(n_layers):\n size_in = layer_dims[i]\n size_out = layer_dims[i + 1]\n layer = nn.Linear(size_in, size_out)\n layers_all.append(layer)\n self.feed_forward = nn.Sequential(*layers_all)\n\n def forward(self, X):\n logits = self.feed_forward(X)\n return logits\n\n\nclass QuestionAnsweringHead(PredictionHead):\n \"\"\"\n A question answering head predicts the start and end of the answer on token level.\n \"\"\"\n\n def __init__(self,\n layer_dims,\n task_name=\"question_answering\",\n no_answer_shift=0,\n top_n_predictions=3,\n context_size=100,\n **kwargs):\n \"\"\"\n :param layer_dims: dimensions of Feed Forward block, e.g. [768,2], for adjusting to BERT embedding. Output should be always 2\n :type layer_dims: List[Int]\n :param task_name: Name of task\n :type task_name: str\n :param no_answer_shift: How much we want to weight giving no answer compared to text answer\n We actually compare in logit space (sum of both start and end logit), so negative\n values result in less no answer predictions and vice versa. normal range = [-5,+5]\n :type no_answer_shift: int\n :param top_n_predictions: When we split a document into multiple passages we can return top n passage answers\n :type top_n_predictions: int\n :param context_size: When we format predictions back to string space we also return surrounding context\n of size context_size\n :type context_size: int\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n \"\"\"\n super(QuestionAnsweringHead, self).__init__()\n self.layer_dims = layer_dims\n self.feed_forward = FeedForwardBlock(self.layer_dims)\n self.num_labels = self.layer_dims[-1]\n self.ph_output_type = \"per_token_squad\"\n self.model_type = (\n \"span_classification\"\n ) # predicts start and end token of answer\n self.task_name = task_name\n self.no_answer_shift = no_answer_shift # how much we want to upweight no answer logit scores compared to text answer ones\n self.top_n_predictions = top_n_predictions #for how many passages we want to get predictions\n self.context_size = context_size\n self.max_ans_len = 1000 # disabling max ans len. Impact on squad performance seems minor\n # each answer is returned with surrounding context. In # characters surrounding the answer\n self.generate_config()\n\n\n @classmethod\n def load(cls, pretrained_model_name_or_path):\n \"\"\"\n Almost identical to a QuestionAnsweringHead. Only difference: we can load the weights from\n a pretrained language model that was saved in the pytorch-transformers style (all in one model).\n \"\"\"\n\n if os.path.exists(pretrained_model_name_or_path) \\\n and \"config.json\" in pretrained_model_name_or_path \\\n and \"prediction_head\" in pretrained_model_name_or_path:\n config_file = os.path.exists(pretrained_model_name_or_path)\n # a) FARM style\n model_file = cls._get_model_file(config_file)\n config = json.load(open(config_file))\n prediction_head = cls(**config)\n logger.info(\"Loading prediction head from {}\".format(model_file))\n prediction_head.load_state_dict(torch.load(model_file, map_location=torch.device(\"cpu\")))\n else:\n # b) pytorch-transformers style\n # load weights from bert model\n # (we might change this later to load directly from a state_dict to generalize for other language models)\n bert_qa = BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)\n\n # init empty head\n head = cls(layer_dims=[bert_qa.config.hidden_size, 2], loss_ignore_index=-1, task_name=\"question_answering\")\n # load weights\n head.feed_forward.feed_forward[0].load_state_dict(bert_qa.qa_outputs.state_dict())\n del bert_qa\n\n return head\n\n def forward(self, X):\n \"\"\"\n One forward pass through the prediction head model, starting with language model output on token level\n\n :param X: Output of language model, of shape [batch_size, seq_length, LM_embedding_dim]\n :type X: torch.tensor\n :return: (start_logits, end_logits), logits for the start and end of answer\n :rtype: tuple[torch.tensor,torch.tensor]\n \"\"\"\n logits = self.feed_forward(X)\n start_logits, end_logits = logits.split(1, dim=-1)\n start_logits = start_logits.squeeze(-1)\n end_logits = end_logits.squeeze(-1)\n return (start_logits, end_logits)\n\n def logits_to_loss(self, logits, start_position, end_position, **kwargs):\n \"\"\"\n Combine predictions and labels to a per sample loss.\n\n :param logits: (start_logits, end_logits), logits for the start and end of answer\n :type logits: tuple[torch.tensor,torch.tensor]\n :param start_position: tensor with indices of START positions per sample\n :type start_position: torch.tensor\n :param end_position: tensor with indices of END positions per sample\n :type end_position: torch.tensor\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n :return: per_sample_loss: Per sample loss : )\n :rtype: torch.tensor\n \"\"\"\n (start_logits, end_logits) = logits\n\n if len(start_position.size()) > 1:\n start_position = start_position.squeeze(-1)\n if len(end_position.size()) > 1:\n end_position = end_position.squeeze(-1)\n # sometimes the start/end positions (the labels read from file) are outside our model predictions, we ignore these terms\n # TODO check if ignored_index is needed. We are checking for start + end validity during construction of samples\n ignored_index = start_logits.size(1)\n start_position.clamp_(0, ignored_index)\n end_position.clamp_(0, ignored_index)\n\n loss_fct = CrossEntropyLoss(ignore_index=ignored_index, reduction=\"none\")\n start_loss = loss_fct(start_logits, start_position)\n end_loss = loss_fct(end_logits, end_position)\n per_sample_loss = (start_loss + end_loss) / 2\n return per_sample_loss\n\n def logits_to_preds(self, logits, **kwargs):\n \"\"\"\n Get the predicted index of start and end token of the answer.\n\n :param logits: (start_logits, end_logits), logits for the start and end of answer\n :type logits: tuple[torch.tensor,torch.tensor]\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n :return: (start_idx, end_idx), start and end indices for all samples in batch\n :rtype: (torch.tensor,torch.tensor)\n \"\"\"\n\n # cast data into useful types/shapes\n (start_logits, end_logits) = logits\n start_logits = start_logits.cpu().numpy()\n end_logits = end_logits.cpu().numpy()\n num_per_batch = start_logits.shape[0]\n segment_ids = kwargs['segment_ids'].data.cpu().numpy()\n sample_ids = kwargs[\"sample_id\"].cpu().numpy()\n passage_shifts = kwargs[\"passage_shift\"].cpu().numpy()\n\n question_shifts = np.argmax(segment_ids > 0,axis=1)\n\n\n no_answer_sum = start_logits[:,0] + end_logits[:,0]\n best_answer_sum = np.zeros(num_per_batch)\n # check if start or end point to the context. Context starts at segment id == 1 (question comes before at segment ids == 0)\n context_start = np.argmax(segment_ids,axis=1)\n context_end = segment_ids.shape[1] - np.argmax(segment_ids[::-1],axis=1)\n start_proposals = self._get_best_textanswer_indices(start_logits, 3)\n end_proposals = self._get_best_textanswer_indices(end_logits, 3)\n best_indices = np.zeros((num_per_batch,2),dtype=int)\n for i_batch in range(num_per_batch):\n # for each sample create mesh of possible start + end combinations and their score as sum of logits\n mesh_idx = np.meshgrid(start_proposals[i_batch,:],end_proposals[i_batch])\n start_comb = mesh_idx[0].flatten()\n end_comb = mesh_idx[1].flatten()\n scores = start_logits[i_batch,start_comb] + end_logits[i_batch,end_comb]\n #iterate over combinations and eliminate impossible ones\n for idx in np.argsort(scores)[::-1]:\n start = start_comb[idx]\n end = end_comb[idx]\n if start < context_start[i_batch]:\n continue\n if end > context_end[i_batch]:\n continue\n if start > end:\n continue\n if(end - start > self.max_ans_len):\n continue\n # maybe need check weather start/end idx refers to start of word and not to a ##... continuation\n\n best_indices[i_batch,0] = start\n best_indices[i_batch,1] = end\n best_answer_sum[i_batch] = scores[idx]\n break # since we take most likely predictions first, we stop when finding a valid prediction\n\n # For each predicted text answer, we want to check weather this question could also be unanswerable with\n # the given passage\n # we need to compare the text answer logits with no-answer logits (at position 0)\n idx_no_answer = no_answer_sum + self.no_answer_shift >= best_answer_sum\n best_indices[idx_no_answer,:] = 0\n best_answer_sum[idx_no_answer] = no_answer_sum[idx_no_answer]\n\n # #probabilities computed through softmaxing logits. Currently unused in downstream code.\n # start_probs = softmax(start_logits, axis=1)\n # end_probs = softmax(end_logits, axis=1)\n # probabilities = (start_probs[range(best_indices.shape[0]), np.squeeze(best_indices[:, 0])] +\n # end_probs[range(best_indices.shape[0]), np.squeeze(best_indices[:, 1])]) / 2\n\n return (best_indices[:, 0], best_indices[:, 1], best_answer_sum, sample_ids, question_shifts, passage_shifts)\n\n def prepare_labels(self, start_position, end_position, **kwargs):\n \"\"\"\n We want to pack labels into a tuple, to be compliant with later functions\n\n :param start_position: indices of answer start positions (in token space)\n :type start_position: torch.tensor\n :param end_position: indices of answer end positions (in token space)\n :type end_position: torch.tensor\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n :return: tuplefied sample_id with corresponding positions\n :rtype: tuple(torch.tensor, torch.tensor,torch.tensor)\n \"\"\"\n return (kwargs[\"sample_id\"], start_position, end_position)\n\n def formatted_preds(self, logits, preds, samples):\n \"\"\"\n Format predictions into actual answer strings (substrings of context). Used for Inference!\n\n :param logits: palceholder to comply with LanguageModel.formatted_preds\n :type logits: None\n :param preds: predictions for each passage, coming from logits_to_preds()\n contains start_idxs, end_idxs, logit_sums, sample_ids, question_shifts, passage_shifts, probabilities\n :type preds: tuple( 7 numpy arrays )\n :param samples: converted samples, to get a hook onto the actual text\n :type samples: List[FARM.data_handler.samples.Sample]\n :param kwargs: placeholder for passing generic parameters\n :type kwargs: object\n :return: Answers to the (ultimate) questions\n :rtype: list(str)\n \"\"\"\n\n all_preds_passage_aggregated = self._aggregate_preds(preds=preds)\n\n result = {}\n result[\"task\"] = \"qa\"\n all_preds = []\n sample_id_to_index = dict([(sample.id,i) for i,sample in enumerate(samples)])\n for current_pred in all_preds_passage_aggregated:\n sampleid_i = current_pred[0, 3]\n try:\n current_sample = samples[sample_id_to_index[sampleid_i]]\n except Exception as e:\n current_sample = None\n logger.warning(f\"Sample id: {sampleid_i} could not be loaded. Error: {e} \")\n if current_sample is not None:\n passage_predictions = []\n for i in range(current_pred.shape[0]):\n passage_pred = {}\n s_i = current_pred[i,0]\n e_i = current_pred[i,1]\n logit_sum_i = current_pred[i,2]\n question_shift_i = current_pred[i,4]\n passage_shift_i = current_pred[i,5]\n passage_pred[\"score\"] = logit_sum_i\n passage_pred[\"probability\"] = -1 # TODO add probabilities that make sense : )\n try:\n #default to returning no answer\n start = 0\n end = 0\n context_start = 0\n answer = \"\"\n context = \"\"\n if(s_i + e_i > 0):\n current_start = int(s_i + passage_shift_i - question_shift_i)\n current_end = int(e_i + passage_shift_i - question_shift_i) + 1\n start = current_sample.tokenized[\"offsets\"][current_start]\n end = current_sample.tokenized[\"offsets\"][current_end]\n # we want the answer in original string space (containing newline, tab or multiple\n # whitespace. So we need to join doc tokens and work with character offsets\n temptext = \" \".join(current_sample.clear_text[\"doc_tokens\"])\n answer = temptext[start:end]\n answer = answer.strip()\n # sometimes we strip trailing whitespaces, so we need to adjust end\n end = start + len(answer)\n context_start = int(np.clip((start-self.context_size),a_min=0,a_max=None))\n context_end = int(np.clip(end +self.context_size,a_max=len(temptext),a_min=None))\n context = temptext[context_start:context_end]\n except IndexError as e:\n logger.info(e)\n passage_pred[\"answer\"] = answer\n passage_pred[\"offset_start\"] = start\n passage_pred[\"offset_end\"] = end\n passage_pred[\"context\"] = context\n passage_pred[\"offset_context_start\"] = start - context_start\n passage_pred[\"offset_context_end\"] = end - context_start\n passage_pred[\"document_id\"] = current_sample.clear_text.get(\"document_id\", None)\n passage_predictions.append(passage_pred)\n\n pred = {}\n pred[\"question\"] = current_sample.clear_text.question_text\n pred[\"question_id\"] = current_sample.clear_text.get(\"qas_id\", None)\n pred[\"ground_truth\"] = current_sample.clear_text.get(\"orig_answer_text\", None)\n pred[\"answers\"] = passage_predictions\n all_preds.append(pred)\n\n result[\"predictions\"] = all_preds\n return result\n\n def _aggregate_preds(self, preds):\n def create_answeridx_string(r):\n start = r[\"pred_start\"] + r[\"passage_shift\"]\n end = r[\"pred_end\"] + r[\"passage_shift\"]\n return f\"{start}-{end}\"\n\n data = {}\n data[\"pred_start\"] = np.concatenate([x[0] for x in preds])\n data[\"pred_end\"] = np.concatenate([x[1] for x in preds])\n data[\"logit_sum\"] = np.concatenate([x[2] for x in preds])\n data[\"sample_id\"] = np.concatenate([x[3] for x in preds])\n data[\"question_shift\"] = np.concatenate([x[4] for x in preds])\n data[\"passage_shift\"] = np.concatenate([x[5] for x in preds])\n df = pd.DataFrame(data=data)\n df.loc[:,\"answer_indices\"] = df.apply(lambda row: create_answeridx_string(row), axis=1)\n\n # we sometimes have multiple predictions for one sample (= paragraph question pair)\n # because we split the paragraph into smaller passages\n # we group all predictions by sample_id\n unique_sample_ids = df.sample_id.unique()\n max_per_sample = []\n for uid in unique_sample_ids:\n group = df.loc[df.sample_id == uid, :]\n idx_text_answers = (group.pred_start + group.pred_end) > 0\n # if we have a text answer in the group we want to discard all no text passages\n # Reasoning: consider how data is constructed from labels: If we split a document into 5 passages and\n # only passage no.3 has the text answer, all remaining passages are labeled as no answer.\n # We want the answer for passage no.3 without regarding the models output for the other passages.\n if np.sum(idx_text_answers) > 0:\n group = group.loc[idx_text_answers,:]\n if self.top_n_predictions == 1:\n max_pred = group.loc[group.logit_sum == np.max(group.logit_sum),:]\n if (max_pred.shape[0] > 1):\n logger.info(f\"Multiple predictions have the exact same probability of occuring: \\n{max_pred.head(5)}\")\n max_pred = max_pred.iloc[0, :]\n else:\n assert isinstance(self.top_n_predictions, int)\n sorted_group = group.sort_values(by=\"logit_sum\",ascending=False)\n filtered_group = sorted_group.drop_duplicates(keep=\"first\", subset=\"answer_indices\")\n max_pred = filtered_group.iloc[:self.top_n_predictions,:]\n max_per_sample.append(max_pred.values)\n return max_per_sample\n\n\n def _get_best_textanswer_indices(self, logits, n_best_size):\n \"\"\"Get the n-best logits from a numpy array without considering the zero index.\n zero index corresponds to no answer, which we deal with separately\"\"\"\n logits_without_zero = logits[:,1:]\n idx_without_zero = np.argsort(logits_without_zero,axis=1)[:,-n_best_size:]\n idx = idx_without_zero + 1\n return idx\n","sub_path":"farm/modeling/prediction_head.py","file_name":"prediction_head.py","file_ext":"py","file_size_in_byte":45855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"213945844","text":"#If you have downloaded the source code from this book’s companion Web site, you will find\n#the following files in the Chapter 07 folder:\n#• GirlNames.txt—This file contains a list of the 200 most popular names given to girls\n#born in the United States from the year 2000 through 2009.\n#• BoyNames.txt—This file contains a list of the 200 most popular names given to boys\n#born in the United States from the year 2000 through 2009.\n#Write a program that reads the contents of the two files into two separate lists. The user\n#should be able to enter a boy’s name, a girl’s name, or both, and the application will display\n#messages indicating whether the names were among the most popular.\n\nb_names = open('boys_names.txt','r')\nboys_name = b_names.read()\ng_names = open('girls_names.txt','r')\ngirls_names = g_names.read()\nall_names = boys_name + girls_names\nprint(all_names)\nboys_name_search = str(input(\"Enter a boy name : \")).lower()\ngirls_name_search = str(input(\"Enter a girl name : \")).lower()\nif boys_name_search in boys_name:\n print(\"You've entered a most popular name among boys name list.\")\nelse:\n print(\"Entered name is not in most popular boys name list\")\nif girls_name_search in girls_names:\n print(\"You've entered a most popular name among girls name list.\")\nelse:\n print(\"Entered name is not in most popular girls name list\")\nif boys_name_search in all_names and girls_name_search in all_names:\n print(\"You've entered a most popular names in boys name list and girls names list.\")\nelse:\n print(\"The names you've entered is not in neither boys nor in girls names lists\")\nb_names.close()\ng_names.close()\n\n","sub_path":"lesson_02/personal/chapter_7/08_names.py","file_name":"08_names.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"433373292","text":"import scrapy\n\nclass HackagePackage(scrapy.Item):\n name = scrapy.Field()\n description = scrapy.Field()\n link = scrapy.Field()\n\n def __init__(self, name: str, description: str, link: str) -> None:\n self.item['name'] = name\n self.item['description'] = description\n self.item['link'] = link\n\n\nclass HackageSpider(scrapy.Spider):\n name = 'hackagespider'\n start_urls = ['https://hackage.haskell.org/packages/browse']\n\n def parse_package_page(self, response):\n name = response.css('h1 a::text').get()\n description = response.css('h1 small::text').get()\n link = response.css('h1 a').attrib['href']\n\n package = HackagePackage(\n name=name,\n description=description,\n link=link\n )\n\n yield package\n\n def parse(self, response):\n for link in response.xpath('//table/tbody/tr/td[1]/a'):\n yield response.follow(link, self.parse_package_page)\n","sub_path":"scrapy/scrape-hackage/hackage-scrape.py","file_name":"hackage-scrape.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"291843841","text":"# -----------------------------------------------------------------------------\n# training script\n# -----------------------------------------------------------------------------\n\nimport torch, torchvision, torch.nn.functional as F\n\ntorch.cuda.empty_cache()\n\nimport argparse\nfrom tqdm import tqdm\nfrom pathlib import Path\n\nfrom Model import DeepInfoMaxLoss\n\nfrom torch.utils.data import DataLoader\nfrom data_classes.multiple_mnist import MultiMNIST\n\nif __name__ == \"__main__\":\n\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n batch_size = 256\n num_epochs = 1000\n num_workers = 4\n save_interval = 100\n version = \"cifar10_v2\"\n lr = 1e-4\n\n # image size (3,32,32)\n # batch size must be an even number\n # shuffle must be True\n # transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),])\n # # train_dataset = torchvision.datasets.cifar.CIFAR10(\"data\", download=True, transform=transform)\n # train_dataset = torchvision.datasets.mnist.MNIST('data', download=True, transform=transform)\n # train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers)\n\n train_dataset = MultiMNIST(train=True, data_root='data', image_size=64, num_digits=4,\n channels=1, to_sort_label=True, dig_to_use=[0, 2, 4, 6],\n nxt_dig_prob=1.0, rand_dig_combine=False,\n split_dig_set=False)\n train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n\n dim = DeepInfoMaxLoss(alpha=0.5, beta=1.0, gamma=0.1).to(device)\n optimizer = torch.optim.Adam(dim.parameters(), lr=lr)\n\n dim.train()\n for epoch in range(1, num_epochs + 1):\n Batch = tqdm(train_loader, total=len(train_dataset) // batch_size)\n for i, (data, target) in enumerate(Batch, 1):\n data = data.to(device)\n\n Y, M = dim.encoder(data)\n\n # shuffle batch to pair each element with another\n M_fake = torch.cat((M[1:], M[0].unsqueeze(0)), dim=0)\n print('m fake: ', M_fake.shape)\n\n # loss = dim(Y, M, M_fake)\n # Batch.set_description(f\"[{epoch:>3d}/{num_epochs:<3d}]Loss/Train: {loss.item():1.5e}\")\n # dim.zero_grad()\n # loss.backward()\n # optimizer.step()\n #\n # # checkpoint and save models\n # if epoch % save_interval == 0:\n # file = Path(f\"./Models/{version}/checkpoint_epoch_{epoch}.pkl\")\n # file.parent.mkdir(parents=True, exist_ok=True)\n # torch.save(dim.state_dict(), str(file))\n","sub_path":"Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"586374256","text":"import numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom dateutil import parser\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator\nimport matplotlib.dates as mdates\nimport statistics\n\nDATA_PATH = '../processed_data/'\n\nprice_data = pd.read_csv(DATA_PATH + 'priceData.csv')\nnope_data = pd.read_csv(DATA_PATH + 'parsedNetDelta2020-08.csv')\ndf = pd.merge(nope_data, price_data, on=\"timestamp\")\n\ndeltaNope = 0.0\ndeltaNope2 = 0.0\ndeltaPrice = 0.0\nlastNope = 0.0\nlastDNope = 0.0\nlastDNope2 = 0.0\nlastPrice = 0.0\ndPriceLag = []\n\nfor index, row in df.iterrows():\n\n if (index == 0):\n df['deltaNope'] = 0.0\n df['deltaPrice'] = 0.0\n df['deltaNope2'] = 0.0\n df['dN-dN2'] = 0.0\n lastNope = row['NOPE_allVolume']\n lastPrice = row['active_underlying_price']\n lastDNope = 0.0\n lastDNope2 = 0.0\n continue\n else:\n # ignore the first entry in each day, as we just want to compare the data within a single day, and not the end\n # of the previous day to the open of the current\n if('9:35' in row['timestamp']):\n #print('Skipping entry:' , row['timestamp'])\n lastNope = row['NOPE_allVolume']\n lastPrice = row['active_underlying_price']\n lastDNope = 0.0\n lastDNope2 = 0.0\n continue\n\n deltaNope = lastNope - row['NOPE_allVolume']\n deltaPrice = lastPrice - row['active_underlying_price']\n deltaNope2 = lastDNope - deltaNope\n\n #print(deltaNope, deltaPrice)\n df.at[index, 'deltaNope'] = deltaNope\n df.at[index, 'deltaPrice'] = deltaPrice\n df.at[index, 'deltaNope2'] = deltaNope2\n\n # use a split interpolate on the second order to function as the modeled second order\n # this is a reduction of cubic spline interpolation used in forward forecasting signal processing\n df.at[index, 'dN-dN2'] = deltaNope - (0.5*lastDNope2 - 0.5*deltaNope2)\n\n dNope = df['deltaNope'].to_numpy()\n dNope2 = df['deltaNope2'].to_numpy()\n dN_sub_dN2 = df['dN-dN2'].to_numpy()\n dPrice = df['deltaPrice'].to_numpy()\n dNopeRaw = df['NOPE_allVolume'].to_numpy()\n\n # shift the data back by 1\n dPriceLag = np.delete(dPrice, 0, 0)\n dPriceLag = np.insert(dPriceLag, dPriceLag.size - 1, 0)\n\n # shift the data back by 2\n dPriceLag2 = np.delete(dPrice, 0, 0)\n dPriceLag2 = np.delete(dPriceLag2, 0, 0)\n dPriceLag2 = np.insert(dPriceLag2, dPriceLag2.size - 1, 0)\n dPriceLag2 = np.insert(dPriceLag2, dPriceLag2.size - 1, 0)\n\n # compute our various correlations\n dN_dP = stats.spearmanr(dNope, dPrice)\n df.at[index, 'corr'] = dN_dP[0]\n print(\"dNope p Value: \", str(dN_dP[1]))\n df.at[index, 'corrLag'] = stats.spearmanr(dNope, dPriceLag)[0]\n df.at[index, 'corrLag_2'] = stats.spearmanr(dNope, dPriceLag2)[0]\n df.at[index, 'corrRaw'] = stats.spearmanr(dNope, dNopeRaw)[0]\n\n dN2_dP = stats.spearmanr(dNope2, dPrice)\n df.at[index, 'corrD2'] = dN2_dP[0]\n print(\"dNope2 p Value: \", str(dN2_dP[1]))\n df.at[index, 'corrD2Lag'] = stats.spearmanr(dNope2, dPriceLag)[0]\n df.at[index, 'corrD2Lag_2'] = stats.spearmanr(dNope2, dPriceLag2)[0]\n\n dN_dN2_dP = stats.spearmanr(dN_sub_dN2, dPrice)\n df.at[index, 'corrdN_sub_dN2'] = dN_dN2_dP[0]\n print(\"dNope - dNope2 p Value: \", str(dN_dN2_dP[1]))\n\n\n dN_dN2_dP_t_plus_5 = stats.spearmanr(dN_sub_dN2, dPriceLag)\n df.at[index, 'corrdN_sub_dN2_lag'] = dN_dN2_dP_t_plus_5[0]\n print(\"dNope - dNope2 T+5 p Value: \", str(dN_dN2_dP_t_plus_5[1]))\n df.at[index, 'corrdN_sub_dN2_lag_2'] = stats.spearmanr(dN_sub_dN2, dPriceLag2)[0]\n\n\n lastNope = row['NOPE_allVolume']\n lastPrice = row['active_underlying_price']\n lastDNope = deltaNope\n lastDNope2 = deltaNope2\n\nfig, axs = plt.subplots(3, 1)\nfig.suptitle('Spearman R Rank Correlation \\n SPY Intra-day deltaNope vs. deltaPrice August 2020')\n\n#axs[3].scatter(df['timestamp'], df['deltaNope'], 2, 'red', label='dNope/dt')\n#axs[3].scatter(df['timestamp'], df['deltaNope2'], 2, 'yellow', label='dNope2/d2t')\n#axs[3].scatter(df['timestamp'], df['dN-dN2'], 2, 'pink', label='dNope/dt - est_dNope2/d2t')\n#axs[3].scatter(df['timestamp'], df['deltaPrice'], 2, 'blue', label='dPrice/dt')\n#axs[3].scatter(df['timestamp'], dPriceLag2, 2, 'pink', label='dPrice/dt')\n\naxs[0].plot(df['timestamp'], df['corr'], 'green', label='Cor(dNope(T),dPrice(T))')\naxs[0].plot(df['timestamp'], df['corrLag'], 'orange', label='Cor(dNope(T), dPrice(T+5))')\naxs[0].plot(df['timestamp'], df['corrLag_2'], 'red', label='Cor(dNope(T), dPrice(T+10))')\naxs[0].plot(df['timestamp'], df['corrRaw'], 'cyan', label='Cor(NOPE(T), dPrice(T))')\naxs[0].xaxis.set_ticks([i*100 for i in range(0,20)])\naxs[0].yaxis.set_ticks([i/4.0 for i in range(-4,4,1)])\naxs[0].get_xaxis().set_ticklabels([])\naxs[0].title.set_text('First Order Differential Analysis')\naxs[0].grid()\n\naxs[0].legend(loc=\"lower right\")\n#axs[0].xlabel(\"Time\")\n#axs[0].ylabel(\"Raw\")\n#axs[0].xticks(np.arange(0, 1605, 75))\n#axs[0].yticks(np.arange(-2.5, 2.5, 0.25))\n\n\naxs[1].plot(df['timestamp'], df['corrD2'], 'black', label='Cor(dNope2(T),dPrice(T))')\naxs[1].plot(df['timestamp'], df['corrD2Lag'], 'blue', label='Cor(dNope2(T),dPrice(T+5))')\naxs[1].plot(df['timestamp'], df['corrD2Lag_2'], 'green', label='Cor(dNope2(T),dPrice(T+10))')\naxs[1].xaxis.set_ticks([i*100 for i in range(0,20)])\naxs[1].yaxis.set_ticks([i/4.0 for i in range(-4,4,1)])\naxs[1].get_xaxis().set_ticklabels([])\naxs[1].title.set_text('Second Order Differential Analysis')\naxs[1].grid()\naxs[1].legend(loc=\"lower right\")\n\naxs[2].plot(df['timestamp'], df['corrdN_sub_dN2'], 'green', label='Cor((dNope - est_dNope2)(T) , dPrice(T))')\naxs[2].plot(df['timestamp'], df['corrdN_sub_dN2_lag'], 'red', label='Cor((dNope - est_dNope2)(T) , dPrice(T+5))')\naxs[2].plot(df['timestamp'], df['corrdN_sub_dN2_lag_2'], 'blue', label='Cor((dNope - est_dNope2)(T) , dPrice(T+10))')\naxs[2].xaxis.set_ticks([i*100 for i in range(0,20)])\naxs[2].yaxis.set_ticks([i/4.0 for i in range(-4,4,1)])\naxs[2].get_xaxis().set_ticklabels([])\naxs[2].title.set_text('Forward Projection First/Second Order Differential Analysis')\naxs[2].grid()\naxs[2].legend(loc=\"lower right\")\n\nplt.show()\n","sub_path":"scripts/differential_analysis.py","file_name":"differential_analysis.py","file_ext":"py","file_size_in_byte":6341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"135155176","text":"#!/usr/bin/python\r\n#coding:utf-8\r\n# ***************************************************************\r\n# 绘制正态分布曲线\r\n# author: pruce\r\n# email: 1756983926@qq.com\r\n# date: 20180919\r\n# ***************************************************************\r\n\r\nimport numpy as np\r\nimport matplotlib.mlab as mlab\r\nimport matplotlib.pyplot as plt\r\n\r\ndef normDistribution():\r\n mu, sigma , num_bins = 0, 1, 50\r\n x = mu + sigma * np.random.randn(1000000)\r\n # 正态分布的数据\r\n n, bins, patches = plt.hist(x, num_bins, normed=True, facecolor = 'black', alpha = 0.5)\r\n # 拟合曲线\r\n y = mlab.normpdf(bins, mu, sigma)\r\n plt.plot(bins, y, 'r--')\r\n plt.xlabel('Expectation')\r\n plt.ylabel('Probability')\r\n plt.title('$N(0,1)$')\r\n\r\n plt.subplots_adjust(left = 0.15)\r\n plt.show()\r\n\r\nnormDistribution()","sub_path":"scripts/statistics/PlotDistribution.py","file_name":"PlotDistribution.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"464313072","text":"from django.forms import *\nfrom models import *\n\nclass PersonForm(ModelForm):\n\n name = CharField()\n\n child1 = CharField()\n\n child2 = CharField()\n\n child3 = CharField()\n\n class Meta:\n model = Person\n\n def save(self, commit=True):\n # Are we saving a new model or editing an existing mode?\n is_new = self.instance.pk is None\n\n # We need to call cleaned_data to comply with cleaning methods of django models\n data = self.cleaned_data\n\n # if the record is new create a new model, otherwise take the model instance\n if is_new:\n parent = Person()\n else:\n parent = self.instance.person\n\n # save master table\n parent.name = data.get('name', '')\n parent.save()\n\n child1 = Child()\n child1.paren = parent\n child1.name = data['child1']\n child1.save()\n\n child2 = Child()\n child2.person = parent\n child2.name = data['child2']\n child2.save()\n\n child3 = Child()\n child3.person = parent\n child3.name = data['child3']\n child3.save()\n\n return parent\n","sub_path":"rahimi/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"512062138","text":"import calendar\r\n\r\nfrom django.http import Http404\r\nfrom django.shortcuts import render\r\nfrom django.utils.decorators import method_decorator\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.shortcuts import render_to_response\r\nfrom django.views.generic.base import TemplateView\r\n\r\nfrom . import helpers, utils\r\nfrom .decorators import jsonify\r\n\r\ndef indexView(request):\r\n \"\"\"\r\n \"\"\"\r\n return render(request, 'presence/start.html')\r\n\r\n\r\n\r\nclass WeekdayView(TemplateView):\r\n \"\"\"\r\n The ChangePasswordView class provides a template-based view used to change\r\n the password of a currently logged in user.\r\n \"\"\"\r\n template_name = 'presence/presence_weekday.html'\r\n\r\n\r\n @method_decorator(login_required)\r\n def get(self, request, *args, **kwargs):\r\n \"\"\"\r\n GET request handler.\r\n \"\"\"\r\n return render(request, self.template_name)\r\n\r\n# @method_decorator(login_required)\r\n# def weekday_view(request):\r\n# \"\"\"\r\n# \"\"\"\r\n# return render_to_response(request, 'presence/presence_weekday.html')\r\n\r\n@method_decorator(login_required)\r\ndef mean_time_view(request):\r\n \"\"\"\r\n \"\"\"\r\n return render_to_response(request, 'presence/mean_time_weekday.html')\r\n\r\n@method_decorator(login_required)\r\ndef start_end_view(request):\r\n \"\"\"\r\n \"\"\"\r\n return render_to_response(request, 'presence/presence_start_end.html')\r\n\r\n@jsonify\r\ndef api_view(request):\r\n \"\"\"\r\n \"\"\"\r\n\r\n data = utils.get_data()\r\n users_info = utils.get_user_data()\r\n return_data = [\r\n {\r\n 'user_id': user_id,\r\n 'name': user_data['name'],\r\n 'avatar': user_data['avatar']\r\n }\r\n for user_id, user_data in users_info.iteritems() if user_id in data\r\n ]\r\n\r\n\r\n return return_data\r\n\r\n@jsonify\r\ndef mean_time_weekday_view(request, user_id):\r\n \"\"\"\r\n \"\"\"\r\n\r\n user_id = int(user_id)\r\n\r\n data = utils.get_data()\r\n if user_id not in data:\r\n raise Http404\r\n\r\n weekdays = helpers.group_by_weekday(data[user_id])\r\n result = [(calendar.day_abbr[weekday], helpers.mean(intervals))\r\n for weekday, intervals in weekdays.items()]\r\n\r\n\r\n return result\r\n\r\n@jsonify\r\ndef presence_start_end_view(request, user_id):\r\n \"\"\"\r\n Returns mean arrival and departure time for each weekday.\r\n \"\"\"\r\n user_id = int(user_id)\r\n data = utils.get_data()\r\n if user_id not in data:\r\n raise Http404\r\n\r\n weekdays = helpers.group_start_end_times_by_weekday(data[user_id])\r\n result = [\r\n (\r\n calendar.day_abbr[weekday], helpers.mean(intervals['start']),\r\n helpers.mean(intervals['end'])\r\n )\r\n for weekday, intervals in weekdays.iteritems()\r\n ]\r\n\r\n return result\r\n\r\n@jsonify\r\ndef presence_weekday_view(request, user_id):\r\n \"\"\"\r\n Returns mean arrival and departure time for each weekday.\r\n \"\"\"\r\n user_id = int(user_id)\r\n data = utils.get_data()\r\n if user_id not in data:\r\n raise Http404\r\n\r\n weekdays = helpers.group_by_weekday(data[user_id])\r\n result = [(calendar.day_abbr[weekday], sum(intervals))\r\n for weekday, intervals in weekdays.items()]\r\n\r\n result.insert(0, ('Weekday', 'Presence (s)'))\r\n\r\n return result\r\n","sub_path":"djanalyzer/apps/presence/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"300180184","text":"import sys\nimport config\nimport db\nfrom PySide import QtGui\nfrom PySide.QtCore import QTimer\nfrom window import Ui_MainWindow\nfrom aggregate import Aggregate\nfrom export import Export\n\n\nclass Cache(object):\n def __init__(self):\n self.cache_window = QtGui.QMainWindow()\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self.cache_window)\n self.cache_window.show()\n\n# Główna część programu\nif __name__ == \"__main__\":\n app = QtGui.QApplication(sys.argv)\n\n cache = Cache() # inicjalizacja okna programu\n configuration = config.Config() # wczytanie konfiguracji\n\n database = db.Db(ip=configuration.db_ip,\n port=configuration.db_port,\n user=configuration.db_user,\n password=configuration.db_password,\n database=configuration.db_name,\n ui=cache.ui) # otwarcie połączenia z bazą danych\n database.prepare(tank_table=configuration.db_tank_table,\n nozzle_table=configuration.db_nozzle_table) # przygotowanie bazy danych\n\n tank_aggregate = Aggregate(values=database.tank_table_values,\n ui=cache.ui) # lista agregacyjna zbiornika paliwa\n nozzle_aggregate = Aggregate(values=database.nozzle_table_values,\n ui=cache.ui) # lista agregacyjna pistoletu paliwowego\n\n export = Export(ui=cache.ui) # inicjalizacja eksportu\n\n # przeliczenie list agregatów\n tank_aggregate.compile_tank()\n nozzle_aggregate.compile_nozzle()\n\n # aktualizacja tablic gui\n tank_aggregate.update_tank_table()\n nozzle_aggregate.update_nozzle_table()\n\n # automatyczne aktualizowanie tablic\n timer = QTimer()\n timer.timeout.connect(database.get_new_data)\n timer.timeout.connect(tank_aggregate.compile_tank_update)\n timer.timeout.connect(nozzle_aggregate.compile_nozzle_update)\n timer.timeout.connect(tank_aggregate.update_tank_table)\n timer.timeout.connect(nozzle_aggregate.update_nozzle_table)\n timer.start(configuration.db_auto_update_interval)\n\n # event dla wyboru ilości krotek agregacji\n cache.ui.radioButton_5.clicked.connect(tank_aggregate.update_tank_table)\n cache.ui.radioButton_15.clicked.connect(tank_aggregate.update_tank_table)\n cache.ui.radioButton_30.clicked.connect(tank_aggregate.update_tank_table)\n cache.ui.radioButton_60.clicked.connect(tank_aggregate.update_tank_table)\n cache.ui.radioButton_5.clicked.connect(nozzle_aggregate.update_nozzle_table)\n cache.ui.radioButton_15.clicked.connect(nozzle_aggregate.update_nozzle_table)\n cache.ui.radioButton_30.clicked.connect(nozzle_aggregate.update_nozzle_table)\n cache.ui.radioButton_60.clicked.connect(nozzle_aggregate.update_nozzle_table)\n\n # event dla eksportu danych\n cache.ui.pushButton_export.clicked.connect(export.to_files)\n\n sys.exit(app.exec_())","sub_path":"cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"470750030","text":"# This file is part of the Reproducible and Reusable Data Analysis Workflow\n# Server (flowServ).\n#\n# Copyright (C) 2019-2020 NYU.\n#\n# flowServ is free software; you can redistribute it and/or modify it under the\n# terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"The workflow repository maintains information about registered workflow\ntemplates. For each template additional basic information is stored in the\nunderlying database.\n\"\"\"\n\nimport errno\nimport git\nimport os\nimport shutil\nimport tempfile\n\nfrom contextlib import contextmanager\n\nfrom flowserv.model.base import WorkflowHandle\nfrom flowserv.model.workflow.manifest import WorkflowManifest\nfrom flowserv.model.workflow.repository import WorkflowRepository\n\nimport flowserv.error as err\nimport flowserv.util as util\nimport flowserv.model.constraint as constraint\n\n\"\"\" \"Default values for the max. attempts parameter and the ID generator\nfunction.\n\"\"\"\nDEFAULT_ATTEMPTS = 100\nDEFAULT_IDFUNC = util.get_short_identifier\n\n\nclass WorkflowManager(object):\n \"\"\"The workflow manager maintains information that is associated with\n workflow templates in a workflow repository.\n \"\"\"\n def __init__(\n self, session, fs, idfunc=None, attempts=None, tmpl_names=None\n ):\n \"\"\"Initialize the database connection, and the generator for workflow\n related file names and directory paths. The optional parameters are\n used to configure the identifier function that is used to generate\n unique workflow identifier as well as the list of default file names\n for template specification files.\n\n By default, short identifiers are used.\n\n Parameters\n ----------\n session: sqlalchemy.orm.session.Session\n Database session.\n fs: flowserv.model.workflow.fs.WorkflowFileSystem\n Generattor for file names and directory paths\n idfunc: func, optional\n Function to generate template folder identifier\n attempts: int, optional\n Maximum number of attempts to create a unique folder for a new\n workflow template\n tmpl_names: list(string), optional\n List of default names for template specification files\n \"\"\"\n self.session = session\n self.fs = fs\n # Initialize the identifier function and the max. number of attempts\n # that are made to generate a unique identifier.\n self.idfunc = idfunc if idfunc is not None else DEFAULT_IDFUNC\n self.attempts = attempts if attempts is not None else DEFAULT_ATTEMPTS\n\n def create_workflow(\n self, source, name=None, description=None, instructions=None,\n specfile=None, manifestfile=None, ignore_postproc=False\n ):\n \"\"\"Add new workflow to the repository. The associated workflow template\n is created in the template repository from either the given source\n directory or a Git repository. The template repository will raise an\n error if neither or both arguments are given.\n\n The method will look for a workflow description file in the template\n base folder with the name flowserv.json, flowserv.yaml, flowserv.yml\n (in this order). The expected structure of the file is:\n\n name: ''\n description: ''\n instructions: ''\n files:\n - source: ''\n target: ''\n specfile: '' or workflowSpec: ''\n\n An error is raised if both specfile and workflowSpec are present in the\n description file.\n\n Raises an error if no workflow name is given or if a given workflow\n name is not unique.\n\n Parameters\n ----------\n source: string\n Path to local template, name or URL of the template in the\n repository.\n name: string, default=None\n Unique workflow name\n description: string, default=None\n Optional short description for display in workflow listings\n instructions: string, default=None\n File containing instructions for workflow users.\n specfile: string, default=None\n Path to the workflow template specification file (absolute or\n relative to the workflow directory)\n manifestfile: string, default=None\n Path to manifest file. If not given an attempt is made to read one\n of the default manifest file names in the base directory.\n ignore_postproc: bool, default=False\n Ignore post-processing workflow specification if True.\n\n Returns\n -------\n flowserv.model.base.WorkflowHandle\n\n Raises\n ------\n flowserv.error.ConstraintViolationError\n flowserv.error.InvalidTemplateError\n flowserv.error.InvalidManifestError\n ValueError\n \"\"\"\n # If a repository Url is given we first clone the repository into a\n # temporary directory that is used as the workflow source directory.\n with clone(source) as sourcedir:\n manifest = WorkflowManifest.load(\n basedir=sourcedir,\n manifestfile=manifestfile,\n name=name,\n description=description,\n instructions=instructions,\n specfile=specfile,\n existing_names=[wf.name for wf in self.list_workflows()]\n )\n # Create identifier and folder for the workflow template. Create a\n # sub-folder for static template files that are copied from the\n # project folder.\n func = self.fs.workflow_basedir\n workflow_id, workflowdir = self.create_folder(dirfunc=func)\n staticdir = self.fs.workflow_staticdir(workflow_id)\n template = manifest.template(staticdir)\n # Copy files from the project folder to the template's static file\n # folder. By default all files in the project folder are copied.\n try:\n manifest.copyfiles(targetdir=staticdir)\n except (IOError, OSError, KeyError) as ex:\n shutil.rmtree(workflowdir)\n raise ex\n # Insert workflow into database and return the workflow handle.\n postproc_spec = template.postproc_spec if not ignore_postproc else None\n workflow = WorkflowHandle(\n workflow_id=workflow_id,\n name=manifest.name,\n description=manifest.description,\n instructions=manifest.instructions,\n workflow_spec=template.workflow_spec,\n parameters=template.parameters,\n modules=template.modules,\n postproc_spec=postproc_spec,\n result_schema=template.result_schema\n )\n self.session.add(workflow)\n # Set the static directory for the workflow handle.\n workflow.set_staticdir(staticdir)\n return workflow\n\n def create_folder(self, dirfunc):\n \"\"\"Create a new unique folder in a base directory using the internal\n identifier function. The path to the created folder is generated using\n the given directory function that takes a unique identifier as the only\n argument.\n\n Returns a tuple containing the identifier and the directory. Raises\n an error if the maximum number of attempts to create the unique folder\n was reached.\n\n Parameters\n ----------\n dirfunc: func\n Function to generate the path for the created folder\n\n Returns\n -------\n (id::string, subfolder::string)\n\n Raises\n ------\n ValueError\n \"\"\"\n identifier = None\n attempt = 0\n while identifier is None:\n # Create a new identifier\n identifier = self.idfunc()\n # Try to generate the subfolder. If the folder exists, set\n # identifier to None to signal failure.\n subfolder = dirfunc(identifier)\n if os.path.isdir(subfolder):\n identifier = None\n else:\n try:\n os.makedirs(subfolder)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n else:\n # Directory must have been created concurrently\n identifier = None\n if identifier is None:\n # Increase number of failed attempts. If the maximum number of\n # attempts is reached raise an errir\n attempt += 1\n if attempt > self.attempts:\n raise RuntimeError('could not create unique folder')\n return identifier, subfolder\n\n def delete_workflow(self, workflow_id):\n \"\"\"Delete the workflow with the given identifier.\n\n Parameters\n ----------\n workflow_id: string\n Unique workflow identifier\n\n Raises\n ------\n flowserv.error.UnknownWorkflowError\n \"\"\"\n # Get the workflow and workflow directory. This will raise an error if\n # the workflow does not exist.\n workflow = self.get_workflow(workflow_id)\n workflowdir = self.fs.workflow_basedir(workflow_id)\n # Delete the workflow from the database and commit changes.\n self.session.delete(workflow)\n self.session.commit()\n # Delete all files that are associated with the workflow if the changes\n # to the database were successful.\n if os.path.isdir(workflowdir):\n shutil.rmtree(workflowdir)\n\n def get_workflow(self, workflow_id):\n \"\"\"Get handle for the workflow with the given identifier. Raises\n an error if no workflow with the identifier exists.\n\n Parameters\n ----------\n workflow_id: string\n Unique workflow identifier\n\n Returns\n -------\n flowserv.model.base.WorkflowHandle\n\n Raises\n ------\n flowserv.error.UnknownWorkflowError\n \"\"\"\n # Get workflow information from database. If the result is empty an\n # error is raised\n workflow = self.session\\\n .query(WorkflowHandle)\\\n .filter(WorkflowHandle.workflow_id == workflow_id)\\\n .one_or_none()\n if workflow is None:\n raise err.UnknownWorkflowError(workflow_id)\n # Set the static directory for the workflow handle.\n workflow.set_staticdir(self.fs.workflow_staticdir(workflow_id))\n return workflow\n\n def list_workflows(self):\n \"\"\"Get a list of descriptors for all workflows in the repository.\n\n Returns\n -------\n list(flowserv.model.base.WorkflowHandle)\n \"\"\"\n workflows = list()\n for wf in self.session.query(WorkflowHandle).all():\n # Set the static directory for the workflow handle.\n wf.set_staticdir(self.fs.workflow_staticdir(wf.workflow_id))\n workflows.append(wf)\n return workflows\n\n def update_workflow(\n self, workflow_id, name=None, description=None, instructions=None\n ):\n \"\"\"Update name, description, and instructions for a given workflow.\n\n Raises an error if the given workflow does not exist or if the name is\n not unique.\n\n Parameters\n ----------\n workflow_id: string\n Unique workflow identifier\n name: string, optional\n Unique workflow name\n description: string, optional\n Optional short description for display in workflow listings\n instructions: string, optional\n Text containing detailed instructions for workflow execution\n\n Returns\n -------\n flowserv.model.base.WorkflowHandle\n\n Raises\n ------\n flowserv.error.ConstraintViolationError\n flowserv.error.UnknownWorkflowError\n \"\"\"\n # Get the workflow from the database. This will raise an error if the\n # workflow does not exist.\n workflow = self.get_workflow(workflow_id)\n # Update workflow properties.\n if name is not None:\n # Ensure that the name is a valid name.\n constraint.validate_name(name)\n # Ensure that the name is unique.\n wf = self.session\\\n .query(WorkflowHandle)\\\n .filter(WorkflowHandle.name == name)\\\n .one_or_none()\n if wf is not None and wf.workflow_id != workflow_id:\n msg = \"workflow '{}' exists\".format(name)\n raise err.ConstraintViolationError(msg)\n workflow.name = name\n if description is not None:\n workflow.description = description\n if instructions is not None:\n workflow.instructions = instructions\n return workflow\n\n\n# -- Helper Methods -----------------------------------------------------------\n\n@contextmanager\ndef clone(source, repository=None):\n \"\"\"Clone a workflow template repository. If source points to a directory on\n local disk it is returned as the 'cloned' source directory. Otherwise, it\n is assumed that source either references a known template in the global\n workflow template repository or points to a git repository. The repository\n is cloned into a temporary directory which is removed when the generator\n resumes after the workflow has been copied to the local repository.\n\n Returns the path to the resulting template source directory on the local\n disk.\n\n Parameters\n ----------\n source: string\n The source is either a path to local template directory, an identifer\n for a template in the global template repository, or the URL for a git\n repository.\n repository: flowserv.model.workflow.repository.WorkflowRepository,\n default=None\n Object providing access to the global workflow repository.\n\n Returns\n -------\n string\n \"\"\"\n if os.path.isdir(source):\n # Return the source if it references a directory on local disk.\n yield source\n else:\n # Clone the repository that matches the given source into a temporary\n # directory on local disk.\n if repository is None:\n repository = WorkflowRepository()\n repourl = repository.get(source)\n sourcedir = tempfile.mkdtemp()\n try:\n git.Repo.clone_from(repourl, sourcedir)\n yield sourcedir\n except (IOError, OSError, git.exc.GitCommandError) as ex:\n raise ex\n finally:\n # Make sure to cleanup by removing the created teporary folder.\n shutil.rmtree(sourcedir)\n","sub_path":"flowserv/model/workflow/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":14625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"210094908","text":"# -*- coding: utf-8 -*-\n# @Time : 2019/10/15 10:59\n# @Author : wangmengmeng\n\"\"\"\ninput：输入包括一个字符串s,字符串s的长度length(1 ≤ length ≤ 50),s只含小写字母('a'-'z')\noutput：\n输出一个整数,表示所有碎片的平均长度,四舍五入保留两位小数。\n如样例所示: s = \"aaabbaaac\"\n所有碎片的平均长度 = (3 + 2 + 3 + 1) / 4 = 2.25\n\"\"\"\ns = input()\nn = len(s)\nk = 0\nfor i in range(n-1):\n if s[i] !=s[i+1]:\n k = k+1\nprint(float(n/(k+1)))","sub_path":"bishi/wangyi_1/test_3.py","file_name":"test_3.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"582261636","text":"# Copyright (c) 2011-2020 Eric Froemling\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n# -----------------------------------------------------------------------------\n\"\"\"Hockey game and support classes.\"\"\"\n\n# ba_meta require api 6\n# (see https://ballistica.net/wiki/meta-tag-system)\n\nfrom __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nimport ba\nfrom bastd.actor.playerspaz import PlayerSpaz\nfrom bastd.actor.scoreboard import Scoreboard\nfrom bastd.actor.powerupbox import PowerupBoxFactory\nfrom bastd.gameutils import SharedObjects\n\nif TYPE_CHECKING:\n from typing import Any, Sequence, Dict, Type, List, Optional, Union\n\n\nclass PuckDiedMessage:\n \"\"\"Inform something that a puck has died.\"\"\"\n\n def __init__(self, puck: Puck):\n self.puck = puck\n\n\nclass Puck(ba.Actor):\n \"\"\"A lovely giant hockey puck.\"\"\"\n\n def __init__(self, position: Sequence[float] = (0.0, 1.0, 0.0)):\n super().__init__()\n shared = SharedObjects.get()\n activity = self.getactivity()\n\n # Spawn just above the provided point.\n self._spawn_pos = (position[0], position[1] + 1.0, position[2])\n self.last_players_to_touch: Dict[int, Player] = {}\n self.scored = False\n assert activity is not None\n assert isinstance(activity, HockeyGame)\n pmats = [shared.object_material, activity.puck_material]\n self.node = ba.newnode('prop',\n delegate=self,\n attrs={\n 'model': activity.puck_model,\n 'color_texture': activity.puck_tex,\n 'body': 'puck',\n 'reflection': 'soft',\n 'reflection_scale': [0.2],\n 'shadow_size': 1.0,\n 'is_area_of_interest': True,\n 'position': self._spawn_pos,\n 'materials': pmats\n })\n ba.animate(self.node, 'model_scale', {0: 0, 0.2: 1.3, 0.26: 1})\n\n def handlemessage(self, msg: Any) -> Any:\n if isinstance(msg, ba.DieMessage):\n assert self.node\n self.node.delete()\n activity = self._activity()\n if activity and not msg.immediate:\n activity.handlemessage(PuckDiedMessage(self))\n\n # If we go out of bounds, move back to where we started.\n elif isinstance(msg, ba.OutOfBoundsMessage):\n assert self.node\n self.node.position = self._spawn_pos\n\n elif isinstance(msg, ba.HitMessage):\n assert self.node\n assert msg.force_direction is not None\n self.node.handlemessage(\n 'impulse', msg.pos[0], msg.pos[1], msg.pos[2], msg.velocity[0],\n msg.velocity[1], msg.velocity[2], 1.0 * msg.magnitude,\n 1.0 * msg.velocity_magnitude, msg.radius, 0,\n msg.force_direction[0], msg.force_direction[1],\n msg.force_direction[2])\n\n # If this hit came from a player, log them as the last to touch us.\n s_player = msg.get_source_player(Player)\n if s_player is not None:\n activity = self._activity()\n if activity:\n if s_player in activity.players:\n self.last_players_to_touch[s_player.team.id] = s_player\n else:\n super().handlemessage(msg)\n\n\nclass Player(ba.Player['Team']):\n \"\"\"Our player type for this game.\"\"\"\n\n\nclass Team(ba.Team[Player]):\n \"\"\"Our team type for this game.\"\"\"\n\n def __init__(self) -> None:\n self.score = 0\n\n\n# ba_meta export game\nclass HockeyGame(ba.TeamGameActivity[Player, Team]):\n \"\"\"Ice hockey game.\"\"\"\n\n name = 'Hockey'\n description = 'Score some goals.'\n available_settings = [\n ba.IntSetting(\n 'Score to Win',\n min_value=1,\n default=1,\n increment=1,\n ),\n ba.IntChoiceSetting(\n 'Time Limit',\n choices=[\n ('None', 0),\n ('1 Minute', 60),\n ('2 Minutes', 120),\n ('5 Minutes', 300),\n ('10 Minutes', 600),\n ('20 Minutes', 1200),\n ],\n default=0,\n ),\n ba.FloatChoiceSetting(\n 'Respawn Times',\n choices=[\n ('Shorter', 0.25),\n ('Short', 0.5),\n ('Normal', 1.0),\n ('Long', 2.0),\n ('Longer', 4.0),\n ],\n default=1.0,\n ),\n ]\n default_music = ba.MusicType.HOCKEY\n\n @classmethod\n def supports_session_type(cls, sessiontype: Type[ba.Session]) -> bool:\n return issubclass(sessiontype, ba.DualTeamSession)\n\n @classmethod\n def get_supported_maps(cls, sessiontype: Type[ba.Session]) -> List[str]:\n return ba.getmaps('hockey')\n\n def __init__(self, settings: dict):\n super().__init__(settings)\n shared = SharedObjects.get()\n self._scoreboard = Scoreboard()\n self._cheer_sound = ba.getsound('cheer')\n self._chant_sound = ba.getsound('crowdChant')\n self._foghorn_sound = ba.getsound('foghorn')\n self._swipsound = ba.getsound('swip')\n self._whistle_sound = ba.getsound('refWhistle')\n self.puck_model = ba.getmodel('puck')\n self.puck_tex = ba.gettexture('puckColor')\n self._puck_sound = ba.getsound('metalHit')\n self.puck_material = ba.Material()\n self.puck_material.add_actions(actions=(('modify_part_collision',\n 'friction', 0.5)))\n self.puck_material.add_actions(conditions=('they_have_material',\n shared.pickup_material),\n actions=('modify_part_collision',\n 'collide', False))\n self.puck_material.add_actions(\n conditions=(\n ('we_are_younger_than', 100),\n 'and',\n ('they_have_material', shared.object_material),\n ),\n actions=('modify_node_collision', 'collide', False),\n )\n self.puck_material.add_actions(conditions=('they_have_material',\n shared.footing_material),\n actions=('impact_sound',\n self._puck_sound, 0.2, 5))\n\n # Keep track of which player last touched the puck\n self.puck_material.add_actions(\n conditions=('they_have_material', shared.player_material),\n actions=(('call', 'at_connect',\n self._handle_puck_player_collide), ))\n\n # We want the puck to kill powerups; not get stopped by them\n self.puck_material.add_actions(\n conditions=('they_have_material',\n PowerupBoxFactory.get().powerup_material),\n actions=(('modify_part_collision', 'physical', False),\n ('message', 'their_node', 'at_connect', ba.DieMessage())))\n self._score_region_material = ba.Material()\n self._score_region_material.add_actions(\n conditions=('they_have_material', self.puck_material),\n actions=(('modify_part_collision', 'collide',\n True), ('modify_part_collision', 'physical', False),\n ('call', 'at_connect', self._handle_score)))\n self._puck_spawn_pos: Optional[Sequence[float]] = None\n self._score_regions: Optional[List[ba.NodeActor]] = None\n self._puck: Optional[Puck] = None\n self._score_to_win = int(settings['Score to Win'])\n self._time_limit = float(settings['Time Limit'])\n\n def get_instance_description(self) -> Union[str, Sequence]:\n if self._score_to_win == 1:\n return 'Score a goal.'\n return 'Score ${ARG1} goals.', self._score_to_win\n\n def get_instance_description_short(self) -> Union[str, Sequence]:\n if self._score_to_win == 1:\n return 'score a goal'\n return 'score ${ARG1} goals', self._score_to_win\n\n def on_begin(self) -> None:\n super().on_begin()\n\n self.setup_standard_time_limit(self._time_limit)\n self.setup_standard_powerup_drops()\n self._puck_spawn_pos = self.map.get_flag_position(None)\n self._spawn_puck()\n\n # Set up the two score regions.\n defs = self.map.defs\n self._score_regions = []\n self._score_regions.append(\n ba.NodeActor(\n ba.newnode('region',\n attrs={\n 'position': defs.boxes['goal1'][0:3],\n 'scale': defs.boxes['goal1'][6:9],\n 'type': 'box',\n 'materials': [self._score_region_material]\n })))\n self._score_regions.append(\n ba.NodeActor(\n ba.newnode('region',\n attrs={\n 'position': defs.boxes['goal2'][0:3],\n 'scale': defs.boxes['goal2'][6:9],\n 'type': 'box',\n 'materials': [self._score_region_material]\n })))\n self._update_scoreboard()\n ba.playsound(self._chant_sound)\n\n def on_team_join(self, team: Team) -> None:\n self._update_scoreboard()\n\n def _handle_puck_player_collide(self) -> None:\n collision = ba.getcollision()\n try:\n puck = collision.sourcenode.getdelegate(Puck, True)\n player = collision.opposingnode.getdelegate(PlayerSpaz,\n True).getplayer(\n Player, True)\n except ba.NotFoundError:\n return\n\n puck.last_players_to_touch[player.team.id] = player\n\n def _kill_puck(self) -> None:\n self._puck = None\n\n def _handle_score(self) -> None:\n \"\"\"A point has been scored.\"\"\"\n\n assert self._puck is not None\n assert self._score_regions is not None\n\n # Our puck might stick around for a second or two\n # we don't want it to be able to score again.\n if self._puck.scored:\n return\n\n region = ba.getcollision().sourcenode\n index = 0\n for index in range(len(self._score_regions)):\n if region == self._score_regions[index].node:\n break\n\n for team in self.teams:\n if team.id == index:\n scoring_team = team\n team.score += 1\n\n # Tell all players to celebrate.\n for player in team.players:\n if player.actor:\n player.actor.handlemessage(ba.CelebrateMessage(2.0))\n\n # If we've got the player from the scoring team that last\n # touched us, give them points.\n if (scoring_team.id in self._puck.last_players_to_touch\n and self._puck.last_players_to_touch[scoring_team.id]):\n self.stats.player_scored(\n self._puck.last_players_to_touch[scoring_team.id],\n 100,\n big_message=True)\n\n # End game if we won.\n if team.score >= self._score_to_win:\n self.end_game()\n\n ba.playsound(self._foghorn_sound)\n ba.playsound(self._cheer_sound)\n\n self._puck.scored = True\n\n # Kill the puck (it'll respawn itself shortly).\n ba.timer(1.0, self._kill_puck)\n\n light = ba.newnode('light',\n attrs={\n 'position': ba.getcollision().position,\n 'height_attenuated': False,\n 'color': (1, 0, 0)\n })\n ba.animate(light, 'intensity', {0: 0, 0.5: 1, 1.0: 0}, loop=True)\n ba.timer(1.0, light.delete)\n\n ba.cameraflash(duration=10.0)\n self._update_scoreboard()\n\n def end_game(self) -> None:\n results = ba.GameResults()\n for team in self.teams:\n results.set_team_score(team, team.score)\n self.end(results=results)\n\n def _update_scoreboard(self) -> None:\n winscore = self._score_to_win\n for team in self.teams:\n self._scoreboard.set_team_value(team, team.score, winscore)\n\n def handlemessage(self, msg: Any) -> Any:\n\n # Respawn dead players if they're still in the game.\n if isinstance(msg, ba.PlayerDiedMessage):\n # Augment standard behavior...\n super().handlemessage(msg)\n self.respawn_player(msg.getplayer(Player))\n\n # Respawn dead pucks.\n elif isinstance(msg, PuckDiedMessage):\n if not self.has_ended():\n ba.timer(3.0, self._spawn_puck)\n else:\n super().handlemessage(msg)\n\n def _flash_puck_spawn(self) -> None:\n light = ba.newnode('light',\n attrs={\n 'position': self._puck_spawn_pos,\n 'height_attenuated': False,\n 'color': (1, 0, 0)\n })\n ba.animate(light, 'intensity', {0.0: 0, 0.25: 1, 0.5: 0}, loop=True)\n ba.timer(1.0, light.delete)\n\n def _spawn_puck(self) -> None:\n ba.playsound(self._swipsound)\n ba.playsound(self._whistle_sound)\n self._flash_puck_spawn()\n assert self._puck_spawn_pos is not None\n self._puck = Puck(position=self._puck_spawn_pos)\n","sub_path":"assets/src/ba_data/python/bastd/game/hockey.py","file_name":"hockey.py","file_ext":"py","file_size_in_byte":14964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"3239105","text":"class Solution:\n def convert(self, s: str, numRows: int) -> str:\n if s == \"\":\n return \"\"\n if numRows <= 1:\n return s\n\n ls = [[] for _ in range(numRows)]\n num_range = list(range(numRows))\n temp = 0\n for char in s:\n curr = num_range[temp]\n ls[curr].append(char)\n if (temp + 1) > (numRows - 1):\n temp = 1\n num_range = list(reversed(num_range))\n else:\n temp+=1\n output = \"\"\n for el in ls:\n output += ''.join(el)\n return output\n","sub_path":"Leet_Code/Medium/6_Zig.py","file_name":"6_Zig.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"58124914","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 20 18:37:06 2020\n\n@author: Pierre-Marie EDLINGER\n\"\"\"\n\nfrom tkinter import *\nfrom tkinter.ttk import *\n\nimport ProgCreationFichierCLS as Prog\nimport FonctionsFichier as FF\n\nDERNIERELIGNE = 10\n\n# définition des gestionnaires\n# d'événements :\n\ndef choixFicDeTravail():\n varNomFicTravail.set(FF.getFichier())\n \"\"\"\n tF = eval(champTC.get())\n varTF.set(str(tF*1.8 +32))\n \"\"\"\n \ndef choixFicCaisse():\n varNomFicCaisses.set(FF.getFichier())\n \ndef choixRepFinal():\n varNomRep.set(FF.getRepertoire())\n \ndef fermeTout():\n global fenPrincipale\n fenPrincipale.quit()\n\ndef creerFichierModele():\n try :\n cheminFic = \"\"\n cheminFic = FF.getOuEnregistrerFichier()\n if cheminFic != \"\":\n Prog.CreerFichierTravail(cheminFic)\n FF.messageInfo(\"Succès\", \"L'opération s'est déroulée avec succès.\")\n except :\n FF.messageWarning(\"Erreur\", \"Une erreur est survenue lors de la création du fichier modèle...\")\n\ndef creerFichiersCLS():\n try:\n if (varNomFicTravail.get() == \"\"):\n FF.messageWarning(\"Renseignements manquants\", \"Choisissez le fichier source.\")\n choixFicDeTravail()\n else :\n if FF.verifieExistanceFichier(varNomFicTravail.get()):\n if (varNomRep.get() == \"\"):\n FF.messageWarning(\"Renseignements manquants\", \"Choisissez un répertoire où placer le(s) fichier(s) à importer dans IBM Lotus.\")\n choixRepFinal()\n else:\n if FF.verifieExistanceRepertoire(varNomRep.get()):\n Prog.LanceCreationFichierCLS(varNomFicTravail.get(), varNomRep.get())\n #FF.messageInfo(\"Succès\", \"L'opération s'est déroulée avec succès.\")\n else:\n choixRepFinal()\n else:\n choixFicDeTravail()\n except :\n FF.messageWarning(\"Erreur\", \"Une erreur est survenue lors de la création du ou des fichier(s) à importer.... Vérifiez qu'il n'y ait pas un Alt+Tab dans un des fichiers.\")\n\ndef creerFichierSupprimeCaisse():\n try:\n if (varNomFicTravail.get() == \"\"):\n FF.messageWarning(\"Renseignements manquants\", \"Choisissez le fichier de travail.\")\n choixFicDeTravail()\n else :\n if FF.verifieExistanceFichier(varNomFicTravail.get()):\n if (varNomRep.get() == \"\"):\n FF.messageWarning(\"Renseignements manquants\", \"Choisissez un répertoire où placer le(s) fichier(s) à importer dans IBM Lotus.\")\n choixRepFinal()\n else:\n if FF.verifieExistanceRepertoire(varNomRep.get()):\n \n if (varNomFicCaisses.get() == \"\"):\n FF.messageWarning(\"Renseignements manquants\", \"Choisissez le fichier des caisses à rendre inactives.\")\n choixFicCaisse()\n else :\n if FF.verifieExistanceFichier(varNomFicCaisses.get()):\n Prog.LanceCreationFichierSupprimeCaisses(varNomFicCaisses.get(), varNomFicTravail.get(), varNomRep.get())\n else:\n choixFicCaisse()\n else:\n choixRepFinal()\n else:\n choixFicDeTravail()\n except :\n FF.messageWarning(\"Erreur\", \"Une erreur est survenue lors de la création du fichier pour rendre des caisses inactives... Vérifiez qu'il n'y ait pas un Alt+Tab dans un des fichiers.\")\n\n\ndef afficheInfos():\n global fenPrincipale\n texte = \"Penser à ouvrir le(s) fichier(s) résultat(s) avec OpenOffice puis enregistrez les au format '.ods' avant de les importer.\\nLe code source de cette application est disponible sur: https://github.com/edlingerpm/DeploiementCLS\\n(copié dans le presse papier).\"\n try :\n fenPrincipale.clipboard_clear()\n fenPrincipale.clipboard_append(\"https://github.com/edlingerpm/DeploiementCLS\")\n FF.messageInfo(\"Information importante\", texte)\n except :\n print(\"Erreur!!!!\")\n None\n\ndef testModif():\n global tableau\n \n if FF.verifieExistanceFichier(varNomFicTravail.get()):\n # try :\n maMatrice = FF.getRensDansFichierExportComplet(varNomFicTravail.get())\n # print(len(maMatrice))\n for i in range(0, len(maMatrice)) :\n # print(maMatrice[i][0])\n tableau.insert('', 'end', iid=maMatrice[i][0], values=(maMatrice[i][1], maMatrice[i][2], maMatrice[i][3]))\n tableau.update\n # except :\n # print(\"Erreur testModif!!!!\")\n # None\n else:\n choixFicDeTravail()\n\n# def pointeur(event):\n# print( \"Clic détecté en X =\" + str(event.x) +\\\n# \", Y =\" + str(event.y))\n \n#========== Programme principal =============\n\n# Création du widget principal (\"parent\") :\nfenPrincipale = Tk()\nfenPrincipale.title(\"Création du fichier d'importation dans IBM Lotus\")\nfenPrincipale.geometry(\"650x130\")\n\n# fenPrincipale.geometry(\"650x200\")\n\n# fenPrincipale.bind(\"\", pointeur) # Button-1 --> clic gauche; Button-2 --> clic molette; Button-3 --> clic droit\n# fenPrincipale.resizable(False, False)\n\ntry: #sert à éviter un message d'erreur si le fichier icone n'existe pas\n fenPrincipale.iconbitmap('icone.ico')\nexcept : None\n\nGAUCHE = 10\nLARGEURBOUTONS = 15\nLIGNEDUBAS = 100\n\n# création des widgets \"enfants\" :\n# can1 = Canvas(fen1,bg='dark grey',height=250, width=250)\n# can1.pack(side=LEFT, padx =5, pady =5)\n#chaine.configure(text = \"test\") # pour changer le texte d'un label\n\n# ---- Gestion de la zone \"où enregistrer les fichiers\"\n# ---- Label\nlblRep1 = Label(fenPrincipale, text = 'Choix du répertoire où enregistrer les fichiers de travail :')\nlblRep1.place(x=GAUCHE, y = 5)\n\n# ---- zone de texte \nvarNomRep = StringVar()\nentr2 = Entry(fenPrincipale, textvariable = varNomRep, width =100)\nvarNomRep.set(\"\")\nentr2.place(x=GAUCHE, y= 25)\n\n# ---- Bouton ------------------\nbtnChoixRep = Button(fenPrincipale, text='...', width =2, command=choixRepFinal)\nbtnChoixRep.place(x=620, y= 23)\n#*********************************************************\n\n# ---- Gestion de la zone \"fichier de travail\"\n# ---- Label\nlblFic2 = Label(fenPrincipale, text = 'Choix du fichier de travail :')\nlblFic2.place(x=GAUCHE, y=50)\n\n# ---- zone de texte\nvarNomFicTravail = StringVar()\nentr1 = Entry(fenPrincipale, textvariable = varNomFicTravail, width =45) # varTC.set(\"\")\nvarNomFicTravail.set(\"\")\nentr1.place(x=GAUCHE, y= 70)\n\n# ---- Bouton ------------------\nbtnFicTravail = Button(fenPrincipale, text='...', width =2, command=choixFicDeTravail)\nbtnFicTravail.place(x=289, y= 68)\n#*********************************************************\n\n# ---- Gestion de la zone \"fichier des caisses\"\n# ---- Label\nlblFic3 = Label(fenPrincipale, text = 'Choix du fichier des caisses à rendre inactives :')\nlblFic3.place(x=GAUCHE +330, y=50)\n\n# ---- zone de texte\nvarNomFicCaisses = StringVar()\nentr2 = Entry(fenPrincipale, textvariable = varNomFicCaisses, width =45) # varTC.set(\"\")\nvarNomFicCaisses.set(\"\")\nentr2.place(x=GAUCHE+330, y= 70)\n\n# ---- Bouton ------------------\nbtnFicCaisse = Button(fenPrincipale, text='...', width =2, command=choixFicCaisse)\nbtnFicCaisse.place(x=620, y= 68)\n#*********************************************************\n\nbtnCreerFicTravail = Button(fenPrincipale, text='Créer un modèle', width =LARGEURBOUTONS, command=creerFichierModele)\nbtnCreerFicTravail.place(x=GAUCHE, y= LIGNEDUBAS)\n\nbtnCreerFichierCLS = Button(fenPrincipale, \n text='Créer le(s) fichier(s) à importer', \n width =LARGEURBOUTONS + 15, \n command=creerFichiersCLS)\nbtnCreerFichierCLS.place(x=115, y= LIGNEDUBAS)\n\nbtnSupprimerCaisses = Button(fenPrincipale, \n text='Créer le fichier pour supprimer caisse(s)', \n width =LARGEURBOUTONS + 20, \n command=creerFichierSupprimeCaisse)\nbtnSupprimerCaisses.place(x=310, y= LIGNEDUBAS)\n\nbtnQuit = Button(fenPrincipale,text='Quitter', width =LARGEURBOUTONS -5, command=fermeTout)\nbtnQuit.place(x=535, y= LIGNEDUBAS)\n\nbtnInfo = Button(fenPrincipale,text='?', width =3, command=afficheInfos)\nbtnInfo.place(x=615, y= LIGNEDUBAS)\n\n\n# btnModif = Button(fenPrincipale,text='Test', width =5, command=testModif)\n# btnModif.place(x=GAUCHE, y= LIGNEDUBAS+30)\n\n\n# lststrMaListe = ListStore(str, str)\n\n# tableau = Treeview(fenPrincipale, columns=('nomfamille', 'prenom', 'da'))\n# tableau.heading('nomfamille', text='Nom de famille')\n# tableau.heading('prenom', text='Prénom')\n# tableau.heading('da', text='DA')\n# tableau['show'] = 'headings' # sans ceci, il y avait une colonne vide à gauche qui a pour rôle d'afficher le paramètre \"text\" qui peut être spécifié lors du insert\n# tableau.place(x=GAUCHE + 50, y= LIGNEDUBAS+30)\n# tableau.insert('', 'end', iid=\"45\", values=(\"1\", \"2\", \"3\"))\n# tableau.insert('', 'end', iid=\"48\", values=(\"4\", \"5\", \"6\"))\n# tableau.set_property(\"editable\", True)\n# tableau.editable = True\n\n\n# démarrage du réceptionnaire d'évènements (boucle principale) :\nfenPrincipale.mainloop()\n\ntry:\n fenPrincipale.destroy()\nexcept:\n None","sub_path":"IHM.py","file_name":"IHM.py","file_ext":"py","file_size_in_byte":9437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"53996377","text":"from HelperLibrary.StorageFunctions import StorageFunctions\n\n\nclass Information:\n def __init__(self, username):\n self.username = username\n\n def execute(self):\n user_data = StorageFunctions(\"users\").retrieve([\"username\"], [self.username])\n user_id = (user_data[0])[0]\n drones_data = StorageFunctions(\"drones\").retrieve([\"user_id\"], [user_id])\n if not drones_data:\n print(\"You have not booked any drones\")\n else:\n print(\"You have booked:\")\n counter = 1\n for booked_data in drones_data:\n origin_data = StorageFunctions(\"locations\").retrieve([\"id\"], [booked_data[8]])\n origin = (origin_data[0])[1]\n destination_data = StorageFunctions(\"locations\").retrieve([\"id\"], [booked_data[9]])\n destination = (destination_data[0])[1]\n print(counter, \": Drone number - \", booked_data[0], \", from \", origin, \" to \", destination, sep='')\n counter += 1\n","sub_path":"BookingSystem/Information.py","file_name":"Information.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"428154970","text":"#!/usr/bin/python\n\n\"\"\"Update an existing highscore in the database.\n\nThis code updates a player's existing highscore in the database,\nif the new score is higher.\n\"\"\"\n\nimport cgi\nimport cgitb\nimport json\n\nimport pymysql\n\nimport processing\n\n\ndef update_score(player_id, level_number, score):\n \"\"\"Update a score in the database.\n\n This function updates an existing score in the\n database for a particular player and level.\n \"\"\"\n\n connection, cursor = processing.connect_to_database()\n cursor.execute(\"UPDATE scores SET score=%s \"\n \"WHERE player_id=%s AND level_id=%s\", (score, player_id, level_number))\n connection.commit()\n\n\ndef is_best(player_id, level_number, score):\n \"\"\"Ensure that the score is the player's highest for that level.\n\n This function checks that the new score is actually better than\n the existing score. If the new score is higher, it returns True.\n If the new score is lower or an old score doesn't exist, it returns\n False.\n \"\"\"\n\n connection, cursor = processing.connect_to_database()\n cursor.execute(\"SELECT score FROM scores \"\n \"WHERE player_id=%s AND level_id=%s\", (player_id, level_number))\n current_best = [(row[0]) for row in cursor.fetchall()]\n\n # There isn't an existing score if length of list is 0\n if len(current_best) != 0 and int(score) > current_best[0]:\n return True\n else:\n return False\n\n\nif __name__ == \"__main__\":\n # Turn on debug mode\n cgitb.enable()\n # Print necessary headers\n print(\"Content-Type: text/html; charset=utf-8\\n\\n\")\n\n form = cgi.FieldStorage()\n player_name = processing.get_player_name(form)\n level_number = processing.get_level_number(form)\n score = processing.get_score(form)\n\n if player_name != None and level_number != None and score != None:\n player_id = processing.get_player_id(player_name)\n\n if player_id != None and is_best(player_id, level_number, score):\n update_score(player_id, level_number, score)\n print(json.dumps([player_name, level_number, score]))\n else:\n # If player doesn't exist of score isn't best\n print(json.dumps(None))\n\n else:\n # If player/level/score not provided\n print(json.dumps(None))","sub_path":"Client-Server Database/updatescore.py","file_name":"updatescore.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"576220832","text":"'''\n44. Wildcard Matching\n\nGiven an input string (s) and a pattern (p), implement wildcard pattern matching with support for '?' and '*'.\n\n'?' Matches any single character.\n'*' Matches any sequence of characters (including the empty sequence).\nThe matching should cover the entire input string (not partial).\n\nNote:\n\ns could be empty and contains only lowercase letters a-z.\np could be empty and contains only lowercase letters a-z, and characters like ? or *.\nExample 1:\n\nInput:\ns = \"aa\"\np = \"a\"\nOutput: false\nExplanation: \"a\" does not match the entire string \"aa\".\nExample 2:\n\nInput:\ns = \"aa\"\np = \"*\"\nOutput: true\nExplanation: '*' matches any sequence.\nExample 3:\n\nInput:\ns = \"cb\"\np = \"?a\"\nOutput: false\nExplanation: '?' matches 'c', but the second letter is 'a', which does not match 'b'.\nExample 4:\n\nInput:\ns = \"adceb\"\np = \"*a*b\"\nOutput: true\nExplanation: The first '*' matches the empty sequence, while the second '*' matches the substring \"dce\".\nExample 5:\n\nInput:\ns = \"acdcb\"\np = \"a*c?b\"\nOutput: false\nAccepted\n228,395\nSubmissions\n943,459\n\n'''\n\n# 2020/04/15, memoization, too hard\n\n'''\nRuntime: 832 ms, faster than 51.16% of Python3 online submissions for Wildcard Matching.\nMemory Usage: 93.4 MB, less than 41.67% of Python3 online submissions for Wildcard Matching.\n'''\n\n\nclass Solution:\n def isMatch(self, s: str, p: str) -> bool:\n memo = {}\n self.dfs(s, 0, p, 0, memo)\n return memo[(0, 0)]\n\n def dfs(self, string, i, pattern, j, memo):\n if (i, j) in memo: return memo[(i, j)]\n if i >= len(string):\n for k in range(j, len(pattern)):\n if pattern[k] != '*':\n memo[(i, j)] = False\n return False\n memo[(i, j)] = True\n return memo[(i, j)]\n if j >= len(pattern):\n memo[(i, j)] = False\n return memo[(i, j)]\n if pattern[j] != '*':\n memo[(i, j)] = self.is_valid(string[i], pattern[j]) \\\n and self.dfs(string, i + 1, pattern, j + 1, memo)\n else:\n memo[(i, j)] = self.dfs(string, i + 1, pattern, j, memo) \\\n or self.dfs(string, i, pattern, j + 1, memo)\n return memo[(i, j)]\n\n def is_valid(self, s, p):\n return s == p or p == '?'\n","sub_path":"0044. Wildcard Matching.py","file_name":"0044. Wildcard Matching.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"42523843","text":"from bs4 import BeautifulSoup\nimport urllib.request\n\n\ntowns={1:'Zagreb+Gl.+Kol.',2:'OGULIN',}\n\n#default is Zagreb if no data is entered\ni_key=1\n\ndef stripped(data,sub=''):\n if data==None:\n data=sub\n else:\n data=data.strip()\n \n return data\n \n\ndef get_data(url):\n response=urllib.request.urlopen(url)\n html=response.read()\n b=BeautifulSoup(html)\n \n b=b.find_all('tr')\n \n print('TRAIN ID - ARRIVAL TIME - LATE - DESTINATION')\n \n for i in b:\n x=i.find_all('td')\n train_id=x[0].a.string.strip()\n \n arrival=x[1].string.strip()\n \n late=stripped(x[2].string,'On Time')\n \n \n from_town=stripped(x[6].string)\n print(train_id+' - '+arrival+' - '+late+' - '+from_town)\n \n\n\ninpt=input('Select number for city: \\n1-Zagreb\\n2-Ogulin\\n:')\n\n\nif inpt.strip()!='':\n i_key=int(inpt)\n\n\n\nif (inpt.strip()!='') and (i_key in towns):\n url='http://vred.hzinfra.hr/hzinfo/Default.asp?KO='+towns[i_key]+'&Category=hzinfo&Service=PANO&LANG=HR&OD1=D&SCREEN=2'\n print('Selected city: '+towns[i_key])\nelse:\n print('You have provided wrong input. Showing trains for Zagreb')\n url='http://vred.hzinfra.hr/hzinfo/Default.asp?KO='+towns[i_key]+'&Category=hzinfo&Service=PANO&LANG=HR&OD1=D&SCREEN=2'\n\n\n\n\nget_data(url)\n\n\n\n\n\n\n\n","sub_path":"hz_get_simple_data_for_2_cities.py","file_name":"hz_get_simple_data_for_2_cities.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"27520230","text":"\"\"\"Wrapper of the Keras model for training without batches\"\"\"\nimport tensorflow as tf\nimport numpy as np\nimport os\n\nfrom copy import deepcopy\nfrom typing import List, Optional, Callable\nfrom tqdm import tqdm\n\nfrom icscreator.kernel.layers import Model\nfrom icscreator.kernel.visualization import BaseVisualizer, EmptyVisualizer\n\n\nclass Trainer(object):\n \"\"\"Class for training models\"\"\"\n\n def __init__(self,\n model: Model,\n optimizer: tf.optimizers.Optimizer,\n loss: tf.losses.Loss,\n model_path: Optional[str] = None,\n metrics: Optional[List[Callable]] = None,\n target_metric: str = \"loss\",\n metric_comparator: Callable = min,\n epochs: int = 1000,\n early_stopping: int = 50,\n visualizer: BaseVisualizer = EmptyVisualizer()):\n self._model = model\n self._optimizer = optimizer\n self._loss = loss\n self._model_path = model_path\n self._metrics = metrics if metrics is not None else []\n self._target_metric = target_metric\n self._metric_comparator = metric_comparator\n self._epochs = epochs\n self._early_stopping = early_stopping\n self._visualizer = visualizer\n self._best_metric_value = None\n self._best_model = None\n self._best_epoch = 0\n\n @property\n def best_model(self) -> Model:\n return self._best_model\n\n def _train_step(self, data: tf.Tensor, target: tf.Tensor) -> (dict, np.ndarray):\n with tf.GradientTape() as tape:\n prediction = self._model(data, sequence=True)\n loss = self._loss(target, prediction)\n gradients = tape.gradient(loss, self._model.trainable_variables)\n self._optimizer.apply_gradients(zip(gradients, self._model.trainable_variables))\n\n prediction = prediction.numpy()\n results = {m.__name__: m(target.numpy(), prediction) for m in self._metrics}\n results[\"loss\"] = loss.numpy()\n return results, prediction\n\n def _prediction_step(self, data: tf.Tensor, target: tf.Tensor) -> (dict, np.ndarray):\n prediction = self._model(data, sequence=True)\n loss = self._loss(target, prediction)\n prediction = prediction.numpy()\n results = {m.__name__: m(target.numpy(), prediction) for m in self._metrics}\n results[\"loss\"] = loss.numpy()\n return results, prediction\n\n @staticmethod\n def _metrics_to_text(epoch: int, results: dict) -> str:\n text = f\"Epoch {epoch}: \" + \", \".join([f\"{k} = {v:.6f}\" for k, v in results.items()])\n return text\n\n def _is_metric_best(self, metric: float) -> bool:\n result = False\n if self._best_metric_value is None or metric == self._metric_comparator(metric, self._best_metric_value):\n self._best_metric_value = metric\n result = True\n return result\n\n def fit(self, train_x: np.ndarray, train_y: np.ndarray, test_x: np.ndarray, test_y: np.ndarray) -> Model:\n desc = self._metrics_to_text(0, {m.__name__: None for m in self._metrics})\n pbar = tqdm(total=self._epochs, desc=desc, ncols=100)\n\n train_x = tf.convert_to_tensor(value=train_x, dtype=self._model.dtype)\n train_y = tf.convert_to_tensor(value=train_y, dtype=self._model.dtype)\n test_x = tf.convert_to_tensor(value=test_x, dtype=self._model.dtype)\n test_y = tf.convert_to_tensor(value=test_y, dtype=self._model.dtype)\n\n for e in range(self._epochs):\n if e - self._best_epoch > self._early_stopping:\n break\n\n train_metrics, _ = self._train_step(train_x, train_y)\n test_metrics, test_prediction = self._prediction_step(test_x, test_y)\n pbar.set_description(self._metrics_to_text(e, test_metrics))\n pbar.update(1)\n\n self._visualizer.draw([test_prediction, test_y],\n train=[train_metrics[self._target_metric]],\n test=[test_metrics[self._target_metric]])\n\n if self._is_metric_best(test_metrics[self._target_metric]):\n self._model.save(self._model_path)\n self._best_epoch = e\n self._best_model = deepcopy(self._model)\n self._visualizer.save_fig(os.path.join(self._model_path, \"figure.jpg\"))\n\n return self._best_model\n","sub_path":"icscreator/kernel/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"268214888","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/mautrix/util/signed_token.py\n# Compiled at: 2019-11-21 01:01:47\n# Size of source mod 2**32: 1335 bytes\nfrom typing import Dict, Optional\nfrom hashlib import sha256\nimport hmac, json, base64\n\ndef _get_checksum(key: str, payload: bytes) -> str:\n hasher = hmac.new((key.encode('utf-8')), msg=payload, digestmod=sha256)\n checksum = base64.urlsafe_b64encode(hasher.digest())\n return checksum.decode('utf-8').rstrip('=')\n\n\ndef sign_token(key: str, payload: Dict) -> str:\n payload_b64 = base64.urlsafe_b64encode(json.dumps(payload).encode('utf-8'))\n checksum = _get_checksum(key, payload_b64)\n payload_str = payload_b64.decode('utf-8').rstrip('=')\n return f\"{checksum}:{payload_str}\"\n\n\ndef verify_token(key: str, data: str) -> Optional[Dict]:\n if not data:\n return\n try:\n checksum, payload = data.split(':', 1)\n except ValueError:\n return\n else:\n payload += (3 - (len(payload) + 3) % 4) * '='\n if checksum != _get_checksum(key, payload.encode('utf-8')):\n return\n payload = base64.urlsafe_b64decode(payload).decode('utf-8')\n try:\n return json.loads(payload)\n except json.JSONDecodeError:\n return","sub_path":"pycfiles/blkmautrix-0.0.18-py3.6/signed_token.cpython-36.py","file_name":"signed_token.cpython-36.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"461873043","text":"import sys\n\nentry = {}\nentry_rev = {}\n\n\ndef definition(vari, num):\n\n if vari in entry:\n del entry_rev[entry[vari]]\n entry[vari] = num\n entry_rev[num] = vari\n\n\ndef calc(alist):\n\n if len(list(filter(lambda z: z in entry.keys(), alist[1:][::2]))) != len(alist[1:][::2]):\n print(\" \".join(alist[1:]) + ' unknown')\n\n else:\n sum = eval(\n \" \".join(map(lambda z: entry[z] if z in entry.keys() else z, alist[1:-1])))\n\n if str(sum) in entry_rev:\n print(\" \".join(alist[1:]) + \" \" + entry_rev[str(sum)])\n\n else:\n print(\" \".join(alist[1:]) + ' unknown ')\n\n\nfor line in sys.stdin:\n func = line.split()[0]\n if func == 'def':\n definition(line.split()[1], line.split()[2])\n\n elif func == 'calc':\n calc(line.split())\n\n elif func == 'clear':\n entry.clear()\n entry_rev.clear()\n else:\n break\n\n# # 변수 3개 이상은?\n# # calc에 value만 넣어서 entry에서 확인\n\n# #calculation function 알고리즈 어디가 틀린지 찾아야함\n\n# #entry_rev를 만듬으로서 dictionary를 list로 접근하는 것에서 (O(n))\n# #O(1)로 바꿈\n\n# # for statement 보다 map(lambda), filter(lambda)가 더 빠르다.\n\n\ndef solution(p):\n next = True\n while(next):\n p += 1\n a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n first = p // 1000\n second = (p % 1000) // 100\n third = (p % 100) // 10\n fourth = p % 10\n count = 0\n number = [first, second, third, fourth]\n for i in number:\n if i in a:\n count += 1\n a.remove(i)\n if count == 4:\n next = False\n return p\n\n\nprint(solution(1987))\n","sub_path":"addingwords.py","file_name":"addingwords.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"197506808","text":"\"\"\"\nScript used for patching settings in\nNUnit assembly configuration files (xml format)\n\"\"\"\nimport xml.etree.ElementTree as ET\n\ndef get_setting_value_element(element_tree, setting_name):\n \"\"\" Get setting value element by name in a config element tree \"\"\"\n try:\n value = element_tree.find(\".//setting[@name='{}']\".format(setting_name)).find(\"value\")\n except:\n raise Exception(\"Setting with name=<{}> could not be found\".format(setting_name))\n return value\n\ndef peek_setting(element_tree, setting_name):\n \"\"\" Get setting text value by name in a config element tree \"\"\"\n value = get_setting_value_element(element_tree, setting_name)\n return value.text\n\ndef poke_setting(element_tree, setting_name, new_text_value):\n \"\"\" Set new setting text value by name in a config element tree \"\"\"\n value = get_setting_value_element(element_tree, setting_name)\n value.text = new_text_value\n\ndef do_replace(config_files, replace_settings):\n \"\"\" \n Set multiple settings in multiple config files\n \\nconfig_files - a list of files to patch\n \\nreplace_settings - a dictionary with settings names \n and new setting values\n \\nExample: do_replace([\"FileA.config\", \"FileB.config\"], {\"Setting1\" : \"Value1\", \"Setting2\" : \"Value2\"})\n \"\"\"\n for config_file in config_files:\n # Open configuration settings file as an element tree\n config_tree_root = ET.parse(config_file)\n print(\"*** Opened file <{}>\".format(config_file))\n\n # Replace all settings\n for setting_name, replace_value in replace_settings.items():\n old_value = peek_setting(config_tree_root, setting_name)\n poke_setting(config_tree_root, setting_name, replace_value)\n new_value = peek_setting(config_tree_root, setting_name)\n print(\"Changed setting <{}> from <{}> to <{}>\".format(setting_name, old_value, new_value))\n\n # Save modified tree to file\n modified_file = \"{}\".format(config_file)\n config_tree_root.write(modified_file)\n print(\"**** Saved changes to file <{}>\".format(modified_file))\n\n\ndef test_do_replace():\n do_replace(\n [\n r\"C:\\wa\\Sally\\trunk\\WIN32\\Output\\x86\\Debug\\CcbDeviceTests.dll.config\", \n r\"C:\\wa\\Sally\\trunk\\WIN32\\Output\\x86\\Debug\\DeflectionDeviceTests.dll.config\"\n ], \n {\n \"RemoteIp\" : \"171.0.14.254\",\n \"PowerBoxRelayIndexPower\" : \"0\"\n }\n )\n","sub_path":"xml_config/patchconfig.py","file_name":"patchconfig.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"480586508","text":"import datetime\nimport random\nfrom typing import Text, Tuple\n\nfrom currency_providers.driver import BaseProviderDriver\n\n\nclass Driver(BaseProviderDriver):\n base_url = \"http://data.fixer.io/api/\"\n access_key = \"1d3ef12f7d656b3e63cbee7e6874702f\"\n\n def __init__(self, base):\n super().__init__(base=base)\n self._mock_data = {}\n for currency in [\"EUR\", \"CHF\", \"USD\", \"GBP\"]:\n currency = self._format_currency(currency)\n self._mock_data[currency] = {}\n for i in range(0, 1400): # 4 años\n date = datetime.datetime.now() - datetime.timedelta(days=i)\n self._mock_data[currency][self._parse_date(date)] = random.random()\n\n @staticmethod\n def _format_currency(currency):\n return currency.upper()\n\n @staticmethod\n def _parse_date(date):\n return date.strftime(\"%Y-%m-%d\")\n\n def rates(self, currency: Text, date: datetime = None) -> Tuple[bool, float, datetime.datetime]:\n currency = self._format_currency(currency)\n if not date:\n date = datetime.datetime.now()\n date_str = self._parse_date(date)\n return True, self._mock_data[currency][date_str], date\n\n def exchanges(self, currency_origin: Text, currency_destination: Text, amount: int) -> Tuple[\n bool, float, datetime.datetime]:\n currency_origin = self._format_currency(currency_origin)\n currency_destination = self._format_currency(currency_destination)\n first_key = next(i for i in self._mock_data[currency_origin])\n amount_origin = self._mock_data[currency_origin][first_key]\n amount_destination = self._mock_data[currency_destination][first_key]\n return True, (amount * (amount_origin / amount_destination)), datetime.datetime.strptime(first_key, \"%Y-%m-%d\")\n\n def time_weighted_rate(self) -> Tuple[bool, float]:\n response, content = self.request.request(url=\"latest\", method=\"get\", data={\"access_key\": \"\"})\n return content\n","sub_path":"currency_providers/providers/mock/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"378671984","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport json\nimport time\nimport paho.mqtt.client as mqtt\nimport traceback\nimport os.path\n######## r3 ZMQ ############\n\nmyclientid_ = \"smallkiosk\"\ndht_sensordata_file_ = \"/tmp/dht11data.txt\"\nquery_sensor_intervall_ = 60\n\ndef sendR3Message(client, topic, datadict, qos=0, retain=False):\n client.publish(topic, json.dumps(datadict), qos, retain)\n\n\ndef decodeR3Payload(payload):\n try:\n return json.loads(payload.decode(\"utf-8\"))\n except Exception as e:\n print(\"Error decodeR3Payload:\" + str(e))\n return {}\n\n# reads data from dht_sensordata_file_\n# which is generated by cron every minute\n# crontab:\n# * * * * * /root/Adafruit_Python_DHT/examples/AdafruitDHT.py 11 30 > /tmp/dht11data.txt.new && mv /tmp/dht11data.txt.new /tmp/dht11data.txt\n\n\ndef getAndPublishDHT11SensorValues(client):\n data = \"\"\n ts = 0\n try:\n ts = int(os.path.getmtime(dht_sensordata_file_))\n with open(dht_sensordata_file_, \"r\") as dhtf:\n data = dhtf.read()\n except:\n return\n\n if data[:0 + 5] == \"Temp=\":\n temp = float(data[5:5 + 4])\n sendR3Message(client, \"realraum/\" + myclientid_ + \"/temperature\",\n {\"Location\": \"LoTHR\", \"Value\": temp, \"Ts\": ts}, retain=True)\n if data[12:12 + 9] == \"Humidity=\":\n humidity = float(data[21:21 + 4])\n sendR3Message(client,\n \"realraum/\" + myclientid_ + \"/relhumidity\",\n {\"Location\": \"LoTHR\",\n \"Percent\": humidity,\n \"Ts\": ts},\n retain=True)\n\ndef onMQTTDisconnect(mqttc, userdata, rc):\n if rc != 0:\n print(\"Unexpected disconnection.\")\n while True:\n time.sleep(5)\n print(\"Attempting reconnect\")\n try:\n mqttc.reconnect()\n break\n except ConnectionRefusedError:\n continue\n else:\n print(\"Clean disconnect.\")\n sys.exit()\n\n# Start zmq connection to publish / forward sensor data\ndef initMQTT():\n client = mqtt.Client(client_id=myclientid_)\n client.connect(\"mqtt.realraum.at\", 1883, keepalive=31)\n client.on_disconnect = onMQTTDisconnect\n return client\n\n\nif __name__ == '__main__':\n client = None\n last_get_sensor_data_ts = time.time()\n try:\n client = initMQTT()\n while True:\n if time.time() - last_get_sensor_data_ts > query_sensor_intervall_:\n getAndPublishDHT11SensorValues(client)\n last_get_sensor_data_ts = time.time()\n client.loop()\n\n except Exception as e:\n traceback.print_exc()\n finally:\n if isinstance(client, mqtt.Client):\n # client_stop_loop()\n client.disconnect()\n","sub_path":"scripts/dht11sensor.py","file_name":"dht11sensor.py","file_ext":"py","file_size_in_byte":2797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"314651743","text":"# imports\nimport numpy as np\nimport emcee as em\nimport corner\nfrom diag_depr import *\nfrom matplotlib import pyplot as plt\nfrom methods import *\nfrom problem_config import *\n\n'''\n'''\n'''\nemcee test case\n'''\n'''\n'''\n# %%\n\n\ndiag = Diagnostic(x, y, e, true_coeffs, total_samples, Post.posterior)\n\n# %%\ndiag.run(walker_list, show_error = False, export_results = True, plt_filename = 'diagnostics/walker_comp.png')\ndiag.export_df('walker_comparison')\n\n# %%\nprint(diag.df)\n\n# %%\nwalker_list = [250]\ndiag.run(walker_list, show_error = True, export_results = True, plt_filename = 'diagnostics/250_error_bands.png')\n# %%\ndiag.show_corner(export_results = True, path = 'diagnostics/250_walker_corner.png')\n\n# %%\ndiag.compare_model_vs_real()\n\n# %%\n\n\na = 0.2\nfor i in range(100):\n plt.plot(diag.fc[[i, i+1], 0], diag.fc[[i, i+1], 2], alpha = a, color = 'teal')\n # plt.scatter(diag.fc[i, 0], diag.fc[i, 2], alpha = a, color = 'black', s = 10)\n if a <= .98:\n a = a + .005\nplt.ylabel('a2')\nplt.xlabel('a0')\nplt.savefig('diagnostics/walker_example.png', dpi = 600)\nplt.show()\n\n\n# # metrop\n# s = Sampler(Post, metropolis, {'scale':1./20., 'iterations':1000000})\n# a_0 = np.array([1, 1, 1])\n# s.sample(a_0)\n# print(s.theta)\n#\n# corner.corner(s.theta)\n","sub_path":"Setup/Deprecated/testing_grounds.py","file_name":"testing_grounds.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"240072522","text":"from ctypes import *\nfrom ctypes.wintypes import *\nimport logging\nimport time\nfrom .Enum import *\nfrom .Constants import *\n\nLOGGER = logging.getLogger(__name__)\n\nSIMCONNECT_OBJECT_ID = DWORD\n\n\ndef IsHR(hr, value):\n _hr = ctypes.HRESULT(hr)\n return ctypes.c_ulong(_hr.value).value == value\n\n\ndef millis():\n return int(round(time.time() * 1000))\n\n\nclass sData(dict):\n __getattr__ = dict.__getitem__\n __setattr__ = dict.__setitem__\n __delattr__ = dict.__delitem__\n\n\nclass Request:\n def __init__(\n self,\n _name,\n _time=None,\n _DATA_DEFINITION_ID=SIMCONNECT_DATA_DEFINITION_ID,\n _DATA_REQUEST_ID=SIMCONNECT_DATA_REQUEST_ID,\n _psc=None,\n ):\n self.DATA_DEFINITION_ID = _DATA_DEFINITION_ID\n self.DATA_REQUEST_ID = _DATA_REQUEST_ID\n self.definitions = []\n self.time = _time\n self.timeout = millis()\n self.name = _name\n self.outData = {}\n self.psc = _psc\n self.psc.out_data[self.DATA_REQUEST_ID] = None\n\n def add(self, name, deff):\n self.definitions.append(deff)\n self.outData[name] = len(self.outData)\n self.psc.AddToDataDefinition(\n self.psc.hSimConnect,\n self.DATA_DEFINITION_ID.value,\n deff[0],\n deff[1],\n SIMCONNECT_DATATYPE.SIMCONNECT_DATATYPE_FLOAT64,\n 0,\n SIMCONNECT_UNUSED,\n )\n\n\nclass SimConnect:\n\n # TODO: update callbackfunction to expand functions.\n def my_dispatch_proc(self, pData, cbData, pContext):\n dwID = pData.contents.dwID\n self.pS = None\n if dwID == SIMCONNECT_RECV_ID.SIMCONNECT_RECV_ID_EVENT:\n evt = cast(pData, POINTER(SIMCONNECT_RECV_EVENT))\n uEventID = evt.contents.uEventID\n if uEventID == self.EventID.EVENT_SIM_START:\n LOGGER.info(\"SIM START\")\n\n elif dwID == SIMCONNECT_RECV_ID.SIMCONNECT_RECV_ID_SIMOBJECT_DATA_BYTYPE:\n pObjData = cast(\n pData, POINTER(SIMCONNECT_RECV_SIMOBJECT_DATA_BYTYPE)\n ).contents\n dwRequestID = pObjData.dwRequestID\n for _request in self.Requests:\n if dwRequestID == _request.DATA_REQUEST_ID.value:\n self.out_data[_request.DATA_REQUEST_ID] = cast(\n pObjData.dwData, POINTER(c_double * 200)\n ).contents\n elif dwID == SIMCONNECT_RECV_ID.SIMCONNECT_RECV_ID_OPEN:\n LOGGER.info(\"SIM OPEN\")\n\n elif dwID == SIMCONNECT_RECV_ID.SIMCONNECT_RECV_ID_QUIT:\n self.quit = 1\n else:\n LOGGER.debug(\"Received: {}\".format(dwID))\n return\n\n def __init__(self, auto_connect=True, library_path=\"./SimConnect.dll\"):\n\n self.EventID = SIMCONNECT_CLIENT_EVENT_ID\n self.DATA_DEFINITION_ID = SIMCONNECT_DATA_DEFINITION_ID\n self.DATA_REQUEST_ID = SIMCONNECT_DATA_REQUEST_ID\n self.GROUP_ID = SIMCONNECT_NOTIFICATION_GROUP_ID\n self.INPUT_GROUP_ID = SIMCONNECT_INPUT_GROUP_ID\n self.CLIENT_DATA_ID = SIMCONNECT_CLIENT_DATA_ID\n self.CLIENT_DATA_DEFINITION_ID = SIMCONNECT_CLIENT_DATA_DEFINITION_ID\n\n self.Requests = []\n self.out_data = {}\n\n self.SimConnect = cdll.LoadLibrary(library_path)\n self.set_attributes()\n\n if auto_connect:\n self.connect()\n\n def set_attributes(self):\n # SIMCONNECTAPI SimConnect_Open(\n # \tHANDLE * phSimConnect,\n # \tLPCSTR szName,\n # \tHWND hWnd,\n # \tDWORD UserEventWin32,\n # \tHANDLE hEventHandle,\n # \tDWORD ConfigIndex)\n\n self.__Open = self.SimConnect.SimConnect_Open\n self.__Open.restype = HRESULT\n self.__Open.argtypes = [POINTER(HANDLE), LPCSTR, HWND, DWORD, HANDLE, DWORD]\n\n # SIMCONNECTAPI SimConnect_Close(\n # \tHANDLE hSimConnect);\n\n self.__Close = self.SimConnect.SimConnect_Close\n self.__Close.restype = HRESULT\n self.__Close.argtypes = [HANDLE]\n\n # SIMCONNECTAPI SimConnect_AddToDataDefinition(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_DATA_DEFINITION_ID DefineID,\n # \tconst char * DatumName,\n # \tconst char * UnitsName,\n # \tSIMCONNECT_DATATYPE DatumType = SIMCONNECT_DATATYPE_FLOAT64,\n # \tfloat fEpsilon = 0,\n # \tDWORD DatumID = SIMCONNECT_UNUSED);\n\n self.AddToDataDefinition = self.SimConnect.SimConnect_AddToDataDefinition\n self.AddToDataDefinition.restype = HRESULT\n self.AddToDataDefinition.argtypes = [\n HANDLE,\n self.DATA_DEFINITION_ID,\n c_char_p,\n c_char_p,\n SIMCONNECT_DATATYPE,\n c_float,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_SubscribeToSystemEvent(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_CLIENT_EVENT_ID EventID,\n # \tconst char * SystemEventName);\n\n self.__SubscribeToSystemEvent = (\n self.SimConnect.SimConnect_SubscribeToSystemEvent\n )\n self.__SubscribeToSystemEvent.restype = HRESULT\n self.__SubscribeToSystemEvent.argtypes = [HANDLE, self.EventID, c_char_p]\n\n # SIMCONNECTAPI SimConnect_CallDispatch(\n # \tHANDLE hSimConnect,\n # \tDispatchProc pfcnDispatch,\n # \tvoid * pContext);\n\n DispatchProc = CFUNCTYPE(c_void_p, POINTER(SIMCONNECT_RECV), DWORD, c_void_p)\n\n self.__CallDispatch = self.SimConnect.SimConnect_CallDispatch\n self.__CallDispatch.restype = HRESULT\n self.__CallDispatch.argtypes = [HANDLE, DispatchProc, c_void_p]\n\n # SIMCONNECTAPI SimConnect_RequestDataOnSimObjectType(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_DATA_REQUEST_ID RequestID,\n # \tSIMCONNECT_DATA_DEFINITION_ID DefineID,\n # \tDWORD dwRadiusMeters,\n # \tSIMCONNECT_SIMOBJECT_TYPE type);\n\n self.__RequestDataOnSimObjectType = (\n self.SimConnect.SimConnect_RequestDataOnSimObjectType\n )\n self.__RequestDataOnSimObjectType.restype = HRESULT\n self.__RequestDataOnSimObjectType.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n self.DATA_DEFINITION_ID,\n DWORD,\n SIMCONNECT_SIMOBJECT_TYPE,\n ]\n\n # SIMCONNECTAPI SimConnect_SetDataOnSimObject(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_DATA_DEFINITION_ID DefineID,\n # \tSIMCONNECT_OBJECT_ID ObjectID,\n # \tSIMCONNECT_DATA_SET_FLAG Flags,\n # \tDWORD ArrayCount,\n # \tDWORD cbUnitSize,\n # \tvoid * pDataSet);\n\n # self.SetDataOnSimObject = self.SimConnect.SimConnect_SetDataOnSimObject\n # self.SetDataOnSimObject.restype = HRESULT\n # self.SetDataOnSimObject.argtypes = [HANDLE, DATA_DEFINE_ID, SIMCONNECT_OBJECT_ID, SIMCONNECT_DATA_SET_FLAG, DWORD, DWORD, ibbuf]\n\n # SIMCONNECTAPI SimConnect_TransmitClientEvent(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_OBJECT_ID ObjectID,\n # \tSIMCONNECT_CLIENT_EVENT_ID EventID,\n # \tDWORD dwData,\n # \tSIMCONNECT_NOTIFICATION_GROUP_ID GroupID,\n # \tSIMCONNECT_EVENT_FLAG Flags);\n\n self.__TransmitClientEvent = self.SimConnect.SimConnect_TransmitClientEvent\n self.__TransmitClientEvent.restype = HRESULT\n self.__TransmitClientEvent.argtypes = [\n HANDLE,\n SIMCONNECT_OBJECT_ID,\n self.EventID,\n DWORD,\n DWORD,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_MapClientEventToSimEvent(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_CLIENT_EVENT_ID EventID,\n # \tconst char * EventName = \"\");\n\n self.__MapClientEventToSimEvent = (\n self.SimConnect.SimConnect_MapClientEventToSimEvent\n )\n self.__MapClientEventToSimEvent.restype = HRESULT\n self.__MapClientEventToSimEvent.argtypes = [HANDLE, self.EventID, c_char_p]\n\n # SIMCONNECTAPI SimConnect_AddClientEventToNotificationGroup(\n # \tHANDLE hSimConnect,\n # \tSIMCONNECT_NOTIFICATION_GROUP_ID GroupID,\n # \tSIMCONNECT_CLIENT_EVENT_ID EventID,\n # \tBOOL bMaskable = FALSE);\n\n self.__AddClientEventToNotificationGroup = (\n self.SimConnect.SimConnect_AddClientEventToNotificationGroup\n )\n self.__AddClientEventToNotificationGroup.restype = HRESULT\n self.__AddClientEventToNotificationGroup.argtypes = [\n HANDLE,\n self.GROUP_ID,\n self.EventID,\n c_bool,\n ]\n\n # SIMCONNECTAPI SimConnect_SetSystemEventState(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID EventID\n # SIMCONNECT_STATE dwState);\n self.__SetSystemEventState = self.SimConnect.SimConnect_SetSystemEventState\n self.__SetSystemEventState.restype = HRESULT\n self.__SetSystemEventState.argtypes = [HANDLE, self.EventID, SIMCONNECT_STATE]\n\n # SIMCONNECTAPI SimConnect_AddClientEventToNotificationGroup(\n # HANDLE hSimConnect,\n # SIMCONNECT_NOTIFICATION_GROUP_ID GroupID\n # SIMCONNECT_CLIENT_EVENT_ID EventID\n # BOOL bMaskable = FALSE);\n self.__AddClientEventToNotificationGroup = (\n self.SimConnect.SimConnect_AddClientEventToNotificationGroup\n )\n self.__AddClientEventToNotificationGroup.restype = HRESULT\n self.__AddClientEventToNotificationGroup.argtypes = [\n HANDLE,\n self.GROUP_ID,\n self.EventID,\n c_bool,\n ]\n\n # SIMCONNECTAPI SimConnect_RemoveClientEvent(\n # HANDLE hSimConnect,\n # SIMCONNECT_NOTIFICATION_GROUP_ID GroupID\n # SIMCONNECT_CLIENT_EVENT_ID EventID);\n self.__RemoveClientEvent = self.SimConnect.SimConnect_RemoveClientEvent\n self.__RemoveClientEvent.restype = HRESULT\n self.__RemoveClientEvent.argtypes = [HANDLE, self.GROUP_ID, self.EventID]\n\n # SIMCONNECTAPI SimConnect_SetNotificationGroupPriority(\n # HANDLE hSimConnect,\n # SIMCONNECT_NOTIFICATION_GROUP_ID GroupID\n # DWORD uPriority);\n self.__SetNotificationGroupPriority = (\n self.SimConnect.SimConnect_SetNotificationGroupPriority\n )\n self.__SetNotificationGroupPriority.restype = HRESULT\n self.__SetNotificationGroupPriority.argtypes = [HANDLE, self.GROUP_ID, DWORD]\n\n # SIMCONNECTAPI SimConnect_ClearNotificationGroup(\n # HANDLE hSimConnect,\n # SIMCONNECT_NOTIFICATION_GROUP_ID GroupID);\n self.__ClearNotificationGroup = (\n self.SimConnect.SimConnect_ClearNotificationGroup\n )\n self.__ClearNotificationGroup.restype = HRESULT\n self.__ClearNotificationGroup.argtypes = [HANDLE, self.GROUP_ID]\n\n # SIMCONNECTAPI SimConnect_RequestNotificationGroup(\n # HANDLE hSimConnect,\n # SIMCONNECT_NOTIFICATION_GROUP_ID GroupID\n # DWORD dwReserved = 0\n # DWORD Flags = 0);\n self.__RequestNotificationGroup = (\n self.SimConnect.SimConnect_RequestNotificationGroup\n )\n self.__RequestNotificationGroup.restype = HRESULT\n self.__RequestNotificationGroup.argtypes = [HANDLE, self.GROUP_ID, DWORD, DWORD]\n\n # SIMCONNECTAPI SimConnect_ClearDataDefinition(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_DEFINITION_ID DefineID);\n self.__ClearDataDefinition = self.SimConnect.SimConnect_ClearDataDefinition\n self.__ClearDataDefinition.restype = HRESULT\n self.__ClearDataDefinition.argtypes = [HANDLE, self.DATA_DEFINITION_ID]\n\n # SIMCONNECTAPI SimConnect_RequestDataOnSimObject(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # SIMCONNECT_DATA_DEFINITION_ID DefineID\n # SIMCONNECT_OBJECT_ID ObjectID\n # SIMCONNECT_PERIOD Period\n # SIMCONNECT_DATA_REQUEST_FLAG Flags = 0\n # DWORD origin = 0\n # DWORD interval = 0\n # DWORD limit = 0);\n self.__RequestDataOnSimObject = (\n self.SimConnect.SimConnect_RequestDataOnSimObject\n )\n self.__RequestDataOnSimObject.restype = HRESULT\n self.__RequestDataOnSimObject.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n self.DATA_DEFINITION_ID,\n SIMCONNECT_OBJECT_ID,\n SIMCONNECT_PERIOD,\n SIMCONNECT_DATA_REQUEST_FLAG,\n DWORD,\n DWORD,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_SetDataOnSimObject(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_DEFINITION_ID DefineID\n # SIMCONNECT_OBJECT_ID ObjectID\n # SIMCONNECT_DATA_SET_FLAG Flags\n # DWORD ArrayCount\n # DWORD cbUnitSize\n # void * pDataSet);\n self.__SetDataOnSimObject = self.SimConnect.SimConnect_SetDataOnSimObject\n self.__SetDataOnSimObject.restype = HRESULT\n self.__SetDataOnSimObject.argtypes = [\n HANDLE,\n self.DATA_DEFINITION_ID,\n SIMCONNECT_OBJECT_ID,\n SIMCONNECT_DATA_SET_FLAG,\n DWORD,\n DWORD,\n c_void_p,\n ]\n\n # SIMCONNECTAPI SimConnect_MapInputEventToClientEvent(\n # HANDLE hSimConnect,\n # SIMCONNECT_INPUT_GROUP_ID GroupID\n # const char * szInputDefinition\n # SIMCONNECT_CLIENT_EVENT_ID DownEventID\n # DWORD DownValue = 0\n # SIMCONNECT_CLIENT_EVENT_ID UpEventID = (SIMCONNECT_CLIENT_EVENT_ID)SIMCONNECT_UNUSED\n # DWORD UpValue = 0\n # BOOL bMaskable = FALSE);\n self.__MapInputEventToClientEvent = (\n self.SimConnect.SimConnect_MapInputEventToClientEvent\n )\n self.__MapInputEventToClientEvent.restype = HRESULT\n self.__MapInputEventToClientEvent.argtypes = [\n HANDLE,\n self.INPUT_GROUP_ID,\n c_char_p,\n self.EventID,\n DWORD,\n self.EventID,\n DWORD,\n c_bool,\n ]\n\n # SIMCONNECTAPI SimConnect_SetInputGroupPriority(\n # HANDLE hSimConnect,\n # SIMCONNECT_INPUT_GROUP_ID GroupID\n # DWORD uPriority);\n self.__SetInputGroupPriority = self.SimConnect.SimConnect_SetInputGroupPriority\n self.__SetInputGroupPriority.restype = HRESULT\n self.__SetInputGroupPriority.argtypes = [HANDLE, self.INPUT_GROUP_ID, DWORD]\n\n # SIMCONNECTAPI SimConnect_RemoveInputEvent(\n # HANDLE hSimConnect,\n # SIMCONNECT_INPUT_GROUP_ID GroupID\n # const char * szInputDefinition);\n self.__RemoveInputEvent = self.SimConnect.SimConnect_RemoveInputEvent\n self.__RemoveInputEvent.restype = HRESULT\n self.__RemoveInputEvent.argtypes = [HANDLE, self.INPUT_GROUP_ID, c_char_p]\n\n # SIMCONNECTAPI SimConnect_ClearInputGroup(\n # HANDLE hSimConnect,\n # SIMCONNECT_INPUT_GROUP_ID GroupID);\n self.__ClearInputGroup = self.SimConnect.SimConnect_ClearInputGroup\n self.__ClearInputGroup.restype = HRESULT\n self.__ClearInputGroup.argtypes = [HANDLE, self.INPUT_GROUP_ID]\n\n # SIMCONNECTAPI SimConnect_SetInputGroupState(\n # HANDLE hSimConnect,\n # SIMCONNECT_INPUT_GROUP_ID GroupID\n # DWORD dwState);\n self.__SetInputGroupState = self.SimConnect.SimConnect_SetInputGroupState\n self.__SetInputGroupState.restype = HRESULT\n self.__SetInputGroupState.argtypes = [HANDLE, self.INPUT_GROUP_ID, DWORD]\n\n # SIMCONNECTAPI SimConnect_RequestReservedKey(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID EventID\n # const char * szKeyChoice1 = \"\"\n # const char * szKeyChoice2 = \"\"\n # const char * szKeyChoice3 = \"\");\n self.__RequestReservedKey = self.SimConnect.SimConnect_RequestReservedKey\n self.__RequestReservedKey.restype = HRESULT\n self.__RequestReservedKey.argtypes = [\n HANDLE,\n self.EventID,\n c_char_p,\n c_char_p,\n c_char_p,\n ]\n\n # SIMCONNECTAPI SimConnect_UnsubscribeFromSystemEvent(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID EventID);\n self.__UnsubscribeFromSystemEvent = (\n self.SimConnect.SimConnect_UnsubscribeFromSystemEvent\n )\n self.__UnsubscribeFromSystemEvent.restype = HRESULT\n self.__UnsubscribeFromSystemEvent.argtypes = [HANDLE, self.EventID]\n\n # SIMCONNECTAPI SimConnect_WeatherRequestInterpolatedObservation(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # float lat\n # float lon\n # float alt);\n self.__WeatherRequestInterpolatedObservation = (\n self.SimConnect.SimConnect_WeatherRequestInterpolatedObservation\n )\n self.__WeatherRequestInterpolatedObservation.restype = HRESULT\n self.__WeatherRequestInterpolatedObservation.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_float,\n c_float,\n c_float,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherRequestObservationAtStation(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # const char * szICAO);\n self.__WeatherRequestObservationAtStation = (\n self.SimConnect.SimConnect_WeatherRequestObservationAtStation\n )\n self.__WeatherRequestObservationAtStation.restype = HRESULT\n self.__WeatherRequestObservationAtStation.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_char_p,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherRequestObservationAtNearestStation(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # float lat\n # float lon);\n self.__WeatherRequestObservationAtNearestStation = (\n self.SimConnect.SimConnect_WeatherRequestObservationAtNearestStation\n )\n self.__WeatherRequestObservationAtNearestStation.restype = HRESULT\n self.__WeatherRequestObservationAtNearestStation.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_float,\n c_float,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherCreateStation(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # const char * szICAO\n # const char * szName\n # float lat\n # float lon\n # float alt);\n self.__WeatherCreateStation = self.SimConnect.SimConnect_WeatherCreateStation\n self.__WeatherCreateStation.restype = HRESULT\n self.__WeatherCreateStation.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_char_p,\n c_char_p,\n c_float,\n c_float,\n c_float,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherRemoveStation(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # const char * szICAO);\n self.__WeatherRemoveStation = self.SimConnect.SimConnect_WeatherRemoveStation\n self.__WeatherRemoveStation.restype = HRESULT\n self.__WeatherRemoveStation.argtypes = [HANDLE, self.DATA_REQUEST_ID, c_char_p]\n\n # SIMCONNECTAPI SimConnect_WeatherSetObservation(\n # HANDLE hSimConnect,\n # DWORD Seconds\n # const char * szMETAR);\n self.__WeatherSetObservation = self.SimConnect.SimConnect_WeatherSetObservation\n self.__WeatherSetObservation.restype = HRESULT\n self.__WeatherSetObservation.argtypes = [HANDLE, DWORD, c_char_p]\n\n # SIMCONNECTAPI SimConnect_WeatherSetModeServer(\n # HANDLE hSimConnect,\n # DWORD dwPort\n # DWORD dwSeconds);\n self.__WeatherSetModeServer = self.SimConnect.SimConnect_WeatherSetModeServer\n self.__WeatherSetModeServer.restype = HRESULT\n self.__WeatherSetModeServer.argtypes = [HANDLE, DWORD, DWORD]\n\n # SIMCONNECTAPI SimConnect_WeatherSetModeTheme(\n # HANDLE hSimConnect,\n # const char * szThemeName);\n self.__WeatherSetModeTheme = self.SimConnect.SimConnect_WeatherSetModeTheme\n self.__WeatherSetModeTheme.restype = HRESULT\n self.__WeatherSetModeTheme.argtypes = [HANDLE, c_char_p]\n\n # SIMCONNECTAPI SimConnect_WeatherSetModeGlobal(\n # HANDLE hSimConnect);\n self.__WeatherSetModeGlobal = self.SimConnect.SimConnect_WeatherSetModeGlobal\n self.__WeatherSetModeGlobal.restype = HRESULT\n self.__WeatherSetModeGlobal.argtypes = [HANDLE]\n\n # SIMCONNECTAPI SimConnect_WeatherSetModeCustom(\n # HANDLE hSimConnect);\n self.__WeatherSetModeCustom = self.SimConnect.SimConnect_WeatherSetModeCustom\n self.__WeatherSetModeCustom.restype = HRESULT\n self.__WeatherSetModeCustom.argtypes = [HANDLE]\n\n # SIMCONNECTAPI SimConnect_WeatherSetDynamicUpdateRate(\n # HANDLE hSimConnect,\n # DWORD dwRate);\n self.__WeatherSetDynamicUpdateRate = (\n self.SimConnect.SimConnect_WeatherSetDynamicUpdateRate\n )\n self.__WeatherSetDynamicUpdateRate.restype = HRESULT\n self.__WeatherSetDynamicUpdateRate.argtypes = [HANDLE, DWORD]\n\n # SIMCONNECTAPI SimConnect_WeatherRequestCloudState(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # float minLat\n # float minLon\n # float minAlt\n # float maxLat\n # float maxLon\n # float maxAlt\n # DWORD dwFlags = 0);\n self.__WeatherRequestCloudState = (\n self.SimConnect.SimConnect_WeatherRequestCloudState\n )\n self.__WeatherRequestCloudState.restype = HRESULT\n self.__WeatherRequestCloudState.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherCreateThermal(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # float lat\n # float lon\n # float alt\n # float radius\n # float height\n # float coreRate = 3.0f\n # float coreTurbulence = 0.05f\n # float sinkRate = 3.0f\n # float sinkTurbulence = 0.2f\n # float coreSize = 0.4f\n # float coreTransitionSize = 0.1f\n # float sinkLayerSize = 0.4f\n # float sinkTransitionSize = 0.1f);\n self.__WeatherCreateThermal = self.SimConnect.SimConnect_WeatherCreateThermal\n self.__WeatherCreateThermal.restype = HRESULT\n self.__WeatherCreateThermal.argtypes = [\n HANDLE,\n self.DATA_REQUEST_ID,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n c_float,\n ]\n\n # SIMCONNECTAPI SimConnect_WeatherRemoveThermal(\n # HANDLE hSimConnect,\n # SIMCONNECT_OBJECT_ID ObjectID);\n self.__WeatherRemoveThermal = self.SimConnect.SimConnect_WeatherRemoveThermal\n self.__WeatherRemoveThermal.restype = HRESULT\n self.__WeatherRemoveThermal.argtypes = [HANDLE, SIMCONNECT_OBJECT_ID]\n\n # SIMCONNECTAPI SimConnect_AICreateParkedATCAircraft(\n # HANDLE hSimConnect,\n # const char * szContainerTitle\n # const char * szTailNumber\n # const char * szAirportID\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AICreateParkedATCAircraft = (\n self.SimConnect.SimConnect_AICreateParkedATCAircraft\n )\n self.__AICreateParkedATCAircraft.restype = HRESULT\n self.__AICreateParkedATCAircraft.argtypes = [\n HANDLE,\n c_char_p,\n c_char_p,\n c_char_p,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AICreateEnrouteATCAircraft(\n # HANDLE hSimConnect,\n # const char * szContainerTitle\n # const char * szTailNumber\n # int iFlightNumber\n # const char * szFlightPlanPath\n # double dFlightPlanPosition\n # BOOL bTouchAndGo\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AICreateEnrouteATCAircraft = (\n self.SimConnect.SimConnect_AICreateEnrouteATCAircraft\n )\n self.__AICreateEnrouteATCAircraft.restype = HRESULT\n self.__AICreateEnrouteATCAircraft.argtypes = [\n HANDLE,\n c_char_p,\n c_char_p,\n c_int,\n c_char_p,\n c_double,\n c_bool,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AICreateNonATCAircraft(\n # HANDLE hSimConnect,\n # const char * szContainerTitle\n # const char * szTailNumber\n # SIMCONNECT_DATA_INITPOSITION InitPos\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AICreateNonATCAircraft = (\n self.SimConnect.SimConnect_AICreateNonATCAircraft\n )\n self.__AICreateNonATCAircraft.restype = HRESULT\n self.__AICreateNonATCAircraft.argtypes = [\n HANDLE,\n c_double,\n c_double,\n SIMCONNECT_DATA_INITPOSITION,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AICreateSimulatedObject(\n # HANDLE hSimConnect,\n # const char * szContainerTitle\n # SIMCONNECT_DATA_INITPOSITION InitPos\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AICreateSimulatedObject = (\n self.SimConnect.SimConnect_AICreateSimulatedObject\n )\n self.__AICreateSimulatedObject.restype = HRESULT\n self.__AICreateSimulatedObject.argtypes = [\n HANDLE,\n c_char_p,\n SIMCONNECT_DATA_INITPOSITION,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AIReleaseControl(\n # HANDLE hSimConnect,\n # SIMCONNECT_OBJECT_ID ObjectID\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AIReleaseControl = self.SimConnect.SimConnect_AIReleaseControl\n self.__AIReleaseControl.restype = HRESULT\n self.__AIReleaseControl.argtypes = [\n HANDLE,\n SIMCONNECT_OBJECT_ID,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AIRemoveObject(\n # HANDLE hSimConnect,\n # SIMCONNECT_OBJECT_ID ObjectID\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AIRemoveObject = self.SimConnect.SimConnect_AIRemoveObject\n self.__AIRemoveObject.restype = HRESULT\n self.__AIRemoveObject.argtypes = [\n HANDLE,\n SIMCONNECT_OBJECT_ID,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_AISetAircraftFlightPlan(\n # HANDLE hSimConnect,\n # SIMCONNECT_OBJECT_ID ObjectID\n # const char * szFlightPlanPath\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__AISetAircraftFlightPlan = (\n self.SimConnect.SimConnect_AISetAircraftFlightPlan\n )\n self.__AISetAircraftFlightPlan.restype = HRESULT\n self.__AISetAircraftFlightPlan.argtypes = [\n HANDLE,\n SIMCONNECT_OBJECT_ID,\n c_char_p,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_ExecuteMissionAction(\n # HANDLE hSimConnect,\n # const GUID guidInstanceId);\n self.__ExecuteMissionAction = self.SimConnect.SimConnect_ExecuteMissionAction\n self.__ExecuteMissionAction.restype = HRESULT\n self.__ExecuteMissionAction.argtypes = []\n\n # SIMCONNECTAPI SimConnect_CompleteCustomMissionAction(\n # HANDLE hSimConnect,\n # const GUID guidInstanceId);\n self.__CompleteCustomMissionAction = (\n self.SimConnect.SimConnect_CompleteCustomMissionAction\n )\n self.__CompleteCustomMissionAction.restype = HRESULT\n self.__CompleteCustomMissionAction.argtypes = []\n\n # SIMCONNECTAPI SimConnect_RetrieveString(\n # SIMCONNECT_RECV * pData,\n # DWORD cbData\n # void * pStringV\n # char ** pszString\n # DWORD * pcbString);\n self.__RetrieveString = self.SimConnect.SimConnect_RetrieveString\n self.__RetrieveString.restype = HRESULT\n self.__RetrieveString.argtypes = []\n\n # SIMCONNECTAPI SimConnect_GetLastSentPacketID(\n # HANDLE hSimConnect,\n # DWORD * pdwError);\n self.__GetLastSentPacketID = self.SimConnect.SimConnect_GetLastSentPacketID\n self.__GetLastSentPacketID.restype = HRESULT\n self.__GetLastSentPacketID.argtypes = [HANDLE, DWORD]\n\n # SIMCONNECTAPI SimConnect_GetNextDispatch(\n # HANDLE hSimConnect,\n # SIMCONNECT_RECV ** ppData\n # DWORD * pcbData);\n self.__GetNextDispatch = self.SimConnect.SimConnect_GetNextDispatch\n self.__GetNextDispatch.restype = HRESULT\n self.__GetNextDispatch.argtypes = []\n\n # SIMCONNECTAPI SimConnect_RequestResponseTimes(\n # HANDLE hSimConnect,\n # DWORD nCount\n # float * fElapsedSeconds);\n self.__RequestResponseTimes = self.SimConnect.SimConnect_RequestResponseTimes\n self.__RequestResponseTimes.restype = HRESULT\n self.__RequestResponseTimes.argtypes = [HANDLE, DWORD, c_float]\n\n # SIMCONNECTAPI SimConnect_InsertString(\n # char * pDest,\n # DWORD cbDest\n # void ** ppEnd\n # DWORD * pcbStringV\n # const char * pSource);\n self.__InsertString = self.SimConnect.SimConnect_InsertString\n self.__InsertString.restype = HRESULT\n self.__InsertString.argtypes = []\n\n # SIMCONNECTAPI SimConnect_CameraSetRelative6DOF(\n # HANDLE hSimConnect,\n # float fDeltaX\n # float fDeltaY\n # float fDeltaZ\n # float fPitchDeg\n # float fBankDeg\n # float fHeadingDeg);\n self.__CameraSetRelative6DOF = self.SimConnect.SimConnect_CameraSetRelative6DOF\n self.__CameraSetRelative6DOF.restype = HRESULT\n self.__CameraSetRelative6DOF.argtypes = []\n\n # SIMCONNECTAPI SimConnect_MenuAddItem(\n # HANDLE hSimConnect,\n # const char * szMenuItem\n # SIMCONNECT_CLIENT_EVENT_ID MenuEventID\n # DWORD dwData);\n self.__MenuAddItem = self.SimConnect.SimConnect_MenuAddItem\n self.__MenuAddItem.restype = HRESULT\n self.__MenuAddItem.argtypes = []\n\n # SIMCONNECTAPI SimConnect_MenuDeleteItem(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID MenuEventID);\n self.__MenuDeleteItem = self.SimConnect.SimConnect_MenuDeleteItem\n self.__MenuDeleteItem.restype = HRESULT\n self.__MenuDeleteItem.argtypes = []\n\n # SIMCONNECTAPI SimConnect_MenuAddSubItem(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID MenuEventID\n # const char * szMenuItem\n # SIMCONNECT_CLIENT_EVENT_ID SubMenuEventID\n # DWORD dwData);\n self.__MenuAddSubItem = self.SimConnect.SimConnect_MenuAddSubItem\n self.__MenuAddSubItem.restype = HRESULT\n self.__MenuAddSubItem.argtypes = []\n\n # SIMCONNECTAPI SimConnect_MenuDeleteSubItem(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_EVENT_ID MenuEventID\n # const SIMCONNECT_CLIENT_EVENT_ID SubMenuEventID);\n self.__MenuDeleteSubItem = self.SimConnect.SimConnect_MenuDeleteSubItem\n self.__MenuDeleteSubItem.restype = HRESULT\n self.__MenuDeleteSubItem.argtypes = []\n\n # SIMCONNECTAPI SimConnect_RequestSystemState(\n # HANDLE hSimConnect,\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # const char * szState);\n self.__RequestSystemState = self.SimConnect.SimConnect_RequestSystemState\n self.__RequestSystemState.restype = HRESULT\n self.__RequestSystemState.argtypes = []\n\n # SIMCONNECTAPI SimConnect_SetSystemState(\n # HANDLE hSimConnect,\n # const char * szState\n # DWORD dwInteger\n # float fFloat\n # const char * szString);\n self.__SetSystemState = self.SimConnect.SimConnect_SetSystemState\n self.__SetSystemState.restype = HRESULT\n self.__SetSystemState.argtypes = []\n\n # SIMCONNECTAPI SimConnect_MapClientDataNameToID(\n # HANDLE hSimConnect,\n # const char * szClientDataName\n # SIMCONNECT_CLIENT_DATA_ID ClientDataID);\n self.__MapClientDataNameToID = self.SimConnect.SimConnect_MapClientDataNameToID\n self.__MapClientDataNameToID.restype = HRESULT\n self.__MapClientDataNameToID.argtypes = []\n\n # SIMCONNECTAPI SimConnect_CreateClientData(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_DATA_ID ClientDataID\n # DWORD dwSize\n # SIMCONNECT_CREATE_CLIENT_DATA_FLAG Flags);\n self.__CreateClientData = self.SimConnect.SimConnect_CreateClientData\n self.__CreateClientData.restype = HRESULT\n self.__CreateClientData.argtypes = [\n HANDLE,\n self.CLIENT_DATA_ID,\n DWORD,\n SIMCONNECT_CREATE_CLIENT_DATA_FLAG,\n ]\n\n # SIMCONNECTAPI SimConnect_AddToClientDataDefinition(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_DATA_DEFINITION_ID DefineID\n # DWORD dwOffset\n # DWORD dwSizeOrType\n # float fEpsilon = 0\n # DWORD DatumID = SIMCONNECT_UNUSED);\n self.__AddToClientDataDefinition = (\n self.SimConnect.SimConnect_AddToClientDataDefinition\n )\n self.__AddToClientDataDefinition.restype = HRESULT\n self.__AddToClientDataDefinition.argtypes = [\n HANDLE,\n self.CLIENT_DATA_DEFINITION_ID,\n DWORD,\n DWORD,\n c_float,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_ClearClientDataDefinition(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_DATA_DEFINITION_ID DefineID);\n self.__ClearClientDataDefinition = (\n self.SimConnect.SimConnect_ClearClientDataDefinition\n )\n self.__ClearClientDataDefinition.restype = HRESULT\n self.__ClearClientDataDefinition.argtypes = [\n HANDLE,\n self.CLIENT_DATA_DEFINITION_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_RequestClientData(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_DATA_ID ClientDataID\n # SIMCONNECT_DATA_REQUEST_ID RequestID\n # SIMCONNECT_CLIENT_DATA_DEFINITION_ID DefineID\n # SIMCONNECT_CLIENT_DATA_PERIOD Period = SIMCONNECT_CLIENT_DATA_PERIOD_ONCE\n # SIMCONNECT_CLIENT_DATA_REQUEST_FLAG Flags = 0\n # DWORD origin = 0\n # DWORD interval = 0\n # DWORD limit = 0);\n self.__RequestClientData = self.SimConnect.SimConnect_RequestClientData\n self.__RequestClientData.restype = HRESULT\n self.__RequestClientData.argtypes = [\n HANDLE,\n self.CLIENT_DATA_ID,\n self.DATA_REQUEST_ID,\n self.CLIENT_DATA_DEFINITION_ID,\n SIMCONNECT_CLIENT_DATA_PERIOD,\n SIMCONNECT_CLIENT_DATA_REQUEST_FLAG,\n DWORD,\n DWORD,\n DWORD,\n ]\n\n # SIMCONNECTAPI SimConnect_SetClientData(\n # HANDLE hSimConnect,\n # SIMCONNECT_CLIENT_DATA_ID ClientDataID\n # SIMCONNECT_CLIENT_DATA_DEFINITION_ID DefineID\n # SIMCONNECT_CLIENT_DATA_SET_FLAG Flags\n # DWORD dwReserved\n # DWORD cbUnitSize\n # void * pDataSet);\n self.__SetClientData = self.SimConnect.SimConnect_SetClientData\n self.__SetClientData.restype = HRESULT\n self.__SetClientData.argtypes = [\n HANDLE,\n self.CLIENT_DATA_ID,\n self.CLIENT_DATA_DEFINITION_ID,\n SIMCONNECT_CLIENT_DATA_SET_FLAG,\n DWORD,\n DWORD,\n c_void_p,\n ]\n\n # SIMCONNECTAPI SimConnect_FlightLoad(\n # HANDLE hSimConnect,\n # const char * szFileName);\n self.__FlightLoad = self.SimConnect.SimConnect_FlightLoad\n self.__FlightLoad.restype = HRESULT\n self.__FlightLoad.argtypes = [HANDLE, c_char_p]\n\n # SIMCONNECTAPI SimConnect_FlightSave(\n # HANDLE hSimConnect,\n # const char * szFileName\n # const char * szTitle\n # const char * szDescription\n # DWORD Flags);\n self.__FlightSave = self.SimConnect.SimConnect_FlightSave\n self.__FlightSave.restype = HRESULT\n self.__FlightSave.argtypes = [HANDLE, c_char_p, c_char_p, c_char_p, DWORD]\n\n # SIMCONNECTAPI SimConnect_FlightPlanLoad(\n # HANDLE hSimConnect,\n # const char * szFileName);\n self.__FlightPlanLoad = self.SimConnect.SimConnect_FlightPlanLoad\n self.__FlightPlanLoad.restype = HRESULT\n self.__FlightPlanLoad.argtypes = [HANDLE, c_char_p]\n\n # SIMCONNECTAPI SimConnect_Text(\n # HANDLE hSimConnect,\n # SIMCONNECT_TEXT_TYPE type\n # float fTimeSeconds\n # SIMCONNECT_CLIENT_EVENT_ID EventID\n # DWORD cbUnitSize\n # void * pDataSet);\n self.__Text = self.SimConnect.SimConnect_Text\n self.__Text.restype = HRESULT\n self.__Text.argtypes = [\n HANDLE,\n SIMCONNECT_TEXT_TYPE,\n c_float,\n self.EventID,\n DWORD,\n c_void_p,\n ]\n\n # SIMCONNECTAPI SimConnect_SubscribeToFacilities(\n # HANDLE hSimConnect,\n # SIMCONNECT_FACILITY_LIST_TYPE type\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.__SubscribeToFacilities = self.SimConnect.SimConnect_SubscribeToFacilities\n self.__SubscribeToFacilities.restype = HRESULT\n self.__SubscribeToFacilities.argtypes = [\n HANDLE,\n SIMCONNECT_FACILITY_LIST_TYPE,\n self.DATA_REQUEST_ID,\n ]\n\n # SIMCONNECTAPI SimConnect_UnsubscribeToFacilities(\n # HANDLE hSimConnect,\n # SIMCONNECT_FACILITY_LIST_TYPE type);\n self.__UnsubscribeToFacilities = (\n self.SimConnect.SimConnect_UnsubscribeToFacilities\n )\n self.__UnsubscribeToFacilities.restype = HRESULT\n self.__UnsubscribeToFacilities.argtypes = [\n HANDLE,\n SIMCONNECT_FACILITY_LIST_TYPE,\n ]\n\n # SIMCONNECTAPI SimConnect_RequestFacilitiesList(\n # HANDLE hSimConnect,\n # SIMCONNECT_FACILITY_LIST_TYPE type\n # SIMCONNECT_DATA_REQUEST_ID RequestID);\n self.____RequestFacilitiesList = (\n self.SimConnect.SimConnect_RequestFacilitiesList\n )\n self.____RequestFacilitiesList.restype = HRESULT\n self.____RequestFacilitiesList.argtypes = [\n HANDLE,\n SIMCONNECT_FACILITY_LIST_TYPE,\n self.DATA_REQUEST_ID,\n ]\n\n self.hSimConnect = HANDLE()\n self.quit = 0\n self.my_dispatch_proc_rd = DispatchProc(self.my_dispatch_proc)\n # self.haveData = False\n\n def connect(self):\n try:\n err = self.__Open(\n byref(self.hSimConnect), LPCSTR(b\"Request Data\"), None, 0, 0, 0\n )\n if IsHR(err, 0):\n LOGGER.debug(\"Connected to Flight Simulator!\")\n # Set up the data definition, but do not yet do anything with itd\n # Request an event when the simulation starts\n self.__SubscribeToSystemEvent(\n self.hSimConnect, self.EventID.EVENT_SIM_START, b\"SimStart\"\n )\n except OSError:\n LOGGER.debug(\"Did not find Flight Simulator running.\")\n exit(0)\n\n def run(self):\n for _request in self.Requests:\n self.out_data[_request.DATA_REQUEST_ID] = None\n if _request.time is not None:\n if (_request.timeout + _request.time) < millis():\n self.request_data(_request)\n _request.timeout = millis()\n\n self.__CallDispatch(self.hSimConnect, self.my_dispatch_proc_rd, None)\n\n def exit(self):\n self.__Close(self.hSimConnect)\n\n def map_to_sim_event(self, name):\n for m in self.EventID:\n if name.decode() == m.name:\n LOGGER.debug(\"Already have event: {}\".format(m))\n return m\n\n names = [m.name for m in self.EventID] + [name.decode()]\n self.EventID = Enum(self.EventID.__name__, names)\n evnt = list(self.EventID)[-1]\n err = self.__MapClientEventToSimEvent(self.hSimConnect, evnt.value, name)\n if IsHR(err, 0):\n return evnt\n else:\n LOGGER.error(\"Error: MapToSimEvent\")\n return None\n\n def add_to_notification_group(self, group, evnt, bMaskable=False):\n self.__AddClientEventToNotificationGroup(\n self.hSimConnect, group, evnt, bMaskable\n )\n\n def request_data(self, _Request):\n self.out_data[_Request.DATA_REQUEST_ID] = None\n self.__RequestDataOnSimObjectType(\n self.hSimConnect,\n _Request.DATA_REQUEST_ID.value,\n _Request.DATA_DEFINITION_ID.value,\n 0,\n SIMCONNECT_SIMOBJECT_TYPE.SIMCONNECT_SIMOBJECT_TYPE_USER,\n )\n\n def get_data(self, _Request, _format=False):\n LOGGER.debug(\"try to get data\")\n\n if self.out_data[_Request.DATA_REQUEST_ID] is None:\n LOGGER.debug(\"no data found\")\n return None\n\n if _format:\n map = {}\n else:\n map = sData\n\n for od in _Request.outData:\n data = self.out_data[_Request.DATA_REQUEST_ID][_Request.outData[od]]\n LOGGER.debug(\"got key {}, value {}\".format(od, data))\n if _format:\n map[od] = data\n else:\n setattr(map, od, data)\n\n return map\n\n def send_data(self, evnt, data=DWORD(0)):\n err = self.__TransmitClientEvent(\n self.hSimConnect,\n SIMCONNECT_OBJECT_ID_USER,\n evnt.value,\n data,\n SIMCONNECT_GROUP_PRIORITY_HIGHEST,\n DWORD(16),\n )\n if IsHR(err, 0):\n LOGGER.debug(\"Event Sent\")\n return True\n else:\n return False\n\n def new_request(self, time=None):\n name = \"Request\" + str(len(self.Requests))\n names = [m.name for m in self.DATA_DEFINITION_ID] + [name]\n self.DATA_DEFINITION_ID = Enum(self.DATA_DEFINITION_ID.__name__, names)\n DEFINITION_ID = list(self.DATA_DEFINITION_ID)[-1]\n\n names = [m.name for m in self.DATA_REQUEST_ID] + [name]\n self.DATA_REQUEST_ID = Enum(self.DATA_REQUEST_ID.__name__, names)\n REQUEST_ID = list(self.DATA_REQUEST_ID)[-1]\n\n _Request = Request(\n _DATA_DEFINITION_ID=DEFINITION_ID,\n _DATA_REQUEST_ID=REQUEST_ID,\n _time=time,\n _name=name,\n _psc=self,\n )\n self.Requests.append(_Request)\n return _Request\n","sub_path":"SimConnect/SimConnect.py","file_name":"SimConnect.py","file_ext":"py","file_size_in_byte":43537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"594356714","text":"#!/usr/bin/env python2\n\nimport random\nfrom sys import argv\n\nif __name__ == '__main__':\n\tcols = int(argv[1])\n\tout = open('./data.csv', 'w')\n\tfor i in range(60000):\n\t\tfor i in range(cols-1):\n\t\t\tout.write(str(random.random()) + \",\")\n\t\tout.write(str(random.random()) + '\\n')\n\tout.close()\n","sub_path":"docker/host/gen_data.py","file_name":"gen_data.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"209516689","text":"import pandas as pd\nimport numpy as np\nimport dateandtime\n\ndef preprocess(data):\n data['MTS'] = data.apply((lambda x: pd.Series({'MTS':dateandtime.convert_dates(x['MTS'])})), axis = 1)\n date = [str(x) for x in data['MTS']]\n date_new = [x.split() for x in date]\n data['date'] = pd.Series([x[0] for x in date_new])\n data['time'] = pd.Series([x[1] for x in date_new])\n time_new = [x.split(':') for x in data['time']]\n data['hours'] = pd.Series([x[0] for x in time_new])\n data['minutes'] = pd.Series([x[1] for x in time_new])\n data['seconds'] = pd.Series([x[2] for x in time_new])\n data['hour_minute'] = pd.Series([(x[0] + ':' + x[1]) for x in time_new])\n return data\n\n","sub_path":"new_process.py","file_name":"new_process.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"284047233","text":"\n'''\nDefine custom losses here and add them to the global_loss_list dict (important!)\nKeep this file to functions with a few lines adding loss components\nDo the actual implementation of the loss components in Loss_implementations.py\n\n'''\nglobal_loss_list = {}\n\nimport tensorflow as tf\n\n##### prototype for merging loss function\nfrom betaLosses import min_beta_loss_rowsplits , min_beta_loss_truth , pre_training_loss, null_loss\nglobal_loss_list['min_beta_loss_rowsplits'] = min_beta_loss_rowsplits\nglobal_loss_list['min_beta_loss_truth'] = min_beta_loss_truth\nglobal_loss_list['pre_training_loss'] = pre_training_loss\nglobal_loss_list['null_loss'] = null_loss\n\n\n\n\n######\n\ndef fraction_loss_with_penalties(truth, pred):\n from Loss_implementations import frac_loss, good_frac_sum_loss, good_frac_range_loss\n fracloss = frac_loss(truth, pred) \n fracsumloss = good_frac_sum_loss(truth,pred) \n fracrangeloss = good_frac_range_loss(truth, pred)\n \n loss = fracloss + fracsumloss + 100.* fracrangeloss\n \n loss = tf.Print(loss, [tf.reduce_mean(loss), \n tf.reduce_mean(fracloss), \n tf.reduce_mean(fracsumloss), \n tf.reduce_mean(fracrangeloss)],\n 'loss, fracloss, fracsumloss, fracrangeloss ')\n return loss\nglobal_loss_list['fraction_loss_with_penalties']=fraction_loss_with_penalties\n\ndef fraction_loss_with_penalties_sort_pred(truth, pred):\n from Loss_implementations import frac_loss_sort_pred, good_frac_sum_loss, good_frac_range_loss\n fraclosssortpred = frac_loss_sort_pred(truth, pred) \n fracsumloss = good_frac_sum_loss(truth,pred) \n fracrangeloss = good_frac_range_loss(truth, pred)\n \n loss = fraclosssortpred + fracsumloss + 100.* fracrangeloss\n \n loss = tf.Print(loss, [tf.reduce_mean(loss), \n tf.reduce_mean(fraclosssortpred), \n tf.reduce_mean(fracsumloss), \n tf.reduce_mean(fracrangeloss)],\n 'loss, fracloss (sort pred), fracsumloss, fracrangeloss ')\n return loss\nglobal_loss_list['fraction_loss_with_penalties_sort_pred']=fraction_loss_with_penalties_sort_pred\n\n\n\n\n\n\n####### for the 'pre'clustering tests\n","sub_path":"modules/Losses.py","file_name":"Losses.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"111605604","text":"from gpiozero import MotionSensor\nimport os\nimport time\nimport tweepy\npir=MotionSensor(4)\ntime.sleep(4)\nif pir.motion_detected:\n print(\"motion detected\")\nelse:\n print(\"no detection of motion\")\n \na=0\nwhile a<=2:\n img=\"/home/cs2017a106/im.jpg\"\n cmd=\"fswebcam -r 1280x720 -S 3 --jpeg 100 \"+img\n os.system(cmd)\n print(\"pic taken\")\n time.sleep(5)\n a+=1\n\ndef get_api(cfg):\n auth = tweepy.OAuthHandler(cfg['consumer_key'], cfg['consumer_secret'])\n auth.set_access_token(cfg['access_token'], cfg['access_token_secret'])\n return tweepy.API(auth)\n\ndef main():\n # Fill in the values noted in previous step here\n cfg = { \n \"consumer_key\" : \"JS7bbVBAo0j5ZDDMq5nJMVJV1\",\n \"consumer_secret\" : \"7bTkI1c06sf8j2uDtRehCAkSQLuhUWACCLuG1FgKgrmImQODxM\",\n \"access_token\" : \"967252071517970433-IsKl2YTxcjtUhE8LkFpXRXzcF6SNl3B\",\n \"access_token_secret\" : \"F3BAuVQfciu7EaSncW7T68WLXSIVROM78Y4G8lsVKg7xK\" \n }\n api = get_api(cfg)\n tweet = \"Nice!!! one\"\n status = api.update_status(status=tweet)\n # Yes, tweet is called 'status' rather confusing\nif __name__ == \"__main__\":\n main()\n print(\"success\")\n","sub_path":"md.py","file_name":"md.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"37584762","text":"# metaDatsetGen imports\nfrom core.config import cfg, createFilenameID\n\n# misc imports\nfrom utils.base import *\nimport pprint\npp = pprint.PrettyPrinter(indent=4)\nimport pickle,cv2,uuid,os,sys\nimport os.path as osp\nimport numpy as np\nfrom numpy import transpose as npt\n\ndef load_mixture_set_single(setID,repetition,size):\n pklName = createFilenameID(setID,str(repetition),str(size)) + \".pkl\"\n # write pickle file of the roidb\n if osp.exists(pklName) is True:\n fid = open(pklName,\"rb\")\n loaded = pickle.load(fid)\n fid.close()\n\n trainData = loaded['train']\n print_each_size(trainData[0])\n testData = loaded['test']\n print_each_size(testData[0])\n else:\n raise ValueError(\"{} does not exists\".format(pklName))\n return trainData,testData\n\n \ndef load_mixture_set(setID,repetition,final_size):\n\n roidbTr = {}\n roidbTe = {}\n l_roidbTr = {}\n l_roidbTe = {}\n annoCountsTr = {}\n annoCountsTe = {}\n\n datasetSizes = cfg.MIXED_DATASET_SIZES\n if final_size not in datasetSizes:\n print(\"invalid dataset size\")\n print(\"valid option sizes include:\")\n print(datasetSizes)\n raise ValueError(\"size {} is not in cfg.MIXED_DATASET_SIZES\".format(final_size))\n sizeIndex = datasetSizes.index(final_size)\n \n for size in datasetSizes[:sizeIndex+1]:\n print(size)\n # create a file for each dataset size\n pklName = createFilenameID(setID,str(repetition),str(size)) + \".pkl\"\n # write pickle file of the roidb\n if osp.exists(pklName) is True:\n fid = open(pklName,\"rb\")\n loaded = pickle.load(fid)\n fid.close()\n\n train = loaded['train']\n test = loaded['test']\n print(pklName)\n # todo: what depends on the old version?\n # old version: returns only one list of sizes from \"final_size\"\n annoCountsTr[size] = train[1]\n annoCountsTe[size] = test[1]\n # if size == final_size: # only save the last count\n # annoCountsTr = train[1]\n # annoCountsTe = test[1]\n for dsID,roidb in train[0].items():\n print(dsID,type(roidb),len(roidb))\n l_roidb = list(roidb)\n if dsID not in roidbTr.keys():\n # sometimes read in as tuple\n # cause unknown\n roidbTr[dsID] = list(l_roidb)\n if size < 5000: print(\"less than 5k @ {}\".format(size))\n if size < 5000: l_roidbTr[dsID] = list(l_roidb)\n else:\n print(\"(a)@ {} roidbTr v.s. l_roidbTr: {} v.s. {}\".format(dsID,len(roidbTr[dsID]),len(l_roidbTr[dsID])))\n roidbTr[dsID].extend(l_roidb)\n print(\"(b)@ {} roidbTr v.s. l_roidbTr: {} v.s. {}\".format(dsID,len(roidbTr[dsID]),len(l_roidbTr[dsID])))\n if size < 5000: print(\"less than 5k @ {}\".format(size))\n if size < 5000: l_roidbTr[dsID].extend(l_roidb)\n print(\"(c)@ {} roidbTr v.s. l_roidbTr: {} v.s. {}\".format(dsID,len(roidbTr[dsID]),len(l_roidbTr[dsID])))\n\n for dsID,roidb in test[0].items():\n print(dsID,type(roidb),len(roidb))\n l_roidb = list(roidb)\n if dsID not in roidbTe.keys():\n roidbTe[dsID] = list(l_roidb)\n if size < 5000: l_roidbTe[dsID] = list(l_roidb)\n else:\n roidbTe[dsID].extend(l_roidb)\n if size < 5000: l_roidbTe[dsID].extend(l_roidb)\n else:\n raise ValueError(\"{} does not exists\".format(pklName))\n # KNOWN ISSUE: the annoCounts* ordering is incorrect.\n # see ymlConfig/default_dataset_index.yml v.s. config DATASET_ORDER \n for ds in roidbTr.keys():\n print(\"@ {} roidbTr v.s. l_roidbTr: {} v.s. {}\".format(ds,len(roidbTr[ds]),len(l_roidbTr[ds])))\n for ds in roidbTe.keys():\n print(\"@ {} roidbTe v.s. l_roidbTe: {} v.s. {}\".format(ds,len(roidbTe[ds]),len(l_roidbTe[ds])))\n return {\"train\":[roidbTr,annoCountsTr,l_roidbTr],\"test\":[roidbTe,annoCountsTe,l_roidbTe]}\n\ndef save_mixture_set_single(roidb,annoCount,setID,repetition,size):\n pklName = createFilenameID(setID,str(repetition),str(size)) + \".pkl\"\n saveInfo = {\"allRoidb\":roidb,\"annoCounts\":annoCount}\n with open(pklName,\"wb\") as f:\n pickle.dump(saveInfo,f)\n \nclass PreviousCounts():\n\n def __init__(self,size,initVal):\n self._prevCounts = [initVal for _ in range(size)]\n\n def __getitem__(self,idx):\n return self._prevCounts[idx]\n\n def __str__(self):\n return str(self._prevCounts)\n\n def update(self,roidbs):\n for idx,roidb in enumerate(roidbs):\n if roidb is None: continue\n self._prevCounts[idx] = len(roidb)\n\n def zero(self):\n self.setAllTo(0)\n\n def setAllTo(self,val):\n for idx in range(8):\n self._prevCounts[idx] = val\n\n\n","sub_path":"lib/utils/mixture_utils.py","file_name":"mixture_utils.py","file_ext":"py","file_size_in_byte":5039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"135875877","text":"import json\n# import logging\n# import sys\nfrom .models import *\n\n\ndef read_json_file():\n movies_data = []\n with open('imdb.json') as f:\n movies_data = json.loads(f.read())\n\n for data in movies_data:\n j = []\n for i in data['genre']:\n j.append(str(i))\n data['genre'] = j\n\n return movies_data\n\n\ndef store_movie_in_db():\n \"\"\"\n Function to store question in database\n :return:\n \"\"\"\n logging.info(\"Inside \" + str(__name__))\n try:\n movies_data = read_json_file()\n # m = Movies()\n for movie_data in movies_data:\n m = Movies()\n m.m_title = movie_data['name']\n m.m_director = movie_data['director']\n m.m_genre = movie_data['genre']\n m.m_imdb_score = movie_data['imdb_score']\n m.m_99popularity = movie_data['99popularity']\n m.save()\n return \"DONE\"\n except Exception:\n # exc_type, exc_obj, tb = sys.exc_info()\n # logging.critical(\"EXCEPTION - \" + str(e) + \" in \" + str(__name__) + \" on line number: \" + str(tb.tb_lineno))\n return None\n\n\n# if __name__ == '__main__':\n# store_question_in_db()\n","sub_path":"movies/add_movies_data_json_to_db.py","file_name":"add_movies_data_json_to_db.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"20267702","text":"import os\nimport unittest\nfrom aibolit.patterns.method_siblings.method_siblings import MethodSiblings\nfrom pathlib import Path\n\n\nclass TestMethodSiblingsSiblings(unittest.TestCase):\n dir_path = Path(os.path.realpath(__file__)).parent\n\n @unittest.skip(\"Not implemented\")\n def test_find_simple_method_siblings(self):\n self.assertEqual(\n [2], MethodSiblings(Path(self.dir_path, 'SimpleMethodSiblings.java')).value()\n )\n\n @unittest.skip(\"Not implemented\")\n def test_find_alternate_method_siblings(self):\n self.assertEqual(\n [2], MethodSiblings(Path(self.dir_path, 'AlternateMethodSiblings.java')).value()\n )\n","sub_path":"test/patterns/method_siblings/test_method_siblings.py","file_name":"test_method_siblings.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"858798","text":"# -*- coding: utf-8 -*-\n\n\nimport requests\nimport time\nimport random\nimport string\nfrom apisms import _5sim, onlinesim\nfrom . import ProxyManager\nfrom python3_anticaptcha import ImageToTextTask\nfrom lxml import html, etree\n\n\ndef rsleep(left = 2, right = 5):\n time.sleep(random.triangular(left, right))\n\ndef prepare():\n pm = ProxyManager.ProxyMN()\n proxy = {'https': 'http://' + pm.GetProxyList(Limit = 1, Type = 'https')[0] + '/'}\n http = requests.Session()\n http.headers.update({\n 'User-agent': 'Mozilla/5.0 (Linux; U; Android 4.4.4; en-US; XT1022 Build/KXC21.5-40) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 UCBrowser/10.7.0.636 U3/0.8.0 Mobile Safari/534.30'\n })\n http.proxies = proxy\n resp = http.get('https://m.vk.com/')\n return http, resp\n\ndef start(session, name, surname, year, month, day, sex, captcha_sid = None, captcha_key = None):\n url = \"https://m.vk.com/join.php?act=start\"\n values = {'act': 'start',\n 'first_name': name,\n 'last_name': surname,\n 'bday': day,\n 'bmonth': month,\n 'byear': year,\n 'sex': sex,\n '_nlm': '1',\n '_ref': 'join'\n }\n if captcha_sid is not None:\n values['captcha_sid'] = captcha_sid\n values['captcha_key'] = captcha_key\n \n response = session.post(url, values)\n return session, response\n\n\ndef get_sms(session, phone_number, _hash):\n values = {'act': 'phone',\n 'hash': _hash,\n '_nlm': '1',\n '_ref': 'join'\n }\n if phone_number.startswith('+77'):\n values['phone_prefix'] = '+77'\n values['phone_number'] = phone_number.split('+77')[1]\n elif phone_number.startswith('+7'):\n values['phone_prefix'] = '+7'\n values['phone_number'] = phone_number.split('+7')[1]\n \n url = \"https://m.vk.com/join?act=phone&hash=\" + _hash\n response = session.post(url, values)\n return session, response\n \n\ndef check_code(session, phone_number, code, _hash):\n url = \"https://m.vk.com/join?act=check_code&hash=\" + _hash\n values = {'act': 'check_code',\n 'hash': _hash,\n 'phone': phone_number,\n 'code': code,\n '_nlm': '1',\n '_ref': 'join'\n }\n response = session.post(url, values)\n return session, response\n\ndef finish(session, key_url, phone_number, password):#session, phone_number, password, key_url):\n \"\"\"a = key_url.split('?')[1]\n values = {}\n for i in a.split('&'):\n b = i.split('=')\n values[b[0]] = b[1]\n for i, j in values.items():\n print(i, j)\n \n values = {\n \n 'expire': '',\n 'recaptcha': '',\n 'captcha_sid': '',\n 'captcha_key': '',\n 'email': phone_number,\n 'pass': password,\n 'join_to_already': 0\n }\"\"\"\n values = {'email': phone_number,\n 'pass': password\n }\n key_url = key_url[:4] + key_url[5:]\n response = session.post(key_url, values)\n return session, response\n \n \n \n#finish(\"https://login.vk.com/?act=login&_origin=https://m.vk.com&ip_h=338750eb054fc82b33&lg_h=bb495da8821fd6e060&role=pda&join_code=12654&join_hash=724ec96c1c2412ec10b45c0be0cb644c&to=am9pbj9hY3Q9ZG9uZQ--&pass=dima_bog_debaga\")\n\n\ndef registration(name = 'Лада', surname = 'Беленова', sex = '1', birthday = None, country = 'kazakhstan', service = '5sim'):\n try:\n session, response = prepare()\n \n rsleep()\n \n if birthday is None:\n byear = str(random.randint(1980, 1998))\n bmonth = str(random.randint(1, 12))\n bday = str(random.randint(1, 28))\n else:\n bday, bmonth, byear = birthday.split('.')\n \n session, response = start(session, name, surname, byear, bmonth, bday, sex)\n while 'captcha' in response.text:\n print('we need captcha')\n pm = ProxyManager.ProxyMN()\n prxlst = pm.GetProxyList(Limit = 5, Type = 'https')\n for i in range(5):\n try:\n proxy = {'https': 'http://' + prxlst[i] + '/'}\n session.proxies = proxy\n session, response = start(session, name, surname, byear, bmonth, bday, sex)\n if 'captcha' not in response.text:\n break\n except Exception:\n print('problems')\n \n print(response.text)\n _hash = response.text.split('action=\"/join?act=phone&hash=')[1].split('\"')[0]\n print('gonna get phone')\n \n \n \n \n \n if service == '5sim':\n ss = _5sim._5simApi()\n \n elif service == 'onlinesim':\n ss = onlinesim.onlinesimApi()\n \n phone_number = ss.get_number(country = country) \n print('number has been got', phone_number) \n session, response = get_sms(session, phone_number, _hash)\n print(response.text)\n sms_code = None\n i = 0\n while sms_code is None:\n time.sleep(1)\n i += 1\n sms_code = ss.check()\n print(sms_code)\n if i >= 60:\n print('no sms')\n ss.ban()\n return registration(name, surname, sex, birthday, country, service)\n session, response = check_code(session, phone_number, sms_code, _hash)\n print(response.text)\n key_url = response.text.split('
![](\"/games/{0}/{8}_box.jpg\" "\\"{9}.Коробка")
' \\\n '{1}![](\"/games/{0}/{1}_0%df.jpg\" "\\"{2},")
' % (num, num)\n num += 1\n quantity -= 1\n for x in range(quantity):\n n = str(num).zfill(2)\n data += '![](\"/games/{0}/{1}_%sf.jpg\" "\\"{2},")
' % (n, n)\n num += 1\n data += '